Continuous Water Quality Monitoring Research Articles

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Design And Development Of A Quality And Quantity Water Monitoring System For Water Tank Based On Internet Of Things

In the context of urban life, the quality and quantity of water stored in tanks play an indispensable role. However, concerns over the lack of effective monitoring of water conditions in tanks have garnered significant attention. This research aims to design a monitoring system utilizing Internet of Things (IoT) technology to ensure continuous monitoring of water quality and quantity within tanks.
 The study zeroes in on developing a solution that overcomes the limitations of traditional methods, known for their time and cost constraints. This solution hinges on a real-time monitoring system that integrates sensors and the NodeMCU ESP32 microcontroller. Measurements of water quality parameters, such as turbidity and total dissolved solids (TDS), along with assessments of water quantity through tank volume, are integrated into the system. Software development is carried out using the Arduino IDE platform.
 Through this research, an increased awareness of the importance of monitoring water quality in tanks is anticipated, and this solution is expected to make a significant contribution to the development of efficient and effective monitoring technology. Thus, a crucial step toward more accurate and up-to-date water monitoring in urban environments can be achieved.

Bio-optical properties of the cyanobacterium Nodularia spumigena

Abstract. In the last century, an increasing number of extreme weather events have been experienced across the globe. These events have also been linked to changes in water quality, especially due to heavy rains, flooding, or droughts. In terms of blue economic activities, harmful algal bloom events can pose a major threat, especially when they become widespread and last for several days. We present and discuss advanced measurements of a bloom dominated by the cyanobacterium Nodularia spumigena conducted by hyperspectral optical technologies via experiments of opportunity. Absorption coefficients, absorbance and fluorescence were measured in the laboratory, and these data are available at https://doi.org/10.4121/21610995.v1 (Wollschläger et al., 2022), https://doi.org/10.4121/21822051.v1 (Miranda et al., 2023) and https://doi.org/10.4121/21904632.v1 (Miranda and Garaba, 2023). Data used to derive the above-water reflectance are available from https://doi.org/10.4121/21814977.v1 (Garaba, 2023) and https://doi.org/10.4121/21814773.v1 (Garaba and Albinus, 2023). Additionally, hyperspectral fluorescence measurements of the dissolved compounds in the water were carried out. These hyperspectral measurements were conducted over a wide spectrum (200–2500 nm). Diagnostic optical features were determined using robust statistical techniques. Water clarity was inferred from Secchi disc measurements (https://doi.org/10.1594/PANGAEA.951239, Garaba and Albinus, 2022). Identification of the cyanobacterium was completed via visual analysis under a microscope. Full sequences of the 16S rRNA and rbcL genes were obtained, revealing a very strong match to N. spumigena; these data are available via GenBank: https://www.ncbi.nlm.nih.gov/nuccore/OP918142/ (Garaba and Bonthond, 2022b) and https://www.ncbi.nlm.nih.gov/nuccore/OP925098 (Garaba and Bonthond, 2022a). The chlorophyll-a and phycocyanin levels determined are available from https://doi.org/10.4121/21792665.v1 (Rohde et al., 2023). Our experiments of opportunity echo the importance of sustainable, simplified, coordinated and continuous water quality monitoring as a way to thrive with respect to the targets set in the United Nations Sustainable Development Goals (e.g. 6, 11, 12 and 14) or the European Union Framework Directives (e.g. the Water Framework Directive and Marine Strategy Framework Directive).

Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring

Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.

Intelligent monitoring of water quality based on image analytics

Owing to the limitations of the spatial arrangement of monitoring stations and time acquisition of satellite remote sensing images, the water quality monitoring of rivers, especially small- and medium-sized rivers, cannot be satisfied in terms of time and space continuity. In this study, we propose a standardization method for the camera images derived at different locations on different time considering the influences of light, shadows, reflections, ripples, leaves and so on. After this algorithm is established, an estimation algorithm based on the comprehensive appearance of water body is proposed, which has the potential of realising real-time, mobile, and continuous monitoring of water quality with low costs. The test results showed that the accuracy of the model was quite high compared to the results of the hydrological monitoring stations. Compared with the single-point detection of water quality monitoring stations, this method has advantages in terms of dynamic detection and small- andmedium-sized water body detection, which can serve as a supplement to traditional detection.

Temporal Analysis of Water Quality for the Nilo Coelho Reservoir, Terra Nova, Pernambuco, Brazil

The Brazilian northeast has a characteristic relationship with water resources because, in this region, water scarcity is associated with natural factors and accentuated by anthropic actions that interfere with water quality. The present work aimed to perform a temporal analysis of the water quality of the Nilo Coelho reservoir, municipality of Terra Nova, a semi-arid region of Pernambuco. Initially, the parameters of dissolved oxygen, nitrate, and phosphorus, available in the system of hydrological response units for Pernambuco (SUPer), following the resolution of the National Environment Council (CONAMA), for the years 1963–2021, were analyzed. Subsequently, land use and occupation mapping were carried out to identify the activities that developed in the region. The result of the analysis of water quality for the Nilo Coelho reservoir classifies it as little compromised, since the parameters are within acceptable limits, with greater variations for phosphorus. Regarding the use and occupation of the soil, there was an increase in water availability and agricultural areas, with a reduction in the caatinga. Continuous monitoring of water quality in the reservoir is recommended, in addition to the application of SUPer and land use and occupation maps as tools for the management of water resources.

A Simplified Equation for Calculating the Water Quality Index (WQI), Kalu River, Sri Lanka

The water supply system plays a major role in the community. The water source is carefully selected based on quality, quantity, and reliability. The quality of water at its sources is continuously deteriorating due to various anthropogenic activities and is a major concern to public health as well. The Kalu River is one of the major water resources in Sri Lanka that supplies potable water to the Kalutara district (a highly populated area) and Rathnapura district. But, there has been no significant research or investigation to examine anthropogenic activities in the river. Due to this, it is difficult to find any proper study related to the overall water quality in the Kalu River. Therefore, this study covers a crucial part related to the water quality of the Kalu River. The spatiotemporal variation of river water quality is highly important not only to processing any treatment activities but also to implementing policy decisions. In this context, water quality management is a global concern as countries strive to meet the United Nations Sustainable Development Goal 6, which aims to ensure the availability and sustainable management of water and sanitation for all. Poor water quality can have severe consequences on human health, ecosystems, and economies. Contaminated water sources pose risks of waterborne diseases, reduced agricultural productivity, and ecological imbalances. Hence, assessing and improving water quality is crucial for achieving sustainable development worldwide. Therefore, this paper presents a comprehensive analysis of spatiotemporal analysis of the water quality of the Kalu River using the water quality data of eight locations for 6 years from 2017 to 2023. Nine water quality parameters, including the pH, electrical conductivity, temperature, chemical oxygen demand, biological oxygen demand, total nitrate, total phosphate, total sulfate, total chlorine, and hardness, were used to develop a simple equation to investigate the water quality index (WQI) of the river. Higher WQI values were not recorded near the famous Kalutara Bridge throughout the years, even though the area is highly urbanized and toured due to religious importance. Overall, the water quality of the river can be considered acceptable based on the results of the WQI. The country lockdowns due to COVID-19 might have impacted the results in 2020; this can be clearly seen with the variation of the annual WQI average, as it clearly indicates decreased levels of the WQI in the years 2020 and 2021, and again, the rise of the WQI level in 2022, as this time period corresponds to the lockdown season and relaxation of the lockdown season in the country. Somehow, for most cases in the Kalu River, the WQI level is well below 25, which can be considered acceptable and suitable for human purposes. But, it may need some attention towards the areas to find possible reasons that are not in the range. Nevertheless, the results suggest the importance of continuous water quality monitoring in the Kalu River.

Spatial-Temporal Dynamics in Potable Water Quality: A Case Study of Mizan-Aman Town, Southwest, Ethiopia

Surface water is one of the sources of global potable water. However, the quality of surface water has been degrading due to an increase in human activities. Hence, the present study was conducted to evaluate the spatial and temporal variability in drinking water physico-chemical and bacteriological characteristics from the different sources in Mizan-Aman, Ethiopia. The physico-chemical characteristics of water at the Kosokol site were found to be statistically different ( p < 0.05 ) from the other sites (Gacheb, Tsit, Reservoir 1, and Reservoir 2 sites). The dry and wet season temperature (27.1 and 23.8°C), turbidity (37.4 and 54.8 NTU), pH (7.6 and 8.1), biological oxygen demand (18.1 and 20.7 mg/l), phosphate (2.4 and 3.5 mg/l), and ammonia (2.2 and 4.8 mg/l) of the water were beyond the acceptable limit set for drinking water by both Ethiopian standards (temperature: <15°C; turbidity: 7 NTU; pH: 6.5–8.5; BOD: 10 mg/l; phosphate: 0.02 mg/l; and ammonia: 1.5 mg/l) and the WHO standards (temperature: <15°C; turbidity: 5 NT U; pH: 6.5–8.5; BOD: 5 mg/l; phosphate: 0.01 mg/l; and ammonia: 1.5 mg/l). The total coliform (366.5 and 494.3 CFU/100 ml) and fecal coliform (209.5 and 278.3 CFU/100 ml) contents of the water in the dry and wet seasons are beyond the acceptable limits for drinking water by Ethiopian standards (total and fecal coliform: 0) and the WHO standards (total and fecal coliform: 0). The physico-chemical and bacteriological characteristics of the water in the wet season were found significantly higher than those ( p < 0.05 ) in the dry season. In general, the water quality changes on a spatial and temporal basis in the study area. Thus, it is important to encourage water quality management works at the upper source sites of the catchment. Furthermore, the city water authority should reinforce safeguarding treatment processes, continuous monitoring of water quality as well as risk assessment and management practices.

Assessment of Infection Prevalence and Intensity of Disease-Causing Parasitic Protozoans Perkinsus marinus and Haplosporidium nelsoni in Georgia Oysters.

Eastern oysters, Crassostrea virginica, are ecologically and economically important coastal species which provide a commercially valuable food product while also improving water quality through filtration, protecting shorelines, and providing habitat. The protozoan parasites Perkinsus marinus and Haplosporidium nesloni commonly infect oysters along the United States Atlantic and Gulf coasts and have been linked to poor oyster health and mass mortality events. In this study, wild oysters were collected from multiple reefs within four tidal creeks along the coast of Georgia to investigate P. marinus and H. nelsoni prevalence and intensity, their potential impact on oyster health, and identify possible drivers of the parasites. A second study occurred on four sites on Sapelo Island, Georgia, with continuous water quality monitoring data to further elucidate potential drivers. Oyster density and condition index, a proxy for health, were measured, and parasites were quantified using a TaqMan probe based quantitative real-time PCR within gill tissue. Real-time PCR showed that 86% of oysters tested were infected by one or both parasites in the coast-wide survey, and 93% of oysters from Sapelo Island were also infected by one or both parasites. Prevalence and infection intensity for both P. marinus and H. nelsoni varied across sites. Overall impacts on oysters were complex-intensity was not linked to oyster metrics in the coastwide study, but oyster condition was negatively correlated with P. marinus prevalence in the Sapelo Island study. Several relationships between both parasites and water quality parameters were identified, providing valuable information about potential drivers that should be investigated further.

Challenges in the conservation of endangered Rucervus eldii eldii McClelland in Keibul Lamjao National Park and Pumlen pat: an analysis of sediment and water quality of the floating natural habitats in the Indo Burma hotspot.

As a part of the monitoring practice of the lone natural habitat of the endemic and endangered Rucervus eldii eldii McClelland or Sangai, the paper reports sediment and water characteristics of Keibul Lamjao National Park-the only floating national park of the world in Manipur, Indo Burma hotspot. The water analysis showed low pH (5.69 ± 0.16), high electrical conductivity (342 ± 13.01 μS m-1), high turbidity (33.2 ± 9.407 NTU), and high phosphate concentrations (0.92 ± 0.11mg L-1) during the study period. The water quality index values calculated indicate that the park water during post-monsoon season is unfit for drinking. Thus, the degrading water quality poses a serious risk to the health of the deer and other species in the park. At present, Sangai in its natural habitat is threatened by pollution, encroachment, phoomdi thickness reduction, and inbreeding depression. To address the problem of inbreeding, Pumlen pat is considered as a second suitable natural habitat for the deer reintroduction program. During the study, water characteristics of the wetland showed similar characteristics as KLNP, i.e., low pH (5.86 ± 0.30), high electrical conductivity (377.6 ± 5.55 μS m-1), high turbidity (32.36 ± 4.91 NTU), and high phosphate concentrations (0.79 ± 0.14mg L-1). Likewise, sediments showed high TP accumulation that ranged from 197.03 ± 0.75 to 332.88 ± 0.99mgkg-1 for KLNP and 245.18 ± 0.85 to 351.48 ± 0.71mgkg-1 for Pumlen pat, respectively. Both, the lone natural habitat and the proposed habitat showed deteriorating water quality. As such, continuous monitoring of KLNP and Pumlen pat water and sediment quality should be a priority during the management practices to protect the endangered deer and health of the habitats for long-term conservation.

IMPLEMENTATION OF AN INTEGRATED MONITORING SYSTEM TO ENSURE THE ENVIRONMENTAL SAFETY OF WATER RESOURCES

Groundwater pollution is a serious environmental problem in Ukraine, where groundwater is used for drinking, industrial, and agricultural purposes. Compliance with hygienic requirements for water quality is especially important during wartime, as underground water is a strategic source of drinking water for the population. Therefore, effective groundwater monitoring and management are crucial for ensuring Ukraine's ecological safety. To address water pollution, continuous monitoring of water quality is necessary, especially near industrial facilities and landfills for solid household waste. However, the existing monitoring systems in Ukraine are not always effective, and complete and accurate information about the state of groundwater is not always available. To effectively manage water resources, it is necessary to consider the possibility of creating an integrated monitoring system that combines various methods and technologies to obtain more complete information about the state of water resources and manage them effectively. An overview of the problem of groundwater pollution in Ukraine was conducted, and the existing methods of groundwater monitoring were analyzed, with their advantages and disadvantages established. The current state of groundwater monitoring in Ukraine was also considered. A brief description of the Integrated Groundwater Monitoring System was presented, and its role in effective groundwater management and the prevention of water pollution was discussed. The possibility of using the Integrated Monitoring System in Ukraine was also considered. It was proposed to use monitoring sensors that measure the parameters of water seeping from the aeration zone to the saturation zone in real-time mode. In conclusion, the role of the Integrated Groundwater Monitoring System in solving the problem of water pollution was emphasized, and recommendations were made for its implementation in practice. Further prospects for research in the field of groundwater monitoring in Ukraine were also outlined.

High-throughput determination of lipopolysaccharide by flow injection analysis using dipicolylamine-type fluorescent probe

Endotoxin (lipopolysaccharide: LPS), a bacterial toxin, is currently quantified by batch analysis using a reagent made from Limulus amebocyte lysate (LAL). Although the LAL method is sensitive, a long measurement time and gelation have hindered its application to online water quality monitoring. In this paper, we report a novel high-throughput analytical technique that combines flow injection analysis (FIA) and a dipicolylamine-type fluorescent probe, which was previously reported to exhibit strong fluorescence emission in response to LPS. A comparison of normal and reverse FIA systems revealed that the normal FIA offers more rapid detection with throughput of 30 samples per hour, which is the highest among known LAL methods and alternative LPS detection methods that have been recently reported. On the other hand, the reverse FIA requires much less reagent (480 μL per sample). Continuous online monitoring of water quality and analysis of real samples spiked with LPS were realized by this technique.

Impact of Artificial Islands and Reefs on Water Quality in Jinmeng Bay, China

Recurrent green tide has been widely studied due to its severe damage to coastal ecosystem. Jinmeng Bay, a popular resort in northeastern China, has suffered from green tide events since 2015, after the constructions of artificial islands and submerged reefs. To investigate the potential impacts of artificial islands and reefs on the water quality in Jinmeng Bay, a MIKE 21 numerical model was established by coupling a hydrodynamic model with a transport model of Chemical Oxygen Demand (COD) and nitrate ion (NO3−). The changes in the water quality in Jinmeng Bay by the initial, first-stage and current construction conditions of artificial islands and submerged reefs were simulated. The model results indicated that: (1) The artificial islands and reefs in Jinmeng Bay hinder the tidal currents and weaken the tidal actions. (2) The weakened tidal actions at the estuary lead to the accretion of COD and NO3−. The neap tides generate a littoral zone with the high concentration of COD and NO3−, and the spring tides maintain the zone at the estuary. (3) NO3− is more sensitive than COD to the variation of hydrodynamic conditions. The NO3− concentration in the north of Conch artificial island is altered significantly, where the construction of the artificial structures decrease the concentration by ~30%, while the demolition of the connection road increases the concentration beyond its initial values by 16~21%. (4) Under the current construction conditions, the rising concentrations of COD (up to 2%) and NO3− (up to 40%) increase the frequency and scale of green tides in Jinmeng Bay considerably. Therefore, continuous monitoring of water quality is required for this region.

Tracing the dominant sources of sediment flowing towards Lake Victoria using geochemical tracers and a Bayesian mixing model

PurposeLake Victoria has been increasingly silting over the past decades, impacting water quality and loss of biodiversity. Sediment control strategies require information on the relative and absolute contributions of sediment from different sources. However, to date, there is no continuous monitoring of sediment flux or water quality in any of the tributaries, prohibiting an assessment of the scale of the problem. The aim of this study was to trace the dominant sources of riverine sediment using geochemical fingerprinting, thereby generating a knowledge base for improving land management and reducing sediment yields in Simiyu River catchment, one of the main contributing rivers to Lake Victoria.Materials and methodsGeochemical tracer concentrations were analyzed in transported sediment from the main river and two tributaries (riverbed sediments) and from soils in five dominant land use types (agricultural land, bush land, forest land, channel banks, and main river banks). Dominant sources to the Simiyu main river sediment were attributed using the Bayesian MixSIAR model.Results and discussionThe mixing model outputs showed that the Simiyu tributary was the dominant source of sediment to the Simiyu main river with 63.2%, while the Duma tributary accounted for 36.8%. Cultivated land was shown to be the main land use source of riverine sediment, accounting for 80.0% and 86.4% in Simiyu and Duma sub-tributaries, respectively, followed by channel banks with 9.0% in both sub-tributaries. Direct unmixing of the Simiyu main river sediment to the land use sources yielded 64.7% contribution of cultivated land and 26.5% of channel banks.ConclusionThe demonstrated application of sediment source tracing provides an important pathway for quantifying the dominant sources of sediment in the rivers flowing towards Lake Victoria. Eroded soil from agricultural areas is the biggest contributor to transported sediment in the Simiyu River. This information is vital for the design of catchment wide management plans that should focus on reducing soil erosion and sediment delivery from farming areas to the river networks, ultimately supporting both food security and water quality in the Lake Victoria Basin.

Advanced Continuous Monitoring System—Tools for Water Resource Management and Decision Support System in Salt Affected Delta

The greatest environmental problem facing the world today is climate change, with a rise in sea level being one of the most important consequences, especially in low-lying coastal areas, such as river deltas where changes are exacerbated by human impacts, leading to increased seawater intrusion into coastal aquifers and the degradation of water quality. Water quality monitoring systems are being developed and deployed to monitor changes in the aquatic environment. With technological progress, traditional sampling-based water monitoring has been supplemented with sensors and automated data acquisition and transmission devices, resulting in the automation of water quality monitoring systems. This paper reviews the recent development and application of automated continuous water quality monitoring systems. It also draws on the results of our own experience in implementing such a system in the Neretva River Delta on the Croatian Adriatic coast. The installed system provides (near) real-time data on parameters such as temperature, pH, EC, TDS, and DO in the water, as well as a number of soil and weather variables, with data available at a high frequency through a developed database and web portal for various stakeholders. Continuous monitoring enables the collection of big data that can be used to develop models for predictions of water quality parameters and to develop guidelines for future management.

Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya

AbstractThe continuous water quality monitoring (WQM) of watersheds and the existing water supplies is a crucial step in realizing sustainable water development and management. However, the conventional approaches are time-consuming, labor intensive, and do not give spatial–temporal variations of the water quality indices. The advancements in remote sensing techniques have enabled WQM over larger temporal and spatial scales. This study used satellite images and an empirical multivariate regression model (EMRM) to estimate chlorophyll-a (Chl-a), total suspended solids (TSS), and turbidity. Furthermore, ordinary Kriging was applied to generate spatial maps showing the distribution of water quality parameters (WQPs). For all the samples, turbidity was estimated with an R2 and Pearson correlation coefficient (r) of 0.763 and 0.818, respectively while TSS estimation gave respective R2 and r values of 0.809 and 0.721. Chl-a was estimated with accuracies of R2 and r of 0.803 and 0.731, respectively. Based on the results, this study concluded that WQPs provide a spatial–temporal view of the water quality in time and space that can be retrieved from satellite data products with reasonable accuracy.

Agricultural Watershed Health Monitoring Using Spatial Based Approach under Tropical Climate Environment

Massive agricultural activities in watershed areas cause severe environmental damage through soil erosion, sedimentation downstream and decreased water quality. A decrease in water quality can damage the aquatic ecosystem, which is an indication of a watershed health level. Hence, continuous monitoring of water quality is needed. This study aims to assess the level of watershed health as a result of agricultural activities. Five water quality indicators such as BOD, COD, pH, Nitrate, and KMnO4 were used in this study. The values of the five indicators were then analyzed quantitatively using scoring with three posibility categories and scores, namely good (score: 5), moderate (score: 3), and poor (score: 1). Categories were determined based on water quality standards for aquatic ecosystems. The results showed a good value category for all indicators. It reveals that in general the watershed is in a healthy condition. Some indicators that showed moderate values (BOD, COD, and KMnO4) in the northern part of the watershed indicated changes in water quality. The results of this study provides a reference for developing a watershed health monitoring system using more complex indicators.

Health risk assessment, water quality indices and reproductive hormone profile among Abakpa citizens in Enugu metropolis

Numerous substances has been attributed to elicit endocrine-disruption and may potentially affect healthy reproductive functions. This study was carried out to determine the health risk assessment, water quality indices and reproductive hormone profile among citizens of Abakpa in Enugu metropolis. Ten (10) water sampling points comprising of wells and streams were analyzed for chemical properties such BOD, COD, DO, pH, TSS, TDS, Total Hardness, and Alkalinity and Heavy metal composition such as Zinc (Zn), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Mercury (Hg) using the American Public Health Association’s (APHA) guidelines and Atomic absorption spectrophotometer (AAS) respectively. Five (5) blood samples comprising males and females was collected across the sampling areas and assayed for reproductive hormone profile such as Testosterone, Follicle Stimulating Hormone, Progesterone, Prolactin, Estradiol and the enzyme Aromatase was using the ELISA kit method. The result of chemical properties such as TSS, TDS, COD, Total hardness and Alkalinity were all within World Health Organization (WHO) stipulated limits, whereas DO, BOD and pH were higher than WHO limits across all sampling points from the sampling area. Some parameters showed significance difference while some sample did not at different sampling locations at 95% confidence level. The result of heavy metals showed very high concentrations of analyzed metals with Hg present in five water samples from Abakpa at concentrations ranging from 0.01 to 1.96 ppm. Pb concentration ranged between 0.06-0.35 ppm; Zn 24.93- 33.65 ppm; Cr 0.00 – 0.23 ppm; Cd. 0.11 – 0.17 ppm across all sampling points. All the result of reproductive hormone showed lower concentration of testosterone in men (1.99 to 2.19 ng/ml) and high concentration in females (2.03 to 2.06 ng/ml) across all sampling points. The concentration of FSH was in the range of 0.67 to 0.77 ng/ml in males, while the level was between 0.71 to 0.83 ng/ml in female. Prolactin results was 3.49 to 3.86 ng/ml for males and 3.44 to 3.59 ng/ml in females. Estradiol levels were 8.48 to 9.54 ng/ml and 9.61 to 9.84 ng/ml for females and males respectively. However, progesterone level were between 34.08 to 37.45 ng/ml for females and 36.48 to 39.56 ng/ml. The aromatase enzyme was averagely below 4.0 Pmol/mg in both male and female. The risk assessment showed low risk exposure but mercury had high risk exposure tendencies of 16.333 when compared to other heavy metals from all samples. Consumption of water in sampled area’s may pose a serious health challenge; high concentration of heavy metals could be attributed to the hormonal imbalance recorded in the research as most heavy metals is seen to reduce the expression and activity of 3β-HSD and 17β-HSD, which could be facilitated by high acidic nature of the water. Thus, interfering with steroidogenesis. Therefore, continuous monitoring of water quality in Abakpa in Enugu metropolis is highly recommended in order to ensure the health safety of humans living in such environment.

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodies. In this work, remote sensing technology integrated with an Artificial Intelligence (AI) algorithm, a new technique called Spatio-Temporal Hybrid Novel Technique (STHNT), was used to predict, and monitor the chemical water quality pollution level through the Water Quality Index (WQI). The Two Bands Regression Empirical (TBRE) water quality model has been used to retrieve water quality parameters from multi-resolution satellite imagery (Sentinel-2 MSI). The Nonlinear Auto-regressive Neural Network (NARNET), which is an Artificial Neural Network (ANN), was set up to predict the water quality index. Based on the model performed on the remote sensing water quality data, it is inferred that NARNET (Coefficient of determination-R2:0.9911, Root Mean Square Error-RMSE:1.693 and Sum of Squares of Error-SSE:14.33) provides significant results in predicting WQI. Therefore, the combined remote sensing technology with artificial intelligence plays a pivotal role in water resource management, which helps in reducing the pollution level in surface water bodies.

Estimation of the capacity for self-purification of transformed rivers of Khanty-Mansi Autonomous Okrug-Yugra (Russia).

This article presents an estimation of the self-purification capacity of rivers flowing through an area of the Ob River basin at Khanty-Mansi Autonomous Okrug-Yugra, which has been significantly transformed by the oil and gas industry. The research was done on the rivers of the Ob River basin in oil fields in 2003-2006 and 2018-2019. The coefficient of mineralization of organic matter has been used as a criterion of the rivers' water self-purification levels. The mineralization coefficient was based on the ratio of numbers of bacteria consuming ammonium nitrogen and polymer substances as well as saprophytic heterotrophic bacteria. According to the results obtained, most rivers draining the oil field area (75% in 2003-2006 and 79% in 2018-2019) were characterized by a low potential for self-purification. The highest level of self-purification criteria was obtained for two rivers: the Ai-Pim River in the Surgut region and the Ovyn'yegan River in the Oktyabrsky region. The high level of self-purification detected with the coeficient of mineralization ensures the maintenance of a certain trophical level and environmental balance of the aquatic ecosystem. Complicated technological processes applied in the oil and gas industry, as well as outdated equipment and poor environmental management, often lead to pipeline accidents and other infrastructure failures. Hazardous pollutants flow into rivers from the watershed area and further into the Gulf of Ob of the Kara Sea. Continuous monitoring of water quality and water resource management based on the results obtained will constitute serious measures to prevent deterioration of aquatic ecosystems. It is necessary to include microbiological parameters in monitoring programs, as this helps to identify the processes of transformation of chemicals found in the river. The use of self-purification assessment methodology for rivers in oil field areas yields effective results in the decision-making process in the sphere of water resource management, significantly reducing ecological risks in the Ob River basin and, as a consequence, in the Arctic region. Integr Environ Assess Manag 2023;19:988-993. © 2022 SETAC.