Health risks of carbon monoxide and mitigation strategies in Abuja municipal area council, Nigeria

Carbon monoxide (CO) pollution from rapid urbanization and increasing vehicle emissions poses a significant public health and environmental threat in the Abuja Municipal Area Council (AMAC), Nigeria. This study addresses a critical gap by conducting an integrated assessment linking CO levels to vegetation health and community impacts to inform localized policies. We analysed CO concentrations and Enhanced Vegetation Index (EVI) values from 2018 to 2024 using satellite data from Google Earth Engine, assessing their relationship with advanced regression models. A concurrent survey of 84 residents, health workers, and policymakers evaluated health impacts and awareness. Results revealed higher CO levels in high-traffic zones and during the dry season, with a complex, non-linear relationship to EVI. The survey showed a strong correlation between CO exposure and respiratory/cardiovascular issues, with most respondents facing financial burdens from related illnesses. Despite high awareness (80%), a significant gap existed in adopting protective measures. The findings underscore the urgent need for integrated strategies in AMAC, including stricter vehicle emission standards, low-emission zones, and expanded green spaces to reduce CO pollution, protect public health, and promote sustainable urban development.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-03-11
DOI
https://doi.org/10.1038/s41598-026-42734-2
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Health risks of carbon monoxide and mitigation strategies in Abuja municipal area council, Nigeria

Adewuyi Taiye Oluwafemi, Okhimamhe Apollonia, Salawu Jibril Olarotimi
Scientific Reports
Air Quality and Health Impacts
article

Health risks of carbon monoxide and mitigation strategies in Abuja municipal area council, Nigeria

Adewuyi Taiye Oluwafemi, Okhimamhe Apollonia, Salawu Jibril Olarotimi
article en

Abstract

Carbon monoxide (CO) pollution from rapid urbanization and increasing vehicle emissions poses a significant public health and environmental threat in the Abuja Municipal Area Council (AMAC), Nigeria. This study addresses a critical gap by conducting an integrated assessment linking CO levels to vegetation health and community impacts to inform localized policies. We analysed CO concentrations and Enhanced Vegetation Index (EVI) values from 2018 to 2024 using satellite data from Google Earth Engine, assessing their relationship with advanced regression models. A concurrent survey of 84 residents, health workers, and policymakers evaluated health impacts and awareness. Results revealed higher CO levels in high-traffic zones and during the dry season, with a complex, non-linear relationship to EVI. The survey showed a strong correlation between CO exposure and respiratory/cardiovascular issues, with most respondents facing financial burdens from related illnesses. Despite high awareness (80%), a significant gap existed in adopting protective measures. The findings underscore the urgent need for integrated strategies in AMAC, including stricter vehicle emission standards, low-emission zones, and expanded green spaces to reduce CO pollution, protect public health, and promote sustainable urban development.

Scientific Reports
Nigerian Defence Academy (NG), Federal University of Technology Minna (NG)
Sustainable cities and communities
Openalex Percentile: Top 30%
Air Quality and Health Impacts
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.