Continuous Water Quality Monitoring Research Articles

Association of Aquatic Invertebrates with the Water Quality in the Ponds of Bhaktapur

Aquatic invertebrates are representative of the health of habitats they live in. This study was conducted to evaluate aquatic invertebrate diversity and relate them with physico-chemical factors in the ponds of Bhaktapur. Rani pokhari, Bhajya pokhari, Na pokhari and Kamal pokhari were selected for the study. Plankton net was swiped out for approximately one minute in each corner of all the ponds. Five subsequent sweeps were taken repeatedly at each site. Invertebrates collected in the net were kept in a plastic container covered with a lid. The collected species were preserved in 70% alcohol. For physico-chemical analysis, water samples were collected from 10cm below the surface water in BOD bottles from each site and taken to the laboratory of Department of Zoology, Bhaktapur Multiple Campus for the further identification and laboratory work. Total 16 species of aquatic invertebrates belonging to five phyla, 10 orders and 16 families were recorded from the ponds of Bhaktapur. Species richness of aquatic invertebrates was found to be 13, 11, four and five in Rani pokhari, Bhajya pokhari, Na pokhari and Kamal pokhari respectively. Phylum Arthropoda was most abundant in all four ponds. The correlation of species richness with physico-chemical parameters showed a positive correlation with water temperature (r = 0.96), electric conductivity (r = 0.98), free carbon dioxide (r = 0.99) and dissolved oxygen (r = 0.51) while the negative correlation with pH (r = -0.83) and alkalinity (r = -0.83). The result obtained is valuable and useful for continuous water quality monitoring activities and the conservation of freshwater fauna in the ponds of Bhaktapur. Further detailed studies on aquatic invertebrates were highly recommended.

Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs

The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO4-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO4-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management.

Caracterización y Análisis de la Calidad del Agua en Entornos Urbanos mediante la implementación de un Sistema Embebido con Tecnología IoT

This study aims to characterize and analyze water quality in urban environments using an IoT embedded system, focusing on San Camilo’s community of Quevedo, Ecuador, where population growth has increased the demand for drinking water. An embedded system was designed to make use of a microcontroller and sensors to monitor critical water quality parameters in real time. The collected data is transmitted to a cloud platform for storage, analysis, and visualization. Spearman's correlation coefficient was preferred for statistical analysis of the data. This allowed for the identification of patterns and relationships between the variables examined. The data revealed significant associations in non-linear data, facilitating a decision regarding the development of predictive models for water management and treatment. The results demonstrate the feasibility and efficiency of using IoT technologies in the continuous monitoring of water quality, offering a scientific basis for the collection and processing of relevant data on the quality of drinking water in residential areas, providing a robust tool to monitor public health and the comfort of the inhabitants.

An uncertainty‐based evaluation of sulphuric compounds transport in reservoirs considering sensitivity analysis

AbstractDeepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE‐QUAL‐W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate‐rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards.

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

GROUNDWATER POLLUTION SOURCE USING PRINCIPAL COMPONENT ANALYSIS IN GUELMA PLAIN, NORTHEAST ALGERIA

This study was conducted in the agricultural region of Guelma, located in northeastern Algeria, where groundwater is the main source of water for human consumption, agriculture, and industry. The objective was to characterize groundwater quality and identify potential pollution sources using multivariate statistical methods, including Principal Component Analysis (PCA), correlation matrix, and the Piper diagram. The analyses revealed strong correlations between certain ions, such as sodium and chloride, suggesting carbonate dissolution processes, such as calcite and dolomite, leading to increased water hardness. The Piper diagram allowed for the classification of water types based on the relative concentrations of major cations (Ca²⁺, Mg²⁺, Na⁺+K⁺) and anions (Cl⁻, SO₄²⁻, CO₃²⁻+HCO₃⁻), showing a predominance of mixed hydrochemical types influenced by both natural and anthropogenic processes.PCA then simplified the interpretation by identifying the most influential variables that could serve as key indicators for the continuous monitoring of water quality. Overall, the results indicate that groundwater chemistry in this region is strongly influenced by human activities and local geological conditions, highlighting the need for sustainable management and continuous monitoring to protect this vital resource.

Salinity and microbiological risk assessment in surface runoff and main tributaries to La Vega Dam in Teuchitlán, Jalisco, Mexico.

Objective: This research aimed to evaluate the water quality of the main tributaries to the La Vega Dam for irrigation purposes through the physicochemical and microbiological parameters. Design/methodology/approach: The 10 main tributaries to the La Vega Dam were selected. The variables evaluated were pH, Temperature, Electrical Conductivity, Total Dissolved Solids and Dissolved Oxygen, boron calcium, magnesium, sodium, SAR, and RCS. Results: 50% of samples presented low sodicity risk, 10% medium risk, 20% and 20% very high risk. In the CSR results, only one site (10%) presented a low risk, and the remaining four tributaries (40%) of this area presented a medium risk. Five sites (50%) located east of the dam presented a high risk. Also, high boron concentration was observed, corresponding to El Salado River. Fecal pollution was detected in 100% of sites, limiting their use as irrigation water. Limitations on study/implications: Continuous water quality monitoring of the main tributaries to and into La Vega Dam should be followed up. Findings/conclusions: The most significant contribution of contamination to the dam was observed in the eastern part, with the entrance of El Salado River. High concentrations of boron were also observed. A high concentration of total and fecal coliforms was also found, in addition to the presence of Pseudomonas spp., which indicates fecal contamination. The effluent at the east of La Vega Dam is unsuitable for irrigation.

Prediction of Physico-Chemical Parameters of Surface Waters Using Autoregressive Moving Average Models: A Case Study of Kis-Balaton Water Protection System, Hungary

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3).

Optimization of biological nitrogen removal in full-scale municipal WWTPs using activated sludge model simulation

The study evaluated the most efficient biological nitrogen removal (BNR) process in four full-scale municipal wastewater treatment plants (WWTPs) by using BioWin, a simulation software based on the activated sludge model (ASM). A series of experiments were conducted to determine the kinetic and stoichiometric parameters for the ASM. Results indicated that autotrophic maximum specific growth rates in the studied WWTPs were generally higher compared to previous findings, likely due to their low COD/N ratios, emphasizing the importance of local parameterization. Continuous water quality monitoring in each plant was employed to validate the model. Dynamic simulation results indicated that the error remained within an acceptable range, with a mean relative error of less than 20%, confirming the reliability of ASM parameters derived from batch experiments. Subsequently, various operational scenarios were analyzed to determine the optimal BNR process for each plant, considering influent flow rate, internal recycling, and methanol addition. Simulation outcomes suggested that O/A (oxic/anoxic) operation is preferable for plants with low organic content (COD = 60–110 mg/L), considering both operational costs and total nitrogen removal rate. Conversely, A/O (anoxic/oxic) operation might be advantageous for plants with higher influent organic matter (COD = 200 mg/L).

Practicalities of community-led continuous water quality monitoring: lessons from Taiwan and UK pilots

Practicalities of community-led continuous water quality monitoring: lessons from Taiwan and UK pilots

Land-Use–Land Cover Changes in the Urban River’s Buffer Zone and Variability of Discharge, Water, and Sediment Quality—A Case of Urban Catchment of the Ngerengere River in Tanzania

The physical integrity of the Ngerengere River and its three tributaries drains within Morogoro Municipality were evaluated by assessing the variations in land-use–land cover (LULC) in the river’s buffer zone, the discharge, and the contamination of river water and sediment from nutrients and heavy metals. Integrated geospatial techniques were used to classify the LULC in the river’s buffer zone. In contrast, the velocity area method and monitoring data from the Wami-Ruvu Basin were used for the discharge measurements. Furthermore, atomic absorption spectrophotometry was used during the laboratory analysis to determine the level of nutrients and heavy metals in the water and river sediment across the 13 sampling locations. The LULC assessment in the river’s buffer during the sampling year of 2023 showed that bare land and built-up areas dominate the river’s buffer, with a coverage of 28% and 38% of the area distribution. The higher discharge across the sampling stations was in the upstream reaches at 3.73 m3/s and 2.36 m3/s at the confluences. The highest concentrations of heavy metals in the water for the dry and wet seasons were 0.09 ± 0.01, 0.25 ± 0.01, 0.03 ± 0.02, 0.73 ± 0.04, 4.07 ± 0.08, and 3.07 ± 0.04 mg/L, respectively, for Pb, Cr, Cd, Cu, Zn, and Ni. The order of magnitude of the heavy metal concentration in the sediments was Zn > Ni > Cr > Cu > Cd > Pb, while the highest NO2−, NO3−, NH3, and PO43− in the water and sediment were 2.05 ± 0.01, 0.394 ± 0.527 0.66 ± 0.05, and 0.63 ± 0.01 mg/L, and 2.64 ± 0.03, 0.63 ± 0.01, 2.36 ± 0.01, and 48.16 ± 0.01 mg/kg, respectively, across all sampling seasons. This study highlights the significant impact of urbanization on river integrity, revealing elevated levels of heavy metal contamination in both water and sediment, the variability of discharge, and alterations in the LULC in the rivers’ buffer. This study recommends the continuous monitoring of the river water quality and quantity of the urban rivers, and the overall land-use plans for conserving river ecosystems.

Mining's legacy: Unraveling the impacts of acid mine drainage on the rivers and streams of the Santa Catarina coal region, Brazil

Acid Mine Drainage (AMD) is a major environmental concern associated with coal mining activities, leading to the deterioration of water quality and posing significant risks to aquatic ecosystems and human health. In this study, we analyzed the physicochemical parameters of water samples collected from rivers and streams affected by mining in the Santa Catarina coal region, Brazil, to assess the extent and severity of AMD impact. Our findings reveal a substantial increase in element concentrations, including Fe, Mn, Al, and major cations (Na, Mg, Ca, and K), with some values exceeding background levels by several orders of magnitude. The majority of the samples exhibited acidic pH values, with only 24% and 34% within the limits established by the Ministry of Health and the geochemical background, respectively. Strong correlations between physicochemical parameters, such as pH and EH, and between acidity, sulfate, and metals, highlight the role of pyrite oxidation and mineral dissolution in generating AMD. Although most samples presented values within the established limits for toxic metals (Cu, Cd, Pb, As, and Hg), high concentrations of Zn were observed, exceeding regulatory and background limits. The results of this study emphasize the need for continuous monitoring and assessment of water quality in the region and contribute to a deeper understanding of the extent and severity of AMD in the Santa Catarina coal region.

A Novel Method for Anomaly Detection and Signal Calibration in Water Quality Monitoring of an Urban Water Supply System

Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to 99.6%. Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management.

On-line biological oxygen demand (BOD) monitoring using a microbial membrane reactor

An on-line biological oxygen demand (BOD) monitoring instrument, utilizing an in-situ microbial membrane reactor, automates membrane cultivation, calibration, and rapid BOD detection, eliminating reagent pollution is presented. The core instrument used was an STM32F103RCT6 microcontroller embedded in the FreeRTOS real-time operating system. A series of practical application tests were carried out by the instrument. The results demonstrate that the instrument provides good stability and accuracy in detecting multiple concentrations of glucose and glutamic acid (GGA) standards, with a relative error range and relative standard deviation range from −3.01 to 11.88% and 4.19 to 6.69%, respectively. The tested water samples had a relative error range from −4.93 to 13.37%. The on-line monitoring instrument for BOD has been tested for almost three months and has shown stable performance, high accuracy, and real-time, in-situ, and on-line BOD detection. In the past three months of application testing, the on-line biological oxygen demand monitoring instrument has shown stable performance, good accuracy, and real-time, in-situ, and on-line BOD measurements. This device has significant environmental benefits for continuous water quality monitoring and the timely monitoring of water pollution.

Delta Blue(green)s: The Impact of Drought and Drought Management Actions on Microcystis in the Sacramento–San Joaquin Delta

Cyanobacterial phytoplankton blooms are more prevalent in the freshwater Sacramento-San Joaquin Delta (Delta) since the late 1990s, including blooms driven by overgrowths of potentially toxigenic organisms of the genus Microcystis. Data from 2014 to 2021 were used to show how flow dynamics, water temperature, and water clarity drive occurrence of Microcystis. We used a Microcystis bloom in the central Delta from 2021 as a case study for how novel monitoring tools can track blooms in real-time and be used post hoc to evaluate the effects of management actions. Microcystis was detected throughout the Delta in all but the highest-flow years, and bloom incidence and severity increased during drier years. In the South Delta, Franks Tract, lower San Joaquin River, and Old River regions, where blooms are most prevalent, higher water temperatures and clarities combined with lower exports from state and federal water projects were the best explanatory factors for the occurrence of Microcystis blooms. Nutrient concentrations were lower in summer than in winter, but only became limiting at high phytoplankton concentrations. We used satellite data and in situ continuous monitoring of flow, phytoplankton communities, and water quality to track hydro-biogeochemical conditions during the 2021 case study Microcystis bloom in the Central Delta. We did not find evidence that changes to Delta outflow regulatory standards contributed to this bloom, but changes in flow caused by a salinity barrier placed in west False River may have exacerbated the bloom. The frequency and severity of droughts are expected to increase in the future as a result of climate change, and our study demonstrates how continued monitoring of cyanotoxins, water quality, and phytoplankton communities could help improve management of cyanobacterial blooms in the Delta and other estuaries.

A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro Hyper-Spectrometer.

The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350-950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum's relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment.

Environmental impacts of tilapia fish cage aquaculture on water physico-chemical parameters of Lake Kivu, Democratic Republic of the Congo

In Africa, cage aquaculture has been growing due to its potential to address food insecurity concerns, provide livelihoods, and contribute to local economies. However, there is a need for continued research on the sustainability and potential ecological effects of cage aquaculture in African lakes and reservoirs. Even with an adequate amount of water, lakes and reservoirs cannot provide ecosystem services if their water quality is not properly managed. The current study on Lake Kivu, DRC focuses on understanding the effects of tilapia cage aquaculture on selected water quality physico-chemical parameters in the Bukavu sub-basin, DRC. The research was conducted in both caged and uncaged sampling stations, on the spatial and temporal scale from April to September 2023 at three bays serving as sampling stations: two caged (Ndendere, Honga) and one non-caged (Nyofu). Some physico-chemical parameters were measured in situ, whereas chlorophyll a and nutrients analysis were performed at the Institut Supérieur Pédagogique (I.S.P) laboratory in Bukavu. The parameters were used to calculate three indices water quality indices: the water quality index (WQI) to classify the water quality at the stations, the organic pollution index (OPI) to determine the level of organic pollution, the Carlson's Trophic Status Index (CTSI) to classify the trophic state of the stations. Chlorophyll a concentration was a measure of algal biomass. All physico-chemical parameters, apart from DO, ammonium and temperature showed no significant differences among stations and depths. Interaction between stations and between seasons was only observed on turbidity. The WQI for all the sampling stations ranged from medium to good quality (51–90). The OPI for all stations showed minimal level of pollution (4.6–5.0) hence lake's water still organically unpolluted. CTSI results indicated the sampling stations are in a eutrophic state (50 to 70). Fish cage aquaculture does not yet pose harm to the water quality of the two Lake Kivu stations under consideration, according to the study's findings. However with the anticipated growth of cage fish farming activities to meet the rising fish demand, continuous monitoring of water quality in the Lake should be done to inform management decisions and for sustainable aquaculture.

Quantifying the combined impacts of anthropogenic CO2 emissions and watershed alteration on estuary acidification at biologically-relevant time scales: a case study from Tillamook Bay, OR, USA.

The impacts of ocean acidification (OA) on coastal water quality have been subject to intensive research in the past decade, but how emissions-driven OA combines with human modifications of coastal river inputs to affect estuarine acidification dynamics is less well understood. This study presents a methodology for quantifying the synergistic water quality impacts of OA and riverine acidification on biologically-relevant timescales through a case study from a small, temperate estuary influenced by coastal upwelling and watershed development. We characterized the dynamics and drivers of carbonate chemistry in Tillamook Bay, OR (USA), along with its coastal ocean and riverine end-members, through a series of synoptic samplings and continuous water quality monitoring from July 2017 to July 2018. Synoptic river sampling showed acidification and increased content in areas with higher proportions of watershed anthropogenic land use. We propagated the impacts of 1). the observed riverine acidification, and 2). modeled OA changes to incoming coastal ocean waters across the full estuarine salinity spectrum and quantified changes in estuarine carbonate chemistry at a 15-minute temporal resolution. The largest magnitude of acidification (-1.4 units) was found in oligo- and mesohaline portions of the estuary due to the poor buffering characteristics of these waters, and was primarily driven by acidified riverine inputs. Despite this, emissions-driven OA is responsible for over 94% of anthropogenic carbon loading to Tillamook Bay and the dominant driver of acidification across most of the estuary due to its large tidal prism and relatively small river discharges. This dominance of ocean-sourced anthropogenic carbon challenges the efficacy of local management actions to ameliorate estuarine acidification impacts. Despite the relatively large acidification effects experienced in Tillamook Bay (-0.16 to -0.23 units) as compared with typical open ocean change (approximately -0.1 units), observations of estuarine would meet existing state standards for . Our analytical framework addresses pressing needs for water quality assessment and coastal resilience strategies to differentiate the impacts of anthropogenic acidification from natural variability in dynamic estuarine systems.

Study of the requirements of an autonomous system for surface water quality monitoring

In recent years, there has been increasing awareness of the preservation, protection and sustainable use of natural resources. Water resources, being one of the most important natural resources, face major threats due to contamination by pollutants of various types and origins. Maintaining the quality of water resources requires more robust, reliable and more frequent monitoring than traditional techniques of data collection based on sporadic, discontinuous and manual processes. The management of large geographical areas, the insufficient spatiotemporal discretization of the values of samples collected by traditional processes and the unpredictability of natural phenomena, require a new approach to data collection procedures. This article, which is the result of ongoing research, defines the technical requirements and technologies used in a continuous and regular monitoring of surface water quality in freshwater systems, whose data acquisition system helps to identify the sources of pollution and the contaminants flow along the waterways. The design of a versatile real-time water quality monitoring system, which, due to its environmental constraints should be based on renewable energies and wireless transfer of energy, will contribute to improve the management and effective protection of water resources.

Phytoplankton community structure and water quality assessment in Xuanwu Lake, China

Phytoplankton community structure influences the water quality of freshwater lakes and attracts the attention worldwide. The physicochemical parameters including dissolved oxygen (DO), total nitrogen, total phosphorus, NH4+-N and CODMn and biological index such as Chl.a concentrations were measured in water of different sites of Xuanwu Lake, China in varied seasons. The Trophic level index (TLI) was calculated to evaluate the trophic status of the lake based on critical water quality parameters. The phytoplankton community structure and biomass in the water was also identified and detected. Obvious change of physicochemical parameters and biological index was detected in varied sampling time. The results showed that TN, TP, NH4+-N, CODMn and Chl.a had higher concentrations in June and August with higher temperature. Obtained Trophic level index values from key water quality indicators revealed that Xuanwu Lake exhibited a state of mesotrophic level in December 2021, mild eutrophic level in October and February, and remained in a state of moderate eutrophication during other periods having higher temperature. Chlorophyta exhibited the highest species diversity and accounted for 48.1% of the entire community. Cyanobacteria had higher density compared to other phytoplankton, which suggesting the potential ecological risk in the lake. These findings aligned with the outcomes of the analysis of the phytoplankton community structure, underscoring a certain degree of correlation between the primary water quality indicators and phytoplankton abundance in Xuanwu Lake. That is, higher discharge of contaminant including nutrients potentially dominated the phytoplankton community structure. The continuous monitoring of phytoplankton community and water quality enabled the assessment of its trophic status of urban lake. Appropriate measures such as adjustment of phytoplankton community structure were proposed to mitigate the eutrophication status of lake. The investigation indicated that phytoplankton variation was the important indicator of water quality and supplied direct evidence for the water quality management and ecosystem restoration of urban lakes.