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

Hourly monitoring of ground-level fine particulate matter (PM
_2.5
) concentrations forms the basis to assess the short-term PM
_2.5
exposure and make rapid responses to pollution events. Satellite remote sensing and ground monitoring stations are able to measure hourly PM
_2.5
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 PM
_2.5
at an hourly scale with spatiotemporal continuity. In this article, we developed a framework to generate hourly seamless PM
_2.5
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 PM
_2.5
, 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 PM
_2.5
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/m
³
) and 0.82 (15.01 μg/m
³
) in the area-based and point-based evaluation, respectively, indicating an excellent model performance. The presented framework maps hourly ground-level PM
_2.5
with spatiotemporal continuity and satisfactory accuracy, and represents an important step towards near real-time monitoring.