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

Thomas Verbeek (UGent)
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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.
Keywords
ROAD TRAFFIC NOISE, SOCIOECONOMIC-STATUS, SOCIAL INEQUALITIES, SPATIAL, AUTOCORRELATION, CHILDRENS EXPOSURE, HEALTH, EQUITY, INJUSTICE, QUALITY, TRANSPORTATION, Air pollution, Noise, Environmental inequality, Environmental justice, Spatial autocorrelation, Spatial regression

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MLA
Verbeek, Thomas. “Unequal Residential Exposure to Air Pollution and Noise : A Geospatial Environmental Justice Analysis for Ghent, Belgium.” SSM-POPULATION HEALTH, vol. 7, 2019, doi:10.1016/j.ssmph.2018.100340.
APA
Verbeek, T. (2019). Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium. SSM-POPULATION HEALTH, 7. https://doi.org/10.1016/j.ssmph.2018.100340
Chicago author-date
Verbeek, Thomas. 2019. “Unequal Residential Exposure to Air Pollution and Noise : A Geospatial Environmental Justice Analysis for Ghent, Belgium.” SSM-POPULATION HEALTH 7. https://doi.org/10.1016/j.ssmph.2018.100340.
Chicago author-date (all authors)
Verbeek, Thomas. 2019. “Unequal Residential Exposure to Air Pollution and Noise : A Geospatial Environmental Justice Analysis for Ghent, Belgium.” SSM-POPULATION HEALTH 7. doi:10.1016/j.ssmph.2018.100340.
Vancouver
1.
Verbeek T. Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium. SSM-POPULATION HEALTH. 2019;7.
IEEE
[1]
T. Verbeek, “Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium,” SSM-POPULATION HEALTH, vol. 7, 2019.
@article{8653294,
  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.}},
  articleno    = {{100340}},
  author       = {{Verbeek, Thomas}},
  issn         = {{2352-8273}},
  journal      = {{SSM-POPULATION HEALTH}},
  keywords     = {{ROAD TRAFFIC NOISE,SOCIOECONOMIC-STATUS,SOCIAL INEQUALITIES,SPATIAL,AUTOCORRELATION,CHILDRENS EXPOSURE,HEALTH,EQUITY,INJUSTICE,QUALITY,TRANSPORTATION,Air pollution,Noise,Environmental inequality,Environmental justice,Spatial autocorrelation,Spatial regression}},
  language     = {{eng}},
  pages        = {{12}},
  title        = {{Unequal residential exposure to air pollution and noise : a geospatial environmental justice analysis for Ghent, Belgium}},
  url          = {{http://doi.org/10.1016/j.ssmph.2018.100340}},
  volume       = {{7}},
  year         = {{2019}},
}

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