Monitoring Of Water Quality Parameters Research Articles

The productivity phenomenon of tilapia floating net cages in Jatiluhur Reservoir in line with the Citarum Harum program

The research was conducted to determine the productivity conditions of tilapia cultivation in the field along with the implementation of reducing the number of floating net cages. Production observations of tilapia culture were carried out on net cages owned by fish farmer in the Jatiluhur Reservoir from 2012 to 2019. The first period-P1 (2013-2015) was before the reduction in the number of net cages, the culture used a double net system and hybrid system. The second period-P2 (2016-2019) when the program started, KJA culture was carried out with a self-maintenance system (100% feeding for tilapia). The value of fish harvest weight ranged from 96.39-102.47 g (P1) and 196.84-278.33 g (P2). The range of survival rate is between 56.99–78.79% (P1), and 60.92–80.41% (P2). The feed efficiency value obtained in the P2 period was 75.77–86.34% while for P1 it was not defined because it was not a 100% feed system (independent). The daily weight gain, productivity and multiple values were as follows: 0.58-1.0 g/day, 0.14-0.45 g/m2, and 2.35-4.56x (P1); 1.43–3.00 g/day, 1.92–2.89 kg/m2 and 6.77–10.80x (P2). The monitored water quality parameters were still within a tolerable range for tilapia floating net cage culture.

Water quality parameter analysis model based on fish behavior

In the process of aquaculture, the quality of water environment directly determines the quality of aquaculture. Deterioration of water quality will directly cause a decline in aquaculture production, and in severe cases, it will cause a large number of deaths of aquatic organisms and serious economic losses to aquaculture enterprises. Therefore, it is important to real-time monitor water quality parameters in aquaculture. In this paper, a water quality monitoring model based on fish behavior is proposed with the oplegnathus punctatus as the research object. We do not need to install complicated equipment, and only need the image data captured by the camera to accomplish the real-time monitoring of water quality parameters. In terms of model, we also added the CBAM attention mechanism, residual module and a series of training strategies, so that the model can focus on the valuable fish behavior information in the image while focusing on the global information. The experimental results show that the accuracy of the model in the validation set can reach 97.2%, and the inference speed can reach 144.36 FPS. And the experiment shows that our model has good generalization performance.

Satellite and Machine Learning Monitoring of Optically Inactive Water Quality Variability in a Tropical River

Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R2 value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.

Seasonal Water Quality Changes and the Eutrophication of Lake Yilong in Southwest China

To better understand the seasonal variation characteristics and trend of water quality in Lake Yilong, we monitored water quality parameters and measured nutrients, including the water temperature (WT), Chlorophyll-a (Chl-a), dissolved oxygen (DO) and pH from September 2016 to May 2020, total nitrogen (TN) and total phosphorus (TP) from October 2016 to August 2018. The results showed that the lake water was well mixed, resulting in no significant thermal stratification. The DO content was decreased in the northwest part of the lake during September and October, resulting in a hypoxic condition. It also varied at different locations of the lake and showed a high heterogeneity and seasonal variability. The Chl-a concentration in Lake Yilong demonstrated seasonal and spatial changes. It was maximum at the center and southwest area of the lake in January. However, in the northwest part of the lake, the maximum value appeared in September and October. The content of TN in the rainy season increased by 75% compared with that in dry season and TP content show a downward trend (from 0.11 mg/L to 0.05 mg/L). The comprehensive nutrition index evaluation shows that the water quality of Lake Yilong in 2016 was middle eutrophic (TLI = 60.56), and that in 2017 (TLI = 56.05) and 2018 (TLI = 56.38) was weak eutrophic, showing that the nutritional status has improved. TN remained at a high level (2.15 ± 0.48 mg/L), water quality needs further improvement. Based on our monitoring and analysis, it is recommended that human activities in the watershed of the lake should be constrained and managed carefully to maintain the water quality of the lake and adopt effective water quality protection and ecological restoration strategies and measures to promote continuous improvement of water quality, for a sustainable social development.

Seasonal variability, predictive modeling and health risks of N-nitrosamines in drinking water of Shanghai

The prevalence of carcinogenic N-nitrosamines in drinking water is of significant concern. In the present study, eight N-nitrosamines from three representative drinking water treatment plants (DWTPs) in Shanghai, China were monitored for an entire year to evaluate their seasonal variability, probabilistic cancer risk and the resulting disease burden. The possibility of employing routinely monitored water quality parameters as predictors of N-nitrosamines was also examined. The results showed that the Taipu River-fed reservoir suffered more serious N-nitrosamine contamination than the Yangtze River-fed reservoirs. Winter witnessed higher levels of N-nitrosamines in both source and finished water. N-nitrosamine concentrations increased from source water to finished water in autumn or winter, but no spatial variations were observed in summer. The total lifetime cancer risk (LCR) posed by N-nitrosamines in finished water was within the acceptable range (1.00 × 10−6 to 1.00 × 10−4), with N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) being the main contributors. Winter and autumn were found to have higher total LCR values. The average individual disability-adjusted life years (DALYs) lost was 4.43 × 10−6 per person-year (ppy), exceeding the reference risk level (1.00 × 10−6 ppy). Liver cancer accounted for 97.1 % of the total disease burden, while bladder and esophagus cancers made a little contribution (2.9 %). A multiple regression model was developed to estimate the total N-nitrosamines in finished water as a function of water quality parameters, and the R2 value was 0.735. This study not only provides fundamental data for public health policy development, but also reveals the necessity to incorporate a seasonal control strategy in DWTPs to minimize the associated health risks induced by N-nitrosamines.

Industrial Wastewater Discharge and Compliance Investigation for Environmentally Resilient Rwanda

While Rwanda is aiming at environmental pollution resilience and green growth, some industries are still discharging untreated effluent into the environment. This study gives a general overview of the compliance level of industrial effluent discharge in Rwanda and the linked negative environmental impacts. It comprises qualitative and quantitative analyses of data obtained from wastewater samples collected from five selected industries in Rwanda. The selected industries had previously been audited and monitored by the Rwanda Environment Management Authority (REMA), due to complains from neighboring residents. The study found that the effluent discharge from wastewater treatment plants (WWTP) for all concerned industries failed to comply with (i) oil and grease (O&G) national and international tolerable parameter limits or the (ii) fecal coliforms national standard. In addition, a compliance level of 66.7% was observed for key water quality monitoring parameters (pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and heavy metals (i.e., lead (Pb), cadmium (Cd), and chromium (Cr)). Following these study findings, one industry was closed by the REMA for deliberately discharging untreated effluent into an adjacent river. This study recommends the adoption of the best available technology for effluent treatment, installation or renovation of existing WWTPs, and the relocation to industrial zones of industries adjacent to fragile environments.

A three-part coupled statistical and physical model to monitor water quality parameters governing disinfection byproduct risk at an urban drinking water intake

Extended drought in the twenty-first century has led to loss of volume for lakes and reservoirs across the globe. In the Colorado River Basin, USA, Lakes Powell and Mead have experienced a 68 and 71% decline in volume, respectively, since 2000. Lake Mead is important to the Las Vegas Valley as it accounts for 90–100% of the source water used to serve the 2.2 million residents and 43 million annual tourists. Lake Powell is also vital to maintaining water quality in Lake Mead as it is located upstream and provides 97% of the water entering Mead. As Lakes Powell and Mead are projected to continue decreasing in volume over the next 5 years, it is important to understand the effects of loss of lake volume on water quality in this highly-managed system. Here, the effects of lake drawdown on water quality parameters that affect disinfection byproduct (DBP) formation in the source water for the Las Vegas Valley are projected over the next 5 years using Federal flow projections, regression modeling to project influent temperature from Lake Powell into Lake Mead, and a three-dimensional hydrodynamic and water quality model for Lake Mead. Results from these modeling efforts include projections for changes in values of water temperature, bromide, and total organic carbon (TOC) at the modeled cell that contains the Las Vegas Valley's urban drinking water intake. Raw water bromide was found to have little change across modeled scenarios; however, raw water temperature is projected to increase because of falling lake surface elevations. Raw water TOC is projected to increase three-fold in the simulated scenario that includes the most loss of volume but remains close to historic values in the remaining simulated scenarios. With these raw water quality projections, water managers can better plan for alterations to the water treatment processes, including mitigation of DBP formation.

Effects of nutrients on the water quality and the algal community of river Nyakomisaro-Riana, Kisii, Kenya

Water quality changes affect the ecological functioning of riverine ecosystems as well as their ability to support organisms and sustain environmental values. The study eighteen months study was conducted from May 2013 to October 2014 to determine the changes in water quality and algal community. Water samples for nutrient and algae composition were collected in triplicates monthly at five sampling sites along the Nyakomisaro-Riana River and laboratory analyses were conducted by following the standard methods described by the American Public Health Association (APHA). Data was oorganized, described and analysed with One-Way Analysis of Variances (ANOVA) and bivariate regression, using the Microsoft Excel 2010 and Minitab software’s and the significance of ddifferences considered at the p-value of  0.05. Tukeys pairwise multiple comparisons were used for post hoc analysis. The level of Total Phosphorous (TP) exhibited a general increasing upstream - downstream trend with an overall mean of 250.99±25.31 µgL-1 and a range of 2.59 - 1281.40 µgL-1. The Soluble Reactive Phosphorous (SRP) concentrations ranged between 1.66 - 632.00 µgL-1 with a spatial mean of 57.24±8.59 µgL-1 within a range of ., while Total Nitrogen (TN) concentration had an overall mean of 996.49±128.60 µgL-1 and ranged between 4.76 - 4332.33 µgL-1. The lowest mean TN concentration (664.87±90.49 µgL-1) was recorded at site NK1 while the highest mean (1349.27±146.48 µgL-1) was recorded at site R1. Nitrate -Nitrogen [NO3-N] concentration ranged between 0.18 - 742.90 µgL-1 with an overall mean of µgL-1 142.28±16.40 µgL-1. Regression analysis showed a positive correlation between Nitrate nitrogen and distance downstream (b = 15.05, a = 112.12, R 2 = 0.40). Out of 135 algal species recorded, 113 had a low percentage occurrence of less than 30%. The algal community comprised of seven genera of diatoms, six genera of chlorophyceae and cyanophyceae and three genera of euglenophyceae. The percentage algal abundance indicated the existence of a climax community,the blue green algae (Microcystis aeruginosa) with a dominance of 34%. This suggested that nutrient loading into Nyakomisaro-Riana River is high. The study recommends that regular monitoring of water quality parameters and algal community changes should be conducted to control the nutrient discharges into the river.

基于虚拟仪器技术的中华绒螯蟹养殖监控系统设计与试验

The measurement and control system of Eriocheir sinensis, the Chinese mitten crab, could increase its survival rate and quality effectively. Based on the analysis and design of the system, the parameter ranges for major factors of water quality during crab breeding process were determined. Sensors of pH, dissolved oxygen (DO), and temperature were used to detect the value of pH, DO, and temperature. LabVIEW, a virtual instrument technology, was used to monitor water quality parameters during crab breeding process, thereby controlling the relay of inlet valve, water valve and oxygen valve by comparing the actual figures with normal parameter. This technology was used to perform the adjustment of water quality parameters respectively to achieve automatic control. In order to test the performance of the system, the monitoring time, stocking density and planting density were taken as experiment factors, and the error rate of the monitoring index was taken as evaluation index. The verification experiment results show that pH, DO and temperature monitoring errors were less than 10%, under the maximum constraint conditions of experiment factors, which meet the design requirements.

Occurrence and modeling of disinfection byproducts in distributed water of a megacity in China: Implications for human health

Disinfection byproducts (DBPs) are initially formed in the process of chlorination in the drinking water treatment plants (DWTPs), then further formed in the distribution system due to the presence of residual chlorine and reactive organic matters. However, in China, DBPs are monitored in the effluent from the DWTPs, but less is known about concentrations of DBPs in tap water since they are usually monitored once per half a year. The smart water service system is establishing real-time monitoring of water indices, although DBPs are an urgent need, they are difficult to monitor in real-time due to their diversity and complicated detection methods. If the correlation between DBP concentration and routinely real-time monitored water quality parameters (e.g., pH value, residual chlorine, ammonia) can be evaluated, the concentration of DBPs can be predicted, which will strengthen the control of tap water safety. This article comprehensively assessed the physicochemical parameters and the occurrence of DBP formation in the tap water with an 18-month investigation in Z city (China). DBP formation in tap water of different seasons and different water sources were compared. Based on the relationship between DBPs and physicochemical parameters, linear prediction and nonlinear prediction models of trihalomethanes (THMs), haloacetonitriles (HANs) and haloacetic acids (HAAs) were established, and the accuracy of these models was verified by measured data. Finally, the toxicity and carcinogenic and non-carcinogenic health risk assessment of DBPs in tap water were analyzed.

Selected Measurement Parameters of Water Quality: Comparative Insight of India and Indonesia

A number of rivers in India and Indonesia are usually used by the people to fulfill their daily needs. To ensure the quality of river water is in reasonable limit, it is important to monitor water quality regularly. The objective of this study was to review and compare the selection of water quality monitoring parameters in India and Indonesia. The used method was descriptive analyses. The results of the study showed several findings: first, in terms of regulation, India was lesser than that of Indonesia; second, India used the water quality index to assess the state of water quality, while Indonesia used the STORET method or the water pollution index; third, India had legally stipulated the main parameters in considering the status of water quality, whereas Indonesia had yet to stipulate it; and forth, there were 10 water quality parameters that were often used in both India and Indonesia, namely pH, BOD, COD, DO, nitrate, phosphate, temperature, TDS, TSS, and total coliform. Therefore, these parameters should be selected as the key parameters to monitor water quality.

Assessment of metal concentrations from recreational rivers in a tropical region (Jengka, Malaysia)

A recreational river may be exposed to some extent of metal pollution. Two rivers, namely Sungai Weh and Sungai Jempul, were selected for this study, as both areas offer recreational activities for residents in Jengka, Pahang, Malaysia. This study examines the concentration of selected metals (Fe, Mn, and Pb), elucidates the possible sources, evaluates the toxicity loads, and estimates the potential health risk. Metal concentrations were analysed using inductively coupled plasma optical emission spectrometry (ICP-OES). The concentrations were found in the sequence of Fe > Pb > Mn. Hierarchical cluster analysis (HCA) suggested that the metals’ origins included both anthropogenic activities and natural sources. About 16.66% of Pb should be removed from the river water to ensure safety, based on the heavy metal toxicity load calculation (HMTL). Children are more vulnerable to non-cancer and cancer risks than adults. The findings indicate that comprehensive monitoring of water quality parameters and thorough exposure assessment should be performed.

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.

Yield improvement of Gracilaria tenuistipitata by optimizing different aspects in coast of cox’s bazar, Bangladesh

This research was designed to find out the effect of different factors such as influence of lunar cycle, harvesting interval, rope type and seeding gap on the production of G. tenuistipitata in coast of Cox’s Bazar. Duration of these experiments were sixty days and all the parameters were recorded fortnightly. Monitoring of water quality parameters indicated that salinity, temperature, transparency, pH and DO were suitable for seaweed cultivation. In determining lunar cycle effect, results envisaged that fresh yield was 14.43% increased when seeding and harvesting time was selected considering the moon cycle. Regarding the selection of harvesting interval, it was found that T3 (30 days interval) was the best to harvest the seaweed whereas T4 (40 days interval) showed decreasing trend in production. Our study also found that semi floating single line showed better yield performance compared to semi floating double line system. In case of influence on seeding gap, it has been found that 20 cm gap between two seed showed the highest yields followed by 10 cm, 30 cm and 40 cm, respectively. Overall, it can be concluded that yield of G. tenuistipitata in coast of Cox’s Bazar could be improved considering those factors.

Hydrobiological Study of Lotic Water of Yamuna River at Yamuna Nagar, Haryana, India during 2018-2019

The current study deals with hydrobiological examination of lotic water of Yamuna river at Yamuna Nagar, Haryana, India to assess the oscillation in the physico-chemical parameters. The monitoring of water quality parameters was performed consecutively during; July, 2018 to June, 2019. The month wise onsite thermal examination, and titrimetric/ volumetric estimation of collected water samples showed seasonality biased pattern throughout the investigation. The mean value of water temperature (oC), dissolved oxygen (mg/l), pH, hardness (mg/l), alkalinity (mg/l) and acidity (mg/l) ranged between 11.9–29.5 oC, 3.4–7.0 mg/l, 6.3– 7.6, 111.25–170.75 mg/l, 64.06–122.0 mg/l and 12.57–25.29 mg/l respectively. The monthly possible source of factors responsible for the spatial fluctuations in the hydrobiological parameters of riverine water was assessed through the advanced numerical tools. The observation reflected the requirement of urgent and systematic management strategies to overcome these invisible scarce and conserve the natural quality of incredible water resources.

Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong

The water quality measurement of marine water is a key research topic for environmental and ocean modelers in the past several decades. Marine water quality is mainly described by its chemical, physical and biological properties. According to the Environmental Protection Department (EPD), Hong Kong, the chemical and physical measurements are integrative parameters for water quality measurements. The existence of undesirable components in marine water degrades the water quality. The increase of natural and anthropogenic events in coastal regions poses the marine ecosystem in danger day by day. Therefore, the prediction of marine water quality indicators is essential. Turbidity is a key indicator of marine water quality, whose prediction is difficult due to its non-linear time series behavior. Therefore, the objective of the present study is to predict turbidity in the marine environment of Hong Kong using Machine Learning (ML) approach. The artificial neural network (ANN), support vector regression (SVR), and Long short-term memory recurrent neural network (LSTM-RNN) have been used as ML tools in the marine environment. The ML model prediction results were compared and the obtained results suggest that the LSTM-RNN model outperforms the ANN and SVR model results with an accuracy of 88.45%. The neural network-based approaches give better prediction accuracy over the SVR model in this study. Thus, it can be concluded that the neural network-based algorithm results can be used to monitor water quality parameters in the marine environment.

Spatial and Temporal Variation of Selected Water Quality Parameters in the Tanzanian Side of Lake Victoria

Lake Victoria’s water quality is increasingly becoming under heavy pressure mainly due to land based activities and aerial inputs which are taking place within the basin. This study was carried out to assess the spatial, including vertical and temporal, variation of the water quality of Lake Victoria on the Tanzanian side. Historical water quality data for the period from 2000 to 2016 was collected. Temperature, pH, Dissolved Oxygen (DO) and Turbidity were selected for study since they are the mostly measured and monitored water quality parameters. Land use-land cover changes were analysed using ArcGIS. ILWIS 3.7 software was used to classify the land use and land cover for the years 2000, 2010 and 2014. MERIS was used to analyse the spatial variation. One-way ANOVA was employed to test the significant variation between different parameters. The results showed that, for the pelagic zone, the range of temperature, pH, DO, and turbidity were 22.8 oC-28.68 oC, 6.3-10.52, 3.42-10.21 mg/l, 1.0 NTU-15.8 NTU respectively. The corresponding values for the littoral zones were 22.3 oC-26.8 oC, 6.47-10.16, 3.99-8.6 mg/l, 1.3-347 NTU respectively. The ANOVA analysis results show that there was a significant variation of NO3(p<0.01). Temperature, pH and DO decreased with the lake depth to the bottom for both zones. For the littoral zone, a strong correlation was observed between temperature and depth, temperature and DO, and between temperature and pH with R2=0.6, p<0.03, R2=-0.78, p<0.01 and R2=0.96, p<0.01, respectively. The bare soil, urban settlements and farm land increased by 38.9%, 8.4% and 10.7% respectively from the year 2000 to 2014 on the Tanzanian side. This could have led to water quality changes. Water quality parameters varied significantly between pelagic and littoral zones. Littoral zones are mostly polluted and thus should be the priority pollution control intervention areas.

Herbal Feed Additives for Gonadal Maturity and Breeding Performance of Female Snow Trout (Schizothorax richardsonii) in Captive Condition

Addressing the issue of gonadal maturity and breeding in captive condition for a commercially important indigenous Coldwater fish, snow trout, Schizothorax richardsonii, field study was conducted with dietary supplementation of the blend of Ashwagandha (Withania somnifera) root powder, dried powder of garlic (Allium sativum), and hing (Ferula asafoetida). Experimental fish of 3 years age were reared in FRP tanks (n = 4) of 1200 L capacity with stocking density of n = 60 in each tank and fed with control (CD) and experimental (TD) diets in duplicate at the feeding rate of 3–5% of their body weight twice in a day. Gonadosomatic index (GSI) and hepatosomatic index (HSI) were recorded for annual breeding cycle coupled with breeding indices in breeding season and regular monitoring of water quality parameters. In female stock, the GSI values ranges from 0.87 to 12.83 and 1.84 to 15.42 in control and treated groups, respectively. Two peaks of GSI values reflects the two breeding season of the species during the month of March and September. Similarly, HSI was lowest during the month of March and September in captive reared female fish and highest during the month of May to June and January to February, with similar trend in both, control treated and groups. In captive condition, 80% specimens were observed mature for spawning in treated group, while only 15% females were ready to spawn in control stock. The fecundity of the fish was observed in the range of 20210-23710 eggs/kg body weight with 80–94.2% fertilization rate. The egg size was observed in the range of 3.5–3.8 mm. The study revealed that 1.0% supplementation of blend of herbs in the diet is beneficial for gonadal maturity of females and better spawning of S. richardsonii in captive Coldwater condition.

Community-Based Monitoring Detects Sources and Risks of Mining-Related Water Pollution in Zimbabwe

Although mining and mineral processing are vital for many economies in the Global South, they are associated with enormous challenges of managing potentially devastating environmental impacts. In contexts where environmental oversight agencies often lack financial and personal capacities to fulfill their role, community-based monitoring might be a valid alternative to monitor potential environmental impacts. In this study, we present the setup and the implementation of a citizen science project to monitor water quality parameters in a river downstream of a coal mining area in Hwange, Western Zimbabwe. In a joint effort over 1.5 years, community monitors and scientists took close to 800 water samples in the Deka River and effluent channels. The data allowed identifying sources of pollution and relating these to past and present mining activities. The primary source of acid mine drainage came from abandoned underground mine sites. Illegal mine water dumping from active mine sites accentuated the problem and resulted in fish kills and food risks for the local population. Concentrations of manganese, nickel and arsenic were exceeding national fresh water guidelines and international drinking water standards. Manganese concentrations exceeded guidelines by a factor of 70 resulting in a public health risk. In this study, we showed that community-based monitoring offers a promising approach to establish a high-quality dataset for assessing mining-related risks if the implementation of sampling protocols is followed tightly. The monitoring scheme significantly improves the collection and interpretation of water quality data in challenging contexts where governmental institutions and industrial players are not enforcing environmental standards.

FORECASTING THE GROUNDWATER TREATMENT PROCESS IN A BIOREACTOR USING FERROBACTERIA

The monitoring of water quality parameters in 90 settlements of eight regions of Ukraine made it possible to state that groundwater is a complex multicomponent system. Existing deironing stations using simplified aeration-filtration technology are not able to remove Fe (II) compounds from water in the presence of humic complexes. Therefore, in modern conditions, the urgent task is to intensify their work through the introduction of new technologies, including biotechnology with the development of appropriate mathematical models. It is shown that much less attention was paid to modeling the kinetics of groundwater treatment processes in bioreactors than to traditional physicochemical methods, for which modern mathematical models were developed.
 The aim of the work is to develop a mathematical model of the kinetics of the process of groundwater treatment in bioreactors. The mathematical model is represented by the Cauchy problem for a nonlinear system of differential equations in partial derivatives of the first order. The system of the Cauchy problem consists of five equations with five unknown functions, which describe the distribution the concentration of ferrum cations, bacteria and the matrix structures in two phases (movable and immobilized) both in space and time. The inverse influence of the characteristics of the process, in particular, the concentration of matrix structures in the inter-pore space, as well as characteristics of the medium with the help of coefficients of mass exchange and porosity, were taken into account. The model allows determining the optimum operation time of a bioreactor between washings