3.1.5. NO<sup>2</sup>

In 2017, the average annual concentration of NO<sup>2</sup> for the whole CCEC was 29.9 µg/m<sup>3</sup> , the range and standard deviation were 35.0 µg/m<sup>3</sup> and 7.5 µg/m<sup>3</sup> , respectively (Figure 6). The number of areas exceeding Grade II of AAQS was about 11.0%. The area with the highest and lowest concentrations of NO<sup>2</sup> were Chengdu city in Sichuan province (53 µg/m<sup>3</sup> ) and Dazu district in Chongqing (18 µg/m<sup>3</sup> ). The distributions of NO<sup>2</sup> were consistent with air pollution transmission channels 1 and 3, which were mainly located in the cities of Chengdu, Meishan, Dazhou, the central districts and Jiangjin district of Chongqing. The rapid development of urbanization in these areas has led to a gradual increase in vehicles, and one of the main sources of NO<sup>2</sup> was traffic emissions [1]. The average annual concentration of NO<sup>2</sup> for the whole CCEC decreased to 24.1 µg/m<sup>3</sup> in 2020, and the range and standard deviation were 24.0 µg/m<sup>3</sup> and 5.9µg/m<sup>3</sup> , respectively. Each area in the CCEC reached Grade II of AAQS, which meant that the control measures of NO<sup>2</sup> in the CCEC also had certain effects in the past years.

The average annual concentrations of NO<sup>2</sup> for each area in the CCEC were lower than interim target 1 of QAGQ (40 µg/m<sup>3</sup> ) in 2020. The number of areas exceeding interim target 2 (30 µg/m<sup>3</sup> ), interim target 3 (20 µg/m<sup>3</sup> ), and the air quality guidelines (10 µg/m<sup>3</sup> ) were 11.1%, 58.3%, and 27.7%, respectively. If all-cause mortality in a population exposed to nitrogen dioxide at the AQG level was arbitrarily set at 100, then it would be 104 and 102 in populations exposed to nitrogen dioxide at the interim target 2 and 3 levels. The results showed that the potential risk to public health caused by NO<sup>2</sup> pollution might still exist.

**Figure 6.** Spatial distribution of NO<sup>2</sup> in CCEC from 2017 to 2020.

### 3.1.6. CO

Figure 7 shows the change in CO in the CCEC from 2017–2020. The results showed that the control of CO in the CCEC had always been effective, since the concentration of CO for the whole CCEC was 1.4 mg/m<sup>3</sup> (the range and standard deviation were 0.9 mg/m<sup>3</sup> and 0.2 mg/m<sup>3</sup> , respectively). The concentration of CO was far below Grade II of AAQS and the air quality guideline in QAGQ, which meant no potential risk to public health. The area with the highest and lowest concentrations of CO was Dazhou city (1.9 mg/m<sup>3</sup> ) and Mianyang city (1.0 mg/m<sup>3</sup> ) in Sichuan province, respectively. In 2020, the concentration of CO for the whole CCEC slightly decreased to 1.1 mg/m<sup>3</sup> ; the range and standard deviation were 0.5 mg/m<sup>3</sup> and 0.1 mg/m<sup>3</sup> , respectively.

In summary, the controls of SO<sup>2</sup> and CO in CCEC were effective. The pollutions of PM10, PM2.5, and NO<sup>2</sup> had obvious improvement, while the control of O<sup>3</sup> was not obvious. The concentrations of PM10, PM2.5, O3, and NO<sup>2</sup> in 2020 were still higher than the air quality guidelines in QAGQ, which meant that the potential risk to public health still exited. The terrain of CCEC was quite complex; the basin in Sichuan province and mountains in Chongqing caused the accumulation of atmospheric pollutants. The pollutions of PM10, PM2.5, and NO<sup>2</sup> were quite severe in 2017. Furthermore, the distributions of PM10, PM2.5, O<sup>3</sup> and NO<sup>2</sup> were consistent with three air pollution transmission channels, which verified the unique geographical and climatic factors influencing the distributions. Since the revised version of "The Environmental Protection Law of People's Republic of China" came into force in 2015, the concentrations of PM2.5 and SO<sup>2</sup> have decreased over time [40]. There were 27 key tasks that had been completed to improve air quality, meet the capacity of atmospheric environment, and control the pollution of PM2.5 and nitrogen oxide in Chongqing city since 2018. The air quality in Sichuan province was also improved by dividing the key areas of air pollution prevention and control, carrying out stricter environmental protection standards, and implementing environmental monitoring systems since 2019. Meanwhile,

the interventions to control COVID-19 might improve air quality. Studies have found reductions in NO<sup>2</sup> and PM2.5 concentrations during the pandemic [41,42]. The air quality of China was also significantly improved due to the anti-epidemic measures [43,44]. The reduction in human activities (traffic and industry) led to the decrease in atmospheric pollutants [45]. With the economic activities resumed, the effect of improvements on air quality will be offset in the short term. Some sustainable policies must be carried out to tackle air pollution in the post-pandemic era [44]. However, the heavily polluted areas still existed in 2020. Thus, the heavily polluted areas caused by PM10, PM2.5, O3, and NO<sup>2</sup> were analyzed by spatial autocorrelation.

**Figure 7.** Spatial distribution of CO in CCEC during 2017 to 2020.

#### *3.2. Spatial Autocorrelation of Air Pollution in CCEC*

3.2.1. Global Spatial Autocorrelation

Table 2 shows the Global Moran0 s I of PM10, PM2.5, NO2, and O<sup>3</sup> during 2017–2020. The Global Moran's I values of PM<sup>10</sup> and PM2.5 were 0.49 and 0.35 (*p* < 0.05, Z > 1.65) in 2017, respectively. The certain aggregation characteristics of PM<sup>10</sup> and PM2.5 with positive spatial correlation were shown in the CCEC, and there was an obvious tendency of aggregation in heavily polluted areas. The Global Moran0 s I values of PM<sup>10</sup> and PM2.5 decreased to 0.21 and 0.06 in 2020, which meant that the spatial aggregations of PM<sup>10</sup> and PM2.5 were changed from aggregation distribution to random distribution, due to the implementation of atmospheric control measures. The Global Moran0 s I value of O<sup>3</sup> increased from 0.21 in 2017 to 0.57 in 2020, and the spatial aggregation became significant. The Global Moran0 s I value of NO<sup>2</sup> did not show any significance, which meant that there was no significant spatial aggregation of NO<sup>2</sup> in CCEC.


**Table 2.** Global Moran0 s I of atmospheric pollutants.

#### 3.2.2. Local Spatial Autocorrelation

The cities of Yibin, Neijiang, Luzhou, Meisan in Sichuan province and Yongchuan district in Chongqing showed the HH type of PM<sup>10</sup> in 2017 (Figure 8), which meant that these areas suffered from heavy pollution of PM10. The areas of Wanzhou district, Fengdu county, and Qianjiang district in Chongqing showed the LL type of PM<sup>10</sup> in 2017, while the Ya'an city in Sichuan province showed the LH type. Meanwhile, the cities of Yibin, Neijiang, Luzhou in Sichuan province and the Yongchuan district in Chongqing showed the HH type of PM2.5 in 2017. Nanchong city in Sichuan province and the districts of Wanzhou, Qianjiang in Chongqing showed the LL type of PM2.5, while Dazhou city in Sichuan province showed the HL type. The results showed that the distribution of particulate matter had obvious regional aggregation; the heavily polluted areas were consistent with the distribution of air pollution transmission channels, which were at the end of the channels. The cities of Zigong, Yibin, Neijiang and Luzhou were traditional industrial bases, and the large-scale and intensification of heavy polluting industries also resulted in the aggregation of heavily polluted areas [27]. In 2020, with the implementation of atmospheric control measures, the number of cities with the HH type of PM<sup>10</sup> decreased obviously, and the spatial aggregation became weak. Meanwhile, the number of cities with the HH type of PM2.5 was barely changed. Based on the results, the cities with the HH type of PM<sup>10</sup> and PM2.5 located at the end of the three transmission channels and the control of PM<sup>10</sup> were better than the control of PM2.5. The southern part of the CCEC still deserved key attention in the future control of particulate matter.

**Figure 8.** LISA cluster of PM<sup>10</sup> and PM2.5 in 2017 and 2020.

The areas of Wanzhou district, Liangpin district, Zhong county, and Fengdu county in Chongqing showed the LL type of O<sup>3</sup> in 2017 (Figure 9). These areas had relatively lighter O<sup>3</sup> pollution. Few cities showed the HH type of O3. Meanwhile, the LH type of O<sup>3</sup> was shown in the cities of Ziyang, Luzhou in Sichuan province and the districts of Tongliang, Yongchuan in Chongqing. The distribution of O<sup>3</sup> in the CCEC was still random in 2017. The HH type of O<sup>3</sup> became more and more obvious year by year, which was mainly located in the cities of Chengdu, Deyang, Neijiang, Ziyang in Sichuan province and districts of Bisan, Tongliang, Yongchuan, and Dazu in Chongqing, which was consistent with the middle reach of the three air pollution transmission channels. Based on the distributions of particulate matter and O3, the degree of pollution in Sichuan province was heavier, due to the low topography of the Sichuan Basin, which was not conducive to the discharge of pollutants. Furthermore, the relatively developed economy, large population density and high industrial density in Sichuan province caused the high emission of pollutants [13]. Therefore, the areas with a high concentration of O<sup>3</sup> should be controlled to prevent the expansion of heavy polluted areas.

**Figure 9.** LISA cluster of O<sup>3</sup> from 2017 to 2020.

In 2017, the districts of Tongnan, Dazu, and Rongchang in Chongqing showed the LL type of NO2, and the city of Ya'an in Sichuan province and district of Qijiang in Chongqing showed the LH type (Figure 10). In 2020, the distribution of NO<sup>2</sup> in CCEC was still random, yet the districts of Nanchuan and Qijiang in Chongqing showed the HH type. Based on the concentration of NO<sup>2</sup> in 2020, the average annual concentration of NO<sup>2</sup> for the whole CCEC was 24.1 µg/m<sup>3</sup> , while the average annual concentration of NO<sup>2</sup> in the districts of Nanchuan and Qijiang in Chongqing was 25.5 µg/m<sup>3</sup> . Since the obvious effect was achieved on NO<sup>2</sup> control in the CCEC, the average annual concentration of NO<sup>2</sup> decreased, and the high concentration of NO<sup>2</sup> in the districts of Nanchuan and Qijiang in Chongqing caused these areas to be the HH type. Therefore, the control of NO<sup>2</sup> in the districts of Nanchuan and Qijiang in Chongqing should be further strengthened in the future.

**Figure 10.** LISA cluster of NO<sup>2</sup> from 2017 to 2020.

#### **4. Conclusions**

We have investigated the temporal and spatial distribution of atmospheric pollutants in the CCEC from 2017 to 2020. The concentrations of PM10, SO2, NO2, and CO met the Grade II of AAQS in 2020, due to the implementation of atmospheric control measures. The average annual concentration of SO<sup>2</sup> for the whole CCEC decreased from 15.4 µg/m<sup>3</sup> in 2017 to 10.3 µg/m<sup>3</sup> in 2020, and the long-term problems of acid rain and sulfur dioxide pollution were basically eliminated. The concentrations of PM<sup>10</sup> and PM2.5 also improved significantly; there were 30.4% and 34.4% reductions for the average annual concentration of PM<sup>10</sup> and PM2.5. The concentration change of O<sup>3</sup> was not obvious, yet 97.2% of the areas met the Grade II of AAQS in 2020. The distributions of PM10, PM2.5, O3, and NO<sup>2</sup> were consistent with three air pollution transmission channels, which meant that the distribution of atmospheric pollutants was influenced by topographic and climatic conditions.

The concentrations of PM10, PM2.5, O3, and NO<sup>2</sup> were far beyond the air quality guidelines in QAGQ in 2020. The purpose of the air quality guidelines was to illustrate the minimum impact of atmospheric pollutants on human health. The concentration levels of PM10, PM2.5, O3, and NO<sup>2</sup> still had certain impacts on human health, and it is necessary to reduce the concentration of these atmospheric pollutants by using interim targets in QAGQ.

Based on the results of spatial autocorrelation of air pollution in the CCEC, the spatial aggregation of PM<sup>10</sup> was significantly reduced, and the number of areas with the HH type of PM<sup>10</sup> decreased in 2020. Meanwhile, the HH type of PM2.5 was mainly located in the southern part of CCEC, and it barely changed in 2020. The spatial aggregation of O<sup>3</sup> became obvious in 2020, and the HH type of O<sup>3</sup> was shown in the central and northwest parts of the CCEC. The spatial aggregation of NO<sup>2</sup> was random during 2017–2020, yet the districts of Nanchuan and Qijiang in Chongqing showed the HH type of NO2.

In summary, the key control areas of particulate matter should focus on the southern part of the CCEC, and the control of industrial pollution sources in the cities of Zigong, Yibin, Neijiang and Luzhou in Sichuan province should be strengthened. It was suggested that the growth rate of coal-fired power plants should be controlled strictly, the proportion of coal and gas in electricity needs to be optimized, and the transformation of the steel

industry to achieve ultra-low emissions should be accelerated. The key control areas of O<sup>3</sup> should focus on the central and northwest parts of the CCEC. It was recommended to reduce the emission of NOx and VOCs in these regions, especially focusing on the sources of scattered polluted enterprises and key industry VOCs emissions. The distribution of NO<sup>2</sup> pollution was random to some extent, yet NO<sup>2</sup> pollution in the southern part of the CCEC is still worth paying attention to.

**Author Contributions:** Conceptualization, N.Q. and F.N.; methodology, H.W. and X.T.; investigation D.J. and T.W.; writing—original draft preparation, N.Q. and Q.T.; writing—review and editing, T.X.; project administration, N.Q.; funding acquisition, N.Q., L.R. and W.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Social Science Fund of China (20XSH015), Science and Technology Research Project of Chongqing Municipal Education Commission (KJQN202100831, KJQN202000834), Natural Science Foundation of Jiangsu Province (BK20210933), and the Start-up Foundation of High-level Talents (2056014) in Chongqing Technology and Business University.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


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