北京市冬季大气化石源CO2典型日变化的14C示踪研究

摘要

城市作为化石源CO2（）排放的热点区域，获得其大气浓度的日变化特征对于深刻理解城市地区的时空变化规律，进而制定合理的节能减排政策至关重要。本研究通过AMS-14C技术，示踪了北京市冬季一个典型日变化事件中大气的变化过程，并探讨了其影响因素。本次日变化事件中大气δ13CO2的值为（−13.9 ± 0.8）‰（−14.8‰ — −12.7‰），Δ14CO2的值为（−151.6 ± 51.3）‰（（−214.2 ± 2.9）‰—（−82.3 ± 3.0）‰），浓度为104.4 ± 44.0 µL ∙ L−1（168.6 ± 2.7 —52.1 ± 3.2 µL ∙ L−1）。浓度具有较大的日变化，夜晚浓度明显高于白天，主要是由于夜间大气混合层高度较低、供暖消耗更多的化石燃料以及在东南风条件下因北京不利的扩散条件而使聚积。此外，在早晚高峰期间，观察到由于交通流量增加引起的较高浓度。同期PM2.5浓度相似的日变化过程进一步验证了本次观测结果的可靠性。%Background, aim, and scope As the main greenhouse gas, how much of the increased atmospheric CO2 derived from the fossil fuel emissions is not only an environmental issue, but also an important scientific question. Traditional statistical methods for estimating the magnitude of emissions incur some uncertainties, especially at regional scale. Radiocarbon (14C), a unique tracer, can be used to distinguish between atmospheric and CO2 from other sources, and have been used to infer the spatio-temporal variations of atmospheric in recent years. Cities as emission hotspots, the diurnal atmospheric 14CO2 observation are important to the understanding of temporal atmospheric fossil fuel CO2 ( ) variability, thus facilitating the mitigation strategies of emissions in China. In this study, one typical diurnal atmospheric 14CO2 observation was carried out at an urban site in Beijing, with the objective to trace the diurnal variations, and to determine the factors influencing them. Materials and methods Beijing, a typical inland city, was selected in this study. It is the most central city in the Beijing-Tianjin-Hebei metropolitan region, with a population of more than 20 million. The city is surrounded by mountains in the west and north and faces the North China Plain to the south. The air sampling site is located on the roof of a building at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Haidian District. The site is located between the North 4th and 5th Ring roads, surrounded by dense office and commercial areas, residential districts, universities and parks. Air samples were collected in aluminum foil sampling bags every 2 hours from 8:00 am (local time) on 15th to 6:00 am (local time) on 16th in January, 2014. The CO2 concentrations and δ13CO2 in the air samples were measured using a Picarro G2131-I CO2 Isotopic Analyzer (Picarro Inc., USA) with cavity ring down spectroscopy (CRDS). This equipment is highly linear and very stable, with very precise CO2 measurements. Each sample was measured for 6 min, and only the average of the data from the last 4 min was used. The air samples in the bags were transferred to a high vacuum system with liquid nitrogen cold trap (−196 ºC) and ethanol-liquid nitrogen cold trap (−90 ºC) to get purified CO2, and then converted into graphite using the zinc-iron method. The 14C levels of the air samples were measured using a 3 MV AMS in Xi’an, China, with a precision of 2—3‰ for 14C measurement. The values of 14C in the air samples were expressed as Δ14C, i.e., the per mil (‰) deviation from the absolute radiocarbon reference standard corrected by the convention of fractionation and decay. concentrations were calculated according to the mass balance of CO2 and 14C. Results The CO2 concentration in the diurnal event was 508.0 ± 38.9 μL ∙ L−1, with high values at night. δ13CO2 values were in the range of −14.8‰ — −12.7‰, with an average of (−13.9 ± 0.8)‰. They were lower than background δ13CO2 value, due to the contribution of fossil fuel emissions. The δ13CO2 values (−13.1 ± 0.3)‰ in daytime were significantly ( p < 0.05) higher than those (−14.5 ± 0.3)‰ at night. The average Δ14CO2 value in this diurnal event was (−151.6 ± 51.3)‰((−214.2 ± 2.9)‰ — (−82.3 ± 3.0)‰), with corresponding concentration of 104.4 ± 44.0 μL ∙ L−1 (168.6 ± 2.7 μL ∙ L−1 — 52.1 ± 3.2 μL ∙ L−1). concentration showed high correlation with CO2, and contributed most of the offset of CO2 compared to background CO2. These results indicated that the diurnal CO2 variations were mainly resulted from the fossil fuel emissions. concentrations showed distinct diurnal variations, with high values at night and low values in daytime. Small peaks of concentrations were observed during the morning and afternoon rush hours, resulted from the emissions from transportation. Discussion The extremely high concentrations at that night resulted from the more fossil fuel consumption for heating and low vertical mixing height at night. Moreover, was readily accumulated when wind direction turned from north to south at that night, because the city is surrounded by mountains in the west and north. Additionally, it is robust for our record, which is indirectly validated by the similar variation trends for simultaneous PM2.5 concentrations in Beijing. Conclusions Our data showed that the diurnal variations of atmospheric in Beijing were controlled by a combination of emission sources, height of vertical mixing, wind direction and topography. Recommendations and perspectives This study provides an example to understand the temporal variational characteristics of atmospheric and their influencing factoring in Chinese cities. Key words: fossil fuel CO2; radiocarbon tracing; Beijing; diurnal variation; wintertime