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首页> 外文期刊>Atmospheric environment >Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China
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Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China

机译：基于气溶胶质谱和1 h分辨率化学数据集的受体模型在中国天津的源分配

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摘要

Source apportionment studies have been performed on online receptor datasets in recent years (called online source apportionment), including mass spectra and online chemical dataset. Single particle aerosol mass spectrometry (SPAMS), an important online technique, has the ability to analyze mass spectrum (MS) and particular size information of a single particle in real time. Clustering methods have been widely applied to MS dataset to investigate the sources of particles, although the receptor models are the common tools to probe the particle sources based on the receptor dataset. This work developed a new method (SPAMS-RM) that employed the receptor model (RM) on an MS dataset from SPAMS to identify particle sources. Particles were measured by SPAMS from July 14 to August 15, 2015, at an urban site in Tianjin, China. Multilinear Engine-2 (ME2) and adaptive resonance theory-based neural networks-2a (ART-2a) were separately used to analyze the single particle MS dataset. This work also evaluated the performance of SPAMS-RM method. Concentrations of chemical components of PM2.5(particulate matter with an aerodynamic diameter of less than 2.5 μm) and gaseous pollutants were measured by independent online instruments (1 h resolution). Source apportionment was separately conducted using two receptor models, Positive Matrix Factorization (PMF) and ME2, based on the 1 h resolution chemical dataset. This method was called online chemical source apportionment (OCSA-RM). ART-2a obtained 19 clusters that merged into five major classes: carbon species, rich-K, sea salt, crustal dust, and industrial metals. SPAMS-RM identified eight sources by interpreting the MS characteristic of factors and investigating the relationship of the temporal trends of factor contributions, chemical species, gaseous pollutants, and particle clusters. OCSA-ME2 and OCSA-PMF both identified seven factors. Source apportionment results between SPAMS-RM and OCSA-ME2/PMF were compared. Each method identified coal combustion, biomass burning, sea salt, nitrate source, sulfate source, vehicle emission, and crustal dust. The SPAMS-RM results showed that nitrate source was the most significant contributor (34%) to the PM followed by sulfate source (17%), coal combustion (14%), crustal dust (11%), vehicle emission (10%), biomass burning-OCEC (7%), and industrial activities & sea salt (4%). Some differences between SPAMS-RM and OCSA-ME2/PMF results existed and might be due to chemical analysis methods and sampling methods. ME2 was used for the first time to identify the PM sources based on the MS dataset from SPAMS and demonstrated its capability when coupled with MS dataset from SPAMS to apportion the source of PM.
机译：近年来，已经对在线受体数据集进行了源分配研究（称为在线源分配），包括质谱和在线化学数据集。单颗粒气溶胶质谱仪（SPAMS）是一项重要的在线技术，具有实时分析单个颗粒的质谱（MS）和特定尺寸信息的能力。尽管受体模型是基于受体数据集探查粒子来源的常用工具，但聚类方法已广泛应用于MS数据集以研究粒子的来源。这项工作开发了一种新方法（SPAMS-RM），该方法在SPAMS的MS数据集上采用受体模型（RM）来识别粒子来源。 2015年7月14日至8月15日，通过SPAMS在中国天津的城市站点对颗粒进行了测量。多线性引擎2（ME2）和基于自适应共振理论的神经网络2a（ART-2a）分别用于分析单粒子MS数据集。这项工作还评估了SPAMS-RM方法的性能。通过独立的在线仪器（分辨率为1 resolutionh）测量PM2.5（空气动力学直径小于2.5μm的颗粒物）的化学成分和气态污染物的浓度。基于1 h分辨率的化学数据集，使用两个受体模型（正矩阵分解（PMF）和ME2）分别进行了源分配。这种方法称为在线化学源分配（OCSA-RM）。 ART-2a获得了19个星团，这些星团已分为五个主要类别：碳物种，富钾，海盐，地壳粉尘和工业金属。 SPAMS-RM通过解释因子的质谱特征并调查因子贡献，化学物质，气态污染物和颗粒簇的时间趋势之间的关系，确定了八个来源。 OCSA-ME2和OCSA-PMF都确定了七个因素。比较了SPAMS-RM和OCSA-ME2 / PMF之间的源分配结果。每种方法都可以识别煤炭燃烧，生物质燃烧，海盐，硝酸盐源，硫酸盐源，车辆排放物和地壳粉尘。 SPAMS-RM结果显示，硝酸盐源是PM的最主要来源（34％），其次是硫酸盐源（17％），煤炭燃烧（14％），地壳粉尘（11％），车辆排放（10％）。 ，生物质燃烧-OCEC（7％）以及工业活动和海盐（4％）。 SPAMS-RM和OCSA-ME2 / PMF结果之间存在一些差异，可能是由于化学分析方法和采样方法所致。 ME2首次用于基于SPAMS的MS数据集识别PM来源，并展示了其与SPAMS的MS数据集结合以分配PM来源的功能。

著录项

  • 来源
    《Atmospheric environment》 |2019年第2期|387-397|共11页
  • 作者

    Xing Peng; Xiaoxi Liu; Xurong Shi; Guoliang Shi; Mei Li; Jiayuan Liu; Yanqi Huangfu; Hong Xu; Ruoyu Ma; Wei Wang; Yinchang Feng;

  • 作者单位

    State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University;

    Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of Chemistry and Biochemistry, University of Colorado Boulder;

    Tianjin Eco-Environmental Monitoring Center;

    College of Computer and Control Engineering, Nankai University;

    Institute of Mass Spectrometer and Atmospheric Environment, Jinan University;

    Guangdong Provincial Engineering Research Center for on-line Source Apportionment System of Air Pollution;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PM; SPAMS; Source apportionment; Receptor model; ART-2a;

    机译：PM;SPAMS;来源分配;受体模型;ART-2a;

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