A comprehensive, open-source data model for wastewater-based epidemiology.

The recent SARS-COV-2 pandemic has sparked the adoption of wastewater-based epidemiology (WBE) as a low-cost way to monitor the health of populations. In parallel, the pandemic has encouraged researchers to openly share their data to serve the public better and accelerate science. However, environmental surveillance data are highly dependent on context and are difficult to interpret meaningfully across sites. This paper presents the second iteration of the Public Health Environmental Surveillance Open Data Model (PHES-ODM), an open-source dictionary and set of data tools to enhance the interoperability of environmental surveillance data and enable the storage of contextual (meta)data. The data model describes how to store environmental surveillance program data, metadata about measurements taken on various specimens (water, air, surfaces, sites, populations) and data about measurement protocols. The model provides software tools that support the collection and use of PHES-ODM formatted data, including performing PCR calculations and data validation, recording data into input templates, generating wide tables for analysis, and producing SQL database definitions. Fully open-source and already adopted by institutions in Canada, the European Union, and other countries, the PHES-ODM provides a path forward for creating robust, interoperable, open datasets for environmental public health surveillance for SARS-CoV-2 and beyond.

Machine learning for modeling N2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability.

Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating N2O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N2O emissions hold substantial potential. However, so far, a comprehensive approach is still overlooked, impeding the advancement of full-scale application. Therefore, this study develops a comprehensive approach for using machine learning to perform online process modeling of N2O emissions. The approach is tested on a long-term N2O emission dataset from a full-scale WWTP. Uniquely, the proposed approach emphasizes not just model accuracy, but it also considers model complexity, computational speed, and interpretability, equipping operators with the insights needed for informed corrective actions. Algorithms with varying levels of complexity and interpretability including k-Nearest Neighbors (kNN), decision trees, ensemble learning models, and deep neural networks (DNN) were considered. Furthermore, a parametric multivariate outlier removal method was adjusted to account for data statistical distributions, significantly reducing data loss. By employing an effective feature selection methodology, a trade-off between data acquisition, model performance, and complexity was found, reducing the number of features by 40 % and decreasing data collection cost, model complexity and computational burden without significant effect on modeling accuracy. The best performing models are kNN (R2 = 0.88), AdaBoost (R2 = 0.94), and DNN (R2 = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N2O mitigation decisions.

Assessing the equivalence of WRRF regulations using dynamic model simulations.

A wide diversity of regulatory practices for wastewater resource recovery plants exists throughout the world. This contribution aims to highlight the implications of choosing particular permitting structures and investigate the equivalence of effluent standards in terms of limit values and compliance assessment specifications. These factors heavily affect the true performance that a treatment plant has to attain and thus the required plant capacity and operation. The dynamic simulations executed in this work, based on a realistic case study and three selected permits from China, The Netherlands and the USA, show the impact of certain compliance specifications like sampling frequency, averaging and tolerable permit exceedances leading to differences in the required design capacity of more than 250% for the same wastewater to be treated. The results also reveal clear differences between permits in their capacity to handle excess variability. The latter is important to avoid overdesign, i.e., when further investment in treatment capacity would result only in marginal effluent quality gains, as well as to create a safe space for testing innovative technologies or ways of operation that might otherwise trigger compliance issues.

Wastewater-based epidemiology: the crucial role of viral shedding dynamics in small communities.

Background: Wastewater surveillance (WWS) of pathogens is a rapidly evolving field owing to the 2019 coronavirus disease pandemic, which brought about a paradigm shift in public health authorities for the management of pathogen outbreaks. However, the interpretation of WWS in terms of clinical cases remains a challenge, particularly in small communities where large variations in pathogen concentrations are routinely observed without a clear relation to clinical incident cases. Methods: Results are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021. We developed a numerical model based on viral kinetics reduction functions to consider both prevalent and incident cases to interpret the WWS data in light of the reported clinical cases in the six surveyed communities. Results: The use of the proposed numerical model with a viral kinetics reduction function drastically increased the interpretability of the WWS data in terms of the clinical cases reported for the surveyed community. In line with our working hypothesis, the effects of viral kinetics reduction modeling were more important in small communities than in larger communities. In all but one of the community cases (where it had no effect), the use of the proposed numerical model led to a change from a +1.5% (for the larger urban center, Quebec City) to a +48.8% increase in the case of a smaller community (Drummondville). Conclusion: Consideration of prevalent and incident cases through the proposed numerical model increases the correlation between clinical cases and WWS data. This is particularly the case in small communities. Because the proposed model is based on a biological mechanism, we believe it is an inherent part of any wastewater system and, hence, that it should be used in any WWS analysis where the aim is to relate WWS measurement to clinical cases.

Sidestream bio-P and mainstream anammox in a BNR process with upstream carbon capture.

The integration of biological phosphorus removal (bio-P) and shortcut nitrogen removal (SNR) processes is challenging because of the conflicting demands on influent carbon: SNR allows for upstream carbon diversion, but this reduction of influent carbon (especially volatile fatty acids [VFAs]) prevents or limits bio-P. The objective of this study was to achieve SNR, either via partial nitritation/anammox (PNA) or partial denitrification/anammox (PdNA), simultaneously with biological phosphorus removal in a process with upstream carbon capture. This study took place in a pilot scale A/B process with a sidestream bio-P reactor and tertiary anammox polishing. Despite low influent rbCOD concentrations from the A-stage effluent, bio-P occurred in the B-stage thanks to the addition of A-stage WAS fermentate to the sidestream reactor. Nitrite accumulation occurred in the B-stage via partial denitrification and partial nitritation (NOB out-selection), depending on operational conditions, and was removed along with ammonia by the tertiary anammox MBBR, with the ability to achieve effluent TIN less than 2 mg/L. PRACTITIONER POINTS: A sidestream reactor with sufficient fermentate addition enables biological phosphorus removal in a B-stage system with little-to-no influent VFA. Enhanced biological phosphorus removal is not inhibited by intermittent aeration and is stable at a wide range of process SRTs. Partial nitritation and partial denitrification are viable routes to produce nitrite within an A/B process with sidestream bio-P, for downstream anammox in a polishing MBBR.

An improved 1D reactive Bürger-Diehl settler model for secondary settling tank denitrification.

An improved 1D reactive settler model is pursued in order to increase the understanding of reactive settling processes and obtain a better prediction of the nitrogen mass balance in wastewater treatment systems. The developed model is based on the 1D Bürger-Diehl settler model with compression function and the Activated Sludge Model No. 1 biological reactions. Specific attention was paid in the model development phase to optimal selection of settling velocity functions and integration of the correct clarifier geometry. A unique measurement campaign was carried out with different operational scenarios to quantify the denitrification in a secondary settling tank. A detailed step-wise calibration effort demonstrated that by choosing an appropriate settling velocity function (power-law structure) and considering the true clarifier geometry allows to accurately capture the biomass concentration profile, total sludge mass, sludge blanket height, and the reaction rates. The resulting model is able to accurately describe total suspended solids (TSS) and nitrate concentration profiles throughout a settling tank under different operational conditions. As such the model can be applied in further scenario analysis and system optimization. PRACTITIONER POINTS: A unique measurement campaign was carried out to obtain detailed data for a reactive settler model development. A 1-D reactive settler model is developed based on the Bürger-Diehl framework including ASM1 biokinetics and the clarifier geometry. An extensive calibration and model selection effort was performed. The model accurately predicts measured concentration profiles in the settling tank. The developed model can be integrated in a plant-wide model to properly calculate the nitrogen mass balance of a WRRF.

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review.

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00835-w.

Influence of MBBR carrier geometrical properties and biofilm thickness restraint on biofilm properties, effluent particle size distribution, settling velocity distribution, and settling behaviour.

The relatively poor settling characteristics of particles produced in moving bed biofilm reactor (MBBR) outline the importance of developing a fundamental understanding of the characterization and settleability of MBBR-produced solids. The influence of carrier geometric properties and different levels of biofilm thickness on biofilm characteristics, solids production, particle size distribution (PSD), and particle settling velocity distribution (PSVD) is evaluated in this study. The analytical ViCAs method is applied to the MBBR effluent to assess the distribution of particle settling velocities. This method is combined with microscopy imaging to relate particle size distribution to settling velocity. Three conventionally loaded MBBR systems are studied at a similar loading rate of 6.0 g/(m2 •day) and with different carrier types. The AnoxK™ K5 carrier, a commonly used carrier, is compared to so-called thickness-restraint carriers, AnoxK™ Z-carriers that are newly designed carriers to limit the biofilm thickness. Moreover, two levels of biofilm thickness, 200 µm and 400 µm, are studied using AnoxK™ Z-200 and Z-400 carriers. Statistical analysis confirms that K5 carriers demonstrated a significantly different biofilm mass, thickness, and density, in addition to distinct trends in PSD and PSVD in comparison with Z-carriers. However, in comparison of thickness-restraint carriers, Z-200 carrier results did not vary significantly compared to the Z-400 carrier. The K5 carriers showed the lowest production of suspended solids (0.7 ± 0.3 g-TSS/day), thickest biofilm (281.1 ± 8.7 µm) and lowest biofilm density (65.0 ± 1.5 kg/m3). The K5 effluent solids also showed enhanced settling behaviour, consisting of larger particles with faster settling velocities.

Mainstream short-cut N removal modelling: current status and perspectives.

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N2O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications.

An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methods.

Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications.

The transition of WRRF models to digital twin applications.

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm.

Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process.

Carbonaceous vs. total biochemical oxygen demand as a basis for WRRF design and performance monitoring.

The standard 5-day biochemical oxygen demand (BOD5 ) measurement of water quality is used widely as a design parameter for water resource recovery facilities (WRRFs). This measure usually includes a component of nitrogenous oxygen demand (NOD) that can cause oversizing of biological processes and under-evaluation of process capacity. Carbonaceous BOD (CBOD5 ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD5 and therefore should be used as a basis for sizing aerobic treatment processes. Nitrogenous oxygen demand or reduced nitrogen content should be used as a loading and process performance parameter for nitrogen removal processes. PRACTITIONER POINTS: Oxygen demand for aerobic biodegradation reactions typically is divided into two major categories-carbonaceous biochemical oxygen demand (CBOD) and nitrogenous oxygen demand (NOD). Use of BOD5 as a design parameter and CBOD5 as an effluent water quality parameter distorts the true performance and loading rate capacity of a treatment plant. Carbonaceous BOD (CBOD5 ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD5 and therefore should be used as a basis for sizing and evaluating the performance of aerobic treatment processes. Nitrogenous oxygen demand or reduced nitrogen content should be used as a loading and process performance parameter for nitrogen removal processes.

Effects of ferric-phosphate forms on phosphorus release and the performance of anaerobic fermentation of waste activated sludge.

Five ferric-phosphate (Fe(III)Ps) with amorphous or crystalline structures were added to waste activated sludge (WAS) for anaerobic fermentation, aiming to investigate effects of Fe(III)Ps forms on phosphorus (P) release and the performance of WAS fermentation. The results revealed that the Fe(III) reduction rate of hexagonal-FePO4 was faster than that of monoclinic-FePO4·2H2O, thanks to its lower crystal field stabilization energy. FePO4·nH2O was reduced to vivianite and part of the phosphate was released as orthophosphate (PO4-P). Giniite (Fe5(PO4)4(OH)3·2H2O) as an iron hydroxyphosphate was transformed to ßFe(III)Fe(II)(PO4)O-like compounds without PO4-P release. In addition, Fe(III)Ps had an adverse effect on the anaerobic fermentation of WAS. The specific hydrolysis rate constant and volatile fatty acids (VFAs) yield decreased by 38.4% and 41.9%, respectively, for the sludge sample with amorphous-FePO4·3H2O, which dropped the most. This study provides new insights into various forms of Fe(III)Ps performance during anaerobic fermentation and is beneficial to enhancing P recovery efficiency.

Nitrification in a biofilm-enhanced highly loaded aerated lagoon.

A full-scale biofilm-enhanced aerated lagoon using fixed submerged media was monitored using automated water quality monitoring stations over the span of one year to quantify its nitrification performance. The system was operating at a high organic loading rate averaging 5.8 g total CBOD5 /m2 of media per day (23.9 g total CBOD5 /m3 of lagoon per day), a total ammonia nitrogen loading rate averaging 0.9 g NH4 -N/m2  day (3.7 g NH4 -N/m3  day), and temperatures ranging from 1.6 to 20.8°C. The system showed an extended seasonal nitrification period compared with a simulated aerated lagoon system of the same dimensions. This extension of complete nitrification with approximately 1 month was observed in the fall despite the decrease of operating temperature down to 4°C. During this maximum nitrification period, substantial denitrification occurred, and the effluent un-ionized ammonia ratio was reduced. A temporary loss of nitrification was also experienced in relation to an episode of elevated suspended solids concentration. Measured biofilm characteristics, namely the detachment dynamics and the biofilm thickness, were used to explain this temporary nitrification loss. During wintertime, a low nitrate production was still observed, suggesting year-long retention of nitrifying bacteria in the biofilm. PRACTITIONER POINTS: Nitrification in a highly loaded biofilm-enhanced aerated lagoon is mainly affected by operating temperature. Maximum nitrification is observed during the warmer months and occurs even at high organic loading rates (>5 g CBOD5 /m2  day). Compared with a simulated suspended growth system, the biofilm-enhanced lagoon shows a significantly extended nitrification period. The extension is observed at the end of the summertime maximum nitrification period. Low amounts of nitrate still produced during winter in the biofilm-enhanced aerated lagoon suggest year-long retention of autotrophic nitrifying biomass in the biofilm. Nitrification in the biofilm-enhanced aerated lagoon is negatively impacted by the presence of important quantities of accumulated solids that resuspend when their digestion starts as temperature increases.

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence.

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

Dynamic grit chamber modelling: dealing with particle settling velocity distributions.

Grit chambers are meant to reduce the impact of inorganic particles on equipment and processes downstream. Despite their important role, characterization and modelling studies of these process units are scarce, leading to a lack of knowledge and suboptimal operation. Thus, this study presents the first dynamic model, based on mass balances and particle settling velocity distributions, for use in a water resource recovery facility (WRRF) simulator for design and optimization of grit removal units.

Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions.

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates. This may not have the capacity to separate the different kinetics. Sequential degradation of substrates is theoretically supported by microbial capacity and the composite nature of substrates (bioaccessibility concept). However, this has not been experimentally assessed. Sequential chemical fractionation has been successfully used to define inputs for an anaerobic digestion model. In this paper, sequential extractions of organic substrates were evaluated in order to compare both models. By removing each fraction (from the most accessible to the least accessible fraction) from three different substrates, anaerobic incubation tests showed that for physically structured substrates, such as activated sludge and wheat straw, sequential approach could better describe experimental results, while this was less important for homogeneous materials such as pulped fruit. Following this, anaerobic incubation tests were performed on five substrates. Cumulative methane production was modelled by the simultaneous and sequential approaches. Results showed that the sequential model could fit the experimental data for all the substrates whereas simultaneous model did not work for some substrates.

Characterizing the settleability of grit particles.

Grit chambers are installed at the headworks of a water resource recovery facility (WRRF) to reduce the impact of grit particles to the equipment and processes downstream. This settling process should thus be designed and operated in an efficient way. Despite the importance of knowing settling characteristics for design and operation of grit chambers, previous grit definitions have been based only on particle size characteristics, and not on settling velocities. Thus, this study presents an evaluation of the performance of two promising settling velocity characterization methods, ViCAs and elutriation, to characterize wastewater particles in view of the design and the optimization of the efficiency of the grit removal unit. PRACTITIONER POINTS: Settling characteristics are the governing parameter for grit chamber design. Since grit particles are vastly heterogeneous, it is preferred to measure these characteristics directly rather than to estimate them from particle size (and assumptions of density, form factor, …). More detailed settling information about grit particles can improve grit chamber design and estimation of removal performance. Adapted ViCAs and elutriation methods for faster settling particles allow studying the particle settling characteristics in a grit chamber. These methods are simple, fast, and cheap and only require small wastewater samples. A relationship was found between the influent TSS concentration and the location of the PSVD curve, with higher TSS concentrations corresponding to higher settling velocities. It was demonstrated that the dynamics of the wastewater characteristics under dry, wet, and snowmelt weather conditions influence grit settling characteristics.