| 舟山市臭氧污染分布特征及来源解析 |
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| 引用本文： | 王俏丽,董敏丽,李素静,吴成志,王刚,陈必新,李伟,高翔,叶荣民.舟山市臭氧污染分布特征及来源解析[J].环境科学,2019,40(3):1143-1151. |
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| 作者姓名： | 王俏丽 董敏丽 李素静 吴成志 王刚 陈必新 李伟 高翔 叶荣民 |
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| 作者单位： | 浙江大学能源工程学院,能源清洁利用国家重点实验室,热能工程研究所,杭州310027;浙江大学化学工程与生物工程学院,生物质化工教育部重点实验室,工业生态与环境研究所,杭州 310027;浙江大学化学工程与生物工程学院,生物质化工教育部重点实验室,工业生态与环境研究所,杭州 310027;三捷环境工程咨询(杭州)有限公司,杭州,310012;浙江大学能源工程学院,能源清洁利用国家重点实验室,热能工程研究所,杭州310027;浙江省舟山海洋生态环境监测站,舟山,316021 |
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| 摘 要： | 臭氧及其前体物在环境空气中传输和反应过程复杂,本研究利用舟山市国控点2014年的监测数据对臭氧污染时空分布开展了统计分析,并利用CMAQ (community multiscale air quality)模型模拟了舟山市2014年臭氧污染形成,选用ISAM(integrated source apportionment method)源追踪算法计算来源贡献率.结果表明,舟山市春秋季节的臭氧浓度相对较高,浓度高值出现在午后13:00～15:00.普陀站的臭氧平均浓度最高而位于中心城区的临城站最低.臭氧总体浓度不高,但易出现单日浓度高值,其中5月臭氧超标率最高.舟山市本地臭氧形成主要受VOCs浓度控制,而源解析结果表明舟山市全年外来源占总贡献的69. 46%.本地源中,工业燃烧源、工艺过程源、道路移动源、非道路移动源的贡献率相差不大,且表现出显著的港口城市特征,船舶源、石化源、储运源分别占总贡献的4. 45%和1. 01%和1. 80%.控制臭氧污染应采取周边区域联防联控的措施,以VOCs排放源为主,不同来源协同调控的措施.
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| 关 键 词： | 臭氧 时空分布 区域多尺度空气质量(CMAQ)模型 源解析 外来源 本地源 |
| 收稿时间： | 2018/5/26 0:00:00 |
| 修稿时间： | 2018/9/25 0:00:00 |
Characteristics of Ozone Pollution Distribution and Source Apportionment in Zhoushan |
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| WANG Qiao-li,DONG Min-li,LI Su-jing,WU Cheng-zhi,WANG Gang,CHEN Bi-xin,LI Wei,GAO Xiang and YE Rong-min.Characteristics of Ozone Pollution Distribution and Source Apportionment in Zhoushan[J].Chinese Journal of Environmental Science,2019,40(3):1143-1151. |
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| Authors: | WANG Qiao-li DONG Min-li LI Su-jing WU Cheng-zhi WANG Gang CHEN Bi-xin LI Wei GAO Xiang and YE Rong-min |
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| Institution: | State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China;Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China,Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China,Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China,Trinity Consultants, Inc. (China office), Hangzhou 310012, China,Trinity Consultants, Inc. (China office), Hangzhou 310012, China,Trinity Consultants, Inc. (China office), Hangzhou 310012, China,Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China,State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China and Zhejiang Provincial Zhoushan Marine Ecological Environmental Monitoring Station, Zhoushan 316021, China |
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| Abstract: | The processes affecting photochemical reactions and regional transport of ozone and its precursors in ambient air are very complicated. In this study, statistical analysis of the spatial and temporal distributions of ozone pollution in Zhoushan was carried out based on monitoring data from state monitoring stations in Zhoushan in 2014. Specifically, ozone formation was simulated by CMAQ (the community multiscale air quality) model, and the source contribution rate was calculated using the Integrated Source Apportionment Method (ISAM) source tracking algorithm. The results showed that ozone pollution was more severe in spring and autumn than in summer and winter, and the highest ozone concentrations mostly appeared during 13:00-15:00 in the afternoon. Putuo Station had the highest ozone concentration while Lincheng Station, located in the downtown area of the city, had the lowest ozone concentration. The overall average ozone concentration was not high; however, peak concentrations that exceeded the standards usually occurred, which occurs most often in May. Local ozone formation in Zhoushan City is controlled by the VOC concentration, and source tracking results showed that non-local sources accounted for 69.46% of the total contribution. Among local emission sources, fuel burning boiler sources, industry process sources, on-road mobile sources, and non-road mobile sources made similar contributions to ozone formation. Moreover, they showed significant characteristics of a port city. The contribution rates from shipping sources, petrochemical sources, and storage and transportation sources were 4.45%, 1.01%, and 1.80%, respectively. In conclusion, control of the ozone pollution in Zhoushan City should be based on simultaneous reduction and coordinated prevention involving multiple sources (VOCs as the main one) both locally and in surrounding areas. |
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| Keywords: | ozone spatial and temporal distribution community multiscale air quality(CMAQ)model source apportionment non-local sources local sources |
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