Socio-technical networks of infrastructure management: Network concepts and motifs for studying digitalization, decentralization, and integrated management.

Networked infrastructure systems - including energy, transportation, water, and wastewater systems - provide essential services to society. Globally, these services are undergoing major transformative processes such as digitalization, decentralization, or integrated management. Such processes not only depend on technical changes in infrastructure systems but also include important social and socio-technical dimensions. In this article, we propose a socio-technical network perspective to study the ensemble of social actors and technical elements involved in an infrastructure system, and their complex relations. We conceptualize structurally explicit socio-technical networks of networked infrastructure systems based on methodological considerations from network analysis and draw on concepts from socio-technical system theories and social-ecological network studies. Based on these considerations, we suggest analytical methods to study basic network concepts such as density, reciprocity, and centrality in a socio-technical network. We illustrate socio-technical motifs, i.e., meaningful sub-structures in socio-technical networks of infrastructure management. Drawing on these, we describe how infrastructure systems can be analyzed in terms of digitalization, decentralization, and integrated management from a socio-technical network perspective. Using the example of urban wastewater systems, we illustrate an empirical application of our approach. The results of an empirical case study in Switzerland demonstrate the potential of socio-technical networks to promote a deeper understanding of complex socio-technical relations in networked infrastructure systems. We contend that such a deeper understanding could improve management practices of infrastructure systems and is becoming even more important for enabling future data-driven, decentralized, and more integrated infrastructure management.

Barriers to the digital transformation of infrastructure sectors.

Digital technologies can be important to policy-makers and public servants, as these technologies can increase infrastructure performance and reduce environmental impacts. For example, utilizing data from sensors in sewer systems can improve their management, which in turn may result in better surface water quality. Whether such big data from sensors is utilized is, however, not only a technical issue, but also depends on different types of social and institutional conditions. Our article identifies individual, organizational, and institutional barriers at the level of sub-states that hinder the evaluation of data from sewer systems. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare 23 Swiss sub-states and find that two barriers at different levels can each hinder data evaluation on their own. More specifically, either a lack of vision at the individual level or a lack of resources at the organizational level hinder the evaluation of data. Findings suggest that taking into account different levels is crucial for understanding digital transformation in public organizations.

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.

Commercial microwave links for urban drainage modelling: The effect of link characteristics and their position on runoff simulations.

Commercial microwave links (CMLs), radio connections widely used in telecommunication networks, can provide path-integrated quantitative precipitation estimates (QPEs) which could complement traditional precipitation observations. This paper assesses the ability of individual CMLs to provide relevant QPEs for urban rainfall-runoff simulations and specifically investigates the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km2) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for urban rainfall-runoff predictions and subsequent modelling performance is assessed by comparing simulated runoffs with measured stormwater discharges. The results show that model performance is related to both the sensitivity of CML to rainfall and CML position. The bias propagated into the runoff predictions is inversely proportional to CML path length. The effect of CML position is especially pronounced during heavy rainfalls, when QPEs from shorter CMLs, located within or close to catchment boundaries, better reproduce runoff dynamics than QPEs from longer CMLs extending far beyond the catchment boundaries. Interestingly, QPEs averaged from all available CMLs best reproduce the runoff temporal dynamics. Adjusting CML QPEs to three rain gauges located 2-3 km outside of the catchment substantially reduces the bias in CML QPEs. Unfortunately, this compromises the ability of the CML QPEs to reproduce runoff dynamics during heavy rainfalls. More experimental case studies are necessary to provide specific recommendations on CML preprocessing methods tailored to different water management tasks, catchments and CML networks.

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.

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.

Adaptation in hindsight: dynamics and drivers shaping urban wastewater systems.

Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25-80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.

Commercial microwave links instead of rain gauges: fiction or reality?

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

Challenges of surveying wastewater drug loads of small populations and generalizable aspects on optimizing monitoring design.

AIMS: Quantifying illicit drug loads through wastewater analysis (WWA) is an alternative approach to estimating population drug use. This study investigated the variability of daily drug loads in wastewater and their relationships to environmental factors over an extended period to: (i) explore the suitability of WWA in small populations and (ii) optimize the monitoring design for future studies. DESIGN, SETTING, PARTICIPANTS: Daily wastewater samples (n = 1369 consecutive days) from a German village with approximately 7160 inhabitants. MEASUREMENTS: Samples were analysed for cocaine and benzoylecgonine with liquid chromatography-tandem mass spectrometry. Time-series analysis was used to explore the effects of weather and other factors on daily cocaine loads. Subsampling was used to assess monitoring design. FINDINGS: Cocaine loads [mean = 652 mgCOC /day, standard deviation (SD) = 498 mgCOC /day] increased over the study period, with higher values during winter and spring. Despite high day-to-day variation, loads were significantly higher during weekends [+161 mgCOC /day, 95% confidence interval (CI) = 115-207 mgCOC /day, P < 10(-4) ] and days with frost (+114 mgCOC /day, 95% CI = 6-223 mgCOC /day, P = 0.039) or snow (+150 mgCOC /day, 95% CI = 46-253 mgCOC /day, P = 0.005). Annual means estimated from 1-week periods were subject to approximately 60% relative error. Increasing sample size and changing sampling from consecutive days to stratified random decreased this uncertainty. CONCLUSIONS: Day-to-day variation and seasonality of drug loads from the few long-term wastewater studies available to date suggest that up to 56 stratified random samples are required to obtain reliable (expected uncertainty around 10%) annual estimates of drug loads. Successfully assessing changes in consumption patterns or relationships to external factors requires larger sample sizes than estimating annual means, which holds true for high-prevalence drugs in small communities and low-prevalence drugs in big cities.

Comparing illicit drug use in 19 European cities through sewage analysis.

The analysis of sewage for urinary biomarkers of illicit drugs is a promising and complementary approach for estimating the use of these substances in the general population. For the first time, this approach was simultaneously applied in 19 European cities, making it possible to directly compare illicit drug loads in Europe over a 1-week period. An inter-laboratory comparison study was performed to evaluate the analytical performance of the participating laboratories. Raw 24-hour composite sewage samples were collected from 19 European cities during a single week in March 2011 and analyzed for the urinary biomarkers of cocaine, amphetamine, ecstasy, methamphetamine and cannabis using in-house optimized and validated analytical methods. The load of each substance used in each city was back-calculated from the measured concentrations. The data show distinct temporal and spatial patterns in drug use across Europe. Cocaine use was higher in Western and Central Europe and lower in Northern and Eastern Europe. The extrapolated total daily use of cocaine in Europe during the study period was equivalent to 356 kg/day. High per capita ecstasy loads were observed in Dutch cities, as well as in Antwerp and London. In general, cocaine and ecstasy loads were significantly elevated during the weekend compared to weekdays. Per-capita loads of methamphetamine were highest in Helsinki and Turku, Oslo and Budweis, while the per capita loads of cannabis were similar throughout Europe. This study shows that a standardized analysis for illicit drug urinary biomarkers in sewage can be applied to estimate and compare the use of these substances at local and international scales. This approach has the potential to deliver important information on drug markets (supply indicator).

Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements.

To check the effectiveness of campaigns preventing drug abuse or indicating local effects of efforts against drug trafficking, it is beneficial to know consumed amounts of substances in a high spatial and temporal resolution. The analysis of drugs of abuse in wastewater (WW) has the potential to provide this information. In this study, the reliability of WW drug consumption estimates is assessed and a novel method presented to calculate the total uncertainty in observed WW cocaine (COC) and benzoylecgonine (BE) loads. Specifically, uncertainties resulting from discharge measurements, chemical analysis and the applied sampling scheme were addressed and three approaches presented. These consist of (i) a generic model-based procedure to investigate the influence of the sampling scheme on the uncertainty of observed or expected drug loads, (ii) a comparative analysis of two analytical methods (high performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry), including an extended cross-validation by influent profiling over several days, and (iii) monitoring COC and BE concentrations in WW of the largest Swiss sewage treatment plants. In addition, the COC and BE loads observed in the sewage treatment plant of the city of Berne were used to back-calculate the COC consumption. The estimated mean daily consumed amount was 107 ± 21 g of pure COC, corresponding to 321 g of street-grade COC.

Assessing wastewater micropollutant loads with approximate Bayesian computations.

Wastewater production, like many other engineered and environmental processes, is inherent stochastic in nature and requires the use of complex stochastic models, for example, to predict realistic patterns of down-the-drain chemicals or pharmaceuticals and personal care products. Up until now, a formal method of statistical inference has been lacking for many of those models, where explicit likelihood functions were intractable. In this Article, we investigate Approximate Bayesian Computation (ABC) methods to infer important parameters of stochastic environmental models. ABC methods have been recently suggested to perform model-based inference in a Bayesian setting when model likelihoods are analytically or computationally intractable and have not been applied to environmental systems analysis or water quality modeling before. In a case study, we investigate the performance of three different algorithms to infer the number of wastewater pulses contained in three high-resolution data series of benzotriazole and total nitrogen loads in sewers. We find that all algorithms perform well and that the uncertainty in the inferred number of corresponding wastewater pulses varies between 6% and 28%. In our case, the results are more sensitive to substance characteristics than to catchment properties. Although the application of ABC methods requires careful tuning and attention to detail, they have a great general potential to update stochastic model parameters with monitoring data and improve their predictive capabilities.

Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: are your conclusions valid? A critical review.

The analysis of 87 peer-reviewed journal articles reveals that sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in sewers and sewage treatment plant influents is mostly carried out according to existing tradition or standard laboratory protocols. Less than 5% of all studies explicitly consider internationally acknowledged guidelines or methods for the experimental design of monitoring campaigns. In the absence of a proper analysis of the system under investigation, the importance of short-term pollutant variations was typically not addressed. Therefore, due to relatively long sampling intervals, potentially inadequate sampling modes, or insufficient documentation, it remains unclear for the majority of reviewed studies whether observed variations can be attributed to "real" variations or if they simply reflect sampling artifacts. Based on results from previous and current work, the present paper demonstrates that sampling errors can lead to overinterpretation of measured data and ultimately, wrong conclusions. Depending on catchment size, sewer type, sampling setup, substance of interest, and accuracy of analytical method, avoidable sampling artifacts can range from "not significant" to "100% or more" for different compounds even within the same study. However, in most situations sampling errors can be reduced greatly, and sampling biases can be eliminated completely, by choosing an appropriate sampling mode and frequency. This is crucial, because proper sampling will help to maximize the value of measured data for the experimental assessment of the fate of PPCPs as well as for the formulation and validation of mathematical models. The trend from reporting presence or absence of a compound in "clean" water samples toward the quantification of PPCPs in raw wastewater requires not only sophisticated analytical methods but also adapted sampling methods. With increasing accuracy of chemical analyses, inappropriate sampling increasingly represents the major source of inaccuracy. A condensed step-by-step Sampling Guide is proposed as a starting point for future studies.

Estimating sewer leakage from continuous tracer experiments.

Direct measurements of sewer leakage with continuous dosing of tracers are often considered too imprecise for practical applications. However, no mathematical framework for data analysis is reported in literature. In this paper, we present an improved experimental design and data analysis procedure together with a comprehensive framework for uncertainty assessment. Test runs in a 700 m-long watertight sewer showed no significant bias and a very high precision of the methodology. The standard error in the results was assessed to 2.6% of the labeled flow with a simplified model. It could be reduced to 1.2% when a dynamic data analysis procedure was applied. The major error contribution was caused by transient transport phenomena, which suggests that careful choosing of the experimental time is more important than the choice of a very specific tracer substance. Although the method is not intended to replace traditional CCTV inspections, it can provide complementary information for rational rehabilitation planning.