Abstract
Accurately predicting the concentration of fine particulate matter (PM(2.5)) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM(2.5) concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM(2.5) prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM(2.5) levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM(2.5) by comparing their complexity and effectiveness. We categorized PM(2.5) DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM(2.5) predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM(2.5) prediction and improve the quality of related studies.