Satellite-Derived 1-km-Resolution PM₁ Concentrations from 2014 to 2018 across China

Abstract

Particulate matter with aerodynamic diameters ≤1 μm (PM 1 ) has a greater impact on the human health but has been less studied due to fewer ground observations. This study attempts to improve the retrieval accuracy and spatial resolution of satellite-based PM 1 estimates using the new ground-based monitoring network in China. Therefore, a space-time extremely randomized trees (STET) model is first developed to estimate PM 1 concentrations at a 1 km spatial resolution from 2014 to 2018 across mainland China. The STET model can derive daily PM 1 concentrations with an average across-validation coefficient of determination of 0.77, a low rootmean-square error of 14.6 μg/m 3 , and a mean absolute error of 8.9 μg/m 3 . PM 1 concentrations are generally low in most areas of China, except for the North China Plain and Sichuan Basin where intense human activities and poor natural conditions are prevalent, especially in winter. Moreover, PM 1 pollution has greatly decreased over the past 5 years, benefiting from emission control in China. The STET model, incorporating the spatiotemporal information, shows superior performance in PM 1 estimates relative to previous studies. This high-resolution and high-quality PM 1 data set in China (i.e., ChinaHighPM 1 ) can be greatly useful for air pollution studies in medium-or small-scale areas.

The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol–planetary boundary layer (PBL) interactions

“…The micropulse lidar (MPL) located in Beijing was operated continuously by Peking University (39.99 • N, 116.31 • E) from March 2016 to December 2018, with a temporal resolution of 15 s and a vertical resolution of 15 m. Due to incomplete laser pulse corrections, the near-surface lidar blind zone is ∼ 0.15 km. Background subtraction, saturation, after-pulse, overlap, and range corrections are applied to raw MPL data to calculate the normalized signals (Yang et al, 2013;Su et al, 2017a). MPL data on rainy days are excluded.…”

Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space–time extremely randomized trees

“…To account for the spatiotemporal heterogeneity of PM 2.5 , the space-time extremely randomized trees (STET) model developed in our previous study for estimating PM 1 (Wei et al, 2019b) is adopted here with further refinements for improving the estimation of PM 2.5 using the high-spatialresolution (1 km) Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product. Note that PM 1 and PM 2.5 emission sources, formation and transport mechanisms, and health impacts differ.…”

“…Note that PM 1 and PM 2.5 emission sources, formation and transport mechanisms, and health impacts differ. Their spatial patterns and distributions also differ, and their particle ratio varies greatly, ranging from less than 0.5 to greater than 0.9 at both spatial and temporal scales, especially in highly polluted regions, as in China (Wei et al, 2019b). The STET model has been improved by using corrected AODs, adding pollutant emissions, updating the feature selection and improving the determination of spatiotemporal information.…”

Significant contribution of organics to aerosol liquid water content in winter in Beijing, China

“…However, directly measuring real-time ALWC is not feasible yet because of technical limitations (Kuang et al, 2018). Four indirect methods have been proposed to calculate realtime ALWC: (1) the aerosol PNSD under dry conditions and ambient RH conditions are first measured, then ALWC is calculated as the difference between dry and ambient aerosol volumes (Stanier et al, 2004); (2) the increased aerosol volume due to water uptake (i.e., ALWC) is calculated according to the measured dry PNSD, size-dependent aerosol hygroscopicity and ambient RH (Kitamori et al, 2009;Bian et al, 2014;; (3) the dry and ambient aerosol volumes are first estimated using the measured aerosol optical enhancement and Ångström exponent, then ALWC is calculated as the difference between dry and ambient aerosol volumes (Kuang et al, 2018); and (4) ALWC is simulated using thermal equilibrium models such as the ISORROPIA thermodynamic model (Nenes et al, 1998), Aerosol Inorganics Model (Wexler and Clegg, 2002), the Simulating Composition of Atmospheric Particles in Equilibrium model (Kim et al, 1993), and the Gibbs Free Energy Minimization model (Ansari and Pandis, 1999) with aerosol chemical composition information as an input.…”