Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan SystemReport as inadecuate




Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System - Download this document for free, or read online. Document in PDF available to download.

School of Information Engineering, China University of Geosciences, Beijing 100083, China





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Academic Editor: Giovanni Pitari

Abstract Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm PM2.5 in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System DMSP-OLS. This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network BP network model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg-m3, with a corresponding average PM2.5 concentration of 155.07 μg-m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. View Full-Text

Keywords: PM2.5; DMSP-OLS; nighttime light data; BP neural-network; Beijing PM2.5; DMSP-OLS; nighttime light data; BP neural-network; Beijing





Author: Runya Li, Xiangnan Liu * and Xuqing Li

Source: http://mdpi.com/



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