Dai Qili, Ding Jing, Song Congbo, Liu Baoshuang, Bi Xiaohui, Wu Jianhui, Zhang Yufen, Feng Yinchang, Hopke Philip K
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
Sci Total Environ. 2021 Mar 10;759:143548. doi: 10.1016/j.scitotenv.2020.143548. Epub 2020 Nov 6.
Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown in Tianjin, China. The data also included gaseous pollutants and hourly PM compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emissions, photonucleation, coal combustion, residential heating and festival emissions. The DN-PMF enhanced the diel patterns of photonucleation and the two traffic factors by enlarging the differences between daytime peak values and nighttime concentrations. The municipal incinerator plant, traffic emissions, and coal combustion have cleaner and more clearly defined directionalities after dispersion normalization. Thus, dispersion normalized PMF is capable of enhancing the source emission patterns. After the COVID-19 lockdown began, PNC of traffic nucleation and traffic emissions decreased by 41% and 44%, respectively, while photonucleation produced more particles likely due to the reduction in the condensation sink. The significant changes in source emissions indicate a substantially reduced traffic volume after the implement of lockdown measures.
因子分析模型利用测量变量的协方差来识别和分配来源。这些模型，特别是正定矩阵因子分解（PMF），已被广泛用于分析颗粒物数量浓度（PNCs）数据集。然而，观测到的PNCs变化和粒径分布受源排放率、大气扩散以及化学和物理转化过程的驱动。由气象诱导稀释引起的观测数据变化降低了获得准确源分配结果的能力。为了减少稀释对源定量估计的影响，一种通过将扩散度量（通风系数）纳入PMF分析（称为扩散归一化PMF，DN-PMF）来提高源分配结果准确性的方法，被应用于在中国天津进行的一次实地测量活动所获得的PNC数据集中，该活动涵盖春节期间以及新冠疫情封锁开始阶段。数据还包括气态污染物和每小时的颗粒物成分数据。分辨出八个因子，并解释为城市焚烧炉、交通核化、二次无机气溶胶（SIA）、交通排放、光致核化、煤炭燃烧、居民供暖和节日排放。DN-PMF通过扩大白天峰值与夜间浓度之间的差异，增强了光致核化和两个交通因子的日变化模式。经过扩散归一化后，城市焚烧厂、交通排放和煤炭燃烧具有更清洁且定义更明确的方向性。因此，扩散归一化PMF能够增强源排放模式。新冠疫情封锁开始后，交通核化和交通排放的PNC分别下降了41%和44%，而光致核化可能由于凝结核的减少产生了更多颗粒物。源排放的显著变化表明封锁措施实施后交通量大幅减少。
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