Bonne Ford

Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter

The negative impacts of fine particulate matter (PM2.5) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. Using the Burnett et al. (2014) integrated exposure response function, we estimate that in the United States, exposure to PM2.5 accounts for approximately 2% of total deaths compared to 14% in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 14% for the U.S. and 2% for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on the order of 20% due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10% due to the satellite observational uncertainty, and 30% or greater uncertainty associated with the application of concentration response functions to estimated exposure.

Spatial and Temporal Estimates of Population Exposure to Wildfire Smoke during the Washington State 2012 Wildfire Season Using Blended Model, Satellite, and In-Situ Data

Abstract 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.

Global burden of mortalities due to chronic exposure to ambient PM2.5 from open combustion of domestic waste

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 PM2.5 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 PM2.5 to all other PM2.5 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 PM2.5 reported in the Global Burden of Disease Study 2010. Exposure to PM2.5 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 PM2.5 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 PM2.5, and over Asia, decreasing resolution from 0.5° × 0.666° to 2° × 2.5° results in 21% fewer mortalities attributed to waste-combustion PM2.5. Owing to coarse model resolution, our global estimates of premature mortality from waste-combustion PM2.5 are likely a lower bound.

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions

Abstract 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 (PM2.5) 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 PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF‐Chem) model, a kriged interpolation of PM2.5 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 PM2.5. A 10 μg/m3 increase in GWR smoke PM2.5 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 PM2.5 exposure method: a 10 μg/m3 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 PM2.5 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.

Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States

Abstract Fine particulate matter (PM2.5) from U.S. anthropogenic sources is decreasing. However, previous studies have predicted that PM2.5 emissions from wildfires will increase in the midcentury to next century, potentially offsetting improvements gained by continued reductions in anthropogenic emissions. Therefore, some regions could experience worse air quality, degraded visibility, and increases in population‐level exposure. We use global climate model simulations to estimate the impacts of changing fire emissions on air quality, visibility, and premature deaths in the middle and late 21st century. We find that PM2.5 concentrations will decrease overall in the contiguous United States (CONUS) due to decreasing anthropogenic emissions (total PM2.5 decreases by 3% in Representative Concentration Pathway [RCP] 8.5 and 34% in RCP4.5 by 2100), but increasing fire‐related PM2.5 (fire‐related PM2.5 increases by 55% in RCP4.5 and 190% in RCP8.5 by 2100) offsets these benefits and causes increases in total PM2.5 in some regions. We predict that the average visibility will improve across the CONUS, but fire‐related PM2.5 will reduce visibility on the worst days in western and southeastern U.S. regions. We estimate that the number of deaths attributable to total PM2.5 will decrease in both the RCP4.5 and RCP8.5 scenarios (from 6% to 4–5%), but the absolute number of premature deaths attributable to fire‐related PM2.5 will double compared to early 21st century. We provide the first estimates of future smoke health and visibility impacts using a prognostic land‐fire model. Our results suggest the importance of using realistic fire emissions in future air quality projections.