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PM 2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan

Author

Listed:
  • Widya Liadira Kusuma

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

  • Wu Chih-Da

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

  • Zeng Yu-Ting

    (National Health Research Inst., No. 35, Keyan Rd, Zhunan, Miaoli County 35053, Taiwan)

  • Handayani Hepi Hapsari

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

  • Jaelani Lalu Muhamad

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

Abstract

Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM 2.5 ). In-situ observations of air pollutants from ground monitoring stations from 2016–2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25–5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The R 2 model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM 2.5 model, the essential predictors for DKI Jakarta were influenced by temperature, NDVI, humidity, and residential area, while those for the Taipei Metropolis region were influenced by PM 10 , NO 2 , SO 2 , UV, rainfall, spring, main road, railroad, airport, proximity to airports, mining areas, and NDVI. The validation of the results of the estimated PM 2.5 distribution use 10-cross validation with indicated R 2 values of 0.62 for DKI Jakarta and 0.84 for Taipei Metropolis. The results of cross-validation show the strength of the model.

Suggested Citation

  • Widya Liadira Kusuma & Wu Chih-Da & Zeng Yu-Ting & Handayani Hepi Hapsari & Jaelani Lalu Muhamad, 2019. "PM 2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan," IJERPH, MDPI, vol. 16(24), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:24:p:4924-:d:294502
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    References listed on IDEAS

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    1. Longxiang Li & Jianhua Gong & Jieping Zhou, 2014. "Spatial Interpolation of Fine Particulate Matter Concentrations Using the Shortest Wind-Field Path Distance," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
    2. 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.
    3. Haiou Yang & Wenbo Chen & Zhaofeng Liang, 2017. "Impact of Land Use on PM 2.5 Pollution in a Representative City of Middle China," IJERPH, MDPI, vol. 14(5), pages 1-14, April.
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    Cited by:

    1. I Gede Nyoman Mindra Jaya & Henk Folmer, 2024. "High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia," Mathematics, MDPI, vol. 12(18), pages 1-29, September.

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