Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City
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...
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| Format: | Article |
| Language: | English |
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Shahid Sadoughi University of Medical Sciences
2024-03-01
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| Series: | Journal of Environmental Health and Sustainable Development |
| Subjects: | |
| Online Access: | http://jehsd.ssu.ac.ir/article-1-670-en.pdf |
| _version_ | 1827319088170401792 |
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| author | Zohre Ebrahimi-Khusfi Mohsen Ebrahimi-Khusfi Ali Reza Nafarzadegan Mojtaba Soleimani-Sardo |
| author_facet | Zohre Ebrahimi-Khusfi Mohsen Ebrahimi-Khusfi Ali Reza Nafarzadegan Mojtaba Soleimani-Sardo |
| author_sort | Zohre Ebrahimi-Khusfi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T00:12:46Z |
| format | Article |
| id | doaj.art-cec4d26731eb4f11a9506483c047e36d |
| institution | Directory Open Access Journal |
| issn | 2476-6267 2476-7433 |
| language | English |
| last_indexed | 2024-04-25T00:12:46Z |
| publishDate | 2024-03-01 |
| publisher | Shahid Sadoughi University of Medical Sciences |
| record_format | Article |
| series | Journal of Environmental Health and Sustainable Development |
| spelling | doaj.art-cec4d26731eb4f11a9506483c047e36d2024-03-13T09:33:27ZengShahid Sadoughi University of Medical SciencesJournal of Environmental Health and Sustainable Development2476-62672476-74332024-03-019121802194Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd CityZohre Ebrahimi-Khusfi0Mohsen Ebrahimi-Khusfi1Ali Reza Nafarzadegan2Mojtaba Soleimani-Sardo3 Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran. Department of Geography, Yazd University, Yazd, Iran. Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran. Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran. 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.http://jehsd.ssu.ac.ir/article-1-670-en.pdfair pollutionparticulate matterdustmachine learningrandom forest. |
| spellingShingle | Zohre Ebrahimi-Khusfi Mohsen Ebrahimi-Khusfi Ali Reza Nafarzadegan Mojtaba Soleimani-Sardo Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City Journal of Environmental Health and Sustainable Development air pollution particulate matter dust machine learning random forest. |
| title | Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City |
| title_full | Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City |
| title_fullStr | Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City |
| title_full_unstemmed | Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City |
| title_short | Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City |
| title_sort | determining effective factors regarding weather and some types of air pollutants in seasonal changes of pm10 concentration using tree based algorithms in yazd city |
| topic | air pollution particulate matter dust machine learning random forest. |
| url | http://jehsd.ssu.ac.ir/article-1-670-en.pdf |
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