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首页 > 过刊浏览>2022年第14卷第1期 >40-49. DOI:10.13878/j.cnki.jnuist.2022.01.005
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2020年新冠疫情前后南昌市大气CO2浓度变化及影响因子分析
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江苏省自然科学基金(BK2020080 2);国家自然科学基金(42105117,42105159)


Atmospheric CO2 concentration and its influence factors during 2020 COVID-19 pandemic in Nanchang
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    摘要:

    大气CO2浓度的变化主要受源汇和大气传输过程影响,因此城市地区的浓度观测包含区域人为源排放的重要信息.为明晰2020年新冠疫情对CO2浓度及人为源排放量的影响,本研究以南昌市大气CO2为研究对象,结合先验排放清单和高精度大气传输模型,对南昌市2020年1月24日至4月30日的小时CO2浓度进行了观测和模拟.基于当地管控政策把研究时段分为两段:一级管控期间(1月24日至3月11日)和二、三级控期间(3月12日至4月30日),并对排放源等主要影响因子进行了量化分析.研究发现:模型能够模拟CO2浓度的小时变化特征,然而由于模型没有考虑排放源的高度信息,尤其是城市中的发电站烟囱等强点源,将对夜晚浓度高估,而正午(12:00-18:00)则无影响.一级管控期间正午CO2浓度(干燥空气中CO2的摩尔分数)观测值和模拟值分别为433.63×10-6和438.22×10-6,其中模拟的人为源浓度贡献值高于观测值约21.9%;而二、三级控期间的观测值和模拟值分别为432.06×10-6和432.24×10-6,其模拟一致性高.浓度对比结果表明所使用的先验排放清单能代表二、三级管控期间人为CO2的排放量,而一级管控期间的排放量则偏高约21.9%,显示出调控措施明显降低了南昌市人为CO2的总排放量.整个时间段植被NEE的平均CO2浓度贡献都小于2×10-6,表明人为源相较于NEE是导致CO2浓度差异的主要因素.二、三级管控期间的边界层相比一级管控期间升高,减少了人为CO2排放量导致的浓度增加幅度,抵消了背景值浓度升高的影响,是两个时间段的浓度观测值接近的主要因素.本研究结果可为城市尺度的温室气体反演提供科学支撑和方法参考.

    Abstract:

    The atmospheric CO2 concentrations are mainly influenced by regional sinks/sources and atmospheric transport processes,thus observations in urban area contain essential information of anthropogenic CO2 emissions.To investigate the effect of COVID-19 on atmospheric CO2 concentration and its anthropogenic emissions,this study chose Nanchang city as the study area and used a priori emission inventory with WRF-STILT (Stochastic Time-Inverted Lagrangian Transport) atmospheric transport model to simulate hourly CO2 concentrations from January 24th to April 30th,2020.In accordance with the government measures to control COVID-19 epidemic,the whole study period was divided into two periods of Level 1 period (from January 24th to March 11th) and Level 2 period (from March 12th to April 30th).Results indicate the model can well capture hourly variations of CO2 concentration,but it overestimated nighttime concentrations due to the negligence of emission source height.During Level 1 period,the observed and simulated afternoon (12:00-18:00) CO2 mole fractions were 433.63×10-6 and 438.22×10-6,respectively,in which the anthropogenic emissions were 21.9% overestimated by simulation compared with observations.While during Level 2 period,the observation and simulation were very close as 432.06×10-6 and 432.24×10-6.The above comparisons indicate that the CO2 emissions can be represented by a priori CO2 emission inventory in Level 2 period,but was overestimated by 21.9% in Level 1 period,and the discrepancy was mainly due to government measures to control COVID-19 pandemic during this period.Besides,the average biological NEE enhancements were generally lower than 2×10-6,indicating a small contribution compared with anthropogenic emissions.The higher PBLH (Planetary Boundary Layer Height) in Level 2 period also offset the enhancement in CO2 emissions,which was also the main reason for the close observations during two periods.Our findings can provide scientific method supports for greenhouse gas emission inversions at urban scale.

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胡诚,艾昕悦,侯波,夏玲君.2020年新冠疫情前后南昌市大气CO2浓度变化及影响因子分析[J].南京信息工程大学学报(自然科学版),2022,14(1):40-49
HU Cheng, AI Xinyue, HOU Bo, XIA Lingjun. Atmospheric CO2 concentration and its influence factors during 2020 COVID-19 pandemic in Nanchang[J]. Journal of Nanjing University of Information Science & Technology, 2022,14(1):40-49

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  • 收稿日期:2022-01-04
  • 在线发布日期: 2022-04-11

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