中文关键词PM_2.5PMF源解析污染事件连续观测贡献分布 英文关键词PM_2.5PMF source apportionmentpollution eventcontinuous monitoringcontribution distribution

作者	单位	E-mail
蔡凡涛	南京信息工程大学环境科学与工程学院, 大气环境与装备技术协同创新中心, 江苏省大气环境监测与污染控制高技术研究重点实验室, 南京 210044	549255079@qq.com
尚玥	南京信息工程大学环境科学与工程学院, 大气环境与装备技术协同创新中心, 江苏省大气环境监测与污染控制高技术研究重点实验室, 南京 210044
戴维	南京信息工程大学环境科学与工程学院, 大气环境与装备技术协同创新中心, 江苏省大气环境监测与污染控制高技术研究重点实验室, 南京 210044
谢鸣捷	南京信息工程大学环境科学与工程学院, 大气环境与装备技术协同创新中心, 江苏省大气环境监测与污染控制高技术研究重点实验室, 南京 210044	mingjie.xie@nuist.edu.cn

中文摘要 为探索PM_2.5组分高时间分辨观测数据在污染事件源解析中的应用，于2017年1~12月对南京市环境空气中PM_2.5组分进行连续观测.分别利用重金属自动分析仪、离子在线分析仪和半连续OC/EC分析仪测定PM_2.5中元素、水溶性离子和碳质组分的小时浓度.选取其中15种元素与5种丰量组分，分别基于3次污染事件（春节期间烟花爆竹燃放、春季沙尘暴和冬季灰霾）和全年的观测结果采用正矩阵因子分析（positive matrix factorization，PMF）模型进行源解析，并比较不同输入数据条件下（PMF_{烟花-沙尘-灰霾}和PMF_全年）的源贡献分布及特征组分平均浓度的估算情况.结果表明，基于不同观测数据的源解析结果在PMF因子类型、组成及贡献分布上均存在较大差异.例如，基于全年观测数据（PMF_全年）的源解析结果中烟花爆竹燃放的平均贡献仅为1.50%，远低于PMF_烟花结果中对应的源贡献（5.24%）；扬尘源的平均相对贡献在PMF_沙尘结果中为8.51%，比PMF_全年结果中扬尘源的贡献（4.45%）高近1倍.主要归结于PMF模型假设来源构成不发生变化，基于长期观测数据的源解析结果易受到排放源变化的影响.另外，烟花爆竹燃放期间，特征组分K的PMF_烟花估算结果[（1.32±1.17）μg·m^-3，P=0.64]较PMF_全年[（1.16±1.19）μg·m^-3，P=0.0090]更接近其观测平均值[（1.36±1.19）μg·m^-3].春季沙尘暴期间，特征组分Fe、Si和Ti的PMF_全年估算结果[（0.061±0.042）~（1.06±0.65）μg·m^-3]均显著低于观测平均值（P<0.05），而它们的PMF_沙尘估算值在峰值区域同观测值高度一致.冬季灰霾污染期间，PM_2.5各丰量组分PMF_全年和PMF_灰霾估算值同观测值的吻合程度均很高（r>0.99）.因此，基于污染事件期间的连续观测结果进行PMF源解析能较准确地反映特征组分及相关排放源的短期变化，有利于提高污染溯源的时效性. 英文摘要 To explore the application of high-temporal-resolution data in PM_2.5 source apportionment during air pollution events, ambient air PM_2.5 components were continuously monitored in urban Nanjing from January to December, 2017. Commercially available instruments for continuous measurements were deployed to obtain hourly concentrations of elements, water-soluble ions, and carbonaceous components of PM_2.5. Data for 15 elements and 5 bulk components during three pollution events(firework combustion during the Spring Festival, a spring sandstorm, and a winter haze event) and across the whole year comprised four datasets for source apportionment using positive matrix factorization(PMF), and the distribution of factor/source contributions and estimations of average concentrations of characteristic components were compared based on different input datasets(PMF_{firework-sand-haze} and PMF_full-year). The results showed that the identified factors/sources, factor profiles, and contributions differed largely between PMF_{firework-sand-haze} and PMF_full-year solutions. For example, the relative average contribution of the firework combustion factor derived from the PMF_full-year solution(was 1.50%) was far less than that of the PMF_firework solution. The dust factor had an average contribution of 8.51% in the PMF_sand solution, which was approximately double that of the PMF_full-year solution. This might be explained by the fact that PMF assumes unvaried source compositions during the measurement campaign, meaning that the source apportionment results based on long-term observations will include bias due to changes in emission sources. Furthermore, during the firework combustion event, the estimated average concentration of K from the PMF_firework solution[(1.32±1.17) μg·m^-3, P=0.64]was closer to measured value[(1.36±1.19) μg·m^-3]than that of the PMF_full-year solution[(1.16±1.19) μg·m^-3, P=0.0090]. For the sand storm event, the concentrations of Fe, Si, and Ti were significantly underestimated by the PMF_full-year solution[(0.061±0.042)-(1.06±0.65) μg·m^-3, P<0.05], while their peak concentrations agreed well between the PMF_sand estimations and the observations. During the winter haze event, all PM_2.5 bulk components were well estimated by both the PMF_full-year and PMF_haze solutions. Based on these results, PMF source apportionment results based on continuous measurement data during pollution events can reasonably reflect short-term variations in characteristic PM_2.5 components and their sources, which can improve the timeliness of air pollution source apportionment.

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