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Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO 2 and Enhanced Vegetation Index (EVI)

Author

Listed:
  • Tianhao Zhang

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Wei Gong

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China)

  • Wei Wang

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Yuxi Ji

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Zhongmin Zhu

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
    College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China)

  • Yusi Huang

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China)

Abstract

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM 2.5 ) is currently quite limited in China. By introducing NO 2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM 2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO 2 and EVI, where cross-validation R 2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM 2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM 2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM 2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO 2 and EVI in GWR models could more effectively estimate PM 2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.

Suggested Citation

  • Tianhao Zhang & Wei Gong & Wei Wang & Yuxi Ji & Zhongmin Zhu & Yusi Huang, 2016. "Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO 2 and Enhanced Vegetation Index (EVI)," IJERPH, MDPI, vol. 13(12), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1215-:d:84645
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    References listed on IDEAS

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    1. Tianhao Zhang & Zhongmin Zhu & Wei Gong & Hao Xiang & Ruimin Fang, 2016. "Characteristics of Fine Particles in an Urban Atmosphere—Relationships with Meteorological Parameters and Trace Gases," IJERPH, MDPI, vol. 13(8), pages 1-16, August.
    2. Tianhao Zhang & Gang Liu & Zhongmin Zhu & Wei Gong & Yuxi Ji & Yusi Huang, 2016. "Real-Time Estimation of Satellite-Derived PM 2.5 Based on a Semi-Physical Geographically Weighted Regression Model," IJERPH, MDPI, vol. 13(10), pages 1-13, September.
    3. Yoram J. Kaufman & Didier Tanré & Olivier Boucher, 2002. "A satellite view of aerosols in the climate system," Nature, Nature, vol. 419(6903), pages 215-223, September.
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