Procter & Gamble, USA
Linear alkylbenzene sulfonate (LAS) has been one of the most heavily used anionic detergent surfactants globally since its introduction to the market in the 1960s. As such, it has a rich information base spanning physico-chemical properties, specific analytical methods applicable to all environmental matrices, acute and chronic toxicity, bioaccumulation, field monitoring data, and assessments using stream mesocosms. In this talk, this information was reviewed in support of an integrated approach that translates chronic toxicity data on pure LAS materials and technical mixtures for comparison to experimental stream mesocosm studies on LAS. Using the toxicity normalization method using local quantitative structure activity relationships (QSARs), chronic laboratory toxicity data for 19 species representing 9 phyla were summarized in various species sensitivity distributions that were also probed to understand the robustness of the SSD itself. The resulting HC5 was 0.19 mg/L (95% confidence interval of 0.06-0.38 mg/L). Leave-one-out and add-one-in Monte Carlo simulations were used to quantitatively and qualitatively evaluate ‘what-if’ scenarios regarding the generation of additional data and clearly demonstrated that the HC5 would not benefit from additional data generation. A high quality experimental stream mesocosm study yielded a long-term NOEC value of 0.27 mg/L suggesting the SSD remained appropriately conservative. In order to provide perspective on the relationships between SSDs, mesocosms, and field studies with regards to application factors, the ecological context of the stream mesocosm was also reviewed. The mesocosm was demonstrated to be an ecological equivalent of natural, low order, relatively unperturbed streams systems. Ecological investigations on trophic dynamics, nutrient processing regional community structure, combined with statistical and biological sensitivity of the test system support the use of low application factors for the mesocosm and SSD outputs as well as their predictive nature to derive safe concentrations for tested chemicals.