Tracking POPs in Global Air from the First 10 Years of the GAPS Network (2005 to 2014)

Abstract

The Global Atmospheric Passive Sampling (GAPS) network, initiated in 2005 across 55 global sites, supports the global monitoring plan (GMP) of the Stockholm Convention on Persistent Organic Pollutants (POPs) by providing information on POP concentrations in air on a global scale. These data inform assessments of the long-range transport potential of POPs and the effectiveness evaluation of chemical regulation efforts, by observing changes in concentrations over time. Currently, measurements spanning 5–10 sampling years are available for 40 sites from the GAPS Network. This study was the first time that POP concentrations in air were reported on a global scale for an extended time period and the first to evaluate worldwide trends with an internally consistent sample set. For consistency between sampling years, site- and sample specific sampling rates were calculated with a new, public online model, which accounts for the effects of wind speed variability. Concentrations for legacy POPs in air between 2005 and 2014 show different trends for different organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). The POPs discussed in this study were chosen due to being the most frequently detected, with detection at the majority of sites. PCB, endosulfan, and hexachlorocyclohexane (HCH) concentrations in air are decreasing at most sites. The global trends reflect global sources and recycling of HCH, ongoing emissions from old stockpiles for PCBs, and recent use restrictions for endosulfan. These chlorinated OCPs continue to present exposure threat to humans and ecosystems worldwide. Concentrations of other OCPs, such as chlordanes, heptachlor and dieldrin, are steady and/or declining slowly at the majority of sites, reflecting a transition from primary to secondary sources (i.e., re-emission from reservoirs where these POPs have accumulated historically) which now control ambient air burdens.

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

“…In comparison, only 60% of the Herkert-modelled median PAS concentrations are within a factor of 3 of the corresponding AAS concentrations due to signicantly higher concentrations for the majority of SVOCs at both Birkenes and Pallas when compared to the Harner-modelled concentrations. This discrepancy was unexpected, as previous studies have found negligible differences in the output concentrations between the two models for most sites/compounds, 8,34 despite their different input parameters. Similar negligible differences were observed in this study at the other four sites, with median PAS concentrations from the two models falling within a factor of 1.5 of each other for all SVOCs at Košetice, Råö, Stórhöf+i, and Zeppelin.…”

“…21 Yet studies have shown that when turbulent airflow conditions exist within the sampler ( e.g. , when the external wind speed exceeds 4.0 m s −1 ), the dependency of R S on v ex is stronger, ranging from sub-linear ( α = 0.63), 34 to linear ( α = 1), 16,19 or greater than linear ( α > 1). 15,20,39,40 As a result, the constant square-root shape of the dependency, regardless of extremely high wind speeds, leads to an underestimation of modelled R S over periods of strong wind.…”

“…This indicates that the Herkert model also underestimates R S at sites with very low wind speeds. Schuster et al 34 suggest this may be an artifact of the MERRA meteorological model underestimating wind speeds at sites where presence of a forest cover may have been incorrectly assumed.…”

“…Medians were computed over the 10 year monitoring period (2009-2018) for gas-phase SVOCs (log K OA < 8.8) and particle-phase SVOCs (log K OA > 11.3) with MONET PAS particle sampling efficiencies of both 100% and 18% where a ¼ 0.5 for laminar ow under all conditions). 21 Yet studies have shown that when turbulent airow conditions exist within the sampler (e.g., when the external wind speed exceeds 4.0 m s À1 ), the dependency of R S on v ex is stronger, ranging from sub-linear (a ¼ 0.63), 34 to linear (a ¼ 1), 16,19 or greater than linear (a > 1). 15,20,39,40 As a result, the constant square-root shape of the dependency, regardless of extremely high wind speeds, leads to an underestimation of modelled R S over periods of strong wind.…”

“…with very low wind speeds. Schuster et al 34 suggest this may be an artifact of the MERRA meteorological model underestimating wind speeds at sites where presence of a forest cover may have been incorrectly assumed. By applying the square-root dependency between the mass transfer coefficient (k V ) and the internal wind speed (v in ) within the Herkert model (a ¼ 0.5, eqn ( 6)), we derived a set of theoretical v in values back-calculated from all of the eld R S derived in this study (blue dots in Fig.…”