Norwegian Institute for Air Research, P.O. Box 100, NO-2027 Kjeller, Norway.
Department of Chemistry, University of Oslo, P.O. Box 1033, NO-0315 Oslo, Norway.
Environ Sci Technol. 2022 Sep 6;56(17):11983-11990. doi: 10.1021/acs.est.2c03047. Epub 2022 Aug 11.
The assessment of long-range transport potential (LRTP) is enshrined in several frameworks for chemical regulation such as the Stockholm Convention. Screening for LRTP is commonly done with the OECD Pov and LRTP Screening Tool employing two metrics, characteristic travel distance (CTD) and transfer efficiency (TE). Here we introduce a set of three alternative metrics and implement them in the Tool's model. Each metric is expressed as a fraction of the emissions in a source region. The three metrics quantify the extent to which the chemical (i) reaches a remote region (dispersion, ϕ1), (ii) is transferred to surface media in the remote region (transfer, ϕ2), and (iii) accumulates in these surface media (accumulation, ϕ3). In contrast to CTD and TE, the emissions fractions metrics can integrate transport via water and air, enabling comprehensive LRTP assessment. Furthermore, since there is a coherent relationship between the three metrics, the new approach provides quantitative mechanistic insight into different phenomena determining LRTP. Finally, the accumulation metric, ϕ3, allows assessment of LRTP in the context of the Stockholm Convention, where the ability of a chemical to elicit adverse effects in surface media is decisive. We conclude that the emission fractions approach has the potential to reduce the risk of false positives/negatives in LRTP assessments.
长距离迁移潜力(LRTP)的评估被纳入了几项化学法规框架中,如《斯德哥尔摩公约》。LRTP 的筛选通常使用经合组织的 POV 和 LRTP 筛选工具进行,该工具采用了两个指标,即特征迁移距离(CTD)和迁移效率(TE)。在这里,我们引入了一组三个替代指标,并将其实施到工具模型中。每个指标都表示为源区排放的一部分。这三个指标量化了化学物质在以下方面的程度:(i)到达偏远地区(分散,ϕ1),(ii)在偏远地区的表面介质中转移(转移,ϕ2),以及(iii)在这些表面介质中积累(积累,ϕ3)。与 CTD 和 TE 不同,排放分数指标可以整合水和空气的传输,从而实现全面的 LRTP 评估。此外,由于这三个指标之间存在一致的关系,因此新方法为确定 LRTP 的不同现象提供了定量的机制见解。最后,积累指标ϕ3允许在《斯德哥尔摩公约》的背景下评估 LRTP,其中化学物质在表面介质中引起不良影响的能力是决定性的。我们得出的结论是,排放分数方法有可能降低 LRTP 评估中假阳性/假阴性的风险。