Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Jianjun Liu,Zhanqing Li,Fuzhong Weng,Maureen C Cribb

doi:10.3390/rs11182120

Abstract

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

Highlights

The concentration of atmospheric particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM2.5) is an important index of air pollution and has been widely used in epidemiological studies, such as the exposure response functions for health effects of air pollutants [1] and an assessment of mortality attributable to pollution [2]
This study presents a multivariable Random forests (RFs) model incorporating aerosol optical depths (AODs) retrieved from a geostationary satellite and meteorological parameters to estimate hourly surface PM2.5 concentrations in central East China in 2016
The CV of the RF model in our study shows that the model estimates PM2.5 concentrations well at the hourly level with an R2 of 0.86 and an RMSE of 17.3 μg m−3

Summary

Introduction

The concentration of atmospheric particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM2.5) is an important index of air pollution and has been widely used in epidemiological studies, such as the exposure response functions for health effects of air pollutants [1] and an assessment of mortality attributable to pollution [2]. Studies on PM2.5 have garnered more and more attention from the public health, government, and scientific communities in recent years (e.g., [5,6]) because PM2.5 has become the primary air pollutant in the rapidly growing megacities of developing countries such as China. Air quality monitoring sites are often sparse and often make measurements at a low spatial resolution. This limits our ability to evaluate the dynamics of air pollution, do human exposure assessments, and contribute to policy making. Various methods have been developed for estimating the spatial and temporal distributions of PM2.5 concentrations on a global scale using satellite-derived column aerosol optical depth (AOD)

Methods

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Discussion

Conclusion