Public Perceptions on Exposure to Traffic-Related Air Pollution Along Nairobi Expressway Corridor

Abstract Traffic-related air pollution is a major global concern, causing millions of deaths annually and contributing to a range of chronic health conditions. This study explored public perceptions of traffic-related air pollution and health risks along the Nairobi Expressway through a survey and multinomial logistic regression analysis. The findings indicated that higher income, education, and longer exposure correlate with greater awareness of vehicle emissions and health impacts. Many respondents reported health issues due to high pollutant levels from traffic emissions. Age, gender, and marital status did not significantly influence perceptions of traffic-related air pollution. The study also highlights the resilience of communities in adopting behaviors to mitigate pollution impacts. There is general support for initiatives aimed at reducing air pollution. The research emphasizes the need for collaborative efforts from policymakers and the public to address traffic-related air pollution. This study offers valuable insights for policymakers, transport engineers, and stakeholders to develop strategies for reducing traffic-related air pollution and promoting sustainable transport, aligning with the United Nations Sustainable Development Goals.

Authors

Institutions

Publication Details

Journal
Civil and Environmental Engineering
Published
2025-05-17
DOI
https://doi.org/10.2478/cee-2025-0031
Citations
1
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
1.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Public Perceptions on Exposure to Traffic-Related Air Pollution Along Nairobi Expressway Corridor

Simpson Nyambane Osano, Caroline Mongina Matara, Elisha Akech, Amir Yusuf
1 citations
Civil and Environmental Engineering
Air Quality and Health Impacts
1.20
article

Public Perceptions on Exposure to Traffic-Related Air Pollution Along Nairobi Expressway Corridor

Simpson Nyambane Osano, Caroline Mongina Matara, Elisha Akech, Amir Yusuf
article en
1 citations

Abstract

Abstract Traffic-related air pollution is a major global concern, causing millions of deaths annually and contributing to a range of chronic health conditions. This study explored public perceptions of traffic-related air pollution and health risks along the Nairobi Expressway through a survey and multinomial logistic regression analysis. The findings indicated that higher income, education, and longer exposure correlate with greater awareness of vehicle emissions and health impacts. Many respondents reported health issues due to high pollutant levels from traffic emissions. Age, gender, and marital status did not significantly influence perceptions of traffic-related air pollution. The study also highlights the resilience of communities in adopting behaviors to mitigate pollution impacts. There is general support for initiatives aimed at reducing air pollution. The research emphasizes the need for collaborative efforts from policymakers and the public to address traffic-related air pollution. This study offers valuable insights for policymakers, transport engineers, and stakeholders to develop strategies for reducing traffic-related air pollution and promoting sustainable transport, aligning with the United Nations Sustainable Development Goals.

Civil and Environmental EngineeringVol. 21(1)
University of Nairobi (KE), Technical University of Kenya (KE), Multimedia University of Kenya (KE)
Openalex Percentile: Top 23%
Air Quality and Health Impacts
1.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.