Water Quality Monitoring Data Research Articles

Integrated STL-DBSCAN Algorithm for Online Hydrological and Water Quality Monitoring Data Cleaning

Integrated STL-DBSCAN Algorithm for Online Hydrological and Water Quality Monitoring Data Cleaning

Analysis of Nitrogen and Phosphorus Pollution in Shenzhen Bay over the Past 40 Years

Based on 40 years of routine water quality monitoring data from Shenzhen Bay and special intensive monitoring since 2021, we analysed the spatiotemporal variations in nitrogen and phosphorus pollution. The results indicate serious levels of nitrogen and phosphorus pollution in Shenzhen Bay. Although recent trends show a decline in concentrations of inorganic nitrogen and reactive phosphate—with the annual average concentration of inorganic nitrogen dropping below 1.0 mg/L since 2020 and reactive phosphate levels near the bay mouth decreasing to approximately 0.020 mg/L—the overall water quality still does not meet China’s Class IV seawater quality standards. Additionally, water quality is poorer during the rainy season, with the average summer concentration of inorganic nitrogen being over 25% higher than in spring and autumn. The water quality is primarily influenced by land-sourced pollutants and tidal effects, generally, with the concentration of DIN and DIP decreased from the estuary of the Shenzhen River, the main seagoing river, to the bay mouth. Near the river’s mouth, concentrations of inorganic nitrogen and reactive phosphate are approximately six and four times higher, respectively, than those near the bay mouth. Due to current emission standards and sewage disposal methods, the proportion of nitrate nitrogen in DIN has significantly increased, exceeding 80% since 2020. To improve the water quality of Shenzhen Bay and to reduce pollution inputs, coordinated land and marine development activities are recommended.

Spatiotemporal Variation Characteristics and Source Identification of Nitrogen in the Baiyangdian Lake Water, China

To study the characteristics and sources of nitrogen in the Baiyangdian Lake, this research conducted water quality monitoring during three hydrological periods (normal period, flood period, and dry period), and 165 pieces of routine water quality monitoring data were collected from the three national control sections for Baiyangdian Lake and its inflow rivers. By integrating water chemical analysis with multivariate statistical techniques, the study comprehensively investigated the spatiotemporal variation patterns of nitrogen in Baiyangdian Lake and identified the sources of nitrogen pollution. The results showed that the concentration of total nitrogen (TN) was highest during the dry period, reaching an average of 0.924 mg/L, and 31.3% of the sites exceeded the national Grade III surface water quality standard, reflecting a potential risk of nitrogen pollution. Based on the ion ratio method and principal component analysis (PCA), the main sources of nitrogen pollution in Baiyangdian Lake were identified as manure and domestic sewage, with agricultural fertilizers also having a certain impact on water nitrogen pollution. In addition, the study also compared the nitrogen concentration in Baiyangdian Lake with several important lakes in China. The results showed that the concentrations of TN and ammonium nitrogen (NH4+-N) in Baiyangdian Lake are lower than those in lakes in areas with similar human activity intensity, indicating that the water quality of Baiyangdian is relatively good. This study can provide a scientific basis for water quality management and pollution prevention for Baiyangdian Lake.

Long term changes in Chalk groundwater quality in East Anglia, UK

Groundwater abstraction quantity from the Chalk is limited to what is sustainable. Water quality data collected in the 1970s and 1980s have been compared with modern water quality monitoring data for 25 public supply abstraction sites. This comparison has established that there has been both substantial (magnitude) and significant (statistical) change in composition at all sites. Identified changes generally represent real changes in groundwater composition. At some sites in the south of the area considered, water that is not the result of present-day recharge processes is being replaced by water from present-day recharge. It is also clear that, at those sites where ancient brackish water is known to be present at depth, it is generally not being mobilised by pumping. In the north of the area, changes are more subtle but there is a tendency of groundwater composition to approach the regional average, apart from calcium which is trending higher. Nitrate has not increased here, suggesting that reduction is a still-active process. Thus, although quantity has been sustained, quality is changing but not to a degree that impacts potable use.

A Groundwater Quality Index Proposed for Assessment of Groundwater Quality: A Case Study of Quang Tri Province, Central Vietnam

Background: Water quality assessment is a crucial task to manage water sources and control water pollution. The purpose of this study is to develop a Groundwater Quality Index (GWQI) to assess groundwater quality. Methods: The GWQI was developed and functioned by weight (wi) and sub-index (qi) of ten selected water quality parameters. Principal Component Analysis (PCA) was applied to a water quality monitoring data set (year 2016 to 2019, 163 samples) at 27 locations in Quang Tri province, aiming to identify the weight of each parameter. Sub-index linear function was established based on the allowable limits for each parameter defined in the National Technical Regulation on drinking water quality. Multiplicate (GWQIM), additive (GWQIA), and reference (GWQIRef) formulas were used to compute the final GWQI indices. The sensitivity and suitability of the proposed GWQI in water quality evaluation were also tested. Results: Among the three GWQI calculation methods, GWQIM was found to be the most suitable in reflecting groundwater quality in the study area. From 2019 to 2022 (152 samples), in the province, 63.2% of GWQIM values were classified as EXCELLENT or GOOD, 17.1% as POOR, and 19.7% as VERY POOR, or UNFIT FOR DRINKING. Conclusion: This improved GWQI can be applied to assess groundwater quality at local or regional scales.

Impact of Landscape Patterns on Water Quality in Urbanized Rivers at Characteristic Scale: A Case of Pearl River Delta, China.

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Repeated conventional flushing to improve water quality in a full‐scale distribution system

AbstractOne of the primary ways utilities prevent and address water quality concerns in distribution systems is via regular hydrant flushing, yet rigorous research to evaluate the impact of full‐scale flushing programs is limited. This study employed time‐series and correlation analyses to evaluate the efficacy of a utility's five‐month repeated conventional flushing program to reduce nitrification and improve disinfectant residual concentrations in the distribution system. Short‐term water quality improvements during flushes were common across the 16 locations flushed, but lasting improvements were inconsistent as demonstrated by continued nitrification and heterogeneous water quality changes. Flushing frequencies and flow rates may need to be tailored to individual sites, even when similar water quality challenges exist. Water quality monitoring data and related analyses helped to prioritize flush sites, maximize efficiency during and between flushes, and inform decisions to implement additional interventions.

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.

Groundwater health risk assessment and its temporal and spatial evolution based on trapezoidal fuzzy number-Monte Carlo stochastic simulation: A case study in western Jilin province

The United States Environmental Protection Agency (USEPA) Four-step-Method (FSM) is a straightforward and extensively utilized tool for evaluating regional health risks, However, the complex and heterogeneous groundwater environment system causes great uncertainty in the assessment process. Triangular stochastic simulation (TSS) possesses certain advantages in solving uncertainty problems, but its inadequacy with discrete data reveals limitations in this aspect. To solve the above problems, this study proposes to construct trapezoidal fuzzy number-Monte Carlo stochastic simulation (TFN-MCSS) to compensate for the shortcomings of the first two methods. This method adopted trapezoidal fuzzy number (TFN) analysis to comprehensively consider the characteristics of a large dispersion of water quality monitoring data and the uncertainty of the human health risk assessment (HHRA) process. Concurrently, to overcome the subjectivity and uncertainty of artificially determining the interval of TFN in traditional methods, the slope was used to select the most probable interval value (TMPIV) of TFN combined with the α-truncated set technique (α-TST) and MCSS. Based on these, a TFN-MCSS was constructed and applied to groundwater HHRA in western Jilin Province. First, the groundwater chemical characteristic determination and water quality evaluation in western Jilin were performed to identify the main pollution indicators, and the health risk effects of pollutants in groundwater of different aquifers at different time periods on adults and children were evaluated using the TFN-MCSS. The uncertainty and sensitivity were analyzed, and the primary risk control indicators were identified and compared to FSM and TSS. The results reveal that TFN-MCSS was more sensitive to data and could reduce the uncertainty of assessment process. It indicated that over a 10-year period, the health risks associated with unconfined groundwater (UW) and confined water (CW) decreased by greater than 52 %. However, the highest total non-carcinogenic risk index (THI) was 1.3-fold higher than the safety threshold, and this posed a health risk.

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.

Variability and driving effect of aquatic gross primary productivity across long-distance inter-basin water diversion project

Long-distance water diversion projects significantly affect the water resource cycle and allocation of regions. However, many unknowns still exist in water ecosystem functionality and energy flow in large-scale inter-basin water diversion projects. This study focused on the gross primary production (GPP) in the Middle-Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the world's longest inter-basin water diversion project. The spatiotemporal distribution, driving factors, and pathways of GPP were comprehensively analyzed based on four years of high-frequency water quality monitoring and satellite re-analysis data from 11 national stations, coupling the Bayesian hierarchical models and multivariate statistical methods. The results showed that the daily average GPP in the main canal of the MRSNWDPC over the years was 2.650 g O2 m−2 d−1, with seasonal peak GPP occurring in summer and generally increasing with the distance along the canal. Five structural equation modeling (SEM) were built to analyze the variations of GPP, revealing the surface pressure (PS) and surface carbon dioxide concentrations (CO2) and pH value were the main driving factors in the main canal. The surface pressure showed significant negative impacts on GPP changes in the canal, while the CO2 and pH showed different direction effects in different sections. The daily average carbon equivalent GPPs in the MRSNWDPC is 0.828 g C m−2 d−1, ranging from 0.600 to 1.028 g C m−2 d−1, and close to the Yangtze River and the East Sea of China. This study provides a scientific basis for a deeper understanding and analysis of the energy flow mechanisms in water ecosystems of mega inter-basin water diversion projects.

Effect of fluoridated water on invasive NHS dental treatments for adults: the LOTUS retrospective cohort study and economic evaluation.

Most water fluoridation studies were conducted on children before the widespread introduction of fluoride toothpastes. There is a lack of evidence that can be applied to contemporary populations, particularly adolescents and adults. To pragmatically assess the clinical and cost effectiveness of water fluoridation for preventing dental treatment and improving oral health in a contemporary population of adults, using a natural experiment design. Retrospective cohort study using routinely collected National Health Service dental claims (FP17) data. National Health Service primary dental care: general dental practices, prisons, community dental services, domiciliary settings, urgent/out-of-hours and specialised referral-only services. Dental patients aged 12 years and over living in England (n = 6,370,280). Individuals exposed to drinking water with a fluoride concentration ≥ 0.7 mg F/l between 2010 and 2020 were matched to non-exposed individuals on key characteristics using propensity scores. Primary: number of National Health Service invasive dental treatments (restorations/'fillings' and extractions) received per person between 2010 and 2020. Secondary: decayed, missing and filled teeth, missing teeth, inequalities, cost effectiveness and return on investment. National Health Service Business Services Authority dental claims data. Water quality monitoring data. Predicted mean number of invasive dental treatments was 3% lower in the optimally fluoridated group than in the sub/non-optimally fluoridated group (incidence rate ratio 0.969, 95% CI 0.967 to 0.971), a difference of -0.173 invasive dental treatments (95% CI -0.185 to -0.161). This magnitude of effect is smaller than what most stakeholders we engaged with (n = 50/54) considered meaningful. Mean decayed, missing and filled teeth were 2% lower in the optimally fluoridated group, with a difference of -0.212 decayed, missing and filled teeth (95% CI -0.229 to -0.194). There was no statistically significant difference in the mean number of missing teeth per person (0.006, 95% CI -0.008 to 0.021). There was no compelling evidence that water fluoridation reduced social inequalities in treatments received or missing teeth; however, decayed, missing and filled teeth data did not demonstrate a typical inequalities gradient. Optimal water fluoridation in England in 2010-20 was estimated to cost £10.30 per person (excluding original setup costs). Mean National Health Service treatment costs for fluoridated patients 2010-20 were 5.5% lower per person, by £22.26 (95% CI -£23.09 to -£21.43), and patients paid £7.64 less in National Health Service dental charges per person (2020 prices). Pragmatic, observational study with potential for non-differential errors of misclassification in fluoridation assignment and outcome measurement and residual and/or unmeasured confounding. Decayed, missing and filled teeth data have not been validated. Water fluoridation cost estimates are based on existing programmes between 2010 and 2020, and therefore do not include the potentially significant capital investment required for new programmes. Receipt of optimal water fluoridation between 2010 and 2020 resulted in very small health effects, which may not be meaningful for individuals, and we could find no evidence of a reduction in social inequalities. Existing water fluoridation programmes in England produced a positive return on investment between 2010 and 2020 due to slightly lower National Health Service treatment costs. These relatively small savings should be evaluated against the projected costs and lifespan of any proposed capital investment in water fluoridation, including new programmes. National Health Service dental data are a valuable resource for research. Further validation and measures to improve quality and completeness are warranted. This trial is registered as ISRCTN96479279, CAG: 20/CAG/0072, IRAS: 20/NE/0144. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: NIHR128533) and is published in full in Public Health Research; Vol. 12, No. 5. See the NIHR Funding and Awards website for further award information.

Spatio-Temporal Variation in the Exceedance of Enterococci in Lake Burley Griffin: An Analysis of 16 Years' Recreational Water Quality Monitoring Data.

Recreational waterbodies with high levels of faecal indicator bacteria (FIB) pose health risks and are an ongoing challenge for urban-lake managers. Lake Burley Griffin (LBG) in the Australian Capital city of Canberra is a popular site for water-based recreation, but analyses of seasonal and long-term patterns in enterococci that exceed alert levels (>200 CFU per 100 mL, leading to site closures) are lacking. This study analysed enterococci concentrations from seven recreational sites from 2001-2021 to examine spatial and temporal patterns in exceedances during the swimming season (October-April), when exposure is highest. The enterococci concentrations varied significantly across sites and in the summer months. The frequency of the exceedances was higher in the 2009-2015 period than in the 2001-2005 and 2015-2021 periods. The odds of alert-level concentrations were greater in November, December, and February compared to October. The odds of exceedance were higher at the Weston Park East site (swimming beach) and lower at the Ferry Terminal and Weston Park West site compared to the East Basin site. This preliminary examination highlights the need for site-specific assessments of environmental and management-related factors that may impact the public health risks of using the lake, such as inflows, turbidity, and climatic conditions. The insights from this study confirm the need for targeted monitoring efforts during high-risk months and at specific sites. The study also advocates for implementing measures to minimise faecal pollution at its sources.

Temporal and spatial water quality impacts of point-source versus catchment-derived nitrogen loads in an urbanised estuary

The Brisbane River estuary is an anthropogenically-impacted waterway in southeast Queensland, Australia. The estuary is over 80 km long and flows through an urbanised region. It receives over 500 t per year of total nitrogen (N) from direct point-source discharges in addition to sporadic flood loads of N from an agriculturally impacted upper catchment. Comprehensive water quality monitoring data for the estuary have been collected from at least 2001. This monitoring data includes ambient nutrient concentrations in the estuary, nutrient concentration and volume of the catchment inflows, and nutrient concentration and volume of point source discharges. This long-term data from a range of sources was used to determine temporal and spatial variations in concentrations, forms, stores and loads of N along the estuary for the period 2001 to 2022. Results showed that, during low-flow periods, the store of N in the mid-upper estuary (33–81 km upstream) is significantly determined by point-source discharges to this reach, and therefore the store of N can be modelled. Model parameters are the daily point source loads, a point source load decay factor, and a background constant store. In the lower estuary (0–33 km upstream) N store can be accurately determined based on dilution with seawater, with point sources not having significant influence on total N in the reach. Total N from large flood events was found to largely pass through the estuary without detectable removal processes, delivering catchment derived N directly to coastal waters. This work informs potential application of nutrient offsets in the estuary, guiding where and when offset options will be effective to mitigate the water quality impacts of point-source nutrients.

Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

Water Quality of Lake Erhai in Southwest China and Its Projected Status in the near Future

The water quality of Lake Erhai has deteriorated in recent decades due to socioeconomic development in the lake basin. After the massive implementation of water environmental protection measures in Lake Erhai in 2016, the trend of water quality deterioration has been curbed and the intensity and frequency of algal blooms has decreased. However, water quality monitoring data show that pollutant concentrations in Lake Erhai still exceed acceptable values, and there is a risk of water quality standard limits being further exceeded in the future. Therefore, it is urgent to systematically study the variability characteristics of water quality in Lake Erhai to provide practical methods to predict the future evolution of water quality. Based on water quality monitoring data from 2009 to 2019, the current water quality characteristics of Lake Erhai were analyzed, and a two-dimensional hydrodynamic and water quality mathematical model was built to predict the water quality in 2025. The results showed that the total phosphorus (TP) concentration declined after 2016, mainly due to the significant reduction of TP entering the lake due to pollution interception. However, the concentrations of the potassium permanganate index (CODMn) and total nitrogen (TN) increased after 2016, demonstrating that the pollution control measures have had little effect on the improvement of CODMn and TN. The spatial and temporal distribution of pollutants showed that the water quality in winter and spring was better than in summer and autumn, and the water quality in the southern lake was better than in the northern lake. This analysis indicates that non-point source pollution remains the main source of pollution in Lake Erhai, and that rainfall is the main driving force of pollutants exceeding the water quality standard. According to the water quality predictions, without additional pollution control measures, pollutant concentrations in Lake Erhai will exceed the Class II water quality standard by 2025. This study analyzes the water quality characteristics, predicts the direction of future water quality changes, and provides a theoretical basis for the future water quality protection of Lake Erhai.

Hydrochemical characteristics of left-bank tributaries of the Dniester within Ternopil Oblast


 
 
 The purpose of this article is to study the chemical composition of water and the hydrochemical regime of the left-bank tributaries of the Dniester within the Ternopil Oblast at 9 monitoring sites on the Zolota Lypa, Koropets, Strypa, Seret, Nichlava, and Zbruch Rivers. Water quality monitoring data from the State Agency of Water Resources of Ukraine for the period from 1993 to 2020 were used. The hydrographic assessment of the territory following the Water Code of Ukraine showed the presence of two medium-sized rivers (Seret and Zbruch) and four small rivers among the studied rivers. The chemical composition of the water was analyzed by the following groups of components: major ions and water salinity; physicochemical parameters; and biogenic components (nitrogen and phosphorus compounds). It was found that the water of most studied rivers is calcium bicarbonate by hydrochemical type. The water of the Nichlava River is calcium bicarbonate-sulfate. The average annual salinity of the water of the studied rivers varies from 476 mg∙dm-3 to 699 mg∙dm-3. It was determined that the water of most rivers is moderately fresh in terms of mineralization. The water of the Nichlava River is fresh with high mineralization. The hydrochemical regime of rivers is generally related to the hydrological regime. The minimum concentrations of the main ions and salinity values of the studied rivers are observed during the summer-autumn low water mark, which is explained by the significant amount of precipitation and the presence of floods during this period. In winter, the maximums of these indicators are observed. Among the biogenic compounds (ammonium, nitrite, nitrate, phosphate), the clearest manifestation of the hydrochemical regime was recorded for nitrate. The minimum NO3– concentrations are observed in the summer and autumn low-water periods when nitrates are consumed for the development of aquatic plants. In the autumn period, with the death of aquatic plants, nitrates accumulate, and during the winter low water, their maximum average content is observed. The analysis of the monitoring system of the State Agency of Water Resources of Ukraine for the content of the studied components in the water of the left-bank tributaries of the Dniester over a multi-year period for all samples showed that their concentrations were within the regulatory requirements, except for one case. Particularly, in the water of the Nichlava – Borshchiv River, an ammonium concentration of 15 mg∙dm-3 was recorded, which was 7.5 times higher than the standard (August 2, 2000).
 
 

A short-cut methodology for the spatial assessment of the biochemical river quality

The deterioration of superficial water quality is a significant concern in water management. Currently, most European rivers do not achieve qualitative standards defined by Directive 2000/60/EC (Water Framework Directive, WFD), while the health status of many surface water bodies remains unknown. Within this context, we propose a new methodology to perform a semi-quantitative analysis of the pressure state of a river, starting from easily accessible data related to anthropic activities. The proposed approach aims to address the endemic scarcity of monitoring records. This study proposes a procedure to (i) evaluate the relative pressure of different human activities, (ii) identify allocation points of different pollutant sources along the river using a raster-based approach, and (iii) determine a spatial biochemical water quality index. The developed index expresses the overall biochemical state of surface water induced by pollutant sources that may simultaneously impact a single river segment. This includes establishments under the so-called Seveso Directive, activities subjected to the IPPC-IED discipline, wastewater treatment plants, and contaminated sites. The methodology has been tested over three rivers in Northern Italy, each exposed to different industrial and anthropogenic pressures: Reno, Enza, and Parma. A comparison with monitored data yielded convincing results, proving the consistency of the proposed index in reproducing the spatial variability of the river water quality. While additional investigations are necessary, the developed methodology can serve as a valuable tool to support decision-making processes and predictive studies in areas lacking or having limited water quality monitoring data.

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms.

Water Quality Monitoring Research Analysis Based on the Remote Sensing Technology

At present, supply and demand contradictions in water resources around the world are imminent and monitoring and governance of the water environment is urgent. This paper aims to combine space and aerospace remote sensing technology and water quality monitoring fields, fully consider the impact of current water body optical polarization on the accuracy of remote sensor optical signals, to identify the lower cost, more accurate measurement data, and easier to operate detection methods. The main analysis and study of three common water quality variables in water quality testing methods and the combination and synthesis of aerospace remote sensing data are proposed. In addition, this paper also selects the relevant water quality testing cases and the impact of optical polarization on the same type of parameter monitoring and performs a parameter analysis, estimating the financial cost that can be saved annually using remote sensing technology for water quality monitoring. Finally, the importance of the combination of space remote sensing data and aerial remote sensing data to improve the accuracy of water quality monitoring data is summarized, and the development prospect of the integration of air remote sensing and space remote sensing technology to overcome the impact of water multi-angle polarization on the accuracy of remote sensing data has prospected.