RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic.
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland.
Environ Sci Process Impacts. 2022 Jun 22;24(6):898-909. doi: 10.1039/d2em00007e.
Passive air sampling (PAS) has been used to monitor semivolatile organic compounds (SVOCs) for the past 20 years, but limitations and uncertainties persist in the derivation of effective sampling volumes, sampling rates, and concentrations. As a result, the comparability of atmospheric levels measured by PAS and concentrations measured by active air sampling (AAS) remains unclear. Long-term PAS data, without conversion into concentrations, provide temporal trends that are similar to, and consistent with, trends from AAS data. However, for more comprehensive environmental and human health assessments of SVOCs, it is also essential to harmonize and pool air concentration data from the major AAS and PAS monitoring networks in Europe. To address this need, we calculated and compared concentration data for 28 SVOCs (including organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs)) at the six monitoring sites in Europe with 10 years of co-located AAS (EMEP) and PAS (MONET) data: Birkenes, Košetice, Pallas, Råö, Stórhöfði, and Zeppelin. Atmospheric SVOC concentrations were derived from PAS data using the two most common computation models. Long-term agreement between the AAS and PAS data was strong for most SVOCs and sites, with 79% of the median PAS-derived concentrations falling within a factor of 3 of their corresponding AAS concentrations. However, in both models it is necessary to set a sampler-dependent correction factor to prevent underestimation of concentrations for primarily particle-associated SVOCs. In contrast, the models overestimate concentrations at sites with wind speeds that consistently exceed 4 m s. We present two recommendations that, if followed, allow MONET PAS to provide sufficiently accurate estimates of SVOC concentrations in air so that they can be deployed together with AAS in regional and global monitoring networks.
被动空气采样（PAS）已被用于监测半挥发性有机化合物（SVOCs）超过 20 年，但在有效采样体积、采样速率和浓度的推导方面仍存在局限性和不确定性。因此，PAS 测量的大气水平与主动空气采样（AAS）测量的浓度之间的可比性仍不清楚。未经浓度转换的长期 PAS 数据提供了与 AAS 数据相似且一致的时间趋势。然而，为了对 SVOCs 进行更全面的环境和人类健康评估，还必须协调和汇集欧洲主要 AAS 和 PAS 监测网络的空气浓度数据。为了满足这一需求，我们计算并比较了在欧洲六个监测站点（Birkenes、Košetice、Pallas、Råö、Stórhöfði 和 Zeppelin）共存 10 年的 AAS（EMEP）和 PAS（MONET）数据的 28 种 SVOCs（包括有机氯农药（OCPs）、多氯联苯（PCBs）、多溴二苯醚（PBDEs）和多环芳烃（PAHs））的浓度数据：大气中 SVOC 浓度是从 PAS 数据中使用最常用的两种计算模型得出的。对于大多数 SVOCs 和站点，AAS 和 PAS 数据之间的长期一致性很强，79%的中值 PAS 衍生浓度与其相应的 AAS 浓度相差在 3 倍以内。然而，在这两种模型中，都需要设置一个与采样器相关的校正因子，以防止对主要与颗粒物结合的 SVOCs 的浓度低估。相比之下，在风速持续超过 4m/s 的站点，模型会高估浓度。我们提出了两项建议，如果遵循这些建议，将允许 MONET PAS 提供足够准确的大气 SVOC 浓度估计，以便与 AAS 一起在区域和全球监测网络中使用。
