Hourly PM2.5 Concentration Monitoring With Spatiotemporal Continuity by the Fusion of Satellite and Station Observations

Abstract

Hourly monitoring of ground-level fine particulate matter (PM2.5) concentrations forms the basis to assess the short-term PM2.5 exposure and make rapid responses to pollution events. Satellite remote sensing and ground monitoring stations are able to measure hourly PM2.5 concentrations, but both of them have strengths and weaknesses: the former features wide spatial coverage, whereas displaying a discontinuous timeline as the retrievals have numerous gaps; conversely, the latter allows for temporally continuous monitoring, but with a limited spatial range around stations being reflected. Thus, efforts are required to map ground-level PM2.5 at an hourly scale with spatiotemporal continuity. In this article, we developed a framework to generate hourly seamless PM2.5 estimates by integrating the aforementioned two data sources with complementary spatiotemporal traits. The satellite-derived aerosol optical depth acquisitions are converted along with auxiliary predictors to retrieve ground-level PM2.5, and then the missing gaps in the retrievals are filled by fusing the satellite-based retrievals and station-based measurements. Meanwhile, we proposed a promising approach to fill the gaps by combining an adapted spatiotemporal fusion model and an error correction method. The validity of the proposed method is confirmed by mapping hourly PM2.5 distributions for 2016 in the Wuhan urban agglomeration, China. The proposed reconstruction method achieved R 2 (root-mean-square error) of 0.87 (6.50 μg/m3) and 0.82 (15.01 μg/m3) in the area-based and point-based evaluation, respectively, indicating an excellent model performance. The presented framework maps hourly ground-level PM2.5 with spatiotemporal continuity and satisfactory accuracy, and represents an important step towards near real-time monitoring.