Effect of water velocity on the uptake of polychlorinatedd biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: an assessment of the efficiency of performance reference compounds (PRCs) in river-like flow conditions

One aim of this study is to determine the impact of water velocity on the uptake of indicator polychlorinated biphenyls (iPCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers. A second aim is to assess the efficiency of performance reference compounds (PRCs) to correct for the impact of water velocity. SR and LDPE samplers were spiked with 11 or 12 PRCs and exposed for 6 weeks to four different velocities (in the range of 1.6 to 37.7 cm s−1) in river-like flow conditions using a channel system supplied with river water. A relationship between velocity and the uptakewas found for each iPCB and enables to determine expected changes in the uptake due to velocity variations. For both samplers, velocity increases from 2 to 10 cm s−1, 30 cm s−1 (interpolated data) and 100 cm s−1 (extrapolated data) lead to increases of the uptake which do not exceed a factor of 2, 3 and 4.5, respectively. Results also showed that the influence of velocity decreased with increasing the octanol-water coefficient partition (log Kow) of iPCBs when SR is used whereas the opposite effect was observed for LDPE. Time-weighted average (TWA) concentrations of iPCBs in water were calculated from iPCB uptake and PRC release. These calculations were performed using either a single PRC or all the PRCs. The efficiency of PRCs to correct the impact of velocity was assessed by comparing the TWA concentrations obtained at the four tested velocities. For SR, a good agreement was found among the four TWA concentrations with both methods (average RSD b 10%). Also for LDPE, PRCs offered a good correction of the impact of water velocity (average RSD of about 10 to 20%). These results contribute to the process of acceptance of passive sampling in routine regulatory monitoring programs.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.