**1. Introduction**

In recent years, haze has attracted the media's attention, and that of the government and population of various countries. It has triggered a wide-ranging discussion on how to coordinate economic development and environmental protection. However, this started a public panic about air pollution and how this affected the physical health of people. Moreover, haze predicts human damage from air pollution [1,2]. For these reasons, haze has aroused the concern of researchers. Therefore, a large amount of experimental data and theoretical reasoning are focused on the cause of haze [3–8], the scope of pollution [9–16], the hazards [2,11,17–22], spatial and temporal distribution, and prevention measures.

With the establishment of many ground detection stations, the detection data of PM2.5 and PM10, which are the primary pollutants of haze, gradually increase, which facilitates the study of its spatial and temporal characteristics [8,14,15,20,22–25]. Researchers have performed analyses of the spatial and temporal evolution of haze in different areas based on satellite images [4,14,26–28]. Gehrig et al. [29] studied the long-term observations of PM2.5 and PM10 in seven regions of Switzerland and obtained the range of PM2.5 concentrations in different regions. Although there were different haze concentrations in different regions,

**Citation:** Yin, L.; Wang, L.; Huang, W.; Liu, S.; Yang, B.; Zheng, W. Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model. *Atmosphere* **2021**, *12*, 1408. https://doi.org/10.3390/ atmos12111408

Academic Editors: Duanyang Liu, Kai Qin and Honglei Wang

Received: 1 September 2021 Accepted: 21 October 2021 Published: 26 October 2021

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the correlation between daily PM2.5 and PM10 concentrations was very high in all regions. In terms of time characteristics, the haze pollution in the Swiss region in winter is the most severe of the four seasons, and the haze pollution in the spring is the lightest. Zhang et al. [30] analyzed the PM2.5 concentration data of 190 cities in China. They found that the significant seasonal variation of PM2.5 occurred in winter and the lowest in summer on the time scale. In terms of the geographical distribution, the PM2.5 concentrations in the northern region are generally higher than in the south. Zhao et al. [27] collected PM2.5 and PM10 concentrations from 30 ground detection stations in Beijing and analyzed haze concentrations' temporal and spatial distribution in winter and spring. The results showed that the concentration of haze in the northern mountains area is lower than that in the south of Beijing, and the haze pollution in urban and rural areas is quite different. The time characteristics of haze showed that there is serious pollution in winter and slight pollution in spring, the highest concentration of PM2.5 and PM10 appears in January, and the lowest concentration appears in April. Zhao et al. [31] compared the time characteristics of urban and rural areas in Beijing.

The concentration prediction of PM2.5 and PM10 as the primary pollutants of haze is also one of the most concerning areas, and researchers have proposed many different prediction models. In the early days of haze prediction, Fuller et al. [32] used empirical models to predict the daily average concentrations of PM2.5 and PM10 in some regions of the U.K., but the scalability was poor due to the model being based on observations in local areas. Dong et al. [33] proposed a hidden Markov-based prediction model to predict PM2.5 concentration. After training, the hidden Markov model can finally predict the PM2.5 concentration value in the next 24 h. Lee et al. [34] combined the MODIS aerosol optical depth (AOD) over England with ground monitoring data to predict haze concentrations in specific areas. As neural networks began to show solid complex-fitting capabilities, researchers began to apply different neural networks to predict haze. Ordieres et al. [35] compared the performance of three neural network structures—multilayer perceptron, radial basis function neural network, and squared multilayer perceptron—with classic predictive models in daily average PM2.5 concentration predictions. The neural networks are significantly better than the classic approaches. Marzano et al. [36] established a recurrent neural network to predict climate phenomena with input from remote sensing satellite imagery.

Unlike previous prediction models, which mainly predict PM2.5/PM10 concentration in a single area [37], this paper proposed a multi-convolution haze-level prediction model to simultaneously predict PM2.5 concentration levels in multiple adjacent areas. This paper used remote sensing satellite images from Beijing as the model's input. The images are cut into nine blocks of the same size and applied in various data processing methods, including radiation correction, geometric correction, area extraction and synthesis, RGB image synthesis, and image cutting. This paper then uses the daily PM2.5 level of nine blocks as output.

In addition to predicting the haze levels in different blocks in Beijing, we also analyzed haze's temporal and spatial characteristics in different areas. Previous researchers have focused on the temporal and spatial characteristics of haze in one area while ignoring the haze correlation between areas bordering each other. Therefore, this paper divided the predicted results into four seasons in chronological order and used frequency histograms to analyze the haze levels in different regions. Furthermore, this paper used the Global Moran's *I* to obtain the correlation between haze in different seasons and geographic locations. It used the Local Moran's *I*, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent regions.
