Reconstruction of Missing Values at PM2.5Monitoring Sites Combining K-ShapeClustering and Conditional Score-BasedDiffusion Models for Imputation

PM 2.5 is a significant contributor to air pollution, and complete air quality monitoring data is the key to effective prevention and control of PM 2.5 .However, there are many missing values in real-time monitoring data due to the instability of the monitoring system, machine failures, or human error.Taking the Yangtze River Delta (YRD) region as an example, this study compared the filling effect of various algorithms in the absence of PM 2.5 concentration ground monitoring data, then selected the optimal algorithm and combined it with the K-Shape clustering partitioning results to fill the missing PM 2.5 concentration data values.The results showed that the Conditional Score-based Diffusion Models for Imputation (CSDI) had better interpolation accuracy than Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (KNN), and Multiple Imputation (MI) in the missing values imputation task.The historical PM 2.5 data from the YRD, when analyzed using CSDI with K-Shape clustering, showed that Partition III had the highest accuracy and Partition II had the lowest.This variance was due to both the clustering accuracy and the inherent characteristics of each partition regarding PM 2.5 fluctuations.Analyzing the daily variation characteristics of PM 2.5 concentrations in different partitions revealed approximately 9 am, 3 pm, and 9 pm as the three main time nodes with large CSDI filling errors in the YRD region.These findings have significant implications for air quality monitoring and PM 2.5 concentration prediction.