Real-time and Seamless Monitoring of Ground-level PM2.5 Using Satellite Remote Sensing

TL;DR

This study develops a real-time, seamless PM2.5 monitoring system using geostationary satellite data and deep learning, overcoming previous limitations of data gaps and low temporal resolution.

Contribution

It introduces a novel spatio-temporal fusion model combining satellite and ground data for real-time PM2.5 monitoring with high accuracy.

Findings

Achieved R2=0.80 in PM2.5 estimation

Successfully filled satellite data gaps with R2=0.75

Demonstrated real-time PM2.5 mapping in Wuhan

Abstract

Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellite-derived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Himawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.5 distributions. The results demonstrate that Himawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R2=0.80, RMSE=17.49 ug/m3) for the estimation of PM2.5. The missing data in satellite-derive PM2.5 are accurately recovered, with R2 between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the real-time and seamless monitoring of ground-level PM2.5.