Microbial biomass and acclimation can affect the removal of organic chemicals in natural surface waters. In order to account for these effects and develop more robust models for biodegradation, we have compiled and curated removal data for un-acclimated (pristine) surface waters on which we developed quantitative structure-activity relationships (QSARs). Global analysis of the very heterogeneous dataset including neutral, anionic, cationic and zwitterionic chemicals (N = 233) using a random forest algorithm showed that useful predictions were possible (Q = 0.4-0.5) though relatively large standard errors were associated (SDEP ∼0.7). Classification of the chemicals based on speciation state and metabolic pathway showed that biodegradation is influenced by the two, and that the dependence of biodegradation on chemical characteristics is non-linear. Class-specific QSAR analysis indicated that shape and charge distribution determine the biodegradation of neutral chemicals (R ∼ 0.6), e.g. through membrane permeation or binding to P450 enzymes, whereas the average biodegradation of charged chemicals is 1 to 2 orders of magnitude lower, for which degradation depends more directly on cellular uptake (R ∼ 0.6). Further analysis showed that specific chemical classes such as peptides and organic halogens are relatively less biodegradable in pristine surface waters, resulting in the need for the microbial consortia to acclimate. Additional literature data was used to verify an acclimation model (based on Monod-type kinetics) capable of extrapolating QSAR predictions to acclimating conditions such as in water treatment, downstream lakes and large rivers under μg L to mg L concentrations. The framework developed, despite being based on multiple assumptions, is promising and needs further validation using experimentation with more standardised and homogenised conditions as well as adequate characterization of the inoculum used.
Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.
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