# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 1 Introduction  
Fine particles (PM2.5) with a diameter of less than 2.5 \(\upmu\)m are a major contributor to air pollution in cities [1; 2; 3; 4; 5]. Chronic exposure to high PM2.5 levels has been linked to negative effects on human organs and can possibly result in cardiovascular illnesses [3]. Prior research [3; 6; 7] identified two types of models, including a deterministic model and a statistical model, used to predict PM2.5 levels. [PERSON], [PERSON], [PERSON], [PERSON], and others predicted the concentration of PM2.5 using deterministic models by simulating the physical transport and chemical reactions of air pollutants [7]. Despite advancements in these methods, the high computational expenses and the unpredictability of the photochemical diffusion process and emissions prevent their use in assessing air quality over large areas [8]. The statistical model utilizes machine learning or deep learning technology to handle the complex connection between PM2.5 levels and external variables [9; 10], achieving prediction accuracy comparable to deterministic models [11; 12]. Statistical models are commonly used to forecast job outcomes due to their impressive efficiency, affordability, and expertise needed [13; 10; 14; 15; 16; 17]. With the improvement of computer capabilities anddeep learning principles, these statistical models have the potential to excel in forecasting PM2.5 levels, thus expanding their application in the field of atmospheric environmental science [9; 10; 18]. Nowadays, machine learning and deep learning models are extensively used to monitor and predict air pollutant levels. Some instances are the LSTM model for both long-term and short-term memory [7], the MLP model for multi-layer perceptron [19; 20], the BPNN model for back propagation neural network [21], the SVR model for support vector regression [21; 22], the RNN model for recurrent neural networks [23; 24; 25; 26; 27], and the SVR model for backpropagation neural networks. The LSTM model, along with GRNN, BPNN, and RNN, is considered one of the deep learning prediction models. The LSTM model, when configured for analyzing historical time series data, can capture temporal autocorrelation, making it a common choice for prediction tasks. The LSTM model is noteworthy for its ability to handle gradient inflation and error back propagation disappearance, as well as its effectiveness in capturing both long-term and short-term information. A number of LSTM-based models for forecasting air quality have been reported, including the Convolutional LSTM extension (C-LSTM) [6], by [PERSON] [7] and others. To forecast 24-h PM2.5 concentration, a TS-LSTME model has been created using the basic LSTM model with sliding prediction [17], convolutional LSTM (GC-LSTM) [17], and depth multiple output LSTM (DM-LSTM) [28; 29; 30]. Both the CNN LSTM and the RLSTM are LSTMs. Convolutional neural networks (CNN) with self-attention mechanisms in 3D can improve the capacity to learn features in comparison to regular CNNs [31; 32; 33; 34]. However, LSTM-based models have several key limitations in PM2.5 concentration prediction tasks. Several research studies have demonstrated the significant impact of spatiotemporal correlation on air pollution fluctuations [3; 11; 16; 22; 32]. Previous studies typically considered the temporal correlation of air pollution. Conversely, the models did not effectively integrate spatial correlation, leading to a decrease in predictive accuracy for PM2.5 concentration. [PERSON] [11] introduced a TS-LSTME model that utilized conventional LSTM models and sliding prediction to forecast the 24-h PM2.5 levels in the Jinjingji region. In the meantime, convolutional LSTM and its different versions acquire spatial characteristics through the utilization of predetermined convolutional kernels and layers of stacked RNN layers. Its actual receptive field is still smaller than the theoretical receptive field, making it unable to effectively learn long-range spatial dependencies; other methods are insufficient in learning long-term time dependence.