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Open AccessArticle

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
Center for International Earth Science Information Network, Earth Institute, Columbia University, Palisades, NY 10964, USA
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2624;
Received: 22 November 2018 / Revised: 9 December 2018 / Accepted: 11 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Monitoring and Modeling: Air Quality Evaluation Studies)
High-spatiotemporal-resolution PM2.5 data are critical to assessing the impacts of prolonged exposure to PM2.5 on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM2.5, providing an effective way to reveal spatiotemporal variations of PM2.5 across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM2.5 measurements were fused to estimate daily ground-level PM2.5 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 PM2.5 and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM2.5 level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM2.5 level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM2.5 concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM2.5 data products we generated can be used to assess the long-term impacts of PM2.5 on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas. View Full-Text
Keywords: urban pollution; remote sensing; PM2.5; AOT urban pollution; remote sensing; PM2.5; AOT
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MDPI and ACS Style

Han, W.; Tong, L.; Chen, Y.; Li, R.; Yan, B.; Liu, X. Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data. Appl. Sci. 2018, 8, 2624.

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