Volume 9, Issue 1 (March 2024)
J Environ Health Sustain Dev 2024, 9(1): 2180-2194 |
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Ebrahimi-Khusfi Z, Ebrahimi-Khusfi M, Nafarzadegan A R, Soleimani-Sardo M. Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City. J Environ Health Sustain Dev 2024; 9 (1) :2180-2194
URL: http://jehsd.ssu.ac.ir/article-1-670-en.html
URL: http://jehsd.ssu.ac.ir/article-1-670-en.html
Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
Abstract: (109 Views)
Introduction: This study was carried out with the aim of determining weather parameters and air pollutants affecting seasonal changes of particulate matter of less than 10 microns (PM10) in Yazd city using Random Forest (RF) and extreme gradient boosting (Xgboost) models.
Materials and Methods: The required data was obtained from 2018 to 2022. Levene’s test was applied to investigate the significant difference in the variance of PM10 values in 4 different seasons, and Boruta algorithm was used to select the best predictive variables. RF and Xgboost models were trained using two-thirds of the input data and were tested using the remaining data set. Their performance was evaluated based on R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe Model Efficiency Coefficient (NSE).
Results: The RF showed a higher performance in predicting PM10 in all the study seasons (R2 > 0.85; RMSE < 22). The contribution of dust concentration and relative humidity in spring PM10 changes was more than other variables. For summer, wind direction and ozone were identified as the most important variables affecting PM10 concentration. In the autumn and winter, air pollutants and dust concentration had the greatest effect on PM10, respectively.
Conclusion: RF model could explain more than 85% of PM10 seasonal variability in Yazd city. It is recommended to use the model to predict the changes of this air pollutant in other regions with similar climatic and environmental conditions. The results can also be useful for providing suitable solutions to reduce PM10 pollution hazards in Yazd city.
Materials and Methods: The required data was obtained from 2018 to 2022. Levene’s test was applied to investigate the significant difference in the variance of PM10 values in 4 different seasons, and Boruta algorithm was used to select the best predictive variables. RF and Xgboost models were trained using two-thirds of the input data and were tested using the remaining data set. Their performance was evaluated based on R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe Model Efficiency Coefficient (NSE).
Results: The RF showed a higher performance in predicting PM10 in all the study seasons (R2 > 0.85; RMSE < 22). The contribution of dust concentration and relative humidity in spring PM10 changes was more than other variables. For summer, wind direction and ozone were identified as the most important variables affecting PM10 concentration. In the autumn and winter, air pollutants and dust concentration had the greatest effect on PM10, respectively.
Conclusion: RF model could explain more than 85% of PM10 seasonal variability in Yazd city. It is recommended to use the model to predict the changes of this air pollutant in other regions with similar climatic and environmental conditions. The results can also be useful for providing suitable solutions to reduce PM10 pollution hazards in Yazd city.
Type of Study: Original articles |
Subject:
Environmental pollution
Received: 2023/11/13 | Accepted: 2024/01/20 | Published: 2024/03/13
Received: 2023/11/13 | Accepted: 2024/01/20 | Published: 2024/03/13
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