臺灣博碩士論文加值系統

中部科學園區(中科)主要是製造半導體、電子和電子外圍設備的高科技園區，而本研究的目標是鑑定中科廠商排放細微顆粒的特性。本研究從2013年1月到9月使用八階微孔均勻沉降微粒採樣器 (MOUDI)去收集沉降微粒中20種金屬(Al, Fe, Na, Mg, Ca, K, In, Mo, Zn, Cr, Cu, Pb, Mn, Ni, Sn, V, Cd, As, Ga and Ge)。
本研究採用下列5種方法找尋高科技產業排放重金屬元素成分: 1.金屬濃度的比較2.累積濃度曲線 3.相關性分析 4.聚類分析 5.期望值理論。1. 經由濃度與其他研究的比較得知Ni (2.18 ng/m3), As (0.16 ng/m3), Ge (0.01 ng/m3), Ga (0.03 ng/m3), Cd (0.12 ng/m3)等金屬的濃度明顯高於其他研究地區。 2.累積濃度曲線將金屬的波峰特性分為三個不同種類：第一種是波峰主要出現在粒徑5.6μm的金屬(Al, Fe, Na, Mg, Ca)、第二種是在所有粒徑下都有可能出現波峰的金屬(K, In, Mo, Zn, Cr, Cu, Pb, Mn, Ni, Sn) 、第三種是主要波峰出現在極細粒徑(小於1μm)的金屬(V, Cd, As, Ga, Ge)。3.相關性分析：可以提供金屬與金屬間是否擁有相似的排放來源。在上風與下風處中，Ga-Ge-As-Cd明顯地有極高的相關性。且移動性元素中，Mn-Ni、Pb-Zn-In-Cr在上風處中有高度相關性，但是在下風處Mn-Ni、Zn-Cu和Pb-As-Cd-Zn-K-Cr有高度的相關性，最後地殼性元素不論在上風或下風處都有高度的相關性。4.使用聚類群集分析(HCA)把金屬分類，可以分為三個群集：第一是地殼性群集、第二是移動性群集、最後是固定性群集。在上風與下風處Al, Fe, Na, Mg, Ca都在同一個群集內，這些金屬都是來自地殼居多。然而上風處的固定性群集有V, As, Ni, Pb, Ga, Ge，但是到了下風處固定性群集的金屬有Ga, Ge, V, As, Cd, Pb, Mn, Ni。上風處的移動性群集有Mn, Cd, Zn, K, Sn, Cr, In, Cu, Mo，然而在下風處移動性群集的金屬有Zn, K, Sn, Cr, In, Cu, Mo。將各元素的分析出現次數彙整後，Ga, Cd 和As出現次數都是5次為最高。5.最後將全部分析方式所獲得的高再現性之元素: As, Ga, Cd進行期望值理論去模擬可能汙染方向推論，其結果顯示As及Ga可能是此區域高科技產業的汙染物，但Cd在期望值結果中，東海採樣點北方沒有貢獻量，所以推論此地區高科技產業與Cd的相關性不高。
先前的研究使用高流量採樣器，研究指出在PM2.5和PM10中的As, Cd, Ge, Pb, Sn, Zn可以當作高科技園區的可能指標汙染物，然而本研究使用MOUDI去鑑定更細的粒狀汙染物，利用期望值理論去確定哪個方向是主要的可能來源，其結果可以解釋在目前極細顆粒中As和Ga是此地區高科技產業可能的汙染物，目前結果顯示高科技產業對Cd的貢獻量並不高。

The Central Taiwan Science Park (CTSP), which consisting of manufacture of semiconductors, electronics and electrical peripherals, was the research target of identifying the characteristics of ultrafine particles mission. This study used a 8-stage micro-orifice uniform deposit impactor (MOUDI) sampler to collect the size distribution of various heavy metals (Al, Fe, Na, Mg, Ca, K, In, Mo, Zn, Cr, Cu, Pb, Mn, Ni, Sn, V, Cd, As, Ga and Ge) in airborne particles from January to September, 2013.
This study used 1.Comparsion concentration of heavy metals with other researches 2. Accumulation curve 3.Correlation analysis 4. HCA 5. Expected value. 1. Comparing with other researches, it indicated that concentration of Ni (2.18 ng/m3), As (0.16 ng/m3), Ge (0.01 ng/m3), Ga (0.03 ng/m3) and Cd (0.12 ng/m3) were both higher other researches. 2. The characteristic size peak distributions was identified by accumulation mode curve and could be classified into three categories: (i) main peak at 5.6μm, (Al, Fe, Na, Mg and Ca), (ii) peak spread around fine, intermediate and coarse modes (K, Zn, In, Mo, Cr, Cu, Pb, Mn, Ni and Sn), and (iii) metals mass peak mainly less than 1μm (V, Cd, As, Ga and Ge). 3. The correlation analysis could both tell which metal had the similar sources and its characteristic of pollution. Both in upwind and in downwind sites, the elements: Ga-Ge-As-Cd observed having the high correlation in two-tailed significant level analysis. The mobile sources elements Mn-Ni, Pb-Zn-In-Cr in upwind had high correlation, while in downwind Mn-Ni, Zn-Cu and Pb-As-Cd-Zn-K-Cr had high correlation while the crustal metals had high correlation in both upwind and in downwind samples. 5. It used hierarchical cluster analysis (HCA) to classify the metals, it could divide three different clusters: (i) crustal cluster, (ii) mobile cluster, (iii) station cluster. In both upwind and downwind samples had Al, Fe, Na. Mg and Ca within the same cluster, those elements both were emit from crustal, so the cluster was the crustal sources while the cluster of station sources had V, As, Ni, Pb, Ga and Ge in upwind, but the cluster of station sources had Ga, Ge, V, As, Cd, Pb, Mn and Ni in downwind. The cluster of mobile sources had Mn, Cd, Zn, K, Sn, Cr, In, Cu and Mo in upwind while the cluster of mobile sources had Cr, Sn, In, K, Cu, Mo and Zn in downwind.5. Finally, combining all the methods, it could realize the metals most frequent were As, Ga and Cd. Using expected value theory to identify the possible sources from high-tech industries in this area.
Previous study used high-volume sampler and indicated that As, Cd, Ge, Pb, Sn and Zn were high-tech park indicator emission in PM2.5 and PM10, while in this study, it used MOUDI to identify the finer particle matter sources and the expected value could show that which direction was the main sources. The results of expected value could explain the UF was the significant pollutants and concluded that Ga and As were the main pollutants in the target area.

LIST OF FIGURES V
Abstract VII
Chapter 1. Introduction 1
Chapter 2. Literature review 7
2.1 Particulate matters in the air 7
2.2 Heavy metals 11
2.3 Size distribution 16
2.4 Hierarchical Cluster Analysis 19
2.5 Expectations theory 21
Chapter 3. Material and Methods 23
3.1 The sampling plan 23
3.1.1 The sampling area 23
3.1.2 The sampling time and frequency 26
3.2 Sampling equipment 26
3.3 Chemical analysis 28
3.3.1 The process of sample pretreatment 28
3.3.2 Analyze the Element concentration 28
3.4 Analysis method of particulate matters (PM) 29
3.4.1 Matrix of correlation analysis 29
3.4.2 Hierarchical cluster analysis (HCA) 30
3.4.3 Size distribution 31
3.4.4 Wind division and event probability estimation 32
Chapter 4. Results and Discussion 34
4.1 Chemical composition analysis 34
4.2 Size distribution 40
4.2.1 Emission peak patterns of particulate 40
4.2.2 Elemental Size Distributions 44
4.3 Correlation Analysis 47
4.4 Multivariate analysis (hierarchical cluster analysis, HCA) 51
4.5 Source apportionment 56
4.6 Summary of metals 61
Chapter 5. Conclusions and suggestions 62
5.1. Conclusions 62
5.2. Suggestions 63
References 64

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