Abstract
Quantitative Structure Biodegradation Relationships (QSBRs) are a tool to predict the biodegradability of chemicals. The objective of this work was to generate reliable biodegradation data for mono-aromatic chemicals in order to evaluate and verify previously developed QSBRs models. A robust biodegradation test method was developed to estimate specific substrate utilization rates, which were used as a proxy for biodegradation rates of chemicals in pure culture. Five representative mono-aromatic chemicals were selected that spanned a wide range of biodegradability. Aerobic biodegradation experiments were performed for each chemical in batch reactors seeded with known degraders. Chemical removal, degrader growth and CO2 production were monitored over time. Experimental data were interpreted using a full carbon mass balance model, and Monod kinetic parameters (Y, Ks, qmax and μmax) for each chemical were determined. In addition, stoichiometric equations for aerobic mineralization of the test chemicals were developed. The theoretically estimated biomass and CO2 yields were similar to those experimentally observed; 35% (s.d ± 8%) of the recovered substrate carbon was converted to biomass, and 65% (s.d ± 8%) was mineralised to CO2. Significant correlations were observed between the experimentally determined specific substrate utilization rates, as represented by qmax and qmax/Ks, at high and low substrate concentrations, respectively, and the first order biodegradation rate constants predicted by a previous QSBR study. Similarly, the correlation between qmax and selected molecular descriptors characterizing the chemicals structure in a previous QSBR study was also significant. These results suggest that QSBR models can be reliable and robust in prioritising chemical half-lives for regulatory screening purposes.
Highlights
Reliable prediction of chemical biodegradation rates can help prioritise evaluation efforts on chemicals that pose the greatest risk to the environment and humans
According to Annex XI of the Registration, Evaluation, Authorisation & Restriction of Chemicals (REACH) directive, the use of a QSAR (Quantitative Structure Activity Relationship) model for regulatory purposes is valid if: (i) the model is developed in accordance to OECD principles, (ii) the evaluated substance is within the applicability domain of the model, (iii) the predicted result is suitable to use for regulatory purposes, and (iv) adequate documentation of the method is provided (Nendza et al, 2013; Echa, 2016)
The qmax values were not correlated with the lipophilic parameter (i.e. LogP of the test chemicals), suggesting that at high substrate concentration, lipophilicity might not be the rate limiting factor. All together these results suggest that the rate of biodegradation of mono-aromatic chemicals seems to be governed by the electronic effects of substituents, kinetic and thermodynamic factors associated with chemical biotransformation, and the shape and size of the chemicals
Summary
Reliable prediction of chemical biodegradation rates can help prioritise evaluation efforts on chemicals that pose the greatest risk to the environment and humans. QSBR models link predictor variables, predominantly physiochemical properties, to response variables that function as associated biodegradation indices of the chemical (e.g., half-life, ThOD, BOD5, rate constants) (Raymond et al, 2001; Wammer and Peters, 2005; Pavan and Worth, 2008; Rücker and Kümmerer, 2012; Xu et al, 2015). Their use in persistence, bioaccumulation and toxicity (PBT), and other ecotoxicological assessments of chemicals can reduce the need for experimental tests on animals and their associated costs (Ho€fer et al, 2004; Martin et al, 2017a). The qualitative data produced by RBTs needed regression models to convert them into quantitative half-life data, the accuracy and reliability of which could be questioned (Arnot et al, 2005; Acharya et al, 2019)
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