IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i12p1215-d84645.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/12/1215/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/13/12/1215/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weisong Li & Wanxu Chen & Jiaojiao Bian & Jun Xian & Li Zhan, 2022. "Impact of Urbanization on Ecosystem Services Balance in the Han River Ecological Economic Belt, China: A Multi-Scale Perspective," IJERPH, MDPI, vol. 19(21), pages 1-18, November.
    2. Huanfeng Shen & Man Zhou & Tongwen Li & Chao Zeng, 2019. "Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM 2.5 Mapping," IJERPH, MDPI, vol. 16(21), pages 1-18, October.
    3. Hongbin Dai & Guangqiu Huang & Jingjing Wang & Huibin Zeng & Fangyu Zhou, 2022. "Spatio-Temporal Characteristics of PM 2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
    4. Wenbo Chen & Fuqing Zhang & Saiwei Luo & Taojie Lu & Jiao Zheng & Lei He, 2022. "Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM 2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    5. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    6. Haiou Yang & Wenbo Chen & Zhaofeng Liang, 2017. "Impact of Land Use on PM 2.5 Pollution in a Representative City of Middle China," IJERPH, MDPI, vol. 14(5), pages 1-14, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Neha Shaw & A. K. Gorai, 2020. "Study of aerosol optical depth using satellite data (MODIS Aqua) over Indian Territory and its relation to particulate matter concentration," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(1), pages 265-279, January.
    3. Prasad, Abhnil Amtesh & Nishant, Nidhi & Kay, Merlinde, 2022. "Dust cycle and soiling issues affecting solar energy reductions in Australia using multiple datasets," Applied Energy, Elsevier, vol. 310(C).
    4. Yumara Martín-Cruz & Álvaro Gómez-Losada, 2023. "Risk Assessment and Source Apportionment of Metals on Atmospheric Particulate Matter in a Suburban Background Area of Gran Canaria (Spain)," IJERPH, MDPI, vol. 20(10), pages 1-18, May.
    5. Suman Moparthy & Dominique Carrer & Xavier Ceamanos, 2019. "Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties," Sustainability, MDPI, vol. 11(5), pages 1-21, March.
    6. Cheng Chen & Oleg Dubovik & Gregory L. Schuster & Mian Chin & Daven K. Henze & Tatyana Lapyonok & Zhengqiang Li & Yevgeny Derimian & Ying Zhang, 2022. "Multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Brigitte Mueller & Xuebin Zhang, 2016. "Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data," Climatic Change, Springer, vol. 134(1), pages 255-267, January.
    8. Hujia Zhao & Huizheng Che & Yanjun Ma & Yangfeng Wang & Hongbin Yang & Yuche Liu & Yaqiang Wang & Hong Wang & Xiaoye Zhang, 2017. "The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China," IJERPH, MDPI, vol. 14(5), pages 1-18, April.
    9. Wei Wang & Zengxin Pan & Feiyue Mao & Wei Gong & Longjiao Shen, 2017. "Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements," IJERPH, MDPI, vol. 14(9), pages 1-12, September.
    10. B. Padmakumari & A. Jaswal & B. Goswami, 2013. "Decrease in evaporation over the Indian monsoon region: implication on regional hydrological cycle," Climatic Change, Springer, vol. 121(4), pages 787-799, December.
    11. Yang Li & Jun Tao & Leiming Zhang & Xiaofang Jia & Yunfei Wu, 2016. "High Contributions of Secondary Inorganic Aerosols to PM 2.5 under Polluted Levels at a Regional Station in Northern China," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    12. Xiang-Dong Chen & En-Hui Wang & Long-Kun Shan & Shao-Chun Zhang & Ce Feng & Yu Zheng & Yang Dong & Guang-Can Guo & Fang-Wen Sun, 2023. "Quantum enhanced radio detection and ranging with solid spins," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    13. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    14. Liang Cheng & Long Li & Longqian Chen & Sai Hu & Lina Yuan & Yunqiang Liu & Yifan Cui & Ting Zhang, 2019. "Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period," IJERPH, MDPI, vol. 16(19), pages 1-25, September.
    15. Xiankang Xu & Kaifang Shi & Zhongyu Huang & Jingwei Shen, 2023. "What Factors Dominate the Change of PM 2.5 in the World from 2000 to 2019? A Study from Multi-Source Data," IJERPH, MDPI, vol. 20(3), pages 1-28, January.
    16. Zhiyuan Fang & Hao Yang & Ye Cao & Kunming Xing & Dong Liu & Ming Zhao & Chenbo Xie, 2021. "Study of Persistent Pollution in Hefei during Winter Revealed by Ground-Based LiDAR and the CALIPSO Satellite," Sustainability, MDPI, vol. 13(2), pages 1-14, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1215-:d:84645. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.