Journal of Environmental Health and Sustainable Development

en Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters Environmental pollution Original articles <div style="text-align: justify;">Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were utilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.</div> Air Pollution, Remote Sensing, Multivariable regression models, PM10 ,Particulate matter, Ahvaz City 2304 2317 http://jehsd.ssu.ac.ir/browse.php?a_code=A-10-787-2&slc_lang=en&sid=1 Morteza Abdullatif Khafaie m.khafaie@live.com 0000-0002-1651-3017 No Environmental Technologies Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Mona Saeidi mona.saeidi64@gmail.com 0009-0004-6831-5858 No Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran Shahin Mohammadi shahingisrs@gmail.com 0000-0002-4848-322X No Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran Hossein Marioryad oryadhsn@gmail.com 0000-0001-6554-5309 No Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran Arsalan Jamshidi jamshidi_a@yahoo.com 0000-0002-3836-4446 Yes Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran