Spatiotemporal variation characteristics, sources and trends of air quality in special region from 2016 to 2020 - A case study of Panzhihua, China

Spatiotemporal variation characteristics, sources and trends of air quality in special region from 2016 to 2020 - A case study of Panzhihua, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal variation characteristics, sources and trends of air quality in special region from 2016 to 2020 - A case study of Panzhihua, China Yan Yang, Xiqiao Wu, Xing Huang, Chaorong Liu, Dan Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4302520/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study based on daily data of six major pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 ) from 2016 to 2020, the spatiotemporal variation characteristics of air quality in Panzhihua and its surrounding cities were analyzed. On this basis, trajectory model is used to analyze the origin and direction of migration of the pollutants in the days with high pollution degree, so as to find a method to prevent and control the air pollution in the cities with special geographical location. The results show that the concentration of pollutants in the study area showed an overall downward trend, but Ozone showed an opposite trend. The air quality in the study area has been significantly improved. Air quality was the best in 2018, followed by 2020. The annual variation trend of PM 2.5 , PM 10 , SO 2 , NO 2 and CO is U-shaped (flat W), while the O 3 is M-shaped. In addition, the monthly average concentration of pollutants in Panzhihua is higher than these in its surrounding cities. O 3 has a significant correlation with its various precursor pollutants, and the air pollution situation is complex and diverse. According to the analysis of pollutant diffusion trajectory, the direction of pollution source in Panzhihua city is southwest and the diffusion direction is east and southeast. Panzhihua air quality spatio-temporal variation analysis of pollutant diffusion trajectory Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction In recent year, due to rapid development of transportation, industry and agriculture, and the speedy changes in people’s lifestyle, as well as the intensification of urbanization (Elbayoumi et al., 2013 ; Tiwari et al., 2015), the world has experienced severe air pollution (Wang et al., 2022). These factors have led to the production of a great quantity of air pollutants and the severe urban pollution, resulting a series of social problems (Larson et al., 1999 ; Siriopoulos et al., 2021; Wang et al., 2021). According to the statistics that the WHO gathered, air pollution causes about 4.2 million deaths worldwide each year (this number is about one in eight of total global deaths) ( https://www.who.int/health-topics/air-pollution#tab=tab_1).Th e report reveals almost 99% of the world’s population breathe air that does not meets world health organization (WHO) standards (WHO, 2021), thus causing a lot of negative economic effects (Huang et al., 2019 ). The Ministry of Ecology and Environment of China issued a new Environmental Air Quality Standards (GB3095-2012) in 2012 (Table 1 ), although this standard has effectively reduced the harm of air pollution to people’s health and improved our air quality (Wang et al., 2019; Bai et al., 2021 ), the negative impacts of air pollution still have not been fundamentally resolved, and air pollutants still endanger people's health. Air pollutants can do great harm to human beings, animals and plants (De Kok et al., 2009 ; Sharma et al., 2020). This is because the fine particulate matters (PM 2.5 , PM 10 ) are small in radius, they exists a large specific surface area, which will help bacteria (Liu et al., 2018 ; Li et al., 2021 ), viruses(Zoran et al., 2020; Maleki et al., 2021 ), heavy metal (Sternbeck et al., 2002; Khan et al., 2020 )attach to fine particulate matters. People and animals will suffer the nervous system (Badeenezhad et al., 2020 ; Costa et al., 2020 ), respiratory system (Ścibor et al., 2021) and immune system (Badeenezhad et al., 2020 ) damage if they are exposed to high concentrations of air pollutants for a long time. For instance ozone can affect the release of biogenic volatile organic compounds(BVOC) in the underlying air (Cai et al., 2021 ). The BVOC affects human health, damage the central nervous system, and cause cancer and teratogenesis (Zhang et al., 2020). Air pollutants interact with or react with normal components of the air, however these reactions to a large extent cause secondary and compound pollution. Photochemical reactions generally occur in summer and produce more harmful secondary pollutants (Liu et al., 2018 ), such as Peroxyacetyl nitrate (Zhang et al., 2021), Sulfate aerosol (Arun et al., 2021 ). In winter, air pollutants will be affected by meteorological conditions, temperature and geographical conditions, causing haze and other meteorological disasters, affecting transportation, and resulting traffic accidents (Dong et al., 2019 ; Song et al., 2022). At present, most of scholars study provincial capitals and important economic development areas, such as Beijing, Wuhan, Hefei, Beijing-Tianjin-Hebei Economic Zone, Yangtze River Delta, Pearl River Delta, Detroit Michigan area, megacities of India and other severe pollution areas (Hu et al. 2014; Zhan et al. 2018; Chen et al. 2019; Xiao et al. 2020; Wen et al. 2021; O'Leary et al. 2022 ; Pal et al. 2022 ; Ren et al. 2022). Some scholars mostly researched on air quality based on the overall air quality changes in countries such as India, Brazil and so on (Pant et al., 2019 ; Siciliano et al., 2020). Many scholars have used air quality monitoring data to conduct extensive research and analysis on air pollution. Furthermore, these studies have generated numerous research methods and analytical models, which are widely used by scholars, such as EMEP MSC-W model (Pisoni et al. 2019), CMAQ model (Liu et al. 2020 ), and air measurement model (Şahi̇n et al. 2021), etc. However, most studies of air pollutants have been researched on simple terrains, or on the analysis of single pollutants. Although some studies have conducted comprehensive analyses of air pollution, the study period is relatively short. Some scholars analyzed the air quality changes in plateau and basin, but these researches were focused on diffusion and the effect of thermal energy on air pollution. (Ivancic and Voncina, 2014 ; Notario et al., 2014 ; Li et al., 2020 ). Based on these circumstances, this study analyzed the air quality spatio-temporal changes from 2016 to 2020 of Panzhihua (PZH), which is a plateau, valley and basin city. In this study, in order to discuss the characteristics of air quality changes in the plateau canyon area, we take PZH city as the center and extend to the surrounding cities. PZH city is the largest mineral resources city in the upper reaches of the Yangtze River and a typical mining city in southwest China. It is located on the edge of the Yunnan-Guizhou Plateau and the Qinghai-Tibet Plateau, and in the Panxi Rift Valley, with very unique terrain conditions. PZH city belongs to the plateau canyon landform, with staggered internal basins, high in the northwest (the southeast edge of the Tibet Plateau) and low in the northeast (Fig. 1 ). The surrounding cities involved in this article are Chuxiong(CX), Kunming (KM), Lijiang (LJ), Liangshanzhou (LSZ), Qujing (QJ), and Zhaotong (ZT). In the six surrounding cites, LSZ is located in the east of the Hengduan Mountains, while the other cities are located on the Yunnan-Guizhou Plateau. There is a large geographical gap between PZH and the surrounding cities, forming a pocket-like geographical terrain. Air pollution characteristics and topographic effect in PZH were still indeterminate. This study analyzes the spatio-temporal change of air pollution and discusses the correlation between major pollutants based on the air quality index (AQI) and six main air pollutants (SO 2 , NO 2 , O 3 , CO, PM 10 , PM 2.5 ) data. The cardinal objective of the present study is to use air quality monitoring data to analyze the spatiotemporal of air qualty and pollution sources of city with special terrain, and to analyze the air pollution characteristics of Yamhara Canyon Basin city. Overall, the main targets were to: (1)explore the spatio-temporal variation characteristics of urban air quality under multiple and complex terrain conditions; (2)clarify the trend of air quality change and the source and diffusion direction of air pollution; (3)provide theoretical support for regional coordinated control of air pollution in special urban groups. Table 1 Air quality concentration limits (GB3095-2012) Pollutant Mean time Limit Unit Level I Level II Sulfur dioxide (SO 2 ) Annual average 20 60 µg/m 3 Daily average 50 150 One hour average 150 500 Nitrogen dioxide (NO 2 ) Annual average 40 40 µg/m 3 Daily average 80 80 One hour average 200 200 Ozone (O 3 ) One hour average 160 200 µg/m 3 Maximum eight-hour average 100 160 Carbonic oxide(CO) One hour average 10 10 mg/m 3 Daily average 4 4 Particulate matter with aerodynamic diameters of < 10 µm (PM 10 ) Annual average 40 70 µg/m 3 Daily average 50 150 Particulate matter with aerodynamic diameters of < 2.5 µm (PM 2.5 ) Annual average 15 35 µg/m 3 Daily average 35 75 2 Materials and methods 2.1 Study sites The seven cities centered on Panzhihua were selected for this paper (Chuxiong, Kunming, Lijiang, Liangshan Prefecture, Qujing, Zhaotong) (Fig. 1 ). PZH’s data were averaged from five state-controlled monitoring sites (Bingcaogang, Nongnongping, Renhe, Sishizhongxiao and Hemenkou) of different land use types. Panzhihua has a longitudes ranging from102°15′E to 108°08′E and has a latitudes from 26°05′N to 27°21′N. To better know this research areas, some basic situations within the region are briefly introduced. Panzhihua is located at the junction of southwest China Sichuan Province and Yunnan Province, it is an important city in the third-tier construction city in China. Panzhihua enjoys the reputation of vanadium and titanium capital of China, with the production of vanadium and titanium ranks third in the world, meanwhile rich in mineral resources and water resources. At the same time, the other six cities are the Southern Silk Road’s important node city. Kunming, it's like spring all the year round, with the title of spring city. Lijiang is an important tourist city with great differences in altitude, rich species, abundant forests, and good ecosystem function. 2.2 Air quality data To know spatial patterns and temporal variations of the main air pollutants in a city centered on Panzhihua (PZH), we analyzed the main pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) monitoring data from January 1st, 2016 to December 31st, 2020 in the city. Moreover, according to the geographical characteristics, the six surrounding cities of PZH were selected to compare air pollution characteristics among the regions, and to exploration air pollution diffusion trajectory. The real-time daily average concentrations of six criteria pollutants in the 7 cities were downloaded from website of Online air quality monitoring and analysis platform ( http://www.aqistudy.com ), air quality data of 5 national control monitoring points in PZH were downloaded from Panzhihua Ecological and Environmental Protection Bureau ( http://sthjj.panzhihua.gov.cn/ ). Data on the daily average AQI (air quality index) and air pollutant concentration of each city provided by the national control monitoring points represent the daily values of the city. AQI is a dimensionless index, which is used to quantitatively express air pollution level in a place during the specific period such as one day or one month. The monthly data is the average of the natural month daily values, and the yearly data is average of the natural yearly daily values. The main six pollutants used this way to describe the monthly and yearly data. In this study, we used the data of backward trajectory and forward trajectory model which are obtained from the National Oceanic and Atmospheric Administration (NOAA) to depict the air pollutants diffused and migration mode in order to uncover formation mechanism of heavy air pollution in special terrain of PZH, China. 2.3 Research method 2.3.1 Statistical analysis In this study, SPSS26.0 (SPSS Inc., chicago IL, USA)was used for statistical analysis of the data, independent sample T test was used to determine the difference of AQI average value, and confidence test was carried out for PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 . In addition, Pearson correlation analysis (Rodgers and Nicewander, 1988 )was used to analyze the relationship between pollutants in the study area. Based on the daily air quality changes of Panzhihua city from 2016 to 2020, the change trend of six major pollutants and the correlation of some pollutants were discussed. In addition, the monitoring data of PZH City was classified, and the daily data were consolidated into monthly average data to discuss the annual change rule of air quality in PZH City. 2.3.2 Theil-Sen estimator Theil-Sen estimator (TSE) (Theil, 1950; Sen, 1968 ) is a good time series estimation method, which can estimate the trend of time series and is not affected by measurement errors and outliers. Therefore, this thesis utilizes this method to detect the change trend of air quality in Panzhihua and its surrounding cities on the inter-annual level. The specific calculation formula is as follows: $${\beta }=\text{M}\text{e}\text{d}\text{i}\text{a}\text{n}\left(\frac{{\text{x}}_{\text{j}}-{\text{x}}_{\text{i}}}{\text{j}-\text{i}}\right), {\forall }_{\text{i}} 0, it stands for an increasing trend; when β = 0, there is no trend change; and when β < 0, it signifies a downward trend. 2.3.3 Mann-Kendall test Mann-kendall is a non-parametric test that can be used to assess the significance of trends in a sequence of practices (Mann, 1945 ; Kendall, 1975 ). This test is suitable for the data whose samples do not follow the normal distribution. It can deal with outliers and missing data well, and also determine whether a process is a natural fluctuation or a definite trend (Gocic and Slavisa 2012 ; Nyikadzino et al., 2020 ; Wang et al., 2021). Moreover, this method is recommended by the World Meteorological Organization and is widely used in the determination of time series trend of hydro-meteorology. Therefore, this thesis uses this method to measure the time series trend of the daily change of air quality index in Panzhihua and its surrounding cities, and the specific calculation formula is as follows: $$\text{S}=\sum _{\text{i}}^{\text{n}-1}\sum _{\text{j}=\text{i}+1}^{\text{n}}\text{s}\text{g}\text{n}({\text{x}}_{\text{j}}-{\text{x}}_{\text{i}})$$ 2 $$\text{s}\text{g}\text{n}\left({\theta }\right)=\left\{\begin{array}{c} 1 \theta >0\\ 0 \theta =0\\ -1 \theta 0 (S < 0), it indicates an upward (downward) trend in the time series; when S = 0, it manifests no trend change; when the sample number n \(\ge\) 10, the following formula can be used to test its significance. The mean and variance of S can be calculated by the following formula: $$\text{E}\left(\text{S}\right)=0$$ 4 $$\text{V}\text{a}\text{r}\left(\text{S}\right)=\frac{1}{18}\left[\text{n}\right(\text{n}-1\left)\right(2\text{n}-5)-\sum _{\text{t}=1}^{\text{n}}{\text{f}}_{\text{t}}({\text{f}}_{\text{t}}-1)(2{\text{f}}_{\text{t}}+5)]$$ 5 In the formula, f t represents the number of x in a given range in any time range t . The formula for calculating the standard normal test statistic Z is as follows: $$\text{Z}=\left\{ \begin{array}{c}\frac{\text{S}-1}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}} S>0\\ 0 S=0\\ \frac{\text{S}+1}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}} SZ 1−α/2 expresses a significant change in time series. This study use significance level of α = 0.05. 2.3.4 Inverse distance weighted interpolation (IDW) Inverse distance weighted is a kind of interpolation algorithm, which is widely used, it mainly combines a group sample point data to determine pixel values (Shepard, 1968). The formula is as follows: $${\text{Z}}_{0}=\frac{{\text{H}}_{\text{i}}^{-\text{p}}{\text{Z}}_{\text{i}}}{\sum _{\text{i}=1}^{\text{n}}{\text{H}}_{\text{i}}^{-\text{p}}}$$ 7 Here, Z 0 is the estimated value; Z i is the attribute value of the i (i = 1,2,3 … n) sampling point, p is the weighted index with a significant impact on the interpolation result, H i is the distance. In this paper we used seven points to analysis the spatial change of air pollution of PZH and its surrounding cities in recent 5 years. 2.3.5 HYSPLIT model The HYSPLIT model (Stein et al., 2015) was used to calculate the reverse trajectory of air quality changes, determine the origin of air pollution and simulate the trajectory of movement. In this study, the 72h backward trajectory was computed by HYSPLIT version 5.1 ( https://www.ready.noaa.gov/ ) at 500 m above ground level and every 6h starting at 8:00 coordinated universal time (UTC). The air data were availabled from the Air Resources Laboratory (ARL). In this paper, we can select Bingcaogang as the starting point of the backward trajectory of Panzhihua city, the residential areas, commercial districts, administrative districts and traffic areas of Bingcaogang are relatively concentrated. 3 Result 3.1 Interannual characteristics of air quality The mean concentrations of AQI, PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 from 2016 to 2020 city of PZH (center city of the study area) in China were 59.63 ± 15.58, 30.72 ± 12.52, 57.02 ± 21.34, 33.31 ± 13.99, 34.67 ± 10.49, 1.45 ± 0.51, 83.31 ± 30.83 (mg/m 3 for CO, µg/m 3 for others except AQI), respectively. In addition, As can be seen from Fig. 2 , air quality in PZH and its surrounding cities except LJ shows a trend of improvement from 2016 to 2020 (β < 0). Among the selected research cities, 6 major pollutants in PZH, including PM 2.5 , PM 10 , SO 2 , NO 2 and CO, show a decreasing trend, O 3 presents a rising trend, and the rising trend is most obvious in several places. In addition, CX and LJ also show a rising trend for O 3 . Although partial pollutants in PZH show a downward trend, the downward trend of PM 2.5 and NO 2 in PZH is weaker than that in other cities. The average of AQI in the study area had a high value in 2017 and 2019. Specially, the air quality of PZH, CX, KM, LJ, LSZ, QJ, and ZT had a downtrend in 2019. Compared with the previous year, the annual average data of air quality index in 2019 increased by 3.16%, 9.63%, 8.94%, 14.04%, 8.27%, and 10.85% respectively in PZH, KM, LJ, CX, LSZ, QJ and ZT. In this study, 2018 is the year with the biggest change in air quality in the study area. Therefore, the improvement of each city in 2018 is introduced in detail below. Within the study area, the concentration of the SO 2 increased the fastest in 2018, with an increase of 5.54% compared with 2017, but it was meet the national control standards every year (Table 1 ). Looking at individual cities within the study area, The concentration of PM 2.5 , PM 10 , SO 2 and CO in KM decreased significantly in 2018, with a decrease rate of 19.85%, 28.17%, 22.30% and 19.77% respectively. In CX, the concentration of PM 10 , NO 2 , SO 2 , CO decreased by 17.01%, 11.17%, 25.29% and 27.08% respectively. And in 2018 the concentration of PM 2.5 , NO 2 , O 3 of LJ decrease by 6.94%, 22.01% and 10.75%, respectively. Annual mean values of PM 2.5 , PM 10 and NO 2 decreased by 20.40%, 22.71% and 19.60%, respectively, from 2017 to 2018. The decrease percentages of annual mean values of PM 2.5 , PM 10 , NO 2 , SO 2 and CO in QJ from 2017 to 2018 were 19.49%, 25.15%, 25.01%, 31.31%, 18.23%. In ZT, from 2017 to 2018, the concentration of PM 2.5 , PM 10 , NO 2 , and SO 2 decreased for 44.75%, 32.25%, 16.64% and 20.90% respectively. In 2018, the concentration of PM 2.5 and PM 10 were the most decreased pollutants in the six main pollutants of PZH, which were decreased by 14.76%, 22.39% respectively (Table 2 ). According to the air quality index (AQI) air quality can be classified into six levels: Excellent (0–50), Good (51–100), Lightly polluted (101–150), Moderately polluted (151–200), Heavily polluted (201–300), and Severely polluted (> 300) (MEP, 2012). Statistical analyses indicated that, PZH and its surrounding cities had excellent air quality about 54.71% of the days, and the days with good air quality account for about 44.22%. Specifically, the pollution days of most of seven cities decreased significantly, however, in several places, PZH, LSZ, QJ and ZT had more days above mild pollution, with 24, 27, 32, 38 days respectively (Table 3 ). The average annual change of air quality in the study area is obvious. The concentration change of various pollutants is also very obvious and the dispersion of air quality is large. In a addition the median number of some pollutants is greater than the average number, indicating that the contribution rate of this pollutant to air pollution is large. From the annual AQI average, the air quality change of PZH city is 2019 > 2016 > 2018 > 2017 > 2020, CX (2019 > 2017 > 2016 > 2018 > 2020), KM (2017 > 2016 > 2019 > 2018 > 2020), LJ (2017 > 2019 > 2020 > 2018 > 2016), LSZ (2016 > 2019 > 2017 > 2020 > 2018), QJ (2016 > 2017 > 2019 > 2020 > 2018), ZT (2017 > 2016 > 2019 > 2018 > 2020) (Fig. 3 ). The air quality and main pollutants (except O 3 ) in PZH and the surrounding cities promoted significantly in the past five years, especially in 2020, except 2019, because in 2019 the air quality index and pollutants concentration had increase. According to national standards and pollutant concentration characteristics, the main pollutants in the study area were fine particulate matter and O 3 . Table 2 statistic of AQI (Air quality index) and concentration of six air pollutants of Panzhihua and its surrounding cities from 2017 to 2018 City AQI(—) PM 2.5 (µg/m³) PM 10 (µg/m³) SO 2 (µg/m³) NO 2 (µg/m³) CO(mg/m³) O 3 (µg/m³) Year 2017 2018 Ratio(%) 2017 2018 Ratio(%) 2017 2018 Ratio(%) 2017 2018 Ratio(%) 2017 2018 Ratio(%) 2017 2018 Ratio(%) 2017 2018 Ratio(%) CX 46.10 ± 16.85 41.72 ± 13.84 9.50 22.53 ± 11.73 19.96 ± 9.25 11.41 39.86 ± 17.30 33.08 ± 15.13 17.01 19.21 ± 7.24 14.35 ± 5.85 25.30 21.01 ± 9.64 18.66 ± 9.38 11.19 0.94 ± 0.19 0.69 ± 0.18 26.60 75.9 ± 31.64 76.09 ± 34.84 -0.25 KM 56.01 ± 17.86 49.08 ± 14.16 12.37 28.48 ± 13.49 22.83 ± 9.61 19.84 58.07 ± 25.36 41.72 ± 16.21 28.16 15.20 ± 5.09 11.81 ± 3.98 22.30 32.13 ± 9.07 31.50 ± 7.33 1.96 0.97 ± 0.20 0.78 ± 0.19 19.59 79.27 ± 34.84 78.65 ± 29.41 0.78 LJ 44.92 ± 12.15 40.12 ± 11.06 10.69 13.52 ± 5.90 12.58 ± 5.81 6.95 27.07 ± 10.50 42.72 ± 9.63 -57.81 11.11 ± 2.03 10.04 ± 2.51 9.63 14.76 ± 5.80 11.51 ± 3.09 22.02 0.70 ± 0.23 0.72 ± 0.14 -2.86 86.65 ± 20.37 77.34 ± 20.21 10.74 LSZ 54.42 ± 18.83 50.31 ± 15.34 7.55 23.30 ± 11.28 18.54 ± 8.35 20.43 37.93 ± 15.94 43.72 ± 11.60 -15.26 17.16 ± 6.83 15.02 ± 4.38 11.65 23.38 ± 7.27 18.79 ± 6.06 19.63 0.87 ± 0.17 0.82 ± 0.18 5.75 93.43 ± 33.31 90.36 ± 26.44 3.29 PZH 61.59 ± 15.53 58.52 ± 13.53 4.98 34.09 ± 14.31 29.06 ± 9.39 14.76 66.55 ± 25.39 44.72 ± 15.35 32.80 35.46 ± 15.50 36.57 ± 13.89 -3.13 35.54 ± 12.22 35.33 ± 9.63 0.59 1.66 ± 0.51 1.45 ± 0.51 12.65 82.91 ± 26.96 85.19 ± 30.69 -2.75 QJ 56.23 ± 18.54 47.26 ± 14.14 15.95 28.27 ± 13.14 22.76 ± 10.38 19.49 53.86 ± 20.29 45.72 ± 14.17 15.11 18.22 ± 6.15 12.51 ± 4.45 31.34 23.33 ± 6.50 17.41 ± 5.50 25.38 1.05 ± 0.25 0.86 ± 0.24 18.10 80.85 ± 37.07 77.96 ± 27.24 3.57 ZT 61.44 ± 23.55 48.13 ± 13.85 21.66 31.13 ± 17.61 17.2 ± 9.65 44.75 56.04 ± 25.36 46.72 ± 19.61 16.63 18.21 ± 10.78 14.40 ± 7.34 20.92 20.67 ± 5.94 17.23 ± 5.21 16.64 0.73 ± 0.23 0.66 ± 0.22 9.59 94.76 ± 32.75 83.86 ± 23.54 11.50 Notice: These ratio in here mean the growth rate in 2018 compared to 2017. Table 3 Statistics of air pollution exceeding standard in PZH and its surrounding cities Pollutant City PM 2.5 PM 10 SO 2 NO 2 CO O 3 Air quality excellent good Total exceed CX 0 0 0 0 0 4 1260 564 3 KM 5 5 0 0 0 9 902 909 16 LJ 0 0 0 0 0 0 1507 318 0 LSZ 11 3 0 0 0 19 971 829 27 PZH 12 2 0 0 1 10 530 1273 23 QJ 9 0 0 0 0 27 937 858 32 ZT 18 4 0 0 0 18 891 898 35 3.2 Seasonal variation The monthly mean is the monthly average obtained by dividing the daily mean by the natural month days, this paper averaged the monthly average of the same month for 5 years. Based on this, the study analyzed the AQI and main pollutants seasonal variation during 2016 to 2020 of PZH and its surrounding cities. Results showed that the AQI higher value appeared in January, April, May, August, November and December, lower value appeared in June, July, September, and October each year (Fig. 4 a). Furthermore, the seasonal trend of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO in PZH and its surrounding cities are consistent, which the maximal value appeared in May or June. The seasonal trend of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO showed a “V” type from January to December (Fig. 4 b, 4 c, 4 d, 4 e, 4 f). Specifically, the SO 2 and NO 2 of PZH were higher than the others cities, but the concentration of these two air pollutants are not higher than the national standards. In this study had a biggest difference which is the change regulation of O 3 . According to the Fig. 4 g, the picture showed that the variation trend of O 3 had two peak and three valley, it same as “M” type. The peak of O 3 variation trend appeared in May and August, the low value appeared in January, July and December. Ozone’s contribution to air pollution tends to be more pronounced in summer. In terms of seasonal scale (March to May is spring, June to August is summer, September to November is autumn, December and January and February are winter), the air pollution change rule of PZH meets spring > winter > summer > autumn. The air pollution change law in CX, KM, LJ, LSZ, QJ and ZT meets spring > winter > autumn > summer (Fig. 5 ). Although the seasonal change law is different between PZH and its surrounding cities, in whole is similar. 3.3 Daily change analysis AQI as an important index of air quality change, so the Mann-Kendall test was used to test the time series trend of the daily data in the seven cities from 2016 to 2020(Fig. 6 ). From the perspective of the overall change trend, several other research points in the study region except PZH have similar change trend.The UF statistics of PZH in 2016 and 2017 appeared an overall downward trend on the annual level, which shows that the air quality presents a trend of improvement at the annual level, and the improvement trend gradually slows down after September. In addition, the average daily trend of air quality in PZH from 2018 to 2020 shows that the air quality has improved after May, but the AQI value from January to April reveals an overall upward trend, indicating that the air quality has deteriorated from January to April. The air quality of cities around Panzhihua shows the following trend: the Air quality index from January to June manifests an overall upward trend, while the daily mean value expresses a significant downward trend after June. According to the daily average change trend of each year, the growth trend of 2019 is the most obvious. There was no intersection between UB and UF in the test level, and there was no obvious mutation of air quality index. 3.4 Correlation analysis According to the distribution situation in the study region, and the O 3 , PM 2.5 and PM 10 were the main pollutants. Therefore, we used the main pollutants data to analysis the correlation and to seek the interaction between the pollutants. On the basis of research finding, the O 3 in the troposphere is a typical secondary pollutant, generated from NO 2 , and volatile organic compounds in the complex chain-type photochemical reactions. Simultaneously, in consideration of O 3 dominate the oxidation strength in the atmosphere, it will then affect the generation of secondary fine particles, and the correlation between the study area O 3 and fine particles was also discussed. Currently many investigators found that the O 3 concentration impact of many factors, such as sunshine time, temperature, humidity and so on. On account of the existing research, we just explored the correlation between the main pollutants to provided scientific insights for the reasonable discharge of all kinds of substances and the reasonable control the major pollutants. According to the month change, the six main pollutants in the study area showed seasonal change, so we performed correlation analyses in the summer and winter. The relationship was tested with Pearson correlation test with the significance leave of 0.05. The relationship between O 3 and PM 2.5 , PM 10 , NO 2 in the study area were significant positive correlation(P < 0.01) in summer. Except LSZ, because in LSZ the relationship between O 3 and PM 2.5 and PM 10 were significant positive correlation, but O 3 was not associated with NO 2 and SO 2 (Table 4 ). However, in winter, there were different correlation between O 3 and PM 2.5 , PM 10 , NO 2 in the study area. A significant negative correlation existed between O 3 and PM 2.5 , PM 10 , SO 2 , and NO 2 in the winter period (P < 0.01) in PZH. However, in CX and ZT the relationship between O 3 and PM 2.5 were significant positive correlation (P < 0.01). In CX, KM and QJ the O 3 was negatively associated with NO 2 . Interestingly, in winter, there was a signification positive correlation between O 3 and SO 2 in QJ and ZT, which were different from other areas. Table 4 Analysis of the correlation between O 3 and PM 2.5 , PM 10 , SO 2 , NO 2 in different seasons Season Cities Pollutant PM 2.5 PM 10 SO 2 NO 2 Summer Chuxiong(CX) O 3 0.710 ** 0.629 ** 0.277 ** 0.546 ** Kunming(KM) 0.658 ** 0.650 ** 0.306 ** 0.501 ** Lijiang(LJ) 0.152 ** 0.350 ** 0.136 ** 0.135 ** Liangshanzhou(LSZ) 0.378 ** 0.394 ** -0.024 0.051 Panzhihua(PZH) 0.709 ** 0.477 ** 0.07 0.325 ** Qujing(QJ) 0.372 ** 0.454 ** 0.206 ** 0.084 Zhaotong(ZT) 0.392 ** 0.582 ** 0.209 ** 0.305 ** Winter Chuxiong(CX) O 3 0.143 ** 0.053 -0.175** -0.013 Kunming(KM) -0.105 -0.014 0.193** -0.150 ** Lijiang(LJ) 0.045 -0.047 -0.081 -0.015 Liangshanzhou(LSZ) -0.039 -0.029 -0.057 -0.222 ** Panzhihua(PZH) -0.154 ** -0.287 ** -0.253 ** -0.453 ** Qujing(QJ) 0.061 0.076 0.125 ** -0.113 ** Zhaotong(ZT) 0.373 ** 0.374 ** 0.238 ** -0.041 Note: ** , the significance is significant (P < 0.01) 3.5 Source and diffusion of pollutants Considering the influence of summer and winter winds, the most severe pollution days in each city in the study area were selected as the representative days, and their diffusion and migration were simulated by using the HYSPLIT trajectory model. Combined with temperature, wind speed, humidity, precipitation and solar radiation on the diffusion of air pollutants, the result showed that, in PZH on December 31, 2020, the source direction of air pollutants was west and southwest, and the diffusion direction was northeast (Fig. 7 a). The largest pollution day in CX is April 14, 2017, the source direction of the pollutants was slightly south of the west, and the diffusion direction was east, with a wide diffusion range and fast migration speed (Fig. 7 b). On December 25, 2017, the source direction of air pollutants in KM was west and southwest, and the direction of diffusion was northeast, and the direction of diffusion was ZT and QJ (Fig. 7 c). On April 13, 2017, the main diffusion direction of air pollutants in LJ was northeast, and the source direction was west. From the perspective of the height change of simulation, the pollution source and diffusion mode were complicated (Fig. 7 d). On December 26, 2020, the biggest pollution situation in LSZ, the direction of the diffusion of the pollutants from the air to the northeast (Sichuan basin direction), the source for the southwest (PZH direction) (Fig. 7 e). December 26, 2017, the biggest pollution happens to QJ, the source of air pollution was in the southwest, diffusion was in the northeast (ZT) (Fig. 7 f). December 10, 2017 is the most serious pollution day in ZT. The source of the pollutants was from the northwest and the direction of diffusion was from the northeast to the Sichuan Basin (Fig. 7 g). The results show that the southwest direction was the primary source direction of pollutants in the study area, and the northeast direction was the main diffusion direction. 4 Discussion 4.1 Improve air quality in PZH and its surrounding cities from 2016 to 2020 Based on our results and analysis, we obtain an in-depth understanding of the temporal and spatial of air pollution and pollutants origin in PZH and its surrounding cities. In PZH, China, it can be found during the period from 2016 to 2020, the air quality had improved. Although the air quality deteriorate in the scope of this study in 2019, it did not exceed the air quality standards. Based on the specific changes in each year, the concentrations of main pollutants have decreased, except the O 3 . The reason may be that the state has strengthened measures to control pollutants (Wang et al., 2022a; Yu and Morotomi, 2022). For example, the state requires factories and enterprises to adopt effective desulfurization technology (Li et al., 2022 ) and discharge automobile exhaust after purification (Dey and Mehta, 2020 ; Salas et al., 2021 ), so that pollutants can be control from human activities (Polednik, 2021 ; Sokhi et al., 2021). In addition, the effective dissemination of ecological civilization has promoted the transformation of people’s way of life and travel in recent years, and improved people’s further understanding of the ecological environment (Wu et al., 2021). From the perspective of economic development, China’s economic development has changed from high-speed development to high-quality development, and the country’s requirements for all walks of life are constantly improving, and so on, these reasons are the effective guarantee of improving air quality (Wang et al., 2022b). As we analyzed above, the air pollutions in Chinese cities are mainly a compound type of pollutions, there is a significant correlation between O 3 and precursor substance (PM 2.5 , PM 10 , SO 2 , NO 2 ), so when precursor substance is effectively controlled, the corresponding content of other pollutants will be reduced. Therefore, effective control is one of the important reasons for improving the pollution situation in the study area. 4.2 Effect of regional characteristics on air quality As the central city of the research area, PZH has a much higher concentration of various pollutants than its surrounding cities, including KM (the provincial capital of Yunnan). In particular, the monthly average concentration of SO 2 , NO 2 , and CO are significantly higher than its surrounding cities. On the one hand, PZH is high in the west and low in the east, the influence of monsoon may lead to the convergence of pollutants from other areas to the interior of PZH (Shu et al., 2022), especially Panxi Valley, which creates favorable conditions for the diffusion of pollutants inward (Poulos and Pielke, 1994 ; Jazcilevich et al., 2005 ). At the same time, the plateau around Panzhihua prevents pollutants that have already entered PZH from spreading outward and diluting, causing pollutants accumulate here, further strengthening the pollution inside PZH (Cao et al., 2020 ). In the other hand, PZH, as a key city of the third line construction, are rich in vanadium and titanium mineral resources, in the process of mining and mineral resources exploitation, a large number of slag and mineral soil will be accumulated, long-term stacking will lead to the loss of water in the slag, under the action of wind, dust and fine particles enter the air, increasing the air pollution in the city (Tian et al., 2021). In addition, a mass of oversized vehicles will be used to transport these resources. Oversize vehicles consume a large amount of fuel, inadequate fuel combustion is probably one of the reasons for the high level of NO 2 and CO in PZH (Gratsea et al., 2017 ; Gerstenberger and Listl, 2019 ). Due to the geographical and topographical factors, in PZH, the air is dry all year round, the wind speed is calm, and the dry season is long. Under the action of these factors, there will often be a temperature inversion phenomenon, so that the air pollutants appear to sink, aggravating the internal pollution situation. PZH has a developed iron and steel industry, which uses a lot of fuel in its production, fuel without desulfurization treatment or the treatment does not meet the use standard, will lead to the increase of the concentration of SO 2 , NO 2 and CO (Zhu et al., 2022). Therefore, PZH should further improve the rational use of energy and resources to meet the standard, so as to improve air quality and reduce the generation of air pollutants, and not aggravate the deterioration of air quality caused by geographical factors. 4.3 Seasonal change analysis Although PZH, the central city in the research region, has a large geographical and topographic difference with its surrounding cities, and is dry all year (Yang et al., 2021) round under the influence of subtropical monsoon climate, it still shows regular changes in the seasonal scale. Except O 3 , the content of other pollutants is low in summer and high in winter, this rule is the same as the overall air pollution in China (Li et al., 2020 ; Shen et al., 2020; Wang et al., 2022). According to the characteristic analysis of the study area, the reasons for the high pollutant concentration in winter are roughly divided into the following situations: In the first case, humans use coal and other materials to warm themselves in winter, resulting in a large number of pollutants, this is more pronounced in the cold plateau areas (such as LSZ) than in other areas. The second situation is that low winter temperature, weak air convection and other reasons will lead to the accumulation of pollutants in the areas where pollution occurs. The third situation is that Chinese residents have an important festival, the Spring Festival, in order to inherit and develop traditional culture, a large number of fireworks will be set off during the festival, leading to the increase of pollutant content. The fourth situation is that the altitude of plateau cities is high and the ultraviolet radiation was strong, under the joint action of precursor gases such as SO 2 , NO 2 and CO with volatile organic compounds and O 3 , secondary inorganic pollutants will be generated, and these substances contribute more to PM 2.5 and PM 10 . A study by (Tao et al., 2014) research in Chengdu showed that the contribution rate of secondary inorganic aerosols to annual PM was 37% ± 18% concentration. In autumn, apart from the influence of industry and transportation, agriculture has become the main source of pollution, because people burn plant straw, leading to increased concentration of pollutants (Liu et al., 2020 ). The reason for the low concentration of pollutants in summer is that the high temperature, active air convection, more rainfall and high relative humidity, resulting in the deposition of pollutants. In addition, summer radiation is strong, as a result of photochemical action, pollution substances will react between components to generate secondary pollutants, resulting in a higher concentration of ozone content in summer. In this paper, it is found that there was a strong correlation between O 3 and SO 2 in summer, indicating that industrial pollution is an important reason for the increase of O 3 concentration (Ma et al., 2021 ; Xia et al., 2021). The air pollution in the study area is complex and diverse, and it’s necessary to further strengthen the control standards of various pollutants. In the process of urban air comprehensive management, it’s necessary for all regions to coordinate management. 4.4 Local and regional source analysis In this study, we found that in addition to PZH, the surrounding cities of KM and QJ are at the leading level in the concentrations of NO 2 , PM 10 and PM 2.5 , indicating that the consumption of fossil fuels by motor vehicles in KM and QJ is relatively high. On the one hand, KM as the capital city of Yunnan Province, it has a large population. On the other hand, the public facilities and infrastructure of the city promote the emergence of PM2.5 to a certain extent (Ma et al., 2021 ). The further increase of urban population will lead to greater pressure on the local air quality and pollution level (Zhang et al., 2022). It will affect people's normal life, lead to the occurrence of various diseases, and pose a great threat to people's lives and health (Allender et al., 2011 ). Moreover, the concentration of SO 2 in LSZ is also in high level, the main reason may be derived from the development of industry, because Xichang, located in LSZ, is an important satellite launch base, large amounts of pollutants will be generated in the process of satellite manufacture and launch. And forest fire is also one of the important reasons for the high concentration of pollutants in LSZ. LJ is an important tourist city, and it has a low concentration of pollutants. Although developed transportation will produce a lot of pollutants, LJ’s extensive forest coverage absorbs kinds of pollutants, thus protecting the atmospheric environment and air quality. Places with large forest coverage rate tend to have relatively low AQI values (Muñoz-Pizza et al., 2020 ). In this study, the area where PZH lacks trees is located in the southeast corner of the Qinghai-Tibet Plateau. Due to the lack of forest resources in the northwest of PZH city, the ability of resistance and absorption external pollutants are weak, resulting in the migration of external pollutants to the center of city. In order to achieve coordination between prevention and control. and promote the construction of a beautiful blue sky in PZH, the government of PZH should not only start from the control of its own pollutants, but also strengthen the control of external pollution sources. 4.5 Pollution characteristics of the study center PZH’s air pollution is more serious than basins. Zhao et al. (2018) research found that PZH air pollution comes from compound pollution, but this is only one aspect of these reasons. The terrain of PZH is more complicated than the general basin, and its geographical position is more distinctive. Because there are many mountains and hills inside PZH, it forms intricate diminutive basins and midget canyons. Some scholars’ studies on street canyons have a serious impact on the diffusion of pollutants (Cui et al., 2017 ; Donateo et al., 2021 ). In comparison, the geographical topography of PZH is far more sophisticated than the impact of urban building on the transmission of air pollutants. Another aspect, PZH has the characteristics of higher altitude, thin and dry air on compared to lower elevation basins. These conditions create favorable conditions for the deposition of pollutants and secondary pollution in the atmosphere. Although basin will appear the phenomenon of air trapped in the haze, it has a more plains which can effectively alleviate when normal air circulation (Guo et al., 2022 ). Therefore, this is why the air pollution in PZH is more complex and changeable than that in simple terrain. 5 Conclusion Through the analysis of the air quality in the plateau canyon area from 2016 to 2020, it is beneficial to further understand the air pollution status under special terrain. In the past five years, the air quality of PZH and its surrounding cities has been improved. Except ozone, the concentration of several pollutants has shown a downward trend. The newly formulated air quality standard has largely promoted the improvement of air quality in this region. On the seasonal scale, the study area is similar to previous research results: the concentration of PM 2.5 , PM 10 , SO 2 , NO 2 , CO is high in winter and low in summer, while the concentration of ozone is high in summer and low in winter. At the same time, there is a complex and significant correlation between O 3 and several precursor pollutants in this research area, and the types of air pollution are complex and diverse. Moreover, this paper uses the trajectory model to discuss the origin and diffusion direction of major pollution date pollutants, and concludes that the source of pollutants in PZH city is mainly comes from the southwest, and the direction of diffusion is east and southeast. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4302520","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296760872,"identity":"092a0a68-eeb8-458c-af89-b030b89f83fa","order_by":0,"name":"Yan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACZhiDvYHhALFaGBvADJ4DxGphgGmRSCBSvXw7+/MHH/cwJG64+fzh4YIaBnlzQloMDvMYNs54BtRyO8fg8IxjDIY7GwhpYeZhbAZ6A6SF4TAPG0OCwQFCDmtmf9j8B6Tl5vEHh3n+EaGF4TCDYTMDSMsNoCN524jQAvLLzJ4DDMYzzwD9wtsnYbiBoMP6jz/48OMAg2zf8eOPP/N8s5En7DAI+O+4AKJSgjj1IGAv30C84lEwCkbBKBhhAAAqTkY2ZdTthQAAAABJRU5ErkJggg==","orcid":"","institution":"China West Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yang","suffix":""},{"id":296760873,"identity":"04a555d1-8dcd-49da-b549-3f35b7838c6d","order_by":1,"name":"Xiqiao Wu","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiqiao","middleName":"","lastName":"Wu","suffix":""},{"id":296760874,"identity":"a7502614-f6fd-4498-9a42-d95e444a7c5e","order_by":2,"name":"Xing Huang","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Huang","suffix":""},{"id":296760875,"identity":"7bc9bcab-089e-496f-a49a-3a31ecd2eb5d","order_by":3,"name":"Chaorong Liu","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chaorong","middleName":"","lastName":"Liu","suffix":""},{"id":296760876,"identity":"27fdada8-8651-4afb-b2d0-7532fd438743","order_by":4,"name":"Dan Luo","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-04-22 02:05:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4302520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4302520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56037552,"identity":"437a410f-a3db-4e76-af4b-b08a3c371c7d","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":530837,"visible":true,"origin":"","legend":"\u003cp\u003eRegional topography and sample points distribution in Panzhihua and its surrounding cities, China\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/38eb7e86cd359ae5473378a6.png"},{"id":56038238,"identity":"2969cafe-898c-4903-9e83-9a6032952ff2","added_by":"auto","created_at":"2024-05-07 18:59:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85098,"visible":true,"origin":"","legend":"\u003cp\u003eThe air quality and main pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e CO and O\u003csub\u003e3\u003c/sub\u003e) annual variation of Panzhihua (PZH) and its surrounding cities from 2016 to 2020, China.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/2609c6377d5b648e59727342.png"},{"id":56037553,"identity":"7c1fb4c1-29b1-42bd-abea-480fdd37b9c9","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":337348,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial-temporal in the air quality of Panzhihua and its surrounding cities, China, from 2016 to 2020\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/64c222036811995a4e32996c.png"},{"id":56037558,"identity":"a28d8991-7545-4862-9eee-726fdaa3ffa9","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":470973,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of monthly concentration of six main pollutants monthly change of different city (PZH, CX, KM, LJ, LSZ, QJ, ZT). The monthly mean here was the weighted average of the corresponding months over 5 years.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/f74ff969c1dc6e063c5db0bc.png"},{"id":56037557,"identity":"af617d66-19f7-4c84-af04-fc446f157b9f","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":734924,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial-temporal in the air quality of Panzhihua and its surrounding cities in different season in recent five years (Spring, March to May, Summer, June to August, Autumn, September to November, Winter, December, January and February).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/3ed992b0aa33506c2614b912.png"},{"id":56037554,"identity":"d9518d2a-2d1c-4260-a777-4026ffbed1ee","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":209208,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of daily air quality change trend in PZH and its surrounding cities. The dotted line represents the significant level of 0.05.（U\u003csub\u003e0.05\u003c/sub\u003e=±1.96）\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/f9b0e0f65d5129b96bbc2789.png"},{"id":56037556,"identity":"d62a5a58-df91-4e10-a1ca-03f48f6afd0a","added_by":"auto","created_at":"2024-05-07 18:51:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1929653,"visible":true,"origin":"","legend":"\u003cp\u003eThe backward trajectories and forward trajectories of PZH(a), CX(b), KM(c), LJ(d), LSZ(e), QJ(f), and ZT(g).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/41981683bbc59e3465c540d7.png"},{"id":62357481,"identity":"793cb7ec-cfa2-4200-be67-671fcf6dc2cc","added_by":"auto","created_at":"2024-08-13 09:26:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4757417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4302520/v1/8a1f6c95-c12c-4766-a8bc-fdb6bb28218f.pdf"}],"financialInterests":"","formattedTitle":"Spatiotemporal variation characteristics, sources and trends of air quality in special region from 2016 to 2020 - A case study of Panzhihua, China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent year, due to rapid development of transportation, industry and agriculture, and the speedy changes in people\u0026rsquo;s lifestyle, as well as the intensification of urbanization (Elbayoumi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tiwari et al., 2015), the world has experienced severe air pollution (Wang et al., 2022). These factors have led to the production of a great quantity of air pollutants and the severe urban pollution, resulting a series of social problems (Larson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Siriopoulos et al., 2021; Wang et al., 2021). According to the statistics that the WHO gathered, air pollution causes about 4.2\u0026nbsp;million deaths worldwide each year (this number is about one in eight of total global deaths) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/air-pollution#tab=tab_1).Th\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/air-pollution#tab=tab_1).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee report reveals almost 99% of the world\u0026rsquo;s population breathe air that does not meets world health organization (WHO) standards (WHO, 2021), thus causing a lot of negative economic effects (Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Ministry of Ecology and Environment of China issued a new Environmental Air Quality Standards (GB3095-2012) in 2012 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), although this standard has effectively reduced the harm of air pollution to people\u0026rsquo;s health and improved our air quality (Wang et al., 2019; Bai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the negative impacts of air pollution still have not been fundamentally resolved, and air pollutants still endanger people's health.\u003c/p\u003e \u003cp\u003eAir pollutants can do great harm to human beings, animals and plants (De Kok et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sharma et al., 2020). This is because the fine particulate matters (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e) are small in radius, they exists a large specific surface area, which will help bacteria (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), viruses(Zoran et al., 2020; Maleki et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), heavy metal (Sternbeck et al., 2002; Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)attach to fine particulate matters. People and animals will suffer the nervous system (Badeenezhad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Costa et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), respiratory system (Ścibor et al., 2021) and immune system (Badeenezhad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) damage if they are exposed to high concentrations of air pollutants for a long time. For instance ozone can affect the release of biogenic volatile organic compounds(BVOC) in the underlying air (Cai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The BVOC affects human health, damage the central nervous system, and cause cancer and teratogenesis (Zhang et al., 2020). Air pollutants interact with or react with normal components of the air, however these reactions to a large extent cause secondary and compound pollution. Photochemical reactions generally occur in summer and produce more harmful secondary pollutants (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), such as Peroxyacetyl nitrate (Zhang et al., 2021), Sulfate aerosol (Arun et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In winter, air pollutants will be affected by meteorological conditions, temperature and geographical conditions, causing haze and other meteorological disasters, affecting transportation, and resulting traffic accidents (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Song et al., 2022).\u003c/p\u003e \u003cp\u003eAt present, most of scholars study provincial capitals and important economic development areas, such as Beijing, Wuhan, Hefei, Beijing-Tianjin-Hebei Economic Zone, Yangtze River Delta, Pearl River Delta, Detroit Michigan area, megacities of India and other severe pollution areas (Hu et al. 2014; Zhan et al. 2018; Chen et al. 2019; Xiao et al. 2020; Wen et al. 2021; O'Leary et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ren et al. 2022). Some scholars mostly researched on air quality based on the overall air quality changes in countries such as India, Brazil and so on (Pant et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Siciliano et al., 2020). Many scholars have used air quality monitoring data to conduct extensive research and analysis on air pollution. Furthermore, these studies have generated numerous research methods and analytical models, which are widely used by scholars, such as EMEP MSC-W model (Pisoni et al. 2019), CMAQ model (Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and air measurement model (Şahi̇n et al. 2021), etc. However, most studies of air pollutants have been researched on simple terrains, or on the analysis of single pollutants. Although some studies have conducted comprehensive analyses of air pollution, the study period is relatively short. Some scholars analyzed the air quality changes in plateau and basin, but these researches were focused on diffusion and the effect of thermal energy on air pollution. (Ivancic and Voncina, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Notario et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on these circumstances, this study analyzed the air quality spatio-temporal changes from 2016 to 2020 of Panzhihua (PZH), which is a plateau, valley and basin city.\u003c/p\u003e \u003cp\u003eIn this study, in order to discuss the characteristics of air quality changes in the plateau canyon area, we take PZH city as the center and extend to the surrounding cities. PZH city is the largest mineral resources city in the upper reaches of the Yangtze River and a typical mining city in southwest China. It is located on the edge of the Yunnan-Guizhou Plateau and the Qinghai-Tibet Plateau, and in the Panxi Rift Valley, with very unique terrain conditions. PZH city belongs to the plateau canyon landform, with staggered internal basins, high in the northwest (the southeast edge of the Tibet Plateau) and low in the northeast (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The surrounding cities involved in this article are Chuxiong(CX), Kunming (KM), Lijiang (LJ), Liangshanzhou (LSZ), Qujing (QJ), and Zhaotong (ZT). In the six surrounding cites, LSZ is located in the east of the Hengduan Mountains, while the other cities are located on the Yunnan-Guizhou Plateau. There is a large geographical gap between PZH and the surrounding cities, forming a pocket-like geographical terrain. Air pollution characteristics and topographic effect in PZH were still indeterminate. This study analyzes the spatio-temporal change of air pollution and discusses the correlation between major pollutants based on the air quality index (AQI) and six main air pollutants (SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, CO, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e) data. The cardinal objective of the present study is to use air quality monitoring data to analyze the spatiotemporal of air qualty and pollution sources of city with special terrain, and to analyze the air pollution characteristics of Yamhara Canyon Basin city.\u003c/p\u003e \u003cp\u003eOverall, the main targets were to: (1)explore the spatio-temporal variation characteristics of urban air quality under multiple and complex terrain conditions; (2)clarify the trend of air quality change and the source and diffusion direction of air pollution; (3)provide theoretical support for regional coordinated control of air pollution in special urban groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAir quality concentration limits (GB3095-2012)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePollutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLimit\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne hour average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne hour average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOzone (O\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne hour average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum eight-hour average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCarbonic oxide(CO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne hour average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParticulate matter with\u003c/p\u003e \u003cp\u003eaerodynamic diameters of\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 \u0026micro;m (PM\u003csub\u003e10\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParticulate matter with\u003c/p\u003e \u003cp\u003eaerodynamic diameters of\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.5 \u0026micro;m (PM\u003csub\u003e2.5\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study sites\u003c/h2\u003e \u003cp\u003eThe seven cities centered on Panzhihua were selected for this paper (Chuxiong, Kunming, Lijiang, Liangshan Prefecture, Qujing, Zhaotong) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PZH\u0026rsquo;s data were averaged from five state-controlled monitoring sites (Bingcaogang, Nongnongping, Renhe, Sishizhongxiao and Hemenkou) of different land use types. Panzhihua has a longitudes ranging from102\u0026deg;15\u0026prime;E to 108\u0026deg;08\u0026prime;E and has a latitudes from 26\u0026deg;05\u0026prime;N to 27\u0026deg;21\u0026prime;N. To better know this research areas, some basic situations within the region are briefly introduced. Panzhihua is located at the junction of southwest China Sichuan Province and Yunnan Province, it is an important city in the third-tier construction city in China. Panzhihua enjoys the reputation of vanadium and titanium capital of China, with the production of vanadium and titanium ranks third in the world, meanwhile rich in mineral resources and water resources. At the same time, the other six cities are the Southern Silk Road\u0026rsquo;s important node city. Kunming, it's like spring all the year round, with the title of spring city. Lijiang is an important tourist city with great differences in altitude, rich species, abundant forests, and good ecosystem function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Air quality data\u003c/h2\u003e \u003cp\u003eTo know spatial patterns and temporal variations of the main air pollutants in a city centered on Panzhihua (PZH), we analyzed the main pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e) monitoring data from January 1st, 2016 to December 31st, 2020 in the city. Moreover, according to the geographical characteristics, the six surrounding cities of PZH were selected to compare air pollution characteristics among the regions, and to exploration air pollution diffusion trajectory. The real-time daily average concentrations of six criteria pollutants in the 7 cities were downloaded from website of Online air quality monitoring and analysis platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.aqistudy.com\u003c/span\u003e\u003cspan address=\"http://www.aqistudy.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), air quality data of 5 national control monitoring points in PZH were downloaded from Panzhihua Ecological and Environmental Protection Bureau (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sthjj.panzhihua.gov.cn/\u003c/span\u003e\u003cspan address=\"http://sthjj.panzhihua.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data on the daily average AQI (air quality index) and air pollutant concentration of each city provided by the national control monitoring points represent the daily values of the city. AQI is a dimensionless index, which is used to quantitatively express air pollution level in a place during the specific period such as one day or one month. The monthly data is the average of the natural month daily values, and the yearly data is average of the natural yearly daily values. The main six pollutants used this way to describe the monthly and yearly data. In this study, we used the data of backward trajectory and forward trajectory model which are obtained from the National Oceanic and Atmospheric Administration (NOAA) to depict the air pollutants diffused and migration mode in order to uncover formation mechanism of heavy air pollution in special terrain of PZH, China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research method\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, SPSS26.0 (SPSS Inc., chicago IL, USA)was used for statistical analysis of the data, independent sample T test was used to determine the difference of AQI average value, and confidence test was carried out for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e. In addition, Pearson correlation analysis (Rodgers and Nicewander, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)was used to analyze the relationship between pollutants in the study area. Based on the daily air quality changes of Panzhihua city from 2016 to 2020, the change trend of six major pollutants and the correlation of some pollutants were discussed. In addition, the monitoring data of PZH City was classified, and the daily data were consolidated into monthly average data to discuss the annual change rule of air quality in PZH City.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Theil-Sen estimator\u003c/h2\u003e \u003cp\u003eTheil-Sen estimator (TSE) (Theil, 1950; Sen, 1968 ) is a good time series estimation method, which can estimate the trend of time series and is not affected by measurement errors and outliers. Therefore, this thesis utilizes this method to detect the change trend of air quality in Panzhihua and its surrounding cities on the inter-annual level. The specific calculation formula is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\beta }=\\text{M}\\text{e}\\text{d}\\text{i}\\text{a}\\text{n}\\left(\\frac{{\\text{x}}_{\\text{j}}-{\\text{x}}_{\\text{i}}}{\\text{j}-\\text{i}}\\right), {\\forall }_{\\text{i}}\u0026lt;\\text{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eβ represents the estimated median slope when \u003cem\u003ei\u003c/em\u003e\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003ej\u003c/em\u003e, X\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and X\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e denote values in different years, and i and j token sequence numbers in different years. When β\u0026thinsp;\u0026gt;\u0026thinsp;0, it stands for an increasing trend; when β\u0026thinsp;=\u0026thinsp;0, there is no trend change; and when β\u0026thinsp;\u0026lt;\u0026thinsp;0, it signifies a downward trend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Mann-Kendall test\u003c/h2\u003e \u003cp\u003eMann-kendall is a non-parametric test that can be used to assess the significance of trends in a sequence of practices (Mann, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). This test is suitable for the data whose samples do not follow the normal distribution. It can deal with outliers and missing data well, and also determine whether a process is a natural fluctuation or a definite trend (Gocic and Slavisa \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nyikadzino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., 2021). Moreover, this method is recommended by the World Meteorological Organization and is widely used in the determination of time series trend of hydro-meteorology. Therefore, this thesis uses this method to measure the time series trend of the daily change of air quality index in Panzhihua and its surrounding cities, and the specific calculation formula is as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{S}=\\sum _{\\text{i}}^{\\text{n}-1}\\sum _{\\text{j}=\\text{i}+1}^{\\text{n}}\\text{s}\\text{g}\\text{n}({\\text{x}}_{\\text{j}}-{\\text{x}}_{\\text{i}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{s}\\text{g}\\text{n}\\left({\\theta }\\right)=\\left\\{\\begin{array}{c} 1 \\theta \u0026gt;0\\\\ 0 \\theta =0\\\\ -1 \\theta \u0026lt;0\\end{array}\\right.$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the calculation result of statistical value S, if S\u0026thinsp;\u0026gt;\u0026thinsp;0 (S\u0026thinsp;\u0026lt;\u0026thinsp;0), it indicates an upward (downward) trend in the time series; when S\u0026thinsp;=\u0026thinsp;0, it manifests no trend change; when the sample number n\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e10, the following formula can be used to test its significance.\u003c/p\u003e \u003cp\u003eThe mean and variance of S can be calculated by the following formula:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{E}\\left(\\text{S}\\right)=0$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\text{V}\\text{a}\\text{r}\\left(\\text{S}\\right)=\\frac{1}{18}\\left[\\text{n}\\right(\\text{n}-1\\left)\\right(2\\text{n}-5)-\\sum _{\\text{t}=1}^{\\text{n}}{\\text{f}}_{\\text{t}}({\\text{f}}_{\\text{t}}-1)(2{\\text{f}}_{\\text{t}}+5)]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, f\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represents the number of \u003cem\u003ex\u003c/em\u003e in a given range in any time range \u003cem\u003et\u003c/em\u003e. The formula for calculating the standard normal test statistic Z is as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\text{Z}=\\left\\{ \\begin{array}{c}\\frac{\\text{S}-1}{\\sqrt{\\text{V}\\text{a}\\text{r}\\left(\\text{S}\\right)}} S\u0026gt;0\\\\ 0 S=0\\\\ \\frac{\\text{S}+1}{\\sqrt{\\text{V}\\text{a}\\text{r}\\left(\\text{S}\\right)}} S\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe positive or negative Z indicates a tendency to increase or decrease. For the given confidence level α, ∣Z∣\u0026gt;Z\u003csub\u003e1\u0026minus;α/2\u003c/sub\u003e expresses a significant change in time series. This study use significance level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Inverse distance weighted interpolation (IDW)\u003c/h2\u003e \u003cp\u003eInverse distance weighted is a kind of interpolation algorithm, which is widely used, it mainly combines a group sample point data to determine pixel values (Shepard, 1968). The formula is as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${\\text{Z}}_{0}=\\frac{{\\text{H}}_{\\text{i}}^{-\\text{p}}{\\text{Z}}_{\\text{i}}}{\\sum _{\\text{i}=1}^{\\text{n}}{\\text{H}}_{\\text{i}}^{-\\text{p}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, Z\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the estimated value; Z\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the attribute value of the \u003cem\u003ei\u003c/em\u003e (i\u0026thinsp;=\u0026thinsp;1,2,3 \u0026hellip; n) sampling point, \u003cem\u003ep\u003c/em\u003e is the weighted index with a significant impact on the interpolation result, H\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the distance. In this paper we used seven points to analysis the spatial change of air pollution of PZH and its surrounding cities in recent 5 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 HYSPLIT model\u003c/h2\u003e \u003cp\u003eThe HYSPLIT model (Stein et al., 2015) was used to calculate the reverse trajectory of air quality changes, determine the origin of air pollution and simulate the trajectory of movement. In this study, the 72h backward trajectory was computed by HYSPLIT version 5.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ready.noaa.gov/\u003c/span\u003e\u003cspan address=\"https://www.ready.noaa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at 500 m above ground level and every 6h starting at 8:00 coordinated universal time (UTC). The air data were availabled from the Air Resources Laboratory (ARL). In this paper, we can select Bingcaogang as the starting point of the backward trajectory of Panzhihua city, the residential areas, commercial districts, administrative districts and traffic areas of Bingcaogang are relatively concentrated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Interannual characteristics of air quality\u003c/h2\u003e \u003cp\u003eThe mean concentrations of AQI, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e from 2016 to 2020 city of PZH (center city of the study area) in China were 59.63\u0026thinsp;\u0026plusmn;\u0026thinsp;15.58, 30.72\u0026thinsp;\u0026plusmn;\u0026thinsp;12.52, 57.02\u0026thinsp;\u0026plusmn;\u0026thinsp;21.34, 33.31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.99, 34.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49, 1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51, 83.31\u0026thinsp;\u0026plusmn;\u0026thinsp;30.83 (mg/m\u003csup\u003e3\u003c/sup\u003e for CO, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for others except AQI), respectively. In addition, As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, air quality in PZH and its surrounding cities except LJ shows a trend of improvement from 2016 to 2020 (β\u0026thinsp;\u0026lt;\u0026thinsp;0). Among the selected research cities, 6 major pollutants in PZH, including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CO, show a decreasing trend, O\u003csub\u003e3\u003c/sub\u003e presents a rising trend, and the rising trend is most obvious in several places. In addition, CX and LJ also show a rising trend for O\u003csub\u003e3\u003c/sub\u003e. Although partial pollutants in PZH show a downward trend, the downward trend of PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e in PZH is weaker than that in other cities. The average of AQI in the study area had a high value in 2017 and 2019. Specially, the air quality of PZH, CX, KM, LJ, LSZ, QJ, and ZT had a downtrend in 2019. Compared with the previous year, the annual average data of air quality index in 2019 increased by 3.16%, 9.63%, 8.94%, 14.04%, 8.27%, and 10.85% respectively in PZH, KM, LJ, CX, LSZ, QJ and ZT.\u003c/p\u003e \u003cp\u003eIn this study, 2018 is the year with the biggest change in air quality in the study area. Therefore, the improvement of each city in 2018 is introduced in detail below. Within the study area, the concentration of the SO\u003csub\u003e2\u003c/sub\u003e increased the fastest in 2018, with an increase of 5.54% compared with 2017, but it was meet the national control standards every year (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Looking at individual cities within the study area, The concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and CO in KM decreased significantly in 2018, with a decrease rate of 19.85%, 28.17%, 22.30% and 19.77% respectively. In CX, the concentration of PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, CO decreased by 17.01%, 11.17%, 25.29% and 27.08% respectively. And in 2018 the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e of LJ decrease by 6.94%, 22.01% and 10.75%, respectively. Annual mean values of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e decreased by 20.40%, 22.71% and 19.60%, respectively, from 2017 to 2018. The decrease percentages of annual mean values of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and CO in QJ from 2017 to 2018 were 19.49%, 25.15%, 25.01%, 31.31%, 18.23%. In ZT, from 2017 to 2018, the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e decreased for 44.75%, 32.25%, 16.64% and 20.90% respectively. In 2018, the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were the most decreased pollutants in the six main pollutants of PZH, which were decreased by 14.76%, 22.39% respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the air quality index (AQI) air quality can be classified into six levels: Excellent (0\u0026ndash;50), Good (51\u0026ndash;100), Lightly polluted (101\u0026ndash;150), Moderately polluted (151\u0026ndash;200), Heavily polluted (201\u0026ndash;300), and Severely polluted (\u0026gt;\u0026thinsp;300) (MEP, 2012). Statistical analyses indicated that, PZH and its surrounding cities had excellent air quality about 54.71% of the days, and the days with good air quality account for about 44.22%. Specifically, the pollution days of most of seven cities decreased significantly, however, in several places, PZH, LSZ, QJ and ZT had more days above mild pollution, with 24, 27, 32, 38 days respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The average annual change of air quality in the study area is obvious. The concentration change of various pollutants is also very obvious and the dispersion of air quality is large. In a addition the median number of some pollutants is greater than the average number, indicating that the contribution rate of this pollutant to air pollution is large. From the annual AQI average, the air quality change of PZH city is 2019\u0026thinsp;\u0026gt;\u0026thinsp;2016\u0026thinsp;\u0026gt;\u0026thinsp;2018\u0026thinsp;\u0026gt;\u0026thinsp;2017\u0026thinsp;\u0026gt;\u0026thinsp;2020, CX (2019\u0026thinsp;\u0026gt;\u0026thinsp;2017\u0026thinsp;\u0026gt;\u0026thinsp;2016\u0026thinsp;\u0026gt;\u0026thinsp;2018\u0026thinsp;\u0026gt;\u0026thinsp;2020), KM (2017\u0026thinsp;\u0026gt;\u0026thinsp;2016\u0026thinsp;\u0026gt;\u0026thinsp;2019\u0026thinsp;\u0026gt;\u0026thinsp;2018\u0026thinsp;\u0026gt;\u0026thinsp;2020), LJ (2017\u0026thinsp;\u0026gt;\u0026thinsp;2019\u0026thinsp;\u0026gt;\u0026thinsp;2020\u0026thinsp;\u0026gt;\u0026thinsp;2018\u0026thinsp;\u0026gt;\u0026thinsp;2016), LSZ (2016\u0026thinsp;\u0026gt;\u0026thinsp;2019\u0026thinsp;\u0026gt;\u0026thinsp;2017\u0026thinsp;\u0026gt;\u0026thinsp;2020\u0026thinsp;\u0026gt;\u0026thinsp;2018), QJ (2016\u0026thinsp;\u0026gt;\u0026thinsp;2017\u0026thinsp;\u0026gt;\u0026thinsp;2019\u0026thinsp;\u0026gt;\u0026thinsp;2020\u0026thinsp;\u0026gt;\u0026thinsp;2018), ZT (2017\u0026thinsp;\u0026gt;\u0026thinsp;2016\u0026thinsp;\u0026gt;\u0026thinsp;2019\u0026thinsp;\u0026gt;\u0026thinsp;2018\u0026thinsp;\u0026gt;\u0026thinsp;2020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The air quality and main pollutants (except O\u003csub\u003e3\u003c/sub\u003e) in PZH and the surrounding cities promoted significantly in the past five years, especially in 2020, except 2019, because in 2019 the air quality index and pollutants concentration had increase. According to national standards and pollutant concentration characteristics, the main pollutants in the study area were fine particulate matter and O\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003estatistic of AQI (Air quality index) and concentration of six air pollutants of Panzhihua and its surrounding cities from 2017 to 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"22\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCity\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAQI(\u0026mdash;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003eCO(mg/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c22\" namest=\"c20\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003eRatio(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.10\u0026thinsp;\u0026plusmn;\u0026thinsp;16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.53\u0026thinsp;\u0026plusmn;\u0026thinsp;11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.96\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.08\u0026thinsp;\u0026plusmn;\u0026thinsp;15.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e25.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e18.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e11.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e26.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;31.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e76.09\u0026thinsp;\u0026plusmn;\u0026thinsp;34.84\u003c/p\u003e \u003c/td\u003e 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colname=\"c22\"\u003e \u003cp\u003e-2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.23\u0026thinsp;\u0026plusmn;\u0026thinsp;18.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.27\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.76\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.86\u0026thinsp;\u0026plusmn;\u0026thinsp;20.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.72\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e31.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e25.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e18.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e80.85\u0026thinsp;\u0026plusmn;\u0026thinsp;37.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e77.96\u0026thinsp;\u0026plusmn;\u0026thinsp;27.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.44\u0026thinsp;\u0026plusmn;\u0026thinsp;23.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.04\u0026thinsp;\u0026plusmn;\u0026thinsp;25.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.72\u0026thinsp;\u0026plusmn;\u0026thinsp;19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e94.76\u0026thinsp;\u0026plusmn;\u0026thinsp;32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e83.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotice: These ratio in here mean the growth rate in 2018 compared to 2017.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of air pollution exceeding standard in PZH and its surrounding cities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePollutant\u003c/span\u003e City\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAir quality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexcellent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003egood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal exceed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePZH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Seasonal variation\u003c/h2\u003e \u003cp\u003eThe monthly mean is the monthly average obtained by dividing the daily mean by the natural month days, this paper averaged the monthly average of the same month for 5 years. Based on this, the study analyzed the AQI and main pollutants seasonal variation during 2016 to 2020 of PZH and its surrounding cities. Results showed that the AQI higher value appeared in January, April, May, August, November and December, lower value appeared in June, July, September, and October each year (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, the seasonal trend of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO in PZH and its surrounding cities are consistent, which the maximal value appeared in May or June. The seasonal trend of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO showed a \u0026ldquo;V\u0026rdquo; type from January to December (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Specifically, the SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e of PZH were higher than the others cities, but the concentration of these two air pollutants are not higher than the national standards. In this study had a biggest difference which is the change regulation of O\u003csub\u003e3\u003c/sub\u003e. According to the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, the picture showed that the variation trend of O\u003csub\u003e3\u003c/sub\u003e had two peak and three valley, it same as \u0026ldquo;M\u0026rdquo; type. The peak of O\u003csub\u003e3\u003c/sub\u003e variation trend appeared in May and August, the low value appeared in January, July and December. Ozone\u0026rsquo;s contribution to air pollution tends to be more pronounced in summer.\u003c/p\u003e \u003cp\u003eIn terms of seasonal scale (March to May is spring, June to August is summer, September to November is autumn, December and January and February are winter), the air pollution change rule of PZH meets spring\u0026thinsp;\u0026gt;\u0026thinsp;winter\u0026thinsp;\u0026gt;\u0026thinsp;summer\u0026thinsp;\u0026gt;\u0026thinsp;autumn. The air pollution change law in CX, KM, LJ, LSZ, QJ and ZT meets spring\u0026thinsp;\u0026gt;\u0026thinsp;winter\u0026thinsp;\u0026gt;\u0026thinsp;autumn\u0026thinsp;\u0026gt;\u0026thinsp;summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although the seasonal change law is different between PZH and its surrounding cities, in whole is similar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Daily change analysis\u003c/h2\u003e \u003cp\u003eAQI as an important index of air quality change, so the Mann-Kendall test was used to test the time series trend of the daily data in the seven cities from 2016 to 2020(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). From the perspective of the overall change trend, several other research points in the study region except PZH have similar change trend.The UF statistics of PZH in 2016 and 2017 appeared an overall downward trend on the annual level, which shows that the air quality presents a trend of improvement at the annual level, and the improvement trend gradually slows down after September. In addition, the average daily trend of air quality in PZH from 2018 to 2020 shows that the air quality has improved after May, but the AQI value from January to April reveals an overall upward trend, indicating that the air quality has deteriorated from January to April. The air quality of cities around Panzhihua shows the following trend: the Air quality index from January to June manifests an overall upward trend, while the daily mean value expresses a significant downward trend after June. According to the daily average change trend of each year, the growth trend of 2019 is the most obvious. There was no intersection between UB and UF in the test level, and there was no obvious mutation of air quality index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation analysis\u003c/h2\u003e \u003cp\u003eAccording to the distribution situation in the study region, and the O\u003csub\u003e3\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were the main pollutants. Therefore, we used the main pollutants data to analysis the correlation and to seek the interaction between the pollutants.\u003c/p\u003e \u003cp\u003eOn the basis of research finding, the O\u003csub\u003e3\u003c/sub\u003e in the troposphere is a typical secondary pollutant, generated from NO\u003csub\u003e2\u003c/sub\u003e, and volatile organic compounds in the complex chain-type photochemical reactions. Simultaneously, in consideration of O\u003csub\u003e3\u003c/sub\u003e dominate the oxidation strength in the atmosphere, it will then affect the generation of secondary fine particles, and the correlation between the study area O\u003csub\u003e3\u003c/sub\u003e and fine particles was also discussed. Currently many investigators found that the O\u003csub\u003e3\u003c/sub\u003e concentration impact of many factors, such as sunshine time, temperature, humidity and so on. On account of the existing research, we just explored the correlation between the main pollutants to provided scientific insights for the reasonable discharge of all kinds of substances and the reasonable control the major pollutants. According to the month change, the six main pollutants in the study area showed seasonal change, so we performed correlation analyses in the summer and winter. The relationship was tested with Pearson correlation test with the significance leave of 0.05. The relationship between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e in the study area were significant positive correlation(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in summer. Except LSZ, because in LSZ the relationship between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were significant positive correlation, but O\u003csub\u003e3\u003c/sub\u003e was not associated with NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, in winter, there were different correlation between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e in the study area. A significant negative correlation existed between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e in the winter period (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in PZH. However, in CX and ZT the relationship between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e were significant positive correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In CX, KM and QJ the O\u003csub\u003e3\u003c/sub\u003e was negatively associated with NO\u003csub\u003e2\u003c/sub\u003e. Interestingly, in winter, there was a signification positive correlation between O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e in QJ and ZT, which were different from other areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the correlation between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e in different seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePollutant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChuxiong(CX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.710\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.629\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.277\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.546\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKunming(KM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.658\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.650\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.306\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.501\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLijiang(LJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.152\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.350\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.136\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.135\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiangshanzhou(LSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.378\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.394\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanzhihua(PZH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.709\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.477\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.325\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQujing(QJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.372\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.454\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.206\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhaotong(ZT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.392\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.582\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.209\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.305\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChuxiong(CX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.175**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKunming(KM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.193**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.150\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLijiang(LJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiangshanzhou(LSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.222\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanzhihua(PZH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.287\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.253\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.453\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQujing(QJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.125\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.113\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhaotong(ZT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.373\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.374\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.238\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: \u003csup\u003e**\u003c/sup\u003e, the significance is significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Source and diffusion of pollutants\u003c/h2\u003e \u003cp\u003eConsidering the influence of summer and winter winds, the most severe pollution days in each city in the study area were selected as the representative days, and their diffusion and migration were simulated by using the HYSPLIT trajectory model. Combined with temperature, wind speed, humidity, precipitation and solar radiation on the diffusion of air pollutants, the result showed that, in PZH on December 31, 2020, the source direction of air pollutants was west and southwest, and the diffusion direction was northeast (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The largest pollution day in CX is April 14, 2017, the source direction of the pollutants was slightly south of the west, and the diffusion direction was east, with a wide diffusion range and fast migration speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). On December 25, 2017, the source direction of air pollutants in KM was west and southwest, and the direction of diffusion was northeast, and the direction of diffusion was ZT and QJ (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). On April 13, 2017, the main diffusion direction of air pollutants in LJ was northeast, and the source direction was west. From the perspective of the height change of simulation, the pollution source and diffusion mode were complicated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). On December 26, 2020, the biggest pollution situation in LSZ, the direction of the diffusion of the pollutants from the air to the northeast (Sichuan basin direction), the source for the southwest (PZH direction) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). December 26, 2017, the biggest pollution happens to QJ, the source of air pollution was in the southwest, diffusion was in the northeast (ZT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). December 10, 2017 is the most serious pollution day in ZT. The source of the pollutants was from the northwest and the direction of diffusion was from the northeast to the Sichuan Basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). The results show that the southwest direction was the primary source direction of pollutants in the study area, and the northeast direction was the main diffusion direction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Improve air quality in PZH and its surrounding cities from 2016 to 2020\u003c/h2\u003e \u003cp\u003eBased on our results and analysis, we obtain an in-depth understanding of the temporal and spatial of air pollution and pollutants origin in PZH and its surrounding cities. In PZH, China, it can be found during the period from 2016 to 2020, the air quality had improved. Although the air quality deteriorate in the scope of this study in 2019, it did not exceed the air quality standards. Based on the specific changes in each year, the concentrations of main pollutants have decreased, except the O\u003csub\u003e3\u003c/sub\u003e. The reason may be that the state has strengthened measures to control pollutants (Wang et al., 2022a; Yu and Morotomi, 2022). For example, the state requires factories and enterprises to adopt effective desulfurization technology (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and discharge automobile exhaust after purification (Dey and Mehta, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Salas et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), so that pollutants can be control from human activities (Polednik, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sokhi et al., 2021). In addition, the effective dissemination of ecological civilization has promoted the transformation of people\u0026rsquo;s way of life and travel in recent years, and improved people\u0026rsquo;s further understanding of the ecological environment (Wu et al., 2021). From the perspective of economic development, China\u0026rsquo;s economic development has changed from high-speed development to high-quality development, and the country\u0026rsquo;s requirements for all walks of life are constantly improving, and so on, these reasons are the effective guarantee of improving air quality (Wang et al., 2022b). As we analyzed above, the air pollutions in Chinese cities are mainly a compound type of pollutions, there is a significant correlation between O\u003csub\u003e3\u003c/sub\u003e and precursor substance (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e), so when precursor substance is effectively controlled, the corresponding content of other pollutants will be reduced. Therefore, effective control is one of the important reasons for improving the pollution situation in the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effect of regional characteristics on air quality\u003c/h2\u003e \u003cp\u003eAs the central city of the research area, PZH has a much higher concentration of various pollutants than its surrounding cities, including KM (the provincial capital of Yunnan). In particular, the monthly average concentration of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO are significantly higher than its surrounding cities. On the one hand, PZH is high in the west and low in the east, the influence of monsoon may lead to the convergence of pollutants from other areas to the interior of PZH (Shu et al., 2022), especially Panxi Valley, which creates favorable conditions for the diffusion of pollutants inward (Poulos and Pielke, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Jazcilevich et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). At the same time, the plateau around Panzhihua prevents pollutants that have already entered PZH from spreading outward and diluting, causing pollutants accumulate here, further strengthening the pollution inside PZH (Cao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the other hand, PZH, as a key city of the third line construction, are rich in vanadium and titanium mineral resources, in the process of mining and mineral resources exploitation, a large number of slag and mineral soil will be accumulated, long-term stacking will lead to the loss of water in the slag, under the action of wind, dust and fine particles enter the air, increasing the air pollution in the city (Tian et al., 2021). In addition, a mass of oversized vehicles will be used to transport these resources. Oversize vehicles consume a large amount of fuel, inadequate fuel combustion is probably one of the reasons for the high level of NO\u003csub\u003e2\u003c/sub\u003e and CO in PZH (Gratsea et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gerstenberger and Listl, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to the geographical and topographical factors, in PZH, the air is dry all year round, the wind speed is calm, and the dry season is long. Under the action of these factors, there will often be a temperature inversion phenomenon, so that the air pollutants appear to sink, aggravating the internal pollution situation. PZH has a developed iron and steel industry, which uses a lot of fuel in its production, fuel without desulfurization treatment or the treatment does not meet the use standard, will lead to the increase of the concentration of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CO (Zhu et al., 2022). Therefore, PZH should further improve the rational use of energy and resources to meet the standard, so as to improve air quality and reduce the generation of air pollutants, and not aggravate the deterioration of air quality caused by geographical factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Seasonal change analysis\u003c/h2\u003e \u003cp\u003eAlthough PZH, the central city in the research region, has a large geographical and topographic difference with its surrounding cities, and is dry all year (Yang et al., 2021) round under the influence of subtropical monsoon climate, it still shows regular changes in the seasonal scale. Except O\u003csub\u003e3\u003c/sub\u003e, the content of other pollutants is low in summer and high in winter, this rule is the same as the overall air pollution in China (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al., 2020; Wang et al., 2022). According to the characteristic analysis of the study area, the reasons for the high pollutant concentration in winter are roughly divided into the following situations: In the first case, humans use coal and other materials to warm themselves in winter, resulting in a large number of pollutants, this is more pronounced in the cold plateau areas (such as LSZ) than in other areas. The second situation is that low winter temperature, weak air convection and other reasons will lead to the accumulation of pollutants in the areas where pollution occurs. The third situation is that Chinese residents have an important festival, the Spring Festival, in order to inherit and develop traditional culture, a large number of fireworks will be set off during the festival, leading to the increase of pollutant content. The fourth situation is that the altitude of plateau cities is high and the ultraviolet radiation was strong, under the joint action of precursor gases such as SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CO with volatile organic compounds and O\u003csub\u003e3\u003c/sub\u003e, secondary inorganic pollutants will be generated, and these substances contribute more to PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. A study by (Tao et al., 2014) research in Chengdu showed that the contribution rate of secondary inorganic aerosols to annual PM was 37% \u0026plusmn; 18% concentration. In autumn, apart from the influence of industry and transportation, agriculture has become the main source of pollution, because people burn plant straw, leading to increased concentration of pollutants (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reason for the low concentration of pollutants in summer is that the high temperature, active air convection, more rainfall and high relative humidity, resulting in the deposition of pollutants. In addition, summer radiation is strong, as a result of photochemical action, pollution substances will react between components to generate secondary pollutants, resulting in a higher concentration of ozone content in summer. In this paper, it is found that there was a strong correlation between O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e in summer, indicating that industrial pollution is an important reason for the increase of O\u003csub\u003e3\u003c/sub\u003e concentration (Ma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xia et al., 2021). The air pollution in the study area is complex and diverse, and it\u0026rsquo;s necessary to further strengthen the control standards of various pollutants. In the process of urban air comprehensive management, it\u0026rsquo;s necessary for all regions to coordinate management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Local and regional source analysis\u003c/h2\u003e \u003cp\u003eIn this study, we found that in addition to PZH, the surrounding cities of KM and QJ are at the leading level in the concentrations of NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, indicating that the consumption of fossil fuels by motor vehicles in KM and QJ is relatively high. On the one hand, KM as the capital city of Yunnan Province, it has a large population. On the other hand, the public facilities and infrastructure of the city promote the emergence of PM2.5 to a certain extent (Ma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The further increase of urban population will lead to greater pressure on the local air quality and pollution level (Zhang et al., 2022). It will affect people's normal life, lead to the occurrence of various diseases, and pose a great threat to people's lives and health (Allender et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, the concentration of SO\u003csub\u003e2\u003c/sub\u003e in LSZ is also in high level, the main reason may be derived from the development of industry, because Xichang, located in LSZ, is an important satellite launch base, large amounts of pollutants will be generated in the process of satellite manufacture and launch. And forest fire is also one of the important reasons for the high concentration of pollutants in LSZ. LJ is an important tourist city, and it has a low concentration of pollutants. Although developed transportation will produce a lot of pollutants, LJ\u0026rsquo;s extensive forest coverage absorbs kinds of pollutants, thus protecting the atmospheric environment and air quality. Places with large forest coverage rate tend to have relatively low AQI values (Mu\u0026ntilde;oz-Pizza et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, the area where PZH lacks trees is located in the southeast corner of the Qinghai-Tibet Plateau. Due to the lack of forest resources in the northwest of PZH city, the ability of resistance and absorption external pollutants are weak, resulting in the migration of external pollutants to the center of city. In order to achieve coordination between prevention and control. and promote the construction of a beautiful blue sky in PZH, the government of PZH should not only start from the control of its own pollutants, but also strengthen the control of external pollution sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Pollution characteristics of the study center\u003c/h2\u003e \u003cp\u003ePZH\u0026rsquo;s air pollution is more serious than basins. Zhao et al. (2018) research found that PZH air pollution comes from compound pollution, but this is only one aspect of these reasons. The terrain of PZH is more complicated than the general basin, and its geographical position is more distinctive. Because there are many mountains and hills inside PZH, it forms intricate diminutive basins and midget canyons. Some scholars\u0026rsquo; studies on street canyons have a serious impact on the diffusion of pollutants (Cui et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Donateo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In comparison, the geographical topography of PZH is far more sophisticated than the impact of urban building on the transmission of air pollutants. Another aspect, PZH has the characteristics of higher altitude, thin and dry air on compared to lower elevation basins. These conditions create favorable conditions for the deposition of pollutants and secondary pollution in the atmosphere. Although basin will appear the phenomenon of air trapped in the haze, it has a more plains which can effectively alleviate when normal air circulation (Guo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this is why the air pollution in PZH is more complex and changeable than that in simple terrain.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThrough the analysis of the air quality in the plateau canyon area from 2016 to 2020, it is beneficial to further understand the air pollution status under special terrain. In the past five years, the air quality of PZH and its surrounding cities has been improved. Except ozone, the concentration of several pollutants has shown a downward trend. The newly formulated air quality standard has largely promoted the improvement of air quality in this region. On the seasonal scale, the study area is similar to previous research results: the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO is high in winter and low in summer, while the concentration of ozone is high in summer and low in winter. At the same time, there is a complex and significant correlation between O\u003csub\u003e3\u003c/sub\u003e and several precursor pollutants in this research area, and the types of air pollution are complex and diverse. Moreover, this paper uses the trajectory model to discuss the origin and diffusion direction of major pollution date pollutants, and concludes that the source of pollutants in PZH city is mainly comes from the southwest, and the direction of diffusion is east and southeast. To some extent, this study supplements the vacancy of the study on the spatio-temporal variation of air quality in this special geographical topographic location, and provides some scientific insights for control and prevention of pollution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are included in the paper or its Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National subject cultivation project of China West Normal University (No. 20A023).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllender, S., et al., 2011. 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Transportation Research Part D: Transport Environ., 91, 102689. https://doi.org/10.1016/j.trd.2020.102689\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Panzhihua, air quality, spatio-temporal variation, analysis of pollutant diffusion trajectory","lastPublishedDoi":"10.21203/rs.3.rs-4302520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4302520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study based on daily data of six major pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e) from 2016 to 2020, the spatiotemporal variation characteristics of air quality in Panzhihua and its surrounding cities were analyzed. On this basis, trajectory model is used to analyze the origin and direction of migration of the pollutants in the days with high pollution degree, so as to find a method to prevent and control the air pollution in the cities with special geographical location. The results show that the concentration of pollutants in the study area showed an overall downward trend, but Ozone showed an opposite trend. The air quality in the study area has been significantly improved. Air quality was the best in 2018, followed by 2020. The annual variation trend of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CO is U-shaped (flat W), while the O\u003csub\u003e3\u003c/sub\u003e is M-shaped. In addition, the monthly average concentration of pollutants in Panzhihua is higher than these in its surrounding cities. O\u003csub\u003e3\u003c/sub\u003e has a significant correlation with its various precursor pollutants, and the air pollution situation is complex and diverse. According to the analysis of pollutant diffusion trajectory, the direction of pollution source in Panzhihua city is southwest and the diffusion direction is east and southeast.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal variation characteristics, sources and trends of air quality in special region from 2016 to 2020 - A case study of Panzhihua, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 18:51:19","doi":"10.21203/rs.3.rs-4302520/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46c3ad02-dd00-418f-b989-674186b556d7","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T09:18:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 18:51:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4302520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4302520","identity":"rs-4302520","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}