臺灣博碩士論文加值系統

研究背景：
空氣污染在印度是一個嚴重的環境和公共衛生問題。雖然大多數研究都集中在城市空氣污染上，但對於許多小城鎮和新興工業化國家的空氣品質下降問題以及其對非傳染性疾病 (NCD) 的影響仍研究不足。本論文重點關注在生態敏感的喜馬拉雅地區喜馬偕爾邦，以調查環境空氣中直徑小於 10 微米的顆粒物 (PM10) 的時空模式，並評估當地人口的疾病負擔與預估的 PM2.5 暴露情形。
材料與方法：
空氣品質數據包括 PM10 和氣象數據，收集自 2011 年至 2015 年全州 7 個區的 20 個空氣監測站，還有區級登記的死亡人數和特定原因死亡人數的百分比。 PM10 的時空模式使用變異數分析 (ANOVA) 進行分析。此外，透過結合風速和風向的雙變量極座標圖以辨別潛在的排放源。接著，使用 2011-2018 年的PM10月平均濃度，並以0.5 和 0.8這兩個比率來估計 PM2.5 的濃度。綜合暴露-反應（IER）模型用於估計在地區層面PM2.5所導致的非傳染性疾病（包括缺血性心臟病（IHD）、腦血管疾病（中風）、慢性阻塞性肺疾病（COPD）、第2型糖尿病和肺癌）早產死亡。
研究結果：
年平均 PM10 和 PM2.5 濃度均高於 WHO 標準。 PM10 在所有空氣監測站點中有明顯的季節性變化，分別在夏季和冬季的濃度較高，而季風季節的濃度較低。工業用地和住宅用地污染相對較重。當地工業、交通和人為排放的排放源汙染主要來自於 PM10 濃度。此外，遠程交通運輸也被認為是期間性的次要污染來源。我們推估有 4,513 (95% CI 3,202-5,357) 和 5,636 (95% CI 4,252-6,364) 的死亡可歸因於預估的 PM2.5 暴露。 IHD 是主要死因，佔 PM2.5 相關早產死亡的 43%，其次是 COPD（34%）、中風（16%）、第 2 型糖尿病（5%）和肺癌（2%）。如果各地區能夠將年平均 PM2.5 濃度降低到先前（2005 年）或新（2021 年）世界衛生組織 (WHO) 指南的 10 µg/m3 或 5 µg/ m3，可以分別降低約 58.4 %及 75.7 %的早產死亡率。
結論：
儘管被指定為生態敏感區，實測的 PM10 和預估的PM2.5 濃度仍遠遠超過 WHO 指南和國家標準。透過改變政策和提高社會意識，可以減少當地工業、車輛和其他人為活動的初級排放。這項研究揭示了 PM2.5 對與非傳染性疾病相關的早產死亡的貢獻，並顯示了獨特的流行病學特徵，例如 IHD 是作為 PM2.5 相關早產死亡的主要原因。最後，州和地區級評估應指導政策制定，以改善空氣污染控制，並改善環境和公共衛生。
關鍵詞：疾病負擔，排放源，喜馬偕爾邦，顆粒物

Background:
Air pollution in India is a serious environmental and public health problem. While most studies focus on urban air pollution, many small towns and newly industrialized states have declining air quality that is poorly studied, as is its contribution to noncommunicable diseases (NCDs). This dissertation focuses on the ecologically sensitive Himalayan region, Himachal Pradesh, to investigate the spatiotemporal patterns of particulate matter less than 10 microns in diameter (PM10) in ambient air and estimation of the attributable burden of disease in the local population with estimated PM2.5 exposure.
Material and Methods:
Air quality data, including PM10 and meteorological data, were obtained from 20 air monitoring stations in 7 districts throughout the state from 2011 to 2015, as were registered deaths at district level and percentage share of cause-specific deaths. Spatiotemporal patterns of PM10 were investigating using analysis of variance (ANOVA). Further, to identify potential emission sources, bivariate polar plots were generated integrating wind speed and direction. Then, monthly mean PM10 was from 2011-2018 was used to estimate the concentration level of PM2.5 by using two ratios 0.5 and 0.8. Integrated exposure-response (IER) model was used to estimate the premature deaths from NCDs including ischemic heart disease (IHD), cerebrovascular diseases (stroke), chronic obstructive pulmonary disease (COPD), type-2 diabetes, and lung cancer attributable to PM2.5 at the district-level.
Results:
Annual mean concentration of PM10 and PM2.5 were above the WHO standards. Seasonal variation in PM10 was evident at all air monitoring sites, with high concentrations during the summer and winter and low concentrations during the monsoon season. Industrial and residential sites were comparatively highly polluted. Emission sources from local industrial, traffic and anthropogenic emissions were all significant contributors to PM10 concentrations. In addition, long-range transport was also a identified as secondary contributor during. We estimated 4,513 (95% CI 3,202-5,357) and 5,636 (95% CI 4,252-6,364) deaths attributable to estimated PM2.5 exposure. IHD was the leading cause of death, accounting for 43% of PM2.5-related premature deaths, followed by COPD (34%), stroke (16%), type-2 diabetes (5%), and lung cancer (2%). Approximately 58.4% and 75.7% reduction in premature mortality can be attained if districts can reduce mean annual PM2.5 concentration to the previous (2005) or new (2021) World Health Organization (WHO) guideline of 10 µg/m3 or 5 µg/m3, respectively.
Conclusion:
Despite being designated an ecologically sensitive region, measured PM10 and estimated PM2.5 concentrations far exceed WHO guidelines and national standards. Primary emissions from local industrial, vehicular, and other anthropogenic activities can be reduced with changes to policy and increased societal awareness. This study revealed the contribution of PM2.5 to premature mortality associated with NCDs and revealed unique epidemiological features, such as IHD as a leading cause of PM2.5-related premature death. Lastly, state- and district-level assessments should guide policymaking for improved air pollution control to improve environmental and public health.
Keywords: Burden of disease, Emission sources, Himachal Pradesh, Particulate matter

Table of Contents
Acknowledgment v
中文摘要 vii
Abstract ix
List of Figures xiii
List of Tables xv
List of abbreviation Error! Bookmark not defined.
1. Introduction 1
1.1 Background 1
1.2 Air pollution pathway 3
1.3 Types of air pollutants 4
1.4 Particulate matter 5
1.5 Air pollution in India 7
1.6 Air pollution-related health impacts studies in India 12
1.7 Air pollution in Himachal Pradesh 16
1.8 Hypothesis 24
1.9 Outline 24
2. Objectives 25
3. Methods 27
3.1 Patterns and sources of PM10 in ecologically sensitive Himalayan region Himachal Pradesh, India 27
3.1.1 Study area and air quality data 27
3.1.2 Meteorological data 30
3.1.3 Spatial and temporal analysis 30
3.1.4 Bivariate Polar Plot and k-means clustering 30
3.2 Burden of disease attributable to ambient fine particulate matter exposure in Himachal Pradesh, India. 33
3.2.1 Ambient PM2.5 data 33
3.2.2 All-cause and cause-specific mortality data 33
3.2.3 Mortality estimation 34
3.2.4 Uncertainty analysis 36
4. Results 37
4.1 Patterns and sources of PM10 in ecologically sensitive Himalayan region Himachal Pradesh, India 37
4.1.1 Spatial variation of PM10 37
4.1.2 Seasonal variations 39
4.1.3 Bivariate polar plot and clustering 40
4.2 Burden of disease attributable to ambient fine particulate matter exposure in Himachal Pradesh, India 47
4.2.1 Estimated PM2.5 concentrations 47
4.2.2 Premature mortality due to PM2.5 47
5. Discussion 60
5.1 Spatial and temporal distribution of PM10 60
5.2 Burden of disease attributable to PM2.5 exposure in Himachal Pradesh 62
6. Conclusion and recommendation 67
7. References 69
8. Appendix I
8.1 Appendix 1. Sample format of air quality data received from Himachal Pradesh State Pollution Control Board I
8.2 Appendix 2. Pictures of the instruments using to collect the data by Himachal Pradesh State Pollution Control Board II
8.3 Appendix 3. Central Pollution Control Board and methodology of data collection III
8.4 Appendix 4. Meteorological data collection from NOAA website VI
8.5 Appendix 5. GBD India Compare platform VI
8.6 Appendix 6. Some pictures from various monitoring sites VIII

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