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

Kalina, Jiří; White, Kevin Bradley; Scheringer, Martin; Příbylová, Petra; Kukučka, Petr; Audy, Ondřej; Martiník, Jakub; Klánová, Jana

doi:10.1039/d2em00007e

Cited by 1 publication

(8 citation statements)

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“…24 However, negligible differences have been observed in the output concentrations compared to the original model for the majority of sites globally. 24,27,28 Therefore, model selection is not expected to significantly influence the temporal trends reported in this study. Temporal trends, in the form of both annual exponential increases/decreases (%) and halving/ doubling times (t 1/2 ), were estimated using the Theil-Sen linear regression estimator 39,40 on log-transformed air concentration data for each combination of sampling site and compound.…”

Section: ■ Introductionmentioning

confidence: 89%

“…Model input parameters specific to MONET samplers are given in Table S2. For more consistent continental-scale meteorological data, site-specific average temperatures over each sampling period were generated from the MERRA-2 model, as described previously for MONET ,, and GAPS. , A more complex model for calculating effective air sampling volumes has recently been developed and is now being used by GAPS . However, negligible differences have been observed in the output concentrations compared to the original model for the majority of sites globally. ,, Therefore, model selection is not expected to significantly influence the temporal trends reported in this study.…”

Section: Methodsmentioning

confidence: 99%

“…For more consistent continental-scale meteorological data, site-specific average temperatures over each sampling period were generated from the MERRA-2 model, as described previously for MONET ,, and GAPS. , A more complex model for calculating effective air sampling volumes has recently been developed and is now being used by GAPS . However, negligible differences have been observed in the output concentrations compared to the original model for the majority of sites globally. ,, Therefore, model selection is not expected to significantly influence the temporal trends reported in this study. Temporal trends, in the form of both annual exponential increases/decreases (%) and halving/doubling times ( t 1/2 ), were estimated using the Theil-Sen linear regression estimator , on log-transformed air concentration data for each combination of sampling site and compound.…”

Section: Methodsmentioning

confidence: 99%

“…It is important to note that De Zilk (NL) has the highest concentrations of many of the POPs, including ∑ 6 PCB, ∑ 12 dl-PCB, ∑ 17 PCDD/F, and ∑ 9 PBDE and is also one of the windiest sites (median wind speed of 5.3 m/s). Significant variation in wind speed is known to affect the calculation of effective sampling volumes of passive air samplers, 38 have recently found that concentrations of POPs from passive air sampling may be overestimated at coastal MONET sites when wind speeds exceed ∼4 m/s, 28 consistent with the observations of Tuduri et al 48 Median wind speeds at several of the coastal sites in this study exceed this threshold (Mace Head, De Zilk, and Stoŕhofdi above 5 m/s; High Muffles, Zmiinyi Island, Lahemaa, and G ̇ordan Lighthouse above 4 m/s) which suggests that the elevated concentrations at some of these sites may be overestimated and should be interpreted with caution. However, this potential overestimation is consistent across the entire data series for each specific site and compound; thus, the influence on the calculated temporal trends is negligible.…”

Section: Environmental Science and Technologymentioning

confidence: 99%

“…However, this potential overestimation is consistent across the entire data series for each specific site and compound; thus, the influence on the calculated temporal trends is negligible. 26,28 Implications for Future Monitoring. The significant decline in concentrations of nearly all POPs over the past 15 years of MONET monitoring demonstrates that regulatory measures in Europe have been successful in reducing the atmospheric burden of these compounds.…”

Section: Environmental Science and Technologymentioning

confidence: 99%

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Spatial and Temporal Trends of Persistent Organic Pollutants across Europe after 15 Years of MONET Passive Air Sampling

White

Kalina

Scheringer

et al. 2023

Environ. Sci. Technol.

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The Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants (POPs) was established to generate long-term data necessary for evaluating the effectiveness of regulatory measures at a global scale. After 15 years of passive air monitoring (2003–2019), MONET is the first network to produce sufficient data for the analysis of continuous long-term temporal trends of POPs in air across the entire European continent. This study reports long-term concentrations of 20 POPs monitored at 32 sites in 27 European countries. As of January 1, 2019, the concentration ranges (pg/m3) were 1.1–52.8 (∑6PCB), 0.3–8.5 (∑12dl-PCB), 0.007–0.175 (∑17PCDD/F), 0.02–2.2 (∑9PBDE), 0.4–24.7 (BDE 209), 0.5–247 (∑6DDT), 1.7–818 (∑4HCH), 15.8–74.7 (HCB), and 5.9–21.5 (PeCB). Temporal trends indicate that concentrations of most POPs have declined significantly over the past 15 years, with median annual decreases ranging from −8.0 to −11.5% (halving times of 6–8 years) for ∑6PCB, ∑17PCDD/F, HCB, PeCB, and ∑9PBDE. Furthermore, no statistically significant differences were observed in either the trends or the concentrations of specific POPs at sites in Western Europe (WEOG) compared to sites in Central and Eastern Europe (CEE), which suggests relatively uniform compound-specific distribution and removal at the continental scale.

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