近年來許多流行病學研究證實暴露於環境中的細懸浮微粒PM2.5會對人體健康造成影響。然而，過去研究對於PM2.5的暴露推估仍然存有不確定性。大多數研究使用政府監測站提供的監測資料，透過各種空間內插方法來推估PM2.5的濃度，例如克利金法和反距離加權法。而PM具有高度的時空變化性，在不同地形條件、天氣型態下，變化性可能會更高。傳統政府監測站由於建置成本高，無法大量設置，以至於無法確實捕捉到PM的高時空變化。即使使用了較為準確的空間內插方法，仍然會受到監測站的分布所影響，使用了不具代表性的監測站，進而導致錯誤評估。本研究將結合環保署與空氣盒子數據開發智慧空污暴露推估模式，降低以往推估的不確定性，可以因時因地選用比較具有代表性的環保署監測站，來推估所暴露的PM2.5濃度。

本研究蒐集了13年的環保署監測站資料與2年多的空氣盒子資料，並建立成資料庫。將蒐集的空氣盒子進行集群分析，以PM2.5、PM10、溫度、相對濕度以及經緯度作為參數，劃分出台灣的暴露分區。本研究以2月、5月、8月、11月代表四個季節，以此四個月份空氣盒子資料得到四季的暴露分區。模式會依據暴露者的暴露時間及所在位置，找出所屬位置之暴露分區，篩選出區域內所有的環保署監測站，以反距離加權法推估暴露濃度，即為本研究所開發的智慧空污暴露推估模式。
本研究將以上四個月份的環保署資料以當月的暴露分區進行推估，透過留一法交叉驗證，與克利金法以及傳統反距離加權法比較，驗證本研究開發模式的準確性。結果顯示本研究開發模式的推估誤差比較低，有較準確的推估能力。最後本研究將模式工具化，可與健保資料庫結合，更精準的評估PM2.5對人體健康造成的影響，提供更有說服力的證據，可供空污管制政策作參考依據。

Spatial data has spatial heterogeneity, when assessing site-specific concentrations, the temporal and spatial variations in air pollutant concentrations may be higher in different weather conditions and areas where the land surface is uneven. Since the existing spatial interpolation methods still have uncertainties, the estimated concentrations are biased. The purpose of this study is to reduce the uncertainty of previous estimates. Combined with EPA and Airbox data, an smart air pollution exposure estimation model was established to estimate exposure to PM2.5 concentrations using a representative EPA monitoring station. The Modeling of site-specific exposures uses inverse distance weighted interpolation between data from a set of representative air quality stations, which are generated through spatial clustering analysis by large amounts of data from low-cost sensors.
This study uses leave-one-out cross-validation to verify the feasibility of the model. And compared with Kriging method and inverse distance weighting method. We found that the developed method can generate a better exposure database by selecting suitable sites for spatial interpolation smartly, with considering clustering of air quality regions that are differentiated by local weather and terrain conditions, compared with traditional spatial interpolation methods, kriging and inverse distance weighting. The developed exposure database will support further analysis of the air pollutants on related health effects.

1. Adams, M.D., Kanaroglou, P.S., and Coulibaly, P. (2016). Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin. Int. J. Geogr. Inf. Sci. 30, 390–404.
2. AssunÇão, R.M., Neves, M.C., Câmara, G., and Freitas, C.D.C. (2006). Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees. Int. J. Geogr. Inf. Sci. 20, 797–811.
3. Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z.J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P., Nieuwenhuijsen, M., et al. (2014). Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. The Lancet 383, 785–795.
4. Bell, M.L., Dominici, F., Ebisu, K., Zeger, S.L., and Samet, J.M. (2007). Spatial and Temporal Variation in PM2.5 Chemical Composition in the United States for Health Effects Studies. Environ. Health Perspect. 115, 989–995.
5. Borghi, F., Spinazzè, A., Rovelli, S., Campagnolo, D., Del Buono, L., Cattaneo, A., and Cavallo, D.M. (2017). Miniaturized Monitors for Assessment of Exposure to Air Pollutants: A Review. Int. J. Environ. Res. Public. Health 14, 909.
6. Buteau, S., Goldberg, M.S., Burnett, R.T., Gasparrini, A., Valois, M.-F., Brophy, J.M., Crouse, D.L., and Hatzopoulou, M. (2018). Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model. Environ. Int. 113, 313–324.
7. Carvalho, M.J., Melo-Gonçalves, P., Teixeira, J.C., and Rocha, A. (2016). Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Phys. Chem. Earth Parts ABC 94, 22–28.
8. Castell, N., Dauge, F.R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., and Bartonova, A. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environ. Int. 99, 293–302.
9. Chen, C.-C., and Yang, C.-Y. (2018). Association between gaseous air pollution and hospital admissions for hypertension in Taipei, Taiwan. J. Toxicol. Environ. Health A 81, 53–59.
10. Chen, C., Li, C., Li, Y., Liu, J., Meng, C., Han, J., Zhang, Y., and Xu, D. (2018). Short-term effects of ambient air pollution exposure on lung function: A longitudinal study among healthy primary school children in China. Sci. Total Environ. 645, 1014–1020.
11. Chen, L., Ho, Y., Lee, H., Wu, H., Liu, H., Hsieh, H., Huang, Y., and Lung, S.C. (2017). An Open Framework for Participatory PM2.5 Monitoring in Smart Cities. IEEE Access 5, 14441–14454.
12. Deng, Q., Lu, C., Li, Y., Sundell, J., and Dan Norbäck (2016). Exposure to outdoor air pollution during trimesters of pregnancy and childhood asthma, allergic rhinitis, and eczema. Environ. Res. 150, 119–127.
13. Dominick, D., Juahir, H., Latif, M.T., Zain, S.M., and Aris, A.Z. (2012). Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmos. Environ. 60, 172–181.
14. Gakidou, E., Afshin, A., Abajobir, A.A., Abate, K.H., Abbafati, C., Abbas, K.M., Abd-Allah, F., Abdulle, A.M., Abera, S.F., Aboyans, V., et al. (2017). Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet 390, 1345–1422.
15. Gao, H., Chen, J., Wang, B., Tan, S.-C., Lee, C.M., Yao, X., Yan, H., and Shi, J. (2011). A study of air pollution of city clusters. Atmos. Environ. 45, 3069–3077.
16. Gramsch, E., Cereceda-Balic, F., Oyola, P., and von Baer, D. (2006). Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and Ozone data. Atmos. Environ. 40, 5464–5475.
17. Guo, C., Zhang, Z., Lau, A.K.H., Lin, C.Q., Chuang, Y.C., Chan, J., Jiang, W.K., Tam, T., Yeoh, E.-K., Chan, T.-C., et al. (2018). Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study. Lancet Planet. Health 2, e114–e125.
18. He, W., Ling, H., Zhang, Z., and Gong, C. (2018). Multi-objective spatially constrained clustering for regionalization with particle swarm optimization. Int. J. Geogr. Inf. Sci. 32, 827–846.
19. Hoek, G., Brunekreef, B., Goldbohm, S., Fischer, P., and van den Brandt, P.A. (2002). Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study. The Lancet 360, 1203–1209.
20. Huang, C., Chen, B., Pan, S., Ho, Y., and Guo, Y.L. (2019). Prenatal exposure to PM2.5 and Congenital Heart Diseases in Taiwan. Sci. Total Environ. 655, 880–886.
21. Hwang, S.-L., Lin, Y.-C., Guo, S.-E., Chou, C.-T., Lin, C.-M., and Chi, M.-C. (2017). Fine particulate matter on hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in southwestern Taiwan during 2006–2012. Int. J. Environ. Health Res. 27, 95–105.
22. Jerrett, M., Burnett, R.T., Ma, R., Pope, C.A., Krewski, D., Newbold, K.B., Thurston, G., Shi, Y., Finkelstein, N., Calle, E.E., et al. (2005). Spatial Analysis of Air Pollution and Mortality in Los Angeles. Epidemiology 16, 727–736.
23. Jung, C.R., Chen, W.T., Lin, Y.T., and Hwang, B.F. (2017). Ambient Air Pollutant Exposures and Hospitalization for Kawasaki Disease in Taiwan: A Case-Crossover Study (2000-2010). Environ. Health Perspect. 125, 670–676.
24. Kim, S.-Y., Sheppard, L., and Kim, H. (2009). Health Effects of Long-Term Air Pollution: Influence of Exposure Prediction Methods. Epidemiology 20, 442–450.
25. Krige, D.G. (1951). A statistical approach to some basic mine valuation problems on the Witwatersrand. J. South. Afr. Inst. Min. Metall. 52, 119–139.
26. Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., Bell, M., Norford, L., and Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 75, 199–205.
27. Lee, H., Honda, Y., Hashizume, M., Guo, Y.L., Wu, C.-F., Kan, H., Jung, K., Lim, Y.-H., Yi, S., and Kim, H. (2015). Short-term exposure to fine and coarse particles and mortality: A multicity time-series study in East Asia. Environ. Pollut. 207, 43–51.
28. Li, B., Li, S., Xiao, C., Zhang, C., Chen, J., Lin, H., Du, Y., and Liu, M. (2018). Time series analysis of death of residents with malignant granules in Shenyang, China. Oncol. Lett. 16, 4507–4511.
29. Liao Duanping, Peuquet Donna J., Duan Yinkang, Whitsel Eric A., Dou Jianwei, Smith Richard L., Lin Hung-Mo, Chen Jiu-Chiuan, and Heiss Gerardo (2006). GIS Approaches for the Estimation of Residential-Level Ambient PM Concentrations. Environ. Health Perspect. 114, 1374–1380.
30. Lim, C.C., Hayes, R.B., Ahn, J., Shao, Y., Silverman, D.T., Jones, R.R., Garcia, C., and Thurston, G.D. (2018). Association between long-term exposure to ambient air pollution and diabetes mortality in the US. Environ. Res. 165, 330–336.
31. Lu, G.Y., and Wong, D.W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Comput. Geosci. 34, 1044–1055.
32. Michanowicz, D.R., Shmool, J.L.C., Cambal, L., Tunno, B.J., Gillooly, S., Olson Hunt, M.J., Tripathy, S., Naumoff Shields, K., and Clougherty, J.E. (2016). A hybrid land use regression/line-source dispersion model for predicting intra-urban NO2. Transp. Res. Part Transp. Environ. 43, 181–191.
33. Miller, K.A., Siscovick, D.S., Sheppard, L., Shepherd, K., Sullivan, J.H., Anderson, G.L., and Kaufman, J.D. (2007). Long-Term Exposure to Air Pollution and Incidence of Cardiovascular Events in Women. N. Engl. J. Med. 356, 447–458.
34. Pan, H.-H., Chen, C.-T., Sun, H.-L., Ku, M.-S., Liao, P.-F., Lu, K.-H., Sheu, J.-N., Huang, J.-Y., Pai, J.-Y., and Lue, K.-H. (2014). Comparison of the Effects of Air Pollution on Outpatient and Inpatient Visits for Asthma: A Population-Based Study in Taiwan. PLOS ONE 9, e96190.
35. Pires, J.C.M., Sousa, S.I.V., Pereira, M.C., Alvim-Ferraz, M.C.M., and Martins, F.G. (2008). Management of air quality monitoring using principal component and cluster analysis—Part I: SO2 and PM10. Atmos. Environ. 42, 1249–1260.
36. Pope, P.T., and Webster, J.T. (1972). The Use of an F-Statistic in Stepwise Regression Procedures. Technometrics 14, 327–340.
37. Pražnikar, J. (2017). Particulate matter time-series and Köppen-Geiger climate classes in North America and Europe. Atmos. Environ. 150, 136–145.
38. Ritz, B., Wilhelm, M., and Zhao, Y. (2006). Air Pollution and Infant Death in Southern California, 1989–2000. Pediatrics 118, 493–502.
39. Rivera-González, L.O., Zhang, Z., Sánchez, B.N., Zhang, K., Brown, D.G., Rojas-Bracho, L., Osornio-Vargas, A., Vadillo-Ortega, F., and O’Neill, M.S. (2015). An assessment of air pollutant exposure methods in Mexico City, Mexico. J. Air Waste Manag. Assoc. 65, 581–591.
40. Rogulski, M. (2017). Low-cost PM monitors as an opportunity to increase the spatiotemporal resolution of measurements of air quality. Energy Procedia 128, 437–444.
41. Schwarz, A.D., Meyer, J., and Dittler, A. (2018). Opportunities for Low-Cost Particulate Matter Sensors in Filter Emission Measurements. Chem. Eng. Technol. 41, 1826–1832.
42. Son, J.-Y., Bell, M.L., and Lee, J.-T. (2010). Individual exposure to air pollution and lung function in Korea: Spatial analysis using multiple exposure approaches. Environ. Res. 110, 739–749.
43. Thurston George D., Ahn Jiyoung, Cromar Kevin R., Shao Yongzhao, Reynolds Harmony R., Jerrett Michael, Lim Chris C., Shanley Ryan, Park Yikyung, and Hayes Richard B. (2016). Ambient Particulate Matter Air Pollution Exposure and Mortality in the NIH-AARP Diet and Health Cohort. Environ. Health Perspect. 124, 484–490.
44. Tsai, S.-S., Chang, C.-C., and Yang, C.-Y. (2013). Fine Particulate Air Pollution and Hospital Admissions for Chronic Obstructive Pulmonary Disease: A Case-Crossover Study in Taipei. Int. J. Environ. Res. Public. Health 10, 6015–6026.
45. Wang, Y.-C., and Lin, Y.-K. (2016). Mortality and emergency room visits associated with ambient particulate matter constituents in metropolitan Taipei. Sci. Total Environ. 569–570, 1427–1434.
46. Wu, C., Hu, W., Zhou, M., Li, S., and Jia, Y. (2019). Data-driven regionalization for analyzing the spatiotemporal characteristics of air quality in China. Atmos. Environ. 203, 172–182.
47. Wu, R., Zhong, L., Huang, X., Xu, H., Liu, S., Feng, B., Wang, T., Song, X., Bai, Y., Wu, F., et al. (2018). Temporal variations in ambient particulate matter reduction associated short-term mortality risks in Guangzhou, China: A time-series analysis (2006–2016). Sci. Total Environ. 645, 491–498.
48. Xie, S., Lawniczak, A.T., and Wang, Z. (2017). Spatially Constrained Clustering to Define Geographical Rating Territories: In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, (Porto, Portugal: SCITEPRESS - Science and Technology Publications), pp. 82–88.
49. Yang, Y., Cao, Y., Li, W., Li, R., Wang, M., Wu, Z., and Xu, Q. (2015). Multi-site time series analysis of acute effects of multiple air pollutants on respiratory mortality: A population-based study in Beijing, China. Sci. Total Environ. 508, 178–187.
50. Yu, H.-L., and Chien, L.-C. (2016). Short-term population-based non-linear concentration–response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis. J. Expo. Sci. Environ. Epidemiol. 26, 197–206.
51. Yu, H., Russell, A., Mulholland, J., Odman, T., Hu, Y., Chang, H.H., and Kumar, N. (2018). Cross-comparison and evaluation of air pollution field estimation methods. Atmos. Environ. 179, 49–60.
52. Yu, Y., Yao, S., Dong, H., Wang, L., Wang, C., Ji, X., Ji, M., Yao, X., and Zhang, Z. (2019). Association between short-term exposure to particulate matter air pollution and cause-specific mortality in Changzhou, China. Environ. Res. 170, 7–15.
53. 郭育良(2014)，細懸浮微粒（PM2.5）之心臟血管疾病流行病學調查研究，環保署/國科會空污防制科研合作計畫報告，計畫編號NSC102－EPA－F－002－002－。
54. 行政院環保署-空氣品質監測網，2019a https://taqm.epa.gov.tw/taqm/tw/b0905.aspx
55. 行政院環保署-空氣品質監測網，2019b https://taqm.epa.gov.tw/taqm/tw/b0101.aspx
56. 行政院環保署-空氣品質監測網，2019c https://taqm.epa.gov.tw/taqm/tw/EpbDataHourly.aspx
57. 台南市長黃偉哲臉書，https://www.facebook.com/taiwanweicher/posts/2450853084966513
58. Edimax網站，https://airbox.edimaxcloud.com/about擷取時間：2019/4/10
59. Edimax網站，https://airbox.edimaxcloud.com/ 擷取時間：2019/4/10
60. PM2.5開放資料入口網站，https://pm25.lass-net.org/