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疫情管控期间深圳市城区VOCs的变化特征及减排效果评估 被引量:4

Characteristics of VOCs and Assessment of Emission Reduction Effect During the Epidemic Lockdown Period in Shenzhen Urban Area
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摘要 2022年3月为防止新冠肺炎疫情(COVID-19)的传播扩散,深圳市采取了严格的管控措施,这为研究深圳市环境空气中人为源挥发性有机物(AVOCs)变化对减排的响应,以及评估当前减排措施的有效性提供了检验机会.基于2022年3月7~27日深圳市莲花站污染物的在线观测数据,对疫情管控前、管控期间和解除管控后AVOCs的变化进行了分析,并利用基于观测的模型(OBM)研究了臭氧生成的敏感性变化,开展了前体物减排效果的评估.结果表明,受区域影响及气象条件的干扰,深圳市城区AVOCs的平均值在疫情管控期间并没有较管控前明显下降;但疫情管控期间“海陆风”影响下早高峰AVOCs的峰值较非管控期间平均下降了46%,且以芳香烃的降幅最大,下降了59%;在持续偏东风影响下或受区域传输影响AVOCs累积升高过程中,芳香烃也分别平均下降了25%和21%.深圳市疫情管控期间城区仍处于AVOCs控制区,短期内要使O_(3)下降,提高AVOCs的减排比例是最有效的措施,建议AVOCs与NO_(x)的协同减排比例大于1∶2;长期NO_(x)需深度减排到60%以上才有可能进入O_(3)的下降通道.情景模拟分析结果表明,疫情管控期间早高峰AVOCs的降低有利于抑制O_(3)的生成,对NO_(x)控制反而会促进O_(3)的生成,区域影响过程中加强本地芳香烃的控制能显著降低O_(3)生成.深圳市现阶段应加强对工业溶剂的管控,特别是降低溶剂源中对O_(3)生成影响较大的芳香烃;同时对机动车能源结构的改革持续推进,降低燃料燃烧中VOCs的排放. To prevent disease spreading during the COVID-19 epidemic,Shenzhen adopted lockdown measures in March of 2022.This provided an opportunity to study the response of changes in anthropogenic volatile organic compounds(AVOCs)in Shenzhen to emission reduction and to evaluate the effectiveness of current emission reduction measures.This study analyzed the variety of AVOCs before,during,and after the epidemic lockdown based on the online observation data of pollutants at Lianhua Station in Shenzhen from March 7,2022 to March 27,2022.Additionally,the sensitivity of ozone formation and the assessment of the reduction effect of precursors was conducted by an observation based model(OBM).The results showed that:affected by regional influences and the interference of meteorological conditions,the average value of AVOCs in Shenzhen urban areas did not drop significantly during the lockdown period compared to that before the lockdown.However,the peak of AVOCs at the morning peak time under the influence of"sea and land wind"during the epidemic lockdown period dropped by 46% on average compared with that during the non-lockdown period,and the aromatic hydrocarbon component dropped the most by 59%.Additionally,under the influence of continuous easterly wind,or during the accumulation and increase of AVOCs affected by regional transmission,aromatic components also decreased by an average of 25% and 21%,respectively.During the lockdown period of the epidemic in Shenzhen,the O_(3) formation in urban areas was still AVOCs-limited.Increasing the emission reduction ratio of AVOCs was the most effective measure to reduce O_(3) in the short term.In order to ensure the effectiveness of emission reduction,it was recommended that the coordinated emission reduction ratio of AVOCs and NO_(x) should be greater than 1∶2.It was only possible to enter the downward channel of O_(3) if the deep emission reduction was more than 60%.This study revealed that the emission reduction of AVOCs during the morning traffic peak during the epidemic lockdown period was conducive to inhibiting the formation of O_(3),whereas the control of NO_(x) would promote it.Strengthening the control of local aromatic hydrocarbon components during the regional impact process could also significantly reduce O_(3) production.At this stage,Shenzhen should strengthen the management and control of industrial solvents,especially to reduce the aromatic hydrocarbon components in the solvent source that have a greater impact on the generation of O_(3).Further,Shenzhen should continue to promote the reform of the energy structure of motor vehicles to reduce the emission of VOCs in fuel combustion.
作者 云龙 林楚雄 李成柳 邱志诚 古添发 李光程 张明棣 郭键锋 YUN Long;LIN Chu-xiong;LI Cheng-liu;QIU Zhi-cheng;GU Tian-fa;LI Guang-cheng;ZHANG Ming-di;GUO Jian-feng(Shenzhen Sub-branch of Guangdong Ecological and Environmental Monitoring Center,Shenzhen 518049,China;Guangzhou OnePoint Environmental Consultant Co.,Ltd.,Guangzhou 510220,China)
出处 《环境科学》 EI CAS CSCD 北大核心 2023年第7期3788-3796,共9页 Environmental Science
基金 国家重点研发计划项目(2018YFC0213106)。
关键词 新冠肺炎疫情(COVID-19) 挥发性有机物(VOCs) 敏感性分析 减排评估 基于观测的模型(OBM) COVID-19 epidemic volatile organic compounds(VOCs) sensitivity analysis reduction effect assessment observation based model(OBM)
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