Geomatics and Environmental Engineering, vol. 20, no. 5

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Vol. 20 No. 5 (2026): Geomatics and Environmental Engineering

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Submitted
Jun 26, 2025
Accepted
May 18, 2026
Published
Jul 30, 2026
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Estimation of PM2.5 Particulate Matter Using MATLAB and GWR Models Based on Remote Sensing Data in Baghdad City, Iraq

https://doi.org/10.7494/geom.2026.20.5.41
Shaimaa Taleb Alnasrawy
University of Technology, College of Civil Engineering, Baghdad, Iraq
https://orcid.org/0000-0002-4710-1736
Nadia A. Aziz
University of Technology, College of Civil Engineering, Baghdad, Iraq
https://orcid.org/0000-0003-1978-7432

Corresponding Author(s) : Shaimaa Taleb Alnasrawy

shaimaa.t.kadhum@uotechnology.edu.iq

Geomatics and Environmental Engineering, Vol. 20 No. 5 (2026): Geomatics and Environmental Engineering

Abstract

Precise and effective air quality forecasting plays a crucial role in predicting and mitigating health risks in communities worldwide. This research focuses on estimating particulate matter (PM2.5) concentrations using geographically weighted regression (GWR) and MATLAB models, utilizing remote sensing data in Baghdad city, Iraq. The estimation process incorporates multiple influencing factors, including aerosol optical depth (AOD), population density, normalized difference vegetation index (NDVI), relative humidity, temperature, wind speed, and elevation. The analysis of the relationship between influencing factors and PM2.5 concentration using grey relational analysis revealed that aerosol optical depth (AOD) exhibited the strongest correlation, whereas population density was the weakest. Furthermore, the results indicated that the PM2.5 concentration model developed using MATLAB, based on remote sensing data and incorporating eight influencing factors, demonstrated the highest accuracy, achieving an R2 value of 0.98 and a root mean square error (RMSE) of 1.7 × 10−10. The performance of the predictive equation for PM2.5 concentration, derived using MATLAB, was assessed by comparing its estimations with ground station measurements obtained from independent locations and time periods. The model achieved a coefficient of determination (R2) of 0.77, indicating that it accounts for 77% of the variability in the observed values. This outcome signifies a strong correlation between the predicted and measured concentrations, confirming that the model effectively represents the underlying relationships with a satisfactory degree of accuracy.

Keywords

air pollution PM2.5 remote sensing data influencing factors predictive models
Alnasrawy, S. T., & Aziz, N. A. (2026). Estimation of PM2.5 Particulate Matter Using MATLAB and GWR Models Based on Remote Sensing Data in Baghdad City, Iraq. Geomatics and Environmental Engineering, 20(5), 41–58. https://doi.org/10.7494/geom.2026.20.5.41
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