doi:  10.3878/j.issn.1006-9895.1609.16173
长三角工业区夏季近地层臭氧和颗粒物污染相互关系研究

An Analysis on the Relationship between Ground-Level Ozone and Particulate Matter in an Industrial Area in the Yangtze River Delta during Summer Time
摘要点击 456  全文点击 464  投稿时间:2016-05-10  修订日期:2016-09-27
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基金:  国家重点研发计划项目2016YFC0202001、2014CB441202,国家自然科学基金项目41375036、41305135,中国科学院战略先导科技专项(B类)XDB05020103,江苏省普通高校学术学位研究生科研创新计划项目KYLX_0838
中文关键词:  长三角工业区  臭氧  PM2.5  气溶胶光学厚度  大气复合污染  臭氧光化学生成
英文关键词:  Industrial area  Ozone  PM2.5  Aerosol optical depth (AOD)  Complex air pollution  Ozone photochemical formation
                    
作者中文名作者英文名单位
邵平SHAO Ping南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044;中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
辛金元XIN Jinyuan南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044;中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
安俊琳AN Junlin南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044
王俊秀WANG Junxiu南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044
吴方堃WU Fangkun中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
吉东生JI Dongsheng中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
王跃思WANG Yuesi南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044;中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
引用:邵平,辛金元,安俊琳,王俊秀,吴方堃,吉东生,王跃思.2017.长三角工业区夏季近地层臭氧和颗粒物污染相互关系研究[J].大气科学,41(3):618-628,doi:10.3878/j.issn.1006-9895.1609.16173.
Citation:SHAO Ping,XIN Jinyuan,AN Junlin,WANG Junxiu,WU Fangkun,JI Dongsheng,WANG Yuesi.2017.An Analysis on the Relationship between Ground-Level Ozone and Particulate Matter in an Industrial Area in the Yangtze River Delta during Summer Time[J].Chinese Journal of Atmospheric Sciences (in Chinese),41(3):618-628,doi:10.3878/j.issn.1006-9895.1609.16173.
中文摘要:
      利用2013年5月15日到8月31日南京江北工业区(长三角典型工业区)同步的观测资料分析了近地层臭氧(O3)和细颗粒物(PM2.5)、气溶胶光学厚度(AOD)的变化特征及相互间的关系,并结合光化学箱模式分析了AOD对近地层O3生成的影响。结果表明,观测期间PM2.5平均质量浓度为56.2±20.1 μg m-3;AOD(500 nm)均值为1.4±0.9;波长指数α(440~870 nm)均值为1.0±0.3。PM2.5质量浓度24 h均值超国家二级标准20.2%,超标时AOD均值增加14.7%,α平均值增加23.9%,O3体积分数均值减少12.3%。O3超国家二级标准10.1%,超标时段AOD增加34.9%,α变化不显著。高温低湿条件下,O3日变化峰值(y)和PM2.5质量浓度(x)存在较高的线性相关。相对湿度<60%时,两者拟合曲线为y=0.97x+43.96(拟合度R2=0.60),温度>32°C时,两者拟合方程为y=1.24x+30.61(R2=0.64)。夏季长三角工业区呈现高浓度O3与高浓度PM2.5叠加的大气复合污染。O3日变化峰值和AOD变化呈显著负相关。模拟结果显示,O3日变化峰值(y)和AOD(x)呈现极高的负相关[y=-34.28x+181.62,R2 = 0.93或y=220.62·exp(-x/3.17)-19.50,R2=0.99]。
Abstract:
      Based on the data collected from May 15th to August 31st 2013 in an industrial area of Nanjing (a representative industrial area in the Yangtze River delta), characteristics of ozone (O3), PM2.5 and aerosol optical depth (AOD), and the relationships between O3 and PM2.5 and between O3 and AOD were analyzed. The effect of AOD on ozone formation was evaluated by the application of a detailed chemical mechanism model (NCAR MM). The average concentration of PM2.5 was 56.2±20.1 μg m-3, and the average AOD (500 nm) and Angstrom exponent α (440-870 nm) were 1.4±0.9 and 1.0±0.3, respectively. PM2.5 and O3 exceeded NAAQS-II (the National Ambient Air Quality Standard II ) by 20.2% and 10.1%, respectively. When PM2.5 exceeded the NAAQS-II, the average AOD (500 nm) and α (440-870 nm) increased by 14.7% and 23.91%, respectively, and O3 fell by 12.3%. When O3 exceeded the NAAQS-II, the average AOD (500 nm) increased by 34.9%, and the average α (440-870 nm) did not vary significantly. There existed a significant linear correlation between daily ozone maximum concentration (y) and PM2.5 concentration (x) under the condition of high temperature and low relative humidity. When the relative humidity was less than 60%, the linear regression function was y=0.97x+43.96 [R2=0.60 (R2 denotes the degree of fitting)]. When the temperature was over 32°C, the linear regression function was y=1.24x+30.61 (R2=0.64). There existed a negative correlation between daily ozone maximum concentration (y) and ground-observed AOD (x) in general. There existed a good negative correlation between simulated daily ozone maximum concentration (y) and ground-observed AOD (x), and the regression functions could be written as y=-34.28x+181.62 (R2=0.93) and/or y=220.62·exp(-x/3.17)-19.50 (R2=0.99).
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