At this stage, the PM2.5 concentration prediction algorithm ignores the influence of other air pollution factors, and has not realized the time-dependent integration with the influence of other environmental pollutants. In this regard, the PCA-EDWavenet-LSTM algorithm considering other air pollution characteristics is proposed. The algorithm proposes to consider other air pollution factors, combine the influence of other air pollution factors with the times dependence on PM2.5 particle concentration, and establish a PCA-EDWaveNet-LSTM algorithm based on air pollution characteristics. In the empirical analysis of PM2.5 historical concentration prediction in Xi'an, the algorithm is compared with RF_Regression algorithm, SVM algorithm, and LSTM neural network. The results show that the prediction performance of this algorithm is better than various traditional prediction algorithms in PM2.5 concentration prediction.

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