**5. Conclusions**

In this study, we examined the spatial and temporal variation of ambient air quality in northwest China (NWC) for a period of four years (2015–2018). During the study period, the average concentration of PM2.5, PM10, SO2, NO2, CO, and O3 decreased in 92.5%, 96.2%, 92.5%, 64.5%, 88.7%, and 11.3% of the cities in NWC. The annual average concentration of particulate matter (PM2.5 and PM10) exceeded the CAAQS Grade II standards and WHO recommended air quality guidelines in NWC, while the annual average concentration of SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In the case of seasonality, the highest pollution level occurred in winter except for ozone, with varying degrees of spatial distribution. The AQI, the proportion of AQI Class I, and the number of pollution days improved by 21.3%, 114.1%, and 61.8%, respectively, in NWC. The AQI improved in all the seasons, with the maximum improvement in spring followed by summer, winter, and autumn. In NWC, PM10 was a major pollutant for most of the days, followed by O3, PM2.5, NO2, CO, and SO2 with different spatial and temporal variations. A strong correlation occurred between AQI and all the pollutants except O3. Stricter regulations, e.g., a three-year action plan to win the blue sky defense war, sector-specific guidelines, and strict enforcement of environmental legislation, are the keys to pollution-free and breathable air. This paper comprehensively discussed the spatio-temporal characteristics of the ambient air quality in NWC and calls for future detailed assessment focusing on source apportionment, health risk assessment, the impact of meteorology, dispersion modeling, and impact of the chemical processes that influence the air quality.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/atmos13030375/s1, Figure S1: The locations of 53 cities in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) northwest China (NWC). Color represents the different classes of air quality index, e.g., green (0–50, good), yellow (51–100, moderate), orange (101–150, unhealthy for a sensitive group), red (151–200, unhealthy for all), purple (201–300, very unhealthy), and maroon (300+, hazardous); Figure S2: The seasonal (spring (light blue line), summer (orange line), autumn (grey line), and winter (yellow line)) spatial distribution of PM2.5 (a), PM10 (b), SO2 (c), NO2 (d), CO (e), and O3 (f) in 53 cities of northwest China between 2015 and 2018. Descriptions are as follows: light blue line with dots (spring), orange line with dots (summer), grey line with dots (autumn), yellow line with dots (winter), and the blue line (CAAQS, daily mean). The abbreviations are as follows: PM2.5 (fine particulate matter), PM10 (coarse particulate matter), SO2. (sulfur dioxide), NO2 (nitrogen dioxide), CO (carbon monoxide), and O3 (ozone); Figure S3: Annual

(a) and seasonal (spring (b), summer (c), autumn (d), winter (e)) relationship between air quality index (AQI) and criteria pollutants (PM2.5, PM10, SO2, NO2, CO, and O3). The abbreviations are as follows: AQI (air quality index), PM2.5 (fine particulate matter), PM10 (coarse particulate matter), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), CO (carbon monoxide), and O3 (ozone); Table S1: Lists of cities, their rankings in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) between 2015 and 2018; Table S2: Pearson correlation between AQI and six criteria pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in northwest China (NWC) between 2015 and 2018. The abbreviations are as follows: AQI (air quality index), PM2.5 (fine particulate matter), PM10 (coarse particulate matter), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), CO (carbon monoxide), and O3 (ozone).

**Author Contributions:** Conceptualization, S.Z.; data curation/accumulation, S.Z.; investigation and data validation, J.L. and M.B.; methodology, S.Z.; data modelling, S.Z.; GIS mapping, S.Z.; writing—original draft preparation, S.Z.; review and editing, J.L. and M.B.; supervision, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research work is supported by the National Natural Science Foundation of China (No. 21667026), the Social Science Foundation of Xinjiang Production and Construction Corps (No. 18YB13), and the Startup Foundation for Introduction Talent of NUIST (2017r107).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.

**Acknowledgments:** I would like to acknowledge the China National Environmental Monitoring Center (CNEMC) for the provision of air quality data. I am also very thankful to Jianjiang Lu and research scholar Anam Arshad from the School of Chemistry and Chemical Engineering, Shihezi University for helping and guiding me throughout the preparation of this paper.

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

### **References**


**Xing Xiang 1, Guangming Shi 1,2,\* , Xiaodong Wu <sup>1</sup> and Fumo Yang 1,2**

<sup>1</sup> Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; xiangxing@stu.scu.edu.cn (X.X.); wuxiaodong@stu.scu.edu.cn (X.W.); fmyang@scu.edu.cn (F.Y.)

**\*** Correspondence: shigm@scu.edu.cn

**Abstract:** Sichuan Basin is an area with some of the most serious PM2.5 pollution, and it is also a key area for joint prevention and control of air pollution in China. Therefore, it is necessary to clarify the temporal and spatial distribution characteristics of PM2.5 concentration in Sichuan Basin (SCB) and study the influence of meteorological conditions. In this study, the spatial disparity of PM2.5 concentration in SCB and its variation trend from 1 December 2015 to 30 November 2019 were analyzed. The results showed that the spatial disparity of SCB was decreasing and distinct variation trends of PM2.5 concentration were observed in different areas. The PM2.5 concentrations declined rapidly in the western and southern basin (most severely polluted areas), decreased at a slower rate in the central and eastern basin, but unexpectedly increased slightly in the northern and northeastern basin. From the perspective of relative spatial anomalies (RAs), the decreasing (increasing) trend of RAs of PM2.5 concentrations in the western and southern (northern and northeastern) parts of SCB were also prominent. The reduction in spatial disparity and the regionally extraordinary increasing trend could be partly explained by the variations in synoptic circulations. Specifically, the reasons for the decrease in wintertime spatial disparity and the increase in RAs in the northern basin were the reduction in synoptic pattern Type 2 (weak high-pressure system and uniform pressure fields) and Type 3 (high-pressure system to the north) and the growth of Type 6 (weak low-pressure system with high-pressure system to the north). In spring, the reasons were the reduction in Type 1 (weak low-pressure system) and Type 5 (weak low-pressure system to the southwest) and the growth of Type 2. The reduction in Type 2 and the growth in Type 4 (weak high-pressure system to the east) could explain the variation in PM2.5 distribution in autumn. This study showed the importance of implementing more precise and effective emission control measures, especially in relatively cleaner areas, in which the impacts of meteorological conditions might cause fluctuation (even rebounding) in the PM2.5 concentration.

**Keywords:** PM2.5; Sichuan Basin; spatial distribution; spatial disparity; synoptic patterns; meteorological conditions
