ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Toxicology, Pollution and the Environment
Integrated Air Quality Assessment Based on Monitoring Data, Environmental Parameters and Modeling for Sustainable Development in Oman: A Case Study of Sohar Port
1. Sultan Qaboos University, Muscat, Oman
2. Sultan Qaboos University Department of Civil and Architectural Engineering, Muscat, Oman
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract
In this study, we used multi-year ambient air quality monitoring data, weather and AERMOD dispersion modelling data to investigate pollutant behavior and industrial source contributions to the air quality in the Sohar Port and Freezone industrial area. We assessed PM₁₀, NO₂, SO₂ and CO and categorized them according to their dominant source characteristics and atmospheric behavior as well as their importance to air quality management. The finding shows that PM₁₀ concentrations are heavily influenced by regional background dust and resuspension processes while NO₂ and SO₂ are more associated with combustion and industrial point sources. We tested the model performance with the following statistical parameters: fractional bias, normalized mean square error, index of agreement, geometric mean bias, geometric variance, and predicted-to-observed ratios. The validation results suggest that PM₁₀ and SO₂ had a better agreement as compared to NO₂ and CO with the air quality environment. But the interpretation of the results needs to take into account some uncertainty around background concentrations, fugitive emissions, traffic emissions and monitoring representativeness. The study as a whole has provided a practical and regionally relevant approach for analyzing industrial air quality in arid coastal environments and underlines the need for better emission inventories, background characterization and monitoring network optimization.
Summary
Keywords
artificial intelligence, combustion, Emission inventory, Industrial sources, modeling, NOX
Received
25 April 2026
Accepted
25 June 2026
Copyright
© 2026 Ambu-Saidi, Yavari and Nikoo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mohammed Ambu-Saidi; Zeinab Yavari
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.