Development of GIS-based Box Model Tool for Air Quality Mapping with Python and ArcGIS Pro in Kirkuk City, Iraq

• Published in: International Journal of Engineering and Geosciences (IJEG) Vol. 11; no. 1; pp. 212 - 225
• Main Authors: Jumaah, Huda, Valizadeh Kamran, Khalil, Ghanbari, Abolfazl, Jeihouni, Mehrdad
• Format: Journal Article
• Language: English
• Published: 01.10.2025
• ISSN: 2548-0960; 2548-0960
• DOI: 10.26833/ijeg.1710723

Urban environmental health depends heavily on air quality because it directly affects ecosystem sustainability, together with resident health outcomes. The rapid urbanization of Kirkuk in Iraq produces important air pollution problems, which stem from growing vehicle pollution combined with industrial sources and insufficient urban planning. The present paper aims to develop a Geographic Information Systems (GIS)-based model. It’s a novel concept to develop advanced pollutant dispersion models by integrating air pollutants with meteorology and ArcGIS Pro analysis. The importance of this study is that it proposes the GIS-based Box Model to precisely forecast air pollution in fast-growing urban centers such as Kirkuk. It is used to support the concept of sustainable urban planning and can easily connect the air quality data to health scopes, and provides good validation accuracies based on the ground data that it uses. The evaluation based on health effects linked to poor air quality will be performed. The research utilized a spatial distribution map algorithm in ArcGIS Pro with Python programming syntax to process elevation data and weather elements and create predictions about pollution concentrations in affected territories. The study showed that the model produced sufficient results throughout the (80-90%) measurement range. The validation process used ground truth data that achieved measurements with a (90-93%) success. Two pollutants, PM2.5 and PM10, were used in model testing validation analysis; the estimated values by the model were compared with ground truth data. Measurements provided an excellent validation of model-calculated air quality measurements with their corresponding ground truth points, thus showing high potential for accurate air quality monitoring and prediction.