Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application

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.