IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i16p2820-d255599.html
   My bibliography  Save this article

Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

Author

Listed:
  • Wenxuan Xu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China)

  • Yongzhong Tian

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China)

  • Yongxue Liu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Bingxue Zhao

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Yongchao Liu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China)

  • Xueqian Zhang

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China)

Abstract

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O 3 , wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

Suggested Citation

  • Wenxuan Xu & Yongzhong Tian & Yongxue Liu & Bingxue Zhao & Yongchao Liu & Xueqian Zhang, 2019. "Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China," IJERPH, MDPI, vol. 16(16), pages 1-23, August.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:16:p:2820-:d:255599
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/16/2820/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/16/2820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Long, Ruyin & Shao, Tianxiang & Chen, Hong, 2016. "Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors," Applied Energy, Elsevier, vol. 166(C), pages 210-219.
    2. Giardullo, Paolo & Sergi, Vittorio & Carton, Wim & Kenis, Anneleen & Kesteloot, Chris & Kazepov, Yuri & Kobus, Dominik & Maione, Michela & Skotak, Krzysztof & Fuzzi, Sandro & Pollini, Francesca, 2016. "Air quality from a social perspective in four European metropolitan areas: Research hypothesis and evidence from the SEFIRA project," Environmental Science & Policy, Elsevier, vol. 65(C), pages 58-64.
    3. Yuxia Ma & Bingshuang Xiao & Chang Liu & Yuxin Zhao & Xiaodong Zheng, 2016. "Association between Ambient Air Pollution and Emergency Room Visits for Respiratory Diseases in Spring Dust Storm Season in Lanzhou, China," IJERPH, MDPI, vol. 13(6), pages 1-14, June.
    4. Muhammad Usman & Zhiqiang Ma & Muhammad Wasif Zafar & Abdul Haseeb & Rana Umair Ashraf, 2019. "Are Air Pollution, Economic and Non-Economic Factors Associated with Per Capita Health Expenditures? Evidence from Emerging Economies," IJERPH, MDPI, vol. 16(11), pages 1-22, June.
    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. Chao Hu & Jin Fan & Jian Chen, 2022. "Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    2. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.
    3. Ruyin Long & Qin Zhang & Hong Chen & Meifen Wu & Qianwen Li, 2020. "Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors," IJERPH, MDPI, vol. 17(1), pages 1-21, January.

    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. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    2. Wang, Zhen & Wei, Liyuan & Niu, Beibei & Liu, Yong & Bin, Guoshu, 2017. "Controlling embedded carbon emissions of sectors along the supply chains: A perspective of the power-of-pull approach," Applied Energy, Elsevier, vol. 206(C), pages 1544-1551.
    3. Kangjuan Lv & Yu Cheng & Yousen Wang, 2021. "Does regional innovation system efficiency facilitate energy-related carbon dioxide intensity reduction in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 789-813, January.
    4. Chuanlong Li & Yuanqing Li & Kaifang Shi & Qingyuan Yang, 2020. "A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China," IJERPH, MDPI, vol. 17(10), pages 1-13, May.
    5. Le Zhang & Qinyi Gu & Chen Li & Yi Huang, 2022. "Characteristics and Spatial–Temporal Differences of Urban “Production, Living and Ecological” Environmental Quality in China," IJERPH, MDPI, vol. 19(22), pages 1-22, November.
    6. Rezgar FEIZI & Sahar AMIDI & Thais NUNEZ-ROCHA & Isabelle RABAUD, 2022. "Carbon Tax and Emissions Transfer: a Spatial Analysis," LEO Working Papers / DR LEO 2965, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    7. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    8. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    9. Nadiia Charkovska & Mariia Halushchak & Rostyslav Bun & Zbigniew Nahorski & Tomohiro Oda & Matthias Jonas & Petro Topylko, 2019. "A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 907-939, August.
    10. Yiwei Wang & Shuwang Yang & Canmian Liu & Shiying Li, 2018. "How Would Economic Development Influence Carbon Productivity? A Case from Hubei in China," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
    11. Cheng Zhang & Ziwei Zhao & Qunwei Wang, 2022. "Effect of Western Development Strategy on carbon productivity and its influencing mechanisms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(4), pages 4963-5002, April.
    12. Huang, Xiaoling & Tian, Peng, 2023. "Polluting thy neighbor or benefiting thy neighbor: Effects of the clean energy development on haze pollution in China," Energy, Elsevier, vol. 268(C).
    13. Min Lu & Xing Wang & Yuquan Cang, 2018. "Carbon Productivity: Findings from Industry Case Studies in Beijing," Energies, MDPI, vol. 11(10), pages 1-19, October.
    14. Zhongye Sun & Xin Zhang & Yifei Gao, 2023. "The Impact of Financial Development on Renewable Energy Consumption: A Multidimensional Analysis Based on Global Panel Data," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    15. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    16. Xu Zhang & Huaping Sun & Taohong Wang, 2022. "Impact of Financial Inclusion on the Efficiency of Carbon Emissions: Evidence from 30 Provinces in China," Energies, MDPI, vol. 15(19), pages 1-15, October.
    17. Linhong Chen & Yue Zhuo & Zhiming Xu & Xiaocang Xu & Xin Gao, 2019. "Is Carbon Dioxide (CO 2 ) Emission an Important Factor Affecting Healthcare Expenditure? Evidence from China, 2005–2016," IJERPH, MDPI, vol. 16(20), pages 1-14, October.
    18. Yu, Yantuan & Chen, Xudong & Zhang, Ning, 2022. "Innovation and energy productivity: An empirical study of the innovative city pilot policy in China✰," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    19. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    20. Daniel Badulescu & Ramona Simut & Alina Badulescu & Andrei-Vlad Badulescu, 2019. "The Relative Effects of Economic Growth, Environmental Pollution and Non-Communicable Diseases on Health Expenditures in European Union Countries," IJERPH, MDPI, vol. 16(24), pages 1-19, December.

    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:16:y:2019:i:16:p:2820-:d:255599. 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.