Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Aargau5232, Switzerland.
Datalystica Ltd., Park innovAARE, Villigen, Aargau5234, Switzerland.
Environ Sci Technol. 2022 Nov 15;56(22):15290-15297. doi: 10.1021/acs.est.2c02509. Epub 2022 Nov 1.
97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 μg/m). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers.
2019 年,欧盟 97%的城市人口所接触到的年细颗粒物水平高于世界卫生组织(WHO)的指导值(5μg/m)。有机气溶胶(OA)是主要空气污染物之一,了解其来源对于设计具有成本效益的减排策略至关重要。基于气溶胶质谱仪(AMS)或气溶胶化学组成监测仪(ACSM)数据的正定矩阵因子分解(PMF)是 OA 源解析(SA)分析的最常用方法。然而,传统的 PMF 需要大量的人工劳动,这阻碍了其在常规监测应用中的实施。本研究提出了源查找实时(SoFi RT,Datalystica Ltd.)方法,用于高效检索 OA 源。SoFi RT 生成的结果与常规滚动 PMF 结果在因子分布、时间序列、日变化模式以及在雅典、巴黎和苏黎世三个为期三年的 ACSM 数据集上 OA 因子的年相对贡献方面非常吻合。尽管 SoFi RT 的初始化需要对采样点的 OA 源(即,因子的大致数量和相关因子分布)有一定了解,但这种技术最大限度地减少了用户交互。最终,它可以在决策者和空气质量模型所需的时间尺度上提供最新的可靠信息。