探討學童汙染物濃度暴露之社會經濟公平性

Abstract

空氣污染對人體健康有負面影響，而學童因生理發展尚未成熟，對空氣污染的暴露更為敏感。除了生理脆弱性之外，社會經濟地位（socioeconomic status, SES）也被認為是影響空氣污染暴露的重要因素，社會經濟弱勢群體往往面臨較高的污染暴露。然而，既有較少以學校作為分析單位，且交通相關空氣污染雖為都市空污的主要來源之一，但少有研究同時納入學校周邊的交通環境，探討其與社會經濟地位、空氣污染暴露三者之間的關係。 本研究以台北市、台南市及台東縣三個都市化程度不同的城市作為研究案例，以小學作為分析單位，整合2020年全年高時間解析度的PM2.5感測資料、以住宅交易價格作為社會經濟地位代理指標的不動產交易資料，以及與主要道路距離為代表的交通指標資料，採用分組分析、K-means集群分析以及全域與局部Moran's I空間自相關分析，並計算PM2.5超標比例，以不同面向檢視社會經濟地位與PM2.5暴露之間的關係。 研究結果顯示，社會經濟地位與PM2.5暴露之間的關係並非單一方向，而隨城市、季節與空間結構而異。在台北，高社會經濟地位學校周邊反而呈現較高的PM2.5濃度；而在台南與台東，則呈現低社會經濟地位學校暴露較高的型態，較符合既有環境不平等文獻所描述的方向。季節分析顯示，僅依賴年平均濃度可能低估特定時期的暴露不平等。納入交通指標後，距主要道路的遠近並未完全解釋社會經濟地位相關的暴露差異。空間分析結果進一步顯示，台北與台南在PM2.5與房價格之間的雙變量空間關聯同樣呈現相反方向，反映兩個城市不同的都市發展型態。 本研究強調，學童空氣污染暴露的社會經濟不平等並非依循單一固定模式，而需考量都市發展脈絡、空間結構與季節變化等多重因素。研究結果可為未來都市環境規劃與健康政策提供參考，並指出在不同都市情境下檢視環境不平等議題的重要性。

Air pollution has adverse effects on human health, and schoolchildren are particularly sensitive to air pollution exposure because their physiological development is not yet complete. In addition to physiological vulnerability, socioeconomic status (SES) is also considered an important factor influencing air pollution exposure, as socioeconomically disadvantaged groups often face higher levels of pollution exposure. However, relatively few studies have used schools as the unit of analysis, and existing research has mainly been concentrated in North America and Europe, while related studies in Asia remain relatively limited. In addition, although traffic-related air pollution is one of the major sources of urban air pollution, few studies have simultaneously incorporated the traffic environment surrounding schools to examine the relationships among socioeconomic status, air pollution exposure, and traffic conditions. This study uses Taipei, Tainan, and Taitung, which represent different levels of urbanization, as case study areas. Using elementary schools as the unit of analysis, this study integrates high-temporal-resolution PM2.5 sensor data for the full year of 2020, real estate transaction data using residential transaction prices as a proxy indicator of socioeconomic status, and traffic indicator data represented by distance to major roads. The study applies grouping analysis, K-means clustering analysis, Kruskal-Wallis tests, Mann-Whitney U tests, and global and local Moran’s I spatial autocorrelation analyses. In addition, the PM2.5 exceedance ratio is calculated to examine the relationship between socioeconomic status and PM2.5 exposure from multiple perspectives. The results show that the relationship between socioeconomic status and PM2.5 exposure does not follow a single direction, but varies across cities, seasons, and spatial structures. In Taipei, schools located in higher-SES areas showed higher PM2.5 concentrations. In contrast, Tainan and Taitung showed a pattern in which schools in lower-SES areas experienced higher exposure, which is more consistent with the direction described in previous environmental inequality literature. The seasonal analysis indicates that relying only on annual average concentrations may underestimate exposure inequalities during specific periods. After incorporating traffic indicators, distance to major roads did not fully explain SES-related differences in exposure. The spatial analysis further shows that Taipei and Tainan exhibited opposite patterns of bivariate spatial association between PM2.5 and housing prices, reflecting different urban development patterns in the two cities. This study emphasizes that socioeconomic inequalities in schoolchildren’s air pollution exposure do not follow a single fixed pattern, but should be understood in relation to multiple factors, including urban development contexts, spatial structures, and seasonal variations. The findings can provide references for future urban environmental planning and public health policies, and highlight the importance of examining environmental inequality under different urban contexts.