Indicator based multi-criteria decision support systems for wastewater treatment plants.

Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.

Systematic assessment of wastewater resource circularity and sustainable value creation.

The circular use of wastewater has attracted significant attention in recent years. However, there is a lack of universal definitions and measurement tools that are required to achieve the circular economy's full potential. Therefore, a methodology was developed using three indicator typologies, namely resource flow, circular action, and sustainability indicators, to facilitate a robust and holistic circularity assessment. The method uses value propositions to integrate the assessment of intrinsic circularity performance with consequential circularity impacts, by quantifying sustainable value creation (using techniques such as life cycle assessment or cost-benefit analysis). Assessment method capabilities were exhibited by applying the defined steps to a wastewater treatment plant, comparing conventional and novel photobioreactor technologies. The resource flow indicator taxonomy results highlight improved outflow circularity, renewable energy usage, and economic efficiency of the novel system. Action indicators revealed that the photobioreactor technology was successful at achieving its defined circular goals. Lastly, sustainability indicators quantified a reduction of carbon footprint by two thirds and eutrophication by 41%, a M€ 0.5 per year increase of economic value, and that disability adjusted life year impacts are 58% lower. This supports that improving wastewater system circularity using photobioreactor technology results in environmental, economic, and social value for stakeholders.

Tracing wastewater resources: Unravelling the circularity of waste using source, destination, and quality analysis.

Current circularity assessment terminology restricts application to wastewater processes due to the focus on technical systems. Waste stream and wastewater discharge circularity definitions lead to paradoxical assessments that generate results of little value for evidence-based decision making. Therefore, a classification approach was developed to measure inflow and outflow circularity of the main wastewater resource flows using the principle of traceability, adopting the attitude that not all waste is created equally. Applying it to a wastewater treatment plant (12,000 m3/d load) showed how upstream agricultural, industrial, and human practices impact downstream treatment, and the effectiveness of resource cycling within the natural environment. Industrial actions increasing fossil carbon concentration (400 m3/d effluent at 1000 mgC/l) reduced inflow and outflow circularity by 16 % and 10.6 % respectively, as secondary and sludge treatment fossil emissions increase significantly. Alternatively, changes to human and agricultural practices (50 % reduction of detergent and synthetic fertiliser usage) improved phosphorus inflow and nitrogen outflow circularity by 5.2 % and 20.1 % respectively. This approach can educate and assign responsibility to water users for developing robust circular economy policy, shifting the pattern from promoting circularity to discouraging linear actions, overcoming the shared economic and environmental burden of linear water use.

Assessing circularity of multi-sectoral systems under the Water-Energy-Food-Ecosystems (WEFE) nexus.

The Multi-Sectoral Water Circularity Assessment (MSWCA) is a methodological framework developed for circularity assessment of the Water-Energy-Food-Ecosystems nexus. It involves five methodological steps and includes an indicators list for the selection of case-specific indicators. This study expands the MSWCA to provide a systematic approach for selecting indicators, considering system's circular actions and multi-functionality, the capture of implemented changes, the three CE principles and the sustainable development goals. Furthermore, this study differentiates between benchmark and dynamic circularity assessment and applies the expanded MSWCA in a water system of the HYDROUSA H2020 project. The benchmark assessment indicates that the HYDROUSA system achieves a 75% increase of water circularity, 76-80% increase of nutrients circularity and 14% reduction of operational `carbon footprint compared to the baseline scenario. The dynamic assessment highlights that additional measures can improve the system's circularity performance (e.g. water circularity can reach 94%) and mitigate risks occurring from uncontrollable changes.

Where is the greatest potential for resource recovery in wastewater treatment plants?

The restorative and regenerative ability of the circular economy has led to the rapid growth of this concept over the past decade, as it facilitates the broadly adopted principles of sustainable development and beyond, through restorative and regenerative actions. The water sector is poised to benefit from this transition, due to its intrinsic circularity and the resources it handles, predominantly found in wastewater, that are valuable and critical. Currently, the vast range of resource recovery technologies coupled with few industrial examples hinder strategic decision making. Resource recovery on a regional scale improves market share and mitigates investment risk, therefore, a structured approach has been developed for the selection of priority technologies to act as a guide for strategic planning. A representative UK wastewater model acts as the baseline, with multi-criteria analysis used to select resources and create an enhanced resource recovery scenario. It was found that implementing the recovery of 5 'priority resources' (and technology pathways) increased nitrogen and phosphorus recovery by 68% and 71%, respectively. Lastly, the need for a cross-cutting approach for the holistic assessment of circular solutions is discussed.

Water Cycle and Circular Economy: Developing a Circularity Assessment Framework for Complex Water Systems.

Water - the most vital resource, negatively affected by the linear pattern of growth - still tries to find its positioning within the emerging concept of circular economy. Fragmented, sectorial circularity approaches hide the risk of underestimating both the preservation of and impacts to water resources and natural capital. In this study, a game changing circularity assessment framework is developed (i.e. MSWCA). The MSWCA follows a multi-sectoral systems approach, symbiotically managing key water-related socio-economic (i.e. urban water, agro-food, energy, industry and waste handling) and non-economic (i.e. natural environment) sectors. The MSWCA modelling framework enables the investigation of the feedback loops between the nature-managed and human-managed systems to assess water and water-related resources circularity. The three CE principles lie at the core of the developed framework, enabling the consideration of physical, technical, environmental and economic aspects. An indicators database is further developed, including all the relevant data requirements, as well as existing and newly developed indicators assessing multi-sectoral systems' circularity. The MSWCA framework is conceptually applied to a fictional city, facilitating its understanding and practical use.

A knowledge discovery framework to predict the N2O emissions in the wastewater sector.

Data Analytics is being deployed to predict the dissolved nitrous oxide (N2O) concentration in a full-scale sidestream sequence batch reactor (SBR) treating the anaerobic supernatant. On average, the N2O emissions are equal to 7.6% of the NH4-N load and can contribute up to 97% to the operational carbon footprint of the studied nitritation-denitritation and via-nitrite enhanced biological phosphorus removal process (SCENA). The analysis showed that average aerobic dissolved N2O concentration could significantly vary under similar influent loads, dissolved oxygen (DO), pH and removal efficiencies. A combination of density-based clustering, support vector machine (SVM), and support vector regression (SVR) models were deployed to estimate the dissolved N2O concentration and behaviour in the different phases of the SBR system. The results of the study reveal that the aerobic dissolved N2O concentration is correlated with the drop of average aerobic conductivity rate (spearman correlation coefficient equal to 0.7), the DO (spearman correlation coefficient equal to -0.7) and the changes of conductivity between sequential cycles. Additionally, operational conditions resulting in low aerobic N2O accumulation (<0.6 mg/L) were identified; step-feeding, control of initial NH4+ concentrations and aeration duration can mitigate the N2O peaks observed in the system. The N2O emissions during aeration shows correlation with the stripping of accumulated N2O from the previous anoxic cycle. The analysis shows that N2O is always consumed after the depletion of NO2- during denitritation (after the "nitrite knee"). Based on these findings SVM classifiers were constructed to predict whether dissolved N2O will be consumed during the anoxic and anaerobic phases and SVR models were trained to predict the N2O concentration at the end of the anaerobic phase and the average dissolved N2O concentration during aeration. The proposed approach accurately predicts the N2O emissions as a latent parameter from other low-cost sensors that are traditionally deployed in biological batch processes.

A decade of nitrous oxide (N2O) monitoring in full-scale wastewater treatment processes: A critical review.

Direct nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N2O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N2O calculation. The full-scale N2O monitoring campaigns help to expand our knowledge on the N2O production pathways and the triggering operational conditions of processes. The accurate N2O monitoring could help to find better process control solutions to mitigate N2O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N2O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N2O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N2O pathways and triggering operational conditions, techniques using operational data and N2O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N2O emissions and the development of emission factor (EF) databases; the N2O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N2O monitoring campaigns lasting less than one month, have reported N2O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N2O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N2O fluxes behaviour. The acquisition of reliable full-scale N2O monitoring data is significant for the calibration and validation of the mechanistic models -describing the N2O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N2O emission patterns. Finally, a gap between the identification of effective N2O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N2O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N2O data, and iii) better understanding of the trade-offs among N2O emissions, energy consumption and system performance to support the optimization of the WWTPs operation.

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N2O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N2O emissions, using Binary Segmentation. The dependencies between operating variables and N2O emissions fluctuated according to Spearman's rank correlation. The correlation between N2O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N2O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N2O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N2O fluxes. Additionally, the highest ranges of measured N2O fluxes belonged to clusters corresponding with NO3-N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N2O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N2O emissions in each sub-period and can be integrated into N2O emissions data processing at wastewater treatment plants.