Volume 9, Issue 3 (September 2024)
J Environ Health Sustain Dev 2024, 9(3): 2304-2317 |
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Khafaie M A, Saeidi M, Mohammadi S, Marioryad H, Jamshidi A. Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters. J Environ Health Sustain Dev 2024; 9 (3) :2304-2317
URL: http://jehsd.ssu.ac.ir/article-1-735-en.html
URL: http://jehsd.ssu.ac.ir/article-1-735-en.html
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 *
, Mona Saeidi , Shahin Mohammadi , Hossein Marioryad , Arsalan Jamshidi *
Department of Environmental Health Engineering, Faculty of Health, Yasuj. University of Medical Sciences, Yasuj, Iran
Abstract: (254 Views)
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.
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.
Keywords: Air Pollution, Remote Sensing, Multivariable regression models, PM10, Particulate matter, Ahvaz City
Type of Study: Original articles |
Subject:
Environmental pollution
Received: 2024/06/13 | Accepted: 2024/08/10 | Published: 2024/10/1
Received: 2024/06/13 | Accepted: 2024/08/10 | Published: 2024/10/1
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