A combined prediction system for PM2.5 concentration integrating spatio-temporal correlation extracting, multi-objective optimization weighting and non-parametric estimation

Air pollution nowadays has seriously hindered the sustainable development. PM2.5 greatly affects air quality and human health, even facilitates virus transmission, making its concentration prediction is crucial. However, previous studies are limited to single PM2.5 concentration series, neglecting optimization of prediction stability, and lacking uncertainty analysis. To address these issues, this research proposed a combined PM2.5 prediction system (CPPs) based on modular concept. Firstly, the temporal and spatial correlations of PM2.5 were fully extracted by data pretreatment and feature selection modules. Subsequently, the results of single submodel prediction module were integrated by multi-objective slime mould algorithm in combination weighted module, achieving Pareto optimality theoretically. Eventually, interval forecasting module analyzed the predictive uncertainty. Notably, a truly accurate metric for predictive directional accuracy was proposed for the first time. Validating CPPs using PM2.5 data from Shanghai during COVID-19 epidemic showed superior performance in point and interval forecasts. This research achieves optimization of prediction accuracy and stability as well as uncertainty analysis based on multiple data sources, contributes to improved air quality and public health protection.