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一个用于研究气相持久性有机污染物在空气和季节性积雪之间交换的动态模型。

A dynamic model to study the exchange of gas-phase persistent organic pollutants between air and a seasonal snowpack.

作者信息

Hansen Kaj M, Halsall Crispin J, Christensen Jesper H

机构信息

Department of Atmospheric Environment, National Environmental Research Institute, P.O. Box 358, Frederiksborgvej 399, 4000 Roskilde, Denmark.

出版信息

Environ Sci Technol. 2006 Apr 15;40(8):2644-52. doi: 10.1021/es051685b.

Abstract

An arctic snow model was developed to predict the exchange of vapor-phase persistent organic pollutants between the atmosphere and the snowpack over a winter season. Using modeled meteorological data simulating conditions in the Canadian High Arctic, a single-layer snowpack was created on the basis of the precipitation rate, with the snow depth, snow specific surface area, density, and total surface area (TSA) evolving throughout the annual time series. TSA, an important parameter affecting the vapor-sorbed quantity of chemicals in snow, was within a factor of 5 of measured values. Net fluxes for fluorene, phenanthrene, PCB-28 and -52, and alpha- and gamma-HCH (hexachlorocyclohexane) were predicted on the basis of their wet deposition (snowfall) and vapor exchange between the snow and atmosphere. Chemical fluxes were found to be highly dynamic, whereby deposition was rapidly offset by evaporative loss due to snow settling (i.e., changes in TSA). Differences in chemical behavior over the course of the season (i.e., fluxes, snow concentrations) were largely dependent on the snow/air partition coefficients (K(sa)). Chemicals with relatively higher K(sa) values such as alpha- and gamma-HCH were efficiently retained within the snowpack until later in the season compared to fluorene, phenathrene, and PCB-28 and -52. Average snow and air concentrations predicted by the model were within a factor of 5-10 of values measured from arctic field studies, but tended to be overpredicted for those chemicals with higher K(sa) values (i.e., HCHs). Sensitivity analysis revealed that snow concentrations were more strongly influenced by K(sa) than either inclusion of wind ventilation of the snowpack or other changes in physical parameters. Importantly, the model highlighted the relevance of the arctic snowpack in influencing atmospheric concentrations. For the HCHs, evaporative fluxes from snow were more pronounced in April and May, toward the end of the winter, providing evidence that the snowpack plays an important role in influencing the seasonal increase in air concentrations for these compounds at this time of year.

摘要

开发了一个北极雪模型,用于预测冬季大气与积雪之间气相持久性有机污染物的交换。利用模拟加拿大高北极地区条件的气象数据模型,根据降水速率创建了单层积雪,积雪深度、雪比表面积、密度和总表面积(TSA)在全年时间序列中不断变化。TSA是影响雪中化学物质蒸汽吸附量的一个重要参数,其值在测量值的5倍范围内。芴、菲、多氯联苯-28和-52以及α-和γ-六氯环己烷(HCH)的净通量是根据它们的湿沉降(降雪)以及雪与大气之间的蒸汽交换来预测的。发现化学通量具有高度动态性,由于积雪沉降(即TSA的变化),蒸发损失迅速抵消了沉积。季节过程中化学行为的差异(即通量、雪中浓度)在很大程度上取决于雪/气分配系数(K(sa))。与芴、菲以及多氯联苯-28和-52相比,具有相对较高K(sa)值的化学物质,如α-和γ-六氯环己烷,在积雪中能有效保留到季节后期。模型预测的平均雪浓度和空气浓度在北极实地研究测量值的5至10倍范围内,但对于那些具有较高K(sa)值的化学物质(即六氯环己烷)往往预测过高。敏感性分析表明,雪中浓度受K(sa)的影响比积雪的风通风或其他物理参数变化的影响更大。重要的是,该模型突出了北极积雪对影响大气浓度的相关性。对于六氯环己烷,在冬季接近尾声的4月和5月,雪中的蒸发通量更为明显,这证明积雪在每年这个时候对这些化合物的空气浓度季节性增加具有重要影响。

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