Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application
Abstract The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorolo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87702-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571767881728000 |
|---|---|
| author | Xiaolei Zhou Xingyue Wang Ruifeng Guo |
| author_facet | Xiaolei Zhou Xingyue Wang Ruifeng Guo |
| author_sort | Xiaolei Zhou |
| collection | DOAJ |
| description | Abstract The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. Firstly, an atmospheric dispersion model is utilized to predict the distribution concentration of VOCs emitted by enterprises in the park at the target monitoring stations based on the ozone generation mechanism. Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. The model integrates atmospheric pollutants and related meteorological data to explore the nonlinear influence relationship of ozone formation in depth. The performance of the model is validated by the comprehensive evaluation indexes of R2, MAE and RMSE, and the results show that the present model outperforms the IPSO-CNN and SHAP-PSO-CNN models with the performance indexes of R2 of 0.9492, MAE of 0.0061 mg/m3 and RMSE of 0.0084 mg/m3. This study not only advances the understanding of ozone pollution formation mechanisms, but also provides an assessment of the impact of VOCs emissions from enterprises in the park, which provides empirical support for environmental management. |
| format | Article |
| id | doaj-art-01d2918d9d7e48ec9f2415cf0a4a79be |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-01d2918d9d7e48ec9f2415cf0a4a79be2025-02-02T12:18:23ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-87702-4Assessment model of ozone pollution based on SHAP-IPSO-CNN and its applicationXiaolei Zhou0Xingyue Wang1Ruifeng Guo2Shenyang Institute of Computing Technology, Chinese Academy of SciencesShenyang Institute of Computing Technology, Chinese Academy of SciencesShenyang Institute of Computing Technology, Chinese Academy of SciencesAbstract The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. Firstly, an atmospheric dispersion model is utilized to predict the distribution concentration of VOCs emitted by enterprises in the park at the target monitoring stations based on the ozone generation mechanism. Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. The model integrates atmospheric pollutants and related meteorological data to explore the nonlinear influence relationship of ozone formation in depth. The performance of the model is validated by the comprehensive evaluation indexes of R2, MAE and RMSE, and the results show that the present model outperforms the IPSO-CNN and SHAP-PSO-CNN models with the performance indexes of R2 of 0.9492, MAE of 0.0061 mg/m3 and RMSE of 0.0084 mg/m3. This study not only advances the understanding of ozone pollution formation mechanisms, but also provides an assessment of the impact of VOCs emissions from enterprises in the park, which provides empirical support for environmental management.https://doi.org/10.1038/s41598-025-87702-4Ground-level ozone pollutionDeep learningParticle swarm optimizationSHAP analysisFeature selection |
| spellingShingle | Xiaolei Zhou Xingyue Wang Ruifeng Guo Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application Scientific Reports Ground-level ozone pollution Deep learning Particle swarm optimization SHAP analysis Feature selection |
| title | Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application |
| title_full | Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application |
| title_fullStr | Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application |
| title_full_unstemmed | Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application |
| title_short | Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application |
| title_sort | assessment model of ozone pollution based on shap ipso cnn and its application |
| topic | Ground-level ozone pollution Deep learning Particle swarm optimization SHAP analysis Feature selection |
| url | https://doi.org/10.1038/s41598-025-87702-4 |
| work_keys_str_mv | AT xiaoleizhou assessmentmodelofozonepollutionbasedonshapipsocnnanditsapplication AT xingyuewang assessmentmodelofozonepollutionbasedonshapipsocnnanditsapplication AT ruifengguo assessmentmodelofozonepollutionbasedonshapipsocnnanditsapplication |