Analysis on Variation and Potential Source Regions of Greenhouse Gases Concentrations at Akedala Station, China

doi:10.21203/rs.3.rs-743657/v1

The research on the variation of greenhouse gases concentrations in typical regions is one of the significant tasks to cope with climate change. Especially at present, the number of extreme weather events is gradually increasing and the trend of global warming is becoming increasingly obvious, the study on variation of greenhouse gases concentrations and their potential source regions can contribute to a scientific formulation of policies regarding greenhouse gas emission reduction as well as to the coordinated development of human and environment. Based on the data of greenhouse gases of Akedala Station from 2009 to 2019, this research studies characteristics of the time series and seasonal trends of greenhouse gases at this station and testes whether abrupt change exists by applying the linear trend analysis method, the contrastive and statistical analysis method and Mann-Kendall method. In addition, Pearson Correlation Coefficient is used to determine the correlation and homology among the four greenhouse gases and backward trajectories model is also used to explore the potential source regions of greenhouse gases at Akedala Station in different seasons. The greenhouse gases concentrations at Akedala Station show a trend of year-on-year growth, with CO₂ concentrations ranging from 389.80×10^− 6 to 408.79×10^− 6 (molar fraction of substances, same below), CH₄ concentrations ranging from1890.07×10^− 9 to 1976.32×10^− 9, N₂0 concentrations ranging from 321.26×10^− 9 to 332.03×10^− 9, and SF₆ concentrations ranging from 7.04×10^− 12 to 10.07×10^− 12, the growth rate of which is similar to the decadal average growth rate in the northern hemisphere. There exist obvious seasonal variations, with CO₂ concentrations showing high in winter and low in summer and CH₄ showing a distinct “W”- shaped trend while N₂0 and SF₆ showing little difference between the four seasons. A relatively strong correlation and homology exist among the four greenhouse gases except in summer, and the analysis based on backward trajectories model shows that the Akedala Station is influenced by the airflow from northwest or southwest throughout the year. Besides, the concentrations of greenhouse gas are closely related to source region of the emissions, biological and non-biological sources, monsoon, and atmospheric photochemical processes.

Environmental Engineering

Environmental Policy

greenhouse gas

seasonal variation

backward trajectory analysis

In recent times, the technology of human society has developed very rapidly. With the accelerating global industrialization following the industrial revolution, the concentrations of greenhouse gases in the atmosphere have shown a significant trend of increase (IPCC, 2014; WMO, 2020). Carbon dioxide (CO₂), Methane (CH₄), Nitrous oxide (N₂O) and Sulfur hexafluoride (SF₆) are the gases in the atmosphere with long life and potential to heat up (WMO,2020). Greenhouse gas emissions from human production activities have become an important driver of climate change. The increase in global temperature is caused by the increase in greenhouse gas concentration, which brings about a series of extreme weather events such as droughts, floods, cold waves, and sea level rise, leading to a negative impact on people's production and life and bringing great damage to the natural environment (An X,2012;Ou-Yang C F ༌2014༛He J H༌2020). China is currently one of the world's largest emitters of greenhouse gases, making the country's climate risk level much higher and the frequency of extreme weather events gradually increasing༈Cheng S Y,2015༉. In order to better conduct research on the changes of greenhouse gases concentrations in the atmosphere, the observation of background atmosphere station is very important. The World Data Centre for Greenhouse Gases (WDCGG) aggregates observations from global and regional background stations, which contributes to global climate change analysis, and also provides basic research data needed for policy development at the international level, such as Kyoto Protocol and Montreal Protocol.

At present, China has already committed to peak carbon dioxide emissions before 2030 and to achieve carbon neutrality before 2060, aiming to achieve the goal of 2℃ warming in the Paris Agreement. Greenhouse gases, mainly CO₂, exchange and interact between different layers of the atmosphere, between land and atmosphere and between ocean and atmosphere, and the rising amount of greenhouse gases in the atmosphere is an important reason of the greenhouse effect. Therefore, the study of decadal characteristics and seasonal distribution of greenhouse gases in typical regions is essential for the formulation of policies for conserving energy and reducing emission. In addition, it is also of great importance to actively response to climate change such as global warming(Climate Change 2013).

Li (2012) analyzed the seasonal variation of CO₂ concentration in Shangri-La regional background atmosphere station and found that the concentration was high in April and May and lowest in July, with the average concentration of 394.78×10^− 6 in spring, 386.82 ×10^− 6 in summer, 386.46×10^− 6 in autumn, and 390.74×10^− 6 in winter. By using cluster analysis, the study also revealed that the air masses mainly originated from the southwest. Liu et al. (2014) analyzed the observation data of CO₂ and CH₄ in Waliguan Station from 2009 to 2010 and found that the average concentration of CO₂ was 390.72 × 10^− 6 and CH₄ was 1851.11 × 10^− 9. In addition, the time series of CO₂ and CH₄ concentrations were researched by using Fourier analysis method. CO₂ had a strong peak at 90d on the power spectrum, which proved the existence of seasonal variation, and its power spectrum had obvious peaks at 12h and 24h, proving the existence of obvious daily variation. In contrast, the power spectrum of CH₄ didn’t show a peak at 24h, which proved that there was no daily variation. Luan (2015) conducted a systematic analysis of greenhouse gas concentrations as well as source and sink at the Longfengshan Station and concluded that the daily variation is obvious, with low concentrations in daytime and high concentrations at nights. And the amplitude of it is relatively large especially in summer, with daily variation amplitudes of 41.2 ± 4.1×10^− 6 and 131 ± 37×10^− 9. In addition, the study also showed that meteorological factors such as surface winds had an important impact on greenhouse gases concentrations, with the E sector in summer increasing the average CO₂ concentration from 387.7 ± 0.5×10^− 6 to 13.0 ± 3.2×10^− 6. Boru MAI et al. (2020) screened the background values by using a local robust regression procedure for CO₂ concentrations in the Pearl River Delta and found that they showed characteristics of high in winter and spring and low in summer, and the study also used backward trajectory and concentration weight trajectory models to reflect potential source areas.

For regions like Asia, the monitoring stations are not uniformly distributed, so the assessment of greenhouse gases appears great uncertainty and there is a lack of high-precision data as well(Zhang,2011). China’s greenhouse gas observations at regional background stations have been standardized since 2006 and have characteristics of long-term, fixed-point and being access to network. Among the seven background atmosphere stations in China, Waliguan Station’s research is more in-depth(Zhou,2006;Cheng,2018,, while the work on greenhouse gas variation as well as source and sink in other stations needs to be further improved(Xu,2011;He,2020;Xia,2016). Besides, the spatial and temporal distribution of greenhouse gases in the Northwest dry area and Central Asia is less studied, and the timescale being researched is also relatively short. Moreover, the correlation between the changes of CO₂, CH₄, N₂O and SF₆ concentrations in the atmosphere and their potential source regions are not studied enough.

In this research, the greenhouse gas data of Akedala Station (AKDL) from 2009 to 2019 is selected, and the linear trend analysis method, the contrastive and statistical analysis method and Mann-Kendall method are applied to explore characteristics of the time series and seasonal trends of greenhouse gases at this station and tested whether abrupt change exists. In addition, Pearson Correlation Coefficient is used to determine the correlation and homology among the four greenhouse gases and backward trajectories model is applied to explore the potential source regions of greenhouse gases at Akedala Station in different seasons.

2.1 Overview of the Station and Data Sources

Akedala Background Atmosphere Station (AKDL, 47˚06′N, 87˚58′E, 563.3m above sea level) is located in the Gobi wetland in the eastern part of Fuhai County, Altay Prefecture, Xinjiang, also the northern margin of Junggar Basin. Within 50km around the station are alluvial flat land and Gobi. The nearest village is 5 km away from the site, with few people around and no trails of complex human activities. Belonging to the Kuk Ayush Township in Fuhai County, the Akedala Station is 65 km from the main city, Beitun, 120 km from the city of Altay in the north, and 43 km from the city of Fuhai County in the west (Feng, 2021). Akedala Station is located in an area of temperate continental arid climate, which is a typical arid and semi-arid region. The climate in this region is characterized by hot summer with little rain, large temperature difference between day and night, relatively cold winter, short spring and autumn and frequent windy days. The vegetation type of its underlying surface is desert steppe, which is representative in North Xinjiang and in the hinterland of Asia and Europe. Akedala Station is located in an open area with high atmospheric visibility, and also far away from densely populated residential areas. In summer, the land around the station is mainly used to grow corps, including corn, sunflower, alfalfa, etc., while in winter, those planting areas will become pastures for herders nearby. On the roads around the station appear few vehicles. The nearby residents heat their homes with coal usually in winter (From mid-October to the end of March the following year). The geographical location of Akedala Station is shown in Fig. 1.

2.2 Data Sources and Quality Control

The sampling point is located at Akedala Regional Background Atmosphere Station, which is one of the field test bases of the China Meteorological Administration. The samples of greenhouse gases were collected and analyzed according to the method recommended by WMO/GAW, using a portable sampler and Flask rigid glass bottles, with a sampling frequency of 1 time per week. The time for sampling was chosen to be around 14:00 Beijing time with wind speed faster than 2m/s, avoiding rain, fog, sand and dust during this period. The samples were sent to the greenhouse gas laboratory of the China Meteorological Administration for the analysis of trace components of greenhouse gases. The data used in this study were obtained from the China Meteorological Administration after quality control and the study period was from September 2009 to December 2019. Specific data is shown in Table 1.

Table 1

Greenhouse gas data quality at the AKDL station
AKDL	CO₂	CH₄	N₂O	SF₆
Time series	September 2009 - December 2019
Total	464
Effective value	428	460	460	426
Percentage of effective value	92.24%	99.14%	99.14%	91.81%
Missing month	July-december 2014; January-march 2016; July 2016; June 2018;	July-december 2014; January-march 2016;	July-december 2014; January-march 2016;	July-december 2014; January-march 2016;July 2016;

The data for the same period of the Waliguan Global Background Atmosphere Station, Hohenpeissenberg Global Background Atmosphere Station in Germany, and Tae-ahn Peninsula Regional Background Atmosphere Station in Korea were obtained from the WMO World Data Centre for Greenhouse Gases (https://gaw.kishou.go.jp/) for the comparative analysis of time series changes. The data sources and location of each station are shown in Table 2.

Table 2

The data sources and location of four stations
GAW ID	Station	Contributuor	Country/ territory	Latitude(north:+;south:-)	Longitude(east:+;wedt:-)	Elenation(m)
AKDL	Akedala	CMA	China	47.10	87.93	563
WLG	Mr.waliguan	NOAA	China	36.17	100.54	3816
HPB	Hohenpeissenberg	NOAA	Germany	47.80	11.01	985
TAP	Tae-ahn Peninsula	NOAA	Repubilc of Korea	36.73	126.13	20

2.3 Research Methods

The trend analysis method, contrastive and statistical analysis method were used for analysis of time series characteristics of greenhouse gases, and Mann-Kendall method was used to study abrupt change (Mann, 1945; Kendall, 1975; Wei, 2007, Xing et al., 2015, Yang et al., 2018). Pearson Correlation Coefficient was used to research the correlation of between greenhouse gases, and HYSPLIT backward trajectory analysis was adopted to study of potential source regions.

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 Figs. 2 and 3. Figure 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 difference in CO₂ concentration between AKDL, WLG and HPB stations was not significant, but 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 Hamet 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 332.03×10⁻⁹at 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 ×10⁻⁹yr ⁻¹ at WLG, 1.08 ×10⁻⁹yr ⁻¹ at AKDL, 0.96 ×10⁻⁹yr ⁻¹ at HPB and 1.01 ×10⁻⁹yr⁻¹at TAP(Fig.4,c), not significantly different from the decadal average growth rate of 0.96 ×10⁻⁹yr⁻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. Figure 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, which was roughly consistent with the trend of CO₂ concentration at the Waliguan Station and Shangdianzi Station. 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, 2012).

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 U_F and U_B 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 U_F and U_B 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 (U_0.001=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 Table 3. The Akedala Station is a regional background atmosphere station, compared with the Waliguan global background atmosphere station located on the Tibetan Plateau, the Pearson correlations here varied considerably under different seasons, but there was also a very clear correlation (Zhang, 2020). 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 N₂0, CO₂ and SF₆, CH₄ and N₂0, and CH₄ and SF₆ were significant throughout the year as well as in all seasons respectively. The Pearson correlation coefficient between N₂O and SF₆ was 0.935 (p < 0.01) throughout the year, 0.964 (p < 0.01) in spring, 0.931 (p < 0.01) in summer, 0.936 (p < 0.01) in autumn and 0.932 (p < 0.01) in winter, showing a distinct positive correlation with a good homology independent of seasonal factors.

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.

Table 3

Pearson correlation coefficients were calculated between the greenhouse gases concentrations throughout the year and in each season.
Pearson correlation coefficient		Spring	Summer	Autumn	Winter
CO₂- CH₄	0.746^**	0.880^**	0.328^*	0.870^**	0.860^**
CO₂- N₂0	0.690^**	0.815^**	0.668^**	0.794^**	0.886^**
CO₂- SF₆	0.598^**	0.816^**	0.523^**	0.791^**	0.877^**
CH₄- N₂0	0.670^**	0.829^**	0.834^**	0.828^**	0.615^**
CH₄- SF₆	0.633^**	0.858^**	0.868^**	0.830^**	0.631^**
N₂0- SF₆	0.935^**	0.964^**	0.931^**	0.936^**	0.932^**
Note: * Represents Significant Correlation at 0.05 level, * *Represents significant correlation at 0.01 level.

3.5 Analysis of Potential Source Regions in Different Reasons

Table 4

Trajectory numbers and percentage based on all trajectories (bold values represent the polluted clusters).
Season	Clusters	The Percentage of All Trajectories (%)	The Source Area of Air Masses
	1	34.48	northeast Xinjiang, China
	2	26.60	eastern Kazakhstan
Winter	3	14.72	Mongol Uls
	4	6.59	northeast Xinjiang, China
	5	10.28	southern Russia
	6	9.34	northeast Xinjiang, China
	1	13.78	northeast Xinjiang, China
	2	6.05	southern Russia
Spring	3	8.20	southern Russia
	4	25.81	eastern Kazakhstan
	5	27.89	eastern Kazakhstan
	6	18.28	northeast Xinjiang, China
	1	25.44	eastern Kazakhstan
	2	41.79	eastern Kazakhstan
Summer	3	2.36	eastern Kazakhstan
	4	15.14	southern Russia
	5	8.75	eastern Kazakhstan
	6	6.53	northeast Xinjiang, China
	1	19.22	northeast Xinjiang, China
	2	25.45	eastern Kazakhstan
Autumn	3	7.97	northeast Xinjiang, China
	4	22.72	eastern Kazakhstan
	5	4.35	southern Russia
	6	20.30	eastern Kazakhstan

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, it is easy to notice the differences in the trajectories of air masses in the four seasons at Akedala Station, which have obvious variation in different seasons. 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 (Table 4), and air pressures of most of air masses were above 800hPa (Fig. 9).

In winter, air masses 1, 2 and 3 played a dominant role, accounting for 75.80% of all trajectories (Table 4). Air mass 1 came from the northern Xinjiang, the southeast of Akedala Station, which was in line with the direction of Urumqi, the provincial capital of Xinjiang. It was possible that the air mass from Urumqi crossed the margin of Gurbantunggut Desert and reached Akedala Station after a short-distance transport (Li, 2020), and 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(Zhao,2021). 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 (Table 4). 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. 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 (Table 4), and air mass 4 came from southern Russia and reached here after a long distance. In this season, almost all air masses came from the west, which was closely related to the combined effect of westerly winds. 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 (Table 4), 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 monsoon, 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 (Table 4). 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).

CO₂, CH4, N₂O and SF₆, as common greenhouse gases, are among the key contributors to global climate change and extreme weather events. In this study, greenhouse gases concentrations at Akedala Station from 2009 to 2019 were analyzed, and conclusions were drawn as follows.

(1) Contrastive analysis was carried out to study the annual average concentrations and growth rates of CO₂, CH₄, N₂O and SF₆ in four regions, which are at the same latitude in the northern hemisphere, located in central Europe, central Asia, and eastern Asia respectively. It was found that the greenhouse gas concentrations at the four stations showed a clear increasing trend in the decade. The growth rates of CO₂ concentration in the four regions were 2.37 ×10^− 6 yr ^− 1 (WLG), 1.90 ×10^− 6 yr ^− 1 (AKDL), 2.53×10^− 6 yr ^− 1 (HPB) and 2.85 ×10^− 6 yr ^− 1 (TAP). The growth rates of CH₄ concentration were 9.12 ×10^− 9 yr ^− 1 (WLG), 8.62 ×10^− 9 yr ^− 1 (AKDL), 7.22 ×10^− 9 yr ^− 1 (HPB) and 7.87 ×10^− 9 yr ^− 1 (TAP). N₂O concentration growth rates were 0.95 ×10^− 9 yr ^− 1 (WLG), 1.08 ×10^− 9 yr ^− 1 (AKDL), 0.96 ×10^− 9 yr ^− 1 (HPB) and1.01 ×10^− 9 yr ^− 1 (TAP). Changes in greenhouse gas concentrations were closely related to factors including biological and non-biological sources, atmospheric boundary layer conditions and sources regions. The results of the M-K test for greenhouse gases concentrations at Akedala Station showed a significant rising trend during the observation period, and the changes might be linked to the local economic development and policies regarding environmental protection and management. The results indicated that CO₂ concentration showed a distinct increase since 2014, while CH₄, N₂O, and SF6 showed a clear increase since 2012.

(2) The greenhouse gases concentrations at Akedala Station showed obvious seasonal variations, with CO₂ concentration being high in winter and low in summer, CH₄ concentration showing a clear “W”-shaped trend, and N₂0 and SF₆ concentrations showing little difference between the four seasons. A strong correlation and homology were observed between the different greenhouse gases concentrations at the Akedala Station in spring, autumn and winter, while a poor homology was observed in summer.

(3) The results of the backward trajectory analysis showed that the Akedala Station was influenced by strong northwesterly airflow in all seasons, with eastern Kazakhstan being one of the main sources of the airflow. And the station was also affected by airflow from the northern Xinjiang. The airflow from the south of Russia was noticeably stronger in summer, which might have an impact on the greenhouse gases concentrations at Akedala Station.

Funding

This work was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (grant no. 2019QZKK010206).

Acknowledgements

Sincere gratitude would be expressed to China Meteorological Administration for providing the quality-controlled Flask bottle sampling data of the Akedala Background Station, and the same gratitude would be sent to the field staff of the Akedala Station for their hard work.

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No competing interests reported.

Analysis on Variation and Potential Source Regions of Greenhouse Gases Concentrations at Akedala Station, China

Status:

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Abstract

Figures

1. Introduction

2. Material And Methods