Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters

Morteza Abdullatif  Khafaie; Mona Saeidi; Shahin  Mohammadi; Hossein  Marioryad; Arsalan  Jamshidi

doi:10.18502/jehsd.v9i3.16579

Morteza Abdullatif Khafaie Environmental Technologies Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Mona Saeidi Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran.
Shahin Mohammadi Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Hossein Marioryad Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran.
Arsalan Jamshidi Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran.

DOI: https://doi.org/10.18502/jehsd.v9i3.16579

Keywords: Air Pollution, Remote Sensing, Multivariable regression models, PM10 , Particulate matter, Ahvaz City.

Abstract

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 u tilized 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.