Clustering micropollutants based on initial biotransformations for improved prediction of micropollutant removal during conventional activated sludge treatment

The lack of fundamental insights on the fate of micropollutants during conventional activated sludge treatment in wastewater treatment plants (WWTPs) presents one of the biggest challenges in optimizing their removal. To address this challenge, we designed a study to identify the drivers of micropollutant removal in WWTPs. We calculated the removal efficiency of micropollutants across the activated sludge process of full-scale wastewater treatment plants from literature-reported data. Our final dataset consisted of 529 independent observations for 84 micropollutants along with a set of associated activated sludge process parameters. We used the Eawag pathway prediction system to predict initial biotransformations for each of the 84 micropollutants and hierarchical clustering to group chemicals based on similarities in their predicted initial sets of biotransformations. We then applied stability selection to generate well-performing models that can be interpreted to uncover the key factors contributing to the range of removal efficiencies calculated for each cluster of micropollutants. The key factors considered in stability selection included six physicochemical properties of the micropollutants and six activated sludge process parameters. The sludge-water partitioning coefficient, molecular weight, solids retention time, influent micropollutant concentration, and dissolved oxygen levels were consistently identified as key factors of micropollutant removal for clusters of micropollutants that undergo certain types of initial biotransformations. Our findings highlight the importance of considering initial biotransformations when evaluating micropollutant removal and identify important process parameters that determine the fate of micropollutants during activated sludge treatment

Exploring the Evolution of Organofluorine-Containing Compounds during Simulated Photolithography Experiments

One potential source of per- and polyfluoroalkyl substances (PFASs) in electronics fabrication wastewater are the organofluorine-containing compounds used in photolithography materials such as photoresists and top antireflective coatings (TARCs). However, the exact identities of these constituents are unknown and transformation reactions that may occur during photolithography may result in the formation of unknown or unexpected PFASs. To address this knowledge gap, we acquired five commercially relevant photolithography materials, characterized the occurrence of organofluorine-containing compounds in each material, and performed simulated photolithography experiments to stimulate any potential transformation reactions. We found that photoresists and TARCs have total fluorine (TF) concentrations in the g L–1 range, similar to the levels of other industrial and commercial products. However, the target and suspect PFASs present in these materials can only explain up to 20% of the TF in a material. We evaluated wastewater samples collected after simulated photolithography experiments and used a mass balance approach to assess the extent of transformations. Although a number of target, suspect, and nontarget PFASs were identified in the wastewater samples, the extent of transformation was limited and the fluorine contained in the PFASs could not explain more than an additional 1% of the TF in the photolithography materials

Relative contribution of ammonia oxidizing bacteria and other members of nitrifying activated sludge communities to micropollutant biotransformation

Improved micropollutant (MP) biotransformation during biological wastewater treatment has been associated with high ammonia oxidation activities, suggesting co-metabolic biotransformation by ammonia oxidizing bacteria as an underlying mechanism. The goal of this study was to clarify the contribution of ammonia oxidizing bacteria to increased MP degradation in nitrifying activated sludge (NAS) communities using a series of inhibition experiments. To this end, we treated a NAS community with two different ammonia oxidation inhibitors, namely octyne (OCT), a mechanistic inhibitor that covalently binds to ammonia monooxygenases, and allylthiourea (ATU), a copper chelator that depletes copper ions from the active center of ammonia monooxygenases. We investigated the biotransformation of 79 structurally different MPs by the inhibitor-treated and untreated sludge communities. Fifty-five compounds exhibited over 20% removal in the untreated control after a 46 h-incubation. Of these, 31 compounds were significantly inhibited by either ATU and/or OCT. For 17 of the 31 MPs, the inhibition by ATU at 46 h was substantially higher than by OCT despite the full inhibition of ammonia oxidation by both inhibitors. This was particularly the case for almost all thioether and phenylurea compounds tested, suggesting that in nitrifying activated sludge communities, ATU does not exclusively act as an inhibitor of bacterial ammonia oxidation. Rather, ATU also inhibited enzymes contributing to MP biotransformation but not to bulk ammonia oxidation. Thus, inhibition studies with ATU tend to overestimate the contribution of ammonia-oxidizing bacteria to MP biotransformation in nitrifying activated sludge communities. Biolog tests revealed only minor effects of ATU on the heterotrophic respiration of common organic substrates by the sludge community, suggesting that ATU did not affect enzymes that were essential in energy conservation and central metabolism of heterotrophs. By comparing ATU- and OCT-treated samples, as well as before and after ammonia oxidation was recovered in OCT-treated samples, we were able to demonstrate that ammonia-oxidizing bacteria were highly involved in the biotransformation of four compounds: asulam, clomazone, monuron and trimethoprim

Can meta-omics help to establish causality between contaminant biotransformations and genes or gene products?

There is increasing interest in using meta-omics association studies to investigate contaminant biotransfor- mations. The general strategy is to characterize the complete set of genes, transcripts, or enzymes from in situ environmental communities and use the abundances of particular genes, transcripts, or enzymes to establish associations with the communities' potential to biotransform one or more contaminants. The associations can then be used to generate hypotheses about the underlying biological causes of particular biotransformations. While meta-omics association studies are undoubtedly powerful, they have a tendency to generate large numbers of non-causal associations, making it potentially difficult to identify the genes, transcripts, or enzymes that cause or promote a particular biotransformation. In this perspective, we describe general scenarios that could lead to pervasive non-causal associations or conceal causal associa- tions. We next explore our own published data for evidence of pervasive non-causal associations. Finally, we evaluate whether causal associations could be identified despite the discussed limitations. Analysis of our own published data suggests that, despite their limitations, meta-omics association studies might still be useful for improving our understanding and predicting the contaminant biotransformation capacities of microbial communities.ISSN:2053-1400ISSN:2053-141

Evaluating the environmental parameters that determine aerobic biodegradation half-lives of pesticides in soil with a multivariable approach

Aerobic biodegradation half-lives (half-lives) are key parameters used to evaluate pesticide persistence in soil. However, half-life estimates for individual pesticides often span several orders of magnitude, reflecting the impact that various environmental or experimental parameters have on half-lives in soil. In this work, we collected literature-reported half-lives for eleven pesticides along with associated metadata describing the environmental or experimental conditions under which they were derived. We then developed a multivariable framework to discover relationships between the half-lives and associated metadata. We first compared data for the herbicide atrazine collected from 95 laboratory and 65 field studies. We discovered that atrazine application history and soil texture were the parameters that have the largest influence on the observed half-lives in both types of studies. We then extended the analysis to include ten additional pesticides with data collected exclusively from laboratory studies. We found that, when data were available, pesticide application history and biomass concentrations were always positively associated with half-lives. The relevance of other parameters varied among the pesticides, but in some cases the variability could be explained by the physicochemical properties of the pesticides. For example, we found that the relative significance of the organic carbon content of soil for determining half-lives depends on the relative solubility of the pesticide. Altogether, our analyses highlight the reciprocal influence of both environmental parameters and intrinsic physicochemical properties for determining half-lives in soil. (C) 2018 Elsevier Ltd. All rights reserved

pH-Dependent Biotransformation of Ionizable Organic Micropollutants in Activated Sludge

Removal of micropollutants (MPs) during activated sludge treatment can mainly be attributed to biotransformation and sorption to sludge flocs, whereby the latter process is known to be of minor importance for polar organic micropollutants. In this work, we investigated the influence of pH on the biotransformation of MPs with cationic-neutral speciation in an activated sludge microbial community. We performed batch biotransformation, sorption control, and abiotic control experiments for 15 MPs with cationic-neutral speciation, one control MP with neutral-anionic speciation, and two neutral MPs at pHs 6, 7, and 8. Biotransformation rate constants corrected for sorption and abiotic processes were estimated from measured concentration time series with Bayesian inference. We found that biotransformation is pH-dependent and correlates qualitatively with the neutral fraction of the ionizable MPs. However, a simple speciation model based on the assumption that only the neutral species is efficiently taken up and biotransformed by the cells tends to overpredict the effect of speciation. Therefore, additional mechanisms such as uptake of the ionic species and other more complex attenutation mechanisms are discussed. Finally, we observed that the sorption coefficients derived from our control experiments were small and showed no notable pH-dependence. From this we conclude that pH-dependent removal of polar, ionizable organic MPs in activated sludge systems is less likely an effect of pH-dependent sorption but rather of pH-dependent biotransformation. The latter has the potential to cause marked differences in the removal of polar, ionizable MPs at different operational pHs during activated sludge treatment

Micropollutant Biotransformation Kinetics Associate with WWTP Process Parameters and Microbial Community Characteristics

The objective of this work was to identify relevant wastewater treatment plant (WWTP) parameters and underlying microbial processes that influence the biotransformation of a diverse set of micropollutants. To do this, we determined biotransformation rate constants for ten organic micropollutants in batch reactors seeded with activated sludge from ten diverse WWTPs. The estimated biotransformation rate constants for each compound ranged between one and four orders of magnitude among the ten WWTPs. The biotransformation rate constants were tested for statistical associations with various WWTP process parameters, <i>amoA</i> transcript abundance, and acetylene-inhibited monooxygenase activity. We determined that (i) ammonia removal associates with oxidative micropollutant biotransformation reaction rates; (ii) archaeal but not bacterial <i>amoA</i> transcripts associate with both ammonia removal and oxidative micropollutant biotransformation reaction rates; and (iii) the activity of acetylene-inhibited monooxygenases (including ammonia monooxygenase) associates with ammonia removal and the biotransformation rate of isoproturon, but does not associate with all oxidative micropollutant biotransformations. In combination, these results lead to the conclusion that ammonia removal and <i>amoA</i> transcript abundance can potentially be predictors of oxidative micropollutant biotransformation reactions, but that the biochemical mechanism is not necessarily linked to ammonia monooxygenase activity