Water Quality Management Strategies Research Articles

The impact of temperature on the growth of Pseudomonas aeruginosa in mineral waters originated from different wells: A predictive approach

This study aimed to evaluate the behavior of Pseudomonas aeruginosa (PSA) in natural mineral water sourced from three different extraction wells and stored at various temperatures (10, 12, 20, 23, and 30 °C) to calculate the kinetic growth parameters of this microorganism through predictive modeling. The physicochemical characterization of waters was also evaluated at the time of collection, and included the analysis of 40 different minerals, and quality parameters such as pH, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity (PSU), and temperature (T). PSA survived in raw mineral water incubated at 12, 20, 23, and 30 °C; however, no growth was observed at 10 °C. Growth curves started with an initial population of ~ 2.5–3 log CFU/mL, and final PSA populations ranged from 3.5 to 4.9 log CFU/mL. The maximum specific growth rate (μmax) at 30 °C varied among the wells, with Well P-07 showing the highest growth rate (0.2 h−1), followed by Well P-08 (0.195 h−1) and well P-01 (0.133 h−1). At 12 °C, well P-01 exhibited the highest growth rate (μmax = 0.22 h−1), indicating a influence of mineral composition in the growth of PSA. The lag time (λ) also varied, with minimum values of 2.4 ± 0.1 h at 30 °C and maximum values of 41.6 ± 0.2 h at 12 °C. From these primary estimated parameters, it was possible to obtain five robust secondary models to describe the influence of temperature on the maximum growth rates and lag phase of PSA in the well. The estimated PSA growth parameters at 20 and 23 °C were subjected to a hierarchical cluster analysis and correlation plots to verify the influence of the physicochemical composition of the waters on the PSA behavior at each well's specific annual average temperature. This analysis confirmed a positive relationship (p < 0.05) between the presence of minerals (Ca, Fe, Sr, Mn, Na) and ions (SO4−3, Cl−) and the PSA lag phase time. These results underscore the need for tailored water quality management strategies that consider chemical composition and temperature to address specific microbial contamination risks.

Multivariate Analysis of Water Quality in the Seybouse River: Implications for Pollution Management

ABSTRACTHeavy metal contamination in water bodies is a pervasive and persistent environmental challenge in many parts of the world, especially in developing countries. This study investigates the use of multivariate analysis methods for monitoring variations in water quality along a spatial gradient and for the interpretation of pollution levels at different sampling sites. We assessed the water quality of the Seybouse River and identified possible sources of pollution using three complementary multivariate analysis techniques (PCA, NMDS, and K‐means clustering). The results indicate a longitudinal gradient in water quality associated with industrial and agricultural activities in the middle and lower Seybouse River. Physico‐chemical and heavy metal analyses show high water turbidity with elevated concentrations of iron and chromium. We show that the contamination stems from four different sources, which can be categorized into different pollution levels. Our results suggest that complementary multivariate methods are a robust approach to identifying and categorizing significant sources of pollution in rivers, enabling the development of future successful water quality management strategies based on water pollution levels. This study highlights the importance of monitoring water quality and taking effective measures to control and mitigate pollution from various sources to ensure the safety of the environment and human health.

The Impact of Acute Ammonia Nitrogen Stress on Serum Biochemical Indicators and Spleen Gene Expression in Juvenile Yellowfin Tuna (Thunnus albacares).

The presence of ammonia nitrogen in water has a significant impact on the serum and spleen of fish, potentially leading to changes in substances such as proteins in the serum while also causing damage to the immune function of the spleen. To investigate the effects of ammonia nitrogen (NH3-N) stress on juvenile yellowfin tuna (Thunnus albacares), this study established three NH3-N concentrations, 0, 5, and 10 mg/L, denoted as L0, L1, and L2, respectively. Serum and spleen samples were collected at 6, 24, and 36 h. The effects of different NH3-N concentrations and exposure times on the physiological status of juvenile fish were investigated by analyzing serum biochemical indices and splenic gene expression. The results indicate that in the L1 group, the serum high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), complement 3 (C3) and complement 4 (C4) levels, and acid phosphatase (ACP) activity showed a trend of initially increasing and then decreasing. In the L2 group, the serum low-density lipoprotein cholesterol (LDL-C), HDL-C, and C4 levels and ACP activity also displayed an initially rising and then declining trend, while TG, C3, and creatinine (CRE) levels and alkaline phosphatase (AKP) activity showed an upward trend. In the L1 group, glutathione peroxidase 1b (GPX1b), interleukin 10 (IL-10), interleukin 6 receptor (IL-6r), and tumor necrosis factor alpha (TNF-α) gene expression levels in the spleen exhibited a trend of first increasing and then decreasing. In the L2 group, IL-10, IL-6r, tumor necrosis factor beta (TNF-β), caspase 2 (casp2), and caspase 9 (casp9) gene expression levels in the spleen also showed an initial increase followed by a decrease. When NH3-N levels are below 5 mg/L, it is recommended to limit stress exposure to no more than 36 h for the juvenile fish. For concentrations ranging from 5 to 10 mg/L, stress should be strictly controlled to within 24 h. Exposure to high NH3-N levels may affect biochemical indicators such as serum lipid metabolism, immunity, and metabolism in juvenile fish, and may damage the expression of antioxidant, immune gene, and apoptosis factors in the spleen. This study aims to deepen our understanding of the effects of NH3-N on juvenile tuna, with the goal of establishing effective water quality monitoring and management strategies. This will ensure the quality of aquaculture water, reduce the harm caused by NH3-N to juvenile yellowfin tuna, and enhance aquaculture efficiency and product quality.

Divergent roles of landscape and young streamflow fraction in stream water quality over seven catchments

Landscape patterns and water age are considered to play similar roles in influencing water quality, as human-caused landscape fragmentation usually leads to complex hydrological pathways and older water ages. The young streamflow fraction (Fyw), which can significantly alter water age, is believed to disproportionately impact water quality. Landscape and Fyw thus may play different roles in streamwater quality, but this assumption has not been examined. Here we examined the roles of Fyw (estimated by the amplitudes of seasonal cycles of oxygen isotope ratios in precipitation (Ap) and streamwater (As)) and landscape pattern (reflected by common landscape metrics) in streamwater quality over 7 forest-dominated catchments in eastern China. Landscape metrics, indicating land use arrangement, were closely related to multiple water quality parameters, suggesting a close association between stream water quality (sinks) and land use (sources). The Fyw had a positive relationship with ammonia nitrogen (R2 = 0.51, P = 0.044) and a negative relationship with nitrate nitrogen (R2 = 0.62, P = 0.022). The Fyw, determined by the heterogeneous-independent As/Ap, offers the possibility of modeling nitrogen compounds across multiple spatial scales. Importantly, the water quality parameters that significantly correlated with Fyw did not correlate with the landscape metrics. Differences in correlations of landscape and Fyw to water quality parameters imply that landscape and Fyw differed in their roles in water quality parameters, as well as the consideration for different water quality management strategies for different contaminants.

Enhancing efficiency and quality control: The impact of Digital Twins in drinking water networks.

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Spatial and temporal dynamics of microbes and genes in drinking water reservoirs: Distribution and potential for taste and odor generation

Numerous reservoirs encounter challenges related to taste and odor issues, often attributed to odorous compounds such as geosmin (GSM) and 2-methylisoborneol (2-MIB). In this study, two large reservoirs located in northern and southern China were investigated. The Jinpen (JP) reservoir had 45.99 % Actinomycetes and 14.82 % Cyanobacteria, while the Xikeng (XK) reservoir contained 37.55 % Actinomycetes and 48.27 % Cyanobacteria. Most of the 2-MIB produced in surface layers of the two reservoirs in summer originated from Cyanobacteria, most of the 2-MIB produced in winter and in the bottom water originated from Actinomycetes. Mic gene abundance in the XK reservoir reached 5.42 × 104 copies/L in winter. The abundance of GSM synthase was notably high in the bottom layer and sediment of both reservoirs, while 2-MIB synthase was abundant in the surface layer of the XK reservoir, echoing the patterns observed in mic gene abundance. The abundance of odor-producing enzymes in the two reservoirs was inhibited by total nitrogen, temperature significantly influenced Actinomycetes abundance in the JP reservoir, whereas dissolved oxygen had a greater impact in the XK reservoir. Overall, this study elucidates the molecular mechanisms underlying odor compounding, providing essential guidance for water quality management strategies and the improvement of urban water reservoir quality.

Seasonal Effects of Constructed Wetlands on Water Quality Characteristics in Jinshan Lake: A Gate Dam Lake (Zhenjiang City, China).

Urban lakes commonly suffer from nutrient over-enrichment, resulting in water quality deterioration and eutrophication. Constructed wetlands are widely employed for ecological restoration in such lakes but their efficacy in water purification noticeably fluctuates with the seasons. This study takes the constructed wetland of Jinshan Lake as an example. By analyzing the water quality parameters at three depths during both summer and winter, this study explores the influence of the constructed wetland on the water quality of each layer during different seasons and elucidates the potential mechanisms underlying these seasonal effects. The results indicate that the constructed wetland significantly enhances total nitrogen (TN) concentration during summer and exhibits the capacity for nitrate-nitrogen removal in winter. However, its efficacy in removing total phosphorus (TP) is limited, and may even serve as a potential phosphorus (P) source for the lake during winter. Water quality test results of different samples indicated they belong to Class III or IV. Restrictive factors varied across seasons: nitrate-nitrogen and BOD5 jointly affected water quality in winter, whereas TP predominantly constrained water quality in summer. These results could provide a reference for water quality monitoring and management strategies of constructed wetlands in different seasons in Jiangsu Province.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52–86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.

Water Quality Assessment and Management Strategies for Nishan Reservoir, Sihe River, and Yihe River Based on Scientific Evaluation

Water Quality Assessment and Management Strategies for Nishan Reservoir, Sihe River, and Yihe River Based on Scientific Evaluation

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022–2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.

Effect of two-dam coexistence on thermal regime: A special withdrawal measure in Fengman reservoir, China

The Fengman Reservoir in China employs a distinctive approach termed two-dam coexistence to mitigate the risks associated with aging and hazardous dams. This method involves the partial demolition of the old dam and construction of a new dam downstream, which allows the remaining part of the old dam to act as a retaining wall for selective water withdrawal. Through a comprehensive study, long-term water temperature data were analyzed, along with the characteristics of the two-dam coexistence system, to comprehend its effects on thermal stratification in the main basin near the dams and the withdrawal water temperature (WWT) regime. The results revealed that the metalimnion shifted upwards, strengthening stratification, and the temperature gradient expanded from 0–0.9 °C/m to 0–10.5 °C/m compared to that under the one-dam condition. Vertical mixing was reduced, causing the temperature difference between surface and bottom waters to range from 6.8 to 26.8 °C. This enhanced the stratification stability by 1.8 times compared with the one-dam condition, with some annual variability. Thus, the buoyancy frequency decreased accordingly. Furthermore, two-dam coexistence influenced the WWT, resulting in higher WWT during the warming period (averaging 4.3 °C from May to June) and lower WWT during the freeze-up period (averaging 0.1 °C from January to February) than that of the one-dam condition. The most significant temperature difference between the main basin near the old dam and the one near the new dam occurred at the bottom of the reservoir (ranging from 3.5 to 12.0 °C), confirming that the old dam functioned as a retaining wall, allowing the thermal regime to change. Owing to the two-dam coexistence, the withdrawal layer shifted by 20.0 m from near the intake to close to the demolition elevation. These changes in stable stratification and variations in WWT play crucial roles in reservoir management. This study offers practical insights for treating similar aging and hazardous reservoirs and serves as a reference for optimizing water quality management strategies.

Analysis of Water Quality Prediction in the Yangtze River Delta under the River Chief System

Water quality prediction is essential for effective water resource management and pollution prevention. In China, research on predictive analytics for various water bodies has not kept pace with environmental needs. This study addresses this gap by conducting a comprehensive analysis and modeling of water quality monitoring data from multiple distributed water bodies specifically within the Yangtze River Delta. Using a novel approach, this paper introduces a distributed water quality prediction system enhanced by a CNN-LSTM joint model. This model synergistically combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks to robustly extract and utilize spatiotemporal data, thereby significantly improving the accuracy of predicting dynamic water quality trends. Notably, the excellent predictive performance of the joint model enables its prediction results to achieve RMSE and MAPE as low as 1.08% and 6.8%, respectively. Empirical results from this study highlight the system’s superior predictive performance. Based on these findings, this paper offers targeted recommendations for water quality monitoring, treatment, and management strategies tailored to the specific needs of the Yangtze River Delta. These contributions are poised to aid policymakers and environmental managers in making more informed decisions.

Comparative evaluation of spatiotemporal variations of surface water quality using water quality indices and GIS

AbstractThere is a need for a comprehensive comparative analysis of spatiotemporal variations in surface water quality, particularly in regions facing multiple pollution sources. While previous research has explored the use of individual water quality indices (WQIs), there is limited understanding of how different WQIs perform in assessing water quality dynamics in complex environmental settings. The objective of this study is to evaluate the effectiveness of three WQIs (Canadian Council of Ministers of the Environment (CCME), National Sanitation Foundation (NSF) and System for Evaluation of the Quality of rivers (SEQ-Eau) and a national water quality regulation in assessing water quality dynamics. The pilot study area is the Acısu Creek in Antalya City of Turkey, where agricultural practices and discharge of treated wastewater effluents impair the water quality. A year-long intensive monitoring study was conducted includig on-site measurements, analysis of numerous physicochemical and bacteriological parameters. The CCME and SEQ-Eau indices classified water quality as excellent/good at the upstream, gradually deteriorating to very poor downstream, showing a strong correlation. However, the NSF index displayed less accuracy in evaluating water quality for certain monitoring stations/sessions due to eclipsing and rigidity problems. The regulatory approach, which categorized water quality as either moderate or good for different sampling sessions/stations, was also found less accurate. The novelty of this study lies in its holistic approach to identify methodological considerations that influence the performance of WQIs. Incorporating statistical analysis, artificial intelligence or multi-criteria decision-making methods into WQIs is recommended for enhanced surface water quality assessment and management strategies.

Market instruments and institutions for reducing pollution in the Ganga River Basin

ABSTRACT In this article, we take an integrated approach to water quality management of the Ganga River, considering the hydrological and ecological integrity of the basin and the economic viability of wastewater treatment options. This basically meant giving due consideration to the quantum and pattern of surface water flows in different river stretches, the vulnerability of groundwater to pollution, the dynamics of surface water–groundwater interactions, and prioritizing regions facing scarcity of water for competitive uses and environmental water stress as well as pollution, while devising strategies for water quality management. The use of market-based instruments and institutions for creating incentives and disincentives to affect behavioral changes was explored, following the identification of regions that are high-risk vis-à-vis groundwater pollution and river pollution.

Nitrate sources and transformations in a river-reservoir system: Response to extreme flooding and various land use

Nitrate (NO3−) pollution poses a global aquatic threat, promoting eutrophication and endangering human health. Large floods can swiftly drive significant nitrogen load into rivers and downstream water bodies, nevertheless, precisely quantifying NO3− sources in watersheds with complex land use patterns remains challenging. In June 2020, heavy and persistent rainfall triggered severe floods across southeastern China, causing Lake Qiandao, the largest reservoir in this region, to reach its highest recorded level. This study investigated NO3− sources and transformations within the Xin'an River-Lake Qiandao system, particularly focusing on the impact of the catastrophic summer flooding event. Through extensive sampling, we identified a distinct nitrogen concentration gradient: from the pristine Xin'an River source, characterized by 95 % forest cover and consistently low nitrogen levels for the initial 125 km, to a gradual rise as the river traversed human-impacted regions, culminating in peak concentrations within residential and agricultural areas. Isotopic analysis identified a general upward trend in nitrate stable isotope (δ15N–NO3–) associated with the expansion of agricultural and urban lands. Mean values of δ15N–NO3– gradually increased from headwaters and forest-dominant catchments (+2.88 ‰) to agricultural regions (+6.64 ‰) and residential areas (+7.10 ‰). A Bayesian modeling identified that during the flood season, soil erosion and chemical fertilizers were the primary contributors, collectively accounting for 74 % of NO3− sources in the Xin'an River. However, accumulated sewage in tributaries and the overflow of sewage from treatment plants, exacerbated by the flood, significantly impacted Lake Qiandao, particularly in densely populated urban areas, contributing 53 % of the total NO3− input. Additionally, NO3− levels and isotopic values in Lake Qiandao were influenced by a mixture of sources and nitrogen cycling processes, including nitrification and algae assimilation following the flood event. In contrast, during baseflow conditions, the contribution of domestic sewage and livestock wastewaters increased to 41 % in the upper river, while Lake Qiandao remained affected by non-point source pollution, with soil erosion and chemical fertilizers contributing 58 % to the total nitrogen pollution. This study sheds light on the complex dynamics of NO3− dynamics within river–reservoir systems, particularly in the context of extreme flooding, emphasizing the critical need for comprehensive water quality management strategies along the entire watershed.

A Study of Physico-Chemical Properties in the Sombrero and Orashi Rivers in the Niger Delta

This study evaluates the physico-chemical parameters in parts of the Sombrero and Orashi Rivers (Agba-Ndele and Ikiri) in the Niger Delta over three months (June to August 2024). Parameters were obtain from the down, mid and up stream, and determined based on standard oceanographic methods PHA, 2004. The surface water samples were collected at a depth of above 15-20cm dark coloured glass bottles measuring 250ml, were used for DO/BOD and water collected were also used for general purpose analysis. The results indicate that Agba-Ndele River exhibited higher turbidity (2.43–2.04 NTU), total suspended solids (3.41–4.12 mg/l), and biological oxygen demand (0.7–3.7 mg/l), alongside lower dissolved oxygen (3.3–7.07 mg/l) levels. In contrast, the Ikiri River displayed lower turbidity (0.80–1.86 NTU), total suspended solids (1.60–3.88 mg/l), and biological oxygen demand (1.5–2.1 mg/l), with higher dissolved oxygen levels (6.3–6.22 mg/l). Chemical oxygen demand ranged from 0.873–5.06 mg/l for Agba-Ndele and 0.813–4.42 mg/l for Ikiri. Both rivers had alkaline pH values (8.43–9.98), with electrical conductivity measuring 12.36–14.20 µs for Agba-Ndele and 4.56–12.99 µs for Ikiri. The result further showed that Nutrient analysis indicated phosphate levels of 5.582–6.740 mg/l for Agba-Ndele and 5.083–7.011 mg/l for Ikiri, while nitrate concentrations were 0.950–2.927 mg/l for Agba-Ndele and 1.373–3.469 mg/l for Ikiri. The findings indicate that both rivers face distinct water quality challenges, showing signs of stress from industrial activities, agricultural runoff, urbanization, and other anthropogenic activities. This research underscores the need for targeted water quality management strategies to safeguard these vital ecosystems and support community health and resilience in the Niger Delta.

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (&lt;10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.

New records on toxic cyanobacteria from Brazil: Exploring their occurrence and geography

Cyanobacterial Harmful Algal Blooms (CyanoHABs) pose a significant threat to communities globally, impacting ecosystems and public health. This study provides an in-depth review of the current state of cyanotoxins and the distribution of CyanoHABs species in Brazil, while also detailing the methods used for their detection. Four hundred and twenty-one incidents were analyzed from 1993 to 2021, compiling cyanotoxin records and toxic CyanoHABs occurrences. The investigation begins with the first detection of microcystins in 1994 and highlights pivotal moments, like the 1996 “Caruaru Syndrome” outbreak. This event encouraged research and updated cyanotoxin-monitoring guidelines. The Brazilian drought period of 2015–2016 exacerbated cyanobacterial growth and saxitoxin levels, coinciding with Zika-related microcephaly. This study delves into methods used for cyanotoxin analysis, including ELISA, bioassays, HPLC, and LC-MS. Additionally, we investigated the toxicity of 37 cyanobacterial strains isolated from various Brazilian environments. Extracts were tested against Artemia salina and analyzed by LC-MS. Results revealed toxicity in extracts from 49 % of cyanobacterial strains. LC-MS results were analyzed using GNPS MS/MS molecular networking for comparing experimental spectra with those of cyanotoxin standards against in-house databases and the existing literature. Our research underscores the variability in cyanotoxin production among species and over time, extending beyond microcystins. LC-MS results, interpreted through the GNPS platform, revealed six cyanotoxin groups in Brazilian strains. Yet, compounds present in 75 % of the toxic extracts remained unidentified. Further research is crucial for fully comprehending the impact of potentially harmful organisms on water quality and public health management strategies. The study highlights the urgent need for continuously monitoring cyanobacteria and the cyanotoxin inclusion of management in public health policies.

Appling fluorescence spectroscopy with absolute principal component coefficient to explore dynamic migration of DOM fractions from an urbanized river during torrential rainfall

This research delves into the dynamic interplay between urbanization and the characteristics of Dissolved Organic Matter (DOM) in the Anyang River, particularly under the stress of torrential rain. The motivation stems from a critical need to decipher how urban landscapes influence water quality, focusing on the intricate transformations and movements of DOM. Employing advanced fluorescence spectroscopy techniques like Excitation-Emission Matrices (EEM) and Parallel Factor Analysis (PARAFAC), the study meticulously differentiates DOM compositions in urban and agricultural settings. It unveils a pronounced distinction, with urban streams showing elevated proteinaceous DOM from wastewater, contrasting with the humic substances prevalent in agricultural runoff. The analysis also captures how intense rainfall events catalyze significant shifts in DOM profiles, thereby emphasizing the need for tailored water quality management strategies in urbanized catchments. This comprehensive approach not only bridges gaps in understanding the urban impact on riverine ecosystems but also sets a foundation for future research and policy development in the face of escalating environmental changes.