Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance

Short-Term total and wildfire fine particulate matter exposure and work loss in California

BACKGROUND

Few studies investigated the impact of particulate matter (PM2.5) 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 PM2.5 exposure with work loss due to sickness among adults living in California.

METHODS

We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM2.5 exposure on work loss using logistic regression models.

RESULTS

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 PM2.5 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 PM2.5 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).

CONCLUSIONS

Our findings suggest that short-term ambient PM2.5 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 PM2.5 standards (annual average of 12 µg/m3) could be further strengthened to protect the health of the citizens of California.

Evaluation of associations between estimates of particulate matter exposure and new onset type 2 diabetes in the REGARDS cohort

BACKGROUND

Studies of PM_2.5 and type 2 diabetes employ differing methods for exposure assignment, which could explain inconsistencies in this growing literature. We hypothesized associations between PM_2.5 and new onset type 2 diabetes would differ by PM_2.5 exposure data source, duration, and community type.

METHODS

We identified participants of the US-based REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort who were free of diabetes at baseline (2003-2007); were geocoded at their residence; and had follow-up diabetes information. We assigned PM_2.5 exposure estimates to participants for periods of 1 year prior to baseline using three data sources, and 2 years prior to baseline for two of these data sources. We evaluated adjusted odds of new onset diabetes per 5 µg/m³ increases in PM_2.5 using generalized estimating equations with a binomial distribution and logit link, stratified by community type.

RESULTS

Among 11,208 participants, 1,409 (12.6%) had diabetes at follow-up. We observed no associations between PM_2.5 and diabetes in higher and lower density urban communities, but within suburban/small town and rural communities, increases of 5 µg/m³ PM_2.5 for 2 years (Downscaler model) were associated with diabetes (OR [95% CI] = 1.65 [1.09, 2.51], 1.56 [1.03, 2.36], respectively). Associations were consistent in direction and magnitude for all three PM_2.5 sources evaluated.

SIGNIFICANCE

1- and 2-year durations of PM_2.5 exposure estimates were associated with higher odds of incident diabetes in suburban/small town and rural communities, regardless of exposure data source. Associations within urban communities might be obfuscated by place-based confounding.

Tripling of western US particulate pollution from wildfires in a warming climate

SignificanceRecord-setting fires in the western United States over the last decade caused severe air pollution, loss of human life, and property damage. Enhanced drought and increased biomass in a warmer climate may fuel larger and more frequent wildfires in the coming decades. Applying an empirical statistical model to fires projected by Earth System Models including climate-ecosystem-socioeconomic interactions, we show that fine particulate pollution over the US Pacific Northwest could double to triple during late summer to fall by the late 21st century under intermediate- and low-mitigation scenarios. The historic fires and resulting pollution extremes of 2017-2020 could occur every 3 to 5 y under 21st-century climate change, posing challenges for air quality management and threatening public health.

The impact of social disadvantage on autonomic physiology of latinx adolescents: The role of environmental risks

The experience of poverty embodies complex, multidimensional stressors that may adversely affect physiological and psychological domains of functioning. Compounded by racial/ethnic discrimination, the financial aspect of family poverty typically coincides with additional social and physical environmental risks such as pollution exposure, housing burden, elevated neighborhood unemployment, and lower neighborhood education levels. In this study, we investigated the associations of multidimensional social disadvantage throughout adolescence with autonomic nervous system (ANS) functioning at 17 years. Two hundred and twenty nine low-income Mexican-American adolescents (48.6% female) and their parents were assessed annually between the ages of 10 and 16. Participants' census tracts were matched with corresponding annual administrative data of neighborhood housing burden, education, unemployment, drinking water quality, and fine particulate matter. We combined measures of adolescents' electrodermal response and respiratory sinuses arrhythmia at rest and during a social exclusion challenge (Cyberball) to use as ANS indices of sympathetic and parasympathetic activity, respectively. Controlling for family income-to-needs, youth exposed to greater cumulative water and air pollution from ages 10-16 displayed altered patterns of autonomic functioning at rest and during the social challenge. Conversely, youth living in areas with higher housing burden displayed healthy patterns of autonomic functioning. Altogether, results suggest that toxin exposure in youths' physical environments disrupts the ANS, representing a plausible mechanism by which pollutants and social disadvantage influence later physical and mental health.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.

Correlation between Exposure to Fine Particulate Matter (PM2.5) during Pregnancy and Congenital Anomalies: Its Surgical Perspectives

BACKGROUND

Fine particulate matter (PM_2.5) can easily penetrate blood vessels and tissues through the human respiratory tract and cause various health problems. Some studies reported that particular matter (PM) exposure during pregnancy is associated with low birth weight or congenital cardiovascular anomalies. This study aimed to investigate the correlation between the degree of exposure to PM ≤ 2.5 μm (PM_2.5) during pregnancy and congenital anomalies relevant to the field of pediatric surgery.

METHODS

Mother-infant dyads with registered addresses in the Metropolitan City were selected during 3 years. The electronic medical records of mothers and neonates were retrospectively analyzed, with a focus on maternal age at delivery, date of delivery, gestation week, presence of diabetes mellitus (DM) or hypertension, parity, the residence of the mother and infant, infant sex, birth weight, Apgar score, and presence of congenital anomaly. The monthly PM_2.5 concentration from the first month of pregnancy to the delivery was computed based on the mothers' residences.

RESULTS

PM_2.5 exposure concentration in the second trimester was higher in the congenital anomaly group than in the non-congenital anomaly group (24.82 ± 4.78 µg/m³, P = 0.023). PM_2.5 exposure concentration did not affect the incidence of nervous, cardiovascular, and gastrointestinal anomalies. While statistically insignificant, the groups with nervous, cardiovascular, gastrointestinal, musculoskeletal, and other congenital anomalies were exposed to higher PM_2.5 concentrations in the first trimester compared with their respective counterparts. The effect of PM_2.5 concentration on the incidence of congenital anomalies was significant even after adjusting for the mother's age, presence of DM, hypertension, and parity. The incidence of congenital anomalies increased by 26.0% (95% confidence interval of 4.3% and 49.2%) per 7.23 µg/m³ elevation of PM_2.5 interquartile range in the second trimester.

CONCLUSIONS

The congenital anomaly group was exposed to a higher PM_2.5 concentration in the second trimester than the non-congenital anomaly group. The PM_2.5 exposure concentration level in the first trimester tended to be higher in groups with anomalies than those without anomalies. This suggests that continuous exposure to a high PM_2.5 concentration during pregnancy influences the incidence of neonatal anomalies in surgical respects.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

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.

Data Science in Environmental Health Research

PURPOSE OF REVIEW

Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research.

RECENT FINDINGS

We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research.

SUMMARY

In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data

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 (PM_2.5). 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 PM_2.5 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 PM_2.5 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 PM_2.5 data can improve the spatial coverage, especially in areas where PM_2.5 ground monitors are lacking, and studying the connections between PM_2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Fine Particulate Matter Pollution and Risk of Community-Acquired Sepsis

While air pollution has been associated with health complications, its effect on sepsis risk is unknown. We examined the association between fine particulate matter (PM_2.5) air pollution and risk of sepsis hospitalization. We analyzed data from the 30,239 community-dwelling adults in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort linked with satellite-derived measures of PM_2.5 data. We defined sepsis as a hospital admission for a serious infection with ≥2 systemic inflammatory response (SIRS) criteria. We performed incidence density sampling to match sepsis cases with 4 controls by age (±5 years), sex, and race. For each matched group we calculated mean daily PM_2.5 exposures for short-term (30-day) and long-term (one-year) periods preceding the sepsis event. We used conditional logistic regression to evaluate the association between PM_2.5 exposure and sepsis, adjusting for education, income, region, temperature, urbanicity, tobacco and alcohol use, and medical conditions. We matched 1386 sepsis cases with 5544 non-sepsis controls. Mean 30-day PM_2.5 exposure levels (Cases 12.44 vs. Controls 12.34 µg/m³; p = 0.28) and mean one-year PM_2.5 exposure levels (Cases 12.53 vs. Controls 12.50 µg/m³; p = 0.66) were similar between cases and controls. In adjusted models, there were no associations between 30-day PM_2.5 exposure levels and sepsis (4th vs. 1st quartiles OR: 1.06, 95% CI: 0.85–1.32). Similarly, there were no associations between one-year PM_2.5 exposure levels and sepsis risk (4th vs. 1st quartiles OR: 0.96, 95% CI: 0.78–1.18). In the REGARDS cohort, PM_2.5 air pollution exposure was not associated with risk of sepsis.

Fine particulate matter and incident coronary heart disease in the REGARDS cohort

Chronic exposure to fine particulate matter (PM_2.5) 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 PM_2.5 on risk for CHD do not generalize to more diverse samples.

METHODS

Therefore, we estimated the relationship between chronic exposure to PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Investigating the effects of environmental factors on autism spectrum disorder in the USA using remotely sensed data

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 PM_2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were performed to determine whether the unhealthy AQI, PM_2.5, 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 PM_2.5, 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.

Air quality measurements-From rubber bands to tapping the rainbow

It is axiomatic that good measurements are integral to good public policy for environmental protection. The generalized term for "measurements" includes sampling and quantitation, data integrity, documentation, network design, sponsorship, operations, archiving, and accessing for applications. Each of these components has evolved and advanced over the last 200 years as knowledge of atmospheric chemistry and physics has matured. Air quality was first detected by what people could see and smell in contaminated air. Gaseous pollutants were found to react with certain materials or chemicals, changing the color of dissolved reagents such that their light absorption at selected wavelengths could be related to both the pollutant chemistry and its concentration. Airborne particles have challenged the development of a variety of sensory devices and laboratory assays for characterization of their enormous range of physical and chemical properties. Advanced electronics made possible the sampling, concentration, and detection of gases and particles, both in situ and in laboratory analysis of collected samples. Accurate and precise measurements by these methods have made possible advanced air quality management practices that led to decreasing concentrations over time. New technologies are leading to smaller and cheaper measurement systems that can further expand and enhance current air pollution monitoring networks.

IMPLICATIONS

Ambient air quality measurement systems have a large influence on air quality management by determining compliance, tracking trends, elucidating pollutant transport and transformation, and relating concentrations to adverse effects. These systems consist of more than just instrumentation, and involve extensive support efforts for siting, maintenance, calibration, auditing, data validation, data management and access, and data interpretation. These requirements have largely been attained for criteria pollutants regulated by National Ambient Air Quality Standards, but they are rarely attained for nonroutine measurements and research studies.

The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA

This study aimed to assess the association between exposure to fine particulate matter (PM_2.5) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM_2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM_2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM_2.5 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 PM_2.5 and RSC incidence rate per 1 μg/m³ PM_2.5 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 PM_2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM_2.5 is 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 PM_2.5.

Fine Particulate Matter (PM2.5) and the Risk of Stroke in the REGARDS Cohort

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.

Fine particulate air pollution and premature ventricular contractions: The REasons for Geographic And Racial Differences in Stroke (REGARDS) Study

BACKGROUND

It is unknown if higher levels of ambient particulate matter (PM) exposure increase the risk for premature ventricular contractions (PVC) in a population-based study of men and women, and if this relationship varies by race or sex.

METHODS

We examined the association of PM <2.5µm in diameter (PM_2.5) concentration with PVCs in 26,121 (mean age=64±9.3 years; 55% female; 41% black) participants from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Estimates of short- (2-week) and long-term (1-year) PM_2.5 exposures were computed prior to the baseline visit using geographic information system data on the individual level at the coordinates of study participants' residences. PVCs were identified from baseline electrocardiograms.

RESULTS

PVCs were detected in 1719 (6.6%) study participants. Short- (OR=1.08, 95%CI=1.03, 1.14) and long- (OR=1.06, 95%CI=1.01, 1.12) term PM_2.5 exposures were associated with PVCs. Interactions were not detected by race or sex. An interaction between short-term PM_2.5 exposure and PVCs was detected for those with cardiovascular disease (OR=1.16, 95%CI=1.06, 1.27) compared with those without cardiovascular disease (OR=1.05, 95%CI=0.99, 1.12; p-interaction=0.027).

CONCLUSION

Our findings suggest that PM_2.5 exposure is associated with an increased risk for PVCs in a biracial population-based study of men and women. We also have identified persons with cardiovascular disease as an at-risk population for PVCs when increases in short-term PM_2.5 concentration occur.

Heat Maps of Hypertension, Diabetes Mellitus, and Smoking in the Continental United States

BACKGROUND

Geographic variations in cardiovascular mortality are substantial, but descriptions of geographic variations in major cardiovascular risk factors have relied on data aggregated to counties. Herein, we provide the first description of geographic variation in the prevalence of hypertension, diabetes mellitus, and smoking within and across US counties.

METHODS AND RESULTS

We conducted a cross-sectional analysis of baseline risk factor measurements and latitude/longitude of participant residence collected from 2003 to 2007 in the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). Of the 30 239 participants, all risk factor measurements and location data were available for 28 887 (96%). The mean (±SD) age of these participants was 64.8(±9.4) years; 41% were black; 55% were female; 59% were hypertensive; 22% were diabetic; and 15% were current smokers. In logistic regression models stratified by race, the median(range) predicted prevalence of the risk factors were as follows: for hypertension, 49% (45%-58%) among whites and 72% (68%-78%) among blacks; for diabetes mellitus, 14% (10%-20%) among whites and 31% (28%-41%) among blacks; and for current smoking, 12% (7%-16%) among whites and 18% (11%-22%) among blacks. Hypertension was most prevalent in the central Southeast among whites, but in the west Southeast among blacks. Diabetes mellitus was most prevalent in the west and central Southeast among whites but in south Florida among blacks. Current smoking was most prevalent in the west Southeast and Midwest among whites and in the north among blacks.

CONCLUSIONS

Geographic disparities in prevalent hypertension, diabetes mellitus, and smoking exist within states and within counties in the continental United States, and the patterns differ by race.

Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches

Air pollution is a major environmental and health problem, especially in urban agglomerations. Estimating personal exposure to fine particulate matter (PM2.5) remains a great challenge because it requires numerous point measurements to explain the daily spatial variation in pollutant levels. Furthermore, meteorological variables have considerable effects on the dispersion and distribution of pollutants, which also depends on spatio-temporal emission patterns. In this study we developed a hybrid interpolation technique that combined the inverse distance-weighted (IDW) method with Kriging with external drift (KED), and applied it to daily PM2.5 levels observed at 10 monitoring stations. This provided us with downscaled high-resolution maps of PM2.5 for the Island of Montreal. For the KED interpolation, we used spatio-temporal daily meteorological estimates and spatial covariates as land use and vegetation density. Different KED and IDW daily estimation models for the year 2010 were developed for each of the six synoptic weather classes. These clusters were developed using principal component analysis and unsupervised hierarchical classification. The results of the interpolation models were assessed with a leave-one-station-out cross-validation. The performance of the hybrid model was better than that of the KED or the IDW alone for all six synoptic weather classes (the daily estimate for R(2) was 0.66-0.93 and for root mean square error (RMSE) 2.54-1.89 μg/m(3)).

Local Variability in the Impacts of Residential Particulate Matter and Pest Exposure on Children's Wheezing Severity: A Geographically Weighted Regression Analysis of Environmental Health Justice

Two assumptions have underpinned environmental justice over the past several decades: 1) uneven environmental exposures yield correspondingly unequal health impacts and 2) these effects are stable across space. To test these assumptions, relationships for residential pest and PM2.5 exposures with children's wheezing severity are examined using global (ordinary least squares) and local (geographically weighted regression [GWR]) models using cross-sectional observational survey data from El Paso (Texas) children. In the global model, having pests and higher levels of PM2.5 were weakly associated with greater wheezing severity. The local model reveals two types of asthmogenic socio-environments where environmental exposures more powerfully predict greater wheezing severity. The first is a lower-income context where children are disproportionately exposed to pests and PM2.5 and the second is a higher-income socio-environment where children are exposed to lower levels of PM2.5, yet PM2.5is counterintuitively associated with more severe wheezing. Findings demonstrate that GWR is a powerful tool for understanding relationships between environmental conditions, social characteristics and health inequalities.

Ambient fine particulate matter air pollution and leisure-time physical inactivity among US adults

OBJECTIVES

There is mounting evidence documenting the adverse health effects of short- and long-term exposure to ambient fine particulate matter (PM2.5) air pollution, but population-based evidence linking PM2.5 and health behaviour remains lacking. This study examined the relationship between ambient PM2.5 air pollution and leisure-time physical inactivity among US adults 18 years of age and above.

STUDY DESIGN

Retrospective data analysis.

METHODS

Participant-level data (n = 2,381,292) from the Behavioral Risk Factor Surveillance System 2003-2011 surveys were linked with Wide-ranging Online Data for Epidemiologic Research air quality data by participants' residential county and interview month/year. Multilevel logistic regressions were performed to examine the effect of ambient PM2.5 air pollution on participants' leisure-time physical inactivity, accounting for various individual and county-level characteristics. Regressions were estimated on the overall sample and subsamples stratified by sex, age cohort, race/ethnicity and body weight status.

RESULTS

One unit (μg/m(3)) increase in county monthly average PM2.5 concentration was found to be associated with an increase in the odds of physical inactivity by 0.46% (95% confidence interval = 0.34%-0.59%). The effect was similar between the sexes but to some extent (although not always statistically significant) larger for younger adults, Hispanics, and overweight/obese individuals compared with older adults, non-Hispanic whites or African Americans, and normal weight individuals, respectively.

CONCLUSIONS

Ambient PM2.5 air pollution is found to be associated with a modest but measurable increase in individuals' leisure-time physical inactivity, and the relationship tends to differ across population subgroups.

The public health exposome: a population-based, exposure science approach to health disparities research

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.

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

BACKGROUND

Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.

METHODS

We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV).

RESULTS

The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007).

CONCLUSIONS

Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.

Environmental Public Health Applications Using Remotely Sensed Data

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature using NASA satellite observations, 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.

Fine particulate matter and incident cognitive impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort

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 (PM_2.5) concentration to determine whether PM_2.5 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 PM_2.5 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 PM_2.5 was related to the odds of incident cognitive impairment. A 10 µg/m³ increase in PM_2.5 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/m³ increase in PM_2.5 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 PM_2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression

Most of currently reported models for predicting PM(2.5) 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 PM(2.5), 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 PM(2.5) 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.