Air-quality-sensitive urban design on NO2 Mitigation in the high-rise high-density City of London

Elevated nitrogen dioxide (NO2) remains a persistent urban air-quality and public-health concern. In the UK, despite progressive emissions-control policies, exceedances have historically occurred in central London. This study quantifies neighbourhood-scale NO2 variability in the high-rise, high-density City of London and evaluates how air-quality-sensitive urban design, alongside traffic measures, influences concentrations in a morphology-constrained street network. We developed machine-learning land-use regression (LUR) models to predict NO2 distribution: the Random Forest (RF) model performed best (adjusted R2 = 0.88; RMSE = 7.6 μg m−3; MAE = 6.25 μg m−3). The RF-LUR model was then applied to assess air-quality-sensitive urban design scenarios. Increased built fraction associated with population growth was associated with widespread NO2 increases (up to 5.5 μg m−3), consistent with reduced dispersion in dense street-canyons. A 50% reduction in heavy-vehicle activity was associated with corridor-focused NO2 decreases of 1–6 μg m−3. Introducing additional high-rise buildings increased height variability and was associated with NO2 reductions up to ∼7 μg m−3 in the eastern cluster. Targeted tree-canopy expansion was associated with hotspot reductions of up to ∼4 μg m−3 but produced small local increases in some areas. The combined intervention package was associated with the largest overall improvement, with NO 2 reductions up to ∼7 μg m−3. The results show that integrating urban morphology within machine-learning LUR models can provide a practical, fine-scale evidence base for spatial targeting and comparative appraisal of NO2 mitigation options in high-density urban districts.

air quality · City of London · land use regression · Machine learning · Nitrogen dioxide · urban air pollution · Urban design · Air Quality and Health Impacts · Urban Transport and Accessibility · Wind and Air Flow Studies

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