Ibiyemi Sesi Okelola¹, Ibrahim Ayinla Mahmud¹, Nathan Rimamsanati Yohanna²,³, Moses Adondua Abah2,3 and Micheal Oladosu Abimbola3,4
1School of Engineering and Built Environment, University of Greater Manchester, Bolton, United Kingdom
2Department of Biochemistry, Faculty of Biosciences, Federal University Wukari, Taraba State, Nigeria
3ResearchHub Nexus Institute, Nigeria
4Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Lagos State, Nigeria
(✉) Corresponding Author: Ibiyemi Sesi Okelola; io5aes@greatermanchester.ac.uk
Received: Aug 3, 2026/ Revised: Sept 18 2026/Accepted: Sept 19, 2026
Highlights
- Urban air pollution arises from transport, industry, power generation, waste burning, and domestic fuel use.
- Source apportionment helps identify and quantify major contributors to urban pollution.
- Receptor models such as PMF and CMB support source-specific pollution assessment.
- Atmospheric and chemical transport models improve prediction of pollutant dispersion and regional transport.
- Machine learning and satellite observations can strengthen spatial air-quality monitoring.
- Integrated management requires coordinated action in transport, energy, waste management, and urban planning.
- Low-cost sensors, AI, and adaptive monitoring can improve long-term exposure assessment and public health protection.
Abstract
Urban air pollution presents a complex environmental and public health challenge driven by rapid population growth, expanding industrial activities, and increasing transportation demands. This review evaluates the integrated framework required for effective urban air quality management, focusing on source apportionment, atmospheric modeling, emission control strategies, and implementation challenges. Urban pollutants consist of primary emissions (e.g., road traffic, industrial activities, power generation, waste burning, and domestic fuel combustion) and secondary pollutants (e.g., ozone and secondary organic aerosols) formed via atmospheric oxidation and photochemical reactions. Because total pollutant mass does not reflect specific source toxicities or origins, source apportionment techniques including receptor models (Positive Matrix Factorization, Chemical Mass Balance), bottom-up emission inventories, and chemical tracer analyses are critical for quantifying specific source contributions. Furthermore, atmospheric prediction relies on Gaussian dispersion models for local street-canyon dynamics, chemical transport models for regional transboundary reactions, and emerging data-driven machine learning or satellite remote-sensing approaches to address spatial coverage gaps. Translating these technical insights into policy requires coordinated interventions across transportation, clean energy, waste management, and urban planning. However, persistent implementation bottlenecks remain, including sparse monitoring networks, weak emission inventories, technical constraints, and fragmented regulatory enforcement. Next-generation management must adopt an adaptive, evidence-based cycle integrating low-cost sensor networks, satellite observations, and artificial intelligence to refine exposure assessment and ensure sustained public health protection.
Keywords: Air Quality Management, , Receptor Modeling (PMF, CMB), Atmospheric Dispersion, Chemical Transport, Particulate Matter (PM2.5), and Air Pollution
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How to cite this article
Okolosi, I. S., Mahmud, I. A., Yohanna, N. R., Abah, M. A., & Abimbola, M. O. (2026). Air quality management in urban areas: A review of source apportionment, modeling, and control strategies. Chemical and Environmental Science Archives, 6(3), 50–64. https://doi.org/10.47587/CESA.2026.6303
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