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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳柏華 | zh_TW |
| dc.contributor.advisor | Albert Y. Chen | en |
| dc.contributor.author | 劉映汝 | zh_TW |
| dc.contributor.author | Ying-Ru Liu | en |
| dc.date.accessioned | 2025-02-21T16:15:32Z | - |
| dc.date.available | 2025-02-22 | - |
| dc.date.copyright | 2025-02-21 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-12-23 | - |
| dc.identifier.citation | Aix, M. L., Schmitz, S., & Bicout, D. J. (2023). Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter. Science of the Total Environment, 889(April). https://doi.org/10.1016/j.scitotenv.2023.164063
Bisignano, A., Carotenuto, F., Zaldei, A., & Giovannini, L. (2022). Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway. Atmospheric Environment, 275, 119008. https://doi.org/10.1016/j.atmosenv.2022.119008 Brzozowski, K., Ryguła, A., & Maczyński, A. (2019). The use of low-cost sensors for air quality analysis in road intersections. Transportation Research Part D: Transport and Environment, 77(November), 198–211. https://doi.org/10.1016/j.trd.2019.10.019 Department, H. K. E. P. (2015). Hong Kong Air Quality Objectives Pollutant. Department, Hong Kong Environmental Protection, 1. Duvall, R. M., Clements, A. L., Hagler, G., Kamal, A., Kilaru, V., Goodman, L., Frederick, S., Barkjohn, K. K., VonWald, I., Greene, D., & Dye, T. (2021a). Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory Supplemental and Informational Monitoring Applications. EPA/600/R-20/280, 2021. 1–79. Duvall, R. M., Clements, A. L., Hagler, G., Kamal, A., Kilaru, V., Goodman, L., Frederick, S., Barkjohn, K. K., VonWald, I., Greene, D., & Dye, T. (2021b). Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory Supplemental and Informational Monitoring Applications. EPA/600/R-20/280, 2021. 1–79. Entities, H.-L. E. G. on the N. Z. E. C. of N.-S. (2022). Integrity Matters: Net Zero Commitments by Business, Financial Institutions, Cities and Regions. United Nations, 15–16. Gómez-Losada, Á., & Pires, J. C. M. (2024). Air quality assessment during the low emission zone implementation in Madrid (Spain). Urban Climate, 55. https://doi.org/10.1016/j.uclim.2024.101995 Intergovernmental Panel on Climate Change. (2022). Climate Change 2022 - Mitigation of Climate Change. In Intergovernmental Panel on Climate Change (Issue 1). International Agency for Research on Cancer. (2013). Outdoor air pollution a leading environmental cause of cancer deaths. World Health Organization, 1. Kang, Y., Aye, L., Ngo, T. D., & Zhou, J. (2022). Performance evaluation of low-cost air quality sensors: A review. Science of the Total Environment, 818, 151769. https://doi.org/10.1016/j.scitotenv.2021.151769 Khreis, H., Nieuwenhuijsen, M. J., Zietsman, J., & Ramani, T. (2020). Traffic-related air pollution: Emissions, human exposures, and health-The way forward. Traffic-Related Air Pollution, 597–620. https://doi.org/10.1016/B978-0-12-818122-5.00025-9 Ku, D., Bencekri, M., Kim, J., Lee, S., & Lee, S. (2020). Review of European low emission zone policy. Chemical Engineering Transactions, 78, 241–246. https://doi.org/10.3303/CET2078041 Liang, L. (2021a). Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges. Environmental Research, 197(March), 111163. https://doi.org/10.1016/j.envres.2021.111163 Liang, L. (2021b). Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges. Environmental Research, 197(November 2020), 111163. https://doi.org/10.1016/j.envres.2021.111163 Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S., Gao, J., Wainwright, D., Neale, D., Kan, R., Kirkwood, J., & Morawska, L. (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185(March), 109438. https://doi.org/10.1016/j.envres.2020.109438 Lo, W., Ho, C., Tseng, E., Hwang, J., Chan, C., & Lin, H. (2022). Long-term exposure to ambient fine particulate matter (PM2.5) and associations with cardiopulmonary diseases and lung cancer in Taiwan: a nationwide longitudinal cohort study. International Journal OfEpidemiology, 51(April), 1230–1242. London, M. of. (2023). Inner London Ultra Low Emission Zone- one year report. Lurkin, V., Hambuckers, J., & Van Woensel, T. (2021). Urban low emissions zones: A behavioral operations management perspective. Transportation Research Part A, 144, 222–240. https://doi.org/10.1016/j.tra.2020.11.015 Malina, C., & Scheffler, F. (2015). The impact of Low Emission Zones on particulate matter concentration and public health. Transportation Research Part A: Policy and Practice, 77, 372–385. https://doi.org/10.1016/j.tra.2015.04.029 Ministry of Justice, J. (1968). Air Pollution Control Act. Ministry of Justice, Japan, 97, 1–59. Molina Rueda, E., Carter, E., L’Orange, C., Quinn, C., & Volckens, J. (2023). Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution. Environmental Science and Technology Letters, 10(3), 247–253. https://doi.org/10.1021/acs.estlett.3c00030 National Environmental Agency. (2018). EPD Report 2018. National Environmental Agency, 9~27. Nishitateno, S., & Burke, P. J. (2024). Effects of Low Emission Zones on Air Quality, New Vehicle Registrations, and Birthweights: Evidence from Japan. In Environmental and Resource Economics (Vol. 87, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10640-024-00875-w Ouimette, J., Arnott, W. P., Laven, P., Whitwell, R., Radhakrishnan, N., Dhaniyala, S., Sandink, M., Tryner, J., & Volckens, J. (2024). Fundamentals of low-cost aerosol sensor design and operation. Aerosol Science and Technology, 58(1), 1–15. https://doi.org/10.1080/02786826.2023.2285935 Parliament, E. (2024). Revision of EU air quality legislation. European Parliament, 1–6. Poulhès, A., & Proulhac, L. (2021). The Paris Region low emission zone, a benefit shared with residents outside the zone. Transportation Research Part D: Transport and Environment, 98(July). https://doi.org/10.1016/j.trd.2021.102977 Ravindra, K., Singh, V., & Mor, S. (2024). Why we should have a universal air quality index? Environment International, 187, 1–6. https://doi.org/10.1016/J.ENVINT.2024.108698 Research, K. legislation I. (2019). Clean Air Conservation Act. Ministry of Environment, 1(8404). Sayahi, T., Butterfield, A., & Kelly, K. E. (2019). Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors. Environmental Pollution, 245, 932–940. https://doi.org/10.1016/j.envpol.2018.11.065 Tassinari, F. (2024). Low emission zones and traffic congestion: Evidence from Madrid Central. Transportation Research Part A: Policy and Practice, 185(September 2023), 104099. https://doi.org/10.1016/j.tra.2024.104099 United Nations Environment Programme. (2021). Regulating Air Quality: The first global assessment of air pollution legislation. United Nations Environment Programme, 12–17. Wang, A., Machida, Y., deSouza, P., Mora, S., Duhl, T., Hudda, N., Durant, J. L., Duarte, F., & Ratti, C. (2023a). Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings. Atmospheric Environment, 301(October 2022). https://doi.org/10.1016/j.atmosenv.2023.119692 Wang, A., Machida, Y., deSouza, P., Mora, S., Duhl, T., Hudda, N., Durant, J. L., Duarte, F., & Ratti, C. (2023b). Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings. Atmospheric Environment, 301(February). https://doi.org/10.1016/j.atmosenv.2023.119692 Weichenthal, S., Van Ryswyk, K., Goldstein, A., Shekarrizfard, M., & Hatzopoulou, M. (2016). Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model. Environmental Pollution, 208, 241–248. https://doi.org/10.1016/j.envpol.2015.04.011 World Health Organization. (2021). WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. ISBN. World Health Organization, 134–139. World Health Organization. (2024). Air quality, energy and health. World Health Organization. Ye, J., Qin, Z., & Chen, X. (2021). Adapt by adopting cleaner vehicles? — Evidence from a low-emission zone policy in Nanchang, China. China Economic Review, 66(February), 101598. https://doi.org/10.1016/j.chieco.2021.101598 Yu, C., & Morotomi, T. (2022). The effect of the revision and implementation for environmental protection law on ambient air quality in China. Journal of Environmental Management, 306(October 2021), 1–13. https://doi.org/10.1016/j.jenvman.2022.114437 Zhang, Y., Andre, M., Liu, Y., Wu, L., Jing, B., & Mao, H. (2018). Evaluation of low emission zone policy on vehicle emission reduction in Beijing, China. IOP Conference Series: Earth and Environmental Science, 121(5). https://doi.org/10.1088/1755-1315/121/5/052070 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96722 | - |
| dc.description.abstract | 近年來,全球興起劃設空氣品質維護區的浪潮,希望以減少交通相關的空氣汙染的方式來提升市區整體空氣品質。目前各國在評估空氣品質維護區內的空氣品質多直接使用標準測站測值進行全面評估,難以捕捉空氣汙染高解析度的空間分布變化。交通相關空氣污染 (TRAP) 會對道路使用者的健康產生不利影響,因此持續監測暴露的重要性已被強調。 其中以PM2.5與對敏感族群用路者(孩童、老人、其他敏感族群)的心血管、呼吸道疾病最為明顯。
本研究旨在探討使用校正後微型感測器評估空氣品質維護區內空氣污染對人體健康影響之可行性,並尋找影響交通相關的空氣汙染的關鍵因素(車流、天氣因子)。微型感測器因其低成本、易於操作以及能夠提供高解析度的時間、空間監測的特色而被作為周邊未設大型標準測站選點的替代方案。 本研究經美國環境保護署提出之感測器測試協定標準評估後,納入天氣因子建構適當微型感測器校正模型。接著使用校正好的微型感測器實地監測台北市空氣品質維護區內交通相關的空氣汙染污染物(PM2.5)濃度,並與周邊交通、交通車流因子進行關聯性分析。 將空氣品質維護區的空氣汙染污染物(PM2.5)濃度與全球空氣品質標準進行比較,我們的日平均PM2.5濃度在空氣品質指標分類中多數時間為良好。此外,影響每小時三台感測器的測值的主要因子為相對濕度、平均溫度與風向。交通車流因子的表現較不顯著。本研究可應用於評估空氣品質維護區內交通相關的空氣汙染污染之個人暴露及健康危害評估。 | zh_TW |
| dc.description.abstract | In recent years, the establishment of Low-Emission Zones (LEZs) globally has aimed to improve urban air quality by mitigating traffic-related air pollution (TRAP), which is known to adversely impact the health of road users. Currently, many countries use monitoring station measurements for comprehensive assessments of air quality within LEZs, which often fail to capture high-resolution spatial variations in TRAP. This study aims to explore the feasibility of using calibrated Low-Cost Sensors (LCS) to assess the health impacts of TRAP within LEZs and to identify key factors influencing TRAP distribution. LCS has been proposed as an alternative solution for areas without large monitoring stations due to their advantages of cost efficiency, compact sizes, ease of operation, and the ability to provide high-resolution monitoring spatially and temporally.
This study aims to explore the feasibility of using calibrated Low-Cost Sensors (LCS) to assess the health impacts of air pollution within LEZs and to identify key factors influencing TRAP (traffic flow and meteorological factors). Low-cost sensors (LCS) have been proposed as an alternative solution for areas without large monitoring stations due to their advantages of cost efficiency, compact sizes, ease of operation, and the ability to provide high-resolution monitoring spatially and temporally. After evaluating the three LCSs according to the U.S. Environmental Protection Agency's sensor testing protocol and incorporating meteorological factors to construct an appropriate calibration model, the calibrated LCS was used to monitor TRAP (PM2.5) concentrations within the Taipei LEZ. The comparison of daily PM2.5 concentrations within the LEZ with global air quality standards shows satisfactory conditions. Furthermore, the main meteorological factors affecting the readings of the three sensors were relative humidity, average temperature, and wind direction, and traffic factors were total traffic flow and average speed for certain VDs. This research provides valuable insights for improving TRAP monitoring within LEZs and offers guidance for future studies at the intersection of TRAP exposure and the effectiveness of LEZ policy. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-21T16:15:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-21T16:15:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 ii
致謝 iii 中文摘要 v Abstract vi Contents viii Chapter 1 Introduction 1 Chapter 2 Literature review 3 2.1 Air Quality in Cities 3 2.2 Ambient Air Pollution 5 2.3 Traffic-Related Air Pollution (TRAP) 7 2.4 Monitoring tools 9 2.5 Low-cost Sensor (LCS) calibration 10 2.6 Trend of Low-Emission Zones (LEZs) 14 2.7 Summary 19 Chapter 3 Methodology 21 3.1 Research process 21 3.2 Low-cost Sensors Data 22 3.2.1 Low-cost Sensor Setup and Deployments 23 3.2.2 Base testing 23 3.2.3 Sensor Calibration 28 3.3 Traffic factors 30 3.4 Summary 30 Chapter 4 Results 31 4.1 Data collection 31 4.2 Data Description 36 4.3 Base Testing 50 4.4 Sensor Calibration 53 4.4.1 Field Deployments 59 4.4.2 Traffic Factors 62 4.4.3 Summary 64 Chapter 5 Conclusion 65 5.1 Findings 65 5.2 Contributions 65 5.3 Limitation 66 5.4 Future work 67 Reference 69 | - |
| dc.language.iso | en | - |
| dc.subject | 空氣品質維護區 | zh_TW |
| dc.subject | 校正模型 | zh_TW |
| dc.subject | 交通車流因子 | zh_TW |
| dc.subject | 微型感測器 | zh_TW |
| dc.subject | 交通相關的空氣污染 | zh_TW |
| dc.subject | traffic factors | en |
| dc.subject | Low-cost sensors | en |
| dc.subject | Traffic-Related Air Pollution | en |
| dc.subject | Low emission zone | en |
| dc.subject | calibration model | en |
| dc.title | 運用校正後微型感測器監測在空氣品質維護區的交通相關的空氣汙染 | zh_TW |
| dc.title | Monitoring Traffic-Related Air Pollution in Low-Emission Zones through Calibrated Low-Cost Sensor | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 桑國忠;關百宸 | zh_TW |
| dc.contributor.oralexamcommittee | Kuo-Chung Shang;Pai-Chen Guan | en |
| dc.subject.keyword | 微型感測器,交通相關的空氣污染,空氣品質維護區,校正模型,交通車流因子, | zh_TW |
| dc.subject.keyword | Low-cost sensors,Traffic-Related Air Pollution,Low emission zone,calibration model,traffic factors, | en |
| dc.relation.page | 74 | - |
| dc.identifier.doi | 10.6342/NTU202404710 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-12-23 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2025-02-22 | - |
| 顯示於系所單位: | 土木工程學系 | |
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