WHO(2021)全球空氣品質準則報告指出，空氣污染相關的疾病對於全球人類的健康造成極大的損害，同時也對人類賴以生存的環境產生極大的負擔，根據環境局之PM2.5污染濃度資料顯示雲嘉南及高屏地區的空氣品質狀況較差，為更需要關注其空氣品質狀態之地區。然而目前對於PM2.5及影響因子的研究大多是討論PM2.5濃度與影響因子間的關聯性，但在討論兩者關係前，先了解污染濃度及各項影響因子的空間分布狀況及資料特徵後再驗證兩者的關聯性是更具意義的；多數文獻對於污染濃度影響的討論大多透過土地使用、交通與建築等面向進行切入，在植栽對於PM2.5 污染濃度影響的研究多以綠覆率等尺度較大的因子進行研究，對於植栽特性與PM2.5污染濃度影響著墨則相對較少，故本研究先透過文獻回顧了解PM2.5潛在的影響因子，同時也納入植栽特性作為影響因子的一部分，並在PM2.5污染濃度較高的時間區段下對污染濃度資料及各項影響因子進行集群分析以了解各項資料的特徵，再進一步針對不同組合狀況建立回歸模型以了解PM2.5污染濃度與各項影響因子之關聯性。
研究結果發現在污染濃最高的組別中工業土地使用面積占比及建物平均高度兩項影響因子對PM2.5污染濃度具有顯著的影響，而在污染濃度次高的組別中污染濃度則是受到建物平均高度、住宅及商業土地使用面積占比及樹冠覆蓋面積占比與植被覆蓋面積占比之顯著影響，在汙染濃度較低的組別中則是受到一般道路面積占比及工業土地使用面積占比之影響，根據上述結果本研究，對後續研究提出以下幾點建議：1.更加完整的運用現有的測站資料，並且更大膽的依據空汙因子的特性選擇更適當的分析範圍；並可以嘗試更完整的全區分析。2.拓展分析時間的尺度，往污染濃度變化的方向更進一步的分析。3.除了線性迴歸，可依據汙染物與地方特性使用不同的迴歸方式。4.嘗試在準備資料中，使用更新的技術利於更直接的反應當下的情況。

The WHO (2021) report emphasizes that global air quality poses a significant threat to health and the environment, particularly in the Yunlin-Chiayi-Tainan and Kaohsiung-Pingtung regions where PM2.5 pollution is pronounced. Current research predominantly focuses on the correlation between PM2.5 concentrations and influencing factors. However, it is more meaningful to first understand the spatial distribution and characteristics of both the concentrations and factors before validating their interrelations. Planting characteristics are relatively underexplored in existing studies. This research, through a literature review of potential influencing factors, including planting characteristics, employs cluster analysis to explore distinct features during various periods. Results reveal that in the high pollution group, industrial land and building height significantly influence PM2.5. In the moderately polluted group, building height, residential-commercial land ratio, tree crown, and vegetation coverage demonstrate notable effects. The low pollution group is influenced by general road and industrial land ratios. Recommendations include a more comprehensive utilization of existing station data, selection of appropriate analysis scopes based on air pollution factor characteristics, expansion of temporal analysis scales, utilization of multivariate regression approaches, and incorporation of updated technologies for improved real-time data accuracy.

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