Abstract
Fine particulate matter (PM2.5) seriously affects the environment, climate, and human health. Over the past decades, the Beijing–Tianjin–Hebei region (BTH) has been severely affected by pollutant gas and PM2.5 emissions caused by heavy industrial production, topography, and other factors and has been one of the most polluted areas in China. Currently, the long-term, large-scale, and high spatial resolution monitoring PM2.5 concentrations ([PM2.5]) using satellite remote sensing technology is an important task for the prevention and control of air pollution. The aerosol optical depth (AOD) retrieved by satellites combined with a variety of auxiliary information was widely used to estimate [PM2.5]. In this study, a two-stage statistical regression [linear mixed effects (LME) + geographically weighted regression (GWR)] model, combined with the latest high spatial resolution (1 km) AOD product and meteorological and land use parameters, was constructed to estimate [PM2.5] in BTH from 2013 to 2020. The model was fitted annually, and the ranges of coefficient of determination (R2), root mean square prediction errors (RMSPE), and relative prediction error (RPE) for the model cross-validation were 0.85–0.95, 7.87–29.90 μg/m3, and 19.19%–32.71%, respectively. Overall, the model obtained relatively good performance and could effectively estimate [PM2.5] in BTH. The [PM2.5] showed obvious temporal characteristic within a year (high in winter and low in summer) and spatial characteristic (high in the southern plain and low in the northern mountain). During the investigated period of 2013–2020, the high pollutant areas ([PM2.5] > 75 μg/m3) in 2020 significantly narrowed compared to 2013, and the annual average [PM2.5] in BTH fell below 55 μg/m3, with a drop of 54.04%. In particular, the [PM2.5] in winter season dropped sharply from 2015 to 2017 and declined steadily after 2017. Our results suggested that significant achievements have been made in air pollution control over the past 8 years, and they still need to be maintained. The research can provide scientific basis and support for the prevention and control of air pollution in BTH and beyond.
Highlights
Fine particulate matter (PM2.5, particles with aerodynamic diameter less than 2.5 μm) are suspended in the atmosphere as a composite of solid and liquid particles
Most of the aerosol optical depth (AOD) products used to predict [PM2.5] were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multiangle Imaging SpectroRadiometer (MISR), and Advanced Himawari Imager (AHI) that the nominal spatial resolutions for AOD retrieved by their algorithms are 10 or 3 km, 17.6 or 4.4, 0.75 and 5 km, respectively (Lee et al, 2011; Hu et al, 2014a; Yao et al, 2018; Wang et al, 2020)
A new high spatial resolution (1-km) MODIS Collection 6 (C6) daily AOD product (MCD19A2) was released in 2018, which was generated based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and demonstrated excellent performance in estimating [PM2.5] (Lyapustin et al, 2018; Zhang Z. et al, 2019; Choi et al, 2019)
Summary
Fine particulate matter (PM2.5, particles with aerodynamic diameter less than 2.5 μm) are suspended in the atmosphere as a composite of solid and liquid particles. Satellite-derived AOD can provide valuable information for the estimation of ground-level PM2.5 pollution due to its large spatial coverage, high spatial resolution, and reliable repeated measurement, especially suitable for those places without PM2.5 monitoring station on the surface (Schaap et al, 2009; Yeganeh et al, 2017; Stowell et al, 2020). A new high spatial resolution (1-km) MODIS Collection 6 (C6) daily AOD product (MCD19A2) was released in 2018, which was generated based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and demonstrated excellent performance in estimating [PM2.5] (Lyapustin et al, 2018; Zhang Z. et al, 2019; Choi et al, 2019)
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