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Evolution of the sewage treatment plant model SimpleTreat: Use of realistic biodegradability tests in probabilistic model simulations
Author(s) -
Franco Antonio,
Struijs Jaap,
Gouin Todd,
Price Oliver R
Publication year - 2013
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.1413
Subject(s) - probabilistic logic , activated sludge , biodegradation , environmental science , sewage sludge , sewage treatment , triclosan , sewage , environmental engineering , biochemical engineering , waste management , computer science , engineering , chemistry , medicine , organic chemistry , pathology , artificial intelligence
Given the large number of chemicals under regulatory scrutiny, models play a crucial role in the screening phase of the environmental risk assessment. The sewage treatment plant (STP) model SimpleTreat 3.1 is routinely applied as part of the European Union System for the Evaluation of Substances to estimate the fate and elimination of organic chemicals discharged via sewage. SimpleTreat estimates tend to be conservative and therefore only useful for lower‐tier assessments. A probabilistic version of SimpleTreat was built on the updated version of the model (SimpleTreat 3.2, presented in a parallel article in this issue), embracing likeliest as well as worst‐case conditions in a statistically robust way. Probabilistic parameters representing the variability of sewage characteristics, STP design, and operational parameters were based on actual STP conditions for activated sludge plants in Europe. An evaluation study was carried out for 4 chemicals with distinct sorption and biodegradability profiles: tonalide, triclosan, trimethoprim, and linear alkylbenzene sulfonate. Simulations incorporated information on biodegradability simulation studies with activated sludge (OECD 314B and OECD 303A tests). Good agreement for both median values and variability ranges was observed between model estimates and monitoring data. The uncertainty analysis highlighted the importance of refined data on partitioning and biodegradability in activated sludge to achieve realistic estimates. The study indicates that the best strategy to refine the exposure assessment of down‐the‐drain chemicals is by integrating higher‐tier laboratory data with probabilistic STP simulations and, if possible, by comparing them with monitoring data for validation. Integr Environ Assess Manag 2013;9:569–579. © 2013 SETAC