Network design for water quality monitoring of surface freshwaters: a review

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Long-term study of phytoplankton dynamics in a supply reservoir reveals signs of trophic state shift linked to changes in hydrodynamics associated with flow management and extreme events

This study analyses over a decade (2009-2022) of monitoring data to understand the impact of hydrological characteristics on water quality and phytoplankton dynamics in Prospect Reservoir, a critical water supply for Greater Sydney, Australia, known for its excellent water quality. Water quality and phytoplankton dynamics were related to hydrodynamics, linked to flow management and the water quality of inflows. Phytoplankton biovolume increased after a prolonged drawdown and subsequent refill event, mainly driven by dinoflagellates, and corresponded to increases in total phosphorus and water temperature. The hydrological period following the 2019/2020 summer bushfires (post-bushfire) that impacted connected reservoirs, was marked by increased flow activity and nutrient loading, leading to significant shifts in the phytoplankton community. Functional group classification and ordination analysis indicated a transition from taxa typically dominant in oligotrophic conditions to meso‑eutrophic. This transition correlated with elevated nutrient levels and chlorophyll-a (Chl-a), and reduced Secchi depth and dissolved oxygen, providing evidence of eutrophication. Q index indicated good water quality post-bushfire, contrasting with a eutrophic status assessment using Chl-a. Our findings highlight the importance of analysing long-term datasets encompassing varied hydroclimatological conditions for a deeper understanding of reservoir behaviour. A comprehensive approach to water quality assessment is recommended, combining functional group classification, Q index and Chl-a measurements for effective reservoir health assessment. This research provides novel insights into the effects of disturbances such as bushfires, on water quality and phytoplankton dynamics in an underrepresented geographic region, offering valuable knowledge for managing water resources amidst growing climate variability.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Secure water quality prediction system using machine learning and blockchain technologies

Water is important for every organism, especially human survival. 2-3 % of fresh water is available on the earth's surface. Discharge of contaminated municipal sewage, removal of degradable wastes and industrial effluents has polluted freshwater resources like an ocean, river, pond, channel, or lake. Hence, this precious resource must be carefully maintained and preserved before consumption. In this research, machine learning models such as Linear Regression, Generalized Linear Model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), classification and regression trees, and Random Forest were used to predict the water quality parameter of Chittar Pattanam Channel, Kanyakumari district, Tamil Nadu in India by giving latitude and longitude. The results showed that the Random Forest (RF) algorithm was better than other models in terms of prediction accuracy with a mean absolute error of 0.56, mean square error of 0.33, and root mean square error of 0.56. Blockchain technologies were used to provide security in the machine learning model. In this work, more than one authorized person is involved in the prediction process, and the authorized person is verified by his signature using Secure Hash Algorithm-256 (SHA). To generate an unpredictable and unique key, SHA-2 uses the size of hash values is 256,384 and 512, a message size is 1024, total rounds are 80 and a word size is 64bits. RSA (Rivest-Shamir-Adleman) technique is used for performing data transfer of keys and encrypting and decrypting data. This study implements a secure water quality prediction system to reduce pollution and improve water quality.

Monitoring multiple parameters in complex water scenarios using a low-cost open-source data acquisition platform

Water monitoring faces challenges that are driven by the infrastructure, protection, financial resources, science and innovation policies, among others. A modular, low-cost, fully open-source and small-sized Unmanned Surface Vessel (USV) called EMAC-USV (EMAC: Estación de Monitoreo Ambiental Costero), is proposed for monitoring bathymetry and water quality parameters (i.e. temperature, suspended solids concentration and hydrocarbon concentration) in complex water scenarios. A detailed description of each part of the platform as well as all electronic connections and functioning is presented.The field works were carried out in two small waste stabilization ponds and in a portion of the main tidal channel of the Bahía Blanca port. The EMAC-USV is the result of a cautious design, regarding the balancing performance, communications, payload capacity, among others.

Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country

Water quality monitoring networks (WQMNs) that capture both the temporal and spatial dimensions are essential to provide reliable data for assessing water quality trends in surface waters, as well as for supporting initiatives to control anthropogenic activities. Meeting these monitoring goals as efficiently as possible is crucial, especially in developing countries where the financial resources are limited and the water quality degradation is accelerating. Here, we asked if sampling frequency could be reduced while maintaining the same degree of information as with bimonthly sampling in the São Paulo State (Brazil) WQMN. For this purpose, we considered data from 2004 to 2018 for 56 monitoring sites distributed into four out of 22 of the state's water resources management units (UGRHIs, "Unidades de Gerenciamento de Recursos Hídricos"). We ran statistical tests for identifying data redundancy among two-month periods in the dry and wet seasons, followed by objective criteria to develop a sampling frequency recommendation. Our results showed that the reduction would be feasible in three UGRHIs, with the number of annual samplings ranging from two to four (instead of the original six). In both seasons, dissolved oxygen and Escherichia coli required more frequent sampling than the other analyzed parameters to adequately capture variability. The recommendation was compatible with flexible monitoring strategies observed in well-structured WQMNs worldwide, since the suggested sampling frequencies were not the same for all UGRHIs. Our approach can contribute to establishing a methodology to reevaluate WQMNs, potentially resulting in less costly and more adaptive strategies in São Paulo State and other developing areas with similar challenges.

Temporal characteristics and trends of nitrogen loadings in lake Taihu, China and its influencing mechanism at multiple timescales

Climate warming impact on excessive nitrogen (N) load in sediment favours cyanobacterial blooms in eutrophic waters. The nitrate (NO3--N) and ammonium (NH4+-N) are two forms of N loads that contribute to algae blooms. However, little attention is paid to the impact of environmental factors on N loads variations at different time scales. This paper used a well-calibrated and validated EFDC model to investigate the temporal patterns and trends of ammonium and nitrate from June 2016 to June 2017. This paper presented the relationship and effects between these variations and environmental factors using data from satellite and reanalysis-based observations obtained for six meteorological parameters. The relationship and effects between these variations and environmental factors were also examined at different timescales (i.e., daily, monthly and seasonal scales). Model calibration results indicated that measured values reasonably matched simulated values. The validation results revealed that relative error (RE) values were within an acceptable range. The REs of ammonium at East Taihu (S12) and Xu Lake (S23) sampling sites were 55.83% and 57.61%, while that of nitrate was 24.37% (S12) and 41.08%, respectively. The daily analysis of NH4+-N and NO3--N variations was 7.318 ± 3.876 (g/m2/day) and 0.0275 ± 0.222 (g/m2/day), respectively. The monthly analysis showed NH4+-N and NO3-N range from 2.04 to 12.04 (g/m2/day) and 0.0008 to 0.064 (g/m2/day), respectively. The magnitude NH4+-N and NO3--N varied and showed distinct inter-monthly variations. , The relationship between sediment fluxes and meteorological parameters showed the magnitude of correlation coefficient (r) and strength of correlation varied significantly. At daily scales, the relationship of NH4+-N and NO3--N had a significant positive correlation with all meteorological parameters. At monthly, the correlation coefficient (r) of NH4+-N and NO3-N were heterogenous. At daily and monthly scales, air temperature and wind speed are the main drivers affecting sediment N loads' dynamics; however, the influence of relative humidity, precipitation, and evaporation on N loads are smaller. The study demonstrates the contribution of meteorological conditions to the magnitude and timing of N loadings variability in water bodies. The findings provide more insight into lake ecosystem protection and environmental remediation.

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R² values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model's performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

A sophisticated model for rating water quality

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique is to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. The IEWQI model was developed based on Cork Harbour, Ireland. The developed IEWQI model was applied to four coastal waterbodies in Ireland, for assessing water quality using 2021 water quality data for the summer and winter seasons in order to evaluate model sensitivity in terms of spatio-temporal resolution of various waterbodies. The model efficiency and uncertainty were also analysed in this research. In terms of different spatio-temporal magnitudes of various domains, the model shows higher sensitivity in four application domains during the summer and winter. In addition, the results of uncertainty reveal that the IEWQI model architecture may be effective for reducing model uncertainty in order to avoid model eclipsing and ambiguity problems. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.

Where to measure water quality ? Application to nitrogen pollution in a catchment in France

Information on the water quality of rivers can be used to judge the effectiveness of past policies or to guide future environmental policies. Consequently, the location of water quality monitoring stations (WQMSs) plays an important role in river pollution control. In the 2000s, a literature developed on the optimization of WQMS location to identify pollution hot spots, average quality, or to minimize the detection time of a potential source of accidental pollution. This article is part of a new literature aimed at locating WQMSs in order to optimize the economic value of information (EVOI) generated by water quality monitoring networks (WQMNs). The field of study is a catchment in northeastern France where the purpose of quality measurement is to define a policy of reduction of agricultural nitrogen fertilizers in order to reach the standard of 50 mg/l of nitrate at the WQMS. Agro-hydrological and economic models estimate the net benefit of input reduction depending on the location of the WQMS on the basis of different assumptions concerning the ecological damage generated by nitrate. We show that the magnitude of the ecological damage and, consequently, the perception of the contamination generated by nitrate in water, play a decisive role on the optimal location of the WQMS, as well as on the benefit of the economic optimization of locations, compared to traditional optimization. Locating WQMSs in a way that maximizes EVOI will be more attractive for very high or very low levels of damage. However, in this context, linking damage to nitrate concentration or to concentration coupled with riparian population density alone will have little impact.

Pre-selection of monitoring stations for marine water quality using affinity propagation: A case study of Xincun Lagoon, hainan, China

The development, protection, and restoration of bays require works in scientific research and applications, and the success of which depends on a well deployment of monitoring stations for marine water quality. However, for bays without historical data, it is difficult to carry out related research on deployment of the monitoring stations, resulting in very few research works. This paper has introduced the affinity propagation (AP) clustering algorithm and achieved good results by correcting the preferences. The results show that under the given preference, that is, when the value of M is -6800, the number of monitoring stations in the Xincun lagoon area is 24. Simultaneous the sensitivity analysis of preferences shows that the number of exemplars decreases with lower preferences, that is, when M decreased from -4000 to -12000, the number also decreased from 70 to 36. However, some exemplars remain unchanged or being changed to adjacent positioning. This shows the stability of computation results and the rationality of AP. The research results can be well applied to other bays, even open waters.

Evaluation of Chemcatcher® passive samplers for pesticide monitoring using high-frequency catchment scale data

Passive samplers (PS) have been proposed as an enhanced water quality monitoring solution in rivers, but their performance against high-frequency data over the longer term has not been widely explored. This study compared the performance of Chemcatcher® passive sampling (PS) devices with high-frequency sampling (HFS: 7-hourly to daily) in two dynamic rivers over 16 months. The evaluation was based on the acid herbicides MCPA (2-methyl-4-chlorophenoxyacetic acid), mecoprop-P, fluroxypyr and triclopyr. The impact of river discharge parameters on Chemcatcher® device performance was also explored. Mixed effects modelling showed that time-weighted mean concentration (TWMC) and flow-weighted mean concentration (FWMC) values obtained by the HFS approach were both significantly higher (p < 0.001) than TWMC values determined from PS regardless of river or pesticide. Modelling also showed that TWMC_PS values were more similar to TWMC_HFS than FWMC_HFS values. However, further testing revealed that MCPA TWMC values from HFS and PS were not significantly different (p > 0.05). There was little indication that river flow parameters altered PS performance-some minor effects were not significant or consistent. Despite this, the PS recovery of very low concentrations indicated that Chemcatcher® devices may be used to evaluate the presence/absence and magnitude of acid herbicides in hydrologically dynamic rivers in synoptic type surveys where space and time coverage is required. However, a period of calibration of the devices in each river would be necessary if they were intended to provide a quantitative review of pesticide concentration as compared with HFS approaches.

Optimization of water quality monitoring programs by data mining

Water quality monitoring programs are essential planning and management tools, but they face many challenges in the developing world. The scarcity of financial and human resources and the unavailability of infrastructure often make it impossible to meet the legal requirements of water monitoring. Many approaches to optimizing water quality monitoring programs have already been proposed. However, few investigations have developed and tested data mining for this purpose. This article has developed data-based models to reduce the number of water quality parameters of monitoring programs using data mining. The objective was to extract patterns from the database, expressed by association rules, which together with field parameters, measured with automatic probes, can estimate laboratory variables. This approach was applied in 35 monitoring stations along 27 river basins throughout Brazil. The data are from fifty years of monitoring (1971-2021), constituting 6328 observations of 60 water quality parameters investigated in different environmental contexts, water quality, and the structuring of monitoring programs. With the applied approach it was possible to estimate 56% of the laboratory parameters in the monitoring stations investigated. The influence of environmental characteristics on the optimization capacity of monitoring programs was evident. The methodology used was not influenced by different water quality levels and anthropogenic impacts. However, the number of parameters was the most influential element in optimization. Monitoring programs with 20 or more water quality variables have the highest potential (≥44%) of optimization by this methodology. Results demonstrate that this approach is a promising alternative that can reduce the frequency of analyses measured in the laboratory and increase the spatial and temporal coverage of water quality monitoring networks.

Linking catchment structural units (CSUs) with water quality: Implications for ambient monitoring network design and data interpretation

Climate induced changes in runoff regimes and ongoing anthropogenic modification of land use and land cover (LULC) are shifting ambient water quality signals worldwide. Modulation of these signals by the physical catchment structure over different scales adds complexity to interpreting and analyzing measured data. Further bias may be introduced where monitoring networks are not representative of the structure of catchments in a given region. Here, we present a new environmental regionalization method to assess the representativeness of water quality monitoring (WQM) networks and to identify key structural drivers linked to water quality signals. Unique numerical codes were generated at the pixel level to provide wall-to-wall coverage of key Catchment Structural Units (CSUs) based on LULC, surficial geology, wetlands and slope. CSU codes were generated for all tributary (AT) catchments >20 km² in Southern Alberta (n = 289), Canada, to determine the representativeness of an existing WQM network (54 tributary catchments) and to assess the explanatory power of CSUs with respect to water quality signals. Cluster analysis (CA) and multi-dimensional scaling (MDS) on the percent area of CSUs in the AT catchments identified six primary structural clusters in Southern Alberta. A clear gradient in catchment structure was evident progressing downstream from the Rocky Mountain headwaters through the foothills and prairie/plains region. Montane and grassland regions were found to be potentially under-represented by the current WQM program whereas catchments dominated by agriculture were likely over-represented. The disproportionate impact of specific CSU combinations on water quality was illustrated where the CA and MDS analyses indicated that even small percentages of urban areas and badland type topography results in elevated concentrations of total recoverable metals, nutrients and major ions. The application of the CSU approach in Southern Alberta demonstrates its value as an alternative method to assess and/or redesign existing WQM networks and to link water quality data to the structural composition of catchments. The general availability of the required data to generate CSUs provides universal potential for the approach to help assess other WQM programs and to contextualize data records. Applying the CSU approach when developing new ambient WQM networks can also help reduce the potential of over-monitoring similarly structured catchments as well as ensuring that all structural classes are represented by the data being generated.

Water quality monitoring with purpose: Using a novel framework and leveraging long-term data

Water quality monitoring programs are developed to meet goals including attaining regulatory compliance, evaluating long-term environmental changes, or quantifying the impact of an emergency event. Methods for developing these programs often fail to address multiple aspects of development (hazard identification, parameter selection, monitoring locations/frequency) simultaneously. We develop a framework for monitoring program development that is both versatile and systematic, the Hazard Based Water Quality Monitoring Planning framework, and apply it to the Quabbin watershed in Massachusetts, USA. We use a novel application of dataset deconstruction of long-term water quality datasets and the Seasonal Kendall test for trends to evaluate the effects of sampling frequency on long-term trend detection at several watershed sites. Results showed that when sampling frequency is decreased, ability to detect statistically significant trends often decreases. Absolute error in trend slopes between biweekly (twice monthly) and reduced sampling frequencies was relatively small for specific conductance and turbidity but was high for total coliform, likely due to interannual variation in rainfall and temperature We found that no one sampling reduction method resulted in a consistently lower absolute error compared to the "truth" (biweekly sampling), highlighting the importance of evaluating conditions that may affect water quality at sites in different parts of a watershed. We demonstrate the framework's usefulness, particularly for parameter and sampling frequency selection, using methods that can be readily applied to other watershed systems.

Spatial optimization of the water quality monitoring network in São Paulo State (Brazil) to improve sampling efficiency and reduce bias in a developing sub-tropical region

Water quality monitoring networks (WQMNs) are essential to provide good data for management decisions. Nevertheless, some WQMNs may not appropriately reflect the conditions of the water bodies and their temporal/spatial dimensions, more particularly in developing countries. Also, some WQMNs may use more resources to attain management goals than necessary and can be improved. Here we analyzed the São Paulo State (Brazil) WQMN design in order to evaluate and increase its spatial representativeness based on cluster analysis and stratified sampling strategy focused on clear monitoring goals. We selected water resources management units (UGRHIs) representative of contrasting land uses in the state, with bimonthly data from 2004 to 2018 in 160 river/stream sites. Cluster analysis indicated monitoring site redundancy above 20% in most of the UGRHIs. We identified heterogeneous spatial strata based on land use, hydrological, and geological features through a stratified sampling strategy. We identified that monitoring sites overrepresented more impacted areas. Thus, the network is biased against determination of baseline conditions and towards highly modified aquatic systems. Our proposed spatial strategy suggested the reduction of the number of sites up to 12% in the UGRHIs with the highest population densities, while others would need expansions based on their environmental heterogeneity. The final densities ranged from 1.6 to 13.4 sites/1,000km². Our results illustrate a successful approach to be considered in the São Paulo WQMN strategy, as well as providing a methodology that can be broadly applied in other developing countries.

Long-Term Series of Chlorophyll-a Concentration in Brazilian Semiarid Lakes from Modis Imagery

By monitoring the chlorophyll a concentration (chla), it is possible to keep track of the eutrophication status of a lake and to describe the temporal dynamics of the phytoplankton biomass. Such monitoring must be both extensive and intensive to account for the short- and long-term biomass variations. This may be achieved by the remote estimation of chla through an orbital sensor with high temporal resolution. In this study, we used MODIS imagery to produce 21-year time series of chla for three strategic lakes of the Brazilian semi-arid region: Eng. Armando Ribeiro Gonçalves, Castanhão, and Orós. We used data collected in 13 lakes of the region to test new and published regression models for chla estimation. The selected model was validated and applied to daily MODIS images for the three largest lakes. The resulting chla time series revealed that the temporal dynamics of the phytoplankton biomass is associated with the hydraulic regime of the lakes, with chla plummeting upon intense water renewal and keeping high during persistent dry periods. The intense rainy season of 2004 reduced the phytoplankton biomass and its effects even extended to the subsequent years. Our results encourage the exploration of the MODIS archived imagery in limnological studies.

Potable water quality assessment of traditionally used springs in a hilly town of Bhaderwah, Jammu and Kashmir, India

The quality of spring water and its suitability for human consumption is determined by examining its physicochemical and microbiological characteristics. Preliminary investigations were conducted to determine the potability of seven traditionally used springs in the highly populated hill town of Bhaderwah in Union Territory of Jammu and Kashmir, India. The water analysis was performed for various physico-chemical and microbial parameters during April 2019-March 2020. Water temperature, TDS, EC, pH, DO, free CO₂, total alkalinity, total hardness, Ca²⁺, Mg²⁺, Na⁺, K⁺, CO₃^2-, HCOֿ₃, Cl‾, NOֿ_3, PO₄^3-, SO₄^2-, total coliforms, and thermotolerant coliforms were all measured. Eleven physical and chemical characteristics were used to generate the Water Quality Index. The Piper diagram demonstrated the predominance of Ca²⁺-HCOֿ₃ water types, whereas the Schoeller diagram indicated that all springs had a similar lithological origin. The chemical composition of springs tested met the required criteria for drinking water quality. The microbiological indicators, on the other hand, did not satisfy the criteria except for Eidgah spring, which lacked thermotolerant coliforms. Our results on spring water potability indicate that the town's most dependable springs are susceptible to anthropogenic contamination and therefore need treatment prior to use. Apart from frequent monitoring, the responsible municipal corporation is expected to develop comprehensive plans to rehabilitate and revitalise these vulnerable drinking water sources.

Citizen-led sampling to monitor phosphate levels in freshwater environments using a simple paper microfluidic device

Contamination of waterways is of increasing concern, with recent studies demonstrating elevated levels of antibiotics, antidepressants, household, agricultural and industrial chemicals in freshwater systems. Thus, there is a growing demand for methods to rapidly and conveniently monitor contaminants in waterways. Here we demonstrate how a combination of paper microfluidic devices and handheld mobile technology can be used by citizen scientists to carry out a sustained water monitoring campaign. We have developed a paper-based analytical device and a 3 minute sampling workflow that requires no more than a container, a test device and a smartphone app. The contaminant measured in these pilots are phosphates, detectable down to 3 mg L^-1. Together these allow volunteers to successfully carry out cost-effective, high frequency, phosphate monitoring over an extended geographies and periods.

Continuous high-frequency pesticide monitoring to observe the unexpected and the overlooked

Synthetic Plant Protection Products (PPPs) are a key element for a large part of today's global food systems. However, the transport of PPPs and their transformation products (TPs) to water bodies has serious negative effects on aquatic ecosystems. Small streams in agricultural catchments may experience pronounced concentration peaks given the proximity to fields and poor dilution capacity. Traditional sampling approaches often prevent a comprehensive understanding of PPPs and TPs concentration patterns being limited by trade-offs between temporal resolution and duration of the observation period. These limitations result in a knowledge gap for accurate ecotoxicological risk assessment and the achievement of optimal monitoring strategies for risk mitigation. We present here high-frequency PPPs and TPs concentration time-series measured with the autonomous MS²Field platform that combines continuous sampling and on-site measurements with a high-resolution mass spectrometer, which allows for overcoming temporal trade-offs. In a small agricultural catchment, we continuously measured 60 compounds at 20 minutes resolution for 41 days during the growing season. This observation period included 8 large and 15 small rain events and provided 2560 concentration values per compound. To identify similarities and differences among the compound-specific concentration time-series, we analysed the entire dataset with positive matrix factorisation. Six factors sufficiently captured the overall complexity in concentration dynamics. While one factor reflected dilution during rainfall, five factors identified PPPs groups that seemed to share a common history of recent applications. The investigation per event of the concentration time-series revealed a surprising complexity of dynamic patterns; physico-chemical properties of the compounds did not influence the (dis)similarity of chemographs. Some PPPs concentration peaks led while others lagged by several hours the water level peaks during large events. During small events, water level peaks always preceded concentration peaks, which were generally only observed when the water levels had almost receded to pre-event levels. Thus, monitoring schemes relying on rainfall or water level as proxies for triggering sampling may lead to systematic biases. The high temporal resolution revealed that the Swiss national monitoring integrating over 3.5 days underestimated critical concentration peaks by a factor of eight to more than 32, captured 3 out of 11 exceedances of legal acute quality standards (the relevant values in the Swiss Water Protection Law) and recorded 1 out of 9 exceedances of regulatory acceptable concentrations (the relevant values for the PPPs registration process). MS²Field allowed for observing unexpected and overlooked pesticide dynamics with consequences for further research but also for monitoring. The large variability in timing of concentration peaks relative to water level calls for more in-depth analyses regarding the respective transport mechanisms. To perform these analyses, spatially distributed sampling and time-series of geo-referenced PPPs application data are needed.

Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches

Water quality degradation is one of the major problems with artificial lakes in estuaries. Long-term spatiotemporal patterns of water quality in a South Korean estuarine reservoir were analyzed using seasonal datasets from 2002 to 2020, and some functional changes in relations of trophic state variables due to the construction of serial weirs in the upper river were also investigated. A total of 19 water quality parameters were used for the study, including indicators of organic matter, nutrients, suspended solids, water clarity, and fecal pollution. In addition, chlorophyll-a (CHL-a) was used to assess algal biomass. An empirical regression model, trophic state index deviation (TSID), and principal component analysis (PCA) were applied. Longitudinal fluctuations in nutrients, organic matter, sestonic CHL-a, and suspended solids were found along the axis of the riverine (Rz), transition (Tz), and lacustrine zones (Lz). The degradation of water quality was seasonally caused by resuspension of sediments, monsoon input due to rainfall inflow, and intensity of Asian monsoon, and was also related to intensive anthropic activities within the catchment. The empirical model and PCA showed that light availability was directly controlled by non-algal turbidity, which was a more important regulator of CHL-a than total nitrogen (TN) and total phosphorus (TP). The TSID supported our hypothesis on the non-algal turbidity. We also found that the construction of serial upper weirs influenced nutrient regime, TSS, CHL-a level, and trophic state in the estuarine reservoir, resulting in lower TP and TN but high CHL-a and high TN/TP ratios. The proportions of both dissolved color clay particles and blue-green algae in the TSID additionally increased. Overall, the long-term patterns of nutrients, suspended solids, and algal biomass changed due to seasonal runoff, turnover time, and reservoir zones along with anthropic impacts of the upper weir constructions, resulting in changes in trophic state variables and their mutual relations in the estuarine reservoir.

Spatio-temporal changes in water quality in the Guarapiranga reservoir (São Paulo, Brazil): insights from a long-term monitoring data series

The provision of drinking water in metropolises is a challenge that requires programs for continuous monitoring of water quality and processes that impact the land cover of the watershed. In this work, we investigated through multivariate statistical analysis the temporal and spatial trends of several variables, not yet explored in a data series that includes 42 years (1978-2020) of monitoring in the hydrographic basin of the Guarapiranga reservoir, in the São Paulo Metropolitan Region-SPMR (Brazil). This reservoir is the source of drinking water for 3.8 million people and plays a strategic role in the social, environmental, and economic structure at SPMR. Our results point to the continuous degradation of water quality in the reservoir, although with different causes and spatio-temporal aspects. Between the 1970s and 1980s, variables associated with erosion/silting played a more critical role. From the 1990s, the introduction of N and P intensified, and the concentration of thermotolerant coliforms increased. The loss of quality is mainly associated with the progressive advance of urban settlements without planning combined with the inefficient initiatives to control domestic sewage pollution. If there is no rapid and comprehensive intervention, there is a risk that the Guarapiranga reservoir may become unsuitable for drinking water supply and other types of use in the future. This scenario will represent a critical obstacle to regional development and the quality of life of the population.

Strategies for identifying pollution sources in a headwater catchment based on multi-scale water quality monitoring

Rural headwater catchments are important to describe the connectivity of pollution sources to water bodies. Strategies to optimize water quality monitoring networks, as parameter definition, sampling, and statistical approach, have been widely discussed. The objectives of this study were to describe the spatial and temporal dynamics (intra- and inter-events) of water quality and to establish its implications for environmental monitoring programs. The monitoring was carried out in a rural headwater catchment (1.2 km²) with shallow soils, high slopes, and intense agricultural activity in Southern Brazil. To better describe the impact of agriculture on water resources, the monitoring strategy was based on definition of the best set of parameters and different sampling frequency to incorporate intra- and inter-event variability and statistical analysis approach. We also analyzed parameters in different sub-basins with physiographic traits. Three hydrological compartments were analyzed: surface flow, groundwater, and base flow. Physico-chemical parameters, the concentration of elements associated with agricultural activity, and biological parameters were evaluated. Total phosphorus and turbidity were the parameters most affected by agricultural activity. They reflected on the inter- and intra-events, the impacts of soil and water degradation by agricultural activity, and the precarious rural sanitation conditions. Spatiotemporal variability of the parameters characterizes the different mechanisms for transferring pollutants from diffuse sources to water bodies. Spatial and temporal patterns in water quality changes were used to discuss environmental monitoring strategies, such as parameter and sampling frequency definition, to improve soil and water conservation programs at the catchment scale.

The study on optimal design of river monitoring network using modified approaching degree model: a case study of the Liaohe River, Northeast China

This paper proposes a quantitative method to optimize the existing river monitoring network based on a modified approaching degree model, T test, and Euclidean distance. In this study, the Liaohe River located in Liaoning province, China, was taken as a research object. Samples were collected from 8 sampling sites throughout the monitoring network, and water quality parameters were analyzed every 2 months from January 2009 to December 2010. The results show that the average concentrations of the ammonia nitrogen (NH₄⁺-N) and chemical oxygen demand (COD) were beyond grade III of the Environmental Quality Standards for Surface Water of China (GB3838-2002), and they were the main water quality parameters. After optimization, the number of monitoring sections along the Liaohe River was reduced to five from the original eight, thus saving 37.5% of the monitoring cost; meanwhile, there is no significant difference between the un-optimized and optimized monitoring networks, and the optimized monitoring network remains to be able to perform as good as the original one. In addition, the total data attainment rate was improved greatly, and the duplicate setting degree of monitoring points decreased significantly compared with other optimal methods. The optimized monitoring network proves to be more efficient, reasonable, and economically feasible, so this quantitative method can help optimize the changing orderly river monitoring networks.

Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA-Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation's greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km². Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as "Integrated Water Science Basins"; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

Designing Dual-Effect Nanohybrids for Removing Heavy Metals and Different Kinds of Anions from the Natural Water

In the present study, well-designed nanohybrids are used to act as effective dual-function adsorbents for removing both anions and heavy metals from natural water, at the same time. In this trend, Zn-Al LDHs and graphene oxide are applied to build up building blocks to produce a series of nanohybrids. These nanohybrids were characterized by X-ray diffraction, thermal analyses, Fourier transform infrared spectroscopy, Raman spectroscopy, and scanning and transmission electron microscopy. These techniques confirmed that the prepared nanohybrids contained nanolayered structures with three-dimensional porous systems. These porous systems were identified by the nitrogen adsorption-desorption isotherms and water purification experiments. The obtained results indicated that these nanohybrids included suitable structures to act as dual function materials. The first function was achieved by removing more than 80% of both cadmium and lead from the natural water. The second function was accomplished by eliminating of 100% of hydrogen phosphate and bromide anions alongside with 80%-91% of sulfate, chloride, and fluoride anions. To conclude, these well-designed nanohybrids convert two-dimensional nanolayered structures to three-dimensional porous networks to work as dual-function materials for removing of heavy metals and different kinds of anions naturally found in the fresh tap water sample with no parameters optimization.

Cross-scale controls on the in-stream dynamics of nitrate and turbidity in semiarid agricultural waterway networks

Stream and riparian zone networks embedded in agricultural landscapes provide a potential intervention point to ameliorate the negative effects of agricultural runoff by reducing transport of nitrate (NO₃^-) and suspended sediments (SS) downstream. However, our ability to support and promote NO₃^- and SS attenuation is limited by our understanding of vegetative and hydrogeomorphic controls in realistic management contexts. In addition, agricultural landscapes are heterogenous on multiple management scales, from farm field to regional water management scales, and the effect of these heterogeneities and how they interact across scales to affect vegetative and hydrogeomorphic controls is poorly explored in many settings. This is especially true in irrigated agricultural settings, where stream and riparian networks are entwined with and sensitive to water management systems. To fill these gaps, we related the vegetative and hydrogeomorphic features of 67 waterway reaches across two water management districts in the California Central Valley to reach-scale NO₃^- and turbidity attenuation and district-scale water quality patterns. We found that in-stream NO₃^- attenuation was rare, but, when it did occur, it was promoted by shallow and wide riparian banks, low flows, and high channel-edge denitrification potential. Nitrate concentrations were consistently higher in upstream reaches compared to water district outlets, suggesting that while exports from the district were low, agricultural runoff may impair within-district water resources. Turbidity attenuation was highly variable and unrelated to vegetative or hydrogeomorphic features, suggesting that onfield controls are crucial to managing suspended sediments. We conclude that waterway networks have the potential to mitigate the effects of agricultural NO₃^- runoff in this setting, but that more effective monitoring and adoption of NO₃^- attenuating features is needed. Using our findings, we make specific management and monitoring recommendations at both reach and water district scales.

Combining Chemometrics and Sensors: Toward New Applications in Monitoring and Environmental Analysis

For many years, an extensive array of chemometric methods have provided a platform upon which a quantitative description of environmental conditions can be obtained. Applying chemometric methods to environmental data allows us to identify and describe the interrelations between certain environmental drivers. They also provide an insight into the interrelationships between these drivers and afford us a greater understanding of the potential impact that these drivers can place upon the environment. However, an effective marriage of these two systems has not been performed. Therefore, it is the aim of this review to highlight the advantages of using chemometrics and sensors to identify hidden trends in environmental parameters, which allow the state of the environment to be effectively monitored. Despite the combination of chemometrics and sensors, to capture new developments and applications in the field of environmental sciences, these methods have not been extensively used. Importantly, although different parameters and monitoring procedures are required for different environments (e.g., air, water, soil), they are not distinct, separate entities. Contemporary developments in the use of chemometrics afford us the ability to predict changes in different aspects of the environment using instrumental methods. This review also provides an insight into the prevailing trends and the future of environmental sensing, highlighting that chemometrics can be used to enhance our ability to monitor the environment. This enhanced ability to monitor environmental conditions and to predict trends would be beneficial to government and research agencies in their ability to develop environmental policies and analysis procedures.

Screening of the microelements composition of drinking well water of Transcarpathian region, Ukraine

Background

For Transcarpathian region, with a pronounced landscape diversity of territories and significant areas of nature reserves, well water is an important source of drinking water. Screening of the microelement composition of drinking well water in Transcarpathia has not been carried out before. The microelement composition of such well water can be considered as the natural (baseline) indicator of quality.

Methods

We screened of the microelement (Cu, Zn, Fe, P, Ca, Mg, Mn, Mo, Co, As, Se, I, Br and F) composition of drinking well water in Transcarpathian region (for all 13 districts). Standard methods were used to determination the concentration of chemical elements in well water: electrothermal atomic absorption spectroscopy (Cu, Zn, Fe, Mn, Mo, Co), flame photometry (Ca, Mg), inverse voltammetry (total iodine), potentiometry (F, Br), fluorimetric (Se) and spectrophotometric methods (P, As).

Results

The content of chemical elements in well water varies over a wide range, in particular for Cu: 3.27–11.6 μg⋅L⁻¹, Zn: 8.16–38.2 μg⋅L⁻¹; Fe: 37.9–411 μg⋅L⁻¹; P: 51.4–193 μg⋅L⁻¹; Ca: 85–139 mg⋅L⁻¹, Mg: 10.2–18.9 mg⋅L⁻¹, Mn: 22.8–78.1 μg⋅L⁻¹; Mo: 2.33–8.96 μg⋅L⁻¹; Co: 1.72–3.38 μg⋅L⁻¹; As: 2.9–17.4 μg⋅L⁻¹; Se: 0.95–3.6 μg⋅L⁻¹; I: 0.94–4.4 μg⋅L⁻¹; Br: 712–3098 μg⋅L⁻¹; F: 71–149 μg⋅L⁻¹. The tendency that the content of microelements (Cu, Zn, Mn, Mo, Co, As, Se, I, Br, and F) in well waters of different landscape zones was evaluated through Spearman's coefficients. The content of trace elements in the waters of different landscape zones increases in the series: lowland landscape > foothill landscape > mountainous landscape (Spearman's coefficients: Cu – 0.62, Zn – 0.85, Mn – 0.69, Mo – 0.83, Co – 0.79, P – 0.74, As – 0.72, Se – 0.75, I – 0.80, Br – 0.91, F – 0.73).

Conclusion

It is shown that the chemical composition of well water can be considered as the natural (baseline) indicator of quality of groundwater in Transcarpathia, taking into account topographic and geochemical features of the territories. Among the main problems of drinking well water in Transcarpathia are the relatively high content of Fe (typical for different landscape zones), as well as the low content of Se, I and F. The correlation of inter-elements content of chemical elements in drinking well waters of Transcarpathia has been revealed. The most pronounced correlations are observed for such pairs of microelements: Co–Mo (r > 0.95), I–Br (r > 0.92), Zn–Br (r > 0.89), Cu–Co (r > 0.84), Se–Co (r > 0.84), Mo–P (r > 0.84), Mo–I (r > 0.82) and Zn–Mo (r > 0.80).

Methodological design for the macro-location of a micropollutants monitoring network in tropical rivers: a case study in Cauca River

Establishing scientifically the macro-location of a micropollutants monitoring network in tropical Andean rivers is a complex process, because information gathering is restricted by high-cost of analysis and limited availability of analytical techniques, which lead to inadequate sampling strategies that hinder the representativeness of samples. Thus, this work proposes a methodology for determining the number of representative sampling sections in a micropollutant monitoring network to characterise the ecological risk in tropical Andean torrential rivers. The proposed methodology consists of four stages: identification of the potential sampling units by Spline interpolation; calculation of the number of representative sections for a stratified sampling with an acceptable level of confidence and error; spatial allocation of the potential sampling units into sections by hierarchical cluster analysis; and representation of the spatial distribution of the sampling sections through a geographic information system (GIS). The proposed methodology is dynamic, and therefore, it can be revisited as more data are obtained in the subsequent years; it has the possibility of being applied to other inter-Andean valley rivers that interact with the tropical Andean sloppy mountains and serves as a tool for decision making by environmental authorities regarding the optimisation of the existing monitoring networks in terms of micropollutants to promote sustainable management of water resources. The proposed methodology is applied in the Upper Cauca River Basin (UCRB), which is located in southwest Colombia, South America.

Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

Mutagenicity as a parameter in surface water monitoring programs-opportunity for water quality improvement

Effect-based analyses are being recognized as excellent tools to a comprehensive and reliable water quality evaluation to complement physical and chemical parameters. The Salmonella/microsome mutagenicity test was introduced in the São Paulo State water quality-monitoring program in 1999 and waters from 104 sites used to the production of drinking water were analyzed. Samples were tested after organic extraction, using the microsuspension version of the Salmonella/microsome assay with strains TA98 and TA100 with and without S9-mammalian metabolic system. Of the 1720 water samples analyzed in 20 years, 20% were positive; TA98 was the most sensitive strain, detecting alone 99%. Results were presented in hazard categories to facilitate water managers' understanding and general public communication. Hot spots of mutagenicity were identified, and pollution sources investigated. A flow scheme with instructions of how to proceed in case of mutagenic samples was developed and implemented in the monitoring program. Enforcement actions were taken to reduce exposure of humans and aquatic biota to mutagenic compounds. The results presented provide scientific basis for the incorporation of the Salmonella/microsome assay in a regulatory framework, and to guide water-quality managers. The inclusion of a mutagenicity assay using standardized conditions proved to be an opportunity to improve the quality of water, and the strategy presented here could be applied by any environmental agency around the world. Environ. Mol. Mutagen. 61:200-211, 2020. © 2019 Wiley Periodicals, Inc.

Estimation of water quality profiles in deep lakes based on easily measurable constituents at the water surface using artificial neural networks coupled with stationary wavelet transform

This study proposes a novel framework to accurately estimate water quality profiles in deep lakes based on parameters measured at the water surface, considering Boulder Basin of Lake Mead as a case study. Hourly-measured meteorological data were used to compute heat exchange between lake and atmosphere. Heat fluxes combined with every 6-hour measured water temperature, conductivity, and dissolved oxygen (DO) profiles, from the water surface to a depth of 100 m over a 48-month period, were used to train seven different artificial neural network-based methods for estimating water quality profiles. Effects of different factors influencing lake water quality, including lake-atmosphere interactions, wind-induced mixing, thermocline depth, winter turnover, oxygen depletion and other factors were investigated in different methods. A method employing stationary wavelet transform with a depth-progressive estimation of temperature, conductivity, and DO generated the smallest average relative errors of 0.52%, 0.22%, and 0.62%, respectively in the water column over a 48-month period. Abrupt changes in temperature, conductivity, and DO profiles due to thermal stratification, winter turnover, and oxygen hypoxia increased estimation errors. The largest errors occurred near the interface between the epilimnion and metalimnion, where vertical mixing intensity significantly decreased.

Methodological proposal for the allocation of water quality monitoring stations using strategic decision analysis

In order to fill a gap in the monitoring of water quality in Brazil, the objective of this study was to propose a methodology to support the allocation of water quality monitoring stations in river basins. To achieve this goal, eight criteria were selected and weighted according to their degree of importance. It was taken into account the opinion of water resources management experts. In addition, a decision support system was designed so that the methodology could be used in the allocation of water quality monitoring stations by researchers and management bodies of water resources, to be fully implemented in geographic information system environment. In order to demonstrate the potential of the proposed methodology, which can be used in places that have or not existing monitoring networks, it has been applied in the Minas Gerais portion of the Doce river basin. Because the area already has a monitoring network with 65 stations in operation under the responsibility of the Minas Gerais Water Management Institute (IGAM), an expansion of the network was suggested and a simulation of a scenario was performed considering that the study area did not have an established network. The results of the analyses consisted of maps of suitability, indicating the locations with greater and lesser suitability for the establishment of the stations. With the application of the methodology, seven new sites were proposed so that the study area had the density recommended by the National Water Agency (ANA), and it was verified that the Caratinga River Water Resources Management Unit (UGRH5 Caratinga) has the most deficiency of stations among the six units evaluated in the Minas Gerais portion of the Doce river basin. In the simulated scenario considering the non-existence of a network, the adequacy map obtained was compared with the existing monitoring network and it was possible to classify the stations according to the purpose for which they were established, such as monitoring environments under anthropic activities or establishing benchmarks for the water bodies. Overall, the proposed methodology proved itself robust, and although the results were specific to one basin, the criteria and decision support system used are fully applicable to other areas of study.

Optimization of River and Lake Monitoring Programs Using a Participative Approach and an Intelligent Decision-Support System

We developed a holistic intelligent decision-support system (IDSS) to provide decision-support for all steps in planning, managing and optimizing water quality monitoring programs (WQMPs). The IDSS is connected to a previously developed database, EnkiTM. The IDSS integrates tacit and explicit knowledge on WQMPs to standardize decision making and to make decisions transparent and transferable. The optimization features of the IDSS were tested on a lake and a river WQMP from two case studies in Canada. We illustrate how the IDSS provides decision support to understanding the underlying rationale of the existing WQMPs, validating and storing data, selecting optimization procedures proposed in the literature, applying the optimization procedures and finalizing the optimization procedure. We demonstrated that the IDSS/EnkiTM is necessary to take and document decisions during all phases of a WQMP to obtain a clear idea of when and why changes are made and determine actionable tasks in the optimization process.

Anthropogenic activity-induced water quality degradation in the Loktak lake, a Ramsar site in the Indo-Burma biodiversity hotspot

Wetland contributes to human well-being and poverty alleviation. The increase in human population leads to more demand for water and degradation of the water bodies around the globe, resulting in scarcity of water. The aim of the present study was to assess the impact of anthropogenic activity on the water quality of the Loktak lake. Water samples were collected seasonally, namely, monsoon, post monsoon, winter and pre-monsoon, during 2013-2014 from 10 sites. For each water sample, 20 physicochemical parameters were analysed using the American Public Health Association method. Furthermore, 11 significant parameter values were used to develop the water quality index (WQI). The result shows high concentrations of nitrite (5.45-11.83 mg/l) and nitrate (93.67-177.75 mg/l) in rivers which is beyond the permissible limit and higher compared to the Loktak. Highest turbidity was observed at Langthabal with 21 NTU, which is above the permissible limit. The WQI of the Loktak ranged from 64 to 77, while for rivers they ranged from 53 to 95, which indicates that the water is in a very poor state. The WQI values of rivers are higher compared with those of the lake, and it was identified that water from the rivers is a major reason for increase in pollution in the lake water. The study suggests the need for long-term monitoring of the lake aquatic ecosystem and identification of pollution sites for proper management of the lake water. The WQI is an important tool to enable the public and decision makers to evaluate the water quality of the Loktak lake.

Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece

Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.

Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems.

Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations

The optimized design of water quality monitoring networks can not only minimize the pollution detection time and maximize the detection probability for river systems but also reduce redundant monitoring locations. In addition, it can save investments and costs for building and operating monitoring systems as well as satisfy management requirements. This paper aims to use the beneficial features of multi-objective discrete particle swarm optimization (MODPSO) to optimize the design of water quality monitoring networks. Four optimization objectives: minimum pollution detection time, maximum pollution detection probability, maximum centrality of monitoring locations and reservation of particular monitoring locations, are proposed. To guide the convergence process and keep reserved monitoring locations in the Pareto frontier, we use a binary matrix to denote reserved monitoring locations and develop a new particle initialization procedure as well as discrete functions for updating particle’s velocity and position. The storm water management model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We define three pollution detection thresholds and simulate pollution events respectively to obtain all the pollution detection time for all the potential monitoring locations when a pollution event occurs randomly at any potential monitoring locations. Compared to the results of an enumeration search method, we confirm that our algorithm could obtain the Pareto frontier of optimized monitoring network design, and the reserved monitoring locations are included to satisfy the management requirements. This paper makes fundamental advancements of MODPSO and enables it to optimize the design of water quality monitoring networks with reserved monitoring locations.

Optimal river monitoring network using optimal partition analysis: a case study of Hun River, Northeast China

River monitoring networks play an important role in water environmental management and assessment, and it is critical to develop an appropriate method to optimize the monitoring network. In this study, an effective method was proposed based on the attainment rate of National Grade III water quality, optimal partition analysis and Euclidean distance, and Hun River was taken as a method validation case. There were 7 sampling sites in the monitoring network of the Hun River, and 17 monitoring items were analyzed once a month during January 2009 to December 2010. The results showed that the main monitoring items in the surface water of Hun River were ammonia nitrogen (NH₄⁺-N), chemical oxygen demand, and biochemical oxygen demand. After optimization, the required number of monitoring sites was reduced from seven to three, and 57% of the cost was saved. In addition, there were no significant differences between non-optimized and optimized monitoring networks, and the optimized monitoring networks could correctly represent the original monitoring network. The duplicate setting degree of monitoring sites decreased after optimization, and the rationality of the monitoring network was improved. Therefore, the optimal method was identified as feasible, efficient, and economic.

Physicochemical and microbiological assessment of spring water in central Himalayan region

In the present study, water quality of 16 springs, located along National Highway-58 from Rishikesh to Badrinath in India, was assessed by determining various physicochemical and microbiological parameters in three different seasons, i.e., pre-monsoon, monsoon, and post-monsoon. For majority of the springs, the pH was slightly alkaline with temperature ranging between 10 and 27 °C. All other parameters such as total hardness (TH), total alkalinity (TA), chloride, phosphate, nitrate, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), and biochemical oxygen demand (BOD) were found to lie within the acceptable limit prescribed by various standard national and international agencies. The principal component analysis reveals that water quality of springs mainly depends on mineral contents of water, as there is a loading of TH, TA, EC, TDS, and other mineral components during one or other season of a year. The positive correlation coefficients determined among mineral components of spring water further substantiate this fact. No loading of DO, BOD, nitrate, and phosphate indicates an absence of anthropogenic pollution in the studied area. No trace metals were detected in any of the springs. Most probable number (MPN) index for coliforms was found to be above the acceptable limit for all the springs in one or more seasons of a year, except the one in Pandukeshwar. Plate-based assay revealed the presence of pathogens like Salmonella, Shigella, Vibrio, and Pseudomonas in some spring water. The findings of the present work reveal that due to high MPN index and presence of other pathogenic bacteria, water from most of the springs cannot be considered completely safe for direct human consumption in its raw form.

Assessment and management of pesticide pollution at a river basin level part II: Optimization of pesticide monitoring networks on surface aquatic ecosystems by data analysis methods

The high cost of extensive pesticide monitoring studies, required for the protection of water resources, and the necessity of early identification of environmental threats, highlighted the need for prioritization of pesticides and sampling sites to be monitored. The aim of this study was to develop an optimum surface water monitoring network at a catchment scale including only the sites of a catchment vulnerable to pesticide pollution. The identification of sampling sites vulnerable to pesticide pollution (VPS) was based on the data of an intensive monitoring survey of 302 pesticides in 102 stationary sampling sites located on the surface water network of a river basin. In the proposed methodology the left-censored data of the analytical results derived from the above mentioned monitoring campaign were included in the statistical analyses by transforming all the raw data into categorical variables and arranging them in ordinal scales based on ecotoxicological thresholds derived from pesticide toxicity tests on aquatic non-target organisms. The categorized data were subjected to Categorical Principal Component Analysis with Optimal Scaling. For the identification of the VPS, the Squared Mahalanobis Distance criterion was applied on the extracted values (scores) of the significant principal components. With this methodology a 46% reduction in the number of the monitoring stations was achieved. This approach will be valuable in establishing more cost effective monitoring schemes in the future in other basins and in developing targeted measures to eliminate or limit the effect of critical pollution sources in surface aquatic systems. Moreover, by applying the proposed methodology, historical monitoring data can be used to initiate more efficient pesticide monitoring campaigns in the future.

Assessment of spatial variations in the surface water quality of the Velhas River Basin, Brazil, using multivariate statistical analysis and nonparametric statistics

The Velhas River sub-basin, which is located in the third-largest river basin in Brazil (São Francisco), is in an advanced state of degradation. In this work, the surface water quality of the Velhas River Basin was studied at 65 monitoring sites; 16 water quality parameters were sampled quarterly for 11 years (2008 to 2013). Cluster analysis (CA) and a nonparametric Kruskal-Wallis test were associated with the analysis of violations to water quality standards to interpret the water quality data set from the Velhas River Basin and assess its spatial variations. The CA grouped the 65 monitoring sites into four groups. The Kruskal-Wallis test identified significant differences (p < 0.05) between the groups formed by CA. The results show that watercourses located in the upper region of the Velhas River Basin are more affected by the release of industrial effluent and domestic sewage, and the lower region is more affected by diffuse pollution and erosion. This association between multivariate statistical techniques and nonparametric tests was effective for the classification and processing of large water quality datasets and the identification of major differences between water pollution sources in the basin. Therefore, these results provide an understanding of the factors affecting water quality in the Velhas River Basin. The results can aid in decision-making by water managers and these methods can be applied to other river basins.

An investigation on water quality variability and identification of ideal monitoring locations by using entropy based disorder indices

Optimizing between information needs and information gathered from water quality monitoring networks involve complicated decision making processes and management strategies. The present study investigates upon entropy based variability of water quality using disorder indices. Employing the Shannon's diversity index and the principle of maximum entropy (POME), the study identifies locations which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface run-off, which lead to water quality variability in a waterbody over a monitoring period. A case study on 19 sampling locations over the monitoring period of 2017-2018 has been done on the entire expanse of Deepor Beel (DB), a wetland of international importance designated as a Ramsar site in 2002. Geospatial analysis and isoinformation lines have been employed to generate geospatial maps that clearly depict ideal monitoring locations which encountered highest variability in their water quality over the monitoring period with respect to physico-chemical parameters, BOD, COD and heavy metals. Results indicated 5 sampling locations DB1, DB10, DB11, DB12 and DB18 for physico-chemical parameters and 4 sampling locations DB1, DB4, DB15 and DB17 for heavy metals encountered highest variability and are recommended ideal monitoring locations. The present study introduces an innovative approach to derive maximum useful information by pinpointing sampling stations for regular monitoring purposes. Risk assessment due to heavy metals was also done by using average daily dose and hazard index in the monitoring period. Cr and Pb were critical to human health on consumption.

Detection of phosphorus species in water: technology and strategies

This review highlights recent advances in methods of detection of total phosphorus in water, including photoelectric strategies, spectroscopy techniques, and modeling algorithms.

Hotspots and main drivers of fecal pollution in Neusiedler See, a large shallow lake in Central Europe

To minimize the risk of negative consequences for public health from fecal pollution in lakes, the continuous surveillance of microbiological water quality parameters, alongside other environmental variables, is necessary at defined bathing sites. Such routine surveillance may prove insufficient to elucidate the main drivers of fecal pollution in a complex lake/watershed ecosystem, and it may be that more comprehensive monitoring activities are required. In this study, the aims were to identify the hotspots and main driving factors of fecal pollution in a large shallow Central European lake, the Neusiedler See, and to determine to what degree its current monitoring network can be considered representative spatially. A stochastic and geostatistical analysis of a huge data set of water quality data (~ 164,000 data points, representing a 22-year time-series) of standard fecal indicator bacteria (SFIB), water quality and meteorological variables sampled at 26 sampling sites was conducted. It revealed that the hotspots of fecal pollution are exclusively related to sites with elevated anthropogenic activity. Background pollution from wildlife or diffuse agricultural run-off at more remote sites was comparatively low. The analysis also showed that variability in the incidence of SFIB was driven mainly by meteorological phenomena, above all, temperature, number of sunny hours, and wind (direction and speed). Due to antagonistic effects and temporal undersampling, the influence of precipitation on SFIB variance could not be clearly determined. Geostatistical analysis did reveal that the current spatial sampling density is insufficient to cover SFIB variance over the whole lake, and that the sites are therefore in the most part representative of local phenomena. Suggestions for the future monitoring and managing of fecal pollution are offered. The applied statistical approach may also serve as a model for the study of other such areas, and in general indicate a method for dealing with similarly large and spatiotemporally heterogeneous datasets.