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Unequal residential exposure to air pollution and noise: A geospatial environmental justice analysis for Ghent Belgium

机译：不平等的住宅暴露在空气污染和噪音中：比利时根特的地理空间环境正义分析

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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.
机译：随着越来越多的关于空气污染和噪声对健康的影响的经验证据，这些影响的合理分配越来越受到关注。现有的环境不平等研究通常侧重于单个环境影响，应用有限范围的协变量或不对空间自相关进行校正。本文介绍了根特（比利时）的地理空间数据分析，将住宅暴露于空气污染和噪音中的因素与社会经济变量和住房变量相结合。全球结果表明，家庭收入较低，失业率更高，外来人口增加，出租房屋增多和居住人口流动性更高的社区更容易受到空气污染的影响，而没有受到噪音的影响。多元回归模型可以解释空气污染的暴露，表明居民的流动性和租赁房屋的百分比是最强的预测指标，从而强调了住房市场在解释哪些人最容易受到威胁的作用。应用空间回归模型可以得到更好的模型，但是降低了所有协变量的重要性，从而使收入和居民流动性成为空气污染暴露的唯一重要预测指标。尽管传统的多元回归模型对于解释噪声暴露并不重要，但空间回归模型却可以说明收入的显着贡献。这意味着收入是整个城市地区空气污染和噪声暴露的有力预测指标。结果为讨论环境正义和采取政策行动的需要提供了良好的起点。该研究还强调了在分析环境不平等时考虑空间自相关的重要性。

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