Water Quality Variables Research Articles

Spatial-temporal Variation in Water Quality of Rain-source Rivers in Shenzhen from 2015 to 2021 and Its Response to Rainfall

Rain-source urban rivers have the characteristics of small water capacity， lack of dynamic water supply， and being easily polluted. This study analyzed the spatial and temporal distribution characteristics of river water quality and the response of characteristic pollutants to rainfall based on daily rainfall data and 21 water quality indicators of nine major river basins in Shenzhen （excluding Shenzhen-Shantou） from 2015 to 2021 by using the single-factor assessment method， comprehensive pollution index method， hierarchical cluster analysis， and Pearson correlation. The results showed that： ① in 2015， the water quality of most sections in the whole region was inferior Class V water. After October 2018， the overall water quality of rivers was greatly improved， which was consistent with the background of Shenzhen's special water control activities in 2018. By 2021， the water quality of approximately 62% of sections reached Class Ⅰ-Ⅲ water standards. ② The water pollution in the densely populated western part of Shenzhen was more serious than that in the eastern part， and the water pollution in the lower reaches of the estuaries and tributaries was more serious than that in the upper reaches. ③ The water quality of the Pingshan River， Guanlan River， Longgang River， and Maozhou River was significantly affected by rainfall. ④ The main characteristic pollution indexes of the Shenzhen River were DO， permanganate index， COD， BOD5， NH4+-N， TP， petroleum， and anionic surfactant. For the Pingshan River and Longgang River， rainfall increased the concentrations of TP and NH4+-N. For the Maozhou River， rainfall increased the concentrations of TP and COD. For the Shenzhen River， rainfall increased the concentrations of COD， TP， and NH4+-N. The above results reveal the spatio-temporal variation in rain-source river water quality in Shenzhen and its response to non-point source pollution caused by rainfall events and provide a scientific reference for building a higher quality water environment in Shenzhen.

Assessing dependence between land use/land cover and water quality

Changes in land use/land cover (LULC) directly or indirectly affect water quality in watercourses and impoundments. Sustainable management strategies aimed to enhance ecosystem health and community well-being require an accurate water-quality evaluation. This study looks into the correlation between temporal changes in LULC, represented by selected landscape variables (land cover area and proportion, patch density, Euclidean nearest-neighbor distance, mean shape index, and Shannon index), and water quality variables (nitrate, total phosphorus, and total suspended solids) at catchment scale. To compare the watershed-size influence, this analysis was performed at two different spatial scales represented by two Uruguayan basins of different sizes, San Salvador (3,118 km2) and Del Tala (160 km2). Partial Least Squares and Random Forest unsupervised machine-learning models were employed for this analysis. By exploiting a non-model-biased method based on game theory (SHAP), the LULC characteristics were quantified and ranked based on their level of importance in the water-quality evaluation. The main outcomes of this study proved that patch density is one of the most influencing metrics in both watersheds and for both models. Agricultural land use is the most critical one at both catchments and agricultural with a forage crop land uses are the most important ones for both algorithms. Furthermore, it is possible to state that the adopted techniques are valuable tools that can provide an adequate overview of the water‐quality behavior in space and time and the correlations between water-quality variables and LULC.

Strengthening rural community water safety planning in Pacific Island countries: evidence and lessons from Solomon Islands, Vanuatu, and Fiji.

Pacific Island Countries (PICs) collectively have the lowest rates of access to safely managed or basic drinking water and sanitation globally. They are also the least urbanised, have dynamic socioeconomic and increasing climate-linked challenges. Community-based water managers need to respond to variability in water availability and quality caused by a range of hazards. Water Safety Planning (WSP), a widely adopted approach to assessing water supply, offers a risk-based approach to mitigating both existing and future hazards. WSP is adaptable, and making modifications to prescribed WSP to adapt it to the local context is common practice. Within the Pacific Community Water Management Plus research project, we used formative research and co-development processes to understand existing local modifications, whether further modifications are required, and, to develop additional modifications to WSP in Fiji, Vanuatu and Solomon Islands. The types of additional local modifications we recommend reflect the unique context of PICs, including adjusting for community management of water supplies and required collective action, community governance systems, levels of social cohesion in communities, and preferred adult-learning pedagogies. Incorporating modifications that address these factors into future WSP will improve the likelihood of sustained and safe community water services in Pacific and similar contexts.

Effects of water quality on fish parasite biodiversity and physiological responses in the host fish Clarias gariepinus from a eutrophic lake subjected to acid mine drainage in South Africa.

Elevated concentrations of contaminants have negative impacts on aquatic organisms and their parasites. Changes in parasite infections have been proposed as a technique for monitoring the health of aquatic ecosystems. Furthermore, alterations in physiological responses (biomarkers) of organisms have also been used to delineate ecosystem quality. Lake Heritage is situated along the Crocodile River in Muldersdrift, Gauteng, South Africa, and is subject to contamination by acid mine drainage. Clarias gariepinus is a well-studied bioindicator species and host to numerous endoparasites and ectoparasites. The aims of this study were to delineate the health status of Lake Heritage through a multifaceted approach by comparing the water quality, biomarker responses, and parasite biodiversity of C. gariepinus, compared to unexposed laboratory-reared fish. Physical and chemical water quality parameters were determined using a hand-held probe, test kits, and element analysis with inductively coupled plasma-mass spectrometry. Biomarker responses in the gill, liver, and muscle tissues from C. gariepinus were assessed for total protein, metallothioneins, superoxide dismutase (SOD), and reduced glutathione (GSH) concentrations and activities of acetylcholinesterase and catalase. Results for water quality variables showed higher pH, nitrate, hardness, and copper levels compared with the South African Target Water Quality Guidelines. Catalase activity and concentrations of SOD and reduced GSH showed a response in C. gariepinus to the water quality. Ectoparasites had lower prevalence and mean intensity than endoparasites. However, there were no differences in the physiological responses between infected and uninfected hosts. The study shows that the eutrophic conditions in Lake Heritage cause biomarker responses in the host when compared to host fish in laboratory conditions. Integr Environ Assess Manag 2024;20:1539-1553. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Assessment of Sarda Sahayak Canal water quality for irrigation using different indices

Suitability of canal water for irrigation is a useful tool for careful management of canal water resources that can impact soils and thereby effect agricultural production. The present work was conducted at Kunda Tehshil in Pratapgarh district of Uttar Pradesh, India for the assessment of variation in water quality of Sarda Sahayak Canal. Canal water samples were collected and important parameters deciding irrigation water quality were analysed viz., pH, electrical conductivity (EC), chloride, calcium, magnesium, carbonate, bicarbonate, sodium, potassium and sodium, sodium adsorption ratio (SAR), residual sodium carbonate (RSC), permeability index levels (PI) and Irrigation Water Quality Index (IWQI). Correlations between studied parameters were deduced to derive at relationships existent between different water quality parameters. Results showed that monthly variation of water quality of the Sarda Sahayak Canal were under permissible limits and classified as good for irrigation with respect to pH, Na%, SAR, RSC and Cl parameters. EC values ll water samples were under medium /good range for irrigation whereas irrigation water quality index of 50% of water samples were under no restriction category and the rest 50% samples showed low restriction category, and hence canal water could be satisfactorily used for irrigation purposes. Further, soils of the studied area were saline, and hence canal water could be safely used for leaching the excess salts out from root zone to achieve high yields without risking and any other soil related problems that could arise out of the use of canal water.

Temporal variations of the multifaceted biodiversity and assembly mechanisms in lake fish assemblages

AbstractUnderstanding long‐term changes in fish diversity and community assembly rules is crucial for freshwater conservation. Growing evidence indicates that studying functional and phylogenetic diversity beyond purely taxonomic considerations can provide different but complementary information on community assembly. Here, the taxonomic, functional, and phylogenetic β‐diversity of fish communities, as well as the community assembly mechanisms, were explored in five impounded lakes of the China's South‐to‐North Water Diversion Project (SNWDP) from the 1980s to the 2010s. We found that (1) there was an obvious trend of species homogenization in the five impounded lakes, but the long‐term transformations of different dimensional β‐diversity were divergent; (2) water quality and land use variables have greater impacts on multidimensional β‐diversity; and (3) community assembly process in taxonomic and functional dimensions were dominated by random process in both periods, while shifting from limiting similarity to habitat filtering in the phylogenetic dimension. These results highlight that functional and phylogenetic diversity are important additional ecological indices for assessing the patterns of fish diversity in lakes.

Spatio-Temporal Variation of Water Quality in Lixia River Watershed Associated with the Operation of a Water Diversion Project

Spatio-Temporal Variation of Water Quality in Lixia River Watershed Associated with the Operation of a Water Diversion Project

Water Quality Index of Arkavathi River and Surrounding Groundwater

Abstract Aim: Finding out the water quality of Arkavathi river, a tributary of the Kaveri system and surrounding groundwater is the aim of the present work using the water quality index (WQI) method. Materials and Methods: WQIs contribute a single number which indicates altogether quality of water at a particular sampling point based on varied water quality parameters. An effort has been ready to invent a WQI using 11 water quality parameters such as pH, temperature, dissolve oxygen, alkalinity, chloride, iron, nitrate, fluoride, total hardness, total dissolved solids, and biochemical oxygen demand measured at five various locations along the Arkavathi river just downstream of wastewater disposal points of Ramanagar town and six groundwater samples surrounding the river basin. Weighted arithmetic WQI technique was used to discover overall WQI. Results: The values of WQI for downstream of wastewater disposal points of Ramanagar town rivers vary from 33.78 to 76.82 and for groundwater vary from 38.6241.77 to 65.95. Based on this, the river quality and groundwater at some of the sampling stations were not suitable for drinking. Conclusions: The quality of water is substandard because mainly in the river samples because of the disposal of untreated sewage and industrial effluent generated from the Ramanagar town is directly without any treatment, improper sanitation condition in the nearby city banks of the river, and also, wastewater discharge from surrounding villages, human activities, urban runoff, and agriculture activities.

How the catchment-river-lake continuum shapes the downstream water quality

Lakes play a crucial role in the nutrient cycling of Earth, despite covering only a small fraction of the planet’s surface. Their interactions with their surrounding catchment areas significantly impact ecosystems and regulatory services. The connection between a lake and its catchment, especially the drainage ratio (catchment area to lake surface area), shapes the characteristics of lakes and their response to catchment processes. Within the catchment area, geological, land cover, and land use factors influence the composition of stream water that flows into the lake. These factors play a role in transporting various substances, both organic and inorganic, to the streams. Lakes act as dynamic filters, altering the chemical composition of water that flows through them. This study aims to investigate how a large, shallow lake impacts the quality of the river water as it passes through. It builds on an analysis of nutrient (carbon, nitrogen, phosphorus, silicon) fluxes into Lake Võrtsjärv, using six years of monthly monitoring data from five main inflows and the outflow. The research explores how catchment characteristics and hydrology affect nutrient concentrations and loadings into the lake, as well as the retention or release of substances by the lake. Findings reveal that catchment characteristics, such as land use and forest cover, significantly influence water quality parameters. Different inflows showed variations in water quality, and annual variations were observed, largely correlated with precipitation and discharge. Võrtsjärv plays a critical role in retaining or releasing nutrients, with varying impacts depending on the water budget of the lake. In years with a positive water balance, the lake retains all nutrients, whereas in dry years only inflowing N and P loads exceed their outflow. Overall, this study underscores the importance of lakes as integral components of catchment ecosystems, shedding light on their complex interactions with the environment and the implications for water quality. It emphasizes the need for careful consideration of land use and hydrological factors in managing and preserving these vital aquatic systems.

Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach

The Albufera of Valencia, a Mediterranean coastal lagoon, has been in a turbid state since 1974, with only four episodes of temporary water transparency in spring. Despite its average depth of 1 m and oligohaline waters, excessive turbidity, fish grazing, and toxic compounds have inhibited submerged macrophyte growth. In spring 2018, a significant area of Myriophyllum spicatum emerged and exhibited significant density until its complete disappearance in August 2018. Using Sentinel-2 and Landsat-8 imagery, we monitored water quality variables and vegetation density during these months. Our study revealed the first unique occurrence of M. spicatum in more than 40 years. A period of increased transparency from summer 2017 compared to previous years seems to be the cause of this appearance, while high summer temperatures caused its disappearance. While improving water quality may help restore submerged macrophyte meadows, our results suggest that summer maximum temperatures may inhibit recovery. Remote sensing, particularly the NDVI index, proved successful in monitoring aquatic vegetation and understanding species-environment relationships. This methodology can enhance future monitoring efforts for macrophyte colonization and expand knowledge of their occurrence and causes of disappearance in this and other similar areas.

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

Protozoan and helminths infestation of Nile tilapia Oreochromis niloticus and its correlation with certain water quality variables along river Nile in the area of Greater Cairo

Pollutants and parasites represent stressors for fish at the individual, population, or community levels. The current study outlines the seasonal infestation pattern of Oreochromis niloticus by protozoan and helminths parasites linked to some physicochemical parameters (pH, dissolved oxygen, total dissolved solids, and electrical conductivity), and metals (Cd, Pb, Fe, Zn, and Ca), at nine selected sites in River Nile (Greater Cairo area) from summer 2019 till spring 2020. Most of the studied criteria in Nile water samples showed normal levels during all seasons compared to the recommended limits of EPA. The parasitological examinations recorded three protozoan categories: Myxobolus (8 species), hemoflagellates (Trypanosoma mukasi) and Ciliates (Trichodina compacta), and three helminths; Clinostomum sp., Acanthocephala sp. and Euclinostomum ardeola. The highest incidence of infection was determined for Clinostomum sp., followed in descending order by Myxobolus sp., Acanthocephala sp., T. mukasi, E. ardeolathen, and T. compacta. Notably, this study introduces the novel identification of new species of Myxobolus in the blood of Oreochromis niloticus. The histopathological examination of gills, muscles, and kidneys reveals serious changes and the presence of encysted trematodes, metacercariae, and cysts of protozoan parasites. Additionally, the study employs cluster analysis based on site similarity in water variables and canonical correspondence analysis, explaining 98.7 % of the variables and indicating correlations between parasite infestation and environmental factors. These analytical approaches reveal the impact of land use activities on water variables and the influence of adjacent activities on fish parasite infestation patterns. In conclusion, this study provides a comprehensive perspective by considering various factors to enhance our understanding of pollutants and parasites affecting fish in the River Nile.

Variaciones fisicoquímicas del agua de una gran presa Hidroeléctrica Mexicana, Región Centro semi-árida

The Zimapan hydropower plant built in 1995 was the first project to take advantage of Mexico's City wastewater for electric power generation. This tropical reservoir receives pollutants that affect water quality from municipal, agricultural, and industrial wastewater through the Tula and San Juan rivers. The study considered the changes in the physicochemical parameters (surface to 20 m depth) for May (wet warm), November (wet cold-after rains), and February (dry cold) in seven dam sites: two lotic, two transitional, and three lentic sites. Mean values ranges were temperature 20.71-24.38 °C, 1.59-7.56 mg/L dissolved oxygen, Secchi disk transparency 2.26-3.98 m, pH 7.12-8.40, electrical conductivity 48-1334 µS/cm, total nitrogen 14.20-30.74 mg/L, total phosphorus 10.45-15.83 mg/L, and dissolved organic carbon 9.11-20.63 mg/L. In November, all the reservoir surface water was hypoxic due to increased dissolved organic carbon by river input. Even though dissolved oxygen has a seasonal variation, this reservoir has a sustainable fishery of tilapia and black bass (~400 tons/y). The dam is meromictic and hypereutrophic, with seasonal water quality variations with no significant spatial variations. Water quality variation observed in this study can be useful to Mexican decision-makers on water and fisheries management, prevent massive fish kills, and be a support guide for commercial and sport fishers.

Assessing intra and interannual variability of water quality in the Sundarban mangrove dominated estuarine ecosystem using remote sensing and hybrid machine learning models

Coastal water bodies are susceptible to high levels of pollution as a result of population growth, rapid urbanization, the expansion of industrial activities, including ports, and expanding tourism-related activities. Untreated sewage disposal from municipalities, industrial wastewater, and numerous recreational and commercial activities along the coast not only deteriorates the water quality but also poses a significant threat to the marine ecosystem, food chain, and public health. Therefore, continuous monitoring of coastal water quality is essential for the sustainable upkeep of marine and aquatic ecosystems. The study aims to assess the intra- and interannual variability of coastal water quality in the Sundarbans estuarine system, straddling the borders of India and Bangladesh. The study employed Landsat 8 OLI satellite data and in-situ measurements of water quality parameters, including temperature, pH, dissolved oxygen, phosphate, total phosphate, nitrate, and nitrite. These measurements were obtained from 100 sampling locations across the entire Sundarban estuarine ecosystem. The data collection spanned the pre-monsoon, monsoon, and post-monsoon periods in the years 2021 and 2022. Several sophisticated machine learning models, including Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were employed to precisely forecast various crucial water quality indicators. Cross-validation results showed that the random forest model is most promising, with R2 values ranging between 0.50 and 0.96, followed by the artificial neural network (R2 values ranging between 0.36 and 0.97), the support vector machine (R2 values ranging between 0.29 and 0.97), and the KNN (R2 values ranging between 0.26 and 0.94). Subsequently, a random forest algorithm was customized for the study area to compute the coastal water quality index on a seasonal scale for the years 2021 and 2022. Results reveal that the concentrations of dissolved oxygen, phosphate, total phosphate, nitrate, nitrite, pH, and water temperature were relatively higher during the pre-monsoon and post-monsoon seasons compared to the monsoon season. In situ and remote sensing data analysis confirmed that Sundarbans estuarine water quality was relatively good during the monsoon period as compared to the rest of the year. In addition, research also highlighted the significance of monsoonal rainfall in the watershed for maintaining coastal water quality. It is estimated that the coastal water was particularly vulnerable due to the combined effects of industrial waste water and municipal sewage discharge. Ultimately, the study demonstrates the effectiveness of machine learning models and remote sensing in predicting the condition of coastal waters. The findings of this research can offer significant perspectives to decision-makers in developing a sustainable strategy for the management of coastal water resources.

Water quality and microecosystem of water tanks in karst mountainous area, Southwest China.

Karst mountainous areas in Southwest China, the world's largest bare karst area, are faced with growing water shortages. Rainwater harvesting plays an important role in alleviating water shortage. However, there remains a substantial gap in the research regarding the water quality of tanks. Water samples were seasonally collected from ten tanks to investigate the physicochemical properties, microbial communities, and their key influencing factors. The result showed that pH, turbidity, chroma, DOC, and CODMn exceeded drinking water guidelines. The alkaline pH value and the deterioration of sensory properties was the main feature of tank water, from which the over-standard rate of the uncleaned water tanks was higher. Moreover, principal component analyses suggested that tank water quality was influenced by human activities, catchment areas, and material cycling processes within the tanks, of which in-tank microbial activities were the most important driving factors in water quality variation. Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, and Verrucomicrobia were the predominant bacterial phyla in water tanks. Acinetobacter, Cyanobium-PCC-6307, CL500-29-marine-group, Candidatus-Aquiluna, and Exiguobacterium were the most abundant genera. The bacterial communities were significantly affected by the management practices. Higher relative abundance of Cyanobacteria and lower relative abundance of Proteobacteria was detected in the uncleaned tanks, which was a sign of tank water quality deterioration. The microbial community structure was closely related to the environmental factors. There was evidence that the water quality was affected by the existence of a microecosystem dominated by photosynthetic microorganisms in the water tanks. In addition, Acinetobacter, Enterobacter, Pseudomonas, and Legionella identified as the potential opportunistic pathogenic genera were frequently detected but the relative abundances except Acinetobacter were low in the tanks. Overall, our findings indicated that management style influences water quality and bacterial communities of tank water.

Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water

Microbial communities in surface waters are affected by environmental conditions and can influence changes in water quality. To explore the hypothesis that the microbiome in agricultural waters associates with spatiotemporal variations in overall water quality and, in turn, has implications for resource monitoring and management, we characterized the relationships between the microbiota and physicochemical properties in a model irrigation pond as a factor of sampling time (i.e., 9:00, 12:00, 15:00) and location within the pond (i.e., bank vs. interior sites and cross-sectional depths at 0, 1, and 2 m). The microbial communities, which were defined by 16S rRNA gene sequencing analysis, significantly varied based on all sampling factors (PERMANOVA P < 0.05 for each). While the relative abundances of dominant phyla (e.g., Proteobacteria and Bacteroidetes) were relatively stable throughout the pond, subtle yet significant increases in α-diversity were observed as the day progressed (ANOVA P < 0.001). Key water quality properties that also increased between the morning and afternoon (i.e., pH, dissolved oxygen, and temperature) positively associated with relative abundances of Cyanobacteria, though were inversely proportional to Verrucomicrobia. These properties, among additional parameters such as bioavailable nutrients (e.g., NH3, NO3, PO4), chlorophyll, phycocyanin, conductivity, and colored dissolved organic matter, exhibited significant relationships with relative abundances of various bacterial genera as well. Further investigation of the microbiota in underlying sediments revealed significant differences between the bank and interior sites of the pond (P < 0.05 for α- and β-diversity). Overall, our findings emphasize the importance of accounting for time of day and water sampling location and depth when surveying the microbiomes of irrigation ponds and other small freshwater sources.

Diel variation of plankton in the highly impacted freshwater zone of Hooghly estuary in relation to ecological alteration.

Plankton are promising ecological monitoring tool that responds quickly to any sort of aquatic ecological alteration, of which many of them are much susceptible to ecological variations. Therefore, monitoring shifts in plankton composition can indicate changes in water quality and aid to identify potential pollution sources. In the present study, the variation in plankton dynamics in relation to ecological variables were monitored in the freshwater zone of the Hooghly estuary from May 2020 to April 2021. The study was conducted in the interval of every six hours. i.e., at 6 A.M., 12 P.M., 6 P.M., and 12 A.M. The present finding revealed the occurrence of 54 phytoplankton and 20 zooplankton taxa/species. Diel variation revealed that among different time intervals, the highest abundance of phytoplankton was recorded 28,307 cells l-1 at 12 P.M, while the lowest was recorded 10,632 cells l-1 at 6 A.M. However, the highest zooplankton abundance was observed 804 ind l-1 at 6 A.M., and the lowest was recorded 156 ind l-1 at 6 P.M. The ANOVA (p < 0.05) analysis indicated significant diel variation for many planktonic genera. The CCA exhibited that most of the phytoplankton were influenced by multiple water quality variables such as temperature, turbidity, calcium, pH, salinity, DO, and nutrients. However, the majority of the zooplankton were affected by turbidity, total phosphorus, sulphate, calcium and available nitrogen. Significant seasonal variation in plankton composition has also been observed. The present study will help to determine the varying diel pattern of planktons in retort to alterations in the water quality parameters and varying ecological niches.

Towards a generic model evaluation metric for non-normally distributed measurements in water quality and ecosystem models

Model evaluation is a crucial process in model development and quantified model evaluation metrics play a pivotal role in model calibration and validation. However, in current water quality and ecosystem models, conventional model evaluation metrics are derived from hydrological models and often overlook the non-normal distribution of water quality and ecological state variables. In this study, we proposed a series of metrics that consider non-normal distributions by modifying components of the Kling-Gupta efficiency. Subsequently, we tested the existing metrics and newly proposed metrics using four different synthetic datasets and the actual simulation case of total phosphorus, chlorophyll a, and dissolved oxygen concentrations. The results demonstrate that the newly proposed metric, Fu-Zhang efficiency, which replaces the ratio of standard deviations with the ratio of interquartile ranges to measure dispersion similarity, is more suitable for evaluating simulation results with lots of outliers in the measurements. Our study reveals the hidden perils in conventional integrated model evaluation metrics of water quality and ecosystem models and calls for the development of comprehensive and feasible quantitative evaluation frameworks to drive the next paradigm shift.

Multi-scenario analysis in the Apulia shoreline: A multi-tiers analytical framework for the combined evaluation and management of coastal erosion and water quality risks

Ongoing climate change is causing threats to coastal areas, according to scenarios of Intergovernmental Panel on Climate Change (IPCC). The coastal areas are becoming highly exposed to erosion as a direct consequence of natural and anthropogenic processes that occur at different spatial-temporal scales. Against this interplay, coastal managers are calling for integrated tools supporting a multi-scenario evaluation of risks arising from natural and anthropogenic stress. A multi-tier analytical framework, exploiting the openly available Earth Observation databases, was developed, allowing the combination of remote sensing, GIS and Bayesian Network to evaluate the probability and uncertainty of coastal erosion risks and connected water quality variation, against ‘what-if’ scenarios, representing different management measures (i.e., Nature-Based Solution) and climate change impacts (e.g., higher incident waves due to increased storminess). Based on the available data for the Municipality of Ugento (Italy), the designed framework was applied over the 2009–2018 timeframe, allowing to capture local-scale shoreline erosion dynamics and driving forces. Results from scenario analysis revealed, to a minor extent, a nexus between oceanographic drivers, shoreline evolution, and water quality changes, with increasing probability of high erosion/accretion and higher turbidity under the simulated rising maximum significant wave height. However, the implementation of Nature-Based Solutions (i.e., circular approach exploiting beached Posidonia oceanica leaves) resulted in significant positive effects, stabilizing the shoreline by reducing high erosion and accretion rates. Despite data constraints, outcomes of the performed assessment could represent valuable information to drive adaptive policy pathways in the context of coastal management along the Ugento shoreline.

Deployment of interpretable machine learning in a water treatment device - feasibility exploration of performance enhancement

Machine learning (ML) provides a promising tool for predictive control and unattended operation of water treatment processes, especially in decentralized water supply due to its high variability of water quality and scarcity of manpower. However, model deployment after ML development is a challenge that hinders their implementation. Herein, this study constructed a novel electrocoagulation membrane cathode reactor (ECMCR) as an example of decentralized water treatment due to its highly integratable. Nine ML models were established to determine the ECMCR operational current density (CD) and the Bayesian technique was employed for adaptive hyperparameters tuning of each ML model. Light gradient boosting machine (LightGBM) with turbidity, humic acid (HA), pH, temperature and time as input performed best (R2 = 0.99, MSE = 0.0004) during training, whereas the random forest (RF) was effective and robust for test data (R2 = 0.98, MSE = 0.0056) with same inputs. The mean decrease impurity (MDI) and Shapley Additive exPlanation (SHAP) method indicated that the RF model made predictions based primarily on influent turbidity, which implied a correct “understanding” of the RF model. Finally, the model was deployed to the ECMCR to explore the regulation capability, and the HA removal and turbidity removal of the ECMCR increased by 23.68 % and 23.83 %, respectively. The results demonstrate that ML technology could assist in achieving the desired performance of drinking water treatment devices without manual involvement.