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Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium
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Verbeek, Thomas (2019) Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium. SSM - Population Health, 7 . 100340. doi:10.1016/j.ssmph.2018.100340 ISSN 2352-8273.
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Official URL: http://dx.doi.org/10.1016/j.ssmph.2018.100340
Abstract
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.
Item Type: | Journal Article | ||||||||
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Subjects: | T Technology > TD Environmental technology. Sanitary engineering | ||||||||
Divisions: | Faculty of Social Sciences > Sociology | ||||||||
Library of Congress Subject Headings (LCSH): | Air -- Pollution -- Ghent (Belgium), Noise pollution -- Ghent (Belgium) | ||||||||
Journal or Publication Title: | SSM - Population Health | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 2352-8273 | ||||||||
Official Date: | April 2019 | ||||||||
Dates: |
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Volume: | 7 | ||||||||
Article Number: | 100340 | ||||||||
DOI: | 10.1016/j.ssmph.2018.100340 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 21 December 2018 | ||||||||
Date of first compliant Open Access: | 21 December 2018 |
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