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长三角地区背景大气CO_(2)浓度变化特征

Characterization of background atmospheric CO_(2) mole fractions in Yangtze River Delta,China
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摘要 二氧化碳(CO_(2))排放增加是全球变暖的主导因素,其中人为活动排放对全球CO_(2)浓度增长的贡献越来越大,然而当前我国对城市区域的大气CO_(2)监测相对缺乏.本文利用长三角地区背景站点(大明山站,DMS)2020年9月—2022年1月的CO_(2)在线观测数据,结合地面风的影响,对观测数据进行筛分,采用后向轨迹分析方法,探讨了长江三角洲地区大气CO_(2)季节浓度及源汇特征.结果表明,观测期间DMS大气CO_(2)平均浓度为(421.9±10.2)×10^(-6).CO_(2)浓度在冬季出现峰值(428.7±9.1)×10^(-6),夏季出现谷值(416.7±5.5)×10^(-6).春、夏、秋季,日变化波动分别在01:00、02:00、21:00出现峰值,在14:00、14:00、13:00出现谷值,而冬季呈相反趋势,峰值出现在13:00,谷值出现在00:00.DMS的CO_(2)浓度与风速呈显著负相关.春秋季,来自NE-ENE风向的局地风,夏冬季来自SSW-WSW的局地风,携带了较高浓度的CO_(2).白天DMS盛行上升风,不同风向CO_(2)浓度差异较大,浓度和风级呈正相关,夜晚DMS盛行下沉风,不同风向CO_(2)浓度差异不大,和风级呈负相关.此外,基于72 h后向轨迹的聚类分析发现,西南、西北和东南的气团向DMS传输的CO_(2)浓度比较均一,这表明DMS能够充分观测长三角区域混合均匀的大气本底特征.另外,潜在源区分析表明,DMS周边城市是重要的CO_(2)潜在源,人为排放对DMS大气CO_(2)浓度存在重要影响. The increase of carbon dioxide(CO_(2))is the main cause for global warming and the anthropogenic emissions are increasingly responsible for the increasing global CO_(2) budget.However,the observations of atmospheric CO_(2) measurements in urban area in China are relative sparse considering the vast emission from cities.In this study,the online CO_(2) observation data from September 2020 to January 2022 at the background station(Daming Mountain Station,DMS)in the Yangtze River Delta region(YRD)were comprehensively analyzed and discussed.Based on the locally meteorological conditions,the background CO_(2) data were obtained after the data filtering.Backward trajectory analysis method was used to preliminarily explore the seasonal concentration and source-sink characteristics of atmospheric CO_(2).The results showed that average atmospheric CO_(2) mole fraction was(421.9±10.2)×10^(-6) during the study period.Obviously seasonal variation was found with a peak appeared in winter(428.7±9.1)×10^(-6) and a trough appeared in summer(416.7±5.5)×10^(-6).Diurnal variation showed the maximum at 01:00,02:00,21:00(LT,local tome)and the minimum at 14:00,14:00 and 13:00 LT in spring,summer,and autumn,respectively.However,in winter,an opposite trend was found with the peak appeared at 13:00 LT and trough appeared at 00:00 LT.There was a significantly negative correlation between CO_(2) mole fraction and wind speed in most directions at DMS.Local surface wind from NE-ENE in spring and autumn and SSW-WSW in summer and winter brought high CO_(2) mole fraction to DMS.Rising winds prevailed at DMS during the daytime had large differences in CO_(2) concentrations in different wind directions and the CO_(2) concentration was positive correlated with the wind level,while downwind prevailed at DMS during the night had little difference in CO_(2) concentrations in different wind directions,and a negative correlation was found between CO_(2) concentration and the wind level.In addition,cluster analysis of 72 h back trajectories found that air masses transported from southwest,northwest,and southeast almost accompanied with similar CO_(2) mole fraction,indicating that DMS can adequately represent background air characteristics of YRD.The source area analysis indicated that cities around DMS were important potential sources of CO_(2),and their anthropogenic emissions had strong effect on the CO_(2) mole fraction at DMS.
作者 胡智伟 刘硕 臧昆鹏 郭娜 林溢 陈圆圆 陈啸鸣 兰文港 温军 潘凤梅 陈丽涵 李珊 鲁嫣冉 郭朋 方双喜 HU Zhiwei;LIU Shuo;ZANG Kunpeng;GUO Na;LIN Yi;CHEN Yuanyuan;CHEN Xiaoming;LAN Wengang;WEN Jun;PAN Fengmei;CHEN Lihan;LI Shan;LU Yanran;GUO Peng;FANG Shuangxi(Department of Environment,Zhejiang University of Technology,Hangzhou 310014;Zhejiang Carbon Neutral Innovation Institute&Zhejiang International Cooperation Base for Science and Technology on Carbon Emission Reduction and Monitoring,Zhejiang University of Technology,Hangzhou 310014;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University Information&Scientific Technology,Nanjing 210044)
出处 《环境科学学报》 CAS CSCD 北大核心 2024年第6期236-248,共13页 Acta Scientiae Circumstantiae
基金 国家重点研发计划项目(No.2020YFA0607502) 国家自然科学基金项目(No.42275113) 浙江省自然科学基金资助项目(No.LQ23D050004)。
关键词 二氧化碳 原位监测 波动特征 季节变化 后向轨迹 carbon dioxide In-situ monitoring fluctuation characteristics seasonal variation backward trajectory
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