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Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City

Author

Listed:
  • Kyung-Duk Min

    (Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea)

  • Ho-Jang Kwon

    (Department of Preventive Medicine, Dankook University College of Medicine, Cheonan 31116, Korea)

  • KyooSang Kim

    (Department of Occupational Environmental Medicine, Seoul Medical Center, Seoul 02053, Korea)

  • Sun-Young Kim

    (Institute of Health and Environment, Seoul National University, Seoul 08826, Korea)

Abstract

Introduction : Many studies have reported the association between air pollution and human health based on regulatory air pollution monitoring data. However, because regulatory monitoring networks were not designed for epidemiological studies, the collected data may not provide sufficient spatial contrasts for assessing such associations. Our goal was to develop a monitoring design supplementary to the regulatory monitoring network in Seoul, Korea. This design focused on the selection of 20 new monitoring sites to represent the variability in PM 2.5 across people’s residences for cohort studies. Methods : We obtained hourly measurements of PM 2.5 at 37 regulatory monitoring sites in 2010 in Seoul, and computed the annual average at each site. We also computed 313 geographic variables representing various pollution sources at the regulatory monitoring sites, 31,097 children’s homes from the Atopy Free School survey, and 412 community service centers in Seoul. These three types of locations represented current, subject, and candidate locations. Using the regulatory monitoring data, we performed forward variable selection and chose five variables most related to PM 2.5 . Then, k-means clustering was applied to categorize all locations into several groups representing a diversity in the spatial variability of the five selected variables. Finally, we computed the proportion of current to subject location in each cluster, and randomly selected new monitoring sites from candidate sites in the cluster with the minimum proportion until 20 sites were selected. Results : The five selected geographic variables were related to traffic or urbanicity with a cross-validated R 2 value of 0.69. Clustering analysis categorized all locations into nine clusters. Finally, one to eight new monitoring sites were selected from five clusters. Discussion : The proposed monitoring design will help future studies determine the locations of new monitoring sites representing spatial variability across residences for epidemiological analyses.

Suggested Citation

  • Kyung-Duk Min & Ho-Jang Kwon & KyooSang Kim & Sun-Young Kim, 2017. "Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City," IJERPH, MDPI, vol. 14(7), pages 1-12, June.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:686-:d:102549
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    References listed on IDEAS

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    1. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    2. A. Lee & A. Szpiro & S.Y. Kim & L. Sheppard, 2015. "Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 255-267, June.
    3. Hwa-Lung Yu & Chih-Hsih Wang & Ming-Che Liu & Yi-Ming Kuo, 2011. "Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods," IJERPH, MDPI, vol. 8(6), pages 1-17, June.
    4. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
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