A framework for modelling in-sewer thermal-hydraulic dynamic anomalies driven by stormwater runoff and seasonal effects.

Rain-induced surface runoff and seasons lead to short- to medium-term anomalies in combined storm- and wastewater flows and temperatures, and influence treatment processes in wastewater resource recovery facilities (WRRF). Additionally, the implementation of decentralized heat recovery (HR) technologies for energy reuse in buildings affect energy-related processes across the urban water cycle and WRRFs heat inflows. However, quantitative insights on thermal-hydraulic dynamics in sewers at network scale and across different scales are very rare. To enhance the understanding of thermal-hydraulic dynamics and the water-energy nexus across the urban water cycle we present a modular framework that couples thermal-hydraulic processes: i) on the surface, ii) in the public sewer network, iii) in households (including in-building HR systems), and iv) in lateral connections. We validate the proposed framework using field measurements at full network scale, present modelling results of extended time periods to illustrate the effect of seasons and precipitation events simultaneously, and quantify the impact of decentralized HR devices on thermal-hydraulics. Simulation results suggest that the presented framework can predict temperature dynamics consistently all year long including short- to long-term variability of in-sewer temperature. The study provides quantitative evidence that the impact of household HR technologies on WRRF inflow heat budgets is reduced by approximately 20% during wet-weather periods in comparison to dry-weather conditions. The presented framework has potential to support multiple research initiatives that will improve the understanding of the water-energy nexus, pollutant dispersion and degradation, and support maintenance campaigns at network scale.

The value of human data annotation for machine learning based anomaly detection in environmental systems.

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

A distributed heat transfer model for thermal-hydraulic analyses in sewer networks.

Thermal-hydraulic considerations in urban drainage networks are essential to utilise available heat capacities from waste- and stormwater. However, available models are either too detailed or too coarse; fully coupled thermal-hydrodynamic modelling tools are lacking. To predict efficiently water-energy dynamics across an entire urban drainage network, we suggest the SWMM-HEAT model, which extends the EPA-StormWater Management Model with a heat-balance component. This enables conducting more advanced thermal-hydrodynamic simulation at full network scale than currently possible. We demonstrate the usefulness of the approach by predicting temperature dynamics in two independent real-world cases under dry weather conditions. We furthermore screen the sensitivity of the model parameters to guide the choice of suitable parameters in future studies. Comparison with measurements suggest that the model predicts temperature dynamics adequately, with RSR values ranging between 0.71 and 1.1. The results of our study show that modelled in-sewer wastewater temperatures are particularly sensitive to soil and headspace temperature, and headspace humidity. Simulation runs are generally fast; a five-day period simulation at high temporal resolution of a network with 415 nodes during dry weather was completed in a few minutes. Future work should assess the performance of the model for different applications and perform a more comprehensive sensitivity analysis under more scenarios. To facilitate the efficient estimation of available heat budgets in sewer networks and the integration into urban planning, the SWMM-HEAT code is made publicly available.

In-building heat recovery mitigates adverse temperature effects on biological wastewater treatment: A network-scale analysis of thermal-hydraulics in sewers.

Heat recovery from wastewater is a robust and straightforward strategy to reduce water-related energy consumption. Its implementation, though, requires a careful assessment of its impacts across the entire wastewater system as adverse effects on the water and resource recovery facility and competition among heat recovery strategies may arise. A model-based assessment of heat recovery from wastewater therefore implies extending the modeling spatial scope, with the aim of enabling thermal-hydraulic simulations from the household tap along its entire flow path down to the wastewater resource recovery facility. With this aim in mind, we propose a new modeling framework interfacing thermal-hydraulic simulations of (i) households, (ii) private lateral connections, and (iii) the main public sewer network. Applying this framework to analyze the fate of wastewater heat budgets in a Swiss catchment, we find that heat losses in lateral connections are large and cannot be overlooked in any thermal-hydraulic analysis, due to the high-temperature, low-flow wastewater characteristics maximizing heat losses to the environment. Further, we find that implementing shower drain heat recovery devices in 50% of the catchment's households lower the wastewater temperature at the recovery facility significantly less - only 0.3 K - than centralized in-sewer heat recovery, due to a significant thermal damping effect induced by lateral connections and secondary sewer lines. In-building technologies are thus less likely to adversely affect biological wastewater treatment processes. The proposed open-source modeling framework can be applied to any other catchment. We thereby hope to enable more efficient heat recovery strategies, maximizing energy harvesting while minimising impacts on biological wastewater treatment.

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.

Modeling in-sewer transformations at catchment scale - implications on drug consumption estimates in wastewater-based epidemiology.

To which extent illicit drugs are transformed during in-sewer transport, depends on a number of factors: i) substance-specific transformation rates, ii) environmental conditions, iii) point of discharge (location of drug user) and iv) sewer network properties, primarily hydraulic residence time (HRT) and the ratio of biofilm contact area to wastewater volume (A/Veq). Assessing associated uncertainties typically requires numerous simulations. Therefore, we propose a new two-step modeling framework: 1) Quantify hydrodynamic conditions. This computationally demanding step was performed once in SWMM to derive HRT and A/Veq for each potential point of discharge (node) in three catchments of different size. 2) Estimate biomarker loss. In this step, Monte Carlo simulations were performed for defined scenarios. Depending on assumptions about drug user distribution and prevalence, a number of nodes was sampled. For each node an empirical first-order transformation model was applied with flow-path-corresponding HRT and A/Veq from step 1. Biotic and abiotic transformation rates were sampled from distributions combining variability of different biofilms. In our modeling study, median losses were >30% for amphetamine, 6-monoacetylmorphine and 6-acetylcodeine in all three catchments with high uncertainty (5%-100% loss), which would imply a systematic underestimation of consumption when neglecting in-sewer processes. Median losses for 21 other investigated biomarkers were <10% with different uncertainty ranges - "no substantial transformation" was confirmed for nine substances in a real sewer segment with a 2-h residence time. Transferability of these results must be tested for other catchments. To further reduce uncertainty, mainly additional knowledge on transformation rates, particularly in biofilm, and their distribution across a sewer network is needed to update model input objectively. Our approach allows efficient testing and, furthermore, can be expanded for many other human biomarkers. Accounting for biomarker stability during in-sewer transport will avoid biased estimates and further improve wastewater-based epidemiology.

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.

Assessing antibiotic resistance of microorganisms in sanitary sewage.

The release of antimicrobial substances into surface waters is of growing concern due to direct toxic effects on all trophic levels and the promotion of antibiotic resistance through sub-inhibitory concentration levels. This study showcases (1) the variation of antibiotics in sanitary sewage depending on different timescales and (2) a method to assess the antibiotic resistance based on an inhibition test. The test is based on the measurement of the oxygen uptake rate (OUR) in wastewater samples with increasing concentrations of the selected antibiotic agents. The following antibiotics were analysed in the present study: clarithromycin (CLA) was selected due to its high toxicity to many microorganisms (low EC50), ciprofloxacin (CIP) which is used to generally fight all bacteria concerning interstitial infections and doxycyclin (DOX) having a broad spectrum efficacy. Results show that CLA inhibited the OUR by approximately 50% at a concentration of about 10 mg L⁻¹, because Gram-negative bacteria such as Escherichia coli are resistant, whereas CIP inhibited about 90% of the OUR at a concentration equal to or greater than 10 mg L⁻¹. In the case of DOX, a moderate inhibition of about 38% at a concentration of 10 mg L⁻¹ was identified, indicating a significant antibiotic resistance. The results are consistent with the corresponding findings from the Clinical and Laboratory Standards Institute. Thus, the presented inhibition test provides a simple but robust alternative method to assess antibiotic resistance in biofilms instead of more complex clinical tests.

Description and design considerations of a randomized clinical trial investigating the effect of a multidisciplinary cognitive-behavioural intervention for patients undergoing lumbar spinal fusion surgery.

BACKGROUND: The ideal rehabilitation strategy following lumbar spinal fusion surgery has not yet been established. This paper is a study protocol, describing the rationale behind and the details of a cognitive-behavioural rehabilitation intervention for lumbar spinal fusion patients based on the best available evidence. Predictors of poor outcome following spine surgery have been identified to provide targets for the intervention, and the components of the intervention were structured in accordance with the cognitive-behavioural model. The study aims to compare the clinical and economical effectiveness of a cognitive-behavioural rehabilitation strategy to that of usual care for patients undergoing lumbar spinal fusion surgery. METHODS/DESIGN: The study is a randomized clinical trial including 96 patients scheduled for lumbar spinal fusion surgery due to degenerative disease or spondylolisthesis. Patients were recruited in the period October 2011 to July 2013, and the follow-up period is one year from date of surgery. Patients are allocated on a 1:2 ratio (control: intervention) to either treatment as usual (control group), which implies surgery and the standard postoperative rehabilitation, or in addition to this, a patient education focusing on pain behaviour and pain coping (intervention group). It takes place in a hospital setting, and consists of six group-based sessions, managed by a multidisciplinary team of health professionals.The primary outcomes are disability (Oswestry Disability Index) and sick leave, while secondary outcomes include coping (Coping Strategies Questionnaire), fear-avoidance belief (Fear Avoidance Belief Questionnaire), pain (Low Back Pain Rating Scale, pain index), mobility during hospitalization (Cumulated Ambulation Score), generic health-related quality of life (EQ-5D) and resource use. Outcomes are measured using self report questionnaires, medical records and national registers. DISCUSSION: It is expected that the intervention can provide better functional outcome, less pain and earlier return to work after lumbar spinal fusion surgery. By combining knowledge and evidence from different knowledge areas, the project aims to provide new knowledge that can create greater consistency in patient treatment. We expect that the results can make a significant contribution to development of guidelines for good rehabilitation of patients undergoing lumbar spinal fusion. TRIAL REGISTRATION: Current Controlled Trials ISRCTN42281022.