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Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration

Author

Listed:
  • Chin-Yu Hsu

    (National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan)

  • Jhao-Yi Wu

    (Department of Forestry and Natural Resources, National Chiayi University, Chiayi 60004, Taiwan)

  • Yu-Cheng Chen

    (National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan)

  • Nai-Tzu Chen

    (National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan)

  • Mu-Jean Chen

    (National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan)

  • Wen-Chi Pan

    (Institute of Environmental and Occupational Health Sciences, National Yang-Ming University, Taipei 11221, Taiwan)

  • Shih-Chun Candice Lung

    (Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
    Department of Atmospheric Sciences, National Taiwan University, Taipei 10617, Taiwan
    Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei 10055, Taiwan)

  • Yue Leon Guo

    (Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei 10055, Taiwan)

  • Chih-Da Wu

    (Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan)

Abstract

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O 3 concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O 3 concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R 2 of 0.74 and a 10-fold cross-validated R 2 of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O 3 concentration variation in Asian areas.

Suggested Citation

  • Chin-Yu Hsu & Jhao-Yi Wu & Yu-Cheng Chen & Nai-Tzu Chen & Mu-Jean Chen & Wen-Chi Pan & Shih-Chun Candice Lung & Yue Leon Guo & Chih-Da Wu, 2019. "Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration," IJERPH, MDPI, vol. 16(7), pages 1-12, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1300-:d:221864
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    Citations

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    Cited by:

    1. Chin-Yu Hsu & Yu-Ting Zeng & Yu-Cheng Chen & Mu-Jean Chen & Shih-Chun Candice Lung & Chih-Da Wu, 2020. "Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration," IJERPH, MDPI, vol. 17(19), pages 1-14, September.
    2. Yanzhuo Liu & Shanshan Song & Chunjuan Bi & Junli Zhao & Di Xi & Ziqi Su, 2019. "Occurrence, Distribution and Risk Assessment of Mercury in Multimedia of Soil-Dust-Plants in Shanghai, China," IJERPH, MDPI, vol. 16(17), pages 1-19, August.

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