臭氧生成涉及前驅物，棘手程度已逐漸超越PM2.5，成為指標型之空氣污染物。本研究基於臭氧與其前驅物間光動態平衡(photo-stationary state, PSS)概念，以建成環境為主體，建構地表臭氧生成與消耗框架。針對2006、2016、2021年度臭氧高污染事件，以臺北、新北、基隆、桃園與新竹地區空氣品質監測站周邊1.5公里為範圍，使用監測站之臭氧與其前驅物濃度資料，搭配國土利用調查資料、三維建物與道路圖資等資料集，建立迴歸模型。採用分量迴歸（Quantile Regression, QR）與最小平方法（Ordinary Least Squares, OLS），系統性分析建成環境因子如何影響臭氧及其前驅物NOₓ 與 NMHC。研究發現，相比於OLS模型，QR 模型提供了更多的細節。特別是汙染物濃度分位數低於 25% 或高於 75% 的極端情況，往往更顯著、更具影響力。建成環境因子對臭氧生成與消耗的影響在不同濃度分位數下亦呈現顯著差異 (p<0.001)，例如在低濃度臭氧地區，農業活動顯著影響當地生物源 VOCS 的釋放。而交通與污染設施對臭氧及前驅物的影響強度與方向性，均隨污染物濃度而變化。研究突破了過往空氣污染研究專注於分析平均值的侷限性，更全面討論建成環境因子在不同污染濃度條件下的影響關係。提出基於迴歸模型與空間差異的城市空氣污染防制建議，為空間規劃提供更多環境數據，以數據支持健康與永續城市的發展、應對全球氣候變遷帶來的挑戰。

This study examines how built environment factors impact ozone production and consumption, using data from Taipei, Keelung, Taoyuan, and Hsinchu. Employing Quantile Regression (QR) and Ordinary Least Squares (OLS), the results show significant variations (p<0.001) across different quantiles. QR reveals more detailed effects, especially in extreme conditions where pollutant concentrations are notably low or high, highlighting the nuanced influence of urban design on air quality. These insights are crucial for developing targeted urban planning strategies to control air pollution.

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