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Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model

Qin, Kai and Rao, Lanlan and Xu, Jian and Bai, Yang and Zou, Jiaheng and Hao, Nan and Li, Shenshen and Yu, Chao (2017) Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model. Remote Sensing, 9 (9), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs9090950 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/9/9/950

Abstract

People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO2. The mean values of OMI tropospheric NO2 columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO2. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R2 (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO2 shows a reasonable spatial pattern, with high annual mean values (>40 μg/m3), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO2 distinguish densely populated areas with high levels of human exposure from others.

Item URL in elib:https://elib.dlr.de/114044/
Document Type:Article
Title:Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Qin, KaiSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaUNSPECIFIED
Rao, LanlanSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaUNSPECIFIED
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Bai, YangCollege of Environment and Planning, Henan University, Kaifeng, ChinaUNSPECIFIED
Zou, JiahengSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaUNSPECIFIED
Hao, NanEuropean Organization for the Exploitation of Meteorological Satellites, Darmstadt, GermanyUNSPECIFIED
Li, ShenshenInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, ChinaUNSPECIFIED
Yu, ChaoInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, ChinaUNSPECIFIED
Date:September 2017
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:9
DOI :10.3390/rs9090950
Page Range:pp. 1-20
Editors:
EditorsEmail
Müller, RichardUNSPECIFIED
Kokhanovsky, Alexander A.UNSPECIFIED
Deneke, HartwigUNSPECIFIED
Pfeifroth, UweUNSPECIFIED
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:NO2; ground level; OMI; GTWR; China
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Project Climatic relevance of atmospheric tracer gases, aerosols and clouds, R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:18 Sep 2017 10:51
Last Modified:14 Dec 2019 04:26

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