Integrating Traffic Simulation and Air Quality Modelling to Reduce Vehicle Emissions at Urban Intersections

Ajayi, Samuel A.; Adams, Charles A.; Nnene, Obiora A.; Dumedah, Gift; Zuidgeest, Mark H.P; Obiri-Yeboah, Abena A.; Ambunda, Robert O. · 2026 · Crossref

DOI: 10.21203/rs.3.rs-9513688/v1

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Summary

This study addresses the challenge of estimating and mitigating vehicular emissions in rapidly growing African cities, specifically focusing on urban intersections in Lagos, Nigeria. The research is motivated by the lack of locally developed emission standards and the significant health risks posed by traffic-related air pollution in sub-Saharan Africa. The authors aim to evaluate the effectiveness of integrating traffic microsimulation with air quality modeling to identify strategies for reducing carbon monoxide (CO) and nitrogen oxides (NOx) emissions. The methodology involved a multi-step approach centered on an unsignalized dual-carriageway intersection in the Egbeda area of Lagos. First, field data on traffic volume, speed, and vehicle composition were collected using pneumatic tubes and mobile tracking apps. These parameters were used to calibrate a PTV VISSIM traffic microsimulation model, validated using the Geoffrey E. Havers (GEH) statistical test. Direct emission measurements were conducted using portable gas analyzers, while ambient pollutant concentrations (CO, NO2, PM2.5) were monitored over seven days. To assess spatial dispersion, the study integrated VISSIM outputs with the AERMOD Gaussian dispersion model, utilizing meteorological data from NASA and terrain data processed via AERMET and AERMAP. Four scenarios were simulated to test emission reduction strategies: reducing traffic volume by 50% (SC1), reducing heavy goods vehicles (SC2), reducing passenger cars (SC3), and introducing intersection signalization (SC4). The results demonstrated significant emission reductions across all tested scenarios. Scenario 1 (50% volume reduction) achieved a 41% reduction in CO and 53% in NOx. Scenario 2 (HGV reduction) yielded 60% and 54% reductions, respectively, while Scenario 3 (passenger car reduction) resulted in 46% and 68% reductions. The introduction of signalization (SC4) improved traffic flow by reducing delays and increasing vehicle speeds, thereby lowering emission levels. Field measurements indicated that peak pollutant levels occurred during morning rush hours, with CO concentrations frequently exceeding World Health Organization guidelines. The AERMOD dispersion modeling revealed that pollutants dispersed predominantly from southwest to northeast, driven by prevailing wind patterns, with high concentrations localized near the roadway. The significance of this study lies in its demonstration of a robust framework for integrating detailed traffic simulation with instantaneous exhaust emission estimates and Gaussian dispersion models. This approach provides accurate, spatially explicit data for assessing air quality impacts in urban environments. The findings offer evidence-based strategies for urban planners and policymakers in developing nations to mitigate traffic-related air pollution through targeted traffic management interventions, such as volume control and signalization, addressing a critical gap in environmental monitoring and regulation in African cities.

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• traffic density