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Spatial–Temporal Distribution Variation of Ground-Level Ozone in China’s Pearl River Delta Metropolitan Region

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  • An Zhang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Jinhuang Lin

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)

  • Wenhui Chen

    (College of Geographical Science, Fujian Normal University, Fuzhou 350007, China)

  • Mingshui Lin

    (College of Tourism, Fujian Normal University, Fuzhou 350117, China)

  • Chengcheng Lei

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

Long-term exposure to ozone pollution will cause severe threats to residents’ physical and mental health. Ground-level ozone is the most severe air pollutant in China’s Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial–temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD’s 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial–temporal kriging (STK) interpolation, and the STK model’s simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone’s spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.

Suggested Citation

  • An Zhang & Jinhuang Lin & Wenhui Chen & Mingshui Lin & Chengcheng Lei, 2021. "Spatial–Temporal Distribution Variation of Ground-Level Ozone in China’s Pearl River Delta Metropolitan Region," IJERPH, MDPI, vol. 18(3), pages 1-13, January.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:872-:d:483781
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    References listed on IDEAS

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    3. Weicong Fu & Ziru Chen & Zhipeng Zhu & Qunyue Liu & Cecil C. Konijnendijk Van den Bosch & Jinda Qi & Mo Wang & Emily Dang & Jianwen Dong, 2018. "Spatial and Temporal Variations of Six Criteria Air Pollutants in Fujian Province, China," IJERPH, MDPI, vol. 15(12), pages 1-20, December.
    4. Gneiting T., 2002. "Nonseparable, Stationary Covariance Functions for Space-Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 590-600, June.
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