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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 詹長權 院長(Chang-Chuan Chan) | |
dc.contributor.author | Pornpun Watcharavitoon | en |
dc.contributor.author | 彭萍 | zh_TW |
dc.date.accessioned | 2021-07-11T14:49:30Z | - |
dc.date.available | 2022-08-31 | |
dc.date.copyright | 2020-09-10 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-11 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78287 | - |
dc.description.abstract | The severe air pollution problem in Bangkok (BKK) is an issue of great concern in Thailand. This study aims to examine the effect of meteorological factors (ambient temperature, pressure, wind speed and wind direction) on air pollutants (PM10, CO8hr, O3_1hr, NO2, and SO2). The temporal and spatial trends of the air pollutants, particularly particulate matter (PM2.5 and PM10), were illustrated to understand their patterns. The PM2.5/PM10 ratios were characterized for industrial, residential, roadside and background sites in wet and dry seasons. The variation of particulate matter during the “lockdown” period for COVID-19 control in Bangkok was evaluated to describe the pollutant behaviour. The datasets of air pollutants and meteorological parameters in Bangkok used in this study were obtained from the Pollution Control Department of the Ministry of Natural Resources and Environment, Thailand. Long–term data from 1996-2009 were analysed to identify relationships between the air pollutants and meteorological factors. A stepwise multiple linear regression model was applied to find significant meteorological factors affecting the air pollutants. A stepwise multiple linear regression model was used to analyse the significant factors affecting PM10, CO8hr, O3_1hr, NO2, and SO2 levels at two groups of sites; results showed a decreased association with meteorological parameters and an increased association with the type of studied area and season. In contrast, O3_1hr levels exhibited a decreased association with the area studied. The coefficients of determination (r2) for PM10, CO8hr, O3_1hr, NO2, and SO2 regression models were 0.54, 0.68, 0.5, 0.64, and 0.22, respectively. In terms of particulate matter, short term datasets from 2018-2019 were examined using ordinary kriging and ANOVA. The monthly mean PM2.5 concentrations was highest at roadsides in the dry season (35.2 ± 19.1μg /m3), and lowest at industrial sites in the wet season (17.5 ± 10.5 μg /m3); similar trends occurred for seasonal variation in PM10 concentrations. The PM2.5/PM10 ratios ranged from 0.52 to 0.68; the highest value was detected in the dry season, while the lowest was in the wet season. It was concluded that the particulate matter concentrations significantly changed in both time and space. Transportation and transformation of pollutants were the primary causes of PM2.5 and PM10 concentrations in Bangkok. Values exceeding safe levels occurred during the northeast monsoon season because of dry weather and the northeast wind direction, transporting the PM2.5 created from open burning of biomass in agriculture areas. Remarkably, a combination of low temperature, high pressure and stagnant wind can lead to the high PM2.5 levels. The ordinary kriging results showed that during the COVID-19 lockdown period (April–May 2020), the PM2.5 and PM10 dramatically decreased compared with values for the same months of 2019. This was attributed to the work-from-home policy and other lifestyle changes that decreased particulate matter sources. In conclusion, this study characterized the variation of air pollutants in Bangkok as spatio-temporally heterogeneous. Traffic emissions are the main cause of spatial variation of air pollutants, and the meteorological parameters might be important factors affecting temporal variation. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:49:30Z (GMT). No. of bitstreams: 1 U0001-1008202000480500.pdf: 9864698 bytes, checksum: 81aaf880583d9de8f31e7fdccc6e1784 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents |
TABLE OF CONTENTS Abstract i Acknowledgements iii Table of Contents iv List of Tables viii List of Figures ix List of Abbreviations xiii CHAPTER 1 INTRODUCTION 1 1.1 Rationale 1 1.2 Research objectives 4 1.3 Scope of the study 5 CHAPTER 2. LITERATURE REVIEWS 8 2.1 General Information 8 2.2 Source of air pollution 9 2.2.1. Mobile Sources 9 2.2.2 Stationary and Area Sources 10 2.2.3 Agricultural Sources 10 2.2.4 Natural Sources 10 2.3 Definition of ambient aerosols 11 2.4 Related Research 11 CHAPTER 3. METHODOLOGY 18 3.1 Descriptions of Locations and Seasons 18 3.2 Air Quality and Meteorological Data 19 3.3 Ordinary Kriging 23 3.4 Cluster analysis and Bivariate polar plot 24 3.5 Statistical analysis 26 CHAPTER 4. RESULTS AND DISCUSSION 28 4.1 Air Quality and Standards 28 4.2 Spatial Variations 31 4.2.1 Ambient air quality between roadside and residential sites 31 4.2.2 PM2.5, PM10 and PM2.5 / PM10 ratio in Bangkok 34 4.2.3 Covid-19 effect to PM2.5 and PM10 concentrations 39 4.3 Trends Variations 42 4.3.1 Long-term Trends of ambient air quality 42 4.3.2 PM2.5 and PM10 concentrations, and PM2.5/PM10 ratios in different areas 47 4.4 Seasonal Variations 50 4.4.1 Ambient air quality between roadside and residential sites 50 4.4.2 PM2.5 , PM10 concentrations and PM2.5/PM10 ratio 52 4.5 Diurnal Variations 56 4.5.1 Ambient air quality between roadside sites and residential sites 56 4.5.2 Weekday-Weekend Variations and Potential Ozone Estimates 57 4.5.3 PM2.5 and PM10 concentration 61 4.5.4 PM2.5 and PM10 average concentration during COVID-19 62 4.6 Correlations and Regression Models 73 4.6.1 Correlations between Air Pollutants and Meteorological Factors 73 4.6.2 Regression Models of Air Pollutants and Meteorological Factors 75 4.6.3 Scatter plot showing the PM10 and PM2.5 predicted and monitoring data 77 4.7 Cluster analysis and Bivariate polar plot with PM2.5 and PM10 sources 80 4.8 PM2.5 concentrations and PM10 concentrations under the lockdown 84 CHAPTER 5 CONCLUSIONS 86 5.1 Temporal and spatial variations in ambient air quality during 1996-2009 in Bangkok, Thailand 86 5.2 Temporal and Spatial variations with Meteorological effect to PM2.5 / PM10 ratio in Bangkok, Thailand 87 5.3 Impact of the lockdown on air pollution during COVID-19 in Bangkok 88 REFERENCES 90 APPENDIX 98 The number of days that PM10, O3-1hr, and SO2 levels exceeded NAAQS thresholds after cross tabulation using the variables location, day type (weekday or weekend), and season. | |
dc.title | 泰國曼谷地區之空氣污染暴露評估 | zh_TW |
dc.title | Exposure Assessment of Air Pollution in Bangkok, Thailand | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 吳章甫 所長 (Chang-Fu Wu),黃耀輝 (Yaw-Huei Hwang),邱嘉斌(Chia-Pin Chio),張大元(Ta-Yuan Chang) | |
dc.subject.keyword | 曼谷,時空變化,環境空氣質量,新冠肺炎, | zh_TW |
dc.subject.keyword | Bangkok,temporal and spatial variations,ambient air quality,COVID – 19, | en |
dc.relation.page | 105 | |
dc.identifier.doi | 10.6342/NTU202002751 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-11 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 環境與職業健康科學研究所 | zh_TW |
顯示於系所單位: | 環境與職業健康科學研究所 |
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