Water Quality Monitoring Data Research Articles

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.

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.

Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Dynamic analysis of port water quality: insights from Zhanjiang Port, China

Abstract The management of port water quality is crucial to marine ecological balance and has been of great concern. In the present study, the water quality monitoring data in Zhanjiang Port from 2015 to 2022 were utilized to analyze the spatiotemporal characteristics and reveal the correlation between different parameters. The structural equation model has been applied to profile the dominant factors of water quality level. The results showed that the port water quality was generally worse in summer and better in winter. Variations in total phosphorus (TP), chemical oxygen demand (COD) and total nitrogen (TN) content directly led to water quality changes in Zhanjiang Port, where an increase in TP content resulted in a significant decrease in water quality level (path coefficient is 2.87). Permanganate index (CODMn) and ammonia nitrogen content indirectly affected the water quality level, while changes in pH and dissolved oxygen (DO) showed no impact. Ammonia nitrogen, pH and DO contents were significantly associated with TP. Human activities and industrial production were identified as the main sources of water quality pollution. The increasing trend of certain water quality parameters highlights the urgency of implementing timely measures to improve water quality conditions in Zhanjiang Bay, China.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named “Prediction - Detection - Judgment” is proposed with a method framework of “Time series increment - Hierarchical clustering - Bayes' theorem model”. Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Optimizing Recharge Area Delineation for Small- to Medium-Sized Groundwater Systems through Coupling Methods and Numerical Modeling: A Case Study of Linfen City, China

In previous investigations, the demarcation of capture zones within a specific research area predominantly relied on a singular method, leading to pronounced limitations and uncertainties. To address this challenge, an extensive field survey was conducted, focusing on the systematic classification of water sources in the Linfen City region. Building upon this classification, an intricate fusion of a hydrogeological analysis and formulaic methodology was employed. This integrated approach, coupled with independent numerical simulation methods, was applied to delineate recharge areas for both alluvial fan pore water in piedmont regions and exposed karst water in small- to medium-sized water sources. Simultaneously, precise spatial interpolation was carried out on water quality monitoring data from supply wells within the water source area for the year 2020. This meticulous analysis facilitated the determination of the spatial distribution characteristics of hydrochemical elements. To assess the water quality within the capture zone, the class III groundwater quality standards of China were employed as a pivotal tool for validating the results of the delineation of water source recharge areas. In the final analysis, a comparative study between the integrated coupling method and numerical simulation outcomes revealed the successful delineation of the boundaries for the water supply areas of Tumen and Caojiapo in Linfen City, covering areas of 5.5 km2 and 22.29 km2, respectively. Simultaneously, the combination of the three methods accurately outlined the boundary of the Hexi water supply area, encompassing an area of 2.5224 km2. These results vividly illustrate that the amalgamation of various methodologies proves more beneficial for the precise delineation of capture zones, particularly in diverse types and scales of groundwater sources. The synergy exhibited by these three methods underscores their collective efficacy, providing a more comprehensive and intuitive delineation of the recharge areas for small- to medium-sized water sources. Consequently, these findings significantly enhance the practical application value of the study and hold promise in making substantial contributions to local groundwater security and management initiatives.

Seasonal Variation and Key Variables Affecting Surface Water Quality in North Vam Nao Area, Vietnam

Surface water is essential to life, but it is seriously influenced by anthropogenic and natural impacts. Extraction of inside information (for examples sources and key variables influencing water quality) from monitoring data of surface water quality is a crucial task. This study was to intensively evaluate surface water quality in North Vam Nao using Principal Component Analysis (PCA) and Cluster Analysis (CA). The water quality parameters from seven locations during the period 2010–2022 including temperature, pH, total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrate (NO3−-N), ammonium (NH4+-N), orthophosphate (PO43−-P) and coliforms were collected. The results show that surface water quality is seasonally varied, except for pH. The PCA results predicted the main sources of water pollution were possibly hydrological conditions (rainfall, erosion) and human activities (domestic, agricultural and industrial activities). CA classified the water quality into three clusters for both dry and wet seasons. The common pollutants in both seasons were total suspended solids, organic matters, nutrients, and coliform. All the parameters temperature, pH, TSS, DO, BOD, COD, NH4+-N, NO3−-N, PO43−-P and coliform are key surface water parameters needed to be monitored. The results of the current study could be used for designing surface water monitoring at the study area.

Identification of pollution source and prediction of water quality based on deep learning techniques

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Monitoring of the water quality of Lake Blidinje and examination of the prognostic model

Abstract The largest mountainous lake in Bosnia and Herzegovina, Lake Blidinje, is located 1,185 meters above sea level. Polymictic lakes are those in which the entire water column is frequently mixed. Shallow lakes function very differently than deep lakes in many ways because of the strong sediment– water interaction and the possibility for significant water impact on vegetation. In the multiple regression analysis, two models were developed with coefficients of multiple determination (R2 = 0.720 and R2 = 0.497) for the independent variable chlorophyll a. The resulting models are compared with water quality monitoring data from 2017 to 2019.

Impact of wastewater reuse on contaminants of emerging concern in an effluent-dominated river

Contaminants of emerging concern such as pharmaceuticals, personal care products, per- and polyfluoroalkyl substances, and plasticizers, are ubiquitous in effluent-dominated rivers and have potential adverse effects on humans and aquatic life. Demands on water supply have prompted conservation and water reuse measures, impacting the discharge in these rivers, yet the effects of these management decisions on water quality are largely intuited and not quantified. This research examines how changes in water reuse practices will impact concentrations of contaminants of emerging concern, specifically carbamazepine, diclofenac, galaxolide, gemfibrozil, 4-nonylphenol, and perfluorooctane sulfonic acid (PFOS), in the effluent-dominated Los Angeles River (Los Angeles County, California). A water quality module was added to a calibrated hydrologic model of the system and parametrized with observed water quality monitoring data in EPA SWMM. Results indicate that water reuse (i.e., reduced effluent flow) will consistently improve in-stream water quality for all compounds studied except PFOS. However, the improvements are often not substantial enough to mitigate high concentrations directly downstream of treated effluent discharge points. Concentrations of these pharmaceuticals are substantially reduced through attenuation as dilution and degradation occur downstream, though the rate of this attenuation is variable and based on the contaminant. In contrast, concentrations of PFOS increase under some wastewater reuse scenarios and decrease under others but remain below the recommended environmental screening levels. Our work also highlights that management decisions regarding water quantity should integrate water quality modeling to help identify priority monitoring locations and constituents.

Effect of landscape pattern on river water quality under different regional delineation methods: A case study of Northwest Section of the Yellow River in China

Study regionA typical arid irrigation area of the Yellow River, Ningxia, Northwest China. Study focusAt present, few studies have evaluated the effects of nested watershed and runoff process on the relationships between landscape patterns and water quality. In this study, we aimed to quantify the relationships between landscape pattern and river water quality using two delineation methods. One method involved a water quality monitoring site only corresponding only to its own sub-watershed, while the other method involved a water quality monitoring site corresponding to all of its upstream sub-watersheds. New hydrological insights for the regionWe collected water quality monitoring data, including DO, CODMn, NH3-N, TP, TN, pH for the irrigation period (May, 2021 – August, 2021) and the non-irrigation period (December, 2021 – March, 2022) for seven sub-watersheds. Our results revealed that when a water quality monitoring site corresponded to all the sub-basins involved in the runoff process, the correlations between landscape composition and water quality parameters were stronger. Additionally, the degree to which landscape configuration explained the overall water quality was greater in such cases. We also found that the buffer zone scale accounted for more than 97.3% of the overall water quality variation. These results highlight the importance of aligning water quality monitoring sites with the corresponding sub-watersheds and emphasize the significance of land-use management in the riparian scale near the Yellow River for water quality protection.

Evolution Characteristics of Water Quality in Plain Reservoirs and Its Relationship with the Economic Development Response: A Case Study of Daheiting Reservoir in Northern China

In order to explore the evolution characteristics of TP and NH3-N in Daheiting reservoir since its construction, and their response to economic development, the monitoring data of water quality from 1992 to 2018 and statistical data of socio-economic development in Qianxi County were analyzed to examine the interannual evolution of TP and NH3-N and their correlation with upstream water quality, various economic indicators, and the scale of cage fish culture. The results show that, influenced by economic development, the evolution process of TP and NH3-N in Daheiting reservoir can be divided into three stages. In Stage I, the economic development of Qianxi County was slow, and the water quality of upstream water and the reservoir was good, with TP and NH3-N concentrations remaining relatively stable. In Stage II, Qianxi County entered a period of rapid economic development, and the TP and NH3-N in upstream water and Daheiting reservoir both increased significantly, with TP exceeding the standard limit. In Stage III, the intensity of external pollution control increased, and all cages were removed from the reservoir. Both TP and NH3-N showed a downward trend, but TP still exceeded the standard limit. Pearson correlation analysis and RDA analysis revealed that the levels of TP and NH3-N in Daheiting reservoir were mainly affected by the water quality of upstream water and the development of primary industry (including cage fish culture).

Remote sensing inversion of water quality parameters in the Yellow River Delta

In recent years, with the rapid socio-economic development of the Yellow River Delta (YRD), the pressure on the supply of water resources has continued to rise. The development of oil-based industries has also led to a series of ecological and environmental problems, such as wetland degradation and water quality deterioration. As an increasing number of rivers are getting polluted, resulting in the deterioration of their water quality, monitoring, managing, and protecting water resources in the YRD is particularly important. In this study, water quality monitoring data and simultaneous Sentinel-2 image data from April 30, 2020, to October 26, 2021, were used to construct an experimental sample in the YRD. Water quality parameters (WQPs) concentrations were correlated with Sentinel-2 image element spectral reflectance and sensitive bands were selected. An empirical method based on the characteristic bands was used to invert a total of six water quality indicators, namely dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-H), total phosphorus (TP), total nitrogen (TN) and turbidity. The results show: (1) A total of five water quality inversion models for DO, TN, CODMn, TP and TN were effective in the areas of the Guangli River, the Tiaohe and the Branch River. The inversion accuracies of the five inversion models (R2of 0.6099, 0.9271, 0.9581, 0.8784 and 0.7387; RMSE of 1.2723, 0.3413, 0.9923, 0.0118 and 1.8476; RPD of 1.53, 2.08, 3.56, 2.76 and 1.53) indicated the feasibility of the water quality inversion method based on Sentinel-2 data using statistical theory for monitoring water quality concentration in the YRD. (2) The spatial distribution of water quality in the YRD was generally characterized by high water quality in the upper reaches and low water quality in the middle and lower reaches (except for some seasonal variations). Among them, the water quality of the upper reaches of the Guangli River was poor, with opposite trends in DO and TN concentrations. In the Tiaohe, CODMn and TP concentrations were not strongly correlated. However, CODMn and TP concentrations were high in the middle reaches where water quality was the worst. The TN concentrations in the Branch River decreased between 2020 and 2021, but the water quality is still in Category V. Therefore, continued attention and appropriate water quality management measures in the YRD are required. Further, by measuring water quality indicators at monitoring stations, regression-fitting equations for WQPs were established to obtain complementary multi-platform observations. Thus, the water quality conditions in the YRD region can be evaluated more accurately and quickly. The research results not only provide an important reference basis for the identification and monitoring of pollution sources, prevention and treatment of water environment pollution in the YRD, but also provide water security for socio-economic and ecological environment security.

Influence of Denitrification on Cyanobacterial Blooms Trends in Lake Taihu, China

Denitrification is a major process in aquatic ecosystems, and it competes with cyanobacterial growth for nitrogen. However, the effect of denitrification on cyanobacterial blooms under the background of climate change remains unclear. This study explored the interaction between lake denitrification and formation of cyanobacterial blooms, using the historical water quality monitoring data of North Lake Taihu over five years from 2017 to 2021 and via incubation experiments of cyanobacteria and sediment denitrification. The monitoring data showed that algal biomass (Chla as a proxy) primarily peaked during summer and autumn. The seasonal variations in total N concentration showed a completely opposite trend than that of algal biomass, which peaked in winter and spring. Nitrate was the major component of dissolved inorganic nitrogen, and the nitrate concentration was approximately zero in summer and autumn. The total phosphorus concentration varied in the same way as the Chla concentration. The experimental results showed that Cyanobacteria did not grow when the temperature was below 20℃. In comparison, denitrification showed a significant linear relationship with temperatures between 10-25℃ (R2=0.99) and reached the maximum value of (62.98±21.36) μmol·(kg·h)-1 in Lake Taihu at 25℃. Additionally, the nitrate concentration threshold at the maximum denitrification rate was 4 mg·L-1. Cyanobacteria assimilate nitrate for growth, thereby reducing the concentration of nitrate required for denitrification. This study indicated that the advance in lake temperature warming due to climate change may result in earlier growth of cyanobacteria, thereby leading to large amounts of N being assimilated by algae before denitrification, further affecting the dynamics of cyanobacterial blooms. The present results are scientifically important for explaining the mechanism of cyanobacterial bloom rebound in Lake Taihu under the background of recent climate changes.