AMBIENT AIR POLLUTION CONCENTRATION VARIATIONS IN INDONESIA USING GEOSPATIAL TECHNOLOGIES A CASE STUDY OF SURABAYA AND JAKARTA

Widya, Liadira Kusuma (2019) AMBIENT AIR POLLUTION CONCENTRATION VARIATIONS IN INDONESIA USING GEOSPATIAL TECHNOLOGIES A CASE STUDY OF SURABAYA AND JAKARTA. Masters thesis, National Cheng kung University.

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Abstract

Air pollution has emerged as a major health, environmental, economic and social problem worldwide. In this study, geospatial technology combined with Land Use Regression (LUR), Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR) approaches were applied to assess the spatial-temporal distribution of several types of air pollutants, including Fine Particulate Matter (PM2.5) for the Jakarta area, and Coarse Particulate Matter (PM10) and Nitrogen Dioxide (NO2) for the Surabaya region. In-situ observations of ambient air pollution were conducted from 2016 to 2018 for the South and Central Jakarta Cities, and from 2010 to 2018 for the Surabaya City, which was used as the dependent variable. Meanwhile, the allocation of land use/land cover and greenness surrounding the monitoring stations from the buffer range of 250 to 5000 meters was collected as spatial predictors using Geographic Information System (GIS) and remote sensing techniques. A supervised stepwise variable selection procedure was used to identify the important predictor variables for developing the LUR, GWR, and GTWR models. A 10-fold cross validation was applied to confirm the model robustness. According to the obtained model R2, the GTWR models explained a better goodness-of-fit than the LUR and the GWR models, while the number of model R2 obtained from GTWR reached 86% for PM2.5, 51% for PM10 variations and 48% for NO2. The cross-validated R2 was 87% for PM2.5, 52% for PM10, and 52% for NO2 confirmed the model robustness. According to the results of the PM2.5 model, the essential predictors for DKI Jakarta were temperature, NDVI, humidity, and residential area. In the PM10 model, four predictors variables were selected, they were public facility, industry and warehousing, paddy field, and NDVI. On the other hand, paddy field, residential area, rainfall, and temperature played the most important roles in explaining NO2 variations.

Item Type: Thesis (Masters)
Additional Information: RTG 025.069 1 Sir d-1 • Widya, Liadira Kusuma
Uncontrolled Keywords: Air Pollutions, GIS, Remote Sensing, Geospatial Technologies, Indonesia
Subjects: G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems.
G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data
G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing
G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions: Faculty of Civil Engineering and Planning > Geomatics Engineering > 29101-(S2) Master Thesis
Depositing User: Liadira Kusuma Widya
Date Deposited: 14 Aug 2020 08:16
Last Modified: 17 Nov 2020 12:07
URI: http://repository.its.ac.id/id/eprint/78038

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