Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics

Highlights

• Focuses on the spatial and temporal distribution characteristics of severe haze in China based on pollution-related data from the Ministry of Environmental Protection.

• Analyzes the interaction between haze and the economy and energy structure in 31 provinces of China, and provides reference for the treatment and the prevention and control of air pollution in China.

• The haze were concentrated in Beijing-Tianjin-Hebei region, Shandong Province, the northern northwest region, southeastern Sichuan province, and Chongqing.

• According to the spatial autocorrelation, haze distribution has obvious seasonality, more in winter and less in summer.

• The concentration of SO₂ and absorbable particles is relatively high in northern cities, while that of southern cities is relatively low, and changes with seasonal changes

Abstract

In recent years, the occurrence and frequency of haze have constantly been increasing, bringing severe threats to people's daily lives. To this end, this paper discusses the spatial and temporal distribution characteristics of severe haze in China, analyzes the interaction between haze pollution and the influence of economy and energy structure on haze in 31 provinces of China. It provides references for the treatment of haze weather and the prevention and control of air pollution in China. This paper mainly adopts the spatial autocorrelation method. The data processed mainly includes API (Air Pollution Index) and meteorological station data. Combined with the statistical yearbook data, this paper conducts multi-aspect research and exploration. By using statistical methods to study the haze distribution in China, we found that the haze and PM_2.5 concentrations were mainly distributed in Beijing-Tianjin-Hebei, Shandong Province, the northern northwest, southeastern Sichuan, and Chongqing. Haze distribution has obvious seasonality, more in winter and less in summer. There are also regional differences in the concentration distribution of urban pollutants. The concentration of SO₂ and absorbable particles are relatively high in northern cities. In contrast, that of southern cities is relatively low and changes with seasonal changes.

Introduction

Along with the development of the society, increasing the speed of economic growth in China, people's living standards and quality are improved, and the population and the number of motor vehicles increases rapidly. Society has reached severe air pollution in some cities, often present in the atmosphere a grey one. At the beginning of the 21st century, air pollution is even more severe in China. The haze weather is one of the typical situations (Chen et al., 2020; Wu et al., 2021; Yin et al., 2021; Zhang et al., 2021a).

Haze is one of the weather phenomena of air pollution. If there is haze in the air, the air will contain many particulate matters, such as dust, smoke, etc. These substances make the air relatively cloudy, and the horizontal visibility also decreases, generally below 10 km (Zheng et al., 2016a). Different from floating dust, sand dust, haze, and other weather phenomena, due to the long duration and comprehensive coverage of haze, aerosol particles in the atmosphere attached to various harmful substances, causing great harm to society and the public (Ramanathan et al., 2001). Combining many existing studies, haze hazards can be roughly divided into the following four aspects: (1) It affects people's health (Kumar et al., 2019a). (2) Affect mental health (Ambade et al., 2021). (3) Affect traffic safety (Ambade et al., 2020). (4) Affect the regional climate and ecological environment (Kumar et al., 2019b). In modern society, environmental and social effects caused by the severity of weather conditions have attracted the attention of various aspects of society (including the public and the scientific community). Therefore, they have also become a hot topic at present (Tang et al., 2020b; Li et al., 2015; Li et al., 2020; Zheng et al., 2017).

Classical statistics has a wide range of applications in academic research, among which scientists favor ArcGIS software combined with the spatial and temporal distribution characteristics of haze. These methods make an essential contribution to studying haze spatial and temporal distribution characteristics (Li et al., 2016; Zhang et al., 2021b; Wang et al., 2021b). Yang et al. (Liu et al., 2017) analyzed haze's temporal and spatial distribution characteristics and the impact of urbanization speed on the frequency of haze occurrence, mainly using meteorological data and relevant economic statistics. Wu Dui et al. (Wu et al., 2007) used the statistics of haze days in the Chinese mainland for more than 50 years to analyze. The results showed that haze days had prominent distribution characteristics in geographical distribution and found that haze was closely related to human economic activities. Based on the data from 1497 national environmental monitoring stations, Liu L et al. (Liu et al., 2020) used the kriging interpolation method and HYSPLIT model to analyze spatiotemporal trends and typical regional pollutant transport in China from 2015 to 2018. Tian H et al. (Tian et al., 2021) evaluated the performance of six machine learning models for estimating PM_2.5 concentrations in the Pearl River Delta (PRD) from August 2014 to December 2019. Moreover, multi-source data were adopted for reliable daily PM_2.5 concentration estimation, including meteorology, vegetation, topography, and point of interest (POI). The results show that the tree-structured models (i.e., Random Forest (RF) and Gradient Boosting Regression Tree (GBRT)) generally produce better estimations than other models. Behera R R et al. (Behera et al., 2021) examine the possible sources of ambient pollutants and seasonal variations within the nine selected stations, including the residential, commercial, and industrial sites from January 2016 to December 2018 in Paradip city using the seasonal air quality index (AQI), principal component analysis (PCA) and hierarchical cluster analysis (HCA) to explain the pollutants dispersion and spatial variations. Wang B et al. (Wang et al., 2021a) used principal component analysis and showed that the primary source of PM_2.5 in the Tangshan suburb was construction dust, wind dust, stationary source, and mobile source. However, the ratio for pollution sources was different for indoor and outdoor PM_2.5.

The use of classical statistics to study haze's temporal and spatial distribution is imperfect because the objects studied by classical statistics must be uncorrelated and independent of each other (Kim et al., 2003). That is, classical statistics cannot obtain the spatial attributes of data objects. On the other hand, the haze has spatial attributes, so it is imperfect to use classical statistics to study haze's temporal and spatial characteristics (Zheng et al., 2016a; Zheng et al., 2017; Yin et al., 2019; Zheng et al., 2013). The emerging spatial statistics method solves the problems mentioned above. It mainly analyzes and researches data with spatial attributes (Li et al., 2017; Zheng et al., 2015; Zheng et al., 2016b; Liu et al., 2018; Zheng et al., 2021a; Zheng et al., 2021b; Zheng et al., 2021c; Ma et al., 2021; Zheng et al., 2021d; Tang et al., 2021; Tang et al., 2020a). Furthermore, the theory assumes a specific connection between the research units adjacent to each other in the geographical position (Kang et al., 2011).

Due to the advancing trend of the information age in Chinese cities, the geographic information system has been used widely and has an opportunity for improvement. Spatial statistics is more closely related to geographic information systems. Therefore, the application and scope of spatial statistics have also been continuously developed and valued. Generally, spatial statistics consists of three major components: spatial point patterns, geostatistics [39.40], and spatial econometrics from the China Ministry of Environmental Protection (http://datacenter.mep.gov.cn/report/air_daily/air_ dairy.jsp) (Zou, 2017). The main research focuses of the three components are different. From spatial econometrics, spatial information processing is usually combined with regional science and theoretical geography. Therefore, spatial statistics and classical methods are related, not completely independent. Spatial statistics also use the relevant techniques and methods of statistical probability analysis. The unique feature is that it has characteristics that classical statistical methods do not have: it can process data with spatial attributes and process data with multiple spatial attributes. The core of the research is the dependence of the data and the degree of association.

To date, most studies of haze are of a certain city or a regional haze of time and space distribution points or the study of a haze, are rarely to haze in China as a whole to study the distribution of the time and space. Based on China's meteorological stations and air pollution data, this article uses spatial statistical methods to analyze and study haze distribution characteristics and spatial correlation. The processed data mainly includes API and weather station data. The time frame of the hazing study and the number of haze days will be determined after preliminary data processing of meteorological factors. From a statistical point of view, the temporal and spatial distribution of China's haze, its influencing factors, and the spatial and temporal distribution of air pollution are studied.