**1. Introduction**

Currently, atmospheric pollution has become an important environmental problem, which places risk on human health. Many studies show that the release of atmospheric pollutants may cause many adverse health effects such as increased risks of cardiovascular and pulmonary diseases, decreased semen quality, and coronary heart disease [1–3]. The rapid developments of transportation and industry cause the discharge of atmospheric pollutants. The emissions of particulate matter with an aerodynamic diameter equal to or less than 2.5 µm (PM2.5) and sulfur dioxide (SO2) by coal-fired power plants accounted for 6% and 33% of national total emissions in 2010, respectively [4], and the usage of coal accounts for 69% of the total energy consumption [5]. Intense vehicular traffic causes the large emissions of nitrogen dioxide (NO2) and carbon dioxide [6]. In order to assess the degree of

**Citation:** Qi, N.; Tan, X.; Wu, T.; Tang, Q.; Ning, F.; Jiang, D.; Xu, T.; Wu, H.; Ren, L.; Deng, W. Temporal and Spatial Distribution Analysis of Atmospheric Pollutants in Chengdu–Chongqing Twin-City Economic Circle. *Int. J. Environ. Res. Public Health* **2022**, *19*, 4333. https:// doi.org/10.3390/ijerph19074333

Academic Editors: Xun Wang, Zhiyuan Wang, Xin Zhao and Paul B. Tchounwou

Received: 21 February 2022 Accepted: 31 March 2022 Published: 4 April 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

air pollution, six pollutants such as particulate matter with an aerodynamic diameter equal to or less than 10 µm (PM10), PM2.5, SO2, NO2, ozone (O3) and carbon monoxide (CO) have been selected to characterize the levels of air pollution. The World Health Organization (WHO) develops the WHO Global Air Quality Guidelines (GAQG) to reduce atmospheric pollutants in order to decrease the enormous health burden resulting from exposure to atmospheric pollution worldwide. In China, the Ambient Air Quality Standard (AAQS) is also set up to protect and improve the living environment and ecological health, and to ensure human health [7].

In January 2020, the construction of the "Chengdu–Chongqing twin-city Economic Circle" (CCEC) was first proposed by the sixth meeting of the Central Finance and Economics Commission, which aims to turn the Chengdu–Chongqing area into an economic circle with its own strengths and distinctive features, as well as a new driver and an important growth engine of the country's high-quality development. CCEC is made up of some cities in Sichuan province and some districts or counties in Chongqing municipality, which is the urbanization area with the highest development level and the greatest development potential in the western region of China. The high-quality development of CCEC can effectively enhance the economic development and the population-carrying capacity of the urbanization area, which is of great significance to the protection of the ecological environment in the upper reaches of the Yangtze River and of western China. It is also an important part of the implementation of the "Yangtze River Economic Belt" and "the Belt and Road Initiative". The whole area of the CCEC is 185,000 square kilometers, which includes 29 districts and counties in Chongqing and 15 cities in Sichuan province (Figure 1).

**Figure 1.** Map of air pollution transmission channels in the Chengdu–Chongqing twin-city Economic Circle (CCEC) [8–10].

For many years, the atmospheric pollution of CCEC has been particularly serious and complex. Due to the unique topography and climate in Sichuan Basin [11], the atmospheric pollutants accumulate in large quantities and cause the Sichuan Basin to become one of the most heavily polluted areas in China [12,13]. Furthermore, Chongqing is a mountainous city, and its environmental quality is significantly affected by the factors of pollution and dense built environment [14]. The research has shown that there are three air pollution transmission channels in Sichuan Basin due to the effect of the east Asian atmospheric circulation and the Qinghai Tibet Plateau flow field [8–10]: (1) Guangyuan → Mianyang → Deyang → Chengdu→ Ya'an; (2) Bazhong → Nanchong→ Suining →Ziyang → Meishan →Leshan; (3) Northern of Chongqing → Dazhou → Guang'an →Nanchong → Suining → Ziyang → Neijiang → Luzhou. Additionally, the pollution of O<sup>3</sup> has become more and more serious in Sichuan Province since 2015, while the particulate matter has shown characteristics of secondary pollutants [15,16]. The primary pollutants in the atmosphere of Chongqing were PM10, nitrogen oxides and SO<sup>2</sup> in 2007–2014, which showed significant regional differences in air quality [17]. Environmental quality and public health determine the prospect of sustainable development [18]. The prevention and reduction of air pollution has become one of the key issues of current concern for high quality development of the economy in the CCEC.

The aim of this research is to explore the spatiotemporal distribution and pollution degrees of atmospheric pollutants from 2017 to 2020 in the CCEC, and to find the main pollution areas. Based on the results, the reasonable suggestions for pollution control in CCEC were propounded, and the theoretical basis for the coordinated governance of the atmosphere and water environment was provided.

#### **2. Data Sources and Methods**

#### *2.1. Data Sources*

In this study, the relevant data of ambient air quality in each area (city in Sichuan province or district and county in Chongqing) of CCEC from 2017 to 2020 were obtained from the ecological and environmental bulletin of Chongqing (http://sthjj.cq.gov.cn/hjzl\_ 249/hjzkgb/, accessed on 31 January 2022) and from nthe ecological and environmental bulletin of each city in Sichuan Province (http://sthjt.sc.gov.cn/sthjt/c104157/hjglnew. shtml, accessed on 31 January 2022). The atmospheric pollutants that were chosen mainly include PM10, PM2.5, SO2, NO2, O<sup>3</sup> and CO.

#### *2.2. Evaluation Standards*

The evaluation standards for the concentration of atmospheric pollutants were based on the Grade II of AAQS [7,19] (http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/ dqhjzlbz/201203/t20120302\_224165.shtml, accessed on 31 January 2022) and GAQG [20] (https://www.who.int/publications/i/item/9789240034433, accessed on 31 January 2022. The average annual concentrations of PM10, PM2.5, SO<sup>2</sup> and NO<sup>2</sup> were adopted, the 24 h average value of the 95th position for CO was adopted, and the 8 h average value of the maximum daily concentration of the 90th position for O<sup>3</sup> was adopted (Table 1). The areal interpolation was used to draw the figures of spatial distribution. The indexes in GAQG are stricter than that in AAQS, which is based on the latest evidence of human health caused by air pollution. The purpose of GAQG is to propose new air quality levels, and their interim targets play a role in guiding emission reduction and promoting air quality to reach the level of air quality guidelines. The purpose of the AAQS is based on air quality management. It aims at promoting harmonious and sustainable development between humans and nature [21]; thus, the indexes in AAQS are much closer to the interim target 1 of GAQG.


**Table 1.** Limit values of atmospheric pollutants in AAQS and GAQG [7,19,20].

\* No average annual concentration of SO<sup>2</sup> was given in GAQG.

#### *2.3. Methods*

Spatial autocorrelation refers to the presence of systematic spatial variation in a mapped variable. The map shows positive spatial autocorrelation where adjacent observations have similar data values. The spatial autocorrelation is often used to detect whether the distribution of variables has spatial dependency, heterogeneity and constitutive properties. Moran's I is one of the important indexes used to analyze spatial correlation (Equation (1)) [22,23].

$$I = \frac{n\sum\_{i=1}^{n}\sum\_{j=1}^{n} w\_{ij}(\mathbf{x}\_{i}-\overline{\mathbf{x}})\left(\mathbf{x}\_{j}-\overline{\mathbf{x}}\right)}{\sum\_{i=1}^{n}\sum\_{j=1}^{n} w\_{ij}\sum\_{i=1}^{n}(\mathbf{x}\_{i}-\overline{\mathbf{x}})^{2}}\tag{1}$$

where n represents the number of the cities and districts; *wij* represents the spatial relationship between region *i* and *j*; *x<sup>i</sup>* and *x<sup>j</sup>* , respectively, are the concentration values of certain atmospheric pollutant in each city; *x* is average concentration value of a certain atmospheric pollutant by study region, *x* = <sup>1</sup> *<sup>n</sup>* ∑ *n i*=1 *xi* . The range of Moran's I lies between −1 and 1. If the Moran's I index >0, this implies a positive spatial correlation. Inversely, if the Moran's I index <0, this indicates a negative correlation [24]. The smaller the value, the stronger the spatial divergence [25].

An alternative approach to measure the relationship typology and intensity are provided by the local indicator of spatial association (LISA) (Equation (2)) [26]. It has four types of distributions, which includes high–high (HH) type, high–low (HL) type, low–high (LH) type and low–low (LL) type. High–high type or low–low type represents spatial clusters of similar high or low concentration values of atmospheric pollutants. Low–high type or high–low type indicates spatial outliers with low concentration values of atmospheric pollutants surrounding high concentration values of atmospheric pollutants or vice versa.

$$L\_i = \frac{\left(\mathbf{x}\_i - \overline{\mathbf{x}}\right)}{S^2} \sum\_{j=1}^{N} w\_{ij} (\mathbf{x}\_i - \overline{\mathbf{x}}) \tag{2}$$

where *S* <sup>2</sup> = <sup>1</sup> *<sup>n</sup>* ∑ *n i*=1 (*x<sup>i</sup>* − *x*) 2 ; S 2 is the concentration variance of a certain atmospheric pollutant. If *L<sup>i</sup>* > 0, this implies the HH type or LL type. If *L<sup>i</sup>* < 0, this indicates the HL type or LH type [27].

#### **3. Result and Discussions**

*3.1. Temporal and Spatial Changes of the Concentrations of Atmospheric Pollutants* 3.1.1. PM<sup>10</sup>

Figure 2 shows the change in PM<sup>10</sup> for each area in the CCEC during 2017–2020. The average annual concentration of PM<sup>10</sup> for the whole CCEC was 72.0 µg/m<sup>3</sup> , and the range and standard deviation were 42.0 µg/m<sup>3</sup> and 9.5 µg/m<sup>3</sup> , respectively, in 2017. The areas with the highest annual average PM<sup>10</sup> concentration were Zigong city in Sichuan province and Jiangjin district in Chongqing (89 µg/m<sup>3</sup> ), and the concentrations were 27.0% higher than Grade II of AAQS. The area with lowest annual average PM<sup>10</sup> concentration was Qianjiang district in Chongqing (47 µg/m<sup>3</sup> ). The number of areas exceeding Grade II of AAQS was about 42.0%. The areas with a higher concentration were mainly located in the cities of Chengdu, Deyang, Leshan, Zigong in Sichuan province and the districts of Jiangjin, Qijiang in Chongqing, and the distribution of PM<sup>10</sup> was consistent with three air pollution transmission channels. In 2020, the average annual concentration of PM<sup>10</sup> for the whole CCEC decreased to 50.1 µg/m<sup>3</sup> , while the range and standard deviation were 32.0 µg/m<sup>3</sup> and 7.2 µg/m<sup>3</sup> , respectively. All cities (districts and counties) met the Grade II of AAQS. The area with the highest concentration of PM<sup>10</sup> was Chengdu city in Sichuan province (64 µg/m<sup>3</sup> ), while the area with the lowest concentration of PM<sup>10</sup> was Ya'an city in Sichuan province (33 µg/m<sup>3</sup> ).

**Figure 2.** Spatial distribution of PM<sup>10</sup> in CCEC from 2017 to 2020.

Studies have found that particulate matter in the atmosphere is harmful to human health [28,29]. Based on the interim targets and air quality guideline of PM<sup>10</sup> in QAGQ, the average annual concentrations of PM<sup>10</sup> for each area in the CCEC were higher than the air quality guideline (15 µg/m<sup>3</sup> ), which caused the potential risk to public health. The numbers of areas exceeding interim target 2 (50 µg/m<sup>3</sup> ) and interim target 3 (30 µg/m<sup>3</sup> ) were 55.6% and 44.4% respectively. If mortality in a population exposed to PM<sup>10</sup> at the air quality guideline level was arbitrarily set at 100, then it would be 114 and 106 in populations exposed to PM<sup>10</sup> at the interim target 2 and 3 levels.
