3.1 Time Series Analysis of Greenhouse Gases Concentrations
The location and time series variation of greenhouse gases concentrations from 2009 to 2019 at Akedala Station, Waliguan Station, Hohenpeissenberg Station, and Tae-ahn Peninsula Station are shown in Fig. 2 and 3. Fig. 3 shows that CO2, CH4, N2O, and SF6 concentrations at these four stations have all experienced an increasing trend and had a pronounced seasonal periodic variation.
The CO2 concentration ranged from 387.38×10−6 to 411.06×10−6 at Waliguan Station, from 389.80×10−6 to 408.79×10−6 at Akedala Station, from 387.88×10−6 to 413.21×10−6 at Hohenpeissenberg Station and from 390.24×10−6 to 418.67×10−6 at Tae-ahn Peninsula Station. The CO2 concentration at TAP station was significantly higher than the other three stations, with the concentration in these stations ranked from high to low as TAP>HPB>AKDL>WLG. And the growth rates were 2.37×10−6 yr −1 at WLG, 1.90 ×10−6 yr −1 at AKDL, 2.53×10−6 yr −1at HPB and 2.85 ×10−6 yr −1 at TAP (Fig. 4, a). The growth rate of CO2 in the Northern Hemisphere was 2.27×10−6 yr −1 in the last decade with the growth rate of Akedala Station slightly lower than the average level, which may be related to the location of the background station and the source and sink. The WLG Station is located on the Qinghai-Tibet Plateau, which is sparsely populated and is less affected by the source region of emission (Liu, 2014). the HPB Station is located in the mountainous region of southern Germany, and it is an important crop production area. And the Altay Prefecture of Xinjiang, where the AKDL Station is located, has a long heating period, which may be a factor influencing the concentration of CO2, while the TAP Station is located in the coastal area of South Korea, with a relatively developed economy and more human activities(Chung, 2013; KJ Lee et al., 1993; Jeeyoung Ham et al., 2019).
The CH4 concentration variation range was from 1899.92×10−9 to 1991.08×10−9 at WLG, from1890.07×10−9 to 1976.32×10−9 at AKDL, from 1912.42×10−9 to 1984.67×10−9 at HPB and from 1912.35×10−9 to 1991.07×10−9 at TAP. Overall, the CH4 concentration at HPB Station and TAP Station were slightly higher than those at WLG Station and AKDL Station. The growth rates were 9.12 ×10−9 yr −1 at WLG, 8.62 ×10−9 yr −1 at AKDL, 7.22 ×10−9 yr −1 at HPB, and 7.87 ×10−9 yr −1 at TAP (Fig. 4,b)with a decadal average growth rate of 7.3 ×10−9 yr −1 for the Northern Hemisphere (WMO, 2020), and the growth rates of WLG Station and AKDL Station were significantly higher than those of HPB Station and TAP Station. The variation and growth of CH4 concentration were affected by source region, biological factors and photochemical factors (Zhang, 2018). HPB Station and TAP Station are located in wet areas, which are major crop production areas and urban zones (Kim,2020, Dlugokencky,2013), while WLG Station and AKDL Station are relatively less influenced by source regions, so differences in concentrations arose. The location of HPB Station and TAP Station might have strong photochemical action, resulting in relatively low growth rates of CH4 concentration, and the relatively high growth rates of CO2 were due to the formation of CO2 from CH4 under photochemical action (Zhang, 2021).
The variation range of N2O concentration was from 322.88×10−9 to 332.43×10−9 at WLG, from 321.26×10−9 to 332.03×10−9 at AKDL, from323.46×10−9 to 333.16×10−9 at HPB and from323.35×10−9 to 333.42×10−9at TAP, with the growth rate of 0.95 ×10−9 yr −1 at WLG, 1.08 ×10−9 yr −1 at AKDL, 0.96 ×10−9 yr −1 at HPB and 1.01 ×10−9 yr −1at TAP(Fig. 4,c), not significantly different from the decadal average growth rate of 0.96 ×10−9 yr −1in the Northern Hemisphere. The SF6 concentration ranged from 6.97×10−12 to 10.19×10−12 at WLG, from 7.04×10−12 to 10.07×10−12 at AKDL, from 7.01×10−12 to 10.21×10−12 at HPB and from 7.14×10−12 to 10.36×10−12 at TAP(Fig. 4,d). Although the content of N2O and SF6 are relatively low in the atmosphere, it is quite evident that they had a clear rising trend in the last decade, and the results from four stations had a strong consistency on this point. More attention is needed for the variation of N2O and SF6 concentrations in the atmosphere.
3.2 Seasonal Variation of Greenhouse gases Concentrations
Affected by biological and non-biological sources, the emissions of greenhouse gases showed a pronounced seasonal variation. Fig. 5. represents the variation of the average values in every months of greenhouse gases concentrations at Akedala Station over the years. At this Station, CO2 and CH4 concentrations showed more obvious seasonal variation while N2O and SF6 concentrations showed less. The CO2 concentration rose to the highest level in March and April, with a highest value of 406.0×10−6 in April, and then fell to the lowest level in August, with the lowest value of 389.0×10−6. The change of CH4 concentration presented an approximate "W"-shaped trend, with a downward trend from January to June, and a rising to a peak in July and August, followed by a gradual decrease till to the bottom in September, and an increase afterwards. The “W”- shaped trend was roughly similar to the trend of CH4 concentrations at Stations like Shangdianzi Station and Longfenshan Station (K. Mueller, 2018; Dai, 2018). The CH4 concentration showed two peaks in July and January of the following year (Zhang, 2011). The CH4 concentration at Akedala Station reached 1924.6×10−9 in August and 1975.6×10−9 in January. The monthly average concentration of N2O and SF6 showed fluctuations, with insignificant seasonal variations and small fluctuating range.
In China, winter is from December to February, spring is from March to May, summer is from June to August, and winter is from September to November. Based on the average values in every months of greenhouse gases concentrations at Akedala Station, the analysis of the data of different seasons was carried out. Figure 6 shows the seasonal variation of greenhouse gases at Akedala Station. It can be seen that there were obvious seasonal differences in CO2 and CH4 concentrations, while N2O and SF6 concentrations didn’t vary significantly from season to season. The CO2 concentration at Akedala Station in the four seasons was in the order of winter, spring, autumn and summer from high to low, which were roughly consistent with the variation of CO2 concentration at Shangdianzi Station (Zhang, 2020). Similar to Shangdianzi Station, Akedala Station is located in the northern part of Xinjiang where winter is cold and residents need to heat their homes by means like burning coal. The burning of fossil fuels increased the CO2 content in the atmosphere in winter, while in summer, ground living plants consumed CO2 in the atmosphere through comparatively strong photosynthesis, so CO2 concentration showed obvious seasonal variation (Ou-Yang et al., 2014). CH4 concentration showed an overall trend of high in winter and autumn and low in spring and summer, and this trend was opposite to the seasonal variation at Waliguan Station and Shangdianzi Station (Zhang,2013; Fang,2017). The air masses in winter and autumn were mostly influenced by the northwest airflow, and in winter the airflow from the northern economic zone increased significantly. In summer, both rainfall and UV light were more intense, contributing to the photochemical action of CH4(Xu, 2011).
N2O concentrations in summer were slightly lower than those in the other three seasons, while SF6 concentrations remained essentially unchanged in all seasons, which were mainly influenced by various factors such as photochemical action and long-range air mass movement.
The seasonal variation of greenhouse gases at Akedala Station reflected the influence and effect of the periodic variations in the terrestrial biosphere in typical regions of Northwest China and Central Asia on the atmosphere.
3.3 Mann-Kendall Test on Greenhouse Gases Concentrations
Mann-Kendall method is frequently used to analyze the internal characteristics of hydrological time series. The results of M-K test for greenhouse gases concentrations at Akedala Station are shown in Fig. 7. The UF and UB of CO2 concentrations at the Akedala Station were higher than 0 from 2009 to 2017 and lower than 0 from 2017 to 2019, with a clear rising trend of the annual average concentration over the 11 years, reaching a maximum in 2018 and then a decrease in 2019. The change trend of CH4, N2O and SF6 concentrations were roughly the same, and UF and UB were higher than 0 during the observation period, indicating that their concentrations showed a rising trend. The results of the M-K test for CO2, N2O and SF6 exceeded the 0.05 trend line since 2012 and exceeded the 0.001 significance level since about 2014 (U0.001=2.56), proving that the growth of CO2, N2O, and SF6 concentrations showed a non-significant growth from 2009 to 2012, while showed a very obvious increasing trend from 2012 to 2019. And the result for CH4 exceeded the 0.05 trend line in 2014 and showed an evident increase since that year. Since 2000, with the gradual advancement of The Great Western Development Strategy, Xinjiang's economic and social development has been promoted significantly. According to the Xinjiang Statistical Yearbook, the GDP of the region increased rapidly from 427.705 billion yuan in 2009 to 1219.9 billion yuan in 2018, which had a clear synchronous change with the rising trend of greenhouse gas concentration. Both GDP and population growth became important anthropogenic sources of greenhouse gases emissions. In 2012, the opening year of the 12th Five-Year Plan, Xinjiang's GDP reached 750.531 billion yuan, achieving a 12% growth, of which the above-scale industrial added value reached 285.006 billion RMB, an increase of 12.7%. The rapid economic growth might become one of the important reasons for the increasing emission of greenhouse gases such as CO2. According to the Xinjiang Statistical Yearbook, Xinjiang's GDP grew to 727.346 billion yuan in 2014 compared with the previous year, with a growth rate of 10%, and above-scale industrial added value reached 315.129 billion yuan. It is worth nothing that CO2 concentration might have shown a decreasing trend since 2019, which might be related to the strong control policies in the source regions. Since 2018, the Altay Prefecture has realized electric heating in the whole region for environmental protection and ecological construction. In addition, some progress has been made in mine management, and work related to the disposal of abandoned mines has been started in order to realize the goal of building a beautiful China.
3.4 Correlation Analysis of Greenhouse Gases Concentrations
To better explore the potential source regions of greenhouse gases at Akedala Station, it is necessary to make further investigation of the correlation between various greenhouse gases concentrations. Correlation analysis was performed on the monthly averages of greenhouse gases concentrations at the Akedala Station from 2009 to 2019, and the numbers of data involved in the analysis totaled 32 of spring, 34 of summer, 34 of autumn and 32 of winter. Pearson correlation coefficients were calculated between the greenhouse gases concentrations throughout the year and in each season, and the results are presented in Tab 2. Pearson correlation between greenhouse gases was more significant in spring, autumn and winter than in summer at the Akedala Station, which was particularly evident between CO2 and CH4. Pearson correlation coefficients between CO2 and CH4 were 0.746 (p<0.01), 0.880 (p<0.01), 0.328 (p<0.05), 0.870 (p<0.01), and 0.860 (p<0.01) for a year-round, spring, summer, autumn, and winter respectively. From the data, it is obvious that CO2 and CH4 were significantly correlated except in summer. The reason for the exception in summer was mainly because the CO2 concentration in the atmosphere in summer was more influenced by source-sink effect of terrestrial ecosystems, while the photochemical action of CH4 was stronger in summer, and the OH free radical concentrations was higher in this season, leading to the conversion of part of CH4 into CO2 through photochemical action. Pearson correlations between CO2 and N2O, CO2 and SF6, CH4 and N2O, and CH4 and SF6 were significant throughout the year as well as in all seasons respectively.
In summary, the Akedala background station is located at the margin of Junggar Basin, the south of which is the core economic region of northern Xinjiang. Greenhouse gases concentrations were affected by anthropogenic greenhouse gas emissions and terrestrial ecosystem. The homology of greenhouse gases was more obvious in spring, autumn and winter, while the influence of terrestrial ecosystems and photochemical action in summer made the homology of greenhouse gases less obvious in this season. Therefore, the subsequent analysis of air mass pathways and potential source regions needs to be researched according to different seasons.
3.5 Analysis of Potential Source Regions in Different Reasons
The variation of greenhouse gases concentrations at background atmosphere station is also closely related to the external air masses. In order to study the effects of long-range transport and atmospheric boundary layer conditions on the variation of greenhouse gases concentrations at Akedala Station in different seasons, a backward trajectory model was used to trace the trajectories of air masses in different seasons. Based on the result of the backward trajectory, six main trajectories were identified for each season (Fig. 8), and their pressure distribution was shown in Fig. 9 From Fig. 8, differences in the trajectories of air masses in four seasons at Akedala station with significant seasonal transport characteristics. The Akedala Station was mainly influenced by the air mass from the northwest, accounting for 46.22%, 61.90%, 52.90% and 72.82% of all trajectories in winter, spring, summer and autumn respectively (Tab 3), and air pressures of most of air masses were above 800hPa (Fig. 9). Therefore, the northwest path originating from Central Asia and parallel to the valley of the Irtysh River is the main channel for the movement of air masses.
In winter, air masses 1, 2 and 3 played a dominant role, accounting for 75.80% of all trajectories (Tab.3). Air mass 1 came from the northern Xinjiang, arriving at Akedala station from the southeast., which was in line with the direction of Urumqi, the provincial capital of Xinjiang. In winter, under the influence of Mongolian high pressure, part of the airflow from Urumqi and other places crosses the Guerbantonggute Desert, and greenhouse gases are transported to Akedala Station via the air mass over a short distance (Li, 2020). The air mass 1 might have high greenhouse gases concentrations due to the need to burn biomass fuel for heating in northern Xinjiang. Air mass 2 arrived here after a long distance. It came from the industrial zone of eastern Kazakhstan, where heavy industries such as mining and non-ferrous metal smelting were concentrated. Under the combined effect of westerly winds, it was transported eastward to arrive the Akedala Station, potentially bringing more greenhouse gases. Air mass 3 came from Mongolia. Air masses 4 and 6 came from the northern Xinjiang, passing through cities with developed industries such as Karamay, and air mass 5 came from the southern part of Russia after a long-distance transport. Except for air mass 5, the air pressures of the rest air masses were all above 750 hPa (Fig. 9).
In the spring, air masses 4, 5, and 6 played a dominant role, and they accounted for 72.07% of all trajectories (Tab.3). Air masses 4 and 5 came from the eastern part of Kazakhstan, while air mass 6 came from the northern part of Xinjiang, located to the southeast of Akedala Station. Therefore, the greenhouse gas concentrations in the spring were mainly affected by industrial emissions transported from eastern Kazakhstan for a long distance and from northern Xinjiang for a short distance, which might carry some of the greenhouse gases to Akedala Station (Zhao,2021). Air mass 1 crossed the industrially developed cities of Karamay, while air masses 2 and 3 came from the south of Russia, passed the border of Mongolia and reached Akedala Station from the northeast. The air pressures of these two air masses were below 750 hpa, which were relatively low, and the air pressures of other air masses were above 750hpa (Fig. 9).
In summer, air masses 1, 2 and 4 became the dominant, accounting for 82.37% of all trajectories. Air masses 1 and 2 both came from the eastern part of Kazakhstan, accounting for about 67.23% of all trajectories (Tab.3), and air mass 4 came from southern Russia and reached here after a long distance. Because the Akedala station is located in the arid zone of Central Asia, far from the ocean, it is significantly affected by the westerly wind belt in summer, and almost all air masses come from Central Asia or further away from Russia. There existed noticeable difference in terms of pressure distribution. Air masses 2 and 4 were dominant and air pressures of them were mainly above 860hpa, while those of others were basically below 860hpa. (Fig. 9).
In autumn, the predominant air masses were number 2, 4 and 6, accounting for about 68.47% of all trajectories (Tab 3), mainly from the eastern part of Kazakhstan. Air masses 2 and 4 were formed when airflow from distant Kazakhstan reached the Akedala Station under the influence of the westerly wind, so their air pressures were relatively close to each other. In addition, air masses 1 and 3, which came from the northern Xinjiang, accounted for 27.19% of the total trajectory (Tab 3). Air mass 5 reached the Akedala Station over the long distance of Altai Mountains. Since southern Russia was sparsely populated, the amount of greenhouse gases carried in the airflow might be relatively low, and the air mass pressure was significantly lower (Fig. 9).