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首页> 外文期刊>Environmental Science & Technology >Dependence of Persistence and Long-Range Transport Potential on Gas-Particle Partitioning in Multimedia Models
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Dependence of Persistence and Long-Range Transport Potential on Gas-Particle Partitioning in Multimedia Models

机译:持久性和远距离传输势对多媒体模型中气体颗粒划分的依赖性

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摘要

A novel approach of modeling gas-particle partitioning using polyparameter linear free energy relationships (ppLFERs) is implemented into three different multimedia box models (the OECD Pov and LRTP screening tool (The Tool), ChemRange, and CliMoChem) and compared to approaches based on the octanol-air partition coefficient (K_(Oa)). In all three multimedia models, calculated overall persistence is not strongly influenced (differences < 3%) by the gas-particle partitioning approach selected. The long-range transport potential (LRTP) is more sensitive to the choice of the gas-particle partitioning model. In CliMoChem, the LRTP of polar chemicals is higher if the ppLFER gas-particle partitioning approach is applied, with differences up to a factor of 2. Modeled concentrations of polar chemicals in Arctic air are also higher in the ppLFER version of CliMoChem. The model results obtained with the ppLFER approach are in good agreement with measured concentrations of a-HCH, methoxychlor, and trifluralin in Arctic air, whereas results from the K_(OA)-based version of the model are in some cases lower by a factor of 10-100. If the required chemical property data are available, the ppLFER approach holds considerable potential to improve the gas-particle partitioning description for polar chemicals in multimedia models.
机译:使用多参数线性自由能关系(ppLFER)对气体颗粒分配进行建模的新方法被实施到三个不同的多媒体盒模型(OECD Pov和LRTP筛选工具(The Tool),ChemRange和CliMoChem)中,并与基于辛醇-空气分配系数(K_(Oa))。在所有三个多媒体模型中,所选择的气体颗粒分配方法均不会对计算的总体持久性产生重大影响(差异<3%)。远距离迁移势(LRTP)对气体颗粒分配模型的选择更为敏感。在CliMoChem中,如果使用ppLFER气体颗粒分配方法,则极性化学品的LRTP会更高,差异最多可达2倍。在ppLFER版本的CliMoChem中,北极空气中极性化学品的模拟浓度也更高。用ppLFER方法获得的模型结果与北极空气中a-HCH,甲氧基氯和三氟拉林的测量浓度高度吻合,而基于K_(OA)的模型的结果在某些情况下降低了一个因子10-100。如果可以获得所需的化学性质数据,则ppLFER方法具有很大的潜力,可以改善多媒体模型中极性化学物质的气体颗粒分配描述。

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