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

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.

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

“…For the geophysical approach (PM 2.5_Dal_NA and PM 2.5_Dal_GL ), satellite AOD provides observational constraints over the globe with fine spatial resolution, which outperforms unconstrained model simulations ( i.e. PM 2.5_CMAQ ), though the model simulated relationship between AOD-PM 2.5 often introduces large uncertainties (Jin et al 2019). For the AQS-Remote Sensing merged approach (PM 2.5_CDC ), incorporating satellite-AOD better resolves urban-rural gradients of PM 2.5 than the product spatially interpolated from AQS observations (i.e.…”

“…Satellite-derived PM 2.5 is valuable for filling the spatial gaps over regions with sparse monitors (van Donkelaar et al 2014(van Donkelaar et al , 2016, providing observational constraints to models (Anenberg et al 2017, Lacey et al 2017), and improving the predictive power of statistical models (Beckerman et al 2013). However, using satellite AOD to predict PM 2.5 , especially at shorter time scales, is challenging due to retrieval uncertainties (Martin 2008, Jin et al 2019, missing data due to the inability to retrieve over cloud and snow Christopher 2008, Levy et al 2009), and the dependence of PM 2.5 -AOD relationship on aerosol speciation, vertical distributions, and aerosol optical properties (Chin et al 2002, Gupta et al 2006, Jin et al 2019.…”

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

“…Compared with the existing methods [15,[20][21][22][23][24][25][26]30,31], although just having a modest improvement (4% for the empirical method) for the test correlation between the simulated GAC and PM 2.5 , this proposed method provides a more flexible framework to fuse the influence of multiple scaling, shift, and other potential factors on the conversion that involves complex atmospheric and chemical processes of aerosols; this method is convenient to train and use in support of the AD tool, and makes the conversion easily adjusted or improved using the data of additional influential factors if available. The polynomial conversion from proxy GAC to PM 2.5 was conducted to employ the stable RSE loss function to obtain a slightly better correlation than linear conversion (0.58 vs. 0.56).…”

“…For empirical conversion, planetary boundary layer height has been used as the divisor to adjust satellite AOD given AOD obtained through the integration of aerosol extinction coefficient by boundary mixing height in most studies [15,[21][22][23][24][25]. Several studies [20,26] employed chemical transport models (CTM) such as the Community Multiscale Air Quality (CMAQ) to simulate the relationship between modeled PM 2.5 and AOD and then used such a relationship as a linear converter of satellite AOD. However, although most CTMs can provide the simulated vertical distribution of aerosols at varied spatial and temporal scales [27], they just have coarse spatial resolution and may considerably underestimate aerosol mass concentrations [28,29], and may be not available for some study regions and periods.…”

Environmental Degradation and Public Opinion: The Case of Air Pollution in Vietnam

“…PM 2.5 is another pollutant for which there is satellite data available. However, satellite-derived PM 2.5 has substantial geographic gaps and are subject to various sources of uncertainties (Jin et al., 2019), especially over regions with sparse ground-based measurements such as Vietnam. Where PM 2.5 data are reliably measured in Vietnam, however, there is a reasonably strong correlation between it and NO 2 ( r = .…”