空氣汙染所造成的健康、環境、經濟及社會影響為當前重要的全球性議題。本研究應用土地利用回歸(Land Use Regression)、地理加權回歸(Geographically Weighted Regression)及地理時間加權回歸(Geographically and Temporally Weighted Regression),，以模擬印尼兩大主要城市空氣汙染之變化情形，研究標的包含PM2.5在雅加達以及PM10和NO2在泗水的時空變異。本研究透過現地監測站分別取得2016至2018年雅加達及2010至2018年泗水地區之空污監測數據，做為模型分析之依變數。同時，利用地理資訊系統及遙測技術計算測站周邊250到5000公尺環域內的土地利用(土地覆蓋)與綠覆率作為解釋變數。進而透過監督式逐步變數選擇法，以篩選空氣污染之重要預測變數，進而建立LUR、GWR及GTWR模型。最後透過十折交叉驗證以確立模型的穩定性。研究結果顯示，比較模型之R2可知，GTWR的配適度優於LUR及GWR；其中利用GTWR所建模型之R2分別為PM2.5的0.86、PM10的0.51以及NO2的0.48；交叉驗證之R2依序為0.87、0.52及0.52，再次確定本研究所建模型具有一定之可信程度。在模型選入之變數方面，PM2.5模型共選入溫度、濕度、NDVI及住宅區；PM10模型選入公共設施、工業區、倉儲、稻田及NDVI；而NO2模型則選入稻田、住宅區、降雨及溫度等變數做為重要之空間預測變數。

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.

Achakulwisut, P. et al. (2019) ‘Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO 2 pollution: estimates from global datasets’, The Lancet Planetary Health. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY NC ND 4.0 license, 3(4), pp. e166–e178. doi: 10.1016/S2542-5196(19)30046-4.
Aldrian, E. and Dwi Susanto, R. (2003) ‘Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature’, International Journal of Climatology, 23(12), pp. 1435–1452. doi: 10.1002/joc.950.
Autrup, H. (2010) ‘Ambient air pollution and adverse health effects’, Procedia - Social and Behavioral Sciences, 2(5), pp. 7333–7338. doi: 10.1016/j.sbspro.2010.05.089.
Azhari, A., Latif, M. T. and Mohamed, A. F. (2018) ‘Road traffic as an air pollutant contributor within an industrial park environment’, Atmospheric Pollution Research, 9(4), pp. 680–687. doi: 10.1016/j.apr.2018.01.007.
Bai, Y. et al. (2016) ‘A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD’, Remote Sensing, 8(3). doi: 10.3390/rs8030262.
BAPPEKO (City Development Planning Bureau of Surabaya) (2014) ‘Land Use Map of Suarabaya in 2014’. Surabaya: BAPPEKO Surabaya.
Barry Ryan, P. et al. (1988) ‘The boston residential NO2characterization study: I. preliminary evaluation of the survey methodology’, Journal of the Air Pollution Control Association, 38(1), pp. 22–27. doi: 10.1080/08940630.1988.10466348.
Beelen, R. et al. (2009) ‘Mapping of background air pollution at a fine spatial scale across the European Union’, Science of the Total Environment. Elsevier B.V., 407(6), pp. 1852–1867. doi: 10.1016/j.scitotenv.2008.11.048.
Berlyand, M. E. (1991) ‘Prediction and Regulation of Air Pollution’, in Atmospheric Sciences Library. Kluwer Academic Publishers, pp. 141–142. doi: 10.1007/978-94-011-3768-3.
Bingham, N. H. and Fry, J. M. (2010) Regression Linear Model in Statistics. London Dordrectht Heidelberg New York: Springer. doi: 10.1007/978-1-84882-969-5.
BMKG (Meteorological Climatological and Gephysical Agency) (2015) Kualitas Udara. Available at: https://www.bmkg.go.id/kualitas-udara/informasi-partikulat-pm25.bmkg (Accessed: 21 February 2019).
Bogaert, P. et al. (2009) ‘Spatiotemporal modelling of ozone distribution in the State of California’, Atmospheric Environment. Elsevier Ltd, 43(15), pp. 2471–2480. doi: 10.1016/j.atmosenv.2009.01.049.
BPS-Statistics Indonesia (2019) Statistical Yearbook of Indonesia 2019. Jakarta: BPS-Statistics Indonesia. Available at: https://www.bps.go.id/publication/download.html?nrbvfeve=ZGFhYzFiYTE4Y2FlMWU5MDcwNmVlNThh&xzmn=aHR0cHM6Ly93d3cuYnBzLmdvLmlkL3B1YmxpY2F0aW9uLzIwMTkvMDcvMDQvZGFhYzFiYTE4Y2FlMWU5MDcwNmVlNThhL3N0YXRpc3Rpay1pbmRvbmVzaWEtMjAxOS5odG1s&twoadfnoarfeauf=MjAxOS0xMi0.
BPS (Statistics of Central Jakarta) (2018) Jakarta Pusat in Figure. 1102001.31. Edited by BPS-Statistics of Jakarta Pusat. Jakarta Pusat: Statistics of Jakarta Pusat.
BPS (Statistics of South Jakarta) (2018) Jakarta Selatan Municipaity in Figures. 1102001.31. Edited by I. Rani and S. Haryoko. Jakarta Selatan: BPS-Statistics of Jakarta Selatan Municipality.
BPS (Statistics Surabaya) (2014) Surabaya in Figures. 1102001.35. Edited by Statistics-Surabaya. Surabaya: Statistics Bureau of Surabaya.
Briggs, D. (2005) ‘The role of GIS: Coping with space (and time) in air pollution exposure assessment’, Journal of Toxicology and Environmental Health - Part A, 68(13–14), pp. 1243–1261. doi: 10.1080/15287390590936094.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1996) ‘Geographically Weighted Regression: A Method for Exploring Spatial Nonstationary’, Geographical Analysis, 28(4). Available at: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1996.tb00936.x.
Cao, J. et al. (2011) ‘Association between long-term exposure to outdoor air pollution and mortality in China: A cohort study’, Journal of Hazardous Materials. Elsevier B.V., 186(2–3), pp. 1594–1600. doi: 10.1016/j.jhazmat.2010.12.036.
Chang, L. F., Lin, C. H. and Su, M. D. (2008) ‘Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation’, Water SA, 34(2), pp. 209–216. doi: 10.4314/wsa.v34i2.183641.
Didan, K. (2015) ‘MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006’, NASA EOSDIS Land Processes DAAC. doi: 10.5067/MODIS/MOD13Q1.006.
Dockery, D. W. et al. (1993) ‘An Association Between Air Pollution and mortality in Six U.S. Cities’, Journal of Medicine, 329(24), pp. 1753–1759. Available at: https://www.nejm.org/doi/pdf/10.1056/NEJM199312093292401?articleTools=true.
European Commision (2010) ‘Climate Change and Air Pollution’, Science for Environment Policy, (24), pp. 3–8. doi: 10.1007/978-3-319-61346-8_1.
Fang, C. et al. (2015) ‘Estimating the impact of urbanization on air quality in China using spatial regression models’, Sustainability, 7(11), pp. 15570–15592. doi: 10.3390/su71115570.
Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationship.
Ge, L. et al. (2016) ‘Construction of a seasonal difference-geographically and temporally weighted regression (SD-GTWR) model and comparative analysis with GWR-based models for hemorrhagic fever with renal syndrome (HFRS) in Hubei Province (China)’, International Journal of Environmental Research and Public Health, 13(11). doi: 10.3390/ijerph13111062.
Gidlöf-Gunnarsson, A. and Öhrström, E. (2007) ‘Noise and well-being in urban residential environments: The potential role of perceived availability to nearby green areas’, Landscape and Urban Planning, 83(2–3), pp. 115–126. doi: 10.1016/j.landurbplan.2007.03.003.
Givoni, B. (1991) ‘IMPACT OF PLANTED AREAS ON URBAN ENVIRONMENTAL QUALITY : A REVIEW’, Atmospheric Environmnet, 25B(3), pp. 289–299. Available at: https://reader.elsevier.com/reader/sd/pii/095712729190001U?token=21D9C84B5F471235BBF2A618D847B175347D5C60665EE066461906B511DE4763BD0BBA5D20473CCC2A4BAA08541B3A31.
González, L. o et al. (2015) ‘An Assessment of Air Pollutant Exposure Methods in Mexico City, Mexico’, J Air Waste Manag Assoc, 65(5), pp. 581–591. doi: 10.1080/10962247.2015.1020974.
Greenstone, M. and Fan, Q. (2019) Indonesia ’ s Worsening Air Quality and its Impact on Life Expectancy. Chicago. Available at: https://aqli.epic.uchicago.edu/wp-content/uploads/2019/03/Indonesia-Report.pdf.
Gu, Y. et al. (2017) ‘The Interaction between Ambient PM 10 and NO 2 on Mortality in Guangzhou , China’, International Journal of Environmental Research and Public Health. doi: 10.3390/ijerph14111381.
Guo, Y. et al. (2017) ‘Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model’, Remote Sensing of Environment. Elsevier Inc., 198, pp. 140–149. doi: 10.1016/j.rse.2017.06.001.
Han, L., Zhou, W. and Li, W. (2015) ‘Increasing impact of urban fine particles (PM2.5) on areas surrounding Chinese cities’, Scientific Reports. Nature Publishing Group, 5, pp. 11–16. doi: 10.1038/srep12467.
Hananto, V. R. and Putra, I. G. N. A. W. (2018) ‘A Dashboard System for Monitoring Air Pollution in Surabaya based on PM2.5’, Journal of Information Systems Engineering and Business Intelligence, 4(2), p. 139. doi: 10.20473/jisebi.4.2.139-146.
Handmer, J. et al. (2012) ‘Changes in impacts of climate extremes: Human systems and ecosystems’, Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, 9781107025, pp. 231–290. doi: 10.1017/CBO9781139177245.007.
Heald, C. L. and Spracklen, D. V. (2015) ‘Land Use Change Impacts on Air Quality and Climate’, Chemical Reviews, 115(10), pp. 4476–4496. doi: 10.1021/cr500446g.
HEI (2010) ‘Outdoor Air Pollution and Health in the Developing Countries of Asia: A Comprehensive Review’, Special Report 18, Boston, Massachusets, (November), p. 284.
Hoek, G. et al. (2008) ‘A review of land-use regression models to assess spatial variation of outdoor air pollution’, Atmospheric Environment. Elsevier Ltd, 42(33), pp. 7561–7578. doi: 10.1016/j.atmosenv.2008.05.057.
Hsu, C.-Y. et al. (2018) ‘Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations’, Remote Sensing, 10(12), p. 1971. doi: 10.3390/rs10121971.
Huang, B., Wu, B. and Barry, M. (2010) ‘Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices’, International Journal of Geographical Information Science, 24(3), pp. 383–401. doi: 10.1080/13658810802672469.
Indonesian Government (1999) ‘Presiden republik indonesia’, Peraturan Pemerintah Republik Indonesia Nomor 41 Tahun 1999 Tentang Pengendalian Pencemaran Udara, pp. 27–28. Available at: http://ditjenpp.kemenkumham.go.id/arsip/ln/1999/pp41-1999.pdf.
Kayes, I. et al. (2019) ‘The relationships between meteorological parameters and air pollutants in an urban environment’, 5(3), pp. 265–278. doi: 10.22034/gjesm.2019.03.01.
Kondo, M. C. et al. (2018) ‘Urban green space and its impact on human health’, International Journal of Environmental Research and Public Health, 15(3). doi: 10.3390/ijerph15030445.
Kusumaningtyas, S. D. A. et al. (2018) ‘The recent state of ambient air quality in Jakarta’, Aerosol and Air Quality Research, 18(9), pp. 2343–2354. doi: 10.4209/aaqr.2017.10.0391.
Kwak, H. et al. (2017) ‘Identifying the correlation between rainfall , traffic flow performance and air pollution concentration in Seoul using a path analysis’, Transportation Research Procedia. Elsevier B.V., 25, pp. 3556–3567. doi: 10.1016/j.trpro.2017.05.288.
Kwak, H. Y. et al. (2017) ‘Identifying the correlation between rainfall, traffic flow performance and air pollution concentration in Seoul using a path analysis’, Transportation Research Procedia. Elsevier B.V., 25, pp. 3552–3563. doi: 10.1016/j.trpro.2017.05.288.
Langville, A. N. and Stewart, W. J. (2004) ‘The Kronecker product and stochastic automata networks’, Journal of Computational and Applied Mathematics, 167(2), pp. 429–447. doi: 10.1016/j.cam.2003.10.010.
Lee, J. H. et al. (2015) ‘LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction’, Science of the Total Environment. Elsevier B.V., 514(17), pp. 178–184. doi: 10.1016/j.scitotenv.2015.01.091.
Li, L., Gong, J. and Zhou, J. (2014) ‘Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance’, PLoS ONE, 9(5), pp. 1–10. doi: 10.1371/journal.pone.0096111.
Li, X. et al. (2015) ‘The application of semicircular-buffer-based land use regression models incorporating wind direction in predicting quarterly NO2 and PM10 concentrations’, Atmospheric Environment, 103, pp. 18–24. doi: 10.1016/j.atmosenv.2014.12.004.
Liu, H. L. and Shen, Y. S. (2014) ‘The impact of green space changes on air pollution and microclimates: A case study of the taipei metropolitan area’, Sustainability (Switzerland), 6(12), pp. 8827–8855. doi: 10.3390/su6128827.
Liu, W. et al. (2015) ‘Land use regression models coupled with meteorology to model spatial and temporal variability of NO 2 and PM 10 in Changsha, China’, Atmospheric Environment, 116(2), pp. 272–280. doi: 10.1016/j.atmosenv.2015.06.056.
Macnaughton, P. et al. (2017) ‘Impact of particular matter exposure and surrounding “Greenness” on chronic absenteeism in Massachusetts public schools’, International Journal of Environmental Research and Public Health, 14(2). doi: 10.3390/ijerph14020207.
Mannucci, P. M. and Franchini, M. (2017) ‘Health effects of ambient air pollution in developing countries’, International Journal of Environmental Research and Public Health, 14(9), pp. 1–8. doi: 10.3390/ijerph14091048.
Ngui, A. N. and Caron, J. (2012) ‘Using geographically weighted regression to assess spatial distribution of mental health in an urban environment.’, Advances in psychology research (Vol 95)., (February), pp. 167–185. Available at: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=psyc9&NEWS=N&AN=2012-28293-009.
Oanh, N. T. K. et al. (2011) ‘CHARACTERIZATION OF PARTICULATE MATTER EMISSION FROM OPEN BURNING OF RICE STRAW’, Atmospheric Environment, 45(2), pp. 493–502. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018782/pdf/nihms246946.pdf.
P., R. and Tang L., L. H. (2009) ‘Cross Validation’, in LIU L., Ö. M. T. (ed.) Encyclopedia of Database Systems. Boston: Springer, pp. 536–538. doi: https://doi.org/10.1007/978-0-387-39940-9.
Park, S. H. and Ko, D. W. (2018) ‘Investigating the effects of the built environment on PM2.5 and PM10: A case study of Seoul Metropolitan city, South Korea’, Sustainability (Switzerland), 10(12), pp. 1–11. doi: 10.3390/su10124552.
Peña, M. S. B. and Rollins, A. (2017) ‘Environmental Exposures and Cardiovascular Disease: A Challenge for Health and Development in Low- and Middle- Income Countries’, Cardiology Clinics, 35(1), pp. 71–76. doi: 10.1016/j.ccl.2016.09.001.
Peng, Y. et al. (2019) ‘A Geographically and Temporally Weighted Regression Model for Spatial Downscaling of MODIS Land Surface Temperatures over Urban Heterogeneous Regions’, IEEE Transactions on Geoscience and Remote Sensing. IEEE, 57(7), pp. 5012–5027. doi: 10.1109/TGRS.2019.2895351.
Pope, C. A. et al. (1995) ‘Particulate air pollution as a predictor of mortality in a prospective study of U.S. Adults’, American Journal of Respiratory and Critical Care Medicine, 151(3 I), pp. 669–674. doi: 10.1164/ajrccm/151.3_pt_1.669.
Quackenboss, J. J. et al. (1986) ‘Personal Exposure to Nitrogen Dioxide: Relationship to Indoor/Outdoor Air Quality and Activity Patterns’, Environmental Science and Technology, 20(8), pp. 775–783. doi: 10.1021/es00150a003.
Rahayu, Y. E., Ahyudanari, E. and Pratomoadmojo, N. A. (2016) ‘Land Use Development and its Impact on Airport Access Road’, Procedia - Social and Behavioral Sciences, 227(November 2015), pp. 31–37. doi: 10.1016/j.sbspro.2016.06.039.
Rotatori, M., Salvatori, R. and Salzano, R. (2011) ‘Planning Air Pollution Monitoring Networks in Industrial Areas by Means of Remote Sensed Images and GIS Techniques’, IntechOpen. doi: http://dx.doi.org/10.5772/16416.
Shairsingh, K. K. et al. (2019) ‘Urban land use regression models: can temporal deconvolution of traffic pollution measurements extend the urban LUR to suburban areas?’, Atmospheric Environment. Elsevier, 196(May 2018), pp. 143–151. doi: 10.1016/j.atmosenv.2018.10.013.
Statistics of DKI Jakarta Province (2018) ‘DKI Jakarta Province in Figures’. Available at: https://jakarta.bps.go.id/publication/2018/08/16/67d90391b7996f51d1c625c4/provinsi-dki-jakarta-dalam-angka-2018.html.
Statistics of Jawa Timur Province (BPS) (2018) Provinsi Jawa Timur Dalam Angka 2018. BPS-Statistics of Jawa Timur Province. Available at: https://jatim.bps.go.id/publication/2018/08/16/9999b727d316c006ee2fd7e7/provinsi-jawa-timur-dalam-angka-2018.html.
Syafei, A. D., Fujiwara, A. and Zhang, J. (2014) ‘Spatial and Temporal Factors of Air Quality in Surabaya City: An Analysis based on a Multilevel Model’, Procedia - Social and Behavioral Sciences. Elsevier B.V., 138(0), pp. 612–622. doi: 10.1016/j.sbspro.2014.07.246.
Tai, A. P. K., Mickley, L. J. and Jacob, D. J. (2010) ‘Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change’, Atmospheric Environment. Elsevier Ltd, 44(32), pp. 3976–3984. doi: 10.1016/j.atmosenv.2010.06.060.
Thurston, G. D. (2008) ‘Outdoor Air Pollution: Sources, Atmospheric Transport, and Human Health Effects’, in International Encyclopedia of Public Health. Second Edi. New York: Academic Press, pp. 700–712. doi: 10.1016/B978-012373960-5.00275-6.
United States Environmental Protetion Agency (USEPA) (2009) Integrated Science Assessment for Particulate Matter. Washington, DC. Available at: https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=216546#Download.
Verma, S. S. and Desai, B. (2016) ‘Effect of Meteorological Conditions on Air Pollution of Surat City’, Journal of International Environmental Application and Science, 8(5), pp. 358–367.
Weng, Q. and Yang, S. (2006) ‘Urban air pollution patterns, land use, and thermal landscape: An examination of the linkage using GIS’, Environmental Monitoring and Assessment, 117(1–3), pp. 463–489. doi: 10.1007/s10661-006-0888-9.
WHO (2000) ‘Chapter 7.1 Nitrogen dioxide’, Air Quality Guidelines for Europe-Second Edition, 3(2), pp. 175–180.
WHO (World Health Organization) (2006) WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Geneva: WHO Press. Available at: https://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf;jsessionid=2F2216A46C624152F597D6F49F4009EB?sequence=1.
World Health Organization (WHO) (2015) World Health Statistics. Luxembourg: World Health Organization. Available at: https://www.who.int/docs/default-source/gho-documents/world-health-statistic-reports/world-health-statistics-2015.pdf?sfvrsn=afb0629f_2.
Wu, C.-D. et al. (2017) ‘Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability’, Environmental Pollution. Elsevier Ltd, 224, pp. 148–157. doi: 10.1016/j.envpol.2017.01.074.
Xu, S. et al. (2019) ‘Strategies of Method Selection for Fine Scale PM2.5 Mapping in Intra-Urban Area Under Crowdsourcing Monitoring’, Atmospheric Measurement Techniques Discussions, pp. 1–21. doi: 10.5194/amt-2018-402.
Zhang, G., Rui, X. and Fan, Y. (2018) ‘Critical review of methods to estimate PM 2.5 concentrations within specified research region’, ISPRS International Journal of Geo-Information, 7(9). doi: 10.3390/ijgi7090368.
Zhang, Z. et al. (2018) ‘National scale spatiotemporal land-use regression model for PM2 . 5 , PM10 and NO2 concentration in China National scale spatiotemporal land-use regression model for PM 2 . 5 , PM 10 and NO 2 concentration in China’, Atmospheric Environment. Elsevier, 192(August), pp. 48–54. doi: 10.1016/j.atmosenv.2018.08.046.