引用本文:杜青清,吴丽萍,赵雪艳,等.临沂市冬季环境空气PM2.5中水溶性离子污染特征及来源分析[J].环境科学研究,2019,32(8):1348-1357.
DU Qingqing,WU Liping,ZHAO Xueyan,et al.Characteristics and Sources Analysis of Water-Soluble Ions of Ambient Air PM2.5 in Winter in Linyi City[J].Research of Environmental Sciences,2019,32(8):1348-1357.]
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临沂市冬季环境空气PM2.5中水溶性离子污染特征及来源分析
杜青清1,3, 吴丽萍1, 赵雪艳3, 王静3, 欧盛菊1, 孟凡胜4, 张霞2
1. 天津城建大学环境与市政工程学院, 天津 300384;2. 中国环境监测总站, 北京 100012;3. 中国环境科学研究院, 环境基准与评估国家重点实验室, 北京 100012;4. 山西大学环境与资源学院, 山西 太原 030006
摘要:
为探究临沂市冬季环境空气PM2.5中水溶性离子污染特征及来源,于2016年12月11日—2017年1月9日在临沂大学、兰山区政府、高新区翠湖嘉园、汤庄办事处、河东区政府、临沂开发区6个采样点开展样品采集.结果表明:①采样期间全市ρ(PM2.5)日均值的平均值为144.86 μg/m3ρ(PM2.5)日均值在2016年12月20日和2017年1月4日出现峰值,分别为304.46和341.65 μg/m3.②水溶性离子日均质量浓度大小顺序依次为ρ(NO3-) > ρ(SO42-) > ρ(NH4+) > ρ(Cl-) > ρ(K+) > ρ(Ca2+) > ρ(Na+) > ρ(F-) > ρ(Mg2+) > ρ(NO2-),其中,在PM2.5w(NO3-)、w(SO42-)、w(NH4+)分别为22.33%、16.57%、13.62%,说明NO3-、SO42-和NH4+是临沂市PM2.5的主要组成部分.③临沂市污染天和非污染天ρ(PM2.5)日均值分别为164.00和56.86 μg/m3.随污染水平增加,PM2.5w(NO3-)明显增高,w(SO42-)和w(NH4+)基本不变,说明w(NO3-)的增加导致ρ(PM2.5)的升高.污染天和非污染天的NOR(氮氧化率)分别为0.28和0.11,SOR(硫氧化率)分别为0.34和0.28,说明污染越重,NOR和SOR越高,并且NOx的气-粒转化速率较SO2慢.污染天ρ(Cl-)和ρ(K+)分别为7.22和1.77 μg/m3,分别是非污染天的2.5和3.0倍.④采样期间非污染天和污染天的N/S〔ρ(NO3-)/ρ(SO42-)〕分别为0.85和1.39,说明非污染天时固定源对PM2.5的贡献相对较大,而污染天时移动源对PM2.5的贡献相对较大.⑤通过PMF模型法解析出3个因子.因子1对PM2.5中水溶性离子的贡献率为56.13%,代表二次源和生物质燃烧源;因子2的贡献率为25.22%,代表工业源和垃圾焚烧源;因子3的贡献率为18.65%,代表扬尘源.研究显示,临沂市冬季PM2.5污染严重,水溶性离子来源复杂,应采取多源控制的污染防治对策.
关键词:  大气颗粒物  PM2.5  水溶性离子  来源解析
DOI:10.13198/j.issn.1001-6929.2018.12.01
分类号:X513
基金项目:国家科技支撑计划项目(No.2014BAC23B01)
Characteristics and Sources Analysis of Water-Soluble Ions of Ambient Air PM2.5 in Winter in Linyi City
DU Qingqing1,3, WU Liping1, ZHAO Xueyan3, WANG Jing3, OU Shengju1, MENG Fansheng4, ZHANG Xia2
1. School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China;2. China National Environmental Monitoring Centre, Beijing 100012, China;3. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China;4. College of Environment & Resource Sciences of Shanxi University, Taiyuan 030006, China
Abstract:
To explore the pollution characteristics of soluble inorganic ions (WSⅡs) in PM2.5 from December 11th, 2016 to January 9th, 2017 in Linyi City, PM2.5 samples were collected during wintertime at 6 sampling sites (Linyi University, Lanshan District Government, High-tech Zone Cuihu Jiayuan, Tangzhuang Office, Hedong District Government, Linyi Development Zone) in Linyi City, and the WSⅡs associated with PM2.5 were analyzed. (1) The results showed that the average concentration of PM2.5 was 144.86 μg/m3 during sampling periods, with the peak values of 304.46 μg/m3 on December 20th, 2016 and 341.65 μg/m3 on January 4th, 2017, respectively. (2) The daily average mass concentration order of water-soluble ions was ρ(NO3-) > ρ(SO42-) > ρ(NH4+) > ρ(Cl-) > ρ(K+) > ρ(Ca2+) > ρ(Na+) > ρ(F-) > ρ(Mg2+) > ρ(NO2-). NO3-, SO42- and NH4+ accounted for 22.33%, 16.57% and 13.62% of the total mass of PM2.5, respectively, which indicated that NO3-, SO42-, NH4+ were the main components of PM2.5 in Linyi City. (3) The daily averages of PM2.5 mass concentrations in polluted and non-polluted days were 164.00 and 56.86 μg/m3, respectively. The mass percentage of NO3- increased obviously with the increase of PM2.5 pollution levels, and the mass percentage of SO42- and NH4+ remained largely unchanged, indicating that the obvious increase of the mass percentage of NO3- led to the increase of the mass concentration of PM2.5. The NOR values of polluted and non-polluted days were 0.28 and 0.11, and SOR values were 0.34 and 0.28, respectively, showing the conversion rate from NOx to NO3- was slower than that from SO2 to SO42-. The average concentrations of Cl- and K+ on polluted days were 7.22 and 1.77 μg/m3 respectively, which were 2.5 and 3.0 times higher than those on non-polluted days. (4) The ρ(NO3-)/ρ(SO42-) (N/S) values of non-polluted days and polluted days were 0.85 and 1.39 respectively, indicating that coal combustion contributed more to PM2.5 on non-polluted days, and mobile source contributed more on polluted days. (5) Three factors were isolated by PMF. The contributions of each factor to WSⅡs in PM2.5 were 56.13% (secondary source and biomass burning), 25.22% (industries and waste incineration), 18.65% (fugitive dust). In conclusion, the PM2.5 pollution was heavy during winter in Linyi City, and the sources of WSⅡs varied. Different countermeasures should be taken according to the pollution characteristics.
Key words:  fine particulate matter  PM2.5  water-soluble ions  source apportionment