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High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function., Environmental Science & Technology, 57 (12), ISSN:0013-936X, ISSN:1520-5851

This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.

It is abundantly evident among domain experts that human activities are profoundly shifting the health and functioning of freshwater ecosystems away from their natural state. Aquatic ecosystems are suffering biodiversity loss and increasingly act as a sink and conduit for anthropogenic pollution. The detailed extent and impact of this remain unclear due to major deficits in our capability, capacity, and willingness to adequately monitor ambient water quality at scale. To reverse this trend, and to understand our ability to restore and return ambient water bodies to a more sustainable baseline, we need to make a societal commitment to increase our monitoring of freshwaters globally. In this primer, we discuss the current status, gaps, and future needs for coordinated water monitoring programs. In the absence of sustained periodic monitoring, intervention, and management, the health of aquatic ecosystems and environments, and consequently our own health, prosperity, and well-being, will be permanently and irreversibly damaged.

Floating photovoltaics (FPV) is emerging as a promising renewable energy technology which enables the production of electricity on surface waters. While this technology could potentially make an important contribution to the energy transition, the current uncertainty about the water quality effects of FPV deployment poses a major barrier to FPV project development and implementation. In this study, we investigated the water quality effects of three distinctive FPV system designs, implemented as part of a 1-year pilot project at the Oostvoornse lake, the Netherlands. A water quality monitoring campaign was set up to monitor on a continuous basis a set of key water quality parameters, including light intensity, water temperature and dissolved oxygen concentration. The measurements were conducted below each of the three FPV systems and contrasted with reference measurements at open water adjacent to the systems. Our monitoring results show that of the water quality parameters considered, the impact of the FPV systems on light intensity was found to be most pronounced, with a light reduction between 73% and 100% relative to the reference measurements. We found limited evidence to corroborate that the FPV systems induced changes to the water temperature and dissolved oxygen concentration. However, it must be noted that this study took place under highly specific conditions due the limited size of the FPV pilot systems and the brackish water of the Oostvoornse lake. This means that the water quality effects reported here may not be representative for a larger scale application of the FPV designs, and may not be one-on-one transferable to other, non-brackish project locations elsewhere.

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.

This repository presents the design and fabrication files required for building the OpenFish soft robotic fish. The detailed build instructions are described in our HardwareX paper: "OpenFish: Biomimetic Design of a Soft Robotic Fish for High Speed Locomotion".

Water Quality Index (WQI) is a unique and effective rating technique for assessing the quality of water. Nevertheless, most of the indices are not applicable to all water types as these are dependent on core physico-chemical water parameters that can make them biased and sensitive towards specific attributes including: (i) time, location and frequency for data sampling; (ii) number, variety and weights allocation of parameters. Therefore, there is a need to evaluate these indices to eliminate uncertainties that make them unpredictable and which may lead to manipulation of the water quality classes. The present study calculated five WQIs for two temporal periods: (i) June to December 2019 obtained in real time (using the Internet of Things (IoT) nodes) at inlet and outlet streams of Rawal Dam; (ii) 2012–2019 obtained from the Rawal Dam Water Filtration Plant, collected through GIS-based grab sampling. The computed WQIs categorized the collected datasets as ‘Very Poor’, primarily owing to the uneven distribution of the water samples that has led to class imbalance in the data. Additionally, this study investigates the classification of water quality using machine learning algorithms namely: Decision Tree (DT), k-Nearest Neighbor (KNN), Logistic Regression (LogR), Multilayer Perceptron (MLP) and Naive Bayes (NB); based on the parameters including: pH, dissolved oxygen, conductivity, turbidity, fecal coliform and temperature. The classification results showed that the DT algorithm outperformed other models with a classification accuracy of 99%. Although WQI is a popular method used to assess the water quality, there is a need to address the uncertainties and biases introduced by the limitations of data acquisition (such as specific location/area, type and number of parameters or water type) leading to class imbalance. This can be achieved by developing a more refined index that considers various other factors such as topographical and hydrological parameters with spatial temporal variations combined machine learning techniques to effectively contribute in estimation of water quality for all regions. HIGHLIGHTS Evaluated five WQI based on six physico-chemical parameters to analyze their sensitivity toward selected location, type and frequency for data sampling.; Computed WQIs categorized the dataset as ‘Very Poor’ because of the uneven distribution of water samples leading to class imbalance.; Five ML models used in which Decision Tree classification accuracy is 99%.; For refined index topographical and hydrological parameters should be considered.

In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.

Disinfection of water systems by chloramination is a method frequently used in North America as an alternative to chlorination. In such a case, monochloramine is used as the primary chlorine source for disinfection. Regular monitoring of the residual concentrations of this species is essential to ensure adequate disinfection. An amperometric sensor for monochloramine would provide fast, reagent-free analysis; however, the presence of dissolved oxygen in water complicates sensor development. In this work, we used in-situ pH control as a method to eliminate oxygen interference by conversion of monochloramine to dichloramine. Unlike monochloramine, the electrochemical reduction of dichloramine occurs outside the oxygen reduction potential window and is therefore not affected by the oxygen concentration. Potential sweep methods were used to investigate the conversion of monochloramine to dichloramine at pH 3. The pH control method was used to calibrate monochloramine concentrations between 1 and 10 ppm, with a detection limit of 0.03 ppm. Tests were carried out in high alkalinity samples, wherein it was found that the sensitivity of this method effectively remained unchanged. Monochloramine was also quantified in the presence of common interferents (copper, phosphate, and iron) which also had no significant impact on the analysis.

Monitoring water quality is an important challenge in both developed and developing countries. Remote sensing data can form a highly frequent dataset with acceptable spatial coverage that can be used to remotely monitor water quality. This paper presents a novel automated model for remotely monitoring water quality to address the problem of insufficient samples and save the time and cost of sample collection. The proposed model estimates both optical and non-optical water quality parameters via Sentinel-2A data. A bio-inspired hybrid model of a Binary Whale Optimization Algorithm (BWOA) and Artificial Neural Network (ANN) (BWOA-ANN) is applied to determine the relationship between extracted reflectance values from Sentinel-2A images and analyzed samples. The novelty of this model is to solve two main problems of remote water quality monitoring: poor applicability and low non-optical parameter estimation accuracy. For the first problem, a proposed fully automated model with band selection using the BWOA to automatically select the optimal features (Sentinel-2A bands) that are suitable for each water quality parameter. The second problem is addressed by automatically detecting the relationship between non-optical parameters, such as the total phosphorus, and optical parameters, such as chlorophyll-a. Three datasets with different locations, seasons, and parameters were selected to test the proposed BWOA-ANN. The experimental results demonstrated good regression with a mean ${R}^{2}$ value of 0.916 for optical parameters and 0.890 for non-optical parameters. The proposed model was found to outperform the ANN with an ${R}^{2}$ value higher by 40% and 52% for the optical and non-optical parameters, respectively.

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed, which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity, and chlorophyll during data collection. The cross-correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to significantly reduce the UAV data noise. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of the multiple linear regression method (MLR), support vector machine method (SVM), and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

The chloride ion (Cl−) can adversely affect an aquatic ecosystem, but it is not clear how Cl− moves with runoff and how its transport processes are related to land uses and land cover. This study investigated how the loading characteristics of Cl− vary depending on storm events and land cover in a temperate region. We monitored Cl− concentrations in three study watersheds that have different compositions of urban and agricultural land uses. In addition, a Mass First Flush ratio (MFFn) was determined to quantify the effect of first flush on Cl− loading. Overall, the observed concentrations and loadings in this study were found to be less than those reported in cold northern regions. The monitoring data showed that Cl− concentrations and loads observed in an urban watershed were significantly larger than those of a rural watershed. The results suggest water management plans should focus on urbanized areas and their storm water to efficiently reduce chloride loading to downstream waterbodies. However, a further study is recommended to identify the sources and pathways of Cl− loaded to waterbodies. HIGHLIGHTS Cl− loading varies depending on storm events and land cover.; The first flush effect on Cl− loading was strong in urban storm runoff.; The urban watersheds had significantly larger Cl− loads than the rural watershed.

Monitoring provides data and information necessary for water quality assessment, but often it is prohibitive, especially when frequent sampling is required. In this study, we explored feasible sampling intervals for improved efficiency of nonpoint source (NPS) pollution assessment. We compared NPS pollutant loads calculated with concentration samples collected at 1, 2, 3, 4, and 6-hour intervals for the first 24 hours of 13 storm events and investigated the effect of different sampling intervals on load estimation for three watersheds that have different land uses. When compared to load estimates made from concentrations sampled at the reference (1-hour) interval, differences in load estimates were less than 10% in the cases of the 2-hour and 3-hour intervals in the urbanized and agricultural watersheds, respectively, except in the case of suspended solids (SS). When it comes to the total load estimation, up to 3-hour interval sampling provided load estimates with acceptable accuracy, except for SS. Thus, the 3-hour sampling interval was considered feasible for long-term pollutant load assessment, while the 2-hour sampling interval was suggested for SS. Such findings are expected to facilitate NPS pollution assessment by providing information required to improve monitoring efficiency. HIGHLIGHTS The efficiency and accuracy of different water sampling intervals were assessed for improved feasibility of nonpoint source pollutant monitoring.; The sampling intervals of equal to or less than 3 hours could provide accurate and efficient pollutant load estimates in agricultural areas.; Urbanized areas required relatively frequent water sampling to satisfy the required accuracy.

The H2S test was created to assess the microbial quality of drinking water in low-resource settings, but the original version of the H2S test lacks sensitivity and specificity for faecal indicator bacteria. There is evidence that a modified media formula of the H2S test may be more sensitive and specific for the faecal indicator bacterium Escherichia coli (E. coli) and less sensitive to organisms of non-faecal origin. This research established the detection threshold and operational range of the H2S test, to increase its sensitivity and specificity for E. coli. A total of 20 modifications of the H2S test, and the original test, were assayed against 20 confirmed and pure culture bacteria of faecal and non-faecal origin at varying concentrations. Additionally, some of the H2S test modifications were evaluated against standard methods for drinking-water analysis. Results indicate that using a modified version of the H2S test containing L-cystine and 2-mercaptopyridine, and bile salts or penicillin G, E. coli will produce H2S. In addition, this research reveals which organisms react positively to the original and modified versions of the H2S test. The modified versions of the H2S test can be promoted as a simple screening test for microbial drinking-water safety in low-resource settings.

A specific study was designed and carried out at the Loire River Basin in France, in order to estimate the measurement uncertainty, including the contribution of sampling. Following recommendations from the Eurachem Guide on Uncertainty arising from sampling, the duplicate method was selected. The study was conducted in 2017 under routine and operational conditions with the accredited laboratories selected by the Loire-Bretagne Water Agency. In overall, 35 monitoring stations were chosen for estimating the spatial variability, and among them 11 monitoring stations were singled out for estimating the temporal variability from April to December. To reduce both the number of analysis and the costs of this study, an unbalanced design was used. Finally, a robust analysis of variance was used to evaluate the measurement uncertainty by means of RANOVA2 software. For some parameters (e.g., calcium, dissolved organic carbon, nitrates, phosphates, suspended matter, metals), the expanded measurement uncertainty estimated using robust ANOVA was found to be lower than 20 % and was thus used to calculate the lower and upper confidence interval limits at 95 %. For parameters having an expanded measurement uncertainty greater than 20 % (e.g., most pesticides, chlorophyll a, biochemical oxygen demand, ammonium, nitrites, Kjeldahl nitrogen), the lower and upper confidence interval limits at 95 % were established using an alternative approach: the expanded uncertainty factor (U-F) derived from log-transformed data.

Water quality monitoring and prediction are important parts of Cyber Physical Systems. Considering the complexity, diversity, and strong non-linearity of water quality data, a single water quality prediction model is difficult to have a significant effect on different data. To solve this problem, a new water quality prediction method based on the preferred classification is proposed in this study. A preferred classifier is established to integrate back propagation neural network, support vector machines for regression and long short-term memory due to the fact that these three prediction models can take into account the different characteristics of water quality data. When new data input, the proposed method preferentially selects the prediction model that is most suitable for the data, and then uses the selected model for prediction. Finally, the proposed method is applied in two actual datasets: Songhua River and Victoria Bay. Experimental results demonstrate that the water quality prediction method based on preferred classification achieves better performance than any of the three single prediction models.

Long-term dynamic monitoring of the water quality of freshwater resources is of great significance to the stable and orderly operation of human society. Most studies only use one of the measured data from the monitoring station and the remote sensing satellite data as the data source. However, a single data source will cause inaccuracy and incompatibility of the water quality monitoring results. Few studies start from practical applications to generate digital images of water quality changes. Furthermore, the performance of shallow neural networks in water quality monitoring is not often ideal. Considering the above problems, we proposed a long short-term memory network model (LSTM) to invert four key water parameters including pondus hydrogenii (PH), dissolved oxygen (DO), chemical oxygen demand (CODMn) and ammonia-nitrogen (NH3-H). Moreover, the model was applied to the satellite images of various periods to generate the inverted image of each water quality parameter. The proposed model has exhibited excellent performance in the water quality assessment of the project, with the coefficient of determination (R2), the relative root-mean-square error (rRMSE), and the mean relative error (MRE) values of 0.83, 0.16, and 0.18, respectively. And the inverted images are also consistent with the official information.

The Cheadle Center at UCSB manages the North Campus Open Space (NCOS), which is a project that has restored 136 acres of upland and wetland habitats that existed before the area was converted into the Ocean Meadows Golf Course in the 1960s. The NCOS restoration project began in 2017 with a fine-scale grading of the site in order to recreate the salt marsh and use the excavated soil to rebuild the upland habitats to the southwest, which are now called the NCOS Mesa. In addition to re-establishing native biodiversity, a key goal of the restoration is to utilize the site as an educational, scientific, and recreational resource. This archived version of the February 2022 newsletter includes updates on a newly funded Research and Monitoring Mentorship Program focusing on soil research, Kids in Nature field trips, parking lot and outdoor classroom construction, and the planned grand opening of the Mesa Trail and other landscape features. The feature story focuses on aquatic macroinvertebrate and water quality monitoring. Community photos include Sora (Porzana carolina), Northern Shoveler (Spatula clypeata), Bufflehead (Bucephala albeola), Wrentit (Chamaea fasciata), Red Maid (Calandrinia ciliata), Blue-eyed Grass (Sisyrinchium bellum) and Succulent Lupine (Lupinus succulentus).&nbsp

Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring. © 2022 Elsevier Ltd

Mirim lagoon is the second largest lacustrine water body in Southern Brazil, providing water for local communities. However, algae growth and water quality in the lagoon and in tributaries rivers is influenced by nutrient’s increase. In this context, this study performs the empirical and semi-empirical chlorophyll-a (Chl-a) modeling using remote sensing and in situ data for water quality assessment. Water quality data were collected at 15 sample locations in the lagoon on the date of Sentinel-2 satellite overpass. Surface reflectance data were derived from the Sen2Cor atmospheric correction method and correlated with Chl-a concentration. The best model presented a Pearson’s correlation coefficient = 0.81 and Mean Absolute Error = 0.13µg.L-1. Low Chl-a concentration is observed at the Northern lagoon, possibly due to suspended solids presence. The same occurs in the left margin, being associated with the influence of land use for agriculture. High Chl-a concentrations are associated with shallower and lentic areas. The mean Chl-a concentration predicted by the model was 17.34μg.L-1, similar to the observed value in situ (16.32μg.L-1). Overall, the empirical model developed can be applied as a tool to reduce costs and efforts in fieldwork measures and to understand eutrophication in this river-lake transition ecosystem.

Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring. © 2022 Elsevier Ltd

This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.

Intermittent rivers and streams are aquatic ecosystems that lose hydrological connectivity during drought periods. Anthropogenic pressures combined with climate change accelerate the spatial and temporal intermittency of these ecosystems, promoting alterations in ecology and ecosystem functions. This study aimed to assess the ecological status of Ribeira de Silveirinhos, located in the municipalities of Valongo and Gondomar (north of Portugal), using the metrics described in the Water Framework Directive. Thus, five sampling sites were selected along Ribeira de Silveirinhos. Sampling was done in three different periods, spring of 2019 and 2020, and autumn of 2019. At each site, physical and chemical water parameters were measured, and the benthic macroinvertebrates community was characterized. Physical and chemical parameters showed low pH values at upstream sites (where the dry phase occurs and is surrounded by Eucalyptus monoculture) and high concentrations of phosphorus at the downstream sites (subjected to several anthropogenic activities, iron waters, and agriculture). The macroinvertebrate community revealed low values of specific abundance and richness, especially during the dry period. Sensitive taxa (Ephemeroptera, Plecoptera, and Trichoptera) were negatively correlated with flow intermittency. Results showed that Ribeira de Silveirinhos is classified between “poor” and “moderate” in terms of ecological status. Intermittent streams are undervalued, so their protection is at risk. Thus, it is important to consider the specific ecological functioning of these ecosystems and to adjust the planning and management models in order to guarantee ecological quality and conservation processes.

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

[EN] The growing social awareness and consequent concern for the environment has driven environmental analytical chemistry to a position of great prominence. In recent times, this position has translated into taking advantage of the great benefits provided by cloud computing and the Internet of Things (IoT), which are especially appropriate when devices such as chemical sensors are used. The use of such sensors is very common when in situ monitoring of environmental parameters is performed, but until recently, it was limited to the deployment of a small number of sensors. Currently, this approach has given way to genuine smart sensing systems (for instance, fully consolidated wireless sensor networks) that are able to provide a substantial amount of information. This type of sensor (the so-called smart sensor) is fundamentally characterized by (a) low consumption, versatility, and autonomy, (b) ease of integration with cloud solutions, (c) durability and reliability of IoT platforms and sensors, and (d) easy installation and deployment of sensor nodes. For all these reasons, and given the increasing importance and use of this type of device, a revision of the recent literature relating the development of smart sensors with environmental issues has been conducted, with major contributions being discussed, most notably those addressing the continuous in-line monitoring of water quality.

Many people in the recent days have suffering from number of diseases due to unsafe and impure drinking water, especially in rural areas. As typical laboratory experiments and official water quality tests take considerable amount of time to obtain results and due to non availability of a simple device that can measure such water quality parameters in real time, therefore in this paper a remote pH level monitoring system for Baghdad drinking water system is proposed. A PH level sensing and monitoring nodes are distributed at different location. These nodes are proactively measured pH level of water and send data to the maintenance center to give them overall picture about pH level via global position system (GSM). This proposed system gives a robust, low-cost and effective method for the drinking water maintenance center to measure and monitoring the water quality in real time environment.

RESUMO O cerne do presente trabalho consistiu em aplicar ferramentas de análise exploratória multivariada objetivando avaliar o número de estações de monitoramento de qualidade da água e a frequência de amostragem. Para tal, utilizou-se banco de dados disponibilizado pelo Instituto Mineiro de Gestão das Águas (IGAM) referente à Bacia do Rio das Velhas, na região central mais populosa de Minas Gerais. Foram utilizadas as técnicas de análise das componentes principais (ACP) e a rede neural de Kohonen, que culminaram na significativa redução da frequência de amostragem, em alguns casos de mensal para anual ou semestral, e na redução do número de estações de monitoramento de 36 para 33. Os resultados permitem abrir a possibilidade do emprego dos métodos utilizados como ferramentas de gestão de recursos hídricos de bacias hidrográficas visando à otimização dos programas de monitoramento de qualidade de água. ABSTRACT The core of this work consisted of applying multivariate exploratory analysis tools to evaluate the number of water quality monitoring stations and the sampling frequency. In such way, the database provided by the Minas Gerais Institute of Water Management (IGAM) on the river basin of Rio das Velhas, in the most populous central region of Minas Gerais state, was used. The Principal Components Analysis and the Kohonen neural network techniques were applied, resulting in a significant reduction in sampling frequency, in some cases from monthly to annual or semi-annual, and in the reduction of the number of monitoring stations from 36 to 33. The results open the possibility of using these methods as watershed water resources management tools aimed at optimization of water quality monitoring programs.

International audience; We present here the first estimates of floating macro-litter in surface waters from the Rhone River, based on monthly visual observations during 1-year period (2016-2017). Plastic represented 77% of the identified items, confirming its predominance in riverine floating litter. Fragments (2.5-50 cm) and Single Use Plastics (i.e. bags, bottles and cover/packaging) were among the most abundant items. Frequent non-plastic floating litter were paper items such as packaging material and newspapers, and metal items (mostly cans), representing 14% and 5% of total litter, respectively. A lower-end estimate resulted in ∼223,000 plastic items (∼0.7 t of plastic) transported annually by the Rhone surface waters to the Gulf of Lion (NW Mediterranean Sea). Floating macro-plastics are only a fraction of the total plastic export by the Rhone. Our study highlights the current discrepancy between field observations and theoretical estimations. Improvements are needed to harmonize data collection methodologies for field studies and model validation.

In recent years, there has been increasing awareness of the preservation, protection and sustainable use of natural resources. Water resources, being one of the most important natural resources, face major threats due to contamination by pollutants of various types and origins. Maintaining the quality of water resources requires more robust, reliable and more frequent monitoring than traditional techniques of data collection based on sporadic, discontinuous and manual processes. The management of large geographical areas, the insufficient spatiotemporal discretization of the values of samples collected by traditional processes and the unpredictability of natural phenomena, require a new approach to data collection procedures. This article, which is the result of ongoing research, defines the technical requirements and technologies used in a continuous and regular monitoring of surface water quality in freshwater systems, whose data acquisition system helps to identify the sources of pollution and the contaminants flow along the waterways. The design of a versatile real-time water quality monitoring system, which, due to its environmental constraints should be based on renewable energies and wireless transfer of energy, will contribute to improve the management and effective protection of water resources. This work was supported by Centro2020, Portugal 2020 and European Union (EU) under the grants, CENTRO-01-0145-FEDER-024052E – Libélula: Mobile robotic surface water quality monitoring system. CENTRO-01-0145-FEDER-024052 info:eu-repo/semantics/publishedVersion

Understanding the fate of fecal pollution in the landscape is required for microbial risk analysis. The aim of this study was to assess the patterns and dynamics of beta-D-glucuronidase (GLUC), which has been suggested as a surrogate for fecal pollution monitoring, in a stream draining an agricultural headwater catchment. Automated enzymatic on-site measurements of stream water and sediments were made over two years (2014-2016) to quantify the sources and pathways of GLUC in a stream. The event water fraction of streamflow was estimated by stable isotopes. Samples from field sediments on a hillslope, streambed sediment and stream water were analyzed for GLUC and with a standard E. coli assay. The results showed ten times higher GLUC and E. coli concentrations during the summer than during the winter for all compartments (field and streambed sediments and stream water). The E. coli concentrations in the streambed sediment were approximately 100 times those of the field sediments. Of the total GLUC load in the study period, 39% were transported during hydrological events (increased streamflow due to rainfall or snowmelt); of these, 44% were transported when the stream contained no recent rainwater. The results suggested that a large proportion of the GLUC and E. coli in the stream water stemmed from resuspended streambed sediments. Moreover, the results strongly indicated the existence of remnant populations of GLUC-active organisms in the catchment. usc Refereed/Peer-reviewed

Made available in DSpace on 2019-10-04T12:36:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-04-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tiete River cascading system, Sao Paulo State, Brazil. It was verified that only QAA(v5) based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAA(v5) outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved. Sao Paulo State Univ, Dept Cartog, Presidente Prudente, SP, Brazil Sao Paulo State Univ, Dept Environm Engn, Sao Jose Dos Campos, SP, Brazil Natl Inst Space Res, Remote Sensing Div, Sao Jose Dos Campos, SP, Brazil Sao Paulo State Univ, Dept Cartog, Presidente Prudente, SP, Brazil Sao Paulo State Univ, Dept Environm Engn, Sao Jose Dos Campos, SP, Brazil FAPESP: 2012/19821-1 FAPESP: 2015/21586-9

Abnormal water quality will increase the occlusion rate among fish schools, which causes difficulties in fish detection and tracking. In order to solve this problem, a multiple fish tracking algorithm for red snapper is proposed in this paper. In the detection stage, we use the Otsu adaptive segmentation algorithm to extract fish targets based on the background subtraction method, following which the fish tracking feature parameters can be obtained based on the fish geometric features. In the tracking stage, the Kalman filter is employed to first estimate the motion state, and then the cost function is constructed from the position of the fish body, target area, and the direction information. Finally, fish school tracking is realized by the interframe relationship matrix. We applied several tracking methods with various schemes to experimental videos of swimming fish schools in different environments. The experimental results show that the proposed tracking algorithm exhibits improved performance and robustness.

The Volga is the longest river in Europe and 16th longest in the world. The riverine landscape of the Volga is of exceptional scientific and economic importance to Russia; the basin contains approximately 40% of the Russian population and relates to 45% of the country's industrial and agricultural produce. The Volga River drains an area of 1.4 million km2, covering various biomes from taiga to semidesert. Anthropogenic impacts in the 20th century include pollution as well as hydropower production and navigation purposes, incurring a cost for its historically important migratory fish (e.g., sturgeons) and related fisheries. River basin management in Russia, since 2006, is based on the water code that determines federal competencies in water management. Extensive water quality monitoring programmes provide feedback to regional managers. Monitoring of biological parameters is spatially limited and should be extended in order to provide sufficient data for informed management. Some initiatives have been implemented in recent decades in order to restore the ecological health of the river and manage fisheries resources (e.g., restocking programmes and the definition of total allowable catches). As recreational fishing is popular but presently unregulated in Russia, we suggest additional monitoring. Finally, the headwaters and lower river floodplain of the Volga have remained as free‐flowing and relatively undisturbed systems. Because reference conditions with low levels of anthropogenic disturbance cannot be found in Central European lowland rivers, both the headwaters and lower Volga floodplains below Volgograd are of great importance on European level.

This paper considers newly emerging wireless technologies that can be explored for potential exploitation in the future wireless sensor network solutions for water quality monitoring applications. The reviewed technologies promise to address long-standing issues that confront existing wireless sensor network solutions devoted to the monitoring of water quality parameters. Such issues include energy efficiency and long-range water quality data communication. These issues can be attributed to the shortcomings of legacy communication networks commonly combined with wireless sensor network solutions, due to a discrepancy caused by traditionally powering the sensors nodes using batteries. In particular, some of the legacy communication networks suffer from short communication range, while others are limited by high power consumption. These shortcomings provide scope for the utilization of newly emerging solutions identified in this study to advance the field of a wireless sensor network for water quality monitoring. To achieve this, three key categories of communication networks have been recommended in this paper, including their architectural design and network deployment for water quality monitoring applications. Also, this paper provides future directions on the identified communication networks to enhance their performance for the next-generation of wireless sensor network solutions for water quality monitoring applications.

Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–α, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (μS/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters.

In recent years, fishery has developed rapidly. For the vital interests of the majority of fishermen, this paper makes full use of Internet of Things and air–water amphibious UAV technology to provide an integrated system that can meet the requirements of fishery water quality monitoring and prediction evaluation. To monitor target water quality in real time, the water quality monitoring of the system is mainly completed by a six-rotor floating UAV that carries water quality sensors. The GPRS module is then used to realize remote data transmission. The prediction of water quality transmission data is mainly realized by the algorithm of time series comprehensive analysis. The evaluation rules are determined according to the water quality evaluation standards to evaluate the predicted water quality data. Finally, the feasibility of the system is proved through experiments. The results show that the system can effectively evaluate fishery water quality under different weather conditions. The prediction accuracy of the pH, dissolved oxygen content, and ammonia nitrogen content of fishery water quality can reach 99%, 98%, and 99% on sunny days, and reach 92%, 98%, and 91% on rainy days.

Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with &lt, 10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors’ signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.

Disinfection of water systems by chloramination is a method frequently used in North America as an alternative to chlorination. In such a case, monochloramine is used as the primary chlorine source for disinfection. Regular monitoring of the residual concentrations of this species is essential to ensure adequate disinfection. An amperometric sensor for monochloramine would provide fast, reagent-free analysis; however, the presence of dissolved oxygen in water complicates sensor development. In this work, we used in-situ pH control as a method to eliminate oxygen interference by conversion of monochloramine to dichloramine. Unlike monochloramine, the electrochemical reduction of dichloramine occurs outside the oxygen reduction potential window and is therefore not affected by the oxygen concentration. Potential sweep methods were used to investigate the conversion of monochloramine to dichloramine at pH 3. The pH control method was used to calibrate monochloramine concentrations between 1 and 10 ppm, with a detection limit of 0.03 ppm. Tests were carried out in high alkalinity samples, wherein it was found that the sensitivity of this method effectively remained unchanged. Monochloramine was also quantified in the presence of common interferents (copper, phosphate, and iron) which also had no significant impact on the analysis.

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

Recently, remote sensing using survey-grade UAVs has been gaining tremendous momentum in applications for the coastal hydro-environment. UAV-based remote sensing provides high spatial and temporal resolutions and flexible operational availability compared to other means, such as satellite imagery or point-based in situ measurements. As strict requirements and government regulations are imposed for every UAV survey, detailed survey planning is essential to ensure safe operations and seamless coordination with other activities. This study established a comprehensive framework for the planning of efficient UAV deployments in coastal areas, which was based on recent on-site survey experiences with a portable unmanned aerial vehicle (pUAV) that was carrying a heavyweight spectral sensor. The framework was classified into three main categories: (i) pre-survey considerations (i.e., administrative preparation and UAV airframe details); (ii) execution strategies (i.e., parameters and contingency planning); and (iii) environmental effects (i.e., weather and marine conditions). The implementation and verification of the framework were performed using a UAV–airborne spectral sensing exercise for water quality monitoring in Singapore. The encountered challenges and the mitigation practices that were developed from the actual field experiences were integrated into the framework to advance the ease of UAV deployment for coastal monitoring and improve the acquisition process of high-quality remote sensing images.

Watershed ecological compensation is an important economic tool for solving the protection–development conflict. However, establishing a sound ecological compensation plan for rational water resource use at the watershed scale remains challenging. Monthly water samples were collected between April 2019 and July 2020 at 28 points to analyze the spatiotemporal variation in water quality in the Taoxi Creek watershed, which is important for local water security. The Soil and Water Assessment Tool was used to simulate water supply, demand, and spatial flow at the watershed scale. Water quality restoration cost was integrated with ecosystem service flow to quantify the watershed ecological compensation. The ecological compensation using water quality restoration cost based on pollutant treatment cost and water quality target level was CNY 11.9 million (USD 188 million). Taoxi Creek was identified as the major supplier of water ecosystem services for downstream residents, and the ecological compensation based on ecosystem service flow was CNY 18.9–47.2 million (USD 3.0–7.5 million). Combining both calculations, the watershed should annually receive CNY 30.8–59.1 million (USD 4.9–9.3 million) of economic compensation from downstream ecosystem beneficiaries. This study provides a scientific basis for improving the ecological compensation scheme in the Taoxi Creek watershed and a reference for ecological compensation formulation in other watersheds.

Monitoring bacterial communities in a drinking water treatment plant (DWTP) may help to understand their regular operations. Bacterial community dynamics in an advanced full-scale DWTP were analyzed by 16S rRNA metabarcoding, and microbial water quality indicators were determined at nine different stages of potabilization: river water and groundwater intake, decantation, sand filtration, ozonization, carbon filtration, reverse osmosis, mixing chamber and post-chlorination drinking water. The microbial content of large water volumes (up to 1100 L) was concentrated by hollow fiber ultrafiltration. Around 10 million reads were obtained and grouped into 10,039 amplicon sequence variants. Metabarcoding analysis showed high bacterial diversity at all treatment stages and above all in groundwater intake, followed by carbon filtration and mixing chamber samples. Shifts in bacterial communities occurred downstream of ozonization, carbon filtration, and, more drastically, chlorination. Proteobacteria and Bacteroidota predominated in river water and throughout the process, but in the final drinking water, the strong selective pressure of chlorination reduced diversity and was clearly dominated by Cyanobacteria. Significant seasonal variation in species distribution was observed in decantation and carbon filtration samples. Some amplicon sequence variants related to potentially pathogenic genera were found in the DWTP. However, they were either not detected in the final water or in very low abundance (

Santa Rosa watershed, where the Santa Rosa River and Cabuyao River are located, is growing with increasing urbanization and commercialization in their surroundings. Water quality monitoring is an important tool in understanding the possible impacts of domestic, industrial, and commercial discharges, and agricultural run-off on river systems and their tributaries. With the integration of absorbance and fluorescence measurements, we can further examine the effects of land use and climate change on dissolved organic matter (DOM) sources found in river systems. In this study, these two rivers exhibit poor quality with varying values in each sampling station and period. DOM sources change from terrestrial to endogenous sources within the sampling period. High aromaticity and molecular size were observed in all downstream sampling stations. This is supported by the high values of humic-like substances. Fluorescence index values showed temporal changes from terrestrial to endogenous DOM sources from November 2019 to February 2020. This is also confirmed by the increasing trend in the biological index. The variation in all sampling stations can be attributed to varying land use, hydrological, and climatological changes such as typhoon Tisoy, and Taal Volcano eruption observed during the sampling period.

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH3-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.

Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water.

Multifunctional Aquatic Drones for surface waters (MaqD) is an operational tool, a "Water-mark" for "live,online dynamic monitoring" of aquatic environments in-situ. For surface running water, for lakes and / oroffshore, MAqD in the image below represents a radically new approach for in-situ robust, reliable andenvironmentally responsible water monitoring.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Fundação de Amparo a Pesquisa do Estado de Minas Gerais O Aquífero Bauru é considerado a unidade hidrogeológica mais importante de Araguari, Minas Gerais. A água deste aquífero é intensamente explorada devido ao baixo custo de captação, que está relacionado ao fato de ser um aquífero freático, pouco profundo e local, submetido a uma abundante recarga. No entanto, essas características o torna potencialmente vulnerável aos agentes poluidores. Este trabalho teve como objetivo oferecer um panorama geral sobre a qualidade da água bruta do aquífero Bauru, na zona urbana de Araguari, identificando possíveis fontes de poluição. As amostras de água foram coletadas em poços da concessionária de abastecimento público. Os critérios para seleção dos poços foram: distribuição espacial, operacionalidade, estrutura e zonas de risco. As amostras foram coletadas a montante do ponto de aplicação do desinfetante, garantindo os aspectos qualitativos da água bruta. Foram amostrados 68 poços, correspondendo a 80 % do poços em operação. Foram realizadas 2 campanhas de campo, sendo a primeira em novembro de 2012 e a segunda em dezembro de 2014. A amostras foram submetidas a ensaios de pH, nitrato, nitrito, fósforo, DQO, DBO5, cor aparente, turbidez, sólidos totais, sólidos dissolvidos totais, sólidos suspensos totais, sólidos voláteis totais e fixos totais. Os resultados das etapas de campo e laboratoriais permitiram o mapeamento dos parâmetros de qualidade e a identificação das áreas críticas em termos de poluição. Os resultados indicaram que vazamentos de esgoto sanitário, em áreas que apresentam elevados índices de vulnerabilidade, podem ser a causa da presença nitrato e sólidos voláteis em alguns poços analisados. Apesar disso, a água bruta do Aquífero Bauru ainda apresenta boa qualidade com propriedades físico- químicas e bacteriológicas adequadas a diversos usos. Apesar das concentrações encontradas ainda serem inferiores aos limites estabelecidos pela legislação vigente, fica evidente a importância do monitoramento contínuo da qualidade da água subterrânea extraída para o abastecimento urbano. The Bauru Aquifer is considered the most important hydrogeological unit of the city of Araguari, Minas Gerais. The water from this aquifer is exploited due to low cost, which is related to the fact of being a shallow and local subjected to an abundant recharging. However, these characteristics makes it potentially vulnerable to pollutants. This work aimed to provide an overview of the quality of raw water from the aquifer Bauru underlying the urban area of Araguari, identifying possible sources of pollution. Water samples were collected in wells of public water utility. The criteria for selection of the wells were: spatial distribution, operation, structure and risk areas. The samples were collected upstream of disinfectant application point, ensuring the qualitative aspects of raw water. 68 wells were sampled corresponding to 80% of the wells in operation. Were held two field campaigns, the first in November 2012 and the second in December 2014. The samples were subjected to pH tests, nitrate, nitrite, phosphorus, COD, BOD, apparent color, turbidity, total solids, solids total dissolved, total suspended solids, total volatile solids and total fixed. The results of field and laboratory steps allowed the mapping of quality parameters and the identification of critical areas in terms of pollution. The results indicated that wastewater leaks in areas that have high levels of vulnerability may be the cause of nitrate and volatile solids present in some wells examined. Nevertheless, the raw water from the aquifer Bauru still has good quality with physical-chemical and bacteriological appropriate to different uses. Although the concentrations found are still below the limits established by law, it is clear the importance of continuous monitoring of groundwater quality extracted for urban supply. Dissertação (Mestrado)

The Cheadle Center at UCSB manages the North Campus Open Space (NCOS), which is a project that has restored 136 acres of upland and wetland habitats that existed before the area was converted into the Ocean Meadows Golf Course in the 1960s. The NCOS restoration project began in 2017 with a fine-scale grading of the site in order to recreate the salt marsh and use the excavated soil to rebuild the upland habitats to the southwest, which are now called the NCOS Mesa. In addition to re-establishing native biodiversity, a key goal of the restoration is to utilize the site as an educational, scientific, and recreational resource. This archived version of theFebruary 2020newsletter includesupdates on Visitor Plaza construction. The feature story honors some of the community donors who have made the project possible. Community photos include Sora (Porzana carolina), Say’s Phoebe (Sayornis saya), Red-Shouldered Hawk (Buteo lineatus), and Northern Shoveler (Spatula clypeata).