Abstract
Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.
Highlights
Exposure to ambient fine particulate matter (PM2.5) is estimated to cause more than 8 million attributable deaths worldwide in 2015 (Burnett et al, 2018) and is associated with an increase in the risk of cardiovascular and respiratory disease (Dominici et al, 2006; Peng et al, 2009)
While we find high Aerosol optical depth (AOD) over some populated urban areas such as New York City (NYC), high AODMAIAC is found over central New York State (NYS), away from major anthropogenic sources
We attribute the opposite seasonal cycle in PM2.5_MAIAC and AODMAIAC to three factors: (1) weak boundary layer ventilation in winter that leads to sharp vertical gradients of aerosol distribution (Kim et al, 2015), (2) higher RH in summer that leads to larger hygroscopic growth, and (3) model overestimates of PM2.5 in wintertime and underestimates of PM2.5 in summertime, leading to an overestimate of the winter-to-summer decrease in PM2.5_CMAQ/AODCMAQ
Summary
Exposure to ambient fine particulate matter (PM2.5) is estimated to cause more than 8 million attributable deaths worldwide in 2015 (Burnett et al, 2018) and is associated with an increase in the risk of cardiovascular and respiratory disease (Dominici et al, 2006; Peng et al, 2009). The satellite-derived PM2.5 is calculated by taking the product of satellite AOD with the modeled ratio of PM2.5 to AOD (van Donkelaar et al, 2006): PM2.5_sat This geophysical approach has the advantage of broad spatial coverage that is not limited by the availability of in situ measurements (van Donkelaar et al, 2006) and has been integral for studying the global burden of disease attributable to ambient air pollution (Cohen et al, 2017). The hygroscopic growth factor for organic carbon (OC) is especially uncertain, varying by organic species, and is poorly represented in models (Ming et al, 2005; Jimenez et al, 2009; Latimer and Martin, 2018) The impacts of these uncertainties on aerosol radiative forcing have been studied extensively, but their impacts on deriving surface PM2.5 from satellite-based column AOD have not yet been quantified. The overarching goal of the comprehensive uncertainty analysis is to assess the relative importance of each uncertain factor, thereby advancing the process-level understanding of the relationship between satellite AOD and surface PM2.5 air quality
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