Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Warmer is healthier: effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities.

PubMed

Cox, Louis A; Popken, Douglas A; Ricci, Paolo F

2013-08-01

Recent studies have indicated that reducing particulate pollution would substantially reduce average daily mortality rates, prolonging lives, especially among the elderly (age ≥ 75). These benefits are projected by statistical models of significant positive associations between levels of fine particulate matter (PM2.5) levels and daily mortality rates. We examine the empirical correspondence between changes in average PM2.5 levels and temperatures from 1999 to 2000, and corresponding changes in average daily mortality rates, in each of 100 U.S. cities in the National Mortality and Morbidity Air Pollution Study (NMMAPS) data base, which has extensive PM2.5, temperature, and mortality data for those 2 years. Increases in average daily temperatures appear to significantly reduce average daily mortality rates, as expected from previous research. Unexpectedly, reductions in PM2.5 do not appear to cause any reductions in mortality rates. PM2.5 and mortality rates are both elevated on cold winter days, creating a significant positive statistical relation between their levels, but we find no evidence that reductions in PM2.5 concentrations cause reductions in mortality rates. For all concerned, it is crucial to use causal relations, rather than statistical associations, to project the changes in human health risks due to interventions such as reductions in particulate air pollution. Copyright © 2013 Elsevier Inc. All rights reserved.

Concentration-response of short-term ozone exposure and hospital admissions for asthma in Texas.

PubMed

Zu, Ke; Liu, Xiaobin; Shi, Liuhua; Tao, Ge; Loftus, Christine T; Lange, Sabine; Goodman, Julie E

2017-07-01

Short-term exposure to ozone has been associated with asthma hospital admissions (HA) and emergency department (ED) visits, but the shape of the concentration-response (C-R) curve is unclear. We conducted a time series analysis of asthma HAs and ambient ozone concentrations in six metropolitan areas in Texas from 2001 to 2013. Using generalized linear regression models, we estimated the effect of daily 8-hour maximum ozone concentrations on asthma HAs for all ages combined, and for those aged 5-14, 15-64, and 65+years. We fit penalized regression splines to evaluate the shape of the C-R curves. Using a log-linear model, estimated risk per 10ppb increase in average daily 8-hour maximum ozone concentrations was highest for children (relative risk [RR]=1.047, 95% confidence interval [CI]: 1.025-1.069), lower for younger adults (RR=1.018, 95% CI: 1.005-1.032), and null for older adults (RR=1.002, 95% CI: 0.981-1.023). However, penalized spline models demonstrated significant nonlinear C-R relationships for all ages combined, children, and younger adults, indicating the existence of thresholds. We did not observe an increased risk of asthma HAs until average daily 8-hour maximum ozone concentrations exceeded approximately 40ppb. Ozone and asthma HAs are significantly associated with each other; susceptibility to ozone is age-dependent, with children at highest risk. C-R relationships between average daily 8-hour maximum ozone concentrations and asthma HAs are significantly curvilinear for all ages combined, children, and younger adults. These nonlinear relationships, as well as the lack of relationship between average daily 8-hour maximum and peak ozone concentrations, have important implications for assessing risks to human health in regulatory settings. Copyright © 2017. Published by Elsevier Ltd.

A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

PubMed Central

Li, Huashan; Cao, Fei; Wang, Xianlong; Ma, Weibin

2014-01-01

Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China. PMID:24605046

[Quantitative estimation of evapotranspiration from Tahe forest ecosystem, Northeast China].

PubMed

Qu, Di; Fan, Wen-Yi; Yang, Jin-Ming; Wang, Xu-Peng

2014-06-01

Evapotranspiration (ET) is an important parameter of agriculture, meteorology and hydrology research, and also an important part of the global hydrological cycle. This paper applied the improved DHSVM distributed hydrological model to estimate daily ET of Tahe area in 2007 using leaf area index and other surface data extracted TM remote sensing data, and slope, aspect and other topographic indices obtained by using the digital elevation model. The relationship between daily ET and daily watershed outlet flow was built by the BP neural network, and a water balance equation was established for the studied watershed, together to test the accuracy of the estimation. The results showed that the model could be applied in the study area. The annual total ET of Tahe watershed was 234.01 mm. ET had a significant seasonal variation. The ET had the highest value in summer and the average daily ET value was 1.56 mm. The average daily ET in autumn and spring were 0.30, 0.29 mm, respectively, and winter had the lowest ET value. Land cover type had a great effect on ET value, and the broadleaf forest had a higher ET ability than the mixed forest, followed by the needle leaf forest.

Development of sustainable precision farming systems for swine: estimating real-time individual amino acid requirements in growing-finishing pigs.

PubMed

Hauschild, L; Lovatto, P A; Pomar, J; Pomar, C

2012-07-01

The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation. Thus, the amino acid requirements estimated by model are animal- and time-dependent and follow, in real time, the individual DFI and BW growth patterns. The proposed model can follow the average feed intake and feed weight trajectory of each individual pig in real time with good accuracy. Based on these trajectories and using classical factorial equations, the model makes it possible to estimate dynamically the AA requirements of each animal, taking into account the intake and growth changes of the animal.

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

Modelling of Sub-daily Hydrological Processes Using Daily Time-Step Models: A Distribution Function Approach to Temporal Scaling

NASA Astrophysics Data System (ADS)

Kandel, D. D.; Western, A. W.; Grayson, R. B.

2004-12-01

Mismatches in scale between the fundamental processes, the model and supporting data are a major limitation in hydrologic modelling. Surface runoff generation via infiltration excess and the process of soil erosion are fundamentally short time-scale phenomena and their average behaviour is mostly determined by the short time-scale peak intensities of rainfall. Ideally, these processes should be simulated using time-steps of the order of minutes to appropriately resolve the effect of rainfall intensity variations. However, sub-daily data support is often inadequate and the processes are usually simulated by calibrating daily (or even coarser) time-step models. Generally process descriptions are not modified but rather effective parameter values are used to account for the effect of temporal lumping, assuming that the effect of the scale mismatch can be counterbalanced by tuning the parameter values at the model time-step of interest. Often this results in parameter values that are difficult to interpret physically. A similar approach is often taken spatially. This is problematic as these processes generally operate or interact non-linearly. This indicates a need for better techniques to simulate sub-daily processes using daily time-step models while still using widely available daily information. A new method applicable to many rainfall-runoff-erosion models is presented. The method is based on temporal scaling using statistical distributions of rainfall intensity to represent sub-daily intensity variations in a daily time-step model. This allows the effect of short time-scale nonlinear processes to be captured while modelling at a daily time-step, which is often attractive due to the wide availability of daily forcing data. The approach relies on characterising the rainfall intensity variation within a day using a cumulative distribution function (cdf). This cdf is then modified by various linear and nonlinear processes typically represented in hydrological and erosion models. The statistical description of sub-daily variability is thus propagated through the model, allowing the effects of variability to be captured in the simulations. This results in cdfs of various fluxes, the integration of which over a day gives respective daily totals. Using 42-plot-years of surface runoff and soil erosion data from field studies in different environments from Australia and Nepal, simulation results from this cdf approach are compared with the sub-hourly (2-minute for Nepal and 6-minute for Australia) and daily models having similar process descriptions. Significant improvements in the simulation of surface runoff and erosion are achieved, compared with a daily model that uses average daily rainfall intensities. The cdf model compares well with a sub-hourly time-step model. This suggests that the approach captures the important effects of sub-daily variability while utilizing commonly available daily information. It is also found that the model parameters are more robustly defined using the cdf approach compared with the effective values obtained at the daily scale. This suggests that the cdf approach may offer improved model transferability spatially (to other areas) and temporally (to other periods).

[Relationship between sulfur dioxide pollution and upper respiratory outpatients in Jiangbei, Ningbo].

PubMed

Wu, Yifeng; Zhao, Fengmin; Qian, Xujun; Xu, Guozhang; He, Tianfeng; Shen, Yueping; Cai, Yibiao

2015-07-01

To describe the daily average concentration of sulfur dioxide (SO2) in Ningbo, and to analysis the health impacts it caused in upper respiratory disease. With outpatients log and air pollutants monitoring data matched in 2011-2013, the distributed lag non-linear models were used to analysis the relative risk of the number of upper respiratory patients associated with SO2, and also excessive risk, and the inferred number of patients due to SO2 pollution. The daily average concentration of SO2 didn't exceed the limit value of second class area. The coefficient of upper respiratory outpatient number and daily average concentration of SO2 matched was 0.44,with the excessive risk was 10% to 18%, the lag of most SO2 concentrations was 4 to 6 days. It could be estimated that about 30% of total upper respiratory outpatients were caused by SO2 pollution. Although the daily average concentration of SO2 didn't exceed the standard in 3 years, the health impacts still be caused with lag effect.

The impact of reforestation in the northeast United States on precipitation and surface temperature

NASA Astrophysics Data System (ADS)

Clark, Allyson

Since the 1920s, forest coverage in the northeastern United States has recovered from disease, clearing for agricultural and urban development, and the demands of the timber industry. Such a dramatic change in ground cover can influence heat and moisture fluxes to the atmosphere, as measured in altered landscapes in Australia, Israel, and the Amazon. In this study, the impacts of recent reforestation in the northeastern United States on summertime precipitation and surface temperature were quantified by comparing average modern values to 1950s values. Weak positive (negative) relationships between reforestation and average monthly precipitation and daily minimum temperatures (average daily maximum surface temperature) were found. There was no relationship between reforestation and average surface temperature. Results of the observational analysis were compared with results obtained from reforestation scenarios simulated with the BUGS5 global climate model. The single difference between the model runs was the amount of forest coverage in the northeast United States; three levels of forest were defined - a grassland state, with 0% forest coverage, a completely forested state, with approximately 100% forest coverage, and a control state, with forest coverage closely resembling modern forest coverage. The three simulations were compared, and had larger magnitude average changes in precipitation and in all temperature variables. The difference in magnitudes between the model simulations observations was much larger than the difference in the amount of reforestation in each case. Additionally, unlike in observations, a negative relationship was found between average daily minimum temperature and amount of forest coverage, implying that additional factors influence temperature and precipitation in the real world that are not accounted for in the model.

Solar energy potential in the United Arab Emirates

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

Khalil, A.; Alnajjar, A.

1995-12-31

In the present study, the global, direct and diffuse components of solar radiation as well as temperature, relative humidity and wind speed have been continuously monitored and analyzed on hourly, daily and monthly basis. Experimental data is compared to the predictions of different theoretical models as functions of declination and hour angles. Correlations are obtained describing the variation of hourly, daily and monthly averages of total and diffuse solar radiation using polynomial expressions. Empirical correlations describing the dependence of the daily average diffuse to total radiation ratio on the clearness index are also obtained. Data of daily diffuse to totalmore » radiation ratio is compared to correlations obtained by other investigators. The comparison shows a reasonable agreement with some scatter due to the seasonal dependence of the correlation. Comparison of calculations with experimental measurements under clear sky conditions show excellent agreement with a maximum error of 8%. The measured ratio of hourly to daily insolation is in excellent agreement with the model of Hottel which is expressed as a function of the clearness index, hour and the sunset hour angles.« less

12 CFR Appendix G to Part 226 - Open-End Model Forms and Clauses

Code of Federal Regulations, 2011 CFR

2011-01-01

... RESERVE SYSTEM TRUTH IN LENDING (REGULATION Z) Pt. 226, App. G Appendix G to Part 226—Open-End Model Forms... charges]. We do not add in any new [purchases/advances/loans]. This gives us the daily balance. Then, we... “average daily balance” we take the beginning balance of your account each day, add any new [purchases...

Depressive Mood, Anger, and Daily Cortisol of Caregivers on High- and Low-Stress Days.

PubMed

Leggett, Amanda N; Zarit, Steven H; Kim, Kyungmin; Almeida, David M; Klein, Laura Cousino

2015-11-01

This study examines the association of daily cortisol with depressive mood and anger. Depressive mood, anger and 2 markers of cortisol, area under the curve (AUC), and cortisol awakening response (CAR) were examined for caregivers (N = 164) of individuals with dementia (IWDs) across 8 days, some of which IWDs attended an adult day service (ADS) program. Caregivers were primarily female (86.7%) with a mean age of 61.99. First, multilevel models were run with CAR and AUC each as separate covariates of anger and depressive mood. A second set of models examined contextual factors of caregivers (i.e., care-related stressors and amount of ADS use) were added to the models for anger and depressive mood (Model 2). On days where caregivers had AUCs below their average they expressed higher anger scores. However in Model 2, anger was associated with more care-related stressors, but not ADS use or daily cortisol. Caregivers who on average had smaller CARs were more likely to be depressed. In Model 2, depressed mood was associated with more care-related stressors and a low average CAR. We found that hypocortisol patterns, reflective of chronic stress experienced by caregivers, are associated with negative mood. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Streamflow simulation studies of the Hillsborough, Alafia, and Anclote Rivers, west-central Florida

USGS Publications Warehouse

Turner, J.F.

1979-01-01

A modified version of the Georgia Tech Watershed Model was applied for the purpose of flow simulation in three large river basins of west-central Florida. Calibrations were evaluated by comparing the following synthesized and observed data: annual hydrographs for the 1959, 1960, 1973 and 1974 water years, flood hydrographs (maximum daily discharge and flood volume), and long-term annual flood-peak discharges (1950-72). Annual hydrographs, excluding the 1973 water year, were compared using average absolute error in annual runoff and daily flows and correlation coefficients of monthly and daily flows. Correlations coefficients for simulated and observed maximum daily discharges and flood volumes used for calibrating range from 0.91 to 0.98 and average standard errors of estimate range from 18 to 45 percent. Correlation coefficients for simulated and observed annual flood-peak discharges range from 0.60 to 0.74 and average standard errors of estimate range from 33 to 44 percent. (Woodard-USGS)

Associations between daily outpatient visits for respiratory diseases and ambient fine particulate matter and ozone levels in Shanghai, China.

PubMed

Wang, Yiyi; Zu, Yaqun; Huang, Lin; Zhang, Hongliang; Wang, Changhui; Hu, Jianlin

2018-09-01

Air pollution in China has been very serious during the recent decades. However, few studies have investigated the effects of short-term exposure to PM 2.5 and O 3 on daily outpatient visits for respiratory diseases. We examined the effects of PM 2.5 and O 3 on the daily outpatient visits for respiratory diseases, explored the sensitivities of different population subgroups and analyzed the relative risk (RR) of PM 2.5 and O 3 in different seasons in Shanghai during 2013-2016. The generalized linear model (GLM) was applied to analyze the exposure-response relationship between air pollutants (daily average PM 2.5 and daily maximum 8-h average O 3 ), and daily outpatient visits due to respiratory diseases. The sensitivities of males and females at the ages of 15-60 yr-old and 60+ yr-old to the pollutants were also studied for the whole year and for the cold and warm months, respectively. Finally, the results of the single-day lagged model were compared with that of the moving average lag model. At lag 0 day, the RR of respiratory outpatients increased by 0.37% with a 10 μg/m 3 increase in PM 2.5 . Exposure to PM 2.5 (RR, 1.0047, 95% CI, 1.0032-1.0062) was more sensitive for females than for males (RR, 1.0025, 95% CI, 1.0008-1.0041), and was more sensitive for the 15-60 yr-old (RR, 1.0041, 95% CI, 1.0027-1.0055) than the 60+ yr-old age group (RR, 1.0031, 95% CI, 1.0014-1.0049). O 3 was not significantly associated with respiratory outpatient visits during the warm periods, but was negatively associated during the cold periods. PM 2.5 was more significantly in the cold periods than that in the warm periods. The results indicated that control of PM 2.5 , compared to O 3 , in the cold periods would be more beneficial to the respiratory health in Shanghai. In addition, the single-day lagged model underestimated the relationship between PM 2.5 and O 3 and outpatient visits for respiratory diseases compared to the moving average lag model. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

PubMed

Alam, Md Saniul; McNabola, Aonghus

2015-05-01

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

Evaluation of Satellite and Model Precipitation Products Over Turkey

NASA Astrophysics Data System (ADS)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14.72 mm/month and 10.75 mm/month, respectively) compared to gauges IWD error (21.58 mm/month). These results show that, on average, ECMWF forecast data have higher skill than TRMM observations. Overall, both ECMWF forecast data and TRMM observations show good potential for catchment scale hydrological analysis.

Application of agrometeorological spectral model in rice area in southern Brazil

NASA Astrophysics Data System (ADS)

Leivas, Janice F.; de C. Teixeira, Antonio Heriberto; Andrade, Ricardo G.; de C. Victoria, Daniel; Bayma-Silva, Gustavo; Bolfe, Edson L.

2015-10-01

The southern region is responsible for 70% of rice production in Brazil. In this study, rice areas of Rio Grande do Sul were selected, using the land use classification, scale 1: 100,000, provided by Brazilian Institute of Geography and Statistics (IBGE). MODIS Images were used and meteorological data, available by National Institute of Meteorology (INMET). The period of analysis was crop season 2011/2012, October to March. To obtain evapotranspiration was applied agrometeorological-spectral model SAFER (Simple Algorithm For Retrieving Evapotranspiration). From the analysis of the results, on planting and cultivation period , the average evapotranspiration (ET) daily was 1.93 +/- 0.96 mm.day-1. In the vegetative development period of rice, the daily ET has achieved 4.94 mm.day-1, with average value 2,31+/- 0.97 mm.day-1. In the period of harvest, evapotranspiration daily average was 1.84 +/- 0.80 mm.day-1. From results obtained, the estimation of evapotranspiration from satellite images may assist in monitoring the culture during the cycle, assisting in estimates of water productivity and crop yield.

Short-term effects of multiple ozone metrics on daily mortality in a megacity of China.

PubMed

Li, Tiantian; Yan, Meilin; Ma, Wenjun; Ban, Jie; Liu, Tao; Lin, Hualiang; Liu, Zhaorong

2015-06-01

Epidemiological studies have widely demonstrated association between ambient ozone and mortality, though controversy remains, and most of them only use a certain metric to assess ozone levels. However, in China, few studies have investigated the acute effects of ambient ozone, and rare studies have compared health effects of multiple daily metrics of ozone. The present analysis aimed to explore variability of estimated health effects by using multiple temporal ozone metrics. Six metrics of ozone, 1-h maximum, maximum 8-h average, 24-h average, daytime average, nighttime average, and commute average, were used in a time-series study to investigate acute mortality associated with ambient ozone pollution in Guangzhou, China, using 3 years of daily data (2006-2008). We used generalized linear models with Poisson regression incorporating natural spline functions to analyze the mortality, ozone, and covariate data. We also examined the association by season. Daily 1- and 8-h maximum, 24-h average, and daytime average concentrations yielded statistically significant associations with mortality. An interquartile range (IQR) of O3 metric increase of each ozone metric (lag 2) corresponds to 2.92 % (95 % confidence interval (CI) 0.24 to 5.66), 3.60 % (95 % CI, 0.92 to 8.49), 3.03 % (95 % CI, 0.57 to 15.8), and 3.31 % (95 % CI, 0.69 to 10.4) increase in daily non-accidental mortality, respectively. Nighttime and commute metrics were weakly associated with increased mortality rate. The associations between ozone and mortality appeared to be more evident during cool season than in the warm season. Results were robust to adjustment for co-pollutants, weather, and time trend. In conclusion, these results indicated that ozone, as a widespread pollutant, adversely affects mortality in Guangzhou.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

PubMed Central

Hoover, Stephen; Jackson, Eric V.; Paul, David; Locke, Robert

2016-01-01

Summary Background Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Objective Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. Methods We used five years of retrospective daily NICU census data for model development (January 2008 – December 2012, N=1827 observations) and one year of data for validation (January – December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. Results The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Conclusions Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning. PMID:27437040

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

PubMed

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Predicting apricot phenology using meteorological data.

PubMed

Ruml, Mirjana; Milatović, Dragan; Vulić, Todor; Vuković, Ana

2011-09-01

The main objective of this study was to develop feasible, easy to apply models for early prediction of full flowering (FF) and maturing (MA) in apricot (Prunus armeniaca L.). Phenological data for 20 apricot cultivars grown in the Belgrade region were modeled against averages of daily temperature records over ten seasons for FF and eight seasons for MA. A much stronger correlation was found between the phenological timing and temperature at the very beginning than at the end of phenophases. Also, the length of developmental periods were better correlated to daily maximum than to daily minimum and mean air temperatures. Using prediction models based on daily maximum temperatures averaged over 30-, 45- and 60-day periods, starting from 1 January for FF prediction and from the date of FF for MA prediction, the onset of examined phenophases in apricot cultivars could be predicted from a few weeks to up to 2 months ahead with acceptable accuracy. The mean absolute differences between the observations and cross-validated predictions obtained by 30-, 45- and 60-day models were 8.6, 6.9 and 5.7 days for FF and 6.1, 3.6 and 2.8 days for MA, respectively. The validity of the results was confirmed using an independent data set for the year 2009.

Predicting apricot phenology using meteorological data

NASA Astrophysics Data System (ADS)

Ruml, Mirjana; Milatović, Dragan; Vulić, Todor; Vuković, Ana

2011-09-01

The main objective of this study was to develop feasible, easy to apply models for early prediction of full flowering (FF) and maturing (MA) in apricot ( Prunus armeniaca L.). Phenological data for 20 apricot cultivars grown in the Belgrade region were modeled against averages of daily temperature records over ten seasons for FF and eight seasons for MA. A much stronger correlation was found between the phenological timing and temperature at the very beginning than at the end of phenophases. Also, the length of developmental periods were better correlated to daily maximum than to daily minimum and mean air temperatures. Using prediction models based on daily maximum temperatures averaged over 30-, 45- and 60-day periods, starting from 1 January for FF prediction and from the date of FF for MA prediction, the onset of examined phenophases in apricot cultivars could be predicted from a few weeks to up to 2 months ahead with acceptable accuracy. The mean absolute differences between the observations and cross-validated predictions obtained by 30-, 45- and 60-day models were 8.6, 6.9 and 5.7 days for FF and 6.1, 3.6 and 2.8 days for MA, respectively. The validity of the results was confirmed using an independent data set for the year 2009.

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

NASA Astrophysics Data System (ADS)

Ausati, Shadi; Amanollahi, Jamil

2016-10-01

Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

On nonstationarity and antipersistency in global temperature series

NASA Astrophysics Data System (ADS)

KäRner, O.

2002-10-01

Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

Characteristics of Atmospheric Pollution in Handan, China

NASA Astrophysics Data System (ADS)

Zhang, P.; Wang, L.; Zhao, X.; Yang, J.; Wei, Z.; Su, J.; Zhang, F.; Meng, C.

2013-12-01

Handan, located in the southern edge of Hebei province, is one of the cities with worst air pollution in China. Based on the data from our comprehensive air quality monitoring station in Handan from August 2012 to January 2013, a series studies on the characteristics of air pollution in Handan were conducted. The daily mean concentration of PM10 and PM2.5 was 231.5 μg/m3 and 125.8 μg/m3 which exceeded daily National Ambient Air Quality Standard II (NAAQS) of China by 54.3% and 67.7% respectively. The highest daily concentration of them was 863.9 μg/m3 and 643.0 μg/m3, appeared on January 11, 2013, exceeding NAAQS by 475.9% and 757.3% respectively. Mean ratio of PM2.5/PM10 was 0.53. High PM2.5/PM10 ratio frequently occurred in winter, especially January (0.63) and February (0.65). Average daily concentration of SO2, NOx, NO2 and CO was 118 μg/m3, 133 μg/m3, 60.4 μg/m3 and 3210 μg/m3 respectively. The maximum daily average concentration of them was 393 μg/m3, 352 μg/m3, 135 μg/m3, 9660 μg/m3 which was 2.62, 3.52, 2.69, 2.42 times of daily NAAQS. The average concentration of total water soluble ions (TWSI) in PM2.5 from October 13 to December 21, 2012 was 69.57 μg/m3 which accounted for 61.67% of PM2.5. NO3-, SO42-, Cl- and NH4+ were the most important components of water soluble ionic composition in PM2.5.their concentration was 21.20 μg/m3, 16.96 μg/m3,8.43 μg/m3 and 14.81 μg/m3, accounted for 18.8%, 15.03% ,7.47% and 13.13% in PM2.5, respectively. Concentration of NO3- and SO42- had a good correlation (R2 = 0.807). The daily average concentration of OC and EC was 22.17 μg/m3, 6.29 μg/m3, accounted for 19.65%, 5.58% in PM2.5 respectively. The average ratio of OC/EC was 3.44, which shows that there is secondary organic carbon (SOC) in carbonaceous aerosol. Chemical characteristics of PM2.5 in Beijing, Tianjin and Handan were very similar. Most of Daily visibility values (67.4%) were lower than 5 km from August 2012 to January 2013. Daily visibility above 16 km was very scarce in Handan. Mean value of daily visibility was only 4.4×3.5 km in the range of 0.3 to 15.6 km. Average daily value of BC, NO, O3, RH, temperature, pressure was 9.3 μg/m3, 35.7 ppb, 20.6 ppb, 64.4%, 12.1 degree and 1011.2 hPa respectively. During the most polluted period from January 6 to January 31, 2013, mean daily visibility was 0.9 km. Average value of BC, NO, RH, temperature and pressure was 20.4 μg/m3, 98.4 ppb, 89.2%, -1.9 degree and 1015.9 hPa respectively. Visibility showed negative correlation with BC, RH, NO2, PM2.5, NOx, PM10, NO, CO, SO2, pressure and showed positive correlation with O3 and temperature. The most related four parameters with visibility were BC, RH, NO2 and PM2.5. The least related four parameters with visibility were O3, temperature, SO2 and pressure. Empirical model was developed to investigate the complex relationships between visibility, meteorological and pollutant parameters. The modeling result was as following: The model computed visibility had good consistence with the observed values.

[Estimating and projecting the acute effect of cold spells on excess mortality under climate change in Guangzhou].

PubMed

Sun, Q H; Wang, W T; Wang, Y W; Li, T T

2018-04-06

Objective: To estimate future excess mortality attributable to cold spells in Guangzhou, China. Methods: We collected the mortality data and metrological data from 2009-2013 of Guangzhou to calculated the association between cold spell days and non-accidental mortality with GLM model. Then we projected future daily average temperatures (2020-2039 (2020s) , 2050-2069 (2050s) , 2080-2099 (2080s) ) with 5 GCMs models and 2 RCPs (RCP4.5 and RCP8.5) to identify cold spell days. The baseline period was the 1980s (1980-1999). Finally, calculated the yearly cold spells related excess death of 1980s, 2020s, 2050s, and 2080s with average daily death count of non-cold spell days, exposure-response relationship, and yearly number of cold spell days. Results: The average of daily non-accidental mortality in Guangzhou from 2009 to 2013 was 96, and the average of daily average was 22.0 ℃. Cold spell days were associated with 3.3% (95% CI: 0.4%-6.2%) increase in non-accidental mortality. In 1980s, yearly cold spells related deaths were 34 (95% CI: 4-64). In 2020s, the number will increase by 0-10; in 2050s, the number will increase by 1-9; and in 2080s, will increase by 1-9 under the RCP4.5 scenario. In 2020s, the number will increase by 0-9; in 2050s, the number will increase by 1-6; and in 2080s, will increase by 0-11 under the RCP8.5 scenario. Conclusion: The cold spells related non-accidental deaths in Guangzhou will increase in future under climate change.

Daily Self-Disclosure and Sleep in Couples

PubMed Central

Kane, Heidi S.; Slatcher, Richard B.; Reynolds, Bridget M.; Repetti, Rena L.; Robles, Theodore F.

2014-01-01

Objective An emerging literature provides evidence for the association between romantic relationship quality and sleep, an important factor in health and well-being. However, we still know very little about the specific relationship processes that affect sleep behavior. Therefore, the goal of this study was to examine how self-disclosure, an important relational process linked to intimacy, relationship satisfaction and health, is associated with sleep behavior. Method As part of a larger study of family processes, wives (n=46) and husbands (n=38) from 46 cohabiting families completed 56 days of daily diaries. Spouses completed evening diaries assessing daily self-disclosure, relationship satisfaction, and mood and morning diaries assessing the prior night's sleep. Multilevel modeling was used to explore the effects of both daily variation in and average levels across the 56 days of self-disclosure on sleep. Results Daily variation in self-disclosure predicted sleep outcomes for wives, but not for husbands. On days when wives self-disclosed more to their spouses than their average level, their subjective sleep quality and sleep efficiency that night improved. Furthermore, daily self-disclosure buffered the negative effect of daily negative mood on sleep latency for wives, but not husbands. In contrast, higher average levels of self-disclosure predicted less waking during the night for husbands, but not for wives. Conclusion The association between self-disclosure and sleep is one mechanism by which daily relationship functioning may influence health and well-being. Gender may play a role in how self-disclosure is associated with sleep. PMID:25068453

Application of Biological Simulation Models in Estimating Feed Efficiency of Finishing Steers

USDA-ARS?s Scientific Manuscript database

Data on individual daily feed intake, bi-weekly BW, and carcass composition were obtained on 1,212 crossbred steers. Within animal regressions of cumulative feed intake and BW on linear and quadratic days on feed were used to quantify initial and ending BW, average daily feed intake (OFI) and ADG o...

Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.

PubMed

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

2016-04-19

To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius rv was performed and the R(2) between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R(2) increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

Safta, C.; Ricciuto, D. M.; Sargsyan, K.; Najm, H. N.; Debusschere, B.; Thornton, P. E.

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

Effects of microclimatic variables on the symptoms and signs onset of Moniliophthora roreri, causal agent of Moniliophthora pod rot in cacao

PubMed Central

Tixier, Philippe; Germon, Amandine; Rakotobe, Veromanitra; Phillips-Mora, Wilbert; Maximova, Siela; Avelino, Jacques

2017-01-01

Moniliophthora Pod Rot (MPR) caused by the fungus Moniliophthora roreri (Cif.) Evans et al., is one of the main limiting factors of cocoa production in Latin America. Currently insufficient information on the biology and epidemiology of the pathogen limits the development of efficient management options to control MPR. This research aims to elucidate MPR development through the following daily microclimatic variables: minimum and maximum temperatures, wetness frequency, average temperature and relative humidity in the highly susceptible cacao clone Pound-7 (incidence = 86% 2008–2013 average). A total of 55 cohorts totaling 2,268 pods of 3–10 cm length, one to two months of age, were tagged weekly. Pods were assessed throughout their lifetime, every one or two weeks, and classified in 3 different categories: healthy, diseased with no sporulation, diseased with sporulating lesions. As a first step, we used Generalized Linear Mixed Models (GLMM) to determine with no a priori the period (when and for how long) each climatic variable was better related with the appearance of symptoms and sporulation. Then the significance of the candidate variables was tested in a complete GLMM. Daily average wetness frequency from day 14 to day 1, before tagging, and daily average maximum temperature from day 4 to day 21, after tagging, were the most explanatory variables of the symptoms appearance. The former was positively linked with the symptoms appearance when the latter exhibited a maximum at 30°C. The most important variables influencing sporulation were daily average minimum temperature from day 35 to day 58 and daily average maximum temperature from day 37 to day 48, both after tagging. Minimum temperature was negatively linked with the sporulation while maximum temperature was positively linked. Results indicated that the fungal microclimatic requirements vary from the early to the late cycle stages, possibly due to the pathogen’s long latent period. This information is valuable for development of new conceptual models for MPR and improvement of control methods. PMID:28972981

Effects of microclimatic variables on the symptoms and signs onset of Moniliophthora roreri, causal agent of Moniliophthora pod rot in cacao.

PubMed

Leandro-Muñoz, Mariela E; Tixier, Philippe; Germon, Amandine; Rakotobe, Veromanitra; Phillips-Mora, Wilbert; Maximova, Siela; Avelino, Jacques

2017-01-01

Moniliophthora Pod Rot (MPR) caused by the fungus Moniliophthora roreri (Cif.) Evans et al., is one of the main limiting factors of cocoa production in Latin America. Currently insufficient information on the biology and epidemiology of the pathogen limits the development of efficient management options to control MPR. This research aims to elucidate MPR development through the following daily microclimatic variables: minimum and maximum temperatures, wetness frequency, average temperature and relative humidity in the highly susceptible cacao clone Pound-7 (incidence = 86% 2008-2013 average). A total of 55 cohorts totaling 2,268 pods of 3-10 cm length, one to two months of age, were tagged weekly. Pods were assessed throughout their lifetime, every one or two weeks, and classified in 3 different categories: healthy, diseased with no sporulation, diseased with sporulating lesions. As a first step, we used Generalized Linear Mixed Models (GLMM) to determine with no a priori the period (when and for how long) each climatic variable was better related with the appearance of symptoms and sporulation. Then the significance of the candidate variables was tested in a complete GLMM. Daily average wetness frequency from day 14 to day 1, before tagging, and daily average maximum temperature from day 4 to day 21, after tagging, were the most explanatory variables of the symptoms appearance. The former was positively linked with the symptoms appearance when the latter exhibited a maximum at 30°C. The most important variables influencing sporulation were daily average minimum temperature from day 35 to day 58 and daily average maximum temperature from day 37 to day 48, both after tagging. Minimum temperature was negatively linked with the sporulation while maximum temperature was positively linked. Results indicated that the fungal microclimatic requirements vary from the early to the late cycle stages, possibly due to the pathogen's long latent period. This information is valuable for development of new conceptual models for MPR and improvement of control methods.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas

PubMed Central

Olvera, Hector A.; Jimenez, Omar; Provencio-Vasquez, Elias

2014-01-01

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available. PMID:25313294

Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study

PubMed Central

Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang

2016-01-01

Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. Design A time-series study using regional death registry between 2009 and 2010. Setting 8 districts in a large metropolitan area in Northern China. Participants 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Main outcome measures Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. Results The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (−1.09 to 4.28 vs −1.08 to 3.93) and the PCs-based model (−2.23 to 4.07 vs −2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, −1.12 to 4.85 versus −1.11 versus 4.83. Conclusions The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. PMID:27531727

The use of alternative pollutant metrics in time-series studies of ambient air pollution and respiratory emergency department visits.

PubMed

Darrow, Lyndsey A; Klein, Mitchel; Sarnat, Jeremy A; Mulholland, James A; Strickland, Matthew J; Sarnat, Stefanie E; Russell, Armistead G; Tolbert, Paige E

2011-01-01

Various temporal metrics of daily pollution levels have been used to examine the relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels. We obtained hourly measurements of ambient particulate matter (PM₂.₅), carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃) from air monitoring networks in 20-county Atlanta for the time period 1993-2004. For each pollutant, we created (1) a daily 1-h maximum; (2) a 24-h average; (3) a commute average; (4) a daytime average; (5) a nighttime average; and (6) a daily 8-h maximum (only for O₃). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day's pollutant metrics. Variability was greatest across O₃ metrics, with the 8-h maximum, 1-h maximum, and daytime metrics yielding strong positive associations and the nighttime O₃ metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O₃). With the exception of daytime metric, all of the CO and NO₂ metrics were positively associated with respiratory emergency department visits. Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (for example, the use of a daily 8-h maximum O₃ as opposed to a 24-h average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.

Improving estuary models by reducing uncertainties associated with river flows

NASA Astrophysics Data System (ADS)

Robins, Peter E.; Lewis, Matt J.; Freer, Jim; Cooper, David M.; Skinner, Christopher J.; Coulthard, Tom J.

2018-07-01

To mitigate against future changes to estuaries such as water quality, catchment and estuary models can be coupled to simulate the transport of harmful pathogenic viruses, pollutants and nutrients from their terrestrial sources, through the estuary and to the coast. To predict future changes to estuaries, daily mean river flow projections are typically used. We show that this approach cannot resolve higher frequency discharge events that have large impacts to estuarine dilution, contamination and recovery for two contrasting estuaries. We therefore characterise sub-daily scale flow variability and propagate this through an estuary model to provide robust estimates of impacts for the future. River flow data (35-year records at 15-min sampling) were used to characterise variabilities in storm hydrograph shapes and simulate the estuarine response. In particular, we modelled a fast-responding catchment-estuary system (Conwy, UK), where the natural variability in hydrograph shapes generated large variability in estuarine circulation that was not captured when using daily-averaged river forcing. In the extreme, the freshwater plume from a 'flash' flood (lasting <12 h) was underestimated by up to 100% - and the response to nutrient loading was underestimated further still. A model of a slower-responding system (Humber, UK), where hydrographs typically last 2-4 days, showed less variability in estuarine circulation and good approximation with daily-averaged flow forcing. Our result has implications for entire system impact modelling; when we determine future changes to estuaries, some systems will need higher resolution future river flow estimates.

Forecast of Frost Days Based on Monthly Temperatures

NASA Astrophysics Data System (ADS)

Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.

2009-04-01

Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.

Modeling Of In-Vehicle Human Exposure to Ambient Fine Particulate Matter

PubMed Central

Liu, Xiaozhen; Frey, H. Christopher

2012-01-01

A method for estimating in-vehicle PM2.5 exposure as part of a scenario-based population simulation model is developed and assessed. In existing models, such as the Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM), in-vehicle exposure is estimated using linear regression based on area-wide ambient PM2.5 concentration. An alternative modeling approach is explored based on estimation of near-road PM2.5 concentration and an in-vehicle mass balance. Near-road PM2.5 concentration is estimated using a dispersion model and fixed site monitor (FSM) data. In-vehicle concentration is estimated based on air exchange rate and filter efficiency. In-vehicle concentration varies with road type, traffic flow, windspeed, stability class, and ventilation. Average in-vehicle exposure is estimated to contribute 10 to 20 percent of average daily exposure. The contribution of in-vehicle exposure to total daily exposure can be higher for some individuals. Recommendations are made for updating exposure models and implementation of the alternative approach. PMID:23101000

Factors associated with feed intake of Angus steers

USDA-ARS?s Scientific Manuscript database

Estimates of variance components were obtained from 475 records of average (AFI) and residual feed intake (RFI). Covariates in various (8) models included average daily gain (G), age (A) and weight (W) on test, and slaughter (S) and ultrasound (U) carcass measures (fat thickness, ribeye area and ma...

Seasonal and daily snowmelt runoff estimates utilizing satellite data. [Wind River Mountains, Wyoming

NASA Technical Reports Server (NTRS)

1980-01-01

Methods using snowcovered area to update seasonal forecasts as snowmelt progresses are also being used in quasi-operational situations. The input of snowcovered area to snowmelt models for short term perdictions was attempted in two ways; namely, the modification of existing hydrologic models and/or the use of models that were specifically designed to use snowcovered area. A daily snowmelt runoff model was used with LANDSAT data to simulate discharge on remote basins in the Wind River Mountains of Wyoming. Daily predicted and actual flows compare closely, and, summarized over the entire snowmelt season (April 1 - September 30), the average difference is only three percent. The model and snowcovered area data are currently being tested on additional watersheds to determine the method's transferability.

Feeling old today? Daily health, stressors, and affect explain day-to-day variability in subjective age.

PubMed

Kotter-Grühn, Dana; Neupert, Shevaun D; Stephan, Yannick

2015-01-01

Subjective age is an important correlate of health, well-being, and longevity. So far, little is known about short-term variability in subjective age and the circumstances under which individuals feel younger/older in daily life. This study examined whether (a) older adults' felt age fluctuates on a day-to-day basis, (b) daily changes in health, stressors, and affect explain fluctuations in felt age, and (c) the daily associations between felt age and health, stressors, or affect are time-ordered. Using an eight-day daily diary approach, N = 43 adults (60-96 years, M = 74.65, SD = 8.19) filled out daily questionnaires assessing subjective age, health, daily stressors, and affect. Data were analysed using multilevel modelling. Subjective age, health, daily stressors, affect. Intra-individual variability in felt age was not explained by time but by short-term variability in other variables. Specifically, on days when participants experienced more than average health problems, stress, or negative affect they felt older than on days with average health, stress, or negative affect. No time-ordered effects were found. Bad health, many stressors, and negative affective experiences constitute circumstances under which older adults feel older than they typically do. Thus, daily measures of subjective age could be markers of health and well-being.

Daily self-disclosure and sleep in couples.

PubMed

Kane, Heidi S; Slatcher, Richard B; Reynolds, Bridget M; Repetti, Rena L; Robles, Theodore F

2014-08-01

An emerging literature provides evidence for the association between romantic relationship quality and sleep, an important factor in health and well-being. However, we still know very little about the specific relationship processes that affect sleep behavior. Therefore, the goal of this study was to examine how self-disclosure, an important relational process linked to intimacy, relationship satisfaction, and health, is associated with sleep behavior. As part of a larger study of family processes, wives (n = 46) and husbands (n = 38) from 46 cohabiting families completed 56 days of daily diaries. Spouses completed evening diaries assessing daily self-disclosure, relationship satisfaction, and mood and morning diaries assessing the prior night's sleep. Multilevel modeling was used to explore the effects of both daily variation in and average levels across the 56 days of self-disclosure on sleep. Daily variation in self-disclosure predicted sleep outcomes for wives, but not for husbands. On days when wives self-disclosed more to their spouses than their average level, their subjective sleep quality and sleep efficiency that night improved. Furthermore, daily self-disclosure buffered the effect of high negative mood on sleep latency for wives, but not husbands. In contrast, higher average levels of self-disclosure predicted less waking during the night for husbands, but not for wives. The association between self-disclosure and sleep is one mechanism by which daily relationship functioning may influence health and well-being. Gender may play a role in how self-disclosure is associated with sleep.

Self-reporting of internal medicine house staff work hours.

PubMed

Saunders, David L; Kehoe, Kimberly C; Rinehart, Vivian H; Berg, Benjamin W

2005-01-01

The 80-hour workweek became a reality for residency programs nationwide on July 1, 2003. In this review of administrative data, we examine the self-reporting of work hours by a cohort of Internal Medicine residents. Data was collected from 27 residents in training at Tripler Army Medical Center over a 4 month period from September 1 to December 31 2002. House staff reported their hours on a daily basis by responding to an email message, as well as on a monthly basis utilizing the Army's UCAPERs (Uniform Chart of Account Personnel System) mandatory monthly workload tracking system. Data from the two separate reporting systems was compared for accuracy, completeness and internal consistency. Compliance with daily reporting was variable (67-97% with overall compliance rate of 86%) but lower when compared with the mandatory military monthly reporting system (95-100%). There were large differences in reporting of average weekly work hours among individual residents when monthly reporting was compared to daily reporting of data with higher averages with monthly data reporting. Weekly totals averaged nearly 12 hours higher when reported monthly compared to reporting on a daily basis (p < 0.0001). A total of 18 residents reported that they worked more than 80 hours per week during one month using monthly data, while only 7 reported that they averaged more than 80 hours with the daily reporting data. When average weekly hours reported on a daily basis were compared with the total number of inpatient days worked over the four month period using a simple regression model, there was a significant relationship with average hours increasing with increasing number of inpatient days worked (adjusted R square = 0. 19, p = 0.01). Little internal consistency was found in the comparison of daily versus monthly work hour reporting, indicating that self-reporting may not provide accurate data. Complying with the 80-hour workweek is crucial for residency programs to maintain accreditation, and thus programs will need a way to accurately capture consistent resident work hour data. Further studies are indicated to determine the most accurate way of assessing house staff work hours.

Daily stressor reactivity during adolescence: The buffering role of parental warmth.

PubMed

Lippold, Melissa A; Davis, Kelly D; McHale, Susan M; Buxton, Orfeu M; Almeida, David M

2016-09-01

This study examined youth stressor reactivity in the form of links between daily stressors and adolescents' negative affect, physical health symptoms, and cortisol patterns. We also tested whether youth gender and parental warmth moderated these linkages. Participants were the children of employees in the information technology division of a large company (N = 132, mean age = 13.39 years, 55% female). Youth completed daily diary telephone interviews on 8 consecutive evenings and provided saliva samples at 4 time points over 4 days to assess daily stressors and youth physiological and affective functioning. Parental warmth was assessed during in-home interviews. Multilevel modeling was used to account for interdependencies in the data. Youth who experienced more daily stressors, on average, reported more negative affect and physical health symptoms, on average. Furthermore, on days youth reported more stressors than usual (compared to their own across-day average), they also exhibited more physical health symptoms, reduced evening cortisol decline (e.g., flatter slopes), higher bedtime cortisol, and more negative affect. Girls had stronger within-person linkages between daily stressors and daily negative affect than boys. Parental warmth moderated these within-person linkages: Youth who experienced more parental warmth had lower negative affect and steeper cortisol decline than usual on less stressful days. However, youth who experienced less parental warmth had higher negative affect and their cortisol levels declined less, even on days with lower-than-usual stress. Daily stressors are associated with youth's affective and physiological functioning, but parental warmth can support youth's stress recovery. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Daily Stressor Reactivity during Adolescence: The Buffering Role of Parental Warmth

PubMed Central

Lippold, Melissa; Davis, Kelly D.; McHale, Susan M.; Buxton, Orfeu; Almeida, David M.

2016-01-01

Objective This study examined youth stressor reactivity in the form of links between daily stressors and adolescents’ negative affect, physical health symptoms, and cortisol patterns. We also tested whether youth gender and parental warmth moderated these linkages. Method Participants were the children of employees in the Information Technology division of a large company (N = 132, mean age = 13.39 years, 55% female). Youth completed daily diary telephone interviews on 8 consecutive evenings and provided saliva samples at 4 time points over 4 days to assess daily stressors and youth physiological and affective functioning. Parental warmth was assessed during in-home interviews. Multi-level modeling was used to account for interdependencies in the data. Results Youth who experienced more daily stressors, on average, reported more negative affect and physical health symptoms, on average. Further, on days youth reported more stressors than usual (compared to their own across-day average), they also exhibited more physical health symptoms, reduced evening cortisol decline (e.g., flatter slopes), higher bedtime cortisol, and more negative affect. Girls had stronger within-person linkages between daily stressors and daily negative affect than boys. Parental warmth moderated these within-person linkages: Youth who experienced more parental warmth had lower negative affect and steeper cortisol decline than usual on less stressful days. Yet, youth who experienced less parental warmth had higher negative affect and their cortisol levels declined less, even on days with lower-than-usual stress. Conclusions Daily stressors are associated with youth's affective and physiological functioning, but parental warmth can support youth's stress recovery. PMID:27175577

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Comparative Effectiveness of Two Walking Interventions on Participation, Step Counts, and Health.

PubMed

Smith-McLallen, Aaron; Heller, Debbie; Vernisi, Kristin; Gulick, Diana; Cruz, Samantha; Snyder, Richard L

2017-03-01

To (1) compare the effects of two worksite-based walking interventions on employee participation rates; (2) compare average daily step counts between conditions, and; (3) examine the effects of increases in average daily step counts on biometric and psychologic outcomes. We conducted a cluster-randomized trial in which six employer groups were randomly selected and randomly assigned to condition. Four manufacturing worksites and two office-based worksite served as the setting. A total of 474 employees from six employer groups were included. A standard walking program was compared to an enhanced program that included incentives, feedback, competitive challenges, and monthly wellness workshops. Walking was measured by self-reported daily step counts. Survey measures and biometric screenings were administered at baseline and 3, 6, and 9 months after baseline. Analysis used linear mixed models with repeated measures. During 9 months, participants in the enhanced condition averaged 726 more steps per day compared with those in the standard condition (p < .001). A 1000-step increase in average daily steps was associated with significant weight loss for both men (-3.8 lbs.) and women (-2.1 lbs.), and reductions in body mass index (-0.41 men, -0.31 women). Higher step counts were also associated with improvements in mood, having more energy, and higher ratings of overall health. An enhanced walking program significantly increases participation rates and daily step counts, which were associated with weight loss and reductions in body mass index.

Understanding the gap between cognitive abilities and daily living skills in adolescents with autism spectrum disorders with average intelligence.

PubMed

Duncan, Amie W; Bishop, Somer L

2015-01-01

Daily living skills standard scores on the Vineland Adaptive Behavior Scales-2nd edition were examined in 417 adolescents from the Simons Simplex Collection. All participants had at least average intelligence and a diagnosis of autism spectrum disorder. Descriptive statistics and binary logistic regressions were used to examine the prevalence and predictors of a "daily living skills deficit," defined as below average daily living skills in the context of average intelligence quotient. Approximately half of the adolescents were identified as having a daily living skills deficit. Autism symptomatology, intelligence quotient, maternal education, age, and sex accounted for only 10% of the variance in predicting a daily living skills deficit. Identifying factors associated with better or worse daily living skills may help shed light on the variability in adult outcome in individuals with autism spectrum disorder with average intelligence. © The Author(s) 2013.

On the impacts of computing daily temperatures as the average of the daily minimum and maximum temperatures

NASA Astrophysics Data System (ADS)

Villarini, Gabriele; Khouakhi, Abdou; Cunningham, Evan

2017-12-01

Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of 10+ % when focusing on extremes and values above (below) high (low) thresholds. Moreover, the effects of the data processing method on trend estimation are generally small, even though the use of the daily minimum and maximum readings reduces the power of trend detection ( 5-10% fewer trends detected in comparison with the reference data).

Influence of air temperature on the first flowering date of Prunus yedoensis Matsum

PubMed Central

Shi, Peijian; Chen, Zhenghong; Yang, Qingpei; Harris, Marvin K; Xiao, Mei

2014-01-01

Climate change is expected to have a significant effect on the first flowering date (FFD) in plants flowering in early spring. Prunus yedoensis Matsum is a good model plant for analyzing this effect. In this study, we used a degree day model to analyze the effect of air temperatures on the FFDs of P. yedoensis at Wuhan University from a long-time series from 1951 to 2012. First, the starting date (=7 February) is determined according to the lowest correlation coefficient between the FFD and the daily average accumulated degree days (ADD). Second, the base temperature (=−1.2°C) is determined according to the lowest root mean square error (RMSE) between the observed and predicted FFDs based on the mean of 62-year ADDs. Finally, based on this combination of starting date and base temperature, the daily average ADD of every year was calculated. Performing a linear fit of the daily average ADD to year, we find that there is an increasing trend that indicates climate warming from a biological climatic indicator. In addition, we find that the minimum annual temperature also has a significant effect on the FFD of P. yedoensis using the generalized additive model. This study provides a method for analyzing the climate change on the FFD in plants' flowering in early spring. PMID:24558585

Analysis of the solar radiation data for Beer Sheva, Israel, and its environs

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

Kudish, A.I.; Ianetz, A.

The solar radiation climate of Beer Sheva, Israel, is reported upon in detail. The database utilized in this analysis consisted of global radiation on a horizontal surface, normal incidence beam radiation, and global radiation on a south-facing surface tilted at 40{degree}. Monthly-average hourly and daily values are reported for each of these three types of measured radiations, together with the calculated monthly-average daily values for the components of the global radiation, viz. the horizontal beam and diffuse radiations. The monthly-average hourly and daily clearness index values have also been calculated and analyzed. Monthly-average daily frequency distributions of the clearness indexmore » values are reported for each month. The solar radiation climate of Beer Sheva has also been compared to those reported for a number of countries in this region. The annual-average daily global radiation incident on a horizontal surface is 18.91 MG/m{sup 2} and that for normal incidence beam radiation is 21.17 MG/m{sup 2}. The annual-average daily fraction of the horizontal global radiation that is beam is 0.72. The annual-average daily value for the clearness index is 0.587 and the average frequency of clear days annually is 58.6%. The authors conclude, based upon the above analysis, that Beer Sheva and its environs are characterized by relatively high, average-daily irradiation rates, both global and beam, and a relatively high frequency of clear days.« less

EnviroAtlas - Biological nitrogen fixation in natural/semi-natural ecosystems by 12-digit HUC for the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean biological nitrogen fixation in natural/semi-natural ecosystems per 12-digit Hydrologic Unit (HUC) in 2006. Biological N fixation (BNF) in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems (Cleveland et al. 1999). AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate (average annual daily temperature, average annual minimum daily temperature, average annual maximum daily temperature, and annual precipitation) and land use/land cover variables in the conterminous US (Sanford and Selnick 2013). Data describing annual average minimum and maximum daily temperatures and total precipitation for 2006 were acquired from the PRISM climate dataset (http://prism.oregonstate.edu). Average annual climate data were then calculated for individual 12-digit USGS Hydrologic Unit Codes (HUC12s; http://water.usgs.gov/GIS/huc.html; 22 March 2011 release) using the Zonal Statistics tool in ArcMap 10.0. AET for individual HUC12s was estimated using equations described in Sanford and Selnick (2013). BNF in natural/semi-natural ecosystems within individual HUC12s was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr-1) and scaled to the proportion

The Influence of Weather Conditions on Joint Pain in Older People with Osteoarthritis: Results from the European Project on OSteoArthritis.

PubMed

Timmermans, Erik J; Schaap, Laura A; Herbolsheimer, Florian; Dennison, Elaine M; Maggi, Stefania; Pedersen, Nancy L; Castell, Maria Victoria; Denkinger, Michael D; Edwards, Mark H; Limongi, Federica; Sánchez-Martínez, Mercedes; Siviero, Paola; Queipo, Rocio; Peter, Richard; van der Pas, Suzan; Deeg, Dorly J H

2015-10-01

This study examined whether daily weather conditions, 3-day average weather conditions, and changes in weather conditions influence joint pain in older people with osteoarthritis (OA) in 6 European countries. Data from the population-based European Project on OSteoArthritis were used. The American College of Rheumatology classification criteria were used to diagnose OA in older people (65-85 yrs). After the baseline interview, at 6 months, and after the 12-18 months followup interview, joint pain was assessed using 2-week pain calendars. Daily values for temperature, precipitation, atmospheric pressure, relative humidity, and wind speed were obtained from local weather stations. Multilevel regression modelling was used to examine the pain-weather associations, adjusted for several confounders. The study included 810 participants with OA in the knee, hand, and/or hip. After adjustment, there were significant associations of joint pain with daily average humidity (B = 0.004, p < 0.01) and 3-day average humidity (B = 0.004, p = 0.01). A significant interaction effect was found between daily average humidity and temperature on joint pain. The effect of humidity on pain was stronger in relatively cold weather conditions. Changes in weather variables between 2 consecutive days were not significantly associated with reported joint pain. The associations between pain and daily average weather conditions suggest that a causal relationship exist between joint pain and weather variables, but the associations between day-to-day weather changes and pain do not confirm causation. Knowledge about the relationship between joint pain in OA and weather may help individuals with OA, physicians, and therapists to better understand and manage fluctuations in pain.

Work and Non-Work Physical Activity Predict Real-Time Smoking Level and Urges in Young Adults.

PubMed

Nadell, Melanie J; Mermelstein, Robin J; Hedeker, Donald; Marquez, David X

2015-07-01

Physical activity (PA) and smoking are inversely related. However, evidence suggests that some types of PA, namely work-related PA, may show an opposite effect. Despite growing knowledge, there remains a paucity of studies examining the context of these behaviors in naturalistic settings or in young adults, a high-risk group for escalation. Participants were 188 young adults (mean age = 21.32; 53.2% female; 91% current smokers) who participated in an electronic diary week to assess daily smoking and urges and a PA recall to examine daily PA. PA was coded into non-work-related and work-related activity to examine differential effects. We considered both participants' weekly average PA and their daily deviations from their average. Mixed-effects regression models revealed that higher weekly average non-work PA was associated with lower smoking level and urges. Daily deviations in non-work PA did not predict urges; however, increased daily non-work PA relative to participants' weekly average was associated with lower smoking for females but higher levels for males. Regarding work PA, only higher weekly average work PA was associated with higher smoking level for both genders; work PA did not predict urges. Results extend previous literature by documenting differential associations between non-work and work PA and young adult smoking and suggest that young adults engaged in work PA should be considered a high-risk group for escalation. Findings provide theoretical and clinical implications for the use of PA in intervention and highlight the necessity of considering PA as a multidimensional construct when examining its links to health behavior. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

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

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Monthly mean forecast experiments with the GISS model

NASA Technical Reports Server (NTRS)

Spar, J.; Atlas, R. M.; Kuo, E.

1976-01-01

The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global and hemispheric energetics, zonally averaged meridional and vertical profiles, forecast error statistics, and monthly mean synoptic fields. Although it generated a realistic mean meridional structure, the model did not adequately reproduce the observed interannual variations in the large scale monthly mean energetics and zonally averaged circulation. The monthly mean sea level pressure field was not predicted satisfactorily, but annual changes in the Icelandic low were simulated. The impact of temporal sea surface temperature variations on the forecasts was investigated by comparing two parallel forecasts for January 1974, one using climatological ocean temperatures and the other observed daily ocean temperatures. The use of daily updated sea surface temperatures produced no discernible beneficial effect.

Gender, Emotion Work, and Relationship Quality: A Daily Diary Study

PubMed Central

Curran, Melissa A.; McDaniel, Brandon T.; Pollitt, Amanda M.; Totenhagen, Casey J.

2015-01-01

We use the gender relations perspective from feminist theorizing to investigate how gender and daily emotion work predict daily relationship quality in 74 couples (148 individuals in dating, cohabiting, or married relationships) primarily from the southwest U.S. Emotion work is characterized by activities that enhance others’ emotional well-being. We examined emotion work two ways: trait (individuals’ average levels) and state (individuals’ daily fluctuations). We examined actor and partner effects of emotion work and tested for gender differences. As outcome variables, we included six types of daily relationship quality: love, commitment, satisfaction, closeness, ambivalence, and conflict. This approach allowed us to predict three aspects of relationship quality: average levels, daily fluctuations, and volatility (overall daily variability across a week). Three patterns emerged. First, emotion work predicted relationship quality in this diverse set of couples. Second, gender differences were minimal for fixed effects: Trait and state emotion work predicted higher average scores on, and positive daily increases in, individuals’ own positive relationship quality and lower average ambivalence. Third, gender differences were more robust for volatility: For partner effects, having a partner who reported higher average emotion work predicted lower volatility in love, satisfaction, and closeness for women versus greater volatility in love and commitment for men. Neither gender nor emotion work predicted average levels, daily fluctuations, or volatility in conflict. We discuss implications and future directions pertaining to the unique role of gender in understanding the associations between daily emotion work and volatility in daily relationship quality for relational partners. PMID:26508808

Gender, Emotion Work, and Relationship Quality: A Daily Diary Study.

PubMed

Curran, Melissa A; McDaniel, Brandon T; Pollitt, Amanda M; Totenhagen, Casey J

2015-08-01

We use the gender relations perspective from feminist theorizing to investigate how gender and daily emotion work predict daily relationship quality in 74 couples (148 individuals in dating, cohabiting, or married relationships) primarily from the southwest U.S. Emotion work is characterized by activities that enhance others' emotional well-being. We examined emotion work two ways: trait (individuals' average levels) and state (individuals' daily fluctuations). We examined actor and partner effects of emotion work and tested for gender differences. As outcome variables, we included six types of daily relationship quality: love, commitment, satisfaction, closeness, ambivalence, and conflict. This approach allowed us to predict three aspects of relationship quality: average levels, daily fluctuations, and volatility (overall daily variability across a week). Three patterns emerged. First, emotion work predicted relationship quality in this diverse set of couples. Second, gender differences were minimal for fixed effects: Trait and state emotion work predicted higher average scores on, and positive daily increases in, individuals' own positive relationship quality and lower average ambivalence. Third, gender differences were more robust for volatility: For partner effects, having a partner who reported higher average emotion work predicted lower volatility in love, satisfaction, and closeness for women versus greater volatility in love and commitment for men. Neither gender nor emotion work predicted average levels, daily fluctuations, or volatility in conflict. We discuss implications and future directions pertaining to the unique role of gender in understanding the associations between daily emotion work and volatility in daily relationship quality for relational partners.

[Time-series analysis of ambient PM₁₀ pollution on residential mortality in Beijing].

PubMed

Xue, Jiang-li; Wang, Qi; Cai, Yue; Zhou, Mai-geng

2012-05-01

To explore the short-term impact of ambient PM(10) on daily non-accidental death, cardiovascular and respiratory death of residents in Beijing. Mortality data of residents in Beijing during 2006 to 2009 were obtained from public health surveillance and information service center of Chinese Center for Disease Control and Prevention, contemporaneous data of average daily air concentration of PM(10), SO(2), NO(2) were obtained from Beijing Environment Protection Bureau (year 2005 - 2006) and public website of Beijing environmental protection (year 2007 - 2009), respectively, contemporaneous meteorological data were obtained from china meteorological data sharing service system. Generalized addictive model (GAM) of time serial analysis was applied. In additional to the control of confounding factors such as long-term trend, day of the week effect, meteorological factors, lag effect and the effects of other atmospheric pollutants were also analyzed. During year 2006 to 2009, the number of average daily non-accidental death, respiratory disease caused death, cardiovascular and cerebrovascular diseases caused death among Beijing residents were 140.1, 15.0, 65.8, respectively;contemporaneous medians of average daily air concentration of PM(10), SO(2), NO(2) were 123.0, 26.0, 58.0 µg/m(3), respectively;contemporaneous average atmosphere pressure, temperature and relative humidity were 10.1 kPa, 13.5°C and 51.9%, respectively. An exposure-response relationship between exposure to ambient PM(10) and increased daily death number was found as every 10 µg/m(3) increase in daily average concentration of PM(10), there was a 0.1267% (95%CI: 0.0824% - 0.1710%) increase in daily non-accidental death of residents, 0.1365% (95%CI: 0.0010% - 0.2720%) increase in respiratory death and 0.1239% (95%CI: 0.0589% - 0.1889%) increase in cardiovascular death. Ambient PM(10) had greatest influence on daily non-accidental and cardiovascular death of the same day, while its greatest influence on respiratory death occurred 5 days later. The ambient PM(10) pollution increased daily non-accidental, respiratory disease caused, cardiovascular and cerebrovascular diseases caused deaths among residents in Beijing, and lag effect existed as for the effect of ambient PM(10) pollution on respiratory disease caused death.

Reconstruction of missing daily streamflow data using dynamic regression models

NASA Astrophysics Data System (ADS)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

Comparative studies on growth traits of Sanga and Friesian-Sanga crossbred calves raised on natural pasture on the Accra Plains of Ghana.

PubMed

Sottie, E T; Darfour-Oduro, K A; Okantah, S A

2009-03-01

Data collected from 1993 to 2006 at the Animal Research Institute of Ghana was used to compare the performance of Sanga and Friesian-Sanga crossbred calves on natural pasture. Performance traits analyzed were birth weight (BWT), weaning weight adjusted to 210 days (WW7), preweaning average daily gain to 210 days (ADG 1), weight at 12 months adjusted to 365 days (W12), weight at 18 months adjusted to 540 days (W18) and postweaning average daily gain (ADG 2, from weaning to 540 days). Effects in the model describing these traits were breed, season, sex and first-order interactions between these effects. With the exception of heavier birth weight of Friesian-Sanga crossbred calves (19.98 kg vs. 19.18 kg), body weights of Sangas at weaning, 12 months and 18 months exceeded those of the Friesian-Sanga crossbred calves by 3.76 kg, 35.06 kg and 46.24 kg respectively. The Sangas were also superior in preweaning average daily gain (0.35 kg/day vs. 0.26 kg/day) and postweaning average daily gain (0.28 kg/day vs. 0.21 kg/day). There was a tendency of increasing weight difference between the two breeds with advancing age. It was suggested that improved nutrition such as supplementary feeding would be necessary for crossbreds to express their potential for growth.

Looking for a relevant potential evapotranspiration model at the watershed scale

NASA Astrophysics Data System (ADS)

Oudin, L.; Hervieu, F.; Michel, C.; Perrin, C.; Anctil, F.; Andréassian, V.

2003-04-01

In this paper, we try to identify the most relevant approach to calculate Potential Evapotranspiration (PET) for use in a daily watershed model, to try to bring an answer to the following question: "how can we use commonly available atmospheric parameters to represent the evaporative demand at the catchment scale?". Hydrologists generally see the Penman model as the ideal model regarding to its good adequacy with lysimeter measurements and its physically-based formulation. However, in real-world engineering situations, where meteorological stations are scarce, hydrologists are often constrained to use other PET formulae with less data requirements or/and long-term average of PET values (the rationale being that PET is an inherently conservative variable). We chose to test 28 commonly used PET models coupled with 4 different daily watershed models. For each test, we compare both PET input options: actual data and long-term average data. The comparison is made in terms of streamflow simulation efficiency, over a large sample of 308 watersheds. The watersheds are located in France, Australia and the United States of America and represent varied climates. Strikingly, we find no systematic improvements of the watershed model efficiencies when using actual PET series instead of long-term averages. This suggests either that watershed models may not conveniently use the climatic information contained in PET values or that formulae are only awkward indicators of the real PET which watershed models need.

Using dew points to estimate savings during a planned cooling shutdown

NASA Astrophysics Data System (ADS)

Friedlein, Matthew T.; Changnon, David; Musselman, Eric; Zielinski, Jeff

2005-12-01

In an effort to save money during the summer of 2003, Northern Illinois University (NIU) administrators instituted a four-day working week and stopped air conditioning buildings for the three-day weekends (Friday through Sunday). Shutting down the air conditioning systems caused a noticeable drop in electricity usage for that part of the campus that features in our study, with estimated total electricity savings of 1,268,492 kilowatt-hours or 17% of the average usage during that eight-week period. NIU's air conditioning systems, which relied on evaporative cooling to function, were sensitive to dew point levels. Greatest savings during the shutdown period occurred on days with higher dew points. An examination of the regional dew point climatology (1959 2003) indicated that the average summer daily dew point for 2003 was 14.9°C (58.8°F), which fell in the lowest 20% of the distribution. Based on the relationship between daily average dew points and electrical usage, a predictive model that could estimate electrical daily savings was created. This model suggests that electrical savings related to any future three-day shutdowns over summer could be much greater in more humid summers. Studies like this demonstrate the potential value of applying climatological information and of integrating this information into practical decision-making.

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

PubMed

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

2014-09-01

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

Impact of increasing heat waves on U.S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory

NASA Astrophysics Data System (ADS)

Shen, L.; Mickley, L. J.; Gilleland, E.

2016-04-01

We develop a statistical model using extreme value theory to estimate the 2000-2050 changes in ozone episodes across the United States. We model the relationships between daily maximum temperature (Tmax) and maximum daily 8 h average (MDA8) ozone in May-September over 2003-2012 using a Point Process (PP) model. At ~20% of the sites, a marked decrease in the ozone-temperature slope occurs at high temperatures, defined as ozone suppression. The PP model sometimes fails to capture ozone-Tmax relationships, so we refit the ozone-Tmax slope using logistic regression and a generalized Pareto distribution model. We then apply the resulting hybrid-extreme value theory model to projections of Tmax from an ensemble of downscaled climate models. Assuming constant anthropogenic emissions at the present level, we find an average increase of 2.3 d a-1 in ozone episodes (>75 ppbv) across the United States by the 2050s, with a change of +3-9 d a-1 at many sites.

Intimate partner violence in Madrid: a time series analysis (2008-2016).

PubMed

Sanz-Barbero, Belén; Linares, Cristina; Vives-Cases, Carmen; González, José Luis; López-Ossorio, Juan José; Díaz, Julio

2018-06-02

This study analyzes whether there are time patterns in different intimate partner violence (IPV) indicators and aims to obtain models that can predict the behavior of these time series. Univariate autoregressive moving average models were used to analyze the time series corresponding to the number of daily calls to the 016 telephone IPV helpline and the number of daily police reports filed in the Community of Madrid during the period 2008-2015. Predictions were made for both dependent variables for 2016. The daily number of calls to the 016 telephone IPV helpline decreased during January 2008-April 2012 and increased during April 2012-December 2015. No statistically significant change was observed in the trend of the number of daily IPV police reports. The number of IPV police reports filed increased on weekends and on Christmas holidays. The number of calls to the 016 IPV help line increased on Mondays. Using data from 2008 to 2015, the univariate autoregressive moving average models predicted 64.2% of calls to the 016 telephone IPV helpline and 73.2% of police reports filed during 2016 in the Community of Madrid. Our results suggest the need for an increase in police and judicial resources on nonwork days. Also, the 016 telephone IPV helpline should be especially active on work days. Copyright © 2018 Elsevier Inc. All rights reserved.

Ambient temperature and emergency room admissions for acute coronary syndrome in Taiwan

NASA Astrophysics Data System (ADS)

Liang, Wen-Miin; Liu, Wen-Pin; Chou, Sze-Yuan; Kuo, Hsien-Wen

2008-01-01

Acute coronary syndrome (ACS) is an important public health problem around the world. Since there is a considerable seasonal fluctuation in the incidence of ACS, climatic temperature may have an impact on the onset of this disease. The objective of this study was to assess the relationship between the average daily temperature, diurnal temperature range and emergency room (ER) admissions for ACS in an ER in Taichung City, Taiwan. A longitudinal study was conducted which assessed the correlation of the average daily temperature and the diurnal temperature range to ACS admissions to the ER of the city’s largest hospital. Daily ER admissions for ACS and ambient temperature were collected from 1 January 2000 to 31 March 2003. The Poisson regression model was used in the analysis after adjusting for the effects of holiday, season, and air pollutant concentrations. The results showed that there was a negative significant association between the average daily temperature and ER admissions for ACS. ACS admissions to the ER increased 30% to 70% when the average daily temperature was lower than 26.2°C. A positive association between the diurnal temperature range and ACS admissions was also noted. ACS admissions increased 15% when the diurnal temperature range was over 8.3°C. The data indicate that patients suffering from cardiovascular disease must be made aware of the increased risk posed by lower temperatures and larger changes in temperature. Hospitals and ERs should take into account the increased demand of specific facilities during colder weather and wider temperature variations.

26 CFR 1.142(a)(5)-1 - Exempt facility bonds: Sewage facilities.

Code of Federal Regulations, 2011 CFR

2011-04-01

...; however, for property treating wastewater reasonably expected to have an average daily raw wasteload... the extent the treatment is for wastewater having an average daily raw wasteload concentration of BOD...—(i) Exception to BOD limit. A facility treating wastewater with an average daily raw wasteload...

26 CFR 1.142(a)(5)-1 - Exempt facility bonds: Sewage facilities.

Code of Federal Regulations, 2012 CFR

2012-04-01

...; however, for property treating wastewater reasonably expected to have an average daily raw wasteload... the extent the treatment is for wastewater having an average daily raw wasteload concentration of BOD...—(i) Exception to BOD limit. A facility treating wastewater with an average daily raw wasteload...

26 CFR 1.142(a)(5)-1 - Exempt facility bonds: Sewage facilities.

Code of Federal Regulations, 2014 CFR

2014-04-01

...; however, for property treating wastewater reasonably expected to have an average daily raw wasteload... the extent the treatment is for wastewater having an average daily raw wasteload concentration of BOD...—(i) Exception to BOD limit. A facility treating wastewater with an average daily raw wasteload...

26 CFR 1.142(a)(5)-1 - Exempt facility bonds: Sewage facilities.

Code of Federal Regulations, 2013 CFR

2013-04-01

...; however, for property treating wastewater reasonably expected to have an average daily raw wasteload... the extent the treatment is for wastewater having an average daily raw wasteload concentration of BOD...—(i) Exception to BOD limit. A facility treating wastewater with an average daily raw wasteload...

26 CFR 1.142(a)(5)-1 - Exempt facility bonds: Sewage facilities.

Code of Federal Regulations, 2010 CFR

2010-04-01

...; however, for property treating wastewater reasonably expected to have an average daily raw wasteload... the extent the treatment is for wastewater having an average daily raw wasteload concentration of BOD...—(i) Exception to BOD limit. A facility treating wastewater with an average daily raw wasteload...

40 CFR 439.22 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2013 CFR

2013-07-01

... average limitation for BOD5 that is less than the equivalent of 45 mg/L. (1) The long-term average daily... subject to this subpart, calculation of the long-term average daily BOD5 load in the influent to the... this section is higher than a concentration value reflecting a reduction in the long-term average daily...

40 CFR 439.22 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2014 CFR

2014-07-01

... average limitation for BOD5 that is less than the equivalent of 45 mg/L. (1) The long-term average daily... subject to this subpart, calculation of the long-term average daily BOD5 load in the influent to the... this section is higher than a concentration value reflecting a reduction in the long-term average daily...

40 CFR 439.22 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2012 CFR

2012-07-01

... average limitation for BOD5 that is less than the equivalent of 45 mg/L. (1) The long-term average daily... subject to this subpart, calculation of the long-term average daily BOD5 load in the influent to the... this section is higher than a concentration value reflecting a reduction in the long-term average daily...

The effect of flow data resolution on sediment yield estimation and channel design

NASA Astrophysics Data System (ADS)

Rosburg, Tyler T.; Nelson, Peter A.; Sholtes, Joel S.; Bledsoe, Brian P.

2016-07-01

The decision to use either daily-averaged or sub-daily streamflow records has the potential to impact the calculation of sediment transport metrics and stream channel design. Using bedload and suspended load sediment transport measurements collected at 138 sites across the United States, we calculated the effective discharge, sediment yield, and half-load discharge using sediment rating curves over long time periods (median record length = 24 years) with both daily-averaged and sub-daily streamflow records. A comparison of sediment transport metrics calculated with both daily-average and sub-daily stream flow data at each site showed that daily-averaged flow data do not adequately represent the magnitude of high stream flows at hydrologically flashy sites. Daily-average stream flow data cause an underestimation of sediment transport and sediment yield (including the half-load discharge) at flashy sites. The degree of underestimation was correlated with the level of flashiness and the exponent of the sediment rating curve. No consistent relationship between the use of either daily-average or sub-daily streamflow data and the resultant effective discharge was found. When used in channel design, computed sediment transport metrics may have errors due to flow data resolution, which can propagate into design slope calculations which, if implemented, could lead to unwanted aggradation or degradation in the design channel. This analysis illustrates the importance of using sub-daily flow data in the calculation of sediment yield in urbanizing or otherwise flashy watersheds. Furthermore, this analysis provides practical charts for estimating and correcting these types of underestimation errors commonly incurred in sediment yield calculations.

Sedentary Behavior as a Daily Process Regulated by Habits and Intentions

PubMed Central

Conroy, David E.; Maher, Jaclyn P.; Elavsky, Steriani; Hyde, Amanda L.; Doerksen, Shawna E.

2014-01-01

Objective Sedentary behavior is a health risk but little is known about the motivational processes that regulate daily sedentary behavior. This study was designed to test a dual-process model of daily sedentary behavior, with an emphasis on the role of intentions and habits in regulating daily sedentary behavior. Methods College students (N = 128) self-reported on their habit strength for sitting and completed a 14-day ecological momentary assessment study that combined daily diaries for reporting motivation and behavior with ambulatory monitoring of sedentary behavior using accelerometers. Results Less than half of the variance in daily sedentary behavior was attributable to between-person differences. People with stronger sedentary habits reported more sedentary behavior on average. People whose intentions for limiting sedentary behavior were stronger, on average, exhibited less self-reported sedentary behavior (and marginally less monitored sedentary behavior). Daily deviations in those intentions were negatively associated with changes in daily sedentary behavior (i.e., stronger than usual intentions to limit sedentary behavior were associated with reduced sedentary behavior). Sedentary behavior also varied within-people as a function of concurrent physical activity, the day of week, and the day in the sequence of the monitoring period. Conclusions Sedentary behavior was regulated by both automatic and controlled motivational processes. Interventions should target both of these motivational processes to facilitate and maintain behavior change. Links between sedentary behavior and daily deviations in intentions also indicate the need for ongoing efforts to support controlled motivational processes on a daily basis. PMID:23477579

Documentation of a deep percolation model for estimating ground-water recharge

USGS Publications Warehouse

Bauer, H.H.; Vaccaro, J.J.

1987-01-01

A deep percolation model, which operates on a daily basis, was developed to estimate long-term average groundwater recharge from precipitation. It has been designed primarily to simulate recharge in large areas with variable weather, soils, and land uses, but it can also be used at any scale. The physical and mathematical concepts of the deep percolation model, its subroutines and data requirements, and input data sequence and formats are documented. The physical processes simulated are soil moisture accumulation, evaporation from bare soil, plant transpiration, surface water runoff, snow accumulation and melt, and accumulation and evaporation of intercepted precipitation. The minimum data sets for the operation of the model are daily values of precipitation and maximum and minimum air temperature, soil thickness and available water capacity, soil texture, and land use. Long-term average annual precipitation, actual daily stream discharge, monthly estimates of base flow, Soil Conservation Service surface runoff curve numbers, land surface altitude-slope-aspect, and temperature lapse rates are optional. The program is written in the FORTRAN 77 language with no enhancements and should run on most computer systems without modifications. Documentation has been prepared so that program modifications may be made for inclusions of additional physical processes or deletion of ones not considered important. (Author 's abstract)

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

PubMed

Koo, Choongwan; Hong, Taehoon; Lee, Minhyun; Park, Hyo Seon

2013-05-07

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country's climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

EVALUATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL VERSION 4.5: UNCERTAINTIES AND SENSITIVITIES IMPACTING MODEL PERFORMANCE: PART I - OZONE

EPA Science Inventory

This study examines ozone (O₃) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-hr average O₃ levels are largely underpredicted when observed O_...

Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study.

PubMed

Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang; Cao, Yang

2016-08-16

To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. A time-series study using regional death registry between 2009 and 2010. 8 districts in a large metropolitan area in Northern China. 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (-1.09 to 4.28 vs -1.08 to 3.93) and the PCs-based model (-2.23 to 4.07 vs -2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, -1.12 to 4.85 versus -1.11 versus 4.83. The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Evaluation of the Environmental Bias on Accelerometer-Measured Total Daily Activity Counts and Owner Survey Responses in Dogs with Osteoarthritis.

PubMed

Katz, Erin M; Scott, Ruth M; Thomson, Christopher B; Mesa, Eileen; Evans, Richard; Conzemius, Michael G

2017-11-01

Objective  To determine if environmental variables affect the average daily activity counts (AC) of dogs with osteoarthritis (OA) and/or owners' perception of their dog's clinical signs or quality of life. Methods  The AC and Canine Brief Pain Inventory (CBPI) owner questionnaires of 62 dogs with OA were compared with daily environmental variables including the following: average temperature (°C), high temperature (°C), low temperature (°C), relative humidity (%), total precipitation (mm), average barometric pressure (hPa) and total daylight hours. Results  Daily AC significantly correlated with average temperature and total daylight hours, but average temperature and total daylight hours accounted for less than 1% of variation in AC. No other significant relationships were found between daily AC and daily high temperature, low temperature, relative humidity, total precipitation or average barometric pressure. No statistical relationship was found between daily AC and the CBPI, nor between environmental variables and the CBPI. Canine Brief Pain Inventory scores for pain severity and pain interference decreased significantly over the test period. Clinical Significance  The relationship between daily AC and average temperature and total daylight hours was significant, but unlikely to be clinically significant. Thus, environmental variables do not appear to have a clinically relevant bias on AC or owner CBPI questionnaires. The decrease over time in CBPI pain severity and pain interference values suggests owners completing the CBPI in this study were influenced by a caregiver placebo effect. Schattauer GmbH Stuttgart.

Patient casemix classification for medicare psychiatric prospective payment.

PubMed

Drozd, Edward M; Cromwell, Jerry; Gage, Barbara; Maier, Jan; Greenwald, Leslie M; Goldman, Howard H

2006-04-01

For a proposed Medicare prospective payment system for inpatient psychiatric facility treatment, the authors developed a casemix classification to capture differences in patients' real daily resource use. Primary data on patient characteristics and daily time spent in various activities were collected in a survey of 696 patients from 40 inpatient psychiatric facilities. Survey data were combined with Medicare claims data to estimate intensity-adjusted daily cost. Classification and Regression Trees (CART) analysis of average daily routine and ancillary costs yielded several hierarchical classification groupings. Regression analysis was used to control for facility and day-of-stay effects in order to compare hierarchical models with models based on the recently proposed payment system of the Centers for Medicare & Medicaid Services. CART analysis identified a small set of patient characteristics strongly associated with higher daily costs, including age, psychiatric diagnosis, deficits in daily living activities, and detox or ECT use. A parsimonious, 16-group, fully interactive model that used five major DSM-IV categories and stratified by age, illness severity, deficits in daily living activities, dangerousness, and use of ECT explained 40% (out of a possible 76%) of daily cost variation not attributable to idiosyncratic daily changes within patients. A noninteractive model based on diagnosis-related groups, age, and medical comorbidity had explanatory power of only 32%. A regression model with 16 casemix groups restricted to using "appropriate" payment variables (i.e., those with clinical face validity and low administrative burden that are easily validated and provide proper care incentives) produced more efficient and equitable payments than did a noninteractive system based on diagnosis-related groups.

SIMULATING REGIONAL-SCALE AIR QUALITY WITH DYNAMIC CHANGES IN REGIONAL CLIMATE AND CHEMICAL BOUNDARY CONDITIONS

EPA Science Inventory

This poster compares air quality modeling simulations under current climate and a future (approximately 2050) climate scenario. Differences in predicted ozone episodes and daily average PM_2.5 concentrations are presented, along with vertical ozone profiles. Modeling ...

Does leisure time moderate or mediate the effect of daily stress on positive affect? An examination using eight-day diary data

PubMed Central

Qian, Xinyi Lisa; Yarnal, Careen M.; Almeida, David M.

2013-01-01

This study tested the applicability of moderation and mediation models to leisure time as a stress coping resource. Analyzing eight-day diary data (N=2,022), we examined the within-person process of using leisure time to cope with daily stressors. We found that relatively high daily stress frequency, while reducing positive affect, prompted an individual to allocate more time to leisure than usual, which then increased positive affect, thus partially remedying the damage by high daily stress frequency. This within-person process, however, is significantly stronger among those with less leisure time on average than leisure-rich individuals. The findings support a partial counteractive mediation model, demonstrate between-person difference in the within-person coping process, and reveal the importance of positive affect as a coping outcome. PMID:25221350

Does leisure time moderate or mediate the effect of daily stress on positive affect? An examination using eight-day diary data.

PubMed

Qian, Xinyi Lisa; Yarnal, Careen M; Almeida, David M

2014-01-01

This study tested the applicability of moderation and mediation models to leisure time as a stress coping resource. Analyzing eight-day diary data (N=2,022), we examined the within -person process of using leisure time to cope with daily stressors. We found that relatively high daily stress frequency, while reducing positive affect, prompted an individual to allocate more time to leisure than usual, which then increased positive affect, thus partially remedying the damage by high daily stress frequency. This within-person process, however, is significantly stronger among those with less leisure time on average than leisure-rich individuals. The findings support a partial counteractive mediation model, demonstrate between-person difference in the within-person coping process, and reveal the importance of positive affect as a coping outcome.

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

NASA Astrophysics Data System (ADS)

Mirbaha, Babak; Saffarzadeh, Mahmoud; AmirHossein Beheshty, Seyed; Aniran, MirMoosa; Yazdani, Mirbahador; Shirini, Bahram

2017-10-01

Analysis of vehicle speed with different weather condition and traffic characteristics is very effective in traffic planning. Since the weather condition and traffic characteristics vary every day, the prediction of average speed can be useful in traffic management plans. In this study, traffic and weather data for a two-lane highway located in Northwest of Iran were selected for analysis. After merging traffic and weather data, the linear regression model was calibrated for speed prediction using STATA12.1 Statistical and Data Analysis software. Variables like vehicle flow, percentage of heavy vehicles, vehicle flow in opposing lane, percentage of heavy vehicles in opposing lane, rainfall (mm), snowfall and maximum daily wind speed more than 13m/s were found to be significant variables in the model. Results showed that variables of vehicle flow and heavy vehicle percent acquired the positive coefficient that shows, by increasing these variables the average vehicle speed in every weather condition will also increase. Vehicle flow in opposing lane, percentage of heavy vehicle in opposing lane, rainfall amount (mm), snowfall and maximum daily wind speed more than 13m/s acquired the negative coefficient that shows by increasing these variables, the average vehicle speed will decrease.

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.

PubMed

Butland, Barbara K; Armstrong, Ben; Atkinson, Richard W; Wilkinson, Paul; Heal, Mathew R; Doherty, Ruth M; Vieno, Massimo

2013-11-13

Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003-2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

Crowdsourcing urban air temperatures from smartphone battery temperatures

NASA Astrophysics Data System (ADS)

Overeem, Aart; Robinson, James C. R.; Leijnse, Hidde; Steeneveld, Gert-Jan; Horn, Berthold K. P.; Uijlenhoet, Remko

2014-05-01

Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. It has been shown that a straightforward heat transfer model can be employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real-time temperature monitoring in densely populated areas. Battery temperature data were collected by users of an Android application for cell phones (opensignal.com). The application automatically sends battery temperature data to a server for storage. In this study, battery temperatures are averaged in space and time to obtain daily averaged battery temperatures for each city separately. A regression model, which can be related to a physical model, is employed to retrieve daily air temperatures from battery temperatures. The model is calibrated with observed air temperatures from a meteorological station of an airport located in or near the city. Time series of air temperatures are obtained for each city for a period of several months, where 50% of the data is for independent verification. The methodology has been applied to Buenos Aires, London, Los Angeles, Paris, Mexico City, Moscow, Rome, and Sao Paulo. The evolution of the retrieved air temperatures often correspond well with the observed ones. The mean absolute error of daily air temperatures is less than 2 degrees Celsius, and the bias is within 1 degree Celsius. This shows that monitoring air temperatures employing an Android application holds great promise. This study will particularly focus on new results: The methodology has been applied to data from three cities in the Netherlands (Amsterdam, Rotterdam, and Utrecht) for the period June - August 2013. It is shown that on average 282 battery temperature readings per day are already sufficient to accurately estimate daily-averaged air temperatures. Results clearly deteriorate when on average only 80 battery temperature readings are available. Since 75% of the world's population has a cell phone, 20% of the land surface of the earth has cellular telephone coverage, and 500 million devices use the Android operating system, there is a huge potential for measuring air temperatures employing cell phones. This could eventually lead to real-time world-wide temperature maps over the continents.

LakeVOC; A Deterministic Model to Estimate Volatile Organic Compound Concentrations in Reservoirs and Lakes

USGS Publications Warehouse

Bender, David A.; Asher, William E.; Zogorski, John S.

2003-01-01

This report documents LakeVOC, a model to estimate volatile organic compound (VOC) concentrations in lakes and reservoirs. LakeVOC represents the lake or reservoir as a two-layer system and estimates VOC concentrations in both the epilimnion and hypolimnion. The air-water flux of a VOC is characterized in LakeVOC in terms of the two-film model of air-water exchange. LakeVOC solves the system of coupled differential equations for the VOC concentration in the epilimnion, the VOC concentration in the hypolimnion, the total mass of the VOC in the lake, the volume of the epilimnion, and the volume of the hypolimnion. A series of nine simulations were conducted to verify LakeVOC representation of mixing, dilution, and gas exchange characteristics in a hypothetical lake, and two additional estimates of lake volume and MTBE concentrations were done in an actual reservoir under environmental conditions. These 11 simulations showed that LakeVOC correctly handled mixing, dilution, and gas exchange. The model also adequately estimated VOC concentrations within the epilimnion in an actual reservoir with daily input parameters. As the parameter-input time scale increased (from daily to weekly to monthly, for example), the differences between the measured-averaged concentrations and the model-estimated concentrations generally increased, especially for the hypolimnion. This may be because as the time scale is increased from daily to weekly to monthly, the averaging of model inputs may cause a loss of detail in the model estimates.

40 CFR Table 2 to Subpart Mmmmm of... - Operating Limits for New or Reconstructed Flame Lamination Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... scrubber, maintain the daily average pressure drop across the venturi within the operating range value... . . . You must . . . 1. Scrubber a. Maintain the daily average scrubber inlet liquid flow rate above the minimum value established during the performance test. b. Maintain the daily average scrubber effluent pH...

40 CFR Table 2 to Subpart Mmmmm of... - Operating Limits for New or Reconstructed Flame Lamination Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... . . . You must . . . 1. Scrubber a. Maintain the daily average scrubber inlet liquid flow rate above the minimum value established during the performance test. b. Maintain the daily average scrubber effluent pH... scrubber, maintain the daily average pressure drop across the venturi within the operating range value...

Declines in Outpatient Antimicrobial Use in Canada (1995–2010)

PubMed Central

Finley, Rita; Glass-Kaastra, Shiona K.; Hutchinson, Jim; Patrick, David M.; Weiss, Karl; Conly, John

2013-01-01

Background With rising reports of antimicrobial resistance in outpatient communities, surveillance of antimicrobial use is imperative for supporting stewardship programs. The primary objective of this article is to assess the levels of antimicrobial use in Canada over time. Methods Canadian antimicrobial use data from 1995 to 2010 were acquired and assessed by four metrics: population-adjusted prescriptions, Defined Daily Doses, spending on antimicrobials (inflation-adjusted), and average Defined Daily Doses per prescription. Linear mixed models were built to assess significant differences among years and antimicrobial groups, and to account for repeated measurements over time. Measures were also compared to published reports from European countries. Results Temporal trends in antimicrobial use in Canada vary by metric and antimicrobial grouping. Overall reductions were seen for inflation-adjusted spending, population-adjusted prescription rates and Defined Daily Doses, and increases were observed for the average number of Defined Daily Doses per prescription. The population-adjusted prescription and Defined Daily Doses values for 2009 were comparable to those reported by many European countries, while the average Defined Daily Dose per prescription for Canada ranked high. A significant reduction in the use of broad spectrum penicillins occurred between 1995 and 2004, coupled with increases in macrolide and quinolone use, suggesting that replacement of antimicrobial drugs may occur as new products arrive on the market. Conclusions There have been modest decreases of antimicrobial use in Canada over the past 15 years. However, continued surveillance of antimicrobial use coupled with data detailing antimicrobial resistance within bacterial pathogens affecting human populations is critical for targeting interventions and maintaining the effectiveness of these products for future generations. PMID:24146863

A senstitivity study of the ground hydrologic model using data generated by an atmospheric general circulation model. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Sun, S. F.

1985-01-01

The Ground Hydrologic Model (GHM) developed for use in an atmospheric general circulation model (GCM) has been refined. A series of sensitivity studies of the new version of the GHM were conducted for the purpose of understanding the role played by various physical parameters in the GHM. The following refinements have been made: (1) the GHM is coupled directly with the planetary boundary layer (PBL); (2) a bulk vegetation layer is added with a more realistic large-scale parameterization; and (3) the infiltration rate is modified. This version GHM has been tested using input data derived from a GCM simulation run for eight North America regions for 45 days. The results are compared with those of the resident GHM in the GCM. The daily average of grid surface temperatures from both models agree reasonably well in phase and magnitude. However, large difference exists in one or two regions on some days. The daily average evapotranspiration is in general 10 to 30% less than the corresponding value given by the resident GHM.

Association of fluoride in water for consumption and chronic pain of body parts in residents of San Kamphaeng district, Chiang Mai, Thailand.

PubMed

Namkaew, Montakarn; Wiwatanadate, Phongtape

2012-09-01

To assess the dose response of fluoride exposure from water and chronic pain. Using a retrospective cohort design, the study was conducted in two sub-districts of San Kamphaeng district, Poo-kha and On-tai. Five hundred and thirty-four residents aged ≥50 years of age were interviewed about their sources of drinking water and assessed for chronic pain. Each water source was sampled for fluoride measurement, from which the average daily fluoride dose was estimated. Binary logistic regression with forward stepwise (likelihood ratio) model selection technique was used to examine the association between the average daily fluoride dose and chronic pain. We found associations between the average daily fluoride dose and lower back pain [odds ratio (OR) = 5.12; 95% confidence interval (CI), 1.59-16.98], and between the high fluoride area vs. the low fluoride area (OR = 1.58; 95% CI, 1.10-2.28; relative risk= 1.22 with 95% CI, 1.14-1.31) to lower back pain. Other risk factors, such as family history of body pain and a history of injury of the lower body, were also associated with lower back pain. However, there were no relationships between the average daily fluoride dose and leg and knee pains. To prevent further lower back pain, we recommend that the water in this area be treated to reduce its fluoride content. © 2012 Blackwell Publishing Ltd.

Temperature-induced excess mortality in Moscow, Russia.

PubMed

Revich, Boris; Shaposhnikov, Dmitri

2008-05-01

After considering the observed long-term trends in average monthly temperatures distribution in Moscow, the authors evaluated how acute mortality responded to changes in daily average, minimum and maximum temperatures throughout the year, and identified vulnerable population groups, by age and causes of death. A plot of the basic mortality-temperature relationship indicated that this relationship was V-shaped with the minimum around 18 degrees C. Each 1 degree C increment of average daily temperature above 18 degrees C resulted in an increase in deaths from all non-accidental causes by 2.8%, from coronary heart disease by 2.7%, from cerebrovascular diseases by 4.7%, and from respiratory diseases by 8.7%, with a lag of 0 or 1 day. Each 1 degrees C drop of average daily temperature from +18 degrees C to -10 degrees C resulted in an increase in deaths from all non-accidental causes by 0.49%, from coronary heart disease by 0.57%, from cerebrovascular diseases by 0.78%, and from respiratory diseases by 1.5%, with lags of maximum association varying from 3 days for non-accidental mortality to 6 days for cerebrovascular mortality. In the age group 75+ years, corresponding risks were consistently higher by 13-30%. The authors also estimated the increase in non-accidental deaths against the variation of daily temperatures. For each 1 degrees C increase of variation of temperature throughout the day, mortality increased by 0.3-1.9%, depending on other assumptions of the model.

Temperature-induced excess mortality in Moscow, Russia

NASA Astrophysics Data System (ADS)

Revich, Boris; Shaposhnikov, Dmitri

2008-05-01

After considering the observed long-term trends in average monthly temperatures distribution in Moscow, the authors evaluated how acute mortality responded to changes in daily average, minimum and maximum temperatures throughout the year, and identified vulnerable population groups, by age and causes of death. A plot of the basic mortality temperature relationship indicated that this relationship was V-shaped with the minimum around 18°C. Each 1°C increment of average daily temperature above 18°C resulted in an increase in deaths from all non-accidental causes by 2.8%, from coronary heart disease by 2.7%, from cerebrovascular diseases by 4.7%, and from respiratory diseases by 8.7%, with a lag of 0 or 1 day. Each 1°C drop of average daily temperature from +18°C to -10°C resulted in an increase in deaths from all non-accidental causes by 0.49%, from coronary heart disease by 0.57%, from cerebrovascular diseases by 0.78%, and from respiratory diseases by 1.5%, with lags of maximum association varying from 3 days for non-accidental mortality to 6 days for cerebrovascular mortality. In the age group 75+ years, corresponding risks were consistently higher by 13 30%. The authors also estimated the increase in non-accidental deaths against the variation of daily temperatures. For each 1°C increase of variation of temperature throughout the day, mortality increased by 0.3 1.9%, depending on other assumptions of the model.

Optimising Habitat-Based Models for Wide-Ranging Marine Predators: Scale Matters

NASA Astrophysics Data System (ADS)

Scales, K. L.; Hazen, E. L.; Jacox, M.; Edwards, C. A.; Bograd, S. J.

2016-12-01

Predicting the responses of marine top predators to dynamic oceanographic conditions requires habitat-based models that sufficiently capture environmental preferences. Spatial resolution and temporal averaging of environmental data layers is a key aspect of model construction. The utility of surfaces contemporaneous to animal movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently under debate, as is the optimal spatial resolution for predictive products. Using movement simulations with built-in environmental preferences (correlated random walks, multi-state hidden Markov-type models) together with modeled (Regional Oceanographic Modeling System, ROMS) and remotely-sensed (MODIS-Aqua) datasets, we explored the effects of degrading environmental surfaces (3km - 1 degree, daily - climatological) on model inference. We simulated the movements of a hypothetical wide-ranging marine predator through the California Current system over a three month period (May-June-July), based on metrics derived from previously published blue whale Balaenoptera musculus tracking studies. Results indicate that models using seasonal or climatological data fields can overfit true environmental preferences, in both presence-absence and behaviour-based model formulations. Moreover, the effects of a degradation in spatial resolution are more pronounced when using temporally averaged fields than when using daily, weekly or monthly datasets. In addition, we observed a notable divergence between the `best' models selected using common methods (e.g. AUC, AICc) and those that most accurately reproduced built-in environmental preferences. These findings have important implications for conservation and management of marine mammals, seabirds, sharks, sea turtles and large teleost fish, particularly in implementing dynamic ocean management initiatives and in forecasting responses to future climate-mediated ecosystem change.

Optimising Habitat-Based Models for Wide-Ranging Marine Predators: Scale Matters

NASA Astrophysics Data System (ADS)

Scales, K. L.; Hazen, E. L.; Jacox, M.; Edwards, C. A.; Bograd, S. J.

2016-02-01

Predicting the responses of marine top predators to dynamic oceanographic conditions requires habitat-based models that sufficiently capture environmental preferences. Spatial resolution and temporal averaging of environmental data layers is a key aspect of model construction. The utility of surfaces contemporaneous to animal movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently under debate, as is the optimal spatial resolution for predictive products. Using movement simulations with built-in environmental preferences (correlated random walks, multi-state hidden Markov-type models) together with modeled (Regional Oceanographic Modeling System, ROMS) and remotely-sensed (MODIS-Aqua) datasets, we explored the effects of degrading environmental surfaces (3km - 1 degree, daily - climatological) on model inference. We simulated the movements of a hypothetical wide-ranging marine predator through the California Current system over a three month period (May-June-July), based on metrics derived from previously published blue whale Balaenoptera musculus tracking studies. Results indicate that models using seasonal or climatological data fields can overfit true environmental preferences, in both presence-absence and behaviour-based model formulations. Moreover, the effects of a degradation in spatial resolution are more pronounced when using temporally averaged fields than when using daily, weekly or monthly datasets. In addition, we observed a notable divergence between the `best' models selected using common methods (e.g. AUC, AICc) and those that most accurately reproduced built-in environmental preferences. These findings have important implications for conservation and management of marine mammals, seabirds, sharks, sea turtles and large teleost fish, particularly in implementing dynamic ocean management initiatives and in forecasting responses to future climate-mediated ecosystem change.

Parameter regionalisation methods for a semi-distributed rainfall-runoff model: application to a Northern Apennine region

NASA Astrophysics Data System (ADS)

Neri, Mattia; Toth, Elena

2017-04-01

The study presents the implementation of different regionalisation approaches for the transfer of model parameters from similar and/or neighbouring gauged basin to an ungauged catchment, and in particular it uses a semi-distributed continuously-simulating conceptual rainfall-runoff model for simulating daily streamflows. The case study refers to a set of Apennine catchments (in the Emilia-Romagna region, Italy), that, given the spatial proximity, are assumed to belong to the same hydrologically homogeneous region and are used, alternatively, as donors and regionalised basins. The model is a semi-distributed version of the HBV model (TUWien model) in which the catchment is divided in zones of different altitude that contribute separately to the total outlet flow. The model includes a snow module, whose application in the Apennine area has been, so far, very limited, even if snow accumulation and melting phenomena do have an important role in the study basins. Two methods, both widely applied in the recent literature, are applied for regionalising the model: i) "parameters averaging", where each parameter is obtained as a weighted mean of the parameters obtained, through calibration, on the donor catchments ii) "output averaging", where the model is run over the ungauged basin using the entire set of parameters of each donor basin and the simulated outputs are then averaged. In the first approach, the parameters are regionalised independently from each other, in the second one, instead, the correlation among the parameters is maintained. Since the model is a semi-distributed one, where each elevation zone contributes separately, the study proposes to test also a modified version of the second approach ("output averaging"), where each zone is considered as an autonomous entity, whose parameters are transposed to the ungauged sub-basin corresponding to the same elevation zone. The study explores also the choice of the weights to be used for averaging the parameters (in the "parameters averaging" approach) or for averaging the simulated streamflow (in the "output averaging" approach): in particular, weights are estimated as a function of the similarity/distance of the ungauged basin/zone to the donors, on the basis of a set of geo-morphological catchment descriptors. The predictive accuracy of the different regionalisation methods is finally assessed by jack-knife cross-validation against the observed daily runoff for all the study catchments.

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Treesearch

Kevin Schaefer; Christopher R. Schwalm; Chris Williams; M. Altaf Arain; Alan Barr; Jing M. Chen; Kenneth J. Davis; Dimitre Dimitrov; Timothy W. Hilton; David Y. Hollinger; Elyn Humphreys; Benjamin Poulter; Brett M. Raczka; Andrew D. Richardson; Alok Sahoo; Peter Thornton; Rodrigo Vargas; Hans Verbeeck; Ryan Anderson; Ian Baker; T. Andrew Black; Paul Bolstad; Jiquan Chen; Peter S. Curtis; Ankur R. Desai; Michael Dietze; Danilo Dragoni; Christopher Gough; Robert F. Grant; Lianhong Gu; Atul Jain; Chris Kucharik; Beverly Law; Shuguang Liu; Erandathie Lokipitiya; Hank A. Margolis; Roser Matamala; J. Harry McCaughey; Russ Monson; J. William Munger; Walter Oechel; Changhui Peng; David T. Price; Dan Ricciuto; William J. Riley; Nigel Roulet; Hanqin Tian; Christina Tonitto; Margaret Torn; Ensheng Weng; Xiaolu Zhou

2012-01-01

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States...

Flexible C : N ratio enhances metabolism of large phytoplankton when resource supply is intermittent

NASA Astrophysics Data System (ADS)

Talmy, D.; Blackford, J.; Hardman-Mountford, N. J.; Polimene, L.; Follows, M. J.; Geider, R. J.

2014-04-01

Phytoplankton cell size influences particle sinking rate, food web interactions and biogeographical distributions. We present a model in which the uptake, storage and assimilation of nitrogen and carbon are explicitly resolved in different sized phytoplankton cells. In the model, metabolism and cellular C : N ratio are influenced by accumulation of carbon polymers such as carbohydrate and lipid, which is greatest when cells are nutrient starved, or exposed to high light. Allometric relations and empirical datasets are used to constrain the range of possible C : N, and indicate larger cells can accumulate significantly more carbon storage compounds than smaller cells. When forced with extended periods of darkness combined with brief exposure to saturating irradiance, the model predicts organisms large enough to accumulate significant carbon reserves may on average synthesize protein and other functional apparatus up to five times faster than smaller organisms. The advantage of storage in terms of average daily protein synthesis rate is greatest when modeled organisms were previously nutrient starved, and carbon storage reservoirs saturated. Small organisms may therefore be at a disadvantage in terms of average daily growth rate in environments that involve prolonged periods of darkness and intermittent nutrient limitation. We suggest this mechanism is a significant constraint on phytoplankton C : N variability and cell size distribution in different oceanic regimes.

40 CFR 439.12 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2011 CFR

2011-07-01

... percent reduction in the long-term average daily BOD5 load of the raw (untreated) process wastewater, multiplied by a variability factor of 3.0. (1) The long-term average daily BOD5 load of the raw process... concentration value reflecting a reduction in the long-term average daily COD load in the raw (untreated...

40 CFR 439.12 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2010 CFR

2010-07-01

... percent reduction in the long-term average daily BOD5 load of the raw (untreated) process wastewater, multiplied by a variability factor of 3.0. (1) The long-term average daily BOD5 load of the raw process... concentration value reflecting a reduction in the long-term average daily COD load in the raw (untreated...

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

PubMed

Voukantsis, Dimitris; Karatzas, Kostas; Kukkonen, Jaakko; Räsänen, Teemu; Karppinen, Ari; Kolehmainen, Mikko

2011-03-01

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM₂.₅ for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration. Copyright © 2011 Elsevier B.V. All rights reserved.

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

Yamankaradeniz, R.; Horuz, I.

In this study, the characteristics of solar assisted heat pump are investigated theoretically and experimentally for clear days during the seven months of the winter season in Istanbul/Turkey. A theoretical model was developed and a computer program was written on this basis. The characteristics such as: daily average collector efficiency and solar radiation, monthly average heat transfer at the condenser, monthly average cooling capacity, the mean COP and the mean COP for total system were examined. The theoretical results were found to be in good agreement with the experimental values.

Strong influence of deposition and vertical mixing on secondary organic aerosol concentrations in CMAQ and CAMx

NASA Astrophysics Data System (ADS)

Shu, Qian; Koo, Bonyoung; Yarwood, Greg; Henderson, Barron H.

2017-12-01

Differences between two air quality modeling systems reveal important uncertainties in model representations of secondary organic aerosol (SOA) fate. Two commonly applied models (CMAQ: Community Multiscale Air Quality; CAMx: Comprehensive Air Quality Model with extensions) predict very different OA concentrations over the eastern U.S., even when using the same source data for emissions and meteorology and the same SOA modeling approach. Both models include an option to output a detailed accounting of how each model process (e.g., chemistry, deposition, etc.) alters the mass of each modeled species, referred to as process analysis. We therefore perform a detailed diagnostic evaluation to quantify simulated tendencies (Gg/hr) of each modeled process affecting both the total model burden (Gg) of semi-volatile organic compounds (SVOC) in the gas (g) and aerosol (a) phases and the vertical structures to identify causes of concentration differences between the two models. Large differences in deposition (CMAQ: 69.2 Gg/d; CAMx: 46.5 Gg/d) contribute to significant OA bias in CMAQ relative to daily averaged ambient concentration measurements. CMAQ's larger deposition results from faster daily average deposition velocities (VD) for both SVOC (g) (VD,cmaq = 2.15 × VD,camx) and aerosols (VD,cmaq = 4.43 × Vd,camx). Higher aerosol deposition velocity would be expected to cause similar biases for inert compounds like elemental carbon (EC), but this was not seen. Daytime low-biases in EC were also simulated in CMAQ as expected but were offset by nighttime high-biases. Nighttime high-biases were a result of overly shallow mixing in CMAQ leading to a higher fraction of EC total atmospheric mass in the first layer (CAMx: 5.1-6.4%; CMAQ: 5.6-6.9%). Because of the opposing daytime and nighttime biases, the apparent daily average bias for EC is reduced. For OA, there are two effects of reduced vertical mixing: SOA and SVOC are concentrated near the surface, but SOA yields are reduced near the surface by nighttime enhancement of NOx. These results help to characterize model processes in the context of SOA and provide guidance for model improvement.

40 CFR 439.22 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2011 CFR

2011-07-01

... limitation for BOD5 that is less than the equivalent of 45 mg/L. (1) The long-term average daily BOD5 load of... to this subpart, calculation of the long-term average daily BOD5 load in the influent to the... this section is higher than a concentration value reflecting a reduction in the long-term average daily...

40 CFR 439.52 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2012 CFR

2012-07-01

..., kg) per day, must reflect not less than 74 percent reduction in the long-term average daily COD load... long-term average daily BOD5 or COD mass loading of the raw process wastewater (i.e., the base number..., calculation of the long-term average daily BOD5 or COD load in the influent to the wastewater treatment system...

40 CFR 439.22 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2010 CFR

2010-07-01

... limitation for BOD5 that is less than the equivalent of 45 mg/L. (1) The long-term average daily BOD5 load of... to this subpart, calculation of the long-term average daily BOD5 load in the influent to the... this section is higher than a concentration value reflecting a reduction in the long-term average daily...

40 CFR 439.52 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2014 CFR

2014-07-01

..., kg) per day, must reflect not less than 74 percent reduction in the long-term average daily COD load... long-term average daily BOD5 or COD mass loading of the raw process wastewater (i.e., the base number..., calculation of the long-term average daily BOD5 or COD load in the influent to the wastewater treatment system...

40 CFR 439.52 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2013 CFR

2013-07-01

..., kg) per day, must reflect not less than 74 percent reduction in the long-term average daily COD load... long-term average daily BOD5 or COD mass loading of the raw process wastewater (i.e., the base number..., calculation of the long-term average daily BOD5 or COD load in the influent to the wastewater treatment system...

40 CFR 439.12 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2013 CFR

2013-07-01

... than 90 percent reduction in the long-term average daily BOD5 load of the raw (untreated) process wastewater, multiplied by a variability factor of 3.0. (1) The long-term average daily BOD5 load of the raw..., calculation of the long-term average daily BOD5 load in the influent to the wastewater treatment system must...

40 CFR 439.12 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2012 CFR

2012-07-01

... than 90 percent reduction in the long-term average daily BOD5 load of the raw (untreated) process wastewater, multiplied by a variability factor of 3.0. (1) The long-term average daily BOD5 load of the raw..., calculation of the long-term average daily BOD5 load in the influent to the wastewater treatment system must...

40 CFR 439.12 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2014 CFR

2014-07-01

... than 90 percent reduction in the long-term average daily BOD5 load of the raw (untreated) process wastewater, multiplied by a variability factor of 3.0. (1) The long-term average daily BOD5 load of the raw..., calculation of the long-term average daily BOD5 load in the influent to the wastewater treatment system must...

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

Fluctuation Dynamics of Exchange Rates on Indian Financial Market

NASA Astrophysics Data System (ADS)

Sarkar, A.; Barat, P.

Here we investigate the scaling behavior and the complexity of the average daily exchange rate returns of the Indian Rupee against four foreign currencies namely US Dollar, Euro, Great Britain Pound and Japanese Yen. Our analysis revealed that the average daily exchange rate return of the Indian Rupee against the US Dollar exhibits a persistent scaling behavior and follow Levy stable distribution. On the contrary the average daily exchange rate returns of the other three foreign currencies show randomness and follow Gaussian distribution. Moreover, it is seen that the complexity of the average daily exchange rate return of the Indian Rupee against US Dollar is less than the other three exchange rate returns.

Modeling physical and chemical climate of the northeastern United States for a geographic information system

Treesearch

Scott V. Ollinger; John D. Aber; Anthony C. Federer; Gary M. Lovett; Jennifer M. Ellis

1995-01-01

A model of physical and chemical climate was developed for New York and New England that can be used in a GIs for integration with ecosystem models. The variables included are monthly average maximum and minimum daily temperatures, precipitation, humidity, and solar radiation, as well as annual atmospheric deposition of sulfur and nitrogen. Equations generated from...

Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States

USGS Publications Warehouse

Hay, L.E.; Clark, M.P.

2003-01-01

This paper examines the hydrologic model performance in three snowmelt-dominated basins in the western United States to dynamically- and statistically downscaled output from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP). Runoff produced using a distributed hydrologic model is compared using daily precipitation and maximum and minimum temperature timeseries derived from the following sources: (1) NCEP output (horizontal grid spacing of approximately 210 km); (2) dynamically downscaled (DDS) NCEP output using a Regional Climate Model (RegCM2, horizontal grid spacing of approximately 52 km); (3) statistically downscaled (SDS) NCEP output; (4) spatially averaged measured data used to calibrate the hydrologic model (Best-Sta) and (5) spatially averaged measured data derived from stations located within the area of the RegCM2 model output used for each basin, but excluding Best-Sta set (All-Sta). In all three basins the SDS-based simulations of daily runoff were as good as runoff produced using the Best-Sta timeseries. The NCEP, DDS, and All-Sta timeseries were able to capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all three basins, the NCEP-, DDS-, and All-Sta-based simulations of runoff showed little skill on a daily basis. When the precipitation and temperature biases were corrected in the NCEP, DDS, and All-Sta timeseries, the accuracy of the daily runoff simulations improved dramatically, but, with the exception of the bias-corrected All-Sta data set, these simulations were never as accurate as the SDS-based simulations. This need for a bias correction may be somewhat troubling, but in the case of the large station-timeseries (All-Sta), the bias correction did indeed 'correct' for the change in scale. It is unknown if bias corrections to model output will be valid in a future climate. Future work is warranted to identify the causes for (and removal of) systematic biases in DDS simulations, and improve DDS simulations of daily variability in local climate. Until then, SDS based simulations of runoff appear to be the safer downscaling choice.

Characteristics of the Surface Turbulent Flux and the Components of Radiation Balance over the Grasslands in the Southeastern Tibetan Plateau

NASA Astrophysics Data System (ADS)

Li, H.; Xiao, Z.; Wei, J.

2016-12-01

Characteristics of the Surface Turbulent Flux and the Components of Radiation Balance over the Grasslands in the Southeastern Tibetan PlateauHongyi Li 1, Ziniu Xiao 2 and Junhong Wei31 China Meteorological Administration Training Centre, Beijing, China2 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 3Theory of Atmospheric Dynamics and Climate, Institute for Atmospheric and Environmental Sciences, Goethe University of Frankfurt, Campus Riedberg, GermanyAbstract:Based on the field observation data over the grasslands in the southeastern Tibetan Plateau and the observational datasets in Nyingchi weather station for the period from May 20 to July 9, 2013, the variation characteristics of the basic meteorological elements in Nyingchi weather station, the surface turbulent fluxes and the components of radiation balance over the grasslands, as well as their relationships, are analyzed in this paper. The results show that in Nyingchi weather station, the daily variations of relative humidity and average total cloud cover are consistent with that of precipitation, but that those of daily average air temperature, daily average ground temperature, daily average wind speed and daily sunshine duration have an opposite change to that of precipitation. During the observation period, latent heat exchange is greater than sensible heat exchange, and latent heat flux is significantly higher when there is rainfall, but sensible heat flux and soil heat flux are lower. The daily variation of the total solar radiation (DR) is synchronous with that of sensible heat flux, and the daily variations of reflective solar radiation (UR), long wave radiation by earth (ULR), net radiation (Rn) and surface albedo are consistent with DR, but that of the long wave radiation by atmosphere (DLR) has an opposite change. The diurnal variations of sensible heat flux, latent heat flux, soil heat flux and the components of surface radiation balance over the grasslands are characterized by higher values at noon and lower values in the morning and evening. Keywords: surface turbulent flux, components of radiation balance, grasslands, southeastern Tibetan Plateau

Less Daily Computer Use is Related to Smaller Hippocampal Volumes in Cognitively Intact Elderly.

PubMed

Silbert, Lisa C; Dodge, Hiroko H; Lahna, David; Promjunyakul, Nutta-On; Austin, Daniel; Mattek, Nora; Erten-Lyons, Deniz; Kaye, Jeffrey A

2016-01-01

Computer use is becoming a common activity in the daily life of older individuals and declines over time in those with mild cognitive impairment (MCI). The relationship between daily computer use (DCU) and imaging markers of neurodegeneration is unknown. The objective of this study was to examine the relationship between average DCU and volumetric markers of neurodegeneration on brain MRI. Cognitively intact volunteers enrolled in the Intelligent Systems for Assessing Aging Change study underwent MRI. Total in-home computer use per day was calculated using mouse movement detection and averaged over a one-month period surrounding the MRI. Spearman's rank order correlation (univariate analysis) and linear regression models (multivariate analysis) examined hippocampal, gray matter (GM), white matter hyperintensity (WMH), and ventricular cerebral spinal fluid (vCSF) volumes in relation to DCU. A voxel-based morphometry analysis identified relationships between regional GM density and DCU. Twenty-seven cognitively intact participants used their computer for 51.3 minutes per day on average. Less DCU was associated with smaller hippocampal volumes (r = 0.48, p = 0.01), but not total GM, WMH, or vCSF volumes. After adjusting for age, education, and gender, less DCU remained associated with smaller hippocampal volume (p = 0.01). Voxel-wise analysis demonstrated that less daily computer use was associated with decreased GM density in the bilateral hippocampi and temporal lobes. Less daily computer use is associated with smaller brain volume in regions that are integral to memory function and known to be involved early with Alzheimer's pathology and conversion to dementia. Continuous monitoring of daily computer use may detect signs of preclinical neurodegeneration in older individuals at risk for dementia.

Indirect Validation of Probe Speed Data on Arterial Corridors

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

Eshragh, Sepideh; Young, Stanley E.; Sharifi, Elham

This study aimed to estimate the accuracy of probe speed data on arterial corridors on the basis of roadway geometric attributes and functional classification. It was assumed that functional class (medium and low) along with other road characteristics (such as weighted average of the annual average daily traffic, average signal density, average access point density, and average speed) were available as correlation factors to estimate the accuracy of probe traffic data. This study tested these factors as predictors of the fidelity of probe traffic data by using the results of an extensive validation exercise. This study showed strong correlations betweenmore » these geometric attributes and the accuracy of probe data when they were assessed by using average absolute speed error. Linear models were regressed to existing data to estimate appropriate models for medium- and low-type arterial corridors. The proposed models for medium- and low-type arterials were validated further on the basis of the results of a slowdown analysis. These models can be used to predict the accuracy of probe data indirectly in medium and low types of arterial corridors.« less

Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland.

PubMed

de Hoogh, Kees; Héritier, Harris; Stafoggia, Massimo; Künzli, Nino; Kloog, Itai

2018-02-01

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM 2.5 ) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM 2.5 monitoring data was supplemented by imputing PM 2.5 concentrations at PM 10 sites, using PM 2.5 /PM 10 ratios at co-located sites. Daily PM 2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM 2.5 in cells with AOD but without PM 2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM 2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM 2.5 concentrations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Influence of topographic complexity on solar insolation estimates for the Colorado River, Grand Canyon, AZ

USGS Publications Warehouse

Yard, M.D.; Bennett, G.E.; Mietz, S.N.; Coggins, L.G.; Stevens, L.E.; Hueftle, S.; Blinn, D.W.

2005-01-01

Rugged topography along the Colorado River in Glen and Grand Canyons, exemplifies features common to canyon-bound streams and rivers of the arid southwest. Physical relief influences regulated river systems, especially those that are altered, and have become partially reliant on aquatic primary production. We measured and modeled instantaneous solar flux in a topographically complex environment to determine where differences in daily, seasonal and annual solar insolation occurred in this river system. At a system-wide scale, topographic complexity generates a spatial and temporal mosaic of varying solar insolation. This solar variation is a predictable consequence of channel orientation, geomorphology, elevation angles and viewshed. Modeled estimates for clear conditions corresponded closely with observed measurements for both instantaneous photosynthetic photon flux density (PPFD: ??mol m-2 s-1) and daily insolation levels (relative error 2.3%, CI ??0.45, S.D. 0.3, n = 29,813). Mean annual daily insolation levels system-wide were estimated to be 36 mol m-2 d -1 (17.5 S.D.), and seasonally varied on average from 13.4-57.4 mol m-2 d-1, for winter and summer, respectively. In comparison to identical areas lacking topographic effect (idealized plane), mean daily insolation levels were reduced by 22% during summer, and as much as 53% during winter. Depending on outlying topography, canyon bound regions having east-west (EW) orientations had higher seasonal variation, averaging from 8.1 to 61.4 mol m-2 d-1, for winter and summer, respectively. For EW orientations, 70% of mid-channel sites were obscured from direct incidence during part of the year; and of these sites, average diffuse light conditions persisted for 19.3% of the year (70.5 days), and extended upwards to 194 days. This predictive model has provided an initial quantitative step to estimate and determine the importance of autotrophic production for this ecosystem, as well as a broader application for other canyon systems. ?? 2004 Published by Elsevier B.V.

Source apportionment of PM2.5 organic carbon in the San Joaquin Valley using monthly and daily observations and meteorological clustering.

PubMed

Skiles, Matthew J; Lai, Alexandra M; Olson, Michael R; Schauer, James J; de Foy, Benjamin

2018-06-01

Two hundred sixty-three fine particulate matter (PM 2.5 ) samples collected on 3-day intervals over a 14-month period at two sites in the San Joaquin Valley (SJV) were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and organic molecular markers. A unique source profile library was applied to a chemical mass balance (CMB) source apportionment model to develop monthly and seasonally averaged source apportionment results. Five major OC sources were identified: mobile sources, biomass burning, meat smoke, vegetative detritus, and secondary organic carbon (SOC), as inferred from OC not apportioned by CMB. The SOC factor was the largest source contributor at Fresno and Bakersfield, contributing 44% and 51% of PM mass, respectively. Biomass burning was the only source with a statistically different average mass contribution (95% CI) between the two sites. Wintertime peaks of biomass burning, meat smoke, and total OC were observed at both sites, with SOC peaking during the summer months. Exceptionally strong seasonal variation in apportioned meat smoke mass could potentially be explained by oxidation of cholesterol between source and receptor and trends in wind transport outlined in a Residence Time Analysis (RTA). Fast moving nighttime winds prevalent during warmer months caused local emissions to be replaced by air mass transported from the San Francisco Bay Area, consisting of mostly diluted, oxidized concentrations of molecular markers. Good agreement was observed between SOC derived from the CMB model and from non-biomass burning WSOC mass, suggesting the CMB model is sufficiently accurate to assist in policy development. In general, uncertainty in monthly mass values derived from daily CMB apportionments were lower than that of CMB results produced with monthly marker composites, further validating daily sampling methodologies. Strong seasonal trends were observed for biomass and meat smoke OC apportionment, and monthly mass averages had lowest uncertainty when derived from daily CMB apportionments. Copyright © 2018 Elsevier Ltd. All rights reserved.

Non-Invasive Investigation of Bone Adaptation in Humans to Mechanical Loading

NASA Technical Reports Server (NTRS)

Whalen, R.

1999-01-01

Experimental studies have identified peak cyclic forces, number of loading cycles, and loading rate as contributors to the regulation of bone metabolism. We have proposed a theoretical model that relates bone density to a mechanical stimulus derived from average daily cumulative peak cyclic 'effective' tissue stresses. In order to develop a non-invasive experimental model to test the theoretical model we need to: (1) monitor daily cumulative loading on a bone, (2) compute the internal stress state(s) resulting from the imposed loading, and (3) image volumetric bone density accurately, precisely, and reproducibly within small contiguous volumes throughout the bone. We have chosen the calcaneus (heel) as an experimental model bone site because it is loaded by ligament, tendon and joint contact forces in equilibrium with daily ground reaction forces that we can measure; it is a peripheral bone site and therefore more easily and accurately imaged with computed tomography; it is composed primarily of cancellous bone; and it is a relevant site for monitoring bone loss and adaptation in astronauts and the general population. This paper presents an overview of our recent advances in the areas of monitoring daily ground reaction forces, biomechanical modeling of the forces on the calcaneus during gait, mathematical modeling of calcaneal bone adaptation in response to cumulative daily activity, accurate and precise imaging of the calcaneus with quantitative computed tomography (QCT), and application to long duration space flight.

Estimating wheat and maize daily evapotranspiration using artificial neural network

NASA Astrophysics Data System (ADS)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities

PubMed Central

Gutiérrez-Zapata, Diana María; Galeano-Vasco, Luis Fernando; Cerón-Muñoz, Mario Fernando

2016-01-01

Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material). PMID:26812150

Comparison of hybrid spectral-decomposition artificial neural network models for understanding climatic forcing of groundwater levels

NASA Astrophysics Data System (ADS)

Abrokwah, K.; O'Reilly, A. M.

2017-12-01

Groundwater is an important resource that is extracted every day because of its invaluable use for domestic, industrial and agricultural purposes. The need for sustaining groundwater resources is clearly indicated by declining water levels and has led to modeling and forecasting accurate groundwater levels. In this study, spectral decomposition of climatic forcing time series was used to develop hybrid wavelet analysis (WA) and moving window average (MWA) artificial neural network (ANN) models. These techniques are explored by modeling historical groundwater levels in order to provide understanding of potential causes of the observed groundwater-level fluctuations. Selection of the appropriate decomposition level for WA and window size for MWA helps in understanding the important time scales of climatic forcing, such as rainfall, that influence water levels. Discrete wavelet transform (DWT) is used to decompose the input time-series data into various levels of approximate and details wavelet coefficients, whilst MWA acts as a low-pass signal-filtering technique for removing high-frequency signals from the input data. The variables used to develop and validate the models were daily average rainfall measurements from five National Atmospheric and Oceanic Administration (NOAA) weather stations and daily water-level measurements from two wells recorded from 1978 to 2008 in central Florida, USA. Using different decomposition levels and different window sizes, several WA-ANN and MWA-ANN models for simulating the water levels were created and their relative performances compared against each other. The WA-ANN models performed better than the corresponding MWA-ANN models; also higher decomposition levels of the input signal by the DWT gave the best results. The results obtained show the applicability and feasibility of hybrid WA-ANN and MWA-ANN models for simulating daily water levels using only climatic forcing time series as model inputs.

Evaluation of Near-Tropopause Ozone Distributions in the Global Modeling Initiative Combined Stratosphere/Troposphere Model with Ozonesonde Data

NASA Technical Reports Server (NTRS)

Considine, David B.; Logan, Jennifer A.; Olsen, Mark A.

2008-01-01

The NASA Global Modeling Initiative has developed a combined stratosphere/troposphere chemistry and transport model which fully represents the processes governing atmospheric composition near the tropopause. We evaluate model ozone distributions near the tropopause, using two high vertical resolution monthly mean ozone profile climatologies constructed with ozonesonde data, one by averaging on pressure levels and the other relative to the thermal tropopause. Model ozone is high biased at the SH tropical and NH midlatitude tropopause by approx. 45% in a 4 deg. latitude x 5 deg. longitude model simulation. Increasing the resolution to 2 deg. x 2.5 deg. increases the NH tropopause high bias to approx. 60%, but decreases the tropical tropopause bias to approx. 30%, an effect of a better-resolved residual circulation. The tropopause ozone biases appear not to be due to an overly vigorous residual circulation or excessive stratosphere/troposphere exchange, but are more likely due to insufficient vertical resolution or excessive vertical diffusion near the tropopause. In the upper troposphere and lower stratosphere, model/measurement intercomparisons are strongly affected by the averaging technique. NH and tropical mean model lower stratospheric biases are less than 20%. In the upper troposphere, the 2 deg. x 2.5 deg. simulation exhibits mean high biases of approx. 20% and approx. 35% during April in the tropics and NH midlatitudes, respectively, compared to the pressure averaged climatology. However, relative-to-tropopause averaging produces upper troposphere high biases of approx. 30% and 70% in the tropics and NH midlatitudes. This is because relative-to-tropopause averaging better preserves large cross-tropopause O3 gradients, which are seen in the daily sonde data, but not in daily model profiles. The relative annual cycle of ozone near the tropopause is reproduced very well in the model Northern Hemisphere midlatitudes. In the tropics, the model amplitude of the near tropopause annual cycle is weak. This is likely due to the annual amplitude of mean vertical upwelling near the tropopause, which analysis suggests is approx. 30% weaker than in the real atmosphere.

Characterization and disaggregation of daily rainfall in the Upper Blue Nile Basin in Ethiopia

NASA Astrophysics Data System (ADS)

Engida, Agizew N.; Esteves, Michel

2011-03-01

SummaryIn Ethiopia, available rainfall records are mainly limited to daily time steps. Though rainfall data at shorter time steps are important for various purposes like modeling of erosion processes and flood hydrographs, they are hardly available in Ethiopia. The objectives of this study were (i) to study the temporal characteristics of daily rains at two stations in the region of the Upper Blue Nile Basin (UBNB) and (ii) to calibrate and evaluate a daily rainfall disaggregation model. The analysis was based on rainfall data of Bahir Dar and Gonder Meteorological Stations. The disaggregation model used was the Modified Bartlett-Lewis Rectangular Pulse Model (MBLRPM). The mean daily rainfall intensity varied from about 4 mm in the dry season to 17 mm in the wet season with corresponding variation in raindays of 0.4-26 days. The observed maximum daily rainfall varied from 13 mm in the dry month to 200 mm in the wet month. The average wet/dry spell length varied from 1/21 days in the dry season to 6/1 days in the rainy season. Most of the rainfall occurs in the afternoon and evening periods of the day. Daily rainfall disaggregation using the MBLRPM alone resulted in poor match between the disaggregated and observed hourly rainfalls. Stochastic redistribution of the outputs of the model using Beta probability distribution function improved the agreement between observed and calculated hourly rain intensities. In areas where convective rainfall is dominant, the outputs of MBLRPM should be redistributed using relevant probability distributions to simulate the diurnal rainfall pattern.

Performance assessment of different day-of-the-year-based models for estimating global solar radiation - Case study: Egypt

NASA Astrophysics Data System (ADS)

Hassan, Gasser E.; Youssef, M. Elsayed; Ali, Mohamed A.; Mohamed, Zahraa E.; Shehata, Ali I.

2016-11-01

Different models are introduced to predict the daily global solar radiation in different locations but there is no specific model based on the day of the year is proposed for many locations around the world. In this study, more than 20 years of measured data for daily global solar radiation on a horizontal surface are used to develop and validate seven models to estimate the daily global solar radiation by day of the year for ten cities around Egypt as a case study. Moreover, the generalization capability for the best models is examined all over the country. The regression analysis is employed to calculate the coefficients of different suggested models. The statistical indicators namely, RMSE, MABE, MAPE, r and R2 are calculated to evaluate the performance of the developed models. Based on the validation with the available data, the results show that the hybrid sine and cosine wave model and 4th order polynomial model have the best performance among other suggested models. Consequently, these two models coupled with suitable coefficients can be used for estimating the daily global solar radiation on a horizontal surface for each city, and also for all the locations around the studied region. It is believed that the established models in this work are applicable and significant for quick estimation for the average daily global solar radiation on a horizontal surface with higher accuracy. The values of global solar radiation generated by this approach can be utilized in the design and estimation of the performance of different solar applications.

Forecasting daily attendances at an emergency department to aid resource planning

PubMed Central

Sun, Yan; Heng, Bee Hoon; Seow, Yian Tay; Seow, Eillyne

2009-01-01

Background Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. Methods Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. Results By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50. After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. Conclusion Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning. PMID:19178716

The air quality forecast in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) System: model evaluation and improvement

NASA Astrophysics Data System (ADS)

Wu, Q.

2013-12-01

The MM5-SMOKE-CMAQ model system, which is developed by the United States Environmental Protection Agency(U.S. EPA) as the Models-3 system, has been used for the daily air quality forecast in the Beijing Municipal Environmental Monitoring Center(Beijing MEMC), as a part of the Ensemble Air Quality Forecast System for Beijing(EMS-Beijing) since the Olympic Games year 2008. In this study, we collect the daily forecast results of the CMAQ model in the whole year 2010 for the model evaluation. The results show that the model play a good model performance in most days but underestimate obviously in some air pollution episode. A typical air pollution episode from 11st - 20th January 2010 was chosen, which the air pollution index(API) of particulate matter (PM10) observed by Beijing MEMC reaches to 180 while the prediction of PM10-API is about 100. Taking in account all stations in Beijing, including urban and suburban stations, three numerical methods are used for model improvement: firstly, enhance the inner domain with 4km grids, the coverage from only Beijing to the area including its surrounding cities; secondly, update the Beijing stationary area emission inventory, from statistical county-level to village-town level, that would provide more detail spatial informance for area emissions; thirdly, add some industrial points emission in Beijing's surrounding cities, the latter two are both the improvement of emission. As the result, the peak of the nine national standard stations averaged PM10-API, which is simulated by CMAQ as daily hindcast PM10-API, reach to 160 and much near to the observation. The new results show better model performance, which the correlation coefficent is 0.93 in national standard stations average and 0.84 in all stations, the relative error is 15.7% in national standard stations averaged and 27% in all stations. The time series of 9 national standard in Beijing urban The scatter diagram of all stations in Beijing, the red is the forecast and the blue is new result.

Accuracy assessment of a net radiation and temperature index snowmelt model using ground observations of snow water equivalent in an alpine basin

NASA Astrophysics Data System (ADS)

Molotch, N. P.; Painter, T. H.; Bales, R. C.; Dozier, J.

2003-04-01

In this study, an accumulated net radiation / accumulated degree-day index snowmelt model was coupled with remotely sensed snow covered area (SCA) data to simulate snow cover depletion and reconstruct maximum snow water equivalent (SWE) in the 19.1-km2 Tokopah Basin of the Sierra Nevada, California. Simple net radiation snowmelt models are attractive for operational snowmelt runoff forecasts as they are computationally inexpensive and have low input requirements relative to physically based energy balance models. The objective of this research was to assess the accuracy of a simple net radiation snowmelt model in a topographically heterogeneous alpine environment. Previous applications of net radiation / temperature index snowmelt models have not been evaluated in alpine terrain with intensive field observations of SWE. Solar radiation data from two meteorological stations were distributed using the topographic radiation model TOPORAD. Relative humidity and temperature data were distributed based on the lapse rate calculated between three meteorological stations within the basin. Fractional SCA data from the Landsat Enhanced Thematic Mapper (5 acquisitions) and the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) (2 acquisitions) were used to derive daily SCA using a linear regression between acquisition dates. Grain size data from AVIRIS (4 acquisitions) were used to infer snow surface albedo and interpolated linearly with time to derive daily albedo values. Modeled daily snowmelt rates for each 30-m pixel were scaled by the SCA and integrated over the snowmelt season to obtain estimates of maximum SWE accumulation. Snow surveys consisting of an average of 335 depth measurements and 53 density measurements during April, May and June, 1997 were interpolated using a regression tree / co-krig model, with independent variables of average incoming solar radiation, elevation, slope and maximum upwind slope. The basin was clustered into 7 elevation / average-solar-radiation zones for SWE accuracy assessment. Model simulations did a poor job at estimating the spatial distribution of SWE. Basin clusters where the solar radiative flux dominated the melt flux were simulated more accurately than those dominated by the turbulent fluxes or the longwave radiative flux.

Verification of computer analysis models for suspension bridges.

DOT National Transportation Integrated Search

2009-08-01

The Vincent Thomas Bridge, connecting Terminal Island with San Pedro, serves both Los Angeles : and Long Beach ports, two busiest ports in the west coast of USA. The bridge carries an : overwhelming number of traffic with an Annual Average Daily Traf...

Accuracy of silicon versus thermopile radiometers for daily and monthly integrated total hemispherical solar radiation

NASA Astrophysics Data System (ADS)

Stoffel, Thomas L.; Myers, Daryl R.

2010-08-01

Measurement stations for solar radiation resource assessment data are expensive and labor intensive. For this reason, long-term solar radiation measurements are not widely available. Growing interest in solar renewable energy systems has generated a great number of questions about the quality of data obtained from inexpensive silicon photodiode radiometers versus costly thermopile radiometers. We analyze a year of daily total and monthly mean global horizontal irradiance measurements derived from 1-minute averages of 3-second samples of pyranometer signals. The data were collected simultaneously from both types of radiometers at the Solar Radiation Research Laboratory (SRRL) operated by the National Renewable Energy Laboratory in Golden, Colorado. All broadband radiometers in service at SRRL are calibrated annually using an outdoor method with reference radiometers traceable to the World Radiometric Reference. We summarized the data by daily total and monthly mean daily total amounts of solar radiation. Our results show that systematic and random errors (identified in our outdoor calibration process) in each type of radiometer cancel out over periods of one day or more. Daily total and mean monthly daily total solar energy measured by the two pyranometer types compare within 1% to 2%. The individual daily variations among different models of thermopile radiometers may be up to twice as large, up to +/-5%, being highest in the winter (higher average solar zenith angle conditions) and lowest in summer, consistent with the lower solar zenith angle conditions.

Short-term impacts of floods on enteric infectious disease in Qingdao, China, 2005-2011.

PubMed

Zhang, F; Liu, Z; Gao, L; Zhang, C; Jiang, B

2016-11-01

The current study aimed to examine the relationship between floods and the three enteric infectious diseases, namely bacillary dysentery (BD), hand-foot-mouth disease (HFMD) and other infectious diarrhoea (OID) in Qingdao, China. Relative risks (RRs) and 95% confidence intervals (CIs) of floods on BD, HFMD and OID were calculated using a quasi-Poisson generalized linear model, adjusting for daily average temperature, daily average relative humidity, and seasonal and long-term temporal trends. Two separate models within two different periods were designed. Model 1 for the summer period showed that floods were positively associated with BD for 4- to 12-day lags, with the greatest effects for 7-day (RR 1·41, 95% CI 1·22-1·62) and 11-day (RR 1·42, 95% CI 1·22-1·64) lags. Similar findings were found in model 2 for the whole study period for 5- to 12-day lags. However, HFMD and OID were not significantly associated with floods in both models. Results from this study will provide insight into the health risks associated with floods and may help inform public health precautionary measures for such disasters.

Influence of wind speed averaging on estimates of dimethylsulfide emission fluxes

DOE PAGES

Chapman, E. G.; Shaw, W. J.; Easter, R. C.; ...

2002-12-03

The effect of various wind-speed-averaging periods on calculated DMS emission fluxes is quantitatively assessed. Here, a global climate model and an emission flux module were run in stand-alone mode for a full year. Twenty-minute instantaneous surface wind speeds and related variables generated by the climate model were archived, and corresponding 1-hour-, 6-hour-, daily-, and monthly-averaged quantities calculated. These various time-averaged, model-derived quantities were used as inputs in the emission flux module, and DMS emissions were calculated using two expressions for the mass transfer velocity commonly used in atmospheric models. Results indicate that the time period selected for averaging wind speedsmore » can affect the magnitude of calculated DMS emission fluxes. A number of individual marine cells within the global grid show DMS emissions fluxes that are 10-60% higher when emissions are calculated using 20-minute instantaneous model time step winds rather than monthly-averaged wind speeds, and at some locations the differences exceed 200%. Many of these cells are located in the southern hemisphere where anthropogenic sulfur emissions are low and changes in oceanic DMS emissions may significantly affect calculated aerosol concentrations and aerosol radiative forcing.« less

Flexible C : N ratio enhances metabolism of large phytoplankton when resource supply is intermittent

NASA Astrophysics Data System (ADS)

Talmy, D.; Blackford, J.; Hardman-Mountford, N. J.; Polimene, L.; Follows, M. J.; Geider, R. J.

2014-09-01

Phytoplankton cell size influences particle sinking rate, food web interactions and biogeographical distributions. We present a model in which the uptake, storage and assimilation of nitrogen and carbon are explicitly resolved in different-sized phytoplankton cells. In the model, metabolism and cellular C : N ratio are influenced by the accumulation of carbon polymers such as carbohydrate and lipid, which is greatest when cells are nutrient starved, or exposed to high light. Allometric relations and empirical data sets are used to constrain the range of possible C : N, and indicate that larger cells can accumulate significantly more carbon storage compounds than smaller cells. When forced with extended periods of darkness combined with brief exposure to saturating irradiance, the model predicts organisms large enough to accumulate significant carbon reserves may on average synthesize protein and other functional apparatus up to five times faster than smaller organisms. The advantage of storage in terms of average daily protein synthesis rate is greatest when modeled organisms were previously nutrient starved, and carbon storage reservoirs saturated. Small organisms may therefore be at a disadvantage in terms of average daily growth rate in environments that involve prolonged periods of darkness and intermittent nutrient limitation. We suggest this mechanism is a significant constraint on phytoplankton C : N variability and cell size distribution in different oceanic regimes.

Daily mean temperature estimate at the US SUFRAD stations as an average of the maximum and minimum temperatures

DOE PAGES

Chylek, Petr; Augustine, John A.; Klett, James D.; ...

2017-09-30

At thousands of stations worldwide, the mean daily surface air temperature is estimated as a mean of the daily maximum (T max) and minimum (T min) temperatures. In this paper, we use the NOAA Surface Radiation Budget Network (SURFRAD) of seven US stations with surface air temperature recorded each minute to assess the accuracy of the mean daily temperature estimate as an average of the daily maximum and minimum temperatures and to investigate how the accuracy of the estimate increases with an increasing number of daily temperature observations. We find the average difference between the estimate based on an averagemore » of the maximum and minimum temperatures and the average of 1440 1-min daily observations to be - 0.05 ± 1.56 °C, based on analyses of a sample of 238 days of temperature observations. Considering determination of the daily mean temperature based on 3, 4, 6, 12, or 24 daily temperature observations, we find that 2, 4, or 6 daily observations do not reduce significantly the uncertainty of the daily mean temperature. The bias reduction in a statistically significant manner (95% confidence level) occurs only with 12 or 24 daily observations. The daily mean temperature determination based on 24 hourly observations reduces the sample daily temperature uncertainty to - 0.01 ± 0.20 °C. Finally, estimating the parameters of population of all SURFRAD observations, the 95% confidence intervals based on 24 hourly measurements is from - 0.025 to 0.004 °C, compared to a confidence interval from - 0.15 to 0.05 °C based on the mean of T max and T min.« less

Daily mean temperature estimate at the US SUFRAD stations as an average of the maximum and minimum temperatures

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

Chylek, Petr; Augustine, John A.; Klett, James D.

At thousands of stations worldwide, the mean daily surface air temperature is estimated as a mean of the daily maximum (T max) and minimum (T min) temperatures. In this paper, we use the NOAA Surface Radiation Budget Network (SURFRAD) of seven US stations with surface air temperature recorded each minute to assess the accuracy of the mean daily temperature estimate as an average of the daily maximum and minimum temperatures and to investigate how the accuracy of the estimate increases with an increasing number of daily temperature observations. We find the average difference between the estimate based on an averagemore » of the maximum and minimum temperatures and the average of 1440 1-min daily observations to be - 0.05 ± 1.56 °C, based on analyses of a sample of 238 days of temperature observations. Considering determination of the daily mean temperature based on 3, 4, 6, 12, or 24 daily temperature observations, we find that 2, 4, or 6 daily observations do not reduce significantly the uncertainty of the daily mean temperature. The bias reduction in a statistically significant manner (95% confidence level) occurs only with 12 or 24 daily observations. The daily mean temperature determination based on 24 hourly observations reduces the sample daily temperature uncertainty to - 0.01 ± 0.20 °C. Finally, estimating the parameters of population of all SURFRAD observations, the 95% confidence intervals based on 24 hourly measurements is from - 0.025 to 0.004 °C, compared to a confidence interval from - 0.15 to 0.05 °C based on the mean of T max and T min.« less

Effects of episodic rainfall on a subterranean estuary

NASA Astrophysics Data System (ADS)

Yu, Xiayang; Xin, Pei; Lu, Chunhui; Robinson, Clare; Li, Ling; Barry, D. A.

2017-07-01

Numerical simulations were conducted to examine the effect of episodic rainfall on nearshore groundwater dynamics in a tidally influenced unconfined coastal aquifer, with a focus on both long-term (yearly) and short-term (daily) behavior of submarine groundwater discharge (SGD) and seawater intrusion (SWI). The results showed nonlinear interactions among the processes driven by rainfall, tides, and density gradients. Rainfall-induced infiltration increased the yearly averaged fresh groundwater discharge to the ocean but reduced the extents of the saltwater wedge and upper saline plume as well as the total rate of seawater circulation through both zones. Overall, the net effect of the interactions led to an increase of the SGD. The nearshore groundwater responded to individual rainfall events in a delayed and cumulative fashion, as evident in the variations of daily averaged SGD and salt stored in the saltwater wedge (quantifying the extent of SWI). A generalized linear model (GLM) along with a Gamma distribution function was developed to describe the delayed and prolonged effect of rainfall events on short-term groundwater behavior. This model validated with results of daily averaged SGD and SWI from the simulations of groundwater and solute transport using independent rainfall data sets, performed well in predicting the behavior of the nearshore groundwater system under the combined influence of episodic rainfall, tides, and density gradients. The findings and developed GLM form a basis for evaluating and predicting SGD, SWI, and associated mass fluxes from unconfined coastal aquifers under natural conditions, including episodic rainfall.

Diameter-growth model across shortleaf pine range using regression tree analysis

Treesearch

Daniel Yaussy; Louis Iverson; Anantha Prasad

1999-01-01

Diameter growth of a tree in most gap-phase models is limited by light, nutrients, moisture, and temperature. Growing-season temperature is represented by growing degree days (gdd), which is the sum of the average daily temperatures above a baseline temperature. Gap-phase models determine the north-south range of a species by the gdd limits at the north and south...

Numerical modeling of incised-valley deposits in Tokyo lowland for the last 13 kyrs

NASA Astrophysics Data System (ADS)

Kubo, Y.; Syvitski, J. P.; Hutton, E. W.; Tanabe, S.

2006-12-01

A coupled-simulation by the hydrologic model HydroTrend and the stratigraphic model SedFlux is applied to the incised-valley-fill deposits in the Tokyo lowland for the last 13,000 years. The postglacial sediments supplied by paleo Tonegawa River have formed deltaic deposits controlled by eustatic sea-level rise after LGM. The effects of changes in sea level, climate, and morphology on the resultant architecture of the deposits are simulated and analyzed by the numerical models. Synthetic sediment flux from the paleo Tonegawa is computed by the hydrologic model HydroTrend. The model predicts variation in average rate of sediment production over geological time scale from changes in drainage area, precipitation, temperature and morphology. Random variation based on statistic climate data is added to the predicted average values to provide daily sediment discharge. The model prediction indicates that, despite 80% increase in drainage area in the past, competing effects of decreased precipitation resulted in relatively stable sediment discharge over the last 13,000 years. On the other hand, variation in daily sediment discharge shows drastic increase during infrequent storm events. Possible occurrence of hyperpycnal flows at the river mouth was indicated during such storms, which produced daily sediment load ten times larger than average yearly sediment discharge. The estimated sediment supply is used as input to the process-based forward-model 2D-SedFlux. SedFlux is able to simulate transport and deposition of sediments by such processes as river plume, bedload dumping and ocean storms with changing boundary conditions of sea level and basement morphology. The simulation is based on the initial paleo-morphology reconstructed from integrated core analysis from the area. 2D-SedFlux successfully predicts the formation of transgressive deposits and subsequent prograding delta deposits, and the results are comparable to general architecture of incised-valley fills in the area. Detailed comparison between the model predictions and field data shows some minor differences, which are then used to revise the local sea level curve.

Modeling Air Temperature/Water Temperature Relations Along a Small Mountain Stream Under Increasing Urban Influence

NASA Astrophysics Data System (ADS)

Fedders, E. R.; Anderson, W. P., Jr.; Hengst, A. M.; Gu, C.

2017-12-01

Boone Creek is a headwater stream of low to moderate gradient located in Boone, North Carolina, USA. Total impervious surface coverage in the 5.2 km2 catchment drained by the 1.9 km study reach increases from 13.4% in the upstream half of the reach to 24.3% in the downstream half. Other markers of urbanization, including culverting, lack of riparian shade vegetation, and bank armoring also increase downstream. Previous studies have shown the stream to be prone to temperature surges on short timescales (minutes to hours) caused by summer runoff from the urban hardscaping. This study investigates the effects of urbanization on the stream's thermal regime at daily to yearly timescales. To do this, we developed an analytical model of daily average stream temperatures based on daily average air temperatures. We utilized a two-part model comprising annual and biannual components and a daily component consisting of a 3rd-order Markov process in order to fit the thermal dynamics of our small, gaining stream. Optimizing this model at each of our study sites in each studied year (78 total site-years of data) yielded annual thermal exchange coefficients (K) for each site. These K values quantify the strength of the relationship between stream and air temperature, or inverse thermal stability. In a uniform, pristine catchment environment, K values are expected to decrease downstream as the stream gains discharge volume and, therefore, thermal inertia. Interannual average K values for our study reach, however, show an overall increase from 0.112 furthest upstream to 0.149 furthest downstream, despite a near doubling of stream discharge between these monitoring points. K values increase only slightly in the upstream, less urban, half of the reach. A line of best fit through these points on a plot of reach distance versus K value has a slope of 2E-6. But the K values of downstream, more urbanized sites increase at a rate of 2E-5 per meter of reach distance, an order of magnitude greater. This indicates a possible tipping point in the stream temperature-water temperature relationship at which increased urbanization overpowers increasing stream thermal inertia.

Spatial disaggregation of POWER-NASA air temperatures and effects on grass reference evapotranspiration in Sicily, Italy

NASA Astrophysics Data System (ADS)

Negm, Amro; Minacapilli, Mario; Provenzano, Giuseppe

2017-04-01

The accurate estimation of grass reference evapotranspiration (ET0) is important for many fields, including hydrology and irrigation water management. Being direct measure of ET0 difficult, expensive and time consuming, application of simplified approaches and web-based meteorological information are often preferred. The Prediction of Worldwide Energy Resource project developed by the American National Aeronautics and Space Administration (POWER-NASA) provides meteorological observations and surface energy fluxes on 1° latitude by 1° longitude grid, with a continuous daily coverage and for the entire globe. However, the broad spatial resolution of these data represents a limiting factor, for example when they have to be used for local estimations of reference ET0. In this work, a procedure for the spatial disaggregation of POWER-NASA daily average air temperature was proposed. In particular, a daily scaling factor was initially defined as the ratio between disaggregated average air temperature and the corresponding native value. This ratio was then modeled with a cosine function, characterized by three parameters depending on elevation, so to account for seasonal and regional variability. The proposed model was calibrated with three years of ground measurements (2006-2008) and then validated over six years (2009-2014). The suitability of the procedure was finally assessed by applying two simplified empirical models to estimate ET0 (Turc, 1961; Hargreaves, 1975). When compared to ET0 values obtained with FAO-56 PM equation, both simplified equations associated to downscaled meteorological observations, were characterized by RMSE ranging between 0.44 and 1.08 mm (average of 0.72-0.74 mm), and average MBE of -0.06 (Turc equation) and 0.13 mm (Hargreaves equation). These results indicated the strength of the proposed procedure to estimate ET0, even for regions characterized by the lack of detailed meteorological information.

Anxiety-related visits to New Jersey emergency departments after September 11, 2001.

PubMed

Adinaro, David J; Allegra, John R; Cochrane, Dennis G; Cable, Gregory

2008-04-01

The purpose of this study was to examine the effect of September 11, 2001 on anxiety-related visits to selected Emergency Departments (EDs). We performed a retrospective analysis of consecutive patients seen by emergency physicians in 15 New Jersey EDs located within a 50-mile radius of the World Trade Center from July 11 through December 11 in each of 6 years, 1996--2001. We chose by consensus all ICD-9 (International Classification of Diseases, 9th revision) codes related to anxiety. We used graphical methods, Box-Jenkins modeling, and time series regression to determine the effect of September 11 to 14 on daily rates of anxiety-related visits. We found that the daily rate of anxiety-related visits just after September 11th was 93% higher (p < 0.0001) than the average for the remaining 150 days for 2001. This represents, on average, one additional daily visit for anxiety at each ED. We concluded that there was an increase in anxiety-related ED visits after September 11, 2001.

Parametric recursive system identification and self-adaptive modeling of the human energy metabolism for adaptive control of fat weight.

PubMed

Őri, Zsolt P

2017-05-01

A mathematical model has been developed to facilitate indirect measurements of difficult to measure variables of the human energy metabolism on a daily basis. The model performs recursive system identification of the parameters of the metabolic model of the human energy metabolism using the law of conservation of energy and principle of indirect calorimetry. Self-adaptive models of the utilized energy intake prediction, macronutrient oxidation rates, and daily body composition changes were created utilizing Kalman filter and the nominal trajectory methods. The accuracy of the models was tested in a simulation study utilizing data from the Minnesota starvation and overfeeding study. With biweekly macronutrient intake measurements, the average prediction error of the utilized carbohydrate intake was -23.2 ± 53.8 kcal/day, fat intake was 11.0 ± 72.3 kcal/day, and protein was 3.7 ± 16.3 kcal/day. The fat and fat-free mass changes were estimated with an error of 0.44 ± 1.16 g/day for fat and -2.6 ± 64.98 g/day for fat-free mass. The daily metabolized macronutrient energy intake and/or daily macronutrient oxidation rate and the daily body composition change from directly measured serial data are optimally predicted with a self-adaptive model with Kalman filter that uses recursive system identification.

A Community Terrain-Following Ocean Modeling System (ROMS)

DTIC Science & Technology

2015-09-30

funded NOPP project titled: Toward the Development of a Coupled COAMPS-ROMS Ensemble Kalman filter and adjoint with a focus on the Indian Ocean and the...surface temperature and surface salinity daily averages for 31-Jan-2014. Similarly, Figure 3 shows the sea surface height averaged solution for 31-Jan... temperature (upper panel; Celsius) and surface salinity (lower panel) for 31-Jan-2014. The refined solution for the Hudson Canyon grid is overlaid on

Intraindividual change and variability in daily stress processes: Findings from two measurement-burst diary studies

PubMed Central

Sliwinski, Martin J.; Almeida, David M.; Smyth, Joshua; Stawski, Robert S.

2010-01-01

There is little longitudinal information on aging-related changes in emotional responses to negative events. The present manuscript examined intraindividual change and variability in the within-person coupling of daily stress and negative affect (NA) using data from two-measurement burst daily diary studies. Three main findings emerged. First, average reactivity to daily stress increased longitudinally, and this increase was evident across most the adult lifespan. Second, individual differences in emotional reactivity to daily stress exhibited long-term temporal stability, but this stability was greatest in midlife and decreased in old age. And third, reactivity to daily stress varied reliably within-persons (across-time), with individual exhibiting higher levels of reactivity during times when reporting high levels of global subject stress in previous month. Taken together, the present results emphasize the importance of modeling dynamic psychosocial and aging processes that operate across different time scales for understanding age-related changes in daily stress processes. PMID:20025399

Modeling the Response of Anopheles gambiae (Diptera: Culicidae) Populations in the Kenya Highlands to a Rise in Mean Annual Temperature.

PubMed

Wallace, Dorothy; Prosper, Olivia; Savos, Jacob; Dunham, Ann M; Chipman, Jonathan W; Shi, Xun; Ndenga, Bryson; Githeko, Andrew

2017-03-01

A dynamical model of Anopheles gambiae larval and adult populations is constructed that matches temperature-dependent maturation times and mortality measured experimentally as well as larval instar and adult mosquito emergence data from field studies in the Kenya Highlands. Spectral classification of high-resolution satellite imagery is used to estimate household density. Indoor resting densities collected over a period of one year combined with predictions of the dynamical model give estimates of both aquatic habitat and total adult mosquito densities. Temperature and precipitation patterns are derived from monthly records. Precipitation patterns are compared with average and extreme habitat estimates to estimate available aquatic habitat in an annual cycle. These estimates are coupled with the original model to produce estimates of adult and larval populations dependent on changing aquatic carrying capacity for larvae and changing maturation and mortality dependent on temperature. This paper offers a general method for estimating the total area of aquatic habitat in a given region, based on larval counts, emergence rates, indoor resting density data, and number of households.Altering the average daily temperature and the average daily rainfall simulates the effect of climate change on annual cycles of prevalence of An. gambiae adults. We show that small increases in average annual temperature have a large impact on adult mosquito density, whether measured at model equilibrium values for a single square meter of habitat or tracked over the course of a year of varying habitat availability and temperature. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Retrieving air humidity, global solar radiation, and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part III: reference evapotranspiration

NASA Astrophysics Data System (ADS)

Lobit, P.; Gómez Tagle, A.; Bautista, F.; Lhomme, J. P.

2017-07-01

We evaluated two methods to estimate evapotranspiration (ETo) from minimal weather records (daily maximum and minimum temperatures) in Mexico: a modified reduced set FAO-Penman-Monteith method (Allen et al. 1998, Rome, Italy) and the Hargreaves and Samani (Appl Eng Agric 1(2): 96-99, 1985) method. In the reduced set method, the FAO-Penman-Monteith equation was applied with vapor pressure and radiation estimated from temperature data using two new models (see first and second articles in this series): mean temperature as the average of maximum and minimum temperature corrected for a constant bias and constant wind speed. The Hargreaves-Samani method combines two empirical relationships: one between diurnal temperature range ΔT and shortwave radiation Rs, and another one between average temperature and the ratio ETo/Rs: both relationships were evaluated and calibrated for Mexico. After performing a sensitivity analysis to evaluate the impact of different approximations on the estimation of Rs and ETo, several model combinations were tested to predict ETo from daily maximum and minimum temperature alone. The quality of fit of these models was evaluated on 786 weather stations covering most of the territory of Mexico. The best method was found to be a combination of the FAO-Penman-Monteith reduced set equation with the new radiation estimation and vapor pressure model. As an alternative, a recalibration of the Hargreaves-Samani equation is proposed.

Stability Analysis of Receiver ISB for BDS/GPS

NASA Astrophysics Data System (ADS)

Zhang, H.; Hao, J. M.; Tian, Y. G.; Yu, H. L.; Zhou, Y. L.

2017-07-01

Stability analysis of receiver ISB (Inter-System Bias) is essential for understanding the feature of ISB as well as the ISB modeling and prediction. In order to analyze the long-term stability of ISB, the data from MGEX (Multi-GNSS Experiment) covering 3 weeks, which are from 2014, 2015 and 2016 respectively, are processed with the precise satellite clock and orbit products provided by Wuhan University and GeoForschungsZentrum (GFZ). Using the ISB calculated by BDS (BeiDou Navigation Satellite System)/GPS (Global Positioning System) combined PPP (Precise Point Positioning), the daily stability and weekly stability of ISB are investigated. The experimental results show that the diurnal variation of ISB is stable, and the average of daily standard deviation is about 0.5 ns. The weekly averages and standard deviations of ISB vary greatly in different years. The weekly averages of ISB are relevant to receiver types. There is a system bias between ISB calculated from the precise products provided by Wuhan University and GFZ. In addition, the system bias of the weekly average ISB of different stations is consistent with each other.

Average ovarian hormone levels, rather than daily values and their fluctuations, are related to facial preferences among women.

PubMed

Marcinkowska, Urszula M; Kaminski, Gwenael; Little, Anthony C; Jasienska, Grazyna

2018-05-24

Hormones are of crucial importance for human behavior. Cyclical changes of ovarian hormones throughout women's menstrual cycle are suggested to underlie fluctuation in masculinity preference for both faces and bodies. In this study we tested this hypothesis based on daily measurements of estradiol and progesterone throughout menstrual cycle, and multiple measurements of women's preference towards masculinity of faces and bodies of men. We expected that due to a large variation among daily hormonal levels we would not observe a direct effect of daily hormone levels, but rather that average levels of ovarian hormones throughout the cycle (a reliable marker of a probability of conception) would better predict women's preferences. We found a negative relationship between average progesterone levels and facial masculinity preference, but only among women who were in long-term relationships. There was no relationship between facial masculinity preference and either of the estradiol or progesterone daily levels. Similarly, only average levels of hormones were significantly related to facial symmetry preference. For women who were in relationships estradiol was positively related to symmetry preference, while for single women this relationship was opposite. For body masculinity preference there were no significant relationships with neither averaged nor daily hormonal levels. Taken together, our results further suggest that overall cycle levels of ovarian hormones (averaged for a cycle) are better predictors of facial masculinity and symmetry preference than daily levels assessed during preferences' tests. Importantly, including information about relationship status in the investigations of hormonal bases of preferences is crucial. Copyright © 2018 Elsevier Inc. All rights reserved.

Organization of vertical shear of wind and daily variability of monsoon rainfall

NASA Astrophysics Data System (ADS)

Gouda, K. C.; Goswami, P.

2016-10-01

Very little is known about the mechanisms that govern the day to day variability of the Indian summer monsoon (ISM) rainfall; in the current dominant view, the daily rainfall is essentially a result of chaotic dynamics. Most studies in the past have thus considered monsoon in terms of its seasonal (June-September) or monthly rainfall. We show here that the daily rainfall in June is associated with vertical shear of horizontal winds at specific scales. While vertical shear had been used in the past to investigate interannual variability of seasonal rainfall, rarely any effort has been made to examine daily rainfall. Our work shows that, at least during June, the daily rainfall variability of ISM rainfall is associated with a large scale dynamical coherence in the sense that the vertical shear averaged over large spatial extents are significantly correlated with area-averaged daily rainfall. An important finding from our work is the existence of a clearly delineated monsoon shear domain (MSD) with strong coherence between area-averaged shear and area-averaged daily rainfall in June; this association of daily rainfall is not significant with shear over only MSD. Another important feature is that the association between daily rainfall and vertical shear is present only during the month of June. Thus while ISM (June-September) is a single seasonal system, it is important to consider the dynamics and variation of June independently of the seasonal ISM rainfall. The association between large-scale organization of circulation and daily rainfall is suggested as a basis for attempting prediction of daily rainfall by ensuring accurate simulation of wind shear.

Scaling analysis on Indian foreign exchange market

NASA Astrophysics Data System (ADS)

Sarkar, A.; Barat, P.

2006-05-01

In this paper, we investigate the scaling behavior of the average daily exchange rate returns of the Indian Rupee against four foreign currencies: namely, US Dollar, Euro, Great Britain Pound and Japanese Yen. The average daily exchange rate return of the Indian Rupee against US Dollar is found to exhibit a persistent scaling behavior and follow Levy stable distribution. On the contrary, the average daily exchange rate returns of the other three foreign currencies do not show persistency or antipersistency and follow Gaussian distribution.

Bayesian multinomial probit modeling of daily windows of ...

EPA Pesticide Factsheets

Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregnancy for 12 individual types of CHDs with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from weeks 2-8 of pregnancy are assigned (specific to each location and pregnancy date) using predictions from the downscaler pollution model. Results are compared to an aggregated exposure model which defines exposure as the average value over pregnancy weeks 2-8. Increased PM2.5 exposure during pregnancy days 53 and 50-51 for pulmonary valve stenosis and tetralogy of Fallot, respectively, are associated with an increased probability of development of each CHD. The largest estimated effect is seen for atrioventricular septal defects on pregnancy day 14. The aggregated exposure model fails to identify any significant windows of susceptibility during pregnancy weeks 2-8 for the considered CHDs. Considering daily PM2.5 exposures in a new modeling framework revealed positive associations for defects that the standard aggregated exposure model was unable to identify. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policie

Feasibility Study of Grid Connected PV-Biomass Integrated Energy System in Egypt

NASA Astrophysics Data System (ADS)

Barakat, Shimaa; Samy, M. M.; Eteiba, Magdy B.; Wahba, Wael Ismael

2016-10-01

The aim of this paper is to present a feasibility study of a grid connected photovoltaic (PV) and biomass Integrated renewable energy (IRE) system providing electricity to rural areas in the Beni Suef governorate, Egypt. The system load of the village is analyzed through the environmental and economic aspects. The model has been designed to provide an optimal system configuration based on daily data for energy availability and demands. A case study area, Monshaet Taher village (29° 1' 17.0718"N, 30° 52' 17.04"E) is identified for economic feasibility in this paper. HOMER optimization model plan imputed from total daily load demand, 2,340 kWh/day for current energy consuming of 223 households with Annual Average Insolation Incident on a Horizontal Surface of 5.79 (kWh/m2/day) and average biomass supplying 25 tons / day. It is found that a grid connected PV-biomass IRE system is an effective way of emissions reduction and it does not increase the investment of the energy system.

Association of total daily physical activity with disability in community-dwelling older persons: a prospective cohort study

PubMed Central

2012-01-01

Background Based on findings primarily using self-report measures, physical activity has been recommended to reduce disability in old age. Collecting objective measures of total daily physical activity in community-dwelling older adults is uncommon, but might enhance the understanding of the relationship of physical activity and disability. We examined whether greater total daily physical activity was associated with less report of disability in the elderly. Methods Data were from the Rush Memory and Aging Project, a longitudinal prospective cohort study of common, age-related, chronic conditions. Total daily physical activity was measured in community-dwelling participants with an average age of 82 using actigraphy for approximately 9 days. Disability was measured via self-reported basic activities of daily living (ADL). The odds ratio and 95% Confidence Interval (CI) were determined for the baseline association of total daily physical activity and ADL disability using a logistic regression model adjusted for age, education level, gender and self-report physical activity. In participants without initial report of ADL disability, the hazard ratio and 95% CI were determined for the relationship of baseline total daily physical activity and the development of ADL disability using a discrete time Cox proportional hazard model adjusted for demographics and self-report physical activity. Results In 870 participants, the mean total daily physical activity was 2. 9 × 105 counts/day (range in 105 counts/day = 0.16, 13. 6) and the mean hours/week of self-reported physical activity was 3.2 (SD = 3.6). At baseline, 718 (82.5%) participants reported being independent in all ADLs. At baseline, total daily physical activity was protective against disability (OR per 105 counts/day difference = 0.55; 95% CI = 0.47, 0.65). Of the participants without baseline disability, 584 were followed for 3.4 years on average. Each 105 counts/day additional total daily physical activity was associated with reduced hazard of developing disability by 25% (HR = 0.75, 95% CI = 0.66, 0.84). The results were unchanged after controlling for important covariates including cognition, depressive symptoms, and chronic health conditions. Conclusions Greater total daily physical activity is independently associated with less disability even after controlling for self-reported physical activity. PMID:23072476

Daily mood ratings via text message as a proxy for clinic based depression assessment.

PubMed

Aguilera, Adrian; Schueller, Stephen M; Leykin, Yan

2015-04-01

Mobile and automated technologies are increasingly becoming integrated into mental health care and assessment. The purpose of this study was to determine how automated daily mood ratings are related to the Patient Health Questionnaire-9 (PHQ-9), a standard measure in the screening and tracking of depressive symptoms. There was a significant relationship between daily mood scores and PHQ-9 scores, and between one-week average mood scores and PHQ-9 scores, controlling for linear change in depression scores. PHQ-9 scores were not related to the average of two week mood ratings. This study also constructed models using variance, maximum, and minimum values of mood ratings in the preceding week and two-week periods as predictors of PHQ-9. None of these variables significantly predicted PHQ-9 scores when controlling for daily mood ratings and the corresponding averages for each period. This study only assessed patients who were in treatment for depression, therefore findings might not generalize to the relationship between text message mood ratings for those who are not depressed. The sample was also predominantly Spanish speaking and low-income making generalizability to other populations uncertain. Our results show that automatic text message based mood ratings can be a clinically useful proxy for the PHQ-9. Importantly, this approach avoids the limitations of the PHQ-9 administration, which include length and a higher requirement for literacy. Copyright © 2015 Elsevier B.V. All rights reserved.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

[Forest lighting fire forecasting for Daxing'anling Mountains based on MAXENT model].

PubMed

Sun, Yu; Shi, Ming-Chang; Peng, Huan; Zhu, Pei-Lin; Liu, Si-Lin; Wu, Shi-Lei; He, Cheng; Chen, Feng

2014-04-01

Daxing'anling Mountains is one of the areas with the highest occurrence of forest lighting fire in Heilongjiang Province, and developing a lightning fire forecast model to accurately predict the forest fires in this area is of importance. Based on the data of forest lightning fires and environment variables, the MAXENT model was used to predict the lightning fire in Daxing' anling region. Firstly, we studied the collinear diagnostic of each environment variable, evaluated the importance of the environmental variables using training gain and the Jackknife method, and then evaluated the prediction accuracy of the MAXENT model using the max Kappa value and the AUC value. The results showed that the variance inflation factor (VIF) values of lightning energy and neutralized charge were 5.012 and 6.230, respectively. They were collinear with the other variables, so the model could not be used for training. Daily rainfall, the number of cloud-to-ground lightning, and current intensity of cloud-to-ground lightning were the three most important factors affecting the lightning fires in the forest, while the daily average wind speed and the slope was of less importance. With the increase of the proportion of test data, the max Kappa and AUC values were increased. The max Kappa values were above 0.75 and the average value was 0.772, while all of the AUC values were above 0.5 and the average value was 0. 859. With a moderate level of prediction accuracy being achieved, the MAXENT model could be used to predict forest lightning fire in Daxing'anling Mountains.

Observational Evaluation of Simulated Land-Atmosphere Coupling on the U.S. Southern Great Plains

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Klein, S. A.

2014-12-01

In a recent study of observed features of land-atmosphere coupling (LAC) at the ARM Southern Great Plains (ARM SGP) site in northern Oklahoma (Phillips and Klein, 2014 Journal of Geophysical Research), we identified statistically significant interactions between 1997-2008 summertime daily averages of soil moisture (at 10 cm depth) and a number of surface atmospheric variables, such as surface evaporation, relative humidity, and temperature. Here we will report on an evaluation of similar features of LAC simulated by version 5 of the global Community Atmosphere Model (CAM5), coupled to its native CLM4 land model, and downscaled to the vicinity of the ARM SGP site. In these case studies, the CAM5 was initialized from a 6-hourly atmospheric reanalysis for each day of the years 2008 and 2009 (where the CLM4 land state was equilibrated to the atmospheric model state), thus permitting a close comparison of the modeled and observed summer daily average features of the LAC in these years. Correlation coefficients R and "sensitivity indices" I (a measure of the comparative change of an atmospheric variable for a one-standard-deviation change in soil moisture) provided quantitative measures of the respective coupling strengths. Such a comparison of observed versus modeled LAC is complicated by differences in atmospheric forcings of the land; for example, the CAM5's summertime precipitation is too scant, and thus the model's upper soil layer often is drier than observed. The modeled daily average covariations of soil moisture with lower atmospheric variables also display less coherence (lower R values), but sometimes greater "sensitivity" (higher I values) than are observed at the ARM SGP site. Since the observational estimate of LAC may itself be sensitive to soil moisture measurement biases, we also will report on a planned investigation of the dependence of LAC on several alternative choices of soil moisture data sets local to the ARM SGP site. AcknowledgmentsThis work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Estimation of daily PM10 concentrations in Italy (2006-2012) using finely resolved satellite data, land use variables and meteorology.

PubMed

Stafoggia, Massimo; Schwartz, Joel; Badaloni, Chiara; Bellander, Tom; Alessandrini, Ester; Cattani, Giorgio; De' Donato, Francesca; Gaeta, Alessandra; Leone, Gianluca; Lyapustin, Alexei; Sorek-Hamer, Meytar; de Hoogh, Kees; Di, Qian; Forastiere, Francesco; Kloog, Itai

2017-02-01

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM 10 measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM 10 concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM 10 =0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM 10 levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM 10 concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes

PubMed Central

Lin, Chao-Yuan; Chiang, Mon-Ling; Lin, Cheng-Yu

2016-01-01

Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents. PMID:27271642

Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes.

PubMed

Lin, Chao-Yuan; Chiang, Mon-Ling; Lin, Cheng-Yu

2016-06-02

Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.

Dust layer effects on the atmospheric radiative budget and heating rate profiles

NASA Astrophysics Data System (ADS)

Perrone, Maria Rita; Tafuro, A. M.; Kinne, S.

2012-11-01

The effect of mineral aerosol optical properties and vertical distribution on clear-sky, instantaneous and daily-average aerosol direct radiative effects (DREs) and heating rates (HRs) is analyzed in the solar (S, 0.3-4 μm) and terrestrial (T, 4-80 μm) spectral domain, respectively. The used radiative transfer model is based on lidar, sun-sky photometer, and radiosonde measurements. The study focuses on the Sahara dust outbreak of July 16, 2009 which advected dust particles from north-western Africa over south-eastern Italy. Clear-sky, instantaneous aerosol DREs and HRs undergo large changes within few hours, for the variability of the dust aerosol properties and vertical distribution. The daily-average, clear-sky aerosol S-DRE is near -5 Wm-2 and -12 Wm-2 at the top of the atmosphere (ToA) and surface (sfc), respectively. The daily-average aerosol T-DRE offsets the S-DRE by about one third at the ToA and by about one half at the surface. The daily average aerosol HR integrated over the whole aerosol column is 0.5 and -0.3 K day-1 in the S and T domain, respectively. Thus, the all-wave integrated HR is 0.2 K day-1. These results highlight the importance of accounting for the interaction of dust particles with T and S radiation. Sensitivity tests indicate that the uncertainties of the aerosol refractive index, size distribution, and vertical distribution have on average a large impact on aerosol HRs in the S and T domain, respectively. Refractive index and aerosol size distribution uncertainties also have a large impact on S- and T-DREs. The aerosol vertical distribution that has a negligible impact on aerosol S-DREs, is important for aerosol T-DREs. It is also shown that aerosol HRs and DREs in the terrestrial domain are affected by the water vapour vertical distribution.

Storm orientation impacts on atmospheric river induced precipitation efficiency

NASA Astrophysics Data System (ADS)

Mehran, A.; Lettenmaier, D. P.

2016-12-01

Atmospheric Rivers (ARs) along the Pacific North coast are often associated with heavy winter precipitation and flooding. We analyze 35 years (1981 2016) of landfalling ARs over a transect along the U.S. West Coast consisting of four river basins from coastal Washington to Southern California (Chehalis, Russian, Santa Ana, and Santa Margarita Rivers) to assess the impact of storm orientation on precipitation rainout efficiency. We define precipitation rainout efficiency as the correlation coefficient between the net integrated vapor transport and precipitation rate. We use 6-hourly climate data from the Climate Forecast System Reanalysis (CFSR) for each of the landfalling ARs. We compute storm orientation from CFSR wind vectors (daily averaged over atmospheric levels between 1000 hPa and 300 hPa) associated with each AR event. We also compute integrated vapor transport (IVT) by multiplying precipitable water by the wind vector and compare with daily averaged precipitation averaged over the river basins, where daily precipitation is taken from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) to evaluate the impact of storm orientation on rainfall efficiency. We calculate the local topographic orientation of each river basin (slope and aspect) from ArcGIS, which we related to storm orientation. To evaluate the impact of storm orientation on rainout efficiency over the Russian River basin (Northern California), we first calculated approaching IVT (for all of AR induced precipitations from 1981 to 2016) and daily averaged precipitation rate. Next, we calculated the correlation coefficient between IVT and precipitation rate (for all AR induced rainouts over the Russian River basin). Finally, by considering the local topographical changes (slope and aspect from ArcGIS) and integrating them into an effective IVT, we compared the correlation coefficients between actual and effective IVT and basin-average precipitation. We find that over the Russian River basin, the rainout efficiency increases from 55 to 75 % when we account for storm orientation relative to topography.

Weather, season, and daily stroke admissions in Hong Kong

NASA Astrophysics Data System (ADS)

Goggins, William B.; Woo, Jean; Ho, Suzanne; Chan, Emily Y. Y.; Chau, P. H.

2012-09-01

Previous studies examining daily temperature and stroke incidence have given conflicting results. We undertook this retrospective study of all stroke admissions in those aged 35 years old and above to Hong Kong public hospitals from 1999 through 2006 in order to better understand the effects of meteorological conditions on stroke risk in a subtropical setting. We used Poisson Generalized Additive Models with daily hemorrhagic (HS) and ischemic stroke (IS) counts separately as outcomes, and daily mean temperature, humidity, solar radiation, rainfall, air pressure, pollutants, flu consultation rates, day of week, holidays, time trend and seasonality as predictors. Lagged effects of temperature, humidity and pollutants were also considered. A total of 23,457 HS and 107,505 IS admissions were analyzed. Mean daily temperature had a strong, consistent, negative linear association with HS admissions over the range (8.2-31.8°C) observed. A 1°C lower average temperature over the same day and previous 4 days (lags 0-4) being associated with a 2.7% (95% CI: 2.0-3.4%, P < .0.0001) higher admission rate after controlling for other variables. This association was stronger among older subjects and females. Higher lag 0-4 average change in air pressure from previous day was modestly associated with higher HS risk. The association between IS and temperature was weaker and apparent only below 22°C, with a 1°C lower average temperature (lags 0-13) below this threshold being associated with a 1.6% (95% CI:1.0-2.2%, P < 0.0001) higher IS admission rate. Pollutant levels were not associated with HS or IS. Future studies should examine HS and IS risk separately.

Systematic review of the relationship of Helicobacter pylori infection with geographical latitude, average annual temperature and average daily sunshine.

PubMed

Lu, Chao; Yu, Ye; Li, Lan; Yu, Chaohui; Xu, Ping

2018-04-17

Helicobacter pylori (H. pylori) infection is a worldwide threat to human health with high prevalence. In this study, we analyzed the relationship between latitude, average annual temperature, average daily sunshine time and H. pylori infection. The PubMed, ClinicalTrials.gov , EBSCO and Web of Science databases were searched to identify studies reporting H. pylori infection. Latitude 30° was the cut-off level for low and mid-latitude areas. We obtained information for latitude, average annual temperature, average daily sunshine, and Human Development Index (HDI) from reports of studies of the relationships with H. pylori infection. Of the 51 studies included, there was significant difference in H. pylori infection between the low- and mid-latitude areas (P = 0.05). There was no significant difference in the prevalence of H. pylori infection in each 15°-latitude zone analyzed (P = 0.061). Subgroup analysis revealed the highest and lowest H. pylori infection rates in the developing regions at > 30° latitude subgroup and the developed regions at < 30° latitude subgroup, respectively (P < 0.001). Multivariate analysis showed that average annual temperature, average daily sunshine time and HDI were significantly correlated with H. pylori infection (P = 0.009, P < 0.001, P < 0.001), while there was no correlation between H. pylori infection and latitude. Our analysis showed that higher average annual temperature was associated with lower H. pylori infection rates, while average daily sunshine time correlated positively with H. pylori infection. HDI was also found to be a significant factor, with higher HDI associated with lower infection rates. These findings provide evidence that can be used to devise strategies for the prevention and control of H. pylori.

Time Scale Effects in Acute Association between Air-Pollution and Mortality

EPA Science Inventory

We used wavelet analysis and generalized additive models (GAM) to study timescale effects in the acute association between mortality and air-pollution. Daily averages of measured NO₂ concentrations in the metropolitan Paris area are used as indicators of human exposure...

Estimation of average daily traffic on local roads in Kentucky.

DOT National Transportation Integrated Search

2016-07-01

Kentucky Transportation Cabinet (KYTC) officials use annual average daily traffic (AADT) to estimate intersection : performance across the state maintained highway system. KYTC currently collects AADTs for state maintained : roads but frequently lack...

Modelling ranging behaviour of female orang-utans: a case study in Tuanan, Central Kalimantan, Indonesia.

PubMed

Wartmann, Flurina M; Purves, Ross S; van Schaik, Carel P

2010-04-01

Quantification of the spatial needs of individuals and populations is vitally important for management and conservation. Geographic information systems (GIS) have recently become important analytical tools in wildlife biology, improving our ability to understand animal movement patterns, especially when very large data sets are collected. This study aims at combining the field of GIS with primatology to model and analyse space-use patterns of wild orang-utans. Home ranges of female orang-utans in the Tuanan Mawas forest reserve in Central Kalimantan, Indonesia were modelled with kernel density estimation methods. Kernel results were compared with minimum convex polygon estimates, and were found to perform better, because they were less sensitive to sample size and produced more reliable estimates. Furthermore, daily travel paths were calculated from 970 complete follow days. Annual ranges for the resident females were approximately 200 ha and remained stable over several years; total home range size was estimated to be 275 ha. On average, each female shared a third of her home range with each neighbouring female. Orang-utan females in Tuanan built their night nest on average 414 m away from the morning nest, whereas average daily travel path length was 777 m. A significant effect of fruit availability on day path length was found. Sexually active females covered longer distances per day and may also temporarily expand their ranges.

Daily Variations in Objective Nighttime Sleep and Subjective Morning Pain in Older Adults with Insomnia: Evidence of Covariation Over Time

PubMed Central

Dzierzewski, Joseph M.; Williams, Jacob M.; Roditi, Daniela; Marsiske, Michael; McCoy, Karin; McNamara, Joseph; Dautovich, Natalie; Robinson, Michael E.; McCrae, Christina S.

2010-01-01

Objectives To examine the relationship between objectively measured nocturnal sleep and subjective report of morning pain in older adults with insomnia. The goal of the paper was to not only examine the sleep-pain association between-persons (mean-level over 14 days), but also to investigate the within-person, day-to-day association. Design Cross-sectional. Setting North-Central Florida. Participants Fifty community-dwelling older adults (Mage = 69.10 years, SDage = 7.02 years, range = 60 – 90 years) with insomnia participated in the study. Measurements This study employed daily home-based assessment utilizing nightly actigraphic measurement of sleep and daily self-report of pain. Measures were completed over fourteen consecutive days. Results Between persons, average sleep over 14 days was not associated with average levels of rated pain. However, following a night in which an older adult with insomnia experienced above-average total sleep time s/he subsequently reported below-average pain ratings. The model explained approximately 24% of the within-person and 8% of the between-person variance in pain ratings. Conclusions Sleep and pain show day-to-day associations (i.e., covary over time) in older adults with insomnia. Such associations may suggest that common physiological systems underlie both the experience of insomnia and pain. Future research should examine the crossover effects of sleep treatment on pain and of pain treatment on sleep. PMID:20406316

Influence of declining mean annual rainfall on the behavior and yield of sediment and particulate organic carbon from tropical watersheds

NASA Astrophysics Data System (ADS)

Strauch, Ayron M.; MacKenzie, Richard A.; Giardina, Christian P.; Bruland, Gregory L.

2018-04-01

The capacity to forecast climate and land-use driven changes to runoff, soil erosion and sediment transport in the tropics is hindered by a lack of long-term data sets and model study systems. To address these issues we utilized three watersheds characterized by similar shape, geology, soils, vegetation cover, and land use arranged across a 900 mm gradient in mean annual rainfall (MAR). Using this space-for-time design, we quantified suspended sediment (SS) and particulate organic carbon (POC) export over 18 months to examine how large-scale climate trends (MAR) affect sediment supply and delivery patterns (hysteresis) in tropical watersheds. Average daily SS yield ranged from 0.128 to 0.618 t km- 2 while average daily POC ranged from 0.002 to 0.018 t km- 2. For the largest storm events, we found that sediment delivery exhibited similar clockwise hysteresis patterns among the watersheds, with no significant differences in the similarity function between watershed pairs, indicating that: (1) in-stream and near-stream sediment sources drive sediment flux; and (2) the shape and timing of hysteresis is not affected by MAR. With declining MAR, the ratio of runoff to baseflow and inter-storm length between pulse events both increased. Despite increases in daily rainfall and the number of days with large rainfall events increasing with MAR, there was a decline in daily SS yield possibly due to the exhaustion of sediment supply by frequent runoff events in high MAR watersheds. By contrast, mean daily POC yield increased with increasing MAR, possibly as a result of increased soil organic matter decomposition, greater biomass, or increased carbon availability in higher MAR watersheds. We compared results to modeled values using the Load Estimator (LOADEST) FORTRAN model, confirming the negative relationship between MAR and sediment yield. However, because of its dependency on mean daily flow, LOADEST tended to under predict sediment yield, a result of its poor ability to capture the high variability in tropical streamflow. Taken together, results indicate that declines in MAR can have contrasting effects on hydrological processes in tropical watersheds, with consequences for instream ecology, downstream water users, and nearshore habitat.

Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach.

PubMed

Fang, Xin; Fang, Bo; Wang, Chunfang; Xia, Tian; Bottai, Matteo; Fang, Fang; Cao, Yang

2017-01-01

There are concerns that the reported association of ambient fine particulate matter (PM2.5) with mortality might be a mixture of PM2.5 and weather conditions. We evaluated the effects of extreme weather conditions and weather types on mortality as well as their interactions with PM2.5 concentrations in a time series study. Daily non-accidental deaths, individual demographic information, daily average PM2.5 concentrations and meteorological data between 2012 and 2014 were obtained from Shanghai, China. Days with extreme weather conditions were identified. Six synoptic weather types (SWTs) were generated. The generalized additive model was set up to link the mortality with PM2.5 and weather conditions. Parameter estimation was based on Bayesian methods using both the Jeffreys' prior and an informative normal prior in a sensitivity analysis. We estimate the percent increase in non-accidental mortality per 10 μg/m3 increase in PM2.5 concentration and constructed corresponding 95% credible interval (CrI). In total, 336,379 non-accidental deaths occurred during the study period. Average daily deaths were 307. The results indicated that per 10 μg/m3 increase in daily average PM2.5 concentration alone corresponded to 0.26-0.35% increase in daily non-accidental mortality in Shanghai. Statistically significant positive associations between PM2.5 and mortality were found for favorable SWTs when considering the interaction between PM2.5 and SWTs. The greatest effect was found in hot dry SWT (percent increase = 1.28, 95% CrI: 0.72, 1.83), followed by warm humid SWT (percent increase = 0.64, 95% CrI: 0.15, 1.13). The effect of PM2.5 on non-accidental mortality differed under specific extreme weather conditions and SWTs. Environmental policies and actions should take into account the interrelationship between the two hazardous exposures.

Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach

PubMed Central

Wang, Chunfang; Xia, Tian; Bottai, Matteo; Fang, Fang; Cao, Yang

2017-01-01

There are concerns that the reported association of ambient fine particulate matter (PM2.5) with mortality might be a mixture of PM2.5 and weather conditions. We evaluated the effects of extreme weather conditions and weather types on mortality as well as their interactions with PM2.5 concentrations in a time series study. Daily non-accidental deaths, individual demographic information, daily average PM2.5 concentrations and meteorological data between 2012 and 2014 were obtained from Shanghai, China. Days with extreme weather conditions were identified. Six synoptic weather types (SWTs) were generated. The generalized additive model was set up to link the mortality with PM2.5 and weather conditions. Parameter estimation was based on Bayesian methods using both the Jeffreys’ prior and an informative normal prior in a sensitivity analysis. We estimate the percent increase in non-accidental mortality per 10 μg/m3 increase in PM2.5 concentration and constructed corresponding 95% credible interval (CrI). In total, 336,379 non-accidental deaths occurred during the study period. Average daily deaths were 307. The results indicated that per 10 μg/m3 increase in daily average PM2.5 concentration alone corresponded to 0.26–0.35% increase in daily non-accidental mortality in Shanghai. Statistically significant positive associations between PM2.5 and mortality were found for favorable SWTs when considering the interaction between PM2.5 and SWTs. The greatest effect was found in hot dry SWT (percent increase = 1.28, 95% CrI: 0.72, 1.83), followed by warm humid SWT (percent increase = 0.64, 95% CrI: 0.15, 1.13). The effect of PM2.5 on non-accidental mortality differed under specific extreme weather conditions and SWTs. Environmental policies and actions should take into account the interrelationship between the two hazardous exposures. PMID:29121092

40 CFR Table 2 to Subpart Uuu of... - Operating Limits for Metal HAP Emissions From Catalytic Cracking Units

Code of Federal Regulations, 2010 CFR

2010-07-01

... daily average liquid-to-gas ratio above the limit established in the performance test. 4. Option 3: Ni.... Electrostatic precipitator Maintain the daily average Ni operating value no higher than the limit established...; maintain the monthly rolling average of the equilibrium catalyst Ni concentration no higher than the limit...

40 CFR Table 2 to Subpart Uuu of... - Operating Limits for Metal HAP Emissions From Catalytic Cracking Units

Code of Federal Regulations, 2011 CFR

2011-07-01

... daily average liquid-to-gas ratio above the limit established in the performance test. 4. Option 3: Ni.... Electrostatic precipitator Maintain the daily average Ni operating value no higher than the limit established...; maintain the monthly rolling average of the equilibrium catalyst Ni concentration no higher than the limit...

Geographical variation in camper expenditures

Treesearch

Wilbur F. LaPage; Edward G. Fisher

1971-01-01

Daily expenditures by families camping in New Hampshire State parks in 1967 averaged $11.81. Considerable variation was found between the northern, central, and southern regions of the State in both the average amount of money spent and the way in which the money was spent. Daily expenditures in the north were higher, but average visit lengths were shorter, resulting...

New York Bight Study. Report 1. Hydrodynamic Modeling

DTIC Science & Technology

1994-08-01

function of time. Values of these parameters, averaged daily, were computed from meteorological data recorded at the John F. Kennedy ( JFK ) Airport for...Island Sound "exchange coefficient values were obtained as before from meteorological data collected at the JFK Airport . They are shown in Figures 62-63

Surface Ozone Background in the United States: Canadian and Mexican Pollution Influences

EPA Science Inventory

We use a global chemical transport model (GEOS-Chem) with 1° x 1° horizontal resolution to quantify the effects of anthropogenic emissions from Canada, Mexico, and outside North America on daily maximum 8-h average ozone concentrations in U.S.surface air.

ESTIMATING GROUND LEVEL PM 2.5 IN THE EASTERN UNITED STATES USING SATELLITE REMOTE SENSING

EPA Science Inventory

An empirical model based on the regression between daily average final particle (PM2.5) concentrations and aerosol optical thickness (AOT) measurements from the Multi-angle Imaging SpectroRadiometer (MISR) was developed and tested using data from the eastern United States during ...

PM2.5 concentrations observed and modeled for the 2016 southern Appalachian wildfire event

EPA Science Inventory

During November 2016, wildfires in the southern Appalachian region of the United States burned over 125,00 acres leading to a widespread outbreak of elevated levels of fine particulate matter (PM2.5). Daily average concentrations above the current National Ambient Air Quality Sta...

Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate

EPA Science Inventory

Exposure to perchlorate is widespread in the United States and many studies have attempted to character the perchlorate exposure by estimating the average daily intakes of perchlorate. These approaches provided population-based estimates, but did not provide individual-level exp...

Investigating the Impact of Lack of Motorcycle Annual Average Daily Traffic Data in Crash Modeling and the Estimation of Crash Modification Factors

DOT National Transportation Integrated Search

2016-10-01

The development of safety performance functions (SPFs) and crash modification factors (CMFs) requires data on traffic exposure. The analysis of motorcycle crashes can be especially challenging in this regard because few jurisdictions collect motorcyc...

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

Canini, Laetitia; Guedj, Jeremie; Chatterjee, Anushree

In this study, modelling HCV RNA decline kinetics under therapy has proven useful for characterizing treatment effectiveness. Here we model HCV viral kinetics (VK) in 72 patients given a combination of danoprevir, a protease inhibitor, and mericitabine, a nucleoside polymerase inhibitor, for 14 days in the INFORM-1 trial. A biphasic VK model with time-varying danoprevir and mericitabine effectiveness and Bliss independence for characterizing the interaction between both drugs provided the best fit to the VK data. As a result, the average final antiviral effectiveness of the drug combination varied between 0.998 for 100 mg three times daily of danoprevir andmore » 500 mg twice daily of mericitabine and 0.9998 for 600 mg twice daily of danoprevir and 1,000 mg twice daily of mericitabine. Using the individual parameters estimated from the VK data collected over 2 weeks, we were not able to reproduce the low sustained virological response rates obtained in a more recent study where patients were treated with a combination of mericitabine and ritonavir-boosted danoprevir for 24 weeks. In conclusion, this suggests that drug-resistant viruses emerge after 2 weeks of treatment and that longer studies are necessary to provide accurate predictions of longer treatment outcomes.« less

Forecasting daily emergency department visits using calendar variables and ambient temperature readings.

PubMed

Marcilio, Izabel; Hajat, Shakoor; Gouveia, Nelson

2013-08-01

This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources. © 2013 by the Society for Academic Emergency Medicine.

Stochastic generators of multi-site daily temperature: comparison of performances in various applications

NASA Astrophysics Data System (ADS)

Evin, Guillaume; Favre, Anne-Catherine; Hingray, Benoit

2018-02-01

We present a multi-site stochastic model for the generation of average daily temperature, which includes a flexible parametric distribution and a multivariate autoregressive process. Different versions of this model are applied to a set of 26 stations located in Switzerland. The importance of specific statistical characteristics of the model (seasonality, marginal distributions of standardized temperature, spatial and temporal dependence) is discussed. In particular, the proposed marginal distribution is shown to improve the reproduction of extreme temperatures (minima and maxima). We also demonstrate that the frequency and duration of cold spells and heat waves are dramatically underestimated when the autocorrelation of temperature is not taken into account in the model. An adequate representation of these characteristics can be crucial depending on the field of application, and we discuss potential implications in different contexts (agriculture, forestry, hydrology, human health).

Lightning Forcing in Global Fire Models: The Importance of Temporal Resolution

NASA Astrophysics Data System (ADS)

Felsberg, A.; Kloster, S.; Wilkenskjeld, S.; Krause, A.; Lasslop, G.

2018-01-01

In global fire models, lightning is typically prescribed from observational data with monthly mean temporal resolution while meteorological forcings, such as precipitation or temperature, are prescribed in a daily resolution. In this study, we investigate the importance of the temporal resolution of the lightning forcing for the simulation of burned area by varying from daily to monthly and annual mean forcing. For this, we utilize the vegetation fire model JSBACH-SPITFIRE to simulate burned area, forced with meteorological and lightning data derived from the general circulation model ECHAM6. On a global scale, differences in burned area caused by lightning forcing applied in coarser temporal resolution stay below 0.55% compared to the use of daily mean forcing. Regionally, however, differences reach up to 100%, depending on the region and season. Monthly averaged lightning forcing as well as the monthly lightning climatology cause differences through an interaction between lightning ignitions and fire prone weather conditions, accounted for by the fire danger index. This interaction leads to decreased burned area in the boreal zone and increased burned area in the Tropics and Subtropics under the coarser temporal resolution. The exclusion of interannual variability, when forced with the lightning climatology, has only a minor impact on the simulated burned area. Annually averaged lightning forcing causes differences as a direct result of the eliminated seasonal characteristics of lightning. Burned area is decreased in summer and increased in winter where fuel is available. Regions with little seasonality, such as the Tropics and Subtropics, experience an increase in burned area.

Social learning pathways in the relation between parental chronic pain and daily pain severity and functional impairment in adolescents with functional abdominal pain.

PubMed

Stone, Amanda L; Bruehl, Stephen; Smith, Craig A; Garber, Judy; Walker, Lynn S

2017-10-06

Having a parent with chronic pain (CP) may confer greater risk for persistence of CP from childhood into young adulthood. Social learning, such as parental modeling and reinforcement, represents one plausible mechanism for the transmission of risk for CP from parents to offspring. Based on a 7-day pain diary in 154 pediatric patients with functional abdominal CP, we tested a model in which parental CP predicted adolescents' daily average CP severity and functional impairment (distal outcomes) via parental modeling of pain behaviors and parental reinforcement of adolescent's pain behaviors (mediators) and adolescents' cognitive appraisals of pain threat (proximal outcome representing adolescents' encoding of parents' behaviors). Results indicated significant indirect pathways from parental CP status to adolescent average daily pain severity (b = 0.18, SE = 0.08, 95% CI: 0.04, 0.31, p = 0.03) and functional impairment (b = 0.08, SE = 0.04, 95% CI: 0.02, 0.15, p = 0.03) over the 7-day diary period via adolescents' observations of parent pain behaviors and adolescent pain threat appraisal. The indirect pathway through parental reinforcing responses to adolescents' pain did not reach significance for either adolescent pain severity or functional impairment. Identifying mechanisms of increased risk for pain and functional impairment in children of parents with CP ultimately could lead to targeted interventions aimed at improving functioning and quality of life in families with chronic pain. Parental modeling of pain behaviors represents a potentially promising target for family based interventions to ameliorate pediatric chronic pain.

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

NASA Astrophysics Data System (ADS)

Martin, Gill M.; Klingaman, Nicholas P.; Moise, Aurel F.

2017-01-01

This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ˜ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ˜ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

A better understanding of long-range temporal dependence of traffic flow time series

NASA Astrophysics Data System (ADS)

Feng, Shuo; Wang, Xingmin; Sun, Haowei; Zhang, Yi; Li, Li

2018-02-01

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

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

2018-01-01

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

Modelling the interaction between danoprevir and mericitabine in the treatment of chronic HCV infection

DOE PAGES

Canini, Laetitia; Guedj, Jeremie; Chatterjee, Anushree; ...

2015-01-01

In this study, modelling HCV RNA decline kinetics under therapy has proven useful for characterizing treatment effectiveness. Here we model HCV viral kinetics (VK) in 72 patients given a combination of danoprevir, a protease inhibitor, and mericitabine, a nucleoside polymerase inhibitor, for 14 days in the INFORM-1 trial. A biphasic VK model with time-varying danoprevir and mericitabine effectiveness and Bliss independence for characterizing the interaction between both drugs provided the best fit to the VK data. As a result, the average final antiviral effectiveness of the drug combination varied between 0.998 for 100 mg three times daily of danoprevir andmore » 500 mg twice daily of mericitabine and 0.9998 for 600 mg twice daily of danoprevir and 1,000 mg twice daily of mericitabine. Using the individual parameters estimated from the VK data collected over 2 weeks, we were not able to reproduce the low sustained virological response rates obtained in a more recent study where patients were treated with a combination of mericitabine and ritonavir-boosted danoprevir for 24 weeks. In conclusion, this suggests that drug-resistant viruses emerge after 2 weeks of treatment and that longer studies are necessary to provide accurate predictions of longer treatment outcomes.« less

Examining the Dynamic Structure of Daily Internalizing and Externalizing Behavior at Multiple Levels of Analysis

PubMed Central

Wright, Aidan G. C.; Beltz, Adriene M.; Gates, Kathleen M.; Molenaar, Peter C. M.; Simms, Leonard J.

2015-01-01

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are applied to daily diary data (Median = 94 days) of maladaptive behaviors collected from a sample (N = 101) of individuals diagnosed with personality disorders (PDs). Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing–Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Disinhibition structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic. PMID:26732546

Comparison of observed lung retention and urinary excretion of thorium workers and members of the public in India with the values predicted by the ICRP biokinetic model.

PubMed

Jaiswal, D D; Singh, I S; Nair, Suma; Dang, H S; Garg, S P; Pradhan, A S

2004-01-01

The daily intake of natural Th and its contents in lungs, skeleton and liver of an Indian adult population group were estimated using radiochemical neutron activation analysis (RNAA) technique. These data on daily intake (through inhalation and ingestion) were used to compute Th contents in lungs and other systemic organs such as skeleton and liver using the new human respiratory tract model (HRTM) and the new biokinetic model of Th. The theoretically computed Th contents in lungs, skeleton and liver of an average Indian adult are 2.56, 4.00 and 0.17 microg, respectively which are comparable with the corresponding experimentally measured values of 4.31, 3.45 and 0.14 microg in an urban population group living in Mumbai. The measured lung contents of Th in a group of five occupational workers were used to compute their total body Th contents and the corresponding daily urinary excretions. The computed total body contents and daily urinary excretions of Th in the five subjects compared favourably with their measured values. These studies, thus, validate the new biokinetic model of Th in natural as well as in occupational exposures in Indian conditions.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

NASA Astrophysics Data System (ADS)

Ni, X. Y.; Huang, H.; Du, W. P.

2017-02-01

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

Using a GIS to link digital spatial data and the precipitation-runoff modeling system, Gunnison River Basin, Colorado

USGS Publications Warehouse

Battaglin, William A.; Kuhn, Gerhard; Parker, Randolph S.

1993-01-01

The U.S. Geological Survey Precipitation-Runoff Modeling System, a modular, distributed-parameter, watershed-modeling system, is being applied to 20 smaller watersheds within the Gunnison River basin. The model is used to derive a daily water balance for subareas in a watershed, ultimately producing simulated streamflows that can be input into routing and accounting models used to assess downstream water availability under current conditions, and to assess the sensitivity of water resources in the basin to alterations in climate. A geographic information system (GIS) is used to automate a method for extracting physically based hydrologic response unit (HRU) distributed parameter values from digital data sources, and for the placement of those estimates into GIS spatial datalayers. The HRU parameters extracted are: area, mean elevation, average land-surface slope, predominant aspect, predominant land-cover type, predominant soil type, average total soil water-holding capacity, and average water-holding capacity of the root zone.

Estimating Perturbation and Meta-Stability in the Daily Attendance Rates of Six Small High Schools

NASA Astrophysics Data System (ADS)

Koopmans, Matthijs

This paper discusses the daily attendance rates in six small high schools over a ten-year period and evaluates how stable those rates are. “Stability” is approached from two vantage points: pulse models are fitted to estimate the impact of sudden perturbations and their reverberation through the series, and Autoregressive Fractionally Integrated Moving Average (ARFIMA) techniques are used to detect dependencies over the long range of the series. The analyses are meant to (1) exemplify the utility of time series approaches in educational research, which lacks a time series tradition, (2) discuss some time series features that seem to be particular to daily attendance rate trajectories such as the distinct downward pull coming from extreme observations, and (3) present an analytical approach to handle the important yet distinct patterns of variability that can be found in these data. The analysis also illustrates why the assumption of stability that underlies the habitual reporting of weekly, monthly and yearly averages in the educational literature is questionable, as it reveals dynamical processes (perturbation, meta-stability) that remain hidden in such summaries.

Estimation of annual average daily traffic for off-system roads in Florida

DOT National Transportation Integrated Search

1999-07-28

Estimation of Annual Average Daily Traffic (AADT) is extremely important in traffic planning and operations for the state departments of transportation (DOTs), because AADT provides information for the planning of new road construction, determination...

Projections of Temperature-Attributable Premature Deaths in 209 U.S. Cities Using a Cluster-Based Poisson Approach

NASA Technical Reports Server (NTRS)

Schwartz, Joel D.; Lee, Mihye; Kinney, Patrick L.; Yang, Suijia; Mills, David; Sarofim, Marcus C.; Jones, Russell; Streeter, Richard; St. Juliana, Alexis; Peers, Jennifer;

Total daily physical activity and the risk of AD and cognitive decline in older adults

PubMed Central

Boyle, P.A.; Yu, L.; Shah, R.C.; Wilson, R.S.; Bennett, D.A.

2012-01-01

Objective: Studies examining the link between objective measures of total daily physical activity and incident Alzheimer disease (AD) are lacking. We tested the hypothesis that an objective measure of total daily physical activity predicts incident AD and cognitive decline. Methods: Total daily exercise and nonexercise physical activity was measured continuously for up to 10 days with actigraphy (Actical®; Philips Healthcare, Bend, OR) from 716 older individuals without dementia participating in the Rush Memory and Aging Project, a prospective, observational cohort study. All participants underwent structured annual clinical examination including a battery of 19 cognitive tests. Results: During an average follow-up of about 4 years, 71 subjects developed clinical AD. In a Cox proportional hazards model adjusting for age, sex, and education, total daily physical activity was associated with incident AD (hazard ratio = 0.477; 95% confidence interval 0.273–0.832). The association remained after adjusting for self-report physical, social, and cognitive activities, as well as current level of motor function, depressive symptoms, chronic health conditions, and APOE allele status. In a linear mixed-effect model, the level of total daily physical activity was associated with the rate of global cognitive decline (estimate 0.033, SE 0.012, p = 0.007). Conclusions: A higher level of total daily physical activity is associated with a reduced risk of AD. PMID:22517108

Total daily physical activity and the risk of AD and cognitive decline in older adults.

PubMed

Buchman, A S; Boyle, P A; Yu, L; Shah, R C; Wilson, R S; Bennett, D A

2012-04-24

Studies examining the link between objective measures of total daily physical activity and incident Alzheimer disease (AD) are lacking. We tested the hypothesis that an objective measure of total daily physical activity predicts incident AD and cognitive decline. Total daily exercise and nonexercise physical activity was measured continuously for up to 10 days with actigraphy (Actical®; Philips Healthcare, Bend, OR) from 716 older individuals without dementia participating in the Rush Memory and Aging Project, a prospective, observational cohort study. All participants underwent structured annual clinical examination including a battery of 19 cognitive tests. During an average follow-up of about 4 years, 71 subjects developed clinical AD. In a Cox proportional hazards model adjusting for age, sex, and education, total daily physical activity was associated with incident AD (hazard ratio = 0.477; 95% confidence interval 0.273-0.832). The association remained after adjusting for self-report physical, social, and cognitive activities, as well as current level of motor function, depressive symptoms, chronic health conditions, and APOE allele status. In a linear mixed-effect model, the level of total daily physical activity was associated with the rate of global cognitive decline (estimate 0.033, SE 0.012, p = 0.007). A higher level of total daily physical activity is associated with a reduced risk of AD.

A biometeorological model of an encephalitis vector

NASA Astrophysics Data System (ADS)

Raddatz, R. L.

1986-01-01

Multiple linear regression techniques and seven years of data were used to build a biometeorological model of Winnipeg's mean daily levels of Culex tarsalis Coquillett. An eighth year of data was used to test the model. Hydrologic accounting of precipitation, evapotranspiration and runoff provided estimates of wetness while the warmness of the season was gauged in terms of the average temperature difference from normal and a threshold antecedent temperature regime. These factors were found to be highly correlated with the time-series of Cx. tarsalis counts. The impact of mosquito adulticiding measures was included in the model via a control effectiveness parameter. An activity-level adjustment, based on mean daily temperatures, was also made to the counts. This model can, by monitoring the weather, provide forecasts of Cx. tarsalis populations for Winnipeg with a lead-time of three weeks, thereby, contributing to an early warning of an impending Western Equine Encephalitis outbreak.

Respiratory hospitalizations in association with fine PM and its ...

EPA Pesticide Factsheets

Despite observed geographic and temporal variation in particulate matter (PM)-related health morbidities, only a small number of epidemiologic studies have evaluated the relation between PM2.5 chemical constituents and respiratory disease. Most assessments are limited by inadequate spatial and temporal resolution of ambient PM measurements and/or by their approaches to examine the role of specific PM components on health outcomes. In a case-crossover analysis using daily average ambient PM2.5 total mass and species estimates derived from the Community Multiscale Air Quality (CMAQ) model and available observations, we examined the association between the chemical components of PM (including elemental and organic carbon, sulfate, nitrate, ammonium, and other remaining) and respiratory hospitalizations in New York State. We evaluated relationships between levels (low, medium, high) of PM constituent mass fractions, and assessed modification of the PM2.5–hospitalization association via models stratified by mass fractions of both primary and secondary PM components. In our results, average daily PM2.5 concentrations in New York State were generally lower than the 24-hr average National Ambient Air Quality Standard (NAAQS). Year-round analyses showed statistically significant positive associations between respiratory hospitalizations and PM2.5 total mass, sulfate, nitrate, and ammonium concentrations at multiple exposure lags (0.5–2.0% per interquartile range [IQR

Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.

PubMed

Cori, Anne; Boëlle, Pierre-Yves; Thomas, Guy; Leung, Gabriel M; Valleron, Alain-Jacques

2009-08-01

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.

Attributes for MRB_E2RF1 Catchments by Major River Basins in the Conterminous United States: 30-Year Average Daily Minimum Temperature, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This tabular data set represents thecatchment-average for the 30-year (1971-2000) average daily minimum temperature in Celsius multiplied by 100 compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data were the United States Average Monthly or Annual Minimum Temperature, 1971 - 2000 raster data set produced by the PRISM Group at Oregon State University. The MRB_E2RF1 catchments are based on a modified version of the Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011). Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

Catchments by Major River Basins in the Conterminous United States: 30-Year Average Daily Minimum Temperature, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This tabular data set represents thecatchment-average for the 30-year (1971-2000) average daily minimum temperature in Celsius multiplied by 100 compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data were the United States Average Monthly or Annual Minimum Temperature, 1971 - 2000 raster data set produced by the PRISM Group at Oregon State University. The MRB_E2RF1 catchments are based on a modified version of the Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011). Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

76 FR 79579 - Approval and Promulgation of Implementation Plans and Designation of Areas for Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-22

... posting of the availability of the submittal on EPA's Adequacy Web site (at http://www.epa.gov/otaq... average annual fourth-highest daily maximum 8-hour average ozone concentration), if it had a 1-hour design... ozone standard is attained when the three-year average of the annual fourth-highest daily maximum 8-hour...

GIS Tools to Estimate Average Annual Daily Traffic

DOT National Transportation Integrated Search

2012-06-01

This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...

An evaluation of the impact of flooring types on exposures to fine and coarse particles within the residential micro-environment using CONTAM.

PubMed

Bramwell, Lisa; Qian, Jing; Howard-Reed, Cynthia; Mondal, Sumona; Ferro, Andrea R

2016-01-01

Typical resuspension activities within the home, such as walking, have been estimated to contribute up to 25% of personal exposures to PM10. Chamber studies have shown that for moderate walking intensities, flooring type can impact the rate at which particles are re-entrained into the air. For this study, the impact of residential flooring type on incremental average daily (24 h) time-averaged exposure was investigated. Distributions of incremental time-averaged daily exposures to fine and coarse PM while walking within the residential micro-environment were predicted using CONTAM, the multizone airflow and contaminant transport program of the National Institute of Standards and Technology. Knowledge of when and where a person was walking was determined by randomly selecting 490 daily diaries from the EPA's consolidated human activity database (CHAD). On the basis of the results of this study, residential flooring type can significantly impact incremental time-averaged daily exposures to coarse and fine particles (α=0.05, P<0.05, N=490, Kruskal-Wallis test) with high-density cut pile carpeting resulting in the highest exposures. From this study, resuspension from walking within the residential micro-environment contributed 6-72% of time-averaged daily exposures to PM10.

Daily Steps in Midlife and Older Adults: Relationship with Demographic, Self-Rated Health, and Self-Reported Physical Activity

ERIC Educational Resources Information Center

Payn, Tamara; Pfeiffer, Karin A.; Hutto, Brent; Vena, John E.; LaMonte, Michael J.; Blair, Steven N.; Hooker, Steven P.

2008-01-01

The relationship between average daily step counts and age, body mass index (BMI), self-reported physical activity (PA) level, and perceived health was determined in 85 middle-aged and older adults who wore a pedometer for 7 consecutive days. Average daily steps were significantly (p less than 0.05) correlated with BMI (r = -0.26), age (r = -0.44)…

75 FR 43069 - Approval of One-Year Extension for Attaining the 1997 8-Hour Ozone Standard in the Baltimore...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-23

... standard (NAAQS). This extension is based in part on air quality data for the 4th highest daily 8-hour... attainment date if: (a) For the first one-year extension, the area's 4th highest daily 8-hour average in the... 4th highest daily 8-hour value, averaged over both the original attainment year and the first...

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China.

PubMed

Xu, Meimei; Guo, Yuming; Zhang, Yajuan; Westerdahl, Dane; Mo, Yunzheng; Liang, Fengchao; Pan, Xiaochuan

2014-12-12

Few studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10 μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach. We collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well. The averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3 ± 78.1 μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10 μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure. Our results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

Comparison of dew point temperature estimation methods in Southwestern Georgia

Treesearch

Marcus D. Williams; Scott L. Goodrick; Andrew Grundstein; Marshall Shepherd

2015-01-01

Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in...

Quantifying the Spatial Distribution of Evapotranspiration over Canada With a Process Model Using Remote Sensing, Meteorological, and Soil Data

NASA Astrophysics Data System (ADS)

Liu, J.; Chen, J.; Cihlar, J.

2004-12-01

The evapotranspiration (ET) from all Canadian landmass is estimated at daily steps and 1 km resolution using a process model named Boreal Ecosystem Productivity Simulator (BEPS). The model is driven by remotely sensed leaf area index and land cover maps, as well as soil water holding capacity and daily meteorological data. All the major ET components are considered: transpiration from vegetation, evaporation of canopy-intercepted rainfall, evaporation from soil, sublimation of snow in winter and in permafrost and glacier areas, and sublimation of canopy-intercepted snow. In forested areas, the transpiration from both the overstory and understory vegetation is modelled separately. The Penman-Monteith method was applied to sunlit and shaded leaf groups individually in modelling the canopy-level transpiration, a methodological improvement necessary for forest canopies with considerable foliage clumping. The modelled ET map displays pronounced east-west and north-south gradients as well as detailed variations with cover types and vegetation density. It is estimated that, for a relative wet year of 1996, the total ET from all Canada's landmass (excluding inland waters) was 2037 km3. If compared with the total precipitation of 5351 km3 based on the data from a medium range meteorological forecast model, the ratio of ET to precipitation was 38 %. The ET averaged over Canadian land surface was 228 mm/yr in 1996, partitioned into transpiration of 102 mm/yr and evaporation and sublimation of 126 mm/yr. Forested areas contributed the largest fraction of the total national ET at 59 %. Averaged for all cover types, transpiration accounted for 45 % of the total ET, while in forested areas, transpiration was contributed 51 % of ET. Modelled results of daily ET are compared with eddy covariance measurements at three forested sites with a r2 value of 0.61 and a root mean square error of 0.7 mm/day.

Modeling level of urban taxi services using neural network

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

Xu, J.; Wong, S.C.; Tong, C.O.

1999-05-01

This paper is concerned with the modeling of the complex demand-supply relationship in urban taxi services. A neural network model is developed, based on a taxi service situation observed in the urban area of Hong Kong. The input consists of several exogenous variables including number of licensed taxis, incremental charge of taxi fare, average occupied taxi journey time, average disposable income, and population and customer price index; the output consists of a set of endogenous variables including daily taxi passenger demand, passenger waiting time, vacant taxi headway, average percentage of occupied taxis, taxi utilization, and average taxi waiting time. Comparisonsmore » of the estimation accuracy are made between the neural network model and the simultaneous equations model. The results show that the neural network-based macro taxi model can obtain much more accurate information of the taxi services than the simultaneous equations model does. Although the data set used for training the neural network is small, the results obtained thus far are very encouraging. The neural network model can be used as a policy tool by regulator to assist with the decisions concerning the restriction over the number of taxi licenses and the fixing of the taxi fare structure as well as a range of service quality control.« less

Estimation of Apple Intake for the Exposure Assessment of Residual Chemicals Using Korea National Health and Nutrition Examination Survey Database

PubMed Central

2016-01-01

The aims of this study were to develop strategies and algorithms of calculating food commodity intake suitable for exposure assessment of residual chemicals by using the food intake database of Korea National Health and Nutrition Examination Survey (KNHANES). In this study, apples and their processed food products were chosen as a model food for accurate calculation of food commodity intakes uthrough the recently developed Korea food commodity intake calculation (KFCIC) software. The average daily intakes of total apples in Korea Health Statistics were 29.60 g in 2008, 32.40 g in 2009, 34.30 g in 2010, 28.10 g in 2011, and 24.60 g in 2012. The average daily intakes of apples by KFCIC software was 2.65 g higher than that by Korea Health Statistics. The food intake data in Korea Health Statistics might have less reflected the intake of apples from mixed and processed foods than KFCIC software has. These results can affect outcome of risk assessment for residual chemicals in foods. Therefore, the accurate estimation of the average daily intake of food commodities is very important, and more data for food intakes and recipes have to be applied to improve the quality of data. Nevertheless, this study can contribute to the predictive estimation of exposure to possible residual chemicals and subsequent analysis for their potential risks. PMID:27152299

Seasonal trends in Ceratitis capitata reproductive potential derived from live-caught females in Greece

PubMed Central

Kouloussis, Nikos A.; Papadopoulos, Nikos T.; Katsoyannos, Byron I.; Müller, Hans-Georg; Wang, Jane-Ling; Su, Yu-Ru; Molleman, Freerk; Carey, James R.

2012-01-01

Reproductive data of individual insects are extremely hard to collect under natural conditions, thus the study of research questions related to oviposition has not advanced. Patterns of oviposition are often inferred only indirectly, through monitoring of host infestation, whereas the influence of age structure and several other factors on oviposition remains unknown. Using a new approach, in this article, we live-trapped wild Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) females on the Greek island of Chios during two field seasons. For their remaining lifetime, these females were placed individually in small cages and their daily oviposition was monitored. Reproduction rates between cohorts from different collection dates were then compared. The results showed that in the different captive cohorts the average remaining lifetime and reproduction were highly variable within and between seasons. Multivariate regression analysis showed that the month of capture had a significant effect on captive life span, average daily reproduction, and patterns of egg laying. The effect of year was significant on reproduction, but not on captive life span. These differences between sampling periods probably reflect differences in the availability of hosts and other factors that vary during the season and affect age structure and reproduction. Using a non-parametric generalized additive model, we found a statistically significant correlation between the captive life span and the average daily reproduction. These findings and the experimental approach have several important implications. PMID:22791908

Fine Particulate Air Pollution and Daily Mortality. A Nationwide Analysis in 272 Chinese Cities.

PubMed

Chen, Renjie; Yin, Peng; Meng, Xia; Liu, Cong; Wang, Lijun; Xu, Xiaohui; Ross, Jennifer A; Tse, Lap A; Zhao, Zhuohui; Kan, Haidong; Zhou, Maigeng

2017-07-01

Evidence concerning the acute health effects of air pollution caused by fine particulate matter (PM 2.5 ) in developing countries is quite limited. To evaluate short-term associations between PM 2.5 and daily cause-specific mortality in China. A nationwide time-series analysis was performed in 272 representative Chinese cities from 2013 to 2015. Two-stage Bayesian hierarchical models were applied to estimate regional- and national-average associations between PM 2.5 concentrations and daily cause-specific mortality. City-specific effects of PM 2.5 were estimated using the overdispersed generalized additive models after adjusting for time trends, day of the week, and weather conditions. Exposure-response relationship curves and potential effect modifiers were also evaluated. The average of annual mean PM 2.5 concentration in each city was 56 μg/m 3 (minimum, 18 μg/m 3 ; maximum, 127 μg/m 3 ). Each 10-μg/m 3 increase in 2-day moving average of PM 2.5 concentrations was significantly associated with increments in mortality of 0.22% from total nonaccidental causes, 0.27% from cardiovascular diseases, 0.39% from hypertension, 0.30% from coronary heart diseases, 0.23% from stroke, 0.29% from respiratory diseases, and 0.38% from chronic obstructive pulmonary disease. There was a leveling off in the exposure-response curves at high concentrations in most, but not all, regions. The associations were stronger in cities with lower PM 2.5 levels or higher temperatures, and in subpopulations with elder age or less education. This nationwide investigation provided robust evidence of the associations between short-term exposure to PM 2.5 and increased mortality from various cardiopulmonary diseases in China. The magnitude of associations was lower than those reported in Europe and North America.

Improved hydrological-model design by integrating nutrient and water flow

NASA Astrophysics Data System (ADS)

Arheimer, B.; Lindstrom, G.

2013-12-01

The potential of integrating hydrologic and nutrient concentration data to better understand patterns of catchment response and to better design hydrological modeling was explored using a national multi-basin model system for Sweden, called ';S-HYPE'. The model system covers more than 450 000 km2 and produce daily values of nutrient concentration and water discharge in 37 000 catchments from 1961 and onwards. It is based on the processed-based and semi-distributed HYdrological Predictions for the Environment (HYPE) code. The model is used operationally for assessments of water status or climate change impacts and for forecasts by the national warning service of floods, droughts and fire. The first model was launched in 2008, but S-HYPE is continuously improved and released in new versions every second year. Observations are available in 400 sites for daily water discharge and some 900 sites for monthly grab samples of nutrient concentrations. The latest version (2012) has an average NSE for water discharge of 0.7 and an average relative error of 5%, including both regulated and unregulated rivers with catchments from ten to several thousands of km2 and various landuse. The daily relative errors of nutrient concentrations are on average 20% for total Nitrogen and 35% for total Phosphorus. This presentation will give practical examples of how the nutrient data has been used to trace errors or inadequate parameter values in the hydrological model. Since 2008 several parts of the model structure has been reconsidered both in the source code, parameter values and input data of catchment characteristics. In this process water quality has been guiding much of the overall model design of catchment hydrological functions and routing along the river network. The model structure has thus been developed iteratively when evaluating results and checking time-series. Examples of water quality driven improvements will be given for estimation of vertical flow paths, such as separation of the hydrograph in surface flow, snow melt and baseflow, as well as horizontal flow paths in the landscape, such as mixing from various land use, impact from lakes and river channel volume. Overall, the S-HYPE model performance of water discharge increased from NSE 0.55 to 0.69 as an average for 400 gauges between the version 2010 and 2012. Most of this improvement, however, can be referred to improved regulations routines, rating curves for major lakes and parameters correcting ET and precipitation. Nevertheless, integrated water and nutrient modeling put constraints on the hydrological parameter values, which reduce equifinality for the hydrological part without reducing the model performance. The examples illustrates that the credibility of the hydrological model structure is thus improved by integrating water and nutrient flow. This lead to improved understanding of flow paths and water-nutrient process interactions in Sweden, which in turn will be very useful in further model analysis on impact of climate change or measures to reduce nutrient load from rivers to the Baltic Sea.

Developing a method for estimating AADT on all Louisiana roads : [tech summary].

DOT National Transportation Integrated Search

2015-12-01

Annual Average Daily Tra c (AADT), the average daily volume of vehicle tra c on a highway or road, is an : important measure in transportation engineering. AADT is used in highway geometric design, pavement : design, tra c forecasting, and h...

@AACAnatomy twitter account goes live: A sustainable social media model for professional societies.

PubMed

Benjamin, Hannah K; Royer, Danielle F

2018-05-01

Social media, with its capabilities of fast, global information sharing, provides a useful medium for professional development, connecting and collaborating with peers, and outreach. The goals of this study were to describe a new, sustainable model for Twitter use by professional societies, and analyze its impact on @AACAnatomy, the Twitter account of the American Association of Clinical Anatomists. Under supervision of an Association committee member, an anatomy graduate student developed a protocol for publishing daily tweets for @AACAnatomy. Five tweet categories were used: Research, Announcements, Replies, Engagement, and Community. Analytics from the 6-month pilot phase were used to assess the impact of the new model. @AACAnatomy had a steady average growth of 33 new followers per month, with less than 10% likely representing Association members. Research tweets, based on Clinical Anatomy articles with an abstract link, were the most shared, averaging 5,451 impressions, 31 link clicks, and nine #ClinAnat hashtag clicks per month. However, tweets from non-Research categories accounted for the highest impression and engagement metrics in four out of six months. For all tweet categories, monthly averages show consistent interaction of followers with the account. Daily tweet publication resulted in a 103% follower increase. An active Twitter account successfully facilitated regular engagement with @AACAnatomy followers and the promotion of clinical anatomy topics within a broad community. This Twitter model has the potential for implementation by other societies as a sustainable medium for outreach, networking, collaboration, and member engagement. Clin. Anat. 31:566-575, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Perceived health status and daily activity participation of older Malaysians.

PubMed

Ng, Sor Tho; Tengku-Aizan, Hamid; Tey, Nai Peng

2011-07-01

This article investigates the influence of perceived health status on the daily activity participation of older Malaysians. Data from the Survey on Perceptions of Needs and Problems of the Elderly, which was conducted in 1999, were used. The negative binomial regression results show that older persons with good perceived health status reported more varieties of daily activity participation, especially among the uneducated and those with below-average self-esteem. The multinomial logistic regression model suggests that older persons with good perceived health status tended to engage daily in paid work only or with leisure activities, whereas those perceived to have poor health were more likely to engage in leisure activities only or leisure and family role activities. Promotion of a healthy lifestyle at a younger age encourages every person to monitor and take responsibility for their own health, which is a necessary strategy to ensure active participation at an older age, and thus improve their well-being.

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

PubMed

Li, Qiongge; Chan, Maria F

2017-01-01

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

Solving the negative impact of congestion in the postanesthesia care unit: a cost of opportunity analysis.

PubMed

Ruiz-Patiño, Alejandro; Acosta-Ospina, Laura Elena; Rueda, Juan-David

2017-04-01

Congestion in the postanesthesia care unit (PACU) leads to the formation of waiting queues for patients being transferred after surgery, negatively affecting hospital resources. As patients recover in the operating room, incoming surgeries are delayed. The purpose of this study was to establish the impact of this phenomenon in multiple settings. An operational mathematical study based on the queuing theory was performed. Average queue length, average queue waiting time, and daily queue waiting time were evaluated. Calculations were based on the mean patient daily flow, PACU length of stay, occupation, and current number of beds. Data was prospectively collected during a period of 2 months, and the entry and exit time was recorded for each patient taken to the PACU. Data was imputed in a computational model made with MS Excel. To account for data uncertainty, deterministic and probabilistic sensitivity analyses for all dependent variables were performed. With a mean patient daily flow of 40.3 and an average PACU length of stay of 4 hours, average total lost surgical opportunity time was estimated at 2.36 hours (95% CI: 0.36-4.74 hours). Cost of opportunity was calculated at $1592 per lost hour. Sensitivity analysis showed that an increase of two beds is required to solve the queue formation. When congestion has a negative impact on cost of opportunity in the surgical setting, queuing analysis grants definitive actions to solve the problem, improving quality of service and resource utilization. Copyright © 2016 Elsevier Inc. All rights reserved.

Thermal Energy Exchange Model and Water Loss of a Barrel Cactus, Ferocactus acanthodes1

PubMed Central

Lewis, Donald A.; Nobel, Park S.

1977-01-01

The influences of various diurnal stomatal opening patterns, spines, and ribs on the stem surface temperature and water economy of a CAM succulent, the barrel cactus Ferocactus acanthodes, were examined using an energy budget model. To incorporate energy exchanges by shortwave and longwave irradiation, latent heat, conduction, and convection as well as the heat storage in the massive stem, the plant was subdivided into over 100 internal and external regions in the model. This enabled the average surface temperature to be predicted within 1 C of the measured temperature for both winter and summer days. Reducing the stem water vapor conductance from the values observed in the field to zero caused the average daily stem surface temperature to increase only 0.7 C for a winter day and 0.3 C for a summer day. Thus, latent heat loss does not substantially reduce stem temperature. Although the surface temperatures averaged 18 C warmer for the summer day than for the winter day for a plant 41 cm tall, the temperature dependence of stomatal opening caused the simulated nighttime water loss rates to be about the same for the 2 days. Spines moderated the amplitude of the diurnal temperature changes of the stem surface, since the daily variation was 17 C for the winter day and 25 C for the summer day with spines compared with 23 C and 41 C, respectively, in their simulated absence. Ribs reduced the daytime temperature rise by providing 54% more area for convective heat loss than for a smooth circumscribing surface. In a simulation where both spines and ribs were eliminated, the daytime average surface temperature rose by 5 C. PMID:16660148

Developmental models for estimating ecological responses to environmental variability: structural, parametric, and experimental issues.

PubMed

Moore, Julia L; Remais, Justin V

2014-03-01

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.

Impact of downward-mixing ozone on surface ozone accumulation in southern Taiwan.

PubMed

Lin, Ching-Ho

2008-04-01

The ozone that initially presents in the previous day's afternoon mixing layer can remain in the nighttime atmosphere and then be carried over to the next morning. Finally, this ozone can be brought to the ground by downward mixing as mixing depth increases during the daytime, thereby increasing surface ozone concentrations. Variation of ozone concentration during each of these periods is investigated in this work. First, ozone concentrations existing in the daily early morning atmosphere at the altitude range of the daily maximum mixing depth (residual ozone concentrations) were measured using tethered ozonesondes on 52 experimental days during 2004-2005 in southern Taiwan. Daily downward-mixing ozone concentrations were calculated by a box model coupling the measured daily residual ozone concentrations and daily mixing depth variations. The ozone concentrations upwind in the previous day's afternoon mixing layer were estimated by the combination of back air trajectory analysis and known previous day's surface ozone distributions. Additionally, the relationship between daily downward-mixing ozone concentration and daily photochemically produced ozone concentration was examined. The latter was calculated by removing the former from daily surface maximum ozone concentration. The measured daily residual ozone concentrations distributed at 12-74 parts per billion (ppb) with an average of 42 +/- 17 ppb are well correlated with the previous upwind ozone concentration (R2 = 0.54-0.65). Approximately 60% of the previous upwind ozone was estimated to be carried over to the next morning and became the observed residual ozone. The daily downward-mixing ozone contributes 48 +/- 18% of the daily surface maximum ozone concentration, indicating that the downward-mixing ozone is as important as daily photochemically produced ozone to daily surface maximum ozone accumulation. The daily downward-mixing ozone is poorly correlated with the daily photochemically produced ozone and contributes significantly to the daily variation of surface maximum ozone concentrations (R2 = 0.19). However, the contribution of downward-mixing ozone to daily ozone variation is not included in most existing statistical models developed for predicting daily ozone variation. Finally, daily surface maximum ozone concentration is positively correlated with daily afternoon mixing depth, attributable to the downward-mixing ozone.

Modeling the Impact of Land Use Change on Regional Water Flux in Northern Wisconsin-Species Effects on Transpiration and Canopy Average Stomatal Conductance

NASA Astrophysics Data System (ADS)

Ewers, B. E.; Mackay, D. S.; Ahl, D. E.; Burrows, S. N.; Samanta, S. S.; Gower, S. T.

2001-05-01

Land use change has created a diversity of forest cover types in northern Wisconsin. Our objective was to determine if changes in forest cover would result in a significant change in regional water flux. To adequately sample these forest cover types we chose four cover types red pine, sugar maple/basswood, quaking aspen/balsam fir, and northern white-cedar/balsam fir/green alder that represent more than 80 percent of the ground area. The remainder of the ground area is mostly non-forested grassland, shrubland, and open water. Within each cover type we measured sap flux of 8 trees of each species. We scaled point measurements of sap flux to tree transpiration using sensors positioned radially into the conducting sapwood and on both the north and south sides of the tree. We found that aspen/balsam fir had the highest average daily transpiration rates. There was no difference in the northern white-cedar/balsam fir/green alder and red pine cover types. The sugar maple/basswood cover type had the lowest daily average transpiration rate. These changes in transpiration could not be explained by differences in leaf area index. Thus, we calculated canopy average stomatal conductance (GS) using an inversion of the Penman-Monteith equation and tree leaf area. We modified a regional hydrology model to include a simple tree hydraulic sub-model that assumes stomatal regulation of leaf water potential. We tested the behavior of the sub-model by evaluating GS response to vapor pressure deficit, radiation, temperature, and soil moisture for each species. We hypothesize that species with a high canopy average stomatal conductance at low vapor pressure deficit will have to have greater sensitivity to vapor pressure deficit in order to maintain minimal leaf water potential as suggested by the model. Our results indicate that changes to forest cover such as conversion from low transpiring sugar maple/basswood to high transpiring aspen/fir will result in predictable changes to the regional water balance of northern Wisconsin.

Data-Driven Residential Load Modeling and Validation in GridLAB-D

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

Gotseff, Peter; Lundstrom, Blake

Accurately characterizing the impacts of high penetrations of distributed energy resources (DER) on the electric distribution system has driven modeling methods from traditional static snap shots, often representing a critical point in time (e.g., summer peak load), to quasi-static time series (QSTS) simulations capturing all the effects of variable DER, associated controls and hence, impacts on the distribution system over a given time period. Unfortunately, the high time resolution DER source and load data required for model inputs is often scarce or non-existent. This paper presents work performed within the GridLAB-D model environment to synthesize, calibrate, and validate 1-second residentialmore » load models based on measured transformer loads and physics-based models suitable for QSTS electric distribution system modeling. The modeling and validation approach taken was to create a typical GridLAB-D model home that, when replicated to represent multiple diverse houses on a single transformer, creates a statistically similar load to a measured load for a given weather input. The model homes are constructed to represent the range of actual homes on an instrumented transformer: square footage, thermal integrity, heating and cooling system definition as well as realistic occupancy schedules. House model calibration and validation was performed using the distribution transformer load data and corresponding weather. The modeled loads were found to be similar to the measured loads for four evaluation metrics: 1) daily average energy, 2) daily average and standard deviation of power, 3) power spectral density, and 4) load shape.« less

40 CFR 503.43 - Pollutant limits.

Code of Federal Regulations, 2011 CFR

2011-07-01

... accordance with § 503.43(e). (d) Pollutant limit—arsenic, cadmium, chromium, and nickel. (1) The average daily concentration for arsenic, cadmium, chromium, and nickel in sewage sludge fed to a sewage sludge... = Average daily concentration of arsenic, cadmium, chromium, or nickel in sewage sludge. CE = Sewage sludge...

Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

PubMed

Ducret-Stich, Regina E; Tsai, Ming-Yi; Ragettli, Martina S; Ineichen, Alex; Kuenzli, Nino; Phuleria, Harish C

2013-07-01

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO₂), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO₂ concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO₂, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO₂ prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO₂ to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO₂ concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study. Copyright © 2013 Elsevier B.V. All rights reserved.

The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins

NASA Astrophysics Data System (ADS)

He, Zhihua; Vorogushyn, Sergiy; Unger-Shayesteh, Katy; Gafurov, Abror; Kalashnikova, Olga; Omorova, Elvira; Merz, Bruno

2018-03-01

This study refines the method for calibrating a glacio-hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC-based method (1) delivers model-internal consistency comparably to the tri-data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri-data set calibration method. Our findings indicate the potential of the HPC-based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain.

Development of equations to predict the influence of floor space on average daily gain, average daily feed intake and gain : feed ratio of finishing pigs.

PubMed

Flohr, J R; Dritz, S S; Tokach, M D; Woodworth, J C; DeRouchey, J M; Goodband, R D

2018-05-01

Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m2/pig), k (floor space/final BW0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)-(237 350×k 2)-(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)-(388 998×k 2)-(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.

Localized Multi-Model Extremes Metrics for the Fourth National Climate Assessment

NASA Astrophysics Data System (ADS)

Thompson, T. R.; Kunkel, K.; Stevens, L. E.; Easterling, D. R.; Biard, J.; Sun, L.

2017-12-01

We have performed localized analysis of scenario-based datasets for the Fourth National Climate Assessment (NCA4). These datasets include CMIP5-based Localized Constructed Analogs (LOCA) downscaled simulations at daily temporal resolution and 1/16th-degree spatial resolution. Over 45 temperature and precipitation extremes metrics have been processed using LOCA data, including threshold, percentile, and degree-days calculations. The localized analysis calculates trends in the temperature and precipitation extremes metrics for relatively small regions such as counties, metropolitan areas, climate zones, administrative areas, or economic zones. For NCA4, we are currently addressing metropolitan areas as defined by U.S. Census Bureau Metropolitan Statistical Areas. Such localized analysis provides essential information for adaptation planning at scales relevant to local planning agencies and businesses. Nearly 30 such regions have been analyzed to date. Each locale is defined by a closed polygon that is used to extract LOCA-based extremes metrics specific to the area. For each metric, single-model data at each LOCA grid location are first averaged over several 30-year historical and future periods. Then, for each metric, the spatial average across the region is calculated using model weights based on both model independence and reproducibility of current climate conditions. The range of single-model results is also captured on the same localized basis, and then combined with the weighted ensemble average for each region and each metric. For example, Boston-area cooling degree days and maximum daily temperature is shown below for RCP8.5 (red) and RCP4.5 (blue) scenarios. We also discuss inter-regional comparison of these metrics, as well as their relevance to risk analysis for adaptation planning.

Spatiotemporal variability of light-absorbing carbon concentration in a residential area impacted by woodsmoke.

PubMed

Krecl, Patricia; Johansson, Christer; Ström, Johan

2010-03-01

Residential wood combustion (RWC) is responsible for 33% of the total carbon mass emitted in Europe. With the new European targets to increase the use of renewable energy, there is a growing concern that the population exposure to woodsmoke will also increase. This study investigates observed and simulated light-absorbing carbon mass (MLAC) concentrations in a residential neighborhood (Lycksele, Sweden) where RWC is a major air pollution source during winter. The measurement analysis included descriptive statistics, correlation coefficient, coefficient of divergence, linear regression, concentration roses, diurnal pattern, and weekend versus weekday concentration ratios. Hourly RWC and road traffic contributions to MLAC were simulated with a Gaussian dispersion model to assess whether the model was able to mimic the observations. Hourly mean and standard deviation concentrations measured at six sites ranged from 0.58 to 0.74 microg m(-3) and from 0.59 to 0.79 microg m(-3), respectively. The temporal and spatial variability decreased with increasing averaging time. Low-wind periods with relatively high MLAC concentrations correlated more strongly than high-wind periods with low concentrations. On average, the model overestimated the observations by 3- to 5-fold and explained less than 10% of the measured hourly variability at all sites. Large residual concentrations were associated with weak winds and relatively high MLAC loadings. The explanation of the observed variability increased to 31-45% when daily mean concentrations were compared. When the contribution from the boilers within the neighborhood was excluded from the simulations, the model overestimation decreased to 16-71%. When assessing the exposure to light-absorbing carbon particles using this type of model, the authors suggest using a longer averaging period (i.e., daily concentrations) in a larger area with an updated and very detailed emission inventory.

Analysis of a resistance-energy balance method for estimating daily evaporation from wheat plots using one-time-of-day infrared temperature observations

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Idso, S. B.; Reginato, R. J.

1986-01-01

Accurate estimates of evaporation over field-scale or larger areas are needed in hydrologic studies, irrigation scheduling, and meteorology. Remotely sensed surface temperature might be used in a model to calculate evaporation. A resistance-energy balance model, which combines an energy balance equation, the Penman-Monteith (1981) evaporation equation, and van den Honert's (1948) equation for water extraction by plant roots, is analyzed for estimating daily evaporation from wheat using postnoon canopy temperature measurements. Additional data requirements are half-hourly averages of solar radiation, air and dew point temperatures, and wind speed, along with reasonable estimates of canopy emissivity, albedo, height, and leaf area index. Evaporation fluxes were measured in the field by precision weighing lysimeters for well-watered and water-stressed wheat. Errors in computed daily evaporation were generally less than 10 percent, while errors in cumulative evaporation for 10 clear sky days were less than 5 percent for both well-watered and water-stressed wheat. Some results from sensitivity analysis of the model are also given.

Evaluating a Priori Ozone Profile Information Used in TEMPO (Tropospheric Emissions: Monitoring of Pollution) Tropospheric Ozone Retrievals

NASA Technical Reports Server (NTRS)

Johnson, Matthew Stephen

2017-01-01

A primary objective for TOLNet is the evaluation and validation of space-based tropospheric O3 retrievals from future systems such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite. This study is designed to evaluate the tropopause-based O3 climatology (TB-Clim) dataset which will be used as the a priori profile information in TEMPO O3 retrievals. This study also evaluates model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time (NRT) data assimilation model products (NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS-5) Forward Processing (FP) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2)) and full chemical transport model (CTM), GEOS-Chem, simulations. The TB-Clim dataset and model products are evaluated with surface (0-2 km) and tropospheric (0-10 km) TOLNet observations to demonstrate the accuracy of the suggested a priori dataset and information which could potentially be used in TEMPO O3 algorithms. This study also presents the impact of individual a priori profile sources on the accuracy of theoretical TEMPO O3 retrievals in the troposphere and at the surface. Preliminary results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles observed by TOLNet, model-simulated profiles from a full CTM (GEOS-Chem is used as a proxy for CTM O3 predictions) resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly (diurnal cycle evaluation) and daily-averaged (daily variability evaluation) TOLNet observations. Furthermore, it was determined that when large daily-averaged surface O3 mixing ratios are observed (65 ppb), which are important for air quality purposes, TEMPO retrieval values at the surface display higher correlations and less bias when applying CTM a priori profile information compared to all other data products. The primary reason for this is that CTM predictions better capture the spatio-temporal variability of the vertical profiles of observed tropospheric O3 compared to the TB-Clim dataset and other NRT data assimilation models evaluated during this study.

Mapping evapotranspiration based on remote sensing: An application to Canada's landmass

NASA Astrophysics Data System (ADS)

Liu, J.; Chen, J. M.; Cihlar, J.

2003-07-01

The evapotranspiration (ET) from all Canadian landmass in 1996 is estimated at daily steps and 1 km resolution using a process model named boreal ecosystem productivity simulator (BEPS). The model is driven by remotely sensed leaf area index and land cover maps as well as soil water holding capacity and daily meteorological data. All the major ET components are considered: transpiration from vegetation, evaporation of canopy-intercepted rainfall, evaporation from soil, sublimation of snow in winter and in permafrost and glacier areas, and sublimation of canopy-intercepted snow. In forested areas the transpiration from both the overstory and understory vegetation is modeled separately. The Penman-Monteith method was applied to sunlit and shaded leaf groups individually in modeling the canopy-level transpiration, a methodological improvement necessary for forest canopies with considerable foliage clumping. The modeled ET map displays pronounced east-west and north-south gradients as well as detailed variations with cover types and vegetation density. It is estimated that for a relative wet year of 1996, the total ET from all Canada's landmass (excluding inland waters) was 2037 km3. If compared with the total precipitation of 5351 km3 based on the data from a medium range meteorological forecast model, the ratio of ET to precipitation was 38%. The ET averaged over Canadian land surface was 228 mm/yr in 1996, partitioned into transpiration of 102 mm yr-1 and evaporation and sublimation of 126 mm yr-1. Forested areas contributed the largest fraction of the total national ET at 59%. Averaged for all cover types, transpiration accounted for 45% of the total ET, while in forested areas, transpiration contributed 51% of ET. Modeled results of daily ET are compared with eddy covariance measurements at three forested sites with a r2 value of 0.61 and a root mean square error of 0.7 mm/day.

Application of aerosol speciation data as an in situ dust proxy for validation of the Dust Regional Atmospheric Model (DREAM)

NASA Astrophysics Data System (ADS)

Shaw, Patrick

The Dust REgional Atmospheric Model (DREAM) predicts concentrations of mineral dust aerosols in time and space, but validation is challenging with current in situ particulate matter (PM) concentration measurements. Measured levels of ambient PM often contain anthropogenic components as well as windblown mineral dust. In this study, two approaches to model validation were performed with data from preexisting air quality monitoring networks: using hourly concentrations of total PM with aerodynamic diameter less than 2.5 μm (PM 2.5); and using a daily averaged speciation-derived soil component. Validation analyses were performed for point locations within the cities of El Paso (TX), Austin (TX), Phoenix (AZ), Salt Lake City (UT) and Bakersfield (CA) for most of 2006. Hourly modeled PM 2.5 did not validate at all with hourly observations among the sites (combined R < 0.00, N = 24,302 hourly values). Aerosol chemical speciation data distinguished between mineral (soil) dust from anthropogenic ambient PM. As expected, statistically significant improvements in correlation among all stations (combined R = 0.16, N = 343 daily values) were found when the soil component alone was used to validate DREAM. The validation biases that result from anthropogenic aerosols were also reduced using the soil component. This is seen in the reduction of the root mean square error between hourly in situ versus hourly modeled (RMSE hourly = 18.6 μg m -3) and 24-h in situ speciation values versus daily averaged observed (RMSE soil = 12.0 μg m -3). However, the lack of a total reduction in RMSE indicates there is still room for improvement in the model. While the soil component is the theoretical proxy of choice for a dust transport model, the current sparse and infrequent sampling is not ideal for routine hourly air quality forecast validation.

Short-Term Mortality Rates during a Decade of Improved Air Quality in Erfurt, Germany

PubMed Central

Breitner, Susanne; Stölzel, Matthias; Cyrys, Josef; Pitz, Mike; Wölke, Gabriele; Kreyling, Wolfgang; Küchenhoff, Helmut; Heinrich, Joachim; Wichmann, H.-Erich; Peters, Annette

2009-01-01

Background Numerous studies have shown associations between ambient air pollution and daily mortality. Objectives Our goal was to investigate the association of ambient air pollution and daily mortality in Erfurt, Germany, over a 10.5-year period after the German unification, when air quality improved. Methods We obtained daily mortality counts and data on mass concentrations of particulate matter (PM) < 10 μm in aerodynamic diameter (PM10), gaseous pollutants, and meteorology in Erfurt between October 1991 and March 2002. We obtained ultrafine particle number concentrations (UFP) and mass concentrations of PM < 2.5 μm in aerodynamic diameter (PM2.5) from September 1995 to March 2002. We analyzed the data using semiparametric Poisson regression models adjusting for trend, seasonality, influenza epidemics, day of the week, and meteorology. We evaluated cumulative associations between air pollution and mortality using polynomial distributed lag (PDL) models and multiday moving averages of air pollutants. We evaluated changes in the associations over time in time-varying coefficient models. Results Air pollution concentrations decreased over the study period. Cumulative exposure to UFP was associated with increased mortality. An interquartile range (IQR) increase in the 15-day cumulative mean UFP of 7,649 cm−3 was associated with a relative risk (RR) of 1.060 [95% confidence interval (CI), 1.008–1.114] for PDL models and an RR/IQR of 1.055 (95% CI, 1.011–1.101) for moving averages. RRs decreased from the mid-1990s to the late 1990s. Conclusion Results indicate an elevated mortality risk from short-term exposure to UFP. They further suggest that RRs for short-term associations of air pollution decreased as pollution control measures were implemented in Eastern Germany. PMID:19337521

Performance of a system of reservoirs on futuristic front

NASA Astrophysics Data System (ADS)

Saha, Satabdi; Roy, Debasri; Mazumdar, Asis

2017-10-01

Application of simulation model HEC-5 to analyze the performance of the DVC Reservoir System (a multipurpose system with a network of five reservoirs and one barrage) on the river Damodar in Eastern India in meeting projected future demand as well as controlling flood for synthetically generated future scenario is addressed here with a view to develop an appropriate strategy for its operation. Thomas-Fiering model (based on Markov autoregressive model) has been adopted for generation of synthetic scenario (monthly streamflow series) and subsequently downscaling of modeled monthly streamflow to daily values was carried out. The performance of the system (analysed on seasonal basis) in terms of `Performance Indices' (viz., both quantity based reliability and time based reliability, mean daily deficit, average failure period, resilience and maximum vulnerability indices) for the projected scenario with enhanced demand turned out to be poor compared to that for historical scenario. However, judicious adoption of resource enhancement (marginal reallocation of reservoir storage capacity) and demand management strategy (curtailment of projected high water requirements and trading off between demands) was found to be a viable option for improvement of the performance of the reservoir system appreciably [improvement being (1-51 %), (2-35 %), (16-96 %), (25-50 %), (8-36 %) and (12-30 %) for the indices viz., quantity based reliability, time based reliability, mean daily deficit, average failure period, resilience and maximum vulnerability, respectively] compared to that with normal storage and projected demand. Again, 100 % reliability for flood control for current as well as future synthetically generated scenarios was noted. The results from the study would assist concerned authority in successful operation of reservoirs in the context of growing demand and dwindling resource.

Modeling and predicting intertidal variations of the salinity field in the Bay/Delta

USGS Publications Warehouse

Knowles, Noah; Uncles, Reginald J.

1995-01-01

One approach to simulating daily to monthly variability in the bay is the development of intertidal model using tidally-averaged equations and a time step on the order of the day.  An intertidal numerical model of the bay's physics, capable of portraying seasonal and inter-annual variability, would have several uses.  Observations are limited in time and space, so simulation could help fill the gaps.  Also, the ability to simulate multi-year episodes (eg, an extended drought) could provide insight into the response of the ecosystem to such events.  Finally, such a model could be used in a forecast mode wherein predicted delta flow is used as model input, and predicted salinity distribution is output with estimates days and months in advance.  This note briefly introduces such a tidally-averaged model (Uncles and Peterson, in press) and a corresponding predictive scheme for baywide forecasting.

Characterizing energy budget variability at a Sahelian site: a test of NWP model behaviour

NASA Astrophysics Data System (ADS)

Mackie, Anna; Palmer, Paul I.; Brindley, Helen

2017-12-01

We use observations of surface and top-of-the-atmosphere (TOA) broadband radiation fluxes determined from the Atmospheric Radiation Measurement programme mobile facility, the Geostationary Earth Radiation Budget (GERB) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments and a range of meteorological variables at a site in the Sahel to test the ability of the ECMWF Integrated Forecasting System cycle 43r1 to describe energy budget variability. The model has daily average biases of -12 and 18 W m-2 for outgoing longwave and reflected shortwave TOA radiation fluxes, respectively. At the surface, the daily average bias is 12(13) W m-2 for the longwave downwelling (upwelling) radiation flux and -21(-13) W m-2 for the shortwave downwelling (upwelling) radiation flux. Using multivariate linear models of observation-model differences, we attribute radiation flux discrepancies to physical processes, and link surface and TOA fluxes. We find that model biases in surface radiation fluxes are mainly due to a low bias in ice water path (IWP), poor description of surface albedo and model-observation differences in surface temperature. We also attribute observed discrepancies in the radiation fluxes, particularly during the dry season, to the misrepresentation of aerosol fields in the model from use of a climatology instead of a dynamic approach. At the TOA, the low IWP impacts the amount of reflected shortwave radiation while biases in outgoing longwave radiation are additionally coupled to discrepancies in the surface upwelling longwave flux and atmospheric humidity.

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

DOE PAGES

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

2016-04-07

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Short-term forecasting of emergency inpatient flow.

PubMed

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

40 CFR 65.162 - Nonflare control and recovery device monitoring records.

Code of Federal Regulations, 2010 CFR

2010-07-01

...). For catalytic incinerators, record the daily average of the temperature upstream of the catalyst bed and the daily average of the temperature differential across the bed. For halogen scrubbers, record... regeneration stream flow and carbon bed regeneration temperature are monitored, the following records shall be...

7 CFR 760.4 - Normal marketings of milk.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AGRICULTURE SPECIAL PROGRAMS INDEMNITY PAYMENT PROGRAMS Dairy Indemnity Payment Program Payments to Dairy... section are adjusted for any change in the daily average number of cows milked during each pay period the milk is off the market compared with the average number of cows milked daily during the base period. (d...

40 CFR Table 2 to Subpart Nnnnn of... - Operating Limits

Code of Federal Regulations, 2011 CFR

2011-07-01

... vented to a control device. For each . . . You must . . . 1. Caustic scrubber or water scrubber/absorber a. Maintain the daily average scrubber inlet liquid or recirculating liquid flow rate, as appropriate, above the operating limit; andb. Maintain the daily average scrubber effluent pH within the...

40 CFR Table 2 to Subpart Nnnnn of... - Operating Limits

Code of Federal Regulations, 2010 CFR

2010-07-01

... vented to a control device. For each . . . You must . . . 1. Caustic scrubber or water scrubber/absorber a. Maintain the daily average scrubber inlet liquid or recirculating liquid flow rate, as appropriate, above the operating limit; andb. Maintain the daily average scrubber effluent pH within the...

A study of variation characteristics of Gobi broadband emissivity based on field observational experiments in northwestern China

NASA Astrophysics Data System (ADS)

Zheng, Zhi-yuan; Wei, Zhi-gang; Wen, Zhi-ping; Dong, Wen-jie; Li, Zhen-chao; Wen, Xiao-hang; Zhu, Xian; Chen, Chen; Hu, Shan-shan

2018-02-01

Land surface emissivity is a significant variable in energy budgets, land cover assessments, and environment and climate studies. However, the assumption of an emissivity constant is being used in Gobi broadband emissivity (GbBE) parameterization scheme in numerical models because of limited knowledge surrounding the spatiotemporal variation characteristics of GbBE. To address this issue, we analyzed the variation characteristics of GbBE and possible impact factor-surface soil moisture based on long-term continuous and high temporal resolution field observational experiments over a typical Gobi underlying surface in arid and semiarid areas in northwestern China. The results indicate that GbBE has obvious daily and diurnal variation features, especially diurnal cycle characteristics. The multi-year average of the daily average of GbBE is in the range of 0.932 to 0.970 with an average of 0.951 ± 0.008, and the average diurnal GbBE is in the range of 0.880 to 0.940 with an average of 0.906 ± 0.018. GbBE varies with surface soil moisture content. We observed a slight decrease in GbBE with an increase in soil moisture, although this change was not very obvious because of the low soil moisture in this area. Nevertheless, we think that soil moisture must be one of the most significant impact factors on GbBE in arid and semiarid areas. Soil moisture must be taken into account into the parameterization schemes of bare soil broadband emissivity in land surface models. Additional field experiments and studies should be carried out in order to clarify this issue.

Effects of reproductive condition, roost microclimate, and weather patterns on summer torpor use by a vespertilionid bat

PubMed Central

Johnson, Joseph S; Lacki, Michael J

2014-01-01

A growing number of mammal species are recognized as heterothermic, capable of maintaining a high-core body temperature or entering a state of metabolic suppression known as torpor. Small mammals can achieve large energetic savings when torpid, but they are also subject to ecological costs. Studying torpor use in an ecological and physiological context can help elucidate relative costs and benefits of torpor to different groups within a population. We measured skin temperatures of 46 adult Rafinesque's big-eared bats (Corynorhinus rafinesquii) to evaluate thermoregulatory strategies of a heterothermic small mammal during the reproductive season. We compared daily average and minimum skin temperatures as well as the frequency, duration, and depth of torpor bouts of sex and reproductive classes of bats inhabiting day-roosts with different thermal characteristics. We evaluated roosts with microclimates colder (caves) and warmer (buildings) than ambient air temperatures, as well as roosts with intermediate conditions (trees and rock crevices). Using Akaike's information criterion (AIC), we found that different statistical models best predicted various characteristics of torpor bouts. While the type of day-roost best predicted the average number of torpor bouts that bats used each day, current weather variables best predicted daily average and minimum skin temperatures of bats, and reproductive condition best predicted average torpor bout depth and the average amount of time spent torpid each day by bats. Finding that different models best explain varying aspects of heterothermy illustrates the importance of torpor to both reproductive and nonreproductive small mammals and emphasizes the multifaceted nature of heterothermy and the need to collect data on numerous heterothermic response variables within an ecophysiological context. PMID:24558571

SU-E-T-379: Evaluation of An EPID-Based System for Daily Dosimetry Check by Comparison with a Widely-Used Ionization Chamber-Based Device

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

McDonald, D; Koch, N; Peng, J

2015-06-15

Purpose: To examine the feasibility of using Varian’s EPID-based Machine Performance Check (MPC) system to track daily machine output through comparison with Sun Nuclear’s DailyQA3 (DQA) device. Methods: Daily machine outputs for two photon energies (6 and 16MV) and five electron energies (6, 9, 12, 16, 20MeV) were measured for one month using both MPC and DQA. Baselines measurements for MPC were taken at the start of the measurement series, while DQA baselines were set at an earlier date. In order to make absolute comparisons with MPC, all DQA readings were referenced to the average of the first three DQAmore » readings in that series, minimizing systematic differences between the measurement techniques due to baseline differences. In addition to daily output measurements, weekly averages were also calculated and compared. Finally, the electron energy dependence of each measurement technique was examined by comparing energy-specific measurements to the average electron output of all energies each day. Results: For 6 and 16MV photons, the largest absolute percent differences between MPC and DQA were 0.60% and 0.73%, respectively. Weekly averages were within 0.17% and 0.23%, respectively. For all five electron energies, the greatest absolute percent differences between MPC and DQA for each energy ranged from 0.49%–0.83%. Weekly averages ranged from 0.07%–0.28%. DQA energy-specific electron readings matched the average electron output within 0.29% for all days and all energies. MPC energy-specific readings matched the average within 0.21% for 9–20MeV. However, 6MeV showed a larger distribution about the average with four days showing a difference greater than 0.30% and a maximum difference of 0.51%. Conclusion: MPC output measurements correlated well with the widely-used DQA3 for most beam energies, making it a reliable back up technique for daily output monitoring. However, MPC may display an energy dependence for lower electrons energies, requiring additional investigation.« less

A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.

PubMed

Chen, Gongbo; Li, Shanshan; Knibbs, Luke D; Hamm, N A S; Cao, Wei; Li, Tiantian; Guo, Jianping; Ren, Hongyan; Abramson, Michael J; Guo, Yuming

2018-09-15

Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM 2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. To estimate daily concentrations of PM 2.5 across China during 2005-2016. Daily ground-level PM 2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM 2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM 2.5 across China with a resolution of 0.1° (≈10 km) during 2005-2016. The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM 2.5 [10-fold cross-validation (CV) R 2  = 83%, root mean squared prediction error (RMSE) = 28.1 μg/m 3 ]. At the monthly and annual time-scale, the explained variability of average PM 2.5 increased up to 86% (RMSE = 10.7 μg/m 3 and 6.9 μg/m 3 , respectively). Taking advantage of a novel application of modeling framework and the most recent ground-level PM 2.5 observations, the machine learning method showed higher predictive ability than previous studies. Random forests approach can be used to estimate historical exposure to PM 2.5 in China with high accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

Daily tornado frequency distributions in the United States

NASA Astrophysics Data System (ADS)

Elsner, J. B.; Jagger, T. H.; Widen, H. M.; Chavas, D. R.

2014-01-01

The authors examine daily tornado counts in the United States over the period 1994-2012 and find strong evidence for a power-law relationship in the distribution frequency. The scaling exponent is estimated at 1.64 (0.019 s.e.) giving a per tornado-day probability of 0.014% (return period of 71 years) that a tornado day produces 145 tornadoes as was observed on 27 April 2011. They also find that the total number of tornadoes by damage category on days with at least one violent tornado follows an exponential rule. On average, the daily number of tornadoes in the next lowest damage category is approximately twice the number in the current category. These findings are important and timely for tornado hazard models and for seasonal and sub-seasonal forecasts of tornado activity.

Reaching the healthy people goals for reducing childhood obesity: closing the energy gap.

PubMed

Wang, Y Claire; Orleans, C Tracy; Gortmaker, Steven L

2012-05-01

The federal government has set measurable goals for reducing childhood obesity to 5% by 2010 (Healthy People 2010), and 10% lower than 2005-2008 levels by 2020 (Healthy People 2020). However, population-level estimates of the changes in daily energy balance needed to reach these goals are lacking. To estimate needed per capita reductions in youths' daily "energy gap" (calories consumed over calories expended) to achieve Healthy People goals by 2020. Analyses were conducted in 2010 to fit multivariate models using National Health and Nutrition Examination Surveys 1971-2008 (N=46,164) to extrapolate past trends in obesity prevalence, weight, and BMI among youth aged 2-19 years. Differences in average daily energy requirements between the extrapolated 2020 levels and Healthy People scenarios were estimated. During 1971-2008, mean BMI and weight among U.S. youth increased by 0.55 kg/m(2) and by 1.54 kg per decade, respectively. Extrapolating from these trends to 2020, the average weight among youth in 2020 would increase by ∼1.8 kg from 2007-2008 levels. Averting this increase will require an average reduction of 41 kcal/day in youth's daily energy gap. An additional reduction of 120 kcal/day and 23 kcal/day would be needed to reach Healthy People 2010 and Healthy People 2020 goals, respectively. Larger reductions are needed among adolescents and racial/ethnic minority youth. Aggressive efforts are needed to reverse the positive energy imbalance underlying the childhood obesity epidemic. The energy-gap metric provides a useful tool for goal setting, intervention planning, and charting progress. Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A statistical model for estimating stream temperatures in the Salmon and Clearwater River basins, central Idaho

USGS Publications Warehouse

Donato, Mary M.

2002-01-01

A water-quality standard for temperature is critical for the protection of threatened and endangered salmonids, which need cold, clean water to sustain life. The Idaho Department of Environmental Quality has established temperature standards to protect salmonids, yet little is known about the normal range of temperatures of most Idaho streams. A single temperature standard for all streams does not take into account the natural temperature variation of streams or the existence of naturally warm waters. To address these issues and to help the Idaho Department of Environmental Quality revise the existing State temperature standards for aquatic life, temperature data from more than 200 streams and rivers in the salmon and Clearwater River Basins were collected. From these data, a statistical model was developed for estimating stream temperatures on the basis of subbasin and site characteristics and climatic factors. Stream temperatures were monitored hourly for approximately 58 days during July, August, and September 2000 at relatively undisturbed sites in subbasins in the Salmon and Clearwater River Basins in central Idaho. The monitored subbasins vary widely in size, elevation, drainage area, vegetation cover, and other characteristics. The resulting data were analyzed for statistical correlations with subbasin and site characteristics to establish the most important factors affecting stream temperature. Maximum daily average stream temperatures were strongly correlated with elevation and total upstream drainage area; weaker correlations were noted with stream depth and width and aver-age subbasin slope. Stream temperatures also were correlated with certain types of vegetation cover, but these variables were not significant in the final model. The model takes into account seasonal temperature fluctuations, site elevation, total drainage area, average subbasin slope, and the deviation of daily average air temperature from a 30-year normal daily average air temperature. The goodness-of-fit of the model varies with day of the year. Overall, temperatures can be estimated with 95-percent confidence to within approximately plus or minus 4 degrees Celsius. The model performed well when tested on independent stream-temperature data previously collected by the U.S. Geological Survey and other agencies. Although the model provides insight into the natural temperature potential of a wide variety of streams and rivers in the Salmon and Clearwater River Basins, it has limitations. It is based on data collected in only one summer, during which temperatures were higher and streamflows were lower than normal. The effects of changes in streamflow on the effectiveness of the model are not known. Because the model is based on data from minimally disturbed or undisturbed streams, it should not be applied to streams known to be significantly affected by human activities such as disturbance of the streambed, diversion and return of water by irrigation ditches, and removal of riparian vegetation. Finally, because the model is based on data from streams in the Salmon and Clearwater River Basins and reflects climatological and landscape characteristics of those basins, it should not be applied to streams outside this region.

Receptor model source attributions for Utah’s Salt Lake City airshed and the impacts of wintertime secondary ammonium nitrate and ammonium chloride aerosol.

EPA Science Inventory

Communities along Utah’s Wasatch Front are currently developing strategies to reduce daily average PM2.5 levels to below National Ambient Air Quality Standards during wintertime, persistent, multi-day stable atmospheric conditions or cold-air pools. Speciated PM2.5 data from the ...

Passenger Flow Forecasting Research for Airport Terminal Based on SARIMA Time Series Model

NASA Astrophysics Data System (ADS)

Li, Ziyu; Bi, Jun; Li, Zhiyin

2017-12-01

Based on the data of practical operating of Kunming Changshui International Airport during2016, this paper proposes Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict the passenger flow. This article not only considers the non-stationary and autocorrelation of the sequence, but also considers the daily periodicity of the sequence. The prediction results can accurately describe the change trend of airport passenger flow and provide scientific decision support for the optimal allocation of airport resources and optimization of departure process. The result shows that this model is applicable to the short-term prediction of airport terminal departure passenger traffic and the average error ranges from 1% to 3%. The difference between the predicted and the true values of passenger traffic flow is quite small, which indicates that the model has fairly good passenger traffic flow prediction ability.

Modelling Inland Flood Events for Hazard Maps in Taiwan

NASA Astrophysics Data System (ADS)

Ghosh, S.; Nzerem, K.; Sassi, M.; Hilberts, A.; Assteerawatt, A.; Tillmanns, S.; Mathur, P.; Mitas, C.; Rafique, F.

2015-12-01

Taiwan experiences significant inland flooding, driven by torrential rainfall from plum rain storms and typhoons during summer and fall. From last 13 to 16 years data, 3,000 buildings were damaged by such floods annually with a loss US$0.41 billion (Water Resources Agency). This long, narrow island nation with mostly hilly/mountainous topography is located at tropical-subtropical zone with annual average typhoon-hit-frequency of 3-4 (Central Weather Bureau) and annual average precipitation of 2502mm (WRA) - 2.5 times of the world's average. Spatial and temporal distributions of countrywide precipitation are uneven, with very high local extreme rainfall intensities. Annual average precipitation is 3000-5000mm in the mountainous regions, 78% of it falls in May-October, and the 1-hour to 3-day maximum rainfall are about 85 to 93% of the world records (WRA). Rivers in Taiwan are short with small upstream areas and high runoff coefficients of watersheds. These rivers have the steepest slopes, the shortest response time with rapid flows, and the largest peak flows as well as specific flood peak discharge (WRA) in the world. RMS has recently developed a countrywide inland flood model for Taiwan, producing hazard return period maps at 1arcsec grid resolution. These can be the basis for evaluating and managing flood risk, its economic impacts, and insured flood losses. The model is initiated with sub-daily historical meteorological forcings and calibrated to daily discharge observations at about 50 river gauges over the period 2003-2013. Simulations of hydrologic processes, via rainfall-runoff and routing models, are subsequently performed based on a 10000 year set of stochastic forcing. The rainfall-runoff model is physically based continuous, semi-distributed model for catchment hydrology. The 1-D wave propagation hydraulic model considers catchment runoff in routing and describes large-scale transport processes along the river. It also accounts for reservoir storage. Major historical flood events have been successfully simulated along with spatial patterns of flows. Comparison of stochastic discharge statistics w.r.t. observed ones from Hydrological Year Books of Taiwan over all recorded years are also in good agreement.

No direct by maternal effects interaction detected for pre-weaning growth in Romane sheep using a reaction norm model.

PubMed

David, Ingrid; Bouvier, Frédéric; Ricard, Edmond; Ruesche, Julien; Weisbecker, Jean-Louis

2013-09-30

The pre-weaning growth of lambs, an important component of meat production, depends on maternal and direct effects. These effects cannot be observed directly and models used to study pre-weaning growth assume that they are additive. However, it is reasonable to suggest that the influence of direct effects on growth may differ depending on the value of maternal effects i.e. an interaction may exist between the two components. To test this hypothesis, an experiment was carried out in Romane sheep in order to obtain observations of maternal phenotypic effects (milk yield and milk quality) and pre-weaning growth of the lambs. The experiment consisted of mating ewes that had markedly different maternal genetic effects with rams that contributed very different genetic effects in four replicates of a 3 × 2 factorial plan. Milk yield was measured using the lamb suckling weight differential technique and milk composition (fat and protein contents) was determined by infrared spectroscopy at 15, 21 and 35 days after lambing. Lambs were weighed at birth and then at 15, 21 and 35 days. An interaction between genotype (of the lamb) and environment (milk yield and quality) for average daily gain was tested using a restricted likelihood ratio test, comparing a linear reaction norm model (interaction model) to a classical additive model (no interaction model). A total of 1284 weights of 442 lambs born from 166 different ewes were analysed. On average, the ewes produced 2.3 ± 0.8 L milk per day. The average protein and fat contents were 50 ± 4 g/L and 60 ± 18 g/L, respectively. The mean 0-35 day average daily gain was 207 ± 46 g/d. Results of the restricted likelihood ratio tests did not highlight any significant interactions between the genotype of the lambs and milk production of the ewe. Our results support the hypothesis of additivity of maternal and direct effects on growth that is currently applied in genetic evaluation models.

Daily Positive Spillover and Crossover from Mothers’ Work to Youth Health

PubMed Central

Lawson, Katie M.; Davis, Kelly D.; McHale, Susan M.; Hammer, Leslie B.; Buxton, Orfeu M.

2016-01-01

Prior research shows that employees’ work experiences can “spill over” into their family lives and “cross over” to affect family members. Expanding on studies that emphasize negative implications of work for family life, this study examined positive work-to-family spillover and positive and negative crossover between mothers and their children. Participants were 174 mothers in the extended care (nursing home) industry and their children (ages 9-17), both of whom completed daily diaries on the same, eight, consecutive evenings. On each workday, mothers reported whether they had a positive experience at work, youth reported on their mothers’ positive and negative mood after work, and youth rated their own mental (positive and negative affect) and physical health (physical health symptoms, sleep quality, sleep duration). Results of two-level models showed that mothers’ positive mood after work, on average, was directly related to youth reports of more positive affect, better sleep quality, and longer sleep duration. In addition, mothers with more positive work experiences, on average, displayed less negative mood after work, and in turn, adolescents reported less negative affect and fewer physical health symptoms. Results are discussed in terms of daily family system dynamics. PMID:25243577

Daily positive spillover and crossover from mothers' work to youth health.

PubMed

Lawson, Katie M; Davis, Kelly D; McHale, Susan M; Hammer, Leslie B; Buxton, Orfeu M

2014-12-01

Prior research shows that employees' work experiences can "spill over" into their family lives and "cross over" to affect family members. Expanding on studies that emphasize negative implications of work for family life, this study examined positive work-to-family spillover and positive and negative crossover between mothers and their children. Participants were 174 mothers in the extended care (nursing home) industry and their children (ages 9-17), both of whom completed daily diaries on the same 8 consecutive evenings. On each workday, mothers reported whether they had a positive experience at work, youth reported on their mothers' positive and negative mood after work, and youth rated their own mental (positive and negative affect) and physical health (physical health symptoms, sleep quality, sleep duration). Results of 2-level models showed that mothers' positive mood after work, on average, was directly related to youth reports of more positive affect, better sleep quality, and longer sleep duration. In addition, mothers with more positive work experiences, on average, displayed less negative mood after work, and in turn, adolescents reported less negative affect and fewer physical health symptoms. Results are discussed in terms of daily family system dynamics.

Gotta catch’em all! Pokémon GO and physical activity among young adults: difference in differences study

PubMed Central

Suharlim, Christian; Ueda, Peter; Kawachi, Ichiro; Rimm, Eric B

2016-01-01

Objective To estimate the effect of playing Pokémon GO on the number of steps taken daily up to six weeks after installation of the game. Design Cohort study using online survey data. Participants Survey participants of Amazon Mechanical Turk (n=1182) residing in the United States, aged 18 to 35 years and using iPhone 6 series smartphones. Main outcome measures Number of daily steps taken each of the four weeks before and six weeks after installation of Pokémon GO, automatically recorded in the “Health” application of the iPhone 6 series smartphones and reported by the participants. A difference in difference regression model was used to estimate the change in daily steps in players of Pokémon GO compared with non-players. Results 560 (47.4%) of the survey participants reported playing Pokémon GO and walked on average 4256 steps (SD 2697) each day in the four weeks before installation of the game. The difference in difference analysis showed that the daily average steps for Pokémon GO players during the first week of installation increased by 955 additional steps (95% confidence interval 697 to 1213), and then this increase gradually attenuated over the subsequent five weeks. By the sixth week after installation, the number of daily steps had gone back to pre-installation levels. No significant effect modification of Pokémon GO was found by sex, age, race group, bodyweight status, urbanity, or walkability of the area of residence. Conclusions Pokémon GO was associated with an increase in the daily number of steps after installation of the game. The association was, however, moderate and no longer observed after six weeks. PMID:27965211

Local food environments are associated with girls' energy, sugar-sweetened beverage and snack-food intakes.

PubMed

Deierlein, Andrea L; Galvez, Maida P; Yen, Irene H; Pinney, Susan M; Biro, Frank M; Kushi, Lawrence H; Teitelbaum, Susan; Wolff, Mary S

2014-10-01

To describe availability and frequency of use of local snack-food outlets and determine whether reported use of these outlets was associated with dietary intakes. Data were cross-sectional. Availability and frequency of use of three types of local snack-food outlets were reported. Daily dietary intakes were based on the average of up to four 24 h dietary recalls. Multivariable linear regression models estimated average daily intakes of energy, sugar-sweetened beverages (SSB) and snack foods/sweets associated with use of outlets. Multi-site, observational cohort study in the USA, 2004-2006. Girls aged 6-8 years (n 1010). Weekly frequency of use of local snack-food outlets increased with number of available types of outlets. Girls with access to only one type of outlet reported consuming food/beverage items less frequently than girls with access to two or three types of outlets (P <0·001). Girls' daily energy, SSB and snack foods/sweets intakes increased with greater use of outlets. Girls who reported using outlets>1 to 3 times/week consumed 0·27 (95 % CI 0·13, 0·40) servings of SSB more daily than girls who reported no use. Girls who reported using outlets>3 times/week consumed 449·61 (95 % CI 134·93, 764·29) kJ, 0·43 (95 % CI 0·29, 0·58) servings of SSB and 0·38 (95 % CI 0·12, 0·65) servings of snack foods/sweets more daily than those who reported no use. Girls' frequency of use of local snack-food outlets increases with the number of available types of outlets and is associated with greater daily intakes of energy and servings of SSB and snack foods/sweets.

Sequential estimation of surface water mass changes from daily satellite gravimetry data

NASA Astrophysics Data System (ADS)

Ramillien, G. L.; Frappart, F.; Gratton, S.; Vasseur, X.

2015-03-01

We propose a recursive Kalman filtering approach to map regional spatio-temporal variations of terrestrial water mass over large continental areas, such as South America. Instead of correcting hydrology model outputs by the GRACE observations using a Kalman filter estimation strategy, regional 2-by-2 degree water mass solutions are constructed by integration of daily potential differences deduced from GRACE K-band range rate (KBRR) measurements. Recovery of regional water mass anomaly averages obtained by accumulation of information of daily noise-free simulated GRACE data shows that convergence is relatively fast and yields accurate solutions. In the case of cumulating real GRACE KBRR data contaminated by observational noise, the sequential method of step-by-step integration provides estimates of water mass variation for the period 2004-2011 by considering a set of suitable a priori error uncertainty parameters to stabilize the inversion. Spatial and temporal averages of the Kalman filter solutions over river basin surfaces are consistent with the ones computed using global monthly/10-day GRACE solutions from official providers CSR, GFZ and JPL. They are also highly correlated to in situ records of river discharges (70-95 %), especially for the Obidos station where the total outflow of the Amazon River is measured. The sparse daily coverage of the GRACE satellite tracks limits the time resolution of the regional Kalman filter solutions, and thus the detection of short-term hydrological events.

Urban scale air quality modelling using detailed traffic emissions estimates

NASA Astrophysics Data System (ADS)

Borrego, C.; Amorim, J. H.; Tchepel, O.; Dias, D.; Rafael, S.; Sá, E.; Pimentel, C.; Fontes, T.; Fernandes, P.; Pereira, S. R.; Bandeira, J. M.; Coelho, M. C.

2016-04-01

The atmospheric dispersion of NOx and PM10 was simulated with a second generation Gaussian model over a medium-size south-European city. Microscopic traffic models calibrated with GPS data were used to derive typical driving cycles for each road link, while instantaneous emissions were estimated applying a combined Vehicle Specific Power/Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (VSP/EMEP) methodology. Site-specific background concentrations were estimated using time series analysis and a low-pass filter applied to local observations. Air quality modelling results are compared against measurements at two locations for a 1 week period. 78% of the results are within a factor of two of the observations for 1-h average concentrations, increasing to 94% for daily averages. Correlation significantly improves when background is added, with an average of 0.89 for the 24 h record. The results highlight the potential of detailed traffic and instantaneous exhaust emissions estimates, together with filtered urban background, to provide accurate input data to Gaussian models applied at the urban scale.

Association of drinking-water source and use characteristics with urinary antimony concentrations.

PubMed

Makris, Konstantinos C; Andra, Syam S; Herrick, Lisa; Christophi, Costas A; Snyder, Shane A; Hauser, Russ

2013-03-01

Environmental factors, such as storage time, frequency of bottle reuse and temperature, have been shown to facilitate antimony (Sb) leaching from water- and food-packaging materials. The globally escalating consumption of water packaged in Sb-containing bottles, such as that of polyethylene terephthalate (PET), could increase human daily Sb doses. This study set out to investigate the relationship between drinking-water source, use characteristics, and urinary Sb concentrations (U-Sb) accompanied with survey responses of a healthy (n=35) Cypriot participant pool. One spot urine sample was collected during administration of questionnaire, while a second spot urine sample was collected from the same individual about 7 days later. Urinary and water Sb concentrations were measured with an inductively coupled plasma mass spectrometer. Survey responses showed that bottled water summed over various volumes and plastic types, such as polycarbonate and PET contributed to an average 61% of daily water consumption. Water sources such as tap, mobile stations (explained in a following section), and well water contributed to 24%, 14%, and 2% of an individual's daily water consumption pattern, respectively. Average daily potable water use of both bottled and tap water by individuals consisted of 65% drinking-water, while the remaining 35% was water used for preparing cold and hot beverages, such as, tea, coffee, and juices. A significant (P=0.02) association between per capita water consumption from PET bottles and urinary creatinine-unadjusted concentrations was observed, but this relationship did not remain after inclusion of covariates in a multivariate regression model. In the creatinine-adjusted regression model, only gender (female) was a significant (P<0.01) predictor of U-Sb, after adjusting for several covariates. It is proposed that consumption data collection on various water uses and sources among individuals could perhaps decrease the uncertainty associated with derivations of acceptable daily Sb intakes.

Physical Activity and Variation in Momentary Behavioral Cognitions: An Ecological Momentary Assessment Study.

PubMed

Pickering, Trevor A; Huh, Jimi; Intille, Stephen; Liao, Yue; Pentz, Mary Ann; Dunton, Genevieve F

2016-03-01

Decisions to perform moderate-to-vigorous physical activity (MVPA) involve behavioral cognitive processes that may differ within individuals depending on the situation. Ecological momentary assessment (EMA) was used to examine the relationships of momentary behavioral cognitions (ie, self-efficacy, outcome expectancy, intentions) with MVPA (measured by accelerometer). A sample of 116 adults (mean age, 40.3 years; 72.4% female) provided real-time EMA responses via mobile phones across 4 days. Multilevel models were used to test whether momentary behavioral cognitions differed across contexts and were associated with subsequent MVPA. Mixed-effects location scale models were used to examine whether subject-level means and within-subjects variances in behavioral cognitions were associated with average daily MVPA. Momentary behavioral cognitions differed across contexts for self-efficacy (P = .007) but not for outcome expectancy (P = .53) or intentions (P = .16). Momentary self-efficacy, intentions, and their interaction predicted MVPA within the subsequent 2 hours (Ps < .01). Average daily MVPA was positively associated with within-subjects variance in momentary self-efficacy and intentions for physical activity (Ps < .05). Although momentary behavioral cognitions are related to subsequent MVPA, adults with higher average MVPA have more variation in physical activity self-efficacy and intentions. Performing MVPA may depend more on how much behavioral cognitions vary across the day than whether they are generally high or low.

Stock price forecasting based on time series analysis

NASA Astrophysics Data System (ADS)

Chi, Wan Le

2018-05-01

Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment.

PubMed

Gulliver, John; Briggs, David

2011-05-15

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Daily Parental Knowledge of Youth Activities Is Linked to Youth Physical Symptoms and HPA functioning

PubMed Central

Lippold, Melissa A.; Davis, Kelly D.; McHale, Susan M.; Almeida, David M.

2015-01-01

Considerable evidence documents linkages between parental knowledge of youth activities and youth risky behavior. We extended this research to determine whether parental knowledge was associated with youth physical health, including reports of physical symptoms (e.g., headaches, stomachaches) and a biomarker of hypothalamic pituitary adrenocortical (HPA) axis functioning (i.e., salivary cortisol levels). Participants were children of employees in the Information Technology division of a Fortune 500 company (N = 132, Mean Age Youth = 13.39 years, 55% female) who participated in a daily diary study. Data were collected via telephone calls on eight consecutive evenings. On four study days, cortisol samples were collected at 4 time points (waking, 30 min after waking, before dinner, bedtime). Multi-level models revealed that, at the between-person level, youth whose parents had higher average knowledge about their activities, exhibited lower bedtime cortisol levels. Furthermore, at the within-person level, on days when parents displayed more knowledge than usual (relative to their own eight-day average), youth had lower before-dinner cortisol than usual. Linkages between average parental knowledge and physical health symptoms were moderated by youth age: Younger but not older adolescents whose parents were more knowledgeable had fewer physical health symptoms, on average. A next step is to identify the processes that underlie these associations. PMID:26751757

Combined use of land use regression and BenMAP for estimating public health benefits of reducing PM2.5 in Tianjin, China

NASA Astrophysics Data System (ADS)

Chen, Li; Shi, Mengshuang; Li, Suhuan; Bai, Zhipeng; Wang, Zhongliang

2017-03-01

To assess the public health benefits of reducing PM2.5 in Tianjin, we created an annual air quality surface with a land use regression (LUR) model conducted at a high spatial resolution (1 km). The predictors included in the final model were population density, road length within a 1000 m buffer, industrial land area within a 2000m buffer and distance to the coast. The fitting R2 and the leave-one-out-cross-validation (LOOCV) R2 of the PM2.5 LUR models were 0.78 and 0.73, respectively, suggesting that the predicted PM2.5 concentrations fitted well with the measured values for the entire year. Daily air quality surfaces were established based on historic concentration data and interpolation method. We evaluated avoided cases of mortality and morbidity in Tianjin, assuming achievement of China's current air quality daily and annual standards (No. GB3095-2012). Reducing the daily average PM2.5 to the daily Class II standard (75 μg/m3), the avoided emergency department visits, the deaths for cardiovascular disease and the deaths for respiratory disease are 85,000 (95% confidence interval (CI), 17,000-150,000), 2000 (95% CI, 920-3100) and 280 (95% CI, 94-460) per year respectively, and the monetary values are 23-42 million yuan, 180-4800 million yuan and 25-670 million yuan per year in 2015 yuan year respectively. Reducing the annual average PM2.5 to the annual Class II standard (35 μg/m3), the avoided emergency department visits, the deaths for cardiovascular disease and the deaths for respiratory disease are 59,000 (95% CI, 12,000-110,000), 1400 (95% CI, 640-2100) and 200 (95% CI, 66-320) per year respectively, and the monetary values are 16-29 million yuan, 130 to 3400 million yuan and 18 to 480 million yuan per year in 2015 yuan year respectively.

Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.

PubMed

Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee

2005-05-11

The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.

Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula.

PubMed

Silva-Palacios, Inmaculada; Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

2016-02-01

Cupressaceae includes species cultivated as ornamentals in the urban environment. This study aims to investigate airborne pollen data for Cupressaceae on the southwestern Iberian Peninsula over a 21-year period and to analyse the trends in these data and their relationship with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1993 to 2013 in Badajoz (SW Spain). The main pollen season for Cupressaceae lasted, on average, 58 days, ranging from 55 to 112 days, from 24 January to 22 March. Furthermore, a short-term forecasting model has been developed for daily pollen concentrations. The model proposed to forecast the airborne pollen concentration is described by one equation. This expression is composed of two terms: the first term represents the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term is obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological parameters multiplied by a fitting coefficient. Temperature was the main meteorological factor by its influence over daily pollen forecast, being the rain the second most important factor. This model represents a good approach to a continuous balance model of Cupressaceae pollen concentration and is supported by a close agreement between the observed and predicted mean concentrations. The novelty of the proposed model is the analysis of meteorological parameters that are not frequently used in Aerobiology.

Ambient carbon monoxide and cardiovascular mortality: a nationwide time-series analysis in 272 cities in China.

PubMed

Liu, Cong; Yin, Peng; Chen, Renjie; Meng, Xia; Wang, Lijun; Niu, Yue; Lin, Zhijing; Liu, Yunning; Liu, Jiangmei; Qi, Jinlei; You, Jinling; Kan, Haidong; Zhou, Maigeng

2018-01-01

Evidence of the acute health effects of ambient carbon monoxide air pollution in developing countries is scarce and mixed. We aimed to evaluate short-term associations between carbon monoxide and daily cardiovascular disease mortality in China. We did a nationwide time-series analysis in 272 major cities in China from January, 2013, to December, 2015. We extracted daily cardiovascular disease mortality data from China's Disease Surveillance Points system. Data on daily carbon monoxide concentrations for each city were obtained from the National Urban Air Quality Real-time Publishing Platform. City-specific associations between carbon monoxide concentrations and daily mortality from cardiovascular disease, coronary heart disease, and stroke were estimated with over-dispersed generalised linear models. Bayesian hierarchical models were used to obtain national and regional average associations. Exposure-response association curves and potential effect modifiers were evaluated. Two-pollutant models were fit to evaluate the robustness of the effects of carbon monoxide on cardiovascular mortality. The average annual mean carbon monoxide concentration in these cities from 2013 to 2015 was 1·20 mg/m 3 , ranging from 0·43 mg/m 3 to 2·45 mg/m 3 . For a 1 mg/m 3 increase in average carbon monoxide concentrations on the present day and previous day (lag 0-1), we observed significant increments in mortality of 1·12% (95% posterior interval [PI] 0·42-1·83) from cardiovascular disease, 1·75% (0·85-2·66) from coronary heart disease, and 0·88% (0·07-1·69) from stroke. These associations did not vary substantially by city, region, and demographic characteristics (age, sex, and level of education), and the associations for cardiovascular disease and coronary heart disease were robust to the adjustment of criteria co-pollutants. We did not find a threshold below which carbon monoxide exposure had no effect on cardiovascular disease mortality. This analysis is, to our knowledge, the largest study done in a developing country, and provides robust evidence of the association between short-term exposure to ambient carbon monoxide and increased cardiovascular disease mortality, especially coronary heart disease mortality. Public Welfare Research Program. Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

[Prediction of heat-related mortality impacts under climate change scenarios in Shanghai].

PubMed

Guo, Ya-fei; Li, Tian-tian; Cheng, Yan-li; Ge, Tan-xi; Chen, Chen; Liu, Fan

2012-11-01

To project the future impacts of climate change on heat-related mortality in shanghai. The statistical downscaling techniques were applied to simulate the daily mean temperatures of Shanghai in the middle and farther future under the changing climate. Based on the published exposure-reaction relationship of temperature and mortality in Shanghai, we projected the heat-related mortality in the middle and farther future under the circumstance of high speed increase of carbon e mission (A2) and low speed increase of carbon emission (B2). The data of 1961 to 1990 was used to establish the model, and the data of 1991 - 2001 was used to testify the model, and then the daily mean temperature from 2030 to 2059 and from 2070 to 2099 were simulated and the heat-related mortality was projected. The data resources were from U.S. National Climatic Data Center (NCDC), U.S. National Centers for Environmental Prediction Reanalysis Data in SDSM Website and UK Hadley Centre Coupled Model Data in SDSM Website. The explained variance and the standard error of the established model was separately 98.1% and 1.24°C. The R(2) value of the simulated trend line equaled to 0.978 in Shanghai, as testified by the model. Therefore, the temperature prediction model simulated daily mean temperatures well. Under A2 scenario, the daily mean temperature in 2030 - 2059 and 2070 - 2099 were projected to be 17.9°C and 20.4°C, respectively, increasing by 1.1°C and 3.6°C when compared to baseline period (16.8°C). Under B2 scenario, the daily mean temperature in 2030 - 2059 and 2070 - 2099 were projected to be 17.8°C and 19.1°C, respectively, increasing by 1.0°C and 2.3°C when compared to baseline period (16.8°C). Under A2 scenario, annual average heat-related mortality were projected to be 516 cases and 1191 cases in 2030 - 2059 and 2070 - 2099, respectively, increasing 53.6% and 254.5% when compared with baseline period (336 cases). Under B2 scenario, annual average heat-related mortality were projected to be 498 cases and 832 cases in 2030 - 2059 and 2070 - 2099, respectively, increasing 48.2% and 147.6% when compared with baseline period (336 cases). Under the changing climate, heat-related mortality is projected to increase in the future;and the increase will be more obvious in year 2070 - 2099 than in year 2030 - 2059.

An empirical model of the quiet daily geomagnetic field variation

USGS Publications Warehouse

Yamazaki, Y.; Yumoto, K.; Cardinal, M.G.; Fraser, B.J.; Hattori, P.; Kakinami, Y.; Liu, J.Y.; Lynn, K.J.W.; Marshall, R.; McNamara, D.; Nagatsuma, T.; Nikiforov, V.M.; Otadoy, R.E.; Ruhimat, M.; Shevtsov, B.M.; Shiokawa, K.; Abe, S.; Uozumi, T.; Yoshikawa, A.

2011-01-01

An empirical model of the quiet daily geomagnetic field variation has been constructed based on geomagnetic data obtained from 21 stations along the 210 Magnetic Meridian of the Circum-pan Pacific Magnetometer Network (CPMN) from 1996 to 2007. Using the least squares fitting method for geomagnetically quiet days (Kp ??? 2+), the quiet daily geomagnetic field variation at each station was described as a function of solar activity SA, day of year DOY, lunar age LA, and local time LT. After interpolation in latitude, the model can describe solar-activity dependence and seasonal dependence of solar quiet daily variations (S) and lunar quiet daily variations (L). We performed a spherical harmonic analysis (SHA) on these S and L variations to examine average characteristics of the equivalent external current systems. We found three particularly noteworthy results. First, the total current intensity of the S current system is largely controlled by solar activity while its focus position is not significantly affected by solar activity. Second, we found that seasonal variations of the S current intensity exhibit north-south asymmetry; the current intensity of the northern vortex shows a prominent annual variation while the southern vortex shows a clear semi-annual variation as well as annual variation. Thirdly, we found that the total intensity of the L current system changes depending on solar activity and season; seasonal variations of the L current intensity show an enhancement during the December solstice, independent of the level of solar activity. Copyright 2011 by the American Geophysical Union.

Some effects of quiet geomagnetic field changes upon values used for main field modeling

USGS Publications Warehouse

Campbell, W.H.

1987-01-01

The effects of three methods of data selection upon the assumed main field levels for geomagnetic observatory records used in main field modeling were investigated for a year of very low solar-terrestrial activity. The first method concerned the differences between the year's average of quiet day field values and the average of all values during the year. For H these differences were 2-3 gammas, for D they were -0.04 to -0.12???, for Z the differences were negligible. The second method of selection concerned the effects of the daytime internal Sq variations upon the daily mean values of field. The midnight field levels when the Sq currents were a minimum deviated from the daily mean levels by as much as 4-7 gammas in H and Z but were negligible for D. The third method of selection was designed to avoid the annual and semi-annual quiet level changes of field caused by the seasonal changes in the magnetosphere. Contributions from these changes were found to be as much as 4-7 gammas in quiet years and expected to be greater than 10 gammas in active years. Suggestions for improved methods of improved data selection in main field modeling are given. ?? 1987.

40 CFR Table 5 to Subpart Mmmmm of... - Continuous Compliance With Emission Limits and Operating Limits

Code of Federal Regulations, 2010 CFR

2010-07-01

... performance test. v. If you use a venturi scrubber, maintaining the daily average pressure drop across the.... Each new or reconstructed flame lamination affected source using a scrubber a. Maintain the daily average scrubber inlet liquid flow rate above the minimum value established during the performanceb...

78 FR 75396 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-11

... Specify the Exclusion of Odd Lot Transactions From Consolidated Average Daily Volume Calculations for a Limited Period of Time for Purposes of Certain Transaction Pricing on the Exchange Through January 31... specify the exclusion of odd lot transactions from consolidated average daily volume (``CADV...

30 CFR 203.74 - When will MMS reconsider its determination?

Code of Federal Regulations, 2011 CFR

2011-07-01

... Sulfur General Royalty Relief for Pre-Act Deep Water Leases and for Development and Expansion Projects... as calculated under this paragraph. (1) Your current reference price is a weighted-average of daily... calendar months; (2) Your base reference price is a weighted average of daily closing prices on the NYMEX...

40 CFR Table 5 to Subpart Mmmmm of... - Continuous Compliance With Emission Limits and Operating Limits

Code of Federal Regulations, 2011 CFR

2011-07-01

.... Each new or reconstructed flame lamination affected source using a scrubber a. Maintain the daily average scrubber inlet liquid flow rate above the minimum value established during the performanceb. Maintain the daily average scrubber effluent pH within the operating range established during the...

A hybrid model for river water temperature as a function of air temperature and discharge

NASA Astrophysics Data System (ADS)

Toffolon, Marco; Piccolroaz, Sebastiano

2015-11-01

Water temperature controls many biochemical and ecological processes in rivers, and theoretically depends on multiple factors. Here we formulate a model to predict daily averaged river water temperature as a function of air temperature and discharge, with the latter variable being more relevant in some specific cases (e.g., snowmelt-fed rivers, rivers impacted by hydropower production). The model uses a hybrid formulation characterized by a physically based structure associated with a stochastic calibration of the parameters. The interpretation of the parameter values allows for better understanding of river thermal dynamics and the identification of the most relevant factors affecting it. The satisfactory agreement of different versions of the model with measurements in three different rivers (root mean square error smaller than 1oC, at a daily timescale) suggests that the proposed model can represent a useful tool to synthetically describe medium- and long-term behavior, and capture the changes induced by varying external conditions.

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?

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

Ping Yang; Daniel B. Ames; Andre Fonseca

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicatemore » that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.« less

[The short-term effects of air pollution on mortality: the results of the EMECAM project in the city of Pamplona, 1991-95. Estudio Multicéntrico Español sobre la Relación entre la Contaminación Atmosférica y la Mortalidad].

PubMed

Aguinaga Ontoso, I; Guillén Grima, F; Oviedo de Sola, P J; Floristan Floristan, M Y; Laborda Santesteban, M S; Martínez Ramírez, M T; Martínez González, M A

1999-01-01

To assess the short-term impact of air pollution on the daily death rate in the city of Pamplona. Ecological study with a population of 212,000 inhabitants. A time series data analysis is conducted by means of multiple linear regression and Poisson regression, with the daily death rate data, air pollution levels for Particles and SO2, weather parameters of average relative humidity and temperature daily and number of cases weekly of flu for the 1991-1995 period. The average number of deaths daily for non-external causes is that of 4.15 deaths, with a range from zero to 13 deaths. The city of Pamplona has a mean annual temperature of 12.7 degrees C (-2.3 degrees C to 31.6 degrees C) and a relative humidity of 68.5%. In the model, the temperature (with a one-day time lag and a six-day time lag temperature squared) and the humidity (with a one-day time lag) is related to the death rate for all causes. But the death rate for non-external causes is only related in the model with the temperature (one-day time lag, P: 0.035) and five-day time lag with temperature squared (p: 0.028). The timely estimates of the relative particle-related risk show that the highest risk of dying stems from respiratory causes with a relative risk of 1.13. However, none of these relationships is statistically significant. In the case of Sulfur Dioxide, the estimates closely near the zero figure, and none of them is significant. The Temperature has an impact of the death rate for all causes, both external and non-external, and the relative humidity solely has an impact on the death rate for non-external causes. It has not been possible to prove any influence of the daily environmental pollution levels on the daily death rate.

Forecasting asthma-related hospital admissions in London using negative binomial models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-05-01

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Daily torpor and hibernation in birds and mammals

PubMed Central

RUF, THOMAS; GEISER, FRITZ

2014-01-01

Many birds and mammals drastically reduce their energy expenditure during times of cold exposure, food shortage, or drought, by temporarily abandoning euthermia, i.e., the maintenance of high body temperatures. Traditionally, two different types of heterothermy, i.e., hypometabolic states associated with low body temperatures (torpor), have been distinguished: Daily torpor, which lasts less than 24 h and is accompanied by continued foraging, versus hibernation, with torpor bouts lasting consecutive days to several weeks in animals that usually do not forage but rely on energy stores, either food caches or body energy reserves. This classification of torpor types has been challenged however, suggesting that these phenotypes may merely represent the extremes in a continuum of traits. Here, we investigate whether variables of torpor in 214 species, 43 birds and 171 mammals form a continuum or a bimodal distribution. We use Gaussian-mixture cluster analysis as well as phylogenetically informed regressions to quantitatively assess the distinction between hibernation and daily torpor and to evaluate the impact of body mass and geographical distribution of species on torpor traits. Cluster analysis clearly confirmed the classical distinction between daily torpor and hibernation. Overall, heterothermic endotherms are small on average, but hibernators are significantly heavier than daily heterotherms and also are distributed at higher average latitudes (~35°) than daily heterotherms (~25°). Variables of torpor for an average 30-g heterotherm differed significantly between daily heterotherms and hibernators. Average maximum torpor bout duration was >30-fold longer, and mean torpor bout duration >25-fold longer in hibernators. Mean minimum body temperature differed by ~13°C, and the mean minimum torpor metabolic rate was ~35% of the BMR in daily heterotherms but only 6% of basal metabolic rate in hibernators. Consequently, our analysis strongly supports the view that hibernators and daily heterotherms are functionally distinct groups that probably have been subject to disruptive selection. Arguably, the primary physiological difference between daily torpor and hibernation, which leads to a variety of derived further distinct characteristics, is the temporal control of entry into and arousal from torpor, which is governed by the circadian clock in daily heterotherms, but apparently not in hibernators. PMID:25123049

Proxy-based reconstruction of erythemal UV doses over Estonia for 1955 2004

NASA Astrophysics Data System (ADS)

Eerme, K.; Veismann, U.; Lätt, S.

2006-08-01

A proxy-based reconstruction of the erythemally-weighted UV doses for 1955-2004 has been performed for the Tartu-Tõravere Meteorological Station (58°16' N, 26°28' E, 70 m a.s.l.) site. The pyrheliometer-measured daily sum of direct irradiance on partly cloudy and clear days, and the pyranometer-measured daily sum of global irradiance on overcast days were used as the cloudiness influence related proxies. The TOMS ozone data have been used for detecting the daily deviations from the climatic value (averaged annual cycle). In 1998-2004, the biases between the measured and reconstructed daily doses in 55.5% of the cases were within ±10% and in 83.5% of the cases within ±20%, on average. In the summer half-year these amounts were 62% and 88%, respectively. In most years the results for longer intervals did not differ significantly, if no correction was made for the daily deviations of total ozone from its climatic value. The annual and summer half-yearly erythemal doses (contributing, on average, 89% of the annual value) agreed within ±2%, except for the years after major volcanic eruptions and one extremely fine weather year (2002). Using the daily relative sunshine duration as a proxy without detailed correction for atmospheric turbidity results in biases of 2-4% in the summer half-yearly dose in the years after major volcanic eruptions and a few other years of high atmospheric turbidity. The year-to-year variations of the summer half-yearly erythemal dose in 1955-2004 were found to be within 92-111% relative to their average value. Exclusion of eight extreme years reduces this range for the remaining to 95-105.5%. Due to the quasi-periodic alternation of wet and dry periods, the interval of cloudy summers 1976-1993 regularly manifests summer half-yearly erythemal dose values lower than the 1955-2004 average. Since 1996/1997 midwinters have been darker than on average.

The effect of dietary betaine in Eimeria acervulina-infected chicks.

PubMed

Matthews, J O; Southern, L L

2000-01-01

Two experiments were conducted to evaluate the effect of dietary betaine in broiler chicks with either chronic (CHR; 2.5 x 10(5) sporulated oocysts on Day 1, 4, 7, and 10) or acute (ACT; 1.0 x 10(6) sporulated oocysts on Day 1) Eimeria acervulina infections. Three hundred (Experiment 1) or 600 (Experiment 2), 4-d-old male chicks were used in the 14-d experiments. In both experiments, a 2 x 3 factorial arrangement of treatments was used: two levels of betaine (0 or 0.075%) and three levels of coccidiosis infection (uninfected, CHR, or ACT). Each treatment was replicated five (Experiment 1) or 10 (Experiment 2) times with 10 chicks per replicate. In Experiment 1, the ACT infection decreased (P < 0.01) average daily gain and gain:feed, and the CHR infection decreased (P < 0.02) average daily gain. The ACT and CHR infections decreased (P < 0.06) Day 7 plasma carotenoids and Day 14 plasma total protein, and the ACT infection also decreased (P < 0.06) Day 7 plasma total protein. Average daily gain and Day 7 plasma total protein were increased in CHR chicks fed betaine but were decreased in uninfected chicks fed betaine (CHR x betaine; P < 0.09). Chicks fed betaine had decreased (P < 0.06) Day 7 plasma carotenoids. In Experiment 2 the CHR and ACT infections decreased (P < 0.01) average daily gain, average daily feed intake, grain:feed ratio, Days 7 and 14 plasma carotenoids, and Day 7 plasma total protein. Chicks fed betaine had increased (P < 0.07) average daily gains, gain:feed ratios, and lesion scores. Day 14 plasma carotenoids and plasma total protein were decreased in uninfected chicks fed betaine but were increased in CHR chicks fed betaine (CHR x betaine; P < 0.04); plasma carotenoids also were increased in ACT chicks fed betaine (ACT x betaine; P < 0.05). Betaine did not consistently affect growth performance, plasma constituents, or lesion score in CHR or ACT coccidiosis-infected chicks.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2015-04-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Effect of air pollution on pediatric respiratory emergency room visits and hospital admissions.

PubMed

Farhat, S C L; Paulo, R L P; Shimoda, T M; Conceição, G M S; Lin, C A; Braga, A L F; Warth, M P N; Saldiva, P H N

2005-02-01

In order to assess the effect of air pollution on pediatric respiratory morbidity, we carried out a time series study using daily levels of PM10, SO2, NO2, ozone, and CO and daily numbers of pediatric respiratory emergency room visits and hospital admissions at the Children's Institute of the University of Sao Paulo Medical School, from August 1996 to August 1997. In this period there were 43,635 hospital emergency room visits, 4534 of which were due to lower respiratory tract disease. The total number of hospital admissions was 6785, 1021 of which were due to lower respiratory tract infectious and/or obstructive diseases. The three health end-points under investigation were the daily number of emergency room visits due to lower respiratory tract diseases, hospital admissions due to pneumonia, and hospital admissions due to asthma or bronchiolitis. Generalized additive Poisson regression models were fitted, controlling for smooth functions of time, temperature and humidity, and an indicator of weekdays. NO2 was positively associated with all outcomes. Interquartile range increases (65.04 microg/m3) in NO2 moving averages were associated with an 18.4% increase (95% confidence interval, 95% CI = 12.5-24.3) in emergency room visits due to lower respiratory tract diseases (4-day moving average), a 17.6% increase (95% CI = 3.3-32.7) in hospital admissions due to pneumonia or bronchopneumonia (3-day moving average), and a 31.4% increase (95% CI = 7.2-55.7) in hospital admissions due to asthma or bronchiolitis (2-day moving average). The study showed that air pollution considerably affects children's respiratory morbidity, deserving attention from the health authorities.

Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform

PubMed Central

Zhang, Xinyu; Hou, Jie

2017-01-01

Background In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention. Objective The objective of our study was to explore the public’s awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu. Methods We collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. Results In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. Conclusions The daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately. PMID:28974484

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Acute effect of ambient air pollution on stroke mortality in the China air pollution and health effects study.

PubMed

Chen, Renjie; Zhang, Yuhao; Yang, Chunxue; Zhao, Zhuohui; Xu, Xiaohui; Kan, Haidong

2013-04-01

There have been no multicity studies on the acute effects of air pollution on stroke mortality in China. This study was undertaken to examine the associations between daily stroke mortality and outdoor air pollution (particulate matter <10 μm in aerodynamic diameter, sulfur dioxide, and nitrogen dioxide) in 8 Chinese cities. We used Poisson regression models with natural spline-smoothing functions to adjust for long-term and seasonal trends, as well as other time-varying covariates. We applied 2-stage Bayesian hierarchical statistical models to estimate city-specific and national average associations of air pollution with daily stroke mortality. Air pollution was associated with daily stroke mortality in 8 Chinese cities. In the combined analysis, an increase of 10 μg/m(3) of 2-day moving average concentrations of particulate matter <10 μm in aerodynamic diameter, sulfur dioxide, and nitrogen dioxide corresponded to 0.54% (95% posterior intervals, 0.28-0.81), 0.88% (95% posterior intervals, 0.54-1.22), and 1.47% (95% posterior intervals, 0.88-2.06) increase of stroke mortality, respectively. The concentration-response curves indicated linear nonthreshold associations between air pollution and risk of stroke mortality. To our knowledge, this is the first multicity study in China, or even in other developing countries, to report the acute effect of air pollution on stroke mortality. Our results contribute to very limited data on the effect of air pollution on stroke for high-exposure settings typical in developing countries.

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic precipitation, fed into a rainfall-runoff model to derive the flood frequency in the Tirolean Alps in Austria. Given the number of generated events, the simulation framework is able to generate a large variety of rainfall patterns, as well as reproduce the variograms of relevant extreme rainfall events in the region of interest.

Assessment of Daily and Weekly Fatigue among African American Cancer Survivors

PubMed Central

Sobel, Rina M.; McSorley, Anna-Michelle M.; Roesch, Scott C.; Malcarne, Vanessa L.; Hawes, Starlyn M.; Sadler, Georgia Robins

2013-01-01

This investigation evaluates two common measures of cancer-related fatigue, one multidimensional/retrospective and one unidimensional/same-day. Fifty-two African American survivors of diverse cancers completed fatigue visual analogue scales once daily, and the Multidimensional Fatigue Symptom Inventory (MFSI) once weekly, for four weeks. Zero-order correlations showed retrospectivefatigue was significantly related to average, peak, and most recent same-dayfatigue. Multilevel random coefficient modeling showed unidimensional fatigue shared the most variance with the MFSI’s General subscale for three weeks, and with the Vigor subscale for one week. Researchers and clinicians may wish to prioritize multidimensional measures when assessing cancer-related fatigue, if appropriate. PMID:23844922

Integrated water system simulation by considering hydrological and biogeochemical processes: model development, with parameter sensitivity and autocalibration

NASA Astrophysics Data System (ADS)

Zhang, Y. Y.; Shao, Q. X.; Ye, A. Z.; Xing, H. T.; Xia, J.

2016-02-01

Integrated water system modeling is a feasible approach to understanding severe water crises in the world and promoting the implementation of integrated river basin management. In this study, a classic hydrological model (the time variant gain model: TVGM) was extended to an integrated water system model by coupling multiple water-related processes in hydrology, biogeochemistry, water quality, and ecology, and considering the interference of human activities. A parameter analysis tool, which included sensitivity analysis, autocalibration and model performance evaluation, was developed to improve modeling efficiency. To demonstrate the model performances, the Shaying River catchment, which is the largest highly regulated and heavily polluted tributary of the Huai River basin in China, was selected as the case study area. The model performances were evaluated on the key water-related components including runoff, water quality, diffuse pollution load (or nonpoint sources) and crop yield. Results showed that our proposed model simulated most components reasonably well. The simulated daily runoff at most regulated and less-regulated stations matched well with the observations. The average correlation coefficient and Nash-Sutcliffe efficiency were 0.85 and 0.70, respectively. Both the simulated low and high flows at most stations were improved when the dam regulation was considered. The daily ammonium-nitrogen (NH4-N) concentration was also well captured with the average correlation coefficient of 0.67. Furthermore, the diffuse source load of NH4-N and the corn yield were reasonably simulated at the administrative region scale. This integrated water system model is expected to improve the simulation performances with extension to more model functionalities, and to provide a scientific basis for the implementation in integrated river basin managements.

First annual register of allergenic pollen in Talca, Chile.

PubMed

Mardones, P; Grau, M; Araya, J; Córdova, A; Pereira, I; Peñailillo, P; Silva, R; Moraga, A; Aguilera-Insunza, R; Yepes-Nuñez, J J; Palomo, I

2013-01-01

There are no data on atmospheric pollen in Talca. In the present work, our aim is to describe the amount of pollen grain in the atmosphere of the city of Talca likely to cause pollinosis of its inhabitants. A volumetric Hirst sampler (Burkard seven-day recording device) was used to study pollen levels. It was placed in the centre of Talca from May 2007 to April 2008. The highest airborne presence of pollen, as measured in weekly averages, was Platanus acerifolia with a maximum weekly daily average of 203 grains/m³ registered during September and October. The second highest was Acer pseudoplatanus with a maximum weekly daily average of 116 grains/m³. Populus spp. had a maximum weekly daily average 103 grains/m³. Olea europaea reached 19 grains/m³ in November. Grasses presented high levels of pollen counts with a maximum weekly daily average of 27 grains/m³ from the end of August until the end of January. Pollens of Plantago spp. Rumex acetosella and Chenopodium spp. had a similar distribution and were present from October to April with maximum weekly daily average of 7 grains/m³, 7 grains/m³ and 3 grains/m³ respectively. Significant concentrations of Ambrosia artemisiifolia were detected from February until April. The population of Talca was exposed to high concentrations of allergenic pollen, such as P. acerifolia, A. pseudoplatanus, and grasses in the months of August through November. The detection of O. europaea and A. artemisiifolia is important as these are emergent pollens in the city of Talca. Aerobiological monitoring will provide the community with reliable information about the level of allergenic pollens, improving treatment and quality of life of patients with respiratory allergy. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Medication costs across the hospice stay: an evaluation of medication costs in response to the MedPAC proposed reimbursement model.

PubMed

Gibson, Marliese A; Kimbrel, Jason M; Protus, Bridget McCrate; Perdue, Willie J; Arradaza, Nicole

2013-11-01

The Medicare Payment Advisory Committee (MedPAC) recommended that the per diem reimbursement for the Medicare Hospice Benefit change to a U-shaped scheme reflecting spending based on nursing visit frequency. This study investigated the change in drug cost over patients' length of stay (LOS) as current drug cost trends are unknown and were not evaluated in the MedPAC proposed reimbursement scheme. An analysis of patient utilizers of a national pharmacy claims database from 2007 to 2010 was completed to determine the trend in average daily pharmaceutical cost per utilizer (PCPU) over the patient's LOS. The average daily PCPU for 144,119 patients demonstrated a U-shaped curve. Indexed values in the first and last periods were significantly higher than in all other periods overall and by diagnosis (P < .001). Although indexed medication costs showed a U-shaped curve, it is imperative that hospice reimbursement be adequately evaluated for all medication costs including variations within the diagnosis mix. Payer sources and hospices must work together to determine adequate reimbursement models that will provide patients with effective and efficient high-quality care through the end of life.

Digital simulation of the effects of urbanization on runoff in the upper Santa Ana Valley, California

USGS Publications Warehouse

Durbin, Timothy J.

1974-01-01

The Stanford Watershed Model was used to simulate the effects of urbanization on the discharge from five drainage basins in the upper Santa Ana Valley, an area with an average annual precipitation of 15 inches. The drainage basins ranged in size from 3.72 to 83.4 square miles. Using the model, synthetic records of streamflow for each basin were generated to represent various degrees of urban development. Examination of the synthetic records indicated that urbanization has the following effects on streamflow in the area:Average annual runoff from a drainage basin with an effective impervious area of 10 percent of the drainage area is approximately 2 inches, and increases by 1 inch for each increase in effective impervious cover equal to 10 percent of the drainage area. About 30 percent of a fully urbanized area is effectively impervious.Urbanization can increase the magnitude of peak discharge and daily mean discharge with a recurrence interval of 2 years by a factor of three to six.Peak discharges and daily mean discharges that have recurrence intervals greater than a limiting value ranging from 50 to 200 years or more are little affected by urbanization.

Comparison of task-based exposure metrics for an epidemiologic study of isocyanate inhalation exposures among autobody shop workers.

PubMed

Woskie, Susan R; Bello, Dhimiter; Gore, Rebecca J; Stowe, Meredith H; Eisen, Ellen A; Liu, Youcheng; Sparer, Judy A; Redlich, Carrie A; Cullen, Mark R

2008-09-01

Because many occupational epidemiologic studies use exposure surrogates rather than quantitative exposure metrics, the UMass Lowell and Yale study of autobody shop workers provided an opportunity to evaluate the relative utility of surrogates and quantitative exposure metrics in an exposure response analysis of cross-week change in respiratory function. A task-based exposure assessment was used to develop several metrics of inhalation exposure to isocyanates. The metrics included the surrogates, job title, counts of spray painting events during the day, counts of spray and bystander exposure events, and a quantitative exposure metric that incorporated exposure determinant models based on task sampling and a personal workplace protection factor for respirator use, combined with a daily task checklist. The result of the quantitative exposure algorithm was an estimate of the daily time-weighted average respirator-corrected total NCO exposure (microg/m(3)). In general, these four metrics were found to be variable in agreement using measures such as weighted kappa and Spearman correlation. A logistic model for 10% drop in FEV(1) from Monday morning to Thursday morning was used to evaluate the utility of each exposure metric. The quantitative exposure metric was the most favorable, producing the best model fit, as well as the greatest strength and magnitude of association. This finding supports the reports of others that reducing exposure misclassification can improve risk estimates that otherwise would be biased toward the null. Although detailed and quantitative exposure assessment can be more time consuming and costly, it can improve exposure-disease evaluations and is more useful for risk assessment purposes. The task-based exposure modeling method successfully produced estimates of daily time-weighted average exposures in the complex and changing autobody shop work environment. The ambient TWA exposures of all of the office workers and technicians and 57% of the painters were found to be below the current U.K. Health and Safety Executive occupational exposure limit (OEL) for total NCO of 20 microg/m(3). When respirator use was incorporated, all personal daily exposures were below the U.K. OEL.

Handbook of solar energy data for south-facing surfaces in the United States. Volume 2: Average hourly and total daily insolation data for 235 localities. Alaska - Montana

NASA Technical Reports Server (NTRS)

Smith, J. H.

1980-01-01

Average hourly and daily total insolation estimates for 235 United States locations are presented. Values are presented for a selected number of array tilt angles on a monthly basis. All units are in kilowatt hours per square meter.

34 CFR 682.304 - Methods for computing interest benefits and special allowance.

Code of Federal Regulations, 2011 CFR

2011-07-01

... interest benefits and special allowance. (a) General. The Secretary pays a lender interest benefits and..., September 30, and December 31 of each year. A lender may use either the average daily balance method or the... shall use the average daily balance method to determine the balance on which the Secretary computes the...

78 FR 75432 - Self-Regulatory Organizations; New York Stock Exchange LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-11

... Proposed Rule Change To Amend Its Price List To Specify the Exclusion of Odd Lot Transactions From Consolidated Average Daily Volume Calculations for a Limited Period of Time for Purposes of Certain Transaction... transactions from consolidated average daily volume (``CADV'') calculations for a limited period of time for...

34 CFR 682.304 - Methods for computing interest benefits and special allowance.

Code of Federal Regulations, 2010 CFR

2010-07-01

... benefits and special allowance. (a) General. The Secretary pays a lender interest benefits and special..., September 30, and December 31 of each year. A lender may use either the average daily balance method or the... shall use the average daily balance method to determine the balance on which the Secretary computes the...

School Attendance: Focusing on Engagement and Re-Engagement. Practice Notes

ERIC Educational Resources Information Center

Center for Mental Health in Schools at UCLA, 2011

2011-01-01

Every student absence jeopardizes the ability of students to succeed at school and schools to achieve their mission. School attendance is a constant concern in schools. Average daily attendance rates are a common determiner of school funding, so schools funded on the basis of average daily attendance have less resources to do the job. Students who…

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

Light Exposure and Eye Growth in Childhood.

PubMed

Read, Scott A; Collins, Michael J; Vincent, Stephen J

2015-10-01

The purpose of this study was to examine the relationship between objectively measured ambient light exposure and longitudinal changes in axial eye growth in childhood. A total of 101 children (41 myopes and 60 nonmyopes), 10 to 15 years of age participated in this prospective longitudinal observational study. Axial eye growth was determined from measurements of ocular optical biometry collected at four study visits over an 18-month period. Each child's mean daily light exposure was derived from two periods (each 14 days long) of objective light exposure measurements from a wrist-worn light sensor. Over the 18-month study period, a modest but statistically significant association between greater average daily light exposure and slower axial eye growth was observed (P = 0.047). Other significant predictors of axial eye growth in this population included children's refractive error group (P < 0.001), sex (P < 0.01), and age (P < 0.001). Categorized according to their objectively measured average daily light exposure and adjusting for potential confounders (age, sex, baseline axial length, parental myopia, nearwork, and physical activity), children experiencing low average daily light exposure (mean daily light exposure: 459 ± 117 lux, annual eye growth: 0.13 mm/y) exhibited significantly greater eye growth than children experiencing moderate (842 ± 109 lux, 0.060 mm/y), and high (1455 ± 317 lux, 0.065 mm/y) average daily light exposure levels (P = 0.01). In this population of children, greater daily light exposure was associated with less axial eye growth over an 18-month period. These findings support the role of light exposure in the documented association between time spent outdoors and childhood myopia.

Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part B: placing back-calculations in a formal statistical framework.

PubMed

Jones, Hayley E; Hickman, Matthew; Kasprzyk-Hordern, Barbara; Welton, Nicky J; Baker, David R; Ades, A E

2014-07-15

Concentrations of metabolites of illicit drugs in sewage water can be measured with great accuracy and precision, thanks to the development of sensitive and robust analytical methods. Based on assumptions about factors including the excretion profile of the parent drug, routes of administration and the number of individuals using the wastewater system, the level of consumption of a drug can be estimated from such measured concentrations. When presenting results from these 'back-calculations', the multiple sources of uncertainty are often discussed, but are not usually explicitly taken into account in the estimation process. In this paper we demonstrate how these calculations can be placed in a more formal statistical framework by assuming a distribution for each parameter involved, based on a review of the evidence underpinning it. Using a Monte Carlo simulations approach, it is then straightforward to propagate uncertainty in each parameter through the back-calculations, producing a distribution for instead of a single estimate of daily or average consumption. This can be summarised for example by a median and credible interval. To demonstrate this approach, we estimate cocaine consumption in a large urban UK population, using measured concentrations of two of its metabolites, benzoylecgonine and norbenzoylecgonine. We also demonstrate a more sophisticated analysis, implemented within a Bayesian statistical framework using Markov chain Monte Carlo simulation. Our model allows the two metabolites to simultaneously inform estimates of daily cocaine consumption and explicitly allows for variability between days. After accounting for this variability, the resulting credible interval for average daily consumption is appropriately wider, representing additional uncertainty. We discuss possibilities for extensions to the model, and whether analysis of wastewater samples has potential to contribute to a prevalence model for illicit drug use. Copyright © 2014. Published by Elsevier B.V.

Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part B: Placing back-calculations in a formal statistical framework

PubMed Central

Jones, Hayley E.; Hickman, Matthew; Kasprzyk-Hordern, Barbara; Welton, Nicky J.; Baker, David R.; Ades, A.E.

2014-01-01

Concentrations of metabolites of illicit drugs in sewage water can be measured with great accuracy and precision, thanks to the development of sensitive and robust analytical methods. Based on assumptions about factors including the excretion profile of the parent drug, routes of administration and the number of individuals using the wastewater system, the level of consumption of a drug can be estimated from such measured concentrations. When presenting results from these ‘back-calculations’, the multiple sources of uncertainty are often discussed, but are not usually explicitly taken into account in the estimation process. In this paper we demonstrate how these calculations can be placed in a more formal statistical framework by assuming a distribution for each parameter involved, based on a review of the evidence underpinning it. Using a Monte Carlo simulations approach, it is then straightforward to propagate uncertainty in each parameter through the back-calculations, producing a distribution for instead of a single estimate of daily or average consumption. This can be summarised for example by a median and credible interval. To demonstrate this approach, we estimate cocaine consumption in a large urban UK population, using measured concentrations of two of its metabolites, benzoylecgonine and norbenzoylecgonine. We also demonstrate a more sophisticated analysis, implemented within a Bayesian statistical framework using Markov chain Monte Carlo simulation. Our model allows the two metabolites to simultaneously inform estimates of daily cocaine consumption and explicitly allows for variability between days. After accounting for this variability, the resulting credible interval for average daily consumption is appropriately wider, representing additional uncertainty. We discuss possibilities for extensions to the model, and whether analysis of wastewater samples has potential to contribute to a prevalence model for illicit drug use. PMID:24636801

Retrieval of air temperatures from crowd-sourced battery temperatures of cell phones

NASA Astrophysics Data System (ADS)

Overeem, Aart; Robinson, James; Leijnse, Hidde; Uijlenhoet, Remko; Steeneveld, Gert-Jan; Horn, Berthold K. P.

2013-04-01

Accurate air temperature observations are important for urban meteorology, for example to study the urban heat island and adverse effects of high temperatures on human health. The number of available temperature observations is often relatively limited. A new development is presented to derive temperature information for the urban canopy from an alternative source: cell phones. Battery temperature data were collected by users of an Android application for cell phones (opensignal.com). The application automatically sends battery temperature data to a server for storage. In this study, battery temperatures are averaged in space and time to obtain daily averaged battery temperatures for each city separately. A regression model, which can be related to a physical model, is employed to retrieve daily air temperatures from battery temperatures. The model is calibrated with observed air temperatures from a meteorological station of an airport located in or near the city. Time series of air temperatures are obtained for each city for a period of several months, where 50% of the data is for independent verification. Results are presented for Buenos Aires, London, Los Angeles, Paris, Mexico City, Moscow, Rome, and Sao Paulo. The evolution of the retrieved air temperatures often correspond well with the observed ones. The mean absolute error of daily air temperatures is less than 2 degrees Celsius, and the bias is within 1 degree Celsius. This shows that monitoring air temperatures employing an Android application holds great promise. Since 75% of the world's population has a cell phone, 20% of the land surface of the earth has cellular telephone coverage, and 500 million devices use the Android operating system, there is a huge potential for measuring air temperatures employing cell phones. This could eventually lead to real-time world-wide temperature maps.

Comprehensive evaluation of Ensemble Multi-Satellite Precipitation Dataset using the Dynamic Bayesian Model Averaging scheme over the Tibetan plateau

NASA Astrophysics Data System (ADS)

Ma, Yingzhao; Yang, Yuan; Han, Zhongying; Tang, Guoqiang; Maguire, Lane; Chu, Zhigang; Hong, Yang

2018-01-01

The objective of this study is to comprehensively evaluate the new Ensemble Multi-Satellite Precipitation Dataset using the Dynamic Bayesian Model Averaging scheme (EMSPD-DBMA) at daily and 0.25° scales from 2001 to 2015 over the Tibetan Plateau (TP). Error analysis against gauge observations revealed that EMSPD-DBMA captured the spatiotemporal pattern of daily precipitation with an acceptable Correlation Coefficient (CC) of 0.53 and a Relative Bias (RB) of -8.28%. Moreover, EMSPD-DBMA outperformed IMERG and GSMaP-MVK in almost all metrics in the summers of 2014 and 2015, with the lowest RB and Root Mean Square Error (RMSE) values of -2.88% and 8.01 mm/d, respectively. It also better reproduced the Probability Density Function (PDF) in terms of daily rainfall amount and estimated moderate and heavy rainfall better than both IMERG and GSMaP-MVK. Further, hydrological evaluation with the Coupled Routing and Excess STorage (CREST) model in the Upper Yangtze River region indicated that the EMSPD-DBMA forced simulation showed satisfying hydrological performance in terms of streamflow prediction, with Nash-Sutcliffe coefficient of Efficiency (NSE) values of 0.82 and 0.58, compared to gauge forced simulation (0.88 and 0.60) at the calibration and validation periods, respectively. EMSPD-DBMA also performed a greater fitness for peak flow simulation than a new Multi-Source Weighted-Ensemble Precipitation Version 2 (MSWEP V2) product, indicating a promising prospect of hydrological utility for the ensemble satellite precipitation data. This study belongs to early comprehensive evaluation of the blended multi-satellite precipitation data across the TP, which would be significant for improving the DBMA algorithm in regions with complex terrain.

A regional neural network model for predicting mean daily river water temperature

USGS Publications Warehouse

Wagner, Tyler; DeWeber, Jefferson Tyrell

2014-01-01

Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate and land use changes, thereby providing information that is valuable to management of river ecosystems and biota such as brook trout.

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity.

PubMed

Van Hertem, T; Maltz, E; Antler, A; Romanini, C E B; Viazzi, S; Bahr, C; Schlageter-Tello, A; Lokhorst, C; Berckmans, D; Halachmi, I

2013-07-01

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Volatility Behaviors of Financial Time Series by Percolation System on Sierpinski Carpet Lattice

NASA Astrophysics Data System (ADS)

Pei, Anqi; Wang, Jun

2015-01-01

The financial time series is simulated and investigated by the percolation system on the Sierpinski carpet lattice, where percolation is usually employed to describe the behavior of connected clusters in a random graph, and the Sierpinski carpet lattice is a graph which corresponds the fractal — Sierpinski carpet. To study the fluctuation behavior of returns for the financial model and the Shanghai Composite Index, we establish a daily volatility measure — multifractal volatility (MFV) measure to obtain MFV series, which have long-range cross-correlations with squared daily return series. The autoregressive fractionally integrated moving average (ARFIMA) model is used to analyze the MFV series, which performs better when compared to other volatility series. By a comparative study of the multifractality and volatility analysis of the data, the simulation data of the proposed model exhibits very similar behaviors to those of the real stock index, which indicates somewhat rationality of the model to the market application.

Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data

NASA Technical Reports Server (NTRS)

Veraverbeke, Sander; Sedano, Fernando; Hook, Simon J.; Randerson, James T.; Jin, Yufang; Rogers, Brendan

2013-01-01

High temporal resolution information on burned area is a prerequisite for incorporating bottom-up estimates of wildland fire emissions in regional air transport models and for improving models of fire behavior. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the evolution of nine large wildland fires. For each fire, local input parameters for the kriging model were defined using variogram analysis. The accuracy of the kriging model was assessed using high resolution daily fire perimeter data available from the U.S. Forest Service. We also assessed the temporal reporting accuracy of the MODIS burned area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared to 33% for MCD45A1 and 53% for MCD64A1.

Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

NASA Astrophysics Data System (ADS)

Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

2015-04-01

A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built in MATLAB an Eviews programmes. Because of the seasonality of PM10 data SARIMA model was also used. The order of autoregression model was determined according to AIC and BIC criteria. The model performances were evaluated from Fractional Bias, Normalized Mean Square Error (NMSE) and Mean Absolute Percentage Error (MAPE). As expected, the results were encouraging. Keywords: PM10, Autoregression, Forecast Acknowledgement The authors would like to acknowledge the financial support by the Scientific and Technological Research Council of Turkey (TUBITAK, project no:112Y319).

Review the number of accidents in Tehran over a two-year period and prediction of the number of events based on a time-series model

PubMed Central

Teymuri, Ghulam Heidar; Sadeghian, Marzieh; Kangavari, Mehdi; Asghari, Mehdi; Madrese, Elham; Abbasinia, Marzieh; Ahmadnezhad, Iman; Gholizadeh, Yavar

2013-01-01

Background: One of the significant dangers that threaten people’s lives is the increased risk of accidents. Annually, more than 1.3 million people die around the world as a result of accidents, and it has been estimated that approximately 300 deaths occur daily due to traffic accidents in the world with more than 50% of that number being people who were not even passengers in the cars. The aim of this study was to examine traffic accidents in Tehran and forecast the number of future accidents using a time-series model. Methods: The study was a cross-sectional study that was conducted in 2011. The sample population was all traffic accidents that caused death and physical injuries in Tehran in 2010 and 2011, as registered in the Tehran Emergency ward. The present study used Minitab 15 software to provide a description of accidents in Tehran for the specified time period as well as those that occurred during April 2012. Results: The results indicated that the average number of daily traffic accidents in Tehran in 2010 was 187 with a standard deviation of 83.6. In 2011, there was an average of 180 daily traffic accidents with a standard deviation of 39.5. One-way analysis of variance indicated that the average number of accidents in the city was different for different months of the year (P < 0.05). Most of the accidents occurred in March, July, August, and September. Thus, more accidents occurred in the summer than in the other seasons. The number of accidents was predicted based on an auto-regressive, moving average (ARMA) for April 2012. The number of accidents displayed a seasonal trend. The prediction of the number of accidents in the city during April of 2012 indicated that a total of 4,459 accidents would occur with mean of 149 accidents per day during these three months. Conclusion: The number of accidents in Tehran displayed a seasonal trend, and the number of accidents was different for different seasons of the year. PMID:26120405

Radiative Forcing by Contrails

NASA Technical Reports Server (NTRS)

Meerkoetter, R.; Schumann, U.; Doelling, D. R.; Nakajima, T.; Tsushima, Y.

1999-01-01

A parametric study of the instantaneous radiative impact of contrails is presented using three different radiative transfer models for a series of model atmospheres and cloud parameters. Contrails are treated as geometrically and optically thin plane parallel homogeneous cirrus layers in a static atmospheres The ice water content is varied as a function of ambient temperature. The model atmospheres include tropical, mid-latitude, and subarctic summer and winter atmospheres Optically thin contrails cause a positive net forcing at top of the atmosphere. At the surface the radiative forcing is negative during daytime. The forcing increases with the optical depth and the amount of contrail cover. At the top of the atmosphere a mean contrail cover of 0.1% with average optical depth of 0.2 to 0.5 causes about 0.01 to 0.03 W/m(exp 2)a daily mean instantaneous radiative forcing. Contrails cool the surface during the day and heat the surface during the night, and hence reduce the daily temperature amplitude The net effect depends strongly on the daily variation of contrail cloud cover. The indirect radiative forcing due to particle changes in natural cirrus clouds may be of the same magnitude as the direct one due to additional cover.

Participatory Water Resources Modeling in a Water-Scarce Basin (Rio Sonora, Mexico) Reveals Uncertainty in Decision-Making

NASA Astrophysics Data System (ADS)

Mayer, A. S.; Vivoni, E. R.; Halvorsen, K. E.; Kossak, D.

2014-12-01

The Rio Sonora Basin (RSB) in northwest Mexico has a semi-arid and highly variable climate along with urban and agricultural pressures on water resources. Three participatory modeling workshops were held in the RSB in spring 2013. A model of the water resources system, consisting of a watershed hydrology model, a model of the water infrastructure, and groundwater models, was developed deliberatively in the workshops, along with scenarios of future climate and development. Participants were asked to design water resources management strategies by choosing from a range of supply augmentation and demand reduction measures associated with water conservation. Participants assessed water supply reliability, measured as the average daily supply divided by daily demand for historical and future periods, by probing with the climate and development scenarios. Pre- and post-workshop-surveys were developed and administered, based on conceptual models of workshop participants' beliefs regarding modeling and local water resources. The survey results indicate that participants believed their modeling abilities increased and beliefs in the utility of models increased as a result of the workshops. The selected water resources strategies varied widely among participants. Wastewater reuse for industry and aquifer recharge were popular options, but significant numbers of participants thought that inter-basin transfers and desalination were viable. The majority of participants indicated that substantial increases in agricultural water efficiency could be achieved. On average, participants chose strategies that produce reliabilities over the historical and future periods of 95%, but more than 20% of participants were apparently satisfied with reliabilities lower than 80%. The wide range of strategies chosen and associated reliabilities indicate that there is a substantial degree of uncertainty in how future water resources decisions could be made in the region.

Association of internet use and depression among the spinal cord injury population.

PubMed

Tsai, I-Hsuan; Graves, Daniel E; Lai, Ching-Huang; Hwang, Lu-Yu; Pompeii, Lisa A

2014-02-01

To examine the relation between the frequency of Internet use and depression among people with spinal cord injury (SCI). Cross-sectional survey. SCI Model Systems. People with SCI (N=4618) who were interviewed between 2004 and 2010. Not applicable. The frequency of Internet use and the severity of depressive symptoms were measured simultaneously by interview. Internet use was reported as daily, weekly, monthly, or none. The depressive symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9), with 2 published criteria being used to screen for depressive disorder. The diagnostic method places more weight on nonsomatic items (ie, items 1, 2, and 9), and the cut-off method that determines depression by a (PHQ-9) score ≥10 places more weight on somatic factors. The average scores of somatic and nonsomatic items represented the severity of somatic and nonsomatic symptoms, respectively. Our multivariate logistic regression model indicated that daily Internet users were less likely to have depressive symptoms (odds ratio=.77; 95% confidence interval, .64-.93), if the diagnostic method was used. The linear multivariate regression analysis indicated that daily and weekly Internet usage were associated with fewer nonsomatic symptoms; no significant association was observed between daily or weekly Internet usage and somatic symptoms. People with SCI who used the Internet daily were less likely to have depressive symptoms. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

Fine Particulate Air Pollution and Mortality in Nine California Counties: Results from CALFINE

PubMed Central

Ostro, Bart; Broadwin, Rachel; Green, Shelley; Feng, Wen-Ying; Lipsett, Michael

2006-01-01

Many epidemiologic studies provide evidence of an association between daily counts of mortality and ambient particulate matter < 10 μm in diameter (PM10). Relatively few studies, however, have investigated the relationship of mortality with fine particles [PM < 2.5 μm in diameter (PM2.5)], especially in a multicity setting. We examined associations between PM2.5 and daily mortality in nine heavily populated California counties using data from 1999 through 2002. We considered daily counts of all-cause mortality and several cause-specific subcategories (respiratory, cardiovascular, ischemic heart disease, and diabetes). We also examined these associations among several subpopulations, including the elderly (> 65 years of age), males, females, non-high school graduates, whites, and Hispanics. We used Poisson multiple regression models incorporating natural or penalized splines to control for covariates that could affect daily counts of mortality, including time, seasonality, temperature, humidity, and day of the week. We used meta-analyses using random-effects models to pool the observations in all nine counties. The analysis revealed associations of PM2.5 levels with several mortality categories. Specifically, a 10-μg/m3 change in 2-day average PM2.5 concentration corresponded to a 0.6% (95% confidence interval, 0.2–1.0%) increase in all-cause mortality, with similar or greater effect estimates for several other subpopulations and mortality subcategories, including respiratory disease, cardiovascular disease, diabetes, age > 65 years, females, deaths out of the hospital, and non-high school graduates. Results were generally insensitive to model specification and the type of spline model used. This analysis adds to the growing body of evidence linking PM2.5 with daily mortality. PMID:16393654

Understanding the Gap between Cognitive Abilities and Daily Living Skills in Adolescents with Autism Spectrum Disorders with Average Intelligence

ERIC Educational Resources Information Center

Duncan, Amie W.; Bishop, Somer L.

2015-01-01

Daily living skills standard scores on the Vineland Adaptive Behavior Scales-2nd edition were examined in 417 adolescents from the Simons Simplex Collection. All participants had at least average intelligence and a diagnosis of autism spectrum disorder. Descriptive statistics and binary logistic regressions were used to examine the prevalence and…

Air drying of softwood lumber, Fairbanks, Alaska.

Treesearch

George R Sampson; Forrest A. Ruppert

1985-01-01

Air-drying rates for two stacks of 2-inch-thick white spruce were observed in the Fairbanks area during summer 1982. The air-drying rate for the same size lumber was also observed during winter 1982-83. Very little drying occurred during the winter. Drying rates in summer were correlated with average daily temperature and average daily dew point to derive predictive...

76 FR 76799 - Self-Regulatory Organizations; New York Stock Exchange LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-08

... average daily volume (``CADV''). The text of the proposed rule change is available at the Exchange, the... Proposed Rule Change To Amend NYSE Rule 104(a)(1)(A) To Reflect That Designated Market Maker Unit Quoting Requirements Are Based on Consolidated Average Daily Volume December 2, 2011. Pursuant to Section 19(b)(1) of...

Older Adults with Diabetes and Osteoarthritis and Their Spouses: Effects of Activity Limitations, Marital Happiness, and Social Contacts on Partners' Daily Mood

ERIC Educational Resources Information Center

Roper, Susanne Olsen; Yorgason, Jeremy B.

2009-01-01

Using daily diary data from 28 later life couples where one spouse had diabetes and osteoarthritis, we examined crossover effects of target spouses' daily activity limitations and their partners' daily mood. On days when target spouses' daily activity limitations were higher than average, partners' positive mood decreased and negative mood…

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2014-10-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

Impact of Canopy Coupling on Canopy Average Stomatal Conductance Across Seven Tree Species in Northern Wisconsin

NASA Astrophysics Data System (ADS)

Ewers, B. E.; Mackay, D. S.; Samanta, S.; Ahl, D. E.; Burrows, S. S.; Gower, S. T.

2001-12-01

Land use changes over the last century in northern Wisconsin have resulted in a heterogeneous landscape composed of the following four main forest types: northern hardwoods, northern conifer, aspen/fir, and forested wetland. Based on sap flux measurements, aspen/fir has twice the canopy transpiration of northern hardwoods. In addition, daily transpiration was only explained by daily average vapor pressure deficit across the cover types. The objective of this study was to determine if canopy average stomatal conductance could be used to explain the species effects on tree transpiration. Our first hypothesis is that across all of the species, stomatal conductance will respond to vapor pressure deficit so as to maintain a minimum leaf water potential to prevent catostrophic cavitiation. The consequence of this hypothesis is that among species and individuals there is a proportionality between high stomatal conductance and the sensitivity of stomatal conductance to vapor pressure deficit. Our second hypothesis is that species that do not follow the proportionality deviate because the canopies are decoupled from the atmosphere. To test our two hypotheses we calculated canopy average stomatal conductance from sap flux measurements using an inversion of the Penman-Monteith equation. We estimated the canopy coupling using a leaf energy budget model that requires leaf transpiration and canopy aerodynamic conductance. We optimized the parameters of the aerodynamic conductance model using a Monte Carlo technique across six parameters. We determined the optimal model for each species by selecting parameter sets that resulted in the proportionality of our first hypothesis. We then tested the optimal energy budget models of each species by comparing leaf temperature and leaf width predicted by the models to measurements of each tree species. In red pine, sugar maple, and trembling aspen trees under high canopy coupling conditions, we found the hypothesized proportionality between high stomatal conductance and the sensitivity of stomatal conductance to vapor pressure deficit. In addition, the canopy conductance of trembling aspen was twice as high as sugar maple and the aspen trees showed much more variability.

Development of a database-driven system for simulating water temperature in the lower Yakima River main stem, Washington, for various climate scenarios

USGS Publications Warehouse

Voss, Frank; Maule, Alec

2013-01-01

A model for simulating daily maximum and mean water temperatures was developed by linking two existing models: one developed by the U.S. Geological Survey and one developed by the Bureau of Reclamation. The study area included the lower Yakima River main stem between the Roza Dam and West Richland, Washington. To automate execution of the labor-intensive models, a database-driven model automation program was developed to decrease operation costs, to reduce user error, and to provide the capability to perform simulations quickly for multiple management and climate change scenarios. Microsoft© SQL Server 2008 R2 Integration Services packages were developed to (1) integrate climate, flow, and stream geometry data from diverse sources (such as weather stations, a hydrologic model, and field measurements) into a single relational database; (2) programmatically generate heavily formatted model input files; (3) iteratively run water temperature simulations; (4) process simulation results for export to other models; and (5) create a database-driven infrastructure that facilitated experimentation with a variety of scenarios, node permutations, weather data, and hydrologic conditions while minimizing costs of running the model with various model configurations. As a proof-of-concept exercise, water temperatures were simulated for a "Current Conditions" scenario, where local weather data from 1980 through 2005 were used as input, and for "Plus 1" and "Plus 2" climate warming scenarios, where the average annual air temperatures used in the Current Conditions scenario were increased by 1degree Celsius (°C) and by 2°C, respectively. Average monthly mean daily water temperatures simulated for the Current Conditions scenario were compared to measured values at the Bureau of Reclamation Hydromet gage at Kiona, Washington, for 2002-05. Differences ranged between 1.9° and 1.1°C for February, March, May, and June, and were less than 0.8°C for the remaining months of the year. The difference between current conditions and measured monthly values for the two warmest months (July and August) were 0.5°C and 0.2°C, respectively. The model predicted that water temperature generally becomes less sensitive to air temperature increases as the distance from the mouth of the river decreases. As a consequence, the difference between climate warming scenarios also decreased. The pattern of decreasing sensitivity is most pronounced from August to October. Interactive graphing tools were developed to explore the relative sensitivity of average monthly and mean daily water temperature to increases in air temperature for model output locations along the lower Yakima River main stem.

The creation of future daily gridded datasets of precipitation and temperature with a spatial weather generator, Cyprus 2020-2050

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred

2014-05-01

High-resolution gridded daily datasets are essential for natural resource management and the analysis of climate changes and their effects. This study aimed to create gridded datasets of daily precipitation and daily minimum and maximum temperature, for the future (2020-2050). The horizontal resolution of the developed datasets is 1 x 1 km2, covering the area under control of the Republic of Cyprus (5.760 km2). The study is divided into two parts. The first consists of the evaluation of the performance of different interpolation techniques for daily rainfall and temperature data (1980-2010) for the creation of the gridded datasets. Rainfall data recorded at 145 stations and temperature data from 34 stations were used. For precipitation, inverse distance weighting (IDW) performs best for local events, while a combination of step-wise geographically weighted regression and IDW proves to be the best method for large scale events. For minimum and maximum temperature, a combination of step-wise linear multiple regression and thin plate splines is recognized as the best method. Six Regional Climate Models (RCMs) for the A1B SRES emission scenario from the EU ENSEMBLE project database were selected as sources for future climate projections. The RCMs were evaluated for their capacity to simulate Cyprus climatology for the period 1980-2010. Data for the period 2020-2050 from the three best performing RCMs were downscaled, using the change factors approach, at the location of observational stations. Daily time series were created with a stochastic rainfall and temperature generator. The RainSim V3 software (Burton et al., 2008) was used to generate spatial-temporal coherent rainfall fields. The temperature generator was developed in R and modeled temperature as a weakly stationary process with the daily mean and standard deviation conditioned on the wet and dry state of the day (Richardson, 1981). Finally gridded datasets depicting projected future climate conditions were created with the identified best interpolation methods. The difference between the input and simulated mean daily rainfall, averaged over all the stations, was 0.03 mm (2.2%), while the error related to the number of dry days was 2 (0.6%). For mean daily minimum temperature the error was 0.005 ºC (0.04%), while for maximum temperature it was 0.01 ºC (0.04%). Overall, the weather generators were found to be reliable instruments for the downscaling of precipitation and temperature. The resulting datasets indicate a decrease of the mean annual rainfall over the study area between 5 and 70 mm (1-15%) for 2020-2050, relative to 1980-2010. Average annual minimum and maximum temperature over the Republic of Cyprus are projected to increase between 1.2 and 1.5 ºC. The dataset is currently used to compute agricultural production and water use indicators, as part of the AGWATER project (AEIFORIA/GEORGO/0311(BIE)/06), co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation. Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., and O'Connell, P.E.: RainSim: A spatial-temporal stochastic rainfall modelling system. Environ. Model. Software 23, 1356-1369, 2008 Richardson, C.W.: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17, 182-190, 1981.

Precipitation interpolation in mountainous areas

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2015-04-01

Different precipitation interpolation techniques as well as external drift covariates are tested and compared in a 26000 km2 mountainous area in Norway, using daily data from 60 stations. The main method of assessment is cross-validation. Annual precipitation in the area varies from below 500 mm to more than 2000 mm. The data were corrected for wind-driven undercatch according to operational standards. While temporal evaluation produce seemingly acceptable at-station correlation values (on average around 0.6), the average daily spatial correlation is less than 0.1. Penalising also bias, Nash-Sutcliffe R2 values are negative for spatial correspondence, and around 0.15 for temporal. Despite largely violated assumptions, plain Kriging produces better results than simple inverse distance weighting. More surprisingly, the presumably 'worst-case' benchmark of no interpolation at all, simply averaging all 60 stations for each day, actually outperformed the standard interpolation techniques. For logistic reasons, high altitudes are under-represented in the gauge network. The possible effect of this was investigated by a) fitting a precipitation lapse rate as an external drift, and b) applying a linear model of orographic enhancement (Smith and Barstad, 2004). These techniques improved the results only marginally. The gauge density in the region is one for each 433 km2; higher than the overall density of the Norwegian national network. Admittedly the cross-validation technique reduces the gauge density, still the results suggest that we are far from able to provide hydrological models with adequate data for the main driving force.

A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows

DOE PAGES

Wu, Fei; Sioshansi, Ramteen

2017-05-04

Here, we develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number ofmore » stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.« less

Herd-of-origin effect on the post-weaning performance of centrally tested Nellore beef cattle.

PubMed

de Rezende Neves, Haroldo Henrique; Polin dos Reis, Felipe; Motta Paterno, Flávia; Rocha Guarini, Aline; Carvalheiro, Roberto; da Silva, Lilian Regina; de Oliveira, João Ademir; Aidar de Queiroz, Sandra

2014-10-01

The objective of a performance test station is to evaluate the performance of potential breeding bulls earlier in order to decrease the generation interval and increase genetic gain as well. This study evaluates the herd-of-origin influence on end-of-test weight (ETW), average daily weight gain during testing (ADG), average daily weight gain during the adjustment period (ADGadj), rib eye area (REA), marbling (MARB), subcutaneous fat thickness (SFT), conformation (C), early finishing (EF), muscling (M), navel (N) and temperament (T) scores, and scrotal circumference (SC) of Nellore cattle that underwent a performance test. We evaluated 664 animals that participated in the performance tests conducted at the Center for Performance CRV Lagoa between 2007 and 2012. Components of variance for each trait were estimated by an animal model (model 1), using the restricted maximum likelihood method. An alternative animal model (model 2) included, in addition to the fixed effects present in S1, the non-correlated random effect of herd-year (HY). A significant HY effect was observed on ETW, REA, SFT, ADGadj, C, and Cw (p < 0.05). The estimated heritability of all traits decreased when the HY effect was included in the model; also, the bull rank, in deciles, changed significantly for traits ETW, REA, SFT, and C. The adjustment period did not completely remove the environmental effect of herd of origin on ETW, REA, SFT, and C. It is recommended that the herd-of-origin effect should be included in the statistical models used to predict the breeding values of the participants of these performance tests.

Hunter-Gatherer Energetics and Human Obesity

PubMed Central

Pontzer, Herman; Raichlen, David A.; Wood, Brian M.; Mabulla, Audax Z. P.; Racette, Susan B.; Marlowe, Frank W.

2012-01-01

Western lifestyles differ markedly from those of our hunter-gatherer ancestors, and these differences in diet and activity level are often implicated in the global obesity pandemic. However, few physiological data for hunter-gatherer populations are available to test these models of obesity. In this study, we used the doubly-labeled water method to measure total daily energy expenditure (kCal/day) in Hadza hunter-gatherers to test whether foragers expend more energy each day than their Western counterparts. As expected, physical activity level, PAL, was greater among Hadza foragers than among Westerners. Nonetheless, average daily energy expenditure of traditional Hadza foragers was no different than that of Westerners after controlling for body size. The metabolic cost of walking (kcal kg−1 m−1) and resting (kcal kg−1 s−1) were also similar among Hadza and Western groups. The similarity in metabolic rates across a broad range of cultures challenges current models of obesity suggesting that Western lifestyles lead to decreased energy expenditure. We hypothesize that human daily energy expenditure may be an evolved physiological trait largely independent of cultural differences. PMID:22848382

Hunter-gatherer energetics and human obesity.

PubMed

Pontzer, Herman; Raichlen, David A; Wood, Brian M; Mabulla, Audax Z P; Racette, Susan B; Marlowe, Frank W

2012-01-01

Western lifestyles differ markedly from those of our hunter-gatherer ancestors, and these differences in diet and activity level are often implicated in the global obesity pandemic. However, few physiological data for hunter-gatherer populations are available to test these models of obesity. In this study, we used the doubly-labeled water method to measure total daily energy expenditure (kCal/day) in Hadza hunter-gatherers to test whether foragers expend more energy each day than their Western counterparts. As expected, physical activity level, PAL, was greater among Hadza foragers than among Westerners. Nonetheless, average daily energy expenditure of traditional Hadza foragers was no different than that of Westerners after controlling for body size. The metabolic cost of walking (kcal kg(-1) m(-1)) and resting (kcal kg(-1) s(-1)) were also similar among Hadza and Western groups. The similarity in metabolic rates across a broad range of cultures challenges current models of obesity suggesting that Western lifestyles lead to decreased energy expenditure. We hypothesize that human daily energy expenditure may be an evolved physiological trait largely independent of cultural differences.

High average daily intake of PCDD/Fs and serum levels in residents living near a deserted factory producing pentachlorophenol (PCP) in Taiwan: influence of contaminated fish consumption.

PubMed

Lee, C C; Lin, W T; Liao, P C; Su, H J; Chen, H L

2006-05-01

An abandoned pentachlorophenol plant and nearby area in southern Taiwan was heavily contaminated by dioxins, impurities formed in the PCP production process. The investigation showed that the average serum PCDD/Fs of residents living nearby area (62.5 pg WHO-TEQ/g lipid) was higher than those living in the non-polluted area (22.5 and 18.2 pg WHO-TEQ/g lipid) (P<0.05). In biota samples, average PCDD/F of milkfish in sea reservoir (28.3 pg WHO-TEQ/g) was higher than those in the nearby fish farm (0.15 pg WHO-TEQ/g), and Tilapia and shrimp showed the similar trend. The average daily PCDD/Fs intake of 38% participants was higher than 4 pg WHO-TEQ/kg/day suggested by the world health organization. Serum PCDD/F was positively associated with average daily intake (ADI) after adjustment for age, sex, BMI, and smoking status. In addition, a prospective cohort study is suggested to determine the long-term health effects on the people living near factory.

The Association between Air Pollution and Outpatient and Inpatient Visits in Shenzhen, China

PubMed Central

Liu, Yachuan; Chen, Shanen; Xu, Jian; Liu, Xiaojian; Wu, Yongsheng; Zhou, Lin; Cheng, Jinquan; Ma, Hanwu; Zheng, Jing; Lin, Denan; Zhang, Li; Chen, Lili

2018-01-01

Nowadays, air pollution is a severe environmental problem in China. To investigate the effects of ambient air pollution on health, a time series analysis of daily outpatient and inpatient visits in 2015 were conducted in Shenzhen (China). Generalized additive model was employed to analyze associations between six air pollutants (namely SO2, CO, NO2, O3, PM10, and PM2.5) and daily outpatient and inpatient visits after adjusting confounding meteorological factors, time and day of the week effects. Significant associations between air pollutants and two types of hospital visits were observed. The estimated increase in overall outpatient visits associated with each 10 µg/m3 increase in air pollutant concentration ranged from 0.48% (O3 at lag 2) to 11.48% (SO2 with 2-day moving average); for overall inpatient visits ranged from 0.73% (O3 at lag 7) to 17.13% (SO2 with 8-day moving average). Our results also suggested a heterogeneity of the health effects across different outcomes and in different populations. The findings in present study indicate that even in Shenzhen, a less polluted area in China, significant associations exist between air pollution and daily number of overall outpatient and inpatient visits. PMID:29360738

Mid-21st century air quality at the urban scale under the influence of changed climate and emissions: case studies for Paris and Stockholm

NASA Astrophysics Data System (ADS)

Markakis, K.; Valari, M.; Engardt, M.; Lacressonnière, G.; Vautard, R.; Andersson, C.

2015-10-01

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 \\unit{km} horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5 % for Paris and -2 % for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5 % while for Stockholm we observe mixed trends up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily average and maximum ozone and 20 % for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective.

R-Matrix Analysis of Structures in Economic Indices: from Nuclear Reactions to High-Frequency Trading

NASA Astrophysics Data System (ADS)

Firk, Frank W. K.

2014-03-01

It is shown that the R-matrix theory of nuclear reactions is a viable mathematical theory for the description of the fine, intermediate and gross structure observed in the time-dependence of economic indices in general, and the daily Dow Jones Industrial Average in particular. A Lorentzian approximation to R-matrix theory is used to analyze the complex structures observed in the Dow Jones Industrial Average on a typical trading day. Resonant structures in excited nuclei are characterized by the values of their fundamental strength function, (average total width of the states)/(average spacing between adjacent states). Here, values of the ratios (average lifetime of individual states of a given component of the daily Dow Jones Industrial Average)/(average interval between the adjacent states) are determined. The ratios for the observed fine and intermediate structure of the index are found to be essentially constant throughout the trading day. These quantitative findings are characteristic of the highly statistical nature of many-body, strongly interacting systems, typified by daily trading. It is therefore proposed that the values of these ratios, determined in the first hour-or-so of trading, be used to provide valuable information concerning the likely performance of the fine and intermediate components of the index for the remainder of the trading day.

Impacts of sampling design and estimation methods on nutrient leaching of intensively monitored forest plots in the Netherlands.

PubMed

de Vries, W; Wieggers, H J J; Brus, D J

2010-08-05

Element fluxes through forest ecosystems are generally based on measurements of concentrations in soil solution at regular time intervals at plot locations sampled in a regular grid. Here we present spatially averaged annual element leaching fluxes in three Dutch forest monitoring plots using a new sampling strategy in which both sampling locations and sampling times are selected by probability sampling. Locations were selected by stratified random sampling with compact geographical blocks of equal surface area as strata. In each sampling round, six composite soil solution samples were collected, consisting of five aliquots, one per stratum. The plot-mean concentration was estimated by linear regression, so that the bias due to one or more strata being not represented in the composite samples is eliminated. The sampling times were selected in such a way that the cumulative precipitation surplus of the time interval between two consecutive sampling times was constant, using an estimated precipitation surplus averaged over the past 30 years. The spatially averaged annual leaching flux was estimated by using the modeled daily water flux as an ancillary variable. An important advantage of the new method is that the uncertainty in the estimated annual leaching fluxes due to spatial and temporal variation and resulting sampling errors can be quantified. Results of this new method were compared with the reference approach in which daily leaching fluxes were calculated by multiplying daily interpolated element concentrations with daily water fluxes and then aggregated to a year. Results show that the annual fluxes calculated with the reference method for the period 2003-2005, including all plots, elements and depths, lies only in 53% of the cases within the range of the average +/-2 times the standard error of the new method. Despite the differences in results, both methods indicate comparable N retention and strong Al mobilization in all plots, with Al leaching being nearly equal to the leaching of SO(4) and NO(3) with fluxes expressed in mol(c) ha(-1) yr(-1). This illustrates that Al release, which is the clearest signal of soil acidification, is mainly due to the external input of SO(4) and NO(3).

Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform.

PubMed

Yang, Hongxi; Li, Shu; Sun, Li; Zhang, Xinyu; Hou, Jie; Wang, Yaogang

2017-10-03

In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM 2.5 ) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public's awareness and knowledge of modifiable risk factors of lung cancer for prevention. The objective of our study was to explore the public's awareness of the association of PM 2.5 with lung cancer risk in China by analyzing the relationship between the daily PM 2.5 concentration and searches for the term "lung cancer" on an Internet big data platform, Baidu. We collected daily PM 2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM 2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM 2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM 2.5 concentration was poor (all r 2 s <.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM 2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. The daily average PM 2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public's awareness of the association of PM 2.5 with lung cancer risk, to lead the public to seek more information about PM 2.5 and its hazards, and to cope with their environment and its risks appropriately. ©Hongxi Yang, Shu Li, Li Sun, Xinyu Zhang, Jie Hou, Yaogang Wang. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.10.2017.

Summer outdoor temperature and occupational heat-related illnesses in Quebec (Canada)

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

Adam-Poupart, Ariane; Smargiassi, Audrey; Institut national de santé publique du Québec

2014-10-15

Background: Predicted rise in global mean temperature and intensification of heat waves associated with climate change present an increasing challenge for occupational health and safety. Although important scientific knowledge has been gathered on the health effects of heat, very few studies have focused on quantifying the association between outdoor heat and mortality or morbidity among workers. Objective: To quantify the association between occupational heat-related illnesses and exposure to summer outdoor temperatures. Methods: We modeled 259 heat-related illnesses compensated by the Workers' Compensation Board of Quebec between May and September, from 1998 to 2010, with maximum daily summer outdoor temperatures inmore » 16 health regions of Quebec (Canada) using generalized linear models with negative binomial distributions, and estimated the pooled effect sizes for all regions combined, by sex and age groups, and for different time lags with random-effect models for meta-analyses. Results: The mean daily compensation count was 0.13 for all regions of Quebec combined. The relationship between daily counts of compensations and maximum daily temperatures was log-linear; the pooled incidence rate ratio (IRR) of daily heat-related compensations per 1 °C increase in daily maximum temperatures was 1.419 (95% CI 1.326 to 1.520). Associations were similar for men and women and by age groups. Increases in daily maximum temperatures at lags 1 and 2 and for two and three-day lag averages were also associated with increases in daily counts of compensations (IRRs of 1.206 to 1.471 for every 1 °C increase in temperature). Conclusion: This study is the first to quantify the association between occupational heat-related illnesses and exposure to summer temperatures in Canada. The model (risk function) developed in this study could be useful to improve the assessment of future impacts of predicted summer outdoor temperatures on workers and vulnerable groups, particularly in colder temperate zones. - Highlights: • 259 heat-related compensated illnesses were modeled with ambient temperature • An overall risk ratio of 1.419 (95% CI 1.326–1.520) for every 1 °C increase was found • Risk estimates were similar for men and women and by large age groups. • There were little lag effects (IRRs of 1.206 to 1.471 for every 1 °C increase)« less

Effect of daily fluctuations in ambient temperature on reproductive failure traits of Landrace and Yorkshire sows under Thai tropical environmental conditions.

PubMed

Jaichansukkit, Teerapong; Suwanasopee, Thanathip; Koonawootrittriron, Skorn; Tummaruk, Padet; Elzo, Mauricio A

2017-03-01

The aim of this study was to determine the effects of daily ranges and maximum ambient temperatures, and other risk factors on reproductive failure of Landrace (L) and Yorkshire (Y) sows under an open-house system in Thailand. Daily ambient temperatures were added to information on 35,579 litters from 5929 L sows and 1057 Y sows from three commercial herds. The average daily temperature ranges (ADT) and the average daily maximum temperatures (PEAK) in three gestation periods from the 35th day of gestation to parturition were classified. The considered reproductive failure traits were the occurrences of mummified fetuses (MM), stillborn piglets (STB), and piglet death losses (PDL) and an indicator trait for number of piglets born alive below the population mean (LBA). A multiple logistic regression model included farrowing herd-year-season (HYS), breed group of sow (BG), parity group (PAR), number of total piglets born (NTB), ADT1, ADT2, ADT3, PEAK1, PEAK2, and PEAK3 as fixed effects, while random effects were animal, repeated observations, and residual. Yorkshire sows had a higher occurrence of LBA than L sows (P = 0.01). The second to fifth parities sows had lower reproductive failures than other parities. The NTB regression coefficients of log-odds were positive (P < 0.01) for all traits. Narrower ranges of ADT3 increased the occurrence of MM, STB, and PDL (P < 0.01), while higher PEAK3 increased the occurrence of MM, STB, PDL, and LBA (P < 0.001). To reduce the risk of reproductive failures, particularly late in gestation, producers would need to closely monitor their temperature management strategies.

Box Model of a Series of Salt Ponds, as Applied to the Alviso Salt Pond Complex, South San Francisco Bay, California

USGS Publications Warehouse

Lionberger, Megan A.; Schoellhamer, David H.; Shellenbarger, Gregory; Orlando, James L.; Ganju, Neil K.

2007-01-01

This report documents the development and application of a box model to simulate water level, salinity, and temperature of the Alviso Salt Pond Complex in South San Francisco Bay. These ponds were purchased for restoration in 2003 and currently are managed by the U.S. Fish and Wildlife Service to maintain existing wildlife habitat and prevent a build up of salt during the development of a long-term restoration plan. The model was developed for the purpose of aiding pond managers during the current interim management period to achieve these goals. A previously developed box model of a salt pond, SPOOM, which calculates daily pond volume and salinity, was reconfigured to simulate multiple connected ponds and a temperature subroutine was added. The updated model simulates rainfall, evaporation, water flowing between the ponds and the adjacent tidal slough network, and water flowing from one pond to the next by gravity and pumps. Theoretical and measured relations between discharge and corresponding differences in water level are used to simulate most flows between ponds and between ponds and sloughs. The principle of conservation of mass is used to calculate daily pond volume and salinity. The model configuration includes management actions specified in the Interim Stewardship Plan for the ponds. The temperature subroutine calculates hourly net heat transfer to or from a pond resulting in a rise or drop in pond temperature and daily average, minimum, and maximum pond temperatures are recorded. Simulated temperature was compared with hourly measured data from pond 3 of the Napa?Sonoma Salt Pond Complex and monthly measured data from pond A14 of the Alviso Salt-Pond Complex. Comparison showed good agreement of measured and simulated pond temperature on the daily and monthly time scales.

Simulation of streamflow and estimation of recharge to the Edwards aquifer in the Hondo Creek, Verde Creek, and San Geronimo Creek watersheds, south-central Texas, 1951-2003

USGS Publications Warehouse

Ockerman, Darwin J.

2005-01-01

The U.S. Geological Survey, in cooperation with the San Antonio Water System, constructed three watershed models using the Hydrological Simulation Program—FORTRAN (HSPF) to simulate streamflow and estimate recharge to the Edwards aquifer in the Hondo Creek, Verde Creek, and San Geronimo Creek watersheds in south-central Texas. The three models were calibrated and tested with available data collected during 1992–2003. Simulations of streamflow and recharge were done for 1951–2003. The approach to construct the models was to first calibrate the Hondo Creek model (with an hourly time step) using 1992–99 data and test the model using 2000–2003 data. The Hondo Creek model parameters then were applied to the Verde Creek and San Geronimo Creek watersheds to construct the Verde Creek and San Geronimo Creek models. The simulated streamflows for Hondo Creek are considered acceptable. Annual, monthly, and daily simulated streamflows adequately match measured values, but simulated hourly streamflows do not. The accuracy of streamflow simulations for Verde Creek is uncertain. For San Geronimo Creek, the match of measured and simulated annual and monthly streamflows is acceptable (or nearly so); but for daily and hourly streamflows, the calibration is relatively poor. Simulated average annual total streamflow for 1951–2003 to Hondo Creek, Verde Creek, and San Geronimo Creek is 45,400; 32,400; and 11,100 acre-feet, respectively. Simulated average annual streamflow at the respective watershed outlets is 13,000; 16,200; and 6,920 acre-feet. The difference between total streamflow and streamflow at the watershed outlet is streamflow lost to channel infiltration. Estimated average annual Edwards aquifer recharge for Hondo Creek, Verde Creek, and San Geronimo Creek watersheds for 1951–2003 is 37,900 acrefeet (5.04 inches), 26,000 acre-feet (3.36 inches), and 5,940 acre-feet (1.97 inches), respectively. Most of the recharge (about 77 percent for the three watersheds together) occurs as streamflow channel infiltration. Diffuse recharge (direct infiltration of rainfall to the aquifer) accounts for the remaining 23 percent of recharge. For the Hondo Creek watershed, the HSPF recharge estimates for 1992–2003 averaged about 22 percent less than those estimated by the Puente method, a method the U.S. Geological Survey has used to compute annual recharge to the Edwards aquifer since 1978. HSPF recharge estimates for the Verde Creek watershed average about 40 percent less than those estimated by the Puente method.

Simulation of future stream alkalinity under changing deposition and climate scenarios.

PubMed

Welsch, Daniel L; Cosby, B Jack; Hornberger, George M

2006-08-31

Models of soil and stream water acidification have typically been applied under scenarios of changing acidic deposition, however, climate change is usually ignored. Soil air CO2 concentrations have potential to increase as climate warms and becomes wetter, thus affecting soil and stream water chemistry by initially increasing stream alkalinity at the expense of reducing base saturation levels on soil exchange sites. We simulate this change by applying a series of physically based coupled models capable of predicting soil air CO2 and stream water chemistry. We predict daily stream water alkalinity for a small catchment in the Virginia Blue Ridge for 60 years into the future given stochastically generated daily climate values. This is done for nine different combinations of climate and deposition. The scenarios for both climate and deposition include a static scenario, a scenario of gradual change, and a scenario of abrupt change. We find that stream water alkalinity continues to decline for all scenarios (average decrease of 14.4 microeq L-1) except where climate is gradually warming and becoming more moist (average increase of 13 microeq L-1). In all other scenarios, base cation removal from catchment soils is responsible for limited alkalinity increase resulting from climate change. This has implications given the extent that acidification models are used to establish policy and legislation concerning deposition and emissions.

MAX-DOAS tropospheric nitrogen dioxide column measurements compared with the Lotos-Euros air quality model

NASA Astrophysics Data System (ADS)

Vlemmix, T.; Eskes, H. J.; Piters, A. J. M.; Schaap, M.; Sauter, F. J.; Kelder, H.; Levelt, P. F.

2015-02-01

A 14-month data set of MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) tropospheric NO2 column observations in De Bilt, the Netherlands, has been compared with the regional air quality model Lotos-Euros. The model was run on a 7×7 km2 grid, the same resolution as the emission inventory used. A study was performed to assess the effect of clouds on the retrieval accuracy of the MAX-DOAS observations. Good agreement was found between modeled and measured tropospheric NO2 columns, with an average difference of less than 1% of the average tropospheric column (14.5 · 1015 molec cm-2). The comparisons show little cloud cover dependence after cloud corrections for which ceilometer data were used. Hourly differences between observations and model show a Gaussian behavior with a standard deviation (σ) of 5.5 · 1015 molec cm-2. For daily averages of tropospheric NO2 columns, a correlation of 0.72 was found for all observations, and 0.79 for cloud free conditions. The measured and modeled tropospheric NO2 columns have an almost identical distribution over the wind direction. A significant difference between model and measurements was found for the average weekly cycle, which shows a much stronger decrease during the weekend for the observations; for the diurnal cycle, the observed range is about twice as large as the modeled range. The results of the comparison demonstrate that averaged over a long time period, the tropospheric NO2 column observations are representative for a large spatial area despite the fact that they were obtained in an urban region. This makes the MAX-DOAS technique especially suitable for validation of satellite observations and air quality models in urban regions.

Estimated Daily Average Per Capita Water Ingestion by Child and Adult Age Categories Based on USDA's 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals (Journal Article)

EPA Science Inventory

Current water ingestion estimates are important for the assessment of risk to human populations of exposure to water-borne pollutants. This paper reports mean and percentile estimates of the distributions of daily average per capita water ingestion for 12 age range groups. The a...

30 CFR 260.122 - How long will a royalty suspension volume be effective for a lease issued in a sale held after...

Code of Federal Regulations, 2011 CFR

2011-07-01

...) Notwithstanding any royalty suspension volume under this subpart, you must pay royalty at the lease stipulated... average of the daily closing price on the New York Mercantile Exchange (NYMEX) for light sweet crude oil... produced for any period stipulated in the lease during which the arithmetic average of the daily closing...

30 CFR 203.54 - How does my relief arrangement for an oil and gas lease operate if prices rise sharply?

Code of Federal Regulations, 2011 CFR

2011-07-01

... reference price, you must pay the effective royalty rate on all monthly production. (a) Your current reference price is a weighted average of daily closing prices on the NYMEX for light sweet crude oil and... average of daily closing prices on the NYMEX for light sweet crude oil and natural gas during the...

Novel Measure of Opioid Dose and Costs of Care for Diabetes Mellitus: Opioid Dose and Health Care Costs.

PubMed

Gautam, Santosh; Franzini, Luisa; Mikhail, Osama I; Chan, Wenyaw; Turner, Barbara J

2016-03-01

Diabetes mellitus (DM) has well known costly complications but we hypothesized that costs of care for chronic pain treated with opioid analgesic (OA) medications would also be substantial. In a statewide, privately insured cohort of 29,033 adults aged 18 to 64 years with DM and noncancer pain who filled OA prescription(s) from 2008 to 2012, our outcomes were costs for specific health care services and total costs per 6-month intervals after the first filled OA prescription. Average daily OA dose (4 categories) and total dose (quartiles) in morphine-equivalent milligrams were calculated per 6-month interval after the first OA prescription and combined into a novel OA dose measure. Associations of OA measures with costs of care (n = 126,854 6-month intervals) were examined using generalized estimating equations adjusted for clinical conditions, psychotherapeutic drugs, and DM treatment. Incremental costs for each type of health care service and total cost of care increased progressively with average daily and total OA dose versus no OAs. The combined OA measure identified the highest incremental total costs per 6-month interval that were increased by $8,389 for 50- to 99-mg average daily dose plus >900 mg total dose and, by $9,181 and $9,958 respectively, for ≥100 mg average daily dose plus 301- to 900-mg or >900 mg total dose. In this statewide DM cohort, total health care costs per 6-month interval increased progressively with higher average daily OA dose and with total OA dose but the greatest increases of >$8,000 were distinguished by combinations of higher average daily and total OA doses. The higher costs of care for opioid-treated patients appeared for all types of services and likely reflects multiple factors including morbidity from the underlying cause of pain, care and complications related to opioid use, and poorer control of diabetes as found in other studies. Copyright © 2016 American Pain Society. Published by Elsevier Inc. All rights reserved.

How Health Behaviors Relate to Academic Performance via Affect: An Intensive Longitudinal Study

PubMed Central

Flueckiger, Lavinia; Lieb, Roselind; Meyer, Andrea H.; Mata, Jutta

2014-01-01

Objective This intensive longitudinal study examined how sleep and physical activity relate to university students’ affect and academic performance during a stressful examination period. Methods On 32 consecutive days, 72 first-year students answered online questionnaires on their sleep quality, physical activity, positive and negative affect, learning goal achievement, and examination grades. First-year university students are particularly well-suited to test our hypotheses: They represent a relatively homogeneous population in a natural, but controlled setting, and simultaneously deal with similar stressors, such as examinations. Data were analyzed using multilevel structural equation models. Results Over the examination period, better average sleep quality but not physical activity predicted better learning goal achievement. Better learning goal achievement was associated with increased probability of passing all examinations. Relations of average sleep quality and average physical activity with learning goal achievement were mediated by experienced positive affect. In terms of day-to-day dynamics, on days with better sleep quality, participants reported better learning goal achievement. Day-to-day physical activity was not related to daily learning goal achievement. Daily positive and negative affect both mediated the effect of day-to-day sleep quality and physical activity on daily learning goal achievement. Conclusion Health behaviors such as sleep quality and physical activity seem important for both academic performance and affect experience, an indicator of mental health, during a stressful examination period. These results are a first step toward a better understanding of between- and within-person variations in health behaviors, affect, and academic performance, and could inform prevention and intervention programs for university students. PMID:25353638

How health behaviors relate to academic performance via affect: an intensive longitudinal study.

PubMed

Flueckiger, Lavinia; Lieb, Roselind; Meyer, Andrea H; Mata, Jutta

2014-01-01

This intensive longitudinal study examined how sleep and physical activity relate to university students' affect and academic performance during a stressful examination period. On 32 consecutive days, 72 first-year students answered online questionnaires on their sleep quality, physical activity, positive and negative affect, learning goal achievement, and examination grades. First-year university students are particularly well-suited to test our hypotheses: They represent a relatively homogeneous population in a natural, but controlled setting, and simultaneously deal with similar stressors, such as examinations. Data were analyzed using multilevel structural equation models. Over the examination period, better average sleep quality but not physical activity predicted better learning goal achievement. Better learning goal achievement was associated with increased probability of passing all examinations. Relations of average sleep quality and average physical activity with learning goal achievement were mediated by experienced positive affect. In terms of day-to-day dynamics, on days with better sleep quality, participants reported better learning goal achievement. Day-to-day physical activity was not related to daily learning goal achievement. Daily positive and negative affect both mediated the effect of day-to-day sleep quality and physical activity on daily learning goal achievement. Health behaviors such as sleep quality and physical activity seem important for both academic performance and affect experience, an indicator of mental health, during a stressful examination period. These results are a first step toward a better understanding of between- and within-person variations in health behaviors, affect, and academic performance, and could inform prevention and intervention programs for university students.

Image guidance during head-and-neck cancer radiation therapy: analysis of alignment trends with in-room cone-beam computed tomography scans.

PubMed

Zumsteg, Zachary; DeMarco, John; Lee, Steve P; Steinberg, Michael L; Lin, Chun Shu; McBride, William; Lin, Kevin; Wang, Pin-Chieh; Kupelian, Patrick; Lee, Percy

2012-06-01

On-board cone-beam computed tomography (CBCT) is currently available for alignment of patients with head-and-neck cancer before radiotherapy. However, daily CBCT is time intensive and increases the overall radiation dose. We assessed the feasibility of using the average couch shifts from the first several CBCTs to estimate and correct for the presumed systematic setup error. 56 patients with head-and-neck cancer who received daily CBCT before intensity-modulated radiation therapy had recorded shift values in the medial-lateral, superior-inferior, and anterior-posterior dimensions. The average displacements in each direction were calculated for each patient based on the first five or 10 CBCT shifts and were presumed to represent the systematic setup error. The residual error after this correction was determined by subtracting the calculated shifts from the shifts obtained using daily CBCT. The magnitude of the average daily residual three-dimensional (3D) error was 4.8 ± 1.4 mm, 3.9 ± 1.3 mm, and 3.7 ± 1.1 mm for uncorrected, five CBCT corrected, and 10 CBCT corrected protocols, respectively. With no image guidance, 40.8% of fractions would have been >5 mm off target. Using the first five CBCT shifts to correct subsequent fractions, this percentage decreased to 19.0% of all fractions delivered and decreased the percentage of patients with average daily 3D errors >5 mm from 35.7% to 14.3% vs. no image guidance. Using an average of the first 10 CBCT shifts did not significantly improve this outcome. Using the first five CBCT shift measurements as an estimation of the systematic setup error improves daily setup accuracy for a subset of patients with head-and-neck cancer receiving intensity-modulated radiation therapy and primarily benefited those with large 3D correction vectors (>5 mm). Daily CBCT is still necessary until methods are developed that more accurately determine which patients may benefit from alternative imaging strategies. Copyright © 2012 Elsevier Inc. All rights reserved.

The 2011 heat wave in Greater Houston: Effects of land use on temperature.

PubMed

Zhou, Weihe; Ji, Shuang; Chen, Tsun-Hsuan; Hou, Yi; Zhang, Kai

2014-11-01

Effects of land use on temperatures during severe heat waves have been rarely studied. This paper examines land use-temperature associations during the 2011 heat wave in Greater Houston. We obtained high resolution of satellite-derived land use data from the US National Land Cover Database, and temperature observations at 138 weather stations from Weather Underground, Inc (WU) during the August of 2011, which was the hottest month in Houston since 1889. Land use regression and quantile regression methods were applied to the monthly averages of daily maximum/mean/minimum temperatures and 114 land use-related predictors. Although selected variables vary with temperature metric, distance to the coastline consistently appears among all models. Other variables are generally related to high developed intensity, open water or wetlands. In addition, our quantile regression analysis shows that distance to the coastline and high developed intensity areas have larger impacts on daily average temperatures at higher quantiles, and open water area has greater impacts on daily minimum temperatures at lower quantiles. By utilizing both land use regression and quantile regression on a recent heat wave in one of the largest US metropolitan areas, this paper provides a new perspective on the impacts of land use on temperatures. Our models can provide estimates of heat exposures for epidemiological studies, and our findings can be combined with demographic variables, air conditioning and relevant diseases information to identify 'hot spots' of population vulnerability for public health interventions to reduce heat-related health effects during heat waves. Copyright © 2014 Elsevier Inc. All rights reserved.

Environmental triggers of acute myocardial infarction: results of a nationwide multiple-factorial population study.

PubMed

Claeys, Marc J; Coenen, Sarah; Colpaert, Charlotte; Bilcke, Joke; Beutels, Phillip; Wouters, Kristien; Legrand, Victor; Van Damme, Pierre; Vrints, Christiaan

2015-12-01

The objective of this study was to study the independent environmental triggers of ST-elevation myocardial infarction (STEMI) in a multifactorial environmental population model. Daily counts of all STEMI patients who underwent urgent percutaneous coronary intervention over the period 2006-2009 in Belgium were associated with average daily meteorological data and influenza-like illness incidence data. The following meteorological measures were investigated: particulate matter less than 10 μM (PM10) and less than 2.5 μM (PM(2.5)), ozone, black smoke, temperature and relative humidity. During the study period a total of 15,964 STEMI patients (mean age 63, 75% male) were admitted with a daily average admission rate of 11 ± 4 patients. A multivariate Poisson regression analysis showed that only the temperature was significantly correlated with STEMI, with an 8% increase in the risk of STEMI for each 10°C decrease in temperature (adjusted incidence risk ratio (IRR) 0.92, 95% CI 0.89-0.96). The effects of temperature were consistent among several subpopulations but the strongest effect was seen in diabetic patients (IRR 0.85, 95% CI 0.78 -0.95). There was a trend for an incremental risk of STEMI for each 10 μg/m³ PM(2.5) increase and during influenza epidemics with IRR of 1.02 (95% CI 1.00-1.04) and 1.07 (95% CI 0.98-1.16), respectively. In a global environmental model, low temperature is the most important environmental trigger for STEMI, whereas air pollution and influenza epidemics only seem to have a modest effect.

Relations between mothers' daily work, home, and relationship stress with characteristics of mother-child conflict interactions.

PubMed

Nelson, Jackie A; Boyer, Brittany P; Villarreal, Deyaun L; Smith, Olivia A

2017-06-01

This study examined whether daily variations in levels of mothers' work, home, and relationship stress were related to collaborative and oppositional qualities of mother-child conflict interactions across 1 week. Mothers reported on 1 specific conflict interaction with their 5- to 8-year-old child and their work, home, and relationship stress through online surveys each day for 7 consecutive days. Diary data from 142 mothers were analyzed in 6 multilevel models, each including within- and between-family levels of a stressor predicting collaborative or oppositional conflict qualities. Results suggested that families in the sample differed from each other, and also varied during the week, in collaborative and oppositional conflict qualities as well as stress in all 3 domains. Mothers reported a greater degree of oppositional conflict qualities on days characterized by higher perceptions of home chaos. Additionally, mothers who reported higher average levels of negativity in romantic relationships endorsed oppositional conflict qualities to a greater extent than mothers with lower relationship negativity. Two multilevel models including all 3 stressors in relation to collaborative and oppositional conflict revealed that for mothers managing multiple roles, average romantic relationship stress was the most important unique contributor to mother-child conflict qualities and daily relationship stress was particularly influential among mothers with sons compared to those with daughters. Results support the spillover hypothesis of stress within the family system and are discussed in terms of mothers' coping mechanisms and emotional engagement. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Drinking water as a proportion of total human exposure to volatile N-nitrosamines.

PubMed

Hrudey, Steve E; Bull, Richard J; Cotruvo, Joseph A; Paoli, Greg; Wilson, Margaret

2013-12-01

Some volatile N-nitrosamines, primarily N-nitrosodimethylamine (NDMA), are recognized as products of drinking water treatment at ng/L levels and as known carcinogens. The U.S. EPA has identified the N-nitrosamines as contaminants being considered for regulation as a group under the Safe Drinking Water Act. Nitrosamines are common dietary components, and a major database (over 18,000 drinking water samples) has recently been created under the Unregulated Contaminant Monitoring Rule. A Monte Carlo modeling analysis in 2007 found that drinking water contributed less than 2.8% of ingested NDMA and less than 0.02% of total NDMA exposure when estimated endogenous formation was considered. Our analysis, based upon human blood concentrations, indicates that endogenous NDMA production is larger than expected. The blood-based estimates are within the range that would be calculated from estimates based on daily urinary NDMA excretion and an estimate based on methylated guanine in DNA of lymphocytes from human volunteers. Our analysis of ingested NDMA from food and water based on Monte Carlo modeling with more complete data input shows that drinking water contributes a mean proportion of the lifetime average daily NDMA dose ranging from between 0.0002% and 0.001% for surface water systems using free chlorine or between 0.001% and 0.01% for surface water systems using chloramines. The proportions of average daily dose are higher for infants (zero to six months) than other age cohorts, with the highest mean up to 0.09% (upper 95th percentile of 0.3%). © 2013 Society for Risk Analysis.

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

PubMed Central

2013-01-01

Background Culex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species. Methods Mosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm. Results CCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of rS = 0.898) and with temperature during 2 weeks prior to capture (rS = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (rS = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of rS = 0.899 for daily and rS = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with rS = 0.876 for daily and rS = 0.899 for weekly data. Conclusions The study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens/restuans populations. PMID:23634763

US EPA 2012 Air Quality Fused Surface for the Conterminous U.S. Map Service

EPA Pesticide Factsheets

This web service contains a polygon layer that depicts fused air quality predictions for 2012 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations co

The effect of automated text messaging and goal setting on pedometer adherence and physical activity in patients with diabetes: A randomized controlled trial.

PubMed

Polgreen, Linnea A; Anthony, Christopher; Carr, Lucas; Simmering, Jacob E; Evans, Nicholas J; Foster, Eric D; Segre, Alberto M; Cremer, James F; Polgreen, Philip M

2018-01-01

Activity-monitoring devices may increase activity, but their effectiveness in sedentary, diseased, and less-motivated populations is unknown. Subjects with diabetes or pre-diabetes were given a Fitbit and randomized into three groups: Fitbit only, Fitbit with reminders, and Fitbit with both reminders and goal setting. Subjects in the reminders group were sent text-message reminders to wear their Fitbit. The goal-setting group was sent a daily text message asking for a step goal. All subjects had three in-person visits (baseline, 3 and 6 months). We modelled daily steps and goal setting using linear mixed-effects models. 138 subjects participated with 48 in the Fitbit-only, 44 in the reminders, and 46 in the goal-setting groups. Daily steps decreased for all groups during the study. Average daily steps were 7123, 6906, and 6854 for the Fitbit-only, the goal-setting, and the reminders groups, respectively. The reminders group was 17.2 percentage points more likely to wear their Fitbit than the Fitbit-only group. Setting a goal was associated with a significant increase of 791 daily steps, but setting more goals did not lead to step increases. In a population of patients with diabetes or pre-diabetes, individualized reminders to wear their Fitbit and elicit personal step goals did not lead to increases in daily steps, although daily steps were higher on days when goals were set. Our intervention improved engagement and data collection, important goals for activity surveillance. This study demonstrates that new, more-effective interventions for increasing activity in patients with pre-diabetes and diabetes are needed.

The effect of automated text messaging and goal setting on pedometer adherence and physical activity in patients with diabetes: A randomized controlled trial

PubMed Central

Anthony, Christopher; Carr, Lucas; Simmering, Jacob E.; Evans, Nicholas J.; Foster, Eric D.; Segre, Alberto M.; Cremer, James F.; Polgreen, Philip M.

2018-01-01

Introduction Activity-monitoring devices may increase activity, but their effectiveness in sedentary, diseased, and less-motivated populations is unknown. Methods Subjects with diabetes or pre-diabetes were given a Fitbit and randomized into three groups: Fitbit only, Fitbit with reminders, and Fitbit with both reminders and goal setting. Subjects in the reminders group were sent text-message reminders to wear their Fitbit. The goal-setting group was sent a daily text message asking for a step goal. All subjects had three in-person visits (baseline, 3 and 6 months). We modelled daily steps and goal setting using linear mixed-effects models. Results 138 subjects participated with 48 in the Fitbit-only, 44 in the reminders, and 46 in the goal-setting groups. Daily steps decreased for all groups during the study. Average daily steps were 7123, 6906, and 6854 for the Fitbit-only, the goal-setting, and the reminders groups, respectively. The reminders group was 17.2 percentage points more likely to wear their Fitbit than the Fitbit-only group. Setting a goal was associated with a significant increase of 791 daily steps, but setting more goals did not lead to step increases. Conclusion In a population of patients with diabetes or pre-diabetes, individualized reminders to wear their Fitbit and elicit personal step goals did not lead to increases in daily steps, although daily steps were higher on days when goals were set. Our intervention improved engagement and data collection, important goals for activity surveillance. This study demonstrates that new, more-effective interventions for increasing activity in patients with pre-diabetes and diabetes are needed. PMID:29718931

Riverbed Hydrologic Exchange Dynamics in a Large Regulated River Reach

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

Zhou, Tian; Bao, Jie; Huang, Maoyi

Hydrologic exchange flux (HEF) is an important hydrologic component in river corridors that includes both bidirectional (hyporheic) and unidirectional (gaining/losing) surface water – groundwater exchanges. Quantifying HEF rates in a large regulated river is difficult due to the large spatial domains, complexity of geomorphologic features and subsurface properties, and the great stage variations created by dam operations at multiple time scales. In this study, we developed a method that combined numerical modeling and field measurements for estimating HEF rates across the river bed in a 7‐km long reach of the highly regulated Columbia River. A high‐resolution computational fluid dynamics (CFD)more » modeling framework was developed and validated by field measurements and other modeling results to characterize the HEF dynamics across the river bed. We found that about 85% of the time from 2008‐2014 the river was losing water with an annual average net HEF rates across the river bed (Qz) of ‐2.3 m3 s−1 (negative indicating downwelling). June was the only month that the river gained water, with monthly averaged Qz of 0.8 m3 s−1. We also found that the daily dam operations increased the hourly gross gaining and losing rate over an average year of 8% and 2%, respectively. By investigating the HEF feedbacks at various time scales, we suggest that the dam operations could reduce the HEF at seasonal time scale by decreasing the seasonal flow variations, while also enhance the HEF at sub‐daily time scale by generating high frequency discharge variations. These changes could generate significant impacts on biogeochemical processes in the hyporheic zone.« less

Mathematical optimization techniques for managing selective catalytic reduction for a fleet of coal-fired power plants

NASA Astrophysics Data System (ADS)

Alanis Pena, Antonio Alejandro

Major commercial electricity generation is done by burning fossil fuels out of which coal-fired power plants produce a substantial quantity of electricity worldwide. The United States has large reserves of coal, and it is cheaply available, making it a good choice for the generation of electricity on a large scale. However, one major problem associated with using coal for combustion is that it produces a group of pollutants known as nitrogen oxides (NO x). NOx are strong oxidizers and contribute to ozone formation and respiratory illness. The Environmental Protection Agency (EPA) regulates the quantity of NOx emitted to the atmosphere in the United States. One technique coal-fired power plants use to reduce NOx emissions is Selective Catalytic Reduction (SCR). SCR uses layers of catalyst that need to be added or changed to maintain the required performance. Power plants do add or change catalyst layers during temporary shutdowns, but it is expensive. However, many companies do not have only one power plant, but instead they can have a fleet of coal-fired power plants. A fleet of power plants can use EPA cap and trade programs to have an outlet NOx emission below the allowances for the fleet. For that reason, the main aim of this research is to develop an SCR management mathematical optimization methods that, with a given set of scheduled outages for a fleet of power plants, minimizes the total cost of the entire fleet of power plants and also maintain outlet NO x below the desired target for the entire fleet. We use a multi commodity network flow problem (MCFP) that creates edges that represent all the SCR catalyst layers for each plant. This MCFP is relaxed because it does not consider average daily NOx constraint, and it is solved by a binary integer program. After that, we add the average daily NOx constraint to the model with a schedule elimination constraint (MCFPwSEC). The MCFPwSEC eliminates, one by one, the solutions that do not satisfy the average daily NOx constraint and the worst NH 3 slip until it finds the solution that satisfies that requirement. We introduce an algorithm called heuristic MCFPwSEC (HMCFPwSEC). When HMCFPwSEC algorithm starts, we calculate the cost of the edges estimating the average NH3 slip level, but after we have a schedule that satisfies the average daily NOx constraint and the worst NH3 slip, we update the cost of the edges with the average NH3 slip for this schedule. We repeat this process until we have the solution. Because HMCFPwSEC does not guarantee optimality, we compare its results with SGO, which is optimal, using computational experiments. The results between both models are very similar, the only important difference is the time to solve each model. Then, a fleet HMCFPwSEC (FHMCFPwSEC) uses HMCFPwSEC to create the SCR management plan for each plant of the fleet, with a discrete NOx emissions value for each plant. FHMCFPwSEC repeats this process with different discrete levels of NOx emissions, for each plant, in order to create a new problem with schedules with different cost and NO x emissions for each plant of the fleet. Finally, FHMCFPwSEC solves this new problem with a binary integer program, in order to satisfy a NO x emission value for the fleet that also minimizes the total cost for the fleet, and using each plant once. FHMCFPwSEC can work with single cut and also with multi-cut methods. Because FHMCFPwSEC does not guarantee optimality, we compare its results with fleet SGO (FSGO) using computational experiments. The results between both models are very similar, the only important difference is the time to solve each model. In the experiments, FHMCFPwSEC multi-cut targeting new layer always uses less time than FSGO.

Comparison of anchor-based and distributional approaches in estimating important difference in common cold.

PubMed

Barrett, Bruce; Brown, Roger; Mundt, Marlon

2008-02-01

Evaluative health-related quality-of-life instruments used in clinical trials should be able to detect small but important changes in health status. Several approaches to minimal important difference (MID) and responsiveness have been developed. To compare anchor-based and distributional approaches to important difference and responsiveness for the Wisconsin Upper Respiratory Symptom Survey (WURSS), an illness-specific quality of life outcomes instrument. Participants with community-acquired colds self-reported daily using the WURSS-44. Distribution-based methods calculated standardized effect size (ES) and standard error of measurement (SEM). Anchor-based methods compared daily interval changes to global ratings of change, using: (1) standard MID methods based on correspondence to ratings of "a little better" or "somewhat better," and (2) two-level multivariate regression models. About 150 adults were monitored throughout their colds (1,681 sick days.): 88% were white, 69% were women, and 50% had completed college. The mean age was 35.5 years (SD = 14.7). WURSS scores increased 2.2 points from the first to second day, and then dropped by an average of 8.2 points per day from days 2 to 7. The SEM averaged 9.1 during these 7 days. Standard methods yielded a between day MID of 22 points. Regression models of MID projected 11.3-point daily changes. Dividing these estimates of small-but-important-difference by pooled SDs yielded coefficients of .425 for standard MID, .218 for regression model, .177 for SEM, and .157 for ES. These imply per-group sample sizes of 870 using ES, 616 for SEM, 302 for regression model, and 89 for standard MID, assuming alpha = .05, beta = .20 (80% power), and two-tailed testing. Distribution and anchor-based approaches provide somewhat different estimates of small but important difference, which in turn can have substantial impact on trial design.

Growth and mortality of larval Myctophum affine (Myctophidae, Teleostei).

PubMed

Namiki, C; Katsuragawa, M; Zani-Teixeira, M L

2015-04-01

The growth and mortality rates of Myctophum affine larvae were analysed based on samples collected during the austral summer and winter of 2002 from south-eastern Brazilian waters. The larvae ranged in size from 2·75 to 14·00 mm standard length (L(S)). Daily increment counts from 82 sagittal otoliths showed that the age of M. affine ranged from 2 to 28 days. Three models were applied to estimate the growth rate: linear regression, exponential model and Laird-Gompertz model. The exponential model best fitted the data, and L(0) values from exponential and Laird-Gompertz models were close to the smallest larva reported in the literature (c. 2·5 mm L(S)). The average growth rate (0·33 mm day(-1)) was intermediate among lanternfishes. The mortality rate (12%) during the larval period was below average compared with other marine fish species but similar to some epipelagic fishes that occur in the area. © 2015 The Fisheries Society of the British Isles.

Intermediate photovoltaic system application experiment operational performance report: Volume 5, for Beverly High School, Beverly, Mass.

NASA Astrophysics Data System (ADS)

1982-02-01

Performance data for the month of January, 1982 for a grid connected photovoltaic power supply in Massachusetts are presented. Data include: monthly and daily electrical energy produced; monthly and daily solar energy incident on the array; monthly and daily array efficiency; plots of energy produced as a function of power level, voltage, cell temperature and time of day; power conditioner input, output and efficiency for each of two individual units and for the total power conditioning system; photovoltaic system efficiency; capacity factor; PV system to load and grid to load energies and corresponding dollar values; daily energy supplies to the load by the PV system; daily PV system availability; monthly and hourly insolation; monthly and hourly temperature average; monthly and hourly wind speed; wind direction distribution; average heating and cooling degree days; number of freeze/thaw cycles; and the data acquisition mode and recording interval plot.

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

USGS Publications Warehouse

Letcher, Benjamin; Hocking, Daniel; O'Neil, Kyle; Whiteley, Andrew R.; Nislow, Keith H.; O'Donnell, Matthew

2016-01-01

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

The change of sleeping and lying posture of Japanese black cows after moving into new environment.

PubMed

Fukasawa, Michiru; Komatsu, Tokushi; Higashiyama, Yumi

2018-04-25

The environmental change is one of the stressful events in livestock production. Change in environment disturbed cow behavior and cows needed several days to reach stable behavioral pattern, especially sleeping posture (SP) and lying posture (LP) have been used as an indicator for relax and well-acclimated to its environment. The aim of this study examines how long does Japanese black cow required for stabilization of SP and LP after moving into new environment. Seven pregnant Japanese black cows were used. Cows were moved into new tie-stall shed and measured sleeping and lying posture 17 times during 35 experimental days. Both SP and LP were detected by accelerometer fixed on middle occipital and hip-cross, respectively. Daily total time, frequency, and average bout of both SP and LP were calculated. Daily SP time was the shortest on day 1, and increased to the highest on day3. It decreased until day 9, after that stabilized about 65 min /day till the end of experiment. The longest average SP bout was shown on day 1, and it decreased to stabilize till day 7. Daily LP time was changed as same manner as daily SP time. The average SP bout showed the longest on day 1, and it decreased to stable level till day 7. On the other hand, the average LP bout showed the shortest on day1, and it was increased to stable level till on day 7. These results showed that pregnant Japanese black cows needed 1 week to stabilize their SP. However, there were different change pattern between the average SP and LP bout, even though the change pattern of daily SP and LP time were similar.

Long-term mesalamine maintenance in ulcerative colitis: which is more important? Adherence or daily dose.

PubMed

Khan, Nabeel; Abbas, Ali M; Koleva, Yordanka N; Bazzano, Lydia A

2013-05-01

There are limited data about the long-term follow-up of patients with ulcerative colitis (UC) maintained on high versus low doses of mesalamine. We evaluated the best long-term average daily dose that would keep the disease in remission. Nationwide ulcerative colitis data were obtained from the Veterans Affairs health care system for the period 2001 to 2011. Those who started mesalamine maintenance during this period were included. Average daily dose and the level of adherence were assessed for the period between the first mesalamine dispense and the date of first flare defined as the first filling of 40 mg/day or more of oral prednisone or any dose of intravenous steroids. Patients with ulcerative colitis maintained on an average daily dose 2.4 to 2.8 g/day (low dose) were compared with 4.4 to 4.8 g/day (high dose). Adherence was assessed using continuous single interval medication availability indicator. We included 4452 patients with a median follow-up of 6 years. There was no significant reduction in the risk of flares when comparing high versus low average mesalamine dose among patients with high [hazard ratio = 0.96, P = 0.8)] and medium (hazard ratio = 0.74, P = 0.17) adherence. However, there was a significant reduction in the risk of flares with high dose of mesalamine among patients with low adherence (hazard ratio = 0.28, P = 0.003). Our data show that when starting a patient on mesalamine, there is no difference in the long-term flare risk between low versus high average daily dose as long as the patients have a high to moderate level of adherence.

Time series model for forecasting the number of new admission inpatients.

PubMed

Zhou, Lingling; Zhao, Ping; Wu, Dongdong; Cheng, Cheng; Huang, Hao

2018-06-15

Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Hybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

A new method to estimate average hourly global solar radiation on the horizontal surface

NASA Astrophysics Data System (ADS)

Pandey, Pramod K.; Soupir, Michelle L.

2012-10-01

A new model, Global Solar Radiation on Horizontal Surface (GSRHS), was developed to estimate the average hourly global solar radiation on the horizontal surfaces (Gh). The GSRHS model uses the transmission function (Tf,ij), which was developed to control hourly global solar radiation, for predicting solar radiation. The inputs of the model were: hour of day, day (Julian) of year, optimized parameter values, solar constant (H0), latitude, and longitude of the location of interest. The parameter values used in the model were optimized at a location (Albuquerque, NM), and these values were applied into the model for predicting average hourly global solar radiations at four different locations (Austin, TX; El Paso, TX; Desert Rock, NV; Seattle, WA) of the United States. The model performance was assessed using correlation coefficient (r), Mean Absolute Bias Error (MABE), Root Mean Square Error (RMSE), and coefficient of determinations (R2). The sensitivities of parameter to prediction were estimated. Results show that the model performed very well. The correlation coefficients (r) range from 0.96 to 0.99, while coefficients of determination (R2) range from 0.92 to 0.98. For daily and monthly prediction, error percentages (i.e. MABE and RMSE) were less than 20%. The approach we proposed here can be potentially useful for predicting average hourly global solar radiation on the horizontal surface for different locations, with the use of readily available data (i.e. latitude and longitude of the location) as inputs.

Performance Comparison of Big Data Analytics With NEXUS and Giovanni

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Huang, T.; Lynnes, C.

2016-12-01

NEXUS is an emerging data-intensive analysis framework developed with a new approach for handling science data that enables large-scale data analysis. It is available through open source. We compare performance of NEXUS and Giovanni for 3 statistics algorithms applied to NASA datasets. Giovanni is a statistics web service at NASA Distributed Active Archive Centers (DAACs). NEXUS is a cloud-computing environment developed at JPL and built on Apache Solr, Cassandra, and Spark. We compute global time-averaged map, correlation map, and area-averaged time series. The first two algorithms average over time to produce a value for each pixel in a 2-D map. The third algorithm averages spatially to produce a single value for each time step. This talk is our report on benchmark comparison findings that indicate 15x speedup with NEXUS over Giovanni to compute area-averaged time series of daily precipitation rate for the Tropical Rainfall Measuring Mission (TRMM with 0.25 degree spatial resolution) for the Continental United States over 14 years (2000-2014) with 64-way parallelism and 545 tiles per granule. 16-way parallelism with 16 tiles per granule worked best with NEXUS for computing an 18-year (1998-2015) TRMM daily precipitation global time averaged map (2.5 times speedup) and 18-year global map of correlation between TRMM daily precipitation and TRMM real time daily precipitation (7x speedup). These and other benchmark results will be presented along with key lessons learned in applying the NEXUS tiling approach to big data analytics in the cloud.

Adverse Effects of UV-B Radiation on Plants Growing at Schirmacher Oasis, East Antarctica.

PubMed

Singh, Jaswant; Singh, Rudra P

2014-01-01

This study aimed to assess the impacts of ultraviolet-B (UV-B) radiation over a 28-day period on the levels of pigments of Umbilicaria aprina and Bryum argenteum growing in field. The depletion of stratospheric ozone is most prominent over Antarctica, which receives more UV-B radiation than most other parts of the planet. Although UV-B radiation adversely affects all flora, Antarctic plants are better equipped to survive the damaging effects of UV-B owing to defenses provided by UV-B absorbing compounds and other screening pigments. The UV-B radiations and daily average ozone values were measured by sun photometer and the photosynthetic pigments were analyzed by the standard spectrophotometric methods of exposed and unexposed selected plants. The daily average atmospheric ozone values were recorded from 5 January to 2 February 2008. The maximum daily average for ozone (310.7 Dobson Units (DU)) was recorded on 10 January 2008. On that day, average UV-B spectral irradiances were 0.016, 0.071, and 0.186 W m(-2) at wavelengths of 305, 312, and 320 nm, respectively. The minimum daily average ozone value (278.6 DU) was recorded on 31 January 2008. On that day, average UV-B spectral irradiances were 0.018, 0.085, and 0.210 W m(-2) at wavelengths of 305, 312, and 320 nm, respectively. Our results concludes that following prolonged UV-B exposure, total chlorophyll levels decreased gradually in both species, whereas levels of UV-B absorbing compounds, phenolics, and carotenoids gradually increased.

Adverse Effects of UV-B Radiation on Plants Growing at Schirmacher Oasis, East Antarctica

PubMed Central

Singh, Jaswant; Singh, Rudra P.

2014-01-01

This study aimed to assess the impacts of ultraviolet-B (UV-B) radiation over a 28-day period on the levels of pigments of Umbilicaria aprina and Bryum argenteum growing in field. The depletion of stratospheric ozone is most prominent over Antarctica, which receives more UV-B radiation than most other parts of the planet. Although UV-B radiation adversely affects all flora, Antarctic plants are better equipped to survive the damaging effects of UV-B owing to defenses provided by UV-B absorbing compounds and other screening pigments. The UV-B radiations and daily average ozone values were measured by sun photometer and the photosynthetic pigments were analyzed by the standard spectrophotometric methods of exposed and unexposed selected plants. The daily average atmospheric ozone values were recorded from 5 January to 2 February 2008. The maximum daily average for ozone (310.7 Dobson Units (DU)) was recorded on 10 January 2008. On that day, average UV-B spectral irradiances were 0.016, 0.071, and 0.186 W m-2 at wavelengths of 305, 312, and 320 nm, respectively. The minimum daily average ozone value (278.6 DU) was recorded on 31 January 2008. On that day, average UV-B spectral irradiances were 0.018, 0.085, and 0.210 W m-2 at wavelengths of 305, 312, and 320 nm, respectively. Our results concludes that following prolonged UV-B exposure, total chlorophyll levels decreased gradually in both species, whereas levels of UV-B absorbing compounds, phenolics, and carotenoids gradually increased. PMID:24748743

26 CFR 1.468B-6 - Escrow accounts, trusts, and other funds used during deferred exchanges of like-kind property...

Code of Federal Regulations, 2011 CFR

2011-04-01

... average daily principal balances, and a 30-day month convention, as follows: Month Account's avg. daily bal. T's avg. daily bal. T's share*(percent) Monthly interest T's end. bal.** May $5,275,000 $2,100...

A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

NASA Technical Reports Server (NTRS)

White, Jeffrey W.; Hoogenboom, Gerrit; Wilkens, Paul W.; Stackhouse, Paul W., Jr.; Hoell, James M.

2010-01-01

Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at least partially explained by assumptions in ambient aerosol properties. Overall, the NASA/POWER solar radiation data are a promising resource for regional modeling studies where realistic accounting of historic variation is required.

Artificial Intelligence Can Predict Daily Trauma Volume and Average Acuity.

PubMed

Stonko, David P; Dennis, Bradley M; Betzold, Richard D; Peetz, Allan B; Gunter, Oliver L; Guillamondegui, Oscar D

2018-04-19

The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a level 1 trauma center. Trauma admission data from TRACS and weather data from the National Oceanic and Atmospheric Administration (NOAA) was collected for all adult trauma patients from July 2013-June 2016. The ANN was constructed using temporal (time, day of week), and weather factors (daily high, active precipitation) to predict four points of daily trauma activity: number of traumas, number of penetrating traumas, average ISS, and number of immediate OR cases per day. We trained a two-layer feed-forward network with 10 sigmoid hidden neurons via the Levenberg-Marquardt backpropagation algorithm, and performed k-fold cross validation and accuracy calculations on 100 randomly generated partitions. 10,612 patients over 1,096 days were identified. The ANN accurately predicted the daily trauma distribution in terms of number of traumas, number of penetrating traumas, number of OR cases, and average daily ISS (combined training correlation coefficient r = 0.9018+/-0.002; validation r = 0.8899+/- 0.005; testing r = 0.8940+/-0.006). We were able to successfully predict trauma and emergent operative volume, and acuity using an ANN by integrating local weather and trauma admission data from a level 1 center. As an example, for June 30, 2016, it predicted 9.93 traumas (actual: 10), and a mean ISS score of 15.99 (actual: 13.12); see figure 3. This may prove useful for predicting trauma needs across the system and hospital administration when allocating limited resources. Level III STUDY TYPE: Prognostic/Epidemiological.

Gotta catch'em all! Pokémon GO and physical activity among young adults: difference in differences study.

PubMed

Howe, Katherine B; Suharlim, Christian; Ueda, Peter; Howe, Daniel; Kawachi, Ichiro; Rimm, Eric B

2016-12-13

 To estimate the effect of playing Pokémon GO on the number of steps taken daily up to six weeks after installation of the game.  Cohort study using online survey data.  Survey participants of Amazon Mechanical Turk (n=1182) residing in the United States, aged 18 to 35 years and using iPhone 6 series smartphones.  Number of daily steps taken each of the four weeks before and six weeks after installation of Pokémon GO, automatically recorded in the "Health" application of the iPhone 6 series smartphones and reported by the participants. A difference in difference regression model was used to estimate the change in daily steps in players of Pokémon GO compared with non-players.  560 (47.4%) of the survey participants reported playing Pokémon GO and walked on average 4256 steps (SD 2697) each day in the four weeks before installation of the game. The difference in difference analysis showed that the daily average steps for Pokémon GO players during the first week of installation increased by 955 additional steps (95% confidence interval 697 to 1213), and then this increase gradually attenuated over the subsequent five weeks. By the sixth week after installation, the number of daily steps had gone back to pre-installation levels. No significant effect modification of Pokémon GO was found by sex, age, race group, bodyweight status, urbanity, or walkability of the area of residence.  Pokémon GO was associated with an increase in the daily number of steps after installation of the game. The association was, however, moderate and no longer observed after six weeks. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

[Temperature modifies the acute effect of particulate air pollution on mortality in Jiang'an district of Wuhan].

PubMed

Zhu, Y H; Wu, R; Zhong, P R; Zhu, C H; Ma, L

2016-06-01

To analyze the temperature modification effect on acute mortality due to particulate air pollution. Daily non-accidental mortality, cardiovascular mortality, and respiratory mortality data were obtained from Jiang'an District Center for Disease Control and Prevention. Daily meteorological data on mean temperature and relative humidity were collected from China Meteorological Data Sharing Service System. The daily concentration of particulate matter was collected from Wuhan Environmental Monitoring center. By using the stratified time-series models, we analyzed effects of particulate air pollution on mortality under different temperature zone from 2002 to 2010, meanwhile comparing the difference of age, gender and educational level, in Wuhan city of China. High temperature (daily average temperature > 33.4 ℃) obviously enhanced the effect of PM10 on mortality. With 10 μg/m(3) increase in PM10 concentrations, non-accidental, cardiovascular, and respiratory mortality increased 2.95% (95%CI: 1.68%-4.24%), 3.58% (95%CI: 1.72%-5.49%), and 5.07% (95%CI: 2.03%-9.51%) respectively. However, low temperature (daily average temperature <-0.21 ℃) enhanced PM10 effect on respiratory mortality with 3.31% (95% CI: 0.07%-6.64%) increase. At high temperature, PM10 had significantly stronger effect on non-accidental mortality of female aged over 65 and people with high educational level groups. With an increase of 10 μg/m(3), daily non-accidental mortality increased 4.27% (95% CI:2.45%-6.12%), 3.38% (95% CI:1.93%-4.86%) and 3.47% (95% CI:1.79%-5.18%), respectively. Whereas people with low educational level were more susceptible to low temperature. A 10 μg/m(3) increase in PM10 was associated with 2.11% (95% CI: 0.20%-4.04%) for non-accidental mortality. Temperature factor can modify the association between the PM10 level and cause-specific mortality. Moreover, the differences were apparent after considering the age, gender and education groups.

Application of a Two-Dimensional Reservoir Water-Quality Model of Beaver Lake, Arkansas, for the Evaluation of Simulated Changes in Input Water Quality, 2001-2003

USGS Publications Warehouse

Galloway, Joel M.; Green, W. Reed

2007-01-01

Beaver Lake is considered a primary watershed of concern in the State of Arkansas. As such, information is needed to assess water quality, especially nutrient enrichment, nutrient-algal relations, turbidity, and sediment issues within the system. A previously calibrated two-dimensional, laterally averaged model of hydrodynamics and water quality was used for the evaluation of changes in input nutrient and sediment concentrations on the water quality of the reservoir for the period of April 2001 to April 2003. Nitrogen and phosphorus concentrations were increased and decreased and tested independently and simultaneously to examine the nutrient concentrations and algal response in the reservoir. Suspended-solids concentrations were increased and decreased to identify how solids are distributed in the reservoir, which can contribute to decreased water clarity. The Beaver Lake model also was evaluated using a conservative tracer. A conservative tracer was applied at various locations in the reservoir model to observe the fate and transport and how the reservoir might react to the introduction of a conservative substance, or a worst-case spill scenario. In particular, tracer concentrations were evaluated at the locations of the four public water-supply intakes in Beaver Lake. Nutrient concentrations in Beaver Lake increased proportionally with increases in loads from the three main tributaries. An increase of 10 times the calibrated daily input nitrogen and phosphorus in the three main tributaries resulted in daily mean total nitrogen concentrations in the epilimnion that were nearly 4 times greater than the calibration concentrations at site L2 and more than 2 times greater than the calibrated concentrations at site L5. Increases in daily input nitrogen in the three main tributaries independently did not correspond in substantial increases in concentrations of nitrogen in Beaver Lake. The greatest proportional increase in phosphorus occurred in the epilimnion at sites L3 and L4 and the least increase occurred at sites L2 and L5 when calibrated daily input phosphorus concentrations were increased. When orthophosphorus was increased in all three tributaries simultaneously by a factor of 10, daily mean orthophosphorus concentrations in the epilimnion of the reservoir were almost 11 times greater than the calibrated concentrations at sites L2 and L5, and 15 times greater in the epilimnion of the reservoir at sites L3 and L4. Phosphorus concentrations in Beaver Lake increased less when nitrogen and phosphorus were increased simultaneously than when phosphorus was increased independently. The greatest simulated increase in algal biomass (represented as chlorophyll a) occurred when nitrogen and phosphorus were increased simultaneously in the three main tributaries. On average, the chlorophyll a values only increased less than 1 microgram per liter when concentrations of nitrogen or phosphorous were increased independently by a factor of 10 at all three tributaries. In comparison, when nitrogen and phosphorus were increased simultaneously by a factor of 10 for all three tributaries, the chlorophyll a concentration increased by about 10 micrograms per liter on average, with a maximum increase of about 57 micrograms per liter in the epilimnion at site L3 in Beaver Lake. Changes in algal biomass with changes in input nitrogen and phosphorus were variable through time in the Beaver Lake model from April 2001 to April 2003. When calibrated daily input nitrogen and phosphorus concentrations were increased simultaneously for the three main tributaries, the increase in chlorophyll a concentration was the greatest in late spring and summer of 2002. Changes in calibrated daily input inorganic suspended solids concentrations were examined because of the effect they may have on water clarity in Beaver Lake. The increase in total suspended solids was greatest in the hypolimnion at the upstream end of Beaver Lake, and negligible changes

Consistent, high-level ethanol consumption in pig-tailed macaques via a multiple-session, limited-intake, oral self-dosing procedure.

PubMed

Weed, Michael R; Wilcox, Kristin M; Ator, Nancy A; Hienz, Robert D

2008-06-01

Alcohol abuse is a major public health burden that can lead to many adverse health effects such as impaired hepatic, gastrointestinal, central nervous system and immune system function. Preclinical animal models of alcohol abuse allow for experimental control over variables often difficult to control in human clinical studies (e.g., ethanol exposure before or during the study, history of other drug use, access to medical care, nutritional status, etc). Nonhuman primate models in particular provide increased genetic, anatomic and physiologic similarity to humans, relative to rodent models. A small percentage of macaques will spontaneously consume large quantities of ethanol; however, most nonhuman primate models of "voluntary" ethanol intake produce relatively low daily ethanol intake in the majority of monkeys. To facilitate study of chronic exposure to high levels of ethanol intake, a macaque model has been developed that induces consistent, daily high-level ethanol consumption. This multiple-session procedure employed 4 drinking sessions per day, with sessions occurring once every 6 hours. The group average alcohol consumption was 4.6 g/kg/d (SEM 0.4), roughly twice the group average consumption of previous reports. Ethanol drinking sessions produced group mean blood ethanol levels of 95 mg/dl after 60 minutes, and fine motor control was impaired up to 90 minutes after a drinking session. This model of multiple-session, limited access, oral ethanol self-dosing produced consistent, high-level ethanol consumption with each session qualifying as a "binge" drinking session using the definition of "binge" provided by the NIAAA (>80 mg/dl/session). This model of ethanol drinking in macaques will be of great utility in the study of immunological, physiological and behavioral effects of ethanol in nonhuman primates.

Objectively Measured Daily Physical Activity and Postural Changes as Related to Positive and Negative Affect Using Ambulatory Monitoring Assessments.

PubMed

Aggio, Daniel; Wallace, Karen; Boreham, Nicola; Shankar, Aparna; Steptoe, Andrew; Hamer, Mark

2017-09-01

The aim of the study was to determine whether objectively measured daily physical activity and posture of sitting, standing, and sit-to-stand transitions are associated with daily assessments of affect. Participants (N = 51, 49% female) wore ActivPal accelerometers for 24 h/d for seven consecutive days. Time spent sitting, standing, and being physically active and sit-to-stand transitions were derived for each day. Participants also completed a mood inventory each evening. Multilevel models examined within- and between-person associations of daily physical activity with positive and negative affect, adjusting for age, sex, body mass index, education, and sleep duration. Within-person associations showed that a 1-hour increase in daily physical activity was associated with a decrease in negative affect over the same day (B = -0.11, 95% confidence interval [CI], -0.21 to -0.01). Between-person associations indicated a borderline significant association between higher average daily physical activity levels and higher positive affect (B = 1.85, 95% CI = -0.25 to 3.94). There were no between- or within-person associations between sitting, standing, and sit-to-stand transitions with affect. Promoting physical activity may be a potential intervention strategy to acutely suppress negative affective states.

Stress is associated with exercise differently among individuals with higher and lower eating disorder symptoms: An ecological momentary assessment study.

PubMed

Sala, Margarita; Brosof, Leigh C; Rosenfield, David; Fernandez, Katya C; Levinson, Cheri A

2017-12-01

Stress is associated with the maintenance of eating disorders and exercise behaviors. However, it is unclear how stress is associated with exercise and vice-versa among individuals with higher levels of eating disorder symptoms in daily life. The current study tested the moderating effect of eating disorder symptoms on the relationships between (1) daily stress and later exercise behavior and (2) daily exercise behavior and later stress. Female college students [N = 129, mean age = 19.19 (SD = 1.40)] completed the Eating Disorder Inventory-2. Participants then completed measures of stress and exercise four times daily across seven days using an automated telephone ecological momentary assessment system. Data were analyzed using multilevel models. Drive for thinness, bulimic symptoms, and body dissatisfaction significantly moderated the relationship between daily stress and later exercise (ps = .01-.05), such that higher daily stress predicted higher later exercise only in individuals who were low (but not average or high) in drive for thinness, bulimic symptoms, and body dissatisfaction symptoms. Stress is associated with exercise differentially depending on individuals' eating disorder symptoms. Our findings suggest that only individuals with lower levels of eating disorder symptoms exercise when stressed. © 2017 Wiley Periodicals, Inc.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Sources of variation in survival of breeding female wood ducks

USGS Publications Warehouse

Hartke, Kevin M.; Grand, J.B.; Hepp, G.R.; Folk, T.H.

2006-01-01

In waterfowl, reproduction is physiologically demanding and females are exposed to varying risks of mortality at different periods of the breeding cycle. Moreover, differences among females may influence survival within breeding periods. We captured and fitted female Wood Ducks (Aix sponsa) with radio-transmitters before nest initiation during two breeding seasons to estimate survival and investigate sources of variation in survival. We partitioned the breeding season into three periods (preincubation, incubation, postnesting) according to breeding status of individual females, and used information-theoretic methods to compare models in which daily survival varied among periods, between successful and failed nesting females, and with parameters describing individual heterogeneity. Our analysis suggested that daily survival was best modeled as a function of breeding period, differences between successful and failed nesting females during postnesting, and early incubation body condition of successful females during post-nesting. Model-averaged daily survival was 0.9988 (95% CL: 0.9963-0.9996) during preincubation and 1.0 during incubation. Postnesting daily survival was 1.0 for failed nesting females and 0.9948 (0.9773-0.9988) for successful females, suggesting a trade-off between current reproduction and survival. Female age, body condition at capture, nest initiation date, and brood size generally were not useful for explaining variation in survival. Only early incubation body condition was important for modeling survival of successful females during postnesting; however, weight of evidence was limited and the effect on survival was weak. Mortality was greatest for females during preincubation and for females that nested successfully. Results support the hypothesis that brood care is costly for females. ?? The Cooper Ornithological Society 2006.

Development and Evaluation of a Cloud-Gap-Filled MODIS Daily Snow-Cover Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Foster, James L.; Kumar, Sujay V.

2010-01-01

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snowcover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.050 resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells "observable" is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD1 OC1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain averaged bias improvement of -11 %, whereas such improvement using the standard MOD1 OC1 maps is -3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.

Individual differences and day-to-day fluctuations in goal planning and type 1 diabetes management.

PubMed

Wiebe, Deborah J; Baker, Ashley C; Suchy, Yana; Stump, Tammy K; Berg, Cynthia A

2018-04-26

To examine whether individual differences and day-to-day fluctuations in diabetes goal planning are associated with Type 1 diabetes (T1D) management during late adolescence, and whether lapses in daily diabetes goal planning are more disruptive to diabetes management among those with poorer executive functioning (EF). Late adolescents with T1D (N = 236, Mage = 17.77 years) completed survey measures assessing individual differences in levels of diabetes goal planning and adherence, as well as survey and performance-based measures of EF; glycemic control was assessed through glycated hemoglobin (HbA1c) assays. Participants then completed a 2-week daily diary, rating items measuring daily diabetes goal planning, goal effort, and adherence, and recording blood-glucose tests from their glucometer at the end of each day. Analyses of survey measures indicated that higher individual differences in diabetes goal planning were associated with better adherence and glycemic control. Analyses of daily data using hierarchical linear modeling indicated that adolescents displayed higher daily adherence and lower blood-glucose levels on days when they had higher-than-their-average levels of daily goal planning and daily goal effort. EF moderated the association between daily goal planning and daily adherence, indicating that lapses in daily goal planning were more disruptive for adolescents with poorer EF. Both individual differences and day-to-day fluctuations in diabetes goal planning are associated with diabetes management, highlighting the challenges of managing T1D in daily life. Youth in late adolescence with poorer EF may especially benefit from planning to attain diabetes goals on a daily basis. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Estimation of stream temperature in support of fish production modeling under future climates in the Klamath River Basin

USGS Publications Warehouse

Flint, Lorraine E.; Flint, Alan L.

2012-01-01

Stream temperature estimates under future climatic conditions were needed in support of fish production modeling for evaluation of effects of dam removal in the Klamath River Basin. To allow for the persistence of the Klamath River salmon fishery, an upcoming Secretarial Determination in 2012 will review potential changes in water quality and stream temperature to assess alternative scenarios, including dam removal. Daily stream temperature models were developed by using a regression model approach with simulated net solar radiation, vapor density deficit calculated on the basis of air temperature, and mean daily air temperature. Models were calibrated for 6 streams in the Lower, and 18 streams in the Upper, Klamath Basin by using measured stream temperatures for 1999-2008. The standard error of the y-estimate for the estimation of stream temperature for the 24 streams ranged from 0.36 to 1.64°C, with an average error of 1.12°C for all streams. The regression models were then used with projected air temperatures to estimate future stream temperatures for 2010-99. Although the mean change from the baseline historical period of 1950-99 to the projected future period of 2070-99 is only 1.2°C, it ranges from 3.4°C for the Shasta River to no change for Fall Creek and Trout Creek. Variability is also evident in the future with a mean change in temperature for all streams from the baseline period to the projected period of 2070-99 of only 1°C, while the range in stream temperature change is from 0 to 2.1°C. The baseline period, 1950-99, to which the air temperature projections were corrected, established the starting point for the projected changes in air temperature. The average measured daily air temperature for the calibration period 1999-2008, however, was found to be as much as 2.3°C higher than baseline for some rivers, indicating that warming conditions have already occurred in many areas of the Klamath River Basin, and that the stream temperature projections for the 21st century could be underestimating the actual change.

17 CFR 240.12h-6 - Certification by a foreign private issuer regarding the termination of registration of a class of...

Code of Federal Regulations, 2010 CFR

2010-04-01

... certifying to the Commission on Form 15F (17 CFR 249.324) that: (1) The foreign private issuer has had... average daily trading volume of the subject class of securities in the United States for a recent 12-month period has been no greater than 5 percent of the average daily trading volume of that class of securities...

The Influence of Time Spent in Outdoor Play on Daily and Aerobic Step Count in Costa Rican Children

ERIC Educational Resources Information Center

Morera Castro, Maria del Rocio

2011-01-01

The purpose of this study is to examine the influence of time spent in outdoor play (i.e., on weekday and weekend days) on daily (i.e., average step count) and aerobic step count (i.e., average moderate to vigorous physical activity [MVPA] during the weekdays and weekend days) in fifth grade Costa Rican children. It was hypothesized that: (a)…

Association, effects and validation of polymorphisms within the NCAPG - LCORL locus located on BTA6 with feed intake, gain, meat and carcass traits in beef cattle

USDA-ARS?s Scientific Manuscript database

Background: In a previously reported genome-wide association study based on a high-density bovine SNP genotyping array, 8 SNP were nominally associated (P

47 annual records of allergenic fungi spore: predictive models from the NW Iberian Peninsula.

PubMed

Aira, M Jesus; Rodriguez-Rajo, F; Jato, Victoria

2008-01-01

An analysis was carried out of the atmospheric representivity of Cladosporium and Alternaria spores in the north-western Iberian Peninsula, registering mean annual concentrations in excess of 300,000 spores/m(3). During the main sporulation period, the highest average daily concentrations corresponded to Cladosporium herbarum type (1,197 spores/m(3)) while the highest daily value was 7,556 spores/m(3) (Cladosporium cladosporioides type). Alternaria only represents between 0.1-1% of the total spores identified. In these spore types, the intraday variation was more acute inland than along the coastline due to oceanic influence. In the predictive models proposed that use the meteorological parameters with which a higher correlation was obtained (mean and maximum temperature) as predictive variables, it was seen that the predicted values did not reveal any significant differences as compared to those observed in 2006, data that was only used for verification purposes.

Use of Regional Climate Model Output for Hydrologic Simulations

NASA Astrophysics Data System (ADS)

Hay, L. E.; Clark, M. P.; Wilby, R. L.; Gutowski, W. J.; Leavesley, G. H.; Pan, Z.; Arritt, R. W.; Takle, E. S.

2001-12-01

Daily precipitation and maximum and minimum temperature time series from a Regional Climate Model (RegCM2) were used as input to a distributed hydrologic model for a rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; East Fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily data sets of precipitation and maximum and minimum temperature were developed from measured data. These datasets included precipitation and temperature data for all stations that are located within the area of the RegCM2 model output used for each basin, but excluded station data used to calibrate the hydrologic model. Both the RegCM2 output and station data capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all four basins, the RegCM2- and station-based simulations of runoff show little skill on a daily basis (Nash-Sutcliffe (NS) values ranging from 0.05-0.37 for RegCM2 and -0.08-0.65 for station). When the precipitation and temperature biases are corrected in the RegCM2 output and station data sets (Bias-RegCM2 and Bias-station, respectively) the accuracy of the daily runoff simulations improve dramatically for the snowmelt-dominated basins. In the rainfall-dominated basin, runoff simulations based on the Bias-RegCM2 output show no skill (NS value of 0.09) whereas Bias-All simulated runoff improves (NS value improved from -0.08 to 0.72). These results indicate that the resolution of the RegCM2 output is appropriate for basin-scale modeling, but RegCM2 model output does not contain the day-to-day variability needed for basin-scale modeling in rainfall-dominated basins. Future work is warranted to identify the causes for systematic biases in RegCM2 simulations, develop methods to remove the biases, and improve RegCM2 simulations of daily variability in local climate.

The effect of reproductive performance on the dairy cattle herd value assessed by integrating a daily dynamic programming model with a daily Markov chain model.

PubMed

Kalantari, A S; Cabrera, V E

2012-10-01

The objective of this study was to determine the effect of reproductive performance on dairy cattle herd value. Herd value was defined as the herd's average retention payoff (RPO). Individual cow RPO is the expected profit from keeping the cow compared with immediate replacement. First, a daily dynamic programming model was developed to calculate the RPO of all cow states in a herd. Second, a daily Markov chain model was applied to estimate the herd demographics. Finally, the herd value was calculated by aggregating the RPO of all cows in the herd. Cow states were described by 5 milk yield classes (76, 88, 100, 112, and 124% with respect to the average), 9 lactations, 750 d in milk, and 282 d in pregnancy. Five different reproductive programs were studied (RP1 to RP5). Reproductive program 1 used 100% timed artificial insemination (TAI; 42% conception rate for first TAI and 30% for second and later services) and the other programs combined TAI with estrus detection. The proportion of cows receiving artificial insemination after estrus detection ranged from 30 to 80%, and conception rate ranged from 25 to 35%. These 5 reproductive programs were categorized according to their 21-d pregnancy rate (21-d PR), which is an indication of the rate that eligible cows become pregnant every 21 d. The 21-d PR was 17% for RP1, 14% for RP2, 16% for RP3, 18% for RP4, and 20% for RP5. Results showed a positive relationship between 21-d PR and herd value. The most extreme herd value difference between 2 reproductive programs was $77/cow per yr for average milk yield (RP5 - RP2), $13/cow per yr for lowest milk yield (RP5 - RP1), and $160/cow per yr for highest milk yield (RP5 - RP2). Reproductive programs were ranked based on their calculated herd value. With the exception of the best reproductive program (RP5), all other programs showed some level of ranking change according to milk yield. The most dramatic ranking change was observed in RP1, which moved from being the worst ranked for lowest milk yield to the second ranked for highest milk yield. Within a reproductive program, RPO changed based on the stage of lactation at pregnancy. Cows getting pregnant in the early stage of a lactation had higher RPO compared with getting pregnant later in the lactation. However, the RPO at calving was similar for early and late lactation pregnancies. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Parental role modeling of fruits and vegetables at meals and snacks is associated with children’s adequate consumption

PubMed Central

Draxten, Michelle; Fulkerson, Jayne A.; Friend, Sarah; Flattum, Colleen F.; Schow, Robin

2014-01-01

Parental role modeling of healthful eating behaviors has been shown to be positively correlated to children’s dietary intake and preference for fruits and vegetables. However, no study to date has utilized both parent and child report of parental role modeling and assessed role modeling at snacks and dinner. The purpose of this study is to 1) examine associations between parent and child report of parental role modeling of fruit and vegetable consumption at snacks and dinner and 2) determine whether parental role modeling is associated with children meeting daily fruit and vegetable recommendations. Parent-child dyads (N=160) participating in the Healthy Home Offerings via the Mealtime Environment (HOME) Plus study completed baseline surveys that included questions regarding parental role modeling of fruits and vegetables at dinner and snacks. Children also completed 24-hour dietary recalls. Spearman correlations and chi-square/Fisher’s exact test were used to examine relationships between parent and child report of parental role modeling of fruit and vegetable consumption at snacks and dinner and whether children met daily recommended servings of fruits and vegetables. On average, children consumed less than three servings of fruits and vegetables per day with only 23% of children consuming the recommended four daily servings. Similarities between parent and child reports of parental role modeling of fruits and vegetables at snacks and dinner varied by food type (e.g., fruit versus green salad) and whether the role modeling behavior was at snack or dinner. Statistically significant correlations were seen between parent and child report of parental role modeling consumption of fruit at dinner and green salad at dinner. Children who reported parental role modeling of vegetable consumption at snack and green salad at dinner were significantly more likely, than those who did not, to meet the daily fruit and vegetable consumption recommendations. Parents who reported role modeling consumption of fruit at snack were significantly more likely to have children who met daily fruit and vegetable consumption recommendations. Results indicate that children are aware of their parents’ eating behaviors and on occasion report this behavior similarly to their parents. Parents should be encouraged to utilize the opportunity to role model healthful dietary intake, especially at snacks, where consumption of fruits and vegetables may be very low. PMID:24630934

Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique.

PubMed

Lee, Minhyun; Koo, Choongwan; Hong, Taehoon; Park, Hyo Seon

2014-04-15

For the effective photovoltaic (PV) system, it is necessary to accurately determine the monthly average daily solar radiation (MADSR) and to develop an accurate MADSR map, which can simplify the decision-making process for selecting the suitable location of the PV system installation. Therefore, this study aimed to develop a framework for the mapping of the MADSR using an advanced case-based reasoning (CBR) and a geostatistical technique. The proposed framework consists of the following procedures: (i) the geographic scope for the mapping of the MADSR is set, and the measured MADSR and meteorological data in the geographic scope are collected; (ii) using the collected data, the advanced CBR model is developed; (iii) using the advanced CBR model, the MADSR at unmeasured locations is estimated; and (iv) by applying the measured and estimated MADSR data to the geographic information system, the MADSR map is developed. A practical validation was conducted by applying the proposed framework to South Korea. It was determined that the MADSR map developed through the proposed framework has been improved in terms of accuracy. The developed MADSR map can be used for estimating the MADSR at unmeasured locations and for determining the optimal location for the PV system installation.

Potential effects of diurnal temperature oscillations on potato late blight with special reference to climate change.

PubMed

Shakya, S K; Goss, E M; Dufault, N S; van Bruggen, A H C

2015-02-01

Global climate change will have effects on diurnal temperature oscillations as well as on average temperatures. Studies on potato late blight (Phytophthora infestans) development have not considered daily temperature oscillations. We hypothesize that growth and development rates of P. infestans would be less influenced by change in average temperature as the magnitude of fluctuations in daily temperatures increases. We investigated the effects of seven constant (10, 12, 15, 17, 20, 23, and 27°C) and diurnally oscillating (±5 and ±10°C) temperatures around the same means on number of lesions, incubation period, latent period, radial lesion growth rate, and sporulation intensity on detached potato leaves inoculated with two P. infestans isolates from clonal lineages US-8 and US-23. A four-parameter thermodynamic model was used to describe relationships between temperature and disease development measurements. Incubation and latency progression accelerated with increasing oscillations at low mean temperatures but slowed down with increasing oscillations at high mean temperatures (P < 0.005), as hypothesized. Infection efficiency, lesion growth rate, and sporulation increased under small temperature oscillations compared with constant temperatures but decreased when temperature oscillations were large. Thus, diurnal amplitude in temperature should be considered in models of potato late blight, particularly when predicting effects of global climate change on disease development.

High school start times after 8:30 am are associated with later wake times and longer time in bed among teens in a national urban cohort study.

PubMed

Nahmod, Nicole G; Lee, Soomi; Buxton, Orfeu M; Chang, Anne-Marie; Hale, Lauren

2017-12-01

High school start times are a key contributor to insufficient sleep. This study investigated associations of high school start times with bedtime, wake time, and time in bed among urban teenagers. Daily-diary study nested within the prospective Fragile Families and Child Wellbeing Study. Twenty US cities. Four hundred thirteen teenagers who completed ≥1 daily diary report on a school day. Participating teens were asked to complete daily diaries for 7 consecutive days. School-day daily diaries (3.8±1.6 entries per person) were used in analyses (N=1555 school days). High school start time, the main predictor, was categorized as 7:00-7:29 am (15%), 7:30-7:59 am (22%), 8:00-8:29 am (35%), and 8:30 am or later (28%). Multilevel modeling examined the associations of school start times with bedtime, wake time, and time in bed. Models adjusted for age, sex, race/ethnicity, household income, caregiver's education, and school type. Teens with the earliest high school start times (7:00-7:29 am) obtained 46 minutes less time in bed on average compared with teens with high school start times at 8:30 am or later (P<.001). Teens exhibited a dose-response relationship between earlier school start times and shorter time in bed, primarily due to earlier wake times (P<.05). Start times after 8:30 am were associated with increased time in bed, extending morning sleep by 27-57 minutes (P<.05) when compared with teens with earlier school start times. Later school start times are associated with later wake times in our large, diverse sample. Teens starting school at 8:30 am or later are the only group with an average time in bed permitting 8 hours of sleep, the minimum recommended by expert consensus for health and well-being. Copyright © 2017 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Height and calories in early childhood.

PubMed

Griffen, Andrew S

2016-03-01

This paper estimates a height production function using data from a randomized nutrition intervention conducted in rural Guatemala from 1969 to 1977. Using the experimental intervention as an instrument, the IV estimates of the effect of calories on height are an order of magnitude larger than the OLS estimates. Information from a unique measurement error process in the calorie data, counterfactuals results from the estimated model and external evidence from migration studies suggest that IV is not identifying a policy relevant average marginal impact of calories on height. The preferred, attenuation bias corrected OLS estimates from the height production function suggest that, averaging over ages, a 100 calorie increase in average daily calorie intake over the course of a year would increase height by 0.06 cm. Counterfactuals from the model imply that calories gaps in early childhood can explain at most 16% of the height gap between Guatemalan children and the US born children of Guatemalan immigrants. Copyright © 2015 Elsevier B.V. All rights reserved.

Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities.

PubMed

Yin, Peng; Chen, Renjie; Wang, Lijun; Meng, Xia; Liu, Cong; Niu, Yue; Lin, Zhijing; Liu, Yunning; Liu, Jiangmei; Qi, Jinlei; You, Jinling; Zhou, Maigeng; Kan, Haidong

2017-11-21

Few large multicity studies have been conducted in developing countries to address the acute health effects of atmospheric ozone pollution. We explored the associations between ozone and daily cause-specific mortality in China. We performed a nationwide time-series analysis in 272 representative Chinese cities between 2013 and 2015. We used distributed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozone (lagged over 0-3 d) on mortality in each city, and we used hierarchical Bayesian models to combine the city-specific estimates. Regional, seasonal, and demographic heterogeneity were evaluated by meta-regression. At the national-average level, a 10-μg/m 3 increase in 8-h maximum ozone concentration was associated with 0.24% [95% posterior interval (PI): 0.13%, 0.35%], 0.27% (95% PI: 0.10%, 0.44%), 0.60% (95% PI: 0.08%, 1.11%), 0.24% (95% PI: 0.02%, 0.46%), and 0.29% (95% PI: 0.07%, 0.50%) higher daily mortality from all nonaccidental causes, cardiovascular diseases, hypertension, coronary diseases, and stroke, respectively. Associations between ozone and daily mortality due to respiratory and chronic obstructive pulmonary disease specifically were positive but imprecise and nonsignificant. There were no statistically significant differences in associations between ozone and nonaccidental mortality according to region, season, age, sex, or educational attainment. Our findings provide robust evidence of higher nonaccidental and cardiovascular mortality in association with short-term exposure to ambient ozone in China. https://doi.org/10.1289/EHP1849.

Modeling urban air pollution with optimized hierarchical fuzzy inference system.

PubMed

Tashayo, Behnam; Alimohammadi, Abbas

2016-10-01

Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

Negative and Positive Emotion Responses to Daily School Problems: Links to Internalizing and Externalizing Symptoms.

PubMed

Bai, Sunhye; Repetti, Rena L

2018-04-01

Examining emotion reactivity and recovery following minor problems in daily life can deepen our understanding of how stress affects child mental health. This study assessed children's immediate and delayed emotion responses to daily problems at school, and examined their correlations with psychological symptoms. On 5 consecutive weekdays, 83 fifth graders (M = 10.91 years, SD = 0.53, 51% female) completed brief diary forms 5 times per day, providing repeated ratings of school problems and emotions. They also completed a one-time questionnaire about symptoms of depression, and parents and teachers rated child internalizing and externalizing problems. Using multilevel modeling techniques, we assessed within-person daily associations between school problems and negative and positive emotion at school and again at bedtime. On days when children experienced more school problems, they reported more negative emotion and less positive emotion at school, and at bedtime. There were reliable individual differences in emotion reactivity and recovery. Individual-level indices of emotion responses derived from multilevel models were correlated with child psychological symptoms. Children who showed more negative emotion reactivity reported more depressive symptoms. Multiple informants described fewer internalizing problems among children who showed better recovery by bedtime, even after controlling for children's average levels of exposure to school problems. Diary methods can extend our understanding of the links between daily stress, emotions and child mental health. Recovery following stressful events may be an important target of research and intervention for child internalizing problems.

Estimation of stream conditions in tributaries of the Klamath River, northern California

USGS Publications Warehouse

Manhard, Christopher V.; Som, Nicholas A.; Jones, Edward C.; Perry, Russell W.

2018-01-01

Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge. Observed temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams. Klamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.

Summary of hydrologic data collected during 1976 in Dade County, Florida

USGS Publications Warehouse

Hull, John E.

1978-01-01

During 1976 rainfall was 1.58 inches below the long-term average. Ground-water levels ranged from 0.4 foot above to 0.5 foot below average. The highest and lowest ground water for the year were both 1 foot below their long-term averages. In the Hialeah-Miami Springs area, water levels in wells near the centers of the heaviest pumping ranged from 8.0 to 9.5 feet below msl (mean sea level, 1929); and in the southwest well-field area, ground-water levels near the centers of pumping ranged from 2.0 feet above to 3.0 feet below msl. The combined average daily discharge from nine major streams and canals that flow eastward into tidal waters was 1,666 cubic feet per second (cfs), 609 cfs above the combined average daily flow for the 1975 water year. The combined average daily flow through the Tamiami Canal outlets was 783 cfs, 215 cfs above that of the 1975 water year. The 1976 position of the salt fron in the coastal part of the Biscayne aquifer was about the same as in 1975 except in the vicinity of Mowry Canal south of Homestead Air Force Base where the salt front had encroached farther inland. (Woodard-USGS)

Summary of hydrologic data collected during 1977 in Dade County, Florida

USGS Publications Warehouse

Hull, John E.

1979-01-01

During 1977 rainfall was 1.52 inches above the long-term average in Dade County, Fla. Ground-water levels ranged from 0.3 foot above to 0.1 foot below average. The highest and lowest ground-water levels for the year were 1 foot below and 1 foot above their long-term average. In the Hialeah-Miami Springs area , water levels in wells near the centers of the heaviest pumping ranged from 7.2 to 11.9 feet below mean sea level, 1929; and in the Southwest well-field area, ground-water levels near the centers of pumping ranged from 1.0 foot above to 1.5 feet below mean sea level. In 1977 the combined average daily discharge from nine major streams and canals that flow eastward into tidal waters was 1,712 cubic feet per second (cfs), 46 cfs above the combined average daily flow for 1976. The combined average daily flow through the Tamiami Canal outlets was 582 cfs, 201 cfs above that of 1976. The 1977 position of the salt front in the coastal part of the Biscayne aquifer was about the same as in 1976, except south of Homestead Air Force Base where the salt front had encroached farther inland. (Woodard-USGS)

Summary of hydrologic data collected during 1975 in Dade County, Florida

USGS Publications Warehouse

Hull, John E.; Beaven, T.R.

1977-01-01

During the 1975 calendar year rainfall in Dade County, Fla., was 14.89 inches below the long-term average (57.17 in.). Ground-water levels ranged from 0.1 foot above to 1.1 feet below average. The highest and lowest ground-water levels for the year were both 1 foot below their long-term averages. In the Hialeah-Miami Springs area, ground-water levels in wells near the centers of the heaviest pumping ranged from 9.8 to 11.2 feet below mean sea level and in the Southwest well field area, ground-water levels near the centers of pumping ranged from 3.5 feet above to 3.4 feet below mean sea level. The combined average daily discharge from eight major streams and canals that flow into Biscayne Bay was 1,014 cubic feet per second (cfs), 124 cfs above the combined average daily flow for the 1974 water year. The combined average daily flow through the Tamiami Canal outlets was 568 cfs, 202 cfs below that of the 1974 water year. The position of the salt front in 1975 in the coastal part of the Biscayne aquifer was about the same as in 1974 except at Miami International Airport and Homestead Air Force Base where the salt front had encroached farther inland. (Woodard-USGS)

Analytical Assessment of the Relationship between 100MWp Large-scale Grid-connected Photovoltaic Plant Performance and Meteorological Parameters

NASA Astrophysics Data System (ADS)

Sheng, Jie; Zhu, Qiaoming; Cao, Shijie; You, Yang

2017-05-01

This paper helps in study of the relationship between the photovoltaic power generation of large scale “fishing and PV complementary” grid-tied photovoltaic system and meteorological parameters, with multi-time scale power data from the photovoltaic power station and meteorological data over the same period of a whole year. The result indicates that, the PV power generation has the most significant correlation with global solar irradiation, followed by diurnal temperature range, sunshine hours, daily maximum temperature and daily average temperature. In different months, the maximum monthly average power generation appears in August, which related to the more global solar irradiation and longer sunshine hours in this month. However, the maximum daily average power generation appears in October, this is due to the drop in temperature brings about the improvement of the efficiency of PV panels. Through the contrast of monthly average performance ratio (PR) and monthly average temperature, it is shown that, the larger values of monthly average PR appears in April and October, while it is smaller in summer with higher temperature. The results concluded that temperature has a great influence on the performance ratio of large scale grid-tied PV power system, and it is important to adopt effective measures to decrease the temperature of PV plant properly.

Factors Associated With Ambulatory Activity in De Novo Parkinson Disease.

PubMed

Christiansen, Cory; Moore, Charity; Schenkman, Margaret; Kluger, Benzi; Kohrt, Wendy; Delitto, Anthony; Berman, Brian; Hall, Deborah; Josbeno, Deborah; Poon, Cynthia; Robichaud, Julie; Wellington, Toby; Jain, Samay; Comella, Cynthia; Corcos, Daniel; Melanson, Ed

2017-04-01

Objective ambulatory activity during daily living has not been characterized for people with Parkinson disease prior to initiation of dopaminergic medication. Our goal was to characterize ambulatory activity based on average daily step count and examine determinants of step count in nonexercising people with de novo Parkinson disease. We analyzed baseline data from a randomized controlled trial, which excluded people performing regular endurance exercise. Of 128 eligible participants (mean ± SD = 64.3 ± 8.6 years), 113 had complete accelerometer data, which were used to determine daily step count. Multiple linear regression was used to identify factors associated with average daily step count over 10 days. Candidate explanatory variable categories were (1) demographics/anthropometrics, (2) Parkinson disease characteristics, (3) motor symptom severity, (4) nonmotor and behavioral characteristics, (5) comorbidities, and (6) cardiorespiratory fitness. Average daily step count was 5362 ± 2890 steps per day. Five factors explained 24% of daily step count variability, with higher step count associated with higher cardiorespiratory fitness (10%), no fear/worry of falling (5%), lower motor severity examination score (4%), more recent time since Parkinson disease diagnosis (3%), and the presence of a cardiovascular condition (2%). Daily step count in nonexercising people recruited for this intervention trial with de novo Parkinson disease approached sedentary lifestyle levels. Further study is warranted for elucidating factors explaining ambulatory activity, particularly cardiorespiratory fitness, and fear/worry of falling. Clinicians should consider the costs and benefits of exercise and activity behavior interventions immediately after diagnosis of Parkinson disease to attenuate the health consequences of low daily step count.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A170).

SU-E-T-636: ProteusONE Machine QA Procedure and Stabiity Study: Half Year Clinical Operation

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

Freund, D; Ding, X; Wu, H

2015-06-15

Purpose: The objective of this study is to evaluate the stability of ProteusOne, the 1st commercial PBS proton system, throughout the daily QA and monthly over 6 month clinical operation. Method: Daily QA test includes IGRT position/repositioning, output in the middle of SOBP, beam flatness, symmetry, inplane and crossplane dimensions as well as energy range check. Daily range shifter QA consist of output, symmetry and field size checks to make sure its integrity. In 30 mins Daily QA test, all the measurements are performed using the MatriXXPT (IBA dosimetry). The data from these measurement was collected and compare over themore » first 6 month of clinical operation. In addition to the items check in daily QA, the summary also includes the monthly QA gantry star shots, absolute position check using a novel device, XRV-100. Results: Average machine output at the center of the spread out bragg peak was 197.5±.8 cGy and was within 1%of the baseline at 198.4 cGy. Beam flatness was within 1% cross plane with an average of 0.67±0.12% and 2% in-plane with an average of 1.08±0.17% compared to baseline measurements of 0.6 and 1.03, respectively. In all cases the radiation isocenter shift was less than or equal to 1mm. Output for the range shifter was within 2% for each individual measurement and averaged 34.4±.2cGy compare to a baseline reading of 34.5cGy. The average range shifter in and cross plane field size measurements were 19.8±0.5cm and 20.5±0.4cm compared with baseline values of 20.19cm and 20.79cm, respectively. Range shifter field symmetry had an average of less 1% for both in-plane and cross plane measurements. Conclusion: All machine metrics over the past 6 months have proved to be stable. Although, some averages are outside the baseline measurement they are within 1% tolerance and the deviation across all measurements is minimal.« less

Power estimation using simulations for air pollution time-series studies

PubMed Central

2012-01-01

Background Estimation of power to assess associations of interest can be challenging for time-series studies of the acute health effects of air pollution because there are two dimensions of sample size (time-series length and daily outcome counts), and because these studies often use generalized linear models to control for complex patterns of covariation between pollutants and time trends, meteorology and possibly other pollutants. In general, statistical software packages for power estimation rely on simplifying assumptions that may not adequately capture this complexity. Here we examine the impact of various factors affecting power using simulations, with comparison of power estimates obtained from simulations with those obtained using statistical software. Methods Power was estimated for various analyses within a time-series study of air pollution and emergency department visits using simulations for specified scenarios. Mean daily emergency department visit counts, model parameter value estimates and daily values for air pollution and meteorological variables from actual data (8/1/98 to 7/31/99 in Atlanta) were used to generate simulated daily outcome counts with specified temporal associations with air pollutants and randomly generated error based on a Poisson distribution. Power was estimated by conducting analyses of the association between simulated daily outcome counts and air pollution in 2000 data sets for each scenario. Power estimates from simulations and statistical software (G*Power and PASS) were compared. Results In the simulation results, increasing time-series length and average daily outcome counts both increased power to a similar extent. Our results also illustrate the low power that can result from using outcomes with low daily counts or short time series, and the reduction in power that can accompany use of multipollutant models. Power estimates obtained using standard statistical software were very similar to those from the simulations when properly implemented; implementation, however, was not straightforward. Conclusions These analyses demonstrate the similar impact on power of increasing time-series length versus increasing daily outcome counts, which has not previously been reported. Implementation of power software for these studies is discussed and guidance is provided. PMID:22995599

Power estimation using simulations for air pollution time-series studies.

PubMed

Winquist, Andrea; Klein, Mitchel; Tolbert, Paige; Sarnat, Stefanie Ebelt

2012-09-20

Estimation of power to assess associations of interest can be challenging for time-series studies of the acute health effects of air pollution because there are two dimensions of sample size (time-series length and daily outcome counts), and because these studies often use generalized linear models to control for complex patterns of covariation between pollutants and time trends, meteorology and possibly other pollutants. In general, statistical software packages for power estimation rely on simplifying assumptions that may not adequately capture this complexity. Here we examine the impact of various factors affecting power using simulations, with comparison of power estimates obtained from simulations with those obtained using statistical software. Power was estimated for various analyses within a time-series study of air pollution and emergency department visits using simulations for specified scenarios. Mean daily emergency department visit counts, model parameter value estimates and daily values for air pollution and meteorological variables from actual data (8/1/98 to 7/31/99 in Atlanta) were used to generate simulated daily outcome counts with specified temporal associations with air pollutants and randomly generated error based on a Poisson distribution. Power was estimated by conducting analyses of the association between simulated daily outcome counts and air pollution in 2000 data sets for each scenario. Power estimates from simulations and statistical software (G*Power and PASS) were compared. In the simulation results, increasing time-series length and average daily outcome counts both increased power to a similar extent. Our results also illustrate the low power that can result from using outcomes with low daily counts or short time series, and the reduction in power that can accompany use of multipollutant models. Power estimates obtained using standard statistical software were very similar to those from the simulations when properly implemented; implementation, however, was not straightforward. These analyses demonstrate the similar impact on power of increasing time-series length versus increasing daily outcome counts, which has not previously been reported. Implementation of power software for these studies is discussed and guidance is provided.

A case study of potential human health impacts from petroleum coke transfer facilities.

PubMed

Dourson, Michael L; Chinkin, Lyle R; MacIntosh, David L; Finn, Jennifer A; Brown, Kathleen W; Reid, Stephen B; Martinez, Jeanelle M

2016-11-01

Petroleum coke or "petcoke" is a solid material created during petroleum refinement and is distributed via transfer facilities that may be located in densely populated areas. The health impacts from petcoke exposure to residents living in proximity to such facilities were evaluated for a petcoke transfer facilities located in Chicago, Illinois. Site-specific, margin of safety (MOS) and margin of exposure (MOE) analyses were conducted using estimated airborne and dermal exposures. The exposure assessment was based on a combined measurement and modeling program that included multiyear on-site air monitoring, air dispersion modeling, and analyses of soil and surfaces in residential areas adjacent to two petcoke transfer facilities located in industrial areas. Airborne particulate matter less than 10 microns (PM 10 ) were used as a marker for petcoke. Based on daily fence line monitoring, the average daily PM 10 concentration at the KCBX Terminals measured on-site was 32 μg/m 3 , with 89% of 24-hr average PM 10 concentrations below 50 μg/m 3 and 99% below 100 μg/m 3 . A dispersion model estimated that the emission sources at the KCBX Terminals produced peak PM 10 levels attributed to the petcoke facility at the most highly impacted residence of 11 μg/m 3 on an annual average basis and 54 μg/m 3 on 24-hr average basis. Chemical indicators of petcoke in soil and surface samples collected from residential neighborhoods adjacent to the facilities were equivalent to levels in corresponding samples collected at reference locations elsewhere in Chicago, a finding that is consistent with limited potential for off-site exposure indicated by the fence line monitoring and air dispersion modeling. The MOE based upon dispersion model estimates ranged from 800 to 900 for potential inhalation, the primary route of concern for particulate matter. This indicates a low likelihood of adverse health effects in the surrounding community. Implications: Handling of petroleum coke at bulk material transfer facilities has been identified as a concern for the public health of surrounding populations. The current assessment, based on measurements and modeling of two facilities located in a densely populated urban area, indicates that petcoke transport and accumulation in off-site locations is minimal. In addition, estimated human exposures, if any, are well below levels that could be anticipated to produce adverse health effects in the general population.

26 CFR 1.163-10T - Qualified residence interest (temporary).

Code of Federal Regulations, 2010 CFR

2010-04-01

... general. (ii)Example. (g)Selection of method. (h)Average balance. (1)Average balance defined. (2)Average balance reported by lender. (3)Average balance computed on a daily basis. (i)In general. (ii)Example. (4)Average balance computed using the interest rate. (i)In general. (ii)Points and prepaid interest. (iii...

Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation

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

Tessum, C. W.; Hill, J. D.; Marshall, J. D.

We present results from and evaluate the performance of a 12-month, 12 km horizontal resolution year 2005 air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a volatility basis set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary modeling efforts used for regulatory and health-effects analysis, with an annual average daytime ozone (O 3) mean fractional bias (MFB) ofmore » 12% and an annual average fine particulate matter (PM 2.5) MFB of −1%. WRF-Chem, as configured here, tends to overpredict total PM 2.5 at some high concentration locations and generally overpredicts average 24 h O 3 concentrations. Performance is better at predicting daytime-average and daily peak O 3 concentrations, which are more relevant for regulatory and health effects analyses relative to annual average values. Predictive performance for PM 2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 36%), underpredicts particulate nitrate (MFB = −110%) and organic carbon (MFB = −29%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM 2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM 2.5 subspecies. Model predictive performance for PM 2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.« less

Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation

DOE PAGES

Tessum, C. W.; Hill, J. D.; Marshall, J. D.

2015-04-07

We present results from and evaluate the performance of a 12-month, 12 km horizontal resolution year 2005 air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a volatility basis set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary modeling efforts used for regulatory and health-effects analysis, with an annual average daytime ozone (O 3) mean fractional bias (MFB) ofmore » 12% and an annual average fine particulate matter (PM 2.5) MFB of −1%. WRF-Chem, as configured here, tends to overpredict total PM 2.5 at some high concentration locations and generally overpredicts average 24 h O 3 concentrations. Performance is better at predicting daytime-average and daily peak O 3 concentrations, which are more relevant for regulatory and health effects analyses relative to annual average values. Predictive performance for PM 2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 36%), underpredicts particulate nitrate (MFB = −110%) and organic carbon (MFB = −29%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM 2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM 2.5 subspecies. Model predictive performance for PM 2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.« less

Ozone and daily mortality rate in 21 cities of East Asia: how does season modify the association?

PubMed

Chen, Renjie; Cai, Jing; Meng, Xia; Kim, Ho; Honda, Yasushi; Guo, Yue Leon; Samoli, Evangelia; Yang, Xin; Kan, Haidong

2014-10-01

Previous studies in East Asia have revealed that the short-term associations between tropospheric ozone and daily mortality rate were strongest in winter, which is opposite to the findings in North America and Western Europe. Therefore, we investigated the season-varying association between ozone and daily mortality rate in 21 cities of East Asia from 1979 to 2010. Time-series Poisson regression models were used to analyze the association between ozone and daily nonaccidental mortality rate in each city, testing for different temperature lags. The best-fitting model was obtained after adjustment for temperature in the previous 2 weeks. Bayesian hierarchical models were applied to pool the city-specific estimates. An interquartile-range increase of the moving average concentrations of same-day and previous-day ozone was associated with an increase of 1.44% (95% posterior interval (PI): 1.08%, 1.80%) in daily total mortality rate after adjustment for temperature in the previous 2 weeks. The corresponding increases were 0.62% (95% PI: 0.08%, 1.16%) in winter, 1.46% (95% PI: 0.89%, 2.03%) in spring, 1.60% (95% PI: 1.03%, 2.17%) in summer, and 1.12% (95% PI: 0.73%, 1.51%) in fall. We found significant associations between short-term exposure to ozone and higher mortality rate in East Asia that varied considerably from season to season with a significant trough in winter. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A calibrated, high-resolution goes satellite solar insolation product for a climatology of Florida evapotranspiration

USGS Publications Warehouse

Paech, S.J.; Mecikalski, J.R.; Sumner, D.M.; Pathak, C.S.; Wu, Q.; Islam, S.; Sangoyomi, T.

2009-01-01

Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10-year period (1995-2004). These insolation estimates were developed into well-calibrated half-hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2-week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground-based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three-step process: (1) comparison with ground-based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m-2/day (13%). Calibration reduced errors to 1.7 MJ m -2/day (10%), and also removed temporal-related, seasonal-related, and satellite sensor-related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2-km resolution maps of estimated daily reference and potential evapotranspiration for water management-related activities. ?? 2009 American Water Resources Association.

Association between PM10 and respiratory hospital admissions in different seasons in heavily polluted Lanzhou City.

PubMed

An, Xingqin; Yan, Tao; Mi, Shengquan; Sun, Zhaobin; Hou, Qing

2015-01-01

Exposure-response relationship between particulate matter less than 10 μm in diameter (PM10) and human health in different seasons from 2001 to 2005 was examined based on hospital admissions data of respiratory system diseases from four major hospitals in Lanzhou, China. To quantify associations of respiratory system diseases with multiple air pollutants and meteorological conditions, a semiparametric generalized additive model was used in the authors' study by implementing daily ambient sulfur dioxide, nitrogen dioxide, and PM10 data collected from the Lanzhou Environmental Monitoring Station and daily meteorological data from Lanzhou Meteorological Bureau. Results showed that daily averaged PM10 increased per interquartile range the hospital admissions number of respiratory diseases by 3.3% in spring, 1.4% in summer, 3.6% in autumn, and 4.0% in winter from a single-pollutant model, or 3.1%, 1.4%, 3.0%, and 4.0% from a multi-pollutant model, respectively. The effect of PM10 on respiratory hospital admissions was lowest in summer and highest in winter. The relative risks of PM10 on female or the elderly (≥ 65 yrs.) were higher, showing a stronger association of PM10 with respiratory diseases in female and elderly groups than in males and people younger than 65.

Daily stress, presleep arousal, and sleep in healthy young women: a daily life computerized sleep diary and actigraphy study.

PubMed

Winzeler, Katja; Voellmin, Annette; Schäfer, Valérie; Meyer, Andrea H; Cajochen, Christian; Wilhelm, Frank H; Bader, Klaus

2014-03-01

Our study aimed to further elucidate the mediating role of presleep arousal in the relationship between daily stress and sleep by investigating subjective sleep quality and actigraphy-assessed sleep efficiency (SE) on both within- and between-participant levels in a sample of healthy young women. Multilevel modeling was applied on electronically assessed data comprising 14 consecutive nights in 145 healthy young women to assess the relationship between daily stress, presleep (somatic and cognitive) arousal, and sleep on both levels between participants and within participants across days. Higher levels of daily stress were consistently and significantly associated with higher levels of somatic and cognitive arousal. Somatic arousal mediated the relationship between daily stress and worsened subjective sleep quality on the between-participant level, while cognitive arousal mediated the relationship between daily stress and worsened subjective sleep quality on the within-participants level. Unexpectedly, healthy young women showed higher SE following days with above-average stress with somatic arousal mediating this relationship. Our data corroborate the role of presleep arousal mediating the relationship between daily stress and subjective sleep quality. Interestingly this effect was restricted to somatic arousal being relevant on interindividual levels and cognitive arousal on intraindividual levels. For young and healthy individuals who experience high stress and arousal, well-established cognitive-behavioral techniques could be useful to regulate arousal and prevent worse subjective sleep quality. Copyright © 2014 Elsevier B.V. All rights reserved.

Sources of secondary organic aerosols over North China Plain in winter

NASA Astrophysics Data System (ADS)

Xing, L.; Li, G.; Tie, X.; Junji, C.; Long, X.

2017-12-01

Organic aerosol (OA) concentrations are simulated over the North China Plain (NCP) from 10th to 26th January, 2014 using the Weather Research and Forecasting model coupled to chemistry (WRF-CHEM), with the goal of examining the impact of heterogeneous HONO sources on atmospheric oxidation capacity and consequently on SOA formation and SOA formation from different pathways in winter. Generally, the model well reproduced the spatial and temporal distribution of PM2.5, SO2, NO2, and O3 concentrations. The heterogeneous HONO formation contributed a major part of atmospheric HONO concentrations in Beijing. The heterogeneous HONO sources significantly increased the daily maximum OH concentrations by 260% on average in Beijing, which enhanced the atmospheric oxidation capacity and consequently SOA concentrations by 80% in Beijing on average. Under severe haze pollution on January 16th 2014, the regional average HONO concentration over NCP was 0.86 ppb, which increased SOA concentration by 68% on average. The average mass fractions of ASOA (SOA from oxidation of anthropogenic VOCs), BSOA (SOA from oxidation of biogenic VOCs), PSOA (SOA from oxidation of evaporated POA), and GSOA (SOA from irreversible uptake of glyoxal and methylglyoxal) during the simulation period over NCP were 24%, 5%, 26% and 45%, respectively. GSOA contributed most to the total SOA mass over NCP in winter. The model sensitivity simulation revealed that GSOA in winter was mainly from primary residential sources. The regional average of GSOA from primary residential sources constituted 87% of total GSOA mass.

Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

NASA Astrophysics Data System (ADS)

Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru

2017-08-01

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be extended to the water balance study of the whole Heihe River basin.

How Can TOLNet Help to Better Understand Tropospheric Ozone? A Satellite Perspective

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.

2018-01-01

Potential sources of a priori ozone (O3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O3 profile climatology (tropopause-based O3 climatology (TB-Clim), currently proposed for use in the TEMPO O3 retrieval algorithm) derived from ozonesonde observations and O3 profiles from three separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: 1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, hourly) and 2) implemented as a priori information in theoretical TEMPO tropospheric O3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0-10 km) and lowermost tropospheric (LMT, 0-2 km) O3 columns. We found that all sources of a priori O3 profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations. However, larger differences between the a priori profiles and lidar observations were observed when evaluating inter-daily and diurnal variability of tropospheric O3. The TB-Clim O3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O3 columns, using a priori profiles from CTM simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT O3 values were observed, using CTM a priori profiles resulted in TEMPO LMT O3 retrievals with the least bias. The application of time-specific (non-climatological) hourly/daily model predictions as the a priori profile in TEMPO O3 retrievals will be best suited when applying this data to study air quality or event-based processes as the standard retrieval algorithm will still need to use a climatology product. Follow-on studies to this work are currently being conducted to investigate the application of different CTM-predicted O3 climatology products in the standard TEMPO retrieval algorithm. Finally, similar methods to those used in this study can be easily applied by TEMPO data users to recalculate tropospheric O3 profiles provided from the standard retrieval using a different source of a priori.

Comparison of SWAT Hydrological Model Results from TRMM 3B42, NEXRAD Stage III, and Oklahoma Mesonet Data

NASA Astrophysics Data System (ADS)

Tobin, K. J.; Bennett, M. E.

2008-05-01

The Cimarron River Basin (3110 sq km) between Dodge and Guthrie, Oklahoma is located in northern Oklahoma and was used as a test bed to compare the hydrological model performance associated with different methods of precipitation quantification. The Soil and Water Assessment Tool (SWAT) was selected for this project, which is a comprehensive model that, besides quantifying watershed hydrology, can simulate water quality as well as nutrient and sediment loading within stream reaches. An advantage of this location is the extensive monitoring of MET parameters (precipitation, temperature, relative humidity, wind speed, solar radiation) afforded by the Oklahoma Mesonet, which has been documented to improve the performance of SWAT. The utility of TRMM 3B42 and NEXRAD Stage III data in supporting the hydrologic modeling of Cimarron River Basin is demonstrated. Minor adjustments to selected model parameters were made to make parameter values more realistic based on results from previous studies and information and to more realistically simulate base flow. Significantly, no ad hoc adjustments to major parameters such as Curve Number or Available Soil Water were made and robust simulations were obtained. TRMM and NEXRAD data are aggregated into an average daily estimate of precipitation for each TRMM grid cell (0.25 degree X 0.25 degree). Preliminary simulation of stream flow (year 2004 to 2006) in the Cimarron River Basin yields acceptable monthly results with very little adjustment of model parameters using TRMM 3B42 precipitation data (mass balance error = 3 percent; Monthly Nash-Sutcliffe efficiency coefficients (NS) = 0.77). However, both Oklahoma Mesonet rain gauge (mass balance error = 13 percent; Monthly NS = 0.91; Daily NS = 0.64) and NEXRAD Stage III data (mass balance error = -5 percent; Monthly NS = 0.95; Daily NS = 0.69) produces superior simulations even at a sub-monthly time scale; daily results are time averaged over a three day period. Note that all types of precipitation data perform better than a synthetic precipitation dataset generated using a weather simulator (mass balance error = 12 percent; Monthly NS = 0.40). Our study again documents that merged precipitation satellite products, such as TRMM 3B42, can support semi-distributed hydrologic modeling at the watershed scale. However, apparently additional work is required to improve TRMM precipitation retrievals over land to generate a product that yields more robust hydrological simulations especially at finer time scales. Additionally, ongoing work in this basin will compare TRMM results with stream flow model results generated using CMORPH precipitation estimates. Finally, in the future we plan to use simulated, semi-distributed soil moisture values determined by SWAT for comparison with gridded soil moisture estimates from TRMM-TMI that should provide further validation of our modeling efforts.

Seasonal greenhouse gas emissions (methane, carbon dioxide, nitrous oxide) from engineered landfills: Daily, intermediate, and final California cover soils

USDA-ARS?s Scientific Manuscript database

We quantified the seasonal variability of CH4, CO2, and N2O emissions from fresh refuse and daily, intermediate, and final cover materials at two California landfills. Fresh refuse fluxes (g m-2 d-1) averaged CH4 0.053[+/-0.03], CO2 135[+/-117], and N2O 0.063[+/-0.059]. Average CH4 emissions across ...

40 CFR 51.907 - For an area that fails to attain the 8-hour NAAQS by its attainment date, how does EPA interpret...

Code of Federal Regulations, 2011 CFR

2011-07-01

... to 1-year extensions of the attainment date if: (a) For the first 1-year extension, the area's 4th... extension, the area's 4th highest daily 8-hour value, averaged over both the original attainment year and... section, the area's 4th highest daily 8-hour average shall be from the monitor with the highest 4th...

40 CFR 51.907 - For an area that fails to attain the 8-hour NAAQS by its attainment date, how does EPA interpret...

Code of Federal Regulations, 2013 CFR

2013-07-01

... to 1-year extensions of the attainment date if: (a) For the first 1-year extension, the area's 4th... extension, the area's 4th highest daily 8-hour value, averaged over both the original attainment year and... section, the area's 4th highest daily 8-hour average shall be from the monitor with the highest 4th...

40 CFR 51.907 - For an area that fails to attain the 8-hour NAAQS by its attainment date, how does EPA interpret...

Code of Federal Regulations, 2012 CFR

2012-07-01

... to 1-year extensions of the attainment date if: (a) For the first 1-year extension, the area's 4th... extension, the area's 4th highest daily 8-hour value, averaged over both the original attainment year and... section, the area's 4th highest daily 8-hour average shall be from the monitor with the highest 4th...

Alteration of travel patterns with vision loss from glaucoma and macular degeneration.

PubMed

Curriero, Frank C; Pinchoff, Jessie; van Landingham, Suzanne W; Ferrucci, Luigi; Friedman, David S; Ramulu, Pradeep Y

2013-11-01

The distance patients can travel outside the home influences how much of the world they can sample and to what extent they can live independently. Recent technological advances have allowed travel outside the home to be directly measured in patients' real-world routines. To determine whether decreased visual acuity (VA) from age-related macular degeneration (AMD) and visual field (VF) loss from glaucoma are associated with restricted travel patterns in older adults. Cross-sectional study. Patients were recruited from an eye clinic, while travel patterns were recorded during their real-world routines using a cellular tracking device. Sixty-one control subjects with normal vision, 84 subjects with glaucoma with bilateral VF loss, and 65 subjects with AMD with bilateral or severe unilateral loss of VA had their location tracked every 15 minutes between 7 am and 11 pm for 7 days using a tracking device. Average daily excursion size (defined as maximum distance away from home) and average daily excursion span (defined as maximum span of travel) were defined for each individual. The effects of vision loss on travel patterns were evaluated after controlling for individual and geographic factors. In multivariable models comparing subjects with AMD and control subjects, average excursion size and span decreased by approximately one-quarter mile for each line of better-eye VA loss (P ≤ .03 for both). Similar but not statistically significant associations were observed between average daily excursion size and span for severity of better-eye VF loss in subjects with glaucoma and control subjects. Being married or living with someone and younger age were associated with more distant travel, while less-distant travel was noted for older individuals, African Americans, and those living in more densely populated regions. Age-related macular degeneration-related loss of VA, but not glaucoma-related loss of VF, is associated with restriction of travel to more nearby locations. This constriction of life space may impact quality of life and restrict access to services.

Impact of spinal pain on daily living activities in postmenopausal women working in agriculture.

PubMed

Raczkiewicz, Dorota; Owoc, Alfred; Sarecka-Hujar, Beata; Saran, Tomasz; Bojar, Iwona

2017-03-22

Postmenopausal women working in agriculture suffer from spinal pain for two overlapping reasons, the first is related to the menopause and the second to the specificity of rural work, which includes lifting heavy objects and changing weather conditions. Spinal pain affects the daily life of women as well as their ability to work. The objective of the study was to analyse the impact of spinal pain on activities of daily life in Polish postmenopausal women performing agricultural work. The study was conducted in 2016 in Poland and included 1,119 post-menopausal women living in rural areas and working in agriculture. The women assessed the severity of spinal pain in 3 sections: neck, thorax and lumbar. Neck Disability Index (NDI) and Oswestry Low Back Disability Index (ODI) questionnaires were used to assess the impact of spinal pain on daily life activities. Generalized linear models were estimated in statistical analyses. Postmenopausal women working in agriculture suffered most often from pain in the lumbar spine, less frequently in the neck, and the least in the thoracic. The most common was an isolated pain in only one section of the spine. Spinal pain disturbed the most the women's rest, standing, lifting objects, while sleep, concentration, and walking the least. The impact of spinal pain on the activities of daily life, on average, was moderate, and increased with greater pain severity, the earlier the age the pain started, the higher the body weight, the lower education level and if there was a co-existing pain in any of the other spine sections. The impact of spinal pain on daily life activities did not depend on age between 45-65, WHR, age at last menstruation, parity, and number and types of births. The impact of spinal pain on daily life activities in postmenopausal women working in agriculture was assessed as moderate, on average, and depended mainly on spinal pain-related characteristics, such as severity, age at onset and co-existence of pain in any other spinal sections.

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

PubMed

Gençay, R; Qi, M

2001-01-01

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Estimating total maximum daily loads with the Stochastic Empirical Loading and Dilution Model

USGS Publications Warehouse

Granato, Gregory; Jones, Susan Cheung

2017-01-01

The Massachusetts Department of Transportation (DOT) and the Rhode Island DOT are assessing and addressing roadway contributions to total maximum daily loads (TMDLs). Example analyses for total nitrogen, total phosphorus, suspended sediment, and total zinc in highway runoff were done by the U.S. Geological Survey in cooperation with FHWA to simulate long-term annual loads for TMDL analyses with the stochastic empirical loading and dilution model known as SELDM. Concentration statistics from 19 highway runoff monitoring sites in Massachusetts were used with precipitation statistics from 11 long-term monitoring sites to simulate long-term pavement yields (loads per unit area). Highway sites were stratified by traffic volume or surrounding land use to calculate concentration statistics for rural roads, low-volume highways, high-volume highways, and ultraurban highways. The median of the event mean concentration statistics in each traffic volume category was used to simulate annual yields from pavement for a 29- or 30-year period. Long-term average yields for total nitrogen, phosphorus, and zinc from rural roads are lower than yields from the other categories, but yields of sediment are higher than for the low-volume highways. The average yields of the selected water quality constituents from high-volume highways are 1.35 to 2.52 times the associated yields from low-volume highways. The average yields of the selected constituents from ultraurban highways are 1.52 to 3.46 times the associated yields from high-volume highways. Example simulations indicate that both concentration reduction and flow reduction by structural best management practices are crucial for reducing runoff yields.

Estimation of surface-level PM2.5 concentration using aerosol optical thickness through aerosol type analysis method

NASA Astrophysics Data System (ADS)

Chen, Qi-Xiang; Yuan, Yuan; Huang, Xing; Jiang, Yan-Qiu; Tan, He-Ping

2017-06-01

Surface-level particulate matter is closely related to column aerosol optical thickness (AOT). Previous researches have successfully used column AOT and different meteorological parameters to estimate surface-level PM concentration. In this study, the performance of a selected linear model that estimates surface-level PM2.5 concentration was evaluated following the aerosol type analysis method (ATAM) for the first time. We utilized 443 daily average data for Xuzhou, Jiangsu province, collected using Aerosol Robotic Network (AERONET) during the period October 2013 to April 2016. Several parameters including atmospheric boundary layer height (BLH), relative humidity (RH), and effective radius of the aerosol size distribution (Ref) were used to assess the relationship between the column AOT and PM2.5 concentration. By including the BLH, ambient RH, and effective radius, the correlation (R2) increased from 0.084 to 0.250 at Xuzhou, and with the use of ATAM, the correlation increased further to 0.335. To compare the results, 450 daily average data for Beijing, pertaining to the same period, were utilized. The study found that model correlations improved by varying degrees in different seasons and at different sites following ATAM. The average urban industry (UI) aerosol ratios at Xuzhou and Beijing were 0.792 and 0.451, respectively, demonstrating poorer air conditions at Xuzhou. PM2.5 estimation at Xuzhou showed lower correlation (R2 = 0.335) compared to Beijing (R2 = 0.407), and the increase of R2 at Xuzhou and Beijing site following use of ATAM were 33.8% and 12.4%, respectively.

Development of a winter wheat adjustable crop calendar model. [Colorado, Idaho, Oklahoma, Montana, Kansas, Missouri, North Dakota and Texas

NASA Technical Reports Server (NTRS)

Baker, J. R. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Least squares techniques were applied for parameter estimation of functions to predict winter wheat phenological stage with daily maximum temperature, minimum temperature, daylength, and precipitation as independent variables. After parameter estimation, tests were conducted using independent data. It may generally be concluded that exponential functions have little advantage over polynomials. Precipitation was not found to significantly affect the fits. The Robertson triquadratic form, in general use for spring wheat, yielded good results, but special techniques and care are required. In most instances, equations with nonlinear effects were found to yield erratic results when utilized with averaged daily environmental values as independent variables.

Parent training plus contingency management for substance abusing families: A Complier Average Causal Effects (CACE) analysis*

PubMed Central

Stanger, Catherine; Ryan, Stacy R.; Fu, Hongyun; Budney, Alan J.

2011-01-01

Background Children of substance abusers are at risk for behavioral/emotional problems. To improve outcomes for these children, we developed and tested an intervention that integrated a novel contingency management (CM) program designed to enhance compliance with an empirically-validated parent training curriculum. CM provided incentives for daily monitoring of parenting and child behavior, completion of home practice assignments, and session attendance. Methods Forty-seven mothers with substance abuse or dependence were randomly assigned to parent training + incentives (PTI) or parent training without incentives (PT). Children were 55% male, ages 2-7 years. Results Homework completion and session attendance did not differ between PTI and PT mothers, but PTI mothers had higher rates of daily monitoring. PTI children had larger reductions in child externalizing problems in all models. Complier Average Causal Effects (CACE) analyses showed additional significant effects of PTI on child internalizing problems, parent problems and parenting. These effects were not significant in standard Intent-to-Treat analyses. Conclusion Results suggest our incentive program may offer a method for boosting outcomes. PMID:21466925

Predicting objectively assessed physical activity from the content and regulation of exercise goals: evidence for a mediational model.

PubMed

Sebire, Simon J; Standage, Martyn; Vansteenkiste, Maarten

2011-04-01

Grounded in self-determination theory (Deci & Ryan, 2000), the purpose of this work was to examine effects of the content and motivation of adults' exercise goals on objectively assessed moderate-to-vigorous physical activity (MVPA). After reporting the content and motivation of their exercise goals, 101 adult participants (Mage = 38.79 years; SD = 11.5) wore an ActiGraph (GT1M) accelerometer for seven days. Accelerometer data were analyzed to provide estimates of engagement in MVPA and bouts of physical activity. Goal content did not directly predict behavioral engagement; however, mediation analysis revealed that goal content predicted behavior via autonomous exercise motivation. Specifically, intrinsic versus extrinsic goals for exercise had a positive indirect effect on average daily MVPA, average daily MVPA accumulated in 10-min bouts and the number of days on which participants performed 30 or more minutes of MVPA through autonomous motivation. These results support a motivational sequence in which intrinsic versus extrinsic exercise goals influence physical activity behavior because such goals are associated with more autonomous forms of exercise motivation.

The relation between price and daily consumption of cigarettes and bidis: findings from the Tobacco Control Policy Evaluation Wave 1 Survey.

PubMed

Pawar, P S; Pednekar, M S; Gupta, P C; Shang, C; Quah, A C K; Fong, G T

2014-12-01

In India, 14% of the population use smoked tobacco products. Increasing prices of these products is one of the measures to curb their consumption. This study analyzes "unit price" and "daily consumption" of cigarettes and bidis and investigates their relation with each other. A cross-sectional survey was conducted in four states of India (Bihar, West Bengal, Madhya Pradesh and Maharashtra) as a part of the International Tobacco Control Policy (TCP) Evaluation Project (the TCP India Project) during 2010-2011. Information was collected from adult (aged ≥ 15) daily exclusive smokers of cigarette/bidi regarding (a) last purchase (purchase in pack/loose, brand and price) and (b) daily consumption. Average unit price and daily consumption was calculated for different brands and states. Regression model was used to assess the impact of price on daily consumption. Bidis were much less expensive ([symbol in text]0.39) than cigarettes ([symbol in text]3.1). The daily consumption was higher (14) among bidi smokers than cigarette smokers (8). The prices and daily consumption of bidis ([symbol in text]0.33-0.43; 12-15) and cigarettes ([symbol in text]2.9-3.6; 5-9) varied across the four states. The unit prices of bidis and cigarettes did not influence their daily consumption. Smokers purchasing bidis in packs paid substantially less per unit and purchase of bidis and cigarettes in packs influenced their consumption positively. Cigarettes although more expensive than bidis, seem very cheap if compared internationally. Hence, prices of both cigarettes and bidis do not influence their consumption.

User's guide for SBUV/TOMS ozone derivative products

NASA Technical Reports Server (NTRS)

Fleig, A. J.; Wellemeyer, C.; Oslik, N.; Lee, D.; Miller, J.; Magatani, R.

1984-01-01

A series of products are available derived from the total-ozone and ozone vertical profile results for the Solar Backscattered Ultraviolet/Total-Ozone Mapping Spectrometer (SBUV/TOMS) Nimbus-7 operation. Products available are (1) orbital height-latitude cross sections of the SBUV profile data, (2) daily global total ozone contours in polar coordinates, (3) daily averages of total ozone in global 5x5 degree latitude-longitude grid, (4) daily, monthly and quarterly averages of total ozone and profile data in 10 degree latitude zones, (5) tabular presentation of zonal means, (6) daily global total ozone and profile contours in polar coordinates. The ""Derivative Products User's Guide'' describes each of these products in detail, including their derivation and presentation format. Information is provided on how to order the tapes and microfilm from the National Space Science Data Center.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

NASA Astrophysics Data System (ADS)

Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-01

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer.

PubMed

Bondar, M Luiza; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-07

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Psychiatric comorbidity is associated prospectively with diminished opioid analgesia and increased opioid misuse in patients with chronic low back pain

PubMed Central

Wasan, Ajay, D.; Michna, Edward; Edwards, Robert, R.; Katz, Jeff, N.; Nedeljkovic, Srdjan, S.; Dolman, Andrew, J.; Janfaza, David; Isaac, Zach; Jamison, Robert, N.

2015-01-01

Background Opioids are frequently prescribed for chronic low back pain (CLBP), but there is little prospective data on which patient subgroups may benefit. Psychiatric comorbidity, such as high levels of depression and anxiety symptoms (termed, comorbid negative affect [NA]) is a common presentation and may predict diminished opioid analgesia and/or increased opioid misuse. Methods We conducted a 6½-month prospective cohort study of oral opioid therapy, with an active drug/placebo run-in period, in 81 CLBP patients with low, moderate, and high levels of NA. Treatment included an opioid titration phase with a prescribing physician blinded to NA group assignment, and a 4-month continuation phase, during which subjects recorded daily pain levels using an electronic diary. The primary outcome was the percent improvement in average daily pain, summarized weekly. Results There was an overall 25% drop out rate. Despite the high NA group being prescribed a higher average daily dose of morphine equivalents, linear mixed model analysis revealed that the 24 study completers in each of the high and low NA groups had an average 21% vs. 39% improvement in pain, respectively (p<.01). The high NA group also had a significantly greater rate of opioid misuse (39% vs. 8%, p<.05), and significantly more and intense opioid side effects (p<.01). Conclusions These results indicate that the benefit and risk considerations in CLBP patients with high vs. low NA are distinctly different. Thus, negative affect is an important phenotypic variable to characterize at baseline, prior to deciding whether to prescribe opioids for CLBP. PMID:26375824

Water-discharge determinations for the tidal reach of the Willamette River from Ross Island Bridge to Mile 10.3, Portland, Oregon

USGS Publications Warehouse

Dempster, G.R.; Lutz, Gale A.

1968-01-01

Water-discharge, velocity, and slope variations for a 3.7-mile-Iong tidal reach of the Willamette River at Portland, Oreg., were defined from discharge measurements and river stage data collected between July 1962 and January 1965. Observed water discharge during tide-affected flows, during floods, and during backwater from the Columbia River and recorded stages at each end of the river reach were used to determine water discharge from two mathematical models. These models use a finite-difference method to solve the equations of moderately unsteady open-channel streamflow, and discharges are computed by an electronic digital computer. Discharges computed by using the mathematical models compare satisfactorily with observed discharges, except during the period of backwater from the annual flood of the Columbia River. The flow resistance coefficients used in the models vary with discharge; for one model, the coefficients for discharges above 30,000 cfs (cubic feet per second) are 12 and 24 percent less than the coefficient used for discharges below 30,000 cfs. Daily mean discharges were determined by use of one mathematical model for approximately two-thirds of the water year, October 1963 through September 1964. Agreement of computed with routed daily mean discharges is fair; above 30,000 cfs, average differences between the two discharges are about 10 percent, and below 30,000 cfs, computed daily discharges are consistently greater (by as much as 25 percent) than routed discharges. The other model was used to compute discharges for the unusually high flood flows of December 1964.

How Well Can Aerosol Measurements from the Terra Morning Polar Orbiting Satellite Represent the Daily Aerosol Abundance and Properties?

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Holben, B. N.; Tanre, D.; Slutzker, I.; Eck, T. F.; Smirnov, A.; Einaudi, Franco (Technical Monitor)

2000-01-01

The Terra mission, launched at the dawn of 1999, and Aqua mission to be launched soon, will possess innovative measurements of the aerosol daily spatial distribution, distinguish between dust, smoke and regional pollution and measure aerosol radiative forcing of climate. Their polar orbit gives daily global coverage, however measurements are acquired at specific time of the day. To what degree can present measurements from Terra taken between 10:00 and 11:30 AM local time, represent the daily average aerosol forcing of climate? Here we answer this question using 7 years of data from the distributed ground based 50-70 instrument Aerosol Robotic Network (AERONET) This (AERONET) half a million measurement data set shows that Terra aerosol measurements represent the daily average values within 5%. The excellent representation is found for large dust particles or small aerosol particles from Fires or regional pollution and for any range of the optical thickness, a measure of the amount of aerosol in the atmosphere.

Effects of Coffee Intake on Incident Chronic Kidney Disease: Community-Based Prospective Cohort Study.

PubMed

Jhee, Jong Hyun; Nam, Ki Heon; An, Seong Yeong; Cha, Min-Uk; Lee, Misol; Park, Seohyun; Kim, Hyoungnae; Yun, Hae-Ryong; Kee, Youn Kyung; Park, Jung Tak; Chang, Tae-Ik; Kang, Ea Wha; Yoo, Tae-Hyun; Kang, Shin-Wook; Han, Seung Hyeok

2018-06-12

Drinking coffee can raise public health problems, but the association between coffee and kidney disease is unknown. We studied whether coffee intake can affect the development of chronic kidney disease in the general population. We analyzed 8717 subjects with normal renal function recruited from the KoGES cohort. Based on food frequency questionnaire, coffee consumption was categorized into five groups: 0/week, <1 cup/week, 1-6 cups/week, 1 cup/day, and ≥2 cups/day. The primary outcome was incident chronic kidney disease defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m 2 . The mean age of study subjects was 52.0 (8.8) years and 47.8% were male. Among the subjects, 52.8% were daily coffee consumers. During a mean follow-up of 11.3 [5.9-11.5] years, 9.5% of participants developed chronic kidney disease. The incident chronic kidney disease occurred less in daily coffee consumers. Unadjusted HRs was significantly lower in daily coffee consumers. In multivariable Cox model even after adjustment of blood pressure, hypertension, cardiovascular disease, diabetes, and amount of daily intake for caffeine-containing foods such as tea and chocolate, coffee consumers with 1 cup/day (HR, 0.76; 95% CI, 0.63-0.92) and ≥2 cups/day (HR, 0.80; 95% CI, 0.65-0.98) were associated with a lower risk of chronic kidney disease development than non-drinkers. Time-averaged and time-varying Cox models yielded similar results. The rates of decline in eGFR were lower in daily coffee consumers. Our findings suggest that daily coffee intake is associated with decreased risk of the development of CKD. Copyright © 2018. Published by Elsevier Inc.

Short-term exposure to ambient air pollution and coronary heart disease mortality in 8 Chinese cities.

PubMed

Li, Huichu; Chen, Renjie; Meng, Xia; Zhao, Zhuohui; Cai, Jing; Wang, Cuicui; Yang, Changyuan; Kan, Haidong

2015-10-15

Coronary heart disease (CHD) accounted for a large fraction of death globally. The association between air pollution and CHD has been reported, but evidence from highly-polluted regions was scarce. We aimed to estimate the acute effects of outdoor air pollution on daily CHD mortality in China. We collected daily CHD deaths in 8 large Chinese cities from 1996 to 2008. We firstly obtained the city-specific effect estimates of air pollution using generalized additive models with quasi-Poisson regression, controlling for time trends, meteorological indicators and day of the week. The random-effect model in meta-analysis was used to pool the exposure-response relationships. We identified a total of 0.13 million CHD deaths. On average, an increase of 10μg/m(3) in 2-day moving average concentrations of particulate matter≤10μm in aerodynamic diameter (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) was significantly associated with increases of 0.36% [95% confidence intervals (CIs): 0.12%, 0.61%], 0.86% (95% CIs: 0.30%,1.41%) and 1.30% (95% CIs: 0.45%, 2.14%) in daily CHD mortality over the 8 Chinese cities, respectively. The pooled exposure-response curves were almost linear and no apparent thresholds were identified. The effects were more pronounced in cities with lower levels of air pollution. The effects of PM10 and NO2 were more robust than SO2. Our findings contributed to the very limited evidence regarding the hazardous effects of ambient air pollution on CHD mortality in highly-polluted regions such as China. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index.

PubMed

Olatinwo, R O; Paz, J O; Brown, S L; Kemerait, R C; Culbreath, A K; Beasley, J P; Hoogenboom, G

2008-10-01

Tomato spotted wilt virus (TSWV), a member of the genus Tospovirus (family Bunyaviridae), is an important plant virus that causes severe damage to peanut (Arachis hypogaea) in the southeastern United States. Disease severity has been extremely variable in individual fields in Georgia, due to several factors including variability in weather patterns. A TSWV risk index has been developed by the University of Georgia to aid peanut growers with the assessment and avoidance of high risk situations. This study was conducted to examine the relationship between weather parameters and spotted wilt severity in peanut, and to develop a predictive model that integrates localized weather information into the risk index. On-farm survey data collected during 1999, 2002, 2004, and 2005 growing seasons, and derived weather variables during the same years were analyzed using nonlinear and multiple regression analyses. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network. The best model explained 61% of the variation in spotted wilt severity (square root transformed) as a function of the interactions between the TSWV risk index, the average daily temperature in April (TavA), the average daily minimum temperature between March and April (TminMA), the accumulated rainfall in March (RainfallM), the accumulated rainfall in April (RainfallA), the number of rain days in April (RainDayA), evapotranspiration in April (EVTA), and the number of days from 1 January to the planting date (JulianDay). Integrating this weather-based model with the TSWV risk index may help peanut growers more effectively manage tomato spotted wilt disease.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Pokémon GO and Physical Activity in Asia: Multilevel Study.

PubMed

Ma, Ben D; Ng, Sai Leung; Schwanen, Tim; Zacharias, John; Zhou, Mudi; Kawachi, Ichiro; Sun, Guibo

2018-06-15

Physical activity has long been considered as an important component of a healthy lifestyle. Although many efforts have been made to promote physical activity, there is no effective global intervention for physical activity promotion. Some researchers have suggested that Pokémon GO, a location-based augmented reality game, was associated with a short-term increase in players' physical activity on a global scale, but the details are far from clear. The objective of our study was to study the relationship between Pokémon GO use and players' physical activity and how the relationship varies across players with different physical activity levels. We conducted a field study in Hong Kong to investigate if Pokémon GO use was associated with physical activity. Pokémon GO players were asked to report their demographics through a survey; data on their Pokémon GO behaviors and daily walking and running distances were collected from their mobile phones. Participants (n=210) were Hong Kong residents, aged 13 to 65 years, who played Pokémon GO using iPhone 5 or 6 series in 5 selected types of built environment. We measured the participants' average daily walking and running distances over a period of 35 days, from 14 days before to 21 days after game installation. Multilevel modeling was used to identify and examine the predictors (including Pokémon GO behaviors, weather, demographics, and built environment) of the relationship between Pokémon GO use and daily walking and running distances. The average daily walking and running distances increased by 18.1% (0.96 km, approximately 1200 steps) in the 21 days after the participants installed Pokémon GO compared with the average distances over the 14 days before installation (P<.001). However, this association attenuated over time and was estimated to disappear 24 days after game installation. Multilevel models indicated that Pokémon GO had a stronger and more lasting association among the less physically active players compared with the physically active ones (P<.001). Playing Pokémon GO in green space had a significant positive relationship with daily walking and running distances (P=.03). Moreover, our results showed that whether Pokémon GO was played, the number of days played, weather (total rainfall, bright sunshine, mean air temperature, and mean wind speed), and demographics (age, gender, income, education, and body mass index) were associated with daily walking and running distances. Pokémon GO was associated with a short-term increase in the players' daily walking and running distances; this association was especially strong among less physically active participants. Pokémon GO can build new links between humans and green space and encourage people to engage in physical activity. Our results show that location-based augmented reality games, such as Pokémon GO, have the potential to be a global public health intervention tool. ©Ben D Ma, Sai Leung Ng, Tim Schwanen, John Zacharias, Mudi Zhou, Ichiro Kawachi, Guibo Sun. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.06.2018.

Contributions of Kansas rangeland burning to ambient O3: Analysis of data from 2001 to 2016.

PubMed

Liu, Zifei; Liu, Yang; Murphy, James P; Maghirang, Ronaldo

2018-03-15

Prescribed range/pasture burning is a common practice in Kansas to enhance the nutritional value of native grasses and control invading weeds, trees, and brush. A major concern associated with the burning is the contribution of smoke to elevated ground level ambient ozone (O 3 ). The objective of this study is to estimate contributions of Kansas rangeland burning to ambient O 3 mixing ratios through regression analysis (1) between observed O 3 data and available satellite burn activity data from 2001 to 2016; and (2) between observed O 3 data and the smoke contributions to PM 2.5 which were resolved from receptor modeling. Positive correlations were observed between ambient O 3 levels and the acres burned each year estimated from satellite imagery. When burned acres in April were larger than or equal to 1.9 million, O 3 >70ppb occurred at least at one of the ten monitoring sites in Kansas. Statistical regression models of daily maximum 8-hour O 3 mixing ratios were developed at each of the ten monitoring sites using meteorological predictors. The O 3 model residuals that were not explained by the meteorological effect models were affected by PM 2.5 contributors including sulfate/industrial sources and emissions that generated secondary organic particles, such as rangeland burning, which were derived from receptor modeling. The average O 3 model residual on the high O 3 days in April was 21±9ppb, which was likely associated with smoke emissions from burning. Research will continue to obtain daily satellite burn activity data and to correlate burn data with daily O 3 data, so that modeling of O 3 levels can be improved under influences of daily burn activities. Less frequency of high O 3 days was observed in April since 2011, which may be partly due to implementation of the Flint Hills Smoke Management Plan which promoted better timing of burns. Copyright © 2017 Elsevier B.V. All rights reserved.

The long-term salinity field in San Francisco Bay

USGS Publications Warehouse

Uncles, R.J.; Peterson, D.H.

1996-01-01

Data are presented on long-term salinity behaviour in San Francisco Bay, California. A two-level, width averaged model of the tidally averaged salinity and circulation has been written in order to interpret the long-term (days to decades) salinity variability. The model has been used to simulate daily averaged salinity in the upper and lower levels of a 51 segment discretization of the Bay over the 22-yr period 1967-1988. Monthly averaged surface salinity from observations and monthly-averaged simulated salinity are in reasonable agreement. Good agreement is obtained from comparison with daily averaged salinity measured in the upper reaches of North Bay. The salinity variability is driven primarily by freshwater inflow with relatively minor oceanic influence. All stations exhibit a marked seasonal cycle in accordance with the Mediterranean climate, as well as a rich spectrum of variability due to extreme inflow events and extended periods of drought. Monthly averaged salinity intrusion positions have a pronounced seasonal variability and show an approximately linear response to the logarithm of monthly averaged Delta inflow. Although few observed data are available for studies of long-term salinity stratification, modelled stratification is found to be strongly dependent on freshwater inflow; the nature of that dependence varies throughout the Bay. Near the Golden Gate, stratification tends to increase up to very high inflows. In the central reaches of North Bay, modelled stratification maximizes as a function of inflow and further inflow reduces stratification. Near the head of North Bay, lowest summer inflows are associated with the greatest modelled stratification. Observations from the central reaches of North Bay show marked spring-neap variations in stratification and gravitational circulation, both being stronger at neap tides. This spring-neap variation is simulated by the model. A feature of the modelled stratification is a hysteresis in which, for a given spring-neap tidal range and fairly steady inflows, the stratification is higher progressing from neaps to springs than from springs to neaps. The simulated responses of the Bay to perturbations in coastal sea salinity and Delta inflow have been used to further delineate the time-scales of salinity variability. Simulations have been performed about low inflow, steady-state conditions for both salinity and Delta inflow perturbations. For salinity perturbations a small, sinusoidal salinity signal with a period of 1 yr has been applied at the coastal boundary as well as a pulse of salinity with a duration of one day. For Delta inflow perturbations a small, sinusoidally varying inflow signal with a period of 1 yr has been superimposed on an otherwise constant Delta inflow, as well as a pulse of inflow with a duration of one day. Perturbations is coastal salinity dissipate as they move through the Bay. Seasonal perturbations require about 40-45 days to propagate from the coastal ocean to the Delta and to the head of South Bay. The response times of the model to perturbations in freshwater inflow are faster than this in North Bay and comparable in South Bay. In North Bay, time-scales are consistent with advection due to lower level, up-estuary transport of coastal salinity perturbations; for inflow perturbations, faster response times arise from both upper level, down-estuary advection and much faster, down-estuary migration of isohalines in response to inflow volume continuity. In South Bay, the dominant time-scales are governed by tidal dispersion.

Domestic animal hosts strongly influence human-feeding rates of the Chagas disease vector Triatoma infestans in Argentina.

PubMed

Gürtler, Ricardo E; Cecere, María C; Vázquez-Prokopec, Gonzalo M; Ceballos, Leonardo A; Gurevitz, Juan M; Fernández, María Del Pilar; Kitron, Uriel; Cohen, Joel E

2014-01-01

The host species composition in a household and their relative availability affect the host-feeding choices of blood-sucking insects and parasite transmission risks. We investigated four hypotheses regarding factors that affect blood-feeding rates, proportion of human-fed bugs (human blood index), and daily human-feeding rates of Triatoma infestans, the main vector of Chagas disease. A cross-sectional survey collected triatomines in human sleeping quarters (domiciles) of 49 of 270 rural houses in northwestern Argentina. We developed an improved way of estimating the human-feeding rate of domestic T. infestans populations. We fitted generalized linear mixed-effects models to a global model with six explanatory variables (chicken blood index, dog blood index, bug stage, numbers of human residents, bug abundance, and maximum temperature during the night preceding bug catch) and three response variables (daily blood-feeding rate, human blood index, and daily human-feeding rate). Coefficients were estimated via multimodel inference with model averaging. Median blood-feeding intervals per late-stage bug were 4.1 days, with large variations among households. The main bloodmeal sources were humans (68%), chickens (22%), and dogs (9%). Blood-feeding rates decreased with increases in the chicken blood index. Both the human blood index and daily human-feeding rate decreased substantially with increasing proportions of chicken- or dog-fed bugs, or the presence of chickens indoors. Improved calculations estimated the mean daily human-feeding rate per late-stage bug at 0.231 (95% confidence interval, 0.157-0.305). Based on the changing availability of chickens in domiciles during spring-summer and the much larger infectivity of dogs compared with humans, we infer that the net effects of chickens in the presence of transmission-competent hosts may be more adequately described by zoopotentiation than by zooprophylaxis. Domestic animals in domiciles profoundly affect the host-feeding choices, human-vector contact rates and parasite transmission predicted by a model based on these estimates.

Ozone and its projection in regard to climate change

NASA Astrophysics Data System (ADS)

Melkonyan, Ani; Wagner, Patrick

2013-03-01

In this paper, the dependence of ozone-forming potential on temperature was analysed based on data from two stations (with an industrial and rural background, respectively) in North Rhine-Westphalia, Germany, for the period of 1983-2007. After examining the interrelations between ozone, NOx and temperature, a projection of the days with ozone exceedance (over a limit value of a daily maximum 8-h average ≥ 120 μg m-3 for 25 days per year averaged for 3 years) in terms of global climate change was made using probability theory and an autoregression integrated moving average (ARIMA) model. The results show that with a temperature increase of 3 K, the frequency of days when ozone exceeds its limit value will increase by 135% at the industrial station and by 87% at the rural background station.

Anorexia Nervosa in the Context of Daily Experience.

ERIC Educational Resources Information Center

Larson, Reed; Johnson, Craig

1981-01-01

This study investigated the anorectic's experience in daily living using the Experience Sampling Method. Results suggest that anorectics spend more time alone and experience lower average affect than other young single women. (Author/GK)

Pregnancy diet and offspring asthma risk over a 10-year period: the Lifeways Cross Generation Cohort Study, Ireland.

PubMed

Viljoen, Karien; Segurado, Ricardo; O'Brien, John; Murrin, Celine; Mehegan, John; Kelleher, Cecily C

2018-02-20

The association of maternal pregnancy diet with offspring asthma risk have been reported. However, literature on longitudinal patterns of asthma risk relative to intrauterine nutrient exposure is limited. We aimed to establish whether vegetable, oily fish and vitamin D intake during pregnancy are associated with childhood asthma risk over a 10-year period in the Irish Republic. Mother-child pairs (n=897) from the Lifeways prospective birth cohort, with data on nutrient intake during pregnancy and asthma status, respectively, were eligible for inclusion in the analysis. Data on socioeconomic and morbidity indicators over 10 years of follow-up on mothers and the index child were collected through self-administered questionnaires. Asthma status as diagnosed by the general practitioner at any time point over 10 years was related to maternal vegetable, oily fish and vitamin D intake during pregnancy, while adjusting for gestational age, socioeconomic status, smoking at delivery, breast feeding, season of birth and supplement use. Data were modelled with a marginal model on correlated observations over time within individuals. In the fully adjusted model, asthma was inversely associated with higher daily average intake of oily fish (OR 0.23 per serving/day, 95% CI 0.04 to 1.41) and of vegetables (OR 0.96 per serving/day, 95% CI 0.88 to 1.05), but the confidence limits overlapped 1. A higher daily vitamin D intake was associated with reduced odds of asthma (OR 0.93 per μg/day, 95% CI 0.89 to 0.98). This analysis suggests higher daily average intake of vitamin D in pregnancy is associated with asthma risk in offspring over the first 10 years of life. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

An integrated water system model considering hydrological and biogeochemical processes at basin scale: model construction and application

NASA Astrophysics Data System (ADS)

Zhang, Y. Y.; Shao, Q. X.; Ye, A. Z.; Xing, H. T.

2014-08-01

Integrated water system modeling is a reasonable approach to provide scientific understanding and possible solutions to tackle the severe water crisis faced over the world and to promote the implementation of integrated river basin management. Such a modeling practice becomes more feasible nowadays due to better computing facilities and available data sources. In this study, the process-oriented water system model (HEXM) is developed by integrating multiple water related processes including hydrology, biogeochemistry, environment and ecology, as well as the interference of human activities. The model was tested in the Shaying River Catchment, the largest, highly regulated and heavily polluted tributary of Huai River Basin in China. The results show that: HEXM is well integrated with good performance on the key water related components in the complex catchments. The simulated daily runoff series at all the regulated and less-regulated stations matches observations, especially for the high and low flow events. The average values of correlation coefficient and coefficient of efficiency are 0.81 and 0.63, respectively. The dynamics of observed daily ammonia-nitrogen (NH4N) concentration, as an important index to assess water environmental quality in China, are well captured with average correlation coefficient of 0.66. Furthermore, the spatial patterns of nonpoint source pollutant load and grain yield are also simulated properly, and the outputs have good agreements with the statistics at city scale. Our model shows clear superior performance in both calibration and validation in comparison with the widely used SWAT model. This model is expected to give a strong reference for water system modeling in complex basins, and provide the scientific foundation for the implementation of integrated river basin management all over the world as well as the technical guide for the reasonable regulation of dams and sluices and environmental improvement in river basins.

The impact of daily temperature on renal disease incidence: an ecological study.

PubMed

Borg, Matthew; Bi, Peng; Nitschke, Monika; Williams, Susan; McDonald, Stephen

2017-10-27

Extremely high temperatures over many consecutive days have been linked to an increase in renal disease in several cities. This is becoming increasingly relevant with heatwaves becoming longer, more intense, and more frequent with climate change. This study aimed to extend the known relationship between daily temperature and kidney disease to include the incidence of eight temperature-prone specific renal disease categories - total renal disease, urolithiasis, renal failure, acute kidney injury (AKI), chronic kidney disease (CKD), urinary tract infections (UTIs), lower urinary tract infections (LUTIs) and pyelonephritis. Daily data was acquired for maximum, minimum and average temperature over the period of 1 July 2003 to 31 March 2014 during the warm season (October to March) in Adelaide, South Australia. Data for daily admissions to all metropolitan hospitals for renal disease, including 83,519 emergency department admissions and 42,957 inpatient admissions, was also obtained. Renal outcomes were analyzed using time-stratified negative binomial regression models, with the results aggregated by day. Incidence rate ratios (IRR) and 95% confidence intervals (CI) were estimated for associations between the number of admissions and daily temperature. Increases in daily temperature per 1 °C were associated with an increased incidence for all renal disease categories except for pyelonephritis. Minimum temperature was associated with the greatest increase in renal disease followed by average temperature and then maximum temperature. A 1°C increase in daily minimum temperature was associated with an increase in daily emergency department admissions for AKI (IRR 1.037, 95% CI: 1.026-1.048), renal failure (IRR 1.030, 95% CI: 1.022-1.039), CKD (IRR 1.017, 95% CI: 1.001-1.033) urolithiasis (IRR 1.015, 95% CI: 1.010-1.020), total renal disease (IRR 1.009, 95% CI: 1.006-1.011), UTIs (IRR 1.004, 95% CI: 1.000-1.007) and LUTIs (IRR 1.003, 95% CI: 1.000-1.006). An increased frequency of renal disease, including urolithiasis, acute kidney injury and urinary tract infections, is predicted with increasing temperatures from climate change. These results have clinical and public health implications for the management of renal diseases and demand tailored health services. Future research is warranted to analyze individual renal diseases with more comprehensive information regarding renal risk factors, and studies examining mortality for specific renal diseases.

Relationships between fire danger and the daily number and daily growth of active incidents burning in the northern Rocky Mountains, USA

Treesearch

Patrick H. Freeborn; Mark A. Cochrane; W. Matt Jolly

2015-01-01

Daily National Fire Danger Rating System (NFDRS) indices are typically associated with the number and final size of newly discovered fires, or averaged over time and associated with the likelihood and total burned area of large fires. Herein we used a decade (2003-12) of NFDRS indices and US Forest Service (USFS) fire reports to examine daily relationships between fire...

Simulation of semi-arid hydrological processes at different spatial resolutions using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Green, T. R.; Erksine, R. H.; David, O.; Ascough, J. C., II; Kipka, H.; Lloyd, W. J.; McMaster, G. S.

2015-12-01

Water movement and storage within a watershed may be simulated at different spatial resolutions of land areas or hydrological response units (HRUs). Here, effects of HRU size on simulated soil water and surface runoff are tested using the AgroEcoSystem-Watershed (AgES-W) model with three different resolutions of HRUs. We studied a 56-ha agricultural watershed in northern Colorado, USA farmed primarily under a wheat-fallow rotation. The delineation algorithm was based upon topography (surface flow paths), land use (crop management strips and native grass), and mapped soil units (three types), which produced HRUs that follow the land use and soil boundaries. AgES-W model parameters that control surface and subsurface hydrology were calibrated using simulated daily soil moisture at different landscape positions and depths where soil moisture was measured hourly and averaged up to daily values. Parameter sets were both uniform and spatially variable with depth and across the watershed (5 different calibration approaches). Although forward simulations were computationally efficient (less than 1 minute each), each calibration required thousands of model runs. Execution of such large jobs was facilitated by using the Object Modeling System with the Cloud Services Innovation Platform to manage four virtual machines on a commercial web service configured with a total of 64 computational cores and 120 GB of memory. Results show how spatially distributed and averaged soil moisture and runoff at the outlet vary with different HRU delineations. The results will help guide HRU delineation, spatial resolution and parameter estimation methods for improved hydrological simulations in this and other semi-arid agricultural watersheds.

Can brook trout survive climate change in large rivers? If it rains.

PubMed

Merriam, Eric R; Fernandez, Rodrigo; Petty, J Todd; Zegre, Nicolas

2017-12-31

We provide an assessment of thermal characteristics and climate change vulnerability for brook trout (Salvelinus fontinalis) habitats in the upper Shavers Fork sub-watershed, West Virginia. Spatial and temporal (2001-2015) variability in observed summer (6/1-8/31) stream temperatures was quantified in 23 (9 tributary, 14 main-stem) reaches. We developed a mixed effects model to predict site-specific mean daily stream temperature from air temperature and discharge and coupled this model with a hydrologic model to predict future (2016-2100) changes in stream temperature under low (RCP 4.5) and high (RCP 8.5) emissions scenarios. Observed mean daily stream temperature exceeded the 21°C brook trout physiological threshold in all but one main-stem site, and 3 sites exceeded proposed thermal limits for either 63- and 7-day mean stream temperature. We modeled mean daily stream temperature with a high degree of certainty (R 2 =0.93; RMSE=0.76°C). Predicted increases in mean daily stream temperature in main-stem and tributary reaches ranged from 0.2°C (RCP 4.5) to 1.2°C (RCP 8.5). Between 2091 and 2100, the average number of days with mean daily stream temperature>21°C increased within main-stem sites under the RCP 4.5 (0-1.2days) and 8.5 (0-13) scenarios; however, no site is expected to exceed 63- or 7-day thermal limits. During the warmest 10years, ≥5 main-stem sites exceeded the 63- or 7-day thermal tolerance limits under both climate emissions scenarios. Years with the greatest increases in stream temperature were characterized by low mean daily discharge. Main-stem reaches below major tributaries never exceed thermal limits, despite neighboring reaches having among the highest observed and predicted stream temperatures. Persistence of thermal refugia within upper Shavers Fork would enable persistence of metapopulation structure and life history processes. However, this will only be possible if projected increases in discharge are realized and offset expected increases in air temperature. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Generating daily weather data for ecosystem modelling in the Congo River Basin

NASA Astrophysics Data System (ADS)

Petritsch, Richard; Pietsch, Stephan A.

2010-05-01

Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range based on the ratio of values on rainy days and days without rain, respectively. For assessing the impact of our correction, we simulated the ecosystem behaviour using the climate data from Lastourville, Moanda and Mouilla with the mechanistic ecosystem model Biome-BGC. Differences in terms of the carbon, nitrogen and water cycle were subsequently analysed and discussed.

A global low order spectral model designed for climate sensitivity studies

NASA Technical Reports Server (NTRS)

Hanna, A. F.; Stevens, D. E.

1984-01-01

A two level, global, spectral model using pressure as a vertical coordinate is developed. The system of equations describing the model is nonlinear and quasi-geostrophic. A moisture budget is calculated in the lower layer only with moist convective adjustment between the two layers. The mechanical forcing of topography is introduced as a lower boundary vertical velocity. Solar forcing is specified assuming a daily mean zenith angle. On land and sea ice surfaces a steady state thermal energy equation is solved to calculate the surface temperature. Over the oceans the sea surface temperatures are prescribed from the climatological average of January. The model is integrated to simulate the January climate.

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

NASA Astrophysics Data System (ADS)

Zhao, Hong; Li, Changjun; Li, Hongping; Lv, Kebo; Zhao, Qinghui

2016-06-01

The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°-25°N, 105°-125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Simulating the effect of climate change on stream temperature in the Trout Lake Watershed, Wisconsin

USGS Publications Warehouse

Selbig, William R.

2015-01-01

The potential for increases in stream temperature across many spatial and temporal scales as a result of climate change can pose a difficult challenge for environmental managers, especially when addressing thermal requirements for sensitive aquatic species. This study evaluates simulated changes to the thermal regime of three northern Wisconsin streams in response to a projected changing climate using a modeling framework and considers implications of thermal stresses to the fish community. The Stream Network Temperature Model (SNTEMP) was used in combination with a coupled groundwater and surface water flow model to assess forecasts in climate from six global circulation models and three emission scenarios. Model results suggest that annual average stream temperature will steadily increase approximately 1.1 to 3.2 °C (varying by stream) by the year 2100 with differences in magnitude between emission scenarios. Daily mean stream temperature during the months of July and August, a period when cold-water fish communities are most sensitive, showed excursions from optimal temperatures with increased frequency compared to current conditions. Projections of daily mean stream temperature, in some cases, were no longer in the range necessary to sustain a cold water fishery.

Simulating the effect of climate change on stream temperature in the Trout Lake Watershed, Wisconsin.

PubMed

Selbig, William R

2015-07-15

The potential for increases in stream temperature across many spatial and temporal scales as a result of climate change can pose a difficult challenge for environmental managers, especially when addressing thermal requirements for sensitive aquatic species. This study evaluates simulated changes to the thermal regime of three northern Wisconsin streams in response to a projected changing climate using a modeling framework and considers implications of thermal stresses to the fish community. The Stream Network Temperature Model (SNTEMP) was used in combination with a coupled groundwater and surface water flow model to assess forecasts in climate from six global circulation models and three emission scenarios. Model results suggest that annual average stream temperature will steadily increase approximately 1.1 to 3.2°C (varying by stream) by the year 2100 with differences in magnitude between emission scenarios. Daily mean stream temperature during the months of July and August, a period when cold-water fish communities are most sensitive, showed excursions from optimal temperatures with increased frequency compared to current conditions. Projections of daily mean stream temperature, in some cases, were no longer in the range necessary to sustain a cold water fishery. Published by Elsevier B.V.

High concentrations of regional dust from deserts to plains across the central Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Reynolds, R. L.; Munson, S. M.; Fernandez, D. P.; Neff, J. C.

2015-12-01

Regional mineral dust in the American Southwest affects snow-melt rates, biogeochemical cycling, visibility, and public health. We measured total suspended particulates (TSP) across a 500-km-long sampling network of five remote sites in Utah and Colorado, USA, forming a gradient in distance from major dust emitting areas. The two westernmost sites on the Colorado Plateau desert had similar TSP concentrations (2008-2012, daily average=126 μg m-3; max. daily average over a two-week period=700 μg m-3 at Canyonlands National Park, Utah), while the easternmost High Plains site, close to cropped and grazed areas in northeastern Colorado, had an average concentration of 143 μg m-3 in 2011-2012 (max. daily average=656 μg m-3). Such concentrations rank comparably with those of TSP in several African and Asian cities in the paths of frequent dust storms. Dust loadings at the two intervening montane sites decreased from the western slope of the Rocky Mountains (Telluride, daily average=68 μg m-3) to an eastern site (Niwot Ridge, daily average=58 μg m-3). Back-trajectory analyses and satellite retrievals indicated that the three westernmost sites received most dust from large desert-source regions as far as 300 km to their southwest. These sources also sometimes sent dust to the two easternmost sites, which additionally captured dust from sources north and northwest of the central Rocky Mountains as well as locally at the Plains site. The PM10 fraction accounted for <15% of TSP, but most TSP is only slightly larger (typical median size, 15-20 μm) after about 100-800 km transport distances. Correlations between TSP and PM10 values indicate increases in both fractions during regional wind storms, especially related to Pacific frontal systems during late winter to late spring. These measurements and observations indicate that most dust deposition and associated air-quality problems in the interior American West are connected to regional dust sources and not to those in Asia.

Low-flow characteristics of streams in Ohio through water year 1997

USGS Publications Warehouse

Straub, David E.

2001-01-01

This report presents selected low-flow and flow-duration characteristics for 386 sites throughout Ohio. These sites include 195 long-term continuous-record stations with streamflow data through water year 1997 (October 1 to September 30) and for 191 low-flow partial-record stations with measurements into water year 1999. The characteristics presented for the long-term continuous-record stations are minimum daily streamflow; average daily streamflow; harmonic mean flow; 1-, 7-, 30-, and 90-day minimum average low flow with 2-, 5-, 10-, 20-, and 50-year recurrence intervals; and 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 20-, and 10-percent daily duration flows. The characteristics presented for the low-flow partial-record stations are minimum observed streamflow; estimated 1-, 7-, 30-, and 90-day minimum average low flow with 2-, 10-, and 20-year recurrence intervals; and estimated 98-, 95-, 90-, 85- and 80-percent daily duration flows. The low-flow frequency and duration analyses were done for three seasonal periods (warm weather, May 1 to November 30; winter, December 1 to February 28/29; and autumn, September 1 to November 30), plus the annual period based on the climatic year (April 1 to March 31).

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

NASA Astrophysics Data System (ADS)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

Emergency department clinical redesign, team-based care and improvements in hospital performance: A time series analysis.

PubMed

Dinh, Michael M; Green, Timothy C; Bein, Kendall J; Lo, Serigne; Jones, Aaron; Johnson, Terence

2015-08-01

The objective was to evaluate the impact of an ED clinical redesign project that involved team-based care and early senior assessment on hospital performance. This was an interrupted time series analysis performed using daily hospital performance data 6 months before and 8 months after the implementation of the clinical redesign intervention that involved Emergency Consultant-led team-based care, redistribution of ED beds and implementation of a senior nursing coordination roles in the ED. The primary outcome was the daily National Emergency Access Target (NEAT) performance (proportion of total daily ED presentations that were admitted to an inpatient ward or discharged from ED within 4 h of arrival). Secondary outcomes were daily ALOS in ED, inpatient Clinical Emergency Response System (CERS) calls and hospital mortality. Autoregressive Integrated Moving Average analysis was used to model NEAT performance. Hospital mortality was modelled using negative binomial regression. After adjusting for patient volume, inpatient admissions, ambulance, hospital occupancy, weekends ED Consultant numbers, weekends and underlying trends, there was a 17% improvement in NEAT associated with the post-intervention period (95% CI 12, 19% P < 0.001). There was no change in the number of CERS calls and the median daily hospital mortality rate reduced from 1.04% to 0.96% (P = 0.025). An ED-focused clinical redesign project was associated with a 17% improvement in NEAT performance with no evidence of an increase in clinical deterioration on inpatient wards and evidence for an improvement in hospital mortality. © 2015 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.