چکیده مقاله
Air pollution is one of the most challenging environmental problems in industrial areas such as Assaluyeh, Iran In this study, three sources of terrestrial, meteorological, and Sentinel 5P satellite data are integrated from 2021 to 2023 to predict and analyze the concentrations of nitrogen dioxide NO₂ and sulfur dioxide SO₂ to create a single dataset A random forest regression model was designed and trained to estimate surface concentrations, and the SHAP library was used to analyze the importance of meteorological data features and analyze the impact of these features The prediction results show the appropriate performance of the model with R² values of 0 76 and 0 75 and RMSE values of 2 83 μg/m3 and 9 47 μg/m3 for SO₂ and NO₂, respectively Using SHAP analysis, we found that meteorological parameters such as sunshine hours and relative humidity have the greatest impact on changes in nitrogen dioxide pollutants Also, regarding sulfur dioxide pollutants, we concluded that the parameters of maximum temperature and the average total 24 hour solar radiation have the greatest impact By designing and training the aforementioned model, we can have a practical view of how to manage and control pollutant concentrations
کلیدواژهها
نویسندگان
Sahar Beydokhtinejada
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Farinaz Alizadeha
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Mohammad Javad Amiria
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
شیوه ارجاع
Beydokhtinejada, Sahar and Alizadeha, Farinaz and Amiria, Mohammad Javad,1404,Industrial Air Quality Assessment through Multi-Source Data and Random Forest Modeling: A Case Study of Assaluyeh, Iran,The Second National Conference on Future and Environmental Sustainability,Jolfa
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی آینده و پایداری محیط زیست4 آذر 1404 · جلفا