Water Quality Metrics Research Articles

A 30-year dataset of CO2 in flowing freshwaters in the United States

Increasing atmospheric carbon dioxide (CO2) concentrations have been linked to effects in a wide range of ecosystems and organisms, with negative effects of elevated CO2 documented for marine organisms. Less is known about the dynamics of CO2 in freshwaters, but the potential exists for freshwater organisms to be challenged by elevated CO2. In flowing freshwaters CO2 exhibits more variability than in lakes or the ocean, yet spatiotemporally extensive direct measures of CO2 in freshwater are rare. However, CO2 can be estimated from pH, temperature, and alkalinity—commonly collected water quality metrics. We used data from the National Water Quality Monitoring Council along with the program PHREEQC to estimate CO2 in flowing freshwaters across 35,000 sites spanning the lower 48 US states from 1990 through 2020. Site data for water chemistry measurements were spatially joined with the National Hydrology Dataset. Our resulting dataset, CDFLOW, presents an opportunity for researchers to add CO2 to their datasets for further investigation.

Hydrotherapy for Patients With External Fixation: Effect on Infectious Events

Surgery to achieve long bone lengthening and deformity correction in skeletally immature patients is traditionally performed through external fixators. The incidence of infection during treatment is high. Hydrotherapy is highly beneficial during external fixation management, but the infection rates may impact its utilization. The objective of this paper is to document the incidence and duration of infection in patients who participated in a full hydrotherapy program when performed under medical supervision and in accordance with strict follow-up protocols. In this retrospective study, we documented the key characteristics of patients who participated in hydrotherapy after lower limb external fixation surgery including age, sex, diagnosis and surgery site, the incidence and duration of pin site infections, and the percentage of cases, which were able to comply with a full hydrotherapy protocol. The hydrotherapy pool met all daily water quality metrics. Thirty-four children (19 males and 15 females) had a mean±SD age of 10.9±4.6 years. Of the cases, 80% were unilateral and 20% were bilateral. Diagnoses included congenital hereditary (eg, achondroplasia), congenital nonhereditary conditions (eg, fibular hemimelia), and acquired conditions (eg, traumatic). The location of osteotomy site was quite evenly spread between the femur and the tibia/fibula. Three out of the 34 patients were unable to receive hydrotherapy due to continuous secretions; 2 patients did not miss any sessions. The mean number of treatments was 3 per week. During the entire period, a total of 1200 treatments were performed for these patients with only 32 cancellations (about 3%) due to secretions or other signs of infection. Spearman correlation coefficients showed significantly high correlations between the duration of external fixation and the duration of hydrotherapy. There are no significant correlations between infections and other factors (sex, age segment, whether the problem is unilateral or /bilateral, diagnosis, and duration of treatment). This is the first report, to our knowledge, that documents the low prevalence of infectious events during hydrotherapy treatment and demonstrates its feasibility. Level III; retrospective, comparative study.

Coral Reef Arks: An In Situ Mesocosm and Toolkit for Assembling Reef Communities.

Coral reefs thrive and provide maximal ecosystem services when they support a multi-level trophic structure and grow in favorable water quality conditions that include high light levels, rapid water flow, and low nutrient levels. Poor water quality and other anthropogenic stressors have caused coral mortality in recent decades, leading to trophic downgrading and the loss of biological complexity on many reefs. Solutions to reverse the causes of trophic downgrading remain elusive, in part because efforts to restore reefs are often attempted in the same diminished conditions that caused coral mortality in the first place. Coral Arks, positively buoyant, midwater structures, are designed to provide improved water quality conditions and supportive cryptic biodiversity for translocated and naturally recruited corals to assemble healthy reef mesocosms for use as long-term research platforms. Autonomous Reef Monitoring Structures (ARMS), passive settlement devices, are used to translocate the cryptic reef biodiversity to the Coral Arks, thereby providing a "boost" to natural recruitment and contributing ecological support to the coral health. We modeled and experimentally tested two designs of Arks to evaluate the drag characteristics of the structures and assess their long-term stability in the midwater based on their response to hydrodynamic forces. We then installed two designs of Arks structures at two Caribbean reef sites and measured several water quality metrics associated with the Arks environment over time. At deployment and 6 months after, the Coral Arks displayed enhanced metrics of reef function, including higher flow, light, and dissolved oxygen, higher survival of translocated corals, and reduced sedimentation and microbialization relative to nearby seafloor sites at the same depth. This method provides researchers with an adaptable, long-term platform for building reef communities where local water quality conditions can be adjusted by altering deployment parameters such as the depth and site.

Characterization of Sub-Catchment Stream and Shallow Groundwater Nutrients and Suspended Sediment in a Mixed Land Use, Agro-Forested Watershed

Excess nutrients and suspended sediment exports from agricultural watersheds are significant sources of global water quality degradation. An improved understanding of surface water and groundwater pollutant loads is needed to advance practices and policies. A study was conducted in an agricultural-forested catchment of the mid-Atlantic region of the United States. Stream water (SW) and shallow groundwater (SGW) samples were collected monthly between January 2020 and December 2021 from eight sub-catchment study sites. Samples were analyzed for nitrate (NO3-N), nitrite (NO2-N), total ammonia (NH3-N), total nitrogen (TN-N), orthophosphate (PO43-P), and total phosphate (TP-P) concentrations using spectrophotometric methods. Total suspended solids concentrations (mg/L) were quantified gravimetrically and volumetrically to estimate mean particle diameter (MZ, µm), particle surface area (CS, m2/mL), sample skewness (Ski), and particle size distributions (sand/silt/clay%). Results showed significant (p < 0.05) differences in nutrient concentrations and suspended sediment characteristics between SW and SGW between study sites. Differences were attributed to source water type and sub-watershed location. Principal components analysis indicated seasonal effects on water quality in summer months and connected land use with TSS, TN-N, and TP-P concentrations. Study results emphasize the importance of SGW water quality metrics for non-point source loading predictions to inform management decisions in agro-forested watersheds.

Correlation coefficients (R-values) as potential indicators of water quality deterioration for the tropical urban lakes

This study demonstrates the possible application of correlation coefficient (R-values) to reflect the water quality status of tropical urban lakes. Ten water quality parameters were measured from contaminated and unpolluted urban lakes at Kelana Jaya, Malaysia. The correlation analysis on the water quality parameters revealed that the polluted lake had higher numbers of significant correlation coefficients with highly significant levels of P< 0.001, attributable to the broader ranges of water quality metrics acquired from the former. This work provides baseline evidence for the potential use of R-values as prospective markers of water quality degradation in tropical urban lakes.

Water quality and antibiotic resistance in the recreational waters

The overuse and improper disposal of antibiotics results in antibiotic resistance. This raises concern over the presence of antibiotic resistant bacteria (ARB) in waterways and pose health risks of antibiotic resistant infections to water recreationists. The purpose of this study was to monitor water quality, microbial ecology, and antibiotic resistance in water and biofilm on submerged plastics at two public boat launches in southeastern Louisiana. Water and biofilm samples were collected once a month, in triplicate, from two public boat launches in Louisiana, USA for a year. Water quality metrics included nitrate, ammonia, sulfate, phosphate, and organic carbon. Water samples were tested for total and fecal coliform abundance and the presence of ARB. Out of 131 bacterial isolates studied from these two sites, 86% of them tested positive for antibiotic resistance with multi-drug resistance. Antibiotic resistance genes (ARGs) for sulfonamide (sul2), bacitracin (bacA) and ampicillin (ampA) were identified in bacterial isolates from water and biofilm samples at both sites. Molecular genetic diversity analysis identified distinct taxonomic diversity differences in biofilm bacteria compared to the planktonic bacteria in the surrounding water. Biofilm samples showed increased diversity at the phylum, genus, and species levels.

Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Water is requisite on earth for life to survive. The study area of the present work is the river Periyar, the longest river in Kerala, India. The overall objective of the present work is to compare the accuracy and performance of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the training and prediction of dissolved oxygen (DO) concentration and biochemical oxygen demand (BOD) in the river. The models were used to examine secondary data from the past for four water quality metrics generated at five monitoring stations located along the river Periyar to predict DO concentrations and BOD. The performance of the models was examined using root mean square error (RMSE) and coefficient of correlation (R) values. This study revealed that ANN is superior to ANFIS in most scenarios. In most situations, ANFIS appears to get the superiority in training, however, ANN seemed to have the dominant position in testing and validation. The inputs having the highest relevance in the case of DO prediction were identified to be nitrate and phosphate concentrations, whereas total solids and COD were seen to be the most impactful in the case of BOD forecasting employing sensitivity analysis.

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

IoT-Based Smart Water Management Systems for Residential Buildings in Saudi Arabia

Water is a precious resource that can be intelligently managed. Effective water usage demands computerized home water supply management in a culture where water tanks, motors, and pumps are ubiquitous. Water management is crucial for the government and the citizens in countries like Saudi Arabia. The issue is providing a constant, high-quality, low-cost water supply. This study introduces a smart water management (IoT-SWM) system that may be used in structures that do not have access to a constant water supply but instead have water stored in enormous tanks underneath. The GSM module collects water use data from each home in a community and transmits it to the cloud, where it is analyzed. A smart water grid is a hybrid application that uses an inspection mode to identify leaks and measure the resulting height differences to keep track of the tank’s water level. The system automatically deactivates the affected section after detecting any water shortage or malfunction in the system mechanism, such as broken valves, pumps, or pipes. It sends an emergency signal to building managers. It monitors essential water quality elements regularly, and if they fall below acceptable levels, it sends warning signals to the building management, who can take action. Over an extended period, the system monitored and recorded all water quality metrics. The system restarts when the water pump has been reconnected and sends an emergency alert. As a result, the suggested system has been an excellent replacement for Saudi Arabia’s mechanically operated system.

Land Used Mapping using Unmanned Aerial Vehicle (UAV) along Parit Rasipan Drainage System

Excess nutrients accelerate the growth of plants and algae in water sources, leading to environmental issues like flow retardation (flood), a decrease in the amount of oxygen in the water, and a decline in the quality of the water. Therefore, the goal of this study is to identify the qualityParit Rasipan drainage system's eutrophic level in terms of temperature, dissolved oxygen, pH, and turbidity; and to analyse an NDVI (Normalized Difference Vegetation Index) image taken by unmanned aerial vehicle (UAV) and using Agisoft photoscan software. As a result, four sampling locations—residential (S1), industrial (S2), agricultural (S3), and farming (S4)—have been chosen for water quality sampling and analysis along the Parit Rasipan drainage system. The HACH method wasused to analyse water samples that have been obtained according to the Standard Methods for Water and Wastewater Examinations. It was found that agricultural zone waspoor water quality compared residential, industrial and farmingzoneswithmeasurements of 3.82 mg/L, 3.09, 25.57 °C, and 14.6 NTU, respectively, the agricultural zone hasthe highest values for thefour metrics of dissolved oxygen, pH, temperature, and turbidity. This scenario could be due to the usage of the fertilizerto cultivate the oil palmcontributed toParit rasipan drainage system (S3)which resulted in asubstantial amount of eutrophication. Hence, the use of UAVs and the agisoft photoscan programme has considerably improved the mapping of the water quality metrics and eutrophic level.

An integrated approach for an impact assessment of the tank water and groundwater quality in Coimbatore region of South India: implication from anthropogenic activities.

Groundwater in the Coimbatore region is very much essential for irrigation and human beings' day-to-day life activities. This research focuses on the impact of anthropogenic activities on the surface water quality of fourtanks in Coimbatore city and its influences on disturbing the groundwater quality as well. Water quality is degrading as a result of encroachment and other anthropogenic activities. The disposal of municipal garbage and waste from other enterprises into the nearby tank degrades the quality of the water in the tanks. This pollutant-concentrated surface water, together with the leech organism, may percolate via the pore spaces between soil particles, where it will interact with groundwater. The quality of groundwater will be impacted as a result of this interaction. As a result, water quality metrics for both surface and groundwater have been assessed in this study, and spatial interpolation was performed using ArcGIS. The map of the spatial distribution of water quality was produced using ArcGIS. This spatially interpolated water quality map helped researchers understand how the quality of surface and groundwater changed over time. Visual MODFLOW and MT3D were used to simulate the groundwater flow. The MODFLOW program is used to simulate the direction of groundwater flow based on groundwater level and rainfall data. The output of MT3D allowed for accurate calculation of the pollutant movement's amount and direction. In this study, chloride was used as the pollutant transport parameter. A semi-structured interview has been done in the study region to find out more about the availability of drinking water, how waste is disposed of, and how diseases are spread among residents who live close to the tanks.

A 22-Site Comparison of Land-Use Practices, E-coli and Enterococci Concentrations.

Land-use practices can greatly impact water quality. Escherichia (E.) coli and Enterococcus are accepted water quality indicators. However, surprisingly little research has been conducted comparing both organisms' population density relationships to land use practices and water quality. Stream water grab samples were collected monthly (n = 9 months) from 22 stream monitoring sites draining varying land use practice types in a representative mixed-land-use watershed of the northeastern United States. E. coli and enterococci colony forming units (CFU per 100 mL) were estimated (n = 396) and statistically analyzed relative to land use practices, hydroclimate, and pH, using a suite of methods, including correlation analysis, Principal Components Analysis (PCA), and Canonical Correspondence Analysis (CCA). Correlation analyses indicated significant (p < 0.05) relationships between fecal indicator bacteria concentrations, water quality metrics and land use practices but emphasized significant (p < 0.05) negative correlations between pH and instream enterococci concentrations. PCA and CCA results indicated consistent spatial differences between fecal indicator bacteria concentrations, pH, and land use/land cover characteristics. The study showed that pH could be considered an integrated proxy variable for past (legacy) and present land use practice influences. Results also bring to question the comparability of E-coli and enterococci relative to dominant land use practices and variations in pH and provide useful information that will help guide land use practice and water pollutant mitigation decision making.

Impacts of residential fertilizer ordinances on Florida lacustrine water quality

AbstractDespite the assumption that residential fertilizer ordinances improve regional water quality, their impacts across space and time largely remain unknown. Here, we analyze changes in water quality of lakes throughout the State of Florida from 1987 to 2018, comparing trends in water quality parameters before and after implementation of county‐wide fertilizer ordinances. We used a large dataset of publicly collected water quality data and linear mixed models to analyze ordinance impacts on total nitrogen, total phosphorus, chlorophyll a, and Secchi depth across 160 lakes throughout Florida. We further analyze water quality impacts relative to the type of ordinance (winter fertilizer ban, summer ban, nonseasonal ban, no ban). We found fertilizer ordinances favorably impacted lacustrine water quality, and winter (dry season) fertilizer bans had the greatest effect across all water quality metrics. Results of this study can be used to support the effectiveness of fertilizer ordinances across humid tropical and subtropical climate regions.

Evaluating the impact of turbidity, precipitation, and land use on nutrient levels and atrazine concentrations in Illinois surface water as determined by citizen scientists

The objective of this study was to evaluate the impact of turbidity, precipitation, land use, and five-week variation on nutrient levels and atrazine concentrations across Illinois state. To acquire the greatest number of samples in a cost and time-sensitive manner, data were collected by citizen scientists. Volunteers collected data regarding five water quality metrics: nitrites, nitrates, phosphates, atrazine, and turbidity once per week from April 19 until May 17, 2017. A subset (24 %) of volunteers also collected turbidity measurements. Data regarding precipitation was obtained from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS), a long-standing grassroots volunteer network of backyard weather observers. Three ordinal regression analyses were performed: one without a blocking effect, a second with week as a blocking effect, and a third with watershed as a blocking effect. In all cases, turbidity was significantly associated with elevated levels of nitrate (Pseudo R2–0.48 to 0.94) and phosphate (Pseudo R2–0.60 to 0.80), while precipitation was significantly associated with elevated levels of nitrate (Pseudo R2–0.25 to 0.35). While analyzing five-week variation, the nitrite and nitrate levels, but not phosphate or atrazine, tended to increase at each site. Further, nitrite and nitrate levels significantly varied between the four land uses – agricultural, urban, suburban, and park. When data were analyzed by the three most well-sampled watersheds, Kankakee, Des Plaines, and Chicago, it was identified that the nutrient levels in the Kankakee and Chicago watersheds were significantly elevated relative to the Des Plaines watershed. Finally, cluster analysis identified that clusters dominated by agricultural land, and to a lesser extent suburban land use, had the most elevated nutrient concentration and the greatest frequency of atrazine hits. Data collected by citizen scientists can provide insight into the geospatial variability of nutrients and agrichemicals and can do so across large geographies.

Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning.

In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5–8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model (R2 = 0.75) during lockdown over Streeter Phelps (R2 = 0.57). Polynomial regression and Newton’s Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones (R2 = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Based on IOT Water Quality Monitoring System

Water is a basic use for all beings, however as growth in population, pollution, and climate change desalinate water a scarce resource, water quality is becoming extremely relevant, especially for individual buildings. These vintage laboratory-based testing regime have become time-consuming, exorbitant, and cannot provide real-time feedback. Water pollution is one of the major threats to green trade liberalization, so it must be managed to preserve its quality. The design and development of a low-cost system that uses the Internet of Things to monitor water quality metrics in real time, specifically for household use, is the topic of this study. The pH, turbidity, flow, and temperature of drinking water are common environmental monitoring parameters (WQMS) that are evaluated in this work.

Paired field and water measurements from drainage management practices in row-crop agriculture

This paper describes a multi-site and multi-decadal dataset of artificially drained agricultural fields in seven Midwest states and North Carolina, USA. Thirty-nine research sites provided data on three conservation practices for cropland with subsurface tile drainage: saturated buffers, controlled drainage, and drainage water recycling. These practices utilize vegetation and/or infrastructure to minimize off-site nutrient losses and retain water in the landscape. A total of 219 variables are reported, including 90 field measurement variables and 129 management operations and metadata. Key measurements include subsurface drain flow (206 site-years), nitrate-N load (154 site-years) and other water quality metrics, as well as agronomic, soil, climate, farm management and metadata records. Data are published at the USDA National Agricultural Library Ag Data Commons repository and are also available through an interactive website at Iowa State University. These multi-disciplinary data have large reuse potential by the scientific community as well as for design of drainage systems and implementation in the US and globally.

Dammed water quality—Longitudinal stream responses below beaver ponds in the Umpqua River Basin, Oregon

AbstractBeaver‐related restoration (BRR) has gained popularity as a means of improving stream ecosystems, but the effects are not fully understood. Studies of dissolved oxygen (DO) and water temperature, key water quality metrics for salmonids, have demonstrated improved conditions in some cases, but warming and decreased DO have been more commonly reported in meta‐analyses. These results point to the contingencies that can influence outcomes from BRR. We examined water quality related to beaver ponds in a diverse coastal watershed (Umpqua River Basin, OR, USA). We monitored water temperature 0–400 m above and below beaver ponds and at pond surfaces and bottoms across seven study sites from June through September of 2019. DO was also recorded at two sites at pond surfaces and pond bottoms. Downstream monthly mean daily maximum temperatures were warmer than upstream reference locations by up to 1.9°C at beaver dam outlets but this heating signal attenuated with downstream distance. Downstream warming was greatest in June and July and best predicted by pond bottom temperatures. DO at pond surfaces and bottoms were hypoxic (≤5 mg/L) for more than half of the 32‐day monitoring period. Water temperatures increased for short distances below monitored beaver ponds and observed oxygen conditions within ponds were largely unsuitable for salmonid fishes. These findings contrast with some commonly stated expectations of BRR, and we recommend that managers consider these expectations prior to implementation. In some cases, project goals may override water quality concerns but in streams where temperature or DO restoration are objectives, managers may consider using BRR techniques with caution.

Comparison of drone and vessel-based collection of microbiological water samples in marine environments

Many water quality metrics cannot be measured in situ and require collection of a physical sample for laboratory analysis. This includes microbiological samples for detection of fecal coliform bacteria in marine and freshwater systems which are a critical component of food safety programs for human consumption of bivalve shellfish worldwide. Water sample collection programs are typically vessel-based which can be time and resource intensive. In Canada, the Canadian Shellfish Sanitation Program aims to avoid consumption of contaminated molluscan bivalves by monitoring fecal coliform bacteria through vessel-based water sample collection. Uncrewed aerial vehicles or drones are becoming more commonly used for water sample collection given their relatively low cost but are rarely used to support microbiological analyses. A prerequisite for the acceptance of a new collection method for a regulatory program is to determine if the method of sample collection affects results. To assess this potential, we designed, developed, and tested a sampling device attached to the underside of a drone to collect water samples for bacteriological analysis. Drone and vessel-based samples were collected in the same location, at the same 20-cm depth, within a minute apart, at ten different geographic locations in coastal Nova Scotia waters to compare fecal coliform counts. Bacterial count estimates obtained from drone-collected samples were not significantly different than estimates obtained from vessel-collected samples (p < 0.5). Results from this study suggest novel water sampling techniques using drones could supplement or replace traditional vessel-based sampling methods.

Evaluation of a Recirculating Aquaculture System Research Facility Designed to Address Current Knowledge Needs in Atlantic Salmon Production

A better understanding of recirculating aquaculture system (RAS) biosecurity is crucial for the sustainable and ethical production of Atlantic salmon smolt and post-smolt in these systems. This study described and evaluated the performance of a RAS facility for fish infection research with Atlantic salmon as the main animal model. Fish body weight, length, water quality, and system metrics from five independent experimental trials conducted between September 2020 and July 2021 were used to analyze the variation within and between treatments. Statistical power analysis was performed to determine the minimum number of fish required. The fish parameters variability showed that the inter-class correlation coefficient was on average low (0.1) and that the variation within tanks was larger than the variation between the tanks. The power analysis showed that 15 fish were required to be sampled per tank under these study conditions. Variation of water quality and system management metrics among the five experimental trials was higher compared to the variation within the five experimental trials. Moreover, the variation of the water quality parameters controlled by sensors was relatively low, whereas the parameters depending on biofilter maturation level and performance presented a very high variation. Water exchange rate-dependent quality parameters showed a similar variation value, i.e., nitrate and water turbidity. The established baseline for variability and performance presents an important reference for the design and realization of future experiments in RAS facilities. It is foreseen that the current research facility will develop new knowledge to improve the RAS biosecurity in the Atlantic salmon aquaculture industry.