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Comparison of Spatial Modelling Approaches on PM 10 and NO 2 Concentration Variations: A Case Study in Surabaya City, Indonesia

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  • Liadira Kusuma Widya

    (Department of Geomatics, National Cheng Kung University, Tainan City 70101, Taiwan
    Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya City 60111, Indonesia)

  • Chin-Yu Hsu

    (Department of Safety, Health, and Environmental Engineering, Ming Chih University of Technology, New Taipei City 24301, Taiwan)

  • Hsiao-Yun Lee

    (Department of Leisure Industry and Health Promotion, National Taipei University of Nursing and Health Sciences, Taipei City 112303, Taiwan)

  • Lalu Muhamad Jaelani

    (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya City 60111, Indonesia)

  • Shih-Chun Candice Lung

    (Research Center for Environmental Changes, Academia Sinica, Taipei City 11529, Taiwan
    Department of Atmospheric Sciences, National Taiwan University, Taipei City 10617, Taiwan
    Institute of Environmental Health, National Taiwan University, Taipei City 100025, Taiwan)

  • Huey-Jen Su

    (Department of Environmental and Occupational Health, National Cheng Kung University, Tainan City 70101, Taiwan)

  • Chih-Da Wu

    (Department of Geomatics, National Cheng Kung University, Tainan City 70101, Taiwan
    National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County 35053, Taiwan)

Abstract

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM 10 ) and nitrogen dioxide (NO 2 ) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM 10 variations and 46%, 47%, and 48% of NO 2 variations, respectively. The GTWR model performed better (R 2 = 0.51 for PM 10 and 0.48 for NO 2 ) than the other two models (R 2 = 0.49–0.50 for PM 10 and 0.46–0.47 for NO 2 ), LUR and GWR. In the PM 10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO 2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM 10 and NO 2 , was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM 10 and NO 2 concentration variations within areas across Asia.

Suggested Citation

  • Liadira Kusuma Widya & Chin-Yu Hsu & Hsiao-Yun Lee & Lalu Muhamad Jaelani & Shih-Chun Candice Lung & Huey-Jen Su & Chih-Da Wu, 2020. "Comparison of Spatial Modelling Approaches on PM 10 and NO 2 Concentration Variations: A Case Study in Surabaya City, Indonesia," IJERPH, MDPI, vol. 17(23), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8883-:d:453351
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    References listed on IDEAS

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    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.
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    4. Seung-Hoon Park & Dong-Won Ko, 2018. "Investigating the Effects of the Built Environment on PM 2.5 and PM 10 : A Case Study of Seoul Metropolitan City, South Korea," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    5. Hsiao-Lan Liu & Yu-Sheng Shen, 2014. "The Impact of Green Space Changes on Air Pollution and Microclimates: A Case Study of the Taipei Metropolitan Area," Sustainability, MDPI, vol. 6(12), pages 1-29, December.
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    1. Yuan Shi & Alexis Kai-Hon Lau & Edward Ng & Hung-Chak Ho & Muhammad Bilal, 2021. "A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM 2.5 Concentration by Integrating Multisource Datasets," IJERPH, MDPI, vol. 19(1), pages 1-16, December.

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