Global intercomparison of polyurethane foam passive air samplers evaluating sources of variability in SVOC measurements

Abstract

Highlights 12 types of PUF passive air samplers were deployed in an intercomparison exercise. Differences in sampler design lead to small variations in sampler performance. Replicate PUF air samples were sent to 14 laboratories to assess variability in data. Laboratories report very different concentrations of POPs, PAHs in identical samples. Analytical uncertainties must be addressed for comparability of air monitoring data.

Graphical tools for the planning and interpretation of polyurethane foam based passive air sampling campaigns

“…25,40,41 Even though the chamber design was the same in these two studies 38,39 the design of the PAS housing can also be a factor. 42 The PAS-SIM model describes the uptake of gas phase compounds only and does not attempt to simulate the uptake of particle-bound substances. Modelling the particle uptake processes within the PUF-PAS would be extremely complex and require deployment-specic input information, such as data on the local wind conditions, the particles in the sampled atmosphere and the particle size distribution of the target compounds, that is typically not available.…”

“…Large variations in the sampling rate for particle-bound SVOCs when using such a housing design are inevitable. 42 These uncertainties are tolerated by PUF-PAS users, because this housing design allows for relatively high sampling rates and maybe also because they are interested in sampling particle-bound substances. Rather than interpreting data for compounds that are particle-bound during a PUF-PAS's deployment without consideration of the uncertainties this entails, we recommend refraining from such quantitative interpretation altogether.…”

Comparability of semivolatile organic compound concentrations from co-located active and passive air monitoring networks in Europe

“…While the sampling and reporting differences can be resolved by further data treatment, a recent global intercomparison suggests that differences in analytical performance between the laboratories may introduce an additional source of uncertainty that limits data comparability. 27 Compared to EMEP, the MONET PAS data and procedures were more consistent between sites since they are all operated by RECETOX. However, there was a change in the MONET sampling procedure at all sites from a 28-d exposure period (2009)(2010)(2011) to an 84-d exposure period (2011-2018).…”

“…S5 † for all SVOCs). These differences in eld R S between sites are affected by variations in meteorological conditions, frequency and duration of AAS and PAS sampling, analytical procedures and performance (AAS data are from four different laboratories; Table S1 †), 27 and random variation associated with individual values. 35 Differences caused by meteorological factors are partially accounted for in the mathematical models for kinetic sampling 22,23 and random variation in both AAS and PAS data may be reduced by repeated sampling over longer periods of time.…”

“…We have also previously described sources of uncertainty and challenges for the harmonization of AAS and PAS SVOC air sampling, such as differences in sampler design, 11,24,25 including potentially problematic artifacts, 4,26 as well as differences in analytical performance between laboratories generating global SVOC air monitoring data. 27 This study focuses on the nal source of uncertainty for harmonization of AAS and PAS datathe determination of R Susing ten years (2009-2018) of continuous air monitoring data for PAHs, PCBs, OCPs, and PBDEs from the six sites in Europe with co-located EMEP AAS and MONET PAS to derive eld-based R S and compare them with results from the most commonly used models 22,23 for R S determination. This comparison enables us to address the challenge of reducing the uncertainties of PAS data 7 to improve the potential for harmonization of SVOC air monitoring data across Europe.…”