2017年台灣細懸浮微粒(PM2.5) 污染來源推估及化學成分特性變化

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何怡慧(YI-HUEI HE) 查詢紙本館藏

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2017年台灣細懸浮微粒(PM2.5) 污染來源推估及化學成分特性變化

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摘要(中)

本文以正矩陣因子法(Positive Matrix Factorization, PMF)及條件機率函數(Conditional Probability Function, CPF)推估2017年「細懸浮微粒(PM2.5)化學成分監測及分析計畫」(以下稱為「2017年計畫」) 6個測站PM2.5污染來源，並以潛在源貢獻因子法(Potential Source Contribution Function, PSCF)探討每個測站硫酸鹽長程傳輸軌跡起源。針對「2017年計畫」在PM2.5高濃度(≧35 μg m-3)時，硝酸根離子在PM2.5占比提高及台灣西部測站有時出現的凌晨PM2.5高濃度現象，本文彙整上述發現的各種污染物濃度及環境條件。此外，本文也推估原生有機碳和二次有機碳濃度以瞭解彼此濃度分布。
污染源推估結果顯示板橋站以F3 (二次硫酸鹽與工業鍋爐)為最重要污染源，推論受樹林等工業區影響。忠明站最重要污染源為F4 (二次硝酸鹽)，推測與國道1號等交通排放有關。斗六站也是以F3 (二次硝酸鹽)為最重要污染源，可能受到雲林科技園區與國道3號擴散不佳影響。嘉義站最重要污染源為F1 (二次硝酸鹽)，從污染來向推論受嘉太工業區和交通排放影響。F3 (二次硝酸鹽與揚塵)是小港站最重要污染源，推論受大發、臨海工業區等影響。具體來說，花蓮站是以F4 (二次硫酸鹽與工業鍋爐)為最重要污染源，推論與海上船舶及美崙工業區有關。
以PSCF追溯各測站(二次硫酸鹽)因子軌跡起源，僅有板橋站來自大陸山東及江蘇省，其他測站多以本地污染源為主。忠明站傳輸污染與台中焚化廠、台中工業區與彰濱工業區等有關；斗六站與斗六工業區等污染源混合影響有關。嘉義站可能受台南南部科學工業園區（南科）影響；小港站則受仁武垃圾焚化廠、大發工業區、中國鋼鐵、林園工業區等混合影響；花蓮站在東北方海面上有污染貢獻，推論受船舶影響。
當台灣西部發生高濃度PM2.5，NO3-呈現高濃度占比，主要與風速低造成的污染物累積與前日污染殘留影響有關。氣膠水溶性離子成分使用ISORROPIA模式進行模擬，指出以(NH4)2SO4、NH4NO3結合型態為主，顯示有氨氣充足現象；這可由台灣北至南部5個測站過剩NH4+與NO3-莫耳濃度有很好相關性(R2) 0.79、0.89、0.89、0.89、0.90得到驗證。至於偏離過剩NH4+與NO3-線性關係較大的離群值，推測可能與夜間高硝酸根離子和NO2濃度的N2O5水解等有關。顯然地，凌晨PM2.5高濃度現象受到前一天氣體污染物濃度影響，其中，以NOX濃度較高，SO2濃度雖然較低，但和前一天濃度差異百分比也不小，NOX和SO2管制具有相同重要性。
透過EC-tracer方法推估板橋站的二次有機碳占OC約32-38%、忠明站約23-41%、斗六站約44-56%、嘉義站約30-47%、小港站約30-57%、花蓮站約28-46%，顯示台灣地區PM2.5 有機碳組成以原生有機碳為主，來源主要是交通活動相關排放與生質燃燒。
綜合而言，除了板橋站有部分SO42-來自中國北方外，台灣西部地區PM2.5以本地污染源貢獻為主，特別是NO3-。高濃度PM2.5事件的高NO3-濃度占比與風速低及較高NOX有關。至於夜間高濃度NO3-可能與高NO2和較高環境相對濕度導致N2O5水解有關。

摘要(英)

This study used Positive Matrix Factorization (PMF) and Conditional Probability Function (CPF) to identify PM2.5 source contributions of the six stations in the “PM2.5 chemical composition monitoring and analysis study” in 2017 (henceforth referred to “2017 Study”). In addition, the Potential Source Contribution Function (PSCF) was applied to investigate the trajectory origins of sulfates through long-range transport to each site. Given the proportion of PM2.5 nitrate ion was higher in a high PM2.5 event (≧35 μg m-3) and frequent occurrence of high PM2.5 in the western area of Taiwan in the midnight in “2017 Study”, this study summarized the pollutant concentrations and environmental conditions associated with the above findings. Moreover, this study estimated primary and secondary organic carbon concentrations to understand the relative distribution of each other.
The source apportionment results showed that F3 (secondary sulfate and industrial boiler) was the main source factor under the influence of the Shulin Industrial Zone at the Banqiao station. For the Zhongming station, F4 (secondary nitrate) was the most important source factor caused by Freeway 1 and other traffic emissions. F3 (secondary nitrate) was the most significant source factor affected by Yunlin Technology Industrial Park and Freeway 3 during poor ventilation of the Douliu station. Similarly, F1 (secondary nitrate) was the source factor contributed the most and was related to Jiatai Industrial Zone plus intensive traffic emissions at the Chiayi station. For the Xiaogang station, F3 (secondary nitrate and soil dust) was the major source factor under the influences of the Dafa and Linhai Industrial Zones. Specifically, ship emissions from the sea and Meilun Industrial Zone accounted for the worst source factor of F4 (secondary sulfate and Industrial boiler) at the Hualien Station.
PSCF modeling for the trajectory origins of “secondary sulfate” of various stations only to find that the Banqiao Station was under the influence of Shandong and Jiangsu Provinces in China. In contrast, local pollution sources affected other stations. For example, Taichung Incineration Plant, as well as Taichung and Changbin Industrial Zones affected the Zhongming Station. The mixed sources including Douliu Industrial Zone influenced the Douliu Station. The Southern Taiwan Science Park might have affected the Chiayi Station. Moreover, a mix of sources affected the Xiaogang Station such as Renwu Refuse Incineration Plant, Dafa Industrial Zone, China Steel Corporation, and Linyuan Industrial Zone. The ship emissions in the northeastern direction to the Hualien station might have caused the pollution.
The high NO3- proportion in high PM2.5 concentration events were mainly under the influences of pollutant accumulation due to low wind speed and the pollutant left-overs from the previous day. ISORROPIA modeling indicated the abundance of NH4+ such that the main compound forms were (NH4)2SO4 and NH4NO3 of the water-soluble inorganic ions. This was evidenced by the excellent correlations (R2) between excess molar concentrations of NH4+ and NO3- were 0.79, 0.89, 0.89, 0.89, and 0.90 in the five stations from north to south in Taiwan. For the outliers that deviate from the linear relationship between excess NH4+ and NO3- might be related to the occurrence of N2O5 hydrolysis under high NO3- and NO2 concentrations at night. Apparently, the pollutant concentrations of the previous days affected the midnight high PM2.5 concentrations. Among various gas pollutants, NOX concentrations were the highest with great deviations with that of the previous day; however, the control of NOX and SO2 were of equal importance as SO2 was also with great deviations with that of the previous days.
The estimates from the EC-Tracer method showed that the proportions of secondary organic carbon over organic carbon (OC) were 32-38% at the Banqiao station, 23-41% at the Zhongming station, 44-56% at the Douliu station, 30-47% at the Chiayi station, 30-47% at the Xiaogang station, and 28-46% at the Hualien station, respectively. It implies that primary OC is the major OC in Taiwan. The sources are mainly associated with emissions from transportation activities and biomass burning.
In summary, local pollution sources are mainly responsible for PM2.5 concentration especially for NO3- in the western area of Taiwan except part of SO42- transported from northern China. Low wind speed and higher NOX were associated with the high proportion of NO3- in high PM2.5 concentration events. As for the high concentration of NO3- at night, it might be related to N2O5 hydrolysis under high NO2 and relative humidity in the environment.

關鍵字(中)

★ 細懸浮微粒(PM2.5)
★ PM2.5化學成分
★ PMF與CPF來源推估
★ PSCF
★ EC-tracer

關鍵字(英)

★ PM2.5
★ chemical components
★ PMF
★ CPF
★ PSCF
★ EC-Trace r

論文目次

摘要 I
Abstract III
致謝 VI
目錄 VII
表目錄 X
圖目錄 XII
第一章前言 1
1.1 研究緣起 1
1.2 研究目的 2
第二章文獻回顧 3
2.1 細懸浮微粒(PM2.5)重要性之文獻 3
2.1.1 PM2.5對人體的危害 3
2.1.2 PM2.5對環境的影響 4
2.2 細懸浮微粒(PM2.5)形成來源 5
2.2.1 PM2.5氣膠水溶性離子組成與機制 6
2.2.2 PM2.5氣膠碳成分組成與機制 10
2.2.3 PM2.5金屬成分來源 13
2.3台灣細懸浮微粒(PM2.5)特性 15
2.4 受體模式正矩陣因子法PMF(Positive Matrix Factorization)應用 18
2.5 潛在源貢獻因子法(Potential source contribution function, PSCF ) 20
第三章研究方法 23
3.1 研究架構與流程 23
3.2採樣地點與時間 25
3.2.1 採樣地點 26
3.2.2 採樣時間 27
3.3 PM2.5手動採樣儀器 28
3.3.1 MetOne SASS PM2.5採樣器 28
3.3.2 MetOne E-FRM PM2.5採樣器 30
3.4 PM2.5質量濃度和化學成分分析方法 31
3.4.1 濾紙採樣前準備 31
3.4.2 氣膠水溶性離子分析方法 32
3.4.3 氣膠碳成分分析方法 33
3.4.4 質量濃度秤重分析 35
3.4.5 氣膠微粒揮發成分補償方法 36
3.4.6 氣膠金屬元素成分檢驗分析方法 37
3.5 正矩陣因子法PMF(Positive Matrix Factorization)概要 42
3.5.1 輸入資料處理 43
3.5.2 模式操作 44
3.4 條件機率函數CPF(Conditional probability function)推估方法 48
3.5 潛在源貢獻因子法(Potential source contribution function, PSCF ) 49
3.6 ISORROPIA模式概要 50
第四章結果與討論 51
4.1 PMF受體模式推估污染來源 51
4.1.1 板橋站PM2.5污染來源 52
4.1.2 忠明站PM2.5污染來源 61
4.1.3 斗六站PM2.5污染來源 70
4.1.4 嘉義站PM2.5污染來源 79
4.1.5 小港站PM2.5污染來源 87
4.1.6 花蓮站PM2.5污染來源 96
4.1.7 各測站因子數選擇比較 103
4.1.8 各測站PMF污染源推估結果比較 104
4.2 潛在源貢獻因子法(Potential source contribution function, PSCF) 105
4.2.1 台灣各測站軌跡線群集分析 105
4.2.2 台灣各測站二次硫酸鹽因子PSCF分析 109
4.3高硝酸根離子濃度占比、PM2.5水溶性離子組成及凌晨PM2.5高濃度現象 117
4.3.1 台灣北中南部高濃度硝酸根離子環境條件 117
4.3.2 PM2.5水溶性離子組成 125
4.3.3 凌晨PM2.5高濃度 129
4.4 PM2.5二次有機碳成分推估 133
4.4.1 台灣北中南都會區、雲嘉區及東部PM2.5與碳成分相關探討 133
4.4.2 PM2.5二次有機碳成分推估 138
第五章結論與建議 145
5.1 結論 145
5.2 建議 147
第六章參考文獻 148
附錄一 PMF受體模式檢測結果 171
附錄二板橋、忠明與小港站PMF結合CPF對應方向主要的公私場所排放量 173
附錄三台灣民國102年行業別版排放量分類統計表 174
附錄四 2017年1-3月、10-12月火點圖 176
附錄五各測站因子數選擇比較 179
附錄六高濃度硝酸鹽採樣日天氣因子 186
附錄七離群值環境條件 195
附錄八不同季節修正後PM2.5濃度與氣態污染物監測資料 199
附錄九不同季節非燃燒與燃燒反應生成的POC 200
附錄十各測站PM2.5季節成分濃度(μg m-3)與占比(%) 201
附錄十一口試委員意見答覆 202

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指導教授

李崇德(Chung-Te Lee)

審核日期

2019-1-9

推文