Verbeek Thomas
Department of Sociology, University of Warwick, Coventry, United Kingdom.
Centre for Mobility and Spatial Planning, Ghent University, Ghent, Belgium.
SSM Popul Health. 2018 Dec 13;7:100340. doi: 10.1016/j.ssmph.2018.100340. eCollection 2019 Apr.
Following the growing empirical evidence on the health effects of air pollution and noise, the fair distribution of these impacts receives increasing attention. The existing environmental inequality studies often focus on a single environmental impact, apply a limited range of covariates or do not correct for spatial autocorrelation. This article presents a geospatial data analysis on Ghent (Belgium), combining residential exposure to air pollution and noise with socioeconomic variables and housing variables. The global results show that neighborhoods with lower household incomes, more unemployment, more people of foreign origin, more rental houses, and higher residential mobility, are more exposed to air pollution, but not to noise. Multiple regression models to explain exposure to air pollution show that residential mobility and percentage of rental houses are the strongest predictors, stressing the role of the housing market in explaining which people are most at risk. Applying spatial regression models leads to better models but reduces the importance of all covariates, leaving income and residential mobility as the only significant predictors for air pollution exposure. While traditional multiple regression models were not significant for explaining noise exposure, spatial regression models were, and also indicate the significant contribution of income to the model. This means income is a robust predictor for both air pollution and noise exposure across the whole urban territory. The results provide a good starting point for discussions about environmental justice and the need for policy action. The study also underlines the importance of taking spatial autocorrelation into account when analyzing environmental inequality.
随着关于空气污染和噪音对健康影响的实证证据不断增加，这些影响的公平分配受到越来越多的关注。现有的环境不平等研究往往只关注单一的环境影响，使用的协变量范围有限，或者没有对空间自相关性进行校正。本文对根特（比利时）进行了地理空间数据分析，将居民接触空气污染和噪音的情况与社会经济变量和住房变量结合起来。总体结果表明，家庭收入较低、失业率较高、外国人口较多、出租房屋较多以及居住流动性较高的社区，空气污染暴露程度更高，但噪音暴露程度并非如此。用于解释空气污染暴露情况的多元回归模型表明，居住流动性和出租房屋比例是最强的预测因素，这凸显了住房市场在解释哪些人风险最高方面的作用。应用空间回归模型能得到更好的模型，但会降低所有协变量的重要性，仅留下收入和居住流动性作为空气污染暴露的显著预测因素。虽然传统的多元回归模型对解释噪音暴露不显著，但空间回归模型显著，且也表明收入对模型有显著贡献。这意味着收入是整个城市区域空气污染和噪音暴露的有力预测因素。研究结果为有关环境正义和政策行动必要性的讨论提供了一个良好的起点。该研究还强调了在分析环境不平等时考虑空间自相关性的重要性。
