Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical‐weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM 2.5 ) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time‐stratified case‐crossover design. Hospital admissions aggregated by ZIP code were linked with population‐weighted daily average concentrations of smoke PM 2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF‐Chem) model, a kriged interpolation of PM 2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF‐Chem, satellite observations of aerosol optical depth, and kriged PM 2.5 . A 10 μg/m 3 increase in GWR smoke PM 2.5 was associated with an 8% increased risk in asthma‐related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019–1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM 2.5 exposure method: a 10 μg/m 3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026–1.145) and not significant using WRF‐Chem (OR: 0.986, 95%CI: 0.931–1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM 2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

A school-based asthma curriculum designed specifically for urban students has been shown to reduce symptoms, activity limitations, and health care utilization for intervention participants.

In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire‐smoke‐specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite‐based observations, and chemical‐transport model (CTM) simulations. Each of these exposure‐estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge‐regression model and a geographically weighted ridge‐regression model. We evaluate the performance of the three individual exposure‐estimate techniques and the two blended techniques by using leave‐one‐out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite‐based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.

Uncontrolled combustion of domestic waste has been observed in many countries, creating concerns for air quality; however, the health implications have not yet been quantified. We incorporate the Wiedinmyer et al (2014 Environ. Sci. Technol. 48 9523-30) emissions inventory into the global chemical-transport model, GEOS-Chem, and provide a first estimate of premature adult mortalities from chronic exposure to ambient PM 2.5 from uncontrolled combustion of domestic waste. Using the concentration-response functions (CRFs) of Burnett et al (2014 Environ. Health Perspect. 122 397-403), we estimate that waste-combustion emissions result in 270 000 (5th-95th: 213 000-328 000) premature adult mortalities per year. The confidence interval results only from uncertainty in the CRFs and assumes equal toxicity of waste-combustion PM 2.5 to all other PM 2.5 sources. We acknowledge that this result is likely sensitive to choice of chemical-transport model, CRFs, and emission inventories. Our central estimate equates to 9% of adult mortalities from exposure to ambient PM 2.5 reported in the Global Burden of Disease Study 2010. Exposure to PM 2.5 from waste combustion increases the risk of premature mortality by more than 0.5% for greater than 50% of the population. We consider sensitivity simulations to uncertainty in waste-combustion emission mass, the removal of waste-combustion emissions, and model resolution. A factor-of-2 uncertainty in waste-combustion PM 2.5 leads to central estimates ranging from 138 000 to 518 000 mortalities per year for factors-of-2 reductions and increases, respectively. Complete removal of waste combustion would only avoid 191 000 (5th-95th: 151 000-224 000) mortalities per year (smaller than the total contributed premature mortalities due to nonlinear CRFs). Decreasing model resolution from 2°×2.5°to 4°×5°results in 16% fewer mortalities attributed to waste-combustion PM 2.5 , and over Asia, decreasing resolution from 0.5°×0.666°to 2°×2.5°results in 21% fewer mortalities attributed to waste-combustion PM 2.5. Owing to coarse model resolution, our global estimates of premature mortality from waste-combustion PM 2.5 are likely a lower bound.

As anthropogenic emissions continue to decline and emissions from landscape (wild, prescribed, and agricultural) fires increase across the coming century, the relative importance of landscape‐fire smoke on air quality and health in the United States (US) will increase. Landscape fires are a large source of fine particulate matter (PM 2.5 ), which has known negative impacts on human health. The seasonal and spatial distribution, particle composition, and co‐emitted species in landscape‐fire emissions are different from anthropogenic sources of PM 2.5 . The implications of landscape‐fire emissions on the sub‐national temporal and spatial distribution of health events and the relative health importance of specific pollutants within smoke are not well understood. We use a health impact assessment with observation‐based smoke PM 2.5 to determine the sub‐national distribution of mortality and the sub‐national and sub‐annual distribution of asthma morbidity attributable to US smoke PM 2.5 from 2006 to 2018. We estimate disability‐adjusted life years (DALYs) for PM 2.5 and 18 gas‐phase hazardous air pollutants (HAPs) in smoke. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and asthma morbidity (on average 75% across 2006–2018) attributable to smoke PM 2.5 occurs outside the West, due to higher population density in the East. Across the US, smoke‐attributable asthma morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM 2.5 is approximately three orders of magnitude higher than DALYs associated with gas‐phase smoke HAPs. Our results indicate awareness and mitigation of landscape‐fire smoke exposure is important across the US.