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Remote Sens. 2017, 9(9), 950; doi:10.3390/rs9090950

Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Weßling, Germany
3
College of Environment and Planning, Henan University, Kaifeng 475001, China
4
European Organization for the Exploitation of Meteorological Satellites, 64283 Darmstadt, Germany
5
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Authors to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 16 August 2017 / Accepted: 9 September 2017 / Published: 13 September 2017
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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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. View Full-Text
Keywords: NO2; ground level; OMI; GTWR; China NO2; ground level; OMI; GTWR; China
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Qin, K.; Rao, L.; Xu, J.; Bai, Y.; Zou, J.; Hao, N.; Li, S.; Yu, C. Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model. Remote Sens. 2017, 9, 950.

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