Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth

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 (PM 2.5 ). We use a forward geophysical approach to derive ground-level PM 2.5 distributions from satellite AOD at 1 km 2 resolution for 2011 over the northeastern US by applying relationships between surface PM 2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 1212 km2 horizontal resolution). Seasonal average satellite-derived PM 2.5 reveals more spatial detail and best captures observed surface PM 2.5 levels during summer. At the daily scale, however, satellite-derived PM 2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM 2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM 2.5 monitors, we show that uncertainties in modeled PM 2.5 =AOD can explain more than 70% of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM 2.5 evaluated at PM 2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM 2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM 2.5 =AOD relationships. Overall, we estimate that uncertainties in the modeled PM 2.5 =AOD lead to an error of 11 μm -3 in daily satellite-derived PM 2.5 , and uncertainties in satellite AOD lead to an error of 8μgm -3 . Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM 2.5 =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 PM 2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM 2.5 .