Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Mapping chemical footprints of organic micropollutants in European streams

There is increasing awareness that chemical pollution of freshwater systems with complex mixtures of chemicals from domestic sources, agriculture and industry may cause a substantial chemical footprint on water organisms, pushing aquatic ecosystems outside the safe operating space. The present study defines chemical footprints as the risk that chemicals or chemical mixtures will have adverse effects on a specific group of organisms. The aim is to characterise these chemical footprints in European streams based on a unique and uniform screening of more than 600 chemicals in 445 surface water samples, and to derive site- and compound-specific information for management prioritisation purposes. In total, 504 pesticides, biocides, pharmaceuticals and other compounds have been detected, including frequently occurring and site-specific compounds with concentrations up to 74 µg/L. Key finding is that three-quarter of the investigated sites in 22 European river basins exceed established thresholds for chemical footprints in freshwater, leading to expected acute or chronic impacts on aquatic organisms. The largest footprints were recorded on invertebrates, followed by algae and fish. More than 70 chemicals exceed thresholds of chronic impacts on invertebrates. For all organism groups, pesticides and biocides were the main drivers of chemical footprints, while mixture impacts were particularly relevant for invertebrates. No clear significant correlation was found between chemical footprints and the urban discharge fractions, suggesting that effluent-specific quality rather than the total load of treated wastewater in the aquatic environment and the contribution of diffuse sources, e.g. from agriculture, determine chemical footprints.

Identifying pollution dynamics using discrete Fourier transform: From an urban-rural river, Central Mexico

The quality of life and human survival is dependent on sustainable development and sanitation of water bodies in an environment. The present research focuses on cyclicity data of more than 750,000 records of parameters associated with the water quality from a rural-urban river monitoring stations in real-time from River Atoyac in Central Mexico. The events detected in the instrumental records correlated with 2528 laboratory and instrumental determinations. The 64 polluting compounds were grouped into inorganic compounds (metals and metalloids) and organic compounds (pesticides, herbicides, hydrocarbons). Metal associated compounds were grouped along mechanical, pharmaceutical and textile industries which associates itself with the entry of polluting components. The cyclicity of the events was detected through Discrete Fourier Transformation time series analysis identifying the predominant events in each station. These highlight the events at 23-26 h corresponding to a circadian pattern of the metabolism of the city. Likewise, pollution signals were detected at 3.3, 5.5, and 12-14 h, associated with discharges from economic activities. Multivariate statistical techniques were used to identify the circadian extremes of a regionalized cycle of polluting compounds in each of the stations. The results of this research allow pollution prevention using a mathematical analysis of time series of different quality parameters collected at monitoring stations in real-time as a tool for predicting polluting events. The DFT analysis makes it possible to prevent polluting events in different bodies of water, allowing to support the development of public policies based on the supervision and control of pollution.

Water Quality and Pollution Trading: A Sustainable Solution for Future Food Production

Nitrogen, an essential nutrient for plant growth, is commonly added to food crops in the form of manure and synthetic fertilizers. Fertilizer use has significantly increased in the past decades to meet the food demands from a rising population. Although this has boosted food production, it has come at a cost to the environment. Indeed, excess fertilizer ends up in water bodies, a pollution that causes losses in aquatic biodiversity. Better fertilizer management is therefore essential to maintaining water sustainability. Here, we develop and evaluate a nitrogen water quality trading scheme to address this challenge. Nitrogen trading incentivizes farmers to work together to invest in pollution reduction measures in order to keep nitrogen water pollution levels within a standardized limit. We build a mathematical model to represent the nitrogen trading and use it to assess the pollution reduction, the effect on the crop yield, and economical outcomes. The model is applied among local farms in the agricultural county of Suffolk, eastern England. We calculate the nitrogen load to the river from each farm and incorporate the abatement cost into the model. The results show how nitrogen water pollution could be reduced cost-effectively while simultaneously increasing the benefit for the whole catchment. Although the benefit does not increase for all the farms, the increase in benefit for the whole catchment is enough to compensate for this loss. The surplus benefit is equally distributed between all the farms, thus increasing their overall benefit. We discuss how the proposed trading model can create a platform for farmers to participate and reduce their water pollution.

GatorByte - An Internet of Things-Based Low-Cost, Compact, and Real-Time Water Resource Monitoring Buoy

Conventional water resource monitoring systems are usually expensive, have a low-temporal resolution, and lack spatial dimension entirely. These systems are typically available as stations or handheld devices. Pinpointing sources of pollution using these systems can be difficult. This project involves developing a high-resolution free-flowing monitoring buoy that records spatiotemporal water-quality data in flowing stream environments. The system is highly customizable, and even users with limited experience in programming or electronics can tailor GatorByte to their needs. The platform includes a data logger, a cloud-based server, and visualization tools. The data logger uses low-cost sensors, electronic peripherals, a 3D-printed enclosure, and printed circuit boards, with a total cost per unit under $1,000 USD. The data logger uses an NB-IoT-capable Arduino for real-time reporting and visualizing sensor data. The GatorByte records physiochemical water metrics - pH, temperature, dissolved oxygen, electroconductivity, and the current location of the buoy using a GPS module. The data logger also includes micro-SD storage and a Bluetooth module for on-field diagnostics. Using the GatorByte buoy, the collection of variations in water quality data in temporal as well as spatial dimensions can be achieved cost-effectively and reliably, enabling quick detection and resolution of pollution events.

Towards highly economical and accurate wastewater sensors by reduced parts of the LED-visible spectrum

Interest is growing in simple, fast and inexpensive systems to analyze urban wastewater quality in real time. In this research project, a methodology is presented for the characterization of COD, BOD₅, TSS, TN, and TP of wastewater samples, without the need to alter the samples or use chemical reagents, from a few wavelengths, belonging to the different color groups that compose the visible spectrum in isolation: (380-700 nm): violet (380-427 nm), blue (427-476 nm), cyan (476-497 nm), green (497-570 nm), yellow (570-581 nm), orange (581-618 nm), and red (618-700 nm). In this study, about 650 raw and treated urban wastewater samples from over 43 WWTPs and a total of 36 estimation models based on genetic algorithms have been calculated. Seven models were calculated for each pollutant parameter; one model for each color group of the visible spectrum, except for TN, which includes an additional model combining the wavelengths of the violet and red region of the spectrum. All the calculated models showed high accuracy, with an R² between 80 and 85 % for COD, BOD₅ and TSS, and 66-74 % for TN and TP. The tests carried out have shown the accuracy of the models of the different color groups to be very close to each other. However, it is noted that the models making use of the wavelengths between 497 and 570 nm (green) were the ones that showed the best performance in all the parameters under study. This research work lays the foundations for the development of cheaper, faster, and simpler wastewater monitoring and characterization equipment.

A probabilistic decision support tool for prediction and management of rainfall-related poor water quality events for a drinking water treatment plant

A data-driven Bayesian Network (BN) model was developed for a large Australian drinking water treatment plant, whose raw water comes from a river into which a number of upstream dams outflow water and smaller tributaries flow. During wet weather events, the spatial distribution of rainfall has a crucial role on the incoming raw water quality, as runoff from specific sub-catchments usually causes significant turbidity and conductivity issues, as opposed to larger dam outflows which have typically better water quality. The BN relies on a conceptual model developed following expert consultation, as well as a combination of different types (e.g. water quality, flow, rainfall) and amount (e.g. high-frequency, daily, scarce depending on variable) of historical data. The validated model proved to have acceptable accuracy in predicting the probability of different incoming raw water quality ranges, and can be used to assess different scenarios (e.g. timing, flow) of dam water releases, for the purpose of achieving dilution of the tributary's poor-quality water and mitigate related drinking water treatment challenges.

IoT in Water Quality Monitoring-Are We Really Here?

The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?

A Deep Learning Approach to Organic Pollutants Classification Using Voltammetry

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about ×25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments.

A New Scenario-Based Approach for Water Quality and Environmental Impact Assessment Due to Mining Activities

Water quality assessment and its monitoring are necessary for areas of mining activities. In Malaysia, the mining industry is the backbone of the manufacturing and construction sectors. This study used spatio-temporal water quality modeling along a reach with mining activities during high and low discharges at Sungai (river) Lebir and Sungai Aring, situated in Gua Musang, Kelantan, Peninsular Malaysia. The objective was to assess the spatio-temporal environmental impact of mining activities during the wet and dry seasons. Data were collected at different locations along the reach. Point and non-point sources were near the mining site. Overland flow calculation at the mining site was found with the widely used SCS (Soil Conservation Service) curve number method. Several scenarios were analyzed, such as baseline, worst-case, and with-mitigation. The study revealed that baseline values of all parameters were either in a natural condition or slightly polluted, except for aluminum. All parameters were estimated at a high concentration from the mining site to downstream during the worst case of the wet season. Whereas, during the worst case of the dry season, no significant differences were observed compared to baseline values. In the with-mitigation scenario, parameter concentrations were improved and similar to baseline values. Overall, the scenario selection was helpful in the environmental impact assessment. Furthermore, this study will be significant in pre- and post-mining assessment and environmental clearance.

Study and Design of a Machine Learning-Enabled Laser-Based Sensor for Pure and Sea Water Determination Using COMSOL Multiphysics

Accurate detection of salt in water is crucial in many applications. Numerous techniques, using direct and indirect methods, have been employed to design seawater sensors. Among the indirect sensing methods, optical sensors are known to be the most accurate, easy to implement, and suitable for application where the chemical properties of the solution to be tested should stay unchanged. This research presents a novel method for real-time label-free biochemical detection of salty water combining various optics concepts with a machine learning system. COMSOL Multiphysics has been employed to design and simulate the proposed sensor. The designed device uses a laser light emitted from the top of a water container, with a sensing part located on the bottom surface. The laser light initially propagates in the air portion, then refracts when it comes into contact with the air-water interface. Different parameters, including the laser beam wavelength λ and its incident angles θi, the temperature, and the air-water levels are employed to generate a set of data and the multilayer perceptron classifier (MLP) to model prediction. The obtained results validated the concept of the proposed sensor using machine learning. The sensor’s prediction precision under various temperature conditions is R2 = 0.844, the equivalent of an MSE of 0.155.

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase "lighthouse" projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

Large seasonal fluctuations of groundwater radioiodine speciation and concentrations in a riparian wetland in South Carolina

Recent studies evaluating multiple years of groundwater radioiodine (¹²⁹I) concentration in a riparian wetland located in South Carolina, USA identified strong seasonal concentration fluctuations, such that summer concentrations were much greater than winter concentrations. These fluctuations were observed only in the wetlands but not in the upland portion of the plume and only with ¹²⁹I, and not with other contaminants of anthropogenic origin: nitrate/nitrite, strontium-90, technecium-99, tritium, or uranium. This unexplained observation was hypothesized to be the result of strongly coupled processes involving hydrology, water temperature, microbiology, and chemistry. To test this hypothesis, an extensive historical groundwater database was evaluated, and additional measurements of total iodine and iodine speciation were made from recently collected samples. During the summer, the water table decreased by as much as 0.7 m, surface water temperature increased by as much as 15 °C, and total iodine concentrations were consistently greater (up to 680%) than the following winter months. Most of the additional iodine observed in the summer could be attributed to proportional gains in organo-iodine, and not iodide or iodate. Furthermore, ¹²⁹I concentrations were observed to be two-orders-of-magnitude greater at the bottom of the upland aquifer than at the top. A coupled hydrological and biogeochemical conceptual model is proposed to tie these observations together. First, as the surface water temperature increased during the summer, microbial activity was enhanced, which in turn stimulated the formation of mobile organo-I. Hydrological processes were also likely involved in the observed iodine seasonal changes: (1) as the water table decreased in summer, the remaining upland water entering the wetland was comprised of a greater proportion of water containing elevated iodine concentrations from the low depths, and (2) water flow paths in summer changed such that the wells intercepted more of the contaminant plume and less of the diluting rainwater (due to evapotranspiration) and streamwater (as the lower levels promote a predominantly recharging system). These results underscore the importance of coupled processes influencing contaminant concentrations, and the need to assess seasonal contaminant variations to optimize long-term monitoring programs of wetlands.

A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy

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.

Amperometric Monitoring of Dissolution of pH-Responsive EUDRAGIT® Polymer Film Coatings

Electrochemical sensors are powerful tools for the detection and real-time monitoring of a wide variety of analytes. However, the long-term operation of Faradaic sensors in complex media is challenging due to fouling. The protection of the electrode surface during in vivo operation is a key element for improving the monitoring of analytes. Here, we study different EUDRAGIT^® controlled release acrylate copolymers for protecting electrode surfaces. The dissolution of these polymers—namely EUDRAGIT^® L 30 D-55 and EUDRAGIT^® FS 30 D—is triggered by a change in pH of the environment, and it is electrochemically monitored by detecting electrode access by means of a redox probe. The full dissolution of the polymer is achieved within 30 min and the electrode response indicates a complete recovery of the original electrochemical performance. We demonstrate that amperometric sensing is a practical and straightforward technique for real-time and in situ sensing of EUDRAGIT^® dissolution profiles. It will find future applications in determining the protection of polymer electrode coating in real matrices and in vivo applications.

Electrical impedance sensing of organic pollutants with ultrathin graphitic membranes

In this paper we propose an original approach for the real-time detection of industrial organic pollutants in water. It is based on the monitoring of the time evolution of the electrical impedance of low-cost graphitic nanomembranes. The developed approach exploits the high sensitivity of the impedance of 2D graphene-related materials to the adsorbents. We examined sensitivity of the nanomembranes based on pyrolyzed photoresist, pyrolytic carbon (PyC), and multilayer graphene films. In order to realize a prototype of a sensor capable of monitoring the pollutants in water, the membranes were integrated into an ad hoc printed circuit board. We demonstrated the correlation between the sensitivity of the electric impedance to adsorbents and the structure of the nanomembranes, and revealed that the amorphous PyC, being most homogeneous and adhesive to the SiO₂substrate, is the most promising in terms of integration into industrial pollutants sensors.

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

Assessment of an Ultrasonic Water Stage Monitoring Sensor Operating in an Urban Stream

The monitoring of the water stage in streams and rivers is essential for the sustainable management of water resources, particularly for the estimation of river discharges, the protection against floods and the design of hydraulic works. The Institute of Marine Biological Resources and Inland Waters of the Hellenic Centre for Marine Research (HCMR) has developed and operates automatic stations in rivers of Greece, which, apart from their monitoring role, offer opportunities for testing new monitoring equipment. This paper compares the performance of a new ultrasonic sensor, a non-contact water stage monitoring instrument, against a pressure transducer, both installed at the same location in an urban stream of the metropolitan area of Athens. The statistical and graph analysis of the almost one-year concurrent measurements from the two sensors revealed that stage differences never exceeded 7%, while the ultrasonic measurements were most of the time higher than the respective pressure transducer ones during the low flow conditions of the dry period and lower during the wet period of the year, when high flow events occurred. It is also remarkable that diurnal air temperature variations under stable hydrologic conditions had an impact on the measured stage from the ultrasonic sensor, which varied its stage measurements within a small but non-negligible range, while the pressure transducer did not practically fluctuate. Despite a slightly increased sensitivity of the ultrasonic sensor to meteorological conditions, the paper concludes that non-contact sensors for the monitoring of the water stage in rivers can be useful, especially where danger for possible damage of submersible instruments is increased.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

Linking environmental with biological data: Low sampling frequencies of chemical pollutants and nutrients in rivers reduce the reliability of model results

Linking environmental and biological data using ecological models can provide crucial knowledge about the effects of water quality parameters on freshwater ecosystems. However, a model can only be as reliable as its input data. Here, the influence of sampling frequency of temporal variable environmental input data on the reliability of model results when linked to biological data was investigated using Threshold Indicator Taxa Analysis (TITAN) and species sensitivity distributions (SSDs). Large-scale biological data from benthic macroinvertebrates and matching water quality data including four metals and four nutrients of up to 559 site-year combinations formed the initial data sets. To compare different sampling frequencies, the initial water quality data sets (n = 12 samples per year, set as reference) were subsampled (n = 10, 8, 6, 4, 2 and 1), annual mean values calculated and used as input data in the models. As expected, subsampling significantly reduced the reliability of the environmental input data across all eight substances. For TITAN, the use of environmental input data with a reduced reliability led to a considerable (1) loss of information because valid taxa were no longer identified, (2) gain of unreliable taxon-specific change points due to false positive taxa, and (3) bias in the change point estimation. In contrast, the reliability of the SSD results appeared to be much less reduced. However, closer examination of the SSD input data indicated that existing effects were masked by poor model performance. The results confirm that the sampling frequency of water quality data significantly influences the reliability of model results when linked with biological data. For studies limited to low sampling frequencies, the discussion provides recommendations on how to deal with low sampling frequencies of temporally variable water quality data when using them in TITAN, in SSDs, and in other ecological models.

Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

A comprehensive review of catchment water quality monitoring using a tiered framework of integrated sensing technologies

Due to the growing threat of climate change, new advances in water quality monitoring strategies are needed now more than ever. Reliable and robust monitoring practices can be used to improve and better understand catchment processes affecting the water quality. In recent years the deployment of long term in-situ sensors has increased the temporal and spatial data being obtained. Furthermore, the development and research into remote sensing using satellite and aerial imagery has been incrementally integrated into catchments for monitoring areas that previously might have been impossible to monitor, producing high-resolution data that has become imperative to catchment monitoring. The use of modelling in catchments has become relevant as it enables the prediction of events before they occur so that strategic plans can be put in place to deal with or prevent certain threats. This review highlights the monitoring approaches employed in catchments currently and examines the potential for integration of these methods. A framework might incorporate all monitoring strategies to obtain more information about a catchment and its water quality. The future of monitoring will involve satellite, in-situ and air borne devices with data analytics playing a key role in providing decision support tools. The review provides examples of successful use of individual technologies, some combined approaches and identifies gaps that should be filled to achieve an ideal catchment observation system.

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

The overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R² = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

Evaluation of the impact of suspended particles on the UV absorbance at 254 nm (UV254) measurements using a submersible UV-Vis spectrophotometer

There is an increasing need to use online instrumentation for continuous monitoring of water quality. However, industrial applications using online instruments, such as submersible UV-Vis spectrophotometers, may require the use of alternative techniques to remove particle effect rather than performing a physical filtration step. Some submersible UV-Vis spectrophotometers have built-in generic particle compensation algorithms to remove the filtration step. This work studied the influence of suspended particles on the measurements of a submersible UV-Vis spectrophotometer as well as the performance of the built-in particle compensation technique under laboratory-controlled conditions. Simulated water samples were used in the combinations of standard particles from laboratory chemical and natural particles extracted from water systems with ultrapure water and treated water from a drinking water treatment plant. Particle contributions to the UV absorbance at 254 nm (UV₂₅₄) measurements of water samples varied differently when particle types or concentrations changed. The compensated UV₂₅₄, measured by the submersible instrument using the built-in generic particle compensation algorithms, was compared with laboratory UV₂₅₄, analysed by the bench-top instrument with the physical filtration method. The results indicated that the built-in generic compensation algorithms of the submersible UV-Vis spectrophotometer may generate undercompensated UV₂₅₄ or overcompensated UV₂₅₄ for various surface waters. These findings provide in-depth knowledge about the impact of suspended particles on the measurements of submersible UV-Vis spectrophotometers; source water dependence; and why site-specific calibration is often needed to get accurate measurements.

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu²⁺) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu²⁺ concentration data to identify likely EC and Cu²⁺ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

Extreme Hydro-Meteorological Events Influence to Water Quality of Small Rivers in Urban Area: A Case Study in Northeast Poland

This paper presents an impact of hydro-meteorological extreme events and urban catchment to water quality in small rivers in Białystok (Poland). The results from a five-year study have taken into account droughts, continuous precipitation, and storm precipitation causing flash floods. Extreme hydro-meteorological events has a different impact on the physical and chemical parameters of water. It was found that the largest change in water quality occurs on the 2nd day after the rainfall and changed concentration of some chemical parameters persists for a long time. The majority but, what’s important, not all of them are diluted after floods and concentrated after droughts. Flash flooding results in a large increase concentrations of DOC and selected forms of phosphorus. Higher values of EC, Eh, Mg²⁺, HCO₃^-, Cl^-, SiO₃^2-, NO₃^-N, TN were observed during drought compared to the average values from 2014–2018. A high degree of naturalness of the river valley and increased water retention results in a decreased concentration of NH₄⁺-N, DOC and phosphorus forms. The buffer zone plays an important role in limiting the inflow of pollutants and nutrients from the catchment area. That is why it is worth undertaking restoration of river valleys in urban areas.

Nitrate dynamics in springs and headwater streams with agricultural catchments in southwestern Germany

The present study examined the dynamics of nitrate pollution in springs and headwater streams in agriculturally used watersheds. The objectives of the study were to record the pollution dynamics throughout the year as a function of different weather patterns and determine the correlation of these dynamics with the degree of agricultural use of the relevant catchments. Moreover, continuous measuring methods should be compared with regular manual sampling procedures. Seven springs with agricultural catchments and their headstreams were studied over 2 years, as well as a reference water body with a forested catchment. At two of the springs, continuous measurements were additionally performed using ion-selective electrodes. Two agrometeorological stations were installed to record the relevant weather parameters. Every water body with agriculturally used surroundings exhibited increased nitrate values. A significant correlation was found between the NO₃^- concentration and the proportion of arable land in the catchment. The nitrate concentration dynamics exhibited extreme weather-related and seasonal fluctuations. While nitrate maxima in autumn and winter correlated widely with the precipitation curve, heavy rainfall in spring and summer led only to short concentration peaks, followed by lower values, presumably caused by dilution effects. Between the spring and downstream measuring points, the nitrate loads increased by the same extent as the arable land area primarily responsible for their emission. No clear nitrate retention was observed between the springs and headstream measuring points. Grab sampling appeared to be sufficiently accurate to record nitrate concentrations when performed at monthly intervals over a two-year measurement period. However, continuous discharge measurements in parallel seemed necessary to determine the loads. The year of 2015 was characterized by a much drier summer and wetter winter, with universally higher nitrate pollution. If these conditions become more established as climate change progresses, increased nitrate pollution must be expected in future.

Real-Time Water Quality Monitoring with Chemical Sensors

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described.

High frequency fluorescence monitoring reveals new insights into organic matter dynamics of an urban river, Birmingham, UK

Natural organic matter (NOM) is fundamental to many biogeochemical processes in river ecosystems. Currently, however, we have limited knowledge of NOM dynamics across the spectrum of flow conditions as previous studies have focused largely on storm events. Field deployable fluorescence technology offers new opportunities to explore both stochastic and predictable diel NOM dynamics at finer time-steps and for longer periods than was hitherto possible, thus yielding new insight into NOM sources, processing, and pathways. Hourly fluorescence data (humic-like fluorescence [Peak C] and tryptophan-like fluorescence [Peak T]) and a suite of hydro-climatological variables were collected from an urban river (Birmingham, UK). We explored monthly concentration-discharge (C-Q) patterns using segmented regression and assessed hysteretic and flushing behaviour for Peak C, T and turbidity to infer source zone activation. Diel patterns were assessed during low flow periods. Wavelet analysis identified strong diurnal variations in Peak C with early morning peaks while no diel dynamics were apparent for Peak T. Using generalised linear modelling relationships between Peak C periodicity and both solar radiation and time since previous storm/scouring event were identified. Breakpoints and positive slopes for C-Q relationship highlighted chemodynamic behaviour for NOM over most of the monitoring period, with Peak T mobilised more relative to Peak C during high Q. Hysteresis patterns were highly variable but flushing behaviour of Peak T and C suggested exhaustion of humic compounds during long duration events and following successive rainfall events. Peak T flushing was correlated with Q magnitude highlighting the potential for combined sewer overflows to act as important NOM sources despite significant dilution potential. This research highlights the potential of real-time, field deployable fluorescence spectroscopy as a viable method for providing insight into diel and transport driven NOM dynamics.

MIMR Criterion Application: Entropy Approach to Select the Optimal Quality Parameter Set Responsible for River Pollution

Surface water quality has a vital role when defining the sustainability of the ecological environment, public health, and the social and economic development of whole countries. Unfortunately, the rapid growth of the worldwide population together with the current climate change have mostly determined fluvial pollution. Therefore, the employment of effective methodologies, able to rapidly and easily obtain reliable information on the quality of rivers, is becoming fundamental for an efficient use of the resource and for the implementation of mitigation measures and actions. The Water Quality Index (WQI) is among the most widely used methods to provide a clear and complete picture of the contamination status of a river stressed by point and diffuse sources of natural and anthropic origin, leading the policy makers and end-users towards a more and more correct and sustainable management of the water resource. The parameter choice is one of the most important and complex phases and recent statistical techniques do not seem to show great objectivity and accuracy in the identification of the real water quality status. The present paper offers a new approach, based on entropy theory and known as the Maximum Information Minimum Redundancy (MIMR) criterion, to define the optimal subset of chemical, physical, and biological parameters, describing the variation of the river quality level in space and time and thus identifying its pollution sources. An algorithm was implemented for the MIMR criterion and applied to a sample basin of Northeast Italy in order to verify its reliability and accuracy. A comparison with the Principal Component Analysis (PCA) showed how the MIMR is more suitable and objective to obtain the optimal quality parameters set, especially when the amount of investigated variables is small, and can thus be a useful tool for fast and low-cost water quality assessment in rivers.

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Industrial effluents with high concentrations of toxic heavy metals are of great concern because of their persistence and non-degradability. However, poor operation and maintenance of wastewater treatment infrastructure is a great concern in South Africa. In this study, physico-chemical parameters and heavy metals (HMs) concentration of wastewater from five different industries, Leeuwkuil wastewater treatment plant (WWTP) inflow and effluent, and Vaal River water samples were monitored between January and September 2017, to investigate the correlation between heavy metal pollution and the location of industries and ascertain the effectiveness of the municipal WWTP. Physico-chemical variables such as pH, biological oxygen demand (BOD), dissolved oxygen (DO), chemical oxygen demand (COD), total dissolved solids (TDS) and electrical conductivity (EC) exhibited both temporal and spatial variations with the values significantly higher in the industrial samples. Inductively coupled plasma optical emission spectrometry (ICP-OES) results also showed that aluminium (Al), copper (Cu), lead (Pb) and zinc (Zn) were significantly higher in industrial effluents (p < 0.05), with only Zn and Al exhibiting significant seasonal variability. Statistical correlation analysis revealed a poor correlation between physicochemical parameters and the HMs compositional quality of wastewater. However, toxic HMs (Zn, Cu and Pb) concentrations in treated wastewater from WWTP were above the permissible limits. Although the WWTP was effective in maintaining most of the wastewater parameters within South African Green drop Standards, the higher Cu, Zn, Pb and COD in its final effluent is a concern in terms of Vaal river health and biological diversity. Therefore, we recommend continuous monitoring and maintenance of the WWTPs infrastructure in the study area.

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.

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

Water quality dynamic during rainfall episodes: integrated approach to assess diffuse pollution using automatic sampling

Diffuse pollution caused by rainfall events potentially affects water quality in rivers and, therefore, must be investigated in order to improve water quality planning and management recovery strategies. For these, a quali-quantitative approach was used to monitor the water quality parameters in a river located in a semi-urban watershed area based upon automatic sampling. Thirteen water quality parameters were measured during five rainfall events. Events ranged from 2.3 to 56.8 mm and water peak flows from 3.3 to 4.5 m³/s. The pollutographs measured showed a standard pattern for total suspended solids (TSS). However, for the other chemical parameters, as total phosphorous (TP) and dissolved organic carbon (DOC), the dilution effects were more evident. It was possible to observe the rainfall influence mainly for physical parameters indicating a mass transport pattern for diffuse pollutants, which increased, for example, the amount of TSS in the river. Furthermore, hydrological characteristics were relevant considering the pollutant behavior. Antecedent dry periods, ranging from 1.3 days to 21.4 days, and critical time, from 2.0 to 10.4 h, are determinants to evaluate non-traditional water quality impacts in the river. In general, each rainfall episode has its own characteristics, which produces distinct mass contribution and temporal behavior, being challenging in making generalization. Therefore, the results indicate that diffuse pollution has to be considered to establish future decision-making strategies to water resources management.

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful.