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  • 學位論文

土地使用格局對地表溫度及空氣品質影響分析

The Impacts of Land Use Patterns on Land Surface Temperature and Air Quality

指導教授 : 葉佳宗
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摘要


近代熱島效應日益嚴重,熱化現象開始從都市地區蔓延至低度開發區,而空氣品質也為當代嚴重的環境問題,無論高溫或空氣污染的環境皆導致人類健康受衝擊,且降低居住環境品質。特定的土地使用面積比例,以及空間結構會影響溫度以及空氣品質,因此,本研究希望能透過土地使用格局(組成、結構)與地表溫度及空氣品質濃度(NOx、PM10)之分析,了解格局與三者之間的關聯性。本研究以2006年土地使用調查圖作為分類基礎,希望透過分析,了解何種土地使用組成及其結構會促使地表溫度、氮氧化物(NOx)及空氣懸浮微粒(PM10)上升,或對三者具有調節的功能。使用景觀指數分析土地使用的組成及結構。地表溫度則利用LANDSAT_5 TM2衛星影像反演,以反距離權重法(Inverse Distance Weighting)推演氮氧化物(NOx)以及空氣懸浮微粒(PM10)。 透過多元迴歸分析能夠判斷適合的分析季節及網格尺度,而地理加權迴歸則進一步考量空間非穩定條件,修正多元迴歸結果並呈呈現影響係數在研究範圍中的變動。綜觀分析結果,對地表溫度具有調節能力的土地使用為農業、森林及水體,為交通、工業及其他建地促使升溫,森林密度、連結性以及聚集度與溫度成反比;交通的密度、平均面積、連結性和聚集度會促使地表溫度上升。對空氣懸浮微粒(PM10)具有調節效果的土地使用為森林、水體及其他建地,而促使濃度上升的土地使用為農業及交通,其中最具調節能力的森林之密度、連結度、完整性;以及工業的密度、平均面積及完整性則最容易使濃度上升。根據地理加權迴歸的分析結果,土地使用格局與地表溫度的關係需要考量空間的非穩定。

並列摘要


Heat island and air pollution are strengthening day by day, the phenomenon developing from urban to suburban area; however, both of them impacted on public health and living quality. The specific percentages of different types of land uses and arrangements will influence Land Surface Temperature (LST) and air quality; hence, this study explored the relationship between land use patterns (composition, configuration) and LST, air pollution concentration. This study reclassified land use divisions by 2006 Land Use Investigation. By the estimation, finding out which types of land use patterns would increase the phenomenon of rising temperature and air pollution concentration, otherwise, discovered what kinds of land use patterns could regulate the heat island and air quality. LST was estimated by LANDSAT5 TM image, and the NOx and PM10 distribution were mapped by Inverse Distance Weighting (IDW). The research investigated the most appropriate estimating scale and season by Multiple Linear Regression analyzing. In the meanwhile, this study took Geographic Weighted Regression to consider the conditions of non-sationarity, and mapped the varying estimates. The strong negative relationship exist between agriculture, forest, water classifications and LST, and transportation, industry, other built-up area classifications will cause LST raise. The most affecting configurations are the density, connectivity and aggregation of forest, all of them can decrease the heat island. In contrast, when the density, mean area, connectivity and aggregation increase, heat island will be strengthened. Forest, water and other built-up classifications can purify PM10 concentration; in contrast, transportation and agriculture classifications will make PM10 clustering. The most regulative configurations to PM10 are density, connectivity and mean are of forest land, and the density, mean area aggregation of industrial land have the significant impact on PM10 concentration. According to implication of GWR, the estimation between land use patterns and LST needs to consider the regional geographic conditions.

參考文獻


蘇子文(2010)。應用遙感探測探討河川及濕地對都會區環境溫度之影響 。臺北科技大學,土木與防災研究所。
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被引用紀錄


游雅婷(2016)。北臺灣地區移動污染源細懸浮微粒空間濃度與族群健康風險評估〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu201600760

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