Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Yuxin Sun,Xingxing Jiang,Xiran Zhou,Chunlin Jin,Yong Xue,Shuhui Wu

doi:10.3390/rs13214276

Yuxin Sun, Xingxing Jiang + Show 4 more

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https://doi.org/10.3390/rs13214276

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Abstract

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement.

Highlights

With the sustainable development and changes of the economy and society, air pollution, especially particulate matter (PM2.5 ) pollution, has been paid more and more attention by the government and people in general
The improved improved geographically and temporally weighted regression (IGTWR) model is applied to the FY-4A data to estimate hourly PM2.5 and PM10 mass concentrations for 5–6 and 9–10 June 2020 in mainland China
The IGTWR model is employed as the basis for model improvement by adding normalized difference vegetation index (NDVI) data to change the definition of the generalised distance

Summary

Introduction

With the sustainable development and changes of the economy and society, air pollution, especially particulate matter (PM2.5 ) pollution, has been paid more and more attention by the government and people in general. The most accurate means of PM2.5 monitoring are ground-based instruments that can obtain real-time data. The ground detection stations only detect air quality in a point-like distribution and cannot cover large areas. Due to the high cost and maintenance requirements, the stations are mainly distributed in urban areas, making it difficult to achieve full coverage. Site data are greatly affected by special circumstances in a small area near the monitoring station, which makes it difficult to take the overall situation into account

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