• 关键词：青岛  港口  PM_2.5  正定矩阵因子分解（PMF）  源解析  潜在源贡献函数（PSCF）

• 基金项目：中国博士后科学基金项目（No.2019M660986）；中央高校基本科研业务费专项（No.63211074）；国家大气污染控制关键问题研究项目（No.DQGG2021301）

• 张子祎
• 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

• 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

• 孟赫
• 山东省青岛生态环境监测中心，青岛 266003

• 石来元
• 山东省青岛生态环境监测中心，青岛 266003

• 王静
• 山东省青岛生态环境监测中心，青岛 266003

• 吴建会
• 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

• 冯银厂
• 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

• 摘要：港口区域因大气污染物排放量大且污染源复杂，已成为沿海城市大气污染防治的关键区域.为明确青岛港口区域PM_2.5污染特征及主要贡献源类，于2019年在青岛市3个港口区域和1个背景点位采集了不同季节的环境PM_2.5样品，并分析了其化学组分特征；同时，采用正定矩阵因子分析模型（PMF）和潜在源贡献函数（PSCF）分别分析了港口区域PM_2.5的主要贡献源类及各源类潜在的影响区域.结果表明，2019年青岛港口区域ρ（PM_2.5）年均值为64 μg·m^-3，是我国空气质量二级标准的1.8倍，其中，董家口点位最高（74 μg·m^-3），崂山点位最低（55 μg·m^-3）. NO₃^-、OC和SO₄^2-是PM_2.5的主导组分，其中，NO₃^-含量（13.1%）明显高于其它组分.董家口点位ρ（NO₃^-）、ρ（SO₄^2-）、ρ（OC）和ρ（EC）（分别为13.0、7.09、8.98和2.91 μg·m^-3）明显高于其它点位，燃煤、工业特别是钢铁企业及货车等影响可能较为明显.同时，冬季这些组分浓度也显著高于其它 季节，而夏季Na的浓度（0.96 μg·m^-3）和占比（2.13%）明显较高；春季Si和Al的浓度（1.27和0.45 μg·m^-3）和占比（2.79%和1.00%）明显高于其它季节.PMF源解析结果表明，二次硫酸盐和二次有机碳气溶胶（SOA）混合源（22.4%）及二次硝酸盐（20.1%）是港口区域PM_2.5的主要贡献源类，其次为机动车源（16.7%）和扬尘源（14.6%），燃煤源的贡献率为13.8%，而海盐和船舶源的贡献为7.2%.从季节变化来看，春季扬尘贡献（32.1%）较高，夏季二次硫酸盐和二次有机碳气溶胶（SOA）混合源（31.6%）、海盐和船舶源（19.2%）贡献较高，而冬季燃煤（16.6%）、机动车（22.8%）、二次硝酸盐（23.9%）、钢铁及相关冶金源（3.2%）和建筑水泥尘（3.6%）贡献较高.河北省中南部及山东省中西部地区是青岛港口各 源类的主要潜在源区，黄海是船舶排放的主要潜在源区.

• Abstract：The port area has become a key area of the prevention and control of air pollution in coastal cities because of the large amount emissions of air pollutants and complex sources. To clarify the pollution characteristics and the main emission sources of ambient PM_2.5 in the port area， PM_2.5 samples were synchronously collected from three port sites and one background site in different seasons in the Qingdao port area in 2019， and their chemical compositions were then determined. The main emission sources and the potential impact areas of these sources were analyzed using positive matrix factorization （PMF） and potential source contribution function （PSCF）， respectively. The results showed that the annual average value of PM_2.5 in Qingdao port area in 2019 was 64 μg·m^-3， which was 1.8 times higher than the air quality standard in China. Ambient PM_2.5 concentration was the highest in Dongjiakou site （74 μg·m^-3） and the lowest in Laoshan site （55 μg·m^-3）. NO₃^-， OC， and SO₄^2- were the dominant components of ambient PM_2.5， and the proportion of NO₃^- （13.1%） was substantially higher than those of other components. The concentrations of NO₃^-， SO₄^2-， OC， and EC at Dongjiakou （13.0， 7.09， 8.98， and 2.91 μg·m^-3， respectively） were markedly higher than the other sites， probably indicating that the impacts of coal combustion， industry emissions， especially for iron and steel enterprises， and trucks at Dongjiakou site were more obvious. Meanwhile， the concentrations of these components in winter were substantially higher than other seasons. However， the concentration and proportion of Na were higher in summer （0.96 μg·m^-3 and 2.13%）， and the concentrations （1.27 and 0.45 μg·m^-3） and proportions （2.79% and 1.00%） of Si and Al in spring were significantly higher than other seasons. PMF results showed that the mixed source of secondary sulfate and secondary organic aerosol （SOA） （the contribution of 22.4%） and secondary nitrate （20.1%） were the main sources of ambient PM_2.5 in the Qingdao port area， followed by motor-vehicle （16.7%） and fugitive dust （14.6%）. The contribution of coal combustion was 13.8%， while that of the mixed source of sea salt and ships was 7.2%. The contribution of fugitive dust was high in spring （32.1%）， the contribution of the mixed source of secondary sulfate and secondary organic aerosol （SOA） （31.6%）， and the mixed source of sea salt and ships （19.2%） were high in summer， while those of coal combustion （16.6%）， motor-vehicles （22.8%）， secondary nitrate （23.9%）， steel and related metallurgical sources （3.2%）， and construction dust （3.6%） were high in winter. The central and southern areas of Hebei Province and the central and western areas of Shandong Province were the main potential impact areas of pollution sources of Qingdao port， and the Yellow Sea was the main potential impact area for ship emissions.

• Key words：Qingdao  port  PM_2.5  positive matrix factorization （PMF）  source apportionment  potential source contribution function （PSCF）

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