Abstract
ABSTRACT Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m–3 for the former and R2 = 0.65 and RMSE = 15.69 µg m–3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.
Highlights
Ambient fine particulate matter air pollution (≤ 2.5 μm in aerodynamic diameter; PM2.5) has numerous negative effects on human health, including heart disease, stroke, respiratory diseases, and lung cancer (Burnett et al, 2018)
Our results further show that almost all population lives in non-attainment areas in Yangtze River Delta (YRD) using annual mean PM2.5 from reflectance-based approach
For reflectance-based estimates, population-weighted mean of PM2.5 concentrations is 54 μg m–3, and almost all population lives in non-attainment areas in YRD; For aerosol optical depth (AOD)-based estimates, population-weighted mean value is 42 μg m–3, and a small proportion of population (16%) lives in attainment areas scattered across the eastern YRD
Summary
Ambient fine particulate matter air pollution (≤ 2.5 μm in aerodynamic diameter; PM2.5) has numerous negative effects on human health, including heart disease, stroke, respiratory diseases, and lung cancer (Burnett et al, 2018). Aerosol and Air Quality Research | https://aaqr.org (Shin et al, 2019 and references therein) These studies are mainly based on three groups of approaches: statistical methods including machine learning (e.g., Lee et al, 2011; Hu et al, 2017; He and Huang, 2018; Wei et al, 2019a; Xue et al, 2019), chemical transport models (Geng et al, 2015; Di et al, 2016; van Donkelaar et al, 2016), and vertical correction models (Zhang and Li, 2015; Gong et al, 2017; Li et al, 2018; Toth et al, 2019). This limitation can be addressed by combining with chemical transport models (Di et al, 2016; Hu et al, 2017), but a nonnegligible discrepancy in aerosol loading between satellite observations and chemical transport models still exists (Liu, 2005; Carnevale et al, 2011; Crippa et al, 2019)
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