Systematic Handling of Environmental Fate Data for Model Development-Illustrated for the Case of Biodegradation Half-Life Data.

The assessment of environmental hazard indicators such as persistence, mobility, toxicity, or bioaccumulation of chemicals often results in highly variable experimental outcomes. Persistence is particularly affected due to a multitude of influencing environmental factors, with biodegradation experiments resulting in half-lives spanning several orders of magnitude. Also, half-lives may lie beyond the limits of reliable half-life quantification, and the number of available data points per substance may vary considerably, requiring a statistically robust approach for the characterization of data. Here, we apply Bayesian inference to address these challenges and characterize the distributions of reported soil half-lives. Our model estimates the mean, standard deviation, and corresponding uncertainties from a set of reported half-lives experimentally obtained for a single substance. We apply our inference model to 893 pesticides and pesticide transformation products with experimental soil half-lives of varying data quantity and quality, and we infer the half-life distribution for each compound. By estimating average half-lives, their experimental variability, and the uncertainty of the estimations, we provide a reliable data source for building predictive models, which are urgently needed by regulatory authorities to manage existing chemicals and by industry to design benign, nonpersistent chemicals. Our approach can be readily adapted for other environmental hazard indicators.

Gamified online surveys: Assessing experience with self-determination theory.

We developed four online interfaces supporting citizen participation in decision-making. We included (1) learning loops (LLs), good practice in decision analysis, and (2) gamification, to enliven an otherwise long and tedious survey. We investigated the effects of these features on drop-out rate, perceived experience, and basic psychological needs (BPNs): autonomy, competence, and relatedness, all from self-determination theory. We also investigated how BPNs and individual causality orientation influence experience of the four interfaces. Answers from 785 respondents, representative of the Swiss German-speaking population in age and gender, provided insightful results. LLs and gamification increased drop-out rate. Experience was better explained by the BPN satisfaction than by the interface, and this was moderated by respondents' causality orientations. LLs increased the challenge, and gamification enhanced the social experience and playfulness. LLs frustrated all three needs, and gamification satisfied relatedness. Autonomy and relatedness both positively influenced the social experience, but competence was negatively correlated with challenge. All observed effects were small. Hence, using gamification for decision-making is questionable, and understanding individual variability is a prerequisite; this study has helped disentangle the diversity of responses to survey design options.

Bioprinting and plastic compression of large pigmented and vascularized human dermo-epidermal skin substitutes by means of a new robotic platform.

Extensive availability of engineered autologous dermo-epidermal skin substitutes (DESS) with functional and structural properties of normal human skin represents a goal for the treatment of large skin defects such as severe burns. Recently, a clinical phase I trial with this type of DESS was successfully completed, which included patients own keratinocytes and fibroblasts. Yet, two important features of natural skin were missing: pigmentation and vascularization. The first has important physiological and psychological implications for the patient, the second impacts survival and quality of the graft. Additionally, accurate reproduction of large amounts of patient's skin in an automated way is essential for upscaling DESS production. Therefore, in the present study, we implemented a new robotic unit (called SkinFactory) for 3D bioprinting of pigmented and pre-vascularized DESS using normal human skin derived fibroblasts, blood- and lymphatic endothelial cells, keratinocytes, and melanocytes. We show the feasibility of our approach by demonstrating the viability of all the cells after printing in vitro, the integrity of the reconstituted capillary network in vivo after transplantation to immunodeficient rats and the anastomosis to the vascular plexus of the host. Our work has to be considered as a proof of concept in view of the implementation of an extended platform, which fully automatize the process of skin substitution: this would be a considerable improvement of the treatment of burn victims and patients with severe skin lesions based on patients own skin derived cells.

Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2.

BACKGROUND: The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change. OBJECTIVES: We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases. METHODS: We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence. RESULTS: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. DISCUSSION: To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Metabolomic Profiling and Toxicokinetics Modeling to Assess the Effects of the Pharmaceutical Diclofenac in the Aquatic Invertebrate Hyalella azteca.

The exposure of ecologically critical invertebrate species to biologically active pharmaceuticals poses a serious risk to the aquatic ecosystem. Yet, the fate and toxic effects of pharmaceuticals on these nontarget aquatic invertebrates and the underlying mechanisms are poorly studied. Herein, we investigated the toxicokinetic (TK) processes (i.e., uptake, biotransformation, and elimination) of the pharmaceutical diclofenac and its biotransformation in the freshwater invertebrate Hyalella azteca. We further employed mass spectrometry-based metabolomics to assess the toxic effects of diclofenac on the metabolic functions of H. azteca exposed to environmentally relevant concentrations (10 and 100 µg/L). The TK results showed a quick uptake of diclofenac by H. azteca (maximum internal concentration of 1.9 µmol/kg) and rapid formation of the conjugate diclofenac taurine (maximum internal concentration of 80.6 µmol/kg), indicating over 40 times higher accumulation of diclofenac taurine than that of diclofenac in H. azteca. Depuration kinetics demonstrated that the elimination of diclofenac taurine was 64 times slower than diclofenac in H. azteca. Metabolomics results suggested that diclofenac inhibited prostaglandin synthesis and affected the carnitine shuttle pathway at environmentally relevant concentrations. These findings shed light on the significance of the TK process of diclofenac, especially the formation of diclofenac taurine, as well as the sublethal effects of diclofenac on the bulk metabolome of H. azteca. Combining the TK processes and metabolomics provides complementary insights and thus a better mechanistic understanding of the effects of diclofenac in aquatic invertebrates.

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high.

Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2-4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate infection incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.

Predictive models using "cheap and easy" field measurements: Can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions?

The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH4+-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R2 from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH4+-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required.

Evaluation of an in vitro assay to screen for the immunotoxic potential of chemicals to fish.

A wide variety of environmental contaminants has been shown to disrupt immune functions of fish and may compromise their defense capability against pathogens. Immunotoxic effects, however, are rarely considered in ecotoxicological testing strategies. The aim of this study was to systematically evaluate the suitability of an in vitro immuno-assay using selected fish immune parameters to screen for chemicals with known immunotoxic potential and to differentiate them from non-immunotoxicants. Non-stimulated and lipopolysaccharide-stimulated head kidney leukocytes of rainbow trout (Oncorhynchus mykiss) were exposed for 3 h or 19 h to chemicals with different modes of action. As immune parameters, phagocytosis activity, oxidative burst activity and cytokine transcription (IL-1ß, TNFα, IL-10) were examined, accompanied by in silico modelling. The immunotoxicants dexamethasone, benzo(a)pyrene, ethinylestradiol and bisphenol A significantly altered the immune parameters at non-cytotoxic concentrations whereas diclofenac had only weak effects. However, the two baseline chemicals with no known immunotoxic potential, butanol and ethylene glycol, caused significant effects, too. From our results it appears that the in vitro fish leukocyte assay as performed in the present study has only a limited capacity for discriminating between immunotoxicants and non-immunotoxicants.

Ex-ante quantification of nutrient, total solids, and water flows in sanitation systems.

To prioritise sustainable sanitation systems in strategic sanitation planning, indicators such as local appropriateness or resource recovery have to be known at the pre-planning phase. The quantification of resource recovery remains a challenge because existing substance flow models require large amounts of input data and can therefore only be applied for a few options at a time for which implementation examples exist. This paper aims to answer two questions: How can we predict resource recovery and losses of sanitation systems ex-ante at the pre-planning phase? And how can we do this efficiently to consider the entire sanitation system option space? The approach builds on an existing model to create all valid sanitation systems from a set of conventional and emerging technologies and to evaluate their appropriateness for a given application case. It complements the previous model with a Substance Flow Model (SFM) and with transfer coefficients from a technology library to quantify nutrients (phosphorus and nitrogen), total solids (as an indicator for energy and organics), and water flows in sanitation systems ex ante. The transfer coefficients are based on literature data and expert judgement. Uncertainties resulting from the variability of literature data or ignorance of experts are explicitly considered, allowing to assess the robustness of the model output. Any (future) technologies or additional products can easily be added to the library. The model is illustrated with a small didactic example showing how 12 valid system configurations are generated from a few technologies, and how substance flows, recovery ratios, and losses to soil, air, and water are quantified considering uncertainties. The recovery ratios vary between 0 and 28% for phosphorus, 0-10% for nitrogen, 0-26% for total solids, and 0-12% for water. The uncertainties reflect the high variability of the literature data but are comparable to those obtained in studies using a conventional post-ante material flow analysis (generally about 30% variability at the scale of a an urban area). Because the model is fully automated and based on literature data, it can be applied ex-ante to a large and diverse set of possible sanitation systems as shown with a real application case. From the 41 technologies available in the library, 101,548 systems are generated and substance flows are modelled. The resulting recovery ratios range from nothing to almost 100%. The two examples also show that recovery depend on technology interactions and has therefore to be assessed for all possible system configurations and not at the single technology level only. The examples also show that there exist trade-offs among different types of reuse (e.g. energy versus nutrients) or different sustainability indicators (e.g. local appropriateness versus resource recovery). These results show that there is a need for such an automated and generic approach that provides recovery data for all system configurations already at the pre-planning phase. The approach presented enables to integrate transparently the best available knowledge for a growing number of sanitation technologies into a planning process. The resulting resource recovery and loss ratios can be used to prioritise resource efficient systems in sanitation planning, either for the pre-selection or the detailed evaluation of options using e.g. MCDA. The results can also be used to guide future development of technology and system innovations. As resource recovery becomes more relevant and novel sanitation technologies and system options emerge, the approach presents itself as a useful tool for strategic sanitation planning in line with the Sustainable Development Goals (SDGs).

Comparative analysis of sanitation systems for resource recovery: Influence of configurations and single technology components.

Resource recovery and emissions from sanitation systems are critical sustainability indicators for strategic urban sanitation planning. In this context, sanitation systems are the most often structured using technology-driven templates rather than performance-based sustainability indicators. In this work, we answer two questions: Firstly, can we estimate generic resource recovery and loss potentials and their uncertainties for a diverse and large set of sanitation systems? And secondly, can we identify technological aspects of sanitation systems that indicate a better overall resource recovery performance? The aim is to obtain information that can be used as an input into any strategic planning process and to help shape technology development and system design for resource recovery in the future. Starting from 41 technologies, which include novel and conventional options, we build 101,548 valid sanitation system configurations. For each system configuration we quantify phosphorus, nitrogen, total solids, and water flows and use that to calculate recovery potentials and losses to the environment, i.e. the soil, air, or surface water. The four substances cover different properties and serve as a proxy for nutrient, organics, energy, and water resources. For modelling the flows ex-ante, we use a novel approach to consider a large range of international literature and expert data considering uncertainties. Thus all results are generic and can therefore be used as input into any strategic planning process or to help guide future technology development. A detailed analysis of the results allows us to identify factors that influence recovery and losses. These factors include the type of source, the length of systems, and the level of containment in storage and treatment. The factors influencing recovery are related to interactions of different technologies in a system which shows the relevance of a modelling approach that allows to look at all possible system configurations systematically. Based on our analysis, we developed five recommendations for the optimization of resource recovery: (i) prioritize short systems that close the loop at the lowest possible level; (ii) separate waste streams as much as possible, because this allows for higher recovery potentials; (iii) use storage and treatment technologies that contain the products as much as possible, avoid leaching technologies (e.g. single pits) and technologies with high risk of volatilization (e.g. drying beds); (iv) design sinks to optimise recovery and avoid disposal sinks; and (v) combine various reuse options for different side streams (e.g. urine diversion systems that combine reuse of urine and production of biofuel from faeces).

Estimating black soldier fly larvae biowaste conversion performance by simulation of midgut digestion.

Black soldier fly larvae treatment is an emerging technology for the conversion of biowaste into potentially more sustainable and marketable high-value products, according to circular economy principles. Unknown or variable performance for different biowastes is currently one challenge that prohibits the global technology up-scaling. This study describes simulated midgut digestion for black soldier fly larvae to estimate biowaste conversion performance. Before simulation, the unknown biowaste residence time in the three midgut regions was determined on three diets varying in protein and non-fiber carbohydrate content. For the static in vitro model, diet residence times of 15 min, 45 min, and 90 min were used for the anterior, middle, and posterior midgut region, respectively. The model was validated by comparing the ranking of diets based on in vitro digestion products to the ranking found in in vivo feeding experiments. Four artificial diets and five biowastes were digested using the model, and diet digestibility and supernatant nutrient contents were determined. This approach was able to distinguish broadly the worst and best performing rearing diets. However, for some of the diets, the performance estimated based on in vitro results did not match with the results of the feeding experiments. Future studies should try to establish a stronger correlation by considering fly larvae nutrient requirements, hemicellulose digestion, and the diet/gut microbiota. In vitro digestion models could be a powerful tool for academia and industry to increase conversion performance of biowastes with black soldier fly larvae.

How Urban Storm- and Wastewater Management Prepares for Emerging Opportunities and Threats: Digital Transformation, Ubiquitous Sensing, New Data Sources, and Beyond - A Horizon Scan.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a "pull" of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Passive samplers to quantify micropollutants in sewer overflows: accumulation behaviour and field validation for short pollution events.

Contaminants in sewer overflows can contribute to exceedances of environmental quality standards, thus the quantification of contaminants during rainfall events is of relevance. However, monitoring is challenged by i) high spatiotemporal variability of contaminants in events of hard-to-predict durations, and ii) a large number of remote sites, which would imply enormous efforts with traditional sampling equipment. Therefore, we evaluate the applicability of passive samplers (Empore styrene-divinylbenzene reverse phase sulfonated (SDB-RPS)) to monitor a set of 13 polar organic contaminants. We present calibration experiments at high temporal resolution to assess the rate limiting accumulation mechanisms for short events (<36 h), report parameters for typical sewer conditions and compare passive samplers with composite water samples in a field study (three locations, total 10 events). With sampling rates of 0.35-3.5 L/d for 1 h reference time, our calibration results indicate a high sensitivity of passive samplers to sample short, highly variable sewer overflows. The contaminant uptake kinetic shows a fast initial accumulation, which is not well represented with the typical first-order model. Our results indicate that mass transfer to passive samplers is either controlled by the water boundary layer and the sorbent, or by the sorbent alone. Overall, passive sampler concentration estimates are within a factor 0.4 to 3.1 in comparison to composite water samples in the field study. We conclude that passive samplers are a promising approach to monitor a large number of discharge sites although it cannot replace traditional stormwater quality sampling in some cases (e.g. exact load estimates, high temporal resolution). Passive samplers facilitate identifying and prioritizing locations that may require more detailed investigations.

Quantifying physical disintegration of faeces in sewers: Stochastic model and flow reactor experiments.

We present a novel stochastic model for quantifying gross solids (GS) physical disintegration under varying turbulent flow conditions and used a unique experimental setup for model calibration and validation. The stochastic deterioration model predicts faeces size evolution over time. It conceptually entails the two main processes of solid fragmentation, namely breakage and erosion. Model parameters were calibrated on synthetic faeces and validated with real human ones. A cylindrical reactor was used, where turbulent flow was forced by an array of water jets and the physical disintegration of the faeces was monitored using a high speed camera. Image analysis of breakage experiments obtained under backlight illumination allowed determination of the evolution of the solids' size over time. The flow field in the reactor was characterised by particle image velocimetry (PIV) using tracer particles seeded into the water. We found different disintegration behaviours depending on turbulence intensity and water content of the solid. In conditions of low shear stress, dense solids hardly disintegrated. Generally, the model predictions mirrored the broad range in the solids disintegration rate imparted by the high variability in flow conditions and in solids characteristics. It is expected that, similar to our experiments, also in real sewer systems both flow conditions and solid characteristics are highly variable and the stochastic model can be tailored to capture this variability. We thus anticipate that the model can be integrated into existing sewer models predicting sewer flows and solids' movement. From these, shear stress, flow velocities and transport of individual solids can be inferred. The integration of the present solids disintegration model may provide better predictions of hot-spots for solids accumulation and blockages in sewers.

Generation of sanitation system options for urban planning considering novel technologies.

The identification of appropriate sanitation systems is particularly challenging in developing urban areas where local needs are not met by conventional solutions. While structured decision-making frameworks such as Community-Led Urban Environmental Sanitation (CLUES) can help facilitate this process, they require a set of sanitation system options as input. Given the large number of possible combinations of sanitation technologies, the generation of a good set of sanitation system options is far from trivial. This paper presents a procedure for generating a set of locally appropriate sanitation system options, which can then be used in a structured decision-making process. The systematic and partly automated procedure was designed (i) to enhance the reproducibility of option generation; (ii) to consider all types of conventional and novel technologies; (iii) to provide a set of sanitation systems that is technologically diverse; and (iv) to formally account for uncertainties linked to technology specifications and local conditions. We applied the procedure to an emerging small town in Nepal. We assessed the appropriateness of 40 technologies and generated 17,955 appropriate system options. These were classified into 16 system templates including on-site, urine-diverting, biogas, and blackwater templates. From these, a subset of 36 most appropriate sanitation system options were selected, which included both conventional and novel options. We performed a sensitivity analysis to evaluate the impact of different elements on the diversity and appropriateness of the set of selected sanitation system options. We found that the use of system templates is most important, followed by the use of a weighted multiplicative aggregation function to quantify local appropriateness. We also show that the optimal size of the set of selected sanitation system options is equal to or slightly greater than the number of system templates. As novel technologies are developed and added to the already large portfolio of technology options, the procedure presented in this work may become an essential tool for generating and exploring appropriate sanitation system options.

Parameter estimation of hydrologic models using a likelihood function for censored and binary observations.

Observations of a hydrologic system response are needed to accurately model system behaviour. Nevertheless, often very few monitoring stations are operated because collecting such reference data adequately and accurately is laborious and costly. It has been recently suggested to use observations not only from dedicated flow meters but also from simpler sensors, such as level or event detectors, which are available more frequently but only provide censored information. Binary observations can be considered as extreme censoring. It is still unclear, however, how to use censored observations most effectively to learn about model parameters. To this end, we suggest a formal likelihood function that incorporates censored observations, while accounting for model structure deficits and uncertainty in input data. Using this likelihood function, the parameter inference is performed within the Bayesian framework. We demonstrate the implementation of our methodology on a case study of an urban catchment, where we estimate the parameters of a hydrodynamic rainfall-runoff model from binary observations of combined sewer overflows. Our results show, first, that censored observations make it possible to learn about model parameters, with an average decrease of 45% in parameter standard deviation from prior to posterior. Second, the inference substantially improves model predictions, providing higher Nash-Sutcliffe efficiency. Third, the gain in information largely depends on the experimental design, i.e. sensor placement. Given the advent of Internet of Things, we foresee that the plethora of censored data promised to be available can be used for parameter estimation within a formal Bayesian framework.

Influence of Different Sewer Biofilms on Transformation Rates of Drugs.

To estimate drug consumption more reliably, wastewater-based epidemiology would benefit from a better understanding of drug residue stability during in-sewer transport. We conducted batch experiments with real, fresh wastewater and sewer biofilms. Experimental conditions mimic small to medium-sized gravity sewers with a relevant ratio of biofilm surface area to wastewater volume (33 m2 m-3). The influences of biological, chemical, and physical processes on the transformation of 30 illicit drug and pharmaceutical residues were quantified. Rates varied among locations and over time. Three substances were not stable-that is, >20% transformation, mainly due to biological processes-at least for one type of tested biofilm for a residence time ≤2 h: amphetamine, 6-acetylcodeine, and 6-monoacetylmorphine. Cocaine, ecgonine methyl ester, norcocaine, cocaethylene, and mephedrone were mainly transformed by chemical hydrolysis and, hence, also unstable in sewers. In contrast, ketamine, norketamine, O-desmethyltramadol, diclofenac, carbamazepine, and methoxetamine were not substantially affected by in-sewer processes under all tested conditions and residence times up to 12 h. Our transformation rates include careful quantification of uncertainty and can be used to identify situations in which specific compounds are not stable. This will improve accuracy and uncertainty estimates of drug consumption when applied to the back-calculation.

Statistical failure models for water distribution pipes - A review from a unified perspective.

This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective. The way the comparison is structured provides the information needed by scientists and practitioners to choose a suitable failure model for their specific needs. The models are presented in a novel framework consisting of: 1) Clarification of model assumptions. The models originally formulated in different mathematical forms are all presented as failure rate. This enables to see differences and similarities across the models. Furthermore, we present a new conceptual failure rate that an optimal model would represent and to which the failure rate of each model can be compared. 2) Specification of the detailed data assumptions required for unbiased model calibration covering the structure and completeness of the data. 3) Presentation of the different types of probabilistic predictions available for each model. Nine different models and their variations or further developments are presented in this review. For every model an overview of its applications published in scientific journals and the available software implementations is provided. The unified view provides guidance to model selection. Furthermore, the model comparison presented herein enables to identify areas where further research is needed.

A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates.

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific micropollutants. This work is an important step towards developing tools to predict biotransformation rates in WWTPs based on taxonomic composition.

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.