Water Quality Measurements Research Articles

SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QA-QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QA and QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. Sensor data from the stream stations yielded anomaly percentages of 0.1%, 0.1%, and 0.2%, which were lower than the anomaly percentages of 0.5%, 0.6%, and 0.8% for the sensors in Lake and mesocosms, respectively. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QA-QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

Effective Surveillance of Water Quality in Recirculating Aquaculture Systems through the Application of Intelligent Biosensors

Water quality (WQ) is the paramount element influencing fish well-being and productivity in aquaculture farming systems. The survival of fish is mostly reliant on the aquatic environment that sustains them. Consequently, it is vital to possess a comprehensive awareness of the WQ prerequisites for the fish. Optimal WQ in Recirculating Aquaculture Systems (RAS) is essential for cultivating species' effective development and survival. Currently, no laws dictate the parameters to be monitored in RAS, leaving each farmer to choose which parameters to monitor. Historically, WQ measurements have been assessed at certain intervals using portable sensors and laboratory tests, which may be labor-intensive. This study proposes an Effective Surveillance of Water Quality (ESWQ) in RAS using Intelligent Biosensors (IBS). This study examines essential water characteristics (temperature, pH, calcium, magnesium, and Dissolved Oxygen (DO)) for RAS and evaluates the IBS for monitoring these factors. This research provides a potential solution for RAS using IBS, which would enhance ESWQ aspects, facilitate data-driven decision-making, and enable more rapid adaptation to evolving RAS situations.

Phytoplankton community structure in Tunda Island waters, Banten Indonesia as a bioindicator to measure water quality

Mining of sea sand in the water area of Tunda Island can result in changes in the quality of the aquatic environment. Phytoplankton may indicate changes due to their rapid reaction to external influences in water. This study aims to determine the water quality condition in Tunda Island waters using phytoplankton communities. The sample collection included 20 observation stations in Tunda Island waters, Banten province. The analyzed water samples had parameters of depth, temperature, pH, dissolved oxygen, total dissolved solids, salinity, nitrates, and phytoplankton communities. The results obtained by several quality water parameters still meet the quality standards except for nitrates. The composition of phytoplankton consists of 3 classes: Cyanophyceae, Bacillariophyceae, and Dinophyceae. Eight phytoplankton genera of the Bacillariophyceae class dominated during observation: Bacteriastrum sp., Chaetoceros sp., Hemiaulus sp., Lauderia sp., Nitzschia sp., Rhizosolenia sp., Skeletonema sp., Thalassiosira sp., and Thalassiotrix sp. The phytoplankton biological index describes the phytoplankton diversity index as moderate diversity, phytoplankton evenness is classified as unstable communities, and phytoplankton dominance is at a moderate condition. Variations within a phytoplankton group can reflect seasonal dynamics and the impact of changes in the aquatic environment.

Comparison of passive and manual chlorination in small piped water networks in rural Ghana: Technical performance, ease-of-use, and cost

Chlorination is the most common water treatment method globally and leads to proven health benefits. Yet, many rural water supplies in low-income settings are unchlorinated, exposing consumers to waterborne diseases. Insufficient technical and financial capacity of water suppliers in low-resource settings are common barriers to more widespread chlorination. We conducted a case study of two approaches to chlorinate small piped water supplies− passive (inline) chlorination and manual chlorination− and compared their technical performance, ease-of-use, and costs in rural Ghana. Based on 685 water quality measurements across two piped networks over three months, both methods provided adequate free chlorine residuals (i.e., 0.2–2.0 mg/L) most of the time (71% for manual chlorination and 86% for passive chlorination). Follow-up measurements five months later revealed a decline in chlorine levels with the manual approach (47% in the target range) and an increase with the passive (inline) approach (100% in the target range). We observed large fluctuations in chlorine levels over time, particularly with inline chlorination, that pH, temperature, conductivity, and turbidity variations did not fully explain. Temporal changes in chlorine demand and/or inconsistently implemented protocols possibly contributed to these fluctuations. Inline chlorination scored higher for ease-of-use (85%) than manual chlorination (70%) but was less financially viable: it represented an 11% increase in operational expenses, compared to 4% for manual chlorination. Initial equipment and installation cost approximately 6,000 USD for inline chlorination and about 260 USD for manual chlorination. Our results highlight the tradeoffs between passive (inline) and manual chlorination. Although less favorable for ease-of-use, manual chlorination is more viable financially and can achieve comparable performance with strict dosing protocol adherence, suggesting this approach deserves similar consideration as passive chlorination when evaluating options for low-resource settings. Both methods are susceptible to changes in operator behaviors and require external oversight plus support for troubleshooting and recalibration.

Monitoring (micro-)pollutants in wastewater treatment plants: Comparing discharges in wet- and dry-weather

Municipal wastewater treatment plants (WWTPs) are crucial for maintaining good quality of surface water, limiting environmental pollution. However, during wet-weather events, WWTPs become an important point-source discharge due to the activation of the bypass, which releases a mix of untreated wastewater and stormwater. This work aims to assess how the WWTP discharges (effluent and bypass) impact on the receiving surface water body during dry- and wet-weather, monitoring 78 pollutants (7 conventional pollutants, 19 heavy metals, and 52 micropollutants) in each stream (effluent during dry-weather, effluent and bypass during wet-weather), including the influent in dry-weather for comparison. The occurrence, concentration levels and variability, and environmental risk were addressed, with a specific focus on high-resolution (up to 20-min) sampling of the bypass, based on the expected relevant temporal dynamicity. A wider range of pollutants occurred in the bypass, included undetected compounds in the dry-weather influent. Besides, a greater inter-events variability in bypass concentrations was observed, but smaller intra-event variability, with only some pollutants exhibiting a distinct first-flush effect. To address the challenge of a cost-effective bypass monitoring, we explored the applicability of readily measurable water quality parameters (total suspended solids and electrical conductivity) as proxies for micropollutants. Correlations between these parameters and specific pollutant groups suggest a promising path for further investigation and broader application. The magnitude of the rain event also affected concentration levels, with event volume clearly affecting pollutants dilution. The environmental risk assessment revealed a significantly higher risk associated to bypass discharge compared to the effluent, especially for conventional pollutants, metals, and terbutryn, highlighting the urgency of improved bypass management strategies. Overall, this study highlights the contribution of wet-weather discharges from WWTPs, emphasizing the importance of high-frequency bypass monitoring to capture peak pollutant concentrations and accurately assess the environmental risk.

Integrated water quality assessment of open water bodies using empirical equations and remote sensing techniques in Bangweulu Wetland lakes, Zambia

Water quality assessment is essential for conserving aquatic ecosystems and human consumption. However, monitoring several water quality parameters in open water bodies poses significant challenges, resulting in gaps and limitations. While the Water Quality Index (WQI) integrates various water quality parameters, its spatial application has not yet been thoroughly evaluated. To address these issues, this study was conducted to establish an integrated remote sensing approach for comprehensive monitoring of water quality. Utilising Sentinel 2 data alongside on-site/lab water quality measurements, various relationships were established. The calculated mean WQI from on-site/lab data was 35, while from remotely sensed data was 41. Linear relationships (R2) between WQI and various water quality parameters such as chloride (0.712), sodium (0.705), TDS (0.705), turbidity (0.628), and EC (0.704) were observed. These relationships facilitated the transformation of water quality parameter maps into WQI maps, the WQI maps were then integrated into one WQI map showing the overall water quality status of the Bangweulu Wetland lakes. Both on-site/lab and remote sensing methods indicated that the concentrations of water quality parameters in the Bangweulu Wetland lakes is lower (better) than the local and international recommended limits, with a calculated WQI falling within the ’Good’ category. This indicates that the water is fresh, clean, and suitable for various purposes including ecological preservation, agriculture, aquaculture, recreation, industrial use, and human consumption. The findings offer insights into the general status of water quality in open water bodies, identifying hot spots and potential sources of water pollution with implications for future management practices.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Evaluation of Groundwater Quality for Drinking in Various Districts of Jammu and Kashmir with Special Emphasis on Kashmir

Abstract: In India, groundwater is a valuable resource for humans, yet human activity has consistently reduced its quality. For the management of groundwater resources to be sustainable, an assessment of the quality of the groundwater is required. With the use of integrated-weight water quality index and multivariate statistical techniques, this study sought to assess the groundwater quality in the Indian area of Kashmir for drinking purposes. Eight water quality measures were included in the data, which came from 50 observation wells and included pH, total hardness, nitrate (NO3−), iron (Fe), lead (Pb), mercury (Hg), arsenic (As), and coliforms. The integrated-weight water quality index (IWQI), principal component analysis (PCA), and cluster analysis (CA) were used to examine various districts. The majority of the metrics, according to the results, were within acceptable ranges, however some areas had excessive hardness and Fe contamination. More than 76% of samples had significant coliform contamination found in them. With four clusters of sample locations, the CA results showed that geological locations had a significant influence on groundwater quality. A PCA's three main components may account for 64.8% of the variation in groundwater quality. Groundwater quality was categorized as excellent (74.11%), good (45%), poor (6.4%), very poor (2.9), and undrinkable (9.4) based on IWQI values ranging from 4 to 2761. These classifications were linked to coliform contamination. These results have shed light on the region's groundwater quality state, which is helpful in creating a plan to safeguard the water supply.

Machine Learning Techniques for Monitoring Water Quality in Water Resources

More than 66% of the Earth's total surface area is covered by water. Clean water is one of the basic needs of everyday life. Consistent pollution of water bodies can have far-reaching effects on the lives of living organisms. The World Health Organization has reported that the provision of safe drinking water for human consumption is a challenge that has reached alarming levels. This is because nearly 70% of the total water withdrawals worldwide are used in agriculture. To determine the water's suitability for human consumption, Tests are typically conducted by examining the properties of water in terms of physical, biological, and chemical conditions. There are various methods to measure water quality. Recently, an ongoing process has been shown to improve water quality. To solve this problem, this paper is using a machine learning model to Contribution for creation a smart model capable of classifying potable and non-potable water that is released to the water share by the competent authorities and the ability of Machine Learning models to monitor and predict water, especially Managing and Planning Water Resources. datasets were utilized in training and evaluating machine learning models, which includes (3276) samples with nine attributes and two labels indicating water usability According to the in our work the (Random Forest) algorithm have the best Through the results that appeared by accuracy (0.954084, 0. 882484, and 0. 931849 for three sizes of data.) and then (Decision Tree, K-Nearest Neighbor, Logistic Regression, SVC) sequentially. Index Terms—Machine Learning, potable water, Water Quality, water resources.

Teknik Pendederan Udang Galah (Macrobrachium Rosenbergii) di Instalasi Budidaya Air Payau (IBAP) Probolinggo, Jawa Timur

Giant Freshwater Prawn (Macrobrachium rosenbergii) or also known as Giant Freshwater Prawn is a type of crustacean that has the largest size compared to other freshwater prawns. Giant prawns are a freshwater fisheries commodity that has great potential to be developed because it has a high selling value. This Field Work Practice (PKL) activity was carried out at the Brackish Water Cultivation Installation (IBAP) in Probolinggo City, East Java. The results of Field Work Practices (PKL) regarding giant prawn nursery techniques start from preparing nursery ponds, preparing water for rearing media, stocking juveniles, managing water tanks for rearing media, water quality management, feed management, controlling pests and diseases, as well as harvesting and packaging. Water quality measurements include DO with values obtained at 5.1-5.6 mg/L, pH 7.1-8.7, temperature reaching 24-28°C, and salinity ranging from 0-5 ppt. The feed given is Artemia salina, corn bran mixed with flakes in a ratio of 3:1, and commercial feed is in the form of pellets with a frequency of administration twice a day, namely in the morning and evening.

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

High Salinity Intensive Seaweed Growth Rate in Aquaponic Systems

Aquaponics is a closed recirculating aquaculture method combined with hydroponics. This method is very alternative for raising fish while growing plants in specially designed media. The mechanism obtained is very effective and efficient, plants can utilize fish waste as nutrients for plant growth. Poisonous fish waste in ponds can be reduced by plants as nutrients for organic substances. This can enhance profitable bio-integrated food production systems. Plants will serve as vegetation filters. This study aimed to determine the effectiveness of aquaponic treatment in high-salinity water by observing the growth in three species of seaweed namely Gracilaria sp., Sargassum sp., and Kappaphycus alvarezii. The research design used the LSD method and was carried out for 63 days. Data analysis using Analysis of Variance (ANOVA) then continued with the test Duncan's Multiple Range Test. The research method involved making aquaponic media including preparing containers for seaweed cultivation and fish and shrimp farming, observing seaweed growth rates, and measuring water quality. Aquaponic treatment can increase the growth rate of intensively cultivated seaweed. The results showed the best weight growth rate in the aquaponic treatment of Gracilaria sp. with a value of 12.33 grams than Kappaphycus alvarezii 11.33 grams and Sargassum sp. 10.83 grams.

Systematization of a toxicity screening method based on a combination of chemical analysis and the delayed fluorescence algal growth inhibition test for use in emergency environmental surveys.

In recent years, heavy rainfall disasters linked to climate change have become more frequent, raising concerns about the release of chemicals stored in factories. Assessing chemical contamination during such emergencies therefore necessitates the development of a quick and easy method for evaluating hazardous contaminants in combination with toxicity testing. This study proposes a "toxicity screening" method that combines biological response testing and chemical analysis to systematically evaluate hazardous contaminants in emergency situations. The toxicity screening method evaluates the water quality in three steps, including water quality measurements and a delayed fluorescence (DF) assay, metal content measurements and a DF assay, and targeted screening analysis and a DF assay. The efficacy of this method was tested using industrial wastewater from 14 locations. Seven of the samples were non-toxic, while the other seven samples were toxic, displaying no observed effect concentration (NOEC) values ranging from 0.625 to 20%. Two toxic samples in the first phase possessed high total chlorine concentrations (0.4mg L-1) and conductivities (2200 mS m-1), indicating that the main sources of toxicity were residual chlorine and a high salt concentration. In the second phase, metal content analysis identified metals as the toxicity cause in four samples. In the third phase, the organic contaminants were analyzed, and tri-n-octyl phosphate (TNOP) was detected at a concentration of 0.00027mg L-1. The results of solid-phase extraction experiments and exposure tests with TNOP alone indicated that the contribution of TNOP to the toxicity was negligible and that chemicals not adsorbed on the solid-phase extraction cartridges were the cause of toxicity. The proposed method can therefore be considered effective for disaster-related water quality assessment, delivering results within 12days.

Bioremediation of Fishery Waste Using Water Lettuce (Pistia stratiotes L.)

Abstract One of the relatively affordable and safe waste treatment is biological treatment by utilising certain plants as biofilters. The water lettuce (Pistia stratiotes L.) is an aquatic plant that is usually considered a weed by the community. These plants can be used to absorb toxic elements in wastewater. The purpose of this study was to analyse the relationship between Pistia stratiotes L. and the decrease in the organic matter content of fishery processing wastewater. The research method involved varying the effectiveness of the relationship between the bacteria and the water lettuce, the cover rate of the water slide plants and measuring water quality. Water quality measurements include: analysis of BOD5, COD, TSS, pH and temperature. Data analysis with regression and correlation tests using SPSS 28 software. In this study, the density of Pistia stratiotes L. used was 0 %, 25 %, 50 %, 75 % and 100 %. Based on the results, it can be concluded that the effectiveness of the Pistia stratiotes L. in reducing levels of BOD5, COD and TSS is highest at a density of 100 % with the effectiveness values of each 23.7 mg/L, 58.7 mg/L and 2.67 mg/L.

Internet of Things Enhanced Maritime Robot for Ocean Waste Cleanup

Plastic pollution is a major environmental concern, particularly in aquatic habitats. The accumulation of floating debris in water bodies caused by plastic pollution negatively impacts ecosystems, aquatic life, and water quality. The objective of this proposed study is to develop a remote-controlled boat equipped with sensors for measuring water quality and an efficient waste collection system. These sensors provide important information about the environmental conditions of the water body by measuring important factors like pH, dissolved oxygen, and turbidity. The data on water quality helps identify the regions that require immediate attention to prevent and manage pollution, as well as assess the overall health of the ecosystem. The primary goal of the research is to significantly reduce the plastic waste in marine areas, promote sustainable practices, and raise public awareness about the need to address marine pollution, ultimately enhancing a healthier marine ecosystem.

Information extraction of seasonal dissolved oxygen in urban water bodies based on machine learning using sentinel-2 imagery: An open access application in Baiyangdian Lake

Water bodies are crucial components of urban ecology. The development of rapid and timely water-quality assessment tools using easily measured variables is essential for the health management of urban water bodies. In this study, we focused on the dissolved oxygen (DO) of Baiyangdian Lake using 251 sets of empirically measured water quality data and corresponding Sentinel-2 satellite images. Nine machine learning algorithms were then used to develop a rapid detection algorithm for the spatial distribution of the DO concentration in Baiyangdian Lake. This study successfully applied these methods to invert the DO concentration in Baiyangdian Lake during spring, summer, and autumn. The results indicated that extra tree regression (ETR) provided the most accurate and stable results for inverting the DO concentration among the nine machine learning methods. In contrast, AdaBoost regression (ABR), Bayesian ridge regression (BRR), and support vector machines (SVM) exhibit relatively poor regression performance and lack sensitivity to DO concentrations. Moreover, the DO concentration in Baiyangdian Lake ranged from approximately 0 to 12 mg/L, with notable spatiotemporal variations. The highest overall DO concentration was observed in the spring, particularly in the southern region. The DO concentration significantly decreased during summer compared to that in spring, with higher values in the southwestern area and lower values in the northern region. The DO concentration reached its lowest value in autumn, with slightly higher values in the southern region. This study focused on the estimation and inversion of DO concentrations in the water bodies of Baiyangdian Lake. By introducing and comparing the performances of commonly used machine learning models, a rapid estimation of the DO concentration was achieved, thereby overcoming the limitations of traditional water quality monitoring methods in DO inversion. It not only intuitively explained the temporal and spatial variation patterns of DO concentration but also laid a foundation for further in-depth exploration of the interactions between DO and other water quality parameters.

Combined Effects of Local and Regional Drivers on Oyster Spat Density and Growth in Eastern Australia

Up to 85% of shellfish reefs have been lost worldwide, resulting in declining ecosystem services, and increasing restoration demand. However, more information regarding the conditions which maximise oyster settlement and growth is required to optimise restoration. We deployed oyster settlement tiles at 21 intertidal sites throughout Moreton Bay, Australia; a region where > 96% of rock oyster reefs are lost and demand for restoration is high. We quantified effects of variables describing the spatial (from GIS), local habitat (using quadrats and water quality measures), and oyster predator (using underwater videography) characteristics of sites on oyster density and size on tiles. Oyster density was highest at sites with intermediate predator abundance and temperature, highest nearby invertebrate cover, and low and high values of turbidity and nearby rock and algae cover. Conversely, oyster size was highest at sites with intermediate predator density, higher fish species richness and turbidity, and lowest temperatures. Together, this showed that optimal restoration requires sites with 22 to 23 °C average water temperatures, between 10 and 15 oyster predators, and either low (< 2 NTU) or high (> 6 NTU) turbidity levels. Notably, we observed multiple peaks for several variables, suggesting the potential presence of multiple cryptic oyster species on settlement tiles. We found that oysters shared preferred environmental conditions with polychaetes, coralline algae, and tunicates, and were more prevalent and abundant at sites with lower turf algae, barnacle, and mussel cover. Identifying environmental variables influencing oyster population distribution, settlement, and growth can guide the selection and approach of oyster restoration sites.

AI-driven modelling approaches for predicting oxygen levels in aquatic environments

Reliable water quality models are crucial for better water management and pollution control. Biochemical oxygen demand (BOD) and dissolved oxygen (DO) are the widely recognized indicators used to examine the quality of water. Stakeholders often face challenges when it comes to monitoring water quality indicators daily. The conventional approach for testing is laborious and costly. Therefore, there is a need to incorporate alternate concepts for predicting water quality parameters. This article proposes a novel approach “Autoencoder-Deep-Autoregressive (AE-DeepAR)” to predict the concentration of BOD and DO using in-situ measurable water quality parameters as input. The autoencoder is employed to utilize a latent space that accurately captures the fundamental characteristics of the nonlinear data. On the other hand, the DeepAR model is utilized to produce point predictions. This approach is being implemented in the Mahanadi River system for the first time. It is found that conductivity, total suspended solids (TSS), turbidity, and ammoniacal nitrogen (NH3-N) are correlated with BOD. Likewise, turbidity, temperature, and total dissolved solids (TDS) correlate significantly with DO. The performance of the AE-DeepAR model in predicting BOD surpasses that of other models at all stations. The R2 range from 0.90 to 0.93, mean absolute error (MAE) varies from 0.061 to 0.147, mean squared error (MSE) lies between 0.005 to 0.028, and mean absolute percentage error (MAPE) differs from 9.20% to 19.95%. All stations show a greater degree of sensitivity. The uncertainty observed is less in most circumstances, but the unpredictability of the values caused by the occurrence of outliers in severe events may influence the outcome. The PICP value varies between 87 % to 95 % in BOD and 89 % to 95 % in DO. The results reveal that AE-DeepAR can be used as an alternative approach to predict BOD and DO with a high degree of accuracy.

An Examination of Stream Water Quality Data from Monitoring of Forest Harvesting in the Eastern Highlands of Victoria

A large data set measuring surface stream turbidity, dissolved oxygen levels, and water temperature was developed by sampling 32 rivers and streams with forested catchments at weekly intervals for between two and three years. This was in response to allegations of possible water quality impairment by forest harvesting (“logging”). Additionally, nine rivers or streams external to the forests were sampled to form a “reference set”; concern was expressed that the water quality of these may be impaired by upstream forest harvesting. An unlogged forested control catchment was selected from the data set and used as a comparator to help reduce seasonal variation. Division of the data into “logged forested catchments” and “unlogged forested catchments” allowed for us to test the null hypothesis. The null hypothesis was that there was no difference between the means of the logged and unlogged sets by a “Student’s t test of difference between means”. The null hypothesis was supported for the three parameters. There was no discernible deterioration in water quality associated with the presence of logging in the stream catchments. It was concluded that logging in this environment was not a determinant of water quality, and that the presence or absence of logging in these catchments did not affect the measured water quality. Spearman rank correlation analysis was unable to detect any statistically significant correlations between the water quality parameters, suggesting they are substantially independent measures of water quality. The monitoring showed that the small upland streams had generally good-to-excellent water quality. The water quality in these was generally better than the “reference set”—this probably reflected agriculture and cultivation in the proximity of their sampling points.

Diversity of Fish Species as Bioindicators of Water Quality in The Cikapundung River, Bandung City

Human activity along the river impacts the aquatic environment from biotic and abiotic factors. One indicator that can reflect the quality of the waters is fish diversity. Several studies have used fish diversity as a biological indicator to determine the quality of waters, such as lake and river waters. The purpose of this study was to determine the diversity of fish and also to determine the quality of water in the Cikapundung River, Bandung City. The method used in this study was a survey and exploration method, which involved collecting samples at five stations using umbrella traps, cast nets, and fishing gear in the morning and evening. The fish obtained were identified using the Fish Identification Guidebook, and the number of each type was calculated. The biotic data obtained were processed using the Shannon-Weiner diversity index (H'), Evenness index (E), Margalef Abundance index (Dmg), and Simpson Dominance index (C). In contrast, the abiotic data were sent to the water quality testing institution (LPKL). The study's results identified seven species from 5 families: Cyprinus sp., Ancistrus sp., Xiphophorus sp., Barbodes sp. According to the Shannon-Weiner index, the diversity of fish species in the Cikapundung River in Bandung City is relatively moderate, with a value (H') of 1.55. The results of water quality measurements of the Cikapundung River from LPKL based on the class II water quality standards of PP RI Number 22 of 2021 show that the condition of the river is in the moderately polluted category.