Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

EPA Science Inventory

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

Cross-validation of the osmotic pressure based on Pitzer model with air humidity osmometry at high concentration of ammonium sulfate solutions.

PubMed

Wang, Xiao-Lan; Zhan, Ting-Ting; Zhan, Xian-Cheng; Tan, Xiao-Ying; Qu, Xiao-You; Wang, Xin-Yue; Li, Cheng-Rong

2014-01-01

The osmotic pressure of ammonium sulfate solutions has been measured by the well-established freezing point osmometry in dilute solutions and we recently reported air humidity osmometry in a much wider range of concentration. Air humidity osmometry cross-validated the theoretical calculations of osmotic pressure based on the Pitzer model at high concentrations by two one-sided test (TOST) of equivalence with multiple testing corrections, where no other experimental method could serve as a reference for comparison. Although more strict equivalence criteria were established between the measurements of freezing point osmometry and the calculations based on the Pitzer model at low concentration, air humidity osmometry is the only currently available osmometry applicable to high concentration, serves as an economic addition to standard osmometry.

Minimizing Concentration Effects in Water-Based, Laminar-Flow Condensation Particle Counters

PubMed Central

Lewis, Gregory S.; Hering, Susanne V.

2013-01-01

Concentration effects in water condensation systems, such as used in the water-based condensation particle counter, are explored through numeric modeling and direct measurements. Modeling shows that the condensation heat release and vapor depletion associated with particle activation and growth lowers the peak supersaturation. At higher number concentrations, the diameter of the droplets formed is smaller, and the threshold particle size for activation is higher. This occurs in both cylindrical and parallel plate geometries. For water-based systems we find that condensational heat release is more important than is vapor depletion. We also find that concentration effects can be minimized through use of smaller tube diameters, or more closely spaced parallel plates. Experimental measurements of droplet diameter confirm modeling results. PMID:24436507

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

PubMed

Hjort, Jan; Hugg, Timo T; Antikainen, Harri; Rusanen, Jarmo; Sofiev, Mikhail; Kukkonen, Jaakko; Jaakkola, Maritta S; Jaakkola, Jouni J K

2016-05-01

Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

Population pharmacokinetics of busulfan in pediatric and young adult patients undergoing hematopoietic cell transplant: a model-based dosing algorithm for personalized therapy and implementation into routine clinical use.

PubMed

Long-Boyle, Janel R; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J; Dvorak, Christopher C

2015-04-01

Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared with conventional dose guidelines. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration at steady state) and implement a simple model-based tool for the initial dosing of busulfan in children undergoing hematopoietic cell transplantation. Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone hematopoietic cell transplantation with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the nonlinear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Modeling of busulfan time-concentration data indicates that busulfan clearance displays nonlinearity in children, decreasing up to approximately 20% between the concentrations of 250-2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan clearance were actual body weight and age. The percentage of individuals achieving a therapeutic concentration at steady state was significantly higher in subjects receiving initial doses based on the population PK model (81%) than in historical controls dosed on conventional guidelines (52%) (P = 0.02). When compared with the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults.

Prediction of a Therapeutic Dose for Buagafuran, a Potent Anxiolytic Agent by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Starting from Pharmacokinetics in Rats and Human.

PubMed

Yang, Fen; Wang, Baolian; Liu, Zhihao; Xia, Xuejun; Wang, Weijun; Yin, Dali; Sheng, Li; Li, Yan

2017-01-01

Physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) models can contribute to animal-to-human extrapolation and therapeutic dose predictions. Buagafuran is a novel anxiolytic agent and phase I clinical trials of buagafuran have been completed. In this paper, a potentially effective dose for buagafuran of 30 mg t.i.d. in human was estimated based on the human brain concentration predicted by a PBPK/PD modeling. The software GastroPlus TM was used to build the PBPK/PD model for buagafuran in rat which related the brain tissue concentrations of buagafuran and the times of animals entering the open arms in the pharmacological model of elevated plus-maze. Buagafuran concentrations in human plasma were fitted and brain tissue concentrations were predicted by using a human PBPK model in which the predicted plasma profiles were in good agreement with observations. The results provided supportive data for the rational use of buagafuran in clinic.

Ratio-based vs. model-based methods to correct for urinary creatinine concentrations.

PubMed

Jain, Ram B

2016-08-01

Creatinine-corrected urinary analyte concentration is usually computed as the ratio of the observed level of analyte concentration divided by the observed level of the urinary creatinine concentration (UCR). This ratio-based method is flawed since it implicitly assumes that hydration is the only factor that affects urinary creatinine concentrations. On the contrary, it has been shown in the literature, that age, gender, race/ethnicity, and other factors also affect UCR. Consequently, an optimal method to correct for UCR should correct for hydration as well as other factors like age, gender, and race/ethnicity that affect UCR. Model-based creatinine correction in which observed UCRs are used as an independent variable in regression models has been proposed. This study was conducted to evaluate the performance of ratio-based and model-based creatinine correction methods when the effects of gender, age, and race/ethnicity are evaluated one factor at a time for selected urinary analytes and metabolites. It was observed that ratio-based method leads to statistically significant pairwise differences, for example, between males and females or between non-Hispanic whites (NHW) and non-Hispanic blacks (NHB), more often than the model-based method. However, depending upon the analyte of interest, the reverse is also possible. The estimated ratios of geometric means (GM), for example, male to female or NHW to NHB, were also compared for the two methods. When estimated UCRs were higher for the group (for example, males) in the numerator of this ratio, these ratios were higher for the model-based method, for example, male to female ratio of GMs. When estimated UCR were lower for the group (for example, NHW) in the numerator of this ratio, these ratios were higher for the ratio-based method, for example, NHW to NHB ratio of GMs. Model-based method is the method of choice if all factors that affect UCR are to be accounted for.

A growth inhibitory model with SOx influenced effective growth rate for estimation of algal biomass concentration under flue gas atmosphere

USDA-ARS?s Scientific Manuscript database

A theoretical model for the prediction of biomass concentration under real flue gas emission has been developed. The model considers the CO2 mass transfer rate, the critical SOx concentration and its role on pH based inter-conversion of bicarbonate in model building. The calibration and subsequent v...

Measured and Modeled Toxicokinetics in Cultured Fish Cells and Application to In Vitro - In Vivo Toxicity Extrapolation

PubMed Central

Stadnicka-Michalak, Julita; Tanneberger, Katrin; Schirmer, Kristin; Ashauer, Roman

2014-01-01

Effect concentrations in the toxicity assessment of chemicals with fish and fish cells are generally based on external exposure concentrations. External concentrations as dose metrics, may, however, hamper interpretation and extrapolation of toxicological effects because it is the internal concentration that gives rise to the biological effective dose. Thus, we need to understand the relationship between the external and internal concentrations of chemicals. The objectives of this study were to: (i) elucidate the time-course of the concentration of chemicals with a wide range of physicochemical properties in the compartments of an in vitro test system, (ii) derive a predictive model for toxicokinetics in the in vitro test system, (iii) test the hypothesis that internal effect concentrations in fish (in vivo) and fish cell lines (in vitro) correlate, and (iv) develop a quantitative in vitro to in vivo toxicity extrapolation method for fish acute toxicity. To achieve these goals, time-dependent amounts of organic chemicals were measured in medium, cells (RTgill-W1) and the plastic of exposure wells. Then, the relation between uptake, elimination rate constants, and log KOW was investigated for cells in order to develop a toxicokinetic model. This model was used to predict internal effect concentrations in cells, which were compared with internal effect concentrations in fish gills predicted by a Physiologically Based Toxicokinetic model. Our model could predict concentrations of non-volatile organic chemicals with log KOW between 0.5 and 7 in cells. The correlation of the log ratio of internal effect concentrations in fish gills and the fish gill cell line with the log KOW was significant (r>0.85, p = 0.0008, F-test). This ratio can be predicted from the log KOW of the chemical (77% of variance explained), comprising a promising model to predict lethal effects on fish based on in vitro data. PMID:24647349

Chemical-specific screening criteria for interpretation of biomonitoring data for volatile organic compounds (VOCs)--application of steady-state PBPK model solutions.

PubMed

Aylward, Lesa L; Kirman, Chris R; Blount, Ben C; Hays, Sean M

2010-10-01

The National Health and Nutrition Examination Survey (NHANES) generates population-representative biomonitoring data for many chemicals including volatile organic compounds (VOCs) in blood. However, no health or risk-based screening values are available to evaluate these data from a health safety perspective or to use in prioritizing among chemicals for possible risk management actions. We gathered existing risk assessment-based chronic exposure reference values such as reference doses (RfDs), reference concentrations (RfCs), tolerable daily intakes (TDIs), cancer slope factors, etc. and key pharmacokinetic model parameters for 47 VOCs. Using steady-state solutions to a generic physiologically-based pharmacokinetic (PBPK) model structure, we estimated chemical-specific steady-state venous blood concentrations across chemicals associated with unit oral and inhalation exposure rates and with chronic exposure at the identified exposure reference values. The geometric means of the slopes relating modeled steady-state blood concentrations to steady-state exposure to a unit oral dose or unit inhalation concentration among 38 compounds with available pharmacokinetic parameters were 12.0 microg/L per mg/kg-d (geometric standard deviation [GSD] of 3.2) and 3.2 microg/L per mg/m(3) (GSD=1.7), respectively. Chemical-specific blood concentration screening values based on non-cancer reference values for both oral and inhalation exposure range from 0.0005 to 100 microg/L; blood concentrations associated with cancer risk-specific doses at the 1E-05 risk level ranged from 5E-06 to 6E-02 microg/L. The distribution of modeled steady-state blood concentrations associated with unit exposure levels across VOCs may provide a basis for estimating blood concentration screening values for VOCs that lack chemical-specific pharmacokinetic data. The screening blood concentrations presented here provide a tool for risk assessment-based evaluation of population biomonitoring data for VOCs and are most appropriately applied to central tendency estimates for such datasets. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective

PubMed Central

Zou, Bin; Luo, Yanqing; Wan, Neng; Zheng, Zhong; Sternberg, Troy; Liao, Yilan

2015-01-01

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping. PMID:25731103

A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

NASA Astrophysics Data System (ADS)

Wang, Chongyang; Chen, Shuisen; Li, Dan; Wang, Danni; Liu, Wei; Yang, Ji

2017-11-01

Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsat-based TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006-2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3-577.2 mg L-1, N = 84, P value < 0.001) and had an acceptable validation accuracy (TSS: 4.5-474 mg L-1, root mean squared error (RMSE) ≤ 25 mg L-1, N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S model-based atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L-1. Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7-160 mg L-1, RMSE: 11.06 mg L-1, N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106-220.7 mg L-1, RMSE: 26.66 mg L-1, N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium- and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similar red bands and near-infrared bands, such as ALI, HJ-1 A and B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, and GF.

Steady state phosphorus mass balance model during hemodialysis based on a pseudo one-compartment kinetic model.

PubMed

Leypoldt, John K; Agar, Baris U; Akonur, Alp; Gellens, Mary E; Culleton, Bruce F

2012-11-01

Mathematical models of phosphorus kinetics and mass balance during hemodialysis are in early development. We describe a theoretical phosphorus steady state mass balance model during hemodialysis based on a novel pseudo one-compartment kinetic model. The steady state mass balance model accounted for net intestinal absorption of phosphorus and phosphorus removal by both dialysis and residual kidney function. Analytical mathematical solutions were derived to describe time-dependent intradialytic and interdialytic serum phosphorus concentrations assuming hemodialysis treatments were performed symmetrically throughout a week. Results from the steady state phosphorus mass balance model are described for thrice weekly hemodialysis treatment prescriptions only. The analysis predicts 1) a minimal impact of dialyzer phosphorus clearance on predialysis serum phosphorus concentration using modern, conventional hemodialysis technology, 2) variability in the postdialysis-to-predialysis phosphorus concentration ratio due to differences in patient-specific phosphorus mobilization, and 3) the importance of treatment time in determining the predialysis serum phosphorus concentration. We conclude that a steady state phosphorus mass balance model can be developed based on a pseudo one-compartment kinetic model and that predictions from this model are consistent with previous clinical observations. The predictions from this mass balance model are theoretical and hypothesis-generating only; additional prospective clinical studies will be required for model confirmation.

Use of novel inhalation kinetic studies to refine physiologically-based-pharmacokinetic models for ethanol in non-pregnant and pregnant rats

EPA Science Inventory

Ethanol (EtOH) exposure induces a variety of concentration-dependent neurological and developmental effects in the rat. Physiologically-based pharmacokinetic (PBPK) models have been used to predict the inhalation exposure concentrations necessary to produce blood EtOH concentrat...

Use of a macroinvertebrate based biotic index to estimate critical metal concentrations for good ecological water quality.

PubMed

Van Ael, Evy; De Cooman, Ward; Blust, Ronny; Bervoets, Lieven

2015-01-01

Large datasets from total and dissolved metal concentrations in Flemish (Belgium) fresh water systems and the associated macroinvertebrate-based biotic index MMIF (Multimetric Macroinvertebrate Index Flanders) were used to estimate critical metal concentrations for good ecological water quality, as imposed by the European Water Framework Directive (2000). The contribution of different stressors (metals and water characteristics) to the MMIF were studied by constructing generalized linear mixed effect models. Comparison between estimated critical concentrations and the European and Flemish EQS, shows that the EQS for As, Cd, Cu and Zn seem to be sufficient to reach a good ecological quality status as expressed by the invertebrate-based biotic index. In contrast, the EQS for Cr, Hg and Pb are higher than the estimated critical concentrations, which suggests that when environmental concentrations are at the same level as the EQS a good quality status might not be reached. The construction of mixed models that included metal concentrations in their structure did not lead to a significant outcome. However, mixed models showed the primary importance of water characteristics (oxygen level, temperature, ammonium concentration and conductivity) for the MMIF. Copyright © 2014 Elsevier Ltd. All rights reserved.

A tissue dose-based comparative exposure assessment of manganese using physiologically based pharmacokinetic modeling—The importance of homeostatic control for an essential metal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gentry, P. Robinan, E-mail: rgentry@ramboll.com

A physiologically-based pharmacokinetic (PBPK) model (Schroeter et al., 2011) was applied to simulate target tissue manganese (Mn) concentrations following occupational and environmental exposures. These estimates of target tissue Mn concentrations were compared to determine margins of safety (MOS) and to evaluate the biological relevance of applying safety factors to derive acceptable Mn air concentrations. Mn blood concentrations measured in occupational studies permitted verification of the human PBPK models, increasing confidence in the resulting estimates. Mn exposure was determined based on measured ambient air Mn concentrations and dietary data in Canada and the United States (US). Incorporating dietary and inhalation exposuresmore » into the models indicated that increases in target tissue concentrations above endogenous levels only begin to occur when humans are exposed to levels of Mn in ambient air (i.e. > 10 μg/m{sup 3}) that are far higher than those currently measured in Canada or the US. A MOS greater than three orders of magnitude was observed, indicating that current Mn air concentrations are far below concentrations that would be required to produce the target tissue Mn concentrations associated with subclinical neurological effects. This application of PBPK modeling for an essential element clearly demonstrates that the conventional application of default factors to “convert” an occupational exposure to an equivalent continuous environmental exposure, followed by the application of safety factors, is not appropriate in the case of Mn. PBPK modeling demonstrates that the relationship between ambient Mn exposures and dose-to-target tissue is not linear due to normal tissue background levels and homeostatic controls. - Highlights: • Manganese is an essential nutrient, adding complexity to its risk assessment. • Nonlinearities in biological processes are important for manganese risk assessment. • A PBPK model was used to estimate target tissue concentrations of manganese. • An MOS approach also considered target tissue concentrations for ambient exposures. • Relationships between ambient Mn exposures and dose-to-target tissue are not linear.« less

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.

PubMed

Cotten, Cameron; Reed, Jennifer L

2013-01-30

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

PubMed Central

2013-01-01

Background Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. Results In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. Conclusions This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets. PMID:23360254

Modeled summer background concentration nutrients and ...

EPA Pesticide Factsheets

We used regression models to predict background concentration of four water quality indictors: total nitrogen (N), total phosphorus (P), chloride, and total suspended solids (TSS), in the mid-continent (USA) great rivers, the Upper Mississippi, the Lower Missouri, and the Ohio. From best-model linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the land use and stressor variables were all set to zero the y-intercept. Except for total P on the Upper Mississippi River and chloride on the Ohio River, we were able to predict background concentration from significant regression models. In every model with more than one predictor variable, the model included at least one variable representing agricultural land use and one variable representing development. Predicted background concentration of total N was the same on the Upper Mississippi and Lower Missouri rivers (350 ug l-1), which was much lower than a published eutrophication threshold and percentile-based thresholds (25th percentile of concentration at all sites in the population) but was similar to a threshold derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (53 ug l-1) was also lower than published and percentile-based thresholds. Background TSS concentration was higher on the Lower Missouri (30 mg l-1) than the other ri

Pseudo-Boltzmann model for modeling the junctionless transistors

NASA Astrophysics Data System (ADS)

Avila-Herrera, F.; Cerdeira, A.; Roldan, J. B.; Sánchez-Moreno, P.; Tienda-Luna, I. M.; Iñiguez, B.

2014-05-01

Calculation of the carrier concentrations in semiconductors using the Fermi-Dirac integral requires complex numerical calculations; in this context, practically all analytical device models are based on Boltzmann statistics, even though it is known that it leads to an over-estimation of carriers densities for high doping concentrations. In this paper, a new approximation to Fermi-Dirac integral, called Pseudo-Boltzmann model, is presented for modeling junctionless transistors with high doping concentrations.

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning.

PubMed

Liu, Zhijian; Li, Hao; Cao, Guoqing

2017-07-30

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM 2.5 and PM 10 ), temperature, relative humidity, and CO₂ concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups.

A Model to Predict the Breathing Zone Concentrations of Particles Emitted from Surfaces

EPA Science Inventory

Activity based sampling (ABS) is typically performed to assess inhalation exposure to particulate contaminants known to have low, heterogeneous concentrations on a surface. Activity based sampling determines the contaminant concentration in a person's breathing zone as they perfo...

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Applying constraints on model-based methods: Estimation of rate constants in a second order consecutive reaction

NASA Astrophysics Data System (ADS)

Kompany-Zareh, Mohsen; Khoshkam, Maryam

2013-02-01

This paper describes estimation of reaction rate constants and pure ultraviolet/visible (UV-vis) spectra of the component involved in a second order consecutive reaction between Ortho-Amino benzoeic acid (o-ABA) and Diazoniom ions (DIAZO), with one intermediate. In the described system, o-ABA was not absorbing in the visible region of interest and thus, closure rank deficiency problem did not exist. Concentration profiles were determined by solving differential equations of the corresponding kinetic model. In that sense, three types of model-based procedures were applied to estimate the rate constants of the kinetic system, according to Levenberg/Marquardt (NGL/M) algorithm. Original data-based, Score-based and concentration-based objective functions were included in these nonlinear fitting procedures. Results showed that when there is error in initial concentrations, accuracy of estimated rate constants strongly depends on the type of applied objective function in fitting procedure. Moreover, flexibility in application of different constraints and optimization of the initial concentrations estimation during the fitting procedure were investigated. Results showed a considerable decrease in ambiguity of obtained parameters by applying appropriate constraints and adjustable initial concentrations of reagents.

Time-dependent oral absorption models

NASA Technical Reports Server (NTRS)

Higaki, K.; Yamashita, S.; Amidon, G. L.

2001-01-01

The plasma concentration-time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, ka(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted Cmax values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, ka(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted Cmax values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient ka(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.

Modeling of breath methane concentration profiles during exercise on an ergometer*

PubMed Central

Szabó, Anna; Unterkofler, Karl; Mochalski, Pawel; Jandacka, Martin; Ruzsanyi, Vera; Szabó, Gábor; Mohácsi, Árpád; Teschl, Susanne; Teschl, Gerald; King, Julian

2016-01-01

We develop a simple three compartment model based on mass balance equations which quantitatively describes the dynamics of breath methane concentration profiles during exercise on an ergometer. With the help of this model it is possible to estimate the endogenous production rate of methane in the large intestine by measuring breath gas concentrations of methane. PMID:26828421

Application of a Physiologically Based Pharmacokinetic Model to Predict OATP1B1-Related Variability in Pharmacodynamics of Rosuvastatin

PubMed Central

Rose, R H; Neuhoff, S; Abduljalil, K; Chetty, M; Rostami-Hodjegan, A; Jamei, M

2014-01-01

Typically, pharmacokinetic–pharmacodynamic (PK/PD) models use plasma concentration as the input that drives the PD model. However, interindividual variability in uptake transporter activity can lead to variable drug concentrations in plasma without discernible impact on the effect site organ concentration. A physiologically based PK/PD model for rosuvastatin was developed that linked the predicted liver concentration to the PD response model. The model was then applied to predict the effect of genotype-dependent uptake by the organic anion-transporting polypeptide 1B1 (OATP1B1) transporter on the pharmacological response. The area under the plasma concentration–time curve (AUC0–∞) was increased by 63 and 111% for the c.521TC and c.521CC genotypes vs. the c.521TT genotype, while the PD response remained relatively unchanged (3.1 and 5.8% reduction). Using local concentration at the effect site to drive the PD response enabled us to explain the observed disconnect between the effect of the OATP1B1 c521T>C polymorphism on rosuvastatin plasma concentration and the cholesterol synthesis response. PMID:25006781

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Toward On-line Parameter Estimation of Concentric Tube Robots Using a Mechanics-based Kinematic Model

PubMed Central

Jang, Cheongjae; Ha, Junhyoung; Dupont, Pierre E.; Park, Frank Chongwoo

2017-01-01

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments. PMID:28717554

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines.

PubMed

Zhan, Xiaobin; Jiang, Shulan; Yang, Yili; Liang, Jian; Shi, Tielin; Li, Xiwen

2015-09-18

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

A numerical model for a thermally-regenerative ammonia-based flow battery using for low grade waste heat recovery

NASA Astrophysics Data System (ADS)

Wang, Weiguang; Shu, Gequn; Tian, Hua; Zhu, Xiuping

2018-06-01

A stationary and a transient two-dimensional models, based on the universal conservation laws and coupled with electrochemical reactions, are firstly applied to describe a single thermally-regenerative ammonia-based flow battery (TR-AFB), and emphasis is placed on studying the effects of reactant concentrations, physical properties of the electrolyte, flow rates and geometric parameters of flow channels on the battery performance. The model includes several experimental parameters measured by cyclic voltammetry (CV), chronoamperometry (CA) and Tafel plot. The results indicate that increasing NH3 concentration has a decisive effect on the improvement of power production and is beneficial to use higher Cu2+ concentrations, but the endurance of membrane and self-discharge need to be considered at the same time. It is also suggested that appropriately reducing the initial Cu(NH3)42+ concentration can promote power and energy densities and mitigate cyclical fluctuation. The relation between the energy and power densities is given, and the models are validated by some experimental data.

Chemistry Resolved Kinetic Flow Modeling of TATB Based Explosives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vitello, P A; Fried, L E; Howard, W M

2011-07-21

Detonation waves in insensitive, TATB based explosives are believed to have multi-time scale regimes. The initial burn rate of such explosives has a sub-microsecond time scale. However, significant late-time slow release in energy is believed to occur due to diffusion limited growth of carbon. In the intermediate time scale concentrations of product species likely change from being in equilibrium to being kinetic rate controlled. They use the thermo-chemical code CHEETAH linked to an ALE hydrodynamics code to model detonations. They term their model chemistry resolved kinetic flow as CHEETAH tracks the time dependent concentrations of individual species in the detonationmore » wave and calculates EOS values based on the concentrations. A HE-validation suite of model simulations compared to experiments at ambient, hot, and cold temperatures has been developed. They present here a new rate model and comparison with experimental data.« less

Quantification of Concentration of Microalgae Anabaena Cylindrica, Coal-bed Methane Water Isolates Nannochloropsis Gaditana and PW-95 in Aquatic Solutions through Hyperspectral Reflectance Measurement and Analytical Model Establishment

NASA Astrophysics Data System (ADS)

Zhou, Z.; Zhou, X.; Apple, M. E.; Spangler, L.

2017-12-01

Three species of microalgae, Anabaena cylindrica (UTEX # 1611), coal-bed methane water isolates Nannochloropsis gaditana and PW-95 were cultured for the measurements of their hyperspectral profiles in different concentrations. The hyperspectral data were measured by an Analytical Spectral Devices (ASD) spectroradiomter with the spectral resolution of 1 nanometer over the wavelength ranges from 350nm to 1050 nm for samples of microalgae of different concentration. Concentration of microalgae was measured using a Hemocytometer under microscope. The objective of this study is to establish the relation between spectral reflectance and micro-algal concentration so that microalgae concentration can be measured remotely by space- or airborne hyperspectral or multispectral sensors. Two types of analytical models, linear reflectance-concentration model and Lamber-Beer reflectance-concentration model, were established for each species. For linear modeling, the wavelength with the maximum correlation coefficient between the reflectance and concentrations of algae was located and then selected for each species of algae. The results of the linear models for each species are shown in Fig.1(a), in which Refl_1, Refl_2, and Refl_3 represent the reflectance of Anabaena, N. Gaditana, and PW-95 respectively. C1, C2, and C3 represent the Concentrations of Anabaena, N. Gaditana, and PW-95 respectively. The Lamber-Beer models were based on the Lambert-Beer Law, which states that the intensity of light propagating in a substance dissolved in a fully transmitting solvent is directly proportional to the concentration of the substance and the path length of the light through the solution. Thus, for the Lamber-Beer modeling, a wavelength with large absorption in red band was selected for each species. The results of Lambert-Beer models for each species are shown in Fig.1(b). Based on the Lamber-Beer models, the absorption coefficient for the three different species will be quantified.

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection.

PubMed

Sadiq, Muhammad W; Nielsen, Elisabet I; Khachman, Dalia; Conil, Jean-Marie; Georges, Bernard; Houin, Georges; Laffont, Celine M; Karlsson, Mats O; Friberg, Lena E

2017-04-01

The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil-plant transfer models.

PubMed

Boshoff, Magdalena; De Jonge, Maarten; Scheifler, Renaud; Bervoets, Lieven

2014-09-15

The aim of this study was to derive regression-based soil-plant models to predict and compare metal(loid) (i.e. As, Cd, Cu, Pb and Zn) concentrations in plants (grass Agrostis sp./Poa sp. and nettle Urtica dioica L.) among sites with a wide range of metal pollution and a wide variation in soil properties. Regression models were based on the pseudo total (aqua-regia) and exchangeable (0.01 M CaCl2) soil metal concentrations. Plant metal concentrations were best explained by the pseudo total soil metal concentrations in combination with soil properties. The most important soil property that influenced U. dioica metal concentrations was the clay content, while for grass organic matter (OM) and pH affected the As (OM) and Cu and Zn (pH). In this study multiple linear regression models proved functional in predicting metal accumulation in plants on a regional scale. With the proposed models based on the pseudo total metal concentration, the percentage of variation explained for the metals As, Cd, Cu, Pb and Zn were 0.56%, 0.47%, 0.59%, 0.61%, 0.30% in nettle and 0.46%, 0.38%, 0.27%, 0.50%, 0.28% in grass. Copyright © 2014 Elsevier B.V. All rights reserved.

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

PubMed Central

Liu, Zhijian; Li, Hao; Cao, Guoqing

2017-01-01

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM2.5 and PM10), temperature, relative humidity, and CO2 concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups. PMID:28758941

Spatio-temporal Analysis of suspended sediment Concentration in the Yongjiang Estuary Based on GOCI

NASA Astrophysics Data System (ADS)

Kang, Yanyan; Dong, Chuan

2018-01-01

The concentration and spatio-temporal variation of suspended sediment concentration in the estuary area are of great significance to the nearshore engineering, port construction and coastal evolution. Based on multi-period GOCI images and corresponding measured suspended sediment concentration (SSC) data, three inversion models (the linear regression model, the power exponent model and the neural network model) were established after rapid atmospheric correction. The results show that the absolute error of the three models is 0.20, 0.16 and 0.10kg/m3 respectively, and the relative errors are 38%, 23% and 18% respectively. The accuracy of the neural network (8-17-17-1) is the best. The SSC distribution diagrams in an ebb and flow cycle are obtained using this ANN model. The results show that with Yongjiang estuary for segmentation, the high concentration area is located in the north and the lower is in the south around Jintang Island deeper water area. When the tide rises, the water flow disturbs a large amount of sediment, and then the sediment concentration increases and high area high concentrations water body moves along the SE-NW. When the tide falls, flow rate decreases and the sediment concentration decreases. However, with the falling tide, the concentration of suspended sediment in the northern sea areas gradually increases, and is higher than 1kg/m3, and gradually moves along the NW-SE until to the estuary.

Total OH reactivity as a constraint of model calculated peroxy radical production, and the catalytic efficiency of NOx in O3 production in a boreal forest environment.

NASA Astrophysics Data System (ADS)

Javed, M. U.; Hens, K.; Martinez, M.; Kubistin, D.; Novelli, A.; Beygi, Z. H.; Axinte, R.; Nölscher, A. C.; Sinha, V.; Song, W.; Johnson, A. M.; Auld, J.; Bohn, B.; Sander, R.; Taraborrelli, D.; Williams, J.; Fischer, H.; Lelieveld, J.; Harder, H.

2016-12-01

Peroxy radicals play a key role in ozone (O3) production and hydroxyl (OH) recycling influencing the self-cleansing capacity and air quality. Organic peroxy radical (RO2) concentrations are estimated by three different approaches for a boreal forest, based on the field campaign HUMPPA-COPEC 2010 in Southern Finland. RO2 concentrations were simulated by a box model constrained by the comprehensive dataset from the campaign and cross-checked against the photostationary state (PSS) of NOx [= nitric oxide (NO) + nitrogen dioxide (NO2)] calculations. The model simulated RO2 concentrations appear too low to explain the measured PSS of NOx. As the atmospheric RO2 production is proportional to OH loss, the total OH loss rate frequency (total OH reactivity) in the model is underestimated compared to the measurements. The total OH reactivity of the model is tuned to match the observed total OH reactivity by increasing the biogenic volatile organic compound (BVOCs) concentrations for the model simulations. The new-found simulated RO2 concentrations based on the tuned OH reactivity explain the measured PSS of NOx reasonably well. Furthermore, the sensitivity of the NOx lifetime and the catalytic efficiency of NOx (CE) in O3 production, in the context of organic alkyl nitrate (RONO2) formation, was also investigated. Based on the campaign data, it was found that the lifetime of NOx and the CE are reduced and are sensitive to the RONO2 formation under low-NOx conditions, which matches a previous model-based study.

A linked hydrodynamic and water quality model for the Salton Sea

USGS Publications Warehouse

Chung, E.G.; Schladow, S.G.; Perez-Losada, J.; Robertson, Dale M.

2008-01-01

A linked hydrodynamic and water quality model was developed and applied to the Salton Sea. The hydrodynamic component is based on the one-dimensional numerical model, DLM. The water quality model is based on a new conceptual model for nutrient cycling in the Sea, and simulates temperature, total suspended sediment concentration, nutrient concentrations, including PO4-3, NO3-1 and NH4+1, DO concentration and chlorophyll a concentration as functions of depth and time. Existing water temperature data from 1997 were used to verify that the model could accurately represent the onset and breakup of thermal stratification. 1999 is the only year with a near-complete dataset for water quality variables for the Salton Sea. The linked hydrodynamic and water quality model was run for 1999, and by adjustment of rate coefficients and other water quality parameters, a good match with the data was obtained. In this article, the model is fully described and the model results for reductions in external phosphorus load on chlorophyll a distribution are presented. ?? 2008 Springer Science+Business Media B.V.

A Process-Based Model of TCA Cycle Functioning to Analyze Citrate Accumulation in Pre- and Post-Harvest Fruits.

PubMed

Etienne, Audrey; Génard, Michel; Bugaud, Christophe

2015-01-01

Citrate is one of the most important organic acids in many fruits and its concentration plays a critical role in organoleptic properties. The regulation of citrate accumulation throughout fruit development, and the origins of the phenotypic variability of the citrate concentration within fruit species remain to be clarified. In the present study, we developed a process-based model of citrate accumulation based on a simplified representation of the TCA cycle to predict citrate concentration in fruit pulp during the pre- and post-harvest stages. Banana fruit was taken as a reference because it has the particularity of having post-harvest ripening, during which citrate concentration undergoes substantial changes. The model was calibrated and validated on the two stages, using data sets from three contrasting cultivars in terms of citrate accumulation, and incorporated different fruit load, potassium supply, and harvest dates. The model predicted the pre and post-harvest dynamics of citrate concentration with fairly good accuracy for the three cultivars. The model suggested major differences in TCA cycle functioning among cultivars during post-harvest ripening of banana, and pointed to a potential role for NAD-malic enzyme and mitochondrial malate carriers in the genotypic variability of citrate concentration. The sensitivity of citrate accumulation to growth parameters and temperature differed among cultivars during post-harvest ripening. Finally, the model can be used as a conceptual basis to study citrate accumulation in fleshy fruits and may be a powerful tool to improve our understanding of fruit acidity.

A hybrid modeling with data assimilation to evaluate human exposure level

NASA Astrophysics Data System (ADS)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Chemistry resolved kinetic flow modeling of TATB based explosives

NASA Astrophysics Data System (ADS)

Vitello, Peter; Fried, Laurence E.; William, Howard; Levesque, George; Souers, P. Clark

2012-03-01

Detonation waves in insensitive, TATB-based explosives are believed to have multiple time scale regimes. The initial burn rate of such explosives has a sub-microsecond time scale. However, significant late-time slow release in energy is believed to occur due to diffusion limited growth of carbon. In the intermediate time scale concentrations of product species likely change from being in equilibrium to being kinetic rate controlled. We use the thermo-chemical code CHEETAH linked to an ALE hydrodynamics code to model detonations. We term our model chemistry resolved kinetic flow, since CHEETAH tracks the time dependent concentrations of individual species in the detonation wave and calculates EOS values based on the concentrations. We present here two variants of our new rate model and comparison with hot, ambient, and cold experimental data for PBX 9502.

Analysis of the repaglinide concentration increase produced by gemfibrozil and itraconazole based on the inhibition of the hepatic uptake transporter and metabolic enzymes.

PubMed

Kudo, Toshiyuki; Hisaka, Akihiro; Sugiyama, Yuichi; Ito, Kiyomi

2013-02-01

The plasma concentration of repaglinide is reported to increase greatly when given after repeated oral administration of itraconazole and gemfibrozil. The present study analyzed this interaction based on a physiologically based pharmacokinetic (PBPK) model incorporating inhibition of the hepatic uptake transporter and metabolic enzymes involved in repaglinide disposition. Firstly, the plasma concentration profiles of inhibitors (itraconazole, gemfibrozil, and gemfibrozil glucuronide) were reproduced by a PBPK model to obtain their pharmacokinetic parameters. The plasma concentration profiles of repaglinide were then analyzed by a PBPK model, together with those of the inhibitors, assuming a competitive inhibition of CYP3A4 by itraconazole, mechanism-based inhibition of CYP2C8 by gemfibrozil glucuronide, and inhibition of organic anion transporting polypeptide (OATP) 1B1 by gemfibrozil and its glucuronide. The plasma concentration profiles of repaglinide were well reproduced by the PBPK model based on the above assumptions, and the optimized values for the inhibition constants (0.0676 nM for itraconazole against CYP3A4; 14.2 μM for gemfibrozil against OATP1B1; and 5.48 μM for gemfibrozil glucuronide against OATP1B1) and the fraction of repaglinide metabolized by CYP2C8 (0.801) were consistent with the reported values. The validity of the obtained parameters was further confirmed by sensitivity analyses and by reproducing the repaglinide concentration increase produced by concomitant gemfibrozil administration at various timings/doses. The present findings suggested that the reported concentration increase of repaglinide, suggestive of synergistic effects of the coadministered inhibitors, can be quantitatively explained by the simultaneous inhibition of the multiple clearance pathways of repaglinide.

Pharmacokinetic/Pharmacodynamic Relationship of Gabapentin in a CFA-induced Inflammatory Hyperalgesia Rat Model.

PubMed

Larsen, Malte Selch; Keizer, Ron; Munro, Gordon; Mørk, Arne; Holm, René; Savic, Rada; Kreilgaard, Mads

2016-05-01

Gabapentin displays non-linear drug disposition, which complicates dosing for optimal therapeutic effect. Thus, the current study was performed to elucidate the pharmacokinetic/pharmacodynamic (PKPD) relationship of gabapentin's effect on mechanical hypersensitivity in a rat model of CFA-induced inflammatory hyperalgesia. A semi-mechanistic population-based PKPD model was developed using nonlinear mixed-effects modelling, based on gabapentin plasma and brain extracellular fluid (ECF) time-concentration data and measurements of CFA-evoked mechanical hyperalgesia following administration of a range of gabapentin doses (oral and intravenous). The plasma/brain ECF concentration-time profiles of gabapentin were adequately described with a two-compartment plasma model with saturable intestinal absorption rate (K m  = 44.1 mg/kg, V max  = 41.9 mg/h∙kg) and dose-dependent oral bioavailability linked to brain ECF concentration through a transit compartment. Brain ECF concentration was directly linked to a sigmoid E max function describing reversal of hyperalgesia (EC 50, plasma  = 16.7 μg/mL, EC 50, brain  = 3.3 μg/mL). The proposed semi-mechanistic population-based PKPD model provides further knowledge into the understanding of gabapentin's non-linear pharmacokinetics and the link between plasma/brain disposition and anti-hyperalgesic effects. The model suggests that intestinal absorption is the primary source of non-linearity and that the investigated rat model provides reasonable predictions of clinically effective plasma concentrations for gabapentin.

A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

NASA Astrophysics Data System (ADS)

Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

2017-02-01

Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.

PubMed

Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

2017-02-01

Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

The importance of expressing antimicrobial agents on water basis in growth/no growth interface models: a case study for Zygosaccharomyces bailii.

PubMed

Dang, T D T; Vermeulen, A; Mertens, L; Geeraerd, A H; Van Impe, J F; Devlieghere, F

2011-01-31

In a previous study on Zygosaccharomyces bailii, three growth/no growth models have been developed, predicting growth probability of the yeast at different conditions typical for acidified foods (Dang, T.D.T., Mertens, L., Vermeulen, A., Geeraerd, A.H., Van Impe, J.F., Debevere, J., Devlieghere, F., 2010. Modeling the growth/no growth boundary of Z. bailii in acidic conditions: A contribution to the alternative method to preserve foods without using chemical preservatives. International Journal of Food Microbiology 137, 1-12). In these broth-based models, the variables were pH, water activity and acetic acid, with acetic acid concentration expressed in volume % on the total culture medium (i.e., broth). To continue the previous study, validation experiments were performed for 15 selected combinations of intrinsic factors to assess the performance of the model at 22°C (60days) in a real food product (ketchup). Although the majority of experimental results were consistent, some remarkable deviations between prediction and validation were observed, e.g., Z. bailii growth occurred in conditions where almost no growth had been predicted. A thorough investigation revealed that the difference between two ways of expressing acetic acid concentration (i.e., on broth basis and on water basis) is rather significant, particularly for media containing high amounts of dry matter. Consequently, the use of broth-based concentrations in the models was not appropriate. Three models with acetic acid concentration expressed on water basis were established and it was observed that predictions by these models well matched the validation results; therefore a "systematic error" in broth-based models was recognized. In practice, quantities of antimicrobial agents are often calculated based on the water content of food products. Hence, to assure reliable predictions and facilitate the application of models (developed from lab media with high dry matter contents), it is important to express antimicrobial agents' concentrations on a common basis-the water content. Reviews over other published growth/no growth models in literature are carried out and expressions of the stress factors' concentrations (on broth basis) found in these models confirm this finding. Copyright © 2010 Elsevier B.V. All rights reserved.

Adaptive Nonparametric Kinematic Modeling of Concentric Tube Robots.

PubMed

Fagogenis, Georgios; Bergeles, Christos; Dupont, Pierre E

2016-10-01

Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space.

Development and evaluation of a regression-based model to predict cesium-137 concentration ratios for saltwater fish.

PubMed

Pinder, John E; Rowan, David J; Smith, Jim T

2016-02-01

Data from published studies and World Wide Web sources were combined to develop a regression model to predict (137)Cs concentration ratios for saltwater fish. Predictions were developed from 1) numeric trophic levels computed primarily from random resampling of known food items and 2) K concentrations in the saltwater for 65 samplings from 41 different species from both the Atlantic and Pacific Oceans. A number of different models were initially developed and evaluated for accuracy which was assessed as the ratios of independently measured concentration ratios to those predicted by the model. In contrast to freshwater systems, were K concentrations are highly variable and are an important factor in affecting fish concentration ratios, the less variable K concentrations in saltwater were relatively unimportant in affecting concentration ratios. As a result, the simplest model, which used only trophic level as a predictor, had comparable accuracies to more complex models that also included K concentrations. A test of model accuracy involving comparisons of 56 published concentration ratios from 51 species of marine fish to those predicted by the model indicated that 52 of the predicted concentration ratios were within a factor of 2 of the observed concentration ratios. Copyright © 2015 Elsevier Ltd. All rights reserved.

City scale pollen concentration variability

NASA Astrophysics Data System (ADS)

van der Molen, Michiel; van Vliet, Arnold; Krol, Maarten

2016-04-01

Pollen are emitted in the atmosphere both in the country-side and in cities. Yet the majority of the population is exposed to pollen in cities. Allergic reactions may be induced by short-term exposure to pollen. This raises the question how variable pollen concentration in cities are in temporally and spatially, and how much of the pollen in cities are actually produced in the urban region itself. We built a high resolution (1 × 1 km) pollen dispersion model based on WRF-Chem to study a city's pollen budget and the spatial and temporal variability in concentration. It shows that the concentrations are highly variable, as a result of source distribution, wind direction and boundary layer mixing, as well as the release rate as a function of temperature, turbulence intensity and humidity. Hay Fever Forecasts based on such high resolution emission and physical dispersion modelling surpass traditional hay fever warning methods based on temperature sum methods. The model gives new insights in concentration variability, personal and community level exposure and prevention. The model will be developped into a new forecast tool to serve allergic people to minimize their exposure and reduce nuisance, coast of medication and sick leave. This is an innovative approach in hay fever warning systems.

Using a biology-based model (DEBtox) to analyze bioassays in ecotoxicology: opportunities and recommendations.

PubMed

Péry, Alexandre R R; Flammarion, Patrick; Vollat, Bernard; Bedaux, Jacques J M; Kooijman, Sebastiaan A L M; Garric, Jeanne

2002-02-01

The conventional analysis of bioassays does not account for biological significance. However, mathematical models do exist that are realistic from a biological point of view and describe toxicokinetics and effects on test organisms of chemical compounds. Here we studied a biology-based model (DEBtox) that provides an estimate of a no-effect concentration, and we demonstrated the ability of such a model to adapt to different situations. We showed that the basic model can be extended to deal with problems usually faced during bioassays like time-varying concentrations or unsuitable choices of initial concentrations. To reach this goal, we report experimental data from Daphnia magna exposed to zinc. These data also showed the potential benefit of the model in understanding the influence of food on toxicity. We finally make some recommendations about the choice of initial concentrations, and we propose a test with a depuration period to check the relevance and the predictive capacity of the DEBtox model. In our experiments, the model performed well and proved its usefulness as a tool in risk assessment.

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

PubMed Central

Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

2017-01-01

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

Global Monthly CO2 Flux Inversion Based on Results of Terrestrial Ecosystem Modeling

NASA Astrophysics Data System (ADS)

Deng, F.; Chen, J.; Peters, W.; Krol, M.

2008-12-01

Most of our understanding of the sources and sinks of atmospheric CO2 has come from inverse studies of atmospheric CO2 concentration measurements. However, the number of currently available observation stations and our ability to simulate the diurnal planetary boundary layer evolution over continental regions essentially limit the number of regions that can be reliably inverted globally, especially over continental areas. In order to overcome these restrictions, a nested inverse modeling system was developed based on the Bayesian principle for estimating carbon fluxes of 30 regions in North America and 20 regions for the rest of the globe. Inverse modeling was conducted in monthly steps using CO2 concentration measurements of 5 years (2000 - 2005) with the following two models: (a) An atmospheric transport model (TM5) is used to generate the transport matrix where the diurnal variation n of atmospheric CO2 concentration is considered to enhance the use of the afternoon-hour average CO2 concentration measurements over the continental sites. (b) A process-based terrestrial ecosystem model (BEPS) is used to produce hourly step carbon fluxes, which could minimize the limitation due to our inability to solve the inverse problem in a high resolution, as the background of our inversion. We will present our recent results achieved through a combination of the bottom-up modeling with BEPS and the top-down modeling based on TM5 driven by offline meteorological fields generated by the European Centre for Medium Range Weather Forecast (ECMFW).

Application of the Polynomial-Based Least Squares and Total Least Squares Models for the Attenuated Total Reflection Fourier Transform Infrared Spectra of Binary Mixtures of Hydroxyl Compounds.

PubMed

Shan, Peng; Peng, Silong; Zhao, Yuhui; Tang, Liang

2016-03-01

An analysis of binary mixtures of hydroxyl compound by Attenuated Total Reflection Fourier transform infrared spectroscopy (ATR FT-IR) and classical least squares (CLS) yield large model error due to the presence of unmodeled components such as H-bonded components. To accommodate these spectral variations, polynomial-based least squares (LSP) and polynomial-based total least squares (TLSP) are proposed to capture the nonlinear absorbance-concentration relationship. LSP is based on assuming that only absorbance noise exists; while TLSP takes both absorbance noise and concentration noise into consideration. In addition, based on different solving strategy, two optimization algorithms (limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm and Levenberg-Marquardt (LM) algorithm) are combined with TLSP and then two different TLSP versions (termed as TLSP-LBFGS and TLSP-LM) are formed. The optimum order of each nonlinear model is determined by cross-validation. Comparison and analyses of the four models are made from two aspects: absorbance prediction and concentration prediction. The results for water-ethanol solution and ethanol-ethyl lactate solution show that LSP, TLSP-LBFGS, and TLSP-LM can, for both absorbance prediction and concentration prediction, obtain smaller root mean square error of prediction than CLS. Additionally, they can also greatly enhance the accuracy of estimated pure component spectra. However, from the view of concentration prediction, the Wilcoxon signed rank test shows that there is no statistically significant difference between each nonlinear model and CLS. © The Author(s) 2016.

Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York

USGS Publications Warehouse

Coon, William F.

2011-01-01

Simulation of streamflows in small subbasins was improved by adjusting model parameter values to match base flows, storm peaks, and storm recessions more precisely than had been done with the original model. Simulated recessional and low flows were either increased or decreased as appropriate for a given stream, and simulated peak flows generally were lowered in the revised model. The use of suspended-sediment concentrations rather than concentrations of the surrogate constituent, total suspended solids, resulted in increases in the simulated low-flow sediment concentrations and, in most cases, decreases in the simulated peak-flow sediment concentrations. Simulated orthophosphate concentrations in base flows generally increased but decreased for peak flows in selected headwater subbasins in the revised model. Compared with the original model, phosphorus concentrations simulated by the revised model were comparable in forested subbasins, generally decreased in developed and wetland-dominated subbasins, and increased in agricultural subbasins. A final revision to the model was made by the addition of the simulation of chloride (salt) concentrations in the Onondaga Creek Basin to help water-resource managers better understand the relative contributions of salt from multiple sources in this particular tributary. The calibrated revised model was used to (1) compute loading rates for the various land types that were simulated in the model, (2) conduct a watershed-management analysis that estimated the portion of the total load that was likely to be transported to Onondaga Lake from each of the modeled subbasins, (3) compute and assess chloride loads to Onondaga Lake from the Onondaga Creek Basin, and (4) simulate precolonization (forested) conditions in the basin to estimate the probable minimum phosphorus loads to the lake.

Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

Treesearch

Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

2004-01-01

To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

Urinary Concentrations of Parabens and Other Antimicrobial Chemicals and Their Association with Couples’ Fecundity

PubMed Central

Smarr, Melissa M.; Sundaram, Rajeshwari; Honda, Masato; Kannan, Kurunthachalam; Louis, Germaine M. Buck

2016-01-01

Background: Human exposure to parabens and other antimicrobial chemicals is continual and pervasive. The hormone-disrupting properties of these environmental chemicals may adversely affect human reproduction. Objective: We aimed to prospectively assess couples’ urinary concentrations of antimicrobial chemicals in the context of fecundity, measured as time to pregnancy (TTP). Methods: In a prospective cohort of 501 couples, we examined preconception urinary chemical concentrations of parabens, triclosan and triclorcarban in relation to TTP; chemical concentrations were modeled both continuously and in quartiles. Cox’s proportional odds models for discrete survival time were used to estimate fecundability odds ratios (FORs) and 95% confidence intervals (CIs) adjusting for a priori–defined confounders. In light of TTP being a couple-dependent outcome, both partner and couple-based exposure models were analyzed. In all models, FOR estimates < 1.0 denote diminished fecundity (longer TTP). Results: Overall, 347 (69%) couples became pregnant. The highest quartile of female urinary methyl paraben (MP) concentrations relative to the lowest reflected a 34% reduction in fecundity (aFOR = 0.66; 95% CI: 0.45, 0.97) and remained so when accounting for couples’ concentrations (aFOR = 0.63; 95% CI: 0.41, 0.96). Similar associations were observed between ethyl paraben (EP) and couple fecundity for both partner and couple-based models (p-trend = 0.02 and p-trend = 0.05, respectively). No associations were observed with couple fecundity when chemicals were modeled continuously. Conclusions: Higher quartiles of preconception urinary concentrations of MP and EP among female partners were associated with reduced couple fecundity in partner-specific and couple-based exposure models. Citation: Smarr MM, Sundaram R, Honda M, Kannan K, Buck Louis GM. 2016. Urinary concentrations of parabens and other antimicrobial chemicals and their association with couples’ fecundity. Environ Health Perspect 124:730–736; http://dx.doi.org/10.1289/EHP189 PMID:27286252

High-Throughput Physiologically Based Toxicokinetic Models for ToxCast Chemicals

EPA Science Inventory

Physiologically based toxicokinetic (PBTK) models aid in predicting exposure doses needed to create tissue concentrations equivalent to those identified as bioactive by ToxCast. We have implemented four empirical and physiologically-based toxicokinetic (TK) models within a new R ...

Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling.

PubMed

Chen, Ran; Zhang, Yuntao; Sahneh, Faryad Darabi; Scoglio, Caterina M; Wohlleben, Wendel; Haase, Andrea; Monteiro-Riviere, Nancy A; Riviere, Jim E

2014-09-23

Quantitative characterization of nanoparticle interactions with their surrounding environment is vital for safe nanotechnological development and standardization. A recent quantitative measure, the biological surface adsorption index (BSAI), has demonstrated promising applications in nanomaterial surface characterization and biological/environmental prediction. This paper further advances the approach beyond the application of five descriptors in the original BSAI to address the concentration dependence of the descriptors, enabling better prediction of the adsorption profile and more accurate categorization of nanomaterials based on their surface properties. Statistical analysis on the obtained adsorption data was performed based on three different models: the original BSAI, a concentration-dependent polynomial model, and an infinite dilution model. These advancements in BSAI modeling showed a promising development in the application of quantitative predictive modeling in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

Spreading of nonmotile bacteria on a hard agar plate: Comparison between agent-based and stochastic simulations

NASA Astrophysics Data System (ADS)

Rana, Navdeep; Ghosh, Pushpita; Perlekar, Prasad

2017-11-01

We study spreading of a nonmotile bacteria colony on a hard agar plate by using agent-based and continuum models. We show that the spreading dynamics depends on the initial nutrient concentration, the motility, and the inherent demographic noise. Population fluctuations are inherent in an agent-based model, whereas for the continuum model we model them by using a stochastic Langevin equation. We show that the intrinsic population fluctuations coupled with nonlinear diffusivity lead to a transition from a diffusion limited aggregation type of morphology to an Eden-like morphology on decreasing the initial nutrient concentration.

Caffeine Citrate Dosing Adjustments to Assure Stable Caffeine Concentrations in Preterm Neonates.

PubMed

Koch, Gilbert; Datta, Alexandre N; Jost, Kerstin; Schulzke, Sven M; van den Anker, John; Pfister, Marc

2017-12-01

To identify dosing strategies that will assure stable caffeine concentrations in preterm neonates despite changing caffeine clearance during the first 8 weeks of life. A 3-step simulation approach was used to compute caffeine doses that would achieve stable caffeine concentrations in the first 8 weeks after birth: (1) a mathematical weight change model was developed based on published weight distribution data; (2) a pharmacokinetic model was developed based on published models that accounts for individual body weight, postnatal, and gestational age on caffeine clearance and volume of distribution; and (3) caffeine concentrations were simulated for different dosing regimens. A standard dosing regimen of caffeine citrate (using a 20 mg/kg loading dose and 5 mg/kg/day maintenance dose) is associated with a maximal trough caffeine concentration of 15 mg/L after 1 week of treatment. However, trough concentrations subsequently exhibit a clinically relevant decrease because of increasing clearance. Model-based simulations indicate that an adjusted maintenance dose of 6 mg/kg/day in the second week, 7 mg/kg/day in the third to fourth week and 8 mg/kg/day in the fifth to eighth week assures stable caffeine concentrations with a target trough concentration of 15 mg/L. To assure stable caffeine concentrations during the first 8 weeks of life, the caffeine citrate maintenance dose needs to be increased by 1 mg/kg every 1-2 weeks. These simple adjustments are expected to maintain exposure to stable caffeine concentrations throughout this important developmental period and might enhance both the short- and long-term beneficial effects of caffeine treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

PubMed

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

An (almost) solvable model for bacterial pattern formation

NASA Astrophysics Data System (ADS)

Grammaticos, B.; Badoual, M.; Aubert, M.

2007-10-01

We present a simple model for the description of ring-like concentric structures in bacterial colonies. We model the differences between Bacillus subtilis and Proteus mirabilis colonies by using a different dependence of the duration of the consolidation phase on the concentration of agar. We compare our results to experimental data from these two bacterial species colonies and obtain a good agreement. Based on this analysis, we formulate a hypothesis on the connection of the diffusion constant that appears in the model to the experimental agar concentration.

Direct comparison of the pharmacodynamics of four antifungal drugs in a mouse model of disseminated candidiasis using microbiological assays of serum drug concentrations.

PubMed

Maki, Katsuyuki; Holmes, Ann R; Watabe, Etsuko; Iguchi, Yumi; Matsumoto, Satoru; Ikeda, Fumiaki; Tawara, Shuichi; Mutoh, Seitaro

2007-01-01

The aim of this study was to compare the pharmacodynamics of the azole antifungal drugs fluconazole, itraconazole and ketoconazole, and the polyene antifungal amphotericin B, in a mouse model of disseminated Candida albicans infection. In order to directly compare effective serum concentrations of these antifungals, drug concentrations were assayed microbiologically by measuring inhibition of C. albicans mycelial growth (mMIC) in a mouse serum-based assay (serum antifungal titer). Efficacy in the mouse infection model was determined using an organ-based (kidney burden) endpoint. For all four drugs, the serum antifungal titers, 8 hr after administration of single doses of drugs at a range of drug concentrations, correlated closely with C. albicans kidney fungal burden in the mouse model. The results showed that determining serum antifungal titer may be used to accurately represent kidney fungal burden in a mouse model of disseminated candidiasis and allowed direct comparison of the pharmacodynamics of differing classes of antifungal drugs.

Comparison of stationary and personal air sampling with an ...

EPA Pesticide Factsheets

Manganese (Mn) is ubiquitous in the environment and essential for normal growth and development, yet excessive exposure can lead to impairments in neurological function. This study modeled ambient Mn concentrations as an alternative to stationary and personal air sampling to assess exposure for children enrolled in the Communities Actively Researching Exposure Study in Marietta, OH. Ambient air Mn concentration values were modeled using US Environmental Protection Agency’s Air Dispersion Model AERMOD based on emissions from the ferromanganese refinery located in Marietta. Modeled Mn concentrations were compared with Mn concentrations from a nearby stationary air monitor. The Index of Agreement for modeled versus monitored data was 0.34 (48 h levels) and 0.79 (monthly levels). Fractional bias was 0.026 for 48 h levels and −0.019 for monthly levels. The ratio of modeled ambient air Mn to measured ambient air Mn at the annual time scale was 0.94. Modeled values were also time matched to personal air samples for 19 children. The modeled values explained a greater degree of variability in personal exposures compared with time-weighted distance from the emission source. Based on these results modeled Mn concentrations provided a suitable approach for assessing airborne Mn exposure in this cohort. The purpose of the study was to investigate the use of air-dispersion modeling as an approach to exposure assessment for ambient manganese.

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

NASA Astrophysics Data System (ADS)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Comparison of AERMOD and CALPUFF models for simulating SO2 concentrations in a gas refinery.

PubMed

Atabi, Farideh; Jafarigol, Farzaneh; Moattar, Faramarz; Nouri, Jafar

2016-09-01

In this study, concentration of SO2 from a gas refinery located in complex terrain was calculated by the steady-state, AERMOD model, and nonsteady-state CALPUFF model. First, in four seasons, SO2 concentrations emitted from 16 refinery stacks, in nine receptors, were obtained by field measurements, and then the performance of both models was evaluated. Then, the simulated results for SO2 ambient concentrations made by each model were compared with the results of the observed concentrations, and model results were compared among themselves. The evaluation of the two models to simulate SO2 concentrations was based on the statistical analysis and Q-Q plots. Review of statistical parameters and Q-Q plots has shown that, according to the evaluation of estimations made, performance of both models to simulate the concentration of SO2 in the region can be considered acceptable. The results showed the AERMOD composite ratio between simulated values made by models and the observed values in various receptors for all four average times is 0.72, whereas CALPUFF's ratio is 0.89. However, in the complex conditions of topography, CALPUFF offers better agreement with the observed concentrations.

Prediction of Fetal Darunavir Exposure by Integrating Human Ex-Vivo Placental Transfer and Physiologically Based Pharmacokinetic Modeling.

PubMed

Schalkwijk, Stein; Buaben, Aaron O; Freriksen, Jolien J M; Colbers, Angela P; Burger, David M; Greupink, Rick; Russel, Frans G M

2017-07-25

Fetal antiretroviral exposure is usually derived from the cord-to-maternal concentration ratio. This static parameter does not provide information on the pharmacokinetics in utero, limiting the assessment of a fetal exposure-effect relationship. The aim of this study was to incorporate placental transfer into a pregnancy physiologically based pharmacokinetic model to simulate and evaluate fetal darunavir exposure at term. An existing and validated pregnancy physiologically based pharmacokinetic model of maternal darunavir/ritonavir exposure was extended with a feto-placental unit. To parameterize the model, we determined maternal-to-fetal and fetal-to-maternal darunavir/ritonavir placental clearance with an ex-vivo human cotyledon perfusion model. Simulated maternal and fetal pharmacokinetic profiles were compared with observed clinical data to qualify the model for simulation. Next, population fetal pharmacokinetic profiles were simulated for different maternal darunavir/ritonavir dosing regimens. An average (±standard deviation) maternal-to-fetal cotyledon clearance of 0.91 ± 0.11 mL/min and fetal-to-maternal clearance of 1.6 ± 0.3 mL/min was determined (n = 6 perfusions). Scaled placental transfer was integrated into the pregnancy physiologically based pharmacokinetic model. For darunavir 600/100 mg twice a day, the predicted fetal maximum plasma concentration, trough concentration, time to maximum plasma concentration, and half-life were 1.1, 0.57 mg/L, 3, and 21 h, respectively. This indicates that the fetal population trough concentration is higher or around the half-maximal effective darunavir concentration for a resistant virus (0.55 mg/L). The results indicate that the population fetal exposure after oral maternal darunavir dosing is therapeutic and this may provide benefits to the prevention of mother-to-child transmission of human immunodeficiency virus. Moreover, this integrated approach provides a tool to prevent fetal toxicity or enhance the development of more selectively targeted fetal drug treatments.

Dry Particulate Nitrate Deposition in China.

PubMed

Liu, Lei; Zhang, Xiuying; Zhang, Yan; Xu, Wen; Liu, Xuejun; Zhang, Xiaomin; Feng, Junlan; Chen, Xinrui; Zhang, Yuehan; Lu, Xuehe; Wang, Shanqian; Zhang, Wuting; Zhao, Limin

2017-05-16

A limited number of ground measurements of dry particulate nitrate deposition (NO 3 - ) makes it difficult and challenging to fully know the status of the spatial and temporal variations of dry NO 3 - depositions over China. This study tries to expand the ground measurements of NO 3 - concentrations at monitoring sites to a national scale, based on the Ozone Monitoring Instrument (OMI) NO 2 columns, NO 2 profiles from an atmospheric chemistry transport model (Model for Ozone and Related chemical Tracers, version 4, MOZART-4) and monitor-based sources, and then estimates the NO 3 - depositions on a regional scale based on an inferred model. The ground NO 2 concentrations were first derived from NO 2 columns and the NO 2 profiles, and then the ground NO 3 - concentrations were derived from the ground NO 2 concentrations and the relationship between NO 2 and NO 3 - based on Chinese Nationwide Nitrogen Deposition Monitoring Network (NNDMN). This estimated dry NO 3 - depositions over China will be helpful in determining the magnitude and pollution status in regions without ground measurements, supporting the construction plan of environmental monitoring in future.

Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

USGS Publications Warehouse

Nowell, Lisa H.; Crawford, Charles G.; Gilliom, Robert J.; Nakagaki, Naomi; Stone, Wesley W.; Thelin, Gail; Wolock, David M.

2009-01-01

Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992-2001) as part of the U.S. Geological Survey's National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agency's River Reach File 1 and to estimate the probability that whole-fish concentrations exceed benchmarks for protection of fish-eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present-day pesticide concentrations in fish. ?? 2009 SETAC.

Estimation of time-variable fast flow path chemical concentrations for application in tracer-based hydrograph separation analyses

USGS Publications Warehouse

Kronholm, Scott C.; Capel, Paul D.

2016-01-01

Mixing models are a commonly used method for hydrograph separation, but can be hindered by the subjective choice of the end-member tracer concentrations. This work tests a new variant of mixing model that uses high-frequency measures of two tracers and streamflow to separate total streamflow into water from slowflow and fastflow sources. The ratio between the concentrations of the two tracers is used to create a time-variable estimate of the concentration of each tracer in the fastflow end-member. Multiple synthetic data sets, and data from two hydrologically diverse streams, are used to test the performance and limitations of the new model (two-tracer ratio-based mixing model: TRaMM). When applied to the synthetic streams under many different scenarios, the TRaMM produces results that were reasonable approximations of the actual values of fastflow discharge (±0.1% of maximum fastflow) and fastflow tracer concentrations (±9.5% and ±16% of maximum fastflow nitrate concentration and specific conductance, respectively). With real stream data, the TRaMM produces high-frequency estimates of slowflow and fastflow discharge that align with expectations for each stream based on their respective hydrologic settings. The use of two tracers with the TRaMM provides an innovative and objective approach for estimating high-frequency fastflow concentrations and contributions of fastflow water to the stream. This provides useful information for tracking chemical movement to streams and allows for better selection and implementation of water quality management strategies.

Development and validation of a comprehensive model for map of fruits based on enzyme kinetics theory and arrhenius relation.

PubMed

Mangaraj, S; K Goswami, T; Mahajan, P V

2015-07-01

MAP is a dynamic system where respiration of the packaged product and gas permeation through the packaging film takes place simultaneously. The desired level of O2 and CO2 in a package is achieved by matching film permeation rates for O2 and CO2 with respiration rate of the packaged product. A mathematical model for MAP of fresh fruits applying enzyme kinetics based respiration equation coupled with the Arrhenious type model was developed. The model was solved numerically using MATLAB programme. The model was used to determine the time to reach to the equilibrium concentration inside the MA package and the level of O2 and CO2 concentration at equilibrium state. The developed model for prediction of equilibrium O2 and CO2 concentration was validated using experimental data for MA packaging of apple, guava and litchi.

A physiologically based toxicokinetic model for lake trout (Salvelinus namaycush).

PubMed

Lien, G J; McKim, J M; Hoffman, A D; Jenson, C T

2001-01-01

A physiologically based toxicokinetic (PB-TK) model for fish, incorporating chemical exchange at the gill and accumulation in five tissue compartments, was parameterized and evaluated for lake trout (Salvelinus namaycush). Individual-based model parameterization was used to examine the effect of natural variability in physiological, morphological, and physico-chemical parameters on model predictions. The PB-TK model was used to predict uptake of organic chemicals across the gill and accumulation in blood and tissues in lake trout. To evaluate the accuracy of the model, a total of 13 adult lake trout were exposed to waterborne 1,1,2,2-tetrachloroethane (TCE), pentachloroethane (PCE), and hexachloroethane (HCE), concurrently, for periods of 6, 12, 24 or 48 h. The measured and predicted concentrations of TCE, PCE and HCE in expired water, dorsal aortic blood and tissues were generally within a factor of two, and in most instances much closer. Variability noted in model predictions, based on the individual-based model parameterization used in this study, reproduced variability observed in measured concentrations. The inference is made that parameters influencing variability in measured blood and tissue concentrations of xenobiotics are included and accurately represented in the model. This model contributes to a better understanding of the fundamental processes that regulate the uptake and disposition of xenobiotic chemicals in the lake trout. This information is crucial to developing a better understanding of the dynamic relationships between contaminant exposure and hazard to the lake trout.

A comparative human health risk assessment of p-dichlorobenzene-based toilet rimblock products versus fragrance/surfactant-based alternatives.

PubMed

Aronson, Dallas B; Bosch, Stephen; Gray, D Anthony; Howard, Philip H; Guiney, Patrick D

2007-10-01

A comparison of the human health risk to consumers using one of two types of toilet rimblock products, either a p-dichlorobenzene-based rimblock or two newer fragrance/surfactant-based alternatives, was conducted. Rimblock products are designed for global use by consumers worldwide and function by releasing volatile compounds into indoor air with subsequent exposure presumed to be mainly by inhalation of indoor air. Using the THERdbASE exposure model and experimentally determined emission data, indoor air concentrations and daily intake values were determined for both types of rimblock products. Modeled exposure concentrations from a representative p-dichlorobenzene rimblock product are an order of magnitude higher than those from the alternative rimblock products due to its nearly pure composition and high sublimation rate. Lifetime exposure to p-dichlorobenzene or the subset of fragrance components with available RfD values is not expected to lead to non-cancer-based adverse health effects based on the exposure concentrations estimated using the THERdbASE model. A similar comparison of cancer-based effects was not possible as insufficient data were available for the fragrance components.

Comparison of different estimation techniques for biomass concentration in large scale yeast fermentation.

PubMed

Hocalar, A; Türker, M; Karakuzu, C; Yüzgeç, U

2011-04-01

In this study, previously developed five different state estimation methods are examined and compared for estimation of biomass concentrations at a production scale fed-batch bioprocess. These methods are i. estimation based on kinetic model of overflow metabolism; ii. estimation based on metabolic black-box model; iii. estimation based on observer; iv. estimation based on artificial neural network; v. estimation based on differential evaluation. Biomass concentrations are estimated from available measurements and compared with experimental data obtained from large scale fermentations. The advantages and disadvantages of the presented techniques are discussed with regard to accuracy, reproducibility, number of primary measurements required and adaptation to different working conditions. Among the various techniques, the metabolic black-box method seems to have advantages although the number of measurements required is more than that for the other methods. However, the required extra measurements are based on commonly employed instruments in an industrial environment. This method is used for developing a model based control of fed-batch yeast fermentations. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

On-line prediction of the glucose concentration of CHO cell cultivations by NIR and Raman spectroscopy: Comparative scalability test with a shake flask model system.

PubMed

Kozma, Bence; Hirsch, Edit; Gergely, Szilveszter; Párta, László; Pataki, Hajnalka; Salgó, András

2017-10-25

In this study, near-infrared (NIR) and Raman spectroscopy were compared in parallel to predict the glucose concentration of Chinese hamster ovary cell cultivations. A shake flask model system was used to quickly generate spectra similar to bioreactor cultivations therefore accelerating the development of a working model prior to actual cultivations. Automated variable selection and several pre-processing methods were tested iteratively during model development using spectra from six shake flask cultivations. The target was to achieve the lowest error of prediction for the glucose concentration in two independent shake flasks. The best model was then used to test the scalability of the two techniques by predicting spectra of a 10l and a 100l scale bioreactor cultivation. The NIR spectroscopy based model could follow the trend of the glucose concentration but it was not sufficiently accurate for bioreactor monitoring. On the other hand, the Raman spectroscopy based model predicted the concentration of glucose in both cultivation scales sufficiently accurately with an error around 4mM (0.72g/l), that is satisfactory for the on-line bioreactor monitoring purposes of the biopharma industry. Therefore, the shake flask model system was proven to be suitable for scalable spectroscopic model development. Copyright © 2017 Elsevier B.V. All rights reserved.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Quantitative spatially resolved measurement of tissue chromophore concentrations using photoacoustic spectroscopy: application to the measurement of blood oxygenation and haemoglobin concentration.

PubMed

Laufer, Jan; Delpy, Dave; Elwell, Clare; Beard, Paul

2007-01-07

A new approach based on pulsed photoacoustic spectroscopy for non-invasively quantifying tissue chromophore concentrations with high spatial resolution has been developed. The technique is applicable to the quantification of tissue chromophores such as oxyhaemoglobin (HbO(2)) and deoxyhaemoglobin (HHb) for the measurement of physiological parameters such as blood oxygen saturation (SO(2)) and total haemoglobin concentration. It can also be used to quantify the local accumulation of targeted contrast agents used in photoacoustic molecular imaging. The technique employs a model-based inversion scheme to recover the chromophore concentrations from photoacoustic measurements. This comprises a numerical forward model of the detected time-dependent photoacoustic signal that incorporates a multiwavelength diffusion-based finite element light propagation model to describe the light transport and a time-domain acoustic model to describe the generation, propagation and detection of the photoacoustic wave. The forward model is then inverted by iteratively fitting it to measurements of photoacoustic signals acquired at different wavelengths to recover the chromophore concentrations. To validate this approach, photoacoustic signals were generated in a tissue phantom using nanosecond laser pulses between 740 nm and 1040 nm. The tissue phantom comprised a suspension of intralipid, blood and a near-infrared dye in which three tubes were immersed. Blood at physiological haemoglobin concentrations and oxygen saturation levels ranging from 2% to 100% was circulated through the tubes. The signal amplitude from different temporal sections of the detected photoacoustic waveforms was plotted as a function of wavelength and the forward model fitted to these data to recover the concentrations of HbO(2) and HHb, total haemoglobin concentration and SO(2). The performance was found to compare favourably to that of a laboratory CO-oximeter with measurement resolutions of +/-3.8 g l(-1) (+/-58 microM) and +/-4.4 g l(-1) (+/-68 microM) for the HbO(2) and HHb concentrations respectively and +/-4% for SO(2) with an accuracy in the latter in the range -6%-+7%.

Quantitative spatially resolved measurement of tissue chromophore concentrations using photoacoustic spectroscopy: application to the measurement of blood oxygenation and haemoglobin concentration

NASA Astrophysics Data System (ADS)

Laufer, Jan; Delpy, Dave; Elwell, Clare; Beard, Paul

2007-01-01

A new approach based on pulsed photoacoustic spectroscopy for non-invasively quantifying tissue chromophore concentrations with high spatial resolution has been developed. The technique is applicable to the quantification of tissue chromophores such as oxyhaemoglobin (HbO2) and deoxyhaemoglobin (HHb) for the measurement of physiological parameters such as blood oxygen saturation (SO2) and total haemoglobin concentration. It can also be used to quantify the local accumulation of targeted contrast agents used in photoacoustic molecular imaging. The technique employs a model-based inversion scheme to recover the chromophore concentrations from photoacoustic measurements. This comprises a numerical forward model of the detected time-dependent photoacoustic signal that incorporates a multiwavelength diffusion-based finite element light propagation model to describe the light transport and a time-domain acoustic model to describe the generation, propagation and detection of the photoacoustic wave. The forward model is then inverted by iteratively fitting it to measurements of photoacoustic signals acquired at different wavelengths to recover the chromophore concentrations. To validate this approach, photoacoustic signals were generated in a tissue phantom using nanosecond laser pulses between 740 nm and 1040 nm. The tissue phantom comprised a suspension of intralipid, blood and a near-infrared dye in which three tubes were immersed. Blood at physiological haemoglobin concentrations and oxygen saturation levels ranging from 2% to 100% was circulated through the tubes. The signal amplitude from different temporal sections of the detected photoacoustic waveforms was plotted as a function of wavelength and the forward model fitted to these data to recover the concentrations of HbO2 and HHb, total haemoglobin concentration and SO2. The performance was found to compare favourably to that of a laboratory CO-oximeter with measurement resolutions of ±3.8 g l-1 (±58 µM) and ±4.4 g l-1 (±68 µM) for the HbO2 and HHb concentrations respectively and ±4% for SO2 with an accuracy in the latter in the range -6%-+7%.

A review of ocean chlorophyll algorithms and primary production models

NASA Astrophysics Data System (ADS)

Li, Jingwen; Zhou, Song; Lv, Nan

2015-12-01

This paper mainly introduces the five ocean chlorophyll concentration inversion algorithm and 3 main models for computing ocean primary production based on ocean chlorophyll concentration. Through the comparison of five ocean chlorophyll inversion algorithm, sums up the advantages and disadvantages of these algorithm,and briefly analyzes the trend of ocean primary production model.

Modeled sulfate concentrations in North Dakota streams, 1993-2008, based on spatial basin characteristics

USGS Publications Warehouse

Galloway, Joel M.; Vecchia, Aldo V.

2014-01-01

Modeled sulfate concentrations generally were highest (greater than 750 milligrams per liter) in basins in western North Dakota and lowest (less than 250 milligrams per liter) in basins in the upper Sheyenne River and upper James River. Area-weighted means for the basin characteristics also were computed for 10-digit and 8-digit hydrologic units for streams in North Dakota and modeled sulfate concentrations were computed from the characteristics. The resulting distribution of modeled sulfate concentrations was similar to the distribution of estimates for the 12-digit hydrologic units, but less variable because the basin characteristics were averaged over larger areas.

Physiologically Based Pharmacokinetic Model for Terbinafine in Rats and Humans

PubMed Central

Hosseini-Yeganeh, Mahboubeh; McLachlan, Andrew J.

2002-01-01

The aim of this study was to develop a physiologically based pharmacokinetic (PB-PK) model capable of describing and predicting terbinafine concentrations in plasma and tissues in rats and humans. A PB-PK model consisting of 12 tissue and 2 blood compartments was developed using concentration-time data for tissues from rats (n = 33) after intravenous bolus administration of terbinafine (6 mg/kg of body weight). It was assumed that all tissues except skin and testis tissues were well-stirred compartments with perfusion rate limitations. The uptake of terbinafine into skin and testis tissues was described by a PB-PK model which incorporates a membrane permeability rate limitation. The concentration-time data for terbinafine in human plasma and tissues were predicted by use of a scaled-up PB-PK model, which took oral absorption into consideration. The predictions obtained from the global PB-PK model for the concentration-time profile of terbinafine in human plasma and tissues were in close agreement with the observed concentration data for rats. The scaled-up PB-PK model provided an excellent prediction of published terbinafine concentration-time data obtained after the administration of single and multiple oral doses in humans. The estimated volume of distribution at steady state (Vss) obtained from the PB-PK model agreed with the reported value of 11 liters/kg. The apparent volume of distribution of terbinafine in skin and adipose tissues accounted for 41 and 52%, respectively, of the Vss for humans, indicating that uptake into and redistribution from these tissues dominate the pharmacokinetic profile of terbinafine. The PB-PK model developed in this study was capable of accurately predicting the plasma and tissue terbinafine concentrations in both rats and humans and provides insight into the physiological factors that determine terbinafine disposition. PMID:12069977

Gridded Model Information Support System (GMISS) user's guide. Volume 3. Model-concentration data-retrieval subsystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

The Gridded Model Information Support System (GMISS) is a data base management system for selected Regional Oxidant Model (ROM) input data and species concentrations produced by gridded photochemical air pollution models. The Model Concentration Data Retrieval Subsystem allows State and local air pollution control agencies to retrieve these hourly data for use in support of their regulatory programs. These hourly data may be used to calculate initial and boundary conditions for the Empirical Kinetics Modeling Approach (EKMA). They may be used for other modeling application needs as well as to support evaluation of regional emission controls strategies. Both temporal andmore » spatial subsets of the data may be retrieved. The document describes how to invoke and execute the Model Concentration Data Retrieval Subsystem using the full screen menus.« less

Measurement of glucose concentration by image processing of thin film slides

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Heavner, David

2012-02-01

Measurement of glucose concentration is important for diagnosis and treatment of diabetes mellitus and other medical conditions. This paper describes a novel image-processing based approach for measuring glucose concentration. A fluid drop (patient sample) is placed on a thin film slide. Glucose, present in the sample, reacts with reagents on the slide to produce a color dye. The color intensity of the dye formed varies with glucose at different concentration levels. Current methods use spectrophotometry to determine the glucose level of the sample. Our proposed algorithm uses an image of the slide, captured at a specific wavelength, to automatically determine glucose concentration. The algorithm consists of two phases: training and testing. Training datasets consist of images at different concentration levels. The dye-occupied image region is first segmented using a Hough based technique and then an intensity based feature is calculated from the segmented region. Subsequently, a mathematical model that describes a relationship between the generated feature values and the given concentrations is obtained. During testing, the dye region of a test slide image is segmented followed by feature extraction. These two initial steps are similar to those done in training. However, in the final step, the algorithm uses the model (feature vs. concentration) obtained from the training and feature generated from test image to predict the unknown concentration. The performance of the image-based analysis was compared with that of a standard glucose analyzer.

Granular activated carbon adsorption of MIB in the presence of dissolved organic matter.

PubMed

Summers, R Scott; Kim, Soo Myung; Shimabuku, Kyle; Chae, Seon-Ha; Corwin, Christopher J

2013-06-15

Based on the results of over twenty laboratory granular activated carbon (GAC) column runs, models were developed and utilized for the prediction of 2-methylisoborneol (MIB) breakthrough behavior at parts per trillion levels and verified with pilot-scale data. The influent MIB concentration was found not to impact the concentration normalized breakthrough. Increasing influent background dissolved organic matter (DOM) concentration was found to systematically decrease the GAC adsorption capacity for MIB. A series of empirical models were developed that related the throughput in bed volumes for a range of MIB breakthrough targets to the influent DOM concentration. The proportional diffusivity (PD) designed rapid small-scale column test (RSSCT) could be directly used to scale-up MIB breakthrough performance below 15% breakthrough. The empirical model to predict the throughput to 50% breakthrough based on the influent DOM concentration served as input to the pore diffusion model (PDM) and well-predicted the MIB breakthrough performance below a 50% breakthrough. The PDM predictions of throughput to 10% breakthrough well simulated the PD-RSSCT and pilot-scale 10% MIB breakthrough. Copyright © 2013 Elsevier Ltd. All rights reserved.

Source apportionment of airborne particulate matter using organic compounds as tracers

NASA Astrophysics Data System (ADS)

Schauer, James J.; Rogge, Wolfgang F.; Hildemann, Lynn M.; Mazurek, Monica A.; Cass, Glen R.; Simoneit, Bernd R. T.

A chemical mass balance receptor model based on organic compounds has been developed that relates source contributions to airborne fine particle mass concentrations. Source contributions to the concentrations of specific organic compounds are revealed as well. The model is applied to four air quality monitoring sites in southern California using atmospheric organic compound concentration data and source test data collected specifically for the purpose of testing this model. The contributions of up to nine primary particle source types can be separately identified in ambient samples based on this method, and approximately 85% of the organic fine aerosol is assigned to primary sources on an annual average basis. The model provides information on source contributions to fine mass concentrations, fine organic aerosol concentrations and individual organic compound concentrations. The largest primary source contributors to fine particle mass concentrations in Los Angeles are found to include diesel engine exhaust, paved road dust, gasoline-powered vehicle exhaust, plus emissions from food cooking and wood smoke, with smaller contribution from tire dust, plant fragments, natural gas combustion aerosol, and cigarette smoke. Once these primary aerosol source contributions are added to the secondary sulfates, nitrates and organics present, virtually all of the annual average fine particle mass at Los Angeles area monitoring sites can be assigned to its source.

Source apportionment of airborne particulate matter using organic compounds as tracers

NASA Astrophysics Data System (ADS)

Schauer, James J.; Rogge, Wolfgang F.; Hildemann, Lynn M.; Mazurek, Monica A.; Cass, Glen R.; Simoneit, Bernd R. T.

A chemical mass balance receptor model based on organic compounds has been developed that relates sours; contributions to airborne fine particle mass concentrations. Source contributions to the concentrations of specific organic compounds are revealed as well. The model is applied to four air quality monitoring sites in southern California using atmospheric organic compound concentration data and source test data collected specifically for the purpose of testing this model. The contributions of up to nine primary particle source types can be separately identified in ambient samples based on this method, and approximately 85% of the organic fine aerosol is assigned to primary sources on an annual average basis. The model provides information on source contributions to fine mass concentrations, fine organic aerosol concentrations and individual organic compound concentrations. The largest primary source contributors to fine particle mass concentrations in Los Angeles are found to include diesel engine exhaust, paved road dust, gasoline-powered vehicle exhaust, plus emissions from food cooking and wood smoke, with smaller contribution:; from tire dust, plant fragments, natural gas combustion aerosol, and cigarette smoke. Once these primary aerosol source contributions are added to the secondary sulfates, nitrates and organics present, virtually all of the annual average fine particle mass at Los Angeles area monitoring sites can be assigned to its source.

An electrical circuit model for additive-modified SnO2 ceramics

NASA Astrophysics Data System (ADS)

Karami Horastani, Zahra; Alaei, Reza; Karami, Amirhossein

2018-05-01

In this paper an electrical circuit model for additive-modified metal oxide ceramics based on their physical structures and electrical resistivities is presented. The model predicts resistance of the sample at different additive concentrations and different temperatures. To evaluate the model two types of composite ceramics, SWCNT/SnO2 with SWCNT concentrations of 0.3, 0.6, 1.2, 2.4 and 3.8%wt, and Ag/SnO2 with Ag concentrations of 0.3, 0.5, 0.8 and 1.5%wt, were prepared and their electrical resistances versus temperature were experimentally measured. It is shown that the experimental data are in good agreement with the results obtained from the model. The proposed model can be used in the design process of ceramic-based gas sensors, and it also clarifies the role of additive in gas sensing process of additive-modified metal oxide gas sensors. Furthermore the model can be used in the system level modeling of designs in which these sensors are also present.

Models and signal processing for an implanted ethanol bio-sensor.

PubMed

Han, Jae-Joon; Doerschuk, Peter C; Gelfand, Saul B; O'Connor, Sean J

2008-02-01

The understanding of drinking patterns leading to alcoholism has been hindered by an inability to unobtrusively measure ethanol consumption over periods of weeks to months in the community environment. An implantable ethanol sensor is under development using microelectromechanical systems technology. For safety and user acceptability issues, the sensor will be implanted subcutaneously and, therefore, measure peripheral-tissue ethanol concentration. Determining ethanol consumption and kinetics in other compartments from the time course of peripheral-tissue ethanol concentration requires sophisticated signal processing based on detailed descriptions of the relevant physiology. A statistical signal processing system based on detailed models of the physiology and using extended Kalman filtering and dynamic programming tools is described which can estimate the time series of ethanol concentration in blood, liver, and peripheral tissue and the time series of ethanol consumption based on peripheral-tissue ethanol concentration measurements.

Modelling NO2 concentrations at the street level in the GAINS integrated assessment model: projections under current legislation

NASA Astrophysics Data System (ADS)

Kiesewetter, G.; Borken-Kleefeld, J.; Schöpp, W.; Heyes, C.; Thunis, P.; Bessagnet, B.; Gsella, A.; Amann, M.

2013-08-01

NO2 concentrations at the street level are a major concern for urban air quality in Europe and have been regulated under the EU Thematic Strategy on Air Pollution. Despite the legal requirements, limit values are exceeded at many monitoring stations with little or no improvement during recent years. In order to assess the effects of future emission control regulations on roadside NO2 concentrations, a downscaling module has been implemented in the GAINS integrated assessment model. The module follows a hybrid approach based on atmospheric dispersion calculations and observations from the AirBase European air quality data base that are used to estimate site-specific parameters. Pollutant concentrations at every monitoring site with sufficient data coverage are disaggregated into contributions from regional background, urban increment, and local roadside increment. The future evolution of each contribution is assessed with a model of the appropriate scale - 28 × 28 km grid based on the EMEP Model for the regional background, 7 × 7 km urban increment based on the CHIMERE Chemistry Transport Model, and a chemical box model for the roadside increment. Thus, different emission scenarios and control options for long-range transport, regional and local emissions can be analysed. Observed concentrations and historical trends are well captured, in particular the differing NO2 and total NOx = NO + NO2 trends. Altogether, more than 1950 air quality monitoring stations in the EU are covered by the model, including more than 400 traffic stations and 70% of the critical stations. Together with its well-established bottom-up emission and dispersion calculation scheme, GAINS is thus able to bridge the scales from European-wide policies to impacts in street canyons. As an application of the model, we assess the evolution of attainment of NO2 limit values under current legislation until 2030. Strong improvements are expected with the introduction of the Euro 6 emission standard for light duty vehicles; however, for some major European cities, further measures may be required, in particular if aiming to achieve compliance at an earlier time.

Scale-up of a physiologically-based pharmacokinetic model to predict the disposition of monoclonal antibodies in monkeys.

PubMed

Glassman, Patrick M; Chen, Yang; Balthasar, Joseph P

2015-10-01

Preclinical assessment of monoclonal antibody (mAb) disposition during drug development often includes investigations in non-human primate models. In many cases, mAb exhibit non-linear disposition that relates to mAb-target binding [i.e., target-mediated disposition (TMD)]. The goal of this work was to develop a physiologically-based pharmacokinetic (PBPK) model to predict non-linear mAb disposition in plasma and in tissues in monkeys. Physiological parameters for monkeys were collected from several sources, and plasma data for several mAbs associated with linear pharmacokinetics were digitized from prior literature reports. The digitized data displayed great variability; therefore, parameters describing inter-antibody variability in the rates of pinocytosis and convection were estimated. For prediction of the disposition of individual antibodies, we incorporated tissue concentrations of target proteins, where concentrations were estimated based on categorical immunohistochemistry scores, and with assumed localization of target within the interstitial space of each organ. Kinetics of target-mAb binding and target turnover, in the presence or absence of mAb, were implemented. The model was then employed to predict concentration versus time data, via Monte Carlo simulation, for two mAb that have been shown to exhibit TMD (2F8 and tocilizumab). Model predictions, performed a priori with no parameter fitting, were found to provide good prediction of dose-dependencies in plasma clearance, the areas under plasma concentration versu time curves, and the time-course of plasma concentration data. This PBPK model may find utility in predicting plasma and tissue concentration versus time data and, potentially, the time-course of receptor occupancy (i.e., mAb-target binding) to support the design and interpretation of preclinical pharmacokinetic-pharmacodynamic investigations in non-human primates.

Feasibility of the Simultaneous Determination of Monomer Concentrations and Particle Size in Emulsion Polymerization Using in Situ Raman Spectroscopy

PubMed Central

2015-01-01

An immersion Raman probe was used in emulsion copolymerization reactions to measure monomer concentrations and particle sizes. Quantitative determination of monomer concentrations is feasible in two-monomer copolymerizations, but only the overall conversion could be measured by Raman spectroscopy in a four-monomer copolymerization. The feasibility of measuring monomer conversion and particle size was established using partial least-squares (PLS) calibration models. A simplified theoretical framework for the measurement of particle sizes based on photon scattering is presented, based on the elastic-sphere-vibration and surface-tension models. PMID:26900256

A modeling approach to account for toxicokinetic interactions in the calculation of biological hazard index for chemical mixtures.

PubMed

Haddad, S; Tardif, R; Viau, C; Krishnan, K

1999-09-05

Biological hazard index (BHI) is defined as biological level tolerable for exposure to mixture, and is calculated by an equation similar to the conventional hazard index. The BHI calculation, at the present time, is advocated for use in situations where toxicokinetic interactions do not occur among mixture constituents. The objective of this study was to develop an approach for calculating interactions-based BHI for chemical mixtures. The approach consisted of simulating the concentration of exposure indicator in the biological matrix of choice (e.g. venous blood) for each component of the mixture to which workers are exposed and then comparing these to the established BEI values, for calculating the BHI. The simulation of biomarker concentrations was performed using a physiologically-based toxicokinetic (PBTK) model which accounted for the mechanism of interactions among all mixture components (e.g. competitive inhibition). The usefulness of the present approach is illustrated by calculating BHI for varying ambient concentrations of a mixture of three chemicals (toluene (5-40 ppm), m-xylene (10-50 ppm), and ethylbenzene (10-50 ppm)). The results show that the interactions-based BHI can be greater or smaller than that calculated on the basis of additivity principle, particularly at high exposure concentrations. At lower exposure concentrations (e.g. 20 ppm each of toluene, m-xylene and ethylbenzene), the BHI values obtained using the conventional methodology are similar to the interactions-based methodology, confirming that the consequences of competitive inhibition are negligible at lower concentrations. The advantage of the PBTK model-based methodology developed in this study relates to the fact that, the concentrations of individual chemicals in mixtures that will not result in a significant increase in the BHI (i.e. > 1) can be determined by iterative simulation.

A PHYSIOLOGICALLY BASED PHARMACOKINETIC/PHARMACODYNAMIC (PBPK/PD) MODEL FOR ESTIMATION OF CUMULATIVE RISK FROM EXPOSURE TO THREE N-METHYL CARBAMATES: CARBARYL, ALDICARB, AND CARBOFURAN

EPA Science Inventory

A physiologically-based pharmacokinetic (PBPK) model for a mixture of N-methyl carbamate pesticides was developed based on single chemical models. The model was used to compare urinary metabolite concentrations to levels from National Health and Nutrition Examination Survey (NHA...

Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land

NASA Astrophysics Data System (ADS)

Stigter, T. Y.; Ribeiro, L.; Dill, A. M. M. Carvalho

2008-07-01

SummaryFactorial regression models, based on correspondence analysis, are built to explain the high nitrate concentrations in groundwater beneath an agricultural area in the south of Portugal, exceeding 300 mg/l, as a function of chemical variables, electrical conductivity (EC), land use and hydrogeological setting. Two important advantages of the proposed methodology are that qualitative parameters can be involved in the regression analysis and that multicollinearity is avoided. Regression is performed on eigenvectors extracted from the data similarity matrix, the first of which clearly reveals the impact of agricultural practices and hydrogeological setting on the groundwater chemistry of the study area. Significant correlation exists between response variable NO3- and explanatory variables Ca 2+, Cl -, SO42-, depth to water, aquifer media and land use. Substituting Cl - by the EC results in the most accurate regression model for nitrate, when disregarding the four largest outliers (model A). When built solely on land use and hydrogeological setting, the regression model (model B) is less accurate but more interesting from a practical viewpoint, as it is based on easily obtainable data and can be used to predict nitrate concentrations in groundwater in other areas with similar conditions. This is particularly useful for conservative contaminants, where risk and vulnerability assessment methods, based on assumed rather than established correlations, generally produce erroneous results. Another purpose of the models can be to predict the future evolution of nitrate concentrations under influence of changes in land use or fertilization practices, which occur in compliance with policies such as the Nitrates Directive. Model B predicts a 40% decrease in nitrate concentrations in groundwater of the study area, when horticulture is replaced by other land use with much lower fertilization and irrigation rates.

Toxicokinetic and Dosimetry Modeling Tools for Exposure ...

EPA Pesticide Factsheets

New technologies and in vitro testing approaches have been valuable additions to risk assessments that have historically relied solely on in vivo test results. Compared to in vivo methods, in vitro high throughput screening (HTS) assays are less expensive, faster and can provide mechanistic insights on chemical action. However, extrapolating from in vitro chemical concentrations to target tissue or blood concentrations in vivo is fraught with uncertainties, and modeling is dependent upon pharmacokinetic variables not measured in in vitro assays. To address this need, new tools have been created for characterizing, simulating, and evaluating chemical toxicokinetics. Physiologically-based pharmacokinetic (PBPK) models provide estimates of chemical exposures that produce potentially hazardous tissue concentrations, while tissue microdosimetry PK models relate whole-body chemical exposures to cell-scale concentrations. These tools rely on high-throughput in vitro measurements, and successful methods exist for pharmaceutical compounds that determine PK from limited in vitro measurements and chemical structure-derived property predictions. These high throughput (HT) methods provide a more rapid and less resource–intensive alternative to traditional PK model development. We have augmented these in vitro data with chemical structure-based descriptors and mechanistic tissue partitioning models to construct HTPBPK models for over three hundred environmental and pharmace

Physiologically based pharmacokinetic and pharmacodynamic modeling of an antagonist (SM-406/AT-406) of multiple inhibitor of apoptosis proteins (IAPs) in a mouse xenograft model of human breast cancer.

PubMed

Zhang, Tao; Li, Yanyan; Zou, Peng; Yu, Jing-yu; McEachern, Donna; Wang, Shaomeng; Sun, Duxin

2013-09-01

The inhibitors of apoptosis proteins (IAPs) are a class of key apoptosis regulators overexpressed or dysregulated in cancer. SM-406/AT-406 is a potent and selective small molecule mimetic of Smac that antagonizes the inhibitor of apoptosis proteins (IAPs). A physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) model was developed to predict the tissue concentration-time profiles of SM-406, the related onco-protein levels in tumor, and the tumor growth inhibition in a mouse model bearing human breast cancer xenograft. In the whole body physiologically based pharmacokinetic (PBPK) model for pharmacokinetics characterization, a well stirred (perfusion rate-limited) model was used to describe SM-406 pharmacokinetics in the lung, heart, kidney, intestine, liver and spleen, and a diffusion rate-limited (permeability limited) model was used for tumor. Pharmacodynamic (PD) models were developed to correlate the SM-406 concentration in tumor to the cIAP1 degradation, pro-caspase 8 decrease, CL-PARP accumulation and tumor growth inhibition. The PBPK-PD model well described the experimental pharmacokinetic data, the pharmacodynamic biomarker responses and tumor growth. This model may be helpful to predict tumor and plasma SM-406 concentrations in the clinic. Copyright © 2013 John Wiley & Sons, Ltd.

A Hybrid Multi-Scale Model of Crystal Plasticity for Handling Stress Concentrations

DOE PAGES

Sun, Shang; Ramazani, Ali; Sundararaghavan, Veera

2017-09-04

Microstructural effects become important at regions of stress concentrators such as notches, cracks and contact surfaces. A multiscale model is presented that efficiently captures microstructural details at such critical regions. The approach is based on a multiresolution mesh that includes an explicit microstructure representation at critical regions where stresses are localized. At regions farther away from the stress concentration, a reduced order model that statistically captures the effect of the microstructure is employed. The statistical model is based on a finite element representation of the orientation distribution function (ODF). As an illustrative example, we have applied the multiscaling method tomore » compute the stress intensity factor K I around the crack tip in a wedge-opening load specimen. The approach is verified with an analytical solution within linear elasticity approximation and is then extended to allow modeling of microstructural effects on crack tip plasticity.« less

A Hybrid Multi-Scale Model of Crystal Plasticity for Handling Stress Concentrations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Shang; Ramazani, Ali; Sundararaghavan, Veera

Microstructural effects become important at regions of stress concentrators such as notches, cracks and contact surfaces. A multiscale model is presented that efficiently captures microstructural details at such critical regions. The approach is based on a multiresolution mesh that includes an explicit microstructure representation at critical regions where stresses are localized. At regions farther away from the stress concentration, a reduced order model that statistically captures the effect of the microstructure is employed. The statistical model is based on a finite element representation of the orientation distribution function (ODF). As an illustrative example, we have applied the multiscaling method tomore » compute the stress intensity factor K I around the crack tip in a wedge-opening load specimen. The approach is verified with an analytical solution within linear elasticity approximation and is then extended to allow modeling of microstructural effects on crack tip plasticity.« less

Pollutant exposures from natural gas cooking burners: a simulation-based assessment for Southern California.

PubMed

Logue, Jennifer M; Klepeis, Neil E; Lobscheid, Agnes B; Singer, Brett C

2014-01-01

Residential natural gas cooking burners (NGCBs) can emit substantial quantities of pollutants, and they are typically used without venting range hoods. We quantified pollutant concentrations and occupant exposures resulting from NGCB use in California homes. A mass-balance model was applied to estimate time-dependent pollutant concentrations throughout homes in Southern California and the exposure concentrations experienced by individual occupants. We estimated nitrogen dioxide (NO2), carbon monoxide (CO), and formaldehyde (HCHO) concentrations for 1 week each in summer and winter for a representative sample of Southern California homes. The model simulated pollutant emissions from NGCBs as well as NO2 and CO entry from outdoors, dilution throughout the home, and removal by ventilation and deposition. Residence characteristics and outdoor concentrations of NO2 and CO were obtained from available databases. We inferred ventilation rates, occupancy patterns, and burner use from household characteristics. We also explored proximity to the burner(s) and the benefits of using venting range hoods. Replicate model executions using independently generated sets of stochastic variable values yielded estimated pollutant concentration distributions with geometric means varying by <10%. The simulation model estimated that-in homes using NGCBs without coincident use of venting range hoods-62%, 9%, and 53% of occupants are routinely exposed to NO2, CO, and HCHO levels that exceed acute health-based standards and guidelines. NGCB use increased the sample median of the highest simulated 1-hr indoor concentrations by 100, 3,000, and 20 ppb for NO2, CO, and HCHO, respectively. Reducing pollutant exposures from NGCBs should be a public health priority. Simulation results suggest that regular use of even moderately effective venting range hoods would dramatically reduce the percentage of homes in which concentrations exceed health-based standards.

Modeling Human Exposure to Indoor Contaminants: External Source to Body Tissues.

PubMed

Webster, Eva M; Qian, Hua; Mackay, Donald; Christensen, Rebecca D; Tietjen, Britta; Zaleski, Rosemary

2016-08-16

Information on human indoor exposure is necessary to assess the potential risk to individuals from many chemicals of interest. Dynamic indoor and human physicologically based pharmacokinetic (PBPK) models of the distribution of nonionizing, organic chemical concentrations in indoor environments resulting in delivered tissue doses are developed, described and tested. The Indoor model successfully reproduced independently measured, reported time-dependent air concentrations of chloroform released during showering and of 2-butyoxyethanol following use of a volatile surface cleaner. The Indoor model predictions were also comparable to those from a higher tier consumer model (ConsExpo 4.1) for the surface cleaner scenario. The PBPK model successful reproduced observed chloroform exhaled air concentrations resulting from an inhalation exposure. Fugacity based modeling provided a seamless description of the partitioning, fluxes, accumulation and release of the chemical in indoor media and tissues of the exposed subject. This has the potential to assist in health risk assessments, provided that appropriate physical/chemical property, usage characteristics, and toxicological information are available.

Modeling of testosterone regulation by pulse-modulated feedback: An experimental data study

NASA Astrophysics Data System (ADS)

Mattsson, Per; Medvedev, Alexander

2013-10-01

The continuous part of a hybrid (pulse-modulated) model of testosterone feedback regulation is extended with infinite-dimensional and nonlinear dynamics, to better explain the testosterone concentration profiles observed in clinical data. A linear least-squares based optimization algorithm is developed for the purpose of detecting impulses of gonadotropin-realsing hormone from measured concentration of luteinizing hormone. The parameters in the model are estimated from hormone concentration measured in human males, and simulation results from the full closed-loop system are provided.

Application of Biotic Ligand and Toxic Unit modeling approaches to predict improvements in zooplankton species richness in smelter-damaged lakes near Sudbury, Ontario.

PubMed

Khan, Farhan R; Keller, W Bill; Yan, Norman D; Welsh, Paul G; Wood, Chris M; McGeer, James C

2012-02-07

Using a 30-year record of biological and water chemistry data collected from seven lakes near smelters in Sudbury (Ontario, Canada) we examined the link between reductions of Cu, Ni, and Zn concentrations and zooplankton species richness. The toxicity of the metal mixtures was assessed using an additive Toxic Unit (TU) approach. Four TU models were developed based on total metal concentrations (TM-TU); free ion concentrations (FI-TU); acute LC50s calculated from the Biotic Ligand Model (BLM-TU); and chronic LC50s (acute LC50s adjusted by metal-specific acute-to-chronic ratios, cBLM-TU). All models significantly correlated reductions in metal concentrations to increased zooplankton species richness over time (p < 0.01) with a rank based on r(2) values of cBLM-TU > BLM-TU = FI-TU > TM-TU. Lake-wise comparisons within each model showed that the BLM-TU and cBLM-TU models provided the best description of recovery across all seven lakes. These two models were used to calculate thresholds for chemical and biological recovery using data from reference lakes in the same region. A threshold value of TU = 1 derived from the cBLM-TU provided the most accurate description of recovery. Overall, BLM-based TU models that integrate site-specific water chemistry-derived estimates of toxicity offer a useful predictor of biological recovery.

Chemistry Resolved Kinetic Flow Modeling of TATB Based Explosives

NASA Astrophysics Data System (ADS)

Vitello, Peter; Fried, Lawrence; Howard, Mike; Levesque, George; Souers, Clark

2011-06-01

Detonation waves in insensitive, TATB based explosives are believed to have multi-time scale regimes. The initial burn rate of such explosives has a sub-microsecond time scale. However, significant late-time slow release in energy is believed to occur due to diffusion limited growth of carbon. In the intermediate time scale concentrations of product species likely change from being in equilibrium to being kinetic rate controlled. We use the thermo-chemical code CHEETAH linked to ALE hydrodynamics codes to model detonations. We term our model chemistry resolved kinetic flow as CHEETAH tracks the time dependent concentrations of individual species in the detonation wave and calculate EOS values based on the concentrations. A validation suite of model simulations compared to recent high fidelity metal push experiments at ambient and cold temperatures has been developed. We present here a study of multi-time scale kinetic rate effects for these experiments. Prepared by LLNL under Contract DE-AC52-07NA27344.

Measurement, modeling, and analysis of nonmethane hydrocarbons and ozone in the southeast United States national parks

NASA Astrophysics Data System (ADS)

Kang, Daiwen

In this research, the sources, distributions, transport, ozone formation potential, and biogenic emissions of VOCs are investigated focusing on three Southeast United States National Parks: Shenandoah National Park, Big Meadows site (SHEN), Great Smoky Mountains National Park at Cove Mountain (GRSM) and Mammoth Cave National Park (MACA). A detailed modeling analysis is conducted using the Multiscale Air Quality SImulation Platform (MAQSIP) focusing on nonmethane hydrocarbons and ozone characterized by high O3 surface concentrations. Nine emissions perturbation using the Multiscale Air Quality SImulation Platform (MAQSIP) focusing on nonmethane hydrocarbons and ozone characterized by high O 3 surface concentrations. In the observation-based analysis, source classification techniques based on correlation coefficient, chemical reactivity, and certain ratios were developed and applied to the data set. Anthropogenic VOCs from automobile exhaust dominate at Mammoth Cave National Park, and at Cove Mountain, Great Smoky Mountains National Park, while at Big Meadows, Shenandoah National Park, the source composition is complex and changed from 1995 to 1996. The dependence of isoprene concentrations on ambient temperatures is investigated, and similar regressional relationships are obtained for all three monitoring locations. Propylene-equivalent concentrations are calculated to account for differences in reaction rates between the OH and individual hydrocarbons, and to thereby estimate their relative contributions to ozone formation. Isoprene fluxes were also estimated for all these rural areas. Model predictions (base scenario) tend to give lower daily maximum O 3 concentrations than observations by 10 to 30%. Model predicted concentrations of lumped paraffin compounds are of the same order of magnitude as the observed values, while the observed concentrations for other species (isoprene, ethene, surrogate olefin, surrogate toluene, and surrogate xylene) are usually an order of magnitude higher than the predictions. Detailed sensitivity and process analyses in terms of ozone and VOC scenarios including the base scenario are designed and utilized in the model simulations. Model predictions are compared with the observed values at the three locations for the same time period. Detailed sensitivity and process analyses in terms of ozone and VOC budgets, and relative importance of various VOCs species are provided. (Abstract shortened by UMI.)

Air quality and ventilation fan control based on aerosol measurement in the bi-directional undersea Bømlafjord tunnel.

PubMed

Indrehus, Oddny; Aralt, Tor Tybring

2005-04-01

Aerosol, NO and CO concentration, temperature, air humidity, air flow and number of running ventilation fans were measured by continuous analysers every minute for a whole week for six different one-week periods spread over ten months in 2001 and 2002 at measuring stations in the 7860 m long tunnel. The ventilation control system was mainly based on aerosol measurements taken by optical scatter sensors. The ventilation turned out to be satisfactory according to Norwegian air quality standards for road tunnels; however, there was some uncertainty concerning the NO2 levels. The air humidity and temperature inside the tunnel were highly influenced by the outside metrological conditions. Statistical models for NO concentration were developed and tested; correlations between predicted and measured NO were 0.81 for a partial least squares regression (PLS1) model based on CO and aerosol, and 0.77 for a linear regression model based only on aerosol. Hence, the ventilation control system should not solely be based on aerosol measurements. Since NO2 is the hazardous polluter, modelling NO2 concentration rather than NO should be preferred in any further optimising of the ventilation control.

Statistical analysis of PM₁₀ concentrations at different locations in Malaysia.

PubMed

Sansuddin, Nurulilyana; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri Md; Ghazali, Nurul Adyani; Madhoun, Wesam Ahmed Al

2011-09-01

Malaysia has experienced several haze events since the 1980s as a consequence of the transboundary movement of air pollutants emitted from forest fires and open burning activities. Hazy episodes can result from local activities and be categorized as "localized haze". General probability distributions (i.e., gamma and log-normal) were chosen to analyze the PM(10) concentrations data at two different types of locations in Malaysia: industrial (Johor Bahru and Nilai) and residential (Kota Kinabalu and Kuantan). These areas were chosen based on their frequently high PM(10) concentration readings. The best models representing the areas were chosen based on their performance indicator values. The best distributions provided the probability of exceedances and the return period between the actual and predicted concentrations based on the threshold limit given by the Malaysian Ambient Air Quality Guidelines (24-h average of 150 μg/m(3)) for PM(10) concentrations. The short-term prediction for PM(10) exceedances in 14 days was obtained using the autoregressive model.

Modelling for reactor-style aerobic composting based on coupling theory of mass-heat-momentum transport and Contois equation.

PubMed

He, Xueqin; Han, Lujia; Ge, Jinyi; Huang, Guangqun

2018-04-01

This study establishes an optimal mathematical modelling to rationally describe the dynamic changes and spatial distribution of temperature and oxygen concentration in the aerobic composting process using coupling mass-heat-momentum transfer based on the microbial mechanism. Two different conditional composting experiments, namely continuous aeration and intermittent aeration, were performed to verify the proposed model. The results show that the model accurately predicted the dynamic changes in temperature (case I: R 2  = 0.93, RMSE = 1.95 K; case II: R 2  = 0.86, RMSE = 4.69 K) and oxygen concentration (case I: R 2  = 0.90, RMSE = 1.26%; case II: R 2  = 0.75, RMSE = 2.93%) in the central point of compost substrates. It also systematically simulated fluctuations in oxygen concentration caused by boundary conditions and the spatial distribution of the actual temperature and oxygen concentration. The proposed model exhibits good applicability in simulating the actual working conditions of aerobic composting process. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data

NASA Astrophysics Data System (ADS)

Oyama, Youichi; Matsushita, Bunkei; Fukushima, Takehiko; Matsushige, Kazuo; Imai, Akio

The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

Nazif, Amina; Mohammed, Nurul Izma; Malakahmad, Amirhossein; Abualqumboz, Motasem S

2018-01-01

The devastating health effects of particulate matter (PM 10 ) exposure by susceptible populace has made it necessary to evaluate PM 10 pollution. Meteorological parameters and seasonal variation increases PM 10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM 10 concentration levels. The analyses were carried out using daily average PM 10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM 10 concentration levels having coefficient of determination (R 2 ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

[Inversion Model and Daily Variation of Total Phosphorus Concentrations in Taihu Lake Based on GOCI Data].

PubMed

Du, Cheng-gong; Li, Yun-mei; Wang, Qiao; Zhu, Li; Lü, Heng

2016-03-15

The TP concentration is an important index of water quality and an important influencing factor of eutrophication and algae blooms. Remote sensing technology has advantages of wide scope and high time limited efficacy. Monitoring the concentration of TP by satellite remote sensing is important for the study of water quality and eutrophication. In situ datasets collected during the three times of experiments in Taihu Lake between 2013 and 2014 were used to develop the TP inversion model based on GOCI data. The GOCI data in spring, summer, autumn and winter in 2014 were selected to analyze the time and space changes of TP concentration in Taihu Lake. The results showed that the TP algorithm was built up based on the variables, which was to use the eight band combination of GOCI data as variable, and build model using Multi factor linear regression method. The algorithm achieved more accurate TP estimation with R² = 0.898, MAPE = 14.296%, RMSE = 0.026 mg · L⁻¹. Meantime, a analysis on the precision of the model by using the measured sample points and the synchronous satellite images with MAPE = 33.642%, 22.551%, RMSE = 0.076 mg · L⁻¹, 0.028 mg · L⁻¹ on August 5, 2014 and October 24, 2014. Through the analysis of the 30 images on the four days of the four seasons, it showed that the absolute concentration of total phosphorus was different in different seasons. But temporal and spatial distribution of total phosphorus concentration was similar in the morning and afternoon. In spatial distribution, the TP concentration in Meiliang Bay, Zhushan Bay, Gonghu Bay, Xiaomei Port and Changdou Port in the southwest coast was at a continuously high position. The TP concentration change in different regions was influenced by wind direction, wind speed and other factors. The TP concentration highest in the morning, and then gradually decreased, this phenomenon reflected that the TP concentration was affected by temperature and light.

SimpleBox 4.0: Improving the model while keeping it simple….

PubMed

Hollander, Anne; Schoorl, Marian; van de Meent, Dik

2016-04-01

Chemical behavior in the environment is often modeled with multimedia fate models. SimpleBox is one often-used multimedia fate model, firstly developed in 1986. Since then, two updated versions were published. Based on recent scientific developments and experience with SimpleBox 3.0, a new version of SimpleBox was developed and is made public here: SimpleBox 4.0. In this new model, eight major changes were implemented: removal of the local scale and vegetation compartments, addition of lake compartments and deep ocean compartments (including the thermohaline circulation), implementation of intermittent rain instead of drizzle and of depth dependent soil concentrations, adjustment of the partitioning behavior for organic acids and bases as well as of the value for enthalpy of vaporization. In this paper, the effects of the model changes in SimpleBox 4.0 on the predicted steady-state concentrations of chemical substances were explored for different substance groups (neutral organic substances, acids, bases, metals) in a standard emission scenario. In general, the largest differences between the predicted concentrations in the new and the old model are caused by the implementation of layered ocean compartments. Undesirable high model complexity caused by vegetation compartments and a local scale were removed to enlarge the simplicity and user friendliness of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Process model for ammonia volatilization from anaerobic swine lagoons incorporating varying wind speeds and biogas bubbling

USDA-ARS?s Scientific Manuscript database

Ammonia volatilization from treatment lagoons varies widely with the total ammonia concentration, pH, temperature, suspended solids, atmospheric ammonia concentration above the water surface, and wind speed. Ammonia emissions were estimated with a process-based mechanistic model integrating ammonia ...

Population Pharmacokinetics of Busulfan in Pediatric and Young Adult Patients Undergoing Hematopoietic Cell Transplant: A Model-Based Dosing Algorithm for Personalized Therapy and Implementation into Routine Clinical Use

PubMed Central

Long-Boyle, Janel; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J.; Dvorak, Christopher C.

2014-01-01

Background Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared to conventional dosing. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration-at-steady-state, Css) and implement a simple, model-based tool for the initial dosing of busulfan in children undergoing HCT. Patients and Methods Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone HCT with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the non-linear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly, Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Results Modeling of busulfan time-concentration data indicates busulfan CL displays non-linearity in children, decreasing up to approximately 20% between the concentrations of 250–2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan CL were actual body weight and age. The percentage of individuals achieving a therapeutic Css was significantly higher in subjects receiving initial doses based on the population PK model (81%) versus historical controls dosed on conventional guidelines (52%) (p = 0.02). Conclusion When compared to the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults. PMID:25162216

DEVELOPMENT OF A WATERSHED-BASED MERCURY POLLUTION CHARACTERIZATION SYSTEM

EPA Science Inventory

To investigate total mercury loadings to streams in a watershed, we have developed a watershed-based source quantification model ? Watershed Mercury Characterization System. The system uses the grid-based GIS modeling technology to calculate total soil mercury concentrations and ...

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

NASA Astrophysics Data System (ADS)

Marro, Massimo; Salizzoni, Pietro; Soulhac, Lionel; Cassiani, Massimo

2018-06-01

We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol. 156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ, conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: (i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and (ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

NASA Astrophysics Data System (ADS)

Marro, Massimo; Salizzoni, Pietro; Soulhac, Lionel; Cassiani, Massimo

2018-01-01

We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol. 156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ, conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: (i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and (ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

Informing Selection of Nanomaterial Concentrations for ToxCast In Vitro Testing using the Multi-Path Particle Dosimetry Model

EPA Science Inventory

Currently, little justification is provided for nanomaterial testing concentrations in in vitro assays. The in vitro concentrations typically used may be higher than those experienced in exposed humans. Selection of concentration levels for hazard evaluation based on real-world ...

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling.

PubMed

Kornecki, Martin; Strube, Jochen

2018-03-16

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network-either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

PubMed Central

Kornecki, Martin; Strube, Jochen

2018-01-01

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream. PMID:29547557

Fertiliser management effects on dissolved inorganic nitrogen in runoff from Australian sugarcane farms.

PubMed

Fraser, Grant; Rohde, Ken; Silburn, Mark

2017-08-01

Dissolved inorganic nitrogen (DIN) movement from Australian sugarcane farms is believed to be a major cause of crown-of-thorns starfish outbreaks which have reduced the Great Barrier Reef coral cover by ~21% (1985-2012). We develop a daily model of DIN concentration in runoff based on >200 field monitored runoff events. Runoff DIN concentrations were related to nitrogen fertiliser application rates and decreased after application with time and cumulative rainfall. Runoff after liquid fertiliser applications had higher initial DIN concentrations, though these concentrations diminished more rapidly in comparison to granular fertiliser applications. The model was validated using an independent field dataset and provided reasonable estimates of runoff DIN concentrations based on a number of modelling efficiency score results. The runoff DIN concentration model was combined with a water balance cropping model to investigate temporal aspects of sugarcane fertiliser management. Nitrogen fertiliser application in December (start of wet season) had the highest risk of DIN movement, and this was further exacerbated in years with a climate forecast for 'wet' seasonal conditions. The potential utility of a climate forecasting system to predict forthcoming wet months and hence DIN loss risk is demonstrated. Earlier fertiliser application or reducing fertiliser application rates in seasons with a wet climate forecast may markedly reduce runoff DIN loads; however, it is recommended that these findings be tested at a broader scale.

Stabilization of sulfuric acid dimers by ammonia, methylamine, dimethylamine, and trimethylamine

NASA Astrophysics Data System (ADS)

Jen, Coty N.; McMurry, Peter H.; Hanson, David R.

2014-06-01

This study experimentally explores how ammonia (NH3), methylamine (MA), dimethylamine (DMA), and trimethylamine (TMA) affect the chemical formation mechanisms of electrically neutral clusters that contain two sulfuric acid molecules (dimers). Dimers may also contain undetectable compounds, such as water or bases, that evaporate upon ionization and sampling. Measurements were conducted using a glass flow reactor which contained a steady flow of humidified nitrogen with sulfuric acid concentrations of 107 to 109 cm-3. A known molar flow rate of a basic gas was injected into the flow reactor. The University of Minnesota Cluster Chemical Ionization Mass Spectrometer was used to measure the resulting sulfuric acid vapor and cluster concentrations. It was found that, for a given concentration of sulfuric acid vapor, the dimer concentration increases with increasing concentration of the basic gas, eventually reaching a plateau. The base concentrations at which the dimer concentrations saturate suggest NH3 < MA < TMA ≲ DMA in forming stabilized sulfuric acid dimers. Two heuristic models for cluster formation by acid-base reactions are developed to interpret the data. The models provide ranges of evaporation rate constants that are consistent with observations and leads to an analytic expression for nucleation rates that is consistent with atmospheric observations.

Improving Air Pollution Modeling Over The Po Valley Using Saharan Dust Transport Forecasts

NASA Astrophysics Data System (ADS)

Kishcha, P.; Carnevale, C.; Finzi, G.; Pisoni, E.; Volta, M.; Nickovic, S.; Alpert, P.

2012-04-01

Our study shows that Saharan dust can contribute significantly to PM10 concentrations in the Po Valley. This dust contribution should be taken into account when estimating the exceedance of pollution limits. The DREAM dust model has been used for several years for producing operational dust forecasts at Tel-Aviv University, Israel. DREAM has been producing daily forecasts of 3-D distribution of dust concentrations over the Mediterranean region, Middle East, Europe, and over the Atlantic Ocean (http://wind.tau.ac.il/dust8/dust.html). In the current study, DREAM dust forecasts were used to give better model estimates of the contribution of Saharan dust to PM10 concentration over the Po Valley, in Northern Italy. This was carried out by the integration of daily Saharan dust forecasts into a mesoscale Transport Chemical Aerosol Model (TCAM). The Po Valley in Northern Italy is frequently affected by high PM10 concentrations, where both natural and anthropogenic sources play a significant role. Our study of TCAM and DREAM integration was carried out for the period May 15 - June 30, 2007, when four significant dust events were observed. The integrated TCAM-DREAM model performance was evaluated by comparing PM10 measurements with modeled PM10 concentrations. First, Saharan dust impact on TCAM performance was analyzed at eleven remote PM10 sites which had the lowest level of air pollution (PM10 ≤ 14 μg/m3) over the period under consideration. For those remote sites, the observed high PM10 concentrations during dust events stood prominently on the background of low PM10 concentrations. At the remote sites, such a strong deviation from the background level can not be attributed to anthropogenic aerosol emissions because of their distance from anthropogenic sources. The observed maxima in PM10 concentration during dust events is evidence of dust aerosol near the surface in Northern Italy. During all dust events under consideration, the integrated TCAM-DREAM model produced more accurate PM10 concentrations than the base TCAM model. Then, a comparison between modeled concentrations and PM10 measurements was carried out at 230 PM10 monitoring sites, distributed within the model domain. This model-vs.-measurement comparison showed that the integrated TCAM -DREAM model more accurately reproduced PM10 concentrations than the base TCAM model, both in term of correlation and mean error. Our results are of importance to countries which have to pay a penalty for exceeding the pollution limit. By extracting dust contribution from PM10 measurements, these countries could show lower rates of man-made pollution.

Increase in the CO2 exchange rate of leaves of Ilex rotunda with elevated atmospheric CO2 concentration in an urban canyon

NASA Astrophysics Data System (ADS)

Takagi, M.; Gyokusen, Koichiro; Saito, Akira

It was found that the atmospheric carbon dioxide (CO2) concentration in an urban canyon in Fukuoka city, Japan during August 1997 was about 30 µmol mol-1 higher than that in the suburbs. When fully exposed to sunlight, in situ the rate of photosynthesis in single leaves of Ilex rotunda planted in the urban canyon was higher when the atmospheric CO2 concentration was elevated. A biochemically based model was able to predict the in situ rate of photosynthesis well. The model also predicted an increase in the daily CO2 exchange rate for leaves in the urban canyon with an increase in atmospheric CO2 concentration. However, in situ such an increase in the daily CO2 exchange rate may be offset by diminished sunlight, a higher air temperature and a lower relative humidity. Thus, the daily CO2 exchange rate predicted using the model based soleley on the environmental conditions prevailing in the urban canyon was lower than that predicted based only on environmental factors found in the suburbs.

Interannual variability of ammonia concentrations over the United States: sources and implications

NASA Astrophysics Data System (ADS)

Schiferl, Luke D.; Heald, Colette L.; Van Damme, Martin; Clarisse, Lieven; Clerbaux, Cathy; Coheur, Pierre-François; Nowak, John B.; Neuman, J. Andrew; Herndon, Scott C.; Roscioli, Joseph R.; Eilerman, Scott J.

2016-09-01

The variability of atmospheric ammonia (NH3), emitted largely from agricultural sources, is an important factor when considering how inorganic fine particulate matter (PM2.5) concentrations and nitrogen cycling are changing over the United States. This study combines new observations of ammonia concentration from the surface, aboard aircraft, and retrieved by satellite to both evaluate the simulation of ammonia in a chemical transport model (GEOS-Chem) and identify which processes control the variability of these concentrations over a 5-year period (2008-2012). We find that the model generally underrepresents the ammonia concentration near large source regions (by 26 % at surface sites) and fails to reproduce the extent of interannual variability observed at the surface during the summer (JJA). Variability in the base simulation surface ammonia concentration is dominated by meteorology (64 %) as compared to reductions in SO2 and NOx emissions imposed by regulation (32 %) over this period. Introduction of year-to-year varying ammonia emissions based on animal population, fertilizer application, and meteorologically driven volatilization does not substantially improve the model comparison with observed ammonia concentrations, and these ammonia emissions changes have little effect on the simulated ammonia concentration variability compared to those caused by the variability of meteorology and acid-precursor emissions. There is also little effect on the PM2.5 concentration due to ammonia emissions variability in the summer when gas-phase changes are favored, but variability in wintertime emissions, as well as in early spring and late fall, will have a larger impact on PM2.5 formation. This work highlights the need for continued improvement in both satellite-based and in situ ammonia measurements to better constrain the magnitude and impacts of spatial and temporal variability in ammonia concentrations.

Total internal reflection-based planar waveguide solar concentrator with symmetric air prisms as couplers.

PubMed

Xie, Peng; Lin, Huichuan; Liu, Yong; Li, Baojun

2014-10-20

We present a waveguide coupling approach for planar waveguide solar concentrator. In this approach, total internal reflection (TIR)-based symmetric air prisms are used as couplers to increase the coupler reflectivity and to maximize the optical efficiency. The proposed concentrator consists of a line focusing cylindrical lens array over a planar waveguide. The TIR-based couplers are located at the focal line of each lens to couple the focused sunlight into the waveguide. The optical system was modeled and simulated with a commercial ray tracing software (Zemax). Results show that the system used with optimized TIR-based couplers can achieve 70% optical efficiency at 50 × geometrical concentration ratio, resulting in a flux concentration ratio of 35 without additional secondary concentrator. An acceptance angle of ± 7.5° is achieved in the x-z plane due to the use of cylindrical lens array as the primary concentrator.

A simple optical model to estimate suspended particulate matter in Yellow River Estuary.

PubMed

Qiu, Zhongfeng

2013-11-18

Distribution of the suspended particulate matter (SPM) concentration is a key issue for analyzing the deposition and erosion variety of the estuary and evaluating the material fluxes from river to sea. Satellite remote sensing is a useful tool to investigate the spatial variation of SPM concentration in estuarial zones. However, algorithm developments and validations of the SPM concentrations in Yellow River Estuary (YRE) have been seldom performed before and therefore our knowledge on the quality of retrieval of SPM concentration is poor. In this study, we developed a new simple optical model to estimate SPM concentration in YRE by specifying the optimal wavelength ratios (600-710 nm)/ (530-590 nm) based on observations of 5 cruises during 2004 and 2011. The simple optical model was attentively calibrated and the optimal band ratios were selected for application to multiple sensors, 678/551 for the Moderate Resolution Imaging Spectroradiometer (MODIS), 705/560 for the Medium Resolution Imaging Spectrometer (MERIS) and 680/555 for the Geostationary Ocean Color Imager (GOCI). With the simple optical model, the relative percentage difference and the mean absolute error were 35.4% and 15.6 gm(-3) respectively for MODIS, 42.2% and 16.3 gm(-3) for MERIS, and 34.2% and 14.7 gm(-3) for GOCI, based on an independent validation data set. Our results showed a good precision of estimation for SPM concentration using the new simple optical model, contrasting with the poor estimations derived from existing empirical models. Providing an available atmospheric correction scheme for satellite imagery, our simple model could be used for quantitative monitoring of SPM concentrations in YRE.

Active Brownian agents with concentration-dependent chemotactic sensitivity.

PubMed

Meyer, Marcel; Schimansky-Geier, Lutz; Romanczuk, Pawel

2014-02-01

We study a biologically motivated model of overdamped, autochemotactic Brownian agents with concentration-dependent chemotactic sensitivity. The agents in our model move stochastically and produce a chemical ligand at their current position. The ligand concentration obeys a reaction-diffusion equation and acts as a chemoattractant for the agents, which bias their motion towards higher concentrations of the dynamically altered chemical field. We explore the impact of concentration-dependent response to chemoattractant gradients on large-scale pattern formation, by deriving a coarse-grained macroscopic description of the individual-based model, and compare the conditions for emergence of inhomogeneous solutions for different variants of the chemotactic sensitivity. We focus primarily on the so-called receptor-law sensitivity, which models a nonlinear decrease of chemotactic sensitivity with increasing ligand concentration. Our results reveal qualitative differences between the receptor law, the constant chemotactic response, and the so-called log law, with respect to stability of the homogeneous solution, as well as the emergence of different patterns (labyrinthine structures, clusters, and bubbles) via spinodal decomposition or nucleation. We discuss two limiting cases, where the model can be reduced to the dynamics of single species: (I) the agent density governed by a density-dependent effective diffusion coefficient and (II) the ligand field with an effective bistable, time-dependent reaction rate. In the end, we turn to single clusters of agents, studying domain growth and determining mean characteristics of the stationary inhomogeneous state. Analytical results are confirmed and extended by large-scale GPU simulations of the individual based model.

A Mathematical Model of Neutral Lipid Content in terms of Initial Nitrogen Concentration and Validation in Coelastrum sp. HA-1 and Application in Chlorella sorokiniana

PubMed Central

Zhao, Yue; Liu, Zhiyong; Liu, Chenfeng; Hu, Zhipeng

2017-01-01

Microalgae are considered to be a potential major biomass feedstock for biofuel due to their high lipid content. However, no correlation equations as a function of initial nitrogen concentration for lipid accumulation have been developed for simplicity to predict lipid production and optimize the lipid production process. In this study, a lipid accumulation model was developed with simple parameters based on the assumption protein synthesis shift to lipid synthesis by a linear function of nitrogen quota. The model predictions fitted well for the growth, lipid content, and nitrogen consumption of Coelastrum sp. HA-1 under various initial nitrogen concentrations. Then the model was applied successfully in Chlorella sorokiniana to predict the lipid content with different light intensities. The quantitative relationship between initial nitrogen concentrations and the final lipid content with sensitivity analysis of the model were also discussed. Based on the model results, the conversion efficiency from protein synthesis to lipid synthesis is higher and higher in microalgae metabolism process as nitrogen decreases; however, the carbohydrate composition content remains basically unchanged neither in HA-1 nor in C. sorokiniana. PMID:28194424

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

PubMed

de Almeida, Maurício Liberal; Saatkamp, Cassiano Junior; Fernandes, Adriana Barrinha; Pinheiro, Antonio Luiz Barbosa; Silveira, Landulfo

2016-09-01

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

Predicting the Activity Coefficients of Free-Solvent for Concentrated Globular Protein Solutions Using Independently Determined Physical Parameters

PubMed Central

McBride, Devin W.; Rodgers, Victor G. J.

2013-01-01

The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations. PMID:24324733

Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

PubMed Central

Yamamoto, Yumi; Välitalo, Pyry A.; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Beukers, Margot W.; van den Berg, Dirk‐Jan; Hartman, Robin; Wong, Yin Cheong; Danhof, Meindert; van Hasselt, John G. C.

2017-01-01

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. PMID:28891201

CO and NO2 pollution in a long two-way traffic road tunnel: investigation of NO2/NOx ratio and modelling of NO2 concentration.

PubMed

Indrehus, O; Vassbotn, P

2001-02-01

The CO, NO and NO2 concentrations, visibility and air flow velocity were measured using continuous analysers in a long Norwegian road tunnel (7.5 km) with traffic in both directions in April 1994 and 1995. The traffic density was monitored at the same time. The NO2 concentration exceeded Norwegian air quality limits for road tunnels 17% of the time in 1994. The traffic through the tunnel decreased from 1994 to 1995, and the mean NO2 concentration was reduced from 0.73 to 0.22 ppm. The ventilation fan control, based on the CO concentration only, was unsatisfactory and the air flow was sometimes low for hours. Models for NO2 concentration based on CO concentration and absolute air flow velocity were developed and tested. The NO2/NOx ratio showed an increase for NOx levels above 2 ppm; a likely explanation for this phenomenon is NO oxidation by O2. Exposure to high NO2 concentrations may represent a health risk for people with respiratory and cardiac diseases. In long road tunnels with two-way traffic, this study indicates that ventilation fan control based on CO concentration should be adjusted for changes in vehicle CO emission and should be supplemented by air flow monitoring to limit the NO2 concentration.

A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

USDA-ARS?s Scientific Manuscript database

Ensembles of process-based crop models are now commonly used to simulate crop growth and development for climate scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of de...

Wind tunnel simulation of air pollution dispersion in a street canyon.

PubMed

Civis, Svatopluk; Strizík, Michal; Janour, Zbynek; Holpuch, Jan; Zelinger, Zdenek

2002-01-01

Physical simulation was used to study pollution dispersion in a street canyon. The street canyon model was designed to study the effect of measuring flow and concentration fields. A method of C02-laser photoacoustic spectrometry was applied for detection of trace concentration of gas pollution. The advantage of this method is its high sensitivity and broad dynamic range, permitting monitoring of concentrations from trace to saturation values. Application of this method enabled us to propose a simple model based on line permeation pollutant source, developed on the principle of concentration standards, to ensure high precision and homogeneity of the concentration flow. Spatial measurement of the concentration distribution inside the street canyon was performed on the model with reference velocity of 1.5 m/s.

Implicit Versus Explicit Applications of the Tissue Residue Approach, Oral Presentation

EPA Science Inventory

Toxic effect models based on the relationship of toxic effects to chemical concentrations within receptor organism tissues can often be reformulated to describe the relationship of toxic effects to exposure concentrations without actual specification of the tissue concentrations....

The effect of nanoparticles aggregation on the thermal conductivity of nanofluids at very low concentrations: Experimental and theoretical evaluations

NASA Astrophysics Data System (ADS)

Motevasel, Mohsen; Nazar, Ali Reza Solaimany; Jamialahmadi, Mohammad

2018-01-01

Nanoparticles suspended in a base fluid yield increased thermal conductivity, which in turn increases convection heat transfer rate. Prediction of suitable relations for determination of thermal conductivity results in heightened accuracy in the calculation of convection heat transfer coefficient and reduced costs. In the majority of studies performed on the prediction of thermal conductivity, some relations and models were used in which the effect of aggregation of particles, especially at low concentrations was ignored. In this research, the thermal conductivity of the nanofluid is measured experimentally at low volumetric concentrations, within the range of 0.02-0.2% for the nanoparticles of Al2O3, MgO, CuO, and SiC in the base fluid of distilled water. The results obtained from the models are compared by the available models considering and neglecting the effect of aggregation of particles. Within the range of the applied concentrations, the relative absolute average deviation ratio of the thermal conductivity models without considering the aggregation effect in relation with the models considering the aggregate, is observed to be between 2 and 6 times. Therefore, it is recommended that even at low concentrations, the effect of aggregation should be considered in the prediction of thermal conductivity.

Application of a numerical model for the planetary boundary layer to the vertical distribution of radon and its daughter products

NASA Astrophysics Data System (ADS)

Vinod Kumar, A.; Sitaraman, V.; Oza, R. B.; Krishnamoorthy, T. M.

A one-dimensional numerical planetary boundary layer (PBL) model is developed and applied to study the vertical distribution of radon and its daughter products in the atmosphere. The meteorological model contains parameterization for the vertical diffusion coefficient based on turbulent kinetic energy and energy dissipation ( E- ɛ model). The increased vertical resolution and the realistic concentration of radon and its daughter products based on the time-dependent PBL model is compared with the steady-state model results and field observations. The ratio of radon concentration at higher levels to that at the surface has been studied to see the effects of atmospheric stability. The significant change in the vertical profile of concentration due to decoupling of the upper portion of the boundary layer from the shallow lower stable layer is explained by the PBL model. The disequilibrium ratio of 214Bi/ 214Pb broadly agrees with the observed field values. The sharp decrease in the ratio during transition from unstable to stable atmospheric condition is also reproduced by the model.

Modeling of surface dust concentrations using neural networks and kriging

NASA Astrophysics Data System (ADS)

Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.

2016-12-01

Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.

A fugacity-based indoor residential pesticide fate model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bennett, Deborah H.; Furtaw, Edward J.; McKone, Thomas E.

Dermal and non-dietary pathways are potentially significant exposure pathways to pesticides used in residences. Exposure pathways include dermal contact with residues on surfaces, ingestion from hand- and object-to-mouth activities, and absorption of pesticides into food. A limited amount of data has been collected on pesticide concentrations in various residential compartments following an application. But models are needed to interpret this data and make predictions about other pesticides based on chemical properties. In this paper, we propose a mass-balance compartment model based on fugacity principles. We include air (both gas phase and aerosols), carpet, smooth flooring, and walls as model compartments.more » Pesticide concentrations on furniture and toys, and in food, are being added to the model as data becomes available. We determine the compartmental fugacity capacity and mass transfer-rate coefficient for wallboard as an example. We also present the framework and equations needed for a dynamic mass-balance model.« less

Modeling sediment concentration in debris flow by Tsallis entropy

NASA Astrophysics Data System (ADS)

Singh, Vijay P.; Cui, Huijuan

2015-02-01

Debris flow is a natural hazard that occurs in landscapes having high slopes, such as mountainous areas. It can be so powerful that it destroys whatever comes in its way, that is, it can kill people and animals; decimate roads, bridges, railway tracks, homes and other property; and fill reservoirs. Owing to its frequent occurrence, it is receiving considerable attention these days. Of fundamental importance in debris flow modeling is the determination of concentration of debris (or sediment) in the flow. The usual approach to determining debris flow concentration is either empirical or hydraulic. Both approaches are deterministic and therefore say nothing about the uncertainty associated with the sediment concentration in the flow. This paper proposes to model debris flow concentration using the Tsallis entropy theory. Verification of the entropy-based distribution of debris flow concentration using the data and equations reported in the literature shows that the Tsallis entropy-proposed model is capable of mimicking the field observed concentration and has potential for practical application.

Prediction of fish and sediment mercury in streams using landscape variables and historical mining.

PubMed

Alpers, Charles N; Yee, Julie L; Ackerman, Joshua T; Orlando, James L; Slotton, Darrel G; Marvin-DiPasquale, Mark C

2016-11-15

Widespread mercury (Hg) contamination of aquatic systems in the Sierra Nevada of California, U.S., is associated with historical use to enhance gold (Au) recovery by amalgamation. In areas affected by historical Au mining operations, including the western slope of the Sierra Nevada and downstream areas in northern California, such as San Francisco Bay and the Sacramento River-San Joaquin River Delta, microbial conversion of Hg to methylmercury (MeHg) leads to bioaccumulation of MeHg in food webs, and increased risks to humans and wildlife. This study focused on developing a predictive model for THg in stream fish tissue based on geospatial data, including land use/land cover data, and the distribution of legacy Au mines. Data on total mercury (THg) and MeHg concentrations in fish tissue and streambed sediment collected during 1980-2012 from stream sites in the Sierra Nevada, California were combined with geospatial data to estimate fish THg concentrations across the landscape. THg concentrations of five fish species (Brown Trout, Rainbow Trout, Sacramento Pikeminnow, Sacramento Sucker, and Smallmouth Bass) within stream sections were predicted using multi-model inference based on Akaike Information Criteria, using geospatial data for mining history and landscape characteristics as well as fish species and length (r(2)=0.61, p<0.001). Including THg concentrations in streambed sediment did not improve the model's fit, however including MeHg concentrations in streambed sediment, organic content (loss on ignition), and sediment grain size resulted in an improved fit (r(2)=0.63, p<0.001). These models can be used to estimate THg concentrations in stream fish based on landscape variables in the Sierra Nevada in areas where direct measurements of THg concentration in fish are unavailable. Published by Elsevier B.V.

Prediction of fish and sediment mercury in streams using landscape variables and historical mining

USGS Publications Warehouse

Alpers, Charles N.; Yee, Julie L.; Ackerman, Joshua T.; Orlando, James L.; Slotton, Darrell G.; Marvin-DiPasquale, Mark C.

2016-01-01

Widespread mercury (Hg) contamination of aquatic systems in the Sierra Nevada of California, U.S., is associated with historical use to enhance gold (Au) recovery by amalgamation. In areas affected by historical Au mining operations, including the western slope of the Sierra Nevada and downstream areas in northern California, such as San Francisco Bay and the Sacramento River–San Joaquin River Delta, microbial conversion of Hg to methylmercury (MeHg) leads to bioaccumulation of MeHg in food webs, and increased risks to humans and wildlife. This study focused on developing a predictive model for THg in stream fish tissue based on geospatial data, including land use/land cover data, and the distribution of legacy Au mines. Data on total mercury (THg) and MeHg concentrations in fish tissue and streambed sediment collected during 1980–2012 from stream sites in the Sierra Nevada, California were combined with geospatial data to estimate fish THg concentrations across the landscape. THg concentrations of five fish species (Brown Trout, Rainbow Trout, Sacramento Pikeminnow, Sacramento Sucker, and Smallmouth Bass) within stream sections were predicted using multi-model inference based on Akaike Information Criteria, using geospatial data for mining history and landscape characteristics as well as fish species and length (r2 = 0.61, p < 0.001). Including THg concentrations in streambed sediment did not improve the model's fit, however including MeHg concentrations in streambed sediment, organic content (loss on ignition), and sediment grain size resulted in an improved fit (r2 = 0.63, p < 0.001). These models can be used to estimate THg concentrations in stream fish based on landscape variables in the Sierra Nevada in areas where direct measurements of THg concentration in fish are unavailable.

Using empirical orthogonal functions from remote sensing reflectance spectra to predict various phytoplankton pigment concentrations in the Eastern Tropical Atlantic

NASA Astrophysics Data System (ADS)

Bracher, Astrid; Taylor, Bettina; Taylor, Marc; Steinmetz, Francois; Dinter, Tilman; Röttgers, Rüdiger

2014-05-01

Phytoplankton pigments play a major role in photosynthesis and photoprotection. Their composition and abundance give information on characteristics of a phytoplankton community in respect to its acclimation to light, overall biomass and composition of major phytoplankton groups. Most phytoplankton pigments can be measured by applying HPLC techniques to filtered water samples. This method like other mathods analysing water samples in the laboratory is time consuming and therefore only a limited number of samples can be obtained. In order to obtain information on phytoplankton pigment composition with a better temporal and spatial composition, the rationale was to develop a method to get from continuous optical measurements pigment concentrations. We have used remote sensing reflectances (RRS) derived from ship-based hyper-spectral underwater radiometric and from satellite MERIS measurements (using the POLYMER algorithm developed by Steinmetz et al. 2011), sampled in the Eastern Tropical Atlantic, to predict the water surface concentration of various pigments or pigment groups in this area. A statistical model based on Empirical Orthogonal Function (EOF) analysis of these RRS spectra was developed. Then subsequently linear models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables were constructed. The model results, which have been verified by cross validation, show that from the ship-based RRS measurements the surface concentrations of a suite of pigments and pigment groups can be well predicted, even when only a multi-spectral resolution of RRS data is chosen. Based on the MERIS reflectance data, only concentrations of total chlorophyll-a (chl-a), monovinyl-chl-a and the groups of photoprotective and photosynthetic carotenoids can be obtained with high quality. The model constructed on the satellite reflectances as input was also applied to one month of MERIS POLYMER data to predict for the whole Eastern Tropical Atlantic area the concentration of those pigments. Finally, the potential, limitations and future perspectives for the application of our generic method are discussed.

Life support system cost study: Addendum to cost analysis of carbon dioxide concentrators

NASA Technical Reports Server (NTRS)

Yakut, M. M.

1973-01-01

New cost data are presented for the Hydrogen-Depolarized Carbon Dioxide Concentrator (HDC), based on modifying the concentrator to delete the quick disconnect valves and filters included in the system model defined in MDC-G4631. System description, cost data and a comparison between CO2 concentrator costs are presented.

Modelling NO2 concentrations at the street level in the GAINS integrated assessment model: projections under current legislation

NASA Astrophysics Data System (ADS)

Kiesewetter, G.; Borken-Kleefeld, J.; Schöpp, W.; Heyes, C.; Thunis, P.; Bessagnet, B.; Terrenoire, E.; Gsella, A.; Amann, M.

2014-01-01

NO2 concentrations at the street level are a major concern for urban air quality in Europe and have been regulated under the EU Thematic Strategy on Air Pollution. Despite the legal requirements, limit values are exceeded at many monitoring stations with little or no improvement in recent years. In order to assess the effects of future emission control regulations on roadside NO2 concentrations, a downscaling module has been implemented in the GAINS integrated assessment model. The module follows a hybrid approach based on atmospheric dispersion calculations and observations from the AirBase European air quality database that are used to estimate site-specific parameters. Pollutant concentrations at every monitoring site with sufficient data coverage are disaggregated into contributions from regional background, urban increment, and local roadside increment. The future evolution of each contribution is assessed with a model of the appropriate scale: 28 × 28 km grid based on the EMEP Model for the regional background, 7 × 7 km urban increment based on the CHIMERE Chemistry Transport Model, and a chemical box model for the roadside increment. Thus, different emission scenarios and control options for long-range transport as well as regional and local emissions can be analysed. Observed concentrations and historical trends are well captured, in particular the differing NO2 and total NOx = NO + NO2 trends. Altogether, more than 1950 air quality monitoring stations in the EU are covered by the model, including more than 400 traffic stations and 70% of the critical stations. Together with its well-established bottom-up emission and dispersion calculation scheme, GAINS is thus able to bridge the scales from European-wide policies to impacts in street canyons. As an application of the model, we assess the evolution of attainment of NO2 limit values under current legislation until 2030. Strong improvements are expected with the introduction of the Euro 6 emission standard for light duty vehicles; however, for some major European cities, further measures may be required, in particular if aiming to achieve compliance at an earlier time.

Derivation of Hunt equation for suspension distribution using Shannon entropy theory

NASA Astrophysics Data System (ADS)

Kundu, Snehasis

2017-12-01

In this study, the Hunt equation for computing suspension concentration in sediment-laden flows is derived using Shannon entropy theory. Considering the inverse of the void ratio as a random variable and using principle of maximum entropy, probability density function and cumulative distribution function of suspension concentration is derived. A new and more general cumulative distribution function for the flow domain is proposed which includes several specific other models of CDF reported in literature. This general form of cumulative distribution function also helps to derive the Rouse equation. The entropy based approach helps to estimate model parameters using suspension data of sediment concentration which shows the advantage of using entropy theory. Finally model parameters in the entropy based model are also expressed as functions of the Rouse number to establish a link between the parameters of the deterministic and probabilistic approaches.

Modeling the microstructure of surface by applying BRDF function

NASA Astrophysics Data System (ADS)

Plachta, Kamil

2017-06-01

The paper presents the modeling of surface microstructure using a bidirectional reflectance distribution function. This function contains full information about the reflectance properties of the flat surfaces - it is possible to determine the share of the specular, directional and diffuse components in the reflected luminous stream. The software is based on the authorial algorithm that uses selected elements of this function models, which allows to determine the share of each component. Basing on obtained data, the surface microstructure of each material can be modeled, which allows to determine the properties of this materials. The concentrator directs the reflected solar radiation onto the photovoltaic surface, increasing, at the same time, the value of the incident luminous stream. The paper presents an analysis of selected materials that can be used to construct the solar concentrator system. The use of concentrator increases the power output of the photovoltaic system by up to 17% as compared to the standard solution.

Acute Exposure to Perchlorethylene alters Rat Visual Evoked Potentials in Relation to Brain Concentration

EPA Science Inventory

These experiments sought to establish a dose-effect relationship between the concentration of perchloroethylene (PCE) in brain tissue and concurrent changes in visual function. A physiologically-based pharmacokinetic (PBPK) model was implemented to predict concentrations of PCE ...

Development and implementation of a regression model for predicting recreational water quality in the Cuyahoga River, Cuyahoga Valley National Park, Ohio 2009-11

USGS Publications Warehouse

Brady, Amie M.G.; Plona, Meg B.

2012-01-01

The Cuyahoga River within Cuyahoga Valley National Park (CVNP) is at times impaired for recreational use due to elevated concentrations of Escherichia coli (E. coli), a fecal-indicator bacterium. During the recreational seasons of mid-May through September during 2009–11, samples were collected 4 days per week and analyzed for E. coli concentrations at two sites within CVNP. Other water-quality and environ-mental data, including turbidity, rainfall, and streamflow, were measured and (or) tabulated for analysis. Regression models developed to predict recreational water quality in the river were implemented during the recreational seasons of 2009–11 for one site within CVNP–Jaite. For the 2009 and 2010 seasons, the regression models were better at predicting exceedances of Ohio's single-sample standard for primary-contact recreation compared to the traditional method of using the previous day's E. coli concentration. During 2009, the regression model was based on data collected during 2005 through 2008, excluding available 2004 data. The resulting model for 2009 did not perform as well as expected (based on the calibration data set) and tended to overestimate concentrations (correct responses at 69 percent). During 2010, the regression model was based on data collected during 2004 through 2009, including all of the available data. The 2010 model performed well, correctly predicting 89 percent of the samples above or below the single-sample standard, even though the predictions tended to be lower than actual sample concentrations. During 2011, the regression model was based on data collected during 2004 through 2010 and tended to overestimate concentrations. The 2011 model did not perform as well as the traditional method or as expected, based on the calibration dataset (correct responses at 56 percent). At a second site—Lock 29, approximately 5 river miles upstream from Jaite, a regression model based on data collected at the site during the recreational seasons of 2008–10 also did not perform as well as the traditional method or as well as expected (correct responses at 60 percent). Above normal precipitation in the region and a delayed start to the 2011 sampling season (sampling began mid-June) may have affected how well the 2011 models performed. With these new data, however, updated regression models may be better able to predict recreational water quality conditions due to the increased amount of diverse water quality conditions included in the calibration data. Daily recreational water-quality predictions for Jaite were made available on the Ohio Nowcast Web site at www.ohionowcast.info. Other public outreach included signage at trailheads in the park, articles in the park's quarterly-published schedule of events and volunteer newsletters. A U.S. Geological Survey Fact Sheet was also published to bring attention to water-quality issues in the park.

An electrical circuit model for simulation of indoor radon concentration.

PubMed

Musavi Nasab, S M; Negarestani, A

2013-01-01

In this study, a new model based on electric circuit theory was introduced to simulate the behaviour of indoor radon concentration. In this model, a voltage source simulates radon generation in walls, conductivity simulates migration through walls and voltage across a capacitor simulates radon concentration in a room. This simulation considers migration of radon through walls by diffusion mechanism in one-dimensional geometry. Data reported in a typical Greek house were employed to examine the application of this technique of simulation to the behaviour of radon.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

PubMed Central

Saylor, Kyle; Zhang, Chenming

2017-01-01

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down into different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies’ ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. PMID:27473014

A PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODEL FOR TOLUENE IN THE LONG EVANS RAT: BODY COMPOSITION AND PHYSICAL ACTIVITY.

EPA Science Inventory

A physiologically-based pharmacokinetic (PBPK) model for inhaled toluene was developed for Long-Evans rats as a component of an exposure-dose-response (EDR) model for volatile organic compounds. The PBPK model was needed to link airborne toluene exposure to its concentration in b...

Comparing Stream DOC Fluxes from Sensor- and Sample-Based Approaches

NASA Astrophysics Data System (ADS)

Shanley, J. B.; Saraceno, J.; Aulenbach, B. T.; Mast, A.; Clow, D. W.; Hood, K.; Walker, J. F.; Murphy, S. F.; Torres-Sanchez, A.; Aiken, G.; McDowell, W. H.

2015-12-01

DOC transport by streamwater is a significant flux that does not consistently show up in ecosystem carbon budgets. In an effort to quantify stream DOC flux, we analyzed three to four years of high-frequency in situ fluorescing dissolved organic matter (FDOM) concentrations and turbidity measured by optical sensors at the five diverse forested and/or alpine headwater sites of the U.S. Geological Survey (USGS) Water, Energy, and Biogeochemical Budgets (WEBB) program. FDOM serves as a proxy for DOC. We also took discrete samples over a range of hydrologic conditions, using both manual weekly and automated event-based sampling. After compensating FDOM for temperature effects and turbidity interference - which was successful even at the high-turbidity Luquillo, PR site -- we evaluated the DOC-FDOM relation based on discrete sample DOC analyses matched to corrected FDOM at the time of sampling. FDOM was a moderately robust predictor of DOC, with r2 from 0.60 to more than 0.95 among sites. We then formed continuous DOC time series by two independent approaches: (1) DOC predicted from FDOM; and (2) the composite method, based on modeled DOC from regression on stream discharge, season, air temperature, and time, forcing the model to observations and adjusting modeled concentrations between observations by linearly-interpolated model residuals. DOC flux from each approach was then computed directly as concentration times discharge. DOC fluxes based on the sensor approach were consistently greater than the sample-based approach. At Loch Vale, CO (2.5 years) and Panola Mountain GA (1 year), the difference was 5-17%. At Sleepers River, VT (3 years), preliminary differences were greater than 20%. The difference is driven by the highest events, but we are investigating these results further. We will also present comparisons from Luquillo, PR, and Allequash Creek, WI. The higher sensor-based DOC fluxes could result from their accuracy during hysteresis, which is difficult to model. In at least one case the higher sensor-based DOC flux was linked to an unsampled event outside the range of the concentration model. Sensors require upkeep and vigilance with the data, but have the potential to yield more accurate fluxes than sample-based approaches.

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

Evaluation of modeling NO2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

NASA Astrophysics Data System (ADS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-03-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope = 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Evaluation of Modeling NO2 Concentrations Driven by Satellite-Derived and Bottom-Up Emission Inventories Using In-Situ Measurements Over China

NASA Technical Reports Server (NTRS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-01-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slopeD0.74 and 0.64 for the daily mean and daytime only) and the MIX (slopeD1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40% higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0% on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15% in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

A methodology for ecosystem-scale modeling of selenium

USGS Publications Warehouse

Presser, T.S.; Luoma, S.N.

2010-01-01

The main route of exposure for selenium (Se) is dietary, yet regulations lack biologically based protocols for evaluations of risk. We propose here an ecosystem-scale model that conceptualizes and quantifies the variables that determinehow Se is processed from water through diet to predators. This approach uses biogeochemical and physiological factors from laboratory and field studies and considers loading, speciation, transformation to particulate material, bioavailability, bioaccumulation in invertebrates, and trophic transfer to predators. Validation of the model is through data sets from 29 historic and recent field case studies of Se-exposed sites. The model links Se concentrations across media (water, particulate, tissue of different food web species). It can be used to forecast toxicity under different management or regulatory proposals or as a methodology for translating a fish-tissue (or other predator tissue) Se concentration guideline to a dissolved Se concentration. The model illustrates some critical aspects of implementing a tissue criterion: 1) the choice of fish species determines the food web through which Se should be modeled, 2) the choice of food web is critical because the particulate material to prey kinetics of bioaccumulation differs widely among invertebrates, 3) the characterization of the type and phase of particulate material is important to quantifying Se exposure to prey through the base of the food web, and 4) the metric describing partitioning between particulate material and dissolved Se concentrations allows determination of a site-specific dissolved Se concentration that would be responsible for that fish body burden in the specific environment. The linked approach illustrates that environmentally safe dissolved Se concentrations will differ among ecosystems depending on the ecological pathways and biogeochemical conditions in that system. Uncertainties and model sensitivities can be directly illustrated by varying exposure scenarios based on site-specific knowledge. The model can also be used to facilitate site-specific regulation and to present generic comparisons to illustrate limitations imposed by ecosystem setting and inhabitants. Used optimally, the model provides a tool for framing a site-specific ecological problem or occurrence of Se exposure, quantify exposure within that ecosystem, and narrow uncertainties abouthowto protect it by understanding the specifics of the underlying system ecology, biogeochemistry, and hydrology.?? 2010 SETAC.

A methodology for ecosystem-scale modeling of selenium.

PubMed

Presser, Theresa S; Luoma, Samuel N

2010-10-01

The main route of exposure for selenium (Se) is dietary, yet regulations lack biologically based protocols for evaluations of risk. We propose here an ecosystem-scale model that conceptualizes and quantifies the variables that determine how Se is processed from water through diet to predators. This approach uses biogeochemical and physiological factors from laboratory and field studies and considers loading, speciation, transformation to particulate material, bioavailability, bioaccumulation in invertebrates, and trophic transfer to predators. Validation of the model is through data sets from 29 historic and recent field case studies of Se-exposed sites. The model links Se concentrations across media (water, particulate, tissue of different food web species). It can be used to forecast toxicity under different management or regulatory proposals or as a methodology for translating a fish-tissue (or other predator tissue) Se concentration guideline to a dissolved Se concentration. The model illustrates some critical aspects of implementing a tissue criterion: 1) the choice of fish species determines the food web through which Se should be modeled, 2) the choice of food web is critical because the particulate material to prey kinetics of bioaccumulation differs widely among invertebrates, 3) the characterization of the type and phase of particulate material is important to quantifying Se exposure to prey through the base of the food web, and 4) the metric describing partitioning between particulate material and dissolved Se concentrations allows determination of a site-specific dissolved Se concentration that would be responsible for that fish body burden in the specific environment. The linked approach illustrates that environmentally safe dissolved Se concentrations will differ among ecosystems depending on the ecological pathways and biogeochemical conditions in that system. Uncertainties and model sensitivities can be directly illustrated by varying exposure scenarios based on site-specific knowledge. The model can also be used to facilitate site-specific regulation and to present generic comparisons to illustrate limitations imposed by ecosystem setting and inhabitants. Used optimally, the model provides a tool for framing a site-specific ecological problem or occurrence of Se exposure, quantify exposure within that ecosystem, and narrow uncertainties about how to protect it by understanding the specifics of the underlying system ecology, biogeochemistry, and hydrology. © 2010 SETAC.

Population Pharmacokinetics of Topiramate in Japanese Pediatric and Adult Patients With Epilepsy Using Routinely Monitored Data.

PubMed

Takeuchi, Masato; Yano, Ikuko; Ito, Satoko; Sugimoto, Mitsuhiro; Yamamoto, Shota; Yonezawa, Atsushi; Ikeda, Akio; Matsubara, Kazuo

2017-04-01

Topiramate is a second-generation antiepileptic drug used as monotherapy and adjunctive therapy in adults and children with partial seizures. A population pharmacokinetic (PPK) analysis was performed to improve the topiramate dosage adjustment for individualized treatment. Patients whose steady-state serum concentration of topiramate was routinely monitored at Kyoto University Hospital from April 2012 to March 2013 were included in the model-building data. A nonlinear mixed effects modeling program was used to evaluate the influence of covariates on topiramate pharmacokinetics. The obtained PPK model was evaluated by internal model validations, including goodness-of-fit plots and prediction-corrected visual predictive checks, and was externally confirmed using the validation data from January 2015 to December 2015. A total of 177 steady-state serum concentrations from 93 patients were used for the model-building analysis. The patients' age ranged from 2 to 68 years, and body weight ranged from 8.6 to 105 kg. The median serum concentration of topiramate was 1.7 mcg/mL, and half of the patients received carbamazepine coadministration. Based on a one-compartment model with first order absorption and elimination, the apparent volume of distribution was 105 L/70 kg, and the apparent clearance was allometrically related to the body weight as 2.25 L·h·70 kg without carbamazepine or phenytoin. Combination treatment with carbamazepine or phenytoin increased the apparent clearance to 3.51 L·h·70 kg. Goodness-of-fit plots, prediction-corrected visual predictive check, and external validation using the validation data from 43 patients confirmed an appropriateness of the final model. Simulations based on the final model showed that dosage adjustments allometrically scaling to body weight can equalize the serum concentrations in children of various ages and adults. The PPK model, using the power scaling of body weight, effectively elucidated the topiramate serum concentration profile ranging from pediatric to adult patients. Dosage adjustments based on body weight and concomitant antiepileptic drug help obtain the dosage of topiramate necessary to reach an effective concentration in each individual.

Thermodynamic investigation of the interaction between cyclodextrins and preservatives - Application and verification in a mathematical model to determine the needed preservative surplus in aqueous cyclodextrin formulations.

PubMed

Holm, René; Olesen, Niels Erik; Alexandersen, Signe Dalgaard; Dahlgaard, Birgitte N; Westh, Peter; Mu, Huiling

2016-05-25

Preservatives are inactivated when added to conserve aqueous cyclodextrin (CD) formulations due to complex formation between CDs and the preservative. To maintain the desired conservation effect the preservative needs to be added in apparent surplus to account for this inactivation. The purpose of the present work was to establish a mathematical model, which defines this surplus based upon knowledge of stability constants and the minimal concentration of preservation to inhibit bacterial growth. The stability constants of benzoic acid, methyl- and propyl-paraben with different frequently used βCDs were determined by isothermal titration calorimetry. Based upon this knowledge mathematical models were constructed to account for the equilibrium systems and to calculate the required concentration of the preservations, which was evaluated experimentally based upon the USP/Ph. Eur./JP monograph. The mathematical calculations were able to predict the needed concentration of preservation in the presence of CDs; it clearly demonstrated the usefulness of including all underlying chemical equilibria in a mathematical model, such that the formulation design can be based on quantitative arguments. Copyright © 2015 Elsevier B.V. All rights reserved.

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.

PubMed

Kim, Eunji; Ivanov, Ivan; Hua, Jianping; Lampe, Johanna W; Hullar, Meredith Aj; Chapkin, Robert S; Dougherty, Edward R

2017-01-01

Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

Development and evaluation of a physics-based windblown ...

EPA Pesticide Factsheets

A new windblown dust emission treatment was incorporated in the Community Multiscale Air Quality (CMAQ) modeling system. This new model treatment has been built upon previously developed physics-based parameterization schemes from the literature. A distinct and novel feature of this scheme, however, is the incorporation of a newly developed dynamic relation for the surface roughness length relevant to small-scale dust generation processes. Through this implementation, the effect of nonerodible elements on the local flow acceleration, drag partitioning, and surface coverage protection is modeled in a physically based and consistent manner. Careful attention is paid in integrating the new windblown dust treatment in the CMAQ model to ensure that the required input parameters are correctly configured. To test the performance of the new dust module in CMAQ, the entire year 2011 is simulated for the continental United States, with particular emphasis on the southwestern United States (SWUS) where windblown dust concentrations are relatively large. Overall, the model shows good performance with the daily mean bias of soil concentrations fluctuating in the range of ±1 µg m−3 for the entire year. Springtime soil concentrations are in quite good agreement (normalized mean bias of 8.3%) with observations, while moderate to high underestimation of soil concentration is seen in the summertime. The latter is attributed to the issue of representing the convective dust sto

Quantitative thermodynamic predication of interactions between nucleic acid and non-nucleic acid species using Microsoft excel.

PubMed

Zou, Jiaqi; Li, Na

2013-09-01

Proper design of nucleic acid sequences is crucial for many applications. We have previously established a thermodynamics-based quantitative model to help design aptamer-based nucleic acid probes by predicting equilibrium concentrations of all interacting species. To facilitate customization of this thermodynamic model for different applications, here we present a generic and easy-to-use platform to implement the algorithm of the model with Microsoft(®) Excel formulas and VBA (Visual Basic for Applications) macros. Two Excel spreadsheets have been developed: one for the applications involving only nucleic acid species, the other for the applications involving both nucleic acid and non-nucleic acid species. The spreadsheets take the nucleic acid sequences and the initial concentrations of all species as input, guide the user to retrieve the necessary thermodynamic constants, and finally calculate equilibrium concentrations for all species in various bound and unbound conformations. The validity of both spreadsheets has been verified by comparing the modeling results with the experimental results on nucleic acid sequences reported in the literature. This Excel-based platform described here will allow biomedical researchers to rationalize the sequence design of nucleic acid probes using the thermodynamics-based modeling even without relevant theoretical and computational skills. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Aripiprazole Lauroxil

PubMed Central

Hard, Marjie L.; Mills, Richard J.; Sadler, Brian M.; Turncliff, Ryan Z.; Citrome, Leslie

2017-01-01

Abstract Background Aripiprazole lauroxil is an extended-release prodrug of aripiprazole for intramuscular injection, approved for schizophrenia treatment. We developed a population pharmacokinetic (PopPK) model to characterize aripiprazole lauroxil PK and evaluate dosing scenarios likely to be encountered in clinical practice. Methods Data from 616 patients with schizophrenia, collected from 5 clinical studies, were used to construct the PopPK model. The model was subsequently used to evaluate various dose levels and frequency and the impact of dosing delay on aripiprazole concentrations. Findings The results of the model indicate that aripiprazole is released into the systemic circulation after 5 to 6 days, and release continues for an additional 36 days. The slow increase in aripiprazole concentration after injection necessitates the coadministration of oral aripiprazole for 21 days with the first injection. Based on the PopPK model simulations, a dosing interval of 882 mg every 6 weeks results in aripiprazole concentrations that fall within the concentration range associated with the efficacious aripiprazole lauroxil dose range (441–882 mg dosed monthly). A 662-mg monthly dose also resulted in aripiprazole concentrations within the efficacious dose range. Aripiprazole lauroxil administration results in prolonged exposure, such that dose delays of 2 to 4 weeks, depending on the dose regimen, do not require oral aripiprazole supplementation upon resumption of dosing. Conclusions This PopPK model and model-based simulations were effective means for evaluating aripiprazole lauroxil dosing regimens and management of missed doses. Such analyses play an important role in determining the use of this long-acting antipsychotic in clinical practice. PMID:28350572

Quality factor of luminescent solar concentrators and practical concentration limits attainable with semiconductor quantum dots

DOE PAGES

Klimov, Victor I.; Baker, Thomas A.; Lim, Jaehoon; ...

2016-05-09

In this study, luminescent solar concentrators (LSCs) can be utilized as both large-area collectors of solar radiation supplementing traditional photovoltaic cells as well as semitransparent “solar windows” that provide a desired degree of shading and simultaneously serve as power-generation units. An important characteristic of an LSC is a concentration factor (C) that can be thought of as a coefficient of effective enlargement (or contraction) of the area of a solar cell when it is coupled to the LSC. Here we use analytical and numerical Monte Carlo modeling in addition to experimental studies of quantum-dot-based LSCs to analyze the factors thatmore » influence optical concentration in practical devices. Our theoretical model indicates that the maximum value of C achievable with a given fluorophore is directly linked to the LSC quality factor (Q LSC) defined as the ratio of absorption coefficients at the wavelengths of incident and reemitted light. In fact, we demonstrate that the ultimate concentration limit (C 0) realized in large-area devices scales linearly with the LSC quality factor and in the case of perfect emitters and devices without back reflectors is approximately equal to Q LSC. To test the predictions of this model, we conduct experimental studies of LSCs based on visible-light emitting II–VI core/shell quantum dots with two distinct LSC quality factors. We also investigate devices based on near-infrared emitting CuInSe xS 2–x quantum dots for which the large emission bandwidth allows us to assess the impact of varied Q LSC on the concentration factor by simply varying the detection wavelength. In all cases, we find an excellent agreement between the model and the experimental observations, suggesting that the developed formalism can be utilized for express evaluation of prospective LSC performance based on the optical spectra of LSC fluorophores, which should facilitate future efforts on the development of high-performance devices based on quantum dots as well as other types of emitters.« less

Quality factor of luminescent solar concentrators and practical concentration limits attainable with semiconductor quantum dots

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klimov, Victor I.; Baker, Thomas A.; Lim, Jaehoon

In this study, luminescent solar concentrators (LSCs) can be utilized as both large-area collectors of solar radiation supplementing traditional photovoltaic cells as well as semitransparent “solar windows” that provide a desired degree of shading and simultaneously serve as power-generation units. An important characteristic of an LSC is a concentration factor (C) that can be thought of as a coefficient of effective enlargement (or contraction) of the area of a solar cell when it is coupled to the LSC. Here we use analytical and numerical Monte Carlo modeling in addition to experimental studies of quantum-dot-based LSCs to analyze the factors thatmore » influence optical concentration in practical devices. Our theoretical model indicates that the maximum value of C achievable with a given fluorophore is directly linked to the LSC quality factor (Q LSC) defined as the ratio of absorption coefficients at the wavelengths of incident and reemitted light. In fact, we demonstrate that the ultimate concentration limit (C 0) realized in large-area devices scales linearly with the LSC quality factor and in the case of perfect emitters and devices without back reflectors is approximately equal to Q LSC. To test the predictions of this model, we conduct experimental studies of LSCs based on visible-light emitting II–VI core/shell quantum dots with two distinct LSC quality factors. We also investigate devices based on near-infrared emitting CuInSe xS 2–x quantum dots for which the large emission bandwidth allows us to assess the impact of varied Q LSC on the concentration factor by simply varying the detection wavelength. In all cases, we find an excellent agreement between the model and the experimental observations, suggesting that the developed formalism can be utilized for express evaluation of prospective LSC performance based on the optical spectra of LSC fluorophores, which should facilitate future efforts on the development of high-performance devices based on quantum dots as well as other types of emitters.« less

Global and Local Contributions to Mercury Concentrations in Lake Michigan and Impact on Fish Consumption Advisories

EPA Science Inventory

LM2-Mercury, a mercury species mass balance model developed for Lake Michigan, was used to assess mercury cycling in Lake Michigan. A calibrated model (including a hindcast) was used to predict mercury concentrations in the lake based on various sensitivity and management scenari...

Development and Application of Regression Models for Estimating Nutrient Concentrations in Streams of the Conterminous United States, 1992-2001

USGS Publications Warehouse

Spahr, Norman E.; Mueller, David K.; Wolock, David M.; Hitt, Kerie J.; Gronberg, JoAnn M.

2010-01-01

Data collected for the U.S. Geological Survey National Water-Quality Assessment program from 1992-2001 were used to investigate the relations between nutrient concentrations and nutrient sources, hydrology, and basin characteristics. Regression models were developed to estimate annual flow-weighted concentrations of total nitrogen and total phosphorus using explanatory variables derived from currently available national ancillary data. Different total-nitrogen regression models were used for agricultural (25 percent or more of basin area classified as agricultural land use) and nonagricultural basins. Atmospheric, fertilizer, and manure inputs of nitrogen, percent sand in soil, subsurface drainage, overland flow, mean annual precipitation, and percent undeveloped area were significant variables in the agricultural basin total nitrogen model. Significant explanatory variables in the nonagricultural total nitrogen model were total nonpoint-source nitrogen input (sum of nitrogen from manure, fertilizer, and atmospheric deposition), population density, mean annual runoff, and percent base flow. The concentrations of nutrients derived from regression (CONDOR) models were applied to drainage basins associated with the U.S. Environmental Protection Agency (USEPA) River Reach File (RF1) to predict flow-weighted mean annual total nitrogen concentrations for the conterminous United States. The majority of stream miles in the Nation have predicted concentrations less than 5 milligrams per liter. Concentrations greater than 5 milligrams per liter were predicted for a broad area extending from Ohio to eastern Nebraska, areas spatially associated with greater application of fertilizer and manure. Probabilities that mean annual total-nitrogen concentrations exceed the USEPA regional nutrient criteria were determined by incorporating model prediction uncertainty. In all nutrient regions where criteria have been established, there is at least a 50 percent probability of exceeding the criteria in more than half of the stream miles. Dividing calibration sites into agricultural and nonagricultural groups did not improve the explanatory capability for total phosphorus models. The group of explanatory variables that yielded the lowest model error for mean annual total phosphorus concentrations includes phosphorus input from manure, population density, amounts of range land and forest land, percent sand in soil, and percent base flow. However, the large unexplained variability and associated model error precluded the use of the total phosphorus model for nationwide extrapolations.

Are Polar Field Magnetic Flux Concentrations Responsible for Missing Interplanetary Flux?

NASA Astrophysics Data System (ADS)

Linker, Jon A.; Downs, C.; Mikic, Z.; Riley, P.; Henney, C. J.; Arge, C. N.

2012-05-01

Magnetohydrodynamic (MHD) simulations are now routinely used to produce models of the solar corona and inner heliosphere for specific time periods. These models typically use magnetic maps of the photospheric magnetic field built up over a solar rotation, available from a number of ground-based and space-based solar observatories. The line-of-sight field at the Sun's poles is poorly observed, and the polar fields in these maps are filled with a variety of interpolation/extrapolation techniques. These models have been found to frequently underestimate the interplanetary magnetic flux (Riley et al., 2012, in press, Stevens et al., 2012, in press) near the minimum part of the cycle unless mitigating correction factors are applied. Hinode SOT observations indicate that strong concentrations of magnetic flux may be present at the poles (Tsuneta et al. 2008). The ADAPT flux evolution model (Arge et al. 2010) also predicts the appearance of such concentrations. In this paper, we explore the possibility that these flux concentrations may account for a significant amount of magnetic flux and alleviate discrepancies in interplanetary magnetic flux predictions. Research supported by AFOSR, NASA, and NSF.

Study of indoor radon distribution using measurements and CFD modeling.

PubMed

Chauhan, Neetika; Chauhan, R P; Joshi, M; Agarwal, T K; Aggarwal, Praveen; Sahoo, B K

2014-10-01

Measurement and/or prediction of indoor radon ((222)Rn) concentration are important due to the impact of radon on indoor air quality and consequent inhalation hazard. In recent times, computational fluid dynamics (CFD) based modeling has become the cost effective replacement of experimental methods for the prediction and visualization of indoor pollutant distribution. The aim of this study is to implement CFD based modeling for studying indoor radon gas distribution. This study focuses on comparison of experimentally measured and CFD modeling predicted spatial distribution of radon concentration for a model test room. The key inputs for simulation viz. radon exhalation rate and ventilation rate were measured as a part of this study. Validation experiments were performed by measuring radon concentration at different locations of test room using active (continuous radon monitor) and passive (pin-hole dosimeters) techniques. Modeling predictions have been found to be reasonably matching with the measurement results. The validated model can be used to understand and study factors affecting indoor radon distribution for more realistic indoor environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Screening tool to evaluate the vulnerability of down-gradient receptors to groundwater contaminants from uncapped landfills

USGS Publications Warehouse

Baker, Ronald J.; Reilly, Timothy J.; Lopez, Anthony R.; Romanok, Kristin M.; Wengrowski, Edward W

2015-01-01

A screening tool for quantifying levels of concern for contaminants detected in monitoring wells on or near landfills to down-gradient receptors (streams, wetlands and residential lots) was developed and evaluated. The tool uses Quick Domenico Multi-scenario (QDM), a spreadsheet implementation of Domenico-based solute transport, to estimate concentrations of contaminants reaching receptors under steady-state conditions from a constant-strength source. Unlike most other available Domenico-based model applications, QDM calculates the time for down-gradient contaminant concentrations to approach steady state and appropriate dispersivity values, and allows for up to fifty simulations on a single spreadsheet. Sensitivity of QDM solutions to critical model parameters was quantified. The screening tool uses QDM results to categorize landfills as having high, moderate and low levels of concern, based on contaminant concentrations reaching receptors relative to regulatory concentrations. The application of this tool was demonstrated by assessing levels of concern (as defined by the New Jersey Pinelands Commission) for thirty closed, uncapped landfills in the New Jersey Pinelands National Reserve, using historic water-quality data from monitoring wells on and near landfills and hydraulic parameters from regional flow models. Twelve of these landfills are categorized as having high levels of concern, indicating a need for further assessment. This tool is not a replacement for conventional numerically-based transport model or other available Domenico-based applications, but is suitable for quickly assessing the level of concern posed by a landfill or other contaminant point source before expensive and lengthy monitoring or remediation measures are taken. In addition to quantifying the level of concern using historic groundwater-monitoring data, the tool allows for archiving model scenarios and adding refinements as new data become available.

Lactational Transfer of Polychlorinated-Biphenyls (PCBs) and Other Organochlorines in St. Lawrence Beluga Whales (Delphinapterus leucas).

PubMed

Cadieux, Marc A; Muir, Derek C G; Béland, Pierre; Hickie, Brendan E

2016-01-01

This study uses an individual-based contaminant bioaccumulation model for marine mammals to explore factors controlling the transfer of PCBs from mother to calf via nursing in beluga from the St. Lawrence Estuary. Beluga blubber samples (n = 46), along with four matched milk samples from stranded animals over the 1986-1994 period were used for comparison with modelled results. Based on 68 POPs, including 48 PCBs and 20 other organochlorine compounds, milk:blubber ratios were 0.65 between log K OW 3-6.5, then decreased to 0.1 at log K OW 8. Model simulations based on this relationship indicated females were transferring PCBs that were relatively very hydrophobic and highly chlorinated less readily than their lower chlorinated counterparts, resulting in an enriched concentration of very hydrophobic congeners in nursing females relative to adult males. There was very good agreement between observed and modelled male:female PCB concentration ratios. Four females within our dataset (15 %) had male-like ΣPCB concentrations as well as male-like congener profiles, suggesting that these individuals may have had a reduced or limited nursing history.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.; Crawford, Charles G.

2008-01-01

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize the probable levels of atrazine for comparison to specific water-quality benchmarks. Sites with a high probability of exceeding a benchmark for human health or aquatic life can be prioritized for monitoring.

Panel Flutter Emulation Using a Few Concentrated Forces

NASA Astrophysics Data System (ADS)

Dhital, Kailash; Han, Jae-Hung

2018-04-01

The objective of this paper is to study the feasibility of panel flutter emulation using a few concentrated forces. The concentrated forces are considered to be equivalent to aerodynamic forces. The equivalence is carried out using surface spline method and principle of virtual work. The structural modeling of the plate is based on the classical plate theory and the aerodynamic modeling is based on the piston theory. The present approach differs from the linear panel flutter analysis in scheming the modal aerodynamics forces with unchanged structural properties. The solutions for the flutter problem are obtained numerically using the standard eigenvalue procedure. A few concentrated forces were considered with an optimization effort to decide their optimal locations. The optimization process is based on minimizing the error between the flutter bounds from emulated and linear flutter analysis method. The emulated flutter results for the square plate of four different boundary conditions using six concentrated forces are obtained with minimal error to the reference value. The results demonstrated the workability and viability of using concentrated forces in emulating real panel flutter. In addition, the paper includes the parametric studies of linear panel flutter whose proper literatures are not available.

Food Web Bioaccumulation Model for Resident Killer Whales from the Northeastern Pacific Ocean as a Tool for the Derivation of PBDE-Sediment Quality Guidelines.

PubMed

Alava, Juan José; Ross, Peter S; Gobas, Frank A P C

2016-01-01

Resident killer whale populations in the NE Pacific Ocean are at risk due to the accumulation of pollutants, including polybrominated diphenyl ethers (PBDEs). To assess the impact of PBDEs in water and sediments in killer whale critical habitat, we developed a food web bioaccumulation model. The model was designed to estimate PBDE concentrations in killer whales based on PBDE concentrations in sediments and the water column throughout a lifetime of exposure. Calculated and observed PBDE concentrations exceeded the only toxicity reference value available for PBDEs in marine mammals (1500 μg/kg lipid) in southern resident killer whales but not in northern resident killer whales. Temporal trends (1993-2006) for PBDEs observed in southern resident killer whales showed a doubling time of ≈5 years. If current sediment quality guidelines available in Canada for polychlorinated biphenyls are applied to PBDEs, it can be expected that PBDE concentrations in killer whales will exceed available toxicity reference values by a large margin. Model calculations suggest that a PBDE concentration in sediments of approximately 1.0 μg/kg dw produces PBDE concentrations in resident killer whales that are below the current toxicity reference value for 95 % of the population, with this value serving as a precautionary benchmark for a management-based approach to reducing PBDE health risks to killer whales. The food web bioaccumulation model may be a useful risk management tool in support of regulatory protection for killer whales.

FATE 5: A natural attenuation calibration tool for groundwater fate and transport modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nevin, J.P.; Connor, J.A.; Newell, C.J.

1997-12-31

A new groundwater attenuation modeling tool (FATE 5) has been developed to assist users with determining site-specific natural attenuation rates for organic constituents dissolved in groundwater. FATE 5 is based on and represents an enhancement to the Domenico analytical groundwater transport model. These enhancements include use of an optimization routine to match results from the Domenico model to actual measured site concentrations, an extensive database of chemical property data, and calculation of an estimate of the length of time needed for a plume to reach steady state conditions. FATE 5 was developed in Microsoft{reg_sign} Excel and is controlled by meansmore » of a simple, user-friendly graphical interface. Using the Solver routine built into Excel, FATE 5 is able to calibrate the attenuation rate used by the Domenico model to match site-specific data. By calibrating the decay rate to site-specific measurements, FATE 5 can yield accurate predictions of long-term natural attenuation processes within a groundwater within a groundwater plume. In addition, FATE 5 includes a formulation of the transient Domenico solution used to help the user determine if the steady-state assumptions employed by the model are appropriate. The calibrated groundwater flow model can then be used either to (i) predict upper-bound constituent concentrations in groundwater, based on an observed source zone concentration, or (ii) back-calculate a lower-bound SSTL value, based on a user-specified exposure point concentration at the groundwater point of exposure (POE). This paper reviews the major elements of the FATE 5 model - and gives results for real-world applications. Key modeling assumptions and summary guidelines regarding calculation procedures and input parameter selection are also addressed.« less

Development of a Physiologically Based Computational Kidney Model to Describe the Renal Excretion of Hydrophilic Agents in Rats

PubMed Central

Niederalt, Christoph; Wendl, Thomas; Kuepfer, Lars; Claassen, Karina; Loosen, Roland; Willmann, Stefan; Lippert, Joerg; Schultze-Mosgau, Marcus; Winkler, Julia; Burghaus, Rolf; Bräutigam, Matthias; Pietsch, Hubertus; Lengsfeld, Philipp

2013-01-01

A physiologically based kidney model was developed to analyze the renal excretion and kidney exposure of hydrophilic agents, in particular contrast media, in rats. In order to study the influence of osmolality and viscosity changes, the model mechanistically represents urine concentration by water reabsorption in different segments of kidney tubules and viscosity dependent tubular fluid flow. The model was established using experimental data on the physiological steady state without administration of any contrast media or drugs. These data included the sodium and urea concentration gradient along the cortico-medullary axis, water reabsorption, urine flow, and sodium as well as urea urine concentrations for a normal hydration state. The model was evaluated by predicting the effects of mannitol and contrast media administration and comparing to experimental data on cortico-medullary concentration gradients, urine flow, urine viscosity, hydrostatic tubular pressures and single nephron glomerular filtration rate. Finally the model was used to analyze and compare typical examples of ionic and non-ionic monomeric as well as non-ionic dimeric contrast media with respect to their osmolality and viscosity. With the computational kidney model, urine flow depended mainly on osmolality, while osmolality and viscosity were important determinants for tubular hydrostatic pressure and kidney exposure. The low diuretic effect of dimeric contrast media in combination with their high intrinsic viscosity resulted in a high viscosity within the tubular fluid. In comparison to monomeric contrast media, this led to a higher increase in tubular pressure, to a reduction in glomerular filtration rate and tubular flow and to an increase in kidney exposure. The presented kidney model can be implemented into whole body physiologically based pharmacokinetic models and extended in order to simulate the renal excretion of lipophilic drugs which may also undergo active secretion and reabsorption. PMID:23355822

A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5

EPA Science Inventory

Various approaches and tools exist to estimate local and regional PM_2.5 impacts from a single emissions source, ranging from simple screening techniques to Gaussian based dispersion models and complex grid-based Eulerian photochemical transport models. These approache...

New closed-form approximation for skin chromophore mapping.

PubMed

Välisuo, Petri; Kaartinen, Ilkka; Tuchin, Valery; Alander, Jarmo

2011-04-01

The concentrations of blood and melanin in skin can be estimated based on the reflectance of light. Many models for this estimation have been built, such as Monte Carlo simulation, diffusion models, and the differential modified Beer-Lambert law. The optimization-based methods are too slow for chromophore mapping of high-resolution spectral images, and the differential modified Beer-Lambert is not often accurate enough. Optimal coefficients for the differential Beer-Lambert model are calculated by differentiating the diffusion model, optimized to the normal skin spectrum. The derivatives are then used in predicting the difference in chromophore concentrations from the difference in absorption spectra. The accuracy of the method is tested both computationally and experimentally using a Monte Carlo multilayer simulation model, and the data are measured from the palm of a hand during an Allen's test, which modulates the blood content of skin. The correlations of the given and predicted blood, melanin, and oxygen saturation levels are correspondingly r = 0.94, r = 0.99, and r = 0.73. The prediction of the concentrations for all pixels in a 1-megapixel image would take ∼ 20 min, which is orders of magnitude faster than the methods based on optimization during the prediction.

Numerical modeling and performance analysis of zinc oxide (ZnO) thin-film based gas sensor

NASA Astrophysics Data System (ADS)

Punetha, Deepak; Ranjan, Rashmi; Pandey, Saurabh Kumar

2018-05-01

This manuscript describes the modeling and analysis of Zinc Oxide thin film based gas sensor. The conductance and sensitivity of the sensing layer has been described by change in temperature as well as change in gas concentration. The analysis has been done for reducing and oxidizing agents. Simulation results revealed the change in resistance and sensitivity of the sensor with respect to temperature and different gas concentration. To check the feasibility of the model, all the simulated results have been analyze by different experimental reported work. Wolkenstein theory has been used to model the proposed sensor and the simulation results have been shown by using device simulation software.

Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice

PubMed Central

De Cock, R. F. W.; Allegaert, K.; Vanhaesebrouck, S.; Danhof, M.; Knibbe, C. A. J.

2015-01-01

Based on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization. PMID:26248375

An inverse radiation model for optical determination of temperature and species concentration: Development and validation

NASA Astrophysics Data System (ADS)

Ren, Tao; Modest, Michael F.; Fateev, Alexander; Clausen, Sønnik

2015-01-01

In this study, we present an inverse calculation model based on the Levenberg-Marquardt optimization method to reconstruct temperature and species concentration from measured line-of-sight spectral transmissivity data for homogeneous gaseous media. The high temperature gas property database HITEMP 2010 (Rothman et al. (2010) [1]), which contains line-by-line (LBL) information for several combustion gas species, such as CO2 and H2O, was used to predict gas spectral transmissivities. The model was validated by retrieving temperatures and species concentrations from experimental CO2 and H2O transmissivity measurements. Optimal wavenumber ranges for CO2 and H2O transmissivity measured across a wide range of temperatures and concentrations were determined according to the performance of inverse calculations. Results indicate that the inverse radiation model shows good feasibility for measurements of temperature and gas concentration.

Prediction of the partitioning behaviour of proteins in aqueous two-phase systems using only their amino acid composition.

PubMed

Salgado, J Cristian; Andrews, Barbara A; Ortuzar, Maria Fernanda; Asenjo, Juan A

2008-01-18

The prediction of the partition behaviour of proteins in aqueous two-phase systems (ATPS) using mathematical models based on their amino acid composition was investigated. The predictive models are based on the average surface hydrophobicity (ASH). The ASH was estimated by means of models that use the three-dimensional structure of proteins and by models that use only the amino acid composition of proteins. These models were evaluated for a set of 11 proteins with known experimental partition coefficient in four-phase systems: polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate and dextran and considering three levels of NaCl concentration (0.0% w/w, 0.6% w/w and 8.8% w/w). The results indicate that such prediction is feasible even though the quality of the prediction depends strongly on the ATPS and its operational conditions such as the NaCl concentration. The ATPS 0 model which use the three-dimensional structure obtains similar results to those given by previous models based on variables measured in the laboratory. In addition it maintains the main characteristics of the hydrophobic resolution and intrinsic hydrophobicity reported before. Three mathematical models, ATPS I-III, based only on the amino acid composition were evaluated. The best results were obtained by the ATPS I model which assumes that all of the amino acids are completely exposed. The performance of the ATPS I model follows the behaviour reported previously, i.e. its correlation coefficients improve as the NaCl concentration increases in the system and, therefore, the effect of the protein hydrophobicity prevails over other effects such as charge or size. Its best predictive performance was obtained for the PEG/dextran system at high NaCl concentration. An increase in the predictive capacity of at least 54.4% with respect to the models which use the three-dimensional structure of the protein was obtained for that system. In addition, the ATPS I model exhibits high correlation coefficients in that system being higher than 0.88 on average. The ATPS I model exhibited correlation coefficients higher than 0.67 for the rest of the ATPS at high NaCl concentration. Finally, we tested our best model, the ATPS I model, on the prediction of the partition coefficient of the protein invertase. We found that the predictive capacities of the ATPS I model are better in PEG/dextran systems, where the relative error of the prediction with respect to the experimental value is 15.6%.

A simple microbial fuel cell model for improvement of biomedical device powering times.

PubMed

Roxby, Daniel N; Tran, Nham; Nguyen, Hung T

2014-01-01

This study describes a Matlab based Microbial Fuel Cell (MFC) model for a suspended microbial population, in the anode chamber for the use of the MFC in powering biomedical devices. The model contains three main sections including microbial growth, microbial chemical uptake and secretion and electrochemical modeling. The microbial growth portion is based on a Continuously Stirred Tank Reactor (CSTR) model for the microbial growth with substrate and electron acceptors. Microbial stoichiometry is used to determine chemical concentrations and their rates of change and transfer within the MFC. These parameters are then used in the electrochemical modeling for calculating current, voltage and power. The model was tested for typically exhibited MFC characteristics including increased electrode distances and surface areas, overpotentials and operating temperatures. Implantable biomedical devices require long term powering which is the main objective for MFCs. Towards this end, our model was tested with different initial substrate and electron acceptor concentrations, revealing a four-fold increase in concentrations decreased the power output time by 50%. Additionally, the model also predicts that for a 35.7% decrease in specific growth rate, a 50% increase in power longevity is possible.

Physiologically-Based Pharmacokinetic and Pharmacodynamic Modeling for the Inhibition of Acetylcholinesterase by Acotiamide, A Novel Gastroprokinetic Agent for the Treatment of Functional Dyspepsia, in Rat Stomach.

PubMed

Yoshii, Kazuyoshi; Iikura, Minami; Hirayama, Masamichi; Toda, Ryoko; Kawabata, Yoshihiro

2016-02-01

Acotiamide, a gastroprokinetic agent used to treat functional dyspepsia, is transported to at least two compartments in rat stomach. However, the role of these stomach compartments in pharmacokinetics and pharmacodynamics of acotiamide remains unclear. Thus, the purpose of this study was to elucidate the relationship of the blood and stomach concentration of acotiamide with its inhibitory effect on acetylcholinesterase (AChE). Concentration profiles of acotiamide and acetylcholine (ACh) were determined after intravenous administration to rats and analyzed by physiologically-based pharmacokinetic and pharmacodynamic (PBPK/PD) model containing vascular space, precursor pool and deep pool of stomach. Acotiamide was eliminated from the blood and stomach in a biexponential manner. Our PBPK/PD model estimated that acotiamide concentration in the precursor pool exceeded 2 μM at approximately 2 h after administration. Acotiamide inhibited AChE activity in vitro with a 50% inhibitory concentration of 1.79 μM. ACh reached the maximum concentration at 2 h after administration. Our PBPK model well described the profile of acotiamide and ACh concentration in the stomach in the assumption that acotiamide was distributed by carrier mediated process and inhibited AChE in the precursor pool of stomach. Thus, Acotiamide in the precursor pool plays an important role for producing the pharmacological action.

Evaluation of lead concentration by one-box type multimedia model in Lake Biwa-Yodo River basin of Japan.

PubMed

Kondo, Akira; Yamamoto, Megumi; Inoue, Yoshio; Ariyadasa, B H A K T

2013-07-01

A one box type multimedia model was developed and applied for Lake Biwa-Yodo River basin in Japan to assess the distribution of lead in the environment. This model is based on mass balance and includes four environmental media; the atmosphere, the soil, the water body, and the sediment. The mass balance of lead is represented by the summation of mass transfer flux at equilibrium, emission flux, advection flux, and deposition flux or sedimentation flux. In the case of metallic compounds, dissolution rate and exchange equilibrium have also been taken into consideration. Pollutant Release and Transfer Registry (PRTR) in Japan was used as one of the major data source for this study. The emission of lead in Lake Biwa-Yodo River basin is calculated based on five sources of registered emission in PRTR, unregistered emission in PRTR, incinerators, leaded gasoline, and landfills. In this study, we estimated lead emission from 1957 to 2007 to observe the temporal accumulation of lead. Calculated lead concentrations were compared with the measured/observed concentrations. It was found out that the model could closely predict lead concentration in the soil and the water body. The concentration in the atmosphere was underestimated by the calculated concentrations. The reason was attributed to the underestimation of the amount of lead emission from incinerators. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies

EPA Science Inventory

Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, beca...

A physiologically based model for tramadol pharmacokinetics in horses.

PubMed

Abbiati, Roberto Andrea; Cagnardi, Petra; Ravasio, Giuliano; Villa, Roberto; Manca, Davide

2017-09-21

This work proposes an application of a minimal complexity physiologically based pharmacokinetic model to predict tramadol concentration vs time profiles in horses. Tramadol is an opioid analgesic also used for veterinary treatments. Researchers and medical doctors can profit from the application of mathematical models as supporting tools to optimize the pharmacological treatment of animal species. The proposed model is based on physiology but adopts the minimal compartmental architecture necessary to describe the experimental data. The model features a system of ordinary differential equations, where most of the model parameters are either assigned or individualized for a given horse, using literature data and correlations. Conversely, residual parameters, whose value is unknown, are regressed exploiting experimental data. The model proved capable of simulating pharmacokinetic profiles with accuracy. In addition, it provides further insights on un-observable tramadol data, as for instance tramadol concentration in the liver or hepatic metabolism and renal excretion extent. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simultaneous or separated; comparison approach for saccharification and fermentation process in producing bio-ethanol from EFB

NASA Astrophysics Data System (ADS)

Bardant, Teuku Beuna; Dahnum, Deliana; Amaliyah, Nur

2017-11-01

Simultaneous Saccharification Fermentation (SSF) of palm oil (Elaeis guineensis) empty fruit bunch (EFB) pulp were investigated as a part of ethanol production process. SSF was investigated by observing the effect of substrate loading variation in range 10-20%w, cellulase loading 5-30 FPU/gr substrate and yeast addition 1-2%v to the ethanol yield. Mathematical model for describing the effects of these three variables to the ethanol yield were developed using Response Surface Methodology-Cheminformatics (RSM-CI). The model gave acceptable accuracy in predicting ethanol yield for Simultaneous Saccharification and Fermentation (SSF) with coefficient of determination (R2) 0.8899. Model validation based on data from previous study gave (R2) 0.7942 which was acceptable for using this model for trend prediction analysis. Trend prediction analysis based on model prediction yield showed that SSF gave trend for higher yield when the process was operated in high enzyme concentration and low substrate concentration. On the other hand, even SHF model showed better yield will be obtained if operated in lower substrate concentration, it still possible to operate in higher substrate concentration with slightly lower yield. Opportunity provided by SHF to operate in high loading substrate make it preferable option for application in commercial scale.

Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

PubMed

Pires, J C M; Souza, A; Pavão, H G; Martins, F G

2014-09-01

The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

NASA Astrophysics Data System (ADS)

Navares, Ricardo; Aznarte, José Luis

2017-04-01

In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features.

PubMed

Navares, Ricardo; Aznarte, José Luis

2017-04-01

In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things

PubMed Central

Akan, Ozgur B.

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics. PMID:29415019

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

PubMed

Kuscu, Murat; Akan, Ozgur B

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

Short Communication: Is Ethanol-Based Hand Sanitizer Involved in Acute Pancreatitis after Excessive Disinfection?-An Evaluation with the Use of PBPK Model.

PubMed

Huynh-Delerme, Céline; Artigou, Catherine; Bodin, Laurent; Tardif, Robert; Charest-Tardif, Ginette; Verdier, Cécile; Sater, Nessryne; Ould-Elhkim, Mostafa; Desmares, Catherine

2012-01-01

An occupational physician reported to the French Health Products Safety Agency (Afssaps) a case of adverse effect of acute pancreatitis (AP) in a teaching nurse, after multiple demonstrations with ethanol-based hand sanitizers (EBHSs) used in a classroom with defective mechanical ventilation. It was suggested by the occupational physician that the exposure to ethanol may have produced a significant blood ethanol concentration and subsequently the AP. In order to verify if the confinement situation due to defective mechanical ventilation could increase the systemic exposure to ethanol via inhalation route, a physiologically based pharmacokinetic (PBPK) modeling was used to predict ethanol blood levels. Under the worst case scenario, the simulation by PBPK modeling showed that the maximum blood ethanol concentration which can be predicted of 5.9 mg/l is of the same order of magnitude to endogenous ethanol concentration (mean = 1.1 mg/L; median = 0.4 mg/L; range = 0-35 mg/L) in nondrinker humans (Al-Awadhi et al., 2004). The present study does not support the likelihood that EBHS leads to an increase in systemic ethanol concentration high enough to provoke an acute pancreatitis.

Screening tool to evaluate the vulnerability of down-gradient receptors to groundwater contaminants from uncapped landfills

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Ronald J.; Reilly, Timothy J.; Lopez, Anthony

2015-09-15

Highlights: • A spreadsheet-based risk screening tool for groundwater affected by landfills is presented. • Domenico solute transport equations are used to estimate downgradient contaminant concentrations. • Landfills are categorized as presenting high, moderate or low risks. • Analysis of parameter sensitivity and examples of the method’s application are given. • The method has value to regulators and those considering redeveloping closed landfills. - Abstract: A screening tool for quantifying levels of concern for contaminants detected in monitoring wells on or near landfills to down-gradient receptors (streams, wetlands and residential lots) was developed and evaluated. The tool uses Quick Domenicomore » Multi-scenario (QDM), a spreadsheet implementation of Domenico-based solute transport, to estimate concentrations of contaminants reaching receptors under steady-state conditions from a constant-strength source. Unlike most other available Domenico-based model applications, QDM calculates the time for down-gradient contaminant concentrations to approach steady state and appropriate dispersivity values, and allows for up to fifty simulations on a single spreadsheet. Sensitivity of QDM solutions to critical model parameters was quantified. The screening tool uses QDM results to categorize landfills as having high, moderate and low levels of concern, based on contaminant concentrations reaching receptors relative to regulatory concentrations. The application of this tool was demonstrated by assessing levels of concern (as defined by the New Jersey Pinelands Commission) for thirty closed, uncapped landfills in the New Jersey Pinelands National Reserve, using historic water-quality data from monitoring wells on and near landfills and hydraulic parameters from regional flow models. Twelve of these landfills are categorized as having high levels of concern, indicating a need for further assessment. This tool is not a replacement for conventional numerically-based transport model or other available Domenico-based applications, but is suitable for quickly assessing the level of concern posed by a landfill or other contaminant point source before expensive and lengthy monitoring or remediation measures are taken. In addition to quantifying the level of concern using historic groundwater-monitoring data, the tool allows for archiving model scenarios and adding refinements as new data become available.« less

Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

PubMed

Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

2017-05-01

This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

Evaluation of Observation-Fused Regional Air Quality Model Results for Population Air Pollution Exposure Estimation

PubMed Central

Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

2014-01-01

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses. PMID:24747248

Estimation of water table level and nitrate pollution based on geostatistical and multiple mass transport models

NASA Astrophysics Data System (ADS)

Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.

2015-04-01

As the sustainable use of groundwater resources is a great challenge for many countries in the world, groundwater modeling has become a very useful and well established tool for studying groundwater management problems. Based on various methods used to numerically solve algebraic equations representing groundwater flow and contaminant mass transport, numerical models are mainly divided into Finite Difference-based and Finite Element-based models. The present study aims at evaluating the performance of a finite difference-based (MODFLOW-MT3DMS), a finite element-based (FEFLOW) and a hybrid finite element and finite difference (Princeton Transport Code-PTC) groundwater numerical models simulating groundwater flow and nitrate mass transport in the alluvial aquifer of Trizina region in NE Peloponnese, Greece. The calibration of groundwater flow in all models was performed using groundwater hydraulic head data from seven stress periods and the validation was based on a series of hydraulic head data for two stress periods in sufficient numbers of observation locations. The same periods were used for the calibration of nitrate mass transport. The calibration and validation of the three models revealed that the simulated values of hydraulic heads and nitrate mass concentrations coincide well with the observed ones. The models' performance was assessed by performing a statistical analysis of these different types of numerical algorithms. A number of metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash Sutcliffe Model Efficiency (NSE) and Reliability Index (RI) were used allowing the direct comparison of models' performance. Spatiotemporal Kriging (STRK) was also applied using separable and non-separable spatiotemporal variograms to predict water table level and nitrate concentration at each sampling station for two selected hydrological stress periods. The predictions were validated using the respective measured values. Maps of water table level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.

FINAL REPORT: Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the Measurement and Modeling of Electrical Signatures of Microbe-Mineral Transformations Impacting Contaminant Transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

PRODAN, CAMELIA; SLATER, LEE; NTARLAGIANNIS, DIMITRIOS

2012-09-01

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall,more » biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.« less

Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the measurement and modeling of electrical signatures of microbe-mineral transformations impacting contaminant transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prodan, Camelia

2013-06-14

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall,more » biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.« less

A kinetic model to explain the maximum in alpha-amylase activity measurements in the presence of small carbohydrates.

PubMed

Baks, Tim; Janssen, Anja E M; Boom, Remko M

2006-06-20

The effect of the presence of several small carbohydrates on the measurement of the alpha-amylase activity was determined over a broad concentration range. At low carbohydrate concentrations, a distinct maximum in the alpha-amylase activity versus concentration curves was observed in several cases. At higher concentrations, all carbohydrates show a decreasing alpha-amylase activity at increasing carbohydrate concentrations. A general kinetic model has been developed that can be used to describe and explain these phenomena. This model is based on the formation of a carbohydrate-enzyme complex that remains active. It is assumed that this complex is formed when a carbohydrate binds to alpha-amylase without blocking the catalytic site and its surrounding subsites. Furthermore, the kinetic model incorporates substrate inhibition and substrate competition. Depending on the carbohydrate type and concentration, the measured alpha-amylase activity can be 75% lower than the actual alpha-amylase activity. The model that has been developed can be used to correct for these effects in order to obtain the actual amount of active enzyme. 2006 Wiley Periodicals, Inc.

Model-based monitoring of stormwater runoff quality.

PubMed

Birch, Heidi; Vezzaro, Luca; Mikkelsen, Peter Steen

2013-01-01

Monitoring of micropollutants (MP) in stormwater is essential to evaluate the impacts of stormwater on the receiving aquatic environment. The aim of this study was to investigate how different strategies for monitoring of stormwater quality (combining a model with field sampling) affect the information obtained about MP discharged from the monitored system. A dynamic stormwater quality model was calibrated using MP data collected by automatic volume-proportional sampling and passive sampling in a storm drainage system on the outskirts of Copenhagen (Denmark) and a 10-year rain series was used to find annual average (AA) and maximum event mean concentrations. Use of this model reduced the uncertainty of predicted AA concentrations compared to a simple stochastic method based solely on data. The predicted AA concentration, obtained by using passive sampler measurements (1 month installation) for calibration of the model, resulted in the same predicted level but with narrower model prediction bounds than by using volume-proportional samples for calibration. This shows that passive sampling allows for a better exploitation of the resources allocated for stormwater quality monitoring.

Habitat-based PCB environmental quality criteria for the protection of endangered killer whales (Orcinus orca).

PubMed

Alava, Juan José; Ross, Peter S; Lachmuth, Cara; Ford, John K B; Hickie, Brendan E; Gobas, Frank A P C

2012-11-20

The development of an area-based polychlorinated biphenyl (PCB) food-web bioaccumulation model enabled a critical evaluation of the efficacy of sediment quality criteria and prey tissue residue guidelines in protecting fish-eating resident killer whales of British Columbia and adjacent waters. Model-predicted and observed PCB concentrations in resident killer whales and Chinook salmon were in good agreement, supporting the model's application for risk assessment and criteria development. Model application shows that PCB concentrations in the sediments from the resident killer whale's Critical Habitats and entire foraging range leads to PCB concentrations in most killer whales that exceed PCB toxicity threshold concentrations reported for marine mammals. Results further indicate that current PCB sediment quality and prey tissue residue criteria for fish-eating wildlife are not protective of killer whales and are not appropriate for assessing risks of PCB-contaminated sediments to high trophic level biota. We present a novel methodology for deriving sediment quality criteria and tissue residue guidelines that protect biota of high trophic levels under various PCB management scenarios. PCB concentrations in sediments and in prey that are deemed protective of resident killer whale health are much lower than current criteria values, underscoring the extreme vulnerability of high trophic level marine mammals to persistent and bioaccumulative contaminants.

The role of external and internal mass transfer in the process of Cu2+ removal by natural mineral sorbents.

PubMed

Sljivić, M; Smiciklas, I; Plećas, I; Pejanović, S

2011-07-01

The kinetics of Cu2+ sorption on to zeolite, clay and diatomite was investigated as a function of initial metal concentrations. For consideration of the mass transfer phenomena, single resistance models based on both film and intraparticle diffusion were tested and compared. The obtained results suggested that the rate-limiting step in Cu2+ sorption strongly depended on the sorbent type, as well as on initial cation concentration. The decrease in external mass transfer coefficients with the increase in initial metal concentrations was in excellent agreement with expressions based on Sherwood and Schmidt dimensionless numbers. The internal diffusivities through zeolite particles were in the range 1.0 x 10(-11) to 1.0 x 10(-13) m2/min, depending on the Cu2+ concentration and the applied theoretical model.

Contribution of feldspar and marine organic aerosols to global ice nucleating particle concentrations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vergara-Temprado, Jesús; Murray, Benjamin J.; Wilson, Theodore W.

Ice-nucleating particles (INPs) are known to affect the amount of ice in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP concentration changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower concentrations. Many modelling studies use parameterizations for heterogeneous ice nucleation and cloud ice processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP concentration will influence the simulated amount of ice in mixed-phase clouds, leading to errors in top-of-atmosphere radiativemore » flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP concentrations relevant for mixed-phase clouds based on laboratory and field measurements of ice nucleation by K-feldspar (an ice-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP concentrations based on these two species agrees much better with currently available ambient measurements than when INP concentrations are assumed to depend only on temperature or particle size. Underestimation of INP concentrations in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to adequately describe the global and regional distribution of INPs in models, which will guide ice nucleation researchers on where to focus future laboratory and field work.« less

Modeling and Simulation of Lab-on-a-Chip Systems

DTIC Science & Technology

2005-08-12

complex chip geometries (including multiple turns). Variations of sample concentration profiles in laminar diffusion-based micromixers are also derived...CHAPTER 6 MODELING OF LAMINAR DIFFUSION-BASED COMPLEX ELECTROKINETIC PASSIVE MICROMIXERS ...140 6.4.4 Multi-Stream (Inter-Digital) Micromixers

A mathematical model for lactate transport to red blood cells.

PubMed

Wahl, Patrick; Yue, Zengyuan; Zinner, Christoph; Bloch, Wilhelm; Mester, Joachim

2011-03-01

A simple mathematical model for the transport of lactate from plasma to red blood cells (RBCs) during and after exercise is proposed based on our experimental studies for the lactate concentrations in RBCs and in plasma. In addition to the influx associated with the plasma-to-RBC lactate concentration gradient, it is argued that an efflux must exist. The efflux rate is assumed to be proportional to the lactate concentration in RBCs. This simple model is justified by the comparison between the model-predicted results and observations: For all 33 cases (11 subjects and 3 different warm-up conditions), the model-predicted time courses of lactate concentrations in RBC are generally in good agreement with observations, and the model-predicted ratios between lactate concentrations in RBCs and in plasma at the peak of lactate concentration in RBCs are very close to the observed values. Two constants, the influx rate coefficient C (1) and the efflux rate coefficient C (2), are involved in the present model. They are determined by the best fit to observations. Although the exact electro-chemical mechanism for the efflux remains to be figured out in the future research, the good agreement of the present model with observations suggests that the efflux must get stronger as the lactate concentration in RBCs increases. The physiological meanings of C (1) and C (2) as well as their potential applications are discussed.

Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

NASA Astrophysics Data System (ADS)

Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

2014-09-01

This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p < 0.005) improvement in the performance of the ANN time series model and also showed better performance in picking up high concentrations. For the presented case study, the correlation coefficient between observed and predicted concentrations improved from 0.77 to 0.79 for PM2.5 and from 0.63 to 0.69 for PM10 and reduced the root mean squared error (RMSE) from 5.00 to 4.74 for PM2.5 and from 6.77 to 6.34 for PM10. The techniques presented here enable the user to obtain an understanding of potential sources and their transport characteristics prior to the implementation of costly chemical analysis techniques or advanced air dispersion models.

Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012

USGS Publications Warehouse

Stone, Mandy L.; Graham, Jennifer L.; Gatotho, Jackline W.

2013-01-01

Cheney Reservoir, located in south-central Kansas, is the primary water supply for the city of Wichita. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station since 1998 on the North Fork Ninnescah River, the main source of inflow to Cheney Reservoir. Continuously measured water-quality physical properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, and turbidity. Discrete water-quality samples were collected during 1999 through 2009 and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models were published in 2006 that were based on data collected during 1997 through 2003. This report updates those models using discrete and continuous data collected during January 1999 through December 2009. Models also were developed for four new constituents, including additional nutrient species and indicator bacteria. In addition, a conversion factor of 0.68 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the North Ninnescah River upstream from Cheney Reservoir site. Newly developed models and 14 years of hourly continuously measured data were used to calculate selected constituent concentrations and loads during January 1999 through December 2012. The water-quality information in this report is important to the city of Wichita because it allows the concentrations of many potential pollutants of interest to Cheney Reservoir, including nutrients and sediment, to be estimated in real time and characterized over conditions and time scales that would not be possible otherwise. In general, model forms and the amount of variance explained by the models was similar between the original and updated models. The amount of variance explained by the updated models changed by 10 percent or less relative to the original models. Total nitrogen, nitrate, organic nitrogen, E. coli bacteria, and total organic carbon models were newly developed for this report. Additional data collection over a wider range of hydrological conditions facilitated the development of these models. The nitrate model is particularly important because it allows for comparison to Cheney Reservoir Task Force goals. Mean hourly computed total suspended solids concentration during 1999 through 2012 was 54 milligrams per liter (mg/L). The total suspended solids load during 1999 through 2012 was 174,031 tons. On an average annual basis, the Cheney Reservoir Task Force runoff (550 mg/L) and long-term (100 mg/L) total suspended solids goals were never exceeded, but the base flow goal was exceeded every year during 1999 through 2012. Mean hourly computed nitrate concentration was 1.08 mg/L during 1999 through 2012. The total nitrate load during 1999 through 2012 was 1,361 tons. On an annual average basis, the Cheney Reservoir Task Force runoff (6.60 mg/L) nitrate goal was never exceeded, the long-term goal (1.20 mg/L) was exceeded only in 2012, and the base flow goal of 0.25 mg/L was exceeded every year. Mean nitrate concentrations that were higher during base flow, rather than during runoff conditions, suggest that groundwater sources are the main contributors of nitrate to the North Fork Ninnescah River above Cheney Reservoir. Mean hourly computed phosphorus concentration was 0.14 mg/L during 1999 through 2012. The total phosphorus load during 1999 through 2012 was 328 tons. On an average annual basis, the Cheney Reservoir Task Force runoff goal of 0.40 mg/L for total phosphorus was exceeded in 2002, the year with the largest yearly mean turbidity, and the long-term goal (0.10 mg/L) was exceeded in every year except 2011 and 2012, the years with the smallest mean streamflows. The total phosphorus base flow goal of 0.05 mg/L was exceeded every year. Given that base flow goals for total suspended solids, nitrate, and total phosphorus were exceeded every year despite hydrologic conditions, the established base flow goals are either unattainable or substantially more best management practices will need to be implemented to attain them. On an annual average basis, no discernible patterns were evident in total suspended sediment, nitrate, and total phosphorus concentrations or loads over time, in large part because of hydrologic variability. However, more rigorous statistical analyses are required to evaluate temporal trends. A more rigorous analysis of temporal trends will allow evaluation of watershed investments in best management practices.

A novel methodology for interpreting air quality measurements from urban streets using CFD modelling

NASA Astrophysics Data System (ADS)

Solazzo, Efisio; Vardoulakis, Sotiris; Cai, Xiaoming

2011-09-01

In this study, a novel computational fluid dynamics (CFD) based methodology has been developed to interpret long-term averaged measurements of pollutant concentrations collected at roadside locations. The methodology is applied to the analysis of pollutant dispersion in Stratford Road (SR), a busy street canyon in Birmingham (UK), where a one-year sampling campaign was carried out between August 2005 and July 2006. Firstly, a number of dispersion scenarios are defined by combining sets of synoptic wind velocity and direction. Assuming neutral atmospheric stability, CFD simulations are conducted for all the scenarios, by applying the standard k-ɛ turbulence model, with the aim of creating a database of normalised pollutant concentrations at specific locations within the street. Modelled concentration for all wind scenarios were compared with hourly observed NO x data. In order to compare with long-term averaged measurements, a weighted average of the CFD-calculated concentration fields was derived, with the weighting coefficients being proportional to the frequency of each scenario observed during the examined period (either monthly or annually). In summary the methodology consists of (i) identifying the main dispersion scenarios for the street based on wind speed and directions data, (ii) creating a database of CFD-calculated concentration fields for the identified dispersion scenarios, and (iii) combining the CFD results based on the frequency of occurrence of each dispersion scenario during the examined period. The methodology has been applied to calculate monthly and annually averaged benzene concentration at several locations within the street canyon so that a direct comparison with observations could be made. The results of this study indicate that, within the simplifying assumption of non-buoyant flow, CFD modelling can aid understanding of long-term air quality measurements, and help assessing the representativeness of monitoring locations for population exposure studies.

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

Investigation of Primary Factors Affecting the Variation of Modeled Oak Pollen Concentrations: A Case Study for Southeast Texas in 2010

NASA Astrophysics Data System (ADS)

Jeon, Wonbae; Choi, Yunsoo; Roy, Anirban; Pan, Shuai; Price, Daniel; Hwang, Mi-Kyoung; Kim, Kyu Rang; Oh, Inbo

2018-02-01

Oak pollen concentrations over the Houston-Galveston-Brazoria (HGB) area in southeastern Texas were modeled and evaluated against in-situ data. We modified the Community Multi-scale Air Quality (CMAQ) model to include oak pollen emission, dispersion, and deposition. The Oak Pollen Emission Model (OPEM) calculated gridded oak pollen emissions, which are based on a parameterized equation considering a plant-specific factor ( C e ), surface characteristics, and meteorology. The simulation period was chosen to be February 21 to April 30 in the spring of 2010, when the observed monthly mean oak pollen concentrations were the highest in six years (2009-2014). The results indicated C e and meteorology played an important role in the calculation of oak pollen emissions. While C e was critical in determining the magnitude of oak pollen emissions, meteorology determined their variability. In particular, the contribution of the meteorology to the variation in oak pollen emissions increased with the oak pollen emission rate. The evaluation results using in-situ surface data revealed that the model underestimated pollen concentrations and was unable to accurately reproduce the peak pollen episodes. The model error was likely due to uncertainty in climatology-based C e used for the estimation of oak pollen emissions and inaccuracy in the wind fields from the Weather Research and Forecast (WRF) model.

When Spreadsheets Become Software - Quality Control Challenges and Approaches - 13360

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fountain, Stefanie A.; Chen, Emmie G.; Beech, John F.

2013-07-01

As part of a preliminary waste acceptance criteria (PWAC) development, several commercial models were employed, including the Hydrologic Evaluation of Landfill Performance model (HELP) [1], the Disposal Unit Source Term - Multiple Species model (DUSTMS) [2], and the Analytical Transient One, Two, and Three-Dimensional model (AT123D) [3]. The results of these models were post-processed in MS Excel spreadsheets to convert the model results to alternate units, compare the groundwater concentrations to the groundwater concentration thresholds, and then to adjust the waste contaminant masses (based on average concentration over the waste volume) as needed in an attempt to achieve groundwater concentrationsmore » at the limiting point of assessment that would meet the compliance concentrations while maximizing the potential use of the landfill (i.e., maximizing the volume of projected waste being generated that could be placed in the landfill). During the course of the PWAC calculation development, one of the Microsoft (MS) Excel spreadsheets used to post-process the results of the commercial model packages grew to include more than 575,000 formulas across 18 worksheets. This spreadsheet was used to assess six base scenarios as well as nine uncertainty/sensitivity scenarios. The complexity of the spreadsheet resulted in the need for a rigorous quality control (QC) procedure to verify data entry and confirm the accuracy of formulas. (authors)« less

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed.

PubMed

Feng, Zhujing; Schilling, Keith E; Chan, Kung-Sik

2013-06-01

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.

Modelling the influence of total suspended solids on E. coli removal in river water.

PubMed

Qian, Jueying; Walters, Evelyn; Rutschmann, Peter; Wagner, Michael; Horn, Harald

2016-01-01

Following sewer overflows, fecal indicator bacteria enter surface waters and may experience different lysis or growth processes. A 1D mathematical model was developed to predict total suspended solids (TSS) and Escherichia coli concentrations based on field measurements in a large-scale flume system simulating a combined sewer overflow. The removal mechanisms of natural inactivation, UV inactivation, and sedimentation were modelled. For the sedimentation process, one, two or three particle size classes were incorporated separately into the model. Moreover, the UV sensitivity coefficient α and natural inactivation coefficient kd were both formulated as functions of TSS concentration. It was observed that the E. coli removal was predicted more accurately by incorporating two particle size classes. However, addition of a third particle size class only improved the model slightly. When α and kd were allowed to vary with the TSS concentration, the model was able to predict E. coli fate and transport at different TSS concentrations accurately and flexibly. A sensitivity analysis revealed that the mechanisms of UV and natural inactivation were more influential at low TSS concentrations, whereas the sedimentation process became more important at elevated TSS concentrations.

Application of Physiologically Based Absorption Modeling to Characterize the Pharmacokinetic Profiles of Oral Extended Release Methylphenidate Products in Adults

PubMed Central

Yang, Xiaoxia; Duan, John; Fisher, Jeffrey

2016-01-01

A previously presented physiologically-based pharmacokinetic model for immediate release (IR) methylphenidate (MPH) was extended to characterize the pharmacokinetic behaviors of oral extended release (ER) MPH formulations in adults for the first time. Information on the anatomy and physiology of the gastrointestinal (GI) tract, together with the biopharmaceutical properties of MPH, was integrated into the original model, with model parameters representing hepatic metabolism and intestinal non-specific loss recalibrated against in vitro and in vivo kinetic data sets with IR MPH. A Weibull function was implemented to describe the dissolution of different ER formulations. A variety of mathematical functions can be utilized to account for the engineered release/dissolution technologies to achieve better model performance. The physiological absorption model tracked well the plasma concentration profiles in adults receiving a multilayer-release MPH formulation or Metadate CD, while some degree of discrepancy was observed between predicted and observed plasma concentration profiles for Ritalin LA and Medikinet Retard. A local sensitivity analysis demonstrated that model parameters associated with the GI tract significantly influenced model predicted plasma MPH concentrations, albeit to varying degrees, suggesting the importance of better understanding the GI tract physiology, along with the intestinal non-specific loss of MPH. The model provides a quantitative tool to predict the biphasic plasma time course data for ER MPH, helping elucidate factors responsible for the diverse plasma MPH concentration profiles following oral dosing of different ER formulations. PMID:27723791

Mathematical modeling improves EC50 estimations from classical dose-response curves.

PubMed

Nyman, Elin; Lindgren, Isa; Lövfors, William; Lundengård, Karin; Cervin, Ida; Sjöström, Theresia Arbring; Altimiras, Jordi; Cedersund, Gunnar

2015-03-01

The β-adrenergic response is impaired in failing hearts. When studying β-adrenergic function in vitro, the half-maximal effective concentration (EC50 ) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50 . Instead, we have applied a mathematical modeling approach to interpret the full dose-response curve in a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose-response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time-resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180-525 nm) is higher than the classically interpreted EC50 (46-191 nm). Mathematical modeling thus makes it possible to re-interpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible. The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database, and may be accessed at http://jjj.bio.vu.nl/database/nyman. © 2015 FEBS.

Assessing microbial competition in a hydrogen-based membrane biofilm reactor (MBfR) using multidimensional modeling.

PubMed

Martin, Kelly J; Picioreanu, Cristian; Nerenberg, Robert

2015-09-01

The membrane biofilm reactor (MBfR) is a novel technology that safely delivers hydrogen to the base of a denitrifying biofilm via gas-supplying membranes. While hydrogen is an effective electron donor for denitrifying bacteria (DNB), it also supports sulfate-reducing bacteria (SRB) and methanogens (MET), which consume hydrogen and create undesirable by-products. SRB and MET are only competitive for hydrogen when local nitrate concentrations are low, therefore SRB and MET primarily grow near the base of the biofilm. In an MBfR, hydrogen concentrations are greatest at the base of the biofilm, making SRB and MET more likely to proliferate in an MBfR system than a conventional biofilm reactor. Modeling results showed that because of this, control of the hydrogen concentration via the intramembrane pressure was a key tool for limiting SRB and MET development. Another means is biofilm management, which supported both sloughing and erosive detachment. For the conditions simulated, maintaining thinner biofilms promoted higher denitrification fluxes and limited the presence of SRB and MET. The 2-d modeling showed that periodic biofilm sloughing helped control slow-growing SRB and MET. Moreover, the rough (non-flat) membrane assembly in the 2-d model provided a special niche for SRB and MET that was not represented in the 1-d model. This study compared 1-d and 2-d biofilm model applicability for simulating competition in counter-diffusional biofilms. Although more computationally expensive, the 2-d model captured important mechanisms unseen in the 1-d model. © 2015 Wiley Periodicals, Inc.

Simulation of daily pesticide concentrations from watershed characteristics and monthly climatic data

USGS Publications Warehouse

Vecchia, Aldo V.; Crawford, Charles G.

2006-01-01

A time-series model was developed to simulate daily pesticide concentrations for streams in the coterminous United States. The model was based on readily available information on pesticide use, climatic variability, and watershed charac-teristics and was used to simulate concentrations for four herbicides [atrazine, ethyldipropylthiocarbamate (EPTC), metolachlor, and trifluralin] and three insecticides (carbofuran, ethoprop, and fonofos) that represent a range of physical and chemical properties, application methods, national application amounts, and areas of use in the United States. The time-series model approximates the probability distributions, seasonal variability, and serial correlation characteristics in daily pesticide concentration data from a national network of monitoring stations. The probability distribution of concentrations for a particular pesticide and station was estimated using the Watershed Regressions for Pesticides (WARP) model. The WARP model, which was developed in previous studies to estimate the probability distribution, was based on selected nationally available watershed-characteristics data, such as pesticide use and soil characteristics. Normality transformations were used to ensure that the annual percentiles for the simulated concentrations agree closely with the percentiles estimated from the WARP model. Seasonal variability in the transformed concentrations was maintained by relating the transformed concentration to precipitation and temperature data from the United States Historical Climatology Network. The monthly precipitation and temperature values were estimated for the centroids of each watershed. Highly significant relations existed between the transformed concentrations, concurrent monthly precipitation, and concurrent and lagged monthly temperature. The relations were consistent among the different pesticides and indicated the transformed concentrations generally increased as precipitation increased but the rate of increase depended on a temperature-dependent growing-season effect. Residual variability of the transformed concentrations, after removal of the effects of precipitation and temperature, was partitioned into a signal (systematic variability that is related from one day to the next) and noise (random variability that is not related from one day to the next). Variograms were used to evaluate measurement error, seasonal variability, and serial correlation of the historical data. The variogram analysis indicated substantial noise resulted, at least in part, from measurement errors (the differences between the actual concen-trations and the laboratory concentrations). The variogram analysis also indicated the presence of a strongly correlated signal, with an exponentially decaying serial correlation function and a correlation time scale (the time required for the correlation to decay to e-1 equals 0.37) that ranged from about 18 to 66 days, depending on the pesticide type. Simulated daily pesticide concentrations from the time-series model indicated the simulated concentrations for the stations located in the northeastern quadrant of the United States where most of the monitoring stations are located generally were in good agreement with the data. The model neither consistently overestimated or underestimated concentrations for streams that are located in this quadrant and the magnitude and timing of high or low concentrations generally coincided reasonably well with the data. However, further data collection and model development may be necessary to determine whether the model should be used for areas for which few historical data are available.

Computational Fluid Dynamics Modeling of Bacillus anthracis Spore Deposition in Rabbit and Human Respiratory Airways

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kabilan, Senthil; Suffield, Sarah R.; Recknagle, Kurtis P.

Three-dimensional computational fluid dynamics and Lagrangian particle deposition models were developed to compare the deposition of aerosolized Bacillus anthracis spores in the respiratory airways of a human with that of the rabbit, a species commonly used in the study of anthrax disease. The respiratory airway geometries for each species were derived from computed tomography (CT) or µCT images. Both models encompassed airways that extended from the external nose to the lung with a total of 272 outlets in the human model and 2878 outlets in the rabbit model. All simulations of spore deposition were conducted under transient, inhalation-exhalation breathing conditionsmore » using average species-specific minute volumes. The highest exposure concentration was modeled in the rabbit based upon prior acute inhalation studies. For comparison, human simulation was also conducted at the same concentration. Results demonstrated that regional spore deposition patterns were sensitive to airway geometry and ventilation profiles. Due to the complex airway geometries in the rabbit nose, higher spore deposition efficiency was predicted in the upper conducting airways compared to the human at the same air concentration of anthrax spores. As a result, higher particle deposition was predicted in the conducting airways and deep lung of the human compared to the rabbit lung due to differences in airway branching pattern. This information can be used to refine published and ongoing biokinetic models of inhalation anthrax spore exposures, which currently estimate deposited spore concentrations based solely upon exposure concentrations and inhaled doses that do not factor in species-specific anatomy and physiology.« less

Aripiprazole Lauroxil: Pharmacokinetic Profile of This Long-Acting Injectable Antipsychotic in Persons With Schizophrenia.

PubMed

Hard, Marjie L; Mills, Richard J; Sadler, Brian M; Turncliff, Ryan Z; Citrome, Leslie

2017-06-01

Aripiprazole lauroxil is an extended-release prodrug of aripiprazole for intramuscular injection, approved for schizophrenia treatment. We developed a population pharmacokinetic (PopPK) model to characterize aripiprazole lauroxil PK and evaluate dosing scenarios likely to be encountered in clinical practice. Data from 616 patients with schizophrenia, collected from 5 clinical studies, were used to construct the PopPK model. The model was subsequently used to evaluate various dose levels and frequency and the impact of dosing delay on aripiprazole concentrations. The results of the model indicate that aripiprazole is released into the systemic circulation after 5 to 6 days, and release continues for an additional 36 days. The slow increase in aripiprazole concentration after injection necessitates the coadministration of oral aripiprazole for 21 days with the first injection. Based on the PopPK model simulations, a dosing interval of 882 mg every 6 weeks results in aripiprazole concentrations that fall within the concentration range associated with the efficacious aripiprazole lauroxil dose range (441-882 mg dosed monthly). A 662-mg monthly dose also resulted in aripiprazole concentrations within the efficacious dose range. Aripiprazole lauroxil administration results in prolonged exposure, such that dose delays of 2 to 4 weeks, depending on the dose regimen, do not require oral aripiprazole supplementation upon resumption of dosing. This PopPK model and model-based simulations were effective means for evaluating aripiprazole lauroxil dosing regimens and management of missed doses. Such analyses play an important role in determining the use of this long-acting antipsychotic in clinical practice.

Utilization of the concentric circle model in clinical nursing: a review.

PubMed

Kazuma, K

1999-12-01

In this article, I review applications of the concentric circle model in clinical nursing. The concentric circle model is based on the cross-sectional shape of the body extremities at several points, and can be used in the areas of both kinesiology and nutritional science. This model makes it possible to calculate the cross-sectional area of muscles from measurement of the circumference of the extremities and the thickness of adipose (fatty) tissue. Then, changes in muscle strength or nutritional status can be inferred or assessed from these data. This model requires only simple and non-invasive measurements, and this is a significant and essential characteristic for its use by nurses, both in clinical and research applications.

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Agent-Based Computational Modeling to Examine How Individual Cell Morphology Affects Dosimetry

EPA Science Inventory

Cell-based models utilizing high-content screening (HCS) data have applications for predictive toxicology. Evaluating concentration-dependent effects on cell fate and state response is a fundamental utilization of HCS data.Although HCS assays may capture quantitative readouts at ...

Development of Multi-Route Physiologically-based Pharmacokinetic Models for Ethanol in the Adult, Pregnant, and Neonatal Rat

EPA Science Inventory

Biofuel blends of 10% ethanol (EtOH) and gasoline are common in the United States, and higher EtOH concentrations are being considered (15-85%). Currently, no physiologically-based pharmacokinetic (PBPK) models are available to describe the kinetics of EtOH-based biofuels. PBPK...

Modelling mercury accumulation in minerogenic peat combining FTIR-ATR spectroscopy and partial least squares (PLS)

NASA Astrophysics Data System (ADS)

Pérez-Rodríguez, Marta; Horák-Terra, Ingrid; Rodríguez-Lado, Luis; Martínez Cortizas, Antonio

2016-11-01

Despite its potential, infrared spectroscopy combined with multivariate statistics has been seldom used to model peat properties with environmental value, such us the concentration of potentially toxic metals. In this research, we applied attenuated total reflectance (ATR) Fourier-Transform Infrared (FTIR) spectroscopy to evaluate the ability of the technique to predict mercury concentrations in late-Pleistocene/Holocene peat from a minerogenic peatland from Minas Gerais (Brazil). Mercury concentrations were analysed using a Milestone DMA-80 analyzer and attenuated total reflectance FTIR-ATR was performed using a Gladi-ATR (Pike Technologies) in the mid IR spectrum (4000-400 cm- 1). Concentrations were modelled using principal components (PCR) and partial least squares regression (PLS). The performance of the models varied between moderate and very good (R2 0.67-0.90), with low RMSD values (0.35-1.06). A PLS model based on three latent vectors (LV1 to LV3) provided the best (R2 0.90, RMSD 0.35) results. LV1 reflected total organic matter content versus mineral matter (mainly quartz from local fluxes), LV2 was related to dust deposition from regional sources, and LV3 reflected peat organic matter decomposition. Compared to a previous investigation based on geochemical data, the spectroscopy-based PLS model performed better, but it has to be complemented with additional data (as δ13 C ratios) to reliably reproduce the changes of the factors controlling mercury accumulation over time. This, time- and cost-effective, methodology may help to develop multi-core approaches to study the within and between mire (of a similar type and area) variability in mercury accumulation, and probably also other peat properties. Fig. S2 Loadings weights of the three and two significant components from the direct (dPCR) and transposed (trPCR) PCR models. Fig. S3 Depth records of the cumulative effects of the factors involved in the variation of mercury concentrations. Left, MIR-PLS model; centre, MIR-PLS + δ13 C data model; right, geochemical model from Pérez-Rodríguez et al. [44].

Application of a Three-Layer Photochemical Box Model in an Athens Street Canyon.

PubMed

Proyou, Athena G; Ziomas, Loannis C; Stathopoulos, Antony

1998-05-01

The aim of this paper is to show that a photochemical box model could describe the air pollution diurnal profiles within a typical street canyon in the city of Athens. As sophisticated three-dimensional dispersion models are computationally expensive and they cannot serve to simulate pollution levels in the scale of an urban street canyon, a suitably modified three-layer photochemical box model was applied. A street canyon of Athens with heavy traffic was chosen to apply the aforementioned model. The model was used to calculate pollutant concentrations during two days with meteorological conditions favoring pollutant accumulation. Road traffic emissions were calculated based on existing traffic load measurements. Meteorological data, as well as various pollutant concentrations, in order to compare with the model results, were provided by available measurements. The calculated concentrations were found to be in good agreement with measured concentration levels and show that, when traffic load and traffic composition data are available, this model can be used to predict pollution episodes. It is noteworthy that high concentrations persisted, even after additional traffic restriction measures were taken on the second day because of the high pollution levels.

Inter-comparison of dynamic models for radionuclide transfer to marine biota in a Fukushima accident scenario

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vives i Batlle, J.; Beresford, N. A.; Beaugelin-Seiller, K.

We report an inter-comparison of eight models designed to predict the radiological exposure of radionuclides in marine biota. The models were required to simulate dynamically the uptake and turnover of radionuclides by marine organisms. Model predictions of radionuclide uptake and turnover using kinetic calculations based on biological half-life (TB1/2) and/or more complex metabolic modelling approaches were used to predict activity concentrations and, consequently, dose rates of 90Sr, 131I and 137Cs to fish, crustaceans, macroalgae and molluscs under circumstances where the water concentrations are changing with time. For comparison, the ERICA Tool, a model commonly used in environmental assessment, and whichmore » uses equilibrium concentration ratios, was also used. As input to the models we used hydrodynamic forecasts of water and sediment activity concentrations using a simulated scenario reflecting the Fukushima accident releases. Although model variability is important, the intercomparison gives logical results, in that the dynamic models predict consistently a pattern of delayed rise of activity concentration in biota and slow decline instead of the instantaneous equilibrium with the activity concentration in seawater predicted by the ERICA Tool. The differences between ERICA and the dynamic models increase the shorter the TB1/2 becomes; however, there is significant variability between models, underpinned by parameter and methodological differences between them. The need to validate the dynamic models used in this intercomparison has been highlighted, particularly in regards to optimisation of the model biokinetic parameters.« less

A functional form for injected MRI Gd-chelate contrast agent concentration incorporating recirculation, extravasation and excretion

NASA Astrophysics Data System (ADS)

Horsfield, Mark A.; Thornton, John S.; Gill, Andrew; Jager, H. Rolf; Priest, Andrew N.; Morgan, Bruno

2009-05-01

A functional form for the vascular concentration of MRI contrast agent after intravenous bolus injection was developed that can be used to model the concentration at any vascular site at which contrast concentration can be measured. The form is based on previous models of blood circulation, and is consistent with previously measured data at long post-injection times, when the contrast agent is fully and evenly dispersed in the blood. It allows the first-pass and recirculation peaks of contrast agent to be modelled, and measurement of the absolute concentration of contrast agent at a single time point allows the whole time course to be rescaled to give absolute contrast agent concentration values. This measure of absolute concentration could be performed at a long post-injection time using either MRI or blood-sampling methods. In order to provide a model that is consistent with measured data, it was necessary to include both rapid and slow extravasation, together with excretion via the kidneys. The model was tested on T1-weighted data from the descending aorta and hepatic portal vein, and on T*2-weighted data from the cerebral arteries. Fitting of the model was successful for all datasets, but there was a considerable variation in fit parameters between subjects, which suggests that the formation of a meaningful population-averaged vascular concentration function is precluded.

Calibration-free self-absorption model for measuring nitric oxide concentration in a pulsed corona discharge.

PubMed

Du, Yanjun; Ding, Yanjun; Liu, Yufeng; Lan, Lijuan; Peng, Zhimin

2014-08-01

The effect of self-absorption on emission intensity distributions can be used for species concentration measurements. A calculation model is developed based on the Beer-Lambert law to quantify this effect. And then, a calibration-free measurement method is proposed on the basis of this model by establishing the relationship between gas concentration and absorption strength. The effect of collision parameters and rotational temperature on the method is also discussed. The proposed method is verified by investigating the nitric oxide emission bands (A²Σ⁺→X²∏) that are generated by a pulsed corona discharge at various gas concentrations. Experiment results coincide well with the expectations, thus confirming the precision and accuracy of the proposed measurement method.

Modeling and analysis of the solar concentrator in photovoltaic systems

NASA Astrophysics Data System (ADS)

Mroczka, Janusz; Plachta, Kamil

2015-06-01

The paper presents the Λ-ridge and V-trough concentrator system with a low concentration ratio. Calculations and simulations have been made in the program created by the author. The results of simulation allow to choose the best parameters of photovoltaic system: the opening angle between the surface of the photovoltaic module and mirrors, resolution of the tracking system and the material for construction of the concentrator mirrors. The research shows the effect each of these parameters on the efficiency of the photovoltaic system and method of surface modeling using BRDF function. The parameters of concentrator surface (eg. surface roughness) were calculated using a new algorithm based on the BRDF function. The algorithm uses a combination of model Torrance-Sparrow and HTSG. The simulation shows the change in voltage, current and output power depending on system parameters.

Improving Riverine Constituent Concentration and Flux Estimation by Accounting for Antecedent Discharge Conditions

NASA Astrophysics Data System (ADS)

Zhang, Q.; Ball, W. P.

2016-12-01

Regression-based approaches are often employed to estimate riverine constituent concentrations and fluxes based on typically sparse concentration observations. One such approach is the WRTDS ("Weighted Regressions on Time, Discharge, and Season") method, which has been shown to provide more accurate estimates than prior approaches. Centered on WRTDS, this work was aimed at developing improved models for constituent concentration and flux estimation by accounting for antecedent discharge conditions. Twelve modified models were developed and tested, each of which contains one additional variable to represent antecedent conditions. High-resolution ( daily) data at nine monitoring sites were used to evaluate the relative merits of the models for estimation of six constituents - chloride (Cl), nitrate-plus-nitrite (NOx), total Kjeldahl nitrogen (TKN), total phosphorus (TP), soluble reactive phosphorus (SRP), and suspended sediment (SS). For each site-constituent combination, 30 concentration subsets were generated from the original data through Monte Carlo sub-sampling and then used to evaluate model performance. For the sub-sampling, three sampling strategies were adopted: (A) 1 random sample each month (12/year), (B) 12 random monthly samples plus additional 8 random samples per year (20/year), and (C) 12 regular (non-storm) and 8 storm samples per year (20/year). The modified models show general improvement over the original model under all three sampling strategies. Major improvements were achieved for NOx by the long-term flow-anomaly model and for Cl by the ADF (average discounted flow) model and the short-term flow-anomaly model. Moderate improvements were achieved for SS, TP, and TKN by the ADF model. By contrast, no such achievement was achieved for SRP by any proposed model. In terms of sampling strategy, performance of all models was generally best using strategy C and worst using strategy A, and especially so for SS, TP, and SRP, confirming the value of routinely collecting storm-flow samples. Overall, this work provides a comprehensive set of statistical evidence for supporting the incorporation of antecedent discharge conditions into WRTDS for constituent concentration and flux estimation, thereby combining the advantages of two recent developments in water quality modeling.

Comparison of two trajectory based models for locating particle sources for two rural New York sites

NASA Astrophysics Data System (ADS)

Zhou, Liming; Hopke, Philip K.; Liu, Wei

Two back trajectory-based statistical models, simplified quantitative transport bias analysis and residence-time weighted concentrations (RTWC) have been compared for their capabilities of identifying likely locations of source emissions contributing to observed particle concentrations at Potsdam and Stockton, New York. Quantitative transport bias analysis (QTBA) attempts to take into account the distribution of concentrations around the directions of the back trajectories. In full QTBA approach, deposition processes (wet and dry) are also considered. Simplified QTBA omits the consideration of deposition. It is best used with multiple site data. Similarly the RTWC approach uses concentrations measured at different sites along with the back trajectories to distribute the concentration contributions across the spatial domain of the trajectories. In this study, these models are used in combination with the source contribution values obtained by the previous positive matrix factorization analysis of particle composition data from Potsdam and Stockton. The six common sources for the two sites, sulfate, soil, zinc smelter, nitrate, wood smoke and copper smelter were analyzed. The results of the two methods are consistent and locate large and clearly defined sources well. RTWC approach can find more minor sources but may also give unrealistic estimations of the source locations.

Global Reference Atmospheric Model and Trace Constituents

NASA Technical Reports Server (NTRS)

Justus, C.; Johnson, D.; Parker, Nelson C. (Technical Monitor)

2002-01-01

Global Reference Atmospheric Model (GRAM-99) is an engineering-level model of the Earth's atmosphere. It provides both mean values and perturbations for density, temperature, pressure, and winds, as well as monthly- and geographically-varying trace constituent concentrations. From 0-27 km, thermodynamics and winds are based on National Oceanic and Atmospheric Administration Global Upper Air Climatic Atlas (GUACA) climatology. Above 120 km, GRAM is based on the NASA Marshall Engineering Thermosphere (MET) model. In the intervening altitude region, GRAM is based on Middle Atmosphere Program (MAP) climatology that also forms the basis of the 1986 COSPAR Intemationa1 Reference Atmosphere (CIRA). MAP data in GRAM are augmented by a specially-derived longitude variation climatology. Atmospheric composition is represented in GRAM by concentrations of both major and minor species. Above 120 km, MET provides concentration values for N2, O2, Ar, O, He, and H. Below 120 km, species represented also include H2O, O3, N2O, CO, CH, and CO2. Water vapor in GRAM is based on a combination of GUACA, Air Force Geophysics Laboratory (AFGL), and NASA Langley Research Center climatologies. Other constituents below 120 km are based on a combination of AFGL and h4AP/CIRA climatologies. This report presents results of comparisons between GRAM Constituent concentrations and those provided by the Naval Research Laboratory (NRL) climatology of Summers (NRL,/MR/7641-93-7416, 1993). GRAM and NRL concentrations were compared for seven species (CH4, CO, CO2, H2O, N2O, O2, and O3) for months January, April, July, and October, over height range 0-115 km, and latitudes -90deg to + 90deg at 10deg increments. Average GRAM-NRL correlations range from 0.878 (for CO) to 0.975 (for O3), with an average over all seven species of 0.936 (standard deviation 0.049).

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

USGS Publications Warehouse

Francy, Donna S.; Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M.G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection.

PubMed

Francy, Donna S; Stelzer, Erin A; Duris, Joseph W; Brady, Amie M G; Harrison, John H; Johnson, Heather E; Ware, Michael W

2013-03-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Developing confidence in adverse outcome pathway-based ...

EPA Pesticide Factsheets

An adverse outcome pathway (AOP) description linking inhibition of aromatase (cytochrome P450 [cyp] 19) to reproductive dysfunction was reviewed for scientific and technical quality and endorsed by the OECD. An intended application of the AOP framework is to support the use of mechanistic or pathway-based data to infer or predict chemical hazards and apical adverse outcomes. As part of this work, ToxCast high throughput screening data were used to identify a chemicals’ ability to inhibit aromatase activity in vitro. Twenty-four hour in vivo exposures, focused on effects on production and circulating concentrations of 17β-estradiol (E2), key events in the AOP, were conducted to verify in vivo activity. Based on these results, imazalil was selected as a case study chemical to test an AOP-based hazard prediction. A computational model of the fish hypothalamic-pituitary-gonadal-liver axis and a statistically-based model of oocyte growth dynamics were used to predict impacts of different concentrations of imazalil on multiple key events along the AOP, assuming continuous exposure for 21 d. Results of the model simulations were used to select test concentrations and design a fathead minnow reproduction study in which fish were exposed to 20, 60, or 200 µg imazalil/L for durations of 2.5, 10, or 21d. Within 60 h of exposure, female fathead minnows showed significant reductions in ex vivo production of E2, circulating E2 concentrations, and significant increases in

Reactive solute transport in acidic streams

USGS Publications Warehouse

Broshears, R.E.

1996-01-01

Spatial and temporal profiles of Ph and concentrations of toxic metals in streams affected by acid mine drainage are the result of the interplay of physical and biogeochemical processes. This paper describes a reactive solute transport model that provides a physically and thermodynamically quantitative interpretation of these profiles. The model combines a transport module that includes advection-dispersion and transient storage with a geochemical speciation module based on MINTEQA2. Input to the model includes stream hydrologic properties derived from tracer-dilution experiments, headwater and lateral inflow concentrations analyzed in field samples, and a thermodynamic database. Simulations reproduced the general features of steady-state patterns of observed pH and concentrations of aluminum and sulfate in St. Kevin Gulch, an acid mine drainage stream near Leadville, Colorado. These patterns were altered temporarily by injection of sodium carbonate into the stream. A transient simulation reproduced the observed effects of the base injection.

Grey-box modelling of aeration tank settling.

PubMed

Bechman, Henrik; Nielsen, Marinus K; Poulsen, Niels Kjølstad; Madsen, Henrik

2002-04-01

A model of the concentrations of suspended solids (SS) in the aeration tanks and in the effluent from these during Aeration tank settling (ATS) operation is established. The model is based on simple SS mass balances, a model of the sludge settling and a simple model of how the SS concentration in the effluent from the aeration tanks depends on the actual concentrations in the tanks and the sludge blanket depth. The model is formulated in continuous time by means of stochastic differential equations with discrete-time observations. The parameters of the model are estimated using a maximum likelihood method from data from an alternating BioDenipho waste water treatment plant (WWTP). The model is an important tool for analyzing ATS operation and for selecting the appropriate control actions during ATS, as the model can be used to predict the SS amounts in the aeration tanks as well as in the effluent from the aeration tanks.

Efficacy of a surfactant-based wound dressing on biofilm control.

PubMed

Percival, Steven L; Mayer, Dieter; Salisbury, Anne-Marie

2017-09-01

The aim of this study was to evaluate the efficacy of both a nonantimicrobial and antimicrobial (1% silver sulfadiazine-SSD) surfactant-based wound dressing in the control of Pseudomonas aeruginosa, Enterococcus sp, Staphylococcus epidermidis, Staphylococcus aureus, and methicillin-resistant S. aureus (MRSA) biofilms. Anti-biofilm efficacy was evaluated in numerous adapted American Standards for Testing and Materials (ASTM) standard biofilm models and other bespoke biofilm models. The ASTM standard models employed included the Minimum biofilm eradication concentration (MBEC) biofilm model (ASTM E2799) and the Centers for Disease Control (CDC) biofilm reactor model (ASTM 2871). Such bespoke biofilm models included the filter biofilm model and the chamberslide biofilm model. Results showed complete kill of microorganisms within a biofilm using the antimicrobial surfactant-based wound dressing. Interestingly, the nonantimicrobial surfactant-based dressing could disrupt existing biofilms by causing biofilm detachment. Prior to biofilm detachment, we demonstrated, using confocal laser scanning microscopy (CLSM), the dispersive effect of the nonantimicrobial surfactant-based wound dressing on the biofilm within 10 minutes of treatment. Furthermore, the non-antimicrobial surfactant-based wound dressing caused an increase in microbial flocculation/aggregation, important for microbial concentration. In conclusion, this nonantimicrobial surfactant-based wound dressing leads to the effective detachment and dispersion of in vitro biofilms. The use of surfactant-based wound dressings in a clinical setting may help to disrupt existing biofilm from wound tissue and may increase the action of antimicrobial treatment. © 2017 by the Wound Healing Society.

A thermodynamic model for the solubility of HfO2(am) in the aqueous K +– HCO 3 -– CO 3 2-–O -–H 2O system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rai, Dhanpat; Kitamura, Akira; Rosso, Kevin M.

Solubility of HfO2(am) was determined as a function of KHCO3 concentrations ranging from 0.001 mol·kg-1 to 0.1 mol·kg-1. The solubility of HfO2(am) increased dramatically with the increase in KHCO3 concentrations, indicating that Hf(IV) makes strong complexes with carbonate. Thermodynamic equilibrium constants for the formation of Hf-carbonate complexes were determined using both the Pitzer and SIT models. The dramatic increase in Hf concentrations with the increase in KHCO3 concentrations can best be described by the formation of Hf(OH-)2(CO3)22- and Hf(CO3)56-. The log10 K0 values for the reactions [Hf4++2CO32-+2OH-⇌Hf(OH)2(CO3)22-] and [Hf4++5CO32-⇌Hf(CO3)56-], based on the SIT model, were determined to be 44.53±0.46 andmore » 41.53±0.46, respectively, and based on the Pitzer model they were 44.56±0.48 and 40.20±0.48, respectively.« less

Local-Scale Air Quality Modeling in Support of Human Health and Exposure Research (Invited)

NASA Astrophysics Data System (ADS)

Isakov, V.

2010-12-01

Spatially- and temporally-sparse information on air quality is a key concern for air-pollution-related environmental health studies. Monitor networks are sparse in both space and time, are costly to maintain, and are often designed purposely to avoid detecting highly localized sources. Recent studies have shown that more narrowly defining the geographic domain of the study populations and improvements in the measured/estimated ambient concentrations can lead to stronger associations between air pollution and hospital admissions and mortality records. Traditionally, ambient air quality measurements have been used as a primary input to support human health and exposure research. However, there is increasing evidence that the current ambient monitoring network is not capturing sharp gradients in exposure due to the presence of high concentration levels near, for example, major roadways. Many air pollutants exhibit large concentration gradients near large emitters such as major roadways, factories, ports, etc. To overcome these limitations, researchers are now beginning to use air quality models to support air pollution exposure and health studies. There are many advantages to using air quality models over traditional approaches based on existing ambient measurements alone. First, models can provide spatially- and temporally-resolved concentrations as direct input to exposure and health studies and thus better defining the concentration levels for the population in the geographic domain. Air quality models have a long history of use in air pollution regulations, and supported by regulatory agencies and a large user community. Also, models can provide bidirectional linkages between sources of emissions and ambient concentrations, thus allowing exploration of various mitigation strategies to reduce risk to exposure. In order to provide best estimates of air concentrations to support human health and exposure studies, model estimates should consider local-scale features, regional-scale transport, and photochemical transformations. Since these needs are currently not met by a single model, hybrid air quality modeling has recently been developed to combine these capabilities. In this paper, we present the results of two studies where we applied the hybrid modeling approach to provide spatial and temporal details in air quality concentrations to support exposure and health studies: a) an urban-scale air quality accountability study involving near-source exposures to multiple ambient air pollutants, and b) an urban-scale epidemiological study involving human health data based on emergency department visits.

Modeling biotic uptake by periphyton and transient hyporrheic storage of nitrate in a natural stream

USGS Publications Warehouse

Kim, Brian K.A.; Jackman, Alan P.; Triska, Frank J.

1992-01-01

To a convection-dispersion hydrologic transport model we coupled a transient storage submodel (Bencala, 1984) and a biotic uptake submodel based on Michaelis-Menten kinetics (Kim et al., 1990). Our purpose was threefold: (1) to simulate nitrate retention in response to change in load in a third-order stream, (2) to differentiate biotic versus hydrologie factors in nitrate retention, and (3) to produce a research tool whose properties are consistent with laboratory and field observations. Hydrodynamic parameters were fitted from chloride concentration during a 20-day chloride-nitrate coinjection (Bencala, 1984), and biotic uptake kinetics were based on flume studies by Kim et al. (1990) and Triska et al. (1983). Nitrate concentration from the 20-day coinjection experiment served as a base for model validation. The complete transport retention model reasonably predicted the observed nitrate concentration. However, simulations which lacked either the transient storage submodel or the biotic uptake submodel poorly predicted the observed nitrate concentration. Model simulations indicated that transient storage in channel and hyporrheic interstices dominated nitrate retention within the first 24 hours, whereas biotic uptake dominated thereafter. A sawtooth function for Vmax ranging from 0.10 to 0.17 μg NO3-N s−1 gAFDM−1 (grams ash free dry mass) slightly underpredicted nitrate retention in simulations of 2–7 days. This result was reasonable since uptake by other nitrate-demanding processes were not included. The model demonstrated how ecosystem retention is an interaction between physical and biotic processes and supports the validity of coupling separate hydrodynamic and reactive submodels to established solute transport models in biological studies of fluvial ecosystems.

Environmental exposure modeling and monitoring of human pharmaceutical concentrations in the environment

USGS Publications Warehouse

Versteeg, D.J.; Alder, A. C.; Cunningham, V. L.; Kolpin, D.W.; Murray-Smith, R.; Ternes, T.

2005-01-01

Human pharmaceuticals are receiving increased attention as environmental contaminants. This is due to their biological activity and the number of monitoring programs focusing on analysis of these compounds in various environmental media and compartments. Risk assessments are needed to understand the implications of reported concentrations; a fundamental part of the risk assessment is an assessment of environmental exposures. The purpose of this chapter is to provide guidance on the use of predictive tools (e.g., models) and monitoring data in exposure assessments for pharmaceuticals in the environment. Methods to predict environmental concentrations from equations based on first principles are presented. These equations form the basis of existing GIS (geographic information systems)-based systems for understanding the spatial distribution of pharmaceuticals in the environment. The pharmaceutical assessment and transport (PhATE), georeferenced regional exposure assessment tool for European rivers (GREAT-ER), and geographical information system (GIS)-ROUT models are reviewed and recommendations are provided concerning the design and execution of monitoring studies. Model predictions and monitoring data are compared to evaluate the relative utility of each approach in environmental exposure assessments. In summary, both models and monitoring data can be used to define representative exposure concentrations of pharmaceuticals in the environment in support of environmental risk assessments.

Physiologically based pharmacokinetic model for 6-mercpatopurine: exploring the role of genetic polymorphism in TPMT enzyme activity

PubMed Central

Ogungbenro, Kayode; Aarons, Leon

2015-01-01

Aims To extend the physiologically based pharmacokinetic (PBPK) model developed for 6-mercaptopurine to account for intracellular metabolism and to explore the role of genetic polymorphism in the TPMT enzyme on the pharmacokinetics of 6-mercaptopurine. Methods The developed PBPK model was extended for 6-mercaptopurine to account for intracellular metabolism and genetic polymorphism in TPMT activity. System and drug specific parameters were obtained from the literature or estimated using plasma or intracellular red blood cell concentrations of 6-mercaptopurine and its metabolites. Age-dependent changes in parameters were implemented for scaling, and variability was also introduced for simulation. The model was validated using published data. Results The model was extended successfully. Parameter estimation and model predictions were satisfactory. Prediction of intracellular red blood cell concentrations of 6-thioguanine nucleotide for different TPMT phenotypes (in a clinical study that compared conventional and individualized dosing) showed results that were consistent with observed values and reported incidence of haematopoietic toxicity. Following conventional dosing, the predicted mean concentrations for homozygous and heterozygous variants, respectively, were about 10 times and two times the levels for wild-type. However, following individualized dosing, the mean concentration was around the same level for the three phenotypes despite different doses. Conclusions The developed PBPK model has been extended for 6-mercaptopurine and can be used to predict plasma 6-mercaptopurine and tissue concentration of 6-mercaptopurine, 6-thioguanine nucleotide and 6-methylmercaptopurine ribonucleotide in adults and children. Predictions of reported data from clinical studies showed satisfactory results. The model may help to improve 6-mercaptopurine dosing, achieve better clinical outcome and reduce toxicity. PMID:25614061

The effect of binary mixtures of zinc, copper, cadmium, and nickel on the growth of the freshwater diatom Navicula pelliculosa and comparison with mixture toxicity model predictions.

PubMed

Nagai, Takashi; De Schamphelaere, Karel A C

2016-11-01

The authors investigated the effect of binary mixtures of zinc (Zn), copper (Cu), cadmium (Cd), and nickel (Ni) on the growth of a freshwater diatom, Navicula pelliculosa. A 7 × 7 full factorial experimental design (49 combinations in total) was used to test each binary metal mixture. A 3-d fluorescence microplate toxicity assay was used to test each combination. Mixture effects were predicted by concentration addition and independent action models based on a single-metal concentration-response relationship between the relative growth rate and the calculated free metal ion activity. Although the concentration addition model predicted the observed mixture toxicity significantly better than the independent action model for the Zn-Cu mixture, the independent action model predicted the observed mixture toxicity significantly better than the concentration addition model for the Cd-Zn, Cd-Ni, and Cd-Cu mixtures. For the Zn-Ni and Cu-Ni mixtures, it was unclear which of the 2 models was better. Statistical analysis concerning antagonistic/synergistic interactions showed that the concentration addition model is generally conservative (with the Zn-Ni mixture being the sole exception), indicating that the concentration addition model would be useful as a method for a conservative first-tier screening-level risk analysis of metal mixtures. Environ Toxicol Chem 2016;35:2765-2773. © 2016 SETAC. © 2016 SETAC.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing

NASA Astrophysics Data System (ADS)

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture—for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments—as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series—daily Poaceae pollen concentrations over the period 2006-2014—was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.

PubMed

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Nephelometry and turbidimetry to assess concentration and dispersion of coal dust in mines

NASA Astrophysics Data System (ADS)

Yushchenko, VP; Legky, VN; Demidov, DE

2018-03-01

The article considers the model of the optical instrument to measure coal dust concentration in mines based on the turbidimetric and nephelometric methods. The calculated data on the intensity of transmitted and scattered waves depending on coal dust concentration and on the size of coal dust particles are presented.

A multimedia fate and chemical transport modeling system for pesticides: II. Model evaluation

NASA Astrophysics Data System (ADS)

Li, Rong; Scholtz, M. Trevor; Yang, Fuquan; Sloan, James J.

2011-07-01

Pesticides have adverse health effects and can be transported over long distances to contaminate sensitive ecosystems. To address problems caused by environmental pesticides we developed a multimedia multi-pollutant modeling system, and here we present an evaluation of the model by comparing modeled results against measurements. The modeled toxaphene air concentrations for two sites, in Louisiana (LA) and Michigan (MI), are in good agreement with measurements (average concentrations agree to within a factor of 2). Because the residue inventory showed no soil residues at these two sites, resulting in no emissions, the concentrations must be caused by transport; the good agreement between the modeled and measured concentrations suggests that the model simulates atmospheric transport accurately. Compared to the LA and MI sites, the measured air concentrations at two other sites having toxaphene soil residues leading to emissions, in Indiana and Arkansas, showed more pronounced seasonal variability (higher in warmer months); this pattern was also captured by the model. The model-predicted toxaphene concentration fraction on particles (0.5-5%) agrees well with measurement-based estimates (3% or 6%). There is also good agreement between modeled and measured dry (1:1) and wet (within a factor of less than 2) depositions in Lake Ontario. Additionally this study identified erroneous soil residue data around a site in Texas in a published US toxaphene residue inventory, which led to very low modeled air concentrations at this site. Except for the erroneous soil residue data around this site, the good agreement between the modeled and observed results implies that both the US and Mexican toxaphene soil residue inventories are reasonably good. This agreement also suggests that the modeling system is capable of simulating the important physical and chemical processes in the multimedia compartments.

Real-time identification of indoor pollutant source positions based on neural network locator of contaminant sources and optimized sensor networks.

PubMed

Vukovic, Vladimir; Tabares-Velasco, Paulo Cesar; Srebric, Jelena

2010-09-01

A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.

Numerical simulation of trace element transport on subsurface environment pollution in coal mine spoil.

PubMed

Qiang, Xue; Bing, Liang; Hui-yun, Wang; Lei, Liu

2006-01-01

An understanding of the dynamic behavior of trace elements leaching from coal mine spoil is important in predicting the groundwater quality. The relationship between trace element concentrations and leaching times, pH values of the media is studied. Column leaching tests conducted in the laboratory showed that there was a close correlation between pH value and trace element concentrations. The longer the leaching time, the higher the trace element concentrations. Different trace elements are differently affected by pH values of leaching media. A numerical model for water flow and trace element transport has been developed based on analyzing the characteristics of migration and transformation of trace elements leached from coal mine spoil. Solutions to the coupled model are accomplished by Eulerian-Lagrangian localized adjoint method. Numerical simulation shows that rainfall intensity determined maximum leaching depth. As rainfall intensity is 3.6ml/s, the outflow concentrations indicate a breakthrough of trace elements beyond the column base, with peak concentration at 90cm depth. And the subsurface pollution range has a trend of increase with time. The model simulations are compared to experimental results of trace element concentrations, with reasonable agreement between them. The analysis and modeling of trace elements suggested that the infiltration of rainwater through the mine spoil might lead to potential groundwater pollution. It provides theoretical evidence for quantitative assessment soil-water quality of trace element transport on environment pollution.

Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press.

PubMed

De Leersnyder, Fien; Peeters, Elisabeth; Djalabi, Hasna; Vanhoorne, Valérie; Van Snick, Bernd; Hong, Ke; Hammond, Stephen; Liu, Angela Yang; Ziemons, Eric; Vervaet, Chris; De Beer, Thomas

2018-03-20

A calibration model for in-line API quantification based on near infrared (NIR) spectra collection during tableting in the tablet press feed frame was developed and validated. First, the measurement set-up was optimised and the effect of filling degree of the feed frame on the NIR spectra was investigated. Secondly, a predictive API quantification model was developed and validated by calculating the accuracy profile based on the analysis results of validation experiments. Furthermore, based on the data of the accuracy profile, the measurement uncertainty was determined. Finally, the robustness of the API quantification model was evaluated. An NIR probe (SentroPAT FO) was implemented into the feed frame of a rotary tablet press (Modul™ P) to monitor physical mixtures of a model API (sodium saccharine) and excipients with two different API target concentrations: 5 and 20% (w/w). Cutting notches into the paddle wheel fingers did avoid disturbances of the NIR signal caused by the rotating paddle wheel fingers and hence allowed better and more complete feed frame monitoring. The effect of the design of the notched paddle wheel fingers was also investigated and elucidated that straight paddle wheel fingers did cause less variation in NIR signal compared to curved paddle wheel fingers. The filling degree of the feed frame was reflected in the raw NIR spectra. Several different calibration models for the prediction of the API content were developed, based on the use of single spectra or averaged spectra, and using partial least squares (PLS) regression or ratio models. These predictive models were then evaluated and validated by processing physical mixtures with different API concentrations not used in the calibration models (validation set). The β-expectation tolerance intervals were calculated for each model and for each of the validated API concentration levels (β was set at 95%). PLS models showed the best predictive performance. For each examined saccharine concentration range (i.e., between 4.5 and 6.5% and between 15 and 25%), at least 95% of future measurements will not deviate more than 15% from the true value. Copyright © 2018 Elsevier B.V. All rights reserved.

Source Parameter Estimation using the Second-order Closure Integrated Puff Model

DTIC Science & Technology

The sensor measurements are categorized as triggered and non-triggered based on the recorded concentration measurements and a threshold...concentration value. Using each measured value, sources of adjoint material are created from the triggered and non-triggered sensors, and the adjoint transport...equations are solved to predict the adjoint concentration fields. The adjoint source strength is inversely proportional to the concentration measurement

Retrieval of Atmospheric Particulate Matter Using Satellite Data Over Central and Eastern China

NASA Astrophysics Data System (ADS)

Chen, G. L.; Guang, J.; Li, Y.; Che, Y. H.; Gong, S. Q.

2018-04-01

Fine particulate matter (PM2.5) is a particle cluster with diameters less than or equal to 2.5 μm. Over the past few decades, regional air pollution composed of PM2.5 has frequently occurred over Central and Eastern China. In order to estimate the concentration, distribution and other properties of PM2.5, the general retrieval models built by establishing the relationship between aerosol optical depth (AOD) and PM2.5 has been widely used in many studies, including experimental models via statistics analysis and physical models with certain physical mechanism. The statistical experimental models can't be extended to other areas or historical period due to its dependence on the ground-based observations and necessary auxiliary data, which limits its further application. In this paper, a physically based model is applied to estimate the concentration of PM2.5 over Central and Eastern China from 2007 to 2016. The ground-based PM2.5 measurements were used to be as reference data to validate our retrieval results. Then annual variation and distribution of PM2.5 concentration in the Central and Eastern China was analysed. Results shows that the annual average PM2.5 show a trend of gradually increasing and then decreasing during 2007-2016, with the highest value in 2011.

Modeling Complex Marine Ecosystems: An Investigation of Two Teaching Approaches with Fifth Graders

ERIC Educational Resources Information Center

Papaevripidou, M.; Constantinou, C. P.; Zacharia, Z. C.

2007-01-01

This study investigated acquisition and transfer of the modeling ability of fifth graders in various domains. Teaching interventions concentrated on the topic of marine ecosystems either through a modeling-based approach or a worksheet-based approach. A quasi-experimental (pre-post comparison study) design was used. The control group (n = 17)…

Morphine Glucuronidation and Elimination in Intensive Care Patients: A Comparison with Healthy Volunteers.

PubMed

Ahlers, Sabine J G M; Välitalo, Pyry A J; Peeters, Mariska Y M; Gulik, Laura van; van Dongen, Eric P A; Dahan, Albert; Tibboel, Dick; Knibbe, Catherijne A J

2015-11-01

Although morphine is used frequently to treat pain in the intensive care unit, its pharmacokinetics has not been adequately quantified in critically ill patients. We evaluated the glucuronidation and elimination clearance of morphine in intensive care patients compared with healthy volunteers based on the morphine and morphine-3-glucuronide (M3G) concentrations. A population pharmacokinetic model with covariate analysis was developed with the nonlinear mixed-effects modeling software (NONMEM 7.3). The analysis included 3012 morphine and M3G concentrations from 135 intensive care patients (117 cardiothoracic surgery patients and 18 critically ill patients), who received continuous morphine infusions adapted to individual pain levels, and 622 morphine and M3G concentrations from a previously published study of 20 healthy volunteers, who received an IV bolus of morphine followed by a 1-hour infusion. For morphine, a 3-compartment model best described the data, whereas for M3G, a 1-compartment model fits best. In intensive care patients with a normal creatinine concentration, a decrease of 76% was estimated in M3G clearance compared with healthy subjects, conditional on the M3G volume of distribution being the same in intensive care patients and healthy volunteers. Furthermore, serum creatinine concentration was identified as a covariate for both elimination clearance of M3G in intensive care patients and unchanged morphine clearance in all patients and healthy volunteers. Under the assumptions in the model, M3G elimination was significantly decreased in intensive care patients when compared with healthy volunteers, which resulted in substantially increased M3G concentrations. Increased M3G levels were even more pronounced in patients with increased serum creatinine levels. Model-based simulations show that, because of the reduction in morphine clearance in intensive care patients with renal failure, a 33% reduction in the maintenance dose would result in morphine serum concentrations equal to those in healthy volunteers and intensive care patients with normal renal function, although M3G concentrations remain increased. Future pharmacodynamic investigations are needed to identify target concentrations in this population, after which final dosing recommendations can be made.

Measurement and Modeling of Respiration Rate of Tomato (Cultivar Roma) for Modified Atmosphere Storage.

PubMed

Kandasamy, Palani; Moitra, Ranabir; Mukherjee, Souti

2015-01-01

Experiments were conducted to determine the respiration rate of tomato at 10, 20 and 30 °C using closed respiration system. Oxygen depletion and carbon dioxide accumulation in the system containing tomato was monitored. Respiration rate was found to decrease with increasing CO2 and decreasing O2 concentration. Michaelis-Menten type model based on enzyme kinetics was evaluated using experimental data generated for predicting the respiration rate. The model parameters that obtained from the respiration rate at different O2 and CO2 concentration levels were used to fit the model against the storage temperatures. The fitting was fair (R2 = 0.923 to 0.970) when the respiration rate was expressed as O2 concentation. Since inhibition constant for CO2 concentration tended towards negetive, the model was modified as a function of O2 concentration only. The modified model was fitted to the experimental data and showed good agreement (R2 = 0.998) with experimentally estimated respiration rate.

Fractional kalman filter to estimate the concentration of air pollution

NASA Astrophysics Data System (ADS)

Vita Oktaviana, Yessy; Apriliani, Erna; Khusnul Arif, Didik

2018-04-01

Air pollution problem gives important effect in quality environment and quality of human’s life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution’s problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

Predicting herbicide and biocide concentrations in rivers across Switzerland

NASA Astrophysics Data System (ADS)

Wemyss, Devon; Honti, Mark; Stamm, Christian

2014-05-01

Pesticide concentrations vary strongly in space and time. Accordingly, intensive sampling is required to achieve a reliable quantification of pesticide pollution. As this requires substantial resources, loads and concentration ranges in many small and medium streams remain unknown. Here, we propose partially filling the information gap for herbicides and biocides by using a modelling approach that predicts stream concentrations without site-specific calibration simply based on generally available data like land use, discharge and nation-wide consumption data. The simple, conceptual model distinguishes herbicide losses from agricultural fields, private gardens and biocide losses from buildings (facades, roofs). The herbicide model is driven by river discharge and the applied herbicide mass; the biocide model requires precipitation and the footprint area of urban areas containing the biocide. The model approach allows for modelling concentrations across multiple catchments at the daily, or shorter, time scale and for small to medium-sized catchments (1 - 100 km2). Four high resolution sampling campaigns in the Swiss Plateau were used to calibrate the model parameters for six model compounds: atrazine, metolachlor, terbuthylazine, terbutryn, diuron and mecoprop. Five additional sampled catchments across Switzerland were used to directly compare the predicted to the measured concentrations. Analysis of the first results reveals a reasonable simulation of the concentration dynamics for specific rainfall events and across the seasons. Predicted concentration ranges are reasonable even without site-specific calibration. This indicates the transferability of the calibrated model directly to other areas. However, the results also demonstrate systematic biases in that the highest measured peaks were not attained by the model. Probable causes for these deviations are conceptual model limitations and input uncertainty (pesticide use intensity, local precipitation, etc.). Accordingly, the model will be conceptually improved. This presentation will present the model simulations and compare the performance of the original and the modified model versions. Finally, the model will be applied across approximately 50% of the catchments in the Swiss Plateau, where necessary input data is available and where the model concept can be reasonably applied.

Prediction of Intensity Change Subsequent to Concentric Eyewall Events

NASA Astrophysics Data System (ADS)

Mauk, Rachel Grant

Concentric eyewall events have been documented numerous times in intense tropical cyclones over the last two decades. During a concentric eyewall event, an outer (secondary) eyewall forms around the inner (primary) eyewall. Improved instrumentation on aircraft and satellites greatly increases the likelihood of detecting an event. Despite the increased ability to detect such events, forecasts of intensity changes during and after these events remain poor. When concentric eyewall events occur near land, accurate intensity change predictions are especially critical to ensure proper emergency preparations and staging of recovery assets. A nineteen-year (1997-2015) database of concentric eyewall events is developed by analyzing microwave satellite imagery, aircraft- and land-based radar, and other published documents. Events are identified in both the North Atlantic and eastern North Pacific basins. TCs are categorized as single (1 event), serial (>= 2 events) and super-serial (>= 3 events). Key findings here include distinct spatial patterns for single and serial Atlantic TCs, a broad seasonal distribution for eastern North Pacific TCs, and apparent ENSO-related variability in both basins. The intensity change subsequent to the concentric eyewall event is calculated from the HURDAT2 database at time points relative to the start and to the end of the event. Intensity change is then categorized as Weaken (≤ -10 kt), Maintain (+/- 5 kt), and Strengthen (≥ 10 kt). Environmental conditions in which each event occurred are analyzed based on the SHIPS diagnostic files. Oceanic, dynamic, thermodynamic, and TC status predictors are selected for testing in a multiple discriminant analysis procedure to determine which variables successfully discriminate the intensity change category and the occurrence of additional concentric eyewall events. Intensity models are created for 12 h, 24 h, 36 h, and 48 h after the concentric eyewall events end. Leave-one-out cross validation is performed on each set of discriminators to generate classifications, which are then compared to observations. For each model, the top combinations achieve 80-95% overall accuracy in classifying TCs based on the environmental characteristics, although Maintain systems are frequently misclassified. The third part of this dissertation employs the Weather Research and Forecasting model to further investigate concentric eyewall events. Two serial Atlantic concentric eyewall cases (Katrina 2005 and Wilma 2005) are selected from the original study set, and WRF simulations performed using several model designs. Despite strong evidence from multiple sources that serial concentric eyewalls formed in both hurricanes, the WRF simulations did not produce identifiable concentric eyewall structures for Katrina, and only transient structures for Wilma. Possible reasons for the lack of concentric eyewall formation are discussed, including model resolution, microphysics, and data sources.

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information.

PubMed

Pan, Long; Yao, Enjian; Yang, Yang

2016-12-01

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

Comparing Lagrangian and Eulerian models for CO2 transport - a step towards Bayesian inverse modeling using WRF/STILT-VPRM

NASA Astrophysics Data System (ADS)

Pillai, D.; Gerbig, C.; Kretschmer, R.; Beck, V.; Karstens, U.; Neininger, B.; Heimann, M.

2012-10-01

We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which two models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data.

A novel growth mode of Physarum polycephalum during starvation

NASA Astrophysics Data System (ADS)

Lee, Jonghyun; Oettmeier, Christina; Döbereiner, Hans-Günther

2018-06-01

Organisms are constantly looking to forage and respond to various environmental queues to maximize their chance of survival. This is reflected in the unicellular organism Physarum polycephalum, which is known to grow as an optimized network. Here, we describe a new growth pattern of Physarum mesoplasmodium, where sheet-like motile bodies termed ‘satellites’ are formed. This non-network pattern formation is induced only when nutrients are scarce, suggesting that it is a type of emergency response. Our goal is to construct a model to describe the behaviour of satellites based on negative chemotaxis. We conjecture a diffusion-based model which implements detection of a signal molecule above a threshold concentration. Then we calculate how far the satellites must travel until the concentration signal falls below the threshold. These calculated distances are in good agreement with the distances where satellites stop. Based on the Akaike weight analysis, our threshold model is at least 2.3 times more likely to be the better model than the others we have considered. Based on the model, we estimate the diffusion coefficient of this molecule, which corresponds to typical signalling molecules.

SVM-based multisensor data fusion for phase concentration measurement in biomass-coal co-combustion

NASA Astrophysics Data System (ADS)

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2018-05-01

In this paper, the electrical method combines the electrostatic sensor and capacitance sensor to measure the phase concentration of pulverized coal/biomass/air three-phase flow through data fusion technology. In order to eliminate the effects of flow regimes and improve the accuracy of the phase concentration measurement, the mel frequency cepstrum coefficient features extracted from electrostatic signals are used to train the Continuous Gaussian Mixture Hidden Markov Model (CGHMM) for flow regime identification. Support Vector Machine (SVM) is introduced to establish the concentration information fusion model under identified flow regimes. The CGHMM models and SVM models are transplanted on digital signal processing (DSP) to realize on-line accurate measurement. The DSP flow regime identification time is 1.4 ms, and the concentration predict time is 164 μs, which can fully meet the real-time requirement. The average absolute value of the relative error of the pulverized coal is about 1.5% and that of the biomass is about 2.2%.

Modeling Carbon-Black/Polymer Composite Sensors

PubMed Central

Lei, Hua; Pitt, William G.; McGrath, Lucas K.; Ho, Clifford K.

2012-01-01

Conductive polymer composite sensors have shown great potential in identifying gaseous analytes. To more thoroughly understand the physical and chemical mechanisms of this type of sensor, a mathematical model was developed by combining two sub-models: a conductivity model and a thermodynamic model, which gives a relationship between the vapor concentration of analyte(s) and the change of the sensor signals. In this work, 64 chemiresistors representing eight different carbon concentrations (8–60 vol% carbon) were constructed by depositing thin films of a carbon-black/polyisobutylene composite onto concentric spiral platinum electrodes on a silicon chip. The responses of the sensors were measured in dry air and at various vapor pressures of toluene and trichloroethylene. Three parameters in the conductivity model were determined by fitting the experimental data. It was shown that by applying this model, the sensor responses can be adequately predicted for given vapor pressures; furthermore the analyte vapor concentrations can be estimated based on the sensor responses. This model will guide the improvement of the design and fabrication of conductive polymer composite sensors for detecting and identifying mixtures of organic vapors. PMID:22518071

A simplified building airflow model for agent concentration prediction.

PubMed

Jacques, David R; Smith, David A

2010-11-01

A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.

Short Communication: Is Ethanol-Based Hand Sanitizer Involved in Acute Pancreatitis after Excessive Disinfection?—An Evaluation with the Use of PBPK Model

PubMed Central

Huynh-Delerme, Céline; Artigou, Catherine; Bodin, Laurent; Tardif, Robert; Charest-Tardif, Ginette; Verdier, Cécile; Sater, Nessryne; Ould-Elhkim, Mostafa; Desmares, Catherine

2012-01-01

An occupational physician reported to the French Health Products Safety Agency (Afssaps) a case of adverse effect of acute pancreatitis (AP) in a teaching nurse, after multiple demonstrations with ethanol-based hand sanitizers (EBHSs) used in a classroom with defective mechanical ventilation. It was suggested by the occupational physician that the exposure to ethanol may have produced a significant blood ethanol concentration and subsequently the AP. In order to verify if the confinement situation due to defective mechanical ventilation could increase the systemic exposure to ethanol via inhalation route, a physiologically based pharmacokinetic (PBPK) modeling was used to predict ethanol blood levels. Under the worst case scenario, the simulation by PBPK modeling showed that the maximum blood ethanol concentration which can be predicted of 5.9 mg/l is of the same order of magnitude to endogenous ethanol concentration (mean = 1.1 mg/L; median = 0.4 mg/L; range = 0–35 mg/L) in nondrinker humans (Al-Awadhi et al., 2004). The present study does not support the likelihood that EBHS leads to an increase in systemic ethanol concentration high enough to provoke an acute pancreatitis. PMID:22577377

Aircraft- and ground-based assessment of the CCN-AOD relationship and implications on model analysis of ACI and underlying aerosol processes

NASA Astrophysics Data System (ADS)

Shinozuka, Y.; Clarke, A. D.; Nenes, A.; Lathem, T. L.; Redemann, J.; Jefferson, A.; Wood, R.

2014-12-01

Contrary to common assumptions in satellite-based modeling of aerosol-cloud interactions, ∂logCCN/∂logAOD is less than unity, i.e., the number concentration of cloud condensation nuclei (CCN) less than doubles as aerosol optical depth (AOD) doubles. This can be explained by omnipresent aerosol processes. Condensation, coagulation and cloud processing, for example, generally make particles scatter more light while hardly increasing their number. This paper reports on the relationship in local air masses between CCN concentration, aerosol size distribution and light extinction observed from aircraft and the ground at diverse locations. The CCN-to-local-extinction relationship, when averaged over ~1 km distance and sorted by the wavelength dependence of extinction, varies approximately by a factor of 2, reflecting the variability in aerosol intensive properties. This, together with retrieval uncertainties and the variability in aerosol spatio-temporal distribution and hygroscopic growth, challenges satellite-based CCN estimates. However, the large differences in estimated CCN may correspond to a considerably lower uncertainty in cloud drop number concentration (CDNC), given the sublinear response of CDNC to CCN. Overall, our findings from airborne and ground-based observations call for model-based reexamination of aerosol-cloud interactions and underlying aerosol processes.

Realization of BP neural network modeling based on NOXof CFB boiler in DCS

NASA Astrophysics Data System (ADS)

Bai, Jianyun; Zhu, Zhujun; Wang, Qi; Ying, Jiang

2018-02-01

In the CFB boiler installed with SNCR denitrification system, the mass concentration of NO X is difficult to be predicted by the conventional mathematical model, and the step response mathematical model, obtained by using the step disturbance test of ammonia injection,is inaccurate. this paper presents two kinds of BP neural network model, according to the relationship between the generated mass concentration of NO X and the load, the ratio of air to coal without using the SNCR system, as well as the relationship between the tested mass concentration of NO X and the load, the ratio of air to coal and the amount of ammonia using the SNCR system. then itrealized the on-line prediction of the mass concentration of NO X and the remaining mass concentration of NO X after reductionreaction in DCS system. the practical results show that the average error per hour between generation and the prediction of the amount of NO X mass concentration is within 10 mg/Nm3,the reducing reaction of measured and predicted hourly average error is within 2 mg/Nm3, all in error range, which provides a more accurate model for solvingthe problem on NO X automatic control of SNCR system.

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

PubMed

Heddam, Salim

2014-01-01

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling.

Development of a population pharmacokinetic model for carbamazepine based on sparse therapeutic monitoring data from pediatric patients with epilepsy.

PubMed

Carlsson, Kristin Cecilie; Hoem, Nils Ove; Glauser, Tracy; Vinks, Alexander A

2005-05-01

Population models can be important extensions of therapeutic drug monitoring (TDM), as they allow estimation of individual pharmacokinetic parameters based on a small number of measured drug concentrations. This study used a Bayesian approach to explore the utility of routinely collected and sparse TDM data (1 sample per patient) for carbamazepine (CBZ) monotherapy in developing a population pharmacokinetic (PPK) model for CBZ in pediatric patients that would allow prediction of CBZ concentrations for both immediate- and controlled-release formulations. Patient and TDM data were obtained from a pediatric neurology outpatient database. Data were analyzed using an iterative 2-stage Bayesian algorithm and a nonparametric adaptive grid algorithm. Models were compared by final log likelihood, mean error (ME) as a measure of bias, and root mean squared error (RMSE) as a measure of precision. Fifty-seven entries with data on CBZ monotherapy were identified from the database and used in the analysis (36 from males, 21 from females; mean [SD] age, 9.1 [4.4] years [range, 2-21 years]). Preliminary models estimating clearance (Cl) or the elimination rate constant (K(el)) gave good prediction of serum concentrations compared with measured serum concentrations, but estimates of Cl and K(el) were highly correlated with estimates of volume of distribution (V(d)). Different covariate models were then tested. The selected model had zero-order input and had age and body weight as covariates. Cl (L/h) was calculated as K(el) . V(d), where K(el) = [K(i) - (K(s) . age)] and V(d) = [V(i) + (V(s) . body weight)]. Median parameter estimates were V(i) (intercept) = 11.5 L (fixed); V(s) (slope) = 0.3957 L/kg (range, 0.01200-1.5730); K(i) (intercept) = 0.173 h(-1) (fixed); and K(s) (slope) = 0.004487 h(-1) . y(-1) (range, 0.0001800-0.02969). The fit was good for estimates of steady-state serum concentrations based on prior values (population median estimates) (R = 0.468; R(2) = 0.219) but was even better for predictions based on individual Bayesian posterior values (R(2) = 0.991), with little bias (ME = -0.079) and good precision (RMSE = 0.055). Based on the findings of this study, sparse TDM data can be used for PPK modeling of CBZ clearance in children with epilepsy, and these models can be used to predict Cl at steady state in pediatric patients. However, to estimate additional pharmacokinetic model parameters (eg, the absorption rate constant and V(d)), it would be necessary to combine sparse TDM data with additional well-timed samples. This would allow development of more informative PPK models that could be used as part of Bayesian dose-individualization strategies.

Characterization of air pollutant concentrations, fleet emission factors, and dispersion near a North Carolina interstate freeway across two seasons

NASA Astrophysics Data System (ADS)

Saha, Provat K.; Khlystov, Andrey; Snyder, Michelle G.; Grieshop, Andrew P.

2018-03-01

We present field measurement data and modeling of multiple traffic-related air pollutants during two seasons at a site adjoining Interstate 40, near Durham, North Carolina. We analyze spatial-temporal and seasonal trends and fleet-average pollutant emission factors and use our data to evaluate a line source dispersion model. Month-long measurement campaigns were performed in summer 2015 and winter 2016. Data were collected at a fixed near-road site located within 10 m from the highway edge, an upwind background site and, under favorable meteorological conditions, along downwind perpendicular transects. Measurements included the size distribution, chemical composition, and volatility of submicron particles, black carbon (BC), nitrogen oxides (NOx), meteorological conditions and traffic activity data. Results show strong seasonal and diurnal differences in spatial distribution of traffic sourced pollutants. A strong signature of vehicle emissions was observed within 100-150 m from the highway edge with significantly higher concentrations during morning. Substantially higher concentrations and less-sharp near-road gradients were observed in winter for many species. Season-specific fleet-average fuel-based emission factors for NO, NOx, BC, and particle number (PN) were derived based on up- and down-wind roadside measurements. The campaign-average NOx and PN emission factors were 20% and 300% higher in winter than summer, respectively. These results suggest that the combined effect of higher emissions and their slower downwind dispersion in winter dictate the observed higher downwind concentrations and wider highway influence zone in winter for several species. Finally, measurements of traffic data, emission factors, and pollutant concentrations were integrated to evaluate a line source dispersion model (R-LINE). The dispersion model captured the general trends in the spatial and temporal patterns in near-road concentrations. However, there was a tendency for the model to under-predict concentrations near the road in the mornings and over-predict concentrations in the evenings.

A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995-2006.

PubMed

Ocaña-Peinado, Francisco M; Valderrama, Mariano J; Bouzas, Paula R

2013-05-01

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995-2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.

Numerical modeling of sorption kinetics of organic compounds to soil and sediment particles

NASA Astrophysics Data System (ADS)

Wu, Shian-chee; Gschwend, Phillip M.

1988-08-01

A numerical model is developed to simulate hydrophobic organic compound sorption kinetics, based on a retarded intraaggregate diffusion conceptualization of this solid-water exchange process. This model was used to ascertain the sensitivity of the sorption process for various sorbates to nonsteady solution concentrations and to polydisperse soil or sediment aggregate particle size distributions. Common approaches to modeling sorption kinetics amount to simplifications of our model and appear justified only when (1) the concentration fluctuations occur on a time scale which matches the sorption timescale of interest and (2) the particle size distribution is relatively narrow. Finally, a means is provided to estimate the extent of approach of a sorbing system to equilibrium as a function of aggregate size, chemical diffusivity and hydrophobicity, and system solids concentration.

Cadmium transfer from contaminated soils to the human body through rice consumption in southern Jiangsu Province, China.

PubMed

Li, Tianyuan; Chang, Qing; Yuan, Xuyin; Li, Jizhou; Ayoko, Godwin A; Frost, Ray L; Chen, Hongyan; Zhang, Xinjian; Song, Yinxian; Song, Wenzhi

2017-06-21

Consumption of crops grown in cadmium-contaminated soils is an important Cd exposure route to humans. The present study utilizes statistical analysis and in vitro digestion experiments to uncover the transfer processes of Cd from soils to the human body through rice consumption. Here, a model was created to predict the levels of bioaccessible Cd in rice grains using phytoavailable Cd quantities in the soil. During the in vitro digestion, a relatively constant ratio between the total and bioaccessible Cd in rice was observed. About 14.89% of Cd in soils was found to be transferred into rice grains and up to 3.19% could be transferred from rice grains to the human body. This model was able to sufficiently predict rice grain cadmium concentrations based on CaCl 2 extracted zinc and cadmium concentrations in soils (R 2 = 0.862). The bioaccessible Cd concentration in rice grains was also able to be predicted using CaCl 2 extracted cadmium from soil (R 2 = 0.892). The models established in this study demonstrated that CaCl 2 is a suitable indicator of total rice Cd concentrations and bioaccessible rice grain Cd concentrations. The chain model approach proposed in this study can be used for the fast and accurate evaluation of human Cd exposure through rice consumption based on the soil conditions in contaminated regions.

Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring.

PubMed

Kerckhoffs, Jules; Hoek, Gerard; Vlaanderen, Jelle; van Nunen, Erik; Messier, Kyle; Brunekreef, Bert; Gulliver, John; Vermeulen, Roel

2017-11-01

Land-use regression (LUR) models for ultrafine particles (UFP) and Black Carbon (BC) in urban areas have been developed using short-term stationary monitoring or mobile platforms in order to capture the high variability of these pollutants. However, little is known about the comparability of predictions of mobile and short-term stationary models and especially the validity of these models for assessing residential exposures and the robustness of model predictions developed in different campaigns. We used an electric car to collect mobile measurements (n = 5236 unique road segments) and short-term stationary measurements (3 × 30min, n = 240) of UFP and BC in three Dutch cities (Amsterdam, Utrecht, Maastricht) in 2014-2015. Predictions of LUR models based on mobile measurements were compared to (i) measured concentrations at the short-term stationary sites, (ii) LUR model predictions based on short-term stationary measurements at 1500 random addresses in the three cities, (iii) externally obtained home outdoor measurements (3 × 24h samples; n = 42) and (iv) predictions of a LUR model developed based upon a 2013 mobile campaign in two cities (Amsterdam, Rotterdam). Despite the poor model R 2 of 15%, the ability of mobile UFP models to predict measurements with longer averaging time increased substantially from 36% for short-term stationary measurements to 57% for home outdoor measurements. In contrast, the mobile BC model only predicted 14% of the variation in the short-term stationary sites and also 14% of the home outdoor sites. Models based upon mobile and short-term stationary monitoring provided fairly high correlated predictions of UFP concentrations at 1500 randomly selected addresses in the three Dutch cities (R 2 = 0.64). We found higher UFP predictions (of about 30%) based on mobile models opposed to short-term model predictions and home outdoor measurements with no clear geospatial patterns. The mobile model for UFP was stable over different settings as the model predicted concentration levels highly correlated to predictions made by a previously developed LUR model with another spatial extent and in a different year at the 1500 random addresses (R 2 = 0.80). In conclusion, mobile monitoring provided robust LUR models for UFP, valid to use in epidemiological studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of the source area of rooftop scalar measurements in London, UK using wind tunnel and modelling approaches.

NASA Astrophysics Data System (ADS)

Brocklehurst, Aidan; Boon, Alex; Barlow, Janet; Hayden, Paul; Robins, Alan

2014-05-01

The source area of an instrument is an estimate of the area of ground over which the measurement is generated. Quantification of the source area of a measurement site provides crucial context for analysis and interpretation of the data. A range of computational models exists to calculate the source area of an instrument, but these are usually based on assumptions which do not hold for instruments positioned very close to the surface, particularly those surrounded by heterogeneous terrain i.e. urban areas. Although positioning instrumentation at higher elevation (i.e. on masts) is ideal in urban areas, this can be costly in terms of installation and maintenance costs and logistically difficult to position instruments in the ideal geographical location. Therefore, in many studies, experimentalists turn to rooftops to position instrumentation. Experimental validations of source area models for these situations are very limited. In this study, a controlled tracer gas experiment was conducted in a wind tunnel based on a 1:200 scale model of a measurement site used in previous experimental work in central London. The detector was set at the location of the rooftop site as the tracer was released at a range of locations within the surrounding streets and rooftops. Concentration measurements are presented for a range of wind angles, with the spread of concentration measurements indicative of the source area distribution. Clear evidence of wind channeling by streets is seen with the shape of the source area strongly influenced by buildings upwind of the measurement point. The results of the wind tunnel study are compared to scalar concentration source areas generated by modelling approaches based on meteorological data from the central London experimental site and used in the interpretation of continuous carbon dioxide (CO2) concentration data. Initial conclusions will be drawn as to how to apply scalar concentration source area models to rooftop measurement sites and suggestions for their improvement to incorporate effects such as channeling.

Kinetic analysis of reactions of Si-based epoxy resins by near-infrared spectroscopy, 13C NMR and soft-hard modelling.

PubMed

Garrido, Mariano; Larrechi, Maria Soledad; Rius, F Xavier; Mercado, Luis Adolfo; Galià, Marina

2007-02-05

Soft- and hard-modelling strategy was applied to near-infrared spectroscopy data obtained from monitoring the reaction between glycidyloxydimethylphenyl silane, a silicon-based epoxy monomer, and aniline. On the basis of the pure soft-modelling approach and previous chemical knowledge, a kinetic model for the reaction was proposed. Then, multivariate curve resolution-alternating least squares optimization was carried out under a hard constraint, that compels the concentration profiles to fulfil the proposed kinetic model at each iteration of the optimization process. In this way, the concentration profiles of each species and the corresponding kinetic rate constants of the reaction, unpublished until now, were obtained. The results obtained were contrasted with 13C NMR. The joint interval test of slope and intercept for detecting bias was not significant (alpha=5%).

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2001-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Modeled and monitored variation in space and time of PCB-153 concentrations in air, sediment, soil and aquatic biota on a European scale.

PubMed

Hauck, Mara; Huijbregts, Mark A J; Hollander, Anne; Hendriks, A Jan; van de Meent, Dik

2010-08-15

We evaluated various modeling options for estimating concentrations of PCB-153 in the environment and in biota across Europe, using a nested multimedia fate model coupled with a bioaccumulation model. The most detailed model set up estimates concentrations in air, soil, fresh water sediment and fresh water biota with spatially explicit environmental characteristics and spatially explicit emissions to air and water in the period 1930-2005. Model performance was evaluated with the root mean square error (RMSE(log)), based on the difference between estimated and measured concentrations. The RMSE(log) was 5.4 for air, 5.6-6.3 for sediment and biota, and 5.5 for soil in the most detailed model scenario. Generally, model estimations tended to underestimate observed values for all compartments, except air. The decline in observed concentrations was also slightly underestimated by the model for the period where measurements were available (1989-2002). Applying a generic model setup with averaged emissions and averaged environmental characteristics, the RMSE(log) increased to 21 for air and 49 for sediment. For soil the RMSE(log) decreased to 3.5. We found that including spatial variation in emissions was most relevant for all compartments, except soil, while including spatial variation in environmental characteristics was less influential. For improving predictions of concentrations in sediment and aquatic biota, including emissions to water was found to be relevant as well. Copyright 2009 Elsevier B.V. All rights reserved.

Estimating human-equivalent no observed adverse-effect levels for VOCs (volatile organic compounds) based on minimal knowledge of physiological parameters. Technical paper

DOE Office of Scientific and Technical Information (OSTI.GOV)

Overton, J.H.; Jarabek, A.M.

1989-01-01

The U.S. EPA advocates the assessment of health-effects data and calculation of inhaled reference doses as benchmark values for gauging systemic toxicity to inhaled gases. The assessment often requires an inter- or intra-species dose extrapolation from no observed adverse effect level (NOAEL) exposure concentrations in animals to human equivalent NOAEL exposure concentrations. To achieve this, a dosimetric extrapolation procedure was developed based on the form or type of equations that describe the uptake and disposition of inhaled volatile organic compounds (VOCs) in physiologically-based pharmacokinetic (PB-PK) models. The procedure assumes allometric scaling of most physiological parameters and that the value ofmore » the time-integrated human arterial-blood concentration must be limited to no more than to that of experimental animals. The scaling assumption replaces the need for most parameter values and allows the derivation of a simple formula for dose extrapolation of VOCs that gives equivalent or more-conservative exposure concentrations values than those that would be obtained using a PB-PK model in which scaling was assumed.« less

Inverse modeling of Texas NOx emissions using space-based and ground-based NO2 observations

NASA Astrophysics Data System (ADS)

Tang, W.; Cohan, D. S.; Lamsal, L. N.; Xiao, X.; Zhou, W.

2013-11-01

Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite-observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with decoupled direct method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2-based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3-55% increase in modeled NO2 column densities and 1-7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

Inverse modeling of Texas NOx emissions using space-based and ground-based NO2 observations

NASA Astrophysics Data System (ADS)

Tang, W.; Cohan, D.; Lamsal, L. N.; Xiao, X.; Zhou, W.

2013-07-01

Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with Decoupled Direct Method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2 based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3-55% increase in modeled NO2 column densities and 1-7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saylor, Kyle, E-mail: saylor@vt.edu; Zhang, Chenmi

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down intomore » different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. - Highlights: • Modelling of nicotine disposition in the presence of anti-nicotine antibodies • Key vaccine efficacy factors are evaluated in silico in rats and in humans. • Model predicts insufficient antibody binding in past human nicotine vaccines. • Improving immunogenicity and antibody specificity may lead to vaccine success.« less

A model of the atmospheric metal deposition by cosmic dust particles

NASA Astrophysics Data System (ADS)

McNeil, W. J.

1993-11-01

We have developed a model of the deposition of meteoric metals in Earth's atmosphere. The model takes as input the total mass influx of material to the Earth and calculates the deposition rate at all altitudes through solution of the drag and subliminal equations in a Monte Carlo-type computation. The diffusion equation is then solved to give steady state concentration of complexes of specific metal species and kinetics are added to calculate the concentration of individual complexes. Concentrating on sodium, we calculate the Na(D) nightglow predicted by the model, and by introduction of seasonal variations in lower tropospheric ozone based on experimental results, we are able to duplicate the seasonal variation of mid-latitude nightglow data.

Remote sensing of oligotrophic waters: model divergence at low chlorophyll concentrations.

PubMed

Mehrtens, Hela; Martin, Thomas

2002-11-20

The performance of the OC2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) algorithm based on 490- and 555-nm water-leaving radiances at low chlorophyll contents is compared with those of semianalytical models and a Monte Carlo radiative transfer model. We introduce our model, which uses two particle phase functions and scattering coefficient parameterizations to achieve a backscattering ratio that varies with chlorophyll concentration. We discuss the various parameterizations and compare them with existent measurements. The SeaWiFS algorithm could be confirmed within an accuracy of 35% over a chlorophyll range from 0.1 to 1 mg m(-3), whereas for lower chlorophyll concentrations we found a significant overestimation of the OC2 algorithm.

Theoretical study of reactive and nonreactive turbulent coaxial jets

NASA Technical Reports Server (NTRS)

Gupta, R. N.; Wakelyn, N. T.

1976-01-01

The hydrodynamic properties and the reaction kinetics of axisymmetric coaxial turbulent jets having steady mean quantities are investigated. From the analysis, limited to free turbulent boundary layer mixing of such jets, it is found that the two-equation model of turbulence is adequate for most nonreactive flows. For the reactive flows, where an allowance must be made for second order correlations of concentration fluctuations in the finite rate chemistry for initially inhomogeneous mixture, an equation similar to the concentration fluctuation equation of a related model is suggested. For diffusion limited reactions, the eddy breakup model based on concentration fluctuations is found satisfactory and simple to use. The theoretical results obtained from these various models are compared with some of the available experimental data.

The development of a model to predict BW gain of growing cattle fed grass silage-based diets.

PubMed

Huuskonen, A; Huhtanen, P

2015-08-01

The objective of this meta-analysis was to develop and validate empirical equations predicting BW gain (BWG) and carcass traits of growing cattle from intake and diet composition variables. The modelling was based on treatment mean data from feeding trials in growing cattle, in which the nutrient supply was manipulated by wide ranges of forage and concentrate factors. The final dataset comprised 527 diets in 116 studies. The diets were mainly based on grass silage or grass silage partly or completely replaced by whole-crop silages, hay or straw. The concentrate feeds consisted of cereal grains, fibrous by-products and protein supplements. Mixed model regression analysis with a random study effect was used to develop prediction equations for BWG and carcass traits. The best-fit models included linear and quadratic effects of metabolisable energy (ME) intake per metabolic BW (BW0.75), linear effects of BW0.75, and dietary concentrations of NDF, fat and feed metabolisable protein (MP) as significant variables. Although diet variables had significant effects on BWG, their contribution to improve the model predictions compared with ME intake models was small. Feed MP rather than total MP was included in the final model, since it is less correlated to dietary ME concentration than total MP. None of the quadratic terms of feed variables was significant (P>0.10) when included in the final models. Further, additional feed variables (e.g. silage fermentation products, forage digestibility) did not have significant effects on BWG. For carcass traits, increased ME intake (ME/BW0.75) improved both dressing proportion (P0.10) effect on dressing proportion or carcass conformation score, but it increased (P<0.01) carcass fat score. The current study demonstrated that ME intake per BW0.75 was clearly the most important variable explaining the BWG response in growing cattle. The effect of increased ME supply displayed diminishing responses that could be associated with increased energy concentration of BWG, reduced diet metabolisability (proportion of ME of gross energy) and/or decreased efficiency of ME utilisation for growth with increased intake. Negative effects of increased dietary NDF concentration on BWG were smaller compared to responses that energy evaluation systems predict for energy retention. The present results showed only marginal effects of protein supply on BWG in growing cattle.

Improving riverine constituent concentration and flux estimation by accounting for antecedent discharge conditions

NASA Astrophysics Data System (ADS)

Zhang, Qian; Ball, William P.

2017-04-01

Regression-based approaches are often employed to estimate riverine constituent concentrations and fluxes based on typically sparse concentration observations. One such approach is the recently developed WRTDS ("Weighted Regressions on Time, Discharge, and Season") method, which has been shown to provide more accurate estimates than prior approaches in a wide range of applications. Centered on WRTDS, this work was aimed at developing improved models for constituent concentration and flux estimation by accounting for antecedent discharge conditions. Twelve modified models were developed and tested, each of which contains one additional flow variable to represent antecedent conditions and which can be directly derived from the daily discharge record. High-resolution (∼daily) data at nine diverse monitoring sites were used to evaluate the relative merits of the models for estimation of six constituents - chloride (Cl), nitrate-plus-nitrite (NOx), total Kjeldahl nitrogen (TKN), total phosphorus (TP), soluble reactive phosphorus (SRP), and suspended sediment (SS). For each site-constituent combination, 30 concentration subsets were generated from the original data through Monte Carlo subsampling and then used to evaluate model performance. For the subsampling, three sampling strategies were adopted: (A) 1 random sample each month (12/year), (B) 12 random monthly samples plus additional 8 random samples per year (20/year), and (C) flow-stratified sampling with 12 regular (non-storm) and 8 storm samples per year (20/year). Results reveal that estimation performance varies with both model choice and sampling strategy. In terms of model choice, the modified models show general improvement over the original model under all three sampling strategies. Major improvements were achieved for NOx by the long-term flow-anomaly model and for Cl by the ADF (average discounted flow) model and the short-term flow-anomaly model. Moderate improvements were achieved for SS, TP, and TKN by the ADF model. By contrast, no such achievement was achieved for SRP by any proposed model. In terms of sampling strategy, performance of all models (including the original) was generally best using strategy C and worst using strategy A, and especially so for SS, TP, and SRP, confirming the value of routinely collecting stormflow samples. Overall, this work provides a comprehensive set of statistical evidence for supporting the incorporation of antecedent discharge conditions into the WRTDS model for estimation of constituent concentration and flux, thereby combining the advantages of two recent developments in water quality modeling.

Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic.

PubMed

Timkova, Jana; Fojtikova, Ivana; Pacherova, Petra

2017-01-01

The purpose of the study is to determine radon-prone areas in the Czech Republic based on the measurements of indoor radon concentration and independent predictors (rock type and permeability of the bedrock, gamma dose rate, GPS coordinates and the average age of family houses). The relationship between the mean observed indoor radon concentrations in monitored areas (∼22% municipalities) and the independent predictors was modelled using a bagged neural network. Levels of mean indoor radon concentration in the unmonitored areas were predicted using the bagged neural network model fitted for the monitored areas. The propensity to increased indoor radon was determined by estimated probability of exceeding the action level of 300Bq/m 3 . Copyright © 2016 Elsevier Ltd. All rights reserved.

Viscosity studies of water based magnetite nanofluids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anu, K.; Hemalatha, J.

2016-05-23

Magnetite nanofluids of various concentrations have been synthesized through co-precipitation method. The structural and topographical studies made with the X-Ray Diffractometer and Atomic Force Microscope are presented in this paper. The density and viscosity studies for the ferrofluids of various concentrations have been made at room temperature. The experimental viscosities are compared with theoretical values obtained from Einstein, Batchelor and Wang models. An attempt to modify the Rosensweig model is made and the modified Rosensweig equation is reported. In addition, new empirical correlation is also proposed for predicting viscosity of ferrofluid at various concentrations.

Inter-comparison of dynamic models for radionuclide transfer to marine biota in a Fukushima accident scenario.

PubMed

Vives I Batlle, J; Beresford, N A; Beaugelin-Seiller, K; Bezhenar, R; Brown, J; Cheng, J-J; Ćujić, M; Dragović, S; Duffa, C; Fiévet, B; Hosseini, A; Jung, K T; Kamboj, S; Keum, D-K; Kryshev, A; LePoire, D; Maderich, V; Min, B-I; Periáñez, R; Sazykina, T; Suh, K-S; Yu, C; Wang, C; Heling, R

2016-03-01

We report an inter-comparison of eight models designed to predict the radiological exposure of radionuclides in marine biota. The models were required to simulate dynamically the uptake and turnover of radionuclides by marine organisms. Model predictions of radionuclide uptake and turnover using kinetic calculations based on biological half-life (TB1/2) and/or more complex metabolic modelling approaches were used to predict activity concentrations and, consequently, dose rates of (90)Sr, (131)I and (137)Cs to fish, crustaceans, macroalgae and molluscs under circumstances where the water concentrations are changing with time. For comparison, the ERICA Tool, a model commonly used in environmental assessment, and which uses equilibrium concentration ratios, was also used. As input to the models we used hydrodynamic forecasts of water and sediment activity concentrations using a simulated scenario reflecting the Fukushima accident releases. Although model variability is important, the intercomparison gives logical results, in that the dynamic models predict consistently a pattern of delayed rise of activity concentration in biota and slow decline instead of the instantaneous equilibrium with the activity concentration in seawater predicted by the ERICA Tool. The differences between ERICA and the dynamic models increase the shorter the TB1/2 becomes; however, there is significant variability between models, underpinned by parameter and methodological differences between them. The need to validate the dynamic models used in this intercomparison has been highlighted, particularly in regards to optimisation of the model biokinetic parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Computational Fluid Dynamic Model for a Novel Flash Ironmaking Process

NASA Astrophysics Data System (ADS)

Perez-Fontes, Silvia E.; Sohn, Hong Yong; Olivas-Martinez, Miguel

A computational fluid dynamic model for a novel flash ironmaking process based on the direct gaseous reduction of iron oxide concentrates is presented. The model solves the three-dimensional governing equations including both gas-phase and gas-solid reaction kinetics. The turbulence-chemistry interaction in the gas-phase is modeled by the eddy dissipation concept incorporating chemical kinetics. The particle cloud model is used to track the particle phase in a Lagrangian framework. A nucleation and growth kinetics rate expression is adopted to calculate the reduction rate of magnetite concentrate particles. Benchmark experiments reported in the literature for a nonreacting swirling gas jet and a nonpremixed hydrogen jet flame were simulated for validation. The model predictions showed good agreement with measurements in terms of gas velocity, gas temperature and species concentrations. The relevance of the computational model for the analysis of a bench reactor operation and the design of an industrial-pilot plant is discussed.

Multivariate prediction of odor from pig production based on in-situ measurement of odorants

NASA Astrophysics Data System (ADS)

Hansen, Michael J.; Jonassen, Kristoffer E. N.; Løkke, Mette Marie; Adamsen, Anders Peter S.; Feilberg, Anders

2016-06-01

The aim of the present study was to estimate a prediction model for odor from pig production facilities based on measurements of odorants by Proton-Transfer-Reaction Mass spectrometry (PTR-MS). Odor measurements were performed at four different pig production facilities with and without odor abatement technologies using a newly developed mobile odor laboratory equipped with a PTR-MS for measuring odorants and an olfactometer for measuring the odor concentration by human panelists. A total of 115 odor measurements were carried out in the mobile laboratory and simultaneously air samples were collected in Nalophan bags and analyzed at accredited laboratories after 24 h. The dataset was divided into a calibration dataset containing 94 samples and a validation dataset containing 21 samples. The prediction model based on the measurements in the mobile laboratory was able to explain 74% of the variation in the odor concentration based on odorants, whereas the prediction models based on odor measurements with bag samples explained only 46-57%. This study is the first application of direct field olfactometry to livestock odor and emphasizes the importance of avoiding any bias from sample storage in studies of odor-odorant relationships. Application of the model on the validation dataset gave a high correlation between predicted and measured odor concentration (R2 = 0.77). Significant odorants in the prediction models include phenols and indoles. In conclusion, measurements of odorants on-site in pig production facilities is an alternative to dynamic olfactometry that can be applied for measuring odor from pig houses and the effects of odor abatement technologies.

Informing Selection of Nanomaterial Concentrations for ToxCast In Vitro Testing using the Multiple-Path Particle Dosimetry Model - 3rd Annual International Conference on the Environmental Implications of NanoTechnology (ICEIN) & EPA Nano Grantees Meeting (2011)

EPA Science Inventory

Currently, little justification is provided for nanomaterial testing concentrations in in vitro assays. The in vitro concentrations typically used may be higher than those experienced by exposed humans. Selection of concentration levels for hazard evaluation based on real-world e...

A physiologically based pharmacokinetic model for ethylene oxide in mouse, rat, and human.

PubMed

Fennell, T R; Brown, C D

2001-06-15

Ethylene oxide (EO) is widely used as a gaseous sterilant and industrial intermediate and is a direct-acting mutagen and carcinogen. The objective of these studies was to develop physiologically based pharmacokinetic (PB-PK) models for EO to describe the exposure-tissue dose relationship in rodents and humans. We previously reported results describing in vitro and in vivo kinetics of EO metabolism in male and female F344 rats and B6C3F1 mice. These studies were extended by determining the kinetics of EO metabolism in human liver cytosol and microsomes. The results indicate enzymatically catalyzed GSH conjugation via cytosolic glutathione S-transferase (cGST) and hydrolysis via microsomal epoxide hydrolase (mEH) occur in both rodents and humans. The in vitro kinetic constants were scaled to account for cytosolic (cGST) and microsomal (mEH) protein content and incorporated into PB-PK descriptions for mouse, rat, and human. Flow-limited models adequately predicted blood and tissue EO levels, disposition, and elimination kinetics determined experimentally in rats and mice, with the exception of testis concentrations, which were overestimated. Incorporation of a diffusion-limited description for testis improved the ability of the model to describe testis concentrations. The model accounted for nonlinear increases in blood and tissue concentrations that occur in mice on exposure to EO concentrations greater than 200 ppm. Species differences are predicted in the metabolism and exposure-dose relationship, with a nonlinear relationship observed in the mouse as a result of GSH depletion. These models represent an essential step in developing a mechanistically based EO exposure-dose-response description for estimating human risk from exposure to EO. Copyright 2001 Academic Press.

Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

PubMed

Ding, Jinliang; Chai, Tianyou; Wang, Hong

2011-03-01

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Microbial risk assessment of drinking water based on hydrodynamic modelling of pathogen concentrations in source water.

PubMed

Sokolova, Ekaterina; Petterson, Susan R; Dienus, Olaf; Nyström, Fredrik; Lindgren, Per-Eric; Pettersson, Thomas J R

2015-09-01

Norovirus contamination of drinking water sources is an important cause of waterborne disease outbreaks. Knowledge on pathogen concentrations in source water is needed to assess the ability of a drinking water treatment plant (DWTP) to provide safe drinking water. However, pathogen enumeration in source water samples is often not sufficient to describe the source water quality. In this study, the norovirus concentrations were characterised at the contamination source, i.e. in sewage discharges. Then, the transport of norovirus within the water source (the river Göta älv in Sweden) under different loading conditions was simulated using a hydrodynamic model. Based on the estimated concentrations in source water, the required reduction of norovirus at the DWTP was calculated using quantitative microbial risk assessment (QMRA). The required reduction was compared with the estimated treatment performance at the DWTP. The average estimated concentration in source water varied between 4.8×10(2) and 7.5×10(3) genome equivalents L(-1); and the average required reduction by treatment was between 7.6 and 8.8 Log10. The treatment performance at the DWTP was estimated to be adequate to deal with all tested loading conditions, but was heavily dependent on chlorine disinfection, with the risk of poor reduction by conventional treatment and slow sand filtration. To our knowledge, this is the first article to employ discharge-based QMRA, combined with hydrodynamic modelling, in the context of drinking water. Copyright © 2015 Elsevier B.V. All rights reserved.

A PHYSIOLOGICALLY BASED COMPUTATIONAL MODEL OF THE BPG AXIS IN FATHEAD MINNOWS: PREDICTING EFFECTS OF ENDOCRINE DISRUPTING CHEMICAL EXPOSURE ON REPRODUCTIVE ENDPOINTS

EPA Science Inventory

This presentation describes development and application of a physiologically-based computational model that simulates the brain-pituitary-gonadal (BPG) axis and other endpoints important in reproduction such as concentrations of sex steroid hormones, 17-estradiol, testosterone, a...

Physiologically based pharmacokinetic model for quinocetone in pigs and extrapolation to mequindox.

PubMed

Zhu, Xudong; Huang, Lingli; Xu, Yamei; Xie, Shuyu; Pan, Yuanhu; Chen, Dongmei; Liu, Zhenli; Yuan, Zonghui

2017-02-01

Physiologically based pharmacokinetic (PBPK) models are scientific methods used to predict veterinary drug residues that may occur in food-producing animals, and which have powerful extrapolation ability. Quinocetone (QCT) and mequindox (MEQ) are widely used in China for the prevention of bacterial infections and promoting animal growth, but their abuse causes a potential threat to human health. In this study, a flow-limited PBPK model was developed to simulate simultaneously residue depletion of QCT and its marker residue dideoxyquinocetone (DQCT) in pigs. The model included compartments for blood, liver, kidney, muscle and fat and an extra compartment representing the other tissues. Physiological parameters were obtained from the literature. Plasma protein binding rates, renal clearances and tissue/plasma partition coefficients were determined by in vitro and in vivo experiments. The model was calibrated and validated with several pharmacokinetic and residue-depletion datasets from the literature. Sensitivity analysis and Monte Carlo simulations were incorporated into the PBPK model to estimate individual variation of residual concentrations. The PBPK model for MEQ, the congener compound of QCT, was built through cross-compound extrapolation based on the model for QCT. The QCT model accurately predicted the concentrations of QCT and DQCT in various tissues at most time points, especially the later time points. Correlation coefficients between predicted and measured values for all tissues were greater than 0.9. Monte Carlo simulations showed excellent consistency between estimated concentration distributions and measured data points. The extrapolation model also showed good predictive power. The present models contribute to improve the residue monitoring systems of QCT and MEQ, and provide evidence of the usefulness of PBPK model extrapolation for the same kinds of compounds.

Geological modeling of submeter scale heterogeneity and its influence on tracer transport in a fluvial aquifer

NASA Astrophysics Data System (ADS)

Ronayne, Michael J.; Gorelick, Steven M.; Zheng, Chunmiao

2010-10-01

We developed a new model of aquifer heterogeneity to analyze data from a single-well injection-withdrawal tracer test conducted at the Macrodispersion Experiment (MADE) site on the Columbus Air Force Base in Mississippi (USA). The physical heterogeneity model is a hybrid that combines 3-D lithofacies to represent submeter scale, highly connected channels within a background matrix based on a correlated multivariate Gaussian hydraulic conductivity field. The modeled aquifer architecture is informed by a variety of field data, including geologic core sampling. Geostatistical properties of this hybrid heterogeneity model are consistent with the statistics of the hydraulic conductivity data set based on extensive borehole flowmeter testing at the MADE site. The representation of detailed, small-scale geologic heterogeneity allows for explicit simulation of local preferential flow and slow advection, processes that explain the complex tracer response from the injection-withdrawal test. Based on the new heterogeneity model, advective-dispersive transport reproduces key characteristics of the observed tracer recovery curve, including a delayed concentration peak and a low-concentration tail. Importantly, our results suggest that intrafacies heterogeneity is responsible for local-scale mass transfer.

Atomistic Modeling of Quaternary Alloys: Ti and Cu in NiAl

NASA Technical Reports Server (NTRS)

Bozzolo, Guillermo; Mosca, Hugo O.; Wilson, Allen W.; Noebe, Ronald D.; Garces, Jorge E.

2002-01-01

The change in site preference in NiAl(Ti,Cu) alloys with concentration is examined experimentally via ALCHEMI and theoretically using the Bozzolo-Ferrante-Smith (BFS) method for alloys. Results for the site occupancy of Ti and Cu additions as a function of concentration are determined experimentally for five alloys. These results are reproduced with large-scale BFS-based Monte Carlo atomistic simulations. The original set of five alloys is extended to 25 concentrations, which are modeled by means of the BFS method for alloys, showing in more detail the compositional range over which major changes in behavior occur. A simple but powerful approach based on the definition of atomic local environments also is introduced to describe energetically the interactions between the various elements and therefore to explain the observed behavior.

Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients.

PubMed

Xue, Ling; Holford, Nick; Ding, Xiao-Liang; Shen, Zhen-Ya; Huang, Chen-Rong; Zhang, Hua; Zhang, Jing-Jing; Guo, Zhe-Ning; Xie, Cheng; Zhou, Ling; Chen, Zhi-Yao; Liu, Lin-Sheng; Miao, Li-Yan

2017-04-01

The aims of this study are to apply a theory-based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S- and R-warfarin. Clinical data were obtained from 264 patients. Total concentrations for S- and R-warfarin were measured by ultra-high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction-corrected visual predictive checks. Warfarin PK was described using a one-compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S-warfarin, but increased clearance for R-warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory-based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid E max PD model inhibiting PCA synthesis as a function of S-warfarin concentration predicted INR. Small R-warfarin effects was described by competitive antagonism of S-warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S-warfarin. A theory-based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory-based allometric scaling of PK parameters was identified. R-warfarin had a minor effect compared with S-warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo. © 2016 The British Pharmacological Society.

Theory‐based pharmacokinetics and pharmacodynamics of S‐ and R‐warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients

PubMed Central

Xue, Ling; Holford, Nick; Ding, Xiao‐liang; Shen, Zhen‐ya; Huang, Chen‐rong; Zhang, Hua; Zhang, Jing‐jing; Guo, Zhe‐ning; Xie, Cheng; Zhou, Ling; Chen, Zhi‐yao; Liu, Lin‐sheng

2016-01-01

Aims The aims of this study are to apply a theory‐based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S‐ and R‐warfarin. Methods Clinical data were obtained from 264 patients. Total concentrations for S‐ and R‐warfarin were measured by ultra‐high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction‐corrected visual predictive checks. Results Warfarin PK was described using a one‐compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S‐warfarin, but increased clearance for R‐warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory‐based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid Emax PD model inhibiting PCA synthesis as a function of S‐warfarin concentration predicted INR. Small R‐warfarin effects was described by competitive antagonism of S‐warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S‐warfarin. Conclusion A theory‐based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory‐based allometric scaling of PK parameters was identified. R‐warfarin had a minor effect compared with S‐warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo. PMID:27763679

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

PubMed Central

Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M. G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public. PMID:23291550

AIR MONITOR SITING BY OBJECTIVE

EPA Science Inventory

A method is developed whereby measured pollutant concentrations can be used in conjunction with a mathematical air quality model to estimate the full spatial and temporal concentration distributions of the pollutants over a given region. The method is based on the application of ...

Using models to interpret the impact of roadside barriers on near-road air quality

NASA Astrophysics Data System (ADS)

Amini, Seyedmorteza; Ahangar, Faraz Enayati; Schulte, Nico; Venkatram, Akula

2016-08-01

The question this paper addresses is whether semi-empirical dispersion models based on data from controlled wind tunnel and tracer experiments can describe data collected downwind of a sound barrier next to a real-world urban highway. Both models are based on the mixed wake model described in Schulte et al. (2014). The first neglects the effects of stability on dispersion, and the second accounts for reduced entrainment into the wake of the barrier under unstable conditions. The models were evaluated with data collected downwind of a kilometer-long barrier next to the I-215 freeway running next to the University of California campus in Riverside. The data included measurements of 1) ultrafine particle (UFP) concentrations at several distances from the barrier, 2) micrometeorological variables upwind and downwind of the barrier, and 3) traffic flow separated by automobiles and trucks. Because the emission factor for UFP is highly uncertain, we treated it as a model parameter whose value is obtained by fitting model estimates to observations of UFP concentrations measured at distances where the barrier impact is not dominant. Both models provide adequate descriptions of both the magnitude and the spatial variation of observed concentrations. The good performance of the models reinforces the conclusion from Schulte et al. (2014) that the presence of the barrier is equivalent to shifting the line sources on the road upwind by a distance of about HU/u∗ where H is the barrier height, U is the wind velocity at half of the barrier height, and u∗ is the friction velocity. The models predict that a 4 m barrier results in a 35% reduction in average concentration within 40 m (10 times the barrier height) of the barrier, relative to the no-barrier site. This concentration reduction is 55% if the barrier height is doubled.

Terbinafine in combination with other antifungal agents for treatment of resistant or refractory mycoses: investigating optimal dosing regimens using a physiologically based pharmacokinetic model.

PubMed

Dolton, Michael J; Perera, Vidya; Pont, Lisa G; McLachlan, Andrew J

2014-01-01

Terbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergistic in vitro antifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens.

Effective concentration-based serum pharmacodynamics for antifungal azoles in a murine model of disseminated Candida albicans infection.

PubMed

Maki, Katsuyuki; Kaneko, Shuji

2013-12-01

An assessment of the effective in vivo concentrations of antifungal drugs is important in determining their pharmacodynamics, and therefore, their optimal dosage regimen. Here we establish the effective in vivo concentration-based pharmacodynamics of three azole antifungal drugs (fluconazole, itraconazole, and ketoconazole) in a murine model of disseminated Candida albicans infection. A key feature of this study was the use of a measure of mycelial (m) growth rather than of yeast growth, and pooled mouse sera rather than synthetic media as a growth medium, for determining the minimum inhibitory concentrations (MICs) of azoles for C. albicans (denoted serum mMICs). The serum mMIC assay was then used to measure antifungal concentrations and effects as serum antifungal titers in the serum of treated mice. Both serum mMIC and sub-mMIC values reflected the effective in vivo serum concentrations. Supra-mMIC and mMIC effects exhibited equivalent efficacies and were concentration-independent, while the sub-mMIC effect was concentration-dependent. Following administration of the minimum drug dosage that inhibited an increase in mouse kidney fungal burden, the duration periods of these effects were similar for all drugs tested. The average duration of either the mMIC effect including the supra-mMIC effect, the sub-mMIC effect, or the post-antifungal effect (PAFE) were 6.9, 6.5 and 10.6 h, respectively. Our study suggests that the area under the curve for serum drug concentration versus time, between the serum mMIC and the sub-mMIC, and exposure time above the serum sub-mMIC after the mMIC effect, are major pharmacodynamic parameters. These findings have important implications for effective concentration-based pharmacodynamics of fungal infections treated with azoles.

Adsorption behavior of the (+/-)-Tröger's base enantiomers in the phase system of a silica-based packing coated with amylose tri(3,5-dimethyl carbamate) and 2-propanol and molecular modeling interpretation.

PubMed

Mihlbachler, Kathleen; De Jesús, Marco A; Kaczmarski, Krzysztof; Sepaniak, Michael J; Seidel-Morgenstern, Andreas; Guiochon, Georges

2006-04-28

The binary adsorption isotherms of the enantiomers of Tröger's base in the phase system made of Chiral Technologies ChiralPak AD [a silica-based packing coated with amylose tri(3,5-dimethyl carbamate)] as the chiral stationary phase (CSP) and 2-propanol as the mobile phase were measured by the perturbation method. The more retained enantiomer exhibits a S-shaped adsorption isotherm with a clear inflection point, the concentration of the less retained enantiomer having practically no competitive influence on this isotherm: In the entire range of concentrations studied, dq2/dC1 approximately 0. By contrast, the less retained enantiomer has a Langmuir adsorption isotherm when pure. At constant mobile phase concentrations, however, its equilibrium concentration in the adsorbed phase increases with increasing concentration of the more retained enantiomer and dq1/dC2 > 0. This cooperative adsorption behavior, opposed to the classical competitive behavior, is exceedingly rare but was clearly demonstrated in this case. Two adsorption isotherm equations that account for these physical observations were derived. They are based on the formation of an adsorbed multi-layer, as suggested by the isotherm data. The excellent agreement between the experimental overloaded elution profiles of binary mixtures and the profiles calculated with the equilibrium-dispersive model validates this binary isotherm model. The adsorption energies calculated by molecular mechanics (MM) and by molecular dynamics (MD) indicate that the chiral recognition arising from the different interactions between the functional groups of the CSP and the molecules of the Tröger's base enantiomers are mainly driven by their Van der Waals interactions. The MD data suggest that the interactions of the (-)-Tröger's base with the CSP are more favored by 8+/-(5) kJ/mol than those of (+)-Tröger's base. This difference seems to be a contributing factor to the increased retention of the - enantiomer on this chromatographic system. The modeling of the data also indicates that both enantiomers can form high stoichiometry complexes while binding onto the stationary phase, in agreement with the results of the equilibrium isotherm studies.

Are groundwater nitrate concentrations reaching a turning point in some chalk aquifers?

PubMed

Smith, J T; Clarke, R T; Bowes, M J

2010-09-15

In past decades, there has been much scientific effort dedicated to the development of models for simulation and prediction of nitrate concentrations in groundwaters, but producing truly predictive models remains a major challenge. A time-series model, based on long-term variations in nitrate fertiliser applications and average rainfall, was calibrated against measured concentrations from five boreholes in the River Frome catchment of Southern England for the period spanning from the mid-1970s to 2003. The model was then used to "blind" predict nitrate concentrations for the period 2003-2008. To our knowledge, this represents the first "blind" test of a model for predicting nitrate concentrations in aquifers. It was found that relatively simple time-series models could explain and predict a significant proportion of the variation in nitrate concentrations in these groundwater abstraction points (R(2)=0.6-0.9 and mean absolute prediction errors 4.2-8.0%). The study highlighted some important limitations and uncertainties in this, and other modelling approaches, in particular regarding long-term nitrate fertiliser application data. In three of the five groundwater abstraction points (Hooke, Empool and Eagle Lodge), once seasonal variations were accounted for, there was a recent change in the generally upward historical trend in nitrate concentrations. This may be an early indication of a response to levelling-off (and declining) fertiliser application rates since the 1980s. There was no clear indication of trend change at the Forston and Winterbourne Abbas sites nor in the trend of nitrate concentration in the River Frome itself from 1965 to 2008. Copyright 2010 Elsevier B.V. All rights reserved.

Characterization and simulation of fate and transport of selected volatile organic compounds in the vicinities of the Hadnot Point Industrial Area and landfill: Chapter A Supplement 6 in Analyses and historical reconstruction of groundwater flow, contaminant fate and transport, and distribution of drinking water within the service areas of the Hadnot Point and Holcomb Boulevard Water Treatment Plants and vicinities, U.S. Marine Corps Base Camp Lejeune, North Carolina

USGS Publications Warehouse

Jones, L. Elliott; Suárez-Soto, René J.; Anderson, Barbara A.; Maslia, Morris L.

2013-01-01

This supplement of Chapter A (Supplement 6) describes the reconstruction (i.e. simulation) of historical concentrations of tetrachloroethylene (PCE), trichloroethylene (TCE), and benzene3 in production wells supplying water to the Hadnot Base (USMCB) Camp Lejeune, North Carolina (Figure S6.1). A fate and transport model (i.e., MT3DMS [Zheng and Wang 1999]) was used to simulate contaminant migration from source locations through the groundwater system and to estimate mean contaminant concentrations in water withdrawn from water-supply wells in the vicinity of the Hadnot Point Industrial Area (HPIA) and the Hadnot Point landfill (HPLF) area.4 The reconstructed contaminant concentrations were subsequently input into a flow-weighted, materials mass balance (mixing) model (Masters 1998) to estimate monthly mean concentrations of the contaminant in finished water 5 at the HPWTP (Maslia et al. 2013). The calibrated fate and transport models described herein were based on and used groundwater velocities derived from groundwater-flow models that are described in Suárez-Soto et al. (2013). Information data pertinent to historical operations of water-supply wells are described in Sautner et al. (2013) and Telci et al. (2013).

Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?

USGS Publications Warehouse

Burns, Emily E.; Thomas-Oates, Jane; Kolpin, Dana W.; Furlong, Edward T.; Boxall, Alistair B.A.

2017-01-01

Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk-based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs. 

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM2.5 Simulations.

PubMed

Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Yuan, Zibing; Russell, Armistead G; Ou, Jiamin; Zhong, Zhuangmin

2017-04-04

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM 2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM 2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM 2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM 2.5 boundary conditions and primary PM 2.5 emissions are found to be the two major uncertainty sources in PM 2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

Low-level Environmental Metals and Metalloids and Incident Pregnancy Loss

PubMed Central

Buck Louis, Germaine M.; Smarr, Melissa M.; Sundaram, Rajeshwari; Steuerwald, Amy J.; Sapra, Katherine J.; Lu, Zhaohui; Parsons, Patrick J.

2017-01-01

Environmental exposure to metals and metalloids is associated with pregnancy loss in some but not all studies. We assessed arsenic, cadmium, mercury, and lead concentrations in 501 couples upon trying for pregnancy and followed them throughout pregnancy to estimate the risk of incident pregnancy loss. Using Cox proportional hazard models, we estimated hazard ratios (HR) and 95% confidence intervals (CIs) for pregnancy loss after covariate adjustment for each partner modeled individually then we jointly modeled both partners’ concentrations. Incidence of pregnancy loss was 28%. In individual partner models, the highest adjusted HRs were observed for female and male blood cadmium (HR=1.08; CI 0.81, 1.44; HR=1.09; 95% CI 0.84, 1.41, respectively). In couple based models, neither partner’s blood cadmium concentrations were associated with loss (HR=1.01; 95% CI 0.75, 1.37; HR=0.92; CI 0.68, 1.25, respectively). We observed no evidence of a significant relation between metal(loids) at environmentally relevant concentrations and pregnancy loss. PMID:28163209

Low-level environmental metals and metalloids and incident pregnancy loss.

PubMed

Buck Louis, Germaine M; Smarr, Melissa M; Sundaram, Rajeshwari; Steuerwald, Amy J; Sapra, Katherine J; Lu, Zhaohui; Parsons, Patrick J

2017-04-01

Environmental exposure to metals and metalloids is associated with pregnancy loss in some but not all studies. We assessed arsenic, cadmium, mercury, and lead concentrations in 501 couples upon trying for pregnancy and followed them throughout pregnancy to estimate the risk of incident pregnancy loss. Using Cox proportional hazard models, we estimated hazard ratios (HR) and 95% confidence intervals (CIs) for pregnancy loss after covariate adjustment for each partner modeled individually then we jointly modeled both partners' concentrations. Incidence of pregnancy loss was 28%. In individual partner models, the highest adjusted HRs were observed for female and male blood cadmium (HR=1.08; CI 0.81, 1.44; HR=1.09; 95% CI 0.84, 1.41, respectively). In couple based models, neither partner's blood cadmium concentrations were associated with loss (HR=1.01; 95% CI 0.75, 1.37; HR=0.92; CI 0.68, 1.25, respectively). We observed no evidence of a significant relation between metal(loids) at these environmentally relevant concentrations and pregnancy loss. Published by Elsevier Inc.

Modelling NOX concentrations through CFD-RANS in an urban hot-spot using high resolution traffic emissions and meteorology from a mesoscale model

NASA Astrophysics Data System (ADS)

Sanchez, Beatriz; Santiago, Jose Luis; Martilli, Alberto; Martin, Fernando; Borge, Rafael; Quaassdorff, Christina; de la Paz, David

2017-08-01

Air quality management requires more detailed studies about air pollution at urban and local scale over long periods of time. This work focuses on obtaining the spatial distribution of NOx concentration averaged over several days in a heavily trafficked urban area in Madrid (Spain) using a computational fluid dynamics (CFD) model. A methodology based on weighted average of CFD simulations is applied computing the time evolution of NOx dispersion as a sequence of steady-state scenarios taking into account the actual atmospheric conditions. The inputs of emissions are estimated from the traffic emission model and the meteorological information used is derived from a mesoscale model. Finally, the computed concentration map correlates well with 72 passive samplers deployed in the research area. This work reveals the potential of using urban mesoscale simulations together with detailed traffic emissions so as to provide accurate maps of pollutant concentration at microscale using CFD simulations.

Use of sediment-trace element geochemical models for the identification of local fluvial baseline concentrations

USGS Publications Warehouse

Horowitz, A.J.; Elrick, K.A.; Demas, C.R.; Demcheck, D.K.

1991-01-01

Studies have demonstrated the utility of fluvial bed sediment chemical data in assesing local water-quality conditions. However, establishing local background trace element levels can be difficult. Reference to published average concentrations or the use of dated cores are often of little use in small areas of diverse local petrology, geology, land use, or hydrology. An alternative approach entails the construction of a series of sediment-trace element predictive models based on data from environmentally diverse but unaffected areas. Predicted values could provide a measure of local background concentrations and comparison with actual measured concentrations could identify elevated trace elements and affected sites. Such a model set was developed from surface bed sediments collected nationwide in the United States. Tests of the models in a small Louisiana basin indicated that they could be used to establish local trace element background levels, but required recalibration to account for local geochemical conditions outside the range of samples used to generate the nationwide models.

Functional enzyme-based modeling approach for dynamic simulation of denitrification process in hyporheic zone sediments: Genetically structured microbial community model

NASA Astrophysics Data System (ADS)

Song, H. S.; Li, M.; Qian, W.; Song, X.; Chen, X.; Scheibe, T. D.; Fredrickson, J.; Zachara, J. M.; Liu, C.

2016-12-01

Modeling environmental microbial communities at individual organism level is currently intractable due to overwhelming structural complexity. Functional guild-based approaches alleviate this problem by lumping microorganisms into fewer groups based on their functional similarities. This reduction may become ineffective, however, when individual species perform multiple functions as environmental conditions vary. In contrast, the functional enzyme-based modeling approach we present here describes microbial community dynamics based on identified functional enzymes (rather than individual species or their groups). Previous studies in the literature along this line used biomass or functional genes as surrogate measures of enzymes due to the lack of analytical methods for quantifying enzymes in environmental samples. Leveraging our recent development of a signature peptide-based technique enabling sensitive quantification of functional enzymes in environmental samples, we developed a genetically structured microbial community model (GSMCM) to incorporate enzyme concentrations and various other omics measurements (if available) as key modeling input. We formulated the GSMCM based on the cybernetic metabolic modeling framework to rationally account for cellular regulation without relying on empirical inhibition kinetics. In the case study of modeling denitrification process in Columbia River hyporheic zone sediments collected from the Hanford Reach, our GSMCM provided a quantitative fit to complex experimental data in denitrification, including the delayed response of enzyme activation to the change in substrate concentration. Our future goal is to extend the modeling scope to the prediction of carbon and nitrogen cycles and contaminant fate. Integration of a simpler version of the GSMCM with PFLOTRAN for multi-scale field simulations is in progress.

Data fusion algorithm for rapid multi-mode dust concentration measurement system based on MEMS

NASA Astrophysics Data System (ADS)

Liao, Maohao; Lou, Wenzhong; Wang, Jinkui; Zhang, Yan

2018-03-01

As single measurement method cannot fully meet the technical requirements of dust concentration measurement, the multi-mode detection method is put forward, as well as the new requirements for data processing. This paper presents a new dust concentration measurement system which contains MEMS ultrasonic sensor and MEMS capacitance sensor, and presents a new data fusion algorithm for this multi-mode dust concentration measurement system. After analyzing the relation between the data of the composite measurement method, the data fusion algorithm based on Kalman filtering is established, which effectively improve the measurement accuracy, and ultimately forms a rapid data fusion model of dust concentration measurement. Test results show that the data fusion algorithm is able to realize the rapid and exact concentration detection.

Location-allocation models and new solution methodologies in telecommunication networks

NASA Astrophysics Data System (ADS)

Dinu, S.; Ciucur, V.

2016-08-01

When designing a telecommunications network topology, three types of interdependent decisions are combined: location, allocation and routing, which are expressed by the following design considerations: how many interconnection devices - consolidation points/concentrators should be used and where should they be located; how to allocate terminal nodes to concentrators; how should the voice, video or data traffic be routed and what transmission links (capacitated or not) should be built into the network. Including these three components of the decision into a single model generates a problem whose complexity makes it difficult to solve. A first method to address the overall problem is the sequential one, whereby the first step deals with the location-allocation problem and based on this solution the subsequent sub-problem (routing the network traffic) shall be solved. The issue of location and allocation in a telecommunications network, called "The capacitated concentrator location- allocation - CCLA problem" is based on one of the general location models on a network in which clients/demand nodes are the terminals and facilities are the concentrators. Like in a location model, each client node has a demand traffic, which must be served, and the facilities can serve these demands within their capacity limit. In this study, the CCLA problem is modeled as a single-source capacitated location-allocation model whose optimization objective is to determine the minimum network cost consisting of fixed costs for establishing the locations of concentrators, costs for operating concentrators and costs for allocating terminals to concentrators. The problem is known as a difficult combinatorial optimization problem for which powerful algorithms are required. Our approach proposes a Fuzzy Genetic Algorithm combined with a local search procedure to calculate the optimal values of the location and allocation variables. To confirm the efficiency of the proposed algorithm with respect to the quality of solutions, significant size test problems were considered: up to 100 terminal nodes and 50 concentrators on a 100 × 100 square grid. The performance of this hybrid intelligent algorithm was evaluated by measuring the quality of its solutions with respect to the following statistics: the standard deviation and the ratio of the best solution obtained.

Informing Selection of Nanomaterial Concentrations for ...

EPA Pesticide Factsheets

Little justification is generally provided for selection of in vitro assay testing concentrations for engineered nanomaterials (ENMs). Selection of concentration levels for hazard evaluation based on real-world exposure scenarios is desirable. We reviewed published ENM concentrations measured in air in manufacturing and R&D labs to identify input levels for estimating ENM mass retained in the human lung using the Multiple-Path Particle Dosimetry (MPPD) model. Model input parameters were individually varied to estimate alveolar mass retained for different particle sizes (5-1000 nm), aerosol concentrations (0.1, 1 mg/m3), aspect ratios (2, 4, 10, 167), and exposure durations (24 hours and a working lifetime). The calculated lung surface concentrations were then converted to in vitro solution concentrations. Modeled alveolar mass retained after 24 hours is most affected by activity level and aerosol concentration. Alveolar retention for Ag and TiO2 nanoparticles and CNTs for a working lifetime (45 years) exposure duration is similar to high-end concentrations (~ 30-400 μg/mL) typical of in vitro testing reported in the literature. Analyses performed are generally applicable to provide ENM testing concentrations for in vitro hazard screening studies though further research is needed to improve the approach. Understanding the relationship between potential real-world exposures and in vitro test concentrations will facilitate interpretation of toxicological results

A QCM-D study of the concentration- and time-dependent interactions of human LL37 with model mammalian lipid bilayers.

PubMed

Lozeau, Lindsay D; Rolle, Marsha W; Camesano, Terri A

2018-07-01

The human antimicrobial peptide LL37 is promising as an alternative to antibiotics due to its biophysical interactions with charged bacterial lipids. However, its clinical potential is limited due to its interactions with zwitterionic mammalian lipids leading to cytotoxicity. Mechanistic insight into the LL37 interactions with mammalian lipids may enable rational design of less toxic LL37-based therapeutics. To this end, we studied concentration- and time-dependent interactions of LL37 with zwitterionic model phosphatidylcholine (PC) bilayers with quartz crystal microbalance with dissipation (QCM-D). LL37 mass adsorption and PC bilayer viscoelasticity changes were monitored by measuring changes in frequency (Δf) and dissipation (ΔD), respectively. The Voigt-Kelvin viscoelastic model was applied to Δf and ΔD to study changes in bilayer thickness and density with LL37 concentration. At low concentrations (0.10-1.00 μM), LL37 adsorbed onto bilayers in a concentration-dependent manner. Further analyses of Δf, ΔD and thickness revealed that peptide saturation on the bilayers was a threshold for interactions observed above 2.00 μM, interactions that were rapid, multi-step, and reached equilibrium in a concentration- and time-dependent manner. Based on these data, we proposed a model of stable transmembrane pore formation at 2.00-10.0 μM, or transition from a primarily lipid to a primarily protein film with a transmembrane pore formation intermediate state at concentrations of LL37 > 10 μM. The concentration-dependent interactions between LL37 and PC bilayers correlated with the observed concentration-dependent biological activities of LL37 (antimicrobial, immunomodulatory and non-cytotoxic at 0.1-1.0 μM, hemolytic and some cytotoxicity at 2.0-13 μM and cytotoxic at >13 μM). Copyright © 2018 Elsevier B.V. All rights reserved.

Community Multiscale Air Quality (CMAQ) Modeling for Regional and Hemispheric Scales

EPA Science Inventory

The CMAQ model is a Eulerian model that produces gridded values of atmospheric concentration and deposition. Recent updates to the model are highlighted that impact estimates of dry and wet deposition of nitrogen, sulfur and base cations. Output from the CMAQ model is used in t...

A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

PubMed

Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

2014-07-31

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

A Telerobotic System for Transnasal Surgery

PubMed Central

Burgner, Jessica; Rucker, D. Caleb; Gilbert, Hunter B.; Swaney, Philip J.; Russell, Paul T.; Weaver, Kyle D.; Webster, Robert J.

2014-01-01

Mechanics-based models of concentric tube continuum robots have recently achieved a level of sophistication that makes it possible to begin to apply these robots to a variety of real-world clinical scenarios. Endonasal skull base surgery is one such application, where their small diameter and tentacle like dexterity are particularly advantageous. In this paper we provide the medical motivation for an endonasal surgical robot featuring concentric tube manipulators, and describe our model-based design and teleoperation methods, as well as a complete system incorporating image-guidance. Experimental demonstrations using a laparoscopic training task, a cadaver reachability study, and a phantom tumor resection experiment illustrate that both novice and expert users can effectively teleoperate the system, and that skull base surgeons can use the robot to achieve their objectives in a realistic surgical scenario. PMID:25089086

Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees

NASA Astrophysics Data System (ADS)

Sayegh, Arwa; Tate, James E.; Ropkins, Karl

2016-02-01

Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx, traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 °C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper highlights the importance of either setting up local background continuous monitors or improving the quality and resolution of modelled UK background maps and the need to further investigate the influence of ambient air temperature on NOx emissions and roadside NOx concentrations.

Laboratory methodologies for indicators of iron status: strengths, limitations, and analytical challenges.

PubMed

Pfeiffer, Christine M; Looker, Anne C

2017-12-01

Biochemical assessment of iron status relies on serum-based indicators, such as serum ferritin (SF), transferrin saturation, and soluble transferrin receptor (sTfR), as well as erythrocyte protoporphyrin. These indicators present challenges for clinical practice and national nutrition surveys, and often iron status interpretation is based on the combination of several indicators. The diagnosis of iron deficiency (ID) through SF concentration, the most commonly used indicator, is complicated by concomitant inflammation. sTfR concentration is an indicator of functional ID that is not an acute-phase reactant, but challenges in its interpretation arise because of the lack of assay standardization, common reference ranges, and common cutoffs. It is unclear which indicators are best suited to assess excess iron status. The value of hepcidin, non-transferrin-bound iron, and reticulocyte indexes is being explored in research settings. Serum-based indicators are generally measured on fully automated clinical analyzers available in most hospitals. Although international reference materials have been available for years, the standardization of immunoassays is complicated by the heterogeneity of antibodies used and the absence of physicochemical reference methods to establish "true" concentrations. From 1988 to 2006, the assessment of iron status in NHANES was based on the multi-indicator ferritin model. However, the model did not indicate the severity of ID and produced categorical estimates. More recently, iron status assessment in NHANES has used the total body iron stores (TBI) model, in which the log ratio of sTfR to SF is assessed. Together, sTfR and SF concentrations cover the full range of iron status. The TBI model better predicts the absence of bone marrow iron than SF concentration alone, and TBI can be analyzed as a continuous variable. Additional consideration of methodologies, interpretation of indicators, and analytic standardization is important for further improvements in iron status assessment. © 2017 American Society for Nutrition.

Performance analysis of high-concentrated multi-junction solar cells in hot climate

NASA Astrophysics Data System (ADS)

Ghoneim, Adel A.; Kandil, Kandil M.; Alzanki, Talal H.; Alenezi, Mohammad R.

2018-03-01

Multi-junction concentrator solar cells are a promising technology as they can fulfill the increasing energy demand with renewable sources. Focusing sunlight upon the aperture of multi-junction photovoltaic (PV) cells can generate much greater power densities than conventional PV cells. So, concentrated PV multi-junction solar cells offer a promising way towards achieving minimum cost per kilowatt-hour. However, these cells have many aspects that must be fixed to be feasible for large-scale energy generation. In this work, a model is developed to analyze the impact of various atmospheric factors on concentrator PV performance. A single-diode equivalent circuit model is developed to examine multi-junction cells performance in hot weather conditions, considering the impacts of both temperature and concentration ratio. The impacts of spectral variations of irradiance on annual performance of various high-concentrated photovoltaic (HCPV) panels are examined, adapting spectra simulations using the SMARTS model. Also, the diode shunt resistance neglected in the existing models is considered in the present model. The present results are efficiently validated against measurements from published data to within 2% accuracy. Present predictions show that the single-diode model considering the shunt resistance gives accurate and reliable results. Also, aerosol optical depth (AOD) and air mass are most important atmospheric parameters having a significant impact on HCPV cell performance. In addition, the electrical efficiency (η) is noticed to increase with concentration to a certain concentration degree after which it decreases. Finally, based on the model predictions, let us conclude that the present model could be adapted properly to examine HCPV cells' performance over a broad range of operating conditions.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas

PubMed Central

Olvera, Hector A.; Jimenez, Omar; Provencio-Vasquez, Elias

2014-01-01

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available. PMID:25313294

Modeling the reversible, diffusive sink effect in response to transient contaminant sources.

PubMed

Zhao, D; Little, J C; Hodgson, A T

2002-09-01

A physically based diffusion model is used to evaluate the sink effect of diffusion-controlled indoor materials and to predict the transient contaminant concentration in indoor air in response to several time-varying contaminant sources. For simplicity, it is assumed the predominant indoor material is a homogeneous slab, initially free of contaminant, and the air within the room is well mixed. The model enables transient volatile organic compound (VOC) concentrations to be predicted based on the material/air partition coefficient (K) and the material-phase diffusion coefficient (D) of the sink. Model predictions are made for three scenarios, each mimicking a realistic situation in a building. Styrene, phenol, and naphthalene are used as representative VOCs. A styrene butadiene rubber (SBR) backed carpet, vinyl flooring (VF), and a polyurethane foam (PUF) carpet cushion are considered as typical indoor sinks. In scenarios involving a sinusoidal VOC input and a double exponential decaying input, the model predicts the sink has a modest impact for SBR/styrene, but the effect increases for VF/phenol and PUF/naphthalene. In contrast, for an episodic chemical spill, SBR is predicted to reduce the peak styrene concentration considerably. A parametric study reveals for systems involving a large equilibrium constant (K), the kinetic constant (D) will govern the shape of the resulting gasphase concentration profile. On the other hand, for systems with a relaxed mass transfer resistance, K will dominate the profile.

Empirical regression models for estimating nitrogen removal in a stormwater wetland during dry and wet days.

PubMed

Guerra, Heidi B; Park, Kisoo; Kim, Youngchul

2013-01-01

Due to the highly variable hydrologic quantity and quality of stormwater runoff, which requires more complex models for proper prediction of treatment, a relatively few and site-specific models for stormwater wetlands have been developed. In this study, regression models based on extensive operational data and wastewater wetlands were adapted to a stormwater wetland receiving both base flow and storm flow from an agricultural area. The models were calibrated in Excel Solver using 15 sets of operational data gathered from random sampling during dry days. The calibrated models were then applied to 20 sets of event mean concentration data from composite sampling during 20 independent rainfall events. For dry days, the models estimated effluent concentrations of nitrogen species that were close to the measured values. However, overestimations during wet days were made for NH(3)-N and total Kjeldahl nitrogen, which resulted from higher hydraulic loading rates and influent nitrogen concentrations during storm flows. The results showed that biological nitrification and denitrification was the major nitrogen removal mechanism during dry days. Meanwhile, during wet days, the prevailing aerobic conditions decreased the denitrification capacity of the wetland, and sedimentation of particulate organic nitrogen and particle-associated forms of nitrogen was increased.

Critical evaluation and modeling of algal harvesting using dissolved air flotation. DAF Algal Harvesting Modeling

DOE PAGES

Zhang, Xuezhi; Hewson, John C.; Amendola, Pasquale; ...

2014-07-14

In our study, Chlorella zofingiensis harvesting by dissolved air flotation (DAF) was critically evaluated with regard to algal concentration, culture conditions, type and dosage of coagulants, and recycle ratio. Harvesting efficiency increased with coagulant dosage and leveled off at 81%, 86%, 91%, and 87% when chitosan, Al 3+, Fe 3+, and cetyl trimethylammonium bromide (CTAB) were used at dosages of 70, 180, 250, and 500 mg g -1, respectively. The DAF efficiency-coagulant dosage relationship changed with algal culture conditions. In evaluating the influence of the initial algal concentration and recycle ratio revealed that, under conditions typical for algal harvesting, wemore » found that it is possible that the number of bubbles is insufficient. A DAF algal harvesting model was developed to explain this observation by introducing mass-based floc size distributions and a bubble limitation into the white water blanket model. Moreover, the model revealed the importance of coagulation to increase floc-bubble collision and attachment, and the preferential interaction of bubbles with larger flocs, which limited the availability of bubbles to the smaller sized flocs. The harvesting efficiencies predicted by the model agree reasonably with experimental data obtained at different Al 3+ dosages, algal concentrations, and recycle ratios. Based on this modeling, critical parameters for efficient algal harvesting were identified.« less

Critical evaluation and modeling of algal harvesting using dissolved air flotation. DAF Algal Harvesting Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuezhi; Hewson, John C.; Amendola, Pasquale

In our study, Chlorella zofingiensis harvesting by dissolved air flotation (DAF) was critically evaluated with regard to algal concentration, culture conditions, type and dosage of coagulants, and recycle ratio. Harvesting efficiency increased with coagulant dosage and leveled off at 81%, 86%, 91%, and 87% when chitosan, Al 3+, Fe 3+, and cetyl trimethylammonium bromide (CTAB) were used at dosages of 70, 180, 250, and 500 mg g -1, respectively. The DAF efficiency-coagulant dosage relationship changed with algal culture conditions. In evaluating the influence of the initial algal concentration and recycle ratio revealed that, under conditions typical for algal harvesting, wemore » found that it is possible that the number of bubbles is insufficient. A DAF algal harvesting model was developed to explain this observation by introducing mass-based floc size distributions and a bubble limitation into the white water blanket model. Moreover, the model revealed the importance of coagulation to increase floc-bubble collision and attachment, and the preferential interaction of bubbles with larger flocs, which limited the availability of bubbles to the smaller sized flocs. The harvesting efficiencies predicted by the model agree reasonably with experimental data obtained at different Al 3+ dosages, algal concentrations, and recycle ratios. Based on this modeling, critical parameters for efficient algal harvesting were identified.« less

A statistical framework for the validation of a population exposure model based on personal exposure data

NASA Astrophysics Data System (ADS)

Rodriguez, Delphy; Valari, Myrto; Markakis, Konstantinos; Payan, Sébastien

2016-04-01

Currently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytechnique.fr/chimere) under air-quality forecasting platforms (e.g. Prev'Air http://www.prevair.org) or research projects. These data may be combined with more or less sophisticated techniques to provide a fairly good representation of pollutant concentration spatial gradients over urban areas. Here we focus on human exposure to atmospheric contaminants. Based on census data on population dynamics and demographics, modeled outdoor concentrations and infiltration of outdoor air-pollution indoors we have developed a population exposure model for ozone and PM2.5. A critical challenge in the field of population exposure modeling is model validation since personal exposure data are expensive and therefore, rare. However, recent research has made low cost mobile sensors fairly common and therefore personal exposure data should become more and more accessible. In view of planned cohort field-campaigns where such data will be available over the Paris region, we propose in the present study a statistical framework that makes the comparison between modeled and measured exposures meaningful. Our ultimate goal is to evaluate the exposure model by comparing modeled exposures to monitor data. The scientific question we address here is how to downscale modeled data that are estimated on the county population scale at the individual scale which is appropriate to the available measurements. To assess this question we developed a Bayesian hierarchical framework that assimilates actual individual data into population statistics and updates the probability estimate.

Detection of greenhouse-gas-induced climatic change. Progress report, 1 December 1991--30 June 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wigley, T.M.L.; Jones, P.D.

1994-07-01

In addition to changes due to variations in greenhouse gas concentrations, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the enhanced greenhouse effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided bymore » a variety of models, from simple energy balance climate models to ocean General Circulation Models. Appendices A--G contain the following seven papers: (A) Recent global warmth moderated by the effects of the Mount Pinatubo eruption; (B) Recent warming in global temperature series; (C) Correlation methods in fingerprint detection studies; (D) Balancing the carbon budget. Implications for projections of future carbon dioxide concentration changes; (E) A simple model for estimating methane concentration and lifetime variations; (F) Implications for climate and sea level of revised IPCC emissions scenarios; and (G) Sulfate aerosol and climatic change.« less

A physiologically based pharmacokinetic (PBPK) model for predicting the efficacy of drug overdose treatment with liposomes in man.

PubMed

Howell, Brett A; Chauhan, Anuj

2010-08-01

Physiologically based pharmacokinetic (PBPK) models were developed for design and optimization of liposome therapy for treatment of overdoses of tricyclic antidepressants and local anesthetics. In vitro drug-binding data for pegylated, anionic liposomes and published mechanistic equations for partition coefficients were used to develop the models. The models were proven reliable through comparisons to intravenous data. The liposomes were predicted to be highly effective at treating amitriptyline overdoses, with reductions in the area under the concentration versus time curves (AUC) of 64% for the heart and brain. Peak heart and brain drug concentrations were predicted to drop by 20%. Bupivacaine AUC and peak concentration reductions were lower at 15.4% and 17.3%, respectively, for the heart and brain. The predicted pharmacokinetic profiles following liposome administration agreed well with data from clinical studies where protein fragments were administered to patients for overdose treatment. Published data on local cardiac function were used to relate the predicted concentrations in the body to local pharmacodynamic effects in the heart. While the results offer encouragement for future liposome therapies geared toward overdose, it is imperative to point out that animal experiments and phase I clinical trials are the next steps to ensuring the efficacy of the treatment. (c) 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

NASA Astrophysics Data System (ADS)

Touch, M.; Clark, D. P.; Barber, W.; Badea, C. T.

2016-04-01

Spectral CT using a photon-counting x-ray detector (PCXD) can potentially increase accuracy of measuring tissue composition. However, PCXD spectral measurements suffer from distortion due to charge sharing, pulse pileup, and Kescape energy loss. This study proposes two novel artificial neural network (ANN)-based algorithms: one to model and compensate for the distortion, and another one to directly correct for the distortion. The ANN-based distortion model was obtained by training to learn the distortion from a set of projections with a calibration scan. The ANN distortion was then applied in the forward statistical model to compensate for distortion in the projection decomposition. ANN was also used to learn to correct distortions directly in projections. The resulting corrected projections were used for reconstructing the image, denoising via joint bilateral filtration, and decomposition into three-material basis functions: Compton scattering, the photoelectric effect, and iodine. The ANN-based distortion model proved to be more robust to noise and worked better compared to using an imperfect parametric distortion model. In the presence of noise, the mean relative errors in iodine concentration estimation were 11.82% (ANN distortion model) and 16.72% (parametric model). With distortion correction, the mean relative error in iodine concentration estimation was improved by 50% over direct decomposition from distorted data. With our joint bilateral filtration, the resulting material image quality and iodine detectability as defined by the contrast-to-noise ratio were greatly enhanced allowing iodine concentrations as low as 2 mg/ml to be detected. Future work will be dedicated to experimental evaluation of our ANN-based methods using 3D-printed phantoms.

Development and testing of a fast conceptual river water quality model.

PubMed

Keupers, Ingrid; Willems, Patrick

2017-04-15

Modern, model based river quality management strongly relies on river water quality models to simulate the temporal and spatial evolution of pollutant concentrations in the water body. Such models are typically constructed by extending detailed hydrodynamic models with a component describing the advection-diffusion and water quality transformation processes in a detailed, physically based way. This approach is too computational time demanding, especially when simulating long time periods that are needed for statistical analysis of the results or when model sensitivity analysis, calibration and validation require a large number of model runs. To overcome this problem, a structure identification method to set up a conceptual river water quality model has been developed. Instead of calculating the water quality concentrations at each water level and discharge node, the river branch is divided into conceptual reservoirs based on user information such as location of interest and boundary inputs. These reservoirs are modelled as Plug Flow Reactor (PFR) and Continuously Stirred Tank Reactor (CSTR) to describe advection and diffusion processes. The same water quality transformation processes as in the detailed models are considered but with adjusted residence times based on the hydrodynamic simulation results and calibrated to the detailed water quality simulation results. The developed approach allows for a much faster calculation time (factor 10 5 ) without significant loss of accuracy, making it feasible to perform time demanding scenario runs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

PubMed Central

Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian

2018-01-01

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908

A simple, mass balance model of carbon flow in a controlled ecological life support system

NASA Technical Reports Server (NTRS)

Garland, Jay L.

1989-01-01

Internal cycling of chemical elements is a fundamental aspect of a Controlled Ecological Life Support System (CELSS). Mathematical models are useful tools for evaluating fluxes and reservoirs of elements associated with potential CELSS configurations. A simple mass balance model of carbon flow in CELSS was developed based on data from the CELSS Breadboard project at Kennedy Space Center. All carbon reservoirs and fluxes were calculated based on steady state conditions and modelled using linear, donor-controlled transfer coefficients. The linear expression of photosynthetic flux was replaced with Michaelis-Menten kinetics based on dynamical analysis of the model which found that the latter produced more adequate model output. Sensitivity analysis of the model indicated that accurate determination of the maximum rate of gross primary production is critical to the development of an accurate model of carbon flow. Atmospheric carbon dioxide was particularly sensitive to changes in photosynthetic rate. The small reservoir of CO2 relative to large CO2 fluxes increases the potential for volatility in CO2 concentration. Feedback control mechanisms regulating CO2 concentration will probably be necessary in a CELSS to reduce this system instability.

A model of plant isoprene emission based on available reducing power captures responses to atmospheric CO₂.

PubMed

Morfopoulos, Catherine; Sperlich, Dominik; Peñuelas, Josep; Filella, Iolanda; Llusià, Joan; Medlyn, Belinda E; Niinemets, Ülo; Possell, Malcolm; Sun, Zhihong; Prentice, Iain Colin

2014-07-01

We present a unifying model for isoprene emission by photosynthesizing leaves based on the hypothesis that isoprene biosynthesis depends on a balance between the supply of photosynthetic reducing power and the demands of carbon fixation. We compared the predictions from our model, as well as from two other widely used models, with measurements of isoprene emission from leaves of Populus nigra and hybrid aspen (Populus tremula × P. tremuloides) in response to changes in leaf internal CO2 concentration (C(i)) and photosynthetic photon flux density (PPFD) under diverse ambient CO2 concentrations (C(a)). Our model reproduces the observed changes in isoprene emissions with C(i) and PPFD, and also reproduces the tendency for the fraction of fixed carbon allocated to isoprene to increase with increasing PPFD. It also provides a simple mechanism for the previously unexplained decrease in the quantum efficiency of isoprene emission with increasing C(a). Experimental and modelled results support our hypothesis. Our model can reproduce the key features of the observations and has the potential to improve process-based modelling of isoprene emissions by land vegetation at the ecosystem and global scales. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

Transport and concentration controls for chloride, strontium, potassium and lead in Uvas Creek, a small cobble-bed stream in Santa Clara County, California, U.S.A. 2. Mathematical modeling

USGS Publications Warehouse

Jackman, A.P.; Walters, R.A.; Kennedy, V.C.

1984-01-01

Three models describing solute transport of conservative ion species and another describing transport of species which adsorb linearly and reversibly on bed sediments are developed and tested. The conservative models are based on three different conceptual models of the transient storage of solute in the bed. One model assumes the bed to be a well-mixed zone with flux of solute into the bed proportional to the difference between stream concentration and bed concentration. The second model assumes solute in the bed is transported by a vertical diffusion process described by Fick's law. The third model assumes that convection occurs in a selected portion of the bed while the mechanism of the first model functions everywhere. The model for adsorbing species assumes that the bed consists of particles of uniform size with the rate of uptake controlled by an intraparticle diffusion process. All models are tested using data collected before, during and after a 24-hr. pulse injection of chloride, strontium, potassium and lead ions into Uvas Creek near Morgan Hill, California, U.S.A. All three conservative models accurately predict chloride ion concentrations in the stream. The model employing the diffusion mechanism for bed transport predicts better than the others. The adsorption model predicts both strontium and potassium ion concentrations well during the injection of the pulse but somewhat overestimates the observed concentrations after the injection ceases. The overestimation may be due to the convection of solute deep into the bed where it is retained longer than the 3-week post-injection observation period. The model, when calibrated for strontium, predicts potassium equally well when the adsorption equilibrium constant for strontium is replaced by that for potassium. ?? 1984.

Modeling the concentration-dependent permeation modes of the KcsA potassium ion channel.

PubMed

Nelson, Peter Hugo

2003-12-01

The potassium channel from Streptomyces lividans (KcsA) is an integral membrane protein with sequence similarity to all known potassium channels, particularly in the selectivity filter region. A recently proposed model for ion channels containing either n or (n-1) single-file ions in their selectivity filters [P. H. Nelson, J. Chem. Phys. 177, 11396 (2002)] is applied to published KcsA channel K+ permeation data that exhibit a high-affinity process at low concentrations and a low-affinity process at high concentrations [M. LeMasurier et al., J. Gen. Physiol. 118, 303 (2001)]. The kinetic model is shown to provide a reasonable first-order explanation for both the high- and low-concentration permeation modes observed experimentally. The low-concentration mode ([K+]<200 mM) has a 200-mV dissociation constant of 56 mM and a conductance of 88 pS. The high-concentration mode ([K+]>200 mM) has a 200-mV dissociation constant of 1100 mM and a conductance of 500 pS. Based on the permeation model, and x-ray analysis [J. H. Morais-Cabral et al., Nature (London) 414, 37 (2001)], it is suggested that the experimentally observed K+ permeation modes correspond to an n=3 mechanism at high concentrations and an n=2 mechanism at low concentrations. The ratio of the electrical dissociation distances for the high- and low-concentration modes is 3:2, also consistent with the proposed n=3 and n=2 modes. Model predictions for K+ channels that exhibit asymmetric current-voltage (I-V) curves are presented, and further validation of the kinetic model via molecular simulation and experiment is discussed. The qualitatively distinct I-V characteristics exhibited experimentally by Tl+, NH+4, and Rb+ ions at 100 mM concentration can also be explained using the model, but more extensive experimental tests are required for quantitative validation of the model predictions.

Modeling the concentration-dependent permeation modes of the KcsA potassium ion channel

NASA Astrophysics Data System (ADS)

Nelson, Peter Hugo

2003-12-01

The potassium channel from Streptomyces lividans (KcsA) is an integral membrane protein with sequence similarity to all known potassium channels, particularly in the selectivity filter region. A recently proposed model for ion channels containing either n or (n-1) single-file ions in their selectivity filters [P. H. Nelson, J. Chem. Phys. 177, 11396 (2002)] is applied to published KcsA channel K+ permeation data that exhibit a high-affinity process at low concentrations and a low-affinity process at high concentrations [M. LeMasurier et al., J. Gen. Physiol. 118, 303 (2001)]. The kinetic model is shown to provide a reasonable first-order explanation for both the high- and low-concentration permeation modes observed experimentally. The low-concentration mode ([K+]<200 mM) has a 200-mV dissociation constant of 56 mM and a conductance of 88 pS. The high-concentration mode ([K+]>200 mM) has a 200-mV dissociation constant of 1100 mM and a conductance of 500 pS. Based on the permeation model, and x-ray analysis [J. H. Morais-Cabral et al., Nature (London) 414, 37 (2001)], it is suggested that the experimentally observed K+ permeation modes correspond to an n=3 mechanism at high concentrations and an n=2 mechanism at low concentrations. The ratio of the electrical dissociation distances for the high- and low-concentration modes is 3:2, also consistent with the proposed n=3 and n=2 modes. Model predictions for K+ channels that exhibit asymmetric current-voltage (I-V) curves are presented, and further validation of the kinetic model via molecular simulation and experiment is discussed. The qualitatively distinct I-V characteristics exhibited experimentally by Tl+, NH+4, and Rb+ ions at 100 mM concentration can also be explained using the model, but more extensive experimental tests are required for quantitative validation of the model predictions.

Refinement of the probability density function model for preferential concentration of aerosol particles in isotropic turbulence

NASA Astrophysics Data System (ADS)

Zaichik, Leonid I.; Alipchenkov, Vladimir M.

2007-11-01

The purposes of the paper are threefold: (i) to refine the statistical model of preferential particle concentration in isotropic turbulence that was previously proposed by Zaichik and Alipchenkov [Phys. Fluids 15, 1776 (2003)], (ii) to investigate the effect of clustering of low-inertia particles using the refined model, and (iii) to advance a simple model for predicting the collision rate of aerosol particles. The model developed is based on a kinetic equation for the two-point probability density function of the relative velocity distribution of particle pairs. Improvements in predicting the preferential concentration of low-inertia particles are attained due to refining the description of the turbulent velocity field of the carrier fluid by including a difference between the time scales of the of strain and rotation rate correlations. The refined model results in a better agreement with direct numerical simulations for aerosol particles.

Inverse Modeling of Texas NOx Emissions Using Space-Based and Ground-Based NO2 Observations

NASA Technical Reports Server (NTRS)

Tang, Wei; Cohan, D.; Lamsal, L. N.; Xiao, X.; Zhou, W.

2013-01-01

Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite observed NO2 over rural regions. Satellitebased top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with Decoupled Direct Method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2 based DKF inversion. The DKF inversions yield conflicting results: the satellite based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3-55% increase in modeled NO2 column densities and 1-7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

NASA Astrophysics Data System (ADS)

Kiesewetter, G.; Borken-Kleefeld, J.; Schöpp, W.; Heyes, C.; Thunis, P.; Bessagnet, B.; Terrenoire, E.; Fagerli, H.; Nyiri, A.; Amann, M.

2015-02-01

Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter <10 μm) limit values at individual air quality monitoring stations reporting to the AirBase database. The modelling approach relies on a combination of bottom up modelling of emissions, simplified atmospheric chemistry and dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000-2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent.

Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

NASA Astrophysics Data System (ADS)

Kiesewetter, G.; Borken-Kleefeld, J.; Schöpp, W.; Heyes, C.; Thunis, P.; Bessagnet, B.; Terrenoire, E.; Amann, M.

2014-07-01

Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter < 10 μm) limit values at individual air quality monitoring stations reporting to the AirBase database. The modelling approach relies on a combination of bottom up modelling of emissions, simplified atmospheric chemistry and dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000-2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent.

Efficacy of disinfectants and detergents intended for a pig farm environment where Salmonella is present.

PubMed

Gosling, Rebecca J; Mawhinney, Ian; Vaughan, Kelly; Davies, Robert H; Smith, Richard P

2017-05-01

Disinfection is a useful component of disease control, although products and chemical groups vary in their activity against different pathogens. This study investigated the ability of fifteen disinfectants to eliminate pig-associated Salmonella. Active compounds of products included chlorocresol, glutaraldehyde/formaldehyde, glutaraldehyde/quaternary ammonium compounds (QAC), iodine, peracetic acid and potassium peroxomonosulphate. Six detergents were also tested for their ability to dislodge faecal material, and interactions with specific disinfectants. Eight serovars were screened against all products using dilution tests and a monophasic Salmonella Typhimurium strain was selected for further testing. The disinfectants were tested using models to replicate boot dip (faecal suspension) and animal housing (surface contamination) disinfection respectively at the Department for Environment, Food and Rural Affairs Approved Disinfectant General Orders (GO) concentration, half GO and twice GO. Stability over time and ability to eliminate Salmonella in biofilm was also assessed. The most effective products were then field tested. Most products at GO concentration eliminated Salmonella in the faecal suspension model. One glutaraldehyde/QAC and one glutaraldehyde/formaldehyde-based product at GO concentration eliminated Salmonella in the surface contamination model. Chlorocresol-based products were more stable in the faecal suspension model. One chlorocresol and the glutaraldehyde/formaldehyde-based product were most successful in eliminating Salmonella from biofilms. All products tested on farm reduced bacterial log counts; the glutaraldehyde/QAC based product produced the greatest reduction. The type of product and the application concentration can impact on efficacy of farm disinfection; therefore, clearer guidance is needed to ensure the appropriate programmes are used for specific environments. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Spectroscopic investigation of inner filter effect by magnolol solutions

NASA Astrophysics Data System (ADS)

Li, Hongmei; YuzhuHu

2007-12-01

Spectroscopy is useful tool for aggregation studies on fluorephores. One of the major problems with this technique is that the inner filter effect becomes unavoidable since the samples are used at high concentration. In this work, our investigation on magnolol spectroscopic properties shows that the inner filter effect (IFE) of fluorescence plays a critical role in the spectra of magnolol. The strong dependence of the fluorescence parameters on the concentration accounts for the apparent experimental evidence of magnolol aggregation at high concentrations. There are some questions despite the aggregation model based on fluorescent aggregates seems to describe the behavior of the system. The mathematical correction on the emission intensities shows the linear fluorescence-concentration relationship. Furthermore, we propose a mathematic model of excitation spectrum based on the primary IFE (absorption of light of excitation wavelength), which provide a correct explanation of the unusual spectral shift and spectral narrowing in the excitation spectra of magnolol at high concentrations. The shapes of spectra are completely independent on magnolol aggregation and are due only to experimental artifacts, i.e. IFE.

Explicit continuous charge-based compact model for long channel heavily doped surrounding-gate MOSFETs incorporating interface traps and quantum effects

NASA Astrophysics Data System (ADS)

Hamzah, Afiq; Hamid, Fatimah A.; Ismail, Razali

2016-12-01

An explicit solution for long-channel surrounding-gate (SRG) MOSFETs is presented from intrinsic to heavily doped body including the effects of interface traps and fixed oxide charges. The solution is based on the core SRGMOSFETs model of the Unified Charge Control Model (UCCM) for heavily doped conditions. The UCCM model of highly doped SRGMOSFETs is derived to obtain the exact equivalent expression as in the undoped case. Taking advantage of the undoped explicit charge-based expression, the asymptotic limits for below threshold and above threshold have been redefined to include the effect of trap states for heavily doped cases. After solving the asymptotic limits, an explicit mobile charge expression is obtained which includes the trap state effects. The explicit mobile charge model shows very good agreement with respect to numerical simulation over practical terminal voltages, doping concentration, geometry effects, and trap state effects due to the fixed oxide charges and interface traps. Then, the drain current is obtained using the Pao-Sah's dual integral, which is expressed as a function of inversion charge densities at the source/drain ends. The drain current agreed well with the implicit solution and numerical simulation for all regions of operation without employing any empirical parameters. A comparison with previous explicit models has been conducted to verify the competency of the proposed model with the doping concentration of 1× {10}19 {{cm}}-3, as the proposed model has better advantages in terms of its simplicity and accuracy at a higher doping concentration.

Characteristics of white LED transmission through a smoke screen

NASA Astrophysics Data System (ADS)

Zheng, Yunfei; Yang, Aiying; Feng, Lihui; Guo, Peng

2018-01-01

The characteristics of white LED transmission through a smoke screen is critical for visible light communication through a smoke screen. Based on the Mie scattering theory, the Monte Carlo transmission model is established. Based on the probability density function, the white LED sampling model is established according to the measured spectrum of a white LED and the distribution angle of the lambert model. The sampling model of smoke screen particle diameter is also established according to its distribution. We simulate numerically the influence the smoke thickness, the smoke concentration and the angle of irradiance of white LED on transmittance of the white LED. We construct a white LED smoke transmission experiment system. The measured result on the light transmittance and the smoke concentration agreed with the simulated result, and demonstrated the validity of simulation model for visible light transmission channel through a smoke screen.

Multi-model study of mercury dispersion in the atmosphere: vertical and interhemispheric distribution of mercury species

NASA Astrophysics Data System (ADS)

Bieser, Johannes; Slemr, Franz; Ambrose, Jesse; Brenninkmeijer, Carl; Brooks, Steve; Dastoor, Ashu; DeSimone, Francesco; Ebinghaus, Ralf; Gencarelli, Christian N.; Geyer, Beate; Gratz, Lynne E.; Hedgecock, Ian M.; Jaffe, Daniel; Kelley, Paul; Lin, Che-Jen; Jaegle, Lyatt; Matthias, Volker; Ryjkov, Andrei; Selin, Noelle E.; Song, Shaojie; Travnikov, Oleg; Weigelt, Andreas; Luke, Winston; Ren, Xinrong; Zahn, Andreas; Yang, Xin; Zhu, Yun; Pirrone, Nicola

2017-06-01

Atmospheric chemistry and transport of mercury play a key role in the global mercury cycle. However, there are still considerable knowledge gaps concerning the fate of mercury in the atmosphere. This is the second part of a model intercomparison study investigating the impact of atmospheric chemistry and emissions on mercury in the atmosphere. While the first study focused on ground-based observations of mercury concentration and deposition, here we investigate the vertical and interhemispheric distribution and speciation of mercury from the planetary boundary layer to the lower stratosphere. So far, there have been few model studies investigating the vertical distribution of mercury, mostly focusing on single aircraft campaigns. Here, we present a first comprehensive analysis based on various aircraft observations in Europe, North America, and on intercontinental flights. The investigated models proved to be able to reproduce the distribution of total and elemental mercury concentrations in the troposphere including interhemispheric trends. One key aspect of the study is the investigation of mercury oxidation in the troposphere. We found that different chemistry schemes were better at reproducing observed oxidized mercury patterns depending on altitude. High concentrations of oxidized mercury in the upper troposphere could be reproduced with oxidation by bromine while elevated concentrations in the lower troposphere were better reproduced by OH and ozone chemistry. However, the results were not always conclusive as the physical and chemical parameterizations in the chemistry transport models also proved to have a substantial impact on model results.

Evaporation kinetics of surfactant solution droplets on rice (Oryza sativa) leaves

PubMed Central

Cao, Li-Dong; Zheng, Li; Xu, Jun; Li, Feng-Min; Huang, Qi-Liang

2017-01-01

The dynamics of evaporating sessile droplets on hydrophilic or hydrophobic surfaces is widely studied, and many models for these processes have been developed based on experimental evidence. However, few research has been explored on the evaporation of sessile droplets of surfactant or pesticide solutions on target crop leaves. Thus, in this paper the impact of surfactant concentrations on contact angle, contact diameter, droplet height, and evolution of the droplets’ evaporative volume on rice leaf surfaces have been investigated. The results indicate that the evaporation kinetics of surfactant droplets on rice leaves were influenced by both the surfactant concentrations and the hydrophobicity of rice leaf surfaces. When the surfactant concentration is lower than the surfactant CMC (critical micelle concentration), the droplet evaporation time is much longer than that of the high surfactant concentration. This is due to the longer existence time of a narrow wedge region under the lower surfactant concentration, and such narrow wedge region further restricts the droplet evaporation. Besides, our experimental data are shown to roughly collapse onto theoretical curves based on the model presented by Popov. This study could supply theoretical data on the evaporation of the adjuvant or pesticide droplets for practical applications in agriculture. PMID:28472108

PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC

USGS Publications Warehouse

Parkhurst, David L.; Wissmeier, Laurin

2015-01-01

PhreeqcRM is a geochemical reaction module designed specifically to perform equilibrium and kinetic reaction calculations for reactive transport simulators that use an operator-splitting approach. The basic function of the reaction module is to take component concentrations from the model cells of the transport simulator, run geochemical reactions, and return updated component concentrations to the transport simulator. If multicomponent diffusion is modeled (e.g., Nernst–Planck equation), then aqueous species concentrations can be used instead of component concentrations. The reaction capabilities are a complete implementation of the reaction capabilities of PHREEQC. In each cell, the reaction module maintains the composition of all of the reactants, which may include minerals, exchangers, surface complexers, gas phases, solid solutions, and user-defined kinetic reactants.PhreeqcRM assigns initial and boundary conditions for model cells based on standard PHREEQC input definitions (files or strings) of chemical compositions of solutions and reactants. Additional PhreeqcRM capabilities include methods to eliminate reaction calculations for inactive parts of a model domain, transfer concentrations and other model properties, and retrieve selected results. The module demonstrates good scalability for parallel processing by using multiprocessing with MPI (message passing interface) on distributed memory systems, and limited scalability using multithreading with OpenMP on shared memory systems. PhreeqcRM is written in C++, but interfaces allow methods to be called from C or Fortran. By using the PhreeqcRM reaction module, an existing multicomponent transport simulator can be extended to simulate a wide range of geochemical reactions. Results of the implementation of PhreeqcRM as the reaction engine for transport simulators PHAST and FEFLOW are shown by using an analytical solution and the reactive transport benchmark of MoMaS.

Modeling the chemical evolution of nitrogen oxides near roadways

NASA Astrophysics Data System (ADS)

Wang, Yan Jason; DenBleyker, Allison; McDonald-Buller, Elena; Allen, David; Zhang, K. Max

2011-01-01

The chemical evolution of nitrogen dioxide (NO 2) and nitrogen monoxide (NO) in the vicinity of roadways is numerically investigated using a computational fluid dynamics model, CFD-VIT-RIT and a Gaussian-based model, CALINE4. CFD-VIT-RIT couples a standard k- ɛ turbulence model for turbulent mixing and the Finite-Rate model for chemical reactions. CALINE4 employs a discrete parcel method, assuming that chemical reactions are independent of the dilution process. The modeling results are compared to the field measurement data collected near two roadways in Austin, Texas, State Highway 71 (SH-71) and Farm to Market Road 973 (FM-973), under parallel and perpendicular wind conditions during the summer of 2007. In addition to ozone (O 3), other oxidants and reactive species including hydroperoxyl radical (HO 2), organic peroxyl radical (RO 2), formaldehyde (HCHO) and acetaldehyde (CH 3CHO) are considered in the transformation from NO to NO 2. CFD-VIT-RIT is shown to be capable of predicting both NO x and NO 2 profiles downwind. CALINE4 is able to capture the NO x profiles, but underpredicts NO 2 concentrations under high wind velocity. Our study suggests that the initial NO 2/NO x ratios have to be carefully selected based on traffic conditions in order to assess NO 2 concentrations near roadways. The commonly assumed NO 2/NO x ratio by volume of 5% may not be suitable for most roadways, especially those with a high fraction of heavy-duty truck traffic. In addition, high O 3 concentrations and high traffic volumes would lead to the peak NO 2 concentration occurring near roadways with elevated concentrations persistent over a long distance downwind.

Toxicokinetic Triage for Environmental Chemicals | Science ...

EPA Pesticide Factsheets

Toxicokinetic (TK) models are essential for linking administered doses to blood and tissue concentrations. In vitro-to-in vivo extrapolation (IVIVE) methods have been developed to determine TK from limited in vitro measurements and chemical structure-based property predictions, providing a less resource–intensive alternative to traditional in vivo TK approaches. High throughput TK (HTTK) methods use IVIVE to estimate doses that produce steady-state plasma concentrations equivalent to those producing biological activity in in vitro screening studies (e.g., ToxCast). In this study, the domain of applicability and assumptions of HTTK approaches were evaluated using both in vivo data and simulation analysis. Based on in vivo data for 87 chemicals, specific properties (e.g., in vitro HTTK data, physico-chemical descriptors, chemical structure, and predicted transporter affinities) were identified that correlate with poor HTTK predictive ability. For 350 xenobiotics with literature HTTK data, we then differentiated those xenobiotics for which HTTK approaches are likely to be sufficient, from those that may require additional data. For 272 chemicals we also developed a HT physiologically-based TK (HTPBTK) model that requires somewhat greater information than a steady-state model, but allows non-steady state dynamics and can predict chemical concentration time-courses for a variety of exposure scenarios, tissues, and species. We used this HTPBTK model to show that the

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

PubMed

Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

2010-08-01

We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error <3 mg/dL) for prediction horizons of up to 25 min; models based on pairs of bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

[Measurements of the concentration of atmospheric CO2 based on OP/FTIR method and infrared reflecting scanning Fourier transform spectrometry].

PubMed

Wei, Ru-Yi; Zhou, Jin-Song; Zhang, Xue-Min; Yu, Tao; Gao, Xiao-Hui; Ren, Xiao-Qiang

2014-11-01

The present paper describes the observations and measurements of the infrared absorption spectra of CO2 on the Earth's surface with OP/FTIR method by employing a mid-infrared reflecting scanning Fourier transform spectrometry, which are the first results produced by the first prototype in China developed by the team of authors. This reflecting scanning Fourier transform spectrometry works in the spectral range 2 100-3 150 cm(-1) with a spectral resolution of 2 cm(-1). Method to measure the atmospheric molecules was described and mathematical proof and quantitative algorithms to retrieve molecular concentration were established. The related models were performed both by a direct method based on the Beer-Lambert Law and by a simulating-fitting method based on HITRAN database and the instrument functions. Concentrations of CO2 were retrieved by the two models. The results of observation and modeling analyses indicate that the concentrations have a distribution of 300-370 ppm, and show tendency that going with the variation of the environment they first decrease slowly and then increase rapidly during the observation period, and reached low points in the afternoon and during the sunset. The concentrations with measuring times retrieved by the direct method and by the simulating-fitting method agree with each other very well, with the correlation of all the data is up to 99.79%, and the relative error is no more than 2.00%. The precision for retrieving is relatively high. The results of this paper demonstrate that, in the field of detecting atmospheric compositions, OP/FTIR method performed by the Infrared reflecting scanning Fourier transform spectrometry is a feasible and effective technical approach, and either the direct method or the simulating-fitting method is capable of retrieving concentrations with high precision.

PAH concentrations simulated with the AURAMS-PAH chemical transport model over Canada and the USA

NASA Astrophysics Data System (ADS)

Galarneau, E.; Makar, P. A.; Zheng, Q.; Narayan, J.; Zhang, J.; Moran, M. D.; Bari, M. A.; Pathela, S.; Chen, A.; Chlumsky, R.

2014-04-01

The offline Eulerian AURAMS (A Unified Regional Air quality Modelling System) chemical transport model was adapted to simulate airborne concentrations of seven PAHs (polycyclic aromatic hydrocarbons): phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene + triphenylene, and benzo[a]pyrene. The model was then run for the year 2002 with hourly output on a grid covering southern Canada and the continental USA with 42 km horizontal grid spacing. Model predictions were compared to ~5000 24 h-average PAH measurements from 45 sites, most of which were located in urban or industrial areas. Eight of the measurement sites also provided data on particle/gas partitioning which had been modelled using two alternative schemes. This is the first known regional modelling study for PAHs over a North American domain and the first modelling study at any scale to compare alternative particle/gas partitioning schemes against paired field measurements. The goal of the study was to provide output concentration maps of use to assessing human inhalation exposure to PAHs in ambient air. Annual average modelled total (gas + particle) concentrations were statistically indistinguishable from measured values for fluoranthene, pyrene and benz[a]anthracene whereas the model underestimated concentrations of phenanthrene, anthracene and chrysene + triphenylene. Significance for benzo[a]pyrene performance was close to the statistical threshold and depended on the particle/gas partitioning scheme employed. On a day-to-day basis, the model simulated total PAH concentrations to the correct order of magnitude the majority of the time. The model showed seasonal differences in prediction quality for volatile species which suggests that a missing emission source such as air-surface exchange should be included in future versions. Model performance differed substantially between measurement locations and the limited available evidence suggests that the model's spatial resolution was too coarse to capture the distribution of concentrations in densely populated areas. A more detailed analysis of the factors influencing modelled particle/gas partitioning is warranted based on the findings in this study.

Standardizing Acute Toxicity Data for use in Ecotoxicology Models: Influence of Test Type, Life Stage, and Concentration Reporting

EPA Science Inventory

Ecotoxicological models generally have large data requirements and are frequently based on existing information from diverse sources. Standardizing data for toxicological models may be necessary to reduce extraneous variation and to ensure models reflect intrinsic relationships. ...

Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions

NASA Astrophysics Data System (ADS)

Ots, Riinu; Heal, Mathew R.; Young, Dominique E.; Williams, Leah R.; Allan, James D.; Nemitz, Eiko; Di Marco, Chiara; Detournay, Anais; Xu, Lu; Ng, Nga L.; Coe, Hugh; Herndon, Scott C.; Mackenzie, Ian A.; Green, David C.; Kuenen, Jeroen J. P.; Reis, Stefan; Vieno, Massimo

2018-04-01

Evidence is accumulating that emissions of primary particulate matter (PM) from residential wood and coal combustion in the UK may be underestimated and/or spatially misclassified. In this study, different assumptions for the spatial distribution and total emission of PM from solid fuel (wood and coal) burning in the UK were tested using an atmospheric chemical transport model. Modelled concentrations of the PM components were compared with measurements from aerosol mass spectrometers at four sites in central and Greater London (ClearfLo campaign, 2012), as well as with measurements from the UK black carbon network.The two main alternative emission scenarios modelled were Base4x and combRedist. For Base4x, officially reported PM2.5 from the residential and other non-industrial combustion source sector were increased by a factor of four. For the combRedist experiment, half of the baseline emissions from this same source were redistributed by residential population density to simulate the effect of allocating some emissions to the smoke control areas (that are assumed in the national inventory to have no emissions from this source). The Base4x scenario yielded better daily and hourly correlations with measurements than the combRedist scenario for year-long comparisons of the solid fuel organic aerosol (SFOA) component at the two London sites. However, the latter scenario better captured mean measured concentrations across all four sites. A third experiment, Redist - all emissions redistributed linearly to population density, is also presented as an indicator of the maximum concentrations an assumption like this could yield.The modelled elemental carbon (EC) concentrations derived from the combRedist experiments also compared well with seasonal average concentrations of black carbon observed across the network of UK sites. Together, the two model scenario simulations of SFOA and EC suggest both that residential solid fuel emissions may be higher than inventory estimates and that the spatial distribution of residential solid fuel burning emissions, particularly in smoke control areas, needs re-evaluation. The model results also suggest the assumed temporal profiles for residential emissions may require review to place greater emphasis on evening (including discretionary) solid fuel burning.

What is needed to make low-density lipoprotein transport in human aorta computational models suitable to explore links to atherosclerosis? Impact of initial and inflow boundary conditions.

PubMed

De Nisco, Giuseppe; Zhang, Peng; Calò, Karol; Liu, Xiao; Ponzini, Raffaele; Bignardi, Cristina; Rizzo, Giovanna; Deng, Xiaoyan; Gallo, Diego; Morbiducci, Umberto

2018-02-08

Personalized computational hemodynamics (CH) is a promising tool to clarify/predict the link between low density lipoproteins (LDL) transport in aorta, disturbed shear and atherogenesis. However, CH uses simplifying assumptions that represent sources of uncertainty. In particular, modelling blood-side to wall LDL transfer is challenged by the cumbersomeness of protocols needed to obtain reliable LDL concentration profile estimations. This paucity of data is limiting the establishment of rigorous CH protocols able to balance the trade-offs among the variety of in vivo data to be acquired, and the accuracy required by biological/clinical applications. In this study, we analyze the impact of LDL concentration initialization (initial conditions, ICs) and inflow boundary conditions (BCs) on CH models of LDL blood-to-wall transfer in aorta. Technically, in an image-based model of human aorta, two different inflow BCs are generated imposing subject-specific inflow 3D PC-MRI measured or idealized (flat) velocity profiles. For each simulated BC, four different ICs for LDL concentration are applied, imposing as IC the LDL distribution resulting from steady-state simulations with average conditions, or constant LDL concentration values. Based on CH results, we conclude that: (1) the imposition of realistic 3D velocity profiles as inflow BC reduces the uncertainty affecting the representation of LDL transfer; (2) different LDL concentration ICs lead to markedly different patterns of LDL transfer. Given that it is not possible to verify in vivo the proper LDL concentration initialization to be applied, we suggest to carefully set and unambiguously declare the imposed BCs and LDL concentration IC when modelling LDL transfer in aorta, in order to obtain reproducible and ultimately comparable results among different laboratories. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

NASA Astrophysics Data System (ADS)

Cantelli, A.; D'Orta, F.; Cattini, A.; Sebastianelli, F.; Cedola, L.

2015-08-01

A computational model is developed for retrieving the positions and the emission rates of unknown pollution sources, under steady state conditions, starting from the measurements of the concentration of the pollutants. The approach is based on the minimization of a fitness function employing a genetic algorithm paradigm. The model is tested considering both pollutant concentrations generated through a Gaussian model in 25 points in a 3-D test case domain (1000m × 1000m × 50 m) and experimental data such as the Prairie Grass field experiments data in which about 600 receptors were located along five concentric semicircle arcs and the Fusion Field Trials 2007. The results show that the computational model is capable to efficiently retrieve up to three different unknown sources.

Continuum-mechanics-based rheological formulation for debris flow

USGS Publications Warehouse

Chen, Cheng-lung; Ling, Chi-Hai; ,

1993-01-01

This paper aims to assess the validity of the generalized viscoplastic fluid (GVF) model in the light of both the classical relative-viscosity versus concentration relation and the dimensionless stress versus shear-rate squared relations based on kinetic theory, thereby addressing how to evaluate the rheological parameters of the GVF model using Bagnold's data.

Overlay coating degradation by simultaneous oxidation and coating/substrate interdiffusion. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Nesbitt, J. A.

1983-01-01

Degradation of NiCrAlZr overlay coatings on various NiCrAl substrates was examined after cyclic oxidation. Concentration/distance profiles were measured in the coating and substrate after various oxidation exposures at 1150 C. For each stubstrate, the Al content in the coating decreased rapidly. The concentration/distance profiles, and particularly that for Al, reflected the oxide spalling resistance of each coated substrate. A numerical model was developed to simulate diffusion associated with overlay-coating degradation by oxidation and coating/substrate interdiffusion. Input to the numerical model consisted of the Cr and Al content of the coating and substrate, ternary diffusivities, and various oxide spalling parameters. The model predicts the Cr and Al concentrations in the coating and substrate after any number of oxidation/thermal cycles. The numerical model also predicts coating failure based on the ability of the coating to supply sufficient Al to the oxide scale. The validity of the model was confirmed by comparison of the predicted and measured concentration/distance profiles. The model was subsequently used to identify the most critical system parameters affecting coating life.

Impact of planetary boundary layer turbulence on model climate and tracer transport

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.; Ott, L. E.; Pawson, S.

2014-12-01

Planetary boundary layer (PBL) processes are important for weather, climate, and tracer transport and concentration. One measure of the strength of these processes is the PBL depth. However, no single PBL depth definition exists and several studies have found that the estimated depth can vary substantially based on the definition used. In the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model, the PBL depth is particularly important because it is used to calculate the turbulent length scale that is used in the estimation of turbulent mixing. This study analyzes the impact of using three different PBL depth definitions in this calculation. Two definitions are based on the scalar eddy diffusion coefficient and the third is based on the bulk Richardson number. Over land, the bulk Richardson number definition estimates shallower nocturnal PBLs than the other estimates while over water this definition generally produces deeper PBLs. The near surface wind velocity, temperature, and specific humidity responses to the change in turbulence are spatially and temporally heterogeneous, resulting in changes to tracer transport and concentrations. Near surface wind speed increases in the bulk Richardson number experiment cause Saharan dust increases on the order of 1 × 10-4 kg m-2 downwind over the Atlantic Ocean. Carbon monoxide (CO) surface concentrations are modified over Africa during boreal summer, producing differences on the order of 20 ppb, due to the model's treatment of emissions from biomass burning. While differences in carbon dioxide (CO2) are small in the time mean, instantaneous differences are on the order of 10 ppm and these are especially prevalent at high latitude during boreal winter. Understanding the sensitivity of trace gas and aerosol concentration estimates to PBL depth is important for studies seeking to calculate surface fluxes based on near-surface concentrations and to studies projecting future concentrations.

Impact of planetary boundary layer turbulence on model climate and tracer transport

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.; Ott, L. E.; Pawson, S.

2015-07-01

Planetary boundary layer (PBL) processes are important for weather, climate, and tracer transport and concentration. One measure of the strength of these processes is the PBL depth. However, no single PBL depth definition exists and several studies have found that the estimated depth can vary substantially based on the definition used. In the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model, the PBL depth is particularly important because it is used to calculate the turbulent length scale that is used in the estimation of turbulent mixing. This study analyzes the impact of using three different PBL depth definitions in this calculation. Two definitions are based on the scalar eddy diffusion coefficient and the third is based on the bulk Richardson number. Over land, the bulk Richardson number definition estimates shallower nocturnal PBLs than the other estimates while over water this definition generally produces deeper PBLs. The near-surface wind velocity, temperature, and specific humidity responses to the change in turbulence are spatially and temporally heterogeneous, resulting in changes to tracer transport and concentrations. Near-surface wind speed increases in the bulk Richardson number experiment cause Saharan dust increases on the order of 1 × 10-4 kg m-2 downwind over the Atlantic Ocean. Carbon monoxide (CO) surface concentrations are modified over Africa during boreal summer, producing differences on the order of 20 ppb, due to the model's treatment of emissions from biomass burning. While differences in carbon dioxide (CO2) are small in the time mean, instantaneous differences are on the order of 10 ppm and these are especially prevalent at high latitude during boreal winter. Understanding the sensitivity of trace gas and aerosol concentration estimates to PBL depth is important for studies seeking to calculate surface fluxes based on near-surface concentrations and for studies projecting future concentrations.

Convergence of the Bouguer-Beer law for radiation extinction in particulate media

NASA Astrophysics Data System (ADS)

Frankel, A.; Iaccarino, G.; Mani, A.

2016-10-01

Radiation transport in particulate media is a common physical phenomenon in natural and industrial processes. Developing predictive models of these processes requires a detailed model of the interaction between the radiation and the particles. Resolving the interaction between the radiation and the individual particles in a very large system is impractical, whereas continuum-based representations of the particle field lend themselves to efficient numerical techniques based on the solution of the radiative transfer equation. We investigate radiation transport through discrete and continuum-based representations of a particle field. Exact solutions for radiation extinction are developed using a Monte Carlo model in different particle distributions. The particle distributions are then projected onto a concentration field with varying grid sizes, and the Bouguer-Beer law is applied by marching across the grid. We show that the continuum-based solution approaches the Monte Carlo solution under grid refinement, but quickly diverges as the grid size approaches the particle diameter. This divergence is attributed to the homogenization error of an individual particle across a whole grid cell. We remark that the concentration energy spectrum of a point-particle field does not approach zero, and thus the concentration variance must also diverge under infinite grid refinement, meaning that no grid-converged solution of the radiation transport is possible.

Comparison and analysis of theoretical models for diffusion-controlled dissolution.

PubMed

Wang, Yanxing; Abrahamsson, Bertil; Lindfors, Lennart; Brasseur, James G

2012-05-07

Dissolution models require, at their core, an accurate diffusion model. The accuracy of the model for diffusion-dominated dissolution is particularly important with the trend toward micro- and nanoscale drug particles. Often such models are based on the concept of a "diffusion layer." Here a framework is developed for diffusion-dominated dissolution models, and we discuss the inadequacy of classical models that are based on an unphysical constant diffusion layer thickness assumption, or do not correctly modify dissolution rate due to "confinement effects": (1) the increase in bulk concentration from confinement of the dissolution process, (2) the modification of the flux model (the Sherwood number) by confinement. We derive the exact mathematical solution for a spherical particle in a confined fluid with impermeable boundaries. Using this solution, we analyze the accuracy of a time-dependent "infinite domain model" (IDM) and "quasi steady-state model" (QSM), both formally derived for infinite domains but which can be applied in approximate fashion to confined dissolution with proper adjustment of a concentration parameter. We show that dissolution rate is sensitive to the degree of confinement or, equivalently, to the total concentration C(tot). The most practical model, the QSM, is shown to be very accurate for most applications and, consequently, can be used with confidence in design-level dissolution models so long as confinement is accurately treated. The QSM predicts the ratio of diffusion layer thickness to particle radius (the Sherwood number) as a constant plus a correction that depends on the degree of confinement. The QSM also predicts that the time required for complete saturation or dissolution in diffusion-controlled dissolution experiments is singular (i.e., infinite) when total concentration equals the solubility. Using the QSM, we show that measured differences in dissolution rate in a diffusion-controlled dissolution experiment are a result of differences in the degree of confinement on the increase in bulk concentration independent of container geometry and polydisperse vs single particle dissolution. We conclude that the constant diffusion-layer thickness assumption is incorrect in principle and should be replaced by the QSM with accurate treatment of confinement in models of diffusion-controlled dissolution.

Clearance Rate and BP-ANN Model in Paraquat Poisoned Patients Treated with Hemoperfusion

PubMed Central

Hu, Lufeng; Hong, Guangliang; Ma, Jianshe; Wang, Xianqin; Lin, Guanyang; Zhang, Xiuhua; Lu, Zhongqiu

2015-01-01

In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05–1.0 μg/mL) and High-PQ group (1.0–10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R = 0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model. PMID:25695058

Real-solution stability diagrams: A thermodynamic tool for modeling corrosion in wide temperature and concentration ranges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderko, A.; Sanders, S.J.; Young, R.D.

1997-01-01

A method was developed for construction of stability diagrams for metals in the presence of realistically modeled aqueous solutions. The method was based on a comprehensive thermodynamic model that combines the Helgeson-Kirkham-Flowers (HKF) equation of state for standard-state properties with a solution nonideality model based on the activity coefficient expressions developed by Bromley and Pitzer. Composition-dependent nonideality effects were incorporated into the calculation of predominance areas for dissolved and solid species. Using the combined thermodynamic model, stability diagrams can be computed for systems involving concentrated solutions (i.e., with molalities up to 30 mol/kg) at temperatures up to 573 K andmore » pressures up to 100 MPa. Since the diagrams are based on a realistic thermodynamic model for the aqueous phase, they are referred to as real-solution stability diagrams. In addition to customary potential (E) and pH variables, concentrations of various active species (e.g., complexing agents) can be used as independent variables, making it possible to analyze effects of various compounds that promote or inhibit corrosion. Usefulness of the methodology was demonstrated by generating real-solution stability diagrams for five representative systems (i.e., sulfur-water [S-H{sub 2}O], copper-ammonia-water [Cu-NH{sub 3}-H{sub 2}O], titanium-chlorine-calcium-water [Ti-Cl-Ca-H{sub 2}O], iron-sulfur-water [Fe-S-H{sub 2}O], and zinc-water [Zn-H{sub 2}O]).« less

Sexual difference in PCB concentrations of coho salmon (Oncorhynchus kisutch)

USGS Publications Warehouse

Madenjian, Charles P.; Schrank, Candy S.; Begnoche, Linda J.; Elliott, Robert F.; Quintal, Richard T.

2010-01-01

We determined polychlorinated biphenyl (PCB) concentrations in 35 female coho salmon (Oncorhynchus kisutch) and 60 male coho salmon caught in Lake Michigan (Michigan and Wisconsin, United States) during the fall of 1994 and 1995. In addition, we determined PCB concentrations in the skin-on fillets of 26 female and 19 male Lake Michigan coho salmon caught during the fall of 2004 and 2006. All coho salmon were age-2 fish. These fish were caught prior to spawning, and therefore release of eggs could not account for sexual differences in PCB concentrations because female coho salmon spawn only once during their lifetime. To investigate whether gross growth efficiency (GGE) differed between the sexes, we applied bioenergetics modeling. Results showed that, on average, males were 19% higher in PCB concentration than females, based on the 1994–1995 dataset. Similarly, males averaged a 20% higher PCB concentration in their skin-on fillets compared with females. According to the bioenergetics modeling results, GGE of adult females was less than 1% higher than adult male GGE. Thus, bioenergetics modeling could not explain the 20% higher PCB concentration exhibited by the males. Nonetheless, a sexual difference in GGE remained a plausible explanation for the sexual difference in PCB concentrations.

The refractive index of human hemoglobin in the visible range.

PubMed

Zhernovaya, O; Sydoruk, O; Tuchin, V; Douplik, A

2011-07-07

Because the refractive index of hemoglobin in the visible range is sensitive to the hemoglobin concentration, optical investigations of hemoglobin are important for medical diagnostics and treatment. Direct measurements of the refractive index are, however, challenging; few such measurements have previously been reported, especially in a wide wavelength range. We directly measured the refractive index of human deoxygenated and oxygenated hemoglobin for nine wavelengths between 400 and 700 nm for the hemoglobin concentrations up to 140 g l(-1). This paper analyzes the results and suggests a set of model functions to calculate the refractive index depending on the concentration. At all wavelengths, the measured values of the refractive index depended on the concentration linearly. Analyzing the slope of the lines, we determined the specific refraction increments, derived a set of model functions for the refractive index depending on the concentration, and compared our results with those available in the literature. Based on the model functions, we further calculated the refractive index at the physiological concentration within the erythrocytes of 320 g l(-1). The results can be used to calculate the refractive index in the visible range for arbitrary concentrations provided that the refractive indices depend on the concentration linearly.

Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula.

PubMed

Silva-Palacios, Inmaculada; Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

2016-02-01

Cupressaceae includes species cultivated as ornamentals in the urban environment. This study aims to investigate airborne pollen data for Cupressaceae on the southwestern Iberian Peninsula over a 21-year period and to analyse the trends in these data and their relationship with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1993 to 2013 in Badajoz (SW Spain). The main pollen season for Cupressaceae lasted, on average, 58 days, ranging from 55 to 112 days, from 24 January to 22 March. Furthermore, a short-term forecasting model has been developed for daily pollen concentrations. The model proposed to forecast the airborne pollen concentration is described by one equation. This expression is composed of two terms: the first term represents the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term is obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological parameters multiplied by a fitting coefficient. Temperature was the main meteorological factor by its influence over daily pollen forecast, being the rain the second most important factor. This model represents a good approach to a continuous balance model of Cupressaceae pollen concentration and is supported by a close agreement between the observed and predicted mean concentrations. The novelty of the proposed model is the analysis of meteorological parameters that are not frequently used in Aerobiology.

Evaluation of Sulfur Dioxide (SO2) Simulations with the GEOS-5/ GOCART Model during the Frostburg/DISCOVER-AQ Field Campaigns

NASA Astrophysics Data System (ADS)

Buchard-Marchant, V.; da Silva, A.; Colarco, P. R.; Krotkov, N. A.; Dickerson, R. R.; Stehr, J. W.; Spinei, E.; Mount, G. H.; Krask, D.

2011-12-01

Sulfur dioxide (SO2) is a major atmospheric pollutant, with a strong anthropogenic component mostly produced by the combustion of fossil fuel and other industrial activities. As a precursor of sulfate aerosols that affect climate, air quality and human health, this gas needs to be monitored on a global scale. Global climate and chemistry models including aerosol processes along with their radiative effects are important tools for climate and air quality research. Validation of these models against in-situ and satellite measurements are essential to ascertain the credibility of these models and to guide model improvements. In this study the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) module running on-line inside the Goddard Earth Observing System version 5 (GEOS-5) model is used to simulate aerosol and SO2 concentrations. The global GEOS-5 system runs at a nominal 25 km horizontal resolution with 72 vertical levels; a comprehensive atmospheric data assimilation system is used to constrain the meteorological state of the model. Data taken over Maryland in the UM's Frostburg (November 2010) and NASA DISCOVER-AQ (July 2011) field campaigns are used to evaluate the GEOS-5 SO2 concentrations. In this presentation we show comparisons of simulated and measured SO2 concentration, using airborne and ground-based data sets, as well as data from the space-based OMI instrument. Preliminary data analysis indicated the model's overestimation of SO2 concentration at the surface, leading to a close examination of mixing processes in the model and the specification of SO2 anthropogenic emission rates. As a result of this analysis, we have implemented a revision of anthropogenic emission inventories in GEOS-5, and updated the vertical placement of SO2 sources. In this presentation we show how these revisions improve the model agreement with observations not only locally but also in regions outside the area of these field campaigns. In particular, we use the ground-based measurements from the Environmental Protection Agency (EPA) for the year 2010 to evaluate the revised model simulations over North America.

Model simulations of cooking organic aerosol (COA) over the UK using estimates of emissions based on measurements at two sites in London

NASA Astrophysics Data System (ADS)

Ots, Riinu; Vieno, Massimo; Allan, James D.; Reis, Stefan; Nemitz, Eiko; Young, Dominique E.; Coe, Hugh; Di Marco, Chiara; Detournay, Anais; Mackenzie, Ian A.; Green, David C.; Heal, Mathew R.

2016-11-01

Cooking organic aerosol (COA) is currently not included in European emission inventories. However, recent positive matrix factorization (PMF) analyses of aerosol mass spectrometer (AMS) measurements have suggested important contributions of COA in several European cities. In this study, emissions of COA were estimated for the UK, based on hourly AMS measurements of COA made at two sites in London (a kerbside site in central London and an urban background site in a residential area close to central London) for the full calendar year of 2012 during the Clean Air for London (ClearfLo) campaign. Iteration of COA emissions estimates and subsequent evaluation and sensitivity experiments were conducted with the EMEP4UK atmospheric chemistry transport modelling system with a horizontal resolution of 5 km × 5 km. The spatial distribution of these emissions was based on workday population density derived from the 2011 census data. The estimated UK annual COA emission was 7.4 Gg per year, which is an almost 10 % addition to the officially reported UK national total anthropogenic emissions of PM2.5 (82 Gg in 2012), corresponding to 320 mg person-1 day-1 on average. Weekday and weekend diurnal variation in COA emissions were also based on the AMS measurements. Modelled concentrations of COA were then independently evaluated against AMS-derived COA measurements from another city and time period (Manchester, January-February 2007), as well as with COA estimated by a chemical mass balance model of measurements for a 2-week period at the Harwell rural site (˜ 80 km west of central London). The modelled annual average contribution of COA to ambient particulate matter (PM) in central London was between 1 and 2 µg m-3 (˜ 20 % of total measured OA1) and between 0.5 and 0.7 µg m-3 in other major cities in England (Manchester, Birmingham, Leeds). It was also shown that cities smaller than London can have a central hotspot of population density of smaller area than the computational grid cell, in which case higher localized COA concentrations than modelled here may be expected. Modelled COA concentrations dropped rapidly outside of major urban areas (annual average of 0.12 µg m-3 for the Harwell location), indicating that although COA can be a notable component in urban air, it does not have a significant effect on PM concentrations on rural areas. The possibility that the AMS-PMF apportionment measurements overestimate COA concentrations by up to a factor of 2 is discussed. Since COA is a primary emission, any downward adjustments in COA emissions would lead to a proportional linear downward scaling in the absolute magnitudes of COA concentrations simulated in the model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Damao; Wang, Zhien; Heymsfield, Andrew J.

Measurement of ice number concentration in clouds is important but still challenging. Stratiform mixed-phase clouds (SMCs) provide a simple scenario for retrieving ice number concentration from remote sensing measurements. The simple ice generation and growth pattern in SMCs offers opportunities to use cloud radar reflectivity (Ze) measurements and other cloud properties to infer ice number concentration quantitatively. To understand the strong temperature dependency of ice habit and growth rate quantitatively, we develop a 1-D ice growth model to calculate the ice diffusional growth along its falling trajectory in SMCs. The radar reflectivity and fall velocity profiles of ice crystals calculatedmore » from the 1-D ice growth model are evaluated with the Atmospheric Radiation Measurements (ARM) Climate Research Facility (ACRF) ground-based high vertical resolution radar measurements. Combining Ze measurements and 1-D ice growth model simulations, we develop a method to retrieve the ice number concentrations in SMCs at given cloud top temperature (CTT) and liquid water path (LWP). The retrieved ice concentrations in SMCs are evaluated with in situ measurements and with a three-dimensional cloud-resolving model simulation with a bin microphysical scheme. These comparisons show that the retrieved ice number concentrations are within an uncertainty of a factor of 2, statistically.« less

Rheology of Carbon Fibre Reinforced Cement-Based Mortar

NASA Astrophysics Data System (ADS)

Banfill, Phillip F. G.; Starrs, Gerry; McCarter, W. John

2008-07-01

Carbon fibre reinforced cement based materials (CFRCs) offer the possibility of fabricating "smart" electrically conductive materials. Rheology of the fresh mix is crucial to satisfactory moulding and fresh CFRC conforms to the Bingham model with slight structural breakdown. Both yield stress and plastic viscosity increase with increasing fibre length and volume concentration. Using a modified Viskomat NT, the concentration dependence of CFRC rheology up to 1.5% fibre volume is reported.

Mathematical modeling of atmospheric fine particle-associated primary organic compound concentrations

NASA Astrophysics Data System (ADS)

Rogge, Wolfgang F.; Hildemann, Lynn M.; Mazurek, Monica A.; Cass, Glen R.; Simoneit, Bernd R. T.

1996-08-01

An atmospheric transport model has been used to explore the relationship between source emissions and ambient air quality for individual particle phase organic compounds present in primary aerosol source emissions. An inventory of fine particulate organic compound emissions was assembled for the Los Angeles area in the year 1982. Sources characterized included noncatalyst- and catalyst-equipped autos, diesel trucks, paved road dust, tire wear, brake lining dust, meat cooking operations, industrial oil-fired boilers, roofing tar pots, natural gas combustion in residential homes, cigarette smoke, fireplaces burning oak and pine wood, and plant leaf abrasion products. These primary fine particle source emissions were supplied to a computer-based model that simulates atmospheric transport, dispersion, and dry deposition based on the time series of hourly wind observations and mixing depths. Monthly average fine particle organic compound concentrations that would prevail if the primary organic aerosol were transported without chemical reaction were computed for more than 100 organic compounds within an 80 km × 80 km modeling area centered over Los Angeles. The monthly average compound concentrations predicted by the transport model were compared to atmospheric measurements made at monitoring sites within the study area during 1982. The predicted seasonal variation and absolute values of the concentrations of the more stable compounds are found to be in reasonable agreement with the ambient observations. While model predictions for the higher molecular weight polycyclic aromatic hydrocarbons (PAH) are in agreement with ambient observations, lower molecular weight PAH show much higher predicted than measured atmospheric concentrations in the particle phase, indicating atmospheric decay by chemical reactions or evaporation from the particle phase. The atmospheric concentrations of dicarboxylic acids and aromatic polycarboxylic acids greatly exceed the contributions that are due to direct emissions from primary sources, confirming that these compounds are principally formed by atmospheric chemical reactions.

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

PubMed

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Modeling and parameters identification of 2-keto-L-gulonic acid fed-batch fermentation.

PubMed

Wang, Tao; Sun, Jibin; Yuan, Jingqi

2015-04-01

This article presents a modeling approach for industrial 2-keto-L-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.

Computational fluid dynamics modeling of Bacillus anthracis spore deposition in rabbit and human respiratory airways

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kabilan, S.; Suffield, S. R.; Recknagle, K. P.

Three-dimensional computational fluid dynamics and Lagrangian particle deposition models were developed to compare the deposition of aerosolized Bacillus anthracis spores in the respiratory airways of a human with that of the rabbit, a species commonly used in the study of anthrax disease. The respiratory airway geometries for each species were derived respectively from computed tomography (CT) and µCT images. Both models encompassed airways that extended from the external nose to the lung with a total of 272 outlets in the human model and 2878 outlets in the rabbit model. All simulations of spore deposition were conducted under transient, inhalation–exhalation breathingmore » conditions using average species-specific minute volumes. Two different exposure scenarios were modeled in the rabbit based upon experimental inhalation studies. For comparison, human simulations were conducted at the highest exposure concentration used during the rabbit experimental exposures. Results demonstrated that regional spore deposition patterns were sensitive to airway geometry and ventilation profiles. Due to the complex airway geometries in the rabbit nose, higher spore deposition efficiency was predicted in the nasal sinus compared to the human at the same air concentration of anthrax spores. In contrast, higher spore deposition was predicted in the lower conducting airways of the human compared to the rabbit lung due to differences in airway branching pattern. This information can be used to refine published and ongoing biokinetic models of inhalation anthrax spore exposures, which currently estimate deposited spore concentrations based solely upon exposure concentrations and inhaled doses that do not factor in species-specific anatomy and physiology for deposition.« less

Fuzzy rule based estimation of agricultural diffuse pollution concentration in streams.

PubMed

Singh, Raj Mohan

2008-04-01

Outflow from the agricultural fields carries diffuse pollutants like nutrients, pesticides, herbicides etc. and transports the pollutants into the nearby streams. It is a matter of serious concern for water managers and environmental researchers. The application of chemicals in the agricultural fields, and transport of these chemicals into streams are uncertain that cause complexity in reliable stream quality predictions. The chemical characteristics of applied chemical, percentage of area under the chemical application etc. are some of the main inputs that cause pollution concentration as output in streams. Each of these inputs and outputs may contain measurement errors. Fuzzy rule based model based on fuzzy sets suits to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. In this study, the property of fuzzy sets to address the uncertainty in input-output relationship is utilized to obtain the estimate of concentrations of a herbicide, atrazine, in a stream. The data of White river basin, a part of the Mississippi river system, is used for developing the fuzzy rule based models. The performance of the developed methodology is found encouraging.

Simultaneous pre-concentration and separation on simple paper-based analytical device for protein analysis.

PubMed

Niu, Ji-Cheng; Zhou, Ting; Niu, Li-Li; Xie, Zhen-Sheng; Fang, Fang; Yang, Fu-Quan; Wu, Zhi-Yong

2018-02-01

In this work, fast isoelectric focusing (IEF) was successfully implemented on an open paper fluidic channel for simultaneous concentration and separation of proteins from complex matrix. With this simple device, IEF can be finished in 10 min with a resolution of 0.03 pH units and concentration factor of 10, as estimated by color model proteins by smartphone-based colorimetric detection. Fast detection of albumin from human serum and glycated hemoglobin (HBA1c) from blood cell was demonstrated. In addition, off-line identification of the model proteins from the IEF fractions with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was also shown. This PAD IEF is potentially useful either for point of care test (POCT) or biomarker analysis as a cost-effective sample pretreatment method.

Bilirubin-a potential marker of drug exposure in atazanavir-based antiretroviral therapy.

PubMed

Rekić, Dinko; Clewe, Oskar; Röshammar, Daniel; Flamholc, Leo; Sönnerborg, Anders; Ormaasen, Vidar; Gisslén, Magnus; Abelö, Angela; Ashton, Michael

2011-12-01

The objective of this work was to examine the atazanavir-bilirubin relationship using a population-based approach and to assess the possible application of bilirubin as a readily available marker of atazanavir exposure. A model of atazanavir exposure and its concentration-dependent effect on bilirubin levels was developed based on 200 atazanavir and 361 bilirubin samples from 82 patients receiving atazanavir in the NORTHIV trial. The pharmacokinetics was adequately described by a one-compartment model with first-order absorption and lag-time. The maximum inhibition of bilirubin elimination rate constant (I(max)) was estimated at 91% (95% CI, 87-94) and the atazanavir concentration resulting in half of I(max) (IC50) was 0.30 μmol/L (95% CI, 0.24-0.37). At an atazanavir/ritonavir dose of 300/100 mg given once daily, the bilirubin half-life was on average increased from 1.6 to 8.1 h. A nomogram, which can be used to indicate suboptimal atazanavir exposure and non-adherence, was constructed based on model simulations.

An Assessment of the Model of Concentration Addition for Predicting the Estrogenic Activity of Chemical Mixtures in Wastewater Treatment Works Effluents

PubMed Central

Thorpe, Karen L.; Gross-Sorokin, Melanie; Johnson, Ian; Brighty, Geoff; Tyler, Charles R.

2006-01-01

The effects of simple mixtures of chemicals, with similar mechanisms of action, can be predicted using the concentration addition model (CA). The ability of this model to predict the estrogenic effects of more complex mixtures such as effluent discharges, however, has yet to be established. Effluents from 43 U.K. wastewater treatment works were analyzed for the presence of the principal estrogenic chemical contaminants, estradiol, estrone, ethinylestradiol, and nonylphenol. The measured concentrations were used to predict the estrogenic activity of each effluent, employing the model of CA, based on the relative potencies of the individual chemicals in an in vitro recombinant yeast estrogen screen (rYES) and a short-term (14-day) in vivo rainbow trout vitellogenin induction assay. Based on the measured concentrations of the four chemicals in the effluents and their relative potencies in each assay, the calculated in vitro and in vivo responses compared well and ranged between 3.5 and 87 ng/L of estradiol equivalents (E2 EQ) for the different effluents. In the rYES, however, the measured E2 EQ concentrations in the effluents ranged between 0.65 and 43 ng E2 EQ/L, and they varied against those predicted by the CA model. Deviations in the estimation of the estrogenic potency of the effluents by the CA model, compared with the measured responses in the rYES, are likely to have resulted from inaccuracies associated with the measurement of the chemicals in the extracts derived from the complex effluents. Such deviations could also result as a consequence of interactions between chemicals present in the extracts that disrupted the activation of the estrogen response elements in the rYES. E2 EQ concentrations derived from the vitellogenic response in fathead minnows exposed to a series of effluent dilutions were highly comparable with the E2 EQ concentrations derived from assessments of the estrogenic potency of these dilutions in the rYES. Together these data support the use of bioassays for determining the estrogenic potency of WwTW effluents, and they highlight the associated problems for modeling approaches that are reliant on measured concentrations of estrogenic chemicals. PMID:16818252

Noninvasive in vivo glucose sensing using an iris based technique

NASA Astrophysics Data System (ADS)

Webb, Anthony J.; Cameron, Brent D.

2011-03-01

Physiological glucose monitoring is important aspect in the treatment of individuals afflicted with diabetes mellitus. Although invasive techniques for glucose monitoring are widely available, it would be very beneficial to make such measurements in a noninvasive manner. In this study, a New Zealand White (NZW) rabbit animal model was utilized to evaluate a developed iris-based imaging technique for the in vivo measurement of physiological glucose concentration. The animals were anesthetized with isoflurane and an insulin/dextrose protocol was used to control blood glucose concentration. To further help restrict eye movement, a developed ocular fixation device was used. During the experimental time frame, near infrared illuminated iris images were acquired along with corresponding discrete blood glucose measurements taken with a handheld glucometer. Calibration was performed using an image based Partial Least Squares (PLS) technique. Independent validation was also performed to assess model performance along with Clarke Error Grid Analysis (CEGA). Initial validation results were promising and show that a high percentage of the predicted glucose concentrations are within 20% of the reference values.

Atmospheric concentrations, sources and gas-particle partitioning of PAHs in Beijing after the 29th Olympic Games.

PubMed

Ma, Wan-Li; Sun, De-Zhi; Shen, Wei-Guo; Yang, Meng; Qi, Hong; Liu, Li-Yan; Shen, Ji-Min; Li, Yi-Fan

2011-07-01

A comprehensive sampling campaign was carried out to study atmospheric concentration of polycyclic aromatic hydrocarbons (PAHs) in Beijing and to evaluate the effectiveness of source control strategies in reducing PAHs pollution after the 29th Olympic Games. The sub-cooled liquid vapor pressure (logP(L)(o))-based model and octanol-air partition coefficient (K(oa))-based model were applied based on each seasonal dateset. Regression analysis among log K(P), logP(L)(o) and log K(oa) exhibited high significant correlations for four seasons. Source factors were identified by principle component analysis and contributions were further estimated by multiple linear regression. Pyrogenic sources and coke oven emission were identified as major sources for both the non-heating and heating seasons. As compared with literatures, the mean PAH concentrations before and after the 29th Olympic Games were reduced by more than 60%, indicating that the source control measures were effective for reducing PAHs pollution in Beijing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study.

PubMed

Liu, Zhijian; Cheng, Kewei; Li, Hao; Cao, Guoqing; Wu, Di; Shi, Yunjie

2018-02-01

Indoor airborne culturable fungi exposure has been closely linked to occupants' health. However, conventional measurement of indoor airborne fungal concentration is complicated and usually requires around one week for fungi incubation in laboratory. To provide an ultra-fast solution, here, for the first time, a knowledge-based machine learning model is developed with the inputs of indoor air quality data for estimating the concentration of indoor airborne culturable fungi. To construct a database for statistical analysis and model training, 249 data groups of air quality indicators (concentration of indoor airborne culturable fungi, indoor/outdoor PM 2.5 and PM 10 concentrations, indoor temperature, indoor relative humidity, and indoor CO 2 concentration) were measured from 85 residential buildings of Baoding (China) during the period of 2016.11.15-2017.03.15. Our results show that artificial neural network (ANN) with one hidden layer has good prediction performances, compared to a support vector machine (SVM). With the tolerance of ± 30%, the prediction accuracy of the ANN model with ten hidden nodes can at highest reach 83.33% in the testing set. Most importantly, we here provide a quick method for estimating the concentration of indoor airborne fungi that can be applied to real-time evaluation.

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women.

PubMed

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2016-03-01

Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007-2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman's r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen's kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements.

REGIONAL OXIDANT MODEL (ROM) USER'S GUIDE, PART 3: THE CORE MODEL

EPA Science Inventory

The Regional Oxidant Model (ROM) determines hourly concentrations and fates of zone and 34 other chemical species over a scale of 1000 km x 1000 km for ozone "episodes" of up to one month's duration. he model structure, based on phenomenological concepts, consists of 3 1/2 layers...

Model-based monitoring and diagnosis of a satellite-based instrument

NASA Technical Reports Server (NTRS)

Bos, Andre; Callies, Jorg; Lefebvre, Alain

1995-01-01

For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

Model-based monitoring and diagnosis of a satellite-based instrument

NASA Astrophysics Data System (ADS)

Bos, Andre; Callies, Jorg; Lefebvre, Alain

1995-05-01

For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

Impurity concentration and temperature dependence of the refractive indices of Er3+ doped ceramic Y2O3.

PubMed

Joshi, A; Haynes, N D; Zelmon, D E; Stafsudd, O; Shori, R

2012-02-13

The refractive indices and thermo-optic coefficients for varying concentrations of Er3+ doped polycrystalline yttria were measured at a variety of wavelengths and temperatures. A Lorenz oscillator model was employed to model the room temperature indices and thermo-optic coefficients were calculated based on temperature dependent index measurements from 0.45 to 1.064 microns. Some consequences relating to thermal lensing are discussed.

The Constraints, Construction, and Verification of a Strain-Specific Physiologically Based Pharmacokinetic Rat Model.

PubMed

Musther, Helen; Harwood, Matthew D; Yang, Jiansong; Turner, David B; Rostami-Hodjegan, Amin; Jamei, Masoud

2017-09-01

The use of in vitro-in vivo extrapolation (IVIVE) techniques, mechanistically incorporated within physiologically based pharmacokinetic (PBPK) models, can harness in vitro drug data and enhance understanding of in vivo pharmacokinetics. This study's objective was to develop a user-friendly rat (250 g, male Sprague-Dawley) IVIVE-linked PBPK model. A 13-compartment PBPK model including mechanistic absorption models was developed, with required system data (anatomical, physiological, and relevant IVIVE scaling factors) collated from literature and analyzed. Overall, 178 system parameter values for the model are provided. This study also highlights gaps in available system data required for strain-specific rat PBPK model development. The model's functionality and performance were assessed using previous literature-sourced in vitro properties for diazepam, metoprolol, and midazolam. The results of simulations were compared against observed pharmacokinetic rat data. Predicted and observed concentration profiles in 10 tissues for diazepam after a single intravenous (i.v.) dose making use of either observed i.v. clearance (CL iv ) or in vitro hepatocyte intrinsic clearance (CL int ) for simulations generally led to good predictions in various tissue compartments. Overall, all i.v. plasma concentration profiles were successfully predicted. However, there were challenges in predicting oral plasma concentration profiles for metoprolol and midazolam, and the potential reasons and according solutions are discussed. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

International challenge to predict the impact of radioxenon releases from medical isotope production on a comprehensive nuclear test ban treaty sampling station.

PubMed

Eslinger, Paul W; Bowyer, Ted W; Achim, Pascal; Chai, Tianfeng; Deconninck, Benoit; Freeman, Katie; Generoso, Sylvia; Hayes, Philip; Heidmann, Verena; Hoffman, Ian; Kijima, Yuichi; Krysta, Monika; Malo, Alain; Maurer, Christian; Ngan, Fantine; Robins, Peter; Ross, J Ole; Saunier, Olivier; Schlosser, Clemens; Schöppner, Michael; Schrom, Brian T; Seibert, Petra; Stein, Ariel F; Ungar, Kurt; Yi, Jing

2016-06-01

The International Monitoring System (IMS) is part of the verification regime for the Comprehensive Nuclear-Test-Ban-Treaty Organization (CTBTO). At entry-into-force, half of the 80 radionuclide stations will be able to measure concentrations of several radioactive xenon isotopes produced in nuclear explosions, and then the full network may be populated with xenon monitoring afterward. An understanding of natural and man-made radionuclide backgrounds can be used in accordance with the provisions of the treaty (such as event screening criteria in Annex 2 to the Protocol of the Treaty) for the effective implementation of the verification regime. Fission-based production of (99)Mo for medical purposes also generates nuisance radioxenon isotopes that are usually vented to the atmosphere. One of the ways to account for the effect emissions from medical isotope production has on radionuclide samples from the IMS is to use stack monitoring data, if they are available, and atmospheric transport modeling. Recently, individuals from seven nations participated in a challenge exercise that used atmospheric transport modeling to predict the time-history of (133)Xe concentration measurements at the IMS radionuclide station in Germany using stack monitoring data from a medical isotope production facility in Belgium. Participants received only stack monitoring data and used the atmospheric transport model and meteorological data of their choice. Some of the models predicted the highest measured concentrations quite well. A model comparison rank and ensemble analysis suggests that combining multiple models may provide more accurate predicted concentrations than any single model. None of the submissions based only on the stack monitoring data predicted the small measured concentrations very well. Modeling of sources by other nuclear facilities with smaller releases than medical isotope production facilities may be important in understanding how to discriminate those releases from releases from a nuclear explosion. Published by Elsevier Ltd.

Computational Material Processing in Microgravity

NASA Technical Reports Server (NTRS)

2005-01-01

Working with Professor David Matthiesen at Case Western Reserve University (CWRU) a computer model of the DPIMS (Diffusion Processes in Molten Semiconductors) space experiment was developed that is able to predict the thermal field, flow field and concentration profile within a molten germanium capillary under both ground-based and microgravity conditions as illustrated. These models are coupled with a novel nonlinear statistical methodology for estimating the diffusion coefficient from measured concentration values after a given time that yields a more accurate estimate than traditional methods. This code was integrated into a web-based application that has become a standard tool used by engineers in the Materials Science Department at CWRU.

Pdf modeling for premixed turbulent combustion based on the properties of iso-concentration surfaces

NASA Technical Reports Server (NTRS)

Vervisch, L.; Kollmann, W.; Bray, K. N. C.; Mantel, T.

1994-01-01

In premixed turbulent flames the presence of intense mixing zones located in front of and behind the flame surface leads to a requirement to study the behavior of iso-concentration surfaces defined for all values of the progress variable (equal to unity in burnt gases and to zero in fresh mixtures). To support this study, some theoretical and mathematical tools devoted to level surfaces are first developed. Then a database of direct numerical simulations of turbulent premixed flames is generated and used to investigate the internal structure of the flame brush, and a new pdf model based on the properties of iso-surfaces is proposed.

Impact of ionization equilibrium on electrokinetic flow of weak electrolytes in nanochannels

NASA Astrophysics Data System (ADS)

Ji, Ziwei; Huang, Zhuo; Chen, Bowei; He, Yuhui; Tsutsui, Makusu; Miao, Xiangshui

2018-07-01

Weak electrolyte transport in nanochannels or nanopores has been actively explored in recent experiments. In this paper, we establish a new electrokinetic model where the ionization balance effect of weak electrolytes is outlined, and performed numerical calculations for H3PO4 concentration-biased nanochannel systems. By considering the roles of local chemical equilibrium in phosphorous acid ionization, the simulation results show quantitative agreement with experimental observations. Based on the model, we predict that enhanced energy harvesting capacity could be accomplished by utilizing weak electrolytes compared to the conventional strong electrolyte approaches in a concentration gradient-based power-generating system.

Compound parabolic concentrator optical fiber tip for FRET-based fluorescent sensors

NASA Astrophysics Data System (ADS)

Ul Hassan, Hafeez; Nielsen, Kristian; Aasmul, Soren; Bang, Ole

2015-09-01

The Compound Parabolic Concentrator (CPC) optical fiber tip shape has been proposed for intensity based fluorescent sensors working on the principle of FRET (Förster Resonance Energy Transfer). A simple numerical Zemax model has been used to optimize the CPC tip geometry for a step-index multimode polymer optical fiber for an excitation and emission wavelength of 550 nm and 650nm, respectively. The model suggests an increase of a factor of 1.6 to 4 in the collected fluorescent power for an ideal CPC tip, as compared to the plane-cut fiber tip for fiber lengths between 5 and 45mm.

Communication: Modeling electrolyte mixtures with concentration dependent dielectric permittivity

NASA Astrophysics Data System (ADS)

Chen, Hsieh; Panagiotopoulos, Athanassios Z.

2018-01-01

We report a new implicit-solvent simulation model for electrolyte mixtures based on the concept of concentration dependent dielectric permittivity. A combining rule is found to predict the dielectric permittivity of electrolyte mixtures based on the experimentally measured dielectric permittivity for pure electrolytes as well as the mole fractions of the electrolytes in mixtures. Using grand canonical Monte Carlo simulations, we demonstrate that this approach allows us to accurately reproduce the mean ionic activity coefficients of NaCl in NaCl-CaCl2 mixtures at ionic strengths up to I = 3M. These results are important for thermodynamic studies of geologically relevant brines and physiological fluids.

Development of glucose measurement system based on pulsed laser-induced ultrasonic method

NASA Astrophysics Data System (ADS)

Ren, Zhong; Wan, Bin; Liu, Guodong; Xiong, Zhihua

2016-09-01

In this study, a kind of glucose measurement system based on pulsed-induced ultrasonic technique was established. In this system, the lateral detection mode was used, the Nd: YAG pumped optical parametric oscillator (OPO) pulsed laser was used as the excitation source, the high sensitivity ultrasonic transducer was used as the signal detector to capture the photoacoustic signals of the glucose. In the experiments, the real-time photoacoustic signals of glucose aqueous solutions with different concentrations were captured by ultrasonic transducer and digital oscilloscope. Moreover, the photoacoustic peak-to-peak values were gotten in the wavelength range from 1300nm to 2300nm. The characteristic absorption wavelengths of glucose were determined via the difference spectral method and second derivative method. In addition, the prediction models of predicting glucose concentrations were established via the multivariable linear regression algorithm and the optimal prediction model of corresponding optimal wavelengths. Results showed that the performance of the glucose system based on the pulsed-induced ultrasonic detection method was feasible. Therefore, the measurement scheme and prediction model have some potential value in the fields of non-invasive monitoring the concentration of the glucose gradient, especially in the food safety and biomedical fields.

Wet deposition of mercury at a New York state rural site: Concentrations, fluxes, and source areas

NASA Astrophysics Data System (ADS)

Lai, Soon-onn; Holsen, Thomas M.; Hopke, Philip K.; Liu, Peng

Event-based mercury (Hg) precipitation samples were collected with a modified MIC-B sampler between September 2003 and April 2005 at Potsdam, NY to investigate Hg in wet deposition and identify potential source areas using the potential source contribution function (PCSF) and residence time weighted concentration (RTWC) models. The volume-weighted mean (VWM) concentration and wet deposition flux were 5.5ngL-1 and 7.6μgm-2 during the study period, and 5.5ngL-1 and 5.9μgm-2 in 2004, respectively, and show seasonal trends with larger values in the spring and summer. The PSCF model results matched known source areas based on an emission inventory better than did the RTWC results based on the spatial correlation index. Both modeling results identified large Hg source areas that contain a number of coal-fired power plants located in the Upper Ohio River Valley and in southeastern Michigan, as well as in Quebec and Ontario where there are metal production facilities, waste incinerators and paper mills. Emissions from the Atlantic Ocean were also determined to be a potential source.

Update of NOx emission temporal profiles using CMAQ-HDDM

NASA Astrophysics Data System (ADS)

Bae, C.; Lee, J. B.; Kim, H. C.; Kim, B. U.; Kim, S.

2017-12-01

This study demonstrates the impact of revised temporal profiles of NOx emissions on air quality simulations in the Seoul Metropolitan Area (SMA), South Korea. Air pollutants such as ozone and nitrogen oxides can be harmful to the human body even with short-term exposure. Since most of air quality models use predefined temporal profiles which are often outdated or taken from different chemical environment, providing accurate temporal variation of emissions are challenging in prediction of correct local air quality. Considering secondary formation of pollutants are important in mega cities and temporal variations of emissions are not coincident with those of resultant concentrations, we utilized CMAQ-HDDM to link emissions and consequential concentrations from different time steps. Base simulations were conducted using WRF, SMOKE, and CMAQ modeling frame using CREATE 2015 and CAPSS 2013 emissions inventories for East Asia and South Korea, respectively. With current modeling system, modeled NOx concentrations underestimate 4% in the daytime (10-16 LST), but overestimate 30% in the nighttime during May to August 2015. Applying revised temporal profiles based on HDDM sensitivities, model performance was improved significantly. We conclude that the proposed temporal allocation method can be useful to reduce the model-observation discrepancies when the activity data for emission sources are difficult to obtain with a bottom-up approach.

Modeling metal binding to soils: the role of natural organic matter.

PubMed

Gustafsson, Jon Petter; Pechová, Pavlina; Berggren, Dan

2003-06-15

The use of mechanistically based models to simulate the solution concentrations of heavy metals in soils is complicated by the presence of different sorbents that may bind metals. In this study, the binding of Zn, Pb, Cu, and Cd by 14 different Swedish soil samples was investigated. For 10 of the soils, it was found that the Stockholm Humic Model (SHM) was able to describe the acid-base characteristics, when using the concentrations of "active" humic substances and Al as fitting parameters. Two additional soils could be modeled when ion exchange to clay was also considered, using a component additivity approach. For dissolved Zn, Cd, Ca, and Mg reasonable model fits were produced when the metal-humic complexation parameters were identical for the 12 soils modeled. However, poor fits were obtained for Pb and Cu in Aquept B horizons. In two of the soil suspensions, the Lund A and Romfartuna Bhs, the calculated speciation agreed well with results obtained by using cation-exchange membranes. The results suggest that organic matter is an important sorbent for metals in many surface horizons of soils in temperate and boreal climates, and the necessity of properly accounting for the competition from Al in simulations of dissolved metal concentrations is stressed.

A first European scale multimedia fate modelling of BDE-209 from 1970 to 2020.

PubMed

Earnshaw, Mark R; Jones, Kevin C; Sweetman, Andy J

2015-01-01

The European Variant Berkeley Trent (EVn-BETR) multimedia fugacity model is used to test the validity of previously derived emission estimates and predict environmental concentrations of the main decabromodiphenyl ether congener, BDE-209. The results are presented here and compared with measured environmental data from the literature. Future multimedia concentration trends are predicted using three emission scenarios (Low, Realistic and High) in the dynamic unsteady state mode covering the period 1970-2020. The spatial and temporal distributions of emissions are evaluated. It is predicted that BDE-209 atmospheric concentrations peaked in 2004 and will decline to negligible levels by 2025. Freshwater concentrations should have peaked in 2011, one year after the emissions peak with sediment concentrations peaking in 2013. Predicted atmospheric concentrations are in good agreement with measured data for the Realistic (best estimate of emissions) and High (worst case scenario) emission scenarios. The Low emission scenario consistently underestimates measured data. The German unilateral ban on the use of DecaBDE in the textile industry is simulated in an additional scenario, the effects of which are mainly observed within Germany with only a small effect on the surrounding areas. Overall, the EVn-BTER model predicts atmospheric concentrations reasonably well, within a factor of 5 and 1.2 for the Realistic and High emission scenarios respectively, providing partial validation for the original emission estimate. Total mean MEC:PEC shows the High emission scenario predicts the best fit between air, freshwater and sediment data. An alternative spatial distribution of emissions is tested, based on higher consumption in EBFRIP member states, resulting in improved agreement between MECs and PECs in comparison with the Uniform spatial distribution based on population density. Despite good agreement between modelled and measured point data, more long-term monitoring datasets are needed to compare predicted trends in concentration to determine the rate of change of POPs within the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference.

PubMed

Ceriotti, Ferruccio; Fernandez-Calle, Pilar; Klee, George G; Nordin, Gunnar; Sandberg, Sverre; Streichert, Thomas; Vives-Corrons, Joan-Lluis; Panteghini, Mauro

2017-02-01

This paper, prepared by the EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM), is dealing with criteria for allocating measurands to the different models for analytical performance specifications (APS) recognized in the 1st EFLM Strategic Conference Consensus Statement. Model 1, based on the effect of APS on clinical outcome, is the model of choice for measurands that have a central role in the decision-making of a specific disease or clinical situation and where cut-off/decision limits are established for either diagnosing, screening or monitoring. Total cholesterol, glucose, HbA1c, serum albumin and cardiac troponins represent practical examples. Model 2 is based on components of biological variation and should be applied to measurands that do not have a central role in a specific disease or clinical situation, but where the concentration of the measurand is in a steady state. This is best achieved for measurands under strict homeostatic control in order to preserve their concentrations in the body fluid of interest, but it can also be applied to other measurands that are in a steady state in biological fluids. In this case, it is expected that the "noise" produced by the measurement procedure will not significantly alter the signal provided by the concentration of the measurand. This model especially applies to electrolytes and minerals in blood plasma (sodium, potassium, chloride, bicarbonate, calcium, magnesium, inorganic phosphate) and to creatinine, cystatin C, uric acid and total protein in plasma. Model 3, based on state-of-the-art of the measurement, should be used for all the measurands that cannot be included in models 1 or 2.

[Algorithm for estimating chlorophyll-a concentration in case II water body based on bio-optical model].

PubMed

Yang, Wei; Chen, Jin; Mausushita, Bunki

2009-01-01

In the present study, a novel retrieval method for estimating chlorophyll-a concentration in case II waters based on bio-optical model was proposed and was tested with the data measured in the laboratory. A series of reflectance spectra, with which the concentration of each sample constituent (for example chlorophyll-a, NPSS etc.) was obtained from accurate experiments, were used to calculate the absorption and backscattering coefficients of the constituents of the case II waters. Then non-negative least square method was applied to calculate the concentration of chlorophyll-a and non-phytoplankton suspended sediments (NPSS). Green algae was firstly collected from the Kasumigaura lake in Japan and then cultured in the laboratory. The reflectance spectra of waters with different amounts of phytoplankton and NPSS were measured in the dark room using FieldSpec Pro VNIR (Analytical Spectral Devises Inc. , Boulder, CO, USA). In order to validate whether this method can be applied in multispectral data (for example Landsat TM), the spectra measured in the laboratory were resampled with Landsat TM bands 1, 2, 3 and 4. Different combinations of TM bands were compared to derive the most appropriate wavelength for detecting chlorophyll-a in case II water for green algae. The results indicated that the combination of TM bands 2, 3 and 4 achieved much better accuracy than other combinations, and the estimated concentration of chlorophyll-a was significantly more accurate than empirical methods. It is expected that this method can be directly applied to the real remotely sensed image because it is based on bio-optical model.

Population Pharmacokinetics of Hydroxychloroquine in Japanese Patients With Cutaneous or Systemic Lupus Erythematosus.

PubMed

Morita, Shigemichi; Takahashi, Toshiya; Yoshida, Yasushi; Yokota, Naohisa

2016-04-01

Hydroxychloroquine (HCQ) is an effective treatment for patients with cutaneous lupus erythematosus (CLE) or systemic lupus erythematosus (SLE) and has been used for these patients in more than 70 nations. However, in Japan, HCQ has not been approved for CLE or SLE. To establish an appropriate therapeutic regimen and to clarify the pharmacokinetics (PK) of HCQ in Japanese patients with CLE with or without SLE (CLE/SLE), a population pharmacokinetic (PopPK) analysis was performed. In a clinical study of Japanese patients with a diagnosis of CLE irrespective of the presence of SLE, blood and plasma drug concentration-time data receiving multiple oral doses of HCQ sulfate (200-400 mg daily) were analyzed using nonlinear mixed-effects model software. The blood and plasma concentrations of HCQ were analyzed using a high-performance liquid chromatography tandem mass spectrometry method. Model evaluation and validation were performed using goodness-of-fit (GOF) plots, visual predictive check, and a bootstrap. The PopPKs of HCQ in the blood and plasma of 90 Japanese patients with CLE/SLE were well described by a 1-compartment model with first-order absorption and absorption lag time. Body weight was a significant (P < 0.001) covariate of oral clearance of HCQ. The final model was assessed using GOF plots, a bootstrap, and visual predictive check, and this model was appropriate. Simulations based on the final model suggested that the recommended daily doses of HCQ sulfate (200-400 mg) based on the ideal body weight in Japanese patients with CLE/SLE were in the similar concentration ranges. The PopPK models derived from both blood and plasma HCQ concentrations of Japanese patients with CLE/SLE were developed and validated. Based on this study, the dosage regimens of HCQ sulfate for Japanese patients with CLE/SLE should be calculated using the individual ideal body weight.

A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women.

PubMed

Ke, Alice Ban; Nallani, Srikanth C; Zhao, Ping; Rostami-Hodjegan, Amin; Isoherranen, Nina; Unadkat, Jashvant D

2013-04-01

Conducting pharmacokinetic (PK) studies in pregnant women is challenging. Therefore, we asked if a physiologically based pharmacokinetic (PBPK) model could be used to evaluate different dosing regimens for pregnant women. We refined and verified our previously published pregnancy PBPK model by incorporating cytochrome P450 CYP1A2 suppression (based on caffeine PK) and CYP2D6 induction (based on metoprolol PK) into the model. This model accounts for gestational age-dependent changes in maternal physiology and hepatic CYP3A activity. For verification, the disposition of CYP1A2-metabolized drug theophylline (THEO) and CYP2D6-metabolized drugs paroxetine (PAR), dextromethorphan (DEX), and clonidine (CLO) during pregnancy was predicted. Our PBPK model successfully predicted THEO disposition during the third trimester (T3). Predicted mean postpartum to third trimester (PP:T3) ratios of THEO area under the curve (AUC), maximum plasma concentration, and minimum plasma concentration were 0.76, 0.95, and 0.66 versus observed values 0.75, 0.89, and 0.72, respectively. The predicted mean PAR steady-state plasma concentration (Css) ratio (PP:T3) was 7.1 versus the observed value 3.7. Predicted mean DEX urinary ratio (UR) (PP:T3) was 2.9 versus the observed value 1.9. Predicted mean CLO AUC ratio (PP:T3) was 2.2 versus the observed value 1.7. Sensitivity analysis suggested that a 100% induction of CYP2D6 during T3 was required to recover the observed PP:T3 ratios of PAR Css, DEX UR, and CLO AUC. Based on these data, it is prudent to conclude that the magnitude of hepatic CYP2D6 induction during T3 ranges from 100 to 200%. Our PBPK model can predict the disposition of CYP1A2, 2D6, and 3A drugs during pregnancy.

A Physiologically Based Pharmacokinetic Model to Predict Disposition of CYP2D6 and CYP1A2 Metabolized Drugs in Pregnant Women

PubMed Central

Ke, Alice Ban; Nallani, Srikanth C.; Zhao, Ping; Rostami-Hodjegan, Amin; Isoherranen, Nina

2013-01-01

Conducting pharmacokinetic (PK) studies in pregnant women is challenging. Therefore, we asked if a physiologically based pharmacokinetic (PBPK) model could be used to evaluate different dosing regimens for pregnant women. We refined and verified our previously published pregnancy PBPK model by incorporating cytochrome P450 CYP1A2 suppression (based on caffeine PK) and CYP2D6 induction (based on metoprolol PK) into the model. This model accounts for gestational age–dependent changes in maternal physiology and hepatic CYP3A activity. For verification, the disposition of CYP1A2–metabolized drug theophylline (THEO) and CYP2D6–metabolized drugs paroxetine (PAR), dextromethorphan (DEX), and clonidine (CLO) during pregnancy was predicted. Our PBPK model successfully predicted THEO disposition during the third trimester (T3). Predicted mean postpartum to third trimester (PP:T3) ratios of THEO area under the curve (AUC), maximum plasma concentration, and minimum plasma concentration were 0.76, 0.95, and 0.66 versus observed values 0.75, 0.89, and 0.72, respectively. The predicted mean PAR steady-state plasma concentration (Css) ratio (PP:T3) was 7.1 versus the observed value 3.7. Predicted mean DEX urinary ratio (UR) (PP:T3) was 2.9 versus the observed value 1.9. Predicted mean CLO AUC ratio (PP:T3) was 2.2 versus the observed value 1.7. Sensitivity analysis suggested that a 100% induction of CYP2D6 during T3 was required to recover the observed PP:T3 ratios of PAR Css, DEX UR, and CLO AUC. Based on these data, it is prudent to conclude that the magnitude of hepatic CYP2D6 induction during T3 ranges from 100 to 200%. Our PBPK model can predict the disposition of CYP1A2, 2D6, and 3A drugs during pregnancy. PMID:23355638

Terbinafine in Combination with Other Antifungal Agents for Treatment of Resistant or Refractory Mycoses: Investigating Optimal Dosing Regimens Using a Physiologically Based Pharmacokinetic Model

PubMed Central

Dolton, Michael J.; Perera, Vidya; Pont, Lisa G.

2014-01-01

Terbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergistic in vitro antifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens. PMID:24126579

Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation.

PubMed

Burghaus, Rolf; Coboeken, Katrin; Gaub, Thomas; Kuepfer, Lars; Sensse, Anke; Siegmund, Hans-Ulrich; Weiss, Wolfgang; Mueck, Wolfgang; Lippert, Joerg

2011-04-22

Rivaroxaban is an oral, direct Factor Xa inhibitor approved in the European Union and several other countries for the prevention of venous thromboembolism in adult patients undergoing elective hip or knee replacement surgery and is in advanced clinical development for the treatment of thromboembolic disorders. Its mechanism of action is antithrombin independent and differs from that of other anticoagulants, such as warfarin (a vitamin K antagonist), enoxaparin (an indirect thrombin/Factor Xa inhibitor) and dabigatran (a direct thrombin inhibitor). A blood coagulation computer model has been developed, based on several published models and preclinical and clinical data. Unlike previous models, the current model takes into account both the intrinsic and extrinsic pathways of the coagulation cascade, and possesses some unique features, including a blood flow component and a portfolio of drug action mechanisms. This study aimed to use the model to compare the mechanism of action of rivaroxaban with that of warfarin, and to evaluate the efficacy and safety of different rivaroxaban doses with other anticoagulants included in the model. Rather than reproducing known standard clinical measurements, such as the prothrombin time and activated partial thromboplastin time clotting tests, the anticoagulant benchmarking was based on a simulation of physiologically plausible clotting scenarios. Compared with warfarin, rivaroxaban showed a favourable sensitivity for tissue factor concentration inducing clotting, and a steep concentration-effect relationship, rapidly flattening towards higher inhibitor concentrations, both suggesting a broad therapeutic window. The predicted dosing window is highly accordant with the final dose recommendation based upon extensive clinical studies.

Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aman Behal; Sunil Kumar; Goodarz Ahmadi

2007-08-05

Numerical Modeling of Solid Gas Flow, System Identification for purposes of modeling and control, and Wireless Sensor and Actor Network design were pursued as part of this project. Time series input-output data was obtained from NETL's Morgantown CFB facility courtesy of Dr. Lawrence Shadle. It was run through a nonlinear kernel estimator and nonparametric models were obtained for the system. Linear and first-order nonlinear kernels were then utilized to obtain a state-space description of the system. Neural networks were trained that performed better at capturing the plant dynamics. It is possible to use these networks to find a plant modelmore » and the inversion of this model can be used to control the system. These models allow one to compare with physics based models whose parameters can then be determined by comparing them against the available data based model. On a parallel track, Dr. Kumar designed an energy-efficient and reliable transport protocol for wireless sensor and actor networks, where the sensors could be different types of wireless sensors used in CFB based coal combustion systems and actors are more powerful wireless nodes to set up a communication network while avoiding the data congestion. Dr. Ahmadi's group studied gas solid flow in a duct. It was seen that particle concentration clearly shows a preferential distribution. The particles strongly interact with the turbulence eddies and are concentrated in narrow bands that are evolving with time. It is believed that observed preferential concentration is due to the fact that these particles are flung out of eddies by centrifugal force.« less

Nitrate Loads and Concentrations in Surface-Water Base Flow and Shallow Groundwater for Selected Basins in the United States, Water Years 1990-2006

USGS Publications Warehouse

Spahr, Norman E.; Dubrovsky, Neil M.; Gronberg, JoAnn M.; Franke, O. Lehn; Wolock, David M.

2010-01-01

Hydrograph separation was used to determine the base-flow component of streamflow for 148 sites sampled as part of the National Water-Quality Assessment program. Sites in the Southwest and the Northwest tend to have base-flow index values greater than 0.5. Sites in the Midwest and the eastern portion of the Southern Plains generally have values less than 0.5. Base-flow index values for sites in the Southeast and Northeast are mixed with values less than and greater than 0.5. Hypothesized flow paths based on relative scaling of soil and bedrock permeability explain some of the differences found in base-flow index. Sites in areas with impermeable soils and bedrock (areas where overland flow may be the primary hydrologic flow path) tend to have lower base-flow index values than sites in areas with either permeable bedrock or permeable soils (areas where deep groundwater flow paths or shallow groundwater flow paths may occur). The percentage of nitrate load contributed by base flow was determined using total flow and base flow nitrate load models. These regression-based models were calibrated using available nitrate samples and total streamflow or base-flow nitrate samples and the base-flow component of total streamflow. Many streams in the country have a large proportion of nitrate load contributed by base flow: 40 percent of sites have more than 50 percent of the total nitrate load contributed by base flow. Sites in the Midwest and eastern portion of the Southern Plains generally have less than 50 percent of the total nitrate load contributed by base flow. Sites in the Northern Plains and Northwest have nitrate load ratios that generally are greater than 50 percent. Nitrate load ratios for sites in the Southeast and Northeast are mixed with values less than and greater than 50 percent. Significantly lower contributions of nitrate from base flow were found at sites in areas with impermeable soils and impermeable bedrock. These areas could be most responsive to nutrient management practices designed to reduce nutrient transport to streams by runoff. Conversely, sites with potential for shallow or deep groundwater contribution (some combination of permeable soils or permeable bedrock) had significantly greater contributions of nitrate from base flow. Effective nutrient management strategies would consider groundwater nitrate contributions in these areas. Mean annual base-flow nitrate concentrations were compared to shallow-groundwater nitrate concentrations for 27 sites. Concentrations in groundwater tended to be greater than base-flow concentrations for this group of sites. Sites where groundwater concentrations were much greater than base-flow concentrations were found in areas of high infiltration and oxic groundwater conditions. The lack of correspondingly high concentrations in the base flow of the paired surface-water sites may have multiple causes. In some settings, there has not been sufficient time for enough high-nitrate shallow groundwater to migrate to the nearby stream. In these cases, the stream nitrate concentrations lag behind those in the shallow groundwater, and concentrations may increase in the future as more high-nitrate groundwater reaches the stream. Alternatively, some of these sites may have processes that rapidly remove nitrate as water moves from the aquifer into the stream channel. Partitioning streamflow and nitrate load between the quick-flow and base-flow portions of the hydrograph coupled with relative scales of soil permeability can infer the importance of surface water compared to groundwater nitrate sources. Study of the relation of nitrate concentrations to base-flow index and the comparison of groundwater nitrate concentrations to stream nitrate concentrations during times when base-flow index is high can provide evidence of potential nitrate transport mechanisms. Accounting for the surface-water and groundwater contributions of nitrate is crucial to effective management and remediat

Prevalence and Determinants of Suboptimal Vitamin D Levels in a Multiethnic Asian Population.

PubMed

Man, Ryan Eyn Kidd; Li, Ling-Jun; Cheng, Ching-Yu; Wong, Tien Yin; Lamoureux, Ecosse; Sabanayagam, Charumathi

2017-03-22

This population-based cross-sectional study examined the prevalence and risk factors of suboptimal vitamin D levels (assessed using circulating 25-hydroxycholecalciferol (25(OH)D)) in a multi-ethnic sample of Asian adults. Plasma 25(OH)D concentration of 1139 Chinese, Malay and Indians (40-80 years) were stratified into normal (≥30 ng/mL), and suboptimal (including insufficiency and deficiency, <30 ng/mL) based on the 2011 Endocrine Society Clinical Practice Guidelines. Logistic regression models were used to assess the associations of demographic, lifestyle and clinical risk factors with the outcome. Of the 1139 participants, 25(OH)D concentration was suboptimal in 76.1%. In multivariable models, age ≤65 years (compared to age >65 years), Malay and Indian ethnicities (compared to Chinese ethnicity), and higher body mass index, HbA1c, education and income levels were associated with suboptimal 25(OH)D concentration ( p < 0.05). In a population-based sample of Asian adults, approximately 75% had suboptimal 25(OH)D concentration. Targeted interventions and stricter reinforcements of existing guidelines for vitamin D supplementation are needed for groups at risk of vitamin D insufficiency/deficiency.

Prevalence and Determinants of Suboptimal Vitamin D Levels in a Multiethnic Asian Population

PubMed Central

Man, Ryan Eyn Kidd; Li, Ling-Jun; Cheng, Ching-Yu; Wong, Tien Yin; Lamoureux, Ecosse; Sabanayagam, Charumathi

2017-01-01

This population-based cross-sectional study examined the prevalence and risk factors of suboptimal vitamin D levels (assessed using circulating 25-hydroxycholecalciferol (25(OH)D)) in a multi-ethnic sample of Asian adults. Plasma 25(OH)D concentration of 1139 Chinese, Malay and Indians (40–80 years) were stratified into normal (≥30 ng/mL), and suboptimal (including insufficiency and deficiency, <30 ng/mL) based on the 2011 Endocrine Society Clinical Practice Guidelines. Logistic regression models were used to assess the associations of demographic, lifestyle and clinical risk factors with the outcome. Of the 1139 participants, 25(OH)D concentration was suboptimal in 76.1%. In multivariable models, age ≤65 years (compared to age >65 years), Malay and Indian ethnicities (compared to Chinese ethnicity), and higher body mass index, HbA1c, education and income levels were associated with suboptimal 25(OH)D concentration (p < 0.05). In a population-based sample of Asian adults, approximately 75% had suboptimal 25(OH)D concentration. Targeted interventions and stricter reinforcements of existing guidelines for vitamin D supplementation are needed for groups at risk of vitamin D insufficiency/deficiency. PMID:28327512

Factorial Design Based Multivariate Modeling and Optimization of Tunable Bioresponsive Arginine Grafted Poly(cystaminebis(acrylamide)-diaminohexane) Polymeric Matrix Based Nanocarriers.

PubMed

Yang, Rongbing; Nam, Kihoon; Kim, Sung Wan; Turkson, James; Zou, Ye; Zuo, Yi Y; Haware, Rahul V; Chougule, Mahavir B

2017-01-03

Desired characteristics of nanocarriers are crucial to explore its therapeutic potential. This investigation aimed to develop tunable bioresponsive newly synthesized unique arginine grafted poly(cystaminebis(acrylamide)-diaminohexane) [ABP] polymeric matrix based nanocarriers by using L9 Taguchi factorial design, desirability function, and multivariate method. The selected formulation and process parameters were ABP concentration, acetone concentration, the volume ratio of acetone to ABP solution, and drug concentration. The measured nanocarrier characteristics were particle size, polydispersity index, zeta potential, and percentage drug loading. Experimental validation of nanocarrier characteristics computed from initially developed predictive model showed nonsignificant differences (p > 0.05). The multivariate modeling based optimized cationic nanocarrier formulation of <100 nm loaded with hydrophilic acetaminophen was readapted for a hydrophobic etoposide loading without significant changes (p > 0.05) except for improved loading percentage. This is the first study focusing on ABP polymeric matrix based nanocarrier development. Nanocarrier particle size was stable in PBS 7.4 for 48 h. The increase of zeta potential at lower pH 6.4, compared to the physiological pH, showed possible endosomal escape capability. The glutathione triggered release at the physiological conditions indicated the competence of cytosolic targeting delivery of the loaded drug from bioresponsive nanocarriers. In conclusion, this unique systematic approach provides rational evaluation and prediction of a tunable bioresponsive ABP based matrix nanocarrier, which was built on selected limited number of smart experimentation.

Pharmacokinetic Modeling to Simulate the Concentration-Time Profiles After Dermal Application of Rivastigmine Patch.

PubMed

Nozaki, Sachiko; Yamaguchi, Masayuki; Lefèvre, Gilbert

2016-07-01

Rivastigmine is an inhibitor of acetylcholinesterases and butyrylcholinesterases for symptomatic treatment of Alzheimer disease and is available as oral and transdermal patch formulations. A dermal absorption pharmacokinetic (PK) model was developed to simulate the plasma concentration-time profile of rivastigmine to answer questions relative to the efficacy and safety risks after misuse of the patch (e.g., longer application than 24 h, multiple patches applied at the same time, and so forth). The model comprised 2 compartments which was a combination of mechanistic dermal absorption model and a basic 1-compartment model. The initial values for the model were determined based on the physicochemical characteristics of rivastigmine and PK parameters after intravenous administration. The model was fitted to the clinical PK profiles after single application of rivastigmine patch to obtain model parameters. The final model was validated by confirming that the simulated concentration-time curves and PK parameters (Cmax and area under the drug plasma concentration-time curve) conformed to the observed values and then was used to simulate the PK profiles of rivastigmine. This work demonstrated that the mechanistic dermal PK model fitted the clinical data well and was able to simulate the PK profile after patch misuse. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Physiologically based pharmacokinetic model for ethyl tertiary‐butyl ether and tertiary‐butyl alcohol in rats: Contribution of binding to α2u–globulin in male rats and high‐exposure nonlinear kinetics to toxicity and cancer outcomes

PubMed Central

Ring, Caroline; Banton, Marcy I.; Leavens, Teresa L.

2016-01-01

Abstract In cancer bioassays, inhalation, but not drinking water exposure to ethyl tertiary‐butyl ether (ETBE), caused liver tumors in male rats, while tertiary‐butyl alcohol (TBA), an ETBE metabolite, caused kidney tumors in male rats following exposure via drinking water. To understand the contribution of ETBE and TBA kinetics under varying exposure scenarios to these tumor responses, a physiologically based pharmacokinetic model was developed based on a previously published model for methyl tertiary‐butyl ether, a structurally similar chemical, and verified against the literature and study report data. The model included ETBE and TBA binding to the male rat‐specific protein α2u–globulin, which plays a role in the ETBE and TBA kidney response observed in male rats. Metabolism of ETBE and TBA was described as a single, saturable pathway in the liver. The model predicted similar kidney AUC0–∞ for TBA for various exposure scenarios from ETBE and TBA cancer bioassays, supporting a male‐rat‐specific mode of action for TBA‐induced kidney tumors. The model also predicted nonlinear kinetics at ETBE inhalation exposure concentrations above ~2000 ppm, based on blood AUC0–∞ for ETBE and TBA. The shift from linear to nonlinear kinetics at exposure concentrations below the concentration associated with liver tumors in rats (5000 ppm) suggests the mode of action for liver tumors operates under nonlinear kinetics following chronic exposure and is not relevant for assessing human risk. Copyright © 2016 The Authors Journal of Applied Toxicology Published by John Wiley & Sons Ltd PMID:27885692

Physiologically based pharmacokinetic model for ethyl tertiary-butyl ether and tertiary-butyl alcohol in rats: Contribution of binding to α2u-globulin in male rats and high-exposure nonlinear kinetics to toxicity and cancer outcomes.

PubMed

Borghoff, Susan J; Ring, Caroline; Banton, Marcy I; Leavens, Teresa L

2017-05-01

In cancer bioassays, inhalation, but not drinking water exposure to ethyl tertiary-butyl ether (ETBE), caused liver tumors in male rats, while tertiary-butyl alcohol (TBA), an ETBE metabolite, caused kidney tumors in male rats following exposure via drinking water. To understand the contribution of ETBE and TBA kinetics under varying exposure scenarios to these tumor responses, a physiologically based pharmacokinetic model was developed based on a previously published model for methyl tertiary-butyl ether, a structurally similar chemical, and verified against the literature and study report data. The model included ETBE and TBA binding to the male rat-specific protein α2u-globulin, which plays a role in the ETBE and TBA kidney response observed in male rats. Metabolism of ETBE and TBA was described as a single, saturable pathway in the liver. The model predicted similar kidney AUC 0-∞ for TBA for various exposure scenarios from ETBE and TBA cancer bioassays, supporting a male-rat-specific mode of action for TBA-induced kidney tumors. The model also predicted nonlinear kinetics at ETBE inhalation exposure concentrations above ~2000 ppm, based on blood AUC 0-∞ for ETBE and TBA. The shift from linear to nonlinear kinetics at exposure concentrations below the concentration associated with liver tumors in rats (5000 ppm) suggests the mode of action for liver tumors operates under nonlinear kinetics following chronic exposure and is not relevant for assessing human risk. Copyright © 2016 The Authors Journal of Applied Toxicology Published by John Wiley & Sons Ltd. Copyright © 2016 The Authors Journal of Applied Toxicology Published by John Wiley & Sons Ltd.

Comparing Lagrangian and Eulerian models for CO2 transport - a step towards Bayesian inverse modeling using WRF/STILT-VPRM

NASA Astrophysics Data System (ADS)

Pillai, D.; Gerbig, C.; Kretschmer, R.; Beck, V.; Karstens, U.; Neininger, B.; Heimann, M.

2012-01-01

We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which both models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data. Thus suggests that it is reasonable to use STILT as an adjoint model of WRF atmospheric transport.

A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations

NASA Astrophysics Data System (ADS)

Tian, X.; Xie, Z.; Liu, Y.; Cai, Z.; Fu, Y.; Zhang, H.; Feng, L.

2014-12-01

We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

An Eulerian model for scavenging of pollutants by raindrops

NASA Astrophysics Data System (ADS)

Kumar, Sudarshan

An Eulerian model for simulating the coupled processes of gas-phase depletion and aqueousphase accumulation of the pollutant species during a rain event has been formulated. The model is capable of taking into account any realistic vertical profile of pollutant species concentrations and time-dependent initial aqueous-phase concentrations at the cloud base. The model considers the processes of single species absorption and dissociation in the aqueous phase. The coupled partial differential equations constituting the model are discretized into a set of ordinary differential equations by using the Galerkin method with chapeau functions as the basis functions. These equations are solved to obtain the pollutant concentrations of the gas phase and raindrops as well as the pH of raindrops as a function of time and distance below cloud-base. Simulations are performed for scavenging of gaseous HNO 3, H 2O 2, SO 2, formaldehyde and NH 3. For the case of highly soluble HNO 3 and H 2O 2, raindrops are far from equilibrium with the gas phase and their capacity for absorption of these gases is undiminished even as they reach ground level. The gas-phase concentrations for these species decrease exponentially with time and the washout is determined primarily by the rain intensity and mass-transfer coefficient of the gaseous species to the raindrops. The pollutant species concentrations in raindrops are an almost linear function of the distance below the cloud base. For the simulation conditions considered in this study, the half-life periods of these gases for removal from the atmosphere range from 15 to 40 min. For SO 2 and formaldehyde, the aqueous-phase concentrations approach equilibrium as the drops fall to ground level and the gas-phase concentrations show large gradients in the vertical. Half-life periods for SO 2 range from 1.3 to 13 h depending on the initial raindrop pH and rain intensity. For formaldehyde, the half-life ranges from 19 to 63 min. Solubility of NH 3 is a strong function of the raindrop pH. As NH 3 is absorbed, the raindrop pH increases and NH 3 solubility decreases. For pre-acidified drops (pH = 4.6), ammonia solubility is very high and the drops are far from equilibrium with the gas phase throughout the falling period. The half-life for ammonia ranges from 11 min to over 3 h in our simulations.

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment.

PubMed

Gulliver, John; Briggs, David

2011-05-15

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Modeling the population-level effects of hypoxia on a coastal fish: implications of a spatially-explicit individual-based model

NASA Astrophysics Data System (ADS)

Rose, K.; Creekmore, S.; Thomas, P.; Craig, K.; Neilan, R.; Rahman, S.; Wang, L.; Justic, D.

2016-02-01

The northwestern Gulf of Mexico (USA) currently experiences a large hypoxic area ("dead zone") during the summer. The population-level effects of hypoxia on coastal fish are largely unknown. We developed a spatially-explicit, individual-based model to analyze how hypoxia effects on reproduction, growth, and mortality of individual Atlantic croaker could lead to population-level responses. The model follows the hourly growth, mortality, reproduction, and movement of individuals on a 300 x 800 spatial grid of 1 km2 cells for 140 years. Chlorophyll-a concentration and water temperature were specified daily for each grid cell. Dissolved oxygen (DO) was obtained from a 3-D water quality model for four years that differed in their severity of hypoxia. A bioenergetics model was used to represent growth, mortality was assumed stage- and age-dependent, and movement behavior was based on temperature preferences and avoidance of low DO. Hypoxia effects were imposed using exposure-effects sub-models that converted time-varying exposure to DO to reductions in growth and fecundity, and increases in mortality. Using sequences of mild, intermediate, and severe hypoxia years, the model predicted a 20% decrease in population abundance. Additional simulations were performed under the assumption that river-based nutrients loadings that lead to more hypoxia also lead to higher primary production and more food for croaker. Twenty-five percent and 50% nutrient reduction scenarios were simulated by adjusting the cholorphyll-a concentrations used as food proxy for the croaker. We then incrementally increased the DO concentrations to determine how much hypoxia would need to be reduced to offset the lower food production resulting from reduced nutrients. We discuss the generality of our results, the hidden effects of hypoxia on fish, and our overall strategy of combining laboratory and field studies with modeling to produce robust predictions of population responses to stressors under dynamic and multi-stressor conditions.

An isotherm-based thermodynamic model of multicomponent aqueous solutions, applicable over the entire concentration range.

PubMed

Dutcher, Cari S; Ge, Xinlei; Wexler, Anthony S; Clegg, Simon L

2013-04-18

In previous studies (Dutcher et al. J. Phys. Chem. C 2011, 115, 16474-16487; 2012, 116, 1850-1864), we derived equations for the Gibbs energy, solvent and solute activities, and solute concentrations in multicomponent liquid mixtures, based upon expressions for adsorption isotherms that include arbitrary numbers of hydration layers on each solute. In this work, the long-range electrostatic interactions that dominate in dilute solutions are added to the Gibbs energy expression, thus extending the range of concentrations for which the model can be used from pure liquid solute(s) to infinite dilution in the solvent, water. An equation for the conversion of the reference state for solute activity coefficients to infinite dilution in water has been derived. A number of simplifications are identified, notably the equivalence of the sorption site parameters r and the stoichiometric coefficients of the solutes, resulting in a reduction in the number of model parameters. Solute concentrations in mixtures conform to a modified Zdanovskii-Stokes-Robinson mixing rule, and solute activity coefficients to a modified McKay-Perring relation, when the effects of the long-range (Debye-Hückel) term in the equations are taken into account. Practical applications of the equations to osmotic and activity coefficients of pure aqueous electrolyte solutions and mixtures show both satisfactory accuracy from low to high concentrations, together with a thermodynamically reasonable extrapolation (beyond the range of measurements) to extreme concentration and to the pure liquid solute(s).

Statistics of concentrations due to single air pollution sources to be applied in numerical modelling of pollutant dispersion

NASA Astrophysics Data System (ADS)

Tumanov, Sergiu

A test of goodness of fit based on rank statistics was applied to prove the applicability of the Eggenberger-Polya discrete probability law to hourly SO 2-concentrations measured in the vicinity of single sources. With this end in view, the pollutant concentration was considered an integral quantity which may be accepted if one properly chooses the unit of measurement (in this case μg m -3) and if account is taken of the limited accuracy of measurements. The results of the test being satisfactory, even in the range of upper quantiles, the Eggenberger-Polya law was used in association with numerical modelling to estimate statistical parameters, e.g. quantiles, cumulative probabilities of threshold concentrations to be exceeded, and so on, in the grid points of a network covering the area of interest. This only needs accurate estimations of means and variances of the concentration series which can readily be obtained through routine air pollution dispersion modelling.

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated with Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2000-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating fife based on a concentration dependent failure criterion (e.g., surface solute content drops to two percent). The computer code, written in an extension of FORTRAN 77, employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Comparison of Satellite Data with Ground-Based Measurements for Assessing Local Distributions of PM2.5 in Northeast Mexico.

NASA Astrophysics Data System (ADS)

Carmona, J.; Mendoza, A.; Lozano, D.; Gupta, P.; Mejia, G.; Rios, J.; Hernández, I.

2017-12-01

Estimating ground-level PM2.5 from satellite-derived Aerosol Optical Depth (AOD) through statistical models is a promising method to evaluate the spatial and temporal distribution of PM2.5 in regions where there are no or few ground-based observations, i.e. Latin America. Although PM concentrations are most accurately measured using ground-based instrumentation, the spatial coverage is too sparse to determine local and regional variations in PM. AOD satellite data offer the opportunity to overcome the spatial limitation of ground-based measurements. However, estimating PM surface concentrations from AOD satellite data is challenging, since multiple factors can affect the relationship between the total-column of AOD and the surface-concentration of PM. In this study, an Assembled Multiple Linear Regression Model (MLR) and a Neural Network Model (NN) were performed to estimate the relationship between the AOD and ground-concentrations of PM2.5 within the Monterrey Metropolitan Area (MMA). The MMA is located in northeast Mexico and is the third most populated urban area in the country. Episodes of high PM pollution levels are frequent throughout the year at the MMA. Daily averages of meteorological and air quality parameters were determined from data recorded at 5 monitoring sites of the MMA air quality monitoring network. Daily AOD data were retrieved from the MODIS sensor onboard the Aqua satellite. Overall, the best performance of the models was obtained using an AOD at 550 µm from the MYD04_3k product in combination with Temperature, Relative Humidity, Wind Speed and Wind Direction ground-based data. For the MLR performed, a correlation coefficient of R 0.6 and % bias of -6% were obtained. The NN showed a better performance than the MLR, with a correlation coefficient of R 0.75 and % bias -4%. The results obtained confirmed that satellite-derived AOD in combination with meteorological fields may allow to estimate PM2.5 local distributions.

Screening for potentially hazardous PRTR chemicals in the Lake Biwa-Yodo River basin of Japan using a one-box multimedia model.

PubMed

Ariyadasa, B H A K T; Kondo, Akira; Inoue, Yoshio

2015-02-01

A system is needed to predict the behavior, fate, and occurrence of environmental pollutants for effective environmental monitoring. Available monitoring data and computational modeling were used to develop a one-box multimedia model based on the mass balance of the emitted chemicals. Eight physiochemical phenomena in the atmosphere, soil, water, and sediment were considered in this model. This study was carried out in the Lake Biwa-Yodo River basin which provides multiple land uses and also the natural water resource for nearly 13 million of population in the region. Annual emissions for 214 nonmetallic compounds were calculated using the chemical emission data on Japanese pollutant release and transfer registry and used for executing the model simulations for 1997, 2002, and 2008 as input data. The calculated chemical concentrations by the model for all the environmental media were analyzed to determine trends in concentration over this study span. The majority of the chemicals decreased in concentration over time. Among the 214 nonmetallic chemical pollutants, 36 chemicals did not decrease in concentration and were in the top 10 % for concentration on average. Of these 36 pollutants, 7 occur in all 4 environmental media and pose a potential health risk at humans in the Lake Biwa-Yodo River basin.

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

PubMed

Singh, Kunwar P; Gupta, Shikha; Ojha, Priyanka; Rai, Premanjali

2013-04-01

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.

Scientific white paper on concentration-QTc modeling.

PubMed

Garnett, Christine; Bonate, Peter L; Dang, Qianyu; Ferber, Georg; Huang, Dalong; Liu, Jiang; Mehrotra, Devan; Riley, Steve; Sager, Philip; Tornoe, Christoffer; Wang, Yaning

2018-06-01

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.

AgMIP Climate Data and Scenarios for Integrated Assessment. Chapter 3

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; Winter, Jonathan M.; McDermid, Sonali P.; Hudson, Nicholas I.

2015-01-01

Climate change presents a great challenge to the agricultural sector as changes in precipitation, temperature, humidity, and circulation patterns alter the climatic conditions upon which many agricultural systems rely. Projections of future climate conditions are inherently uncertain owing to a lack of clarity on how society will develop, policies that may be implemented to reduce greenhouse-gas (GHG) emissions, and complexities in modeling the atmosphere, ocean, land, cryosphere, and biosphere components of the climate system. Global climate models (GCMs) are based on well-established physics of each climate component that enable the models to project climate responses to changing GHG concentration scenarios (Stocker et al., 2013).The most recent iteration of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) utilized representative concentration pathways (RCPs) to cover the range of plausible GHG concentrations out past the year 2100, with RCP8.5 representing an extreme scenario and RCP4.5 representing a lower concentrations scenario (Moss et al., 2010).

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Hyun-Seob; Thomas, Dennis G.; Stegen, James C.

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accountedmore » for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.« less

Volcanic ash dosage calculator: A proof-of-concept tool to support aviation stakeholders during ash events

NASA Astrophysics Data System (ADS)

Dacre, H.; Prata, A.; Shine, K. P.; Irvine, E.

2017-12-01

The volcanic ash clouds produced by Icelandic volcano Eyjafjallajökull in April/May 2010 resulted in `no fly zones' which paralysed European aircraft activity and cost the airline industry an estimated £1.1 billion. In response to the crisis, the Civil Aviation Authority (CAA), in collaboration with Rolls Royce, produced the `safe-to-fly' chart. As ash concentrations are the primary output of dispersion model forecasts, the chart was designed to illustrate how engine damage progresses as a function of ash concentration. Concentration thresholds were subsequently derived based on previous ash encounters. Research scientists and aircraft manufactures have since recognised the importance of volcanic ash dosages; the accumulated concentration over time. Dosages are an improvement to concentrations as they can be used to identify pernicious situations where ash concentrations are acceptably low but the exposure time is long enough to cause damage to aircraft engines. Here we present a proof-of-concept volcanic ash dosage calculator; an innovative, web-based research tool, developed in close collaboration with operators and regulators, which utilises interactive data visualisation to communicate the uncertainty inherent in dispersion model simulations and subsequent dosage calculations. To calculate dosages, we use NAME (Numerical Atmospheric-dispersion Modelling Environment) to simulate several Icelandic eruption scenarios, which result in tephra dispersal across the North Atlantic, UK and Europe. Ash encounters are simulated based on flight-optimal routes derived from aircraft routing software. Key outputs of the calculator include: the along-flight dosage, exposure time and peak concentration. The design of the tool allows users to explore the key areas of uncertainty in the dosage calculation and to visualise how this changes as the planned flight path is varied. We expect that this research will result in better informed decisions from key stakeholders during volcanic ash events through a deeper understanding of the associated uncertainties in dosage calculations.

StreamVOC - A deterministic source-apportionment model to estimate volatile organic compound concentrations in rivers and streams

USGS Publications Warehouse

Asher, William E.; Bender, David A.; Zogorski, John S.; Bartholomay, Roy C.

2006-01-01

This report documents the construction and verification of the model, StreamVOC, that estimates (1) the time- and position-dependent concentrations of volatile organic compounds (VOCs) in rivers and streams as well as (2) the source apportionment (SA) of those concentrations. The model considers how different types of sources and loss processes can act together to yield a given observed VOC concentration. Reasons for interest in the relative and absolute contributions of different sources to contaminant concentrations include the need to apportion: (1) the origins for an observed contamination, and (2) the associated human and ecosystem risks. For VOCs, sources of interest include the atmosphere (by absorption), as well as point and nonpoint inflows of VOC-containing water. Loss processes of interest include volatilization to the atmosphere, degradation, and outflows of VOC-containing water from the stream to local ground water. This report presents the details of StreamVOC and compares model output with measured concentrations for eight VOCs found in the Aberjona River at Winchester, Massachusetts. Input data for the model were obtained during a synoptic study of the stream system conducted July 11-13, 2001, as part of the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey. The input data included a variety of basic stream characteristics (for example, flows, temperature, and VOC concentrations). The StreamVOC concentration results agreed moderately well with the measured concentration data for several VOCs and provided compound-dependent SA estimates as a function of longitudinal distance down the river. For many VOCs, the quality of the agreement between the model-simulated and measured concentrations could be improved by simple adjustments of the model input parameters. In general, this study illustrated: (1) the considerable difficulty of quantifying correctly the locations and magnitudes of ground-water-related sources of contamination in streams; and (2) that model-based estimates of stream VOC concentrations are likely to be most accurate when the major sources are point sources or tributaries where the spatial extent and magnitude of the sources are tightly constrained and easily determined.

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

PubMed

Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

Design and indoor testing of a compact optical concentrator

NASA Astrophysics Data System (ADS)

Zheng, Cheng; Li, Qiyuan; Rosengarten, Gary; Hawkes, Evatt; Taylor, Robert A.

2017-01-01

We propose and analyze designs for stationary and compact optical concentrators. The designs are based on a catadioptric assembly with a linear focus line. They have a focal distance of around 10 to 15 cm with a concentration ratio (4.5 to 5.9 times). The concentrator employs an internal linear-tracking mechanism, making it suitable for rooftop solar applications. The optical performance of the collector has been simulated with ray tracing software (Zemax), and laser-based indoor experiments were carried out to validate this model. The results show that the system is capable of achieving an average optical efficiency of around 66% to 69% during the middle 6 (sunniest) h of the day. The design process and principles described in this work will help enable a new class of rooftop solar thermal concentrators.

International challenge to predict the impact of radioxenon releases from medical isotope production on a comprehensive nuclear test ban treaty sampling station

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eslinger, Paul W.; Bowyer, Ted W.; Achim, Pascal

Abstract The International Monitoring System (IMS) is part of the verification regime for the Comprehensive Nuclear-Test-Ban-Treaty Organization (CTBTO). At entry-into-force, half of the 80 radionuclide stations will be able to measure concentrations of several radioactive xenon isotopes produced in nuclear explosions, and then the full network may be populated with xenon monitoring afterward (Bowyer et al., 2013). Fission-based production of 99Mo for medical purposes also releases radioxenon isotopes to the atmosphere (Saey, 2009). One of the ways to mitigate the effect of emissions from medical isotope production is the use of stack monitoring data, if it were available, so thatmore » the effect of radioactive xenon emissions could be subtracted from the effect from a presumed nuclear explosion, when detected at an IMS station location. To date, no studies have addressed the impacts the time resolution or data accuracy of stack monitoring data have on predicted concentrations at an IMS station location. Recently, participants from seven nations used atmospheric transport modeling to predict the time-history of 133Xe concentration measurements at an IMS station in Germany using stack monitoring data from a medical isotope production facility in Belgium. Participants received only stack monitoring data and used the atmospheric transport model and meteorological data of their choice. Some of the models predicted the highest measured concentrations quite well (a high composite statistical model comparison rank or a small mean square error with the measured values). The results suggest release data on a 15 min time spacing is best. The model comparison rank and ensemble analysis suggests that combining multiple models may provide more accurate predicted concentrations than any single model. Further research is needed to identify optimal methods for selecting ensemble members and those methods may depend on the specific transport problem. None of the submissions based only on the stack monitoring data predicted the small measured concentrations very well. The one submission that best predicted small concentrations also included releases from nuclear power plants. Modeling of sources by other nuclear facilities with smaller releases than medical isotope production facilities may be important in discriminating those releases from releases from a nuclear explosion.« less

DEVELOPMENT AND EVALUATION OF AN INTEGRATED MODEL TO FACILITATE RISK-BASED CORRECTIVE ACTION AT SUPERFUND SITES

EPA Science Inventory

We developed a numerical model to predict chemical concentrations in indoor environments resulting from soil vapor intrusion and volatilization from groundwater. The model, which integrates new and existing algorithms for chemical fate and transport, was originally...

Phosphorus and Phytoplankton in Lake Michigan: Model Post-audit and Projections

EPA Science Inventory

The eutrophication model, LM3-Eutro, was developed in support of the Lake Michigan Mass Balance Project to simulate chlorophyll-a (phytoplankton), phosphorus and carbon concentrations in the lake. This high-resolution carbon-based model was developed and calibrated using extensi...

Concentration dependence of the cell membrane permeability to cryoprotectant and water and implications for design of methods for post-thaw washing of human erythrocytes.

PubMed

Lahmann, John M; Benson, James D; Higgins, Adam Z

2018-02-01

For more than fifty years the human red blood cell (RBC) has been a widely studied model for transmembrane mass transport. Existing literature spans myriad experimental designs with varying results and physiologic interpretations. In this review, we examine the kinetics and mechanisms of membrane transport in the context of RBC cryopreservation. We include a discussion of the pathways for water and glycerol permeation through the cell membrane and the implications for mathematical modeling of the membrane transport process. In particular, we examine the concentration dependence of water and glycerol transport and provide equations for estimating permeability parameters as a function of concentration based on a synthesis of literature data. This concentration-dependent transport model may allow for design of improved methods for post-thaw removal of glycerol from cryopreserved blood. More broadly, the consideration of the concentration dependence of membrane permeability parameters may be important for other cell types as well, especially for design of methods for equilibration with the highly concentrated solutions used for vitrification. Copyright © 2017 Elsevier Inc. All rights reserved.

Dietary Iron Bioavailability: Agreement between Estimation Methods and Association with Serum Ferritin Concentrations in Women of Childbearing Age

PubMed Central

Dias, Gisele Cristina; Morimoto, Juliana Massami; Marchioni, Dirce Maria Lobo; Colli, Célia

2018-01-01

Predictive iron bioavailability (FeBio) methods aimed at evaluating the association between diet and body iron have been proposed, but few studies explored their validity and practical usefulness in epidemiological studies. In this cross-sectional study involving 127 women (18–42 years) with presumably steady-state body iron balance, correlations were checked among various FeBio estimates (probabilistic approach and meal-based and diet-based algorithms) and serum ferritin (SF) concentrations. Iron deficiency was defined as SF < 15 µg/L. Pearson correlation, Friedman test, and linear regression were employed. Iron intake and prevalence of iron deficiency were 10.9 mg/day and 12.6%. Algorithm estimates were strongly correlated (0.69≤ r ≥0.85; p < 0.001), although diet-based models (8.5–8.9%) diverged from meal-based models (11.6–12.8%; p < 0.001). Still, all algorithms underestimated the probabilistic approach (17.2%). No significant association was found between SF and FeBio from Monsen (1978), Reddy (2000), and Armah (2013) algorithms. Nevertheless, there was a 30–37% difference in SF concentrations between women stratified at extreme tertiles of FeBio from Hallberg and Hulthén (2000) and Collings’ (2013) models. The results demonstrate discordance of FeBio from probabilistic approach and algorithm methods while suggesting two models with best performances to rank individuals according to their bioavailable iron intakes. PMID:29883384

Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?

PubMed

Burns, Emily E; Thomas-Oates, Jane; Kolpin, Dana W; Furlong, Edward T; Boxall, Alistair B A

2017-10-01

Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk-based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs. Environ Toxicol Chem 2017;36:2823-2832. © 2017 SETAC. © 2017 SETAC.

Turbulent reacting flow computations including turbulence-chemistry interactions

NASA Technical Reports Server (NTRS)

Narayan, J. R.; Girimaji, S. S.

1992-01-01

A two-equation (k-epsilon) turbulence model has been extended to be applicable for compressible reacting flows. A compressibility correction model based on modeling the dilatational terms in the Reynolds stress equations has been used. A turbulence-chemistry interaction model is outlined. In this model, the effects of temperature and species mass concentrations fluctuations on the species mass production rates are decoupled. The effect of temperature fluctuations is modeled via a moment model, and the effect of concentration fluctuations is included using an assumed beta-pdf model. Preliminary results obtained using this model are presented. A two-dimensional reacting mixing layer has been used as a test case. Computations are carried out using the Navier-Stokes solver SPARK using a finite rate chemistry model for hydrogen-air combustion.

Evaluation of high-resolution GRAMM-GRAL (v15.12/v14.8) NOx simulations over the city of Zürich, Switzerland

NASA Astrophysics Data System (ADS)

Berchet, Antoine; Zink, Katrin; Oettl, Dietmar; Brunner, Jürg; Emmenegger, Lukas; Brunner, Dominik

2017-09-01

Hourly NOx concentrations were simulated for the city of Zürich, Switzerland, at 10 m resolution for the years 2013-2014. The simulations were generated with the nested mesoscale meteorology and micro-scale dispersion model system GRAMM-GRAL (versions v15.12 and v14.8) by applying a catalogue-based approach. This approach was specifically designed to enable long-term city-wide building-resolving simulations with affordable computation costs. It relies on a discrete set of possible weather situations and corresponding steady-state flow and dispersion patterns that are pre-computed and then matched hourly with actual meteorological observations. The modelling system was comprehensively evaluated using eight sites continuously monitoring NOx concentrations and 65 passive samplers measuring NO2 concentrations on a 2-weekly basis all over the city. The system was demonstrated to fulfil the European Commission standards for air pollution modelling at nearly all sites. The average spatial distribution was very well represented, despite a general tendency to overestimate the observed concentrations, possibly due to a crude representation of traffic-induced turbulence and to underestimated dispersion in the vicinity of buildings. The temporal variability of concentrations explained by varying emissions and weather situations was accurately reproduced on different timescales. The seasonal cycle of concentrations, mostly driven by stronger vertical dispersion in summer than in winter, was very well captured in the 2-year simulation period. Short-term events, such as episodes of particularly high and low concentrations, were detected in most cases by the system, although some unrealistic pollution peaks were occasionally generated, pointing at some limitations of the steady-state approximation. The different patterns of the diurnal cycle of concentrations observed in the city were generally well captured as well. The evaluation confirmed the adequacy of the catalogue-based approach in the context of city-scale air pollution modelling. The ability to reproduce not only the spatial gradients but also the hourly temporal variability over multiple years makes the model system particularly suitable for investigating individualized air pollution exposure in the city.

PAH concentrations simulated with the AURAMS-PAH chemical transport model over Canada and the USA

NASA Astrophysics Data System (ADS)

Galarneau, E.; Makar, P. A.; Zheng, Q.; Narayan, J.; Zhang, J.; Moran, M. D.; Bari, M. A.; Pathela, S.; Chen, A.; Chlumsky, R.

2013-07-01

The off-line Eulerian AURAMS chemical transport model was adapted to simulate the atmospheric fate of seven PAHs: phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene + triphenylene, and benzo[a]pyrene. The model was then run for the year 2002 with hourly output on a~grid covering southern Canada and the continental USA with 42 km horizontal grid spacing. Model predictions were compared to ~ 5000 24 h average PAH measurements from 45 sites, eight of which also provided data on particle/gas partitioning which had been modelled using two alternative schemes. This is the first known regional modelling study for PAHs over a North American domain and the first modelling study at any scale to compare alternative particle/gas partitioning schemes against paired field measurements. Annual average modelled total (gas + particle) concentrations were statistically indistinguishable from measured values for fluoranthene, pyrene and benz[a]anthracene whereas the model underestimated concentrations of phenanthrene, anthracene and chrysene + triphenylene. Significance for benzo[a]pyrene performance was close to the statistical threshold and depended on the particle/gas partitioning scheme employed. On a day-to-day basis, the model simulated total PAH concentrations to the correct order of magnitude the majority of the time. Model performance differed substantially between measurement locations and the limited available evidence suggests that the model spatial resolution was too coarse to capture the distribution of concentrations in densely populated areas. A more detailed analysis of the factors influencing modelled particle/gas partitioning is warranted based on the findings in this study.

Seasonal and diurnal patterns in the dispersion of SO2 from Mt. Nyiragongo

NASA Astrophysics Data System (ADS)

Dingwell, Adam; Rutgersson, Anna; Claremar, Björn; Arellano, Santiago; Yalire, Mathieu M.; Galle, Bo

2016-05-01

Mt. Nyiragongo is an active volcano located in the Democratic Republic of Congo, close to the border of Rwanda and about 15 km north of the city of Goma (∼ 1,000,000 inhabitants). Gases emitted from Nyiragongo might pose a persistent hazard to local inhabitants and the environment. While both ground- and satellite-based observations of the emissions exist, prior to this study, no detailed analysis of the dispersion of the emissions have been made. We have conducted a dispersion study, using a modelling system to determine the geographical distribution of SO2. A combination of a meteorological model (WRF), a Lagrangian particle dispersion model (FLEXPART-WRF) and flux data based on DOAS measurements from the NOVAC-network is used. Since observations can only be made during the day, we use random sampling of fluxes and ensemble modelling to estimate night-time emissions. Seasonal variations in the dispersion follows the migration of the Inter Tropical Convergence Zone. In June-August, the area with the highest surface concentrations is located to the northwest, and in December-February, to the southwest of the source. Diurnal variations in surface concentrations were determined by the development of the planetary boundary layer and the lake-/land breeze cycle around lake Kivu. Both processes contribute to low surface concentrations during the day and high concentrations during the night. However, the strong northerly trade winds in November-March weakened the lake breeze, contributing to higher daytime surface concentrations along the northern shore of Lake Kivu, including the city of Goma. For further analysis and measurements, it is important to include both seasonal and diurnal cycles in order to safely cover periods of high and potentially hazardous concentrations.

Theoretical study of liquid droplet dispersion in a venturi scrubber.

PubMed

Fathikalajahi, J; Talaie, M R; Taheri, M

1995-03-01

The droplet concentration distribution in an atomizing scrubber was calculated based on droplet eddy diffusion by a three-dimensional dispersion model. This model is also capable of predicting the liquid flowing on the wall. The theoretical distribution of droplet concentration agrees well with experimental data given by Viswanathan et al. for droplet concentration distribution in a venturi-type scrubber. The results obtained by the model show a non-uniform distribution of drops over the cross section of the scrubber, as noted by the experimental data. While the maximum of droplet concentration distribution may depend on many operating parameters of the scrubber, the results of this study show that the highest uniformity of drop distribution will be reached when penetration length is approximately equal to one-fourth of the depth of the scrubber. The results of this study can be applied to evaluate the removal efficiency of a venturi scrubber.

A statistical downscaling approach for roadside NO2 concentrations: Application to a WRF-Chem study for Berlin

NASA Astrophysics Data System (ADS)

Kuik, Friderike; Lauer, Axel; von Schneidemesser, Erika; Butler, Tim

2017-04-01

Many European cities continue to struggle with meeting the European air quality limits for NO2. In Berlin, Germany, most of the exceedances in NO2 recorded at monitoring sites near busy roads can be largely attributed to emissions from traffic. In order to assess the impact of changes in traffic emissions on air quality at policy relevant scales, we combine the regional atmosphere-chemistry transport model WRF-Chem at a resolution of 1kmx1km with a statistical downscaling approach. Here, we build on the recently published study evaluating the performance of a WRF-Chem setup in representing observed urban background NO2 concentrations from Kuik et al. (2016) and extend this setup by developing and testing an approach to statistically downscale simulated urban background NO2 concentrations to street level. The approach uses a multilinear regression model to relate roadside NO2 concentrations observed with the municipal monitoring network with observed NO2 concentrations at urban background sites and observed traffic counts. For this, the urban background NO2 concentrations are decomposed into a long term, a synoptic and a diurnal component using the Kolmogorov-Zurbenko filtering method. We estimate the coefficients of the regression model for five different roadside stations in Berlin representing different street types. In a next step we combine the coefficients with simulated urban background concentrations and observed traffic counts, in order to estimate roadside NO2 concentrations based on the results obtained with WRF-Chem at the five selected stations. In a third step, we extrapolate the NO2 concentrations to all major roads in Berlin. The latter is based on available data for Berlin of daily mean traffic counts, diurnal and weekly cycles of traffic as well as simulated urban background NO2 concentrations. We evaluate the NO2 concentrations estimated with this method at street level for Berlin with additional observational data from stationary measurements and mobile measurements conducted during a campaign in summer 2014. The results show that this approach allows us to estimate NO2 concentrations at roadside reasonably well. The approach can be applied when observations show a strong correlation between roadside NO2 concentrations and traffic emissions from a single type of road. The method, however, shows weaknesses for intersections where observed NO2 concentrations are influenced by traffic on several different roads. We then apply this downscaling approach to estimate the impact of different traffic emission scenarios both on urban background and street level NO2 concentrations. References Kuik, F., Lauer, A., Churkina, G., Denier van der Gon, H. A. C., Fenner, D., Mar, K. A., and Butler, T. M.: Air quality modelling in the Berlin-Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data, Geosci. Model Dev., 9, 4339-4363, doi:10.5194/gmd-9-4339-2016, 2016.

Improved simulation of regional CO2 surface concentrations using GEOS-Chem and fluxes from VEGAS

NASA Astrophysics Data System (ADS)

Chen, Z. H.; Zhu, J.; Zeng, N.

2013-08-01

CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However, one persistent problem in using model-observation comparisons for this goal relates to the issue of compatibility. Observations at a single station reflect all underlying processes of various scales. These processes usually cannot be fully resolved by model simulations at the grid points nearest the station due to lack of spatial or temporal resolution or missing processes in the model. In this study the stations in one region were grouped based on the amplitude and phase of the seasonal cycle at each station. The regionally averaged CO2 at all stations in one region represents the regional CO2 concentration of this region. The regional CO2 concentrations from model simulations and observations were used to evaluate the regional model results. The difference of the regional CO2 concentration between observation and modeled results reflects the uncertainty of the large-scale flux in the region where the grouped stations are. We compared the regional CO2 concentrations between model results with biospheric fluxes from the Carnegie-Ames-Stanford Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) models, and used observations from GLOBALVIEW-CO2 to evaluate the regional model results. The results show the largest difference of the regionally averaged values between simulations with fluxes from VEGAS and observations is less than 5 ppm for North American boreal, North American temperate, Eurasian boreal, Eurasian temperate and Europe, which is smaller than the largest difference between CASA simulations and observations (more than 5 ppm). There is still a large difference between two model results and observations for the regional CO2 concentration in the North Atlantic, Indian Ocean, and South Pacific tropics. The regionally averaged CO2 concentrations will be helpful for comparing CO2 concentrations from modeled results and observations and evaluating regional surface fluxes from different methods.

Concentrations of the UV filter ethylhexyl methoxycinnamate in the aquatic compartment: a comparison of modelled concentrations for Swiss surface waters with empirical monitoring data.

PubMed

Straub, Jürg Oliver

2002-05-10

UV filters in sunscreens and cosmetics protect the skin from damage through UV radiation. Many tonnes per year of UV filters are being used in Europe and will be present, at least seasonally, in detectable concentrations in surface waters similar to common pharmaceutically active substances. Predicted environmental concentrations (PECs) of ethylhexyl methoxycinnamate (EHMC; CAS 5466-77-3) were extrapolated for Switzerland, taking into consideration substance-specific environmental fate data and marketing estimates, by crude worst-case reckoning and by applying two environmental models (Mackay Level III; USES 3.0), both configured for Swiss hydrological and area data. By worst-case reckoning the summer PEC is 70.8-81.3 ng/l while for the remaining 8 months of the year the PEC is 13.1-15.1 ng/l. The Level III model results in concentrations of 2.4 ng/l during the summer and 0.44 ng/l during the rest of the year, while the USES 3.0 model gives an average PEC for the whole year of 7.6 ng/l. Pooling summer monitoring data (90 single analyses) from the River Rhine below Basel in the year 1997 (Water Protection Board of Basel) and from Lakes Zurich and Hüttner in 1998 (Poiger et al., in preparation) allowed a derivation of a probabilistic median concentration of 4.6 ng/l, a 95th-percentile concentration of 18.6 ng/l and a 99th-percentile concentration of 33.5 ng/l. The 6-fold range from the median value to the maximum calls for caution in interpreting published monitoring concentrations. Comparison of modelled PECs with realistic median concentrations shows that crude reckoning overestimates actual concentrations by a factor of about 10, probably through insufficient consideration of (further) degradation of EHMC in sewage works, surface waters, sediments or river banks. Both computer models, in contrast, are within the same order of magnitude as the actual summer concentrations. Based on the available data, both these environmental fate and distribution models give realistic PECs.

Development and evaluation of consensus-based sediment effect concentrations for polychlorinated biphenyls

USGS Publications Warehouse

MacDonald, Donald D.; Dipinto, Lisa M.; Field, Jay; Ingersoll, Christopher G.; Long, Edward R.; Swartz, Richard C.

2000-01-01

Sediment-quality guidelines (SQGs) have been published for polychlorinated biphenyls (PCBs) using both empirical and theoretical approaches. Empirically based guidelines have been developed using the screening-level concentration, effects range, effects level, and apparent effects threshold approaches. Theoretically based guidelines have been developed using the equilibrium-partitioning approach. Empirically-based guidelines were classified into three general categories, in accordance with their original narrative intents, and used to develop three consensus-based sediment effect concentrations (SECs) for total PCBs (tPCBs), including a threshold effect concentration, a midrange effect concentration, and an extreme effect concentration. Consensus-based SECs were derived because they estimate the central tendency of the published SQGs and, thus, reconcile the guidance values that have been derived using various approaches. Initially, consensus-based SECs for tPCBs were developed separately for freshwater sediments and for marine and estuarine sediments. Because the respective SECs were statistically similar, the underlying SQGs were subsequently merged and used to formulate more generally applicable SECs. The three consensus-based SECs were then evaluated for reliability using matching sediment chemistry and toxicity data from field studies, dose-response data from spiked-sediment toxicity tests, and SQGs derived from the equilibrium-partitioning approach. The results of this evaluation demonstrated that the consensus-based SECs can accurately predict both the presence and absence of toxicity in field-collected sediments. Importantly, the incidence of toxicity increases incrementally with increasing concentrations of tPCBs. Moreover, the consensus-based SECs are comparable to the chronic toxicity thresholds that have been estimated from dose-response data and equilibrium-partitioning models. Therefore, consensus-based SECs provide a unifying synthesis of existing SQGs, reflect causal rather than correlative effects, and accurately predict sediment toxicity in PCB-contaminated sediments.

A NetCDF version of the two-dimensional energy balance model based on the full multigrid algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Kelin; North, Gerald R.; Stevens, Mark J.

A NetCDF version of the two-dimensional energy balance model based on the full multigrid method in Fortran is introduced for both pedagogical and research purposes. Based on the land-sea-ice distribution, orbital elements, greenhouse gases concentration, and albedo, the code calculates the global seasonal surface temperature. A step-by-step guide with examples is provided for practice.

Modeling radiocesium transport from a river catchment based on a physically-based distributed hydrological and sediment erosion model.

PubMed

Kinouchi, Tsuyoshi; Yoshimura, Kazuya; Omata, Teppei

2015-01-01

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) in March 2011 resulted in the deposition of large quantities of radionuclides, such as (134)Cs and (137)Cs, over parts of eastern Japan. Since then high levels of radioactive contamination have been detected in large areas, including forests, agricultural land, and residential areas. Due to the strong adsorption capability of radiocesium to soil particles, radiocesium migrates with eroded sediments, follows the surface flow paths, and is delivered to more populated downstream regions and eventually to the Pacific Ocean. It is therefore important to understand the transport of contaminated sediments in the hydrological system and to predict changes in the spatial distribution of radiocesium concentrations by taking the land-surface processes related to sediment migration into consideration. In this study, we developed a distributed model to simulate the transport of water and contaminated sediment in a watershed hydrological system, and applied this model to a partially forested mountain catchment located in an area highly contaminated by the radioactive fallout. Observed discharge, sediment concentration, and cesium concentration measured from June 2011 until December 2012 were used for calibration of model parameters. The simulated discharge and sediment concentration both agreed well with observed values, while the cesium concentration was underestimated in the initial period following the accident. This result suggests that the leaching of radiocesium from the forest canopy, which was not considered in the model, played a significant role in its transport from the catchment. Based on the simulation results, we quantified the long-term fate of radiocesium over the study area and estimated that the effective half-life of (137)Cs deposited in the study area will be approximately 22 y due to the export of contaminated sediment by land-surface processes, and the amount of (137)Cs remaining in the catchment will be reduced to 39% of the initial total within 30 y after contamination. This study provides a perspective on the transport of suspended sediments and radiocesium in catchments with similar land use and radiocesium contamination. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bacterial degradation of acetone in an outdoor model stream

USGS Publications Warehouse

Rathbun, R.E.; Stephens, D.W.; Tai, D.Y.

1993-01-01

Diurnal variations of the acetone concentration in an outdoor model stream were measured with and without a nitrate supplement to determine if the nitrate supplement would stimulate bacterial degradation of the acetone. Acetone loss coefficients were computed from the diurnal data using a fitting procedure based on a Lagrangian particle model. The coefficients indicated that bacterial degradation of the acetone was occurring in the downstream part of the stream during the nitrate addition. However, the acetone concentrations stabilized at values considerably above the limit of detection for acetone determination, in contrast to laboratory respirometer studies where the acetone concentration decreased rapidly to less than the detection limit, once bacterial acclimation to the acetone had occurred. One possible explanation for the difference in behavior was the limited 6-hour residence time of the acetone in the model stream.

Favipiravir pharmacokinetics in Ebola-Infected patients of the JIKI trial reveals concentrations lower than targeted

PubMed Central

Nguyen, Thi Huyen Tram; Anglaret, Xavier; Madelain, Vincent; Taburet, Anne-Marie; Baize, Sylvain; Pastorino, Boris; Rodallec, Anne; Piorkowski, Géraldine; Conde, Mamoudou N.; Bore, Joseph Akoi; Carbonnelle, Caroline; Jacquot, Frédéric; Raoul, Hervé; Malvy, Denis; Mentré, France

2017-01-01

Background In 2014–2015, we assessed favipiravir tolerance and efficacy in patients with Ebola virus (EBOV) disease (EVD) in Guinea (JIKI trial). Because the drug had never been used before for this indication and that high concentrations of the drugs were needed to achieve antiviral efficacy against EBOV, a pharmacokinetic model had been used to propose relevant dosing regimen. Here we report the favipiravir plasma concentrations that were achieved in participants in the JIKI trial and put them in perspective with the model-based targeted concentrations. Methods and findings Pre-dose drug concentrations were collected at Day-2 and Day-4 of treatment in 66 patients of the JIKI trial and compared to those predicted by the model taking into account patient’s individual characteristics. At Day-2, the observed concentrations were slightly lower than the model predictions adjusted for patient’s characteristics (median value of 46.1 versus 54.3 μg/mL for observed and predicted concentrations, respectively, p = 0.012). However, the concentrations dropped at Day-4, which was not anticipated by the model (median values of 25.9 and 64.4 μg/mL for observed and predicted concentrations, respectively, p<10−6). There was no significant relationship between favipiravir concentrations and EBOV viral kinetics or mortality. Conclusions Favipiravir plasma concentrations in the JIKI trial failed to achieve the target exposure defined before the trial. Furthermore, the drug concentration experienced an unanticipated drop between Day-2 and Day-4. The origin of this drop could be due to severe sepsis conditions and/or to intrinsic properties of favipiravir metabolism. Dose-ranging studies should be performed in healthy volunteers to assess the concentrations and the tolerance that could be achieved with high doses. Trial registration ClinicalTrials.gov NCT02329054 PMID:28231247

Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juxiu Tong; Bill X. Hu; Hai Huang

2014-03-01

With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations,more » we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study.« less

Indoor Radon Concentration Related to Different Radon Areas and Indoor Radon Prediction

NASA Astrophysics Data System (ADS)

Juhásová Šenitková, Ingrid; Šál, Jiří

2017-12-01

Indoor radon has been observed in the buildings at areas with different radon risk potential. Preventive measures are based on control of main potential radon sources (soil gas, building material and supplied water) to avoid building of new houses above recommended indoor radon level 200 Bq/m3. Radon risk (index) estimation of individual building site bedrock in case of new house siting and building protection according technical building code are obligatory. Remedial actions in buildings built at high radon risk areas were carried out principally by unforced ventilation and anti-radon insulation. Significant differences were found in the level of radon concentration between rooms where radon reduction techniques were designed and those where it was not designed. The mathematical model based on radon exhalation from soil has been developed to describe the physical processes determining indoor radon concentration. The model is focused on combined radon diffusion through the slab and advection through the gap from sub-slab soil. In this model, radon emanated from building materials is considered not having a significant contribution to indoor radon concentration. Dimensional analysis and Gauss-Newton nonlinear least squares parametric regression were used to simplify the problem, identify essential input variables and find parameter values. The presented verification case study is introduced for real buildings with respect to various underground construction types. Presented paper gives picture of possible mathematical approach to indoor radon concentration prediction.

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

NASA Technical Reports Server (NTRS)

Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

1996-01-01

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

NASA Astrophysics Data System (ADS)

Afkhamipour, Morteza; Mofarahi, Masoud; Borhani, Tohid Nejad Ghaffar; Zanganeh, Masoud

2018-03-01

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity ( C P ) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity ({C}_P^E) data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C P collected from various literatures. The AARD between ANN model results and experimental data of C P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.

Determination of effective electromagnetic parameters of concentrated suspensions of ellipsoidal particles using Generalized Differential Effective Medium approximation

NASA Astrophysics Data System (ADS)

Markov, M.; Levin, V.; Markova, I.

2018-02-01

The paper presents an approach to determine the effective electromagnetic parameters of suspensions of ellipsoidal dielectric particles with surface conductivity. This approach takes into account the existence of critical porosity that corresponds to the maximum packing volume fraction of solid inclusions. The approach is based on the Generalized Differential Effective Medium (GDEM) method. We have introduced a model of suspensions containing ellipsoidal inclusions of two types. Inclusions of the first type (phase 1) represent solid grains, and inclusions of the second type (phase 2) contain material with the same physical properties as the host (phase 0). In this model, with increasing porosity the concentration of the host decreases, and it tends to zero near the critical porosity. The proposed model has been used to simulate the effective electromagnetic parameters of concentrated suspensions. We have compared the modeling results for electrical conductivity and dielectric permittivity with the empirical equations. The results obtained have shown that the GDEM model describes the effective electrical conductivity and dielectric permittivity of suspensions in a wide range of inclusion concentrations.

A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.

PubMed

Ewings, Sean M; Sahu, Sujit K; Valletta, John J; Byrne, Christopher D; Chipperfield, Andrew J

2015-06-01

This article presents a new statistical approach to analysing the effects of everyday physical activity on blood glucose concentration in people with type 1 diabetes. A physiologically based model of blood glucose dynamics is developed to cope with frequently sampled data on food, insulin and habitual physical activity; the model is then converted to a Bayesian network to account for measurement error and variability in the physiological processes. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods for simultaneous estimation of all model parameters and prediction of blood glucose concentration. Although there are problems with parameter identification in a minority of cases, most parameters can be estimated without bias. Predictive performance is unaffected by parameter misspecification and is insensitive to misleading prior distributions. This article highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes. The proposed methods represent a new paradigm for analysis of deterministic mathematical models of blood glucose concentration. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Water quality of storm runoff and comparison of procedures for estimating storm-runoff loads, volume, event-mean concentrations, and the mean load for a storm for selected properties and constituents for Colorado Springs, southeastern Colorado, 1992

USGS Publications Warehouse

Von Guerard, Paul; Weiss, W.B.

1995-01-01

The U.S. Environmental Protection Agency requires that municipalities that have a population of 100,000 or greater obtain National Pollutant Discharge Elimination System permits to characterize the quality of their storm runoff. In 1992, the U.S. Geological Survey, in cooperation with the Colorado Springs City Engineering Division, began a study to characterize the water quality of storm runoff and to evaluate procedures for the estimation of storm-runoff loads, volume and event-mean concentrations for selected properties and constituents. Precipitation, streamflow, and water-quality data were collected during 1992 at five sites in Colorado Springs. Thirty-five samples were collected, seven at each of the five sites. At each site, three samples were collected for permitting purposes; two of the samples were collected during rainfall runoff, and one sample was collected during snowmelt runoff. Four additional samples were collected at each site to obtain a large enough sample size to estimate storm-runoff loads, volume, and event-mean concentrations for selected properties and constituents using linear-regression procedures developed using data from the Nationwide Urban Runoff Program (NURP). Storm-water samples were analyzed for as many as 186 properties and constituents. The constituents measured include total-recoverable metals, vola-tile-organic compounds, acid-base/neutral organic compounds, and pesticides. Storm runoff sampled had large concentrations of chemical oxygen demand and 5-day biochemical oxygen demand. Chemical oxygen demand ranged from 100 to 830 milligrams per liter, and 5.-day biochemical oxygen demand ranged from 14 to 260 milligrams per liter. Total-organic carbon concentrations ranged from 18 to 240 milligrams per liter. The total-recoverable metals lead and zinc had the largest concentrations of the total-recoverable metals analyzed. Concentrations of lead ranged from 23 to 350 micrograms per liter, and concentrations of zinc ranged from 110 to 1,400 micrograms per liter. The data for 30 storms representing rainfall runoff from 5 drainage basins were used to develop single-storm local-regression models. The response variables, storm-runoff loads, volume, and event-mean concentrations were modeled using explanatory variables for climatic, physical, and land-use characteristics. The r2 for models that use ordinary least-squares regression ranged from 0.57 to 0.86 for storm-runoff loads and volume and from 0.25 to 0.63 for storm-runoff event-mean concentrations. Except for cadmium, standard errors of estimate ranged from 43 to 115 percent for storm- runoff loads and volume and from 35 to 66 percent for storm-runoff event-mean concentrations. Eleven of the 30 concentrations collected during rainfall runoff for total-recoverable cadmium were censored (less than) concentrations. Ordinary least-squares regression should not be used with censored data; however, censored data can be included with uncensored data using tobit regression. Standard errors of estimate for storm-runoff load and event-mean concentration for total-recoverable cadmium, computed using tobit regression, are 247 and 171 percent. Estimates from single-storm regional-regression models, developed from the Nationwide Urban Runoff Program data base, were compared with observed storm-runoff loads, volume, and event-mean concentrations determined from samples collected in the study area. Single-storm regional-regression models tended to overestimate storm-runoff loads, volume, and event-mean con-centrations. Therefore, single-storm local- and regional-regression models were combined using model-adjustment procedures to take advantage of the strengths of both models while minimizing the deficiencies of each model. Procedures were used to develop single-stormregression equations that were adjusted using local data and estimates from single-storm regional-regression equations. Single-storm regression models developed using model- adjustment proce

An approximate analytical methodology for the concentration CDF and corresponding adverse health effects in 3D heterogeneous aquifers

NASA Astrophysics Data System (ADS)

Zarlenga, Antonio; de Barros, Felipe; Fiori, Aldo

2017-04-01

Predicting solutes displacement in ecosystems is a complex task because of heterogeneity of hydrogeological properties and limited financial resources for characterization. As a consequence, solute transport model predictions are subject to uncertainty and probabilistic methods are invoked. Despite the significant theoretical advances in subsurface hydrology, there is a compelling need to transfer those specialized know-hows into an easy-to-use practical tool. The deterministic approach is able to capture some features of the transport behavior but its adoption in practical applications (e.g. remediation strategies or health risk assessment) is often inadequate because of its inability to accurately model the phenomena triggered by the spatial heterogeneity. The rigorous evaluation of the local contaminant concentration in natural aquifers requires an accurate estimate of the domain properties and huge computational times; those aspects limit the adoption of fully 3D numerical models. In this presentation, we illustrate a physically-based methodology to analytically estimate of the statistics of the solute concentration in natural aquifers and the related health risk. Our methodology aims to provide a simple tool for a quick assessment of the contamination level in aquifers, as function of a few relevant, physically based parameters such as the log conductivity variance, the mean flow velocity, the Péclet number. Solutions of the 3D analytical model adopt the results of previous works: transport model is based on the solutions proposed by Zarlenga and Fiori (2013, 2014) where semi-analytical relations for the statics of local contaminant concentration are carry out through a Lagrangian first-order model. As suggested in de Barros and Fiori (2014), the Beta distribution is assumed for the concentration cumulative density function (CDF). We illustrate the use of the closed-form equations for the probability of local contaminant concentration and health risk in a series of problems of practical relevance. The basic scenario is constituted by a steady state plume in a 3D heterogeneous formation. In this case the non-reactive transport is ruled by interplay of the spreading (lateral and vertical) and dilution. The second scenario considers two different dynamics of degradation in aerobic and anaerobic conditions which allows the contaminant abatement. The final example links the environmental concentration with adverse health effects. For this case, additional information on toxicological and behavioral parameters are required. Despite the simplifying assumptions adopted, the proposed solutions are appealing in applications due to their simplicity and the fact that they allow to easily propagate the uncertainty from different sources in the final risk endpoint. de Barros, F.P., Fiori, A., 2014. First-order based cumulative distribution function for solute concentration in heterogeneous aquifers: theoretical analysis and implications for human health risk assessment. Water Resour. Res. 50, 4018-4037. Zarlenga, A., Fiori, A., 2013. Steady plumes in heterogeneous porous formations: a stochastic lagrangian approach. Water Resour. Res. 49, 864-873. Zarlenga, A., Fiori, A., 2014. Stochastic analytical modeling of the biodegradation of steady plumes. J. Contam. Hydrol. 157, 106-116.

Correcting for the influence of sampling conditions on biomarkers of exposure to phenols and phthalates: a 2-step standardization method based on regression residuals.

PubMed

Mortamais, Marion; Chevrier, Cécile; Philippat, Claire; Petit, Claire; Calafat, Antonia M; Ye, Xiaoyun; Silva, Manori J; Brambilla, Christian; Eijkemans, Marinus J C; Charles, Marie-Aline; Cordier, Sylvaine; Slama, Rémy

2012-04-26

Environmental epidemiology and biomonitoring studies typically rely on biological samples to assay the concentration of non-persistent exposure biomarkers. Between-participant variations in sampling conditions of these biological samples constitute a potential source of exposure misclassification. Few studies attempted to correct biomarker levels for this error. We aimed to assess the influence of sampling conditions on concentrations of urinary biomarkers of select phenols and phthalates, two widely-produced families of chemicals, and to standardize biomarker concentrations on sampling conditions. Urine samples were collected between 2002 and 2006 among 287 pregnant women from Eden and Pélagie cohorts, from which phthalates and phenols metabolites levels were assayed. We applied a 2-step standardization method based on regression residuals. First, the influence of sampling conditions (including sampling hour, duration of storage before freezing) and of creatinine levels on biomarker concentrations were characterized using adjusted linear regression models. In the second step, the model estimates were used to remove the variability in biomarker concentrations due to sampling conditions and to standardize concentrations as if all samples had been collected under the same conditions (e.g., same hour of urine collection). Sampling hour was associated with concentrations of several exposure biomarkers. After standardization for sampling conditions, median concentrations differed by--38% for 2,5-dichlorophenol to +80 % for a metabolite of diisodecyl phthalate. However, at the individual level, standardized biomarker levels were strongly correlated (correlation coefficients above 0.80) with unstandardized measures. Sampling conditions, such as sampling hour, should be systematically collected in biomarker-based studies, in particular when the biomarker half-life is short. The 2-step standardization method based on regression residuals that we proposed in order to limit the impact of heterogeneity in sampling conditions could be further tested in studies describing levels of biomarkers or their influence on health.

EC-QCL mid-IR transmission spectroscopy for monitoring dynamic changes of protein secondary structure in aqueous solution on the example of β-aggregation in alcohol-denaturated α-chymotrypsin.

PubMed

Alcaráz, Mirta R; Schwaighofer, Andreas; Goicoechea, Héctor; Lendl, Bernhard

2016-06-01

In this work, a novel EC-QCL-based setup for mid-IR transmission measurements in the amide I region is introduced for monitoring dynamic changes in secondary structure of proteins. For this purpose, α-chymotrypsin (aCT) acts as a model protein, which gradually forms intermolecular β-sheet aggregates after adopting a non-native α-helical structure induced by exposure to 50 % TFE. In order to showcase the versatility of the presented setup, the effects of varying pH values and protein concentration on the rate of β-aggregation were studied. The influence of the pH value on the initial reaction rate was studied in the range of pH 5.8-8.2. Results indicate an increased aggregation rate at elevated pH values. Furthermore, the widely accessible concentration range of the laser-based IR transmission setup was utilized to investigate β-aggregation across a concentration range of 5-60 mg mL(-1). For concentrations lower than 20 mg mL(-1), the aggregation rate appears to be independent of concentration. At higher values, the reaction rate increases linearly with protein concentration. Extended MCR-ALS was employed to obtain pure spectral and concentration profiles of the temporal transition between α-helices and intermolecular β-sheets. Comparison of the global solutions obtained by the modelled data with results acquired by the laser-based IR transmission setup at different conditions shows excellent agreement. This demonstrates the potential and versatility of the EC-QCL-based IR transmission setup to monitor dynamic changes of protein secondary structure in aqueous solution at varying conditions and across a wide concentration range. Graphical abstract EC-QCL IR spectroscopy for monitoring protein conformation change.

Environmental contaminants in freshwater fish and their risk to piscivorous wildlife based on a national monitoring program

USGS Publications Warehouse

Hinck, J.E.; Schmitt, C.J.; Chojnacki, K.A.; Tillitt, D.E.

2009-01-01

Organochlorine chemical residues and elemental concentrations were measured in piscivorous and benthivorous fish at 111 sites from large U.S. river basins. Potential contaminant sources such as urban and agricultural runoff, industrial discharges, mine drainage, and irrigation varied among the sampling sites. Our objectives were to provide summary statistics for chemical contaminants and to determine if contaminant concentrations in the fish were a risk to wildlife that forage at these sites. Concentrations of dieldrin, total DDT, total PCBs, toxaphene, TCDD-EQ, cadmium, chromium, mercury, lead, selenium, and zinc exceeded toxicity thresholds to protect fish and piscivorous wildlife in samples from at least one site; most exceedences were for total PCBs, mercury, and zinc. Chemical concentrations in fish from the Mississippi River Basin exceeded the greatest number of toxicity thresholds. Screening level wildlife risk analysis models were developed for bald eagle and mink using no adverse effect levels (NOAELs), which were derived from adult dietary exposure or tissue concentration studies and based primarily on reproductive endpoints. No effect hazard concentrations (NEHC) were calculated by comparing the NOAEL to the food ingestion rate (dietary-based NOAEL) or biomagnification factor (tissue-based NOAEL) of each receptor. Piscivorous wildlife may be at risk from a contaminant if the measured concentration in fish exceeds the NEHC. Concentrations of most organochlorine residues and elemental contaminants represented no to low risk to bald eagle and mink at most sites. The risk associated with pentachloroanisole, aldrin, Dacthal, methoxychlor, mirex, and toxaphene was unknown because NOAELs for these contaminants were not available for bald eagle or mink. Risk differed among modeled species and sites. Our screening level analysis indicates that the greatest risk to piscivorous wildlife was from total DDT, total PCBs, TCDD-EQ, mercury, and selenium. Bald eagles were at greater risk to total DDT and total PCBs than mink, whereas risks of TCDD-EQ, mercury, and selenium were greater to mink than bald eagle. ?? Springer Science+Business Media B.V. 2008.

A MULTISTAGE BIOLOGICALLY BASED MODEL FOR MOUSE LIVER TUMORS RESULTING FROM EXPOSURE TO DICHLOROACETIC ACID

EPA Science Inventory

Dichloroacetic Acid (DCA) is a major byproduct of the chlorine disinfection of humic acid containing drinking water sources. It is a hepatocarcinogen in mice and rats at exposure concentrations in drinking water that are at least 4 orders of magnitude above the concentrations in ...