Estimation of High-Resolution Daily Ground-Level PM_2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

• Main Authors: Weihong Han, Ling Tong, Yunping Chen, Runkui Li, Beizhan Yan, Xue Liu
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Applied Sciences
• Subjects: urban pollution; remote sensing; PM_2.5; AOT
• Online Access: https://www.mdpi.com/2076-3417/8/12/2624

High-spatiotemporal-resolution PM_2.5 data are critical to assessing the impacts of prolonged exposure to PM_2.5 on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM_2.5, providing an effective way to reveal spatiotemporal variations of PM_2.5 across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM_2.5 measurements were fused to estimate daily ground-level PM_2.5 concentrations in Beijing for 2013–2017 at 1 km resolution through a linear mixed effect model (LMEM). The results showed a good agreement between the estimated and measured PM_2.5 and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM_2.5 level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM_2.5 level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM_2.5 concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM_2.5 data products we generated can be used to assess the long-term impacts of PM_2.5 on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas.