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008			250709s20249999th u ms t 000 eng d
035			_a.b12462081
099		9	_aAIT RSPR no.UI-24-01
100	1		_aNawapat Choosuwan
245	1	0	_aAssessing air pollution risks from electric vehicles in Bangkok : _btowards mitigation strategies and policy recommendations
260			_aPathum Thani : _bAsian Institute of Technology, _c2024
300			_a140 leave : _bill.+ _e1 online resource
490	1		_aResearch studies project report ; _vno. UI-24-01
500			_aA research study submitted in partial fulfillment of the requirements for the degree of Master of Science in Urban Innovation and Sustainability
502			_aResearch Studies Project Report (M. Sc.) - Asian Institute of Technology, 2024
520			_aAir pollution remains a critical concern in densely populated urban areas, particularly in cities like Bangkok where transportation emissions are a predominant contributor. This study aims to comprehensively understand the current situation and explore the relationship between electric vehicle (EV) adoption and air pollution risk in Bangkok, ultimately proposing effective policy recommendations for promoting EV adoption and improving air quality. The research utilized advanced machine learning models, including XGBoost, Naive Bayes, K-Nearest Neighbors (KNN), and Random Forest to assess air pollutant emission risks. The air pollutant emission risk assessment employs various air pollutants and environmental factors considered, such as CO, SO2, NO2, O3, PM2.5, PM10, wind speed (WS), wind direction (WD), relative humidity (RH), temperature, building density, road density, distance from roads, and the Normalized Difference Vegetation Index (NDVI). Pearson{u2019}s correlation was employed in the study to examine the relationship between EV charging density and air pollutant emission risk in Bangkok CBD. The findings for the current situation reveal a significant increase in Battery Electric Vehicle (BEV) registrations from 2018 to 2023, driven by technological advancements, government incentives, and rising environmental awareness. However, the study also highlights persistently high levels of particulate matter (PM2.5 and PM10) in densely populated and high-traffic areas, indicating the need for more aggressive measures to improve air quality. Key findings indicate that all models consistently identified high and very high-risk zones, particularly in the Central Business District (CBD) and along major roads, underscoring the significant impact of vehicular traffic and dense urbanization on air quality. A notable achievement of this study is the identification of a significant inverse correlation between EV charging capacity and air pollution risk, with areas of high charging capacity showing lower pollution levels. The insights and recommendations provided in this study offer a comprehensive roadmap for policymakers and stakeholders. By addressing infrastructure gaps, enhancing capacity, leveraging successful models, and fostering public-private collaboration, Bangkok can significantly reduce its environmental footprint and promote a healthier urban environment.
650		0	_aAir _xPollution _xGovernment policy _zThailand _zBangkok
650		0	_aUrban pollution _zThailand _zBangkok
700	1		_aPramanik, Malay, _eChairperson
700	0		_aVilas Nitivattananon, _eExamination Committee
700	0		_aEkbordin Winijkul, _eExamination Committee
710	2		_aRoyal Thai Government Fellowship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tResearch studies project report ; _vno. UI-24-01
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B22689
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909			_aBarcode : - _bCREATED : 2025-09-07 _cRECORD # : i13553999 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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