• 关键词：PM_2.5  化学组分  源解析  重污染事件

• 基金项目：国家重点研发计划（No.2016YFC0208500，2016YFC0208501，2016YFC0208502）；天津市科技计划项目（No.15YFYSSF00030）

• 元洁
• 天津市环境监测中心, 天津 300191

• 刘保双
• 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350

• 程渊
• 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350

• 肖致美
• 天津市环境监测中心, 天津 300191

• 徐虹
• 天津市环境监测中心, 天津 300191

• 关玉春
• 天津市环境监测中心, 天津 300191

• 摘要：为了快速分析天津市区冬季以及重污染过程中PM_2.5的化学组成特征及来源，本研究于2017年1月利用在线监测仪器快速采集了天津市区环境受体中PM_2.5及其化学组分的小时数据，并通过PMF（positive matrix factorization，正定矩阵因子分解法）模型解析了天津市区2017年1月及重污染过程中PM_2.5的主要贡献源类，分析了重污染过程中排放源的变化趋势.结果表明：2017年1月天津市区PM_2.5浓度为6.0~449.0 μg·m^-3，平均值为153.3 μg·m^-3.NO₃^-、SO₄^2-、NH₄⁺是PM_2.5中水溶性离子的主要组分，三者之和占水溶性离子总量的88.3%.NH₄⁺与Cl^-、NO₃^-、SO₄^2-均表现出显著的正相关性（r=0.82，0.95，0.97；p<0.01）.NO₃^-和SO₄^2-（r=0.90；p<0.01），Ca²⁺与Mg²⁺（r=0.65；p<0.01）均表现出显著的相关性，说明它们分别具有较高的同源性.OC和EC也是PM_2.5的重要组成部分，两者之和占PM_2.5质量浓度的20.4%.重污染过程中，PM_2.5及其主要离子的浓度显著的增加（p<0.01），并存在较高的二次离子生成.PMF解析结果表明，二次源类是天津市区2017年1月PM_2.5的首要源类，分担率为38.1%，其次为机动车源（分担率为25.6%）、燃煤源（分担率17.1%）、扬尘（分担率10.1%）和生物质燃烧（分担率9.1%）.重污染过程中，二次源是PM_2.5的主要贡献源类，分担率达到39.3%；说明重污染期间存在显著的二次转化及二次粒子的积累过程.重污染发生演变过程中，二次源、机动车源和燃煤源对PM_2.5贡献表现出显著增加的趋势，而扬尘和生物质燃烧的贡献则没有显著增加.

• Abstract：In order to accurately and promptly analyze the characteristic and the sources of chemical components in PM_2.5 during winter and haze events in Tianjin urban area. From January 2017, this study was conducted by utilizing the online monitoring instruments with instantly hourly data collected for ambient PM_2.5 and chemical components. Also, both the major contributors of PM_2.5 and the variation tendency of emission source was resolved by using Positive Matrix Factorization (PMF) model in January 2017 and haze events. The results demonstrate that the concentrations of PM_2.5 in January 2017 in Tianjin range from 6.0 to 449.0 μg·m^-3, with an average of 153.3 μg·m^-3. Moreover, NO₃^-, SO₄^2- and NH₄⁺ are the primary components of water-soluble ions in PM_2.5, the sum of the concentrations of the three water-soluble ions accounts for 88.3% of the total water-soluble ions. There are significant correlations between NH₄⁺and Cl^-, NO₃^-, SO₄^2- (r=0.82, 0.95, 0.97; p<0.01). Between NO₃^-and SO₄^2- (r=0.90; p<0.01), and between Ca²⁺and Mg²⁺ (r=0.65; p<0.01) also indicate significant correlations, implying the high homology. OC and EC are also the important fractions of PM_2.5, consisting of 20.4% in PM_2.5 concentration. During the haze events, the concentrations of PM_2.5 and major water-soluble ions perform the significant increasing (p<0.01), and the probability of secondary transformation is also higher. Furthermore, the results of the PMF source apportionment illustrate that secondary source, vehicle emission, coal combustion, resuspended dust and biomass burning are major sources of PM_2.5 in January 2017, and their contributions are 38.1%, 25.6%, 17.1%, 10.1% and 9.1%, respectively. During haze events, secondary source is still dominated contributor by occupying 39.3% of PM_2.5 concentration, implying there are the significant secondary-reactions and the accumulation processes of secondary particles during haze events. In the evolution-processes of haze events, the contributions of secondary source, and vehicle emission and coal combustion to PM_2.5 increase significantly, while the contributions of resuspended dust and biomass burning have not shown a distinct increase in trends.

• Key words：PM_2.5  chemical components  source apportionment  haze events

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