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January 24, 20260 citationsOpen Access

The Chemical and Socio-Spatial Frontier: Causal Modeling of Non-Exhaust and Industrial PM2.5 Speciation and Longitudinal Equity in the Post- Mitigation ULEZ Environment

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BOBenard Otieno Otieno

Key Points

  • The aim is to assess spatial exposure to specific PM2.5 pollutants and their health impacts on disadvantaged communities.
  • Utilized a quasi-experimental Difference-in-Difference design.
  • Incorporated air quality data with Geographic Information System (GIS) for mapping.
  • Applied Positive Matrix factorization for source apportionment of pollutants.
  • Linked health data through mixed-effects regression analyses.
  • Conducted qualitative interviews with residents to capture experiences related to air quality and equity.
  • Identified significant levels of PM2.5 from non-exhaust and industrial sources exceeding WHO recommendations.
  • Demonstrated a causal relationship between air quality and respiratory health issues in low-income populations.
  • Highlighted persistent equity concerns due to financial burdens following scrappage scheme closures.

Abstract

Abstract Background: London’s Ultra Low Emission Zone (ULEZ) has lessened exhaust discharges, refining air quality, and health outcomes like children’s lung function. However, latest expansions reveal a plateau, with PM2.5 levels exceeding WHO recommendations due to non-exhaust (brake/tire wear) and industrial sources. The scrappage scheme’s closure in September 2024 exacerbates financial burdens on low-income, car owner groups, raising equity concerns. Objectives: This study aims to quantify spatial exposure to speciated PM2.5 pollutants (Industrial: V, Ni, As, non-exhaust: Fe, Cu, Zn) across deprivation deciles, establish causal links to respiratory health and anxiety, and assess post-mitigation residual stress and social isolation in vulnerable populations. Methods: Employing a quasi-experimental Difference-in-Difference design, incorporate LAQN/Defra speciation data with GIS (GWR) for mapping and Positive Matrix factorization for source apportionment. Link to CHILL/NHS health data via mixed-effects regression. Qualitative semi-structured interviews (n=30-40) with Outer London occupants denote recognition justice. Expected Outcome: Findings will inform targeted policies on industrial areas and non-exhaust controls, advocating perpetual equity support. Key words: ULEZ, PM2.5Speciation, non-exhaust emissions, industrial pollution, environmental justice, socio-spatial equity, source apportionment, Difference-in-Difference, recognition justice.

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Cite This Study

Benard Otieno Otieno (2026) studied this question.

synapsesocial.com/papers/6974610cbb9d90c67120aedahttps://doi.org/10.5281/zenodo.18334788
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