Water Quality Information Research Articles

The impact of spatial resolution on inland water quality monitoring from space

Abstract Remote sensing of inland waters can provide timely and global water quality information to a wide variety of stakeholders. One of the parameters that determines the feasibility of using optical space-based instruments for monitoring inland waters is the ground sampling distance (GSD), defined as the width of a pixel projected on the Earth’s surface. We assume that to analyze a body of water with optical imagery, its characteristic width must be larger than 3 times the GSD to obtain an ‘unmixed’ pixel that doesn’t contain signal from the adjacent land. Here we obtain the size distribution of river lengths, river areas, and lake areas—as a function of width—for rivers and lakes in the Western United States (US) and in Australia. We base this analysis on the Surface Water and Ocean Topography River Database (SWORD) and HydroLAKES databases, extrapolated to 5 m-wide features. We show that the fraction of river length and river area larger than a certain width increases sharply as the width decreases, indicating that even small decreases in the GSD result in significant increases in the number of bodies that can be surveyed. On the other hand, the distribution of lake areas shows a ‘knee’ at around 400 m, indicating that gains from GSDs smaller than 130 m will be modest. We found that a satellite instrument with a GSD capability of 18 m can provide coverage of 4.4% of total river lengths, 38% of total river area, and 94% of total lake area within the study areas. We argue that decreasing the GSD incurs penalties associated with loss of signal-to-noise, larger instrument, smaller swath, and longer revisit times.

Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.

Physicochemical and microbial quality of bottled drinking water sold in Embakasi Central, Nairobi, Kenya

ABSTRACT Bottled drinking water is among the fastest-growing options for seemingly safe drinking water in low- and middle-income countries. However, comprehensive water quality information is rarely made available to consumers. The objective of this study was to assess the physicochemical and microbial quality of bottled drinking water at the point of sale in Embakasi Central sub-county, Nairobi County. Using a cross-sectional study design, we assessed eight bottled water brands purchased from local retail outlets (n = 158 bottles). Laboratory analysis was conducted to assess a range of parameters, including Escherichia coli, heterotrophic bacteria, lead, nitrite, nitrate, fluoride, and pH. Across all bottles sampled, 12% exceeded the national standards for microbial safety (detectable E. coli) and 3% presented a high risk to health (>10 colony forming units per/100 mL for E. coli). Select brands were frequently contaminated, with all samples of Brand 5 having elevated fluoride levels. Overall, one in four samples exceeded national standards for contaminants of public health concern. These findings reveal the poor quality of bottled drinking water in a suburb of Nairobi, presenting risks to consumers. There is a need to strengthen risk management and oversight of water packaging facilities in this setting.

POLICY FORMULATION FOR ANTICIPATING THE IMPACT OF ACID RAIN ON PADDY PLANTS USING NORMATIVE JURIDICAL ANALYSIS

Law Number 32 of 2009 Article 57, paragraph 4C that one of the preservations of atmosphere functions regulate efforts to protect against acid rain. One of the effects of acid rain is soil damage to plants, such as what happened to paddy plants. In Indonesia, paddy is one of the priority food sources, but every year, it decreases in several regions. Aims: This study aimed at acid rain's anticipated impact on paddy plants in this case study Bandung Regency. Methodology and results: The approach method in this study is a normative juridical approach, using primary data sources obtained through interviews and journal literature. Then, secondary data from legal literature were analyzed descriptively and qualitatively. The study results show that the Bandung Regency Environmental Service has enforced the law. However, violations still occur by industry and society through vehicle emissions, coupled with the increase in population. One of the efforts to formulate policies to anticipate acid rain in paddy plants is to establish sanctions for violators who still pollute the environment, apply catalytic converters to vehicle exhausts that have the potential to remove NOx, carry out measurements at groundwater measurement stations, water quality information systems (manual or real-time). In addition, the irrigation policy for river water flowing on agricultural land with existing laws is being improved. Conclusion, significance, and impact study: Anticipating the impact of acid rain on rice requires various approaches, both technical and non-technical, as well as further analysis from various policy sectors.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from 0.33 to 33.77%, and most of them were 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

Evaluation of Groundwater Security under Engineering Environment in Geda Township Area,Tibe

Water body is one of the important factors of engineering spatial structure, and also the carrier of spatial activities. The Tibetan Plateau is known as the “third pole in the world”. It has a special geographical location and a large number of projects for engineering infrastructure. Therefore, how to build various major and large-scale projects under the plateau environment needs to be further strengthened to the importance of water bodies. In this paper, through the geological conditions in Geda Township, Tibet, Analysis and performance of water mechanism in the area, through the field discovery, The water direction in the survey area is controlled by the structure and terrain; at the same time, the project water supply guarantee is mainly for the qualitative and quantitative evaluation of water quality and quantity information, the water supply guarantee of the investigation area is mainly divided into surface water supply water guarantee and groundwater water supply guarantee. This paper describes the relevant content of groundwater protection, through the field investigation and field water quality collection, the basic situation in the investigation area is that: the groundwater quality is generally superior to that of the surface water, especially more than Jilin spring water, better water quality and larger amount of water; It is of guiding significance to water quality investigation and geological engineering construction in other areas.

Intelligent Water Quality Testing Device based on Internet of Things Technology

Water quality testing is an important environmental testing standard. However, at present, Internet of Things water quality testing products generally have problems such as single function, complex interface, and short sensor life. The design of this paper is a device with intelligent water quality detection technology, including the independent design of the sensor interface standard, the sensor automatic recognition, plug and play; The sensor is equipped with a lifting mechanical structure to avoid long-term soaking of the sensor and increase the service life of the sensor by 35%. At the same time, combined with intelligent Internet of Things, big data analysis and other technologies, help relevant departments to grasp water quality information in a timely and accurate manner, early warning and forecasting of major regional water pollution accidents, supervision of pollutant discharge, and implementation of control systems to provide help. It is mainly used to monitor the water quality data of small and medium-sized reservoirs, rivers and lakes to achieve intelligent management within the jurisdiction.

Sezonowa i przestrzenna zmienność jakości wody w systemie jezior Lumina-Rosu w delcie Dunaju w Rumunii

The main aim of the present scientific paper is to assess water quality of the surface water of various lakes located in the Lumina-Rosu interdistributary depression, Danube Delta, Romania. As specific objectives, the paper considered to spot the main potential sources of contamination and their environmental impacts on aquatic ecosystems. Equally, the purpose of the article is to emphasize the importance of monitoring the water quality of the Danube Delta, for preserving these unique natural water resources and their ecosystem services (fresh water, biodiversity, flood control, recreation, nutrient cycling, fishing and other traditional activities, ecotourism). Surface water samples collected from several control sections situated on the main tributaries and canals, as well as from lakes were investigated physically (To - C, EC - μS/cm, TDS - mg/L, turbidity - NTU units, TSS - mg/L) and chemically (pH - units, DO - mg/L, N-NO2- - mg/L, N-NO3- - mg/L, P-PO43-- mg/L, Chla - mg/L, SiO2 - mg/L, TOC - mg/L, SO42- - mg/L, S2- - mg/L, H2S - mg/L and synthetic detergents - mg/L). Generally, the hydro-physical-chemical characteristics and water quality assessment of the surface waters of the lakes manifest the combined effect of both processes occurred in the catchment (weathering, sediment supply and transport), as well as the in situ lake processes (photosynthesis, sediment mixing, biogeochemical cycling, evaporation, eutrophication, productivity changes). Overall, the physical and chemical characteristics of the sampling sites investigated at high waters of the Danube are quite similar to those measured at low waters. The differences that mainly occurred within the lakes are more related to the limnological variables and the local environmental conditions. Anyway, a significant seasonal variation was noticed in the lakes, during the dry period, when different physical and chemical characteristics appeared to be influenced by high air temperature, lack of precipitation, low water level regime, low dissolved oxygen level regime, all related to climate change effects etc. Moreover, the anthropogenic factor that left its mark on some physical-chemical characteristics of water should not be omitted either. However, the results obtained within this study did not show such alarming values as might have dangerous effects on the investigated aquatic environments. The water quality information within this case study may be used for improving the understanding of the water quality issues and to better coordinate and plan for future monitoring activities in and around the Danube Delta environment. The results will contribute to updating the existing database with relevant information for a sustainable future of the Danube Delta and in similar areas that are subjected to such environmental challenges.

Distribution of Per‐ and Polyfluoroalkyl Substances in the Rapidly Urbanizing Arroyo Colorado Watershed, Texas

AbstractThis study presents the first report of per‐ and polyfluoroalkyl substances (PFAS) in water samples collected in the Arroyo Colorado (n = 15), irrigation canals (n = 6), stormwater and wastewater retention ponds (n = 7), as well as drinking waters (n = 2) across the Arroyo Colorado watershed. Of the 30 PFAS monitored in this study, 14 were detected in the samples in various combinations. Short‐chain PFAS (less than 8 carbon atoms) were observed in most samples. Water collected from the Arroyo Colorado showed significant spatial variabilities, with high total PFAS concentrations observed near possible point sources ‐ a municipal airport and wastewater treatment facilities. PFAS concentrations were generally higher in water samples collected in stormwater and wastewater retention ponds than in the Arroyo Colorado and irrigation canals. PFAS in stormwater retention ponds likely came from roadway runoff. Short‐chain PFAS were observed in the two municipal water samples, but they were below the current U.S. EPA regulation limits or are not currently regulated. This study provides useful information for water quality in this region and provides insights into PFAS occurrence in a rapidly urbanizing area.

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution, is an effective method to obtain water quality information comprehensively. In this study, we attempted to develop a method for estimating the chlorophyll concentration from aerial images using machine learning that considers brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis. The reflectance of harmful algae bloom (HAB) was different from that of phytoplankton seen under normal conditions; so, the images containing HAB were the causes of error in the estimation of the chlorophyll concentration. First, the images when the bloom occurred were extracted by the discrimination with machine learning. Then, the other images were used for the regression of the concentration. Finally, the coefficient of determination between the estimated chlorophyll concentration when no bloom occurred by the image analysis and the observed value reached 0.84. The proposed method enables the detailed depiction of the spatial distribution of the chlorophyll concentration, which contributes to the improvement in water quality management in reservoirs.

A dissolved oxygen prediction model based on GRU–N-Beats

Dissolved oxygen is one of the most important water quality parameters in aquaculture, and the level determines whether fish can grow healthily. Since there is a delay in equipment control in the aquaculture environment, dissolved oxygen prediction is needed to reduce the loss due to low dissolved oxygen. To solve the problem of insufficient accuracy and poor interpretability of traditional methods in predicting dissolved oxygen from multivariate water quality parameters, this paper proposes an improved N-Beats-based prediction network. First, the maximum expectation algorithm [expectation–maximization (EM)] was used to fill in the original data by fitting the missing values. Second, the discrete wavelet transform (DWT) was used to reduce the overall noise of the sample, then the gated recurrent unit (GRU) feature extraction network was employed to extract the water quality information from the temporal dimension, the N-Beats was utilized to predict the preprocessed data, and the residual operation through Stack was performed to obtain the prediction results. The improved algorithm overcomes the challenge of insufficient prediction accuracy of the traditional algorithm. The GRU–N-Beats network proposed in this paper can extract features from multivariate time dimensions for prediction. The values of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 for the proposed algorithm were 0.171, 0.120, 0.015, and 0.97, respectively. In particular, they were 28.5%, 32.1%, 51.6%, 24.3%, 14.9%, 36.4%, and 19.3% higher than those of long short-term memory (LSTM), GRU, temporal convolutional network (TCN), LSTM–TCN, PatchTST, back-propagation neural network (BPNN), and N-Beats on RMSE, respectively.

Water Quality System Informatics: An Emerging Inter-Discipline of Environmental Engineering

Water quality system informatics (WQSI) is an emerging field that employs cybernetics to collect and digitize data associated with water quality. It involves monitoring the physical, chemical, and biological processes that affect water quality and the ecological impacts and interconnections within water quality systems. WQSI integrates theories and methods from water quality engineering, information engineering, and system control theory, enabling the intelligent management and control of water quality. This integration revolutionizes the understanding and management of water quality systems with greater precision and higher resolution. WQSI is a new stage of development in environmental engineering that is driven by the digital age. This work explores the fundamental concepts, research topics, and methods of WQSI and its features and potential to promote disciplinary development. The innovation and development of WQSI are crucial for driving the digital and intelligent transformation of national industry patterns in China, positioning China at the forefront of environmental engineering and ecological environment research on a global scale.

Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring

The conservation of hydrological resources involves continuously monitoring their contamination. A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q‐learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus‐based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1–3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state‐of‐the‐art approaches.

Applying user-centered design to improve drinking water consumer confidence reports: A Baltimore case study

The US Environmental Protection Agency is revising its policy on drinking water quality reports for consumers. These reports are intended to enhance the public’s “right to know” and to spur action to protect and promote safe water. However, these reports are known to be highly technical and difficult to access compromising their communication value. This study engaged a 10-person focus group to gather evidence on how these reports can be improved. We applied user-centered design principles to understand public drinking water consumer information needs and preferences and to develop new communication tools and methods. Through a set of in-depth interviews, we learned that most participants were unaware of the report until introduced to it during the study. The focus group participants voiced preferences for: better ways to convey technical information; more health information; a clearer understanding of costs and billing; and neighborhood or household level water quality information. Following the interviews, we convened two rounds of small group meetings to create new report designs and to review and refine the designs. The focus group developed a one-page summary statement, water contaminant trend charts, an interactive map, and other recommendations on ways to improve dissemination of the report. The project results, focus group recommendations and designs were submitted to the US Environmental Protection Agency for consideration as the policy for these reports is finalized. We believe these findings provide valuable insights into water quality communication challenges and that this approach is widely applicable and can be informative for water utilities as they prepare future reports.

Application Research on Optimal Allocation of Drainage Rights Based on Computer Big Data in Anhui Province of Huai-River Basin

In this paper, a three-dimensional visualization model of river water body based on Vega is proposed. This system can not only display the water quality information of each monitoring point on the geographic information platform, but also carry out virtual reality roaming of the river. Then the mechanism of interactive allocation of initial water discharge rights is analyzed. According to the principle of interaction and negotiation between different regions and industries, a two-level diagnosis and optimization model is established. Secondly, the optimal equation and diagnostic criteria of comprehensive benefits based on regional economy and society are constructed based on the strict control of the maximum emission permit. The initial emission rights are interactively configured between the primary and secondary levels. Emission reduction targets for each region and industry were proposed. Considering the fairness and effectiveness of the allocation of water discharge rights between regions and industries, the comprehensive benefits of each region and the whole basin are maximized. The feasibility of the scheme is illustrated by an example analysis. It is urgent to carry out the research on the rational allocation of drainage rights in Huaihe River, and the relevant research results will contribute to the sustainable supply of grain in Anhui province, help avoid water disputes around the Huaihe River, and promote the sustainable economic and social development of Anhui province.

Real-time water quality forecasting in rivers using satellite data and dynamic models: an online system for operational management, control and citizen science

Increasingly scarce water resources and growing global populations have exacerbated the problems of water quality in river systems and freshwaters in general. New monitoring methodologies and tools to democratize access to water quality information are needed if we are to reach ambitious societal objectives such as the UN Sustainable Development Goals and the European Green Deal. Here we present a cloud-based system for producing publicly accessible real time water quality forecasts coupled to novel biosensor technology. Short term forecasts of water quality impairments, e.g., as cyanobacteria blooms, sediment plumes and toxic pollution incidents are increasingly relevant both to citizens and stakeholders. Here, we present a new cloud based system that utilizes satellite data to produce real time forecasts of flow and water quality using a chain of dynamic catchment-scale models at multiple locations in a river network. We demonstrate this new system using two case studies: the River Thames and the Essex Colne River (United Kingdom). These rivers are key water supply sources for London and South-East England, respectively and are of high interest to recreational water users. We show how the new system can predict and forecast water quality, estimate toxicity and connect to citizen science observations using an App (www.aquascope.com) for information synthesis and delivery.

UAV Control Based on Pattern Recognition in Aquaculture Application

This study proposes a drone application for the net cage aquaculture industry. A visual control structure is applied to the drone to obtain water-quality information surrounding the net cages. This study integrates a hexacopter, camera, onboard computer, flight control board, servo motor, and global positioning system’s auto-cruise function to adjust the drone position and control the servo motor retractable sensor to reach the desired target at an accurate location. In object identification, a deep learning neural network is used to identify the net cages. An onboard computer calculates the horizontal distance between the drone and the net cage. A “You only look once” (YOLO) neural network is used to detect the net cage images. Considering the hardware calculation speed and ability, an onboard computer is applied to process the flight control board and control the drone. In the mission, an aerial camera detects targets (net cage) and provides visual information to the drone for the target approaching control process. After executing the water-quality measurement, the drone will end the mission and return to the base. This study modifies the architecture of YOLO, compares it with the original model, and then finds a proper architecture for this mission. This study aims to assist cage aquaculture operators by using drones to measure water quality, which can reduce aquaculture’s labor costs.

Perceptions of drinking water: Understanding the role of individualized water quality data in Detroit, Michigan

Understanding water users’ perceptions of drinking water quality and the water service provider are important to understand for effective communication with users. Traditionally, the primary means through which water users receive information about drinking water is via the annual Consumer Confidence Report, which summarizes water quality information at the water system-scale and not at the point-of-use. In this study, we recruited 24 water users from different homes in Detroit, Michigan to assess the effect of access to individualized data on perceptions related to their drinking water quality and service provider. Each participant had a water quality sensor node, which measured five different water quality parameters, temporarily installed in their home for four weeks. Entry interviews were completed at the time of sensor node installation. After four weeks, water quality reports summarizing the individual water quality data collected by the sensor nodes were prepared and shared with participants, after which the exit interviews were completed. We found that access to individualized water quality data positively affected participants’ perceptions of drinking water quality and safety, for example, 92% of participants rated the safety of water at the faucet as at least ‘Somewhat Safe’ in the exit interview compared to 46% in the entry interview. However, participants’ perceptions of the water service provider did not change significantly in response to this information (p > 0.05). Half of the study participants expressed interest in more frequent monitoring and communication, including actionable data that allowed participants to make more informed decisions about how to better manage their water quality at home. We saw evidence of long-term changes in response to access to individualized information with 50% reporting changes in behavior related to drinking water use. We conclude that access to localized water quality data provides actionable information that Detroit, Michigan water users value.

Long-term Change of Water Transparency in Lake Singkarak Observed from Remote Sensing Data

The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. Satellite data has a big potential as a source for providing data to retrieve water quality information. In this study, we used Landsat TM/ETM+ remote sensing data and applied an empirical model for estimating the water transparency (Zsd) to represent water quality in Lake Singkarak. We used 230 scenes of pre-processed Landsat TM/ETM+ images to generate a long-term SD database for Lake Singkarak from 1987 to 2020. The visual and statistical analysis shows the change of Zsd in Lake Singkarak. In the period before 2000, the Zsd were generally lower than 2 meters (m). From 2000 to 2005 the Zsd continuously increased from 1.5m to 4m. Lastly, from 2005 until 2020 Zsd were steady: 2m > Zsd < 4m. The satellite-based Zsd estimations captured the three regimes of water quality in Lake Singkarak. These estimations will thus provide useful data for lake managers and policymakers.

A review of the status, challenges, trends, and prospects of groundwater quality assessment in Nigeria: an evidence-based meta-analysis approach.

With the imminent industrial growth and population increase, Nigeria will continue to experience significant shifts in the quality of water, with a rise in emerging contaminants. This will increase the irregularity and complexity of the water quality information. Therefore, using the PRISMA meta-analysis approach, this review systematically identified the commonly used water quality assessment techniques in Nigeria, the drawback in the application of these techniques as well as the gaps in the area of water quality assessment and monitoring from 2003 to 2023. Recommendations were also made based on the evaluation of a new research direction; through the review of the effectiveness of advanced techniques for monitoring water quality in Nigeria. Sixty-eight published articles were chosen for the meta-analysis while the VOSviewer program was used to perform bibliographic coupling and visualization. The review revealed that the application of machine learning in water quality prediction has not been well explored in Nigeria. This is attributed to limited data availability and poor funding by the government. It was found that southwestern Nigeria has a greater amount of research on groundwater quality monitoring and evaluation than other regions. The variability was explained by variations in the underlying geology, aquifer features; variability in anthropogenic activities, and level of literacy among various geopolitical zones. Further studies should focus on the application of soft-computing and integrated biomonitoring techniques for effective prediction and monitoring of emerging contaminants for improved water quality. Effective collaboration between environmental stakeholders and government agencies is recommended for effective water resource sustainability.