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.
Highlights
IntroductionHourly monitoring of PM2.5 concentrations is in great demand [12], since it forms the basis to estimate short-term PM2.5 exposure and make rapid responses to serious pollution events
The new generation geostationary meteorological satellite of Japan, Himawari-8 [15], was launched on July 2, 2015, and the satellite-derived hourly aerosol optical depth (AOD) product has been routinely released, which can be used as a major predictor to estimate PM2.5 concentrations [16, 17, 13]
We focus on assessing the performance of the proposed reconstruction method
Summary
Hourly monitoring of PM2.5 concentrations is in great demand [12], since it forms the basis to estimate short-term PM2.5 exposure and make rapid responses to serious pollution events. The generation of hourly PM2.5 estimates has been of great significance and worthy of investigation. The new generation geostationary meteorological satellite of Japan, Himawari-8 [15], was launched on July 2, 2015, and the satellite-derived hourly aerosol optical depth (AOD) product has been routinely released, which can be used as a major predictor to estimate PM2.5 concentrations [16, 17, 13]. Based on the presented models, some studies mapped and analyzed the hourly distribution of PM2.5 [25]. Wang, et al [12] applied an improved linear mixed-effect model to retrieve hourly PM2.5 concentrations over the Beijing-Tianjin-Hebei region in China; and Zeng, et al [26] mapped the hourly PM2.5 distributions in Hebei, China, by using a vertical-humidity correction method
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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