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

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Analysis on Variation and Potential Source Regions of Greenhouse Gases Concentrations at Akedala Station, China Analysis on Variation and Potential Source Regions of Greenhouse Gases Concentrations at Akedala Station, China Zhujun Zhao China Meteorological Administration (CMA) Qing He ( [email protected] ) China Meteorological Administration (CMA) Quanwei Zhao China Meteorological Administration (CMA) Hanlin Li China Meteorological Administration (CMA) Zhongqi Lu China Meteorological Administration (CMA) Jianlin Wang Field Scientic Experiment Base of Akedala Atmospheric Background, China Meteorological Administration (CMA) Research Article Keywords: greenhouse gas, seasonal variation, backward trajectory analysis Posted Date: July 28th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-743657/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/19 Abstract The research on the variation of greenhouse gases concentrations in typical regions is one of the signicant 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 scientic 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 Coecient 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 − 6 − 6 growth, with CO2 concentrations ranging from 389.80×10 to 408.79×10 (molar fraction of substances, same below), CH4 − 9 − 9 − 9 − 9 concentrations ranging from1890.07×10 to 1976.32×10 , N20 concentrations ranging from 321.26×10 to 332.03×10 , and − 12 − 12 SF6 concentrations ranging from 7.04×10 to 10.07×10 , 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 inuenced by the airow 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. 1. Introduction 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 signicant trend of increase (IPCC, 2014; WMO, 2020). Carbon dioxide (CO2), Methane (CH4), Nitrous oxide (N2O) and Sulfur hexauoride (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, oods, 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 2014He J H2020). 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 increasingCheng 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 Page 2/19 − 6 Waliguan Station from 2009 to 2010 and found that the average concentration of CO2 was 390.72 × 10 and CH4 was 1851.11 × − 9 10 . 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 − 6 − 6 average CO2 concentration from 387.7 ± 0.5×10 to 13.0 ± 3.2×10 . 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 reect 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, xed-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 Coecient 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. Material And Methods 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 at 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.
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