Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

A novel mathematical template for developing fDOM probe fluorescence signal correction models for freshwaters.

The reliable application of field deployable fluorescent dissolved organic matter (fDOM) probes is hindered by several influencing factors which need to be compensated. This manuscript describes the corrections of temperature, pH, turbidity and inner filter effect on fluorescence signal of a commercial fDOM probe (fDOMs). For this, Australian waters with wide ranging qualities were selected, e.g. dissolved organic carbon (DOC) ranging from ∼1 to ∼30 mg/L, specific UV absorbance at 254 nm from ∼1 to ∼6 L/m/mg and turbidity from ∼1 to ∼ 350 FNU. Laboratory-based model calibration experiments (MCEs) were performed. A model template was developed and used for the development of the correction models. For each factor, data generated through MCEs were used to determine model coefficient (α) values by fitting the generated model to the experimental data. Four discrete factor models were generated by determination of a factor-specific α value. The α values derived for each water of the MCEs subset were consistent for each factor model. This indicated generic nature of the four α values across wide-ranging water qualities. High correlation between fDOMs and DOC were achieved after applying the four-factor compensation models to new data (r, 0.96, p < 0.05). Also, average biases (and %) between DOC predicted through fDOMs and actual DOC were decreased by applying the four-factor compensation model (from 3.54 (60.9%) to 1.28 (16.7%) mg/L DOC). These correction models were incorporated into a Microsoft EXCEL-based software termed EXOf-Correct for ready-to-use applications.

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema.

In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel's experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowledge domain, including the implicit knowledge. Specifically, 115 actual JHA documents and interviews with 18 JHA domain experts were analyzed and used as the source of knowledge for creating a new JHA knowledge base, namely the Job Hazard Analysis Knowledge Graph (JHAKG). To ensure the quality of the developed ontology, a systematic approach to ontology development called METHONTOLOGY was used in this process. The case study performed for validation purposes demonstrates that a JHAKG can operate as a knowledge base that answers queries regarding hazards, external factors, level of risks, and appropriate control measures to mitigate risks. As the JHAKG is a database of knowledge representing a large number of actual JHA cases previously developed and also implicit knowledge that has not been formalized in any explicit forms yet, the quality of JHA documents produced from queries to the database is expectedly higher than the ones produced by an individual safety manager in terms of completeness and comprehensiveness.

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review.

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

Development of an Optical Method to Monitor Nitrification in Drinking Water.

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH2Cl) residual. The decay of monochloramine releases ammonia (NH3), which is converted to nitrite (NO2-) and nitrate (NO3-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach.

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L-1.

Utilization of drinking water treatment sludge in concrete paving blocks: Microstructural analysis, durability and leaching properties.

The management of abundant drinking water treatment sludge (DWTS) in landfill remains an important issue. The reuse of DWTS as construction material could contribute to the development of greener concrete product and to mitigating the detrimental environment effect from excessive production of DWTS. This paper investigates the potential of using DWTS as sand replacement in Concrete Paving Blocks (CPB). Five CPB mixtures were designed and the replacement ratios of sand by DWTS were 0%, 5%, 10%, 15%, and 20%, by weight. Properties of CPB such as compressive strength, water absorption, abrasion resistance, sulfate attack and metal leachability were determined. The results indicated that above 10% of DWTS, the replacement was detrimental to such properties of the CPB. Microstructure analysis proved the addition of DWTS could result in ettringite formation and the interfacial transition zone (ITZ) between the cement matrix and DWTS was more porous than that of sand. In addition, the metal leachability test of CPB demonstrated that the addition of high-copper DWTS into CPB was safe.

Chloramine demand estimation using surrogate chemical and microbiological parameters.

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (Fm) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.

Developing a chloramine decay index to understand nitrification: A case study of two chloraminated drinking water distribution systems.

The management of chloramine decay and the prevention of nitrification are some of the critical issues faced by water utilities that use chloramine as a disinfectant. In this study, potential association between high performance size exclusion chromatography (HPSEC) data obtained with multiple wavelength Ultraviolet (UV) detection from two drinking water distribution systems in Australia and nitrification occurrence was investigated. An increase in the absorbance signal of HPSEC profiles with UV detection at λ=230nm between apparent molecular weights of 200 to 1000Da was observed at sampling sites that experienced rapid chloramine decay and nitrification while its absorbance signal at λ=254nm decreased. A chloramine decay index (C.D.I) defined as the ratio of area beneath the HPSEC spectra at two different wavelengths of 230 and 254nm, was used in assessing chloramine decay occurrences. The C.D.Is of waters at locations that experienced nitrification were consistently higher than locations not experiencing nitrification. A simulated laboratory study showed that the formation of nitrite/nitrate and/or soluble microbial products and/or the release of extracellular polymeric substances (EPS) during nitrification may contribute to the C.D.I. increase. These findings suggest that C.D.I derived from HPSEC with multiple wavelength UV detection could be an informative index to track the occurrence of rapid chloramine decay and nitrification.

Influence of coagulation mechanisms and floc formation on filterability.

Minimizing particles in water is a key goal for improving drinking water quality and safety. The media filtration process, as the last step of the solid-liquid separation process, is largely influenced by the characteristics of flocs, which are formed and controlled within the coagulation process. In a laboratory-based study, the impacts of the physical characteristics of flocs formed using aluminum sulfate on the filtration treatment of two comparative water samples were investigated using a photometric dispersion analyzer and a filterability apparatus. In general, the optimum dosage for maximizing filterability was higher than that for minimizing turbidity under neutral pH conditions. For a monomeric aluminum-based coagulant, the charge neutralization mechanism produced better floc characteristics, including floc growth speed and size, than the sweep flocculation mechanism. In addition, the charge neutralization mechanism showed better performance compared to sweep flocculation in terms of DOC removal and floc filterability improvement for both waters, and showed superiority in turbidity removal only when the raw water had high turbidity. For the different mechanisms, the ways that floc characteristics impacted on floc filterability also differed. The low variation in floc size distribution obtained under the charge neutralization mechanism resulted in the flocs being amenable to removal by filtration processes. For the sweep flocculation mechanism, increasing the floc size improved the settling ability of flocs, resulting in higher filter efficiency.

Characterisation of dissolved organic matter in stormwater using high-performance size exclusion chromatography.

Understanding the complexity of dissolved organic matter (DOM) in stormwater has drawn a lot of interest, since DOM from stormwater causes not only environmental impacts, but also worsens downstream aquatic quality associated with water supply and treatability. This study introduced and employed high-performance size exclusion chromatography (HPSEC) coupled with an ultraviolet-visible (UV-vis) diode array detector to assess changes in stormwater-associated DOM characteristics. Stormwater DOM was also analysed in relation to storm event characteristics, water quality and spectroscopic analysis. Statistical tools were used to determine the correlations within DOM and water quality measurements. Results showed that dissolved organic carbon (DOC) and UV absorbance at 254 nm (UV254) as conventional DOM parameters were found to be correlated well to the changes in stormwater quality during each of the three storm events studied. Both detector wavelengths (210 and 254 nm) and their ratio (A210/A254) were found to provide additional information on the physiochemical properties of stormwater-associated DOM. This study indicated that A210/A254 is an important parameter which could be used to estimate the DOM proportions of functional groups and conjugated carbon species. This study provided also an understanding of stormwater quality constituents through assessing variability and sensitivity for various parameters, and the additional information of rainfall characteristics on runoff quality data for a better understanding of parameter correlations and influences.

High-performance size exclusion chromatography with a multi-wavelength absorbance detector study on dissolved organic matter characterisation along a water distribution system.

This study examined the associations between dissolved organic matter (DOM) characteristics and potential nitrification occurrence in the presence of chloramine along a drinking water distribution system. High-performance size exclusion chromatography (HPSEC) coupled with a multiple wavelength detector (200-280nm) was employed to characterise DOM by molecular weight distribution, bacterial activity was analysed using flow cytometry, and a package of simple analytical tools, such as dissolved organic carbon, absorbance at 254nm, nitrate, nitrite, ammonia and total disinfectant residual were also applied and their applicability to indicate water quality changes in distribution systems were also evaluated. Results showed that multi-wavelength HPSEC analysis was useful to provide information about DOM character while changes in molecule weight profiles at wavelengths less than 230nm were also able to be related to other water quality parameters. Correct selection of the UV wavelengths can be an important factor for providing appropriate indicators associated with different DOM compositions. DOM molecular weight in the range of 0.2-0.5kDa measured at 210nm correlated positively with oxidised nitrogen concentration (r=0.99), and the concentrations of active bacterial cells in the distribution system (r=0.85). Our study also showed that the changes of DOM character and bacterial cells were significant in those sampling points that had decreases in total disinfectant residual. HPSEC-UV measured at 210nm and flow cytometry can detect the changes of low molecular weight of DOM and bacterial levels, respectively, when nitrification occurred within the chloraminated distribution system.

A comprehensive assessment of superabsorbent resin produced using modified quinoa husk and coal fly ash - Preparation, characterization and application.

About 200 million tons of coal fly ash (CFA) is not effectively used in China every year. To enhance the utilization of biomass waste quinoa husk (QH) and solid waste CFA and reduce the preparation cost of superabsorbent resin (SAR), a low-cost, biodegradable modified quinoa husk-g-poly (acrylic acid)/coal fly ash superabsorbent resin (MQH-g-PAA/CFA SAR) was synthesized using modified quinoa husk (MQH), acrylic acid and CFA and used to improve the drought resistance and fertilizer conservation ability of soil. The surface morphology and performance of SAR were characterized by Fourier transform infrared (FTIR) spectrometry, scanning electron microscopy (SEM), and thermogravimetric analysis (TGA), which provided evidence for improving the properties of SAR by grafting MQH and adding CFA. In addition, the synthesis conditions were studied and optimized, together with the contents of initiator, crosslinker, MQH, and CFA to acrylic acid as well as the neutralization degree of acrylic acid. After optimization, the optimum water absorbency of SAR in deionized water, tap water, and physiological saline was 1302, 356, and 91 g/g respectively. The swelling and water-retention mechanisms of SARs were analyzed by a dynamic model and the results were in good agreement with the experimental data. In the soil experiment, the addition of SAR improved the drought resistance ability of soil, and reduced the leaching loss of fertilizer in the soil (from 49.5 % to 36.7 %). Therefore, this material exhibits significant potential in the field of agriculture and offers a novel approach with economic benefit for the utilization of MQH and CFA.

Current trends and future directions of global research on wastewater to energy: a bibliometric analysis and review.

This paper presents a structured bibliometric analysis and review of the research publications recorded in the Web of Science database from 2000 to 2023 to methodically examine the landscape and development of the 'wastewater to energy' research field in relation to global trends, potential hotspots, and future research directions. The study highlights three main research themes in 'wastewater to energy', which are biogas production through anaerobic digestion of sewage sludge, methane generation from microbial wastewater treatment, and hydrogen production from biomass. The analysis reveals activated sludge, biochar, biomethane, biogas upgrading, hydrogen, and circular economy as key topics increasingly gaining momentum in recent research publications as well as representing potential future research directions. The findings also signify transformation to SDGs and circular economy practices, through the integration of on-site renewables and biogas upgrading for energy self-sufficiency, optimising energy recovery from wastewater treatment systems, and fostering research and innovation in 'wastewater to energy' supported by policy incentives. By shedding light on emerging trends, cross-cutting themes, and potential policy implications, this study contributes to informing both knowledge and practices of the 'wastewater to energy' research community.

Multistep, microvolume resin fractionation combined with 3D fluorescence spectroscopy for improved DOM characterization and water quality monitoring.

Conventionally, resin fractionation (RF) method has been widely used to characterize dissolved organic matter (DOM) found in different source waters based on general and broad DOM fractions grouping. In this study, a new refined method using multistep, microvolume resin fractionation combined with excitation emission matrix fluorescence spectroscopy (MSM-RF-EEMS) was developed for further isolation and characterization of subfractions within the primary DOM fractions separated from using the conventional RF method. Subsequently, its feasibility in indicating the occurrence of urban pollution in source waters was also assessed. Results from using the new MSM-RF-EEMS method strongly illustrated that several organic subfractions still exist within the regarded primary pure hydrophobic acid (HoA) fraction including the humic- and fulvic-like organic matters, tryptophan- and tyrosine-like proteins. It was found that by using the MSM-RF-EEMS method, the organic subfractions present within the primary DOM fraction could be easily identified and characterized. Further validation on the HoA fraction using the MSM-RF-EEMS method revealed that the constant association of EEM peak T1 (tryptophan) fraction could specifically be used to indicate the occurrence of urban pollution in source water. The correlation analysis on the presence of EEM peak T2 (tyrosine) fraction could be used as a supplementary proof to further verify the presence of urban pollution in source waters. These findings on using the presence of EEM peaks T1 and T2 within the primary HoA fraction would be significant and useful for developing a sensory device for online water quality monitoring.

Piezoresistivity and AC Impedance Spectroscopy of Cement-Based Sensors: Basic Concepts, Interpretation, and Perspective.

Cement-based sensors include conductive fillers to achieve a sensing capability based on the piezoresistivity phenomenon, in which the electrical resistivity changes with strain. The microstructural characterisation of cement-based sensors can be obtained using a promising non-destructive technique, such as AC impedance spectroscopy (ACIS), which has been recently used by many researchers. This paper reviews the fundamental concepts of piezoresistivity and ACIS in addition to the comparison of equivalent circuit models of cement-based sensors found in the literature. These concepts include piezoresistivity theory, factors affecting piezoresistivity measurement, resistance measurement methodology, strain/damage sensing, causes of piezoresistivity, theories of conduction, AC impedance spectroscopy theory, and the equivalent circuit model. This review aims to provide a comprehensive guide for researchers and practitioners interested in exploring and applying different techniques to self-sensing concrete.

Microbial Desalination Cell for Sustainable Water Treatment: A Critical Review.

In view of increasing threats arising from the shortage of fresh water, there is an urgent need to propose sustainable technologies for the exploitation of unconventional water sources. As a derivative of microbial fuel cells (MFCs), microbial desalination cell (MDC) has the potential of desalinating saline/brackish water while simultaneously generating electricity, as well as treating wastewater. Therefore, it is worth investigating its practicability as a potential sustainable desalination technology. This review article first introduces the fundamentals and annual trends of MDCs. The desalination of diverse types of solutions using MDCs along with their life cycle impact assessment (LCIA)  and economic analysis is studied later. Finally, limitations and areas for improvement, prospects, and potential applications of this technology are discussed. Due to the great advantages of MDCs, improving their design, building materials, efficiency, and throughput will offer them as a significant alternative to the current desalination technologies.

Using reverse phase high performance liquid chromatography as an alternative to resin fractionation to assess the hydrophobicity of natural organic matter.

Resin fractionation is the most widely used technique to isolate and characterize natural organic matter (NOM) based on its hydrophobicity and hydrophilicity, however, it is also recognized as a time consuming technique. This paper describes the use of reverse phase high performance liquid chromatography (RPHPLC) as a rapid assessment technique to determine the hydrophobicity/hydrophilicity of NOM. The optimum column separation condition was achieved and without the need for concentrating the sample prior to analysis and with good reproducibility of the peak retention time and the peak area. The characterization results were further compared with the traditional resin fractionation technique using DAX-8 and XAD-4 resins. The results demonstrated that the polarities defined by the two methods were different but consistent and also that the fractions absorbed onto XAD-4 were less hydrophobic than those absorbed onto DAX-8. The difference in definition between resin fractionation and RPHPLC were further investigated.

Prediction of DOM removal of low specific UV absorbance surface waters using HPSEC combined with peak fitting.

High performance size exclusion chromatography (HPSEC) is used in water quality research primarily to determine the molecular weight distribution of the dissolved organic matter (DOM), but by applying peak fitting to the chromatogram, this technique can also be used as a tool to model and predict DOM removal. Six low specific UV absorbance (SUVA) source waters were treated using coagulation with alum and both the source and treated water samples were analysed using HPSEC. By comparing the molecular weight profiles of the source and treated waters, it was established that several DOM components were not effectively removed by alum coagulation even after high dosage alum treatment. A peak-fitting technique was applied based on the concept of linking the character (molecular weight profile) of the recalcitrant organics in the treated water with those of the source water. This was then applied to predict DOM treatability by determining the areas of the peaks which were assigned to removable organics from the source water molecular weight profile after peak fitting, and this technique quantified the removable and non-removable organics. The prediction was compared with the actual dissolved organic carbon (DOC) removal determined from jar testing and showed good agreement, with variance between 2% and 10%. This confirmed that this prediction approach, which was originally developed for high SUVA waters, can also be applied successfully to predict DOC removal in low SUVA waters.

pH modeling for maximum dissolved organic matter removal by enhanced coagulation.

Correlations between raw water characteristics and pH after enhanced coagulation to maximize dissolved organic matter (DOM) removal using four typical coagulants (FeCl3, Al2(SO4)3, polyaluminum chloride (PACl) and high performance polyaluminum chloride (HPAC)) without pH control were investigated. These correlations were analyzed on the basis of the raw water quality and the chemical and physical fractionations of DOM of thirteen Chinese source waters over three seasons. It was found that the final pH after enhanced coagulation for each of the four coagulants was influenced by the content of removable DOM (i.e. hydrophobic and higher apparent molecular weight (AMW) DOM), the alkalinity and the initial pH of raw water. A set of feed-forward semi-empirical models relating the final pH after enhanced coagulation for each of the four coagulants with the raw water characteristics were developed and optimized based on correlation analysis. The established models were preliminarily validated for prediction purposes, and it was found that the deviation between the predicted data and actual data was low. This result demonstrated the potential for the application of these models in practical operation of drinking water treatment plants.