| PM2.5化学组分连续观测在污染事件源解析中的应用 |
| 摘要点击 4370 全文点击 912 投稿时间：2021-02-23 修订日期：2021-03-25 |
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| 中文关键词 PM2.5 PMF源解析 污染事件 连续观测 贡献分布 |
| 英文关键词 PM2.5 PMF source apportionment pollution event continuous monitoring contribution 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 |
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| 中文摘要 |
| 为探索PM2.5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PM2.5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PM2.5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，PM2.5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 PM2.5 source apportionment during air pollution events, ambient air PM2.5 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 PM2.5. 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(PMFfirework-sand-haze and PMFfull-year). The results showed that the identified factors/sources, factor profiles, and contributions differed largely between PMFfirework-sand-haze and PMFfull-year solutions. For example, the relative average contribution of the firework combustion factor derived from the PMFfull-year solution(was 1.50%) was far less than that of the PMFfirework solution. The dust factor had an average contribution of 8.51% in the PMFsand solution, which was approximately double that of the PMFfull-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 PMFfirework 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 PMFfull-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 PMFfull-year solution[(0.061±0.042)-(1.06±0.65) μg·m-3, P<0.05], while their peak concentrations agreed well between the PMFsand estimations and the observations. During the winter haze event, all PM2.5 bulk components were well estimated by both the PMFfull-year and PMFhaze 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 PM2.5 components and their sources, which can improve the timeliness of air pollution source apportionment. |
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