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• One-dimensional models can adequately surrogate two-dimensional models for overland flow simulations on two-dimensional (2D) domains by mimicking 2D models in mesh generations. • The 1D channel networks is generated to geometrically covers the whole domain without overlapping and intersection, and hydrologically follows the steepest slopes. • A 1D model was applied to 2D complex domains in both laboratory and field scales with complex geometry to compare with the 2D model simulation. High computational demand limits the applications of two-dimensional (2D) shallow water equation models for high resolution overland flow simulations, while one-dimensional (1D) models can achieve higher computing efficiency. This study applied a 1D hydrodynamic model to surrogate 2D models for overland flow simulations on complex 2D domains. With one dimension reduced, the surrogate model simulation would have acceptable accuracy with much higher computing efficiency. The surrogating is fulfilled through mimicking 2D models in mesh generations, so that the 1D channel network is generated in such a way that it geometrically covers the whole domain without overlapping and intersection, and hydrologically follows the steepest slopes. Several benchmark cases on 2D domains in both laboratory and field scales with complex geometry, where no 1D models have been ever applied, are used to compare the 1D and 2D model simulations. The comparisons demonstrate that the 1D model does have potentials to efficiently simulate overland flow on 2D complex domains with accuracy comparable to 2D models. [ABSTRACT FROM AUTHOR]

Meshless numerical models have attracted much attention due to the circumvention of troublesome mesh generation. Current meshless numerical groundwater (GW) models either focus on only pumping in fully saturated zone or merely simulate variably saturated GW flow without pumping. However, these two components are both essential for a GW model to represent practical real-world conditions. This gap is bridged in this study by developing a three-dimensional model, CCHE3D-GW-Meshless, using the meshless weak-strong form method. The new model code was verified with three representative cases, and good agreement was found with the analytical/numerical solutions in all cases. CCHE3D-GW-Meshless was then applied to a field case of a pumping well near a meandering river located at Shellmound, Mississippi, USA. The hydrological properties of the site were obtained through calibration which yielded a generally good match with observed data considering the incompletely known heterogeneity in aquifer properties, indicating the applicability of the model. • A meshless groundwater model using weak-strong form method is developed. • This meshless model can simulate pumping in variably saturated groundwater flow. • This model has been successfully applied to a pumping well near a meandering river. [ABSTRACT FROM AUTHOR]

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. National Environmental Public Health Tracking Program at CDC has collaborated with partners to develop and disseminate county-level daily UV irradiance (2005–2015) and total solar irradiance (1991–2012) data for the contiguous United States. UV irradiance dataset was derived from the Ozone Monitoring Instrument (OMI), and solar irradiance was extracted from National Solar Radiation Data Base (NSRDB) and SolarAnywhere data. Firstly, we produced daily population-weighted UV and solar irradiance datasets at the county level. Then the spatial distributions and long-term trends of UV irradiance, solar irradiance and the ratio of UV irradiance to solar irradiance were analyzed. The national average values across all years are 4300 Wh/m2, 2700 J/m2 and 130 mW/m2 for global horizontal irradiance (GHI), erythemally weighted daily dose of UV irradiance (EDD) and erythemally weighted UV irradiance at local solar noon time (EDR), respectively. Solar, UV irradiances and the ratio of UV to solar irradiance all increased toward the South and in some areas with high altitude, suggesting that using solar irradiance as indicator of UV irradiance in studies covering large geographic regions may bias the true pattern of UV exposure. National annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. Both datasets are available to the public through CDC's Tracking network. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. These datasets help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies. Image 1 • The first UV irradiance dataset for all counties in contiguous US for public access. • National solar and UV irradiances increased significantly over study periods. • UV and solar irradiances, and the ratio increased towards the South and with altitude. • Using solar irradiance as indicator in large geographic regions may bias UV exposure. • These new datasets can improve UV exposure estimates in future epidemiology studies. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. [ABSTRACT FROM AUTHOR]

Chronic exposure to fine particulate matter (PM2.5) is accepted as a causal risk factor for coronary heart disease (CHD). However, most of the evidence for this hypothesis is based upon cohort studies in whites, comprised of either only males or females who live in urban areas. It is possible that many estimates of the effect of chronic exposure to PM2.5 on risk for CHD do not generalize to more diverse samples.Methods: Therefore, we estimated the relationship between chronic exposure to PM2.5 and risk for CHD in among participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort who were free from CHD at baseline (n=17,126). REGARDS is a sample of whites and blacks of both genders living across the continental United States. We fit Cox proportional hazards models for time to CHD to estimate the hazard ratio for baseline 1-year mean PM2.5 exposure, adjusting for environmental variables, demographics, and other risk factors for CHD including the Framingham Risk Score.Results: The hazard ratio (95% CI) for a 2.7-μg/m3 increase (interquartile range) 1-year mean concentration of PM2.5 was 0.94 (0.83-1.06) for combined CHD death and nonfatal MI, 1.13 (0.92-1.40) for CHD death, and 0.85 (0.73-0.99) for nonfatal MI. We also did not find evidence that these associations depended upon overall CHD risk factor burden.Conclusions: Our results do not provide strong evidence for an association between PM2.5 and incident CHD in a heterogeneous cohort, and we conclude that the effects of chronic exposure to fine particulate matter on CHD require further evaluation. [ABSTRACT FROM AUTHOR]

Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river discharges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to estimate and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea, and the rainfall patterns in the Magdalena River watershed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River watershed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001–2014 ( p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths ( r = 0.57–0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed ( r = 0.63–0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Niña conditions while the opposite was the case during El Niño conditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths ( r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters. [ABSTRACT FROM AUTHOR]

Abstract: A major shortcoming of any remotely-sensed land surface temperature (LST) dataset is the lack of observations for cloud-covered areas. A method is presented that uses the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra platform to fill in spatial gaps in the Aqua MODIS LST dataset over the conterminous United States (CONUS) and limited adjacent areas. Over this domain, data are available for only about 50% of all times and pixels for each of the two MODIS sensors. Coverage is highest in summer and lowest in winter, with major regional variations. The relative close temporal proximity (~3h) of the Aqua and Terra overpasses provides an opportunity to combine information from the two data sources, which can reduce the data loss, most of which we assume is cloud-related. We applied the approach to create a ‘merged’ data set that supplements existing Aqua and Terra daytime and nighttime LST products. We used Terra LST data to fill gaps in Aqua data, resulting in a data set tied to the ~1:30AM/PM overpass times, so that the resulting data closely approximate daily minimum and maximum LST values. In order to use Terra LST observations to supplement Aqua data, an adjustment was applied to account for the different overpass times of the two platforms. Terra''s 10:30AM overpass usually senses a cooler surface than does Aqua with its 1:30PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30PM than does Aqua at 1:30AM. Our approach was to determine, by season, mean Aqua and Terra LST values on the CONUS grid, based on data from a multi-year (2003–2008) period. Adding the mean Aqua-Terra LST differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. Using independent offsets for each grid cell and season provides a first-order accounting for factors such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover, which control the diurnal cycle of LST. For the six-year period, the merged data set increases data coverage by 24% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired. [Copyright &y& Elsevier]

• Short-term daily total PM 2.5 exposure is associated with work loss due to sickness. • Association of PM 2.5 with work loss becomes stronger when exposed to higher wildfire smoke. • Federal and state PM 2.5 standards could be further strict to protect public health. Few studies investigated the impact of particulate matter (PM 2.5) on some symptom exacerbations that are not perceived as severe enough to search for medical assistance. We aimed to study the association of short-term daily total PM 2.5 exposure with work loss due to sickness among adults living in California. We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM 2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM 2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM 2.5 exposure on work loss using logistic regression models. About 1.69% (weighted percentage) of adult respondents reported work loss in the week before the survey interview. The odds ratio of work loss was 1.45 (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.03, 2.03) when a 2-week average of daily total PM 2.5 exposure was higher than 12 µg/m3. The OR for work loss was 1.05 (95% CI: 0.98, 1.13) for each 2.56ug/m3 increase in the 2-week average of daily total PM 2.5 exposure, and became stronger among those who were highly exposed to wildfire smoke (OR = 1.06, 95% CI: 1.00, 1.13), compared to those with lower wildfire smoke exposure (OR = 1.04, 95% CI: 0.79, 1.39). Our findings suggest that short-term ambient PM 2.5 exposure is positively associated with work loss due to sickness and the association was stronger among those with higher wildfire smoke exposure. It also indicated that the current federal and state PM 2.5 standards (annual average of 12 µg/m3) could be further strengthened to protect the health of the citizens of California. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies showed that fine particulate matter (PM2.5, particles smaller than 2.5μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM2.5 concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM2.5 exposure estimates. However, the coarse resolutions (e.g., 10km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM2.5 characteristics that are crucial to population-based PM2.5 health effects research. In this paper, a new aerosol product with 1km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM2.5 concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R2 of 0.83, mean prediction error (MPE) of 1.89μg/m3, and square root of the mean squared prediction errors (RMSPE) of 2.73μg/m3 in model fitting, and R2 of 0.67, MPE of 2.54μg/m3, and RMSPE of 3.88μg/m3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1km spatial resolution MAIAC AOD can be used to estimate PM2.5 concentrations. [Copyright &y& Elsevier]