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A review of receptor modelling of industrially emitted particulate matter

机译:工业排放颗粒物的受体建模综述

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摘要

This review summarizes the different receptor models that have been adopted at industrial and urban sites to apportion the sources of particulate matter (PM) from industries. Industrial processes and those associated with industry (such as transportation) are an important source of airborne PM which includes trace elements, organic and elemental carbon, and PAHs. Industry also emits gaseous pollutants which form secondary aerosol in the atmosphere. Most published studies have employed chemical mass balance (CMB), positive matrix factorization (PMF) and/or principal component analysis (PCA) models as source apportionment tools. These receptor models were mostly applied to fine particulate matter (PM_(2.5)) and PM_(10) compositional data, particularly the inorganic constituents. Some studies have combined two or more of these receptor models, which provides useful information on the uncertainties associated with different models. Industry has been reported to contribute from 0 to 70% of PM mass at industrial sites. It appears that some studies are unsuccessful in apportioning PM from industry, e.g., unable to distinguish industrial emissions from other sources. A critical evaluation of the literature data also showed that the choice of appropriate tracers for industry, both generically and for specific industries, varies between different PM source apportionment studies. This is not surprising considering the significant difference in source profiles of PM from different types of industry, which may compromise source apportionment of industrial emissions using CMB with non-local source profiles. It may also affect the attribution of industrial emissions in multivariate statistical models (e.g. PMF and PCA). It is concluded that a general classification of the source "industry" is rarely appropriate for PM source apportionment. Indeed, such studies may even need to consider the different processes within a particular industry, such as a steelworks, which emit PM with significantly different chemical signatures. It is suggested that future source apportionment studies should make every effort to measure source profiles of PM from different industrial processes, and where possible, use multiple models in order to more accurately apportion the source emissions from industry.
机译:这篇综述总结了工业和城市场所采用的不同受体模型,以分配来自工业的颗粒物(PM)来源。工业过程以及与工业相关的过程(例如运输)是空气中PM的重要来源,其中包括痕量元素,有机和元素碳以及PAH。工业也排放气态污染物,这些污染物在大气中形成二次气溶胶。大多数已发表的研究都采用化学物质平衡(CMB),正矩阵分解(PMF)和/或主成分分析(PCA)模型作为来源分配工具。这些受体模型主要应用于细颗粒物(PM_(2.5))和PM_(10)的成分数据,尤其是无机成分。一些研究已经组合了两个或多个这些受体模型,从而提供了与不同模型相关的不确定性的有用信息。据报道,工业占工业现场PM质量的0%至70%。似乎有些研究无法将PM从工业中分配出来,例如,无法区分工业排放与其他来源。对文献数据的严格评估还表明,对于不同行业的PM来源分配研究,一般行业和特定行业的合适示踪剂的选择是不同的。考虑到来自不同行业的PM的排放量差异显着,这可能不足为奇,这可能会损害使用带有非本地排放量分布的CMB的工业排放物的源分配。它还可能影响多元统计模型(例如PMF和PCA)中工业排放的属性。结论是,源“行业”的一般分类很少适用于PM源分配。确实,此类研究甚至可能需要考虑特定行业(例如钢铁厂)中的不同过程,这些过程释放出具有明显不同化学特征的PM。建议未来的源头分配研究应尽一切努力来测量来自不同工业过程的PM的源曲线,并在可能的情况下,使用多种模型以更准确地分配来自工业的源头排放。

著录项

  • 来源
    《Atmospheric environment》 |2014年第11期|109-120|共12页
  • 作者单位

    Division of Environmental Health and Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom;

    Division of Environmental Health and Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom,Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, Jeddah 21589, Saudi Arabia;

    Division of Environmental Health and Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom;

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

    Source apportionment; Industrial emissions; Receptor modelling; Metals; Particulate matter; Steel industry;

    机译:来源分配;工业排放;受体建模;金属;颗粒物;钢铁工业;

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