The variational characteristics of CO2 concentrations and δ13C values at the urban sites in Beijing and Xiamen, China
： 2018 - 05 - 17
133 3 0

Abstract & Keywords
Abstract: Background, aim, and scope To mitigate the global warming, more and more attention of the environmental scientists are focused on atmospheric CO2 increase. Urban areas are hotspots of anthropogenic CO2 emission sources. It is necessary to monitor the feature of CO2 variations in urban areas. The data of CO2 concentration and the stable carbon isotope ratio (δ13C) are important for CO2 reduction in urban areas. Therefore, the variation in CO2 and δ13C in urban areas are observed in this paper. The study presented the data of atmospheric CO2 concentrations and δ13C values at the urban sites in the inland city of Beijing and in the coastal city of Xiamen, to study the seasonal and diurnal variation in CO2 concentration and δ13C during 2014, and their key factors. Materials and methods In this study, we measured the CO2 concentration and its δ13C at the urban sampling sites in Beijing and Xiamen, respectively. Beijing is a typical  inland city, which is located in the northwest of the North China Plain, surrounded by West Mountain and Jundu Mountain. It is the center of the Beijing-Tianjin-Hebei metropolitan region, with a population of more than 20 million.  Xiamen is one of the cities in southeast coastal areas, facing the Taiwan Strait, with a resident population of more than 4 million. There is no direct pollution sources around the sampling sites in Beijing and Xiamen. We used aluminum foil sampling bags to collect the air samples and collected 144 urban air samples in this study. The CO2 concentrations and δ13C of samples were measured by the Picarro G2131-Ⅰ CO2 Isotopic Analyzer (Picarro Inc.) with cavity ring down spectroscopy (CRDS). This analyzer has a characteristic of highly linearity and accuracy in CO2 and δ13C measurements (error <  0.1‰). The CO2 and δ13C value for each sample was measured for 6 minutes, and the average of the data from the last 4 min was used, to avoid equipment instability created by dead volumes when switching a new sample. Results The results showed that the CO2 concentrations in Beijing and Xiamen were high in fall and winter, and low in spring and summer, but the δ13C values are low in fall and winter, and high in spring and summer during the period of our study. Diurnal observations at the urban sites in Beijing and Xiamen showed low CO2 concentrations and high δ13C values in the daytime, while high CO2 concentrations and low δ13C values in the night. Additionally, morning and afternoon rush hour peaks were observed. Discussion The sampling site in Beijing and Xiamen displayed much higher CO2 increase and lower δ13C values than the background site of Waliguan. The obvious difference of CO2 concentration and δ13C values between sampling site and background station may be from the influence of regional emission, so it is necessary to analyze the regional emission sources. Keeling plot method by the additional stable carbon isotope ratio produced by a local source (δs) showed that the increased CO2 in Beijing is mainly affected by coal combustion. The δs in Xiamen was inﬂuenced by fossil fuel like coal and oil in summer, and the reduction of respiration of plants and the change of weather condition during winter is the main reason for the variation in δs in Xiamen. Conclusions  The reason for the variation atmospheric CO2 in the inland city of Beijing and the coastal city of Xiamen were influenced by multivariate factors such as emission sources, height of the vertical mixing, wind direction and topography.  Recommendations and perspectives Using stable carbon isotope technology can understand the main sources of increased atmospheric CO2, but there is still some uncertainty. 14C tracing was needed to further quantify the fossil source CO2 in the future.
Keywords: atmospheric CO2 concentration; stable carbon isotope; seasonal variation; diurnal variation; Keeling plot
CO2浓度的快速增长已经引起科学界的普遍关注。研究表明，近八十万年以来，气温几乎与CO2浓度同步变化（Lüthi et al，2008）。近130年（1880-2012年）全球地表平均温度升高约0.85°C，同时大气CO2浓度也由工业革命前的280.0 ppm（ppm = μL.L-1）增长到2017年的404.0 ppm（GOAST，2017）。目前，国际社会普遍认为以CO2为主的温室气体排放是造成全球増温的重要原因（Rosa and Ribeiro，2001；WMO，2015）。CO2过量排放会加剧全球变暖程度，导致海平面上升、海水酸化、生物多样性锐减等众多问题（IPCC，2014）。为了减缓大气増温幅度，各国家和地区都在致力于加强国际间合作，针对气候变化制定合理减排措施。2015年12月在巴黎气候大会上通过了《巴黎协议》，其目的在于将本世纪的増温幅度控制在工业化前水平2°C以内，并努力将气温升幅控制在1.5°C之内，大气CO2浓度水平不应超过450.0 ppm（UNFCCC，2015）。因此，对于不同区域CO2浓度及其来源研究是制定合理碳减排政策的基础，也是减缓大气 CO2浓度增长的前提。
CO2及其稳定碳同位素（13C）分布具有区域性差异，其浓度的变化状况能够反映出不同区域大气受自然和人为活动影响的程度。Keeling率先在Mauna Loa等地对CO2浓度进行连续监测，发现CO2浓度变化遵循一定的季节变化规律（Keeling，1958）。造成CO2季节变化的因素可能包括植被的年际循环以及人类活动的影响（Keeling，1961；IPCC，2014）。城市是人类活动密集区域，其排放的CO2占总人为排放量的70%左右（Satterthwaite，2008）。城市CO2浓度通常在人口最为密集的市中心最高，邻近郊区较低，周边乡村地区通常接近背景地区CO2水平（Pataki et al，2007；Büns and Kuttler，2012）。CO2的δ13C可以用来估算城市地区光合作用、土壤呼吸、化石燃料燃烧等的相对贡献（Clark-Thorne and Yapp，2003；Widory and Javoy，2003；Bush et al，2007；Pataki et al，2007；Newman et al，2013）。对城市大气CO2浓度及δ13C进行观测有助于辨别城市CO2的不同来源，为控制城市地区CO2浓度的增长提供理论依据（Moore J，2015）。

1   采样与分析方法
1.1   采样地点

1.2   样品采集时间及方法

1.3   大气CO2浓度测量及δ13CO2分析方法
13C是碳的稳定性同位素，也是CO2的重要示踪元素，样品中13C的水平一般用标准物质的相对比值δ表示(‰)：
$${\text{ δ}}^{\text{13}}\text{C= }\left[\text{ }\frac{{\left({}_{\text{ }}{}^{\text{13}}\text{C}/{}_{\text{ }}{}^{\text{12}}\text{C}\right)}_{\text{sample}}}{{\left({}_{\text{ }}{}^{\text{13}}\text{C}/{}_{\text{ }}{}^{\text{12}}\text{C}\right)}_{\text{std}}}\text{ -1}\right]\text{ ×1000}$$ （1）

2   结果与讨论
2.1   大气CO2浓度和δ13C季节变化特征

Fig.1 Seasonal variations in CO2 concentrations at the urban sites in Beijing and Xiamen
2.2   大气CO2浓度和δ13C日变化特征

6月时，北京市和厦门市CO2浓度日均值分别为411.7 ± 12.9ppm和 410.1 ± 8.4ppm；δ13C日均值分别为-10.4 ± 0.9‰和-9.3 ± 0.4‰，两地的日变化差异相对较小。在1月，北京市与厦门市日变化差异明显，CO2浓度日均值分别为508.0±38.9 ppm、430.8 ± 7.8ppm；δ13C日均值分别为-13.9±0.8‰、-11.6 ± 0.4‰，推测可能是与北京市供暖季大量化石燃料消耗有关。

 图2 北京市和厦门市采样点典型日变化大气CO2 浓度及δ13C值Fig.2 Diurnal variations in CO2 concentrations and δ13C values at the urban sites in Beijing and Xiamen
2.3   区域CO2 排放源的δ13C（δs）特征
Keeling曲线结合CO2浓度与稳定性同位素技术，假设背景大气和区域排放贡献的CO2在观测期间不发生改变，则得到的城市CO2浓度是背景CO2浓度与区域排放CO2浓度之和（公式2）（Keeling，1958，1961），基于同位素质量守恒，得到（公式3）：
$${ C}_{\mathrm{m}}={C}_{bg}+{C}_{s }$$ （2）
$${\delta }_{m }{C}_{\mathrm{m}}= {\delta }_{\mathrm{b}\mathrm{g} }{C}_{\mathrm{b}\mathrm{g}}+{\delta }_{s }{C}_{s}$$ （3）
$${\delta }_{m }={C}_{bg}（{\delta }_{bg}-{\delta }_{s}）（1/{C}_{m}）+{\delta }_{s}$$ （4）

Fig.3 The correlation of CO2 concentrations and δ13C values in Beijing (right) and Xiamen (left)
3   结论

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FENG Xue1,2,3,4

WANG Sen1,2*,
wangsen@nwu.edu.cn

NIU Zhenchuan3,4

National Natural Science Foundation of China (41573136, 41773141); CAS “Light of West China” Program (XAB2015A02); Youth Innovation Promotion Association CAS(2016360); CAS (Y354011480, Y652001480, SKLLQGPY1610); Natural Science Foundation of Shaanxi Province, China (2014JQ2-4018)

Journal of Earth Environment