空氣污染對人體健康有害，尤其是細懸浮微粒PM2.5，其濃度會受天氣、交通、土地利用等因素影響，不同地點和高度在不同時間下的濃度差異顯著。隨著人類活動空間的變化，了解PM2.5在三維空間中的分布需求隨之出現。要獲取PM2.5資料直接量測是最準確的，但空氣污染監測站數量不足，獲取平面大範圍資料之方法如線性克利金內插法和土地利用迴歸已經被廣泛應用，但三維空氣污染研究仍有限。本研究旨在利用無人機和摩托車靈活量測平面及三維空間的空氣污染，結合GIS分析方法，發展三維PM2.5分布推估模型，並應用於研究地區。研究區域涵蓋竹南鎮和頭份市，相鄰的行政區域總面積約91平方公里，人口約18萬人，顯示出持續增長的趨勢。該地區包括竹南工業區、頭份工業區和竹南科學園區，土地利用模式多樣化，成為研究三維空氣污染分布的理想地點。為了捕捉PM2.5濃度的變化，地面資料的收集在每天的白天或下午進行，使用安裝在摩托車上的儀器，沿預先規劃的路線行駛約40公里，速度為每小時20到40公里，每15秒記錄一次PM2.5量測值，共進行了七輪採樣，以確保對研究區域的全面覆蓋。無人機採樣則在事先挑選的12個不同位置進行垂直起降，測量高度從0至120m，每10m停留5秒。除了現場資料外，研究還收集了來自各種資料庫的其他地理空間資料，包括環保署空氣品質監測資料庫、中央氣象局、國土測繪中心和交通研究所，這些資料將作為推估模型中的X變數。根據過去的估算和模擬案例，我們將所有X變數轉換為10m × 10m的網格圖像，計算了變數密度分布和每個網格中心到各空間變數的歐幾里得距離。研究將建立兩組使用不同演算法推算之模型，基於地面採樣PM2.5的地面模型和使用無人機資料的垂直模型，模型將使用80%的資料進行訓練，20%進行測試，使用程式特定套件調整模型超參數，並利用計算SHAP value方法排名變數的重要性，逐步將變數添加至模型中。模型間比較排序基於R2、調整後R2、MSE、MAE、RMSE等指標，以及十折交叉驗證與資料分層驗證測試模型表現與穩定度。從地面與垂直模型中選出表現最佳之模型進行整合並調整高度參數後，我們可以推估不同高度下的平面PM2.5濃度分布，並在ArcGIS Pro中使用經驗貝式克里金法進行3D插值，生成PM2.5濃度分布的三維立方體，揭示不同高度下的濃度變化。地面取樣的PM2.5濃度範圍為1.64至128.11 μg/m³，無人機數據顯示濃度在7.22至46.57 μg/m³。在五個地面機器學習模型中，Category-Based Regression (CBR)演算法 模型表現最佳，R2值超過0.9，且誤差最低，被擇出推估地面PM2.5濃度；垂直模型中CBR演算法表現最佳，結合垂直模型和地面推估資料，輸出了每20m一張的水平PM2.5濃度圖。利用ArcGIS Pro的經驗貝氏克里金3D功能，輸出PM2.5濃度立方體，並通過調整角度和切片，詳細分析了局部空氣污染情況。例如，在國家健康研究院周圍，空氣污染濃度隨高度變化顯示非線性趨勢。本研究首次開發並應用了一種基於無人機與摩托車量測資料結合GIS分析的三維PM2.5分布推估方法。相比傳統的土地利用迴歸模型，該方法運用了機器學習技術來處理非線性關係，不僅能精確估算地面及不同高度的空氣污染濃度，還能生成三維立體的污染分布模型，為未來的空氣質量監測和管理提供了新視角與科學依據。此方法學奠定了三維空氣污染研究的基礎，具備應用於更多城市或地區的潛力。

PM2.5 poses significant health risks, especially with increasing urbanization and industrialization, making it crucial to understand air pollution exposure across different elevations. Traditional air quality monitoring stations are limited in number and unevenly distributed, failing to provide precise regional data. To address this, Land Use Regression (LUR) and Machine Learning (ML) algorithms have been developed to estimate air pollution levels, but three-dimensional pollution research remains limited. Unmanned Aerial Vehicles (UAVs), combined with Geographic Information System (GIS) methodologies, have become vital tools for comprehensive three-dimensional air quality assessment, enabling the creation of dynamic air quality models by sampling air at various altitudes. The study area covers Zhunan Township and Toufen City, with a total area of approximately 91 square kilometers and a population of around 180,000, reflecting continuous growth. Ground-level data collection was conducted daily using instruments mounted on a motorcycle along pre-planned routes, with seven rounds ensuring comprehensive coverage. UAV sampling involved deploying instruments atop a hexacopter at 12 different sites, measuring altitudes from 0 to 100 meters. Supplementary data from various databases were integrated, such as meteorology data and land use data. Two models were developed: a ground model using ground sampling data and a three-dimensional model using UAV data, with SHAP (SHapley Additive exPlanations) used to rank variable importance. Using ArcGIS Pro's Empirical Bayesian Kriging 3D functionality, a PM2.5 concentration cube was created, revealing variations at different altitudes, with non-linear trends observed near the National Health Research Institute. This study differs from traditional LUR methods by using ML techniques to handle non-linear relationships, providing a comprehensive view of three-dimensional PM2.5 distribution. Future policies should consider vertical variations to more effectively manage air quality, especially as cities continue to expand. As global research advances towards 3D modeling, integrating vertical variations in urban design and planning becomes increasingly important for effective air quality management.

Akinosho, T. D., Bilal, M., Hayes, E. T., Ajayi, A., Ahmed, A., & Khan, Z. (2023). Deep learning-based multi-target regression for traffic-related air pollution forecasting. MACHINE LEARNING WITH APPLICATIONS, 12, Article 100474. https://doi.org/10.1016/j.mlwa.2023.100474
Alexeeff, S. E., Schwartz, J., Kloog, I., Chudnovsky, A., Koutrakis, P., & Coull, B. A. (2015). Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data. Journal of Exposure Science & Environmental Epidemiology, 25(2), 138-144. https://doi.org/10.1038/jes.2014.40
Bangar, V., Mishra, A. K., Jangid, M., & Rajput, P. (2021). Elemental characteristics and source-apportionment of PM2. 5 during the post-monsoon season in Delhi, India. Frontiers in Sustainable Cities, 3, 648551.
Basu, B., Alam, M. S., Ghosh, B., Gill, L., & McNabola, A. (2019). Augmenting limited background monitoring data for improved performance in land use regression modelling: Using support vector regression and mobile monitoring. Atmospheric Environment, 201, 310-322. https://doi.org/https://doi.org/10.1016/j.atmosenv.2018.12.048
Beelen, R., Hoek, G., Fischer, P., Brandt, P. A. v. d., & Brunekreef, B. (2007). Estimated long-term outdoor air pollution concentrations in a cohort study. Atmospheric Environment, 41(7), 1343-1358. https://doi.org/https://doi.org/10.1016/j.atmosenv.2006.10.020
Beelen, R., Hoek, G., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P. H., Nieuwenhuijsen, M. J., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Marcon, A., Vartiainen, E., Lanki, T., Yli-Tuomi, T., Oftedal, B., . . . Brunekreef, B. (2015). Natural-cause mortality and long-term exposure to particle components: an analysis of 19 European cohorts within the multi-center ESCAPE project. Environ Health Perspect, 123(6), 525-533. https://doi.org/10.1289/ehp.1408095
Bitta, J., Pavlíková, I., Svozilík, V., & Jančík, P. (2018). Air Pollution Dispersion Modelling Using Spatial Analyses. ISPRS International Journal of Geo-Information, 7(12).
Brauer, M., Lencar, C., Tamburic, L., Koehoorn, M., Demers, P., & Karr, C. (2008). A cohort study of traffic-related air pollution impacts on birth outcomes. Environ Health Perspect, 116(5), 680-686. https://doi.org/10.1289/ehp.10952
Bressi, M., Sciare, J., Ghersi, V., Bonnaire, N., Nicolas, J. B., Petit, J. E., Moukhtar, S., Rosso, A., Mihalopoulos, N., & Féron, A. (2013). A one-year comprehensive chemical characterisation of fine aerosol (PM_2.5) at urban, suburban and rural background sites in the region of Paris (France). Atmos. Chem. Phys., 13(15), 7825-7844. https://doi.org/10.5194/acp-13-7825-2013
Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A., 3rd, Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S., Coggins, J., Di, Q., Brunekreef, B., Frostad, J., Lim, S. S., Kan, H., Walker, K. D., Thurston, G. D., Hayes, R. B., . . . Spadaro, J. V. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc Natl Acad Sci U S A, 115(38), 9592-9597. https://doi.org/10.1073/pnas.1803222115
Candice Lung, S.-C., Chen, N., Hwang, J.-S., Hu, S.-C., Wang, W.-C., Wen, T.-Y., & Liu, C.-H. (2020). Panel study using novel sensing devices to assess associations of PM2.5 with heart rate variability and exposure sources. Journal of Exposure Science & Environmental Epidemiology, 30. https://doi.org/10.1038/s41370-020-0254-y
Chang, C.-C., Chang, C.-Y., Wang, J.-L., Pan, X.-X., Chen, Y.-C., & Ho, Y.-J. (2020). An optimized multicopter UAV sounding technique (MUST) for probing comprehensive atmospheric variables. Chemosphere, 254, 126867. https://doi.org/https://doi.org/10.1016/j.chemosphere.2020.126867
Chang, C.-C., Wang, J.-L., Chang, C.-Y., Liang, M.-C., & Lin, M.-R. (2016). Development of a multicopter-carried whole air sampling apparatus and its applications in environmental studies. Chemosphere, 144, 484-492. https://doi.org/https://doi.org/10.1016/j.chemosphere.2015.08.028
Chen, W., Zhang, F., Luo, S., Lu, T., Zheng, J., & He, L. (2022). Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 19(18).
Cichowicz, R., & Dobrzański, M. (2021). 3D Spatial Analysis of Particulate Matter (PM10, PM2.5 and PM1.0) and Gaseous Pollutants (H2S, SO2 and VOC) in Urban Areas Surrounding a Large Heat and Power Plant. Energies, 14(14).
Coker, E. S., Amegah, A. K., Mwebaze, E., Ssematimba, J., & Bainomugisha, E. (2021). A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda. ENVIRONMENTAL RESEARCH, 199, Article 111352. https://doi.org/10.1016/j.envres.2021.111352
Devasekhar, V., & Natarajan, P. (2023). Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 14(4), 927-937.
Di, Q., Amini, H., Shi, L. H., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y. J., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM_2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Article 104909. https://doi.org/10.1016/j.envint.2019.104909
Dimakopoulou, K., Samoli, E., Analitis, A., Schwartz, J., Beevers, S., Kitwiroon, N., Beddows, A., Barratt, B., Rodopoulou, S., Zafeiratou, S., Gulliver, J., & Katsouyanni, K. (2022). Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 19(9), Article 5401. https://doi.org/10.3390/ijerph19095401
Dubey, R., Patra, A. K., & Nazneen. (2022). Vertical profile of particulate matter: A review of techniques and methods. AIR QUALITY ATMOSPHERE AND HEALTH, 15(6), 979-1010. https://doi.org/10.1007/s11869-022-01192-1
Eeftens, M., Odabasi, D., Flückiger, B., Davey, M., Ineichen, A., Feigenwinter, C., & Tsai, M. Y. (2019). Modelling the vertical gradient of nitrogen dioxide in an urban area. Sci Total Environ, 650(Pt 1), 452-458. https://doi.org/10.1016/j.scitotenv.2018.09.039
Gilbert, N. L., Goldberg, M. S., Beckerman, B., Brook, J. R., & Jerrett, M. (2005). Assessing spatial variability of ambient nitrogen dioxide in Montréal, Canada, with a land-use regression model. J Air Waste Manag Assoc, 55(8), 1059-1063. https://doi.org/10.1080/10473289.2005.10464708
Hayes, R. B., Lim, C., Zhang, Y., Cromar, K., Shao, Y., Reynolds, H. R., Silverman, D. T., Jones, R. R., Park, Y., Jerrett, M., Ahn, J., & Thurston, G. D. (2019). PM2.5 air pollution and cause-specific cardiovascular disease mortality. International Journal of Epidemiology, 49(1), 25-35. https://doi.org/10.1093/ije/dyz114
He, M., & Dhaniyala, S. (2012). Vertical and horizontal concentration distributions of ultrafine particles near a highway. Atmospheric Environment, 46, 225-236. https://doi.org/https://doi.org/10.1016/j.atmosenv.2011.09.076
Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol, 41(7), 2422-2428. https://doi.org/10.1021/es0606780
Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42(33), 7561-7578. https://doi.org/https://doi.org/10.1016/j.atmosenv.2008.05.057
Hubbell Bryan, J., Hallberg, A., McCubbin Donald, R., & Post, E. (2005). Health-Related Benefits of Attaining the 8-Hr Ozone Standard. Environmental Health Perspectives, 113(1), 73-82. https://doi.org/10.1289/ehp.7186
Huebert, B., Bates, T., Russell, P., Shi, G., Kim, Y., Kawamura, K., Carmichael, G., & Nakajima, T. (2003). An overview of ACE-Asia: Strategies for quantifying the relationships between Asian aerosols and their climatic impacts. Journal of Geophysical Research, 108. https://doi.org/10.1029/2003JD003550
Ito, K., Thurston, G. D., & Silverman, R. A. (2007). Characterization of PM2.5, gaseous pollutants, and meteorological interactions in the context of time-series health effects models. Journal of Exposure Science & Environmental Epidemiology, 17(2), S45-S60. https://doi.org/10.1038/sj.jes.7500627
Jacob, D. J., Crawford, J. H., Kleb, M. M., Connors, V. S., Bendura, R. J., Raper, J. L., Sachse, G. W., Gille, J. C., Emmons, L., & Heald, C. L. (2003). Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission: Design, execution, and first results. Journal of Geophysical Research: Atmospheres, 108(D20). https://doi.org/https://doi.org/10.1029/2002JD003276
Jacob, D. J., & Winner, D. A. (2009). Effect of climate change on air quality. Atmospheric Environment, 43(1), 51-63. https://doi.org/https://doi.org/10.1016/j.atmosenv.2008.09.051
Janhäll, S. (2015). Review on urban vegetation and particle air pollution – Deposition and dispersion. Atmospheric Environment, 105, 130-137. https://doi.org/https://doi.org/10.1016/j.atmosenv.2015.01.052
Jhun, I., Coull, B. A., Schwartz, J., Hubbell, B., & Koutrakis, P. (2015). The impact of weather changes on air quality and health in the United States in 1994-2012. Environ Res Lett, 10(8). https://doi.org/10.1088/1748-9326/10/8/084009
Jia, H., Liu, Y., Guo, D., He, W., Zhao, L., & Xia, S. (2021). PM2.5-induced pulmonary inflammation via activating of the NLRP3/caspase-1 signaling pathway. Environmental Toxicology, 36(3), 298-307. https://doi.org/https://doi.org/10.1002/tox.23035
Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362-367. https://doi.org/https://doi.org/10.1016/j.envpol.2007.06.012
Kim, S. Y., Sheppard, L., & Kim, H. (2009). Health effects of long-term air pollution: influence of exposure prediction methods. Epidemiology, 20(3), 442-450. https://doi.org/10.1097/EDE.0b013e31819e4331
Kleine Deters, J., Zalakeviciute, R., Gonzalez, M., & Rybarczyk, Y. (2017). Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters. Journal of Electrical and Computer Engineering, 2017(1), 5106045. https://doi.org/https://doi.org/10.1155/2017/5106045
Lao, X. Q., Guo, C., Chang, L. Y., Bo, Y., Zhang, Z., Chuang, Y. C., Jiang, W. K., Lin, C., Tam, T., Lau, A. K. H., Lin, C. Y., & Chan, T. C. (2019). Long-term exposure to ambient fine particulate matter (PM(2.5)) and incident type 2 diabetes: a longitudinal cohort study. Diabetologia, 62(5), 759-769. https://doi.org/10.1007/s00125-019-4825-1
Larkin, A., Anenberg, S., Goldberg, D. L., Mohegh, A., Brauer, M., & Hystad, P. (2023). A global spatial-temporal land use regression model for nitrogen dioxide air pollution. FRONTIERS IN ENVIRONMENTAL SCIENCE, 11, Article 1125979. https://doi.org/10.3389/fenvs.2023.1125979
Lee, B. J., Kim, B., & Lee, K. (2014). Air pollution exposure and cardiovascular disease. Toxicol Res, 30(2), 71-75. https://doi.org/10.5487/tr.2014.30.2.071
Liang, C.-S., Duan, F.-K., He, K.-B., & Ma, Y.-L. (2016). Review on recent progress in observations, source identifications and countermeasures of PM2.5. Environment International, 86, 150-170. https://doi.org/https://doi.org/10.1016/j.envint.2015.10.016
Liu, L., Zhang, Y., Yang, Z., Luo, S., & Zhang, Y. (2021). Long-term exposure to fine particulate constituents and cardiovascular diseases in Chinese adults. J Hazard Mater, 416, 126051. https://doi.org/10.1016/j.jhazmat.2021.126051
Lundberg, S., Erion, G., & Lee, S.-I. (2018). Consistent Individualized Feature Attribution for Tree Ensembles.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA.
Miller, K. A., Siscovick, D. S., Sheppard, L., Shepherd, K., Sullivan, J. H., Anderson, G. L., & Kaufman, J. D. (2007). Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med, 356(5), 447-458. https://doi.org/10.1056/NEJMoa054409
Murillo, J. H., Roman, S. R., Rojas Marin, J. F., Ramos, A. C., Jimenez, S. B., Gonzalez, B. C., & Baumgardner, D. G. (2013). Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America. Atmospheric Pollution Research, 4(2), 181-190. https://doi.org/https://doi.org/10.5094/APR.2013.018
Oliver, M. A., & Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International journal of geographical information systems, 4(3), 313-332. https://doi.org/10.1080/02693799008941549
Philip, S., Martin, R. V., Snider, G., Weagle, C. L., van Donkelaar, A., Brauer, M., Henze, D. K., Klimont, Z., Venkataraman, C., Guttikunda, S. K., & Zhang, Q. (2017). Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models. Environmental Research Letters, 12(4), 044018. https://doi.org/10.1088/1748-9326/aa65a4
Pun, V. C., Kazemiparkouhi, F., Manjourides, J., & Suh, H. H. (2017). Long-Term PM2.5 Exposure and Respiratory, Cancer, and Cardiovascular Mortality in Older US Adults. American Journal of Epidemiology, 186(8), 961-969. https://doi.org/10.1093/aje/kwx166
Raaschou-Nielsen, O., Antonsen, S., Agerbo, E., Hvidtfeldt, U. A., Geels, C., Frohn, L. M., Christensen, J. H., Sigsgaard, T., Brandt, J., & Pedersen, C. B. (2023). PM2.5 air pollution components and mortality in Denmark. Environment International, 171, 107685. https://doi.org/https://doi.org/10.1016/j.envint.2022.107685
Ravindra, K., Bahadur, S. S., Katoch, V., Bhardwaj, S., Kaur-Sidhu, M., Gupta, M., & Mor, S. (2023). Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. Science of The Total Environment, 858, Article 159509. https://doi.org/10.1016/j.scitotenv.2022.159509
Ritz, B., Wilhelm, M., & Zhao, Y. (2006). Air pollution and infant death in southern California, 1989-2000. Pediatrics, 118(2), 493-502. https://doi.org/10.1542/peds.2006-0027
Roldán, J. J., Joossen, G., Sanz, D., Del Cerro, J., & Barrientos, A. (2015). Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses. Sensors, 15(2), 3334-3350.
Ross, Z., Jerrett, M., Ito, K., Tempalski, B., & Thurston, G. D. (2007). A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmospheric Environment, 41(11), 2255-2269. https://doi.org/https://doi.org/10.1016/j.atmosenv.2006.11.012
Song, C., He, J., Wu, L., Jin, T., Chen, X., Li, R., Ren, P., Zhang, L., & Mao, H. (2017). Health burden attributable to ambient PM2.5 in China. Environmental Pollution, 223, 575-586. https://doi.org/https://doi.org/10.1016/j.envpol.2017.01.060
Vernier, J. P., Fairlie, T. D., Deshler, T., Venkat Ratnam, M., Gadhavi, H., Kumar, B. S., Natarajan, M., Pandit, A. K., Akhil Raj, S. T., Hemanth Kumar, A., Jayaraman, A., Singh, A. K., Rastogi, N., Sinha, P. R., Kumar, S., Tiwari, S., Wegner, T., Baker, N., Vignelles, D., . . . Renard, J. B. (2018). BATAL: The Balloon Measurement Campaigns of the Asian Tropopause Aerosol Layer. Bulletin of the American Meteorological Society, 99(5), 955-973. https://doi.org/https://doi.org/10.1175/BAMS-D-17-0014.1
Wang, A., Xu, J. S., Tu, R., Saleh, M., & Hatzopoulou, M. (2020). Potential of machine learning for prediction of traffic related air pollution. TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 88, Article 102599. https://doi.org/10.1016/j.trd.2020.102599
Wolf, K., Cyrys, J., Harciníková, T., Gu, J., Kusch, T., Hampel, R., Schneider, A., & Peters, A. (2017). Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany. Science of The Total Environment, 579, 1531-1540. https://doi.org/https://doi.org/10.1016/j.scitotenv.2016.11.160
Wong, P.-Y., Su, H.-J., Lee, H.-Y., Chen, Y.-C., Hsiao, Y.-P., Huang, J.-W., Teo, T.-A., Wu, C.-D., & Spengler, J. D. (2021). Using land-use machine learning models to estimate daily NO2 concentration variations in Taiwan. Journal of Cleaner Production, 317, 128411. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.128411
Wong, P. Y., Lee, H. Y., Chen, Y. C., Zeng, Y. T., Chern, Y. R., Chen, N. T., Lung, S. C. C., Su, H. J., & Wu, C. D. (2021). Using a land use regression model with machine learning to estimate ground level PM_2.5. Environmental Pollution, 277, Article 116846. https://doi.org/10.1016/j.envpol.2021.116846
Wu, J., M Winer, A., & J Delfino, R. (2006). Exposure assessment of particulate matter air pollution before, during, and after the 2003 Southern California wildfires. Atmospheric Environment, 40(18), 3333-3348. https://doi.org/https://doi.org/10.1016/j.atmosenv.2006.01.056
Wu, S., Deng, F., Wei, H., Huang, J., Wang, X., Hao, Y., Zheng, C., Qin, Y., Lv, H., Shima, M., & Guo, X. (2014). Association of Cardiopulmonary Health Effects with Source-Appointed Ambient Fine Particulate in Beijing, China: A Combined Analysis from the Healthy Volunteer Natural Relocation (HVNR) Study. Environmental Science & Technology, 48(6), 3438-3448. https://doi.org/10.1021/es404778w
Wu, S., Ni, Y., Li, H., Pan, L., Yang, D., Baccarelli, A. A., Deng, F., Chen, Y., Shima, M., & Guo, X. (2016). Short-term exposure to high ambient air pollution increases airway inflammation and respiratory symptoms in chronic obstructive pulmonary disease patients in Beijing, China. Environment International, 94, 76-82. https://doi.org/https://doi.org/10.1016/j.envint.2016.05.004
Xu, X., Qin, N., Zhao, W., Tian, Q., Si, Q., Wu, W., Iskander, N., Yang, Z., Zhang, Y., & Duan, X. (2022). A three-dimensional LUR framework for PM(2.5) exposure assessment based on mobile unmanned aerial vehicle monitoring. Environ Pollut, 301, 118997. https://doi.org/10.1016/j.envpol.2022.118997
Yazdi, M. D., Kuang, Z., Dimakopoulou, K., Barratt, B., Suel, E., Amini, H., Lyapustin, A., Katsouyanni, K., & Schwartz, J. (2020). Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods. REMOTE SENSING, 12(6), Article 914. https://doi.org/10.3390/rs12060914
Yin, P., Brauer, M., Cohen, A., Burnett, R. T., Liu, J., Liu, Y., & Zhou, M. (2015). Ambient fine particulate matter exposure and cardiovascular mortality in China: a prospective cohort study. The Lancet, 386, S6. https://doi.org/https://doi.org/10.1016/S0140-6736(15)00584-X
Zhang, Q., Fu, F., & Tian, R. (2020). A deep learning and image-based model for air quality estimation. Science of The Total Environment, 724, 138178. https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138178
吳昭儀, 吳治達, 陳裕政, 許金玉, & 陳穆貞. (2020). 應用克利金／土地利用迴歸混合模式推估林園臨海石化工業區懸浮微粒之時空分布. In 航測及遙測學刊 (Vol. 25, pp. 11-23).
翁佩詒, 吳治達, & 蘇慧貞. (2021). 結合土地利用迴歸與極限梯度提升演算法發展高雄都會區二氧化氮之推估模型. In 航測及遙測學刊 (Vol. 26, pp. 1-12).
曾于庭, 吳治達, & 龍世俊. (2018). 應用土地利用迴歸模式推估北部空品區細懸浮微粒之時空分布. In 航測及遙測學刊 (Vol. 23, pp. 191-204).
曾芷琳, 蕭雅萍, 曾于庭, 陳穆貞, 龍世俊, & 吳治達. (2019). 整合空間資訊技術與土地利用迴歸模式推估高屏空品區細懸浮微粒之時空分布. In 航測及遙測學刊 (Vol. 24, pp. 79-87).