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Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources. Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors. [ABSTRACT FROM AUTHOR]

Estuarine ecosystems are largely influenced by watersheds directly connected to them. In the Mobile Bay, Alabama watersheds we examined the effect of land cover and land use ( LCLU) changes on discharge rate, water properties, and submerged aquatic vegetation, including freshwater macrophytes and seagrasses, throughout the estuary. LCLU scenarios from 1948, 1992, 2001, and 2030 were used to influence watershed and hydrodynamic models and evaluate the impact of LCLU change on shallow aquatic ecosystems. Overall, our modeling results found that LCLU changes increased freshwater flows into Mobile Bay altering temperature, salinity, and total suspended sediments ( TSS). Increased urban land uses coupled with decreased agricultural/pasture lands reduced TSS in the water column. However, increased urbanization or agricultural/pasture land coupled with decreased forest land resulted in higher TSS concentrations. Higher sediment loads were usually strongly correlated with higher TSS levels, except in areas where a large extent of wetlands retained sediment discharged during rainfall events. The modeling results indicated improved water clarity in the shallow aquatic regions of Mississippi Sound and degraded water clarity in the Wolf Bay estuary. This integrated modeling approach will provide new knowledge and tools for coastal resource managers to manage shallow aquatic habitats that provide critical ecosystem services. [ABSTRACT FROM AUTHOR]

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making. [ABSTRACT FROM PUBLISHER]

There is a well-documented association between ambient fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) morbidity and mortality. Exposure to PM2.5 can cause premature death and harmful and chronic health effects such as heart attack, diabetes, and stroke. The Environmental Protection Agency sets annual PM2.5 standards to reduce these negative health effects. Currently above an annual average level of 12.0 µg/m is considered unhealthy. Methods. We examined the association of long-term exposure to PM2.5 and CVD in a cohort of 44,610 individuals who resided in 12 states recruited into the Southern Community Cohort Study (SCCS). The SCCS was designed to recruit Black and White participants who received care from Federally Qualified Health Centers; hence, they represent vulnerable individuals from low-income families across this vast region. This study tests whether SCCS participants who lived in locations exposed to elevated ambient levels of PM2.5 concentrations were more likely to report a history of CVD at enrollment (2002–2009). Remotely sensed satellite data integrated with ground monitoring data provide an assessment of the average annual PM2.5 in urban and rural locations where the SCCS participants resided. We used multilevel logistic regression to estimate the associations between self-reported CVD and exposure to elevated ambient levels of PM2.5. Results. We found a 13.4 percent increase in the odds of reported CVD with exposure to unhealthy levels of PM2.5 exposure at enrollment. The SCCS participants with medical histories of hypertension, hypercholesterolemia, and smoking had, overall, 385 percent higher odds of reported CVD than those without these clinical risk factors. Additionally, Black participants were more likely to live in locations with higher ambient PM2.5 concentrations and report high levels of clinical risk factors, thus, they may be at a greater future risk of CVD. Conclusions: In the SCCS participants, we found a strong relation between exposures to high ambient levels of PM2.5 and self-reported CVD at enrollment. [ABSTRACT FROM AUTHOR]

Abstract: Most of currently reported models for predicting PM2.5 concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM2.5, aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R 2 of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM2.5 exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields. [Copyright &y& Elsevier]

This paper provides results on the performance of the Prescott Spatial Growth Model to project land use in the Atlanta metropolitan area. The Prescott Spatial Growth Model is a tool that can be applied at fine or coarse spatial scales and can accommodate a wide range of user inputs to develop any number of growth rules for projecting land use. "Blind" simulations and "guided" simulations for the Atlanta region were performed for the 1980-2000 period to evaluate the performance of the model in the quantity and spatial distribution of urban growth. Quantitative comparisons of both the blind and guided simulations with actual land use were used to assess the model's performance. The error in the quantity of land-use classes projected and the spatial arrangement of the classes was assessed. The model provides exceptional flexibility and good overall performance in projecting land use in this study. [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]

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM 2.5 ) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM 2.5 . (3) Results: The study resulted in four key findings: (1) PM 2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m 3 increase in PM 2.5 ; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments., Competing Interests: The authors declare no conflict of interest.

Fine particulate matter (PM 2.5 ) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM 2.5 exposures. This review article surveys publicly available exposure datasets for surface PM 2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM 2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM 2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM 2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM 2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources. Implications : Fine particulate matter (PM 2.5 ) has large impacts on human morbidity and mortality. Even though the methods for generating the PM 2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM 2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM 2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

A public participatory geographical information systems (PPGIS) demographic, environmental, socioeconomic, health status portal was developed for the Stambaugh-Elwood (SE) community in Columbus, OH. We hypothesized that soil at SE residences would have metal concentrations above natural background levels. Three aims were developed that allowed testing of this hypothesis. Aim 1 focused on establishing partnerships between academia, state agencies and communities to assist in the development of a community voice. Aim 2 was to design and conduct soil sampling for residents of the SE community. Aim 3 was to utilize our interactive, customized portal as a risk communication tool by allowing residents to educate themselves as to the potential risks from industrial sources in close proximity to their community. Multiple comparisons of means were used to determine differences in soil element concentration by sampling location at p < 0.05. The results demonstrated that eight metals (As, Cd, Cu, Pb, Mo, Se, Tl, Zn) occurred at statistically-significantly greater levels than natural background levels, but most were below risk-based residential soil screening levels. Results were conveyed to residents via an educational, risk-communication informational card. This study demonstrates that community-led coalitions in collaboration with academic teams and state agencies can effectively address environmental concerns.