Background:The effects of exposure to fine particulate matter (PM2.5) during wildland fires are not well understood in comparison with PM2.5 exposures from other sources.Objectives:We examined the cardiopulmonary effects of short-term exposure to PM2.5 on smoke days in the United States to evaluate whether health effects are consistent with those during non-smoke days.Methods:We examined cardiopulmonary hospitalizations among adults ≥65 y of age, in U.S. counties (n=692) within 200km of 123 large wildfires during 2008–2010. We evaluated associations during smoke and non-smoke days and examined variability with respect to modeled and observed exposure metrics. Poisson regression was used to estimate county-specific effects at lag days 0–6 (L0–6), adjusted for day of week, temperature, humidity, and seasonal trend. We used meta-analyses to combine county-specific effects and estimate overall percentage differences in hospitalizations expressed per 10-μg/m3 increase in PM2.5.Results:Exposure to PM2.5, on all days and locations, was associated with increased hospitalizations on smoke and non-smoke days using modeled exposure metrics. The estimated effects persisted across multiple lags, with a percentage increase of 1.08% [95% confidence interval (CI): 0.28, 1.89] on smoke days and 0.67% (95% CI: −0.09, 1.44) on non-smoke days for respiratory and 0.61% (95% CI: 0.09, 1.14) on smoke days and 0.69% (95% CI: 0.19, 1.2) on non-smoke days for cardiovascular outcomes on L1. For asthma-related hospitalizations, the percentage increase was greater on smoke days [6.9% (95% CI: 3.71, 10.11)] than non-smoke days [1.34% (95% CI: −1.10, 3.77)] on L1.Conclusions:The increased risk of PM2.5-related cardiopulmonary hospitalizations was similar on smoke and non-smoke days across multiple lags and exposure metrics, whereas risk for asthma-related hospitalizations was higher during smoke days. https://doi.org/10.1289/EHP3860

Colorado is regularly impacted by long-range transport of wildfire smoke from upwind regions. This smoke is a major source of ambient PM2.5. Maternal exposure to total PM2.5 during pregnancy has been linked to decreased birth weight and other adverse outcomes, although the impact of wildfire smoke contribution has only recently been investigated. The objective of this study was to estimate associations between adverse pregnancy outcomes and ambient wildfire smoke PM2.5. Wildfire smoke PM2.5 exposures were estimated using a previously published method incorporating ground-based monitors and remote sensing data. Logistic regression models stratified by ZIP code and mixed models with random intercept by ZIP code were used to test for associations. The primary outcomes of interest were preterm birth and birth weight. Secondary outcomes included gestational hypertension, gestational diabetes, neonatal intensive care unit admission, assisted ventilation, small for gestational age, and low birth weight. Exposure to wildfire smoke PM2.5 over the full gestation and during the second trimester were positively associated with pre-term birth (OR = 1.076 (μg/m3)−1 [95% CI = 1.016, 1.139; p = 0.013] and 1.132 (μg/m3)−1 [95% CI = 1.088, 1.178]; p < 0.0001, respectively), while exposure during the first trimester was associated with decreased birth weight (−5.7 g/(μg/m3) [95% CI: −11.1, −0.4; p = 0.036]). Secondary outcomes were mixed.

Background:The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century.Objectives:We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005.Methods:Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993–2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental).Results:We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0–5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0–5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1–3 and 0–5).Conclusions:Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality.Citation:Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993–2005. Environ Health Perspect 124:1735–1743; http://dx.doi.org/10.1289/EHP216

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time–location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.