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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 statistica...

Background While injuries experienced during hurricanes and other tropical cyclones have been relatively well- characterized through traditional surveillance, less is known about tropical cyclones' impacts on non-injury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment. Methods We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 United States counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥ 21 m/s at the county center; secondary analyses considered other wind thresholds and hazards. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models. Results For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval [CI]: 2%, 5%) and respiratory hospitalizations increased 16% (95% CI: 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric. Conclusions In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥ 65 years.

Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm ...

Background Ambient particulate matter has been shown to be associated with declining human health, although the association between fine particulate matter (PM 2.5 ) and stroke is uncertain. Methods We utilized satellite-derived measures of PM 2.5 to examine the association between exposure and stroke in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. We used a time-stratified case-crossover design, with exposure lags of 1 day, 2 days, and 3 days. We examined all strokes, as well as ischemic and hemorrhagic strokes separately. Results Among 30,239 participants in the REGARDS study, 746 incident events were observed: 72 hemorrhagic, 617 ischemic, and 57 of unknown type. Participants exposed to higher levels of PM 2.5 more often resided in urban areas compared to rural, and in the southeastern United States. After adjustment for temperature and relative humidity, no association was observed between PM 2.5 exposure and stroke, regardless of the lag (1-day lag OR = .99, 95% CI: .83-1.19; 2-day lag OR = .95, 95% CI: .80-1.14; 3-day lag OR = .95, 95% CI = .79-1.13). Similar results were observed for the stroke subtypes. Conclusions In this large cohort of African-Americans and whites, no association was observed between PM 2.5 and stroke. The ability to examine this association with a large number of outcomes and by stroke subtype helps fill a gap in the literature examining the association between PM 2.5 and stroke.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 μm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Background Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes. Methods We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008–2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression. Results We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria. Conclusion Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public. Electronic supplementary material The online version of this article (10.1186/s12940-019-0467-5) contains supplementary material, which is available to authorized users.

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in roughly the upper 5 cm with a 30–50-km resolution and a mission accuracy requirement of 0.04 cm3/cm-3. These observations can be used to improve land surface model (LSM) soil moisture states through data assimilation (DA). In this paper, SMOS soil moisture retrievals are assimilated into the Noah LSM via an Ensemble Kalman Filter within the National Aeronautics and Space Administration Land Information System. Bias correction is implemented using cumulative distribution function (cdf) matching, with points aggregated by either land cover or soil type to reduce the sampling error in generating the cdfs. An experiment was run for the warm season of 2011 to test SMOS DA and to compare assimilation methods. Verification of soil moisture analyses in the 0–10-cm upper layer and the 0–1-m root zone was conducted using in situ measurements from several observing networks in central and southeastern United States. This experiment showed that SMOS DA significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0–10-cm layer. Time series at specific stations demonstrates the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared with using a simple uniform correction curve.

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.

While risks of accidental deaths from tropical storms (e.g., drowning, carbon monoxide poisoning) have been well-documented, much less is known about risks for more common causes of mortality (e.g....

Background 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.

This study aimed to assess the association between exposures to outdoor environmental factors and autism spectrum disorder (ASD) prevalence in a diverse and spatially distributed population of 8-year-old children from the USA (n = 2,097,188) using the air quality index (AQI) of the US Environmental Protection Agency as well as satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were performed to determine whether the unhealthy AQI, PM2.5, sunlight, and maximum heat index were related to the odds of ASD prevalence based on gender and race and taking into consideration the confounding factors of smoking and socioeconomic status. The logistic regression odds ratios for ASD per 10% increase in the unhealthy AQI were greater than 1 for all categories, indicating that unhealthy AQI is related to the odds of ASD prevalence. The odds ratio of ASD due to the exposure to the unhealthy AQI was higher for Asians (OR = 2.96, 95% CI = 1.11-7.88) than that for Hispanics (OR = 1.308, 95% CI = 0.607-2.820), and it was higher for Blacks (OR = 1.398, 95% CI = 0.827-2.364) than that for Whites (OR = 1.219, 95% CI = 0.760-1.954). The odds ratio of ASD due to the unhealthy AQI was slightly higher for males (OR = 1.123, 95% CI = 0.771-1.635) than that for females (OR = 1.117, 95% CI = 0.789-1.581). The effects of the unhealthy environmental exposures on the odds ratios of ASD of this study were inconclusive (i.e., statically insignificant; p value > 0.05) for all categories except for Asians. The odds ratios of ASD for Asians were increased by 5, 12, and 14% with increased levels of the environmental exposures of 10 μg/m3 of PM2.5, 1000 kJ/m2 of sunlight, and 1 °F of maximum heat index, respectively. The odds ratios of ASD prevalence for all categories, except for Asians, were increased with the inclusion of the smoking covariate, reflecting the effect of smoking on ASD prevalence besides the unhealthy environmental factors.

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 PM2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM2.5 concentrations 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 PM2.5 and RSC incidence rate per 1 μg/m3 PM2.5 increase 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/m3 increase of PM2.5 were greater than one for all categories excep...

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) 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 PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km 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 PM(sub 2.5) 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 R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

Ground-truthing results are presented for a new 1-km air temperature product downscaled for New York City (NYC) from ∼12 km North American Land Data Assimilation System (NLDAS) air temperature data using 1 km moderate resolution imaging spectroradiometer surface temperature data. The downscaled product was compared against a unique highly spatially resolved ground-level ambient air temperature dataset collected through the New York City Community Air Survey (NYCCAS), a neighborhood level air pollution and temperature monitoring network, for the years 2009 and 2010. This work focuses on the spatial variation in daily minimum temperatures within the five counties that comprise NYC (∼784 km2). Overall, the downscaled daily minimum temperature was well correlated with ground station data, with NYCCAS minimum temperatures being slightly higher. Minimum temperature R2 values were 0.9 and 0.92, and mean absolute errors were 0.69°C and 0.86°C for years 2009 and 2010, respectively. The smallest differences between NYCCAS and the downscaled data were seen at lower temperatures, in less densely urbanized areas, and in areas with higher vegetative cover, suggesting systematic bias in the downscaled data related to land-use. The 1-km dataset discerned neighborhood level temperature differences in high-density urban situations with heterogeneous land cover.

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.

Sunlight may be related to cognitive function through vitamin D metabolism or circadian rhythm regulation. The analysis presented here sought to test whether ground and satellite measures of solar radiation are associated with cognitive decline. The study used a 15-year residential history merged with satellite and ground monitor data to determine sunlight (solar radiation) and air temperature exposure for a cohort of 19,896 cognitively intact black and white participants aged 45+ from the 48 contiguous United States. Exposures of 15, 10, 5, 2, and 1-year were used to predict cognitive status at the most recent assessment in logistic regression models; 1-year insolation and maximum temperatures were chosen as exposure measures. Solar radiation interacted with temperature, age, and gender in its relationships with incident cognitive impairment. After adjustment for covariates, the odds ratio (OR) of cognitive decline for solar radiation exposure below the median vs above the median in the 3rd tertile of maximum temperatures was 1.88 (95 % CI: 1.24, 2.85), that in the 2nd tertile was 1.33 (95 % CI: 1.09, 1.62), and that in the 1st tertile was 1.22 (95 % CI: 0.92, 1.60). We also found that participants under 60 years old had an OR = 1.63 (95 % CI: 1.20, 2.22), those 60-80 years old had an OR = 1.18 (95 % CI: 1.02, 1.36), and those over 80 years old had an OR = 1.05 (0.80, 1.37). Lastly, we found that males had an OR = 1.43 (95 % CI: 1.22, 1.69), and females had an OR = 1.02 (0.87, 1.20). We found that lower levels of solar radiation were associated with increased odds of incident cognitive impairment.

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 (~3 h) 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 ~1:30 AM/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 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than does Aqua at 1:30 AM. 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.

Background Urbanization has been correlated with hypertension (HTN) in developing countries undergoing rapid economic and environmental transitions. Objectives We examined the relationships among living environment (urban, suburban, and rural), day/night land surface temperatures (LST), and blood pressure in selected regions from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Also, the linking of data on blood pressure from REGARDS with National Aeronautics and Space Administration (NASA) science data is relevant to NASA’s strategic goals and missions, particularly as a primary focus of the agency’s Applied Sciences Program. Methods REGARDS is a national cohort of 30,228 people from the 48 contiguous United States with self-reported and measured blood pressure levels. Four metropolitan regions (Philadelphia, PA; Atlanta, GA; Minneapolis, MN; and Chicago, IL) with varying geographic and health characteristics were selected for study. Satellite remotely sensed data were used to characterize the LST and land cover/land use (LCLU) environment for each area. We developed a method for characterizing participants as living in urban, suburban, or rural living environments, using the LCLU data. These data were compiled on a 1-km grid for each region and linked with the REGARDS data via an algorithm using geocoding information. Results REGARDS participants in urban areas have higher systolic and diastolic blood pressure than do those in suburban or rural areas, and also a higher incidence of HTN. In univariate models, living environment is associated with HTN, but after adjustment for known HTN risk factors, the relationship was no longer present. Conclusion Further study regarding the relationship between HTN and living environment should focus on additional environmental characteristics, such as air pollution. The living environment classification method using remotely sensed data has the potential to facilitate additional research linking environmental variables to public health concerns.

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

This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land surface data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the surrounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

Optimal deconvolution (ODC) utilizes the footprint overlap in microwave observations to estimate the earth's brightness temperatures (TB). This paper examines the accuracy of ODC-estimated TB compared with a standard averaging technique. Because brightness temperatures cannot be independently verified, we constructed synthetic True TB for accuracy assessment. We assigned TB at a high spatial resolution (1 km) grid and computed the True TB by spatial averaging of the assigned TB to a lower resolution earth grid (25 km), selected to match the resolution of products generated from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). We used the sensor antenna response function along with the 1-km assigned TB to generate synthetic observations at AMSR-E footprint locations. These synthetic observations were subsequently deconvolved in the ODC technique to estimate TB at the lower resolution earth grid. The ODC-estimated TB and the simple grid cell averages of the synthetic observations were compared with the True TB allowing us to quantify the efficacy of each technique. In areas of high TB contrast (such as boundaries of water bodies), ODC performed significantly better than averaging. In other areas, ODC and averaging techniques produced similar results. A technique similar to ODC can be effective in delineating water bodies with significant clarity. That will allow microwave observations to be utilized near the shorelines, a trouble spot for the currently used averaging techniques.

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500–800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.

A key state variable in land surface–atmosphere interactions is soil moisture, which affects surface energy fluxes, runoff and the radiation balance. Soil moisture modelling relies on parameter estimates that are inadequately measured at the necessarily fine model scales. Hence, model soil moisture estimates are imperfect and often drift away from reality through simulation time. Because of its spatial and temporal nature, remote sensing holds great promise for soil moisture estimation. Much success has been attained in recent years in soil moisture estimation using passive and active microwave sensors, but progress has been slow. One reason for this is the scale disparity between remote sensing data resolution and the hydrologic process scale. Other impediments include vegetation cover and microwave penetration depth. As a result, currently there is no comprehensive method for assimilating remote soil moisture observations within a surface hydrology model at watershed or larger scales. This paper describes a measurement–modelling system for estimating the three-dimensional soil moisture distribution, incorporating remote microwave observations, a surface flux–soil moisture model, a radiative transfer model and Kalman filtering. The surface model, driven by meteorological observations, estimates the vertical and lateral distribution of water. Based on the model soil moisture profiles, microwave brightness temperatures are estimated using the radiative transfer model. A Kalman filter is then applied using modelled and observed brightness temperatures to update the model soil moisture profile. The modelling system has been applied using data from the Southern Great Plains 1997 field experiment. In the presence of highly inaccurate rainfall input, assimilation of remote microwave data results in better agreement with observed soil moisture. Without assimilation, it was seen that the model near-surface soil moisture reached a minimum that was higher than observed, resulting in substantial errors during very dry conditions. Updating moisture profiles daily with remotely sensed brightness temperatures reduced but did not eliminate this bias. Copyright  2002 John Wiley & Sons, Ltd.

This paper compares microwave brightness temperature (T/sub B/) estimated by two radiobrightness models: a multilayer coherent radiative transfer (CRT) model and a single-layer Fresnel reflectance model. Two dielectric mixing schemes were used along with the models to calculate permittivity (real part of the dielectric constant). Model T/sub B/ and permittivity estimates were compared and validated against Huntsville, AL 1998 field experiment measurements. Model differences can be attributed to the mixing scheme, the radiobrightness model, or the vertical profile representation. Two sets of simulations were performed to quantify the sources of variation, one using observed son temperature and moisture profiles as input, and another using uniform profiles. Using uniform profiles, systematic differences in permittivity estimated by the mixing schemes resulted in T/sub B/ differences as large as 15 K. However, for uniform profiles, differences in T/sub B/ estimated by the radiobrightness models for a given permittivity value were less than 2 K. For cases using observed profiles, near-surface drying of the profiles resulted in T/sub B/ values from the CRT model 6-10 K higher than estimates from the Fresnel model, which determines T/sub B/ based on 0-5 cm mean moisture and temperature. Therefore, the major sources of T/sub B/ variations were the dielectric mixing scheme and the shape of the near-surface moisture profile. No radiobrightness/mixing scheme combination exhibited superiority across all plots and times.

A sensitivity analysis was performed to determine the effects of systematically increasing the number of soil layers in a land surface-atmosphere model on the components of the modeled water budget. The study was done for a forested location in central Oklahoma for a 65-day period in spring 1996 using the model called Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS). SHEELS is based on the Biosphere-Atmosphere Transfer Scheme (BATS), except that the subsurface hydrology was substantially changed to improve representation of the soil moisture profile. The soil profile was divided into zones of thickness 0.05 m (upper), 1.25 m (root), and 1.20 m (bottom). The two principal conclusions are that (1) the water budget is very sensitive to the number of layers in the soil profile under wet conditions and (2) the water budget is much more sensitive to the number of layers in the profile than to the range of 2 orders of magnitude in saturated hydraulic conductivity considered in this study. A result of the latter conclusion is that larger errors in modeled water fluxes can occur from using an insufficient number of soil layers than from using an incorrect value of saturated hydraulic conductivity.

Background Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status. Methods Residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine previous-year sunlight radiation exposure for 17,773 black and white participants aged 45+ from the US. Exploratory and confirmatory analyses were performed by randomly dividing the sample into halves. Logistic regression models were used to examine relationships with cardiovascular risk factors. Results The lowest, compared to the highest quartile of insolation exposure was associated with lower high-density lipoprotein levels in adjusted exploratory (−2.7 mg/dL [95% confidence interval: −4.2, −1.2]) and confirmatory (−1.5 mg/dL [95% confidence interval: −3.0, −0.1]) models. The lowest, compared to the highest quartile of insolation exposure was associated with higher systolic blood pressure levels in unadjusted exploratory and confirmatory, as well as the adjusted exploratory model (2.3 mmHg [95% confidence interval: 0.8, 3.8]), but not the adjusted confirmatory model (1.6 mg/dL [95% confidence interval: −0.5, 3.7]). Conclusions The results of this study suggest that lower long-term sunlight exposure has an association with lower high-density lipoprotein levels. However, all associations were weak, thus it is not known if insolation may affect cardiovascular outcomes through these risk factors.

Introduction: Recent work has suggested that there is some association between acute exposures to fine particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) and ischemic stroke; however, the evidence is conflicting. Thus, we assessed whether PM2.5 was associated with ischemic stroke in participants in the Reasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. Methods: We used a time-stratified case-crossover design to determine if exposure to PM2.5 was associated with an increased risk of ischemic stroke. We fit conditional logistic regression models to determine the odds ratio of ischemic stroke for those exposed to moderate (PM2.5 15-40 μg/m3) relative to good (PM2.5 ≤ 15 μg/m3) levels of PM2.5. We adjusted for temperature at the time of exposure, and assessed whether the association differed by region of residence (stroke belt vs. non-belt regions). Results: Among 442 participants who experienced an incident ischemic stroke in REGARDS, we found that there was no association with PM2.5 exposure (OR: 0.89, 95% CI: 0.69-1.15), and that there was no impact of region of residence on these results (p for interaction=0.14). Conclusions: We did not confirm earlier research indicating that there is an acute association between PM2.5 and ischemic stroke. More research is needed to understand these conflicting results, and to assess the impact of longer term exposures of PM2.5 on stroke incidence.

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

Precipitation estimates from radar systems are a crucial component of many hydrometeorological applications, from flash flood forecasting to regional water budget studies. For analyses on large spatial scales and long timescales, it is frequently necessary to use composite reflectivities from a network of radar systems. Such composite products are useful for regional or national studies, but introduce a set of difficulties not encountered when using single radars. For instance, each contributing radar has its own calibration and scanning characteristics, but radar identification may not be retained in the compositing procedure. As a result, range effects on signal return cannot be taken into account. This paper assesses the accuracy with which composite radar imagery can be used to estimate precipitation in the convective environment of Florida during the summer of 1991. Results using Z = 30OR(sup 1.4) (WSR-88D default Z-R relationship) are compared with those obtained using the probability matching method (PMM). Rainfall derived from the power law Z-R was found to he highly biased (+90%-l10%) compared to rain gauge measurements for various temporal and spatial integrations. Application of a 36.5-dBZ reflectivity threshold (determined via the PMM) was found to improve the performance of the power law Z-R, reducing the biases substantially to 20%-33%. Correlations between precipitation estimates obtained with either Z-R relationship and mean gauge values are much higher for areal averages than for point locations. Precipitation estimates from the PMM are an improvement over those obtained using the power law in that biases and root-mean-square errors are much lower. The minimum timescale for application of the PMM with the composite radar dataset was found to be several days for area-average precipitation. The minimum spatial scale is harder to quantify, although it is concluded that it is less than 350 sq km. Implications relevant to the WSR-88D system are discussed.

The principal component of the water cycle over land areas is precipitation. Knowledge of the accuracy of areal precipitation estimates, therefore, is imperative. The manifold observations of hydrometeorological quantities in the Convection and Precipitation/Electrification Experiment (CaPE) that took place in central Florida during the summer of 1991 have provided an opportunity to examine the various water fluxes. This paper deals with only one of them, the daily area-average rainfall as derived from rain gauges. The theory for spatial sampling errors for random gauge design is extended to include rain gauge bias and random errors. The requirement for randomly located gauges turns out to be quite well met with the approximately 100 gauges available each day over the 17 300-km2 CaPE study area. Rain gauge bias, due mainly to wind effects, is estimated to be 6% based on a previous study and random measurement error is calculated from seven pairs of collocated gauges. For the category of daily are...