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

Widya Liadira Kusuma, Wu Chih-Da, Zeng Yu-Ting, Handayani Hepi Hapsari and Jaelani Lalu Muhamad
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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

IJERPH, 2019, vol. 16, issue 24, 1-12

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.

Keywords: air pollutions; fine particulate matter (PM 2.5 ); GIS; remote sensing; land use regression (LUR) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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