ConvFormer-KDE: A Long-Term Point-Interval Prediction Framework for PM Based on Multi-Source Spatial and Temporal Data.

Accurate long-term PM prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM and studying the uncertainty of PM concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications.