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A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy. [ABSTRACT FROM AUTHOR]

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]

This ecological study aimed to assess the association between long-term exposures to outdoor environmental factors and mortality rate from cardiovascular disease (CVD) in a diverse and spatially distributed population from 3,094 counties within the U.S. (n > 3,780,000 CVD deaths) using satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were conducted to determine whether PM2.5, sunlight and maximum heat index were related to the odds of the total CVD death rate based on gender, race, and age taking into consideration the confounding risk factors of diabetes, obesity, leisure- time physical inactivity, smoking and socioeconomic status. The study has shown that elevated levels of PM2.5, sunlight and heat long-term exposures are significantly associated with an increase in the odds ratio of the total CVD mortality. The results suggest a 9.8% (95% CI = 6.3% - 13.4%), 0.9% (95% CI = 0.5% - 1.2%), and 0.7% (95% CI = 0.5% - 11.2%) increase in total CVD mortality associated with 10 μg/m3 increase in PM2.5 concentrations, 1,000 kJ/m2 increases in sunlight, and 1 oF increase in heat index, respectively. The odds ratios for the CVD death rate due to long-term exposures of PM2.5, sunlight, and heat index were significantly greater than 1.0 for all categories except for Asians, Hispanics, and American Indians, indicating that the effect of long-term exposures to particulate matter, sunlight radiation, and maximum heat on CVD mortality is trivial for Asians, Hispanics, and American Indians. Among the categories of age, the group of 65 years and older had the highest odds ratios, suggesting that the age group of 65 years and older are the most vulnerable group to the environmental exposures of PM2.5 (OR = 1.179, 95% CI = 1.124 - 1.237), sunlight (OR = 1.047, 95% CI = 1.041 - 1.053), and maximum heat (OR = 1.014, 95% CI = 1.011 - 1.016). The odds ratios of CVD mortality due to the environmental exposures were higher for Blacks than those for Whites. The odds ratios for all categories were attenuated with the inclusion of diabetes, obesity, leisure-time physical inactivity, smoking, and income covariates, reflecting the effect of other medical conditions, lifestyle, behavioral and socioeconomic factors on the CVD death rate besides the environmental factors. [ABSTRACT FROM AUTHOR]

This study aimed to assess the association between exposure to fine particulate matter (PM2.5) and respiratory system cancer incidence in the US population (n= 295,404,580) using a satellite-derived estimate of PM2.5concentrations. Linear and logistic regression analyses were performed to determine whether PM2.5was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM2.5concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM2.5and RSC incidence rate per 1 μg/m3PM2.5increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R2= 0.36), 2.14 (R2= 0.14), 3.92 (R2= 0.23), 5.02 (R2= 0.21), and 4.15 (R2= 0.28). Similarly, the logistic regression odds ratios per 10 μg/m3increase of PM2.5were greater than one for all categories except for Asians and American Indians, indicating that PM2.5is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56–3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09–2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43–3.14) than for Whites (OR = 1.72, 95% CI = 1.23–2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM2.5. [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]