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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94271
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dc.contributor.advisor施上粟zh_TW
dc.contributor.advisorShang-Shu Shihen
dc.contributor.author麥維中zh_TW
dc.contributor.authorWei-Zhong Maien
dc.date.accessioned2024-08-15T16:32:57Z-
dc.date.available2024-08-27-
dc.date.copyright2024-08-15-
dc.date.issued2024-
dc.date.submitted2024-07-25-
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94271-
dc.description.abstract近年來,越來越多文獻表明懸浮在空氣中的PM2.5對人類會有不小的危害。關於如何改善空氣品質,不少研究指出植生帶對於PM2.5降解存有一定程度的效果。然而,大部分有關植生帶改善空氣中PM2.5濃度的研究,多為探討小範圍內植生帶與非植生帶的PM2.5濃度差異,較少涉及到同時存有植生帶、結構物與水域的複雜環境內的PM2.5濃度變化。都市中的校園內大多含有豐富的植生帶與小範圍的水域,且結構物的密集程度相比於都市街谷區域也較為稀疏。為了解都市與校園內不同基盤間的PM2.5濃度,進而探討PM2.5是否會因為外部風吹拂而讓特定基盤內有滯留現象。本研究選定臺灣大學校本部及周圍共約2.3平方公里之範圍為研究區域,依臺灣空氣污染物排放量清冊內所整理的PM2.5年度排放量資料作為污染場的參考來源,輔以鄰近研究區域的氣象站所記錄之氣象資料,並使用GRAL(Graz Lagrangian Model)與GRAMM(Graz Mesoscale Model)模式來模擬特定時段下PM2.5與風廊的分佈情形。
模擬結果顯示,校園內部植生帶與水域的PM¬2.5濃度明顯較高。檢視特定區域內的植生帶、水域與結構物的PM2.5垂直濃度分佈,可看到在特定高度區間,當高度增加時,植生帶和水域內的PM2.5濃度減少幅度較結構物周圍小,推測原因是植生帶與水域內的PM2.5較難以受到風廊而轉移至其他地方。本研究也設定了四組不同的情境來探討不同基盤下的差異。在更改校園配置的情境與納入降雨的情境下,可看出植生帶與水域的PM2.5沉降量皆大於原始情境。在不同季節的情境下,可觀察到風廊對PM2.5濃度分佈有十足的影響。在更改PM2.5來源的情境下,能發現校園內的植生帶與水域具有滯留校外PM2.5的能力。上列的模擬結果皆側面說明都市校園內植生帶與水域具有滯留PM2.5的功能,並突顯植生帶與水域在小範圍都市內所扮演的角色。
zh_TW
dc.description.abstractRecent literature suggests that PM2.5 in the air significantly negatively influences human health, and some studies indicate that vegetation can effectively reduce PM2.5 impacts. However, most research on how vegetation affects PM2.5 concentrations has focused on small-scale areas. Few studies examine PM2.5 concentration variations in deploying complex landscapes and environments, such as areas with vegetation, buildings, and water bodies. Urban campuses often have abundant vegetation and lower structure density than urban street canyon areas. To better understand PM2.5 concentration variations between different infrastructures in campuses and urban areas and to explore whether external wind fields may detain PM2.5 within specific infrastructures, this study selected an area of approximately 2.3 square kilometers, including and around the National Taiwan University (NTU). Data from the Taiwan Emission Data System (TEDS) was used to reference annual PM2.5 emission sources. The meteorological data from nearby weather stations of the NTU were collected. The Graz Mesoscale Model (GRAMM) and Graz Lagrangian Model (GRAL) were combined to simulate the changing concentration and residence of PM2.5 with several scenarios considering the effects of seasonal wind and rainfall, pollutant sources inside and outside the campus, and allocation of vegetation and water areas.
The results show a significantly higher concentration of PM2.5 within vegetation and water areas on the campus, indicating their detention potential. We examine the vertical distribution of PM2.5 concentrations within specific vegetation, water areas, and buildings. We observe that within specific height ranges, as height increases, the degradation of PM2.5 concentrations on vegetation and water bodies is less than on artificial structures such as buildings. PM2.5 concentrations within vegetation and water areas were found to be less easily transported by wind compared with PM2.5 around structures. This study also designed four scenarios to investigate variations among different landscapes. In the scenario of campus reconfiguration and the incorporation of rainfall, it can be observed that both vegetation and water areas demonstrate higher PM2.5 deposition compared to the non-rainfall baseline model. Wind fields with significant advection effects can distinctly influence the distribution of PM2.5 concentrations in different seasons. In adjusting PM2.5 emission sources, it is evident that vegetation and water areas on campus can catch PM2.5 input from outside. We suggest that greenery and water bodies in urban campuses can trap PM2.5, emphasizing their role in small-scale urban environments.
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dc.description.tableofcontents口試委員審定書 i
謝辭 ii
中文摘要 iii
Abstract iv
目次 vi
圖次 ix
表次 xii
第一章、 緒論 1
1.1、 研究緣起 1
1.2、 研究目的 3
1.3、 研究內容 4
1.3.1、 論文架構 4
1.3.2、 研究流程 5
第二章、 文獻回顧 7
2.1、 PM2.5對人居環境之影響 7
2.2、 都市街谷對PM2.5之影響 8
2.3、 植生帶對PM2.5之影響 10
第三章、 研究方法 13
3.1、 拉格朗日粒子擴散模式( Graz Lagrangian Model, GRAL ) 13
3.1.1、 基礎擴散方程式 13
3.1.2、 垂向與水平擴散 15
3.1.3、 乾沉降與濕沉降 20
3.1.4、 風與大氣條件 22
3.1.5、 植生帶與結構物之各網格污染物與流場分佈 25
3.2、 拉格朗日中尺度擴散模式( Graz Mesoscale Model, GRAMM ) 29
3.2.1、 控制方程式 29
3.3、 數值運算流程 31
第四章、 模式建置 35
4.1、 研究地點 35
4.2、 資料來源與彙整 36
4.2.1、 結構物資料 36
4.2.2、 植生帶資料 37
4.2.3、 水域資料 39
4.2.4、 PM2.5資料 40
4.2.5、 氣象場資料 48
4.3、 模式建立流程 50
4.3.1、 基礎參數設置 50
4.3.2、 地形場建置 52
4.3.3、 污染場建置 53
4.3.4、 氣象場設置 58
第五章、 模擬成果分析與討論 60
5.1、 現況分析及討論 60
5.1.1、 校園內植生帶與非植生帶之差異 60
5.1.2、 校園內與外圍街谷之差異 73
5.2、 模擬情境分析及討論 78
5.2.1、 改變校園配置對校園內外PM2.5分佈之影響 78
5.2.2、 季節差異對校園內外PM2.5分佈之影響 87
5.2.3、 調整污染排放源對校園內外PM2.5分佈之影響 96
5.2.4、 降雨事件對校園內外PM2.5分佈之影響 104
5.2.5、 各模擬情境與原始現況比對 106
第六章、 結論與建議 108
6.1、 結論 108
6.2、 建議 110
參考資料 112
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dc.language.isozh_TW-
dc.subject風廊zh_TW
dc.subjectPM2.5zh_TW
dc.subject滯留現象zh_TW
dc.subject水域zh_TW
dc.subject植生帶zh_TW
dc.subjectvegetationen
dc.subjectwater areasen
dc.subjectdetentionen
dc.subjectwind fieldsen
dc.subjectPM2.5en
dc.title校園風廊系統對周遭環境 PM2.5濃度以及沉降量影響之模擬分析zh_TW
dc.titleSimulation analysis of the impact of campus wind corridor systems on ambient PM2.5 levels and depositionen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee胡明哲;吳清森zh_TW
dc.contributor.oralexamcommitteeMing-Che Hu;Ching-Sen Wuen
dc.subject.keywordPM2.5,風廊,植生帶,水域,滯留現象,zh_TW
dc.subject.keywordPM2.5,wind fields,vegetation,water areas,detention,en
dc.relation.page117-
dc.identifier.doi10.6342/NTU202401886-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2024-07-26-
dc.contributor.author-college工學院-
dc.contributor.author-dept土木工程學系-
顯示於系所單位:土木工程學系

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