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姓名	廖健捷(Chien-Chieh Liao)  查詢紙本館藏	畢業系所	環境工程研究所
論文名稱	利用移動式平台量測台灣北部都會區道路上細懸浮微粒在時間與空間上的變化 (Using a Mobile Platform to Characterize Spatial and Temporal Variation of On-road Fine Particles in Northern Taiwan Urban City)
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摘要(中)	道路交通排放為都市環境中細懸浮微粒的重要貢獻來源。暴露於懸浮微粒環境中將可能對人體健康造成負面效應，而都市居民在通勤過程中易受道路上車輛污染排放的影響。超細懸浮微粒(UFP)和黑碳微粒(BC)為機動車輛排放的指標粒狀污染物之一，在道路環境中的濃度常呈現劇烈的時間與空間變化。傳統固定測站可能因涵蓋密度不足，以至於無法有效的捕捉機動車輛排放在道路環境上的時空變化，並有效推估其對於用路人的暴露劑量，故移動式監測方法逐漸受到重視。本研究建置一移動式即時監測系統用於調查台灣北部都會區道路上機動車輛的污染排放資訊，量測台灣北部都會區道路上PM2.5質量濃度、超細懸浮微粒(UFP)之平均幾何粒徑(GMD)與數目濃度(NC)、肺部沉積表面積(LDSA)、黑碳(BC)以及一氧化碳(CO)，並探討其在空間與時間的變化。在冬季期間，交通尖峰時刻在繁忙道路上懸浮微粒粒徑約為32.89±6.59 [nm]、UFP數目濃度約為61228±38320 [#/cm3]、PM2.5約為23±8 [μg/m3]、BC約為3.93±2.24 [μg/m3]、LDSA約為229.29±119.99 [μm2/cm3]以及CO約為2.44±2.07 [ppm]；在夏季期間微粒粒徑約為39.53±9.62 [nm]、UFP數目濃度約為30318±15844 [cm3]、PM2.5約為18±9 [μg/m3]、BC約為3.93±2.24 [μg/m3]、LDSA約為148.33±87.3 [μm2/cm3]以及CO約為3.46±2.74 [ppm]。量測結果發現PM2.5與道路上交通量沒有一致的變動關係，在道路上各個暫停點所測得的濃度沒有顯著的差異，在一日中亦無明顯變化，較不受到交通尖峰時刻的影響。實驗地區道路整體黑碳平均濃度沒有受到尖峰時刻與季節變化影響，實驗地區整體黑碳濃度屬於較穩定的狀態。但各個暫停點的黑碳BC濃度變化則明顯受交通量變化影響，在空間分佈上高濃度集中在高車流量的道路上。UFP數目濃度容易受到道路上車輛所影響，此外LDSA、CO與UFP數目濃度在道路中的空間變化與交通量有相似的變動趨勢。然而，除道路上車輛數目以外，街道類型與結構也會影響微粒特性在道路環境中的濃度變化。本研究進一步計算LDSA與SMPS所估計微粒表面積濃度的比值(LDSA/SA)，此可作為微粒的不規則狀的指標。無論在冬季或夏季LDSA/SA在兩條實驗路徑上沒有發現顯著的差異。LDSA/SA最低值發生在夏季中午，同時比起其他時段UFP數目濃度、LDSA與BC也伴隨著最低的濃度值。車流量與車種組成相關性分析顯示UFP數目濃度和LDSA與三種類型的車輛有顯著的相關性。CO和GMD也發現顯著的相關性，但是GMD的相關係數(r)呈現負值，而CO僅與轎車與機車有顯著相關。
摘要(英)	On-road vehicle emission is an important source of contribution for PM2.5 in urban areas.The negative health effects of exposure to PM2.5 and other Traffic related air pollutants(TRAP) is well confirmed.For city dwekkers and cimuters can be exposed to higher concentrations of vehicle emissions. Ultrafine particles (UFP) and Black carbon (BC) are the typical surrogates for vehicle emissions, and they could vary dramatically in space and time within urban microenvironment. This exhibits great challenges for estimating human exposure and the resulting potential health effects. Conventional stationary monitoring stations/networks are not enough to effectively capture TRAPs spatial characteristics.Mobile monitoring approaches have been conducted to address poorly represented by ambient stationary monitoring.In this study, a mobile real-time monitoring system was established to characterize the on-road vehicle emissions along the selected route in different period and season, including particulate matter(PM2.5), UFP size distributions, lung deposition surface area(LDSA) , Black carbon(BC) and carbon monoxide (CO) in an urban city of northern Taiwan. The results showed that the concentration of particles in the experimental area were significant differences in the season.In the winter rush hour, the particle GMD was about 32.89 ± 6.59 [nm], the number of UFP was about 61228±38320 [#/cm3], PM2.5 was about 23±8 [μg/m3], BC was about 3.93±2.24 [μg/m3], the LDSA was about 229.29±119.99 [μm2/cm3] and the CO was about 2.44±2.07 [ppm], in the summer, the GMD was about 39.53±9.62 [nm], the number of UFP was about 30318±1584 [#/cm3], PM2.5 was about 18±9 [μg/m3], BC was about 3.93±2.24 [μg/m3], the LDSA was about 148.33±87.3 [μm2/cm3] and the CO is about 3.46±2.74 [ppm] on the busy road. PM2.5 variation was not in agreement with the traffic fluxes and no significant difference among 14 points in 2 routes and in the day.The variation of BC between each point was partially related to traffic flux, like P1 and P7 with higher traffic fluxes and BC level.However, the overall average concentration of BC was not affected by rush hour and seasonal variations show that on road BC in study area was relatively stable.The UFP number conc. , LDSA and CO in R1 exceed 2 times higher than R2, in cause of the R1 with higher traffic flux than R2.The UFP NC,LDSA and CO had same trend and were agreement with the traffic flux in each point. Traffic-related pollutants levels not only depend on the traffic flux, but also the local topography, such as road intersection or buildings environment around the street.The LDSA/SA ratio indicated that the particle’s irregular level, the ratio was not significant difference between R1 and R2 no matter summer and winter showed that on road PM’s characteristic was same in all time.The LDSA/SA ratio in summer noon was lowest level and the UFP、LDSA and BC identically had lowest level in all season and period of day. The number and LDSA conc. had significant correlation with all three types of vehicles. Significant correlations were also found for CO and GMD, however, GMD’s R shows a negative value.
關鍵字(中)	★ 移動量測 ★ 交通相關污物 ★ 超細懸浮微粒 ★ 細懸浮微粒 ★ 時空變化	關鍵字(英)	★ Mobile monitoring ★ Traffic-related air pollution ★ Ultrafine particle ★ Particulate matter ★ Spatial-temporal variance
論文目次	目錄 摘要 II 英文摘要 III 致謝 V 目錄 VI 圖目錄 VIII 表目錄 X 第一章 前言 1 1.1 研究緣起 1 1.2 研究動機 2 1.3 研究目的 4 第二章 文獻回顧 5 2.1 機動車輛排放微粒特性 5 2.1.1 微粒粒徑分佈與數目濃度 5 2.1.2 懸浮微粒表面積濃度 7 2.1.3 交通相關污染物黑碳 8 2.2 移動式量測 9 第三章 研究方法 11 3.1 實驗地點 11 3.2 移動式監測系統 13 3.3 監測儀器原理與數據校正 15 3.3.1 PM2.5的質量濃度 15 3.3.2 微粒粒徑分佈 (PSD) 17 3.3.3 肺部沉積表面積濃度量 (LDSA) 17 3.3.4 黑碳 (Black Carbon) 17 3.3.5 超細懸浮微粒(UFP)數目濃度 19 3.3.6 一氧化碳 (CO) 19 3.4 實驗設計與量測策略 20 第四章 結果與討論 22 4.1 交通量與污染物分析 22 4.1.1 實驗地區交通量與車種組成 22 4.1.2 各暫停點交通量與污染物變化 23 4.2 微粒特性在時空上的變化 30 4.2.1 超細懸浮微粒 (UFP) 30 4.2.2 肺部沉積表面積 (LDSA) 34 4.2.3 細懸浮微粒 (PM2.5) 40 4.2.4 黑碳 (BC) 44 4.2.5 一氧化碳 (CO) 49 第五章 結論 53 參考文獻 55
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指導教授	蕭大智(Ta-Chih Hsiao)	審核日期	2017-7-31
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