Human health risk assessment framework for new water resource recovery-based bio-composite materials.

A new type of bio-composite material is being produced from water-recovered resources such as cellulose fibres from wastewater, calcite from the drinking water softening process, and grass and reed from waterboard sites. These raw materials may be contaminated with pathogens and chemicals such as Escherichia coli, heavy metals, and resin compounds. A novel risk assessment framework is proposed here, addressing human health risks during the production of new bio-composite materials. The developed framework consists of a combination of existing risk assessment methods and is based on three main steps: hazard identification, qualitative risk mapping, and quantitative risk assessment. The HAZOP and Event Tree Analysis methodologies were used for hazard identification and risk mapping stages. Then, human health risks were quantitatively assessed using quantitative chemical risk assessment, evaluating cancer and non-cancer risk, and quantitative microbial risk assessment. The deterministic and the stochastic approaches were performed for this purpose. The contamination of raw materials may pose human health concerns, resulting in cancer risk above the threshold. Microbial risk is also above the safety threshold. Additional analysis would be significant as future research to better assess the microbial risk in biocomposite production. The framework has been effectively used for chemical and microbial risk assessment.

Environmental risk assessment related to using resource recovery-based bio-composite materials in the aquatic environment with new laboratory leaching test data.

The concept of circular economy, aiming at increasing the sustainability of products and services in the water and other sectors, is gaining momentum worldwide. Driven by this concept, novel bio-composite materials produced by recovering resources from different parts of the water cycle are now manufactured in The Netherlands. The new materials are used for different products such as canal bank protection elements, as an alternative to similar elements made of hardwood. As much as these new materials are appealing from the sustainability point of view, they may leach toxic substances into the aquatic environment given some of their ingredients, e.g., cellulose recovered from wastewater treatment. Therefore, a methodology for the assessment of related environmental risks is needed and it does not exist currently. This paper addresses this knowledge gap by presenting a framework for this. The framework is based on European environmental risk assessment guidelines, and it includes four key steps: (i) hazard identification, (ii) dose-response modelling, (iii) exposure assessment and (iv) risk characterisation (i.e. assessment). As part of the first step, laboratory leaching tests were carried out to evaluate the potential release of specific chemical substances such as heavy metals and resin compounds into the aquatic environment. Laboratory test results were then used as input data to evaluate the risk of potential leaching from canal bank protection elements into surface water. A deterministic model was used first to identify the chemicals exceeding the guideline threshold. Subsequently, a stochastic model was applied to evaluate the environmental risks across a range of leachate concentrations and water velocities in the canal, thereby simulating a broader spectrum of possible situations. The risk analyses were conducted for four alternative bio-composite materials made of different ingredients, two different flow conditions (stagnant water and advective flow) in two types of canals (wide ditch and primary watercourse) and for two different water levels based on season conditions (summer and winter conditions). The results obtained from leaching tests identified Cu, Mn, Zn, styrene and furfuryl alcohol as potentially troublesome chemicals. In the case of stagnant water, the absence of a flow rate increases the residence time of the chemicals in the surface water, resulting in a higher PEC/PNEC (i.e. risk) value. However, under stagnant case conditions, environmental risks for all chemicals considered turned out to be below the safety threshold. In the advective case, the existence of a flow rate, even at low velocities simulating the conditions of 'almost no flow,' contributes to increased dilution, resulting in lower PEC/PNEC ratio values. The results presented here, even though representing real-case scenarios, are only indicative as these are based on laboratory leaching tests and a number of assumptions made. Additional field tests involving collecting and analysing water and sediment samples from the canal where the canal bank protection elements are located, over a prolonged period, are required to come up with more conclusive findings.

Limitations of a biokinetic model to predict the seasonal variations of nitrous oxide emissions from a full-scale wastewater treatment plant.

A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (N2O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N2O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N2O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N2O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N2O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA.

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining.

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, "antecedent precipitation history" and "seasonal time occurrence of rainfall," significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h.

A new approach to circularity assessment for a sustainable water sector: Accounting for environmental functional flows and losses.

Resource recovery solutions can reduce the water sector's resource use intensity. With many such solutions being proposed, an assessment method for effective decision-making is needed. The water sector predominantly deals with biogeochemical resources (e.g., nitrogen) that are different from technical resources (e.g., industrial coagulants) in three ways: (1) they move through the environment in natural cycles; (2) they fulfil different human and environmental functions; and (3) they are subject to substantial environmental losses. Whilst several circularity assessment methods exist for technical resources, biogeochemical resources have received less attention. To address this, a well-established material circularity indicator (MCI) method is modified. This is done by redefining the terms: restoration, regeneration, and linear flows to create a new circularity assessment approach. The new approach is demonstrated in a real-life case study involving treated wastewater (TW) fertigation. The new approach reveals that using the original MCI method underestimates the circularity of resource recovery solutions involving biogeochemical resources. This is because, in the original MCI method, only the flows that are reused/recycled for human functions can be considered circular, whereas, in the new approach, one also considers flows such as N2 emission and groundwater infiltration as circular flows. Even though these may not be reuse/recycle type flows, they still contribute towards future resource availability and, thus, towards sustainability. The modified assessment method shows that TW fertigation can significantly improve nitrogen and water circularity. However, careful planning of the fertigation schedule is essential since increasing fertigation frequency leads to lower water but higher nitrogen circularity. Additionally, collecting drainage water for reuse can improve nitrogen circularity. In conclusion, using the modified MCI approach, circularity can be assessed in a manner that is better aligned with sustainability.

Predictive heuristic control: Inferring risks from heterogeneous nowcast accuracy.

Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected, whilst performing poorly in quantifying rainfall depths. No relation was found in the nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks from nowcast data.

Deep learning for detecting macroplastic litter in water bodies: A review.

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.

Towards the long term implementation of real time control of combined sewer systems: a review of performance and influencing factors.

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this 'best-before' characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems.

A review of serious games for urban water management decisions: current gaps and future research directions.

Urban water management (UWM) is a complex problem characterized by multiple alternatives, conflicting objectives, and multiple uncertainties about key drivers like climate change, population growth, and increasing urbanization. Serious games are becoming a popular means to support decision-makers who are responsible for the planning and management of urban water systems. This is evident in the increasing number of articles about serious games in recent years. However, the effectiveness of these games in improving decision-making and the quality of their design and evaluation approaches remains unclear. To understand this better, in this paper, we identified 41 serious games covering the urban water cycle. Of these games, 15 were shortlisted for a detailed review. By using common rational decision-making and game design phases from literature, we evaluated and mapped how the shortlisted games contribute to these phases. Our research shows that current serious game applications have multiple limitations: lack of focus on executing the initial phases of decision-making, limited use of storytelling and adaptive game elements, use of low-quality evaluation design and explicit indicators to measure game outcomes, and lastly, lack of attention to cognitive processes of players playing the game. Addressing these limitations is critical for advancing purposeful game design supporting UWM.

Foul sewer model development using geotagged information and smart water meter data.

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Water quality modeling in sewer networks: Review and future research directions.

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

Developing a fuzzy logic-based risk assessment for groundwater contamination from well integrity failure during hydraulic fracturing.

Recent natural gas development by means of hydraulic fracturing requires a detailed risk analysis to eliminate or mitigate damage to the natural environment. Such geo-energy related subsurface activities involve complex engineering processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. This research seeks to develop a risk framework utilising data for quantitative numerical analysis and expert knowledge for qualitative analysis in the form of fuzzy logic, focusing on hydraulically fractured wells during the well stimulation stage applied to scenarios in the UK and Canada. New fault trees are developed for assessing cement failure in the vertical and horizontal directions, resulting in probabilities of failure of 3.42% and 0.84%, respectively. An overall probability of migration to groundwater during the well injection stage was determined as 0.0006%, compared with a Canadian case study which considered 0.13% of wells failed during any stage of the wells life cycle. It incorporates various data types to represent the complexity of hydraulic fracturing, encouraging a more complete and accurate analysis of risk failures which engineers can directly apply to old and new hydraulic fracturing sites without the necessity for extensive historic and probabilistic data. This framework can be extended to assess risk across all stages of well development, which would lead to a gap in the modelled and actual probabilities narrowing. The framework developed has relevance to other geo-energy related subsurface activities such as CO2 sequestration, geothermal, and waste fluid injection disposal.

A novel machine learning application: Water quality resilience prediction Model.

Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate. This study utilises Artificial Neural Network (ANN) to develop a novel application to predict water quality resilience to simplify resilience evaluation. The Fuzzy Analytic Hierarchy Process method is used to rank water basins based on their level of resilience and to identify the ones that demand prompt restoration strategies. The commonly used 'magnitude * duration of being in failure state' quantification method has been used to formulate and evaluate resilience. A 17-years long water quality dataset from the 22 water basins in the State of São Paulo, Brazil, was used to train and test the ANN model. The overall agreement between the measured and simulated WQI resilience values is satisfactory and hence, can be used by planners and decision makers for improved water management. Moreover, comparative analyses show similarities and differences between the 'level of criticalities' reported in each zone by Environment Agency of the state of São Paulo (CETESB) and by the resilience model in this study.

Predicting non-deposition sediment transport in sewer pipes using Random forest.

Sediment transport in sewers has been extensively studied in the past. This paper aims to propose a new method for predicting the self-cleansing velocity required to avoid permanent deposition of material in sewer pipes. The new Random Forest (RF) based model was implemented using experimental data collected from the literature. The accuracy of the developed model was evaluated and compared with ten promising literature models using multiple observed datasets. The results obtained demonstrate that the RF model is able to make predictions with high accuracy for the whole dataset used. These predictions clearly outperform predictions made by other models, especially for the case of non-deposition with deposited bed criterion that is used for designing large sewer pipes. The volumetric sediment concentration was identified as the most important parameter for predicting self-cleansing velocity.

Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems.

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R2, NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.

Using Complex Network Analysis for Optimization of Water Distribution Networks.

The optimization of water networks supports the decision-making process by identifying the optimal trade-off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto-optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude).

Non-deposition self-cleansing models for large sewer pipes.

Multiple models from the literature and experimental datasets have been developed and collected to predict sediment transport in sewers. However, all these models were developed for smaller sewer pipes, i.e. using experimental data collected on pipes with diameters smaller than 500 mm. To address this issue, new experimental data were collected on a larger, 595 mm pipe located in a laboratory at the University of los Andes. Two new self-cleansing models were developed using this dataset. Both models predict the sewer self-cleansing velocity for the cases of non-deposition with and without deposited bed. The newly developed and existing models were then evaluated and compared on the basis of the most recently collected and previously published datasets. Models were compared in terms of prediction accuracy measured by the root mean squared error and mean absolute percentage error. The results obtained show that in the existing literature, self-cleansing models tend to be overfitted, i.e. have a rather high prediction accuracy when applied to the data collected by the authors, but this accuracy deteriorates quickly when applied to the datasets collected by other authors. The newly developed models can be used for designing both small and large sewer pipes with and without deposited bed condition.

Assessing the global resilience of water quality sensor placement strategies within water distribution systems.

Water quality sensors are often spatially distributed in water distribution systems (WDSs) to detect contamination events and monitor quality parameters (e.g., chlorine residual levels), thereby ensuring safety of a WDS. The performance of a water quality sensor placement strategy (WQSPS) is not only affected by sensor spatial deployment that has been extensively analyzed in literature, but also by possible sensor failures that have been rarely explored so far. However, enumerating all possible sensor failure scenarios is computationally infeasible for a WQSPS with a large number of sensors. To this end, this paper proposes an evolutionary algorithm (EA) based method to systematically and efficiently investigate the WQSPS' global resilience considering all likely sensor failures. First, new metrics are developed in the proposed method to assess the global resilience of a WQSPS. This is followed by a proposal of an efficient optimization approach based on an EA to identify the values of global resilience metrics. Finally, the sensors within the WQSPS are ranked to identify their relative importance in maintaining the WQSPS's detection performance. Two real-world WDSs with four WQSPSs for each case study are used to demonstrate the utility of the proposed method. Results show that: (i) compared to the traditional global resilience analysis method, the proposed EA-based approach identifies improved values of global resilience metrics, (ii) the WQSPSs that deploy sensors close to large demand users are overall more resilient in handling sensor failures relative to other design solutions, thus offering important insight to facilitate the selection of WQSPSs, and (iii) sensor rankings based on the global resilience can identify those sensors whose failure would significantly reduce the WQSPS's performance thereby providing guidance to enable effective water quality sensor management and maintenance.

Exploring trade-offs among the multiple benefits of green-blue-grey infrastructure for urban flood mitigation.

Climate change is presenting one of the main challenges to our planet. In parallel, all regions of the world are projected to urbanise further. Consequently, sustainable development challenges will be increasingly concentrated in cities. A resulting impact is the increment of expected urban flood risk in many areas around the globe. Adaptation to climate change is an opportunity to improve urban conditions through the implementation of green-blue infrastructures, which provide multiple benefits besides flood mitigation. However, this is not an easy task since urban drainage systems are complex structures. This work focuses on a method to analyse the trade-offs when different benefits are pursued in stormwater infrastructure planning. A hydrodynamic model was coupled with an evolutionary optimisation algorithm to evaluate different green-blue-grey measures combinations. This evaluation includes flood mitigation as well as the enhancement of co-benefits. We confirmed optimisation as a helpful decision-making tool to visualise trade-offs among flood management strategies. Our results show that considering co-benefits enhancement as an objective boosts the selection of green-blue infrastructure. However, flood mitigation effectiveness can be diminished when extra benefits are pursued. Finally, we proved that combining green-blue-grey measures is particularly important in urban spaces when several benefits are considered simultaneously.

Assessing the Co-Benefits of green-blue-grey infrastructure for sustainable urban flood risk management.

Green-blue infrastructures in urban spaces offer several co-benefits besides flood risk reduction, such as water savings, energy savings due to less cooling usage, air quality improvement and carbon sequestration. Traditionally, these co-benefits were not included in decision making processes for flood risk management. In this work we present a method to include the monetary analysis of these co-benefits into a cost-benefits analysis of flood risk mitigation measures. This approach was applied to a case study, comparing costs and benefits with and without co-benefits. Different intervention strategies were considered, using green, blue and grey measures and combinations of them. The results obtained illustrate the importance of assessing co-benefits when identifying best adaptation strategies to improve urban flood risk management. Otherwise green infrastructure is likely to appear less efficient than more conventional grey infrastructure. Moreover, a mix of green, blue and grey infrastructures is likely to result in the best adaptation strategy as these three alternatives tend to complement each other. Grey infrastructure has good performance at reducing the risk of flooding, whilst green infrastructure brings in multiple additional benefits that grey infrastructure cannot offer.