Water Quality Variables Research Articles

Melatonin Modulates Haematological and Water Quality Parameters Following a 100 Km Transportation of Clarias gariepinus by Road

Background: The transportation of fish is a common practice in aquaculture. However, the transportation of fish results in significant stress that can cause mortality and disease outbreaks due to compromised immune status. To ameliorate the effect of this stress, it is advocated that a suitable antioxidant be supplemented to fish before subjecting to transportation. Objectives: The present experiment was performed to evaluate the effect of melatonin on haematological parameters and water quality indices of Clarias gariepinus post-transportation. Methods: Forty adult C. gariepinus fishes, weighing an average of 450.46 g and measuring an average of 38.23 cm and 4.46 cm were used for the experiment. They were divided into two groups. Group I (MMF) was supplemented with melatonin in their feed every day for one month while group II (OMF) was not administered melatonin. The subjects were fed a commercial pelleted diet once a day throughout the experiment. They were maintained in a tank made of plastic, and water was originally provided through a flow-through system. Haematological and water quality parameters were determined before and after transportation. Results: After the transportation process, erythrocyte count and packed cell volume of the group I was significantly higher (P<0.05). In comparison to group II, the total leucocyte count, neutrophil count, and neutrophil/lymphocyte ratio of group I were all significantly lower (P<0.05). After transit, group II had considerably higher quantities of nitrite, nitrate, and ammonia (P<0.05) than group I. Following transportation, group II had significantly higher nitrite, nitrate, and ammonia (P<0.05) than group I. However, in group I, dissolved oxygen concentration was greater (P<0.05) than that of group II. Conclusion: Accordingly, it was concluded that melatonin affected various haematological and water quality variables in C. gariepinus and will ameliorate the stress of transportation.

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Spatio-temporal variation in water quality and phytoplankton community structure in Changwang, Meishe, and Wuyuan Rivers in Hainan Island, China.

For the first time, this study explored spatio-temporal variation in water quality and phytoplankton community structure in Changwang, Meishe, and Wuyuan Rivers in tropical Hainan Island, China. Phytoplankton samples and water were collected between March and December 2019 and analyzed using standard methods. Two-way ANOVA revealed significant spatial and seasonal variation in physico-chemical parameters (p < 0.05). Wuyuan had high TP (0.06 ± 0.04mg L-1), TN (1.14 ± 0.71mg L-1), NH4+-N (0.07 ± 0.09mg L-1), Secchi depth (2.28 ± 3.79m), salinity (3.60±5.50 ppt),and EC (332.50 ± 219.10 µS cm-1). At the same time, Meishe had high TP (0.07 ± 0.03mg L-1), TN (1.04 ± 0.74mg L-1), NH4+-N (0.07 ± 0.10mg L-1), EC (327.61 ± 63.22 µS cm-1), and turbidity (40.25 ± 21.16 NTU). In terms of seasons, spring recorded high average TP, TN, NH4+-N, COD, and DO, while summer had a high temperature, Chl-a, salinity, and EC. Generally, the physico-chemical parameters met the China water quality standard limits (GB 3838-2002). Overall, 197 phytoplankton species belonging to Cyanophyta, Chlorophyta, Cryptophyta, Bacillariophyta, Pyrrophyta, Euglenophyta, Xanthophyta, and Chrysophyta were identified, with Cyanophyta being dominant. Phytoplankton density showed spatial changes varying from 18 × 106 cell L-1 to 84 × 106 cell L-1. The phytoplankton diversity ranged from 1.86 to 2.41, indicating a mesotrophic state. One-way ANOSIM showed no significant spatial dissimilarity in phytoplankton composition (R = 0.042, p = 0.771) but indicated a significant seasonal difference (R = 0.265, p = 0.001). Therefore, SIMPER analysis revealed that Lyngbya attenuata, Merismopedia tenuissima, Cyclotella sp., Merismopedia glauca, Merismopedia elegans, and Phormidium tenue contributed to the seasonal differences. Furthermore, CCA demonstrated that TP, TN, NH4+-N, COD, Chl-a, and Secchi depth greatly influenced the phytoplankton community. This study shows the spatio-temporal variation in water quality and phytoplankton communities, useful for managing riverine quality.

A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy

A major concern in the management of reservoirs is water quality because of the negative consequences it has on both environment and human life. Artificial Intelligence (AI) concept produces a reliable framework to recognize complicated and non-linear correlations between input and output data. Although various machine learning (ML) algorithms in recent studies were employed to predict water quality variables, the existing literature lacks exploring the combination of these algorithms, which has the potential to significantly amplify the outcomes achieved by individual models. Thus, the current study aims to bridge this knowledge gap by evaluating the precision of Random Forest Regression (RFR), Support Vector Regression, Multilayer Perceptron (MLP), and Bayesian Maximum Entropy-based Fusion (BMEF) models to estimate such water quality variables as dissolved oxygen (DO) and chlorophyll-a (Chl-a). The comparisons were conducted in two primary stages: (1) a comparison of the outcomes of different ML algorithms with each other, and (2) comparing the ML algorithms' findings with that of the BMEF model, which considers uncertainty. These comparisons were evaluated using robust statistical measures, and, finally, to indicate the utility and efficacy of the newly introduced framework, it was efficiently utilized in Wadi Dayqah Dam, which is situated in Oman. The findings indicated that, throughout both training and testing phases, the BMEF model outperformed individual machine learning models, namely MLP, RFR, and SVR by 5%, 26%, and 10%, respectively, when R2 and Chl-a are considered as evaluation index and water quality variables, respectively. Additionally, as the individual ML models are not capable of predicting electrical conductivity and oxidation-reduction potential efficiently, the BMEF model leads to better results by R2=0.89, which outperforms MLP (R2=0.81), RFR (R2=0.79), and SVR (R2=0.62) for oxidation-reduction potential. Regarding the study limits of the present study, spatio-temporal data should be collected over a long time to increase the data frequency and reduce the uncertainty related to climate variability.

Effects of Hydrological Season on the Relationship between Land Use and Surface Water Quality

In recent decades, land use patterns have changed significantly in highly urbanized areas, which is usually linked with the spatial variation of surface water quality at the catchment scale, but little attention has been paid to how hydrological seasons affect this relationship. Taking Pudong New Area of Shanghai, China, as an example, this paper evaluated the influence of hydrological seasons on the relationship between land use and water quality under different hydrological buffers. It was shown that the contribution of land use to the spatial variation of water quality is approximately 30%. In addition, the explanatory ability was greatest in the average season while it was smaller in the dry and wet seasons. Land uses showed scale effects; at a smaller scale, urban areas, agricultural land and water areas were the most important land uses affected by water quality. As the buffers changed from 500 to 1500 m, the impact of urban areas decreased significantly, while that of agricultural land and water areas increased rapidly; however, when the buffer was greater than 1000 m, the explanatory ability of water areas did not increase further but remained stable. Green space is only significant at the 200 m and 500 m scales, which showed the effect of improving river quality. This study is expected to provide references for future decision making of urban construction, environmental planning and management.

Evaluation of Watershed Water Quality Management According to Flow Conditions through Factor Analysis and Naïve Bayes Classifier

Previous studies on water quality assessment for watershed management have predominantly focused on specific seasonal or annual average values, rather than considering water quality variations based on flow fluctuations. It is crucial to identify the water quality characteristics within a watershed by incorporating flow conditions to establish a customized watershed management approach over different time periods. In this study, a vulnerability analysis was conducted to attain the target water quality (TWQ) in 22 watersheds within the Nakdong River system in South Korea. Additionally, factor analysis (FA) was employed to analyze the characteristics of water quality fluctuations in relation to flow conditions. The FA results categorized the pollution source characteristics of the 22 watersheds into various types, indicating the need for specific pollution source management strategies. These findings enabled an initial decision-making process regarding which water pollution sources to prioritize based on flow conditions. Moreover, detailed analyses of pollution sources were performed for watersheds, where achieving TWQ was challenging. Subsequently, a data-based prediction model was developed using the naïve Bayes classification model to determine the likelihood of achieving TWQ. As a result, this study proposes a technique for water quality management in watersheds by introducing a water quality excess probability model, which employs data-based analysis instead of traditional numerical modeling for watershed water quality assessment and proactive prediction. The study discusses the potential of various data-based tools to reduce development and analysis time, providing a powerful alternative to physical-based models that require extensive input data and are time-consuming. To advance future studies, the establishment of comprehensive water environment big data, improvement of real-time monitoring systems within watersheds, and advancements in spatial and temporal observation technologies are emphasized as essential for the development of an advanced watershed management system.

Impact of mine closure on groundwater resource: Experience from Westrand Basin-South Africa

The mining sector is at the edge of expanding to cater for natural resources that are much needed for technological development and manufacturing. Mushrooming of mines will consequently increase the number of mines closure. Moreover, mines closure have adverse impact on the environment at large and specifically on water resources. This study analyses historical groundwater quality parameters in mine intensive basin of Westrand Basin (WRB) to understand the status of groundwater quality in relation to mining activities and mine closure. Geographic information system (GIS) was used to map the spatio-temporal variation of groundwater quality in the basin and groundwater quality index (GQI) to evaluate its status. The coefficient of variation (CV) was applied to understand the stability of groundwater quality after the mine closure. Results indicated unstable and altered trend with increasing levels of acidity and salts concentration around the mines vicinity following the mine closure. The resultant maps indicated a significant deterioration of groundwater quality around the WRB with concentrations decreasing downstream. Obtained average GQI for the study period of 1996–2015 suggested a moderate groundwater quality at a range of GQI = 64–73. The CV indicated varying water quality at CV > 30% suggesting presence of source of contamination. Observed groundwater quality trends in Westrand basin suggested that mines closure present potential threat on groundwater quality and thus, a need for a robust mine closure plan and implementation.

Use of Microbiological and Chemical Data to Evaluate the Effects of Tourism on Water Quality in Karstic Cenotes in Yucatan, Mexico

Cenotes are spectacular karst formations in Yucatan, Mexico, often used for recreation. However, their impact on water quality has yet to be explored in detail. Therefore, during Easter, water samples were collected from four cenotes to identify variations in water quality associated with the presence of tourists. PCO of water quality, before (PH) and during Holy Week (HW) in 2019, explained 49.02% of the total variation. The indicators contributing to the first principal coordinate’s variation were Sr, K, sulfate, and chloride (0.89). Whereas, alkalinity, temperature, conductivity, nitrate, and ORP contributed to the second PC. PERMANOVA indicated a significant interaction between “cenote” and “condition” factors, and post hoc paired comparisons indicated significant differences between PH and HW conditions. Significant correlations varied among the four cenotes as the result of hydrogeological differences. Whereas, numbers of visitors were correlated with at least one fecal-matter indicator, demonstrating anthropogenic influence on the cenotes’ water quality.

Implications of changing trends in hydroclimatic and water quality parameters on estuarine habitats in the Gulf Coastal Plain

Florida’s low elevation and geographic location make it particularly vulnerable to climate change effects such as sea level rise, increased intensity and frequency of storm events, and altered precipitation. Climate change is expected to exacerbate hydrological cycling with potential widespread implications for estuarine habitats that thrive under specific salinity regimes. We used historical data from sites in the eastern Gulf Coastal Plain, USA to examine trends and trend variability of several climatic, hydrologic, and estuarine water quality variables which have implications on seagrass and oyster habitat extent in downstream estuarine environments. We analyzed temperature, precipitation, low-flow and high-flow metrics (including the highest or lowest daily, 7-day average, and 30-day average) for each season annually over the period 1985–2020. We also analyzed estuarine water clarity metrics and salinity within waterbody segments of four estuary systems within the study area. Hydroclimate results showed that temperature increased at most sites. While there was variation in streamflow, the overall trend was declining streamflow. Declining trends were observed in most water clarity metrics, indicating improved clarity, especially in winter. Salinity generally declined across the study area. While overall streamflow decreased, main river stems to the estuaries had increasing trends in maximum streamflow characteristics, likely contributing to the decrease in estuarine salinity across the region. These results indicate that trends in streamflow (both magnitude and timing) in the watershed affect downstream estuarine water quality. These results have important implications on seagrass and oyster restoration and management efforts in the region, indicating that it is important to understand changing climatic and hydrologic conditions and how they may impact the estuarine resources.

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (&amp;gt;18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables.

Effects of comprehensive landscape patterns on water quality and identification of key metrics thresholds causing its abrupt changes

Comprehensive landscape patterns influence water quality with multiple factors, complex processes, and scale dependence. However, studies identifying landscape thresholds causing abrupt water quality changes and characterizing the contribution of topography to water quality are still limited. Exploring the impact mechanisms of natural geographical and landscape characteristics on spatial and seasonal water quality variations is conducive to watershed water resource protection and ecosystem restoration. Based on water quality monitoring data of Minjiahe River in the typical headwater area of the upstream Dan River in China from 2019 to 2021, we employed redundancy analysis, partial redundancy analysis, and nonparametric change-point analysis to analyze the relationship between stream water quality and multi-spatial scale comprehensive landscape patterns, to obtain the interactive and independent contributions of different landscape categories at multi-spatial scales on water quality, and to find the key landscape threshold leading to abrupt changes in water quality. Results showed that landscape configuration, landscape composition, and topographic factors collectively explain over 89.1% of water quality variation. Most seasonal variations in water quality were primarily caused by landscape configuration. The landscape composition was mainly responsible for the differences in water quality variations among spatial scales. The topographic factors made the least independent contribution and had a potential impact on overall water quality variation. In order to protect the water quality of streams, it is more reasonable to regulate the landscape at different scales. At the sub-catchment scale, interspersion and juxtaposition index (IJI) and landscape shape index (LSI) should be controlled below 82% and 22. At the 100 m riparian scale, farmland, urban land, IJI, and LSI should be controlled below 29%, 6.5%, 92%, and 26, respectively. Our results provide important guidance for optimizing landscape patterns and water conservation in the watershed.

Đánh giá sự thay đổi chất lượng nước mặt theo mùa trên dòng chính và một số sông nhánh thuộc lưu vực sông Cả

The purpose of research is to assess the seasonal changes of water quality in mainstream and in specific tributaries in the Ca River Basin. Water samples were collected in the dry season (May 2021) and in the beginning of the rainy season (September 2021) from 4 sites located on Ca River’s mainstream and 5 sites located on Ca River’s tributaries to analyze water quality parameters. Results indicated that physical parameters (pH, EC,salinity, DO) and and heavy metals (As, Cu) are within the standard values according to the National Technical Regulation on Surface Water Quality QCVN 08-MT:2015/BTNMT for domestic water supply purposes (Columne A1). The concentration of nutrients measured in the tributaries is higher than those in the mainstream due to the contribution of nutrients and organic materials from agricultural runoff. In addition, the water flow in the tributaries is lower than in the mainstream, as a result, the concentration of nutrients tends to be higher in the tributaries. The samples revealed that the concentration of nutrients (NO3-, TN, PO43-, TP) and chemical oxygen demand (COD) appeared higher in the beginning of the rainy season than in the dry season. The increase of nutrient concentrations can be explained by the leaching of nutrients into the river. In general, the water quality of Ca River’s tributaries during the rainy season have a high nutrient concentration and also the risk of eutrophication.

Quantifying and trend analyzing dynamics of water quality variables of two Indus basin rivers of Indian Punjab

Quantifying and trend analyzing dynamics of water quality variables of two Indus basin rivers of Indian Punjab

Autochthonous sources and biogeochemical processes drive the spatiotemporal variation of DOM composition and optical indicators in a karst river

Dissolved organic matter (DOM), as a large carbon pool, is key to biogeochemical cycles, especially in some unique areas. Karst rivers typically exhibit unique biogeochemical characteristics and different human inputs; whether and how these special differences affect DOM distribution, transport and composition are not clear. Here, 134 samples were obtained from the Daning River, a typical karst river located in the mountain landscapes of the Yangtze River, during both the dry and wet seasons. Multiple methods including UV–visible absorption, three-dimensional fluorescence spectroscopy parallel factor analysis (PARAFAC), principal component analysis (PCA) and correlation analysis were used to research the molecular compositions, optical properties and origins of DOM. During the wet season, there was higher aromaticity and intensity of UV absorbing functional groups in DOM. The higher Fmax of the protein-like component in the wet season suggested more biological sources in DOM. PCA indicated that autochthonous sources and biodegradation were the main sources of DOM during the two seasons. During the dry season, forest showed the opposite effect on DOM quality when compared to farmland and urban. The findings demonstrated that monsoonal climate and biogeochemical processes were the primary reasons for the differences in DOM components and sources, which benefits the understanding of carbon biogeochemical processes in the karst rivers. More importantly, the analysis of DOM composition and sources can provide valuable insights into identifying potential contaminants and understanding water quality variations, which is beneficial to the concrete implementation of water protection.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model’s water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a “medium” level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Seawater quality assessment of the marine ecosystem of Punta Galeta in Panama

Seawater quality was described in the Caribbean coast of Panama based on water physicochemical parameters and biological monitoring studies. Samples of water and benthic macroinvertebrates were collected near Punta Galeta, Province of Colón, Panama. The sampling area was sheltered from the action of waves and located between the coral reef and mangroves. Class Polychaeta was themost abundant with 90 % overall presence in Punta Galeta. Taxonomic classes of Bivalvia, Malacostraca and Ophiuroidea only represented 10 % altogether. Overall, seawater quality was suitable for the collected species of macroinvertebrates, even though concentrations of nitrateand phosphate were above the recommended value. Recorded values of temperature, pH and dissolved oxygen indicated that the site is far from reaching hypoxic conditions. Water quality index indicates a good water quality at Punta Galeta. Further studies with more robust and intense sampling programs are required to properly define the seasonal variations of water quality and its link to benthic macroinvertebrates. Similar studies in the Caribbean of Panama are scarce even though they offer valuable information for water resource administrators.

Effects of Environmental and Water Quality Variables on Histamine-Producing Bacteria Concentration and Species in the Northern Gulf of Mexico.

Scombrotoxin (histamine) fish poisoning is a common seafood-borne illness attributed to toxin production by histamine-producing bacteria (HPB) in fish tissues during decomposition. In laboratory studies, growth of HPB and other bacterial species is affected by physical and chemical attributes, but natural communities of HPB are not well understood. To determine how in situ environmental and water quality variables may affect density of HPB in the natural aquatic environment, we compared presence and abundance of HPB to ambient temperature, salinity, dissolved oxygen, fecal coliforms, male-specific coliphage, nutrient concentrations, carbon and nitrogen stable isotope ratios, and C:N in water samples collected from July 2017 to February 2018 along a natural salinity gradient in a tidal river on the coast of northern Gulf of Mexico. HPB in water samples were quantified using a real-time PCR, most probable number method. HPB species were identified via 16S rRNA gene sequences. Temperature and salinity were determined to be the main factors driving HPB presence and concentration. Canonical correspondence analysis revealed that different HPB were associated with different environmental conditions. Photobacterium damselae was found under warmer, higher-salinity conditions; Raoultella planticola was found at colder, lower-salinity conditions; Enterobacter aerogenes was found at warmer, lower-salinity conditions; and Morganella morganii was found at most sites, independent of environmental conditions. These results showed that naturally occurring HPB abundance and species composition can be affected by environmental conditions, which could manifest in various potentials for histamine formation and scombrotoxin fish poisoning risk based on environmental factors. IMPORTANCE This study determined the effects of environmental conditions on presence and abundance of naturally occurring histamine-producing bacteria in the northern Gulf of Mexico. Here, we show that HPB abundance and species composition are related to in situ ambient temperature and salinity, with the magnitude of this effect dependent on the particular HPB species. This finding suggests that environmental conditions at fishing sites could affect the risk of human illness from scombrotoxin (histamine) fish poisoning.

Assessment of drinking water quality in Tirana city, Albania

The analysis of drinking-water for metal contamination is an important step in ensuring humanand environmental health. Samples were analyzed in order to determine the water quality in 30 tap fromTirana, Albania during the year 2018. The concentrations of 26 metals (µg/L) in drinking-water sampleswere determined using ICP-MS for all samples collected from different sites. With purpose to see if theconcentrations of these elements in water have a negative impact on health problems, the results obtainedwere compared with the standard classification proposed by the WHO. From the comparison is obviouslythat the drinking water of Tirana is within the permitted norms regarding the metal content. The data setobtained was subjected to the FA analysis to identify water quality variables. Four dominant factors wereextracted. FA1 represent the association of Na, Mg, Al, K, V, As, Se, Mo, Sb and Ba with lithogenic andanthropogenic emission (elements derived from traffic), and FA3 (Ni, Cu, Zn, Hg and Pb) is probablyrelated to the influence of the old pipelines or their aerial deposition at the source.

Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River

Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R2. The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R2 ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.

Effectiveness of Filter Media Compositions on Water Quality, Growth and Survival Rate of Tilapia (Oreochromis niloticus) Cultured in Recirculation System

Market demand for tilapia (Oreochromis niloticus) is increasing every year. This has encouraged aquaculture companies to apply intensive cultivation systems. However, these applications run the risk of increasing waste and decreasing water quality. One strategy to overcome those problems of water quality is to use recirculation aquaculture system. This study aims to determine the effectiveness of filter media composition on water quality, survival rate, and tilapia growth. The average body weight of the fish used was 3.84 ± 0.05 g per individual. A completely random experiment design with three treatments and three replications were applied. The treatments were combination of several filter media with different compositions, namely A (Dacron + 75% nets + 25% sand), B (Dacron + 50% nets + 50% sand) and C (Dacron + 25% nets + 75% sand).The water quality variables measured were pH, DO, temperature, ammonia, and VTR (Volumetric TAN Removal). The survival rate (SR), relative growth rate (RGR). and total amount feed fed (TFF) of fish were also measured. The results showed that the composition of the filter had a significant effect (P&lt;0.05) between treatment. The best VTR was showed in Treatment C with value 54.65 gm-3d-1.Keywords: Tilapia, recirculation, dacron, nets, sand