Reviewing Explainable Artificial Intelligence Towards Better Air Quality Modelling

The increasing complexity of machine learning models used in environmental studies necessitates robust tools for transparency and interpretability. This paper systematically explores the transformative potential of Explainable Artificial Intelligence (XAI) techniques within the field of air quality research. A range of XAI methodologies, including Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), have been effectively investigated to achieve robust, comprehensible outcomes in modeling air pollutant concentrations worldwide. The integration of advanced feature engineering, visual analytics, and methodologies like DeepLIFT and Layer-Wise Relevance Propagation further enhance the interpretability and reliability of deep learning models. Despite these advancements, a significant proportion of air quality research still overlooks the implementation of XAI techniques, resulting in biases and redundancies within datasets. This review highlights the pivotal role of XAI techniques in facing these challenges, thus promoting precision, transparency, and trust in complex models. Furthermore, it underscores the necessity for a continued commitment to the integration and development of XAI techniques, pushing the boundaries of our understanding and usability of Artificial Intelligence in environmental science. The comprehensive insights offered by XAI can significantly aid in decision-making processes and lead to transformative strides within the fields of Internet of Things and air quality research.