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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

1
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
4
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; https://doi.org/10.3390/app8122624
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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