Corn stover harvest increases herbicide movement to subsurface drains - Root Zone Water Quality Model simulations.

PubMed

Shipitalo, Martin J; Malone, Robert W; Ma, Liwang; Nolan, Bernard T; Kanwar, Rameshwar S; Shaner, Dale L; Pederson, Carl H

2016-06-01

Crop residue removal for bioenergy production can alter soil hydrologic properties and the movement of agrochemicals to subsurface drains. The Root Zone Water Quality Model (RZWQM), previously calibrated using measured flow and atrazine concentrations in drainage from a 0.4 ha chisel-tilled plot, was used to investigate effects of 50 and 100% corn (Zea mays L.) stover harvest and the accompanying reductions in soil crust hydraulic conductivity and total macroporosity on transport of atrazine, metolachlor and metolachlor oxanilic acid (OXA). The model accurately simulated field-measured metolachlor transport in drainage. A 3 year simulation indicated that 50% residue removal reduced subsurface drainage by 31% and increased atrazine and metolachlor transport in drainage 4-5-fold when surface crust conductivity and macroporosity were reduced by 25%. Based on its measured sorption coefficient, approximately twofold reductions in OXA losses were simulated with residue removal. The RZWQM indicated that, if corn stover harvest reduces crust conductivity and soil macroporosity, losses of atrazine and metolachlor in subsurface drainage will increase owing to reduced sorption related to more water moving through fewer macropores. Losses of the metolachlor degradation product OXA will decrease as a result of the more rapid movement of the parent compound into the soil. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

The Study of the Impacts of The agriculture practices on ET by In-situ Measurement and Numeric Modeling in Southern China

NASA Astrophysics Data System (ADS)

Huang, Jinhui Jeanne; Chan, Han

2017-04-01

ABSTRACT Evapotranspiration (ET) has long been regarded as a very important component in energy and mass exchange between hydrosphere, atmosphere and biosphere. It is estimated that about 70% annual precipitation goes back to atmosphere through the process of ET, ET thus plays a significant role in modeling regional and global climate and assessing stresses on natural and agricultural ecosystems. The variation of ET is affected by many processes including hydrological, metrological as well as biological processes. Water used in Agriculture Sector is normally accounted for about 70% of total water consumption. ET may also be enhanced by agriculture practices as it is the key component of water consumption in agriculture practices. A two-year continuous in-situ ET measurement (in half minute time scale) by eddy covariance method (using EC-QCL analyzer and three-dimensional ultrasonic anemometer) was conducted in a large vegetable farmland in the suburb of Yueyang City, Hunan Province. EddyPro software was employed to calculate the actual evapotranspiration, water vapor flux, latent heat flux (LE) and analysis the trend of actual evapotranspiration in different time scales. A RZWQM2 (Root Zone Water Quality Model) model was also developed based on the local metrological data and agriculture practices including planting, harvesting, irrigation practices and fertilization etc., The field observations including in-situ ET measurement are used to calibrate the RZWQM2 model. The calibrated model was further used to study the effects of various agriculture activates on ET. The study shows that the crop density has the greatest effects on the variation of plant transpiration following by irrigation and fertilization. This study provides some scientific basis for the optimization and improvement of agricultural activities in the future. Key words: ET; Agricultural Practices; Eddy Covariance Method; RZWQM2 model

Modeling the long-term effect of winter cover crops on nitrate transport in artificially drained fields across the Midwest U.S.

USDA-ARS?s Scientific Manuscript database

A fall-planted cover crop is a management practice with multiple benefits including reducing nitrate losses from artificially drained fields. We used the Root Zone Water Quality Model (RZWQM) to simulate the impact of a cereal rye cover crop on reducing nitrate losses from drained fields across five...

N loss to drain flow and N2O emissions from a corn-soybean rotation with winter rye.

PubMed

Gillette, K; Malone, R W; Kaspar, T C; Ma, L; Parkin, T B; Jaynes, D B; Fang, Q X; Hatfield, J L; Feyereisen, G W; Kersebaum, K C

2018-03-15

Anthropogenic perturbation of the global nitrogen cycle and its effects on the environment such as hypoxia in coastal regions and increased N 2 O emissions is of increasing, multi-disciplinary, worldwide concern, and agricultural production is a major contributor. Only limited studies, however, have simultaneously investigated NO 3 - losses to subsurface drain flow and N 2 O emissions under corn-soybean production. We used the Root Zone Water Quality Model (RZWQM) to evaluate NO 3 - losses to drain flow and N 2 O emissions in a corn-soybean system with a winter rye cover crop (CC) in central Iowa over a nine year period. The observed and simulated average drain flow N concentration reductions from CC were 60% and 54% compared to the no cover crop system (NCC). Average annual April through October cumulative observed and simulated N 2 O emissions (2004-2010) were 6.7 and 6.0kgN 2 O-Nha -1 yr -1 for NCC, and 6.2 and 7.2kgNha -1 for CC. In contrast to previous research, monthly N 2 O emissions were generally greatest when N loss to leaching were greatest, mostly because relatively high rainfall occurred during the months fertilizer was applied. N 2 O emission factors of 0.032 and 0.041 were estimated for NCC and CC using the tested model, which are similar to field results in the region. A local sensitivity analysis suggests that lower soil field capacity affects RZWQM simulations, which includes increased drain flow nitrate concentrations, increased N mineralization, and reduced soil water content. The results suggest that 1) RZWQM is a promising tool to estimate N 2 O emissions from subsurface drained corn-soybean rotations and to estimate the relative effects of a winter rye cover crop over a nine year period on nitrate loss to drain flow and 2) soil field capacity is an important parameter to model N mineralization and N loss to drain flow. Published by Elsevier B.V.

Development of an irrigation scheduling software based on model predicted crop water stress

USDA-ARS?s Scientific Manuscript database

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress which is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling software based on RZWQM2 model pr...

Analysis of parameter sensitivity and identifiability of root zone water quality model (RZWQM) for dryland sugerbeet modeling

USDA-ARS?s Scientific Manuscript database

Sugarbeet is being considered as one of the most viable feedstock alternatives to corn for biofuel production since herbicide resistant energy beets were deregulated by USDA in 2012. Growing sugarbeets for biofuel production may have significant impacts on soil health and water quality in the north-...

Optimizing ET-based irrigation scheduling for wheat and maize with water constraints

USDA-ARS?s Scientific Manuscript database

Deficit irrigation is proved to increase crop water use efficiency (WUE) in water limited areas, but effective irrigation required better understanding of crop responses to water stress intensity and timing. In this study, the Root Zone Water Quality Model (RZWQM) was first calibrated and validated ...

Improved model quality assessment using ProQ2.

PubMed

Ray, Arjun; Lindahl, Erik; Wallner, Björn

2012-09-10

Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement. Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local. ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson's correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2

ENHANCED STREAM WATER QUALITY MODEL (QUAL2EU)

EPA Science Inventory

The enhanced stream water quality model QUAL2E and QUAL2E-UNCAS (37) permits simulation of several water quality constituents in a branching stream system using a finite difference solution to the one-dimensional advective-dispersive mass transport and reaction equation. The con...

Simulation of Climate Change Impacts on Wheat-Fallow Cropping Systems

USDA-ARS?s Scientific Manuscript database

Agricultural system simulation models are predictive tools for assessing climate change impacts on crop production. In this study, RZWQM2 that contains the DSSAT 4.0-CERES model was evaluated for simulating climate change impacts on wheat growth. The model was calibrated and validated using data fro...

A manufacturing quality assessment model based-on two stages interval type-2 fuzzy logic

NASA Astrophysics Data System (ADS)

Purnomo, Muhammad Ridwan Andi; Helmi Shintya Dewi, Intan

2016-01-01

This paper presents the development of an assessment models for manufacturing quality using Interval Type-2 Fuzzy Logic (IT2-FL). The proposed model is developed based on one of building block in sustainable supply chain management (SSCM), which is benefit of SCM, and focuses more on quality. The proposed model can be used to predict the quality level of production chain in a company. The quality of production will affect to the quality of product. Practically, quality of production is unique for every type of production system. Hence, experts opinion will play major role in developing the assessment model. The model will become more complicated when the data contains ambiguity and uncertainty. In this study, IT2-FL is used to model the ambiguity and uncertainty. A case study taken from a company in Yogyakarta shows that the proposed manufacturing quality assessment model can work well in determining the quality level of production.

UNCERTAINTY ANALYSIS IN WATER QUALITY MODELING USING QUAL2E

EPA Science Inventory

A strategy for incorporating uncertainty analysis techniques (sensitivity analysis, first order error analysis, and Monte Carlo simulation) into the mathematical water quality model QUAL2E is described. The model, named QUAL2E-UNCAS, automatically selects the input variables or p...

Application of OMI NO2 for Regional Air Quality Model Evaluation

NASA Astrophysics Data System (ADS)

Holloway, T.; Bickford, E.; Oberman, J.; Scotty, E.; Clifton, O. E.

2012-12-01

To support the application of satellite data for air quality analysis, we examine how column NO2 measurements from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura satellite relate to ground-based and model estimates of NO2 and related species. Daily variability, monthly mean values, and spatial gradients in OMI NO2 from the Netherlands Royal Meteorological Institute (KNMI) are compared to ground-based measurements of NO2 from the EPA Air Quality System (AQS) database. Satellite data is gridded to two resolutions typical of regional air quality models - 36 km x 36 km over the continental U.S., and 12 km x 12 km over the Upper Midwestern U.S. Gridding is performed using the Wisconsin Horizontal Interpolation Program for Satellites (WHIPS), a publicly available software to support gridding of satellite data to model grids. Comparing daily OMI retrievals (13:45 daytime local overpass time) with ground-based measurements (13:00), we find January and July 2007 correlation coefficients (r-values) generally positive, with values higher in the winter (January) than summer (July) for most sites. Incidences of anti-correlation or low-correlation are evaluated with model simulations from the U.S. EPA Community Multiscale Air Quality Model version 4.7 (CMAQ). OMI NO2 is also used to evaluate CMAQ output, and to compare performance metrics for CMAQ relative to AQS measurements. We compare simulated NO2 across both the U.S. and Midwest study domains with both OMI NO2 (total column CMAQ values, weighted with the averaging kernel) and with ground-based observations (lowest model layer CMAQ values). 2007 CMAQ simulations employ emissions from the Lake Michigan Air Directors Consortium (LADCO) and meteorology from the Weather Research and Forecasting (WRF) model. Over most of the U.S., CMAQ is too high in January relative to OMI NO2, but too low in January relative to AQS NO2. In contrast, CMAQ is too low in July relative to OMI NO2, but too high relative to AQS NO2. These

Simulating the fate of fall- and spring-applied poultry litter nitrogen in corn production

USDA-ARS?s Scientific Manuscript database

Monitoring the fate of N derived from manures applied to fertilize crops is difficult, time consuming, and relatively expensive. But computer simulation models can help understand the interactions among various N processes in the soil-plant system and determine the fate of applied N. The RZWQM2 was ...

Relationship between soybean yield/quality and soil quality in a major soybean-producing area based on a 2D-QSAR model

NASA Astrophysics Data System (ADS)

Gao, Ming; Li, Shiwei

2017-05-01

Based on experimental data of the soybean yield and quality from 30 sampling points, a quantitative structure-activity relationship model (2D-QSAR) was established using the soil quality (elements, pH, organic matter content and cation exchange capacity) as independent variables and soybean yield or quality as the dependent variable, with SPSS software. During the modeling, the full data set (30 and 14 compounds) was divided into a training set (24 and 11 compounds) for model generation and a test set (6 and 3 compounds) for model validation. The R2 values of the resulting models and data were 0.826 and 0.808 for soybean yield and quality, respectively, and all regression coefficients were significant (P < 0.05). The correlation coefficient R2pred of observed values and predicted values of the soybean yield and soybean quality in the test set were 0.961 and 0.956, respectively, indicating that the models had a good predictive ability. Moreover, the Mo, Se, K, N and organic matter contents and the cation exchange capacity of soil had a positive effect on soybean production, and the B, Mo, Se, K and N contents and cation exchange coefficient had a positive effect on soybean quality. The results are instructive for enhancing soils to improve the yield and quality of soybean, and this method can also be used to study other crops or regions, providing a theoretical basis to improving the yield and quality of crops.

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

COMPUTER PROGRAM DOCUMENTATION FOR THE ENHANCED STREAM WATER QUALITY MODEL QUAL2E

EPA Science Inventory

Presented in the manual are recent modifications and improvements to the widely used stream water quality model QUAL-II. Called QUAL2E, the enhanced model incorporates improvements in eight areas: (1) algal, nitrogen, phosphorus, and dissolved oxygen interactions; (2) algal growt...

Simulating the fate of water in field soil crop environment

NASA Astrophysics Data System (ADS)

Cameira, M. R.; Fernando, R. M.; Ahuja, L.; Pereira, L.

2005-12-01

This paper presents an evaluation of the Root Zone Water Quality Model(RZWQM) for assessing the fate of water in the soil-crop environment at the field scale under the particular conditions of a Mediterranean region. The RZWQM model is a one-dimensional dual porosity model that allows flow in macropores. It integrates the physical, biological and chemical processes occurring in the root zone, allowing the simulation of a wide spectrum of agricultural management practices. This study involved the evaluation of the soil, hydrologic and crop development sub-models within the RZWQM for two distinct agricultural systems, one consisting of a grain corn planted in a silty loam soil, irrigated by level basins and the other a forage corn planted in a sandy soil, irrigated by sprinklers. Evaluation was performed at two distinct levels. At the first level the model capability to fit the measured data was analyzed (calibration). At the second level the model's capability to extrapolate and predict the system behavior for conditions different than those used when fitting the model was assessed (validation). In a subsequent paper the same type of evaluation is presented for the nitrogen transformation and transport model. At the first level a change in the crop evapotranspiration (ETc) formulation was introduced, based upon the definition of the effective leaf area, resulting in a 51% decrease in the root mean square error of the ETc simulations. As a result the simulation of the root water uptake was greatly improved. A new bottom boundary condition was implemented to account for the presence of a shallow water table. This improved the simulation of the water table depths and consequently the soil water evolution within the root zone. The soil hydraulic parameters and the crop variety specific parameters were calibrated in order to minimize the simulation errors of soil water and crop development. At the second level crop yield was predicted with an error of 1.1 and 2.8% for

A protocol for parameterization and calibration of RZWQM2 in field research

USDA-ARS?s Scientific Manuscript database

Use of agricultural system models in field research requires a full understanding of both the model and the system it simulates. Since the 1960s, agricultural system models have increased tremendously in their complexity due to greater understanding of the processes simulated, their application to r...

STREAM WATER QUALITY MODEL

EPA Science Inventory

QUAL2K (or Q2K) is a river and stream water quality model that is intended to represent a modernized version of the QUAL2E (or Q2E) model (Brown and Barnwell 1987). Q2K is similar to Q2E in the following respects:

• One dimensional. The channel is well-mixed vertically a...

Overview and Evaluation of the Community Multiscale Air Quality (CMAQ) Modeling System Version 5.2

EPA Science Inventory

A new version of the Community Multiscale Air Quality (CMAQ) model, version 5.2 (CMAQv5.2), is currently being developed, with a planned release date in 2017. The new model includes numerous updates from the previous version of the model (CMAQv5.1). Specific updates include a new...

ENHANCED STREAM WATER QUALITY MODELS QUAL2E AND QUAL2E-UNCAS: DOCUMENTATION AND USER MANUAL

EPA Science Inventory

The manual is a major revision of the original QUAL2E program documentation released in 1985. It includes a description of the recent modifications and improvements to the widely used water quality models QUAL-II and QUAL2E. The enhancements include an extensive capability for un...

Water quality modeling for urban reach of Yamuna river, India (1999-2009), using QUAL2Kw

NASA Astrophysics Data System (ADS)

Sharma, Deepshikha; Kansal, Arun; Pelletier, Greg

2017-06-01

The study was to characterize and understand the water quality of the river Yamuna in Delhi (India) prior to an efficient restoration plan. A combination of collection of monitored data, mathematical modeling, sensitivity, and uncertainty analysis has been done using the QUAL2Kw, a river quality model. The model was applied to simulate DO, BOD, total coliform, and total nitrogen at four monitoring stations, namely Palla, Old Delhi Railway Bridge, Nizamuddin, and Okhla for 10 years (October 1999-June 2009) excluding the monsoon seasons (July-September). The study period was divided into two parts: monthly average data from October 1999-June 2004 (45 months) were used to calibrate the model and monthly average data from October 2005-June 2009 (45 months) were used to validate the model. The R2 for CBODf and TN lies within the range of 0.53-0.75 and 0.68-0.83, respectively. This shows that the model has given satisfactory results in terms of R2 for CBODf, TN, and TC. Sensitivity analysis showed that DO, CBODf, TN, and TC predictions are highly sensitive toward headwater flow and point source flow and quality. Uncertainty analysis using Monte Carlo showed that the input data have been simulated in accordance with the prevalent river conditions.

Evaluation of the Community Multiscale Air Quality (CMAQ) Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Evaluation of the Community Multi-scale Air Quality Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Predicting unsaturated zone nitrogen mass balances in agricultural settings of the United States

USGS Publications Warehouse

Nolan, Bernard T.; Puckett, Larry J.; Ma, Liwang; Green, Christopher T.; Bayless, E. Randall; Malone, Robert W.

2009-01-01

Unsaturated zone N fate and transport were evaluated at four sites to identify the predominant pathways of N cycling: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard and cornfield (Zea mays L.) in the lower Merced River study basin, California; and corn–soybean [Glycine max (L.) Merr.] rotations in study basins at Maple Creek, Nebraska, and at Morgan Creek, Maryland. We used inverse modeling with a new version of the Root Zone Water Quality Model (RZWQM2) to estimate soil hydraulic and nitrogen transformation parameters throughout the unsaturated zone; previous versions were limited to 3-m depth and relied on manual calibration. The overall goal of the modeling was to derive unsaturated zone N mass balances for the four sites. RZWQM2 showed promise for deeper simulation profiles. Relative root mean square error (RRMSE) values for predicted and observed nitrate concentrations in lysimeters were 0.40 and 0.52 for California (6.5 m depth) and Nebraska (10 m), respectively, and index of agreement (d) values were 0.60 and 0.71 (d varies between 0 and 1, with higher values indicating better agreement). For the shallow simulation profile (1 m) in Maryland, RRMSE and d for nitrate were 0.22 and 0.86, respectively. Except for Nebraska, predictions of average nitrate concentration at the bottom of the simulation profile agreed reasonably well with measured concentrations in monitoring wells. The largest additions of N were predicted to come from inorganic fertilizer (153–195 kg N ha−1 yr−1 in California) and N fixation (99 and 131 kg N ha−1 yr−1 in Maryland and Nebraska, respectively). Predicted N losses occurred primarily through plant uptake (144–237 kg N ha−1 yr−1) and deep seepage out of the profile (56–102 kg N ha−1 yr−1). Large reservoirs of organic N (up to 17,500 kg N ha−1 m−1 at Nebraska) were predicted to reside in the unsaturated zone, which has implications for potential future transfer of nitrate to groundwater.

Reflexion on linear regression trip production modelling method for ensuring good model quality

NASA Astrophysics Data System (ADS)

Suprayitno, Hitapriya; Ratnasari, Vita

2017-11-01

Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.

Overview and Evaluation of the Community Multiscale Air Quality Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Evaluation of the Community Multi-scale Air Quality (CMAQ) Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

A Multi-Model Assessment for the 2006 and 2010 Simulations under the AirQuality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part I. Indicators of the Sensitivity of O3 and PM2.5 Formation Regimes

EPA Science Inventory

Under the Air Quality Model Evaluation International Initiative, Phase 2 (AQMEII-2), three online coupled air quality model simulations, with six different configurations, are analyzed for their performance, inter-model agreement, and responses to emission and meteorological chan...

[Coupling SWAT and CE-QUAL-W2 models to simulate water quantity and quality in Shanmei Reservoir watershed].

PubMed

Liu, Mei-Bing; Chen, Dong-Ping; Chen, Xing-Wei; Chen, Ying

2013-12-01

A coupled watershed-reservoir modeling approach consisting of a watershed distributed model (SWAT) and a two-dimensional laterally averaged model (CE-QUAL-W2) was adopted for simulating the impact of non-point source pollution from upland watershed on water quality of Shanmei Reservoir. Using the daily serial output from Shanmei Reservoir watershed by SWAT as the input to Shanmei Reservoir by CE-QUAL-W2, the coupled modeling was calibrated for runoff and outputs of sediment and pollutant at watershed scale and for elevation, temperature, nitrate, ammonium and total nitrogen in Shanmei Reservoir. The results indicated that the simulated values agreed fairly well with the observed data, although the calculation precision of downstream model would be affected by the accumulative errors generated from the simulation of upland model. The SWAT and CE-QUAL-W2 coupled modeling could be used to assess the hydrodynamic and water quality process in complex watershed comprised of upland watershed and downstream reservoir, and might further provide scientific basis for positioning key pollution source area and controlling the reservoir eutrophication.

Comparing measured and modelled soil carbon: which site-specific variables are linked to high stability?

NASA Astrophysics Data System (ADS)

Robertson, Andy; Schipanski, Meagan; Ma, Liwang; Ahuja, Lajpat; McNamara, Niall; Smith, Pete; Davies, Christian

2016-04-01

Changes in soil carbon (C) stocks have been studied in depth over the last two decades, as net greenhouse gas (GHG) sinks are highlighted to be a partial solution to the causes of climate change. However, the stability of this soil C is often overlooked when measuring these changes. Ultimately a net sequestration in soils is far less beneficial if labile C is replacing more stable forms. To date there is no accepted framework for measuring soil C stability, and as a result there is considerable uncertainty associated with the simulated impacts of land management and land use change when using process-based systems models. However, a recent effort to equate measurable soil C fractions to model pools has generated data that help to assess the impacts of land management, and can ultimately help to reduce the uncertainty of model predictions. Our research compiles this existing fractionation data along with site metadata to create a simplistic statistical model able to quantify the relative importance of different site-specific conditions. Data was mined from 23 published studies and combined with original data to generate a dataset of 100+ land use change sites across Europe. For sites to be included they required soil C fractions isolated using the Zimmermann et al. (2007) method and specific site metadata (mean annual precipitation, MAP; mean annual temperature, MAT; soil pH; land use; altitude). Of the sites, 75% were used to develop a generalized linear mixed model (GLMM) to create coefficients where site parameters can be used to predict influence on the measured soil fraction C stocks. The remaining 25% of sites were used to evaluate uncertainty and validate this empirical model. Further, four of the aforementioned sites were used to simulate soil C dynamics using the RothC, DayCent and RZWQM2 models. A sensitivity analysis (4096 model runs for each variable applying Latin hypercube random sampling techniques) was then used to observe whether these models place

Quality of care for patients with diabetes mellitus type 2 in 'model practices' in Slovenia - first results.

PubMed

Petek, Davorina; Mlakar, Mitja

2016-09-01

A new organisation at the primary level, called model practices, introduces a 0.5 full-time equivalent nurse practitioner as a regular member of the team. Nurse practitioners are in charge of registers of chronic patients, and implement an active approach into medical care. Selected quality indicators define the quality of management. The majority of studies confirm the effectiveness of the extended team in the quality of care, which is similar or improved when compared to care performed by the physician alone. The aim of the study is to compare the quality of management of patients with diabetes mellitus type 2 before and after the introduction of model practices. A cohort retrospective study was based on medical records from three practices. Process quality indicators, such as regularity of HbA1c measurement, blood pressure measurement, foot exam, referral to eye exam, performance of yearly laboratory tests and HbA1c level before and after the introduction of model practices were compared. The final sample consisted of 132 patients, whose diabetes care was exclusively performed at the primary care level. The process of care has significantly improved after the delivery of model practices. The most outstanding is the increase of foot exam and HbA1c testing. We could not prove better glycaemic control (p>0.1). Nevertheless, the proposed benchmark for the suggested quality process and outcome indicators were mostly exceeded in this cohort. The introduction of a nurse into the team improves the process quality of care. Benchmarks for quality indicators are obtainable. Better outcomes of care need further confirmation.

Information Quality Evaluation of C2 Systems at Architecture Level

DTIC Science & Technology

2014-06-01

based on architecture models of C2 systems, which can help to identify key factors impacting information quality and improve the system capability at the stage of architecture design of C2 system....capability evaluation of C2 systems at architecture level becomes necessary and important for improving the system capability at the stage of architecture ... design . This paper proposes a method for information quality evaluation of C2 system at architecture level. First, the information quality model is

Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models

USGS Publications Warehouse

Smith, Erik A.; Kiesling, Richard L.; Galloway, Joel M.; Ziegeweid, Jeffrey R.

2014-01-01

Water quality, habitat, and fish in Minnesota lakes will potentially be facing substantial levels of stress in the coming decades primarily because of two stressors: (1) land-use change (urban and agricultural) and (2) climate change. Several regional and statewide lake modeling studies have identified the potential linkages between land-use and climate change on reductions in the volume of suitable lake habitat for coldwater fish populations. In recent years, water-resource scientists have been making the case for focused assessments and monitoring of sentinel systems to address how these stress agents change lakes over the long term. Currently in Minnesota, a large-scale effort called “Sustaining Lakes in a Changing Environment” is underway that includes a focus on monitoring basic watershed, water quality, habitat, and fish indicators of 24 Minnesota sentinel lakes across a gradient of ecoregions, depths, and nutrient levels. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, developed predictive water quality models to assess water quality and habitat dynamics of three select deepwater lakes in Minnesota. The three lakes (Lake Carlos in Douglas County, Elk Lake in Clearwater County, and Trout Lake in Cook County) were assessed under recent (2010–11) meteorological conditions. The three selected lakes contain deep, coldwater habitats that remain viable during the summer months for coldwater fish species. Hydrodynamics and water-quality characteristics for each of the three lakes were simulated using the CE-QUAL-W2 model, which is a carbon-based, laterally averaged, two-dimensional water-quality model. The CE-QUAL-W2 models address the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of temperature and oxygen in lakes. The CE-QUAL-W2 models for all three lakes successfully predicted water temperature, on the basis of the

Regionalized PM2.5 Community Multiscale Air Quality model performance evaluation across a continuous spatiotemporal domain.

PubMed

Reyes, Jeanette M; Xu, Yadong; Vizuete, William; Serre, Marc L

2017-01-01

The regulatory Community Multiscale Air Quality (CMAQ) model is a means to understanding the sources, concentrations and regulatory attainment of air pollutants within a model's domain. Substantial resources are allocated to the evaluation of model performance. The Regionalized Air quality Model Performance (RAMP) method introduced here explores novel ways of visualizing and evaluating CMAQ model performance and errors for daily Particulate Matter ≤ 2.5 micrometers (PM2.5) concentrations across the continental United States. The RAMP method performs a non-homogenous, non-linear, non-homoscedastic model performance evaluation at each CMAQ grid. This work demonstrates that CMAQ model performance, for a well-documented 2001 regulatory episode, is non-homogeneous across space/time. The RAMP correction of systematic errors outperforms other model evaluation methods as demonstrated by a 22.1% reduction in Mean Square Error compared to a constant domain wide correction. The RAMP method is able to accurately reproduce simulated performance with a correlation of r = 76.1%. Most of the error coming from CMAQ is random error with only a minority of error being systematic. Areas of high systematic error are collocated with areas of high random error, implying both error types originate from similar sources. Therefore, addressing underlying causes of systematic error will have the added benefit of also addressing underlying causes of random error.

Development of an Instructional Quality Assurance Model in Nursing Science

ERIC Educational Resources Information Center

Ajpru, Haruthai; Pasiphol, Shotiga; Wongwanich, Suwimon

2011-01-01

The purpose of this study was to develop an instructional quality assurance model in nursing science. The study was divided into 3 phases; (1) to study the information for instructional quality assurance model development (2) to develop an instructional quality assurance model in nursing science and (3) to audit and the assessment of the developed…

Global warming likely reduces crop yield and water availability of the dryland cropping systems in the U.S. central Great Plains

USDA-ARS?s Scientific Manuscript database

We investigated impacts of GCM-projected climate change on dryland crop rotations of wheat-fallow and wheat-corn-fallow in the Central Great Plains (Akron in Colorado, USA) using the CERES 4.0 crop modules in RZWQM2. The climate change scenarios for CO2, temperature, and precipitation were produced ...

Quality models for audiovisual streaming

NASA Astrophysics Data System (ADS)

Thang, Truong Cong; Kim, Young Suk; Kim, Cheon Seog; Ro, Yong Man

2006-01-01

Quality is an essential factor in multimedia communication, especially in compression and adaptation. Quality metrics can be divided into three categories: within-modality quality, cross-modality quality, and multi-modality quality. Most research has so far focused on within-modality quality. Moreover, quality is normally just considered from the perceptual perspective. In practice, content may be drastically adapted, even converted to another modality. In this case, we should consider the quality from semantic perspective as well. In this work, we investigate the multi-modality quality from the semantic perspective. To model the semantic quality, we apply the concept of "conceptual graph", which consists of semantic nodes and relations between the nodes. As an typical of multi-modality example, we focus on audiovisual streaming service. Specifically, we evaluate the amount of information conveyed by a audiovisual content where both video and audio channels may be strongly degraded, even audio are converted to text. In the experiments, we also consider the perceptual quality model of audiovisual content, so as to see the difference with semantic quality model.

Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

EPA Pesticide Factsheets

The uploaded data consists of the BRACE Na aerosol observations paired with CMAQ model output, the updated model's parameterization of sea salt aerosol emission size distribution, and the model's parameterization of the sea salt emission factor as a function of sea surface temperature. This dataset is associated with the following publication:Gantt , B., J. Kelly , and J. Bash. Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 8: 3733-3746, (2015).

The influence of synoptic weather regimes on UK air quality: regional model studies of tropospheric column NO2

NASA Astrophysics Data System (ADS)

Pope, R. J.; Savage, N. H.; Chipperfield, M. P.; Ordóñez, C.; Neal, L. S.

2015-07-01

Synoptic meteorology can have a significant influence on UK air quality. Cyclonic (anticyclonic) conditions lead to the dispersion (accumulation) of air pollutants away from (over) source regions. Meteorology also modifies atmospheric chemistry processes such as photolysis and wet deposition. Previous studies have shown a relationship between observed satellite tropospheric column NO2 and synoptic meteorology in different seasons. Here, we test whether the UK Met Office Air Quality in the Unified Model (AQUM) can reproduce these observations and then use the model to determine the controlling factors. We show that AQUM successfully captures the observed relationships, when sampled under the Lamb Weather Types, an objective classification of midday UK circulation patterns. By using a range of idealised NOx-like tracers with different e-folding lifetimes, we show that under different synoptic regimes the NO2 lifetime in AQUM is approximately 6 h in summer and 12 h in winter. The longer lifetime can explain why synoptic spatial column NO2 variations are more significant in winter compared to summer, due to less NO2 photochemical loss. We also show that cyclonic conditions have more seasonality in column NO2 than anticyclonic conditions as they result in more extreme spatial departures from the wintertime seasonal average. Within a season (summer or winter) under different synoptic regimes, a large proportion of the spatial pattern in the UK column NO2 field can be explained by the idealised model tracers, showing that transport is an important factor in governing the variability of UK air quality on seasonal synoptic timescales.

An innovative modeling approach using Qual2K and HEC-RAS integration to assess the impact of tidal effect on River Water quality simulation.

PubMed

Fan, Chihhao; Ko, Chun-Han; Wang, Wei-Shen

2009-04-01

Water quality modeling has been shown to be a useful tool in strategic water quality management. The present study combines the Qual2K model with the HEC-RAS model to assess the water quality of a tidal river in northern Taiwan. The contaminant loadings of biochemical oxygen demand (BOD), ammonia nitrogen (NH(3)-N), total phosphorus (TP), and sediment oxygen demand (SOD) are utilized in the Qual2K simulation. The HEC-RAS model is used to: (i) estimate the hydraulic constants for atmospheric re-aeration constant calculation; and (ii) calculate the water level profile variation to account for concentration changes as a result of tidal effect. The results show that HEC-RAS-assisted Qual2K simulations taking tidal effect into consideration produce water quality indices that, in general, agree with the monitoring data of the river. Comparisons of simulations with different combinations of contaminant loadings demonstrate that BOD is the most import contaminant. Streeter-Phelps simulation (in combination with HEC-RAS) is also performed for comparison, and the results show excellent agreement with the observed data. This paper is the first report of the innovative use of a combination of the HEC-RAS model and the Qual2K model (or Streeter-Phelps equation) to simulate water quality in a tidal river. The combination is shown to provide an alternative for water quality simulation of a tidal river when available dynamic-monitoring data are insufficient to assess the tidal effect of the river.

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period.

PubMed

Gokhale, Sharad; Raokhande, Namita

2008-05-01

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the 'modified General Finite Line Source Model' (M-GFLSM) of particulates, the 'California Line Source' (CALINE3) model, and the 'California Line Source for Queuing & Hot Spot Calculations' (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime. The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s(-1)), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.

Putting people into water quality modelling.

NASA Astrophysics Data System (ADS)

Strickert, G. E.; Hassanzadeh, E.; Noble, B.; Baulch, H. M.; Morales-Marin, L. A.; Lindenschmidt, K. E.

2017-12-01

Water quality in the Qu'Appelle River Basin, Saskatchewan is under pressure due to nutrient pollution entering the river system from major cities, industrial zones and agricultural areas. Among these stressors, agricultural activities are basin-wide; therefore, they are the largest non-point source of water pollution in this region. The dynamics of agricultural impacts on water quality are complex and stem from decisions and activities of two distinct stakeholder groups, namely grain farmers and cattle producers, which have different business plans, values, and attitudes towards water quality. As a result, improving water quality in this basin requires engaging with stakeholders to: (1) understand their perspectives regarding a range of agricultural Beneficial Management Practices (BMPs) that can improve water quality in the region, (2) show them the potential consequences of their selected BMPs, and (3) work with stakeholders to better understand the barriers and incentives to implement the effective BMPs. In this line, we held a series of workshops in the Qu'Appelle River Basin with both groups of stakeholders to understand stakeholders' viewpoints about alternative agricultural BMPs and their impact on water quality. Workshop participants were involved in the statement sorting activity (Q-sorts), group discussions, as well as mapping activity. The workshop outcomes show that stakeholder had four distinct viewpoints about the BMPs that can improve water quality, i.e., flow and erosion control, fertilizer management, cattle site management, as well as mixed cattle and wetland management. Accordingly, to simulate the consequences of stakeholder selected BMPs, a conceptual water quality model was developed using System Dynamics (SD). The model estimates potential changes in water quality at the farm, tributary and regional scale in the Qu'Appelle River Basin under each and/or combination of stakeholder selected BMPs. The SD model was then used for real

New Methods for Air Quality Model Evaluation with Satellite Data

NASA Astrophysics Data System (ADS)

Holloway, T.; Harkey, M.

2015-12-01

Despite major advances in the ability of satellites to detect gases and aerosols in the atmosphere, there remains significant, untapped potential to apply space-based data to air quality regulatory applications. Here, we showcase research findings geared toward increasing the relevance of satellite data to support operational air quality management, focused on model evaluation. Particular emphasis is given to nitrogen dioxide (NO2) and formaldehyde (HCHO) from the Ozone Monitoring Instrument aboard the NASA Aura satellite, and evaluation of simulations from the EPA Community Multiscale Air Quality (CMAQ) model. This work is part of the NASA Air Quality Applied Sciences Team (AQAST), and is motivated by ongoing dialog with state and federal air quality management agencies. We present the response of satellite-derived NO2 to meteorological conditions, satellite-derived HCHO:NO2 ratios as an indicator of ozone production regime, and the ability of models to capture these sensitivities over the continental U.S. In the case of NO2-weather sensitivities, we find boundary layer height, wind speed, temperature, and relative humidity to be the most important variables in determining near-surface NO2 variability. CMAQ agreed with relationships observed in satellite data, as well as in ground-based data, over most regions. However, we find that the southwest U.S. is a problem area for CMAQ, where modeled NO2 responses to insolation, boundary layer height, and other variables are at odds with the observations. Our analyses utilize a software developed by our team, the Wisconsin Horizontal Interpolation Program for Satellites (WHIPS): a free, open-source program designed to make satellite-derived air quality data more usable. WHIPS interpolates level 2 satellite retrievals onto a user-defined fixed grid, in effect creating custom-gridded level 3 satellite product. Currently, WHIPS can process the following data products: OMI NO2 (NASA retrieval); OMI NO2 (KNMI retrieval); OMI

The influence of synoptic weather regimes on UK air quality: regional model studies of tropospheric column NO2

NASA Astrophysics Data System (ADS)

Pope, R. J.; Savage, N. H.; Chipperfield, M. P.; Ordóñez, C.; Neal, L. S.

2015-10-01

Synoptic meteorology can have a significant influence on UK air quality. Cyclonic conditions lead to the dispersion of air pollutants away from source regions, while anticyclonic conditions lead to their accumulation over source regions. Meteorology also modifies atmospheric chemistry processes such as photolysis and wet deposition. Previous studies have shown a relationship between observed satellite tropospheric column NO2 and synoptic meteorology in different seasons. Here, we test whether the UK Met Office Air Quality in the Unified Model (AQUM) can reproduce these observations and then use the model to explore the relative importance of various factors. We show that AQUM successfully captures the observed relationships when sampled under the Lamb weather types, an objective classification of midday UK circulation patterns. By using a range of idealized NOx-like tracers with different e-folding lifetimes, we show that under different synoptic regimes the NO2 lifetime in AQUM is approximately 6 h in summer and 12 h in winter. The longer lifetime can explain why synoptic spatial tropospheric column NO2 variations are more significant in winter compared to summer, due to less NO2 photochemical loss. We also show that cyclonic conditions have more seasonality in tropospheric column NO2 than anticyclonic conditions as they result in more extreme spatial departures from the wintertime seasonal average. Within a season (summer or winter) under different synoptic regimes, a large proportion of the spatial pattern in the UK tropospheric column NO2 field can be explained by the idealized model tracers, showing that transport is an important factor in governing the variability of UK air quality on seasonal synoptic timescales.

Air Quality Modeling | Air Quality Planning & Standards | US ...

EPA Pesticide Factsheets

2016-06-08

The basic mission of the Office of Air Quality Planning and Standards is to preserve and improve the quality of our nation's air. One facet of accomplishing this goal requires that new and existing air pollution sources be modeled for compliance with the National Ambient Air Quality Standards (NAAQS).

Quality of care for patients with diabetes mellitus type 2 in ‘model practices’ in Slovenia – first results

PubMed Central

Mlakar, Mitja

2016-01-01

Abstract Background A new organisation at the primary level, called model practices, introduces a 0.5 full-time equivalent nurse practitioner as a regular member of the team. Nurse practitioners are in charge of registers of chronic patients, and implement an active approach into medical care. Selected quality indicators define the quality of management. The majority of studies confirm the effectiveness of the extended team in the quality of care, which is similar or improved when compared to care performed by the physician alone. The aim of the study is to compare the quality of management of patients with diabetes mellitus type 2 before and after the introduction of model practices. Methods A cohort retrospective study was based on medical records from three practices. Process quality indicators, such as regularity of HbA1c measurement, blood pressure measurement, foot exam, referral to eye exam, performance of yearly laboratory tests and HbA1c level before and after the introduction of model practices were compared. Results The final sample consisted of 132 patients, whose diabetes care was exclusively performed at the primary care level. The process of care has significantly improved after the delivery of model practices. The most outstanding is the increase of foot exam and HbA1c testing. We could not prove better glycaemic control (p>0.1). Nevertheless, the proposed benchmark for the suggested quality process and outcome indicators were mostly exceeded in this cohort. Conclusion The introduction of a nurse into the team improves the process quality of care. Benchmarks for quality indicators are obtainable. Better outcomes of care need further confirmation. PMID:27703537

Higher Education Quality Assessment Model: Towards Achieving Educational Quality Standard

ERIC Educational Resources Information Center

Noaman, Amin Y.; Ragab, Abdul Hamid M.; Madbouly, Ayman I.; Khedra, Ahmed M.; Fayoumi, Ayman G.

2017-01-01

This paper presents a developed higher education quality assessment model (HEQAM) that can be applied for enhancement of university services. This is because there is no universal unified quality standard model that can be used to assess the quality criteria of higher education institutes. The analytical hierarchy process is used to identify the…

Treated wastewater and Nitrate transport beneath irrigated fields near Dodge city, Kansas

USGS Publications Warehouse

Sophocleous, M.; Townsend, M.A.; Vocasek, F.; Ma, Liwang; Ashok, K.C.

2010-01-01

Use of secondary-treated municipal wastewater for crop irrigation south of Dodge City, Kansas, where the soils are mainly of silty clay loam texture, has raised a concern that it has resulted in high nitratenitrogen concentrations (10-50 mg/kg) in the soil and deeper vadose zone, and also in the underlying deep (20-45 m) ground water. The goal of this field-monitoring project was to assess how and under what circumstances nitrogen (N) nutrients under cultivated corn that is irrigated with this treated wastewater can reach the deep ground water of the underlying High Plains aquifer, and what can realistically be done to minimize this problem. We collected 15.2-m-deep cores for physical and chemical properties characterization; installed neutron moisture-probe access tubes and suction lysimeters for periodic measurements; sampled area monitoring, irrigation, and domestic wells; performed dye-tracer experiments to examine soil preferential-flow processes through macropores; and obtained climatic, crop, irrigation, and N-application rate records. These data and additional information were used in the comprehensive Root Zone Water Quality Model (RZWQM2) to identify key parameters and processes that influence N losses in the study area. We demonstrated that nitrate-N transport processes result in significant accumulations of N in the thick vadose zone. We also showed that nitrate-N in the underlying ground water is increasing with time and that the source of the nitrate is from the wastewater applications. RZWQM2 simulations indicated that macropore flow is generated particularly during heavy rainfall events, but during our 2005-06 simulations the total macropore flow was only about 3% of precipitation for one of two investigated sites, whereas it was more than 13% for the other site. Our calibrated model for the two wastewater-irrigated study sites indicated that reducing current levels of corn N fertilization by half or more to the level of 170 kg/ha substantially

Implications of Modeling Uncertainty for Water Quality Decision Making

NASA Astrophysics Data System (ADS)

Shabman, L.

2002-05-01

The report, National Academy of Sciences report, "Assessing the TMDL Approach to Water Quality Management" endorsed the "watershed" and "ambient water quality focused" approach" to water quality management called for in the TMDL program. The committee felt that available data and models were adequate to move such a program forward, if the EPA and all stakeholders better understood the nature of the scientific enterprise and its application to the TMDL program. Specifically, the report called for a greater acknowledgement of model prediction uncertinaity in making and implementing TMDL plans. To assure that such uncertinaity was addressed in water quality decision making the committee called for a commitment to "adaptive implementation" of water quality management plans. The committee found that the number and complexity of the interactions of multiple stressors, combined with model prediction uncertinaity means that we need to avoid the temptation to make assurances that specific actions will result in attainment of particular water quality standards. Until the work on solving a water quality problem begins, analysts and decision makers cannot be sure what the correct solutions are, or even what water quality goals a community should be seeking. In complex systems we need to act in order to learn; adaptive implementation is a concurrent process of action and learning. Learning requires (1) continued monitoring of the waterbody to determine how it responds to the actions taken and (2) carefully designed experiments in the watershed. If we do not design learning into what we attempt we are not doing adaptive implementation. Therefore, there needs to be an increased commitment to monitoring and experiments in watersheds that will lead to learning. This presentation will 1) explain the logic for adaptive implementation; 2) discuss the ways that water quality modelers could characterize and explain model uncertinaity to decision makers; 3) speculate on the implications

A model for predicting air quality along highways.

DOT National Transportation Integrated Search

1973-01-01

The subject of this report is an air quality prediction model for highways, AIRPOL Version 2, July 1973. AIRPOL has been developed by modifying the basic Gaussian approach to gaseous dispersion. The resultant model is smooth and continuous throughout...

Information quality-control model

NASA Technical Reports Server (NTRS)

Vincent, D. A.

1971-01-01

Model serves as graphic tool for estimating complete product objectives from limited input information, and is applied to cost estimations, product-quality evaluations, and effectiveness measurements for manpower resources allocation. Six product quality levels are defined.

[Hyperspectral Remote Sensing Estimation Models for Pasture Quality].

PubMed

Ma, Wei-wei; Gong, Cai-lan; Hu, Yong; Wei, Yong-lin; Li, Long; Liu, Feng-yi; Meng, Peng

2015-10-01

Crude protein (CP), crude fat (CFA) and crude fiber (CFI) are key indicators for evaluation of the quality and feeding value of pasture. Hence, identification of these biological contents is an essential practice for animal husbandry. As current approaches to pasture quality estimation are time-consuming and costly, and even generate hazardous waste, a real-time and non-destructive method is therefore developed in this study using pasture canopy hyperspectral data. A field campaign was carried out in August 2013 around Qinghai Lake in order to obtain field spectral properties of 19 types of natural pasture using the ASD Field Spec 3, a field spectrometer that works in the optical region (350-2 500 nm) of the electromagnetic spectrum. In additional to the spectral data, pasture samples were also collected from the field and examined in laboratory to measure the relative concentration of CP (%), CFA (%) and CFI (%). After spectral denoising and smoothing, the relationship of pasture quality parameters with the reflectance spectrum, the first derivatives of reflectance (FDR), band ratio and the wavelet coefficients (WCs) was analyzed respectively. The concentration of CP, CFA and CFI of pasture was found closely correlated with FDR with wavebands centered at 424, 1 668, and 918 nm as well as with the low-scale (scale = 2, 4) Morlet, Coiflets and Gassian WCs. Accordingly, the linear, exponential, and polynomial equations between each pasture variable and FDR or WCs were developed. Validation of the developed equations indicated that the polynomial model with an independent variable of Coiflets WCs (scale = 4, wavelength =1 209 nm), the polynomial model with an independent variable of FDR, and the exponential model with an independent variable of FDR were the optimal model for prediction of concentration of CP, CFA and CFI of pasture, respectively. The R2 of the pasture quality estimation models was between 0.646 and 0.762 at the 0.01 significance level. Results suggest

Evaluating Air-Quality Models: Review and Outlook.

NASA Astrophysics Data System (ADS)

Weil, J. C.; Sykes, R. I.; Venkatram, A.

1992-10-01

Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (c about the mean concentration (C) over an ensemble-a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For cC large residuals exist between predicted and observed concentrations, which confuse model evaluations.This paper addresses ways of evaluating model physics in light of the large c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C-from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.In general, c models and predictions of the probability distribution of the fluctuating concentration (c), (c, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that (c approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model-ARAP-that predicts C, c, and (c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications- the uncertainty in the very high and

A Review of Surface Water Quality Models

PubMed Central

Li, Shibei; Jia, Peng; Qi, Changjun; Ding, Feng

2013-01-01

Surface water quality models can be useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body. The modeling results from these models under different pollution scenarios are very important components of environmental impact assessment and can provide a basis and technique support for environmental management agencies to make right decisions. Whether the model results are right or not can impact the reasonability and scientificity of the authorized construct projects and the availability of pollution control measures. We reviewed the development of surface water quality models at three stages and analyzed the suitability, precisions, and methods among different models. Standardization of water quality models can help environmental management agencies guarantee the consistency in application of water quality models for regulatory purposes. We concluded the status of standardization of these models in developed countries and put forward available measures for the standardization of these surface water quality models, especially in developing countries. PMID:23853533

Image quality assessment by preprocessing and full reference model combination

NASA Astrophysics Data System (ADS)

Bianco, S.; Ciocca, G.; Marini, F.; Schettini, R.

2009-01-01

This paper focuses on full-reference image quality assessment and presents different computational strategies aimed to improve the robustness and accuracy of some well known and widely used state of the art models, namely the Structural Similarity approach (SSIM) by Wang and Bovik and the S-CIELAB spatial-color model by Zhang and Wandell. We investigate the hypothesis that combining error images with a visual attention model could allow a better fit of the psycho-visual data of the LIVE Image Quality assessment Database Release 2. We show that the proposed quality assessment metric better correlates with the experimental data.

Air Quality Modeling

EPA Pesticide Factsheets

In this technical support document (TSD) EPA describes the air quality modeling performed to support the Environmental Protection Agency’s Transport Rule proposal (now known as the Cross-State Air Pollution Rule).

Using aircraft and satellite observations to improve regulatory air quality models

NASA Astrophysics Data System (ADS)

Canty, T. P.; Vinciguerra, T.; Anderson, D. C.; Carpenter, S. F.; Goldberg, D. L.; Hembeck, L.; Montgomery, L.; Liu, X.; Salawitch, R. J.; Dickerson, R. R.

2014-12-01

Federal and state agencies rely on EPA approved models to develop attainment strategies that will bring states into compliance with the National Ambient Air Quality Standards (NAAQS). We will describe modifications to the Community Multi-Scale Air Quality (CMAQ) model and Comprehensive Air Quality Model with Extensions (CAMx) frameworks motivated by analysis of NASA satellite and aircraft measurements. Observations of tropospheric column NO2 from OMI have already led to the identification of an important deficiency in the chemical mechanisms used by models; data collected during the DISCOVER-AQ field campaign has been instrumental in devising an improved representation of the chemistry of nitrogen species. Our recent work has focused on the use of: OMI observations of tropospheric O3 to assess and improve the representation of boundary conditions used by AQ models, OMI NO2 to derive a top down NOx emission inventory from commercial shipping vessels that affect air quality in the Eastern U.S., and OMI HCHO to assess the C5H8 emission inventories provided by bioegenic emissions models. We will describe how these OMI-driven model improvements are being incorporated into the State Implementation Plans (SIPs) being prepared for submission to EPA in summer 2015 and how future modeling efforts may be impacted by our findings.

Quality Assurance and Quality Control, Part 2.

PubMed

Akers, Michael J

2015-01-01

The tragedy surrounding the New England Compounding Center and contaminated steroid syringe preparations clearly points out what can happen if quality-assurance and quality-control procedures are not strictly practiced in the compounding of sterile preparations. This article is part 2 of a two-part article on requirements to comply with United States Pharmacopeia general chapters <797> and <1163> with respect to quality assurance of compounded sterile preparations. Part 1 covered documentation requirements, inspection procedures, compounding accuracy checks, and part of a discussion on bacterial endotoxin testing. Part 2 covers sterility testing, the completion from part 1 on bacterial endotoxin testing, a brief dicussion of United States Pharmacopeia <1163>, and advances in pharmaceutical quality systems.

APOLLO: a quality assessment service for single and multiple protein models.

PubMed

Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

2011-06-15

We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Estimating Lightning NOx Emissions for Regional Air Quality Modeling

NASA Astrophysics Data System (ADS)

Holloway, T.; Scotty, E.; Harkey, M.

2014-12-01

Lightning emissions have long been recognized as an important source of nitrogen oxides (NOx) on a global scale, and an essential emission component for global atmospheric chemistry models. However, only in recent years have regional air quality models incorporated lightning NOx emissions into simulations. The growth in regional modeling of lightning emissions has been driven in part by comparisons with satellite-derived estimates of column NO2, especially from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite. We present and evaluate a lightning inventory for the EPA Community Multiscale Air Quality (CMAQ) model. Our approach follows Koo et al. [2010] in the approach to spatially and temporally allocating a given total value based on cloud-top height and convective precipitation. However, we consider alternate total NOx emission values (which translate into alternate lightning emission factors) based on a review of the literature and performance evaluation against OMI NO2 for July 2007 conditions over the U.S. and parts of Canada and Mexico. The vertical distribution of lightning emissions follow a bimodal distribution from Allen et al. [2012] calculated over 27 vertical model layers. Total lightning NO emissions for July 2007 show the highest above-land emissions in Florida, southeastern Texas and southern Louisiana. Although agreement with OMI NO2 across the domain varied significantly depending on lightning NOx assumptions, agreement among the simulations at ground-based NO2 monitors from the EPA Air Quality System database showed no meaningful sensitivity to lightning NOx. Emissions are compared with prior studies, which find similar distribution patterns, but a wide range of calculated magnitudes.

Web 2.0--E-Learning 2.0--Quality 2.0? Quality for New Learning Cultures

ERIC Educational Resources Information Center

Ehlers, Ulf Daniel

2009-01-01

Purpose: The purpose of this paper is to analyse the changes taking place when learning moves from a transmissive learning model to a collaborative and reflective learning model and proposes consequences for quality development. Design/methodology/approach: The paper summarises relevant research in the field of e-learning to outline the…

Improved algorithms in the CE-QUAL-W2 water-quality model for blending dam releases to meet downstream water-temperature targets

USGS Publications Warehouse

Rounds, Stewart A.; Buccola, Norman L.

2015-01-01

Water-quality models allow water resource professionals to examine conditions under an almost unlimited variety of potential future scenarios. The two-dimensional (longitudinal, vertical) water-quality model CE-QUAL-W2, version 3.7, was enhanced and augmented with new features to help dam operators and managers explore and optimize potential solutions for temperature management downstream of thermally stratified reservoirs. Such temperature management often is accomplished by blending releases from multiple dam outlets that access water of different temperatures at different depths. The modified blending algorithm in version 3.7 of CE-QUAL-W2 allows the user to specify a time-series of target release temperatures, designate from 2 to 10 floating or fixed-elevation outlets for blending, impose minimum and maximum head and flow constraints for any blended outlet, and set priority designations for each outlet that allow the model to choose which outlets to use and how to balance releases among them. The modified model was tested with a variety of examples and against a previously calibrated model of Detroit Lake on the North Santiam River in northwestern Oregon, and the results compared well. These updates to the blending algorithms will allow more complicated dam-operation scenarios to be evaluated somewhat automatically with the model, with decreased need for multiple model runs or preprocessing of model inputs to fully characterize the operational constraints.

COMMUNITY MULTISCALE AIR QUALITY ( CMAQ ) MODEL - QUALITY ASSURANCE AND VERSION CONTROL

EPA Science Inventory

This presentation will be given to the EPA Exposure Modeling Workgroup on January 24, 2006. The quality assurance and version control procedures for the Community Multiscale Air Quality (CMAQ) Model are presented. A brief background of CMAQ is given, then issues related to qual...

Better Insight Into Water Resources Management With Integrated Hydrodynamic And Water Quality Models

NASA Astrophysics Data System (ADS)

Debele, B.; Srinivasan, R.; Parlange, J.

2004-12-01

Models have long been used in water resources management to guide decision making and improve understanding of the system. Numerous models of different scales -spatial and temporal - are available. Yet, very few models manage to bridge simulations of hydrological and water quality parameters from both upland watershed and riverine system. Most water quality models, such as QUAL2E and EPD-RIV1 concentrate on the riverine system while CE-QUAL-W2 and WASP models focus on larger waterbodies, such as lakes and reservoirs. On the other hand, the original SWAT model, HSPF and other upland watershed hydrological models simulate agricultural (diffuse) pollution sources with limited number of processes incorporated to handle point source pollutions that emanate from industrial sectors. Such limitations, which are common in most hydrodynamic and water quality models undermine better understanding that otherwise could be uncovered by employing integrated hydrological and water quality models for both upland watershed and riverine system. The SWAT model is a well documented and verified hydrological and water quality model that has been developed to simulate the effects of various management scenarios on the health of the environment in terms of water quantity and quality. Recently, the SWAT model has been extended to include the simulation of hydrodynamic and water quality parameters in the river system. The extended SWAT model (ESWAT) has been further extended to run using diurnally varying (hourly) weather data and produce outputs at hourly timescales. This and other improvements in the ESWAT model have been documented in the current work. Besides, the results from two case studies in Texas will be reported.

Exploring the effects of nitrogen fertilization management alternatives on nitrate loss and crop yields in tile-drained fields in Illinois.

PubMed

Jeong, Hanseok; Bhattarai, Rabin

2018-05-01

It is vital to manage the excessive use of nitrogen (N) fertilizer in corn production, the single largest consumer of N fertilizer in the United States, in order to achieve more sustainable agroecosystems. This study comprehensively explored the effects of N fertilization alternatives on nitrate loss and crop yields using the Root Zone Water Quality Model (RZWQM) in tile-drained fields in central Illinois. The RZWQM was tested for the prediction of tile flow, nitrate loss, and crop yields using eight years (1993-2000) of observed data and showed satisfactory model performances from statistical and graphical evaluations. Our model simulations demonstrated the maximum return to nitrogen (MRTN) rate (193 kgha -1 ), a newly advised N recommendation by the Illinois Nutrient Loss Reduction Strategy (INLRS), can be further reduced. Nitrate loss was reduced by 10.3% and 29.8%, but corn yields decreased by 0.3% and 1.9% at 156 and 150 kgha -1 of N fertilizer rate in the study sites A and E, respectively. Although adjustment of N fertilization timing presented a further reduction in nitrate loss, there was no optimal timing to ensure nitrate loss reduction and corn productivity. For site A, 100% spring application was the most productive and 40% fall, 10% pre-plant, and 50% side dress application generated the lowest nitrate loss. For site E, the conventional N application timing was verified as the best practice in both corn production and nitrate loss reduction. Compared to surface broadcast placement, injected N fertilizer in spring increased corn yield, but may also escalate nitrate loss. This study presented the need of an adaptive N fertilizer management due to the heterogeneity in agricultural systems, and raised the importance of timing and placement of N fertilizer, as well as further reduction in fertilizer rate to devise a better in-field N management practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

The EFQM Excellence Model for Deploying Quality Management: A British-Russian Journey

ERIC Educational Resources Information Center

Steed, Carol; Maslow, Dmitry; Mazaletskaya, Anna

2005-01-01

This paper describes how the Excellence Model[R] developed by the European Foundation for Quality Management (EFQM) can be used and applied within higher education, with practical examples accompanying the Model in a Russian University to raise management quality. (Contains 5 figures, 2 tables, and 1 footnote.)

Combined effects of sleep quality and depression on quality of life in patients with type 2 diabetes.

PubMed

Zhang, Pan; Lou, Peian; Chang, Guiqiu; Chen, Peipei; Zhang, Lei; Li, Ting; Qiao, Cheng

2016-04-05

Poor sleep quality and depression negatively impact the health-related quality of life of patients with type 2 diabetes, but the combined effect of the two factors is unknown. This study aimed to assess the interactive effects of poor sleep quality and depression on the quality of life in patients with type 2 diabetes. Patients with type 2 diabetes (n = 944) completed the Diabetes Specificity Quality of Life scale (DSQL) and questionnaires on sleep quality and depression. The products of poor sleep quality and depression were added to the logistic regression model to evaluate their multiplicative interactions, which were expressed as the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction, and the synergy index (S). Poor sleep quality and depressive symptoms both increased DSQL scores. The co-presence of poor sleep quality and depressive symptoms significantly reduced DSQL scores by a factor of 3.96 on biological interaction measures. The relative excess risk of interaction was 1.08. The combined effect of poor sleep quality and depressive symptoms was observed only in women. Patients with both depressive symptoms and poor sleep quality are at an increased risk of reduction in diabetes-related quality of life, and this risk is particularly high for women due to the interaction effect. Clinicians should screen for and treat sleep difficulties and depressive symptoms in patients with type 2 diabetes.

Innovations in projecting emissions for air quality modeling ...

EPA Pesticide Factsheets

Air quality modeling is used in setting air quality standards and in evaluating their costs and benefits. Historically, modeling applications have projected emissions and the resulting air quality only 5 to 10 years into the future. Recognition that the choice of air quality management strategy has climate change implications is encouraging longer modeling time horizons. However, for multi-decadal time horizons, many questions about future conditions arise. For example, will current population, economic, and land use trends continue, or will we see shifts that may alter the spatial and temporal pattern of emissions? Similarly, will technologies such as building-integrated solar photovoltaics, battery storage, electric vehicles, and CO2 capture emerge as disruptive technologies - shifting how we produce and use energy - or will these technologies achieve only niche markets and have little impact? These are some of the questions that are being evaluated by researchers within the U.S. EPA’s Office of Research and Development. In this presentation, Dr. Loughlin will describe a range of analytical approaches that are being explored. These include: (i) the development of alternative scenarios of the future that can be used to evaluate candidate management strategies over wide-ranging conditions, (ii) the application of energy system models to project emissions decades into the future and to assess the environmental implications of new technologies, (iii) and methodo

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled

Uncertainty, ensembles and air quality dispersion modeling: applications and challenges

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Miller, Erik

The past two decades have seen significant advances in mesoscale meteorological modeling research and applications, such as the development of sophisticated and now widely used advanced mesoscale prognostic models, large eddy simulation models, four-dimensional data assimilation, adjoint models, adaptive and targeted observational strategies, and ensemble and probabilistic forecasts. Some of these advances are now being applied to urban air quality modeling and applications. Looking forward, it is anticipated that the high-priority air quality issues for the near-to-intermediate future will likely include: (1) routine operational forecasting of adverse air quality episodes; (2) real-time high-level support to emergency response activities; and (3) quantification of model uncertainty. Special attention is focused here on the quantification of model uncertainty through the use of ensemble simulations. Application to emergency-response dispersion modeling is illustrated using an actual event that involved the accidental release of the toxic chemical oleum. Both surface footprints of mass concentration and the associated probability distributions at individual receptors are seen to provide valuable quantitative indicators of the range of expected concentrations and their associated uncertainty.

A NEW COMBINED LOCAL AND NON-LOCAL PBL MODEL FOR METEOROLOGY AND AIR QUALITY MODELING

EPA Science Inventory

A new version of the Asymmetric Convective Model (ACM) has been developed to describe sub-grid vertical turbulent transport in both meteorology models and air quality models. The new version (ACM2) combines the non-local convective mixing of the original ACM with local eddy diff...

Innovations in projecting emissions for air quality modeling

EPA Science Inventory

Air quality modeling is used in setting air quality standards and in evaluating their costs and benefits. Historically, modeling applications have projected emissions and the resulting air quality only 5 to 10 years into the future. Recognition that the choice of air quality mana...

Testing a pharmacist-patient relationship quality model among older persons with diabetes.

PubMed

Worley, Marcia M

2006-03-01

Considering recent changes to the Medicare program, pharmacists will have unique opportunities to be reimbursed for providing Medication Therapy Management Services to older persons with diabetes. A high-quality pharmacist-patient relationship can lay the foundation for effective provision of Medication Therapy Management Services and improved care in this cohort. To test a pharmacist-patient relationship quality model in a group of older persons with diabetes from the patient's perspective. Antecedents to relationship quality were pharmacist participative behavior/patient-centeredness of relationship, patient participative behavior, and pharmacist-patient interpersonal communication. Pharmacist-patient relationship commitment was the outcome of relationship quality studied. Data were collected via mailed questionnaire from a random sample of 600 community-dwelling adults in the United States who (1) were 65 years of age and older, (2) had type 1 or type 2 diabetes, (3) used at least one prescription medication to treat their diabetes, and (4) used some type of nonmail order pharmacy as their primary source of obtaining prescription medications. Model relationships were tested using path analysis. The adjusted response rate was 41.6% (221/531). The models explained 47% and 49% of the variance in relationship quality and relationship commitment, respectively. In the relationship quality model, pharmacist participative behavior/patient-centeredness of relationship (beta=.51, P<.001) and pharmacist-patient interpersonal communication (beta=.17, P=.008) had direct effects on relationship quality. In the relationship commitment model, relationship quality had a direct effect on relationship commitment (beta=.60, P<.001). Pharmacist participative behavior/patient-centeredness and pharmacist-patient interpersonal communication had indirect effects on relationship commitment through their effects on relationship quality, which is a mediator in the model. Results affirm

Biological interaction between sleep quality and depression in type 2 diabetes.

PubMed

Zhao, J; Li, X-L; Han, K; Tao, Z-Q; Wu, Z-M

2016-07-01

To explore the interaction of sleep quality and depression among patients with type 2 diabetes mellitus (T2DM). With multistage cluster sampling, the living quality of all participants was investigated. The indicator of interaction was calculated according to the delta method and non-conditional logistic regression model. There were 944 residents involved in the final analysis including 365 males and 579 females. The average age was (64 ± 10.2) years. The rate of poor sleep quality and poor sleep quality combined depression were 33.6% and 40.1%, respectively. Due to poor sleep quality and depression in patients with T2DM, the combined interaction index was 2.48 (95% CI 1.44-4.29), the relative excess risk was 3.42 (95% CI 2.16-4.67), and the attributable proportion was 0.51 (95% CI 0.32-0.70). An additive interaction rather than a multiplicative interaction of poor sleep quality and depression in affecting the quality of life was found in T2DM patients. When both factors existed at the same time, the interaction effect of these 2 factors was greater than the sum of the two factors.

Investigating the Impact of Marine Ship Emissions on Regional Air Quality using OMI Satellite NO2 Observations and the CMAQ Model

NASA Astrophysics Data System (ADS)

Ring, A.; Canty, T. P.; He, H.; Vinciguerra, T.; Lamsal, L. N.; Dickerson, R. R.; Salawitch, R. J.; Cohen, M.; Montgomery, L. N.; Dreessen, J.

2015-12-01

Commercial marine vessels (CMVs) emit significant amounts of NOx, an ozone precursor, which may contribute to negative health consequences for people living in areas near shipping lanes. In coastal US states, many metropolitan areas such as Baltimore and New York City are located near ports with CMVs. Many studies estimate that ships account for ~15-30% of the global anthropogenic NOx emissions. EPA developed emissions inventories are widely used by states to construct model scenarios for testing air quality attainment strategies. Currently, CMV emissions are generated by simply applying growth factors to aggregated emissions data from much earlier years. Satellite retrievals from the Ozone Monitoring Instrument (OMI) have been successfully used to improve the veracity of marine emissions by incorporating observational data from the inventory year. In this study we use OMI NO2 observations and Community Multiscale Air Quality (CMAQ) model outputs to improve the EPA marine emission estimates for the Mid-Atlantic region. Back trajectories from the NOAA Air Resources Laboratory HYSPLIT model are used to identify days with minimal continental influence on OMI tropospheric column NO2 over shipping lanes. We perform sensitivity analyses to quantify the impact of marine emissions on air quality and suggest strategies to better meet the EPA mandated ozone standard.

Water quality modelling of Jadro spring.

PubMed

Margeta, J; Fistanic, I

2004-01-01

Management of water quality in karst is a specific problem. Water generally moves very fast by infiltration processes but far more by concentrated flows through fissures and openings in karst. This enables the entire surface pollution to be transferred fast and without filtration into groundwater springs. A typical example is the Jadro spring. Changes in water quality at the spring are sudden, but short. Turbidity as a major water quality problem for the karst springs regularly exceeds allowable standards. Former practice in problem solving has been reduced to intensive water disinfection in periods of great turbidity without analyses of disinfection by-products risks for water users. The main prerequisite for water quality control and an optimization of water disinfection is the knowledge of raw water quality and nature of occurrence. The analysis of monitoring data and their functional relationship with hydrological parameters enables establishment of a stochastic model that will help obtain better information on turbidity in different periods of the year. Using the model a great number of average monthly and extreme daily values are generated. By statistical analyses of these data possibility of occurrence of high turbidity in certain months is obtained. This information can be used for designing expert system for water quality management of karst springs. Thus, the time series model becomes a valuable tool in management of drinking water quality of the Jadro spring.

Fox Valley Technical College Quality First Process Model.

ERIC Educational Resources Information Center

Fox Valley Technical Coll., Appleton, WI.

An overview is provided of the Quality First Process Model developed by Fox Valley Technical College (FVTC), Wisconsin, to provide guidelines for quality instruction and service consistent with the highest educational standards. The 16-step model involves activities that should be adaptable to any organization. The steps of the quality model are…

Prediction of global and local model quality in CASP8 using the ModFOLD server.

PubMed

McGuffin, Liam J

2009-01-01

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0--an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/. Copyright 2009 Wiley-Liss, Inc.

Water quality modelling of an impacted semi-arid catchment using flow data from the WEAP model

NASA Astrophysics Data System (ADS)

Slaughter, Andrew R.; Mantel, Sukhmani K.

2018-04-01

The continuous decline in water quality in many regions is forcing a shift from quantity-based water resources management to a greater emphasis on water quality management. Water quality models can act as invaluable tools as they facilitate a conceptual understanding of processes affecting water quality and can be used to investigate the water quality consequences of management scenarios. In South Africa, the Water Quality Systems Assessment Model (WQSAM) was developed as a management-focussed water quality model that is relatively simple to be able to utilise the small amount of available observed data. Importantly, WQSAM explicitly links to systems (yield) models routinely used in water resources management in South Africa by using their flow output to drive water quality simulations. Although WQSAM has been shown to be able to represent the variability of water quality in South African rivers, its focus on management from a South African perspective limits its use to within southern African regions for which specific systems model setups exist. Facilitating the use of WQSAM within catchments outside of southern Africa and within catchments for which these systems model setups to not exist would require WQSAM to be able to link to a simple-to-use and internationally-applied systems model. One such systems model is the Water Evaluation and Planning (WEAP) model, which incorporates a rainfall-runoff component (natural hydrology), and reservoir storage, return flows and abstractions (systems modelling), but within which water quality modelling facilities are rudimentary. The aims of the current study were therefore to: (1) adapt the WQSAM model to be able to use as input the flow outputs of the WEAP model and; (2) provide an initial assessment of how successful this linkage was by application of the WEAP and WQSAM models to the Buffalo River for historical conditions; a small, semi-arid and impacted catchment in the Eastern Cape of South Africa. The simulations of

40 CFR Appendix W to Part 51 - Guideline on Air Quality Models

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 2 2010-07-01 2010-07-01 false Guideline on Air Quality Models W Appendix W to Part 51 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS Pt. 51, App. W Appendix W to Part 51—Guideline on Air Quality Models...

Effects of Meteorological Data Quality on Snowpack Modeling

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Robertson, M.; Hedrick, A. R.; Johnson, M.

2017-12-01

Detailed quality control of meteorological inputs is the most time-intensive component of running the distributed, physically-based iSnobal snow model, and the effect of data quality of the inputs on the model is unknown. The iSnobal model has been run operationally since WY2013, and is currently run in several basins in Idaho and California. The largest amount of user input during modeling is for the quality control of precipitation, temperature, relative humidity, solar radiation, wind speed and wind direction inputs. Precipitation inputs require detailed user input and are crucial to correctly model the snowpack mass. This research applies a range of quality control methods to meteorological input, from raw input with minimal cleaning, to complete user-applied quality control. The meteorological input cleaning generally falls into two categories. The first is global minimum/maximum and missing value correction that could be corrected and/or interpolated with automated processing. The second category is quality control for inputs that are not globally erroneous, yet are still unreasonable and generally indicate malfunctioning measurement equipment, such as temperature or relative humidity that remains constant, or does not correlate with daily trends observed at nearby stations. This research will determine how sensitive model outputs are to different levels of quality control and guide future operational applications.

AIR QUALITY MODELING OF AMMONIA: A REGIONAL MODELING PERSPECTIVE

EPA Science Inventory

The talk will address the status of modeling of ammonia from a regional modeling perspective, yet the observations and comments should have general applicability. The air quality modeling system components that are central to modeling ammonia will be noted and a perspective on ...

Quantifying Co-benefits of Renewable Energy through Integrated Electricity and Air Quality Modeling

NASA Astrophysics Data System (ADS)

Abel, D.

2016-12-01

This work focuses on the coordination of electricity sector changes with air quality and health improvement strategies through the integration of electricity and air quality models. Two energy models are used to calculate emission perturbations associated with changes in generation technology (20% generation from solar photovoltaics) and demand (future electricity use under a warmer climate). Impacts from increased solar PV penetration are simulated with the electricity model GridView, in collaboration with the National Renewable Energy Laboratory (NREL). Generation results are used to scale power plant emissions from an inventory developed by the Lake Michigan Air Directors Consortium (LADCO). Perturbed emissions and are used to calculate secondary particulate matter with the Community Multiscale Air Quality (CMAQ) model. We find that electricity NOx and SO2 emissions decrease at a rate similar to the total fraction of electricity supplied by solar. Across the Eastern U.S. region, average PM2.5 is reduced 5% over the summer, with highest reduction in regions and on days of greater PM2.5. A similar approach evaluates the air quality impacts of elevated electricity demand under a warmer climate. Meteorology is selected from the North American Regional Climate Change Assessment Program (NARCCAP) and input to a building energy model, eQUEST, to assess electricity demand as a function of ambient temperature. The associated generation and emissions are calculated on a plant-by-plant basis by the MyPower power sector model. These emissions are referenced to the 2011 National Emissions Inventory to be modeled in CMAQ for the Eastern U.S. and extended to health impact evaluation with the Environmental Benefits Mapping and Analysis Program (BenMAP). All results focus on the air quality and health consequences of energy system changes, considering grid-level changes to meet climate and air quality goals.

Modeling water quality, temperature, and flow in Link River, south-central Oregon

USGS Publications Warehouse

Sullivan, Annett B.; Rounds, Stewart A.

2016-09-09

The 2.1-km (1.3-mi) Link River connects Upper Klamath Lake to the Klamath River in south-central Oregon. A CE-QUAL-W2 flow and water-quality model of Link River was developed to provide a connection between an existing model of the upper Klamath River and any existing or future models of Upper Klamath Lake. Water-quality sampling at six locations in Link River was done during 2013–15 to support model development and to provide a better understanding of instream biogeochemical processes. The short reach and high velocities in Link River resulted in fast travel times and limited water-quality transformations, except for dissolved oxygen. Reaeration through the reach, especially at the falls in Link River, was particularly important in moderating dissolved oxygen concentrations that at times entered the reach at Link River Dam with marked supersaturation or subsaturation. This reaeration resulted in concentrations closer to saturation downstream at the mouth of Link River.

The Grand Challenge of Basin-Scale Groundwater Quality Management Modelling

NASA Astrophysics Data System (ADS)

Fogg, G. E.

2017-12-01

of appropriately upscaling advection-dispersion and reactions at the basin scale (10^2 km). A road map for research and development in groundwater quality management modeling and its application toward securing future groundwater resources will be discussed.

Air Quality Response Modeling for Decision Support | Science ...

EPA Pesticide Factsheets

Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being use

Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

EPA Science Inventory

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality m...

One multi-media environmental system with linkage between meteorology/ hydrology/ air quality models and water quality model

NASA Astrophysics Data System (ADS)

Tang, C.; Lynch, J. A.; Dennis, R. L.

2016-12-01

The biogeochemical processing of nitrogen and associated pollutants is driven by meteorological and hydrological processes in conjunction with pollutant loading. There are feedbacks between meteorology and hydrology that will be affected by land-use change and climate change. Changes in meteorology will affect pollutant deposition. It is important to account for those feedbacks and produce internally consistent simulations of meteorology, hydrology, and pollutant loading to drive the (watershed/water quality) biogeochemical models. In this study, the ecological response to emission reductions in streams in the Potomac watershed was evaluated. Firstly, we simulated the deposition by using the fully coupled Weather Research & Forecasting (WRF) model and the Community Multiscale Air Quality (CAMQ) model; secondly, we created the hydrological data by the offline linked Variable Infiltration Capacity (VIC) model and the WRF model. Lastly, we investigated the water quality by one comprehensive/environment model, namely the linkage of CMAQ, WRF, VIC and the Model of Acidification of Groundwater In Catchment (MAGIC) model from 2002 to 2010.The simulated results (such as NO3, SO4, and SBC) fit well to the observed values. The linkage provides a generally accurate, well-tested tool for evaluating sensitivities to varying meteorology and environmental changes on acidification and other biogeochemical processes, with capability to comprehensively explore strategic policy and management design.

Modeling and Negotiating Service Quality

NASA Astrophysics Data System (ADS)

Benbernou, Salima; Brandic, Ivona; Cappiello, Cinzia; Carro, Manuel; Comuzzi, Marco; Kertész, Attila; Kritikos, Kyriakos; Parkin, Michael; Pernici, Barbara; Plebani, Pierluigi

In this chapter the research problems of specifying and negotiating QoS and its corresponding quality documents are analyzed. For this reason, this chapter is separated into two main sections, Section 6.1 and 6.2, with each dedicated to one of the two problems, i.e., QoS specification and negotiation, respectively. Each section has a similar structure: they first introduce the problem and then, in the remaining subsections, review related work. Finally, the chapter ends with Section 6.3, which identifies research gaps and presents potential research challenges in QoS modelling, specification and negotiation.

Aerothermal modeling program, phase 2

NASA Technical Reports Server (NTRS)

Mongia, H. C.; Patankar, S. V.; Murthy, S. N. B.; Sullivan, J. P.; Samuelsen, G. S.

1985-01-01

The main objectives of the Aerothermal Modeling Program, Phase 2 are: to develop an improved numerical scheme for incorporation in a 3-D combustor flow model; to conduct a benchmark quality experiment to study the interaction of a primary jet with a confined swirling crossflow and to assess current and advanced turbulence and scalar transport models; and to conduct experimental evaluation of the air swirler interaction with fuel injectors, assessments of current two-phase models, and verification the improved spray evaporation/dispersion models.

A parsimonious dynamic model for river water quality assessment.

PubMed

Mannina, Giorgio; Viviani, Gaspare

2010-01-01

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH(4), and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management.

Impacts of Energy Sector Emissions on PM2.5 Air Quality in Northern India

NASA Astrophysics Data System (ADS)

Karambelas, A. N.; Kiesewetter, G.; Heyes, C.; Holloway, T.

2015-12-01

India experiences high concentrations of fine particulate matter (PM2.5), and several Indian cities currently rank among the world's most polluted cities. With ongoing urbanization and a growing economy, emissions from different energy sectors remain major contributors to air pollution in India. Emission sectors impact ambient air quality differently due to spatial distribution (typical urban vs. typical rural sources) as well as source height characteristics (low-level vs. high stack sources). This study aims to assess the impacts of emissions from three distinct energy sectors—transportation, domestic, and electricity—on ambient PM2.5­­ in northern India using an advanced air quality analysis framework based on the U.S. EPA Community Multi-Scale Air Quality (CMAQ) model. Present air quality conditions are simulated using 2010 emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model. Modeled PM2.5 concentrations are compared with satellite observations of aerosol optical depth (AOD) from the Moderate Imaging Spectroradiometer (MODIS) for 2010. Energy sector emissions impacts on future (2030) PM2.5 are evaluated with three sensitivity simulations, assuming maximum feasible reduction technologies for either transportation, domestic, or electricity sectors. These simulations are compared with a business as usual 2030 simulation to assess relative sectoral impacts spatially and temporally. CMAQ is modeled at 12km by 12km and include biogenic emissions from the Community Land Model coupled with the Model of Emissions of Gases and Aerosols in Nature (CLM-MEGAN), biomass burning emissions from the Global Fires Emissions Database (GFED), and ERA-Interim meteorology generated with the Weather Research and Forecasting (WRF) model for 2010 to quantify the impact of modified anthropogenic emissions on ambient PM2.5 concentrations. Energy sector emissions analysis supports decision-making to improve future air quality and public health in

Air quality modeling for effective environmental management in the mining region.

PubMed

Asif, Zunaira; Chen, Zhi; Han, Yi

2018-04-18

Air quality in the mining sector is a serious environmental concern and associated with many health issues. The air quality management in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. A modeling approach called mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model is taken into account through the planet's boundary conditions and assuming that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values as the predicted concentrations of PM 10 , PM 2.5 , TSP, NO 2 and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening of summer, and minimum mixing height (380 m) is attained during the evening of winter. The dust fall (PM coarse) deposition flux is maximum during February and March with the deposition velocity of 4.67 cm/s. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM 2.5 ; 0.71 to 0.82 for PM 10 and from 0.71 to 0.89 for NO 2 . The analyses illustrate that presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. The comparison of MADM and CALPUFF modeling values are made for four different pollutants (PM 2.5 , PM 10 , TSP, and NO 2 ) under three different atmospheric stability classes (stable, neutral and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Reduced-form air quality modeling for community-scale ...

EPA Pesticide Factsheets

Transportation plays an important role in modern society, but its impact on air quality has been shown to have significant adverse effects on public health. Numerous reviews (HEI, CDC, WHO) summarizing findings of hundreds of studies conducted mainly in the last decade, conclude that exposures to traffic emissions near roads are a public health concern. The Community LINE Source Model (C-LINE) is a web-based model designed to inform the community user of local air quality impacts due to roadway vehicles in their region of interest using a simplified modeling approach. Reduced-form air quality modeling is a useful tool for examining what-if scenarios of changes in emissions, such as those due to changes in traffic volume, fleet mix, or vehicle speed. Examining various scenarios of air quality impacts in this way can identify potentially at-risk populations located near roadways, and the effects that a change in traffic activity may have on them. C-LINE computes dispersion of primary mobile source pollutants using meteorological conditions for the region of interest and computes air-quality concentrations corresponding to these selected conditions. C-LINE functionality has been expanded to model emissions from port-related activities (e.g. ships, trucks, cranes, etc.) in a reduced-form modeling system for local-scale near-port air quality analysis. This presentation describes the Community modeling tools C-LINE and C-PORT that are intended to be used by local gove

Production system with process quality control: modelling and application

NASA Astrophysics Data System (ADS)

Tsou, Jia-Chi

2010-07-01

Over the past decade, there has been a great deal of research dedicated to the study of quality and the economics of production. In this article, we develop a dynamic model which is based on the hypothesis of a traditional economic production quantity model. Taguchi's cost of poor quality is used to evaluate the cost of poor quality in the dynamic production system. A practical case from the automotive industry, which uses the Six-sigma DMAIC methodology, is discussed to verify the proposed model. This study shows that there is an optimal value of quality investment to make the production system reach a reasonable quality level and minimise the production cost. Based on our model, the management can adjust its investment in quality improvement to generate considerable financial return.

A Multi-Model Assessment for the 2006 and 2010 Simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of Column Variable Predictions Using Satellite Data

EPA Science Inventory

Within the context of the Air Quality Model Evaluation International Initiative phase 2 (AQMEII2) project, this part II paper performs a multi-model assessment of major column abundances of gases, radiation, aerosol, and cloud variables for 2006 and 2010 simulations with three on...

ProQ3: Improved model quality assessments using Rosetta energy terms

PubMed Central

Uziela, Karolis; Shu, Nanjiang; Wallner, Björn; Elofsson, Arne

2016-01-01

Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/. PMID:27698390

Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

RZWQM predicted effects of soil N testing with incorporated automatic parameter optimization software (PEST) and weather input quality control

USDA-ARS?s Scientific Manuscript database

Among the most promising tools available for determining precise N requirements are soil mineral N tests. Field tests that evaluated this practice, however, have been conducted under only limited weather and soil conditions. Previous research has shown that using agricultural systems models such as ...

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

PubMed

Guevara, M; Tena, C; Soret, A; Serradell, K; Guzmán, D; Retama, A; Camacho, P; Jaimes-Palomera, M; Mediavilla, A

2017-04-15

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km 2 ) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO 2 , O 3 , VOC and PM 2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NO x and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O 3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA. Copyright © 2017 Elsevier B.V. All rights reserved.

A framework for modeling contaminant impacts on reservoir water quality

NASA Astrophysics Data System (ADS)

Jeznach, Lillian C.; Jones, Christina; Matthews, Thomas; Tobiason, John E.; Ahlfeld, David P.

2016-06-01

This study presents a framework for using hydrodynamic and water quality models to understand the fate and transport of potential contaminants in a reservoir and to develop appropriate emergency response and remedial actions. In the event of an emergency situation, prior detailed modeling efforts and scenario evaluations allow for an understanding of contaminant plume behavior, including maximum concentrations that could occur at the drinking water intake and contaminant travel time to the intake. A case study assessment of the Wachusett Reservoir, a major drinking water supply for metropolitan Boston, MA, provides an example of an application of the framework and how hydrodynamic and water quality models can be used to quantitatively and scientifically guide management in response to varieties of contaminant scenarios. The model CE-QUAL-W2 was used to investigate the water quality impacts of several hypothetical contaminant scenarios, including hypothetical fecal coliform input from a sewage overflow as well as an accidental railway spill of ammonium nitrate. Scenarios investigated the impacts of decay rates, season, and inter-reservoir transfers on contaminant arrival times and concentrations at the drinking water intake. The modeling study highlights the importance of a rapid operational response by managers to contain a contaminant spill in order to minimize the mass of contaminant that enters the water column, based on modeled reservoir hydrodynamics. The development and use of hydrodynamic and water quality models for surface drinking water sources subject to the potential for contaminant entry can provide valuable guidance for making decisions about emergency response and remediation actions.

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

INDOOR AIR QUALITY MODELING (CHAPTER 58)

EPA Science Inventory

The chapter discussses indoor air quality (IAQ) modeling. Such modeling provides a way to investigate many IAQ problems without the expense of large field experiments. Where experiments are planned, IAQ models can be used to help design experiments by providing information on exp...

Quality Assurance Model for Digital Adult Education Materials

ERIC Educational Resources Information Center

Dimou, Helen; Kameas, Achilles

2016-01-01

Purpose: This paper aims to present a model for the quality assurance of digital educational material that is appropriate for adult education. The proposed model adopts the software quality standard ISO/IEC 9126 and takes into account adult learning theories, Bloom's taxonomy of learning objectives and two instructional design models: Kolb's model…

Study on an Air Quality Evaluation Model for Beijing City Under Haze-Fog Pollution Based on New Ambient Air Quality Standards

PubMed Central

Li, Li; Liu, Dong-Jun

2014-01-01

Since 2012, China has been facing haze-fog weather conditions, and haze-fog pollution and PM2.5 have become hot topics. It is very necessary to evaluate and analyze the ecological status of the air environment of China, which is of great significance for environmental protection measures. In this study the current situation of haze-fog pollution in China was analyzed first, and the new Ambient Air Quality Standards were introduced. For the issue of air quality evaluation, a comprehensive evaluation model based on an entropy weighting method and nearest neighbor method was developed. The entropy weighting method was used to determine the weights of indicators, and the nearest neighbor method was utilized to evaluate the air quality levels. Then the comprehensive evaluation model was applied into the practical evaluation problems of air quality in Beijing to analyze the haze-fog pollution. Two simulation experiments were implemented in this study. One experiment included the indicator of PM2.5 and was carried out based on the new Ambient Air Quality Standards (GB 3095-2012); the other experiment excluded PM2.5 and was carried out based on the old Ambient Air Quality Standards (GB 3095-1996). Their results were compared, and the simulation results showed that PM2.5 was an important indicator for air quality and the evaluation results of the new Air Quality Standards were more scientific than the old ones. The haze-fog pollution situation in Beijing City was also analyzed based on these results, and the corresponding management measures were suggested. PMID:25170682

Leaf quality and insect herbivory in model tropical plant communities after long-term exposure to elevated atmospheric CO2.

PubMed

Arnone, J A; Zaller, J G; Körner, Ch; Ziegler, C; Zandt, H

1995-09-01

Results from laboratory feeding experiments have shown that elevated atmospheric carbon dioxide can affect interactions between plants and insect herbivores, primarily through changes in leaf nutritional quality occurring at elevated CO 2 . Very few data are available on insect herbivory in plant communities where insects can choose among species and positions in the canopy in which to feed. Our objectives were to determine the extent to which CO 2 -induced changes in plant communities and leaf nutritional quality may affect herbivory at the level of the entire canopy. We introduced equivalent populations of fourth instar Spodoptera eridania, a lepidopteran generalist, to complex model ecosystems containing seven species of moist tropical plants maintained under low mineral nutrient supply. Larvae were allowed to feed freely for 14 days, by which time they had reached the seventh instar. Prior to larval introductions, plant communities had been continuously exposed to either 340 μl CO 2 l -1 or to 610 μl CO 2 l -1 for 1.5 years. No major shifts in leaf nutritional quality [concentrations of N, total non-structural carbohydrates (TNC), sugar, and starch; ratios of: C/N, TNC/N, sugar/N, starch/N; leaf toughness] were observed between CO 2 treatments for any of the species. Furthermore, no correlations were observed between these measures of leaf quality and leaf biomass consumption. Total leaf area and biomass of all plant communities were similar when caterpillars were introduced. However, leaf biomass of some species was slightly greater-and for other species slightly less (e.g. Cecropia peltata)-in communities exposed to elevated CO 2 . Larvae showed the strongest preference for C. peltata leaves, the plant species that was least abundant in all communites, and fed relatively little on plants species which were more abundant. Thus, our results indicate that leaf tissue quality, as described by these parameters, is not necessarily affected by elevated CO 2 under

Impacts of potential CO2-reduction policies on air quality in the United States.

PubMed

Trail, Marcus A; Tsimpidi, Alexandra P; Liu, Peng; Tsigaridis, Kostas; Hu, Yongtao; Rudokas, Jason R; Miller, Paul J; Nenes, Athanasios; Russell, Armistead G

2015-04-21

Impacts of emissions changes from four potential U.S. CO2 emission reduction policies on 2050 air quality are analyzed using the community multiscale air quality model (CMAQ). Future meteorology was downscaled from the Goddard Institute for Space Studies (GISS) ModelE General Circulation Model (GCM) to the regional scale using the Weather Research Forecasting (WRF) model. We use emissions growth factors from the EPAUS9r MARKAL model to project emissions inventories for two climate tax scenarios, a combined transportation and energy scenario, a biomass energy scenario and a reference case. Implementation of a relatively aggressive carbon tax leads to improved PM2.5 air quality compared to the reference case as incentives increase for facilities to install flue-gas desulfurization (FGD) and carbon capture and sequestration (CCS) technologies. However, less capital is available to install NOX reduction technologies, resulting in an O3 increase. A policy aimed at reducing CO2 from the transportation sector and electricity production sectors leads to reduced emissions of mobile source NOX, thus reducing O3. Over most of the U.S., this scenario leads to reduced PM2.5 concentrations. However, increased primary PM2.5 emissions associated with fuel switching in the residential and industrial sectors leads to increased organic matter (OM) and PM2.5 in some cities.

Assessment of Reference Height Models on Quality of Tandem-X dem

NASA Astrophysics Data System (ADS)

Mirzaee, S.; Motagh, M.; Arefi, H.

2015-12-01

The aim of this study is to investigate the effect of various Global Digital Elevation Models (GDEMs) in producing high-resolution topography model using TanDEM-X (TDX) Coregistered Single Look Slant Range Complex (CoSSC) images. We selected an image acquired on Jun 12th, 2012 over Doroud region in Lorestan, west of Iran and used 4 external digital elevation models in our processing including DLR/ASI X-SAR DEM (SRTM-X, 30m resolution), ASTER GDEM Version 2 (ASTER-GDEMV2, 30m resolution), NASA SRTM Version 4 (SRTM-V4, 90m resolution), and a local photogrammetry-based DEM prepared by National Cartographic Center (NCC DEM, 10m resolution) of Iran. InSAR procedure for DEM generation was repeated four times with each of the four external height references. The quality of each external DEM was initially assessed using ICESat filtered points. Then, the quality of, each TDX-based DEM was assessed using the more precise external DEM selected in the previous step. Results showed that both local (NCC) DEM and SRTM X-band performed the best (RMSE< 9m) for TDX-DEM generation. In contrast, ASTER GDEM v2 and SRTM C-band v4 showed poorer quality.

Modeling the Water - Quality Effects of Changes to the Klamath River Upstream of Keno Dam, Oregon

USGS Publications Warehouse

Sullivan, Annett B.; Sogutlugil, I. Ertugrul; Rounds, Stewart A.; Deas, Michael L.

2013-01-01

The Link River to Keno Dam (Link-Keno) reach of the Klamath River, Oregon, generally has periods of water-quality impairment during summer, including low dissolved oxygen, elevated concentrations of ammonia and algae, and high pH. Efforts are underway to improve water quality in this reach through a Total Maximum Daily Load (TMDL) program and other management and operational actions. To assist in planning, a hydrodynamic and water-quality model was used in this study to provide insight about how various actions could affect water quality in the reach. These model scenarios used a previously developed and calibrated CE-QUAL-W2 model of the Link-Keno reach developed by the U.S. Geological Survey (USGS), Watercourse Engineering Inc., and the Bureau of Reclamation for calendar years 2006-09 (referred to as the "USGS model" in this report). Another model of the same river reach was previously developed by Tetra Tech, Inc. and the Oregon Department of Environmental Quality for years 2000 and 2002 and was used in the TMDL process; that model is referred to as the "TMDL model" in this report. This report includes scenarios that (1) assess the effect of TMDL allocations on water quality, (2) provide insight on certain aspects of the TMDL model, (3) assess various methods to improve water quality in this reach, and (4) examine possible water-quality effects of a future warmer climate. Results presented in this report for the first 5 scenarios supersede or augment those that were previously published (scenarios 1 and 2 in Sullivan and others [2011], 3 through 5 in Sullivan and others [2012]); those previous results are still valid, but the results for those scenarios in this report are more current.

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

DockQ: A Quality Measure for Protein-Protein Docking Models

PubMed Central

Basu, Sankar

2016-01-01

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure

Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning.

PubMed

Terrado, Marta; Sabater, Sergi; Chaplin-Kramer, Becky; Mandle, Lisa; Ziv, Guy; Acuña, Vicenç

2016-01-01

There is a growing pressure of human activities on natural habitats, which leads to biodiversity losses. To mitigate the impact of human activities, environmental policies are developed and implemented, but their effects are commonly not well understood because of the lack of tools to predict the effects of conservation policies on habitat quality and/or diversity. We present a straightforward model for the simultaneous assessment of terrestrial and aquatic habitat quality in river basins as a function of land use and anthropogenic threats to habitat that could be applied under different management scenarios to help understand the trade-offs of conservation actions. We modify the InVEST model for the assessment of terrestrial habitat quality and extend it to freshwater habitats. We assess the reliability of the model in a severely impaired basin by comparing modeled results to observed terrestrial and aquatic biodiversity data. Estimated habitat quality is significantly correlated with observed terrestrial vascular plant richness (R(2)=0.76) and diversity of aquatic macroinvertebrates (R(2)=0.34), as well as with ecosystem functions such as in-stream phosphorus retention (R(2)=0.45). After that, we analyze different scenarios to assess the suitability of the model to inform changes in habitat quality under different conservation strategies. We believe that the developed model can be useful to assess potential levels of biodiversity, and to support conservation planning given its capacity to forecast the effects of management actions in river basins. Copyright © 2015 Elsevier B.V. All rights reserved.

Community Multiscale Air Quality Modeling System (CMAQ)

EPA Pesticide Factsheets

CMAQ is a computational tool used for air quality management. It models air pollutants including ozone, particulate matter and other air toxics to help determine optimum air quality management scenarios.

Current Applications of OMI Tropospheric NO2 Data for Air Quality and a Look to the Future

NASA Technical Reports Server (NTRS)

Pickering, Kenneth E.; Bucsela, E.; Allen, D.; Prados, A.; Gleason, J.; Kondragunta, S.

2010-01-01

Ozone Monitoring Instrument (OMI) Tropospheric NO2 products are being used to enhance the ability to monitor changes in NO2 air quality, update emission inventories, and evaluate regional air quality models. Trends in tropospheric column NO2 have been examined over the eastern United States in relation to emissions changes mandated by regulatory actions. Decreases of 20 to 40 percent over the period 2005 to 2008 were noted, largely in response to major emission reductions at power plants. The OMI data have been used to identify regions in which the opposite trend has been found. We have also used OMI NO2 in efforts to improve emission inventories for NOx emissions from soil. Lightning NOx emissions have been added to CMAQ, the US Environmental Protection Agency's regional air quality model. Evaluation of the resulting NO2 columns in the model is being conducted using the OMI NO2 observations. Community Multiscale Air Quality (CMAQ) together with the OMI NO2 data comprise a valuable tool for monitoring and predicting air quality. Looking to the future, we expect that the combination of Global Ozone Monitoring Experiment-2 (GOME-2) (morning) and OMI (afternoon) data sets obtained through use of the same retrieval algorithms will substantially increase the possibility of successful integration of satellite information into regional air quality forecast models. Farther down the road, we anticipate the Geostationary Coastal and Air Pollution Events (GEO-CAPE) platform to supply data possibly on an hourly basis, allowing much more comprehensive analysis of air quality from space.

Thermogravimetric and model-free kinetic studies on CO2 gasification of low-quality, high-sulphur Indian coals

NASA Astrophysics Data System (ADS)

Das, Tonkeswar; Saikia, Ananya; Mahanta, Banashree; Choudhury, Rahul; Saikia, Binoy K.

2016-10-01

Coal gasification with CO2 has emerged as a cleaner and more efficient way for the production of energy, and it offers the advantages of CO2 mitigation policies through simultaneous CO2 sequestration. In the present investigation, a feasibility study on the gasification of three low-quality, high-sulphur coals from the north-eastern region (NER) of India in a CO2 atmosphere using thermogravimetric analysis (TGA-DTA) has been made in order to have a better understanding of the physical and chemical characteristics in the process of gasification of coal. Model-free kinetics was applied to determine the activation energies (E) and pre-exponential factors (A) of the CO2 gasification process of the coals. Multivariate non-linear regression analyses were performed to find out the formal mechanisms, kinetic model, and the corresponding kinetic triplets. The results revealed that coal gasification with CO2 mainly occurs in the temperature range of 800∘-1400∘C and a maximum of at around 1100∘C. The reaction mechanisms responsible for CO2 gasification of the coals were observed to be of the ` nth order with autocatalysis (CnB)' and ` nth order (Fn) mechanism'. The activation energy of the CO2 gasification was found to be in the range 129.07-146.81 kJ mol-1.

COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM (ONE ATMOSPHERE)

EPA Science Inventory

This task supports ORD's strategy by providing responsive technical support of EPA's mission and provides credible state of the art air quality models and guidance. This research effort is to develop and improve the Community Multiscale Air Quality (CMAQ) modeling system, a mu...

Geospatial distribution modeling and determining suitability of groundwater quality for irrigation purpose using geospatial methods and water quality index (WQI) in Northern Ethiopia

NASA Astrophysics Data System (ADS)

Gidey, Amanuel

2018-06-01

Determining suitability and vulnerability of groundwater quality for irrigation use is a key alarm and first aid for careful management of groundwater resources to diminish the impacts on irrigation. This study was conducted to determine the overall suitability of groundwater quality for irrigation use and to generate their spatial distribution maps in Elala catchment, Northern Ethiopia. Thirty-nine groundwater samples were collected to analyze and map the water quality variables. Atomic absorption spectrophotometer, ultraviolet spectrophotometer, titration and calculation methods were used for laboratory groundwater quality analysis. Arc GIS, geospatial analysis tools, semivariogram model types and interpolation methods were used to generate geospatial distribution maps. Twelve and eight water quality variables were used to produce weighted overlay and irrigation water quality index models, respectively. Root-mean-square error, mean square error, absolute square error, mean error, root-mean-square standardized error, measured values versus predicted values were used for cross-validation. The overall weighted overlay model result showed that 146 km2 areas are highly suitable, 135 km2 moderately suitable and 60 km2 area unsuitable for irrigation use. The result of irrigation water quality index confirms 10.26% with no restriction, 23.08% with low restriction, 20.51% with moderate restriction, 15.38% with high restriction and 30.76% with the severe restriction for irrigation use. GIS and irrigation water quality index are better methods for irrigation water resources management to achieve a full yield irrigation production to improve food security and to sustain it for a long period, to avoid the possibility of increasing environmental problems for the future generation.

Towards the Next Generation Air Quality Modeling System ...

EPA Pesticide Factsheets

The community multiscale air quality (CMAQ) model of the U.S. Environmental Protection Agency is one of the most widely used air quality model worldwide; it is employed for both research and regulatory applications at major universities and government agencies for improving understanding of the formation and transport of air pollutants. It is noted, however, that air quality issues and climate change assessments need to be addressed globally recognizing the linkages and interactions between meteorology and atmospheric chemistry across a wide range of scales. Therefore, an effort is currently underway to develop the next generation air quality modeling system (NGAQM) that will be based on a global integrated meteorology and chemistry system. The model for prediction across scales-atmosphere (MPAS-A), a global fully compressible non-hydrostatic model with seamlessly refined centroidal Voronoi grids, has been chosen as the meteorological driver of this modeling system. The initial step of adapting MPAS-A for the NGAQM was to implement and test the physics parameterizations and options that are preferred for retrospective air quality simulations (see the work presented by R. Gilliam, R. Bullock, and J. Herwehe at this workshop). The next step, presented herein, would be to link the chemistry from CMAQ to MPAS-A to build a prototype for the NGAQM. Furthermore, the techniques to harmonize transport processes between CMAQ and MPAS-A, methodologies to connect the chemis

Urban Air Quality Modelling with AURORA: Prague and Bratislava

NASA Astrophysics Data System (ADS)

Veldeman, N.; Viaene, P.; De Ridder, K.; Peelaerts, W.; Lauwaet, D.; Muhammad, N.; Blyth, L.

2012-04-01

The European Commission, in its strategy to protect the health of the European citizens, states that in order to assess the impact of air pollution on public health, information on long-term exposure to air pollution should be available. Currently, indicators of air quality are often being generated using measured pollutant concentrations. While air quality monitoring stations data provide accurate time series information at specific locations, air quality models have the advantage of being able to assess the spatial variability of air quality (for different resolutions) and predict air quality in the future based on different scenarios. When running such air quality models at a high spatial and temporal resolution, one can simulate the actual situation as closely as possible, allowing for a detailed assessment of the risk of exposure to citizens from different pollutants. AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach), a prognostic 3-dimensional Eulerian chemistry-transport model, is designed to simulate urban- to regional-scale atmospheric pollutant concentration and exposure fields. The AURORA model also allows to calculate the impact of changes in land use (e.g. planting of trees) or of emission reduction scenario's on air quality. AURORA is currently being applied within the ESA atmospheric GMES service, PASODOBLE (http://www.myair-eu.org), that delivers information on air quality, greenhouse gases, stratospheric ozone, … At present there are two operational AURORA services within PASODOBLE. Within the "Air quality forecast service" VITO delivers daily air quality forecasts for Belgium at a resolution of 5 km and for the major Belgian cities: Brussels, Ghent, Antwerp, Liege and Charleroi. Furthermore forecast services are provided for Prague, Czech Republic and Bratislava, Slovakia, both at a resolution of 1 km. The "Urban/regional air quality assessment service" provides urban- and regional-scale maps (hourly resolution

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.

A systematic literature review of open source software quality assessment models.

PubMed

Adewumi, Adewole; Misra, Sanjay; Omoregbe, Nicholas; Crawford, Broderick; Soto, Ricardo

2016-01-01

Many open source software (OSS) quality assessment models are proposed and available in the literature. However, there is little or no adoption of these models in practice. In order to guide the formulation of newer models so they can be acceptable by practitioners, there is need for clear discrimination of the existing models based on their specific properties. Based on this, the aim of this study is to perform a systematic literature review to investigate the properties of the existing OSS quality assessment models by classifying them with respect to their quality characteristics, the methodology they use for assessment, and their domain of application so as to guide the formulation and development of newer models. Searches in IEEE Xplore, ACM, Science Direct, Springer and Google Search is performed so as to retrieve all relevant primary studies in this regard. Journal and conference papers between the year 2003 and 2015 were considered since the first known OSS quality model emerged in 2003. A total of 19 OSS quality assessment model papers were selected. To select these models we have developed assessment criteria to evaluate the quality of the existing studies. Quality assessment models are classified into five categories based on the quality characteristics they possess namely: single-attribute, rounded category, community-only attribute, non-community attribute as well as the non-quality in use models. Our study reflects that software selection based on hierarchical structures is found to be the most popular selection method in the existing OSS quality assessment models. Furthermore, we found that majority (47%) of the existing models do not specify any domain of application. In conclusion, our study will be a valuable contribution to the community and helps the quality assessment model developers in formulating newer models and also to the practitioners (software evaluators) in selecting suitable OSS in the midst of alternatives.

Utility of NCEP Operational and Emerging Meteorological Models for Driving Air Quality Prediction

NASA Astrophysics Data System (ADS)

McQueen, J.; Huang, J.; Huang, H. C.; Shafran, P.; Lee, P.; Pan, L.; Sleinkofer, A. M.; Stajner, I.; Upadhayay, S.; Tallapragada, V.

2017-12-01

Operational air quality predictions for the United States (U. S.) are provided at NOAA by the National Air Quality Forecasting Capability (NAQFC). NAQFC provides nationwide operational predictions of ozone and particulate matter twice per day (at 06 and 12 UTC cycles) at 12 km resolution and 1 hour time intervals through 48 hours and distributed at http://airquality.weather.gov. The NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) 12 km weather prediction is used to drive the Community Multiscale Air Quality (CMAQ) model. In 2017, the NAM was upgraded in part to reduce a warm 2m temperature bias in Summer (V4). At the same time CMAQ was updated to V5.0.2. Both versions of the models were run in parallel for several months. Therefore the impact of improvements from the atmospheric chemistry model versus upgrades with the weather prediction model could be assessed. . Improvements to CMAQ were related to improvements to improvements in NAM 2 m temperature bias through increasing the opacity of clouds and reducing downward shortwave radiation resulted in reduced ozone photolysis. Higher resolution operational NWP models have recently been introduced as part of the NCEP modeling suite. These include the NAM CONUS Nest (3 km horizontal resolution) run four times per day through 60 hours and the High Resolution Rapid Refresh (HRRR, 3 km) run hourly out to 18 hours. In addition, NCEP with other NOAA labs has begun to develop and test the Next Generation Global Prediction System (NGGPS) based on the FV3 global model. This presentation also overviews recent developments with operational numerical weather prediction and evaluates the ability of these models for predicting low level temperatures, clouds and capturing boundary layer processes important for driving air quality prediction in complex terrain. The assessed meteorological model errors could help determine the magnitude of possible pollutant errors from CMAQ if used

The Attributive Theory of Quality: A Model for Quality Measurement in Higher Education.

ERIC Educational Resources Information Center

Afshar, Arash

A theoretical basis for defining and measuring the quality of institutions of higher education, namely for accreditation purposes, is developed. The theory, the Attributive Theory of Quality, is illustrated using a calculation model that is based on general systems theory. The theory postulates that quality only exists in relation to the…

Collaborative problem solving with a total quality model.

PubMed

Volden, C M; Monnig, R

1993-01-01

A collaborative problem-solving system committed to the interests of those involved complies with the teachings of the total quality management movement in health care. Deming espoused that any quality system must become an integral part of routine activities. A process that is used consistently in dealing with problems, issues, or conflicts provides a mechanism for accomplishing total quality improvement. The collaborative problem-solving process described here results in quality decision-making. This model incorporates Ishikawa's cause-and-effect (fishbone) diagram, Moore's key causes of conflict, and the steps of the University of North Dakota Conflict Resolution Center's collaborative problem solving model.

VOLATILE ORGANIC COMPOUND EMISSIONS FROM LATEX PAINT-PART 2. TEST HOUSE STUDIES AND INDOOR AIR QUALITY (IAQ) MODELING

EPA Science Inventory

Emission models developed using small chamber data were combined with an Indoor Air Quality (IAQ) model to analyze the impact of volatile organic compound (VOC) emissions from latex paint on indoor environments. Test house experiments were conducted to verify the IAQ model's pred...

Objective assessment of MPEG-2 video quality

NASA Astrophysics Data System (ADS)

Gastaldo, Paolo; Zunino, Rodolfo; Rovetta, Stefano

2002-07-01

The increasing use of video compression standards in broadcasting television systems has required, in recent years, the development of video quality measurements that take into account artifacts specifically caused by digital compression techniques. In this paper we present a methodology for the objective quality assessment of MPEG video streams by using circular back-propagation feedforward neural networks. Mapping neural networks can render nonlinear relationships between objective features and subjective judgments, thus avoiding any simplifying assumption on the complexity of the model. The neural network processes an instantaneous set of input values, and yields an associated estimate of perceived quality. Therefore, the neural-network approach turns objective quality assessment into adaptive modeling of subjective perception. The objective features used for the estimate are chosen according to the assessed relevance to perceived quality and are continuously extracted in real time from compressed video streams. The overall system mimics perception but does not require any analytical model of the underlying physical phenomenon. The capability to process compressed video streams represents an important advantage over existing approaches, like avoiding the stream-decoding process greatly enhances real-time performance. Experimental results confirm that the system provides satisfactory, continuous-time approximations for actual scoring curves concerning real test videos.

Evaluating Regional-Scale Air Quality Models

EPA Science Inventory

Numerical air quality models are being used to understand the complex interplay among emission loading meteorology, and atmospheric chemistry leading to the formation and accumulation of pollutants in the atmosphere. A model evaluation framework is presented here that considers ...

Modelling End-User of Electronic-Government Service: The Role of Information quality, System Quality and Trust

NASA Astrophysics Data System (ADS)

Witarsyah Jacob, Deden; Fudzee, Mohd Farhan Md; Aizi Salamat, Mohamad; Kasim, Shahreen; Mahdin, Hairulnizam; Azhar Ramli, Azizul

2017-08-01

Many governments around the world increasingly use internet technologies such as electronic government to provide public services. These services range from providing the most basic informational website to deploying sophisticated tools for managing interactions between government agencies and beyond government. Electronic government (e-government) aims to provide a more accurate, easily accessible, cost-effective and time saving for the community. In this study, we develop a new model of e-government adoption service by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) through the incorporation of some variables such as System Quality, Information Quality and Trust. The model is then tested using a large-scale, multi-site survey research of 237 Indonesian citizens. This model will be validated by using Structural Equation Modeling (SEM). The result indicates that System Quality, Information Quality and Trust variables proven to effect user behavior. This study extends the current understanding on the influence of System Quality, Information Quality and Trust factors to researchers, practitioners, and policy makers.

Dynamic Assessment of Water Quality Based on a Variable Fuzzy Pattern Recognition Model

PubMed Central

Xu, Shiguo; Wang, Tianxiang; Hu, Suduan

2015-01-01

Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results. PMID:25689998

Dynamic assessment of water quality based on a variable fuzzy pattern recognition model.

PubMed

Xu, Shiguo; Wang, Tianxiang; Hu, Suduan

2015-02-16

Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results.

Conceptual Models, Choices, and Benchmarks for Building Quality Work Cultures.

ERIC Educational Resources Information Center

Acker-Hocevar, Michele

1996-01-01

The two models in Florida's Educational Quality Benchmark System represent a new way of thinking about developing schools' work culture. The Quality Performance System Model identifies nine dimensions of work within a quality system. The Change Process Model provides a theoretical framework for changing existing beliefs, attitudes, and behaviors…

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

NASA Astrophysics Data System (ADS)

Shaw, Amelia R.; Smith Sawyer, Heather; LeBoeuf, Eugene J.; McDonald, Mark P.; Hadjerioua, Boualem

2017-11-01

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

DOE PAGES

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.; ...

2017-10-24

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

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

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Developing hydrological model for water quality in Iraq marshes zone using Landsat-TM

NASA Astrophysics Data System (ADS)

Marghany, Maged; Hasab, Hashim Ali; Mansor, Shattri; Shariff, Abdul Rashid Bin Mohamed

2016-06-01

The Mesopotamia marshlands constitute the largest wetland ecosystem in the Middle East and Western Eurasia. These wetlands are located at the confluence of the Tigris and Euphrates Rivers in southern Iraq. However, there are series reductions in the wetland zones because of neighbor countries, i.e. Turkey, Syria built dams upstream of Tigris and Euphrates Rivers. In addition, the first Gulf war of the 1980s had damaged majority of the marches resources. In fact,the marshes had been reduced in size to less than 7% since 1973 and had deteriorated in water quality parameters. The study integrates Hydrological Model of RMA-2 with Geographic Information System, and remote sensing techniques to map the water quality in the marshlands south of Iraq. This study shows that RMA-2 shows the two dimensional water flow pattern and water quality quantities in the marshlands. It can be said that the integration between Hydrological Model of RMA-2, Geographic Information System, and remote sensing techniques can be used to monitor water quality in the marshlands south of Iraq.

RZWQM2 Simulations of Alternative Cropping Systems With and Without Summer Crops in the Central Great Plains

USDA-ARS?s Scientific Manuscript database

Integration and synthesis of data accruing from complex alternative crop rotation experiments across locations and climates is a challenge to agriculturists. System simulation models are potential tools to address this challenge. In this study, we simulated three long-term (1991 to 2008) dryland c...

Air Quality Forecasts Using the NASA GEOS Model

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;

The impact of working memory and the "process of process modelling" on model quality: Investigating experienced versus inexperienced modellers.

PubMed

Martini, Markus; Pinggera, Jakob; Neurauter, Manuel; Sachse, Pierre; Furtner, Marco R; Weber, Barbara

2016-05-09

A process model (PM) represents the graphical depiction of a business process, for instance, the entire process from online ordering a book until the parcel is delivered to the customer. Knowledge about relevant factors for creating PMs of high quality is lacking. The present study investigated the role of cognitive processes as well as modelling processes in creating a PM in experienced and inexperienced modellers. Specifically, two working memory (WM) functions (holding and processing of information and relational integration) and three process of process modelling phases (comprehension, modelling, and reconciliation) were related to PM quality. Our results show that the WM function of relational integration was positively related to PM quality in both modelling groups. The ratio of comprehension phases was negatively related to PM quality in inexperienced modellers and the ratio of reconciliation phases was positively related to PM quality in experienced modellers. Our research reveals central cognitive mechanisms in process modelling and has potential practical implications for the development of modelling software and teaching the craft of process modelling.

Modeling discharge, temperature, and water quality in the Tualatin River, Oregon

USGS Publications Warehouse

Rounds, Stewart A.; Wood, Tamara M.; Lynch, Dennis D.

1999-01-01

The discharge, water temperature, and water quality of the Tualatin River in northwestern Oregon was simulated with CE-QUAL-W2, a two-dimensional, laterally averaged model developed by the U.S. Army Corps of Engineers. The model was calibrated for May through October periods of 1991, 1992, and 1993. Nine hypothetical scenarios were tested with the model to provide insight for river managers and regulators.

Design and Establishment of Quality Model of Fundamental Geographic Information Database

NASA Astrophysics Data System (ADS)

Ma, W.; Zhang, J.; Zhao, Y.; Zhang, P.; Dang, Y.; Zhao, T.

2018-04-01

In order to make the quality evaluation for the Fundamental Geographic Information Databases(FGIDB) more comprehensive, objective and accurate, this paper studies and establishes a quality model of FGIDB, which formed by the standardization of database construction and quality control, the conformity of data set quality and the functionality of database management system, and also designs the overall principles, contents and methods of the quality evaluation for FGIDB, providing the basis and reference for carry out quality control and quality evaluation for FGIDB. This paper designs the quality elements, evaluation items and properties of the Fundamental Geographic Information Database gradually based on the quality model framework. Connected organically, these quality elements and evaluation items constitute the quality model of the Fundamental Geographic Information Database. This model is the foundation for the quality demand stipulation and quality evaluation of the Fundamental Geographic Information Database, and is of great significance on the quality assurance in the design and development stage, the demand formulation in the testing evaluation stage, and the standard system construction for quality evaluation technology of the Fundamental Geographic Information Database.

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

NASA Technical Reports Server (NTRS)

Rasool, Quazi Z.; Zhang, Rui; Lash, Benjamin; Cohan, Daniel S.; Cooter, Ellen J.; Bash, Jesse O.; Lamsal, Lok N.

2016-01-01

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

[Review on HSPF model for simulation of hydrology and water quality processes].

PubMed

Li, Zhao-fu; Liu, Hong-Yu; Li, Yan

2012-07-01

Hydrological Simulation Program-FORTRAN (HSPF), written in FORTRAN, is one ol the best semi-distributed hydrology and water quality models, which was first developed based on the Stanford Watershed Model. Many studies on HSPF model application were conducted. It can represent the contributions of sediment, nutrients, pesticides, conservatives and fecal coliforms from agricultural areas, continuously simulate water quantity and quality processes, as well as the effects of climate change and land use change on water quantity and quality. HSPF consists of three basic application components: PERLND (Pervious Land Segment) IMPLND (Impervious Land Segment), and RCHRES (free-flowing reach or mixed reservoirs). In general, HSPF has extensive application in the modeling of hydrology or water quality processes and the analysis of climate change and land use change. However, it has limited use in China. The main problems with HSPF include: (1) some algorithms and procedures still need to revise, (2) due to the high standard for input data, the accuracy of the model is limited by spatial and attribute data, (3) the model is only applicable for the simulation of well-mixed rivers, reservoirs and one-dimensional water bodies, it must be integrated with other models to solve more complex problems. At present, studies on HSPF model development are still undergoing, such as revision of model platform, extension of model function, method development for model calibration, and analysis of parameter sensitivity. With the accumulation of basic data and imorovement of data sharing, the HSPF model will be applied more extensively in China.

Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

NASA Astrophysics Data System (ADS)

Gantt, B.; Kelly, J. T.; Bash, J. O.

2015-11-01

Sea spray aerosols (SSAs) impact the particle mass concentration and gas-particle partitioning in coastal environments, with implications for human and ecosystem health. Model evaluations of SSA emissions have mainly focused on the global scale, but regional-scale evaluations are also important due to the localized impact of SSAs on atmospheric chemistry near the coast. In this study, SSA emissions in the Community Multiscale Air Quality (CMAQ) model were updated to enhance the fine-mode size distribution, include sea surface temperature (SST) dependency, and reduce surf-enhanced emissions. Predictions from the updated CMAQ model and those of the previous release version, CMAQv5.0.2, were evaluated using several coastal and national observational data sets in the continental US. The updated emissions generally reduced model underestimates of sodium, chloride, and nitrate surface concentrations for coastal sites in the Bay Regional Atmospheric Chemistry Experiment (BRACE) near Tampa, Florida. Including SST dependency to the SSA emission parameterization led to increased sodium concentrations in the southeastern US and decreased concentrations along parts of the Pacific coast and northeastern US. The influence of sodium on the gas-particle partitioning of nitrate resulted in higher nitrate particle concentrations in many coastal urban areas due to increased condensation of nitric acid in the updated simulations, potentially affecting the predicted nitrogen deposition in sensitive ecosystems. Application of the updated SSA emissions to the California Research at the Nexus of Air Quality and Climate Change (CalNex) study period resulted in a modest improvement in the predicted surface concentration of sodium and nitrate at several central and southern California coastal sites. This update of SSA emissions enabled a more realistic simulation of the atmospheric chemistry in coastal environments where marine air mixes with urban pollution.

Soil Quality Index Determination Models for Restinga Forest

NASA Astrophysics Data System (ADS)

Bonilha, R. M.; Casagrande, J. C.; Soares, R. M.

2012-04-01

The Restinga Forest is a set of plant communities in mosaic, determined by the characteristics of their substrates as a result of depositional processes and ages. In this complex mosaic are the physiognomies of restinga forests of high-stage regeneration (high restinga) and middle stage of regeneration (low restinga), each with its plant characteristics that differentiate them. Located on the coastal plains of the Brazilian coast, suffering internal influences both the continental slopes, as well as from the sea. Its soils come from the Quaternary and are subject to constant deposition of sediments. The climate in the coastal type is tropical (Köppen). This work was conducted in four locations: (1) Anchieta Island, Ubatuba, (2) Juréia-Itatins Ecological Station, Iguape, (3) Vila das Pedrinhas, Comprida Island; and (4) Cardoso Island, Cananeia. The soil samples were collect at a depths of 0 to 5, 0-10, 0-20, 20-40 and 40 to 60cm for the chemical and physical analysis. Were studied the additive and pondering additive models to evaluate soil quality. It was concluded: a) the comparative additive model produces quantitative results and the pondering additive model quantitative results; b) as the pondering additive model, the values of Soil Quality Index (SQI) for soils under forest of restinga are low and realistic, demonstrating the small plant biomass production potential of these soils, as well as their low resilience; c) the values of SQI similar to areas with and without restinga forest give quantitative demonstration of the restinga be considered as soil phase; d) restinga forest, probably, is maintained solely by the cycling of nutrients in a closed nutrient cycling; e) for the determination of IQS for soils under restinga vegetation the use of routine chemical analysis is adequate. Keywords: Model, restinga forest, Soil Quality Index (SQI).

Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool

NASA Astrophysics Data System (ADS)

Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.

2018-06-01

Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.

EPA RESEARCH HIGHLIGHTS -- MODELS-3/CMAQ OFFERS COMPREHENSIVE APPROACH TO AIR QUALITY MODELING

EPA Science Inventory

Regional and global coordinated efforts are needed to address air quality problems that are growing in complexity and scope. Models-3 CMAQ contains a community multi-scale air quality modeling system for simulating urban to regional scale pollution problems relating to troposphe...

Quality Concerns in Technical Education in India: A Quantifiable Quality Enabled Model

ERIC Educational Resources Information Center

Gambhir, Victor; Wadhwa, N. C.; Grover, Sandeep

2016-01-01

Purpose: The paper aims to discuss current Technical Education scenarios in India. It proposes modelling the factors affecting quality in a technical institute and then applying a suitable technique for assessment, comparison and ranking. Design/methodology/approach: The paper chose graph theoretic approach for quantification of quality-enabled…

Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models

NASA Astrophysics Data System (ADS)

Zang, Tianwu

Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with 3˜5A root-mean-square deviation (RMSD) from the solution, the progress of refining these models is quite slow. It is therefore urgently needed to develop effective methods to bring low-quality models to higher-accuracy ranges (e.g., less than 2 A RMSD). In this thesis, I present several novel computational methods to address the high-accuracy refinement problem. First, an enhanced sampling method, named parallel continuous simulated tempering (PCST), is developed to accelerate the molecular dynamics (MD) simulation. Second, two energy biasing methods, Structure-Based Model (SBM) and Ensemble-Based Model (EBM), are introduced to perform targeted sampling around important conformations. Third, a three-step method is developed to blindly select high-quality models along the MD simulation. These methods work together to make significant refinement of low-quality models without any knowledge of the solution. The effectiveness of these methods is examined in different applications. Using the PCST-SBM method, models with higher global distance test scores (GDT_TS) are generated and selected in the MD simulation of 18 targets from the refinement category of the 10th Critical Assessment of Structure Prediction (CASP10). In addition, in the refinement test of two CASP10 targets using the PCST-EBM method, it is indicated that EBM may bring the initial model to even higher-quality levels. Furthermore, a multi-round refinement protocol of PCST-SBM improves the model quality of a protein to the level that is sufficient high for the molecular replacement in X-ray crystallography. Our results justify the crucial position of enhanced sampling in the protein structure prediction and demonstrate that a considerable improvement of low-accuracy structures is still achievable with current force fields.

Air quality modeling for the urban Jackson, Mississippi Region using a high resolution WRF/Chem model.

PubMed

Yerramilli, Anjaneyulu; Dodla, Venkata B; Desamsetti, Srinivas; Challa, Srinivas V; Young, John H; Patrick, Chuck; Baham, Julius M; Hughes, Robert L; Yerramilli, Sudha; Tuluri, Francis; Hardy, Mark G; Swanier, Shelton J

2011-06-01

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting-Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators.

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

A coastal three-dimensional water quality model of nitrogen in Jiaozhou Bay linking field experiments with modelling.

PubMed

Lu, Dongliang; Li, Keqiang; Liang, Shengkang; Lin, Guohong; Wang, Xiulin

2017-01-15

With anthropogenic changes, the structure and quantity of nitrogen nutrients have changed in coastal ocean, which has dramatically influenced the water quality. Water quality modeling can contribute to the necessary scientific grounding of coastal management. In this paper, some of the dynamic functions and parameters of nitrogen were calibrated based on coastal field experiments covering the dynamic nitrogen processes in Jiaozhou Bay (JZB), including phytoplankton growth, respiration, and mortality; particulate nitrogen degradation; and dissolved organic nitrogen remineralization. The results of the field experiments and box model simulations showed good agreement (RSD=20%±2% and SI=0.77±0.04). A three-dimensional water quality model of nitrogen (3DWQMN) in JZB was improved and the dynamic parameters were updated according to field experiments. The 3DWQMN was validated based on observed data from 2012 to 2013, with good agreement (RSD=27±4%, SI=0.68±0.06, and K=0.48±0.04), which testifies to the model's credibility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Klang River water quality modelling using music

NASA Astrophysics Data System (ADS)

Zahari, Nazirul Mubin; Zawawi, Mohd Hafiz; Muda, Zakaria Che; Sidek, Lariyah Mohd; Fauzi, Nurfazila Mohd; Othman, Mohd Edzham Fareez; Ahmad, Zulkepply

2017-09-01

Water is an essential resource that sustains life on earth; changes in the natural quality and distribution of water have ecological impacts that can sometimes be devastating. Recently, Malaysia is facing many environmental issues regarding water pollution. The main causes of river pollution are rapid urbanization, arising from the development of residential, commercial, industrial sites, infrastructural facilities and others. The purpose of the study was to predict the water quality of the Connaught Bridge Power Station (CBPS), Klang River. Besides that, affects to the low tide and high tide and. to forecast the pollutant concentrations of the Biochemical Oxygen Demand (BOD) and Total Suspended Solid (TSS) for existing land use of the catchment area through water quality modeling (by using the MUSIC software). Besides that, to identifying an integrated urban stormwater treatment system (Best Management Practice or BMPs) to achieve optimal performance in improving the water quality of the catchment using the MUSIC software in catchment areas having tropical climates. Result from MUSIC Model such as BOD5 at station 1 can be reduce the concentration from Class IV to become Class III. Whereas, for TSS concentration from Class III to become Class II at the station 1. The model predicted a mean TSS reduction of 0.17%, TP reduction of 0.14%, TN reduction of 0.48% and BOD5 reduction of 0.31% for Station 1 Thus, from the result after purposed BMPs the water quality is safe to use because basically water quality monitoring is important due to threat such as activities are harmful to aquatic organisms and public health.

Developing Quality Indicators and Auditing Protocols from Formal Guideline Models: Knowledge Representation and Transformations

PubMed Central

Advani, Aneel; Goldstein, Mary; Shahar, Yuval; Musen, Mark A.

2003-01-01

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically (1) context-specific and (2) case-mix-adjusted quality indicators that (3) can model global or local levels of detail about the guideline (4) parameterized by defining the reliability of each indicator or element of the guideline. PMID:14728124

The Use of Remote Sensing Data for Modeling Air Quality in the Cities

NASA Astrophysics Data System (ADS)

Putrenko, V. V.; Pashynska, N. M.

2017-12-01

Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.

Quality control of the RMS US flood model

NASA Astrophysics Data System (ADS)

Jankowfsky, Sonja; Hilberts, Arno; Mortgat, Chris; Li, Shuangcai; Rafique, Farhat; Rajesh, Edida; Xu, Na; Mei, Yi; Tillmanns, Stephan; Yang, Yang; Tian, Ye; Mathur, Prince; Kulkarni, Anand; Kumaresh, Bharadwaj Anna; Chaudhuri, Chiranjib; Saini, Vishal

2016-04-01

The RMS US flood model predicts the flood risk in the US with a 30 m resolution for different return periods. The model is designed for the insurance industry to estimate the cost of flood risk for a given location. Different statistical, hydrological and hydraulic models are combined to develop the flood maps for different return periods. A rainfall-runoff and routing model, calibrated with observed discharge data, is run with 10 000 years of stochastic simulated precipitation to create time series of discharge and surface runoff. The 100, 250 and 500 year events are extracted from these time series as forcing for a two-dimensional pluvial and fluvial inundation model. The coupling of all the different models which are run on the large area of the US implies a certain amount of uncertainty. Therefore, special attention is paid to the final quality control of the flood maps. First of all, a thorough quality analysis of the Digital Terrain model and the river network was done, as the final quality of the flood maps depends heavily on the DTM quality. Secondly, the simulated 100 year discharge in the major river network (600 000 km) is compared to the 100 year discharge derived using extreme value distribution of all USGS gauges with more than 20 years of peak values (around 11 000 gauges). Thirdly, for each gauge the modelled flood depth is compared to the depth derived from the USGS rating curves. Fourthly, the modelled flood depth is compared to the base flood elevation given in the FEMA flood maps. Fifthly, the flood extent is compared to the FEMA flood extent. Then, for historic events we compare flood extents and flood depths at given locations. Finally, all the data and spatial layers are uploaded on geoserver to facilitate the manual investigation of outliers. The feedback from the quality control is used to improve the model and estimate its uncertainty.

On Regional Modeling to Support Air Quality Policies

EPA Science Inventory

We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing mete...

An Effect of the Co-Operative Network Model for Students' Quality in Thai Primary Schools

ERIC Educational Resources Information Center

Khanthaphum, Udomsin; Tesaputa, Kowat; Weangsamoot, Visoot

2016-01-01

This research aimed: 1) to study the current and desirable states of the co-operative network in developing the learners' quality in Thai primary schools, 2) to develop a model of the co-operative network in developing the learners' quality, and 3) to examine the results of implementation of the co-operative network model in the primary school.…

Quality management in home care: models for today's practice.

PubMed

Verhey, M P

1996-01-01

In less than a decade, home care providers have been a part of two major transitions in health care delivery. First, because of the advent of managed care and a shift from inpatient to community-based services, home care service delivery systems have experienced tremendous growth. Second, the principles and practices of total quality management and continuous quality improvement have permeated the organization, administration, and practice of home health care. Based on the work of Deming, Juran, and Crosby, the basic tenets of the new quality management philosophy involve a focus on the following five key areas: (1) systems and processes rather than individual performance; (2) involvement, collaboration, and empowerment; (3) internal and external "customers"; (4) data and measurement; and (5) standards, guidelines, and outcomes of care. Home care providers are among those in the forefront who are developing and implementing programs that integrate these foci into the delivery of quality home care services. This article provides a summary of current home care programs that address these five key areas of quality management philosophy and provide models for innovative quality management practice in home care. For further information about each program, readers are referred to the original reports in the home care and quality management journal literature, as cited herein.

Modelling the impacts of agricultural management practices on river water quality in Eastern England.

PubMed

Taylor, Sam D; He, Yi; Hiscock, Kevin M

2016-09-15

Agricultural diffuse water pollution remains a notable global pressure on water quality, posing risks to aquatic ecosystems, human health and water resources and as a result legislation has been introduced in many parts of the world to protect water bodies. Due to their efficiency and cost-effectiveness, water quality models have been increasingly applied to catchments as Decision Support Tools (DSTs) to identify mitigation options that can be introduced to reduce agricultural diffuse water pollution and improve water quality. In this study, the Soil and Water Assessment Tool (SWAT) was applied to the River Wensum catchment in eastern England with the aim of quantifying the long-term impacts of potential changes to agricultural management practices on river water quality. Calibration and validation were successfully performed at a daily time-step against observations of discharge, nitrate and total phosphorus obtained from high-frequency water quality monitoring within the Blackwater sub-catchment, covering an area of 19.6 km(2). A variety of mitigation options were identified and modelled, both singly and in combination, and their long-term effects on nitrate and total phosphorus losses were quantified together with the 95% uncertainty range of model predictions. Results showed that introducing a red clover cover crop to the crop rotation scheme applied within the catchment reduced nitrate losses by 19.6%. Buffer strips of 2 m and 6 m width represented the most effective options to reduce total phosphorus losses, achieving reductions of 12.2% and 16.9%, respectively. This is one of the first studies to quantify the impacts of agricultural mitigation options on long-term water quality for nitrate and total phosphorus at a daily resolution, in addition to providing an estimate of the uncertainties of those impacts. The results highlighted the need to consider multiple pollutants, the degree of uncertainty associated with model predictions and the risk of

Development of Water Quality Modeling in the United States

EPA Science Inventory

This presentation describes historical trends in water quality model development in the United States, reviews current efforts, and projects promising future directions. Water quality modeling has a relatively long history in the United States. While its origins lie in the work...

An Overview of Atmospheric Chemistry and Air Quality Modeling

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.

2017-01-01

This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

Stressor-response modeling using the 2D water quality model and regression trees to predict chlorophyll-a in a reservoir system

USDA-ARS?s Scientific Manuscript database

In order to control algal blooms, stressor-response relationships between water quality metrics, environmental variables, and algal growth should be understood and modeled. Machine-learning methods were suggested to express stressor-response relationships found by application of mechanistic water qu...

THE ATMOSPHERIC MODEL EVALUATION TOOL (AMET); AIR QUALITY MODULE

EPA Science Inventory

This presentation reviews the development of the Atmospheric Model Evaluation Tool (AMET) air quality module. The AMET tool is being developed to aid in the model evaluation. This presentation focuses on the air quality evaluation portion of AMET. Presented are examples of the...

AIR QUALITY SIMULATION MODEL PERFORMANCE FOR ONE-HOUR AVERAGES

EPA Science Inventory

If a one-hour standard for sulfur dioxide were promulgated, air quality dispersion modeling in the vicinity of major point sources would be an important air quality management tool. Would currently available dispersion models be suitable for use in demonstrating attainment of suc...

Satellite remote sensing for modeling and monitoring of water quality in the Great Lakes

NASA Astrophysics Data System (ADS)

Coffield, S. R.; Crosson, W. L.; Al-Hamdan, M. Z.; Barik, M. G.

2017-12-01

Consistent and accurate monitoring of the Great Lakes is critical for protecting the freshwater ecosystems, quantifying the impacts of climate change, understanding harmful algal blooms, and safeguarding public health for the millions who rely on the Lakes for drinking water. While ground-based monitoring is often hampered by limited sampling resolution, satellite data provide surface reflectance measurements at much more complete spatial and temporal scales. In this study, we implemented NASA data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to build robust water quality models. We developed and validated models for chlorophyll-a, nitrogen, phosphorus, and turbidity based on combinations of the six MODIS Ocean Color bands (412, 443, 488, 531, 547, and 667nm) for 2003-2016. Second, we applied these models to quantify trends in water quality through time and in relation to changing land cover, runoff, and climate for six selected coastal areas in Lakes Michigan and Erie. We found strongest models for chlorophyll-a in Lake Huron (R2 = 0.75), nitrogen in Lake Ontario (R2=0.66), phosphorus in Lake Erie (R2=0.60), and turbidity in Lake Erie (R2=0.86). These offer improvements over previous efforts to model chlorophyll-a while adding nitrogen, phosphorus, and turbidity. Mapped water quality parameters showed high spatial variability, with nitrogen concentrated largely in Superior and coastal Michigan and high turbidity, phosphorus, and chlorophyll near urban and agricultural areas of Erie. Temporal analysis also showed concurrence of high runoff or precipitation and nitrogen in Lake Michigan offshore of wetlands, suggesting that water quality in these areas is sensitive to changes in climate.

Does export product quality matter for CO2 emissions? Evidence from China.

PubMed

Gozgor, Giray; Can, Muhlis

2017-01-01

This paper re-estimates the environmental Kuznets curve (EKC) in China. To this end, it uses the unit root tests with structural breaks and the autoregressive-distributed lag (ARDL) estimations over the period 1971-2010. The special role is given to the impact of export product quality on CO 2 emissions in the empirical models. The paper finds that the EKC hypothesis is applicable in China. It also observes the positive effect from energy consumption to CO 2 emissions. In addition, it finds that the export product quality is negatively associated with CO 2 emissions. The paper also argues potential implications.

Community Multiscale Air Quality Model

EPA Science Inventory

The U.S. EPA developed the Community Multiscale Air Quality (CMAQ) system to apply a “one atmosphere” multiscale and multi-pollutant modeling approach based mainly on the “first principles” description of the atmosphere. The multiscale capability is supported by the governing di...

A diagnostic model for studying daytime urban air quality trends

NASA Technical Reports Server (NTRS)

Brewer, D. A.; Remsberg, E. E.; Woodbury, G. E.

1981-01-01

A single cell Eulerian photochemical air quality simulation model was developed and validated for selected days of the 1976 St. Louis Regional Air Pollution Study (RAPS) data sets; parameterizations of variables in the model and validation studies using the model are discussed. Good agreement was obtained between measured and modeled concentrations of NO, CO, and NO2 for all days simulated. The maximum concentration of O3 was also predicted well. Predicted species concentrations were relatively insensitive to small variations in CO and NOx emissions and to the concentrations of species which are entrained as the mixed layer rises.

[Professional quality of life in the clinical governance model of Asturias (Spain)].

PubMed

Díaz Corte, Carmen; Suárez Álvarez, Óscar; Fueyo Gutiérrez, Alejandra; Mola Caballero de Rodas, Pablo; Rancaño García, Iván; Sánchez Fernández, Ana María; Suárez Gutiérrez, Rebeca; Díaz Vázquez, Carlos

2013-01-01

To evaluate professional quality of life in our clinical governance model by comparing differences according to the time since the model's implementation (1-3 years) and the setting (primary or hospital care). A cross-sectional descriptive study was performed. The 35-item, anonymous, self-administered Professional Quality of Life Questionnaire, with three additional questions, was applied. A minimum sample size for each clinical governance unit/area (CGU/CGA) was calculated. Descriptive, univariate and bivariate analyses were performed using the 35 items separately. The subscales of « management support », « workload » and « intrinsic motivation » were used as dependant variables, and the setting and time since implementation of the CGU/CGA as independent variables. Of the study population of 2572 professionals, 1395 (54%) responded (67% in primary care and 51% in hospital care). A total of 87% had been working for 5 years or more in their positions. Thirty-three percent had worked for less than a year in clinical governance. The item with the highest score was job training (8.39 ± 1.42) and that with the lowest was conflicts with peers (3.23 ± 2.2). Primary healthcare professionals showed better results in management support and quality of life at work and hospital professionals in workload. The clinical governance model obtained the best scores at 3 years and the worst at 1 year. These differences were especially favorable for clinical governance in hospitals: professionals working longer perceived a lower workload and more intrinsic motivation and quality of life. A longer time working in the clinical governance model was associated with better perception of professional quality of life, especially in hospital care. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

Quantitative image quality evaluation of MR images using perceptual difference models

PubMed Central

Miao, Jun; Huo, Donglai; Wilson, David L.

2008-01-01

The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality. The Case-PDM threshold for nonperceptible differences in a 2-alternative forced choice study varied with the type of image under study, but was ≈1.1 for diffuse image effects, providing a rule of thumb. Ordering the image quality evaluation models, we found in overall Case-PDM ≈ IDM (Sarnoff Corporation) ≈ SSIM [Wang et al. IEEE Trans. Image Process. 13, 600–612 (2004)] > mean squared error ≈ NR [Wang et al. (2004) (unpublished)] > DCTune (NASA) > IQM (MITRE Corporation). The authors conclude that Case-PDM is very useful in MR image evaluation but that one should probably restrict studies to similar images and similar processing, normally not a limitation in image reconstruction studies. PMID:18649487

INTERDEPENDENCIES OF MULTI-POLLUTANT CONTROL SIMULATIONS IN AN AIR QUALITY MODEL

EPA Science Inventory

In this work, we use the Community Multi-Scale Air Quality (CMAQ) modeling system to examine the effect of several control strategies on simultaneous concentrations of ozone, PM_2.5, and three important HAPs: formaldehyde, acetaldehyde and benzene.

The development of an integrated Indonesian health care model using Kano's model, quality function deployment and balanced scorecard

NASA Astrophysics Data System (ADS)

Jonny, Zagloed, Teuku Yuri M.

2017-11-01

This paper aims to present an integrated health care model for Indonesian health care industry. Based on previous researches, there are two health care models in the industry such as decease- and patient-centered care models. In their developments, the patient-centered care model is widely applied due to its capability in reducing cost and improving quality simultaneously. However, there is still no comprehensive model resulting in cost reduction, quality improvement, patient satisfaction and hospital profitability simultaneously. Therefore, this research is intended to develop that model. In doing so, first, a conceptual model using Kano's Model, Quality Function Deployment (QFD) and Balanced Scorecard (BSC) is developed to generate several important elements of the model as required by stakeholders. Then, a case study of an Indonesian hospital is presented to evaluate the validity of the model using correlation analysis. As a result, it can be concluded that the model is validated implying several managerial insights among its elements such as l) leadership (r=0.85) and context of the organization (r=0.77) improve operations; 2) planning (r=0.96), support process (r=0.87) and continual improvement (r=0.95) also improve operations; 3) operations improve customer satisfaction (r=0.89) and financial performance (r=0.93) and 4) customer satisfaction improves the financial performance (0.98).

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

Identification of water quality degradation hotspots in developing countries by applying large scale water quality modelling

NASA Astrophysics Data System (ADS)

Malsy, Marcus; Reder, Klara; Flörke, Martina

2014-05-01

Decreasing water quality is one of the main global issues which poses risks to food security, economy, and public health and is consequently crucial for ensuring environmental sustainability. During the last decades access to clean drinking water increased, but 2.5 billion people still do not have access to basic sanitation, especially in Africa and parts of Asia. In this context not only connection to sewage system is of high importance, but also treatment, as an increasing connection rate will lead to higher loadings and therefore higher pressure on water resources. Furthermore, poor people in developing countries use local surface waters for daily activities, e.g. bathing and washing. It is thus clear that water utilization and water sewerage are indispensable connected. In this study, large scale water quality modelling is used to point out hotspots of water pollution to get an insight on potential environmental impacts, in particular, in regions with a low observation density and data gaps in measured water quality parameters. We applied the global water quality model WorldQual to calculate biological oxygen demand (BOD) loadings from point and diffuse sources, as well as in-stream concentrations. Regional focus in this study is on developing countries i.e. Africa, Asia, and South America, as they are most affected by water pollution. Hereby, model runs were conducted for the year 2010 to draw a picture of recent status of surface waters quality and to figure out hotspots and main causes of pollution. First results show that hotspots mainly occur in highly agglomerated regions where population density is high. Large urban areas are initially loading hotspots and pollution prevention and control become increasingly important as point sources are subject to connection rates and treatment levels. Furthermore, river discharge plays a crucial role due to dilution potential, especially in terms of seasonal variability. Highly varying shares of BOD sources across

Development of the Next Generation Air Quality Modeling System

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

A Total Quality Leadership Process Improvement Model

DTIC Science & Technology

1993-12-01

Leadership Process Improvement Model by Archester Houston, Ph.D. and Steven L. Dockstader, Ph.D. DTICS ELECTE tleaese oand sale itsFeat ben proe 94-12058...tTl ’AND SIATE COVERID0 Z lits Z40 uerI’Ll12/93 IFinalS.FNR IM F A Total Quality Leadership Process Improvement Model M ARRhOW~ Archester Houston, Ph.D...and Steven L. Dockstader, Ph.D. ?. 7PEJORMING ORG-AN1:AION NAMEIS) AND 00-RESS(ES) L PERFORMIN4 ORAINIZATION Total Quality Leadership OfficeREOTNMR

Voice Quality Modelling for Expressive Speech Synthesis

PubMed Central

Socoró, Joan Claudi

2014-01-01

This paper presents the perceptual experiments that were carried out in order to validate the methodology of transforming expressive speech styles using voice quality (VoQ) parameters modelling, along with the well-known prosody (F 0, duration, and energy), from a neutral style into a number of expressive ones. The main goal was to validate the usefulness of VoQ in the enhancement of expressive synthetic speech in terms of speech quality and style identification. A harmonic plus noise model (HNM) was used to modify VoQ and prosodic parameters that were extracted from an expressive speech corpus. Perception test results indicated the improvement of obtained expressive speech styles using VoQ modelling along with prosodic characteristics. PMID:24587738

Atmospheric Model Evaluation Tool for meteorological and air quality simulations

EPA Pesticide Factsheets

The Atmospheric Model Evaluation Tool compares model predictions to observed data from various meteorological and air quality observation networks to help evaluate meteorological and air quality simulations.

The Effect of an Education Program Utilising PRECEDE Model on the Quality of Life in Patients with Type 2 Diabetes

ERIC Educational Resources Information Center

Taghdisi, M. H.; Borhani, M.; Solhi, M.; Afkari, M. E.; Hosseini, F.

2012-01-01

Background and objective: The problems caused by diabetes have direct and indirect impacts on the quality of life of diabetic patients. An increase of these problems means a decrease in a patient's quality of life. This study was conducted to assess the effect of the educational programme based on the precede model in promoting quality of life of…

Regional Air Quality Model Application of the Aqueous-Phase ...

EPA Pesticide Factsheets

In most ecosystems, atmospheric deposition is the primary input of mercury. The total wet deposition of mercury in atmospheric chemistry models is sensitive to parameterization of the aqueous-phase reduction of divalent oxidized mercury (Hg2+). However, most atmospheric chemistry models use a parameterization of the aqueous-phase reduction of Hg2+ that has been shown to be unlikely under normal ambient conditions or use a non mechanistic value derived to optimize wet deposition results. Recent laboratory experiments have shown that Hg2+ can be photochemically reduced to elemental mercury (Hg) in the aqueous-phase by dissolved organic matter and a mechanism and the rate for Hg2+ photochemical reduction by dicarboxylic acids (DCA) has been proposed. For the first time in a regional scale model, the DCA mechanism has been applied. The HO2-Hg2+ reduction mechanism, the proposed DCA reduction mechanism, and no aqueous-phase reduction (NAR) of Hg2+ are evaluated against weekly wet deposition totals, concentrations and precipitation observations from the Mercury Deposition Network (MDN) using the Community Multiscale Air Quality (CMAQ) model version 4.7.1. Regional scale simulations of mercury wet deposition using a DCA reduction mechanism evaluated well against observations, and reduced the bias in model evaluation by at least 13% over the other schemes evaluated, although summertime deposition estimates were still biased by −31.4% against observations. The use of t

Air Quality Modeling for the Urban Jackson, Mississippi Region Using a High Resolution WRF/Chem Model

PubMed Central

Yerramilli, Anjaneyulu; Dodla, Venkata B.; Desamsetti, Srinivas; Challa, Srinivas V.; Young, John H.; Patrick, Chuck; Baham, Julius M.; Hughes, Robert L.; Yerramilli, Sudha; Tuluri, Francis; Hardy, Mark G.; Swanier, Shelton J.

2011-01-01

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting–Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators. PMID:21776240

Impact of inherent meteorology uncertainty on air quality model predictions

EPA Science Inventory

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

PubMed Central

2012-01-01

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management. PMID:23369363

FIRST RESULTS FROM OPERATIONAL TESTING OF THE U.S. EPA MODELS-3 COMMUNITY MULTISCALE MODEL FOR AIR QUALITY (CMAQ)

EPA Science Inventory

The Models 3 / Community Multiscale Model for Air Quality (CMAQ) has been designed for one-atmosphere assessments for multiple pollutants including ozone (O3), particulate matter (PM10, PM2.5), and acid / nutrient deposition. In this paper we report initial results of our evalu...

Evaluating four N2O emission algorithms in RZWQM2 in response to N rate on an irrigated corn field

USDA-ARS?s Scientific Manuscript database

Nitrous oxide (N2O) emissions from agricultural soils are major contributors to greenhouse gases. Correctly assessing the effects of the interactions between agricultural practices and environmental factors on N2O emissions is required for better crop and nitrogen (N) management. We used an enhanced...

Large-scale structure prediction by improved contact predictions and model quality assessment.

PubMed

Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

2017-07-15

Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Indoor Air Quality Building Education and Assessment Model

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM), released in 2002, is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts

NASA Astrophysics Data System (ADS)

Gan, Chuen-Meei

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

Enhancing population pharmacokinetic modeling efficiency and quality using an integrated workflow.

PubMed

Schmidt, Henning; Radivojevic, Andrijana

2014-08-01

Population pharmacokinetic (popPK) analyses are at the core of Pharmacometrics and need to be performed regularly. Although these analyses are relatively standard, a large variability can be observed in both the time (efficiency) and the way they are performed (quality). Main reasons for this variability include the level of experience of a modeler, personal preferences and tools. This paper aims to examine how the process of popPK model building can be supported in order to increase its efficiency and quality. The presented approach to the conduct of popPK analyses is centered around three key components: (1) identification of most common and important popPK model features, (2) required information content and formatting of the data for modeling, and (3) methodology, workflow and workflow supporting tools. This approach has been used in several popPK modeling projects and a documented example is provided in the supplementary material. Efficiency of model building is improved by avoiding repetitive coding and other labor-intensive tasks and by putting the emphasis on a fit-for-purpose model. Quality is improved by ensuring that the workflow and tools are in alignment with a popPK modeling guidance which is established within an organization. The main conclusion of this paper is that workflow based approaches to popPK modeling are feasible and have significant potential to ameliorate its various aspects. However, the implementation of such an approach in a pharmacometric organization requires openness towards innovation and change-the key ingredient for evolution of integrative and quantitative drug development in the pharmaceutical industry.

Health and quality of life outcomes impairment of quality of life in type 2 diabetes mellitus: a cross-sectional study.

PubMed

Zurita-Cruz, Jessie N; Manuel-Apolinar, Leticia; Arellano-Flores, María Luisa; Gutierrez-Gonzalez, Alejandro; Najera-Ahumada, Alma Gloria; Cisneros-González, Nelly

2018-05-15

Type 2 diabetes mellitus (DM2) is a chronic disease, and for treatment to succeed, it is necessary to harmonize the mental health of the patient with the environment, which impacts quality of life and adherence to medical regimens. The objetive of this study is describe the quality of life of patients with DM2 and the factors relates to its modification. This investigation was a cross-sectional study. Patients over 18 years of age with DM2 were selected. The following variables related to quality of life were studied: age, sex, occupation, marital status, years of DM2 evolution, comorbidities and presence of depression (Beck Depression Inventory). Perceived quality of life was measured with a health-related quality of life (HRQoL) scale, the 36-Item Short-Form Survey (SF-36). Patients were classified according to SF-36 HRQoL score (< 50, 51-75 and > 76 points). Among the 1394 patients included, the median age was 62 years. Global HRQoL had a median of 50.1 points. Bivariate analysis showed that age, marital status, sex, occupation, comorbidities, duration of DM2 and comorbidities had impacts on HRQoL. The logistic regression model identified age (odds ratio [OR] 1.04) and depression (OR 4.4) as independent factors that influenced overall quality of life. Patients with DM2 have poor HRQoL, which is associated with a high frequency of depression. Older age and the presence of depression impair patient HRQoL. R-2013-781-052. Registered 20 December 2014.

ONE-ATMOSPHERE DYNAMICS DESCRIPTION IN THE MODELS-3 COMMUNITY MULTI-SCALE QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

This paper proposes a general procedure to link meteorological data with air quality models, such as U.S. EPA's Models-3 Community Multi-scale Air Quality (CMAQ) modeling system. CMAQ is intended to be used for studying multi-scale (urban and regional) and multi-pollutant (ozon...

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.

A three-dimensional conceptual model of the water quality distribution in the Albuquerque Basin

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

Romero, D.

1995-12-31

It is possible to construct a conceptual model of the Albuquerque Basin`s geochemical characteristics and water quality distribution based on (1) the Hawley and Haase hydrogeological model, (2) water analyses from City of Albuquerque water wells, and (3) sound geological and chemical principles. Previous studies have characterized the water quality and geochemistry of the Albuquerque Basin from a two-dimensional perspective; however, to date, there has been no examination of the variation of water quality with depth within the Albuquerque Basin. The primary focus of this paper is to describe a first attempt at developing a conceptual understanding of the three-dimensionalmore » water quality distribution of the Albuquerque Basin based on the above three building blocks.« less

Path Forward for the Air Quality Model Evaluation International Initiative (AQMEII)

EPA Science Inventory

This article lays out the objectives for Phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII). The inhalation of air pollutants such as ozone and fine particles has been linked to adverse impacts on human health, and the atmospheric deposition of pollutan...

Puget Sound Dissolved Oxygen Modeling Study: Development of an Intermediate Scale Water Quality Model

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

Khangaonkar, Tarang; Sackmann, Brandon S.; Long, Wen

2012-10-01

The Salish Sea, including Puget Sound, is a large estuarine system bounded by over seven thousand miles of complex shorelines, consists of several subbasins and many large inlets with distinct properties of their own. Pacific Ocean water enters Puget Sound through the Strait of Juan de Fuca at depth over the Admiralty Inlet sill. Ocean water mixed with freshwater discharges from runoff, rivers, and wastewater outfalls exits Puget Sound through the brackish surface outflow layer. Nutrient pollution is considered one of the largest threats to Puget Sound. There is considerable interest in understanding the effect of nutrient loads on themore » water quality and ecological health of Puget Sound in particular and the Salish Sea as a whole. The Washington State Department of Ecology (Ecology) contracted with Pacific Northwest National Laboratory (PNNL) to develop a coupled hydrodynamic and water quality model. The water quality model simulates algae growth, dissolved oxygen, (DO) and nutrient dynamics in Puget Sound to inform potential Puget Sound-wide nutrient management strategies. Specifically, the project is expected to help determine 1) if current and potential future nitrogen loadings from point and non-point sources are significantly impairing water quality at a large scale and 2) what level of nutrient reductions are necessary to reduce or control human impacts to DO levels in the sensitive areas. The project did not include any additional data collection but instead relied on currently available information. This report describes model development effort conducted during the period 2009 to 2012 under a U.S. Environmental Protection Agency (EPA) cooperative agreement with PNNL, Ecology, and the University of Washington awarded under the National Estuary Program« less

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.

EVALUATING AND USING AIR QUALITY MODELS

EPA Science Inventory

Grid-based models are being used to assess the magnitude of the pollution problem and to design emission control strategies to achieve compliance with the relevant air quality standards in the United States.

Spatial Allocator for air quality modeling

EPA Pesticide Factsheets

The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.

An assessment model for quality management

NASA Astrophysics Data System (ADS)

Völcker, Chr.; Cass, A.; Dorling, A.; Zilioli, P.; Secchi, P.

2002-07-01

SYNSPACE together with InterSPICE and Alenia Spazio is developing an assessment method to determine the capability of an organisation in the area of quality management. The method, sponsored by the European Space Agency (ESA), is called S9kS (SPiCE- 9000 for SPACE). S9kS is based on ISO 9001:2000 with additions from the quality standards issued by the European Committee for Space Standardization (ECSS) and ISO 15504 - Process Assessments. The result is a reference model that supports the expansion of the generic process assessment framework provided by ISO 15504 to nonsoftware areas. In order to be compliant with ISO 15504, requirements from ISO 9001 and ECSS-Q-20 and Q-20-09 have been turned into process definitions in terms of Purpose and Outcomes, supported by a list of detailed indicators such as Practices, Work Products and Work Product Characteristics. In coordination with this project, the capability dimension of ISO 15504 has been revised to be consistent with ISO 9001. As contributions from ISO 9001 and the space quality assurance standards are separable, the stripped down version S9k offers organisations in all industries an assessment model based solely on ISO 9001, and is therefore interesting to all organisations, which intend to improve their quality management system based on ISO 9001.

[Integrated Quality Management System (IQMS): a model for improving the quality of reproductive health care in rural Kenya].

PubMed

Herrler, Claudia; Bramesfeld, Anke; Brodowski, Marc; Prytherch, Helen; Marx, Irmgard; Nafula, Maureen; Richter-Aairijoki, Heide; Musyoka, Lucy; Marx, Michael; Szecsenyi, Joachim

2015-01-01

To develop a model aiming to improve the quality of services for reproductive health care in rural Kenya and designed to measure the quality of reproductive health services in such a way that allows these services to identify measures for improving their performance. The Integrated Quality Management System (IQMS) was developed on the basis of a pre-existing and validated model for quality promotion, namely the European Practice Assessment (EPA). The methodology for quality assessment and feedback of assessment results to the service teams was adopted from the EPA model. Quality assessment methodology included data assessment through staff, patient surveys and service visitation. Quality is assessed by indicators, and so indicators had to be developed that were appropriate for assessing reproductive health care in rural Kenya. A search of the Kenyan and international literature was conducted to identify potential indicators. These were then rated for their relevance and clarity by a panel of Kenyan experts. 260 indicators were rated as relevant and assigned to 29 quality dimensions and 5 domains. The implementation of IQMS in ten facilities showed that IQMS is a feasible model for assessing the quality of reproductive health services in rural Kenya. IQMS enables these services to identify quality improvement targets and necessary improvement measures. Both strengths and limitations of IQMS will be discussed. Copyright © 2015. Published by Elsevier GmbH.

Meteorological Processes Affecting Air Quality – Research and Model Development Needs

EPA Science Inventory

Meteorology modeling is an important component of air quality modeling systems that defines the physical and dynamical environment for atmospheric chemistry. The meteorology models used for air quality applications are based on numerical weather prediction models that were devel...

Private healthcare quality: applying a SERVQUAL model.

PubMed

Butt, Mohsin Muhammad; de Run, Ernest Cyril

2010-01-01

This paper seeks to develop and test the SERVQUAL model scale for measuring Malaysian private health service quality. The study consists of 340 randomly selected participants visiting a private healthcare facility during a three-month data collection period. Data were analyzed using means, correlations, principal component and confirmatory factor analysis to establish the modified SERVQUAL scale's reliability, underlying dimensionality and convergent, discriminant validity. Results indicate a moderate negative quality gap for overall Malaysian private healthcare service quality. Results also indicate a moderate negative quality gap on each service quality scale dimension. However, scale development analysis yielded excellent results, which can be used in wider healthcare policy and practice. Respondents were skewed towards a younger population, causing concern that the results might not represent all Malaysian age groups. The study's major contribution is that it offers a way to assess private healthcare service quality. Second, it successfully develops a scale that can be used to measure health service quality in Malaysian contexts.

An integrated system dynamics model developed for managing lake water quality at the watershed scale.

PubMed

Liu, Hui; Benoit, Gaboury; Liu, Tao; Liu, Yong; Guo, Huaicheng

2015-05-15

A reliable system simulation to relate socioeconomic development with water environment and to comprehensively represent a watershed's dynamic features is important. In this study, after identifying lake watershed system processes, we developed a system dynamics modeling framework for managing lake water quality at the watershed scale. Two reinforcing loops (Development and Investment Promotion) and three balancing loops (Pollution, Resource Consumption, and Pollution Control) were constituted. Based on this work, we constructed Stock and Flow Diagrams that embedded a pollutant load model and a lake water quality model into a socioeconomic system dynamics model. The Dianchi Lake in Yunnan Province, China, which is the sixth largest and among the most severely polluted freshwater lakes in China, was employed as a case study to demonstrate the applicability of the model. Water quality parameters considered in the model included chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP). The business-as-usual (BAU) scenario and three alternative management scenarios on spatial adjustment of industries and population (S1), wastewater treatment capacity construction (S2), and structural adjustment of agriculture (S3), were simulated to assess the effectiveness of certain policies in improving water quality. Results showed that S2 is most effective scenario, and the COD, TN, and TP concentrations in Caohai in 2030 are 52.5, 10.9, and 0.8 mg/L, while those in Waihai are 9.6, 1.2, and 0.08 mg/L, with sustained development in the watershed. Thus, the model can help support the decision making required in development and environmental protection strategies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fuzzy intelligent quality monitoring model for X-ray image processing.

PubMed

Khalatbari, Azadeh; Jenab, Kouroush

2009-01-01

Today's imaging diagnosis needs to adapt modern techniques of quality engineering to maintain and improve its accuracy and reliability in health care system. One of the main factors that influences diagnostic accuracy of plain film X-ray on detecting pathology is the level of film exposure. If the level of film exposure is not adequate, a normal body structure may be interpretated as pathology and vice versa. This not only influences the patient management but also has an impact on health care cost and patient's quality of life. Therefore, providing an accurate and high quality image is the first step toward an excellent patient management in any health care system. In this paper, we study these techniques and also present a fuzzy intelligent quality monitoring model, which can be used to keep variables from degrading the image quality. The variables derived from chemical activity, cleaning procedures, maintenance, and monitoring may not be sensed, measured, or calculated precisely due to uncertain situations. Therefore, the gamma-level fuzzy Bayesian model for quality monitoring of an image processing is proposed. In order to apply the Bayesian concept, the fuzzy quality characteristics are assumed as fuzzy random variables. Using the fuzzy quality characteristics, the newly developed model calculates the degradation risk for image processing. A numerical example is also presented to demonstrate the application of the model.

Modelling the urban air quality in Hamburg with the new city-scale chemistry transport model CityChem

NASA Astrophysics Data System (ADS)

Karl, Matthias; Ramacher, Martin; Aulinger, Armin; Matthias, Volker; Quante, Markus

2017-04-01

Air quality modelling plays an important role by providing guidelines for efficient air pollution abatement measures. Currently, most urban dispersion models treat air pollutants as passive tracer substances or use highly simplified chemistry when simulating air pollutant concentrations on the city-scale. The newly developed urban chemistry-transport model CityChem has the capability of modelling the photochemical transformation of multiple pollutants along with atmospheric diffusion to produce pollutant concentration fields for the entire city on a horizontal resolution of 100 m or even finer and a vertical resolution of 24 layers up to 4000 m height. CityChem is based on the Eulerian urban dispersion model EPISODE of the Norwegian Institute for Air Research (NILU). CityChem treats the complex photochemistry in cities using detailed EMEP chemistry on an Eulerian 3-D grid, while using simple photo-stationary equilibrium on a much higher resolution grid (receptor grid), i.e. close to industrial point sources and traffic sources. The CityChem model takes into account that long-range transport contributes to urban pollutant concentrations. This is done by using 3-D boundary concentrations for the city domain derived from chemistry-transport simulations with the regional air quality model CMAQ. For the study of the air quality in Hamburg, CityChem was set-up with a main grid of 30×30 grid cells of 1×1 km2 each and a receptor grid of 300×300 grid cells of 100×100 m2. The CityChem model was driven with meteorological data generated by the prognostic meteorology component of the Australian chemistry-transport model TAPM. Bottom-up inventories of emissions from traffic, industry, households were based on data of the municipality of Hamburg. Shipping emissions for the port of Hamburg were taken from the Clean North Sea Shipping project. Episodes with elevated ozone (O3) were of specific interest for this study, as these are associated with exceedances of the World

Simulating maize yield and bomass with spatial variability of soil field capacity

USGS Publications Warehouse

Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

2015-01-01

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Quality assessment of protein model-structures using evolutionary conservation.

PubMed

Kalman, Matan; Ben-Tal, Nir

2010-05-15

Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface. We present ConQuass, a novel quality assessment program based on the consistency between the model structure and the protein's conservation pattern. We show that it can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. We also show that when the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs. A perl implementation of the method, as well as the various perl and R scripts used for the analysis are available at http://bental.tau.ac.il/ConQuass/. nirb@tauex.tau.ac.il Supplementary data are available at Bioinformatics online.

Comparative evaluation of urban storm water quality models

NASA Astrophysics Data System (ADS)

Vaze, J.; Chiew, Francis H. S.

2003-10-01

The estimation of urban storm water pollutant loads is required for the development of mitigation and management strategies to minimize impacts to receiving environments. Event pollutant loads are typically estimated using either regression equations or "process-based" water quality models. The relative merit of using regression models compared to process-based models is not clear. A modeling study is carried out here to evaluate the comparative ability of the regression equations and process-based water quality models to estimate event diffuse pollutant loads from impervious surfaces. The results indicate that, once calibrated, both the regression equations and the process-based model can estimate event pollutant loads satisfactorily. In fact, the loads estimated using the regression equation as a function of rainfall intensity and runoff rate are better than the loads estimated using the process-based model. Therefore, if only estimates of event loads are required, regression models should be used because they are simpler and require less data compared to process-based models.

The NASA Lightning Nitrogen Oxides Model (LNOM): Application to Air Quality Modeling

NASA Technical Reports Server (NTRS)

Koshak, William; Peterson, Harold; Khan, Maudood; Biazar, Arastoo; Wang, Lihua

2011-01-01

Recent improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) and its application to the Community Multiscale Air Quality (CMAQ) modeling system are discussed. The LNOM analyzes Lightning Mapping Array (LMA) and National Lightning Detection Network(TradeMark)(NLDN) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NO(x) (= NO + NO2). The latest LNOM estimates of lightning channel length distributions, lightning 1-m segment altitude distributions, and the vertical profile of lightning NO(x) are presented. The primary improvement to the LNOM is the inclusion of non-return stroke lightning NOx production due to: (1) hot core stepped and dart leaders, (2) stepped leader corona sheath, K-changes, continuing currents, and M-components. The impact of including LNOM-estimates of lightning NO(x) for an August 2006 run of CMAQ is discussed.

A pilot modeling technique for handling-qualities research

NASA Technical Reports Server (NTRS)

Hess, R. A.

1980-01-01

A brief survey of the more dominant analysis techniques used in closed-loop handling-qualities research is presented. These techniques are shown to rely on so-called classical and modern analytical models of the human pilot which have their foundation in the analysis and design principles of feedback control. The optimal control model of the human pilot is discussed in some detail and a novel approach to the a priori selection of pertinent model parameters is discussed. Frequency domain and tracking performance data from 10 pilot-in-the-loop simulation experiments involving 3 different tasks are used to demonstrate the parameter selection technique. Finally, the utility of this modeling approach in handling-qualities research is discussed.

APPLICATION AND EVALUATION OF CMAQ IN THE UNITED STATES: AIR QUALITY FORECASTING AND RETROSPECTIVE MODELING

EPA Science Inventory

Presentation slides provide background on model evaluation techniques. Also included in the presentation is an operational evaluation of 2001 Community Multiscale Air Quality (CMAQ) annual simulation, and an evaluation of PM_2.5 for the CMAQ air quality forecast (AQF) ...

Optimum profit model considering production, quality and sale problem

NASA Astrophysics Data System (ADS)

Chen, Chung-Ho; Lu, Chih-Lun

2011-12-01

Chen and Liu ['Procurement Strategies in the Presence of the Spot Market-an Analytical Framework', Production Planning and Control, 18, 297-309] presented the optimum profit model between the producers and the purchasers for the supply chain system with a pure procurement policy. However, their model with a simple manufacturing cost did not consider the used cost of the customer. In this study, the modified Chen and Liu's model will be addressed for determining the optimum product and process parameters. The authors propose a modified Chen and Liu's model under the two-stage screening procedure. The surrogate variable having a high correlation with the measurable quality characteristic will be directly measured in the first stage. The measurable quality characteristic will be directly measured in the second stage when the product decision cannot be determined in the first stage. The used cost of the customer will be measured by adopting Taguchi's quadratic quality loss function. The optimum purchaser's order quantity, the producer's product price and the process quality level will be jointly determined by maximising the expected profit between them.

The Utility of the OMI HCHO/NO2 in Air Quality Decision-Making Activities

NASA Technical Reports Server (NTRS)

Duncan, Bryan

2010-01-01

I will discuss a novel and practical application of the OMI HCHU and NO2 data products to the "weight of evidence" in the air quality decision-making process (e.g., State Implementation Plan (SIP)) for a city, region, or state to demonstrate that it is making progress toward attainment of the National Ambient Air Quality Standard (NAAQS) for ozone. Any trend, or lack thereof, in the observed OMI HCHO/NO2 may support that an emission control strategy implemented to reduce ozone is or is not occurring for a metropolitan area. In addition, the observed OMI HCHO/NO2 may be used to define new emission control strategies as the photochemical environments of urban areas evolve over time. I will demonstrate the utility of the OMI HCHO/NO2 over the U.S. for air quality applications with support from simulations with both a regional model and a photochemical box model. These results support mission planning of an OMI-like instrument for the proposed GEO-CAPE satellite that has as one of its objectives to study air quality from space. However, I'm attending the meeting as the Aura Deputy Project Scientist, so I don't technically need to present anything to justify the travel.

Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part 1: Ozone”

EPA Science Inventory

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundar...

A comparison of different functions for predicted protein model quality assessment.

PubMed

Li, Juan; Fang, Huisheng

2016-07-01

In protein structure prediction, a considerable number of models are usually produced by either the Template-Based Method (TBM) or the ab initio prediction. The purpose of this study is to find the critical parameter in assessing the quality of the predicted models. A non-redundant template library was developed and 138 target sequences were modeled. The target sequences were all distant from the proteins in the template library and were aligned with template library proteins on the basis of the transformation matrix. The quality of each model was first assessed with QMEAN and its six parameters, which are C_β interaction energy (C_beta), all-atom pairwise energy (PE), solvation energy (SE), torsion angle energy (TAE), secondary structure agreement (SSA), and solvent accessibility agreement (SAE). Finally, the alignment score (score) was also used to assess the quality of model. Hence, a total of eight parameters (i.e., QMEAN, C_beta, PE, SE, TAE, SSA, SAE, score) were independently used to assess the quality of each model. The results indicate that SSA is the best parameter to estimate the quality of the model.

An annual assessment of air quality with the CALIOPE modeling system over Spain.

PubMed

Baldasano, J M; Pay, M T; Jorba, O; Gassó, S; Jiménez-Guerrero, P

2011-05-01

The CALIOPE project, funded by the Spanish Ministry of the Environment, aims at establishing an air quality forecasting system for Spain. With this goal, CALIOPE modeling system was developed and applied with high resolution (4km×4km, 1h) using the HERMES emission model (including emissions of resuspended particles from paved roads) specifically built up for Spain. The present study provides an evaluation and the assessment of the modeling system, coupling WRF-ARW/HERMES/CMAQ/BSC-DREAM8b for a full-year simulation in 2004 over Spain. The evaluation focuses on the capability of the model to reproduce the temporal and spatial distribution of gas phase species (NO(2), O(3), and SO(2)) and particulate matter (PM10) against ground-based measurements from the Spanish air quality monitoring network. The evaluation of the modeling results on an hourly basis shows a strong dependency of the performance of the model on the type of environment (urban, suburban and rural) and the dominant emission sources (traffic, industrial, and background). The O(3) chemistry is best represented in summer, when mean hourly variability and high peaks are generally well reproduced. The mean normalized error and bias meet the recommendations proposed by the United States Environmental Protection Agency (US-EPA) and the European regulations. Modeled O(3) shows higher performance for urban than for rural stations, especially at traffic stations in large cities, since stations influenced by traffic emissions (i.e., high-NO(x) environments) are better characterized with a more pronounced daily variability. NO(x)/O(3) chemistry is better represented under non-limited-NO(2) regimes. SO(2) is mainly produced from isolated point sources (power generation and transformation industries) which generate large plumes of high SO(2) concentration affecting the air quality on a local to national scale where the meteorological pattern is crucial. The contribution of mineral dust from the Sahara desert through

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that

Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

NASA Astrophysics Data System (ADS)

Mathur, R.

2009-12-01

Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing

A dynamic water-quality modeling framework for the Neuse River estuary, North Carolina

USGS Publications Warehouse

Bales, Jerad D.; Robbins, Jeanne C.

1999-01-01

As a result of fish kills in the Neuse River estuary in 1995, nutrient reduction strategies were developed for point and nonpoint sources in the basin. However, because of the interannual variability in the natural system and the resulting complex hydrologic-nutrient inter- actions, it is difficult to detect through a short-term observational program the effects of management activities on Neuse River estuary water quality and aquatic health. A properly constructed water-quality model can be used to evaluate some of the potential effects of manage- ment actions on estuarine water quality. Such a model can be used to predict estuarine response to present and proposed nutrient strategies under the same set of meteorological and hydrologic conditions, thus removing the vagaries of weather and streamflow from the analysis. A two-dimensional, laterally averaged hydrodynamic and water-quality modeling framework was developed for the Neuse River estuary by using previously collected data. Development of the modeling framework consisted of (1) computational grid development, (2) assembly of data for model boundary conditions and model testing, (3) selection of initial values of model parameters, and (4) limited model testing. The model domain extends from Streets Ferry to Oriental, N.C., includes seven lateral embayments that have continual exchange with the main- stem of the estuary, three point-source discharges, and three tributary streams. Thirty-five computational segments represent the mainstem of the estuary, and the entire framework contains a total of 60 computa- tional segments. Each computational cell is 0.5 meter thick; segment lengths range from 500 meters to 7,125 meters. Data that were used to develop the modeling framework were collected during March through October 1991 and represent the most comprehensive data set available prior to 1997. Most of the data were collected by the North Carolina Division of Water Quality, the University of North Carolina

Differences in aquatic habitat quality as an impact of one- and two-dimensional hydrodynamic model simulated flow variables

NASA Astrophysics Data System (ADS)

Benjankar, R. M.; Sohrabi, M.; Tonina, D.; McKean, J. A.

2013-12-01

Aquatic habitat models utilize flow variables which may be predicted with one-dimensional (1D) or two-dimensional (2D) hydrodynamic models to simulate aquatic habitat quality. Studies focusing on the effects of hydrodynamic model dimensionality on predicted aquatic habitat quality are limited. Here we present the analysis of the impact of flow variables predicted with 1D and 2D hydrodynamic models on simulated spatial distribution of habitat quality and Weighted Usable Area (WUA) for fall-spawning Chinook salmon. Our study focuses on three river systems located in central Idaho (USA), which are a straight and pool-riffle reach (South Fork Boise River), small pool-riffle sinuous streams in a large meadow (Bear Valley Creek) and a steep-confined plane-bed stream with occasional deep forced pools (Deadwood River). We consider low and high flows in simple and complex morphologic reaches. Results show that 1D and 2D modeling approaches have effects on both the spatial distribution of the habitat and WUA for both discharge scenarios, but we did not find noticeable differences between complex and simple reaches. In general, the differences in WUA were small, but depended on stream type. Nevertheless, spatially distributed habitat quality difference is considerable in all streams. The steep-confined plane bed stream had larger differences between aquatic habitat quality defined with 1D and 2D flow models compared to results for streams with well defined macro-topographies, such as pool-riffle bed forms. KEY WORDS: one- and two-dimensional hydrodynamic models, habitat modeling, weighted usable area (WUA), hydraulic habitat suitability, high and low discharges, simple and complex reaches

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Useful measures and models for analytical quality management in medical laboratories.

PubMed

Westgard, James O

2016-02-01

The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.

Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals.

PubMed

Zhang, Ying-Ying; Zhou, Xiao-Bin; Wang, Qiu-Zhen; Zhu, Xiao-Yan

2017-05-01

Multivariable logistic regression (MLR) has been increasingly used in Chinese clinical medical research during the past few years. However, few evaluations of the quality of the reporting strategies in these studies are available.To evaluate the reporting quality and model accuracy of MLR used in published work, and related advice for authors, readers, reviewers, and editors.A total of 316 articles published in 5 leading Chinese clinical medical journals with high impact factor from January 2010 to July 2015 were selected for evaluation. Articles were evaluated according 12 established criteria for proper use and reporting of MLR models.Among the articles, the highest quality score was 9, the lowest 1, and the median 5 (4-5). A total of 85.1% of the articles scored below 6. No significant differences were found among these journals with respect to quality score (χ = 6.706, P = .15). More than 50% of the articles met the following 5 criteria: complete identification of the statistical software application that was used (97.2%), calculation of the odds ratio and its confidence interval (86.4%), description of sufficient events (>10) per variable, selection of variables, and fitting procedure (78.2%, 69.3%, and 58.5%, respectively). Less than 35% of the articles reported the coding of variables (18.7%). The remaining 5 criteria were not satisfied by a sufficient number of articles: goodness-of-fit (10.1%), interactions (3.8%), checking for outliers (3.2%), collinearity (1.9%), and participation of statisticians and epidemiologists (0.3%). The criterion of conformity with linear gradients was applicable to 186 articles; however, only 7 (3.8%) mentioned or tested it.The reporting quality and model accuracy of MLR in selected articles were not satisfactory. In fact, severe deficiencies were noted. Only 1 article scored 9. We recommend authors, readers, reviewers, and editors to consider MLR models more carefully and cooperate more closely with statisticians and

Stratospheric Intrusion-Influenced Ozone Air Quality Exceedences Investigated in MERRA-2

NASA Technical Reports Server (NTRS)

Knowland, K. Emma; Ott, Lesley; Duncan, Bryan; Wargan, Krzysztof

2017-01-01

Ozone near the surface is harmful to human health and is a result of the photochemical reaction with both man-made and natural precursor pollutant sources. Therefore, in order to reduce near surface ozone concentrations, communities must reduce anthropogenic pollution sources. However, the injection of stratospheric ozone into the troposphere, known as a stratospheric intrusion, can also lead to concentrations of ground-level ozone exceeding air quality standards. Stratospheric intrusions are dynamical atmospheric features, however, these intrusions have been misrepresented in models and reanalyses until recently, as the features of a stratospheric intrusion are best identified in horizontal resolutions of approximately 50 km or smaller. NASA's Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) reanalysis is a publicly-available high-resolution dataset (50 km) with assimilated ozone that characterizes stratospheric ozone on the same spatiotemporal resolution as the meteorology. We show that stratospheric intrusions that impact surface air quality are well represented in the MERRA-2 reanalysis. This is demonstrated through a case study analysis of stratospheric intrusion events which were identified by the United States Environmental Protection Agency (EPA) to impact surface ozone air quality in spring 2012 in Colorado. The stratospheric intrusions are identified in MERRA-2 by the folding of the dynamical tropopause under the jet stream and subsequent isentropic descent of dry, O3-rich stratospheric air towards the surface where ozone air quality exceedences were observed. The MERRA-2 reanalysis can support air quality agencies for more rapid identification of the impact of stratospheric air on ground-level ozone.

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

THE EMERGENCE OF NUMERICAL AIR QUALITY FORCASTING MODELS AND THEIR APPLICATIONS

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

The choices, choosing model of quality of life: description and rationale.

PubMed

Gurland, Barry J; Gurland, Roni V

2009-01-01

This introductory paper offers a critical review of current models and measures of quality of life, and describes a choices and choosing (c-c) process as a new model of quality of life. Criteria are proposed for judging the relative merits of models of quality of life with preference being given to explicit mechanisms, linkages to a science base, a means of identifying deficits amenable to rational restorative interventions, and with embedded values of the whole person. A conjectured model, based on the processes of gaining access to choices and choosing among them, matches the proposed criteria. The c-c process is an evolved adaptive mechanism dedicated to the pursuit of quality of life, driven by specific biological and psychological systems, and influenced by social and environmental forces. This model strengthens the science base for the field of quality of life, unifies approaches to concept and measurement, and guides the evaluation of impairments of quality of life. Corresponding interventions can be aimed at relieving restrictions or distortions of the c-c process; thus helping people to preserve and improve their quality of life. RELATED WORK: Companion papers detail relevant aspects of the science base, present methods of identifying deficits and distortions of the c-c model so as to open opportunities for rational restorative interventions, and explore empirical analyses of the relationship between health imposed restrictions of c-c and conventional indicators of diminished quality of life. [corrected] (c) 2008 John Wiley & Sons, Ltd.

Development and application of air quality models at the US ...

EPA Pesticide Factsheets

Overview of the development and application of air quality models at the U.S. EPA, particularly focused on the development and application of the Community Multiscale Air Quality (CMAQ) model developed within the Computation Exposure Division (CED) of the National Exposure Research Laboratory (NERL). This presentation will provide a simple overview of air quality model development and application geared toward a non-technical student audience. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Improved protein model quality assessments by changing the target function.

PubMed

Uziela, Karolis; Menéndez Hurtado, David; Shu, Nanjiang; Wallner, Björn; Elofsson, Arne

2018-06-01

Protein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the contact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates. © 2018 Wiley Periodicals, Inc.

Assessment and prediction of air quality using fuzzy logic and autoregressive models

NASA Astrophysics Data System (ADS)

Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

2012-12-01

In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

Poor sleep quality is associated with increased arterial stiffness in Japanese patients with type 2 diabetes mellitus.

PubMed

Osonoi, Yusuke; Mita, Tomoya; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Kanazawa, Akio; Gosho, Masahiko; Fujitani, Yoshio; Watada, Hirotaka

2015-06-18

While poor sleep quality can worsen cardiovascular risk factors such as glucose and lipid profiles in patients with type 2 diabetes mellitus (T2DM), the relationship between sleep quality and atherosclerosis remains largely unknown. The aim of this study was to examine this relationship. The study participants comprised 724 Japanese T2DM outpatients free of history of cardiovascular diseases. The relationships between sleep quality (assessed by the Pittsburgh Sleep Quality Index (PSQI)) and various clinical and laboratory parameters were investigated. The mean PSQI was 5.1 ± 3.0 (±SD). Patients were divided into three groups based on the total PSQI score; subjects with good sleep quality (n = 462), average sleep quality (n = 185), and poor sleep quality (n = 77). In the age/gender-adjusted model, patients with poor sleep quality tended to be obese, evening type and depressed. However, other lifestyles showed no significant trends. Alanine aminotransferase, fasting blood glucose, HbA1c, systolic blood pressure, urinary albumin excretion, and brachial-ankle pulse wave velocity (baPWV) tended to be higher in patients with poor sleep quality. High baPWV was the only parameter that correlated with poor sleep in a model adjusted for several other lifestyle factors. Our study indicates that poor sleep quality in T2DM patients correlates with increased arterial wall stiffness, a marker of atherosclerosis and a risk factor for cardiovascular diseases.

Quality assurance of weather data for agricultural system model input

USDA-ARS?s Scientific Manuscript database

It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

MODELED MESOSCALE METEOROLOGICAL FIELDS WITH FOUR-DIMENSIONAL DATA ASSIMILATION IN REGIONAL SCALE AIR QUALITY MODELS

EPA Science Inventory

This paper addresses the need to increase the temporal and spatial resolution of meteorological data currently used in air quality simulation models, AQSMs. ransport and diffusion parameters including mixing heights and stability used in regulatory air quality dispersion models a...

ROLE OF MODELS IN AIR QUALITY MANAGEMENT DECISIONS

EPA Science Inventory

Within the frame of the US-India bilateral agreement on environmental cooperation, a team of US scientists have been helping India in designing emission control policies to address urban air quality problems. This presentation discusses how air quality models need to be used for ...

[Autism: educational models for a quality life].

PubMed

Tamarit, J

2005-01-15

Our aim is to describe the change that is taking place in the field of education in developmental disabilities from models centred on the clinical symptoms and on the limitations in the adaptive skills to models that focus on valuable personal results in terms of quality of life. In order to understand these changes, we outline some of the key points that have given rise to a particular cultural construction of disability and we also discuss how the situation is changing towards models aimed at achieving important personal results. In autism, as in the other developmental disorders, special emphasis has traditionally been placed on an education focusing on symptoms and on skills, and, although things are now beginning to head in that direction, little attention has been given to education based on the person and his or her quality of life. These changes imply new roles for the professionals attending these people. These roles involve combining technique with empathy and ethics, and they are more firmly based on the active role of individuals with autism, together with their rights, interests and opinions. Models of intervention must pay special attention to the pursuit of valuable personal results, which are oriented towards living a quality life and must involve the active participation of the individuals themselves as well as their relatives.

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Integrating microbial physiology and enzyme traits in the quality model

NASA Astrophysics Data System (ADS)

Sainte-Marie, Julien; Barrandon, Matthieu; Martin, Francis; Saint-André, Laurent; Derrien, Delphine

2017-04-01

Microbe activity plays an undisputable role in soil carbon storage and there have been many calls to integrate microbial ecology in soil carbon (C) models. With regard to this challenge, a few trait-based microbial models of C dynamics have emerged during the past decade. They parameterize specific traits related to decomposer physiology (substrate use efficiency, growth and mortality rates...) and enzyme properties (enzyme production rate, catalytic properties of enzymes…). But these models are built on the premise that organic matter (OM) can be represented as one single entity or are divided into a few pools, while organic matter exists as a continuum of many different compounds spanning from intact plant molecules to highly oxidised microbial metabolites. In addition, a given molecule may also exist in different forms, depending on its stage of polymerization or on its interactions with other organic compounds or mineral phases of the soil. Here we develop a general theoretical model relating the evolution of soil organic matter, as a continuum of progressively decomposing compounds, with decomposer activity and enzyme traits. The model is based on the notion of quality developed by Agren and Bosatta (1998), which is a measure of molecule accessibility to degradation. The model integrates three major processes: OM depolymerisation by enzyme action, OM assimilation and OM biotransformation. For any enzyme, the model reports the quality range where this enzyme selectively operates and how the initial quality distribution of the OM subset evolves into another distribution of qualities under the enzyme action. The model also defines the quality range where the OM can be uptaken and assimilated by microbes. It finally describes how the quality of the assimilated molecules is transformed into another quality distribution, corresponding to the decomposer metabolites signature. Upon decomposer death, these metabolites return to the substrate. We explore here the how

The Total Quality Management Model Department of Personnel State of Colorado,

DTIC Science & Technology

A panel of three members will present the Total Quality Management model recently designed for the Department of Personnel, State of Colorado. This model was selected to increase work quality and productivity of the Department and to exemplify Governor Romer’s commitment to quality work within state government.

Operation quality assessment model for video conference system

NASA Astrophysics Data System (ADS)

Du, Bangshi; Qi, Feng; Shao, Sujie; Wang, Ying; Li, Weijian

2018-01-01

Video conference system has become an important support platform for smart grid operation and management, its operation quality is gradually concerning grid enterprise. First, the evaluation indicator system covering network, business and operation maintenance aspects was established on basis of video conference system's operation statistics. Then, the operation quality assessment model combining genetic algorithm with regularized BP neural network was proposed, which outputs operation quality level of the system within a time period and provides company manager with some optimization advice. The simulation results show that the proposed evaluation model offers the advantages of fast convergence and high prediction accuracy in contrast with regularized BP neural network, and its generalization ability is superior to LM-BP neural network and Bayesian BP neural network.

A software quality model and metrics for risk assessment

NASA Technical Reports Server (NTRS)

Hyatt, L.; Rosenberg, L.

1996-01-01

A software quality model and its associated attributes are defined and used as the model for the basis for a discussion on risk. Specific quality goals and attributes are selected based on their importance to a software development project and their ability to be quantified. Risks that can be determined by the model's metrics are identified. A core set of metrics relating to the software development process and its products is defined. Measurements for each metric and their usability and applicability are discussed.

A Quality Process Approach to Electronic System Reliability: Supplier Quality Assessment Procedure. Volume 2

DTIC Science & Technology

1993-11-01

AND SUPPORT SERVICE QUALITY ....... 16 3.5.7 SUPPLIER QUALITY ............................................................................. 16 3.6...RESULTS ......................................................................................................... 16 3.6.1 PRODUCT AND SERVICE QUALITY RESULTS...10 5.6 Business Process and Support Service Quality 20 5.7 Supplier Quality 20 6.0 Results 180 6.1 Product and Service Quality Results 90 6.2 Business

Modeling the Complex Photochemistry of Biomass Burning Plumes in Plume-Scale, Regional, and Global Air Quality Models

NASA Astrophysics Data System (ADS)

Alvarado, M. J.; Lonsdale, C. R.; Yokelson, R. J.; Travis, K.; Fischer, E. V.; Lin, J. C.

2014-12-01

Forecasting the impacts of biomass burning (BB) plumes on air quality is difficult due to the complex photochemistry that takes place in the concentrated young BB plumes. The spatial grid of global and regional scale Eulerian models is generally too large to resolve BB photochemistry, which can lead to errors in predicting the formation of secondary organic aerosol (SOA) and O3, as well as the partitioning of NOyspecies. AER's Aerosol Simulation Program (ASP v2.1) can be used within plume-scale Lagrangian models to simulate this complex photochemistry. We will present results of validation studies of the ASP model against aircraft observations of young BB smoke plumes. We will also present initial results from the coupling of ASP v2.1 into the Lagrangian particle dispersion model STILT-Chem in order to better examine the interactions between BB plume chemistry and dispersion. In addition, we have used ASP to develop a sub-grid scale parameterization of the near-source chemistry of BB plumes for use in regional and global air quality models. The parameterization takes inputs from the host model, such as solar zenith angle, temperature, and fire fuel type, and calculates enhancement ratios of O3, NOx, PAN, aerosol nitrate, and other NOy species, as well as organic aerosol (OA). We will present results from the ASP-based BB parameterization as well as its implementation into the global atmospheric composition model GEOS-Chem for the SEAC4RS campaign.

Institutional Response to the Swedish Model of Quality Assurance.

ERIC Educational Resources Information Center

Nilsson, Karl-Axel; Wahlen, Staffan

2000-01-01

Evaluates the Swedish model of quality assurance of higher education by examining the response of institutions to 27 quality audits and 19 follow-up interviews. Discusses the relationship between top-down and bottom-up approaches to internal quality assurance and suggests that, with growing professionalization, more limited result-oriented audits…

Assessment of the Quality Management Models in Higher Education

ERIC Educational Resources Information Center

Basar, Gulsun; Altinay, Zehra; Dagli, Gokmen; Altinay, Fahriye

2016-01-01

This study involves the assessment of the quality management models in Higher Education by explaining the importance of quality in higher education and by examining the higher education quality assurance system practices in other countries. The qualitative study was carried out with the members of the Higher Education Planning, Evaluation,…

Indoor Air Quality Building Education and Assessment Model Forms

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

A sediment resuspension and water quality model of Lake Okeechobee

USGS Publications Warehouse

James, R.T.; Martin, J.; Wool, T.; Wang, P.-F.

1997-01-01

The influence of sediment resuspension on the water quality of shallow lakes is well documented. However, a search of the literature reveals no deterministic mass-balance eutrophication models that explicitly include resuspension. We modified the Lake Okeeehobee water quality model - which uses the Water Analysis Simulation Package (WASP) to simulate algal dynamics and phosphorus, nitrogen, and oxygen cycles - to include inorganic suspended solids and algorithms that: (1) define changes in depth with changes in volume; (2) compute sediment resuspension based on bottom shear stress; (3) compute partition coefficients for ammonia and ortho-phosphorus to solids; and (4) relate light attenuation to solids concentrations. The model calibration and validation were successful with the exception of dissolved inorganic nitrogen species which did not correspond well to observed data in the validation phase. This could be attributed to an inaccurate formulation of algal nitrogen preference and/or the absence of nitrogen fixation in the model. The model correctly predicted that the lake is lightlimited from resuspended solids, and algae are primarily nitrogen limited. The model simulation suggested that biological fluxes greatly exceed external loads of dissolved nutrients; and sedimentwater interactions of organic nitrogen and phosphorus far exceed external loads. A sensitivity analysis demonstrated that parameters affecting resuspension, settling, sediment nutrient and solids concentrations, mineralization, algal productivity, and algal stoichiometry are factors requiring further study to improve our understanding of the Lake Okeechobee ecosystem.

Protein single-model quality assessment by feature-based probability density functions.

PubMed

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Evaluation of the Community Multiscale Air Quality model version 5.1

EPA Science Inventory

The Community Multiscale Air Quality model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environment...

Assessment and modeling of groundwater quality using WQI and GIS in Upper Egypt area.

PubMed

Rabeiy, Ragab ElSayed

2017-04-04

The continuous growth and development of population need more fresh water for drinking, irrigation, and domestic in arid countries like Egypt. Evaluation the quality of groundwater is an essential study to ensure its suitability for different purposes. In this study, 812 groundwater samples were taken within the middle area of Upper Egypt (Sohag Governorate) to assess the quality of groundwater for drinking and irrigation purposes. Eleven water parameters were analyzed at each groundwater sample (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - SO 4 2- , Fe 2+ , Mn 2+ , Cl - , electrical conductivity, and pH) to exploit them in water quality evaluation. A classical statistics were applied for the raw data to examine the distribution of physicochemical parameters in the investigated area. The relationship between groundwater parameters was tested using the correlation coefficient where a strong relationship was found between several water parameters such as Ca 2+ and Cl - . Water quality index (WQI) is a mathematical model used to transform many water parameters into a single indicator value which represents the water quality level. Results of WQI showed that 20% of groundwater samples are excellent, 75% are good for drinking, and 7% are very poor water while only 1% of samples are unsuitable for drinking. To test the suitability of groundwater for irrigation, three indices are used; they are sodium adsorption ration (SAR), sodium percentage (Na%), and permeability index (PI). For irrigation suitability, the study proved that most sampling sites are suitable while less than 3% are unsuitable for irrigation. The spatial distribution of the estimated values of WQI, SAR, Na%, PI, and each groundwater parameter was spatially modeled using GIS.

Researches of fruit quality prediction model based on near infrared spectrum

NASA Astrophysics Data System (ADS)

Shen, Yulin; Li, Lian

2018-04-01

With the improvement in standards for food quality and safety, people pay more attention to the internal quality of fruits, therefore the measurement of fruit internal quality is increasingly imperative. In general, nondestructive soluble solid content (SSC) and total acid content (TAC) analysis of fruits is vital and effective for quality measurement in global fresh produce markets, so in this paper, we aim at establishing a novel fruit internal quality prediction model based on SSC and TAC for Near Infrared Spectrum. Firstly, the model of fruit quality prediction based on PCA + BP neural network, PCA + GRNN network, PCA + BP adaboost strong classifier, PCA + ELM and PCA + LS_SVM classifier are designed and implemented respectively; then, in the NSCT domain, the median filter and the SavitzkyGolay filter are used to preprocess the spectral signal, Kennard-Stone algorithm is used to automatically select the training samples and test samples; thirdly, we achieve the optimal models by comparing 15 kinds of prediction model based on the theory of multi-classifier competition mechanism, specifically, the non-parametric estimation is introduced to measure the effectiveness of proposed model, the reliability and variance of nonparametric estimation evaluation of each prediction model to evaluate the prediction result, while the estimated value and confidence interval regard as a reference, the experimental results demonstrate that this model can better achieve the optimal evaluation of the internal quality of fruit; finally, we employ cat swarm optimization to optimize two optimal models above obtained from nonparametric estimation, empirical testing indicates that the proposed method can provide more accurate and effective results than other forecasting methods.

Quality assessment of protein model-structures based on structural and functional similarities

PubMed Central

2012-01-01

Background Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. Results GOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. Conclusions The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best

Quality assessment of protein model-structures based on structural and functional similarities.

PubMed

Konopka, Bogumil M; Nebel, Jean-Christophe; Kotulska, Malgorzata

2012-09-21

Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and

The Feasibility of Quality Function Deployment (QFD) as an Assessment and Quality Assurance Model

ERIC Educational Resources Information Center

Matorera, D.; Fraser, W. J.

2016-01-01

Business schools are globally often seen as structured, purpose-driven, multi-sector and multi-perspective organisations. This article is based on the response of a graduate school to an innovative industrial Quality Function Deployment-based model (QFD), which was to be adopted initially in a Master's degree programme for quality assurance…

NEIGHBORHOOD SCALE MODELING OF PM 2.5 AND AIR TOXICS CONCENTRATION DISTRIBUTIONS TO DRIVE HUMAN EXPOSURE MODELS

EPA Science Inventory

Air quality (AQ) simulation models provide a basis for implementing the National Ambient Air Quality Standards (NAAQS) and are a tool for performing risk-based assessments and for developing environmental management strategies. Fine particulate matter (PM 2.5), its constituent...

Perceptual video quality assessment in H.264 video coding standard using objective modeling.

PubMed

Karthikeyan, Ramasamy; Sainarayanan, Gopalakrishnan; Deepa, Subramaniam Nachimuthu

2014-01-01

Since usage of digital video is wide spread nowadays, quality considerations have become essential, and industry demand for video quality measurement is rising. This proposal provides a method of perceptual quality assessment in H.264 standard encoder using objective modeling. For this purpose, quality impairments are calculated and a model is developed to compute the perceptual video quality metric based on no reference method. Because of the shuttle difference between the original video and the encoded video the quality of the encoded picture gets degraded, this quality difference is introduced by the encoding process like Intra and Inter prediction. The proposed model takes into account of the artifacts introduced by these spatial and temporal activities in the hybrid block based coding methods and an objective modeling of these artifacts into subjective quality estimation is proposed. The proposed model calculates the objective quality metric using subjective impairments; blockiness, blur and jerkiness compared to the existing bitrate only calculation defined in the ITU G 1070 model. The accuracy of the proposed perceptual video quality metrics is compared against popular full reference objective methods as defined by VQEG.

McCook Reservoir Water Quality Model. Numerical Model Investigation

DTIC Science & Technology

1991-09-01

REPT TYPE AND DATES COVERED ad September Cana Final report . LEAND SUBTITLE S. FUNDING NUERS Spinfild VA2261 ThcCook Reservoir Water Quality Model...oxygen injected by the aeration system Manufacturers of diffusers supply OTE information specific to gas flow rate and depth. The depths at which most

48 CFR 246.470-2 - Quality evaluation data.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 246.470-2 Quality evaluation data. The contract administration office shall establish a system for the collection, evaluation, and use of the types of quality evaluation data specified in PGI 246.470-2. [71 FR... 48 Federal Acquisition Regulations System 3 2012-10-01 2012-10-01 false Quality evaluation data...

48 CFR 246.470-2 - Quality evaluation data.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 246.470-2 Quality evaluation data. The contract administration office shall establish a system for the collection, evaluation, and use of the types of quality evaluation data specified in PGI 246.470-2. [71 FR... 48 Federal Acquisition Regulations System 3 2013-10-01 2013-10-01 false Quality evaluation data...

48 CFR 246.470-2 - Quality evaluation data.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 246.470-2 Quality evaluation data. The contract administration office shall establish a system for the collection, evaluation, and use of the types of quality evaluation data specified in PGI 246.470-2. [71 FR... 48 Federal Acquisition Regulations System 3 2014-10-01 2014-10-01 false Quality evaluation data...

48 CFR 246.470-2 - Quality evaluation data.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 246.470-2 Quality evaluation data. The contract administration office shall establish a system for the collection, evaluation, and use of the types of quality evaluation data specified in PGI 246.470-2. [71 FR... 48 Federal Acquisition Regulations System 3 2011-10-01 2011-10-01 false Quality evaluation data...

Improved Algorithms for Blending Dam Releases to Meet Downstream Water-Temperature Targets in the CE-QUAL-W2 Water-Quality Model

NASA Astrophysics Data System (ADS)

Rounds, S. A.; Buccola, N. L.

2014-12-01

The two-dimensional (longitudinal, vertical) water-quality model CE-QUAL-W2, version 3.7, was enhanced with new features to help dam operators and managers efficiently explore and optimize potential solutions for temperature management downstream of thermally stratified reservoirs. Such temperature management often is accomplished by blending releases from multiple dam outlets that access water of different temperatures at different depths in the reservoir. The original blending algorithm in this version of the model was limited to mixing releases from two outlets at a time, and few constraints could be imposed. The new enhanced blending algorithm allows the user to (1) specify a time-series of target release temperatures, (2) designate from 2 to 10 floating or fixed-elevation outlets for blending, (3) impose maximum head constraints as well as minimum and maximum flow constraints for any blended outlet, and (4) set a priority designation for each outlet that allows the model to choose which outlets to use and how to balance releases among them. The modified model was tested against a previously calibrated model of Detroit Lake on the North Santiam River in northwestern Oregon, and the results compared well. The enhanced model code is being used to evaluate operational and structural scenarios at multiple dam/reservoir systems in the Willamette River basin in Oregon, where downstream temperature management for endangered fish is a high priority for resource managers and dam operators. These updates to the CE-QUAL-W2 blending algorithm allow scenarios involving complicated dam operations and/or hypothetical outlet structures to be evaluated more efficiently with the model, with decreased need for multiple/iterative model runs or preprocessing of model inputs to fully characterize the operational constraints.

Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

EPA Science Inventory

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...

Integrated Modelling on Flow and Water Quality Under the Impacts of Climate Change and Agricultural Activities

NASA Astrophysics Data System (ADS)

SHI, J.

2014-12-01

Climate change is expected to have a significant impact on flooding in the UK, inducing more intense and prolonged storms. Frequent flooding due to climate change already exacerbates catchment water quality. Land use is another contributing factor to poor water quality. For example, the move to intensive farming could cause an increase in faecal coliforms entering the water courses. In an effort to understand better the effects on water quality from land use and climate change, the hydrological and estuarine processes are being modelled using SWAT (Soil and Water Assessment Tool), linked to a 2-D hydrodynamic model DIVAST(Depth Integrated Velocity and Solute Transport). The coupled model is able to quantify how much of each pollutant from the catchment reaches the harbour and the impact on water quality within the harbour. The work is focused on the transportation and decay of faecal coliforms from agricultural runoff into the rivers Frome and Piddle in the UK. The impact from the agricultural land use and activities on the catchment river hydrology and water quality are evaluated. The coupled model calibration and validation showed the good model performance on flow and faecal coliform in the watershed and estuary.

Air Quality Modeling and Forecasting over the United States Using WRF-Chem

NASA Astrophysics Data System (ADS)

Boxe, C.; Hafsa, U.; Blue, S.; Emmanuel, S.; Griffith, E.; Moore, J.; Tam, J.; Khan, I.; Cai, Z.; Bocolod, B.; Zhao, J.; Ahsan, S.; Gurung, D.; Tang, N.; Bartholomew, J.; Rafi, R.; Caltenco, K.; Rivas, M.; Ditta, H.; Alawlaqi, H.; Rowley, N.; Khatim, F.; Ketema, N.; Strothers, J.; Diallo, I.; Owens, C.; Radosavljevic, J.; Austin, S. A.; Johnson, L. P.; Zavala-Gutierrez, R.; Breary, N.; Saint-Hilaire, D.; Skeete, D.; Stock, J.; Salako, O.

2016-12-01

WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. The model is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry. The development of WRF-Chem is a collaborative effort among the community led by NOAA/ESRL scientists. The Official WRF-Chem web page is located at the NOAA web site. Our model development is closely linked with both NOAA/ESRL and DOE/PNNL efforts. Description of PNNL WRF-Chem model development is located at the PNNL web site as well as the PNNL Aerosol Modeling Testbed. High school and undergraduate students, representative of academic institutions throughout USA's Tri-State Area (New York, New Jersey, Connecticut), set up WRF-Chem on CUNY CSI's High Performance Computing Center. Students learned the back-end coding that governs WRF-Chems structure and the front-end coding that displays visually specified weather simulations and forecasts. Students also investigated the impact, to select baseline simulations/forecasts, due to the reaction, NO2 + OH + M → HOONO + M (k = 9.2 × 10-12 cm3 molecule-1 s-1, Mollner et al. 2010). The reaction of OH and NO2 to form gaseous nitric acid (HONO2) is among the most influential and in atmospheric chemistry. Till a few years prior, its rate coefficient remained poorly determined under tropospheric conditions because of difficulties in making laboratory measurements at 760 torr. These activities fosters student coding competencies and deep insights into weather forecast and air quality.

Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate Matter

EPA Science Inventory

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and bound...

Pathways Between Discrimination and Quality of Life in Patients with Type 2 Diabetes.

PubMed

Achuko, Obinna; Walker, Rebekah J; Campbell, Jennifer A; Dawson, Aprill Z; Egede, Leonard E

2016-03-01

Discrimination is a social determinant that has been linked to poor physical and mental health outcomes. This study aimed to examine the pathway whereby discrimination influences quality of life in patients with type 2 diabetes. Six hundred fifteen patients were recruited from two adult primary care clinics in the southeastern United States. Measures included perceived discrimination, perceived stress, social support, and social cohesion and were based on a theoretical model for the pathways by which perceived discrimination influences mental and physical health. Quality of life was measured using the SF-12 questionnaire. The final model [χ(2)(106) = 157.35, P = 0.009, R(2) = 0.99, root mean square error of approximation = 0.03, comparative fit index = 0.99] indicates direct effects of higher perceived stress (r = -1.02, P < 0.05) and lower social support (r = 0.36, P < 0.001) significantly related to decreased mental health component score (MCS) of quality of life. Discrimination and social cohesion were not significantly directly related to MCS. However, higher discrimination (r = 0.47, P < 0.001), higher social cohesion (r = 0.14, P < 0.05), and lower social support (r = -0.43, P < 0.001) were significantly directly related to increased stress. No significant paths were found for the physical component score of quality of life. Perceived discrimination was significantly associated with stress and served as a pathway to influence the mental health component of quality of life (MCS). Social support had a direct and an indirect effect on MCS through a negative association with stress. These results suggest that future interventions should be developed to decrease stress and increase social support surrounding discrimination to improve the MCS of quality of life in patients with diabetes.

41 CFR 101-26.803-2 - Reporting quality deficiencies.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 2 2010-07-01 2010-07-01 true Reporting quality... § 101-26.803-2 Reporting quality deficiencies. (a) Quality deficiencies are defined as defects or nonconforming conditions which limit or prohibit the item received from fulfilling its intended purpose. Quality...

College quality and hourly wages: evidence from the self-revelation model, sibling models and instrumental variables.

PubMed

Borgen, Nicolai T

2014-11-01

This paper addresses the recent discussion on confounding in the returns to college quality literature using the Norwegian case. The main advantage of studying Norway is the quality of the data. Norwegian administrative data provide information on college applications, family relations and a rich set of control variables for all Norwegian citizens applying to college between 1997 and 2004 (N = 141,319) and their succeeding wages between 2003 and 2010 (676,079 person-year observations). With these data, this paper uses a subset of the models that have rendered mixed findings in the literature in order to investigate to what extent confounding biases the returns to college quality. I compare estimates obtained using standard regression models to estimates obtained using the self-revelation model of Dale and Krueger (2002), a sibling fixed effects model and the instrumental variable model used by Long (2008). Using these methods, I consistently find increasing returns to college quality over the course of students' work careers, with positive returns only later in students' work careers. I conclude that the standard regression estimate provides a reasonable estimate of the returns to college quality. Copyright © 2014 Elsevier Inc. All rights reserved.

Conceptual Model for Quality of Life among Adults With Congenital or Early Deafness

PubMed Central

Kushalnagar, P; McKee, M; Smith, SR; Hopper, M; Kavin, D; Atcherson, SR

2015-01-01

Background A conceptual model of health-related quality of life (QoL) is needed to describe key themes that impact perceived QoL in adults with congenital or early deafness. Objective: To revise University of Washington Center for Disability Policy and Research's conceptual model of health promotion and QoL, with suggestions for applying the model to improving programs or services that target deaf adults with early deafness. Methods Purposive and theoretical sampling of 35 adults who were born or became deaf early was planned in a 1-year study. In-depth semi-structured interviews probed deaf adult participants' perceptions about quality of life as a deaf individual. Data saturation was reached at the 17th interview with 2 additional interviews for validation, resulting in a total sample of 19 deaf adults. Coding and thematic analysis were conducted to develop the conceptual model. Results Our conceptual model delineates the relationships between health status (self-acceptance, coping with limitations), intrinsic (functional communication skills, navigating barriers/self-advocacy, resilience) and extrinsic (acceptance by others, access to information, educating others) factors in their influence on deaf adult quality of life outcomes at home, college, work, and in the community. Conclusions Findings demonstrate the need for the programs and services to consider not only factors intrinsic to the deaf individual but also extrinsic factors in enhancing perceived quality of life outcomes among people with a range of functional hearing and language preferences, including American Sign Language. PMID:24947577

Conceptual model for quality of life among adults with congenital or early deafness.

PubMed

Kushalnagar, Poorna; McKee, Michael; Smith, Scott R; Hopper, Melinda; Kavin, Denise; Atcherson, Samuel R

2014-07-01

A conceptual model of health-related quality of life (QoL) is needed to describe key themes that impact perceived QoL in adults with congenital or early deafness. To revise University of Washington Center for Disability Policy and Research's conceptual model of health promotion and QoL, with suggestions for applying the model to improving programs or services that target deaf adults with early deafness. Purposive and theoretical sampling of 35 adults who were born or became deaf early was planned in a 1-year study. In-depth semi-structured interviews probed deaf adult participants' perceptions about quality of life as a deaf individual. Data saturation was reached at the 17th interview with 2 additional interviews for validation, resulting in a total sample of 19 deaf adults. Coding and thematic analysis were conducted to develop the conceptual model. Our conceptual model delineates the relationships between health status (self-acceptance, coping with limitations), intrinsic (functional communication skills, navigating barriers/self-advocacy, resilience) and extrinsic (acceptance by others, access to information, educating others) factors in their influence on deaf adult quality of life outcomes at home, college, work, and in the community. Findings demonstrate the need for the programs and services to consider not only factors intrinsic to the deaf individual but also extrinsic factors in enhancing perceived quality of life outcomes among people with a range of functional hearing and language preferences, including American Sign Language. Copyright © 2014 Elsevier Inc. All rights reserved.

A water quality index model using stepwise regression and neural networks models for the Piabanha River basin in Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Villas Boas, M. D.; Olivera, F.; Azevedo, J. S.

2013-12-01

The evaluation of water quality through 'indexes' is widely used in environmental sciences. There are a number of methods available for calculating water quality indexes (WQI), usually based on site-specific parameters. In Brazil, WQI were initially used in the 1970s and were adapted from the methodology developed in association with the National Science Foundation (Brown et al, 1970). Specifically, the WQI 'IQA/SCQA', developed by the Institute of Water Management of Minas Gerais (IGAM), is estimated based on nine parameters: Temperature Range, Biochemical Oxygen Demand, Fecal Coliforms, Nitrate, Phosphate, Turbidity, Dissolved Oxygen, pH and Electrical Conductivity. The goal of this study was to develop a model for calculating the IQA/SCQA, for the Piabanha River basin in the State of Rio de Janeiro (Brazil), using only the parameters measurable by a Multiparameter Water Quality Sonde (MWQS) available in the study area. These parameters are: Dissolved Oxygen, pH and Electrical Conductivity. The use of this model will allow to further the water quality monitoring network in the basin, without requiring significant increases of resources. The water quality measurement with MWQS is less expensive than the laboratory analysis required for the other parameters. The water quality data used in the study were obtained by the Geological Survey of Brazil in partnership with other public institutions (i.e. universities and environmental institutes) as part of the project "Integrated Studies in Experimental and Representative Watersheds". Two models were developed to correlate the values of the three measured parameters and the IQA/SCQA values calculated based on all nine parameters. The results were evaluated according to the following validation statistics: coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike information criterion (AIC) and Final Prediction Error (FPE). The first model was a linear stepwise regression between three independent variables

Modeling prescribed burning experiments and assessing the fire impacts on local to regional air quality

NASA Astrophysics Data System (ADS)

Zhou, L.; Baker, K. R.; Napelenok, S. L.; Elleman, R. A.; Urbanski, S. P.

2016-12-01

Biomass burning, including wildfires and prescribed burns, strongly impact the global carbon cycle and are of increasing concern due to the potential impacts on ambient air quality. This modelling study focuses on the evolution of carbonaceous compounds during a prescribed burning experiment and assesses the impacts of burning on local to regional air quality. The Community Multiscale Air Quality (CMAQ) model is used to conduct 4 and 2 km grid resolution simulations of prescribed burning experiments in southeast Washington state and western Idaho state in summer 2013. The ground and airborne measurements from the field experiment are used to evaluate the model performance in capturing surface and aloft impacts from the burning events. Phase partitioning of organic compounds in the plume are studied as it is a crucial step towards understanding the fate of carbonaceous compounds. The sensitivities of ambient concentrations and deposition to emissions are conducted for organic carbon, elemental carbon and ozone to estimate the impacts of fire on air quality.

Quantifying the air quality-CO2 tradeoff potential for airports

NASA Astrophysics Data System (ADS)

Ashok, Akshay; Dedoussi, Irene C.; Yim, Steve H. L.; Balakrishnan, Hamsa; Barrett, Steven R. H.

2014-12-01

Aircraft movements on the airport surface are responsible for CO2 emissions that contribute to climate change and other emissions that affect air quality and human health. While the potential for optimizing aircraft surface movements to minimize CO2 emissions has been assessed, the implications of CO2 emissions minimization for air quality have not been quantified. In this paper, we identify conditions in which there is a tradeoff between CO2 emissions and population exposure to O3 and secondary PM2.5 - i.e. where decreasing fuel burn (which is directly proportional to CO2 emissions) results in increased exposure. Fuel burn and emissions are estimated as a function of thrust setting for five common gas turbine engines at 34 US airports. Regional air quality impacts, which are dominated by ozone and secondary PM2.5, are computed as a function of airport location and time using the adjoint of the GEOS-Chem chemistry-transport model. Tradeoffs between CO2 emissions and population exposure to PM2.5 and O3 occur between 2-18% and 5-60% of the year, respectively, depending on airport location, engine type, and thrust setting. The total duration of tradeoff conditions is 5-12 times longer at maximum thrust operations (typical for takeoff) relative to 4% thrust operations (typical for taxiing). Per kilogram of additional fuel burn at constant thrust setting during tradeoff conditions, reductions in population exposure to PM2.5 and O3 are 6-13% and 32-1060% of the annual average (positive) population exposure per kilogram fuel burn, where the ranges encompass the medians over the 34 airports. For fuel burn increases due to thrust increases (i.e. for constant operating time), reductions in both PM2.5 and O3 exposure are 1.5-6.4 times larger in magnitude than those due to increasing fuel burn at constant thrust (i.e. increasing operating time). Airports with relatively high population exposure reduction potentials - which occur due to a combination of high duration and

Industrial pollution and the management of river water quality: a model of Kelani River, Sri Lanka.

PubMed

Gunawardena, Asha; Wijeratne, E M S; White, Ben; Hailu, Atakelty; Pandit, Ram

2017-08-19

Water quality of the Kelani River has become a critical issue in Sri Lanka due to the high cost of maintaining drinking water standards and the market and non-market costs of deteriorating river ecosystem services. By integrating a catchment model with a river model of water quality, we developed a method to estimate the effect of pollution sources on ambient water quality. Using integrated model simulations, we estimate (1) the relative contribution from point (industrial and domestic) and non-point sources (river catchment) to river water quality and (2) pollutant transfer coefficients for zones along the lower section of the river. Transfer coefficients provide the basis for policy analyses in relation to the location of new industries and the setting of priorities for industrial pollution control. They also offer valuable information to design socially optimal economic policy to manage industrialized river catchments.

Temporal evolution modeling of hydraulic and water quality performance of permeable pavements

NASA Astrophysics Data System (ADS)

Huang, Jian; He, Jianxun; Valeo, Caterina; Chu, Angus

2016-02-01

A mathematical model for predicting hydraulic and water quality performance in both the short- and long-term is proposed based on field measurements for three types of permeable pavements: porous asphalt (PA), porous concrete (PC), and permeable inter-locking concrete pavers (PICP). The model was applied to three field-scale test sites in Calgary, Alberta, Canada. The model performance was assessed in terms of hydraulic parameters including time to peak, peak flow and water balance and a water quality variable (the removal rate of total suspended solids). A total of 20 simulated storm events were used for model calibration and verification processes. The proposed model can simulate the outflow hydrographs with a coefficient of determination (R2) ranging from 0.762 to 0.907, and normalized root-mean-square deviation (NRMSD) ranging from 13.78% to 17.83%. Comparison of the time to peak flow, peak flow, runoff volume and TSS removal rates between the measured and modeled values in model verification phase had a maximum difference of 11%. The results demonstrate that the proposed model is capable of capturing the temporal dynamics of the pavement performance. Therefore, the model has great potential as a practical modeling tool for permeable pavement design and performance assessment.

Water quality assessment and meta model development in Melen watershed - Turkey.

PubMed

Erturk, Ali; Gurel, Melike; Ekdal, Alpaslan; Tavsan, Cigdem; Ugurluoglu, Aysegul; Seker, Dursun Zafer; Tanik, Aysegul; Ozturk, Izzet

2010-07-01

Istanbul, being one of the highly populated metropolitan areas of the world, has been facing water scarcity since the past decade. Water transfer from Melen Watershed was considered as the most feasible option to supply water to Istanbul due to its high water potential and relatively less degraded water quality. This study consists of two parts. In the first part, water quality data covering 26 parameters from 5 monitoring stations were analyzed and assessed due to the requirements of the "Quality Required of Surface Water Intended for the Abstraction of Drinking Water" regulation. In the second part, a one-dimensional stream water quality model with simple water quality kinetics was developed. It formed a basic design for more advanced water quality models for the watershed. The reason for assessing the water quality data and developing a model was to provide information for decision making on preliminary actions to prevent any further deterioration of existing water quality. According to the water quality assessment at the water abstraction point, Melen River has relatively poor water quality with regard to NH(4)(+), BOD(5), faecal streptococcus, manganese and phenol parameters, and is unsuitable for drinking water abstraction in terms of COD, PO(4)(3-), total coliform, total suspended solids, mercury and total chromium parameters. The results derived from the model were found to be consistent with the water quality assessment. It also showed that relatively high inorganic nitrogen and phosphorus concentrations along the streams are related to diffuse nutrient loads that should be managed together with municipal and industrial wastewaters. Copyright 2010 Elsevier Ltd. All rights reserved.

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

USGS Publications Warehouse

Hanna, R. Blair; Campbell, Sharon G.

2000-01-01

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

Should we trust build-up/wash-off water quality models at the scale of urban catchments?

PubMed

Bonhomme, Céline; Petrucci, Guido

2017-01-01

Models of runoff water quality at the scale of an urban catchment usually rely on build-up/wash-off formulations obtained through small-scale experiments. Often, the physical interpretation of the model parameters, valid at the small-scale, is transposed to large-scale applications. Testing different levels of spatial variability, the parameter distributions of a water quality model are obtained in this paper through a Monte Carlo Markov Chain algorithm and analyzed. The simulated variable is the total suspended solid concentration at the outlet of a periurban catchment in the Paris region (2.3 km 2 ), for which high-frequency turbidity measurements are available. This application suggests that build-up/wash-off models applied at the catchment-scale do not maintain their physical meaning, but should be considered as "black-box" models. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quality Analysis on 3d Buidling Models Reconstructed from Uav Imagery

NASA Astrophysics Data System (ADS)

Jarzabek-Rychard, M.; Karpina, M.

2016-06-01

Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.

48 CFR 246.470-2 - Quality evaluation data.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 3 2010-10-01 2010-10-01 false Quality evaluation data... SYSTEM, DEPARTMENT OF DEFENSE CONTRACT MANAGEMENT QUALITY ASSURANCE Government Contract Quality Assurance 246.470-2 Quality evaluation data. The contract administration office shall establish a system for the...

EVALUATING THE USE OF OUTPUTS FROM COMPREHENSIVE METEOROLOGICAL MODELS IN AIR QUALITY MODELING APPLICATIONS

EPA Science Inventory

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of Meteorological observations, as we...

Development and application of new quality model for software projects.

PubMed

Karnavel, K; Dillibabu, R

2014-01-01

The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects.

Stochastic Models of Quality Control on Test Misgrading.

ERIC Educational Resources Information Center

Wang, Jianjun

Stochastic models are developed in this article to examine the rate of test misgrading in educational and psychological measurement. The estimation of inadvertent grading errors can serve as a basis for quality control in measurement. Limitations of traditional Poisson models have been reviewed to highlight the need to introduce new models using…

Modeling hydrodynamics, temperature and water quality in Henry Hagg Lake, Oregon, 2000-2003

USGS Publications Warehouse

Sullivan, Annette B.; Rounds, Stewart A.

2004-01-01

The two-dimensional model CE-QUAL-W2 was used to simulate hydrodynamics, temperature, and water quality in Henry Hagg Lake, Oregon, for the years 2000 through 2003. Input data included lake bathymetry, meteorologic conditions, tributary inflows, tributary temperature and water quality, and lake outflows. Calibrated constituents included lake hydrodynamics, water temperature, orthophosphate, total phosphorus, ammonia, algae, chlorophyll a, zooplankton, and dissolved oxygen. Other simulated constituents included nitrate, dissolved and particulate organic matter, dissolved solids, and suspended sediment. Two algal groups (blue-green algae, and all other algae) were included in the model to simulate the lakes algal communities. Measured lake stage data were used to calibrate the lakes water balance; calibration of water temperature and water quality relied upon vertical profile data taken in the deepest part of the lake near the dam. The model initially was calibrated with data from 200001 and tested with data from 200203. Sensitivity tests were performed to examine the response of the model to specific parameters and coefficients, including the light-extinction coefficient, wind speed, tributary inflows of phosphorus, nitrogen and organic matter, sediment oxygen demand, algal growth rates, and zooplankton feeding preference factors.

WATER QUALITY MODELING IN THE RIO CHONE ESTUARY

EPA Science Inventory

Water quality in the Rio Chone Estuary, a seasonally inverse, tropical estuary, in Ecuador was characterized by modeling the distribution of biochemical oxygen demand (BOD) and dissolved inorganic nitrogen (DIN) within the water column. These two variables are modeled using modif...

Aircraft model prototypes which have specified handling-quality time histories

NASA Technical Reports Server (NTRS)

Johnson, S. H.

1976-01-01

Several techniques for obtaining linear constant-coefficient airplane models from specified handling-quality time histories are discussed. One technique, the pseudodata method, solves the basic problem, yields specified eigenvalues, and accommodates state-variable transfer-function zero suppression. The method is fully illustrated for a fourth-order stability-axis small-motion model with three lateral handling-quality time histories specified. The FORTRAN program which obtains and verifies the model is included and fully documented.

Quantifying long-term responses of crop yield and nitrate leaching in an intensive farmland using agro-eco-environmental model.

PubMed

Sun, Mei; Huo, Zailin; Zheng, Yanxia; Dai, Xiaoqin; Feng, Shaoyuan; Mao, Xiaomin

2018-02-01

Quantitatively ascertaining and analyzing long-term responses of crop yield and nitrate leaching on varying irrigation and fertilization treatments are focal points for guaranteeing crop yield and reducing nitrogen loss. The calibrated agricultural-hydrological RZWQM2 model was used to explore the long-term (2003-2013) transport processes of water and nitrogen and the nitrate leaching amount into groundwater in summer maize and winter wheat rotation field in typical intensive plant area in the North China Plain, Daxing district of Beijing. Simulation results showed that application rates of irrigation and nitrogen fertilizer have couple effects on crop yields and nitrogen leaching of root zone. When both the irrigation and fertilizer for summer maize and winter wheat were 400mm and 400kgNha -1 , respectively, nitrate leaching into groundwater accounted for 47.9% of application amount of nitrogen fertilizer. When application amount of irrigation is 200mm and fertilization is 200kgNha -1 , NUPE (nitrogen uptake efficiency), NUE (nitrogen use efficiency), NPFP (nitrogen partial factor productivity), and W pi (irrigation water productive efficiency) were in general higher than that under other irrigation and fertilization condition (irrigation from 104-400mm, fertilizer 104-400kgNha -1 ). Irrigation bigger than 200mm could shorten the response time of nitrate leaching in deeper soil layer in different irrigation treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

Modelling the effect of wildfire on forested catchment water quality using the SWAT model

NASA Astrophysics Data System (ADS)

Yu, M.; Bishop, T.; van Ogtrop, F. F.; Bell, T.

2016-12-01

Wildfire removes the surface vegetation, releases ash, increase erosion and runoff, and therefore effects the hydrological cycle of a forested water catchment. It is important to understand chnage and how the catchment recovers. These processes are spatially sensitive and effected by interactions between fire severity and hillslope, soil type and surface vegetation conditions. Thus, a distributed hydrological modelling approach is required. In this study, the Soil and Water Analysis Tool (SWAT) is used to predict the effect of 2001/02 Sydney wild fire on catchment water quality. 10 years pre-fire data is used to create and calibrate the SWAT model. The calibrated model was then used to simulate the water quality for the 10 years post-fire period without fire effect. The simulated water quality data are compared with recorded water quality data provided by Sydney catchment authority. The mean change of flow, total suspended solid, total nitrate and total phosphate are compare on monthly, three month, six month and annual basis. Two control catchment and three burn catchment were analysed.

Multiple Sensitivity Testing for Regional Air Quality Model in summer 2014

NASA Astrophysics Data System (ADS)

Tang, Y.; Lee, P.; Pan, L.; Tong, D.; Kim, H. C.; Huang, M.; Wang, J.; McQueen, J.; Lu, C. H.; Artz, R. S.

2015-12-01

The NOAA Air Resources laboratory leads to improve the performance of the U.S. Air Quality Forecasting Capability (NAQFC). It is operational in NOAA National Centers for Environmental Prediction (NCEP) which focuses on predicting surface ozone and PM2.5. In order to improve its performance, we tested several approaches, including NOAA Environmental Modeling System Global Aerosol Component (NGAC) simulation derived ozone and aerosol lateral boundary conditions (LBC), bi-direction NH3 emission and HMS(Hazard Mapping System)-BlueSky emission with the latest U.S. EPA Community Multi-scale Air Quality model (CMAQ) version and the U.S EPA National Emission Inventory (NEI)-2011 anthropogenic emissions. The operational NAQFC uses static profiles for its lateral boundary condition (LBC), which does not impose severe issue for near-surface air quality prediction. However, its degraded performance for the upper layer (e.g. above 3km) is evident when comparing with aircraft measured ozone. NCEP's Global Forecast System (GFS) has tracer O3 prediction treated as 3-D prognostic variable (Moorthi and Iredell, 1998) after being initialized with Solar Backscatter Ultra Violet-2 (SBUV-2) satellite data. We applied that ozone LBC to the CMAQ's upper layers and yield more reasonable O3 prediction than that with static LBC comparing with the aircraft data in Discover-AQ Colorado campaign. NGAC's aerosol LBC also improved the PM2.5 prediction with more realistic background aerosols. The bi-direction NH3 emission used in CMAQ also help reduce the NH3 and nitrate under-prediction issue. During summer 2014, strong wildfires occurred in northwestern USA, and we used the US Forest Service's BlueSky fire emission with HMS fire counts to drive CMAQ and tested the difference of day-1 and day-2 fire emission estimation. Other related issues were also discussed.

A simple parametric model observer for quality assurance in computer tomography

NASA Astrophysics Data System (ADS)

Anton, M.; Khanin, A.; Kretz, T.; Reginatto, M.; Elster, C.

2018-04-01

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10–15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.

Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

EPA Science Inventory

The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

Mobility Device Quality Affects Participation Outcomes for People With Disabilities: A Structural Equation Modeling Analysis.

PubMed

Magasi, Susan; Wong, Alex; Miskovic, Ana; Tulsky, David; Heinemann, Allen W

2018-01-01

To test the effect that indicators of mobility device quality have on participation outcomes in community-dwelling adults with spinal cord injury, traumatic brain injury, and stroke by using structural equation modeling. Survey, cross-sectional study, and model testing. Clinical research space at 2 academic medical centers and 1 free-standing rehabilitation hospital. Community-dwelling adults (N=250; mean age, 48±14.3y) with spinal cord injury, traumatic brain injury, and stroke. Not applicable. The Mobility Device Impact Scale, Patient-Reported Outcomes Measurement Information System Social Function (version 2.0) scale, including Ability to Participate in Social Roles and Activities and Satisfaction with Social Roles and Activities, and the 2 Community Participation Indicators' enfranchisement scales. Details about device quality (reparability, reliability, ease of maintenance) and device type were also collected. Respondents used ambulation aids (30%), manual (34%), and power wheelchairs (30%). Indicators of device quality had a moderating effect on participation outcomes, with 3 device quality variables (repairability, ease of maintenance, device reliability) accounting for 20% of the variance in participation. Wheelchair users reported lower participation enfranchisement than did ambulation aid users. Mobility device quality plays an important role in participation outcomes. It is critical that people have access to mobility devices and that these devices be reliable. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Value-added strategy models to provide quality services in senior health business.

PubMed

Yang, Ya-Ting; Lin, Neng-Pai; Su, Shyi; Chen, Ya-Mei; Chang, Yao-Mao; Handa, Yujiro; Khan, Hafsah Arshed Ali; Elsa Hsu, Yi-Hsin

2017-06-20

The rapid population aging is now a global issue. The increase in the elderly population will impact the health care industry and health enterprises; various senior needs will promote the growth of the senior health industry. Most senior health studies are focused on the demand side and scarcely on supply. Our study selected quality enterprises focused on aging health and analyzed different strategies to provide excellent quality services to senior health enterprises. We selected 33 quality senior health enterprises in Taiwan and investigated their excellent quality services strategies by face-to-face semi-structured in-depth interviews with CEO and managers of each enterprise in 2013. A total of 33 senior health enterprises in Taiwan. Overall, 65 CEOs and managers of 33 enterprises were interviewed individually. None. Core values and vision, organization structure, quality services provided, strategies for quality services. This study's results indicated four type of value-added strategy models adopted by senior enterprises to offer quality services: (i) residential care and co-residence model, (ii) home care and living in place model, (iii) community e-business experience model and (iv) virtual and physical portable device model. The common part in these four strategy models is that the services provided are elderly centered. These models offer virtual and physical integrations, and also offer total solutions for the elderly and their caregivers. Through investigation of successful strategy models for providing quality services to seniors, we identified opportunities to develop innovative service models and successful characteristics, also policy implications were summarized. The observations from this study will serve as a primary evidenced base for enterprises developing their senior market and, also for promoting the value co-creation possibility through dialogue between customers and those that deliver service. © The Author 2017. Published by Oxford

CMAQ Involvement in Air Quality Model Evaluation International Initiative

EPA Pesticide Factsheets

Description of Air Quality Model Evaluation International Initiative (AQMEII). Different chemical transport models are applied by different groups over North America and Europe and evaluated against observations.

Four-dimensional evaluation of regional air quality models

EPA Science Inventory

We present highlights of the results obtained in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3). Activities in AQMEII3 were focused on evaluating the performance of global, hemispheric and regional modeling systems over Europe and North Ame...

Using the Community Multiscale Air Quality (CMAQ) model to estimate public health impacts of PM2.5 from individual power plants.

PubMed

Buonocore, Jonathan J; Dong, Xinyi; Spengler, John D; Fu, Joshua S; Levy, Jonathan I

2014-07-01

We estimated PM2.5-related public health impacts/ton emitted of primary PM2.5, SO2, and NOx for a set of power plants in the Mid-Atlantic and Lower Great Lakes regions of the United States, selected to include varying emission profiles and broad geographic representation. We then developed a regression model explaining variability in impacts per ton emitted using the population distributions around each plant. We linked outputs from the Community Multiscale Air Quality (CMAQ) model v 4.7.1 with census data and concentration-response functions for PM2.5-related mortality, and monetized health estimates using the value-of-statistical-life. The median impacts for the final set of plants were $130,000/ton for primary PM2.5 (range: $22,000-230,000), $28,000/ton for SO2 (range: $19,000-33,000), and $16,000/ton for NOx (range: $7100-26,000). Impacts of NOx were a median of 34% (range: 20%-75%) from ammonium nitrate and 66% (range: 25%-79%) from ammonium sulfate. The latter pathway is likely from NOx enhancing atmospheric oxidative capacity and amplifying sulfate formation, and is often excluded. Our regression models explained most of the variation in impact/ton estimates using basic population covariates, and can aid in estimating impacts averted from interventions such as pollution controls, alternative energy installations, or demand-side management. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety

PubMed Central

Kern, Lisa M; Abramson, Erika; Kaushal, Rainu

2011-01-01

With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself; (2) the provider using the technology; (3) the organizational setting; and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems. PMID:21857023

On Regional Modeling to Support Air Quality Policies (book chapter)

EPA Science Inventory

We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing meteo...

Development and Application of New Quality Model for Software Projects

PubMed Central

Karnavel, K.; Dillibabu, R.

2014-01-01

The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects. PMID:25478594

Developing a Holistic Model for Quality in Higher Education.

ERIC Educational Resources Information Center

Srikanthan, G.; Dalrymple, John F.

2002-01-01

Proposes a holistic model for quality management in higher education which incorporates both service and academic functions. Discusses the crucial role played by organizational culture in implementation of any quality strategy, and asserts that ideal organizational behavior embodies the "learning communities" concept. (EV)

Recent Advances in WRF Modeling for Air Quality Applications

EPA Science Inventory

The USEPA uses WRF in conjunction with the Community Multiscale Air Quality (CMAQ) for air quality regulation and research. Over the years we have added physics options and geophysical datasets to the WRF system to enhance model capabilities especially for extended retrospective...

Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

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

Castillo-Villar, Krystel K.; Eksioglu, Sandra; Taherkhorsandi, Milad

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges. Here, to address this challenges, this article presents an optimization model which quantifies and controls the impact of biomass quality variability on supply chain related decisions and technology selection. We propose a two-stage stochastic programming model and associated efficient solution procedures for solving large-scale problems to (1) better represent the random nature of the biomassmore » quality (defined by moisture and ash contents) in supply chain modeling, and (2) assess the impact of these uncertainties on the supply chain design and planning. The proposed model is then applied to a case study in the state of Tennessee. Results show that high moisture and ash contents negatively impact the unit delivery cost since poor biomass quality requires the addition of quality control activities. Experimental results indicate that supply chain cost could increase as much as 27%–31% when biomass quality is poor. We assess the impact of the biomass quality on the topological supply chain. Our case study indicates that biomass quality impacts supply chain costs; thus, it is important to consider the impact of biomass quality in supply chain design and management decisions.« less

Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

DOE PAGES

Castillo-Villar, Krystel K.; Eksioglu, Sandra; Taherkhorsandi, Milad

2017-02-20

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges. Here, to address this challenges, this article presents an optimization model which quantifies and controls the impact of biomass quality variability on supply chain related decisions and technology selection. We propose a two-stage stochastic programming model and associated efficient solution procedures for solving large-scale problems to (1) better represent the random nature of the biomassmore » quality (defined by moisture and ash contents) in supply chain modeling, and (2) assess the impact of these uncertainties on the supply chain design and planning. The proposed model is then applied to a case study in the state of Tennessee. Results show that high moisture and ash contents negatively impact the unit delivery cost since poor biomass quality requires the addition of quality control activities. Experimental results indicate that supply chain cost could increase as much as 27%–31% when biomass quality is poor. We assess the impact of the biomass quality on the topological supply chain. Our case study indicates that biomass quality impacts supply chain costs; thus, it is important to consider the impact of biomass quality in supply chain design and management decisions.« less

Take the Reins on Model Quality with ModelCHECK and Gatekeeper

NASA Technical Reports Server (NTRS)

Jones, Corey

2012-01-01

Model quality and consistency has been an issue for us due to the diverse experience level and imaginative modeling techniques of our users. Fortunately, setting up ModelCHECK and Gatekeeper to enforce our best practices has helped greatly, but it wasn't easy. There were many challenges associated with setting up ModelCHECK and Gatekeeper including: limited documentation, restrictions within ModelCHECK, and resistance from end users. However, we consider ours a success story. In this presentation we will describe how we overcame these obstacles and present some of the details of how we configured them to work for us.

MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL AEROSOL COMPONENT 1: MODEL DESCRIPTION

EPA Science Inventory

The aerosol component of the Community Multiscale Air Quality (CMAQ) model is designed to be an efficient and economical depiction of aerosol dynamics in the atmosphere. The approach taken represents the particle size distribution as the superposition of three lognormal subdis...

Modeling the Dynamic Change of Air Quality and its Response to Emission Trends

NASA Astrophysics Data System (ADS)

Zhou, Wei

This thesis focuses on evaluating atmospheric chemistry and transport models' capability in simulating the chemistry and dynamics of power plant plumes, evaluating their strengths and weaknesses in predicting air quality trends at regional scales, and exploring air quality trends in an urban area. First, the Community Mutlti-scale Air Quality (CMAQ) model is applied to simulate the physical and chemical evolution of power plant plumes (PPPs) during the second Texas Air Quality Study (TexAQS) in 2006. SO2 and NOy were observed to be rapidly removed from PPPs on cloudy days but not on cloud-free days, indicating efficient aqueous processing of these compounds in clouds, while the model fails to capture the rapid loss of SO2 and NOy in some plumes on the cloudy day. Adjustments to cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO 2 loss while NOy in the model was insensitive to QC. Second, CMAQ is applied to simulate the ozone (O3) change after the NO x SIP Call and mobile emission controls in the eastern U.S. from 2002 to 2006. Observed downward changes in 8-hour O3 concentrations in the NOx SIP Call region were under-predicted by 26%--66%. The under-prediction in O3 improvements could be alleviated by 5%--31% by constraining NOx emissions in each year based on observed NOx concentrations while temperature biases or uncertainties in chemical reactions had minor impact on simulated O3 trends. Third, changes in ozone production in the Houston area is assessed with airborne measurements from TexAQS 2000 and 2006. Simultaneous declines in nitrogen oxides (NOx=NO+NO2) and highly reactive Volatile Organic Compounds (HRVOCs) were observed in the Houston Ship Channel (HSC). The reduction in HRVOCs led to the decline in total radical concentration by 20-50%. Rapid ozone production rates in the Houston area declined by 40-50% from 2000 to 2006, to which the reduction in NOx and HRVOCs had the similar

Gaia: automated quality assessment of protein structure models.

PubMed

Kota, Pradeep; Ding, Feng; Ramachandran, Srinivas; Dokholyan, Nikolay V

2011-08-15

Increasing use of structural modeling for understanding structure-function relationships in proteins has led to the need to ensure that the protein models being used are of acceptable quality. Quality of a given protein structure can be assessed by comparing various intrinsic structural properties of the protein to those observed in high-resolution protein structures. In this study, we present tools to compare a given structure to high-resolution crystal structures. We assess packing by calculating the total void volume, the percentage of unsatisfied hydrogen bonds, the number of steric clashes and the scaling of the accessible surface area. We assess covalent geometry by determining bond lengths, angles, dihedrals and rotamers. The statistical parameters for the above measures, obtained from high-resolution crystal structures enable us to provide a quality-score that points to specific areas where a given protein structural model needs improvement. We provide these tools that appraise protein structures in the form of a web server Gaia (http://chiron.dokhlab.org). Gaia evaluates the packing and covalent geometry of a given protein structure and provides quantitative comparison of the given structure to high-resolution crystal structures. dokh@unc.edu Supplementary data are available at Bioinformatics online.

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

Evolution of Air Quality Model at the US EPA

EPA Science Inventory

At the US EPA, we have developed an air quality model, CMAQ, in the past 20+ years. Throughout the years, the model has been upgraded with respect to advancement of science. We have extended the model from regional to hemispheric. We have coupled it with meteorological model, WR...

Estimation of contribution ratios of pollutant sources to a specific section based on an enhanced water quality model.

PubMed

Cao, Bibo; Li, Chuan; Liu, Yan; Zhao, Yue; Sha, Jian; Wang, Yuqiu

2015-05-01

Because water quality monitoring sections or sites could reflect the water quality status of rivers, surface water quality management based on water quality monitoring sections or sites would be effective. For the purpose of improving water quality of rivers, quantifying the contribution ratios of pollutant resources to a specific section is necessary. Because physical and chemical processes of nutrient pollutants are complex in water bodies, it is difficult to quantitatively compute the contribution ratios. However, water quality models have proved to be effective tools to estimate surface water quality. In this project, an enhanced QUAL2Kw model with an added module was applied to the Xin'anjiang Watershed, to obtain water quality information along the river and to assess the contribution ratios of each pollutant source to a certain section (the Jiekou state-controlled section). Model validation indicated that the results were reliable. Then, contribution ratios were analyzed through the added module. Results show that among the pollutant sources, the Lianjiang tributary contributes the largest part of total nitrogen (50.43%), total phosphorus (45.60%), ammonia nitrogen (32.90%), nitrate (nitrite + nitrate) nitrogen (47.73%), and organic nitrogen (37.87%). Furthermore, contribution ratios in different reaches varied along the river. Compared with pollutant loads ratios of different sources in the watershed, an analysis of contribution ratios of pollutant sources for each specific section, which takes the localized chemical and physical processes into consideration, was more suitable for local-regional water quality management. In summary, this method of analyzing the contribution ratios of pollutant sources to a specific section based on the QUAL2Kw model was found to support the improvement of the local environment.

Modeling crop residue burning experiments and assessing the fire impacts on air quality

EPA Science Inventory

Prescribed burning is a common land management practice that results in ambient emissions of a variety of primary and secondary pollutants with negative health impacts. The community Multiscale Air Quality (CMAQ) model is used to conduct 2 km grid resolution simulations of prescr...

Modeling white sturgeon movement in a reservoir: The effect of water quality and sturgeon density

USGS Publications Warehouse

Sullivan, A.B.; Jager, H.I.; Myers, R.

2003-01-01

We developed a movement model to examine the distribution and survival of white sturgeon (Acipenser transmontanus) in a reservoir subject to large spatial and temporal variation in dissolved oxygen and temperature. Temperature and dissolved oxygen were simulated by a CE-QUAL-W2 model of Brownlee Reservoir, Idaho for a typical wet, normal, and dry hydrologic year. We compared current water quality conditions to scenarios with reduced nutrient inputs to the reservoir. White sturgeon habitat quality was modeled as a function of temperature, dissolved oxygen and, in some cases, suitability for foraging and depth. We assigned a quality index to each cell along the bottom of the reservoir. The model simulated two aspects of daily movement. Advective movement simulated the tendency for animals to move toward areas with high habitat quality, and diffusion simulated density dependent movement away from areas with high sturgeon density in areas with non-lethal habitat conditions. Mortality resulted when sturgeon were unable to leave areas with lethal temperature or dissolved oxygen conditions. Water quality was highest in winter and early spring and lowest in mid to late summer. Limiting nutrient inputs reduced the area of Brownlee Reservoir with lethal conditions for sturgeon and raised the average habitat suitability throughout the reservoir. Without movement, simulated white sturgeon survival ranged between 45 and 89%. Allowing movement raised the predicted survival of sturgeon under all conditions to above 90% as sturgeon avoided areas with low habitat quality. ?? 2003 Elsevier B.V. All rights reserved.

Monitoring Quality Across Home Visiting Models: A Field Test of Michigan's Home Visiting Quality Assurance System.

PubMed

Heany, Julia; Torres, Jennifer; Zagar, Cynthia; Kostelec, Tiffany

2018-06-05

Introduction In order to achieve the positive outcomes with parents and children demonstrated by many home visiting models, home visiting services must be well implemented. The Michigan Home Visiting Initiative developed a tool and procedure for monitoring implementation quality across models referred to as Michigan's Home Visiting Quality Assurance System (MHVQAS). This study field tested the MHVQAS. This article focuses on one of the study's evaluation questions: Can the MHVQAS be applied across models? Methods Eight local implementing agencies (LIAs) from four home visiting models (Healthy Families America, Early Head Start-Home Based, Parents as Teachers, Maternal Infant Health Program) and five reviewers participated in the study by completing site visits, tracking their time and costs, and completing surveys about the process. LIAs also submitted their most recent review by their model developer. The researchers conducted participant observation of the review process. Results Ratings on the MHVQAS were not significantly different between models. There were some differences in interrater reliability and perceived reliability between models. There were no significant differences between models in perceived validity, satisfaction with the review process, or cost to participate. Observational data suggested that cross-model applicability could be improved by assisting sites in relating the requirements of the tool to the specifics of their model. Discussion The MHVQAS shows promise as a tool and process to monitor implementation quality of home visiting services across models. The results of the study will be used to make improvements before the MHVQAS is used in practice.

Modeling Study on Air Quality Improvement due to Mobile Source Emission control Plan in Seoul Metropolitan Area

NASA Astrophysics Data System (ADS)

Kim, Y. J.; Sunwoo, Y.; Hwang, I.; Song, S.; Sin, J.; Kim, D.

2015-12-01

A very high population and corresponding high number of vehicles in the Seoul Metropolitan Area (SMA) are aggravating the air quality of this region. The Korean government continues to make concerted efforts to improve air quality. One of the major policies that the Ministry of Environment of Korea enforced is "The Special Act for Improvement of Air Quality in SMA" and "The 1st Air Quality Management Plan of SMA". Mobile Source emission controls are an important part of the policy. Thus, it is timely to evaluate the air quality improvement due to the controls. Therefore, we performed a quantitative analysis of the difference in air quality using the Community Multiscale Air Quality (CMAQ) model and December, 2011 was set as the target period to capture the impact of the above control plans. We considered four fuel-type vehicle emission scenarios and compared the air quality improvement differences between them. The scenarios are as follows: no-control, gasoline vehicle control only, diesel vehicle control only, and control of both; utilizing the revised mobile source emissions from the Clean Air Policy Support System (CAPSS), which is the national emission inventory reflecting current policy.In order to improve the accuracy of the modeling data, we developed new temporal allocation coefficients based on traffic volume observation data and spatially reallocated the mobile source emissions using vehicle flow survey data. Furthermore, we calculated the PM10 and PM2.5 emissions of gasoline vehicles which is omitted in CAPSS.The results of the air quality modeling shows that vehicle control plans for both gasoline and diesel lead to a decrease of 0.65ppb~8.75ppb and 0.02㎍/㎥~7.09㎍/㎥ in NO2 and PM10 monthly average concentrations, respectively. The large percentage decreases mainly appear near the center of the metropolis. However, the largest NO2 decrease percentages are found in the northeast region of Gyeonggi-do, which is the province that surrounds the

Influence of air quality model resolution on uncertainty associated with health impacts

NASA Astrophysics Data System (ADS)

Thompson, T. M.; Selin, N. E.

2012-06-01

We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model (CAMx), we ran a modeling episode with meteorological inputs representing conditions as they occurred during August through September 2006, and two emissions inventories (a 2006 base case and a 2018 proposed control scenario, both for Houston, Texas) at 36, 12, 4 and 2 km resolution. The base case model performance was evaluated for each resolution against daily maximum 8-h averaged ozone measured at monitoring stations. Results from each resolution were more similar to each other than they were to measured values. Population-weighted ozone concentrations were calculated for each resolution and applied to concentration response functions (with 95% confidence intervals) to estimate the health impacts of modeled ozone reduction from the base case to the control scenario. We found that estimated avoided mortalities were not significantly different between 2, 4 and 12 km resolution runs, but 36 km resolution may over-predict some potential health impacts. Given the cost/benefit analysis requirements of the Clean Air Act, the uncertainty associated with human health impacts and therefore the results reported in this study, we conclude that health impacts calculated from population weighted ozone concentrations obtained using regional photochemical models at 36 km resolution fall within the range of values obtained using fine (12 km or finer) resolution modeling. However, in some cases, 36 km resolution may not be fine enough to statistically replicate the results achieved using 2 and 4 km resolution. On average, when modeling at 36 km resolution, 7 deaths per ozone month were avoided because of ozone reductions resulting from the proposed emissions reductions (95% confidence interval was 2-9). When modeling at 2, 4 or 12 km finer scale resolution, on

Underground Test Area Activity Quality Assurance Plan Nevada National Security Site, Nevada. Revision 2

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

Krenzien, Susan; Farnham, Irene

This Quality Assurance Plan (QAP) provides the overall quality assurance (QA) requirements and general quality practices to be applied to the U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) Underground Test Area (UGTA) activities. The requirements in this QAP are consistent with DOE Order 414.1D, Change 1, Quality Assurance (DOE, 2013a); U.S. Environmental Protection Agency (EPA) Guidance for Quality Assurance Project Plans for Modeling (EPA, 2002); and EPA Guidance on the Development, Evaluation, and Application of Environmental Models (EPA, 2009). If a participant’s requirement document differs from this QAP, the stricter requirement will take precedence.more » NNSA/NFO, or designee, must review this QAP every two years. Changes that do not affect the overall scope or requirements will not require an immediate QAP revision but will be incorporated into the next revision cycle after identification. Section 1.0 describes UGTA objectives, participant responsibilities, and administrative and management quality requirements (i.e., training, records, procurement). Section 1.0 also details data management and computer software requirements. Section 2.0 establishes the requirements to ensure newly collected data are valid, existing data uses are appropriate, and environmental-modeling methods are reliable. Section 3.0 provides feedback loops through assessments and reports to management. Section 4.0 provides the framework for corrective actions. Section 5.0 provides references for this document.« less

Contribution of regional-scale fire events to ozone and PM2.5 air quality estimated by photochemical modeling approaches

NASA Astrophysics Data System (ADS)

Baker, K. R.; Woody, M. C.; Tonnesen, G. S.; Hutzell, W.; Pye, H. O. T.; Beaver, M. R.; Pouliot, G.; Pierce, T.

2016-09-01

Two specific fires from 2011 are tracked for local to regional scale contribution to ozone (O3) and fine particulate matter (PM2.5) using a freely available regulatory modeling system that includes the BlueSky wildland fire emissions tool, Spare Matrix Operator Kernel Emissions (SMOKE) model, Weather and Research Forecasting (WRF) meteorological model, and Community Multiscale Air Quality (CMAQ) photochemical grid model. The modeling system was applied to track the contribution from a wildfire (Wallow) and prescribed fire (Flint Hills) using both source sensitivity and source apportionment approaches. The model estimated fire contribution to primary and secondary pollutants are comparable using source sensitivity (brute-force zero out) and source apportionment (Integrated Source Apportionment Method) approaches. Model estimated O3 enhancement relative to CO is similar to values reported in literature indicating the modeling system captures the range of O3 inhibition possible near fires and O3 production both near the fire and downwind. O3 and peroxyacetyl nitrate (PAN) are formed in the fire plume and transported downwind along with highly reactive VOC species such as formaldehyde and acetaldehyde that are both emitted by the fire and rapidly produced in the fire plume by VOC oxidation reactions. PAN and aldehydes contribute to continued downwind O3 production. The transport and thermal decomposition of PAN to nitrogen oxides (NOX) enables O3 production in areas limited by NOX availability and the photolysis of aldehydes to produce free radicals (HOX) causes increased O3 production in NOX rich areas. The modeling system tends to overestimate hourly surface O3 at routine rural monitors in close proximity to the fires when the model predicts elevated fire impacts on O3 and Hazard Mapping System (HMS) data indicates possible fire impact. A sensitivity simulation in which solar radiation and photolysis rates were more aggressively attenuated by aerosol in the plume

Developing and Transitioning Numerical Air Quality Models to Improve Air Quality and Public Health Decision-Making in El Salvador and Costa Rica As Part of the Servir Applied Sciences Team

NASA Astrophysics Data System (ADS)

Thomas, A.; Huff, A. K.; Gomori, S. G.; Sadoff, N.

2014-12-01

In order to enhance the capacity for air quality modeling and improve air quality monitoring and management in the SERVIR Mesoamerica region, members of SERVIR's Applied Sciences Team (AST) are developing national numerical air quality models for El Salvador and Costa Rica. We are working with stakeholders from the El Salvador Ministry of the Environment and Natural Resources (MARN); National University of Costa Rica (UNA); the Costa Rica Ministry of the Environment, Energy, and Telecommunications (MINAET); and Costa Rica National Meteorological Institute (IMN), who are leaders in air quality monitoring and management in the Mesoamerica region. Focusing initially on these institutions will build sustainability in regional modeling activities by developing air quality modeling capability that can be shared with other countries in Mesoamerica. The air quality models are based on the Community Multi-scale Air Quality (CMAQ) model and incorporate meteorological inputs from the Weather Research and Forecasting (WRF) model, as well as national emissions inventories from El Salvador and Costa Rica. The models are being optimized for urban air quality, which is a priority of decision-makers in Mesoamerica. Once experimental versions of the modeling systems are complete, they will be transitioned to servers run by stakeholders in El Salvador and Costa Rica. The numerical air quality models will provide decision support for stakeholders to identify 1) high-priority areas for expanding national ambient air monitoring networks, 2) needed revisions to national air quality regulations, and 3) gaps in national emissions inventories. This project illustrates SERVIR's goal of the transition of science to support decision-making through capacity building in Mesoamerica, and it aligns with the Group on Earth Observations' health societal benefit theme. This presentation will describe technical aspects of the development of the models and outline key steps in our successful

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

NASA Technical Reports Server (NTRS)

Li, Hui; Faruque, Fazlay; Williams, Worth; Al-Hamdan, Mohammad; Luvall, Jeffrey C.; Crosson, William; Rickman, Douglas; Limaye, Ashutosh

2009-01-01

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

Aircraft model prototypes which have specified handling-quality time histories

NASA Technical Reports Server (NTRS)

Johnson, S. H.

1978-01-01

Several techniques for obtaining linear constant-coefficient airplane models from specified handling-quality time histories are discussed. The pseudodata method solves the basic problem, yields specified eigenvalues, and accommodates state-variable transfer-function zero suppression. The algebraic equations to be solved are bilinear, at worst. The disadvantages are reduced generality and no assurance that the resulting model will be airplane like in detail. The method is fully illustrated for a fourth-order stability-axis small motion model with three lateral handling quality time histories specified. The FORTRAN program which obtains and verifies the model is included and fully documented.

10 CFR 960.5-2-5 - Environmental quality.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., State, or local environmental requirements. (2) Projected significant adverse environmental impacts that... REPOSITORY Preclosure Guidelines Environment, Socioeconomics, and Transportation § 960.5-2-5 Environmental quality. (a) Qualifying condition. The site shall be located such that (1) the quality of the environment...

DeepQA: improving the estimation of single protein model quality with deep belief networks.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Uncertainty analyses of the calibrated parameter values of a water quality model

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.; Lindenschmidt, K.-E.

2003-04-01