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Temporal variations and potential sources of organophosphate esters in PM_(2.5) in Xinxiang, North China

机译:华北新乡PM_(2.5)中有机磷酸酯的时间变化及潜在来源

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We monitored the concentrations of 10 organophosphate esters (OPEs) in 52 fine particulate matter (PM2.5) samples in Xinxiang, Henan Province, North China, in 2015. During the sampling period, the OPE concentrations in most samples (n = 47) differed minimally and were relatively stable (mean: 2.02 +/- 0.93 ng m(-3)), although several samples (n = 5) had high total OPE (Sigma 10OPE) concentrations (mean: 9.99 +/- 5.69 ng m(-3)), which may have been influenced by high PM2.5 levels. Meanwhile, some samples had high PM2.5 concentrations but low Sigma 10OPE concentrations (i.e. low OPE/PM2.5 ratios) or low PM2.5 concentrations but high Sigma 10OPE concentrations, which might have been influenced by air mass sources. Therefore, we assessed air mass sources using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and wind direction frequency data, and subsequently analysed PM2.5 and OPE sources using a potential source contribution function (PSCF) model. The results revealed that air mass sources couldn't represent the source of specific pollutants, including PM2.5 and OPEs. Generally, both PM2.5 and OPEs were from Henan and Shandong Provinces; however, the major source areas differed, which may have resulted from diverse pollution characteristics in various source areas. The principal component analysis and PSCF results revealed that the 10 OPEs could be segmented into three groups, which were associated with different source areas. These results suggested that pollution characteristics of contaminants in source areas should be considered in source apportionment. (C) 2018 Elsevier Ltd. All rights reserved.
机译:我们在2015年监测了华北河南省新乡市的52个细颗粒物(PM2.5)样品中10种有机磷酸酯(OPE)的浓度。在采样期间,大多数样品中的OPE浓度(n = 47)差异极小且相对稳定(平均值:2.02 +/- 0.93 ng m(-3)),尽管几个样品(n = 5)具有较高的总OPE(Sigma 10OPE)浓度(平均值:9.99 +/- 5.69 ng m(-3) -3)),这可能已受到高PM2.5水平的影响。同时,一些样品的PM2.5浓度高但Sigma 10OPE浓度低(即低OPE / PM2.5比)或PM2.5浓度低但Sigma 10OPE浓度高,这可能已受到空气质量源的影响。因此,我们使用混合单粒子拉格朗日综合轨迹(HYSPLIT)模型和风向频率数据评估了空气质量源,随后使用了潜在的源贡献函数(PSCF)模型分析了PM2.5和OPE源。结果表明,空气质量来源不能代表特定污染物的来源,包括PM2.5和OPEs。通常,PM2.5和OPE均来自河南和山东。然而,主要污染源地区不同,这可能是由于不同污染源地区的污染特征不同所致。主成分分析和PSCF结果表明,这10个OPE可分为三类,分别与不同的来源地区相关联。这些结果表明,在源头分配中应考虑源头区域污染物的污染特征。 (C)2018 Elsevier Ltd.保留所有权利。

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