Modelling Urban Air Traffic Pollution on the Nairobi Expressway Corridor

Abstract

Worldwide, the rapid increase in combustion engine vehicles and city traffic congestion has notably escalated air pollution levels. The emissions released from vehicle exhausts contain harmful elements that adversely affect both human health and the environment. The prediction of traffic related air pollutants and a comprehensive understanding regarding concentration levels of harmful traffic-related air pollutants and how individuals are exposed to them across various settings within cities is crucial. This study’s objective was to assess and understand the air quality situation on the Nairobi expressway corridor through the development of emission prediction models that explain the impact of traffic and meteorological conditions on air pollution along the Nairobi expressway corridor. To gain a comprehensive understanding of the environmental, health, and societal implications of traffic-related air pollution, a spatial dispersion analysis of air pollutant concentrations along the road corridor, an evaluation of the exposure of target populations to the emissions, and an assessment of how the public perceives traffic-related air pollution were carried out. In order to achieve this, data on air quality, weather, traffic, populations exposed and perceptions was collected. Machine learning models were used to create prediction models while air dispersion modelling of the emissions from traffic was achieved by American Meteorological Society-Environmental Protection Agency Regulatory Model (AERMOD) software which produced emission maps for the Nairobi Expressway corridor. Using the population data, the exposure levels for residents, workers, hospital patients and school going children within 500 meters of the expressway were calculated. A questionnaire survey and multinomial logistic regression were utilized to examine perceptions regarding traffic-related air pollution. According to the Machine learning (ML) models and post-hoc SHapley Additive exPlanations(SHAP) analysis, temperature, humidity, and traffic volume were major contributing factors for air pollutants. The findings show that the proposed machine learning model could be used to conduct a thorough investigation of vehicle-induced air pollution in road corridors and could considerably increase the consistency and precision of predictions. AERMOD air dispersion software outputs included the simulation of the 24-hour, 8-hour and annual particulate matter (PM2.5 and PM10), Total Volatile Organic Compounds (TVOCs) and carbon monoxide (CO) concentration values from Nairobi expressway corridor traffic. The simulations indicated that traffic along the Nairobi expressway contributes to emissions along the corridor, despite variations between the modeled and measured values, demonstrating the applicability of AERMOD in air dispersion modeling from mobile sources. The average PM2.5, PM10, TVOCs and equivalent carbon monoxide(eCO) exposure for residents, students, employees and hospital patients were found to be elevated which poses significant health risks to the populations, more so to the vulnerable populations within the road corridors. The survey and multinominal logistic regression revealed that most respondents are aware of traffic-related air pollution and its health impacts. Those with higher incomes, longer exposure durations, and higher education levels showed greater knowledge about vehicle emissions. This study contributes to knowledge on the air quality state in road corridors in Kenya which can guide current and future policies on vehicle emissions. Finally, the study addresses how air pollution along road corridors can be improved through implementation of guidelines, laws and regulations.