Characterizing Spatial Diversity of Passive Sampling Sites for Measuring Levels and Trends of Semivolatile Organic Chemicals

Abstract

Passive air sampling of semivolatile organic compounds (SVOCs) is a relatively inexpensive method that facilitates extensive campaigns with numerous sampling sites. An important question in the design of passive-sampling networks concerns the number and location of samplers. We investigate this question with the example of 17 SVOCs sampled at 14 background sites across the Czech Republic. More than 200 time series (length 5-11 years) were used to characterize SVOC levels and trends in air between 2003 and 2015. Six polychlorinated biphenyls (PCBs), 6 polyaromatic hydrocarbons (PAHs), and 5 organochlorine pesticides (OCPs) at 14 sites were assessed using data from the MONET passive sampling network. Significant decreases were found for most PCBs and OCPs whereas hexachlorobenzene (HCB) and most PAHs showed (mostly insignificant) increases. Spatial variability was rather low for PCBs and OCPs except for dichlorodiphenyltrichloroethane (DDT) and rather high for PAHs. The variability of the SVOC levels and trends depends on characteristics of the sites including their remoteness, landscape, population, and pollution sources. The sites can be grouped in distinct clusters, which helps to identify similar and, thereby, potentially redundant sites. This information is useful when monitoring networks need to be optimized regarding the location and number of sites.

Passive air sampling for semi-volatile organic chemicals

“…not one that is specic to a particular deployment, it may in fact be more transparent to not report volumetric air concentrations: the time-normalised sequestered amount has less uncertainty than the calculated (15, 47, 80, 120, 160, 200, 240, 280, 320 m) concentration, because the latter inherits the uncertainty of the SR. Several early PAS studies had reported spatial results in amount per sampler or amount per sampler per time, 29,280,390,391,432,433,448,469 but only recently has there been a reemergence of support for not necessarily reporting volumetric air concentrations. [514][515][516][517] This approach is clearly advisable when using a PAS whose SR under a given set of circumstances is not well established or even unknown. A good example is the study attaching a PAS to a gull, where it was entirely possible to compare the exposure of different birds using time-normalised sequestered amounts.…”

“…308 A network of PASs across the Czech Republic was used to address the issue of representativeness more comprehensively by not only comparing absolute concentration levels of different SVOCs but also time trends obtained from those sites. 516 Cluster analysis based on levels and trends revealed that the fourteen sites belonged to one of three types, whereby sites belonging to a cluster share characteristics related to remoteness, landscape, population, and pollution sources and therefore also their pollution prole. An analysis like this could assist in optimizing the elimination of sampling sites from a network, by nding the number and location of sites that yield the most information with the least number of sites.…”

Comparability of long-term temporal trends of POPs from co-located active and passive air monitoring networks in Europe

“…[29][30][31][32] Instead, we were limited to the simpler methods previously used for passive air monitoring. 19,33 In this case, by assuming approximate rst-order kinetics [34][35][36] and thus an exponential decrease in POP concentrations over time, the time series can simply be described by compound-specic halflives (years) and rate constants (% change per year).…”

“…41 As previously discussed, direct comparison of active and passive sampling data requires conversion of passive-sampling concentrations from ng per PUF per d to ng m À3 using models. 27,42 However, we have previously shown that it is possible to calculate temporal trends directly from the primary ng per PUF per d concentrations 19,33 and therefore used the same methodology in this study. To compare the corresponding active and passive time series, we computed the value of each exponential trend at the middle point of their overlapping period.…”

“…Although PCBs are still in limited use, their atmospheric levels have decreased over the globe. 7,[33][34][35][36][44][45][46][47] Active EMEP temporal trends for all six indicator PCBs show statistically signicant (p < 0.05) decreases at Birkenes, Košetice, Pallas, Råö and Zeppelin ranging from 2-20% per year. This is supported by similar and consistent decreases in the passive MONET trends for all PCBs and all sites ranging from 1-30% per year.…”