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Where do people spend their leisure time on dusty days? Application of spatiotemporal behavioral responses to particulate matter pollution

Author

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  • Yunwon Choi

    (Seoul National University)

  • Heeyeun Yoon

    (Seoul National University
    Seoul National University)

  • Danya Kim

    (Seoul National University)

Abstract

In recent years, the deterioration of air quality due to high levels of particulate matter (PM: especially PM 10 and PM 2.5) has become a transboundary environmental problem in countries of the East Asia Pacific region. People tend to change their behavior in response to bad air quality, and those changes generate a wide range of socioeconomic implications. Despite their significance, however, the effects of PM on human behavior, especially behaviors involving outdoor activities, have not been sufficiently examined. This study employed panel analyses to empirically investigate differences in the preferences for recreational spaces with changes in the PM 2.5 level in Seoul, South Korea, from March 1 to June 30, 2017. We found a statistically significant relationship between the PM 2.5 level and the number of visitors to particular types of study sites in the daytime on weekdays and all days of the weekends. In both time groups, the number of visitors in open spaces decreased, while those in commercial spaces increased with heightened PM 2.5 level. But the numbers of visitors to indoor sports facilities showed no significant changes with the level of PM 2.5. We also found differing levels of pedestrian volume change according to sub-categories of open and commercial spaces. As an early empirical study of the impact of PM on human behavior, this work will help evaluate environmental policies and make possible safer community development planning.

Suggested Citation

  • Yunwon Choi & Heeyeun Yoon & Danya Kim, 2019. "Where do people spend their leisure time on dusty days? Application of spatiotemporal behavioral responses to particulate matter pollution," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(2), pages 317-339, October.
    • Handle: RePEc:spr:anresc:v:63:y:2019:i:2:d:10.1007_s00168-019-00926-x
    DOI: 10.1007/s00168-019-00926-x
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    References listed on IDEAS

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    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
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    4. Ailshire, Jennifer & Karraker, Amelia & Clarke, Philippa, 2017. "Neighborhood social stressors, fine particulate matter air pollution, and cognitive function among older U.S. adults," Social Science & Medicine, Elsevier, vol. 172(C), pages 56-63.
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    Citations

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    Cited by:

    1. Jeongin Eum & Hyungkyoo Kim, 2021. "Effects of Air Pollution on Assaults: Findings from South Korea," Sustainability, MDPI, vol. 13(20), pages 1-13, October.
    2. Zhengtao Li & Henk Folmer, 2023. "Air pollution and perception-based averting behaviour in the Jinchuan mining area, China," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(2), pages 477-505, April.
    3. Benyong Wei & Bin Hu & Wenhua Qi, 2023. "Fine–Scale Spatiotemporal Distribution Assessment of Indoor Population Based on Single Buildings: A Case in Dongcheng Subdistrict, Xichang, China," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    4. Yeeun Shin & Suyeon Kim & Jinsil Park & Sang-Woo Lee & Kyungjin An, 2022. "Effectiveness of Particulate Matter Forecasting and Warning Systems within Urban Areas," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    5. Graziano Salvalai & Juan Diego Blanco Cadena & Gessica Sparvoli & Gabriele Bernardini & Enrico Quagliarini, 2022. "Pedestrian Single and Multi-Risk Assessment to SLODs in Urban Built Environment: A Mesoscale Approach," Sustainability, MDPI, vol. 14(18), pages 1-30, September.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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