Water Quality Processes Research Articles

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as “Excellent prediction”. The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

A Review on Applications of Artificial Intelligence in Wastewater Treatment

In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

Exploring Water Quality as a Determinant of Small-Scale Fisheries Vulnerability

Water quality is a fundamental indicator of coastal ecosystem health. Maintaining appropriate levels of water quality is critical for the growth of aquatic species and the livelihoods of dependent small-scale fishery (SSF) communities. However, natural (e.g., cyclones, floods) and hu-man-induced (e.g., hydrological changes, varied fishing techniques) factors create cumulative stress on these systems, leading to environmental and socioeconomic challenges. This often manifests as food insecurity, occupational displacement, and biodiversity loss. Despite existing research on coastal sustainability and resilience, the intricate connection between water-quality variations and social–ecological vulnerabilities remains understudied. This paper addresses this gap, focusing on the interplay between water quality changes and the vulnerabilities faced by SSF communities. Using the Chilika Lagoon in India as a case study, this synthesis paper examines water-quality processes and their impact on community vulnerabilities over three decades. It analyses various coping and adaptive responses of the fisher communities and the potential of their actions for creating viable small-scale fisheries. Our findings suggest ways in which SSF communities can respond to these vulnerabilities and help foster knowledge for their transition to viability.

Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example.

The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.

Development of a general protocol for rapid response research on water quality disturbances and its application for monitoring the largest wildfire recorded in New Mexico, USA

Anthropogenic and natural disasters (e.g., wildfires, oil spills, mine spills, sewage treatment facilities) cause water quality disturbances in fluvial networks. These disturbances are highly unpredictable in space-time, with the potential to propagate through multiple stream orders and impact human and environmental health over days to years. Due to challenges in monitoring and studying these events, we need methods to strategize the deployment of rapid response research teams on demand. Rapid response research has the potential to close the gap in available water quality data and process understanding through time-sensitive data collection efforts. This manuscript presents a protocol that can guide researchers in preparing for and researching water quality disturbance events. We tested and refined the protocol by assessing the longitudinal propagation of water quality disturbances from the 2022 Hermit's Peak—Calf Canyon, NM, USA, the largest in the state's recorded history. Our rapid response research allowed us to collect high-resolution water quality data with semi-continuous sensors and synoptic grab sampling. The data collected have been used for traditional peer-reviewed publications and pragmatically to inform water utilities, restoration, and outreach programs.

Longitudinal stream synoptic monitoring tracks chemicals along watershed continuums: a typology of trends.

There are challenges in monitoring and managing water quality due to spatial and temporal heterogeneity in contaminant sources, transport, and transformations. We demonstrate the importance of longitudinal stream synoptic (LSS) monitoring, which can track combinations of water quality parameters along flowpaths across space and time. Specifically, we analyze longitudinal patterns of chemical mixtures of carbon, nutrients, greenhouse gasses, salts, and metals concentrations along 10 flowpaths draining 1,765 km2 of the Chesapeake Bay region. These 10 longitudinal stream flowpaths are drained by watersheds experiencing either urban degradation, forest and wetland conservation, or stream and floodplain restoration. Along the 10 longitudinal stream flowpaths, we monitored over 300 total sampling sites along a combined stream length of 337 km. Synoptic monitoring along longitudinal flowpaths revealed: (1) increasing, decreasing, piecewise, or no trends and transitions in water quality with increasing distance downstream, which provide insights into water quality processes along flowpaths; (2) longitudinal trends and transitions in water quality along flowpaths can be quantified and compared using simple linear and non-linear statistical relationships with distance downstream and/or land use/land cover attributes, (3) attenuation and transformation of chemical cocktails along flowpaths depend on: spatial scales, pollution sources, and transitions in land use and management, hydrology, and restoration. We compared our LSS patterns with others from the global literature to synthesize a typology of longitudinal water quality trends and transitions in streams and rivers based on hydrological, biological, and geochemical processes. Applications of LSS monitoring along flowpaths from our results and the literature reveal: (1) if there are shifts in pollution sources, trends, and transitions along flowpaths, (2) which pollution sources can spread further downstream to sensitive receiving waters such as drinking water supplies and coastal zones, and (3) if transitions in land use, conservation, management, or restoration can attenuate downstream transport of pollution sources. Our typology of longitudinal water quality responses along flowpaths combines many observations across suites of chemicals that can follow predictable patterns based on watershed characteristics. Our typology of longitudinal water quality responses also provides a foundation for future studies, watershed assessments, evaluating watershed management and stream restoration, and comparing watershed responses to non-point and point pollution sources along streams and rivers. LSS monitoring, which integrates both spatial and temporal dimensions and considers multiple contaminants together (a chemical cocktail approach), can be a comprehensive strategy for tracking sources, fate, and transport of pollutants along stream flowpaths and making comparisons of water quality patterns across different watersheds and regions.

Application of Boruta algorithms as a robust methodology for performance evaluation of CMIP6 general circulation models for hydro-climatic studies

Regional climate models are essential for climate change projections and hydrologic modelling studies, especially in watersheds that are overly sensitive to changes in climate. Accurate hydrologic model development is a daunting task in data-sparse regions where climate change’s impact on hydrologic and water quality processes is necessary for a well-informed policy decision on adaptation and hazard mitigation strategies. Novel approaches have been evolving that evaluated GCMs with the objective of improved parameterization to limit uncertainty and improve hydrologic model development. However, conclusions drawn should be purpose-driven based on intended usage. This study provides an overview of the state-of-the-art Boruta random forest as a robust methodology in the performance evaluation of GCMs models for hydroclimatic study. Highlights from the assessment indicate that (1) there is consistency in replicating the three observed climate variables of daily precipitation, maximum and minimum temperature respectively, (2) better temporal correlation (R2 = 0.95) in annual precipitation with a mean bias of 0.638mm/year, when compared to symmetrical uncertainty (SU) (R2 = 0.82), and all models ensembles (AME) (R2 = 0.88) with associated biases of 68.19mm/year and 10.57mm/year, respectively. Evaluation of the multi-year climate extreme indices, trends and magnitude reveal that there is a fair representation of basin-scale observed climate extreme events. However, the Boruta random forest approach exhibited a better statistical trend and magnitude of the extreme event in the basin. The findings of the study revealed enhanced GCM dataset evaluation and present a simple and efficient methodology to examine the limitations associated with the selected GCM ensemble for impact study in hydrology.

Unraveling the Roles of MW/UV/TiO2 Photocatalysis Technologies for Organic Wastewater Treatment

Microwave-induced oxidation and UV/TiO2 photocatalytic technologies are widely used for organic wastewater treatment. Furthermore, the combination of these technologies (MW/UV/TiO2) result in a new advanced oxidation process. As a green and efficient photocatalytic degradation technology, MW/UV/TiO2 is favored for its advantages of high removal rate, short time use, wide concentration range, low cost, good stability, and no secondary pollution. Herein, this paper has summarized insights into the removal process by unveiling the degradation mechanism of organic compounds with MW-assisted technology. Additionally, water quality factors and process parameters affect the photocatalytic efficiencies, consisting of initial concentration, initial volume, TiO2 dosage, UV intensity, microwave power, temperature, pH, and fluid velocity, which have been systematically analyzed. Finally, possible future research directions and guidelines are proposed. Our findings will provide a way forward for the development of effective microwave-assisted remediation technologies that are broadly applicable to various environmental contamination scenarios.

Environmental drivers of spatial and temporal water quality variability in four coastal wetlands of Lake Ontario

Great Lakes coastal wetlands serve as mediation zones between the land and the lake, regulating the fate of materials received from tributaries prior to discharge to the lake nearshore zone. To improve our understanding of water quality processing and nutrient fate in coastal wetlands, we evaluated within- and across-wetland water quality as a function of environmental drivers over a decade (2009–2018) in three drowned river mouth (Carruthers, Duffin’s, and Rouge) and one barrier lagoon (Frenchman’s Bay) wetlands on the north shore of Lake Ontario. Overall, land-use had a weak relative association with most water quality parameters, reflecting no appreciable changes in land-use across the study years. The barrier lagoon wetland Frenchman’s Bay had a distinctly different water quality pattern from the drowned river mouth wetlands, where water quality followed a high to low concentration gradient from near the tributary confluences (high) to the lake-wetland confluence (low) (permutational analysis of variance p-value < 0.001). Notably, we observed significant differences among celled (i.e., natural ponds in wetlands) and non-celled sites in Duffin’s and Rouge marshes, primarily attributed to strong covariation among phosphorus, phosphate, and organic nitrogen concentrations (permutational analysis of variance p-value < 0.001). This water-quality signature seemed to be driven by solar radiation and lake level variability (i.e., seiche inundation), inferring that wetlands may be important sites for the mobilization of legacy phosphorus in sediments under certain climatic conditions, and following seiche events.

Effects of Storm Surge Barrier Closures on Estuary Saltwater Intrusion and Stratification

AbstractGated storm surge barriers have been constructed or proposed in many estuaries worldwide for coastal flood risk reduction. Past studies have shown that, even when open, a barrier system's fixed infrastructure can increase estuary stratification and salt intrusion, potentially affecting water quality and ecological processes. However, surge barrier closures could have a much stronger influence on estuary conditions by temporarily blocking the tidal exchange. In this project, we use an existing regional three‐dimensional hydrodynamic model, with modifications to simulate surge barrier closure and reopening, to study the effects on estuarine salt intrusion and stratification of the Hudson River. Across a range of modeled scenarios of gate closure frequencies, durations and river streamflows, we evaluate the changes caused by gate closures, as well as the recovery time to normal conditions. Our results for the Hudson show long‐duration gate closures (three or more days) with low streamflows temporarily lead to salt intrusion and stratification beyond recent historical extremes. Moreover, monthly frequency closures, which could occur as soon as 2070 under realistic scenarios of sea‐level rise and barrier management, do not allow for recovery under low streamflow conditions and could lead to durable changes to estuary physical conditions. As a result, long duration closures and high‐frequency closures both constitute a threat to municipal water supplies. This study demonstrates a framework for understanding the potential impacts of any proposed surge barrier system and can help improve our understanding of corresponding ecological impacts.

Prediction of Water Quality with Ensemble Learning Algorithms

As monitoring and control of the quality of the water is one of the most important issues in the world since only 74% of the world's population use safely managed water where the water is treated well to reach the minimum limit of safety and quality standards. For observation of the water potability and to take immediate actions to improve the water quality, real-time monitoring and classification process are required. However, monitoring and controlling the quality of the water is not an easy task since it has many requirements such as the collection and analysis of data and measures to be taken. In this paper, we focus on applying machine learning for evaluation of the water quality. We have chosen five ensemble learning algorithms namely, Adaptive Boosting, Random Forest, Extra trees classifier, Gradient Boosting, and Stacking Classifier to evaluate their classification performances in defining the water quality. Results reveal that the Stacking Classifier has the highest performance among the five classifiers that we have studied.

Quantitative study for the effect of water velocity on water quality change

Atmospheric reaeration process plays a key role in degradation of organic matter and self-purification processes affecting dissolved oxygen (DO) concentrations in rivers and streams. Moreover, water quality (WQ) processes can be highly dependent on flow velocity conditions. Where, highly reaeration rates are positively correlated with high flow velocity. Therefore, the present work reports deep insights on the role of flow velocity with reaeration process in the concentration of DO and biochemical oxygen demand (BOD5). This study was applied along the largest agricultural drainage water reuse project, El-Salam Canal in Egypt. The hydrodynamic (HD) characteristics and WQ parameters (DO and BOD5) along the canal were simulated using a one-dimensional HD and WQ model (MIKE 11). The simulated results indicated that the canal was deteriorated towards El-Salam Canal after mixing with El-Serw and Bahr Hadous drains. Statistical regression fitted line and Pearson correlation analysis, were performed on the simulation results of the reaeration rates with flow velocity and WQ values, indicating a significant correlation. Accordingly, water velocity should be taken into account as a key factor for describing the WQ change.

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjön, and Ivösjön) where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and overestimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors.

Comparative Evaluation of the Performance of SWAT, SWAT+, and APEX Models in Simulating Edge of Field Hydrological Processes

Hydrologic and water quality models are often used in assessing the response of environmental processes to human activities and climatic change. However, these models differ in terms of their complexity, requirements, underlying equations, and assumptions, and as such their performance in simulating landscape processes varies. Consequently, a key question that has to be addressed is to select the most suitable model that gives results closest to reality for an intended purpose. In this study, the performance of the basin-wide older version of SWAT is compared with that of the small watershed model APEX to assess the performance of both models at a field scale level. The new restructured version of SWAT (SWAT+) is compared with the older version to determine whether the new changes incorporated in SWAT+ have improved model performance, particularly for small watersheds. The three models were used to simulate the edge of field processes for a 6.6 ha plot located at the USDA-Agricultural Research Station near Riesel, Texas, and to predict water yield, soil, and mineral phosphorous loss from the micro watershed. Results showed that all the uncalibrated models over-predict soil and phosphorous loss in a micro watershed. Uncalibrated SWAT and SWAT+ models simulated water yield satisfactory albeit with low-performance metrics. The calibrated versions simulated water yield with indices close to optimal values. PBIAS as a performance assessment metric was determined to be overly sensitive and prone to numerical errors. SWAT+ will be helpful in the understanding of hydrological and water quality processes at micro watersheds considering that it addresses structural flaws associated with the older version, and the manually calibrated version matches the performance of both APEX and SWAT, despite the latter two undergoing rigorous automatic calibration.

An Experimental Platform to Study Wind, Hydrodynamic, and Biochemical Conditions in the Littoral Zone During Extreme Coastal Storms

Tropical cyclones and other extreme coastal storms cause widespread interruption and damage to meteorological and hydrological measurement stations exactly when researchers need them most. There is a longstanding need to collect collocated and synchronized measurements in areas where storms severely damage civil/coastal infrastructure. To fill this observational gap, researchers led by author Masters developed a state-of-the-art monitoring station called a “Sentinel.” Sentinels are intended for temporary installation on the beach between the mean tidal datum and the sand dunes and are engineered to operate in and measure extreme wind, storm surge, wave, and hazardous water quality conditions. They are envisioned as a shared-use resource—a hardened IoT (Internet of Things) platform set up in the right place at the right time to study wind and wave loads, coastal erosion and morphology changes, water quality, and other processes during extreme coastal storms.

Automated Secchi disk depth measurement based on artificial intelligence object recognition

Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth - the Secchi disk depth (SDD). High frequency SDD measurement is manpower intensive, precluding better understanding of the daily and diurnal variation of water transparency. For the first time, an artificial intelligence based object detection algorithm was employed for the automatic detection of SD from images, mimicking SDD measurement by human eyes. The trained model was validated on a large number of images (about 2000 for a single day in daytime) obtained from a remote-controlled imaging system in a fish farm in a Hong Kong embayment, demonstrating high detection accuracy of 93 %. The work opens up opportunities in the nowcast and forecast of short-term water quality changes (e.g. algal blooms) in coastal waters.

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

Field observations of stratification in stormwater wet ponds

Stratification is one of the fundamental physical processes that may have a significant impact on water quality in stormwater wet ponds. However, the role of thermal and chemical stratifications in governing water quality processes is not fully understood. This is in part due to the lack of detailed field measurements of sufficient governing parameters over time periods that span a wide range of environmental conditions. To fill this gap, a comprehensive 2-year field program was undertaken in two stormwater wet ponds in Calgary, Alberta, Canada, during the ice-free season from May to November in 2018 and 2019. At different locations in each pond, thermal and chemical stratifications were observed, thermocline depth and strength were determined, and continuous water velocity profiles were measured. In addition, the effect of local weather conditions on stratification, thermocline, and hydrodynamics was investigated. The results showed that the ponds had vertical water temperature differences >1 °C for 99% of the time, May to August. In addition, salt-laden inflows from road deicing salts led to strong chemical stratification up to five times stronger in the sediment forebays than in the main cells in spring. Wind-induced surface currents were insignificant, scaling at 0.3% of the wind speed with negligible impact on vertical mixing in the ponds. Our results demonstrate that the ponds' strong and prolonged stratification decreased pollutant retention capacity and caused the water at depth to become anoxic, degrading the quality of the water discharged downstream. Hence, additional consideration of stratification is required when designing new stormwater ponds.

Climbing the Effluent Filtration Tree: Modelling, Mechanisms &amp; Applications—A Monograph

Particle filtration is a major building block in effluent treatment facilities for water reuse in agriculture, industry, and the community. Yet, its incorporation in modern hybrid treatment systems still lacks basic know-how for process optimization. This paper aims to provide a profound understanding of particle filtration vis-à-vis its various reuse applications. The methodology used follows a road map depicted as a growing tree, representing the author’s research from roots to top: roots—basic modeling, mechanisms; tree trunk—filter design approach for reuse; branches—enhanced particle removal; and tree crown—pretreatment, bioparticle, and nanoparticle removal. Contact deep-bed filtration process optimization, algorithms for economically optimal filter design, tertiary filtration and membrane pretreatment, and related energy issues are being discussed. Some of the conclusions are that pilot plant planning should be primarily derived from particle surface interactions with filter media, based on measurements of mineral particle or bioparticle size, shape, and physicochemical characteristics, and applying attachment-detachment models. Filter design optimization would comprise of selecting efficient water quality processes first, followed by economic optimization for final design parameters. A holistic approach in the design of filtration facilities, standing alone or incorporated in hybrid systems, is also considered.

Time series forecasting of temperature and turbidity due to global warming in river Ganga at and around Varanasi, India.

The fluctuation in the river ecosystem network due to climate change-induced global warming affects aquatic organisms, water quality, and other ecological processes. Assessment of climate change-induced global warming impacts on regional hydrological processes is vital for effective water resource management and planning. The global warming effect on river water quality has been analyzed in this work. The river Ganga stretch near the Varanasi region has been chosen as the study area for this analysis. The air temperature has been predicted using the seasonal autoregressive integrated moving average (SARIMA) and the Prophet model. The Prophet model has shown better accuracy with a root mean square percent error (RMSPE) value of 3.2% compared to the SARIMA model, which has an RMPSE value of 7.54%. The river temperature, turbidity, and nighttime radiance values have been predicted for the years 2022 and 2025 using the long short-term memory (LSTM) algorithm. The anthropogenic effect on the river has been evaluated by using the nighttime radiance imageries. The predicted average river temperature shows an increment of 0.58°C and 0.63°C for the city and non-city river stretches, respectively, in 2025 compared to 2022. Similarly, the river turbidity shows an increment of 1.21 nephelometric turbidity units (NTU) and 1.17 NTU for the city and non-city stretch, respectively, in 2025 compared to 2022. For future predicted years, the nighttime radiance values for the region situated near the city river stretch show a significant rise compared to the region that lies nearby the non-city river stretch.