Water Quality Characteristics Research Articles

GIS Constructed Water Monitoring Network System Via IOT

Monitoring the quality of water plays a vital role in the advancement towards intelligent and smart agriculture. It also facilitates the seamless transition to automated monitoring of essential components of human daily needs, as new technologies continue to be developed and adopted in both agricultural and human daily life, particularly in relation to water. Assessing water stress requires accurate measurement of water usage [1]. The objective of this study is to optimize the distribution strategy and achieve more efficient and rapid distribution, while also making better use of technology to address the urgent need for water conservation. One of the criteria for the second filtering phase, which utilized a realtime data collection system, focused on the implementation of water quality monitoring and characterization methods. Water measurement sensors and the Internet of Things (IoT) can provide immediate information on water flow and pressure rates. This information is crucial for the analysis and surveillance of water [2]. At the RCEW campus, online smart water monitoring is imperative to ensure a more efficient and sustainable use of water. By combining the Internet of Things and geographic information systems (GIS), an online smart water monitoring prototype was developed specifically for RCEW [3]. This cutting-edge technology has proven to be highly successful in providing real-time information on water parameters, saving both time and money, while enabling the continuous study of water consumption patterns. This innovative technology seamlessly integrates IoT and GIS for effective water surveillance [4,5].

Ecological impact of pharmaceutical pollutants and options of river health improvements - A risk analysis-based approach

Pharmaceuticals are one of the emerging pollutants (EPs) in river waters across the world. Due to their toxic effects on aquatic organisms, they have drawn the global attention of the scientific community concerned with river ecosystems. This paper reviews the existing occurrence data for various pharmaceutical pollutants (PPs) reported in river waters in some part of the world and their ecological impacts. Using algae, macroinvertebrates (MI), and fish as biotic indicator groups in water to reflect river health conditions, an attempt has been made to assess the ecological risk due to the presence of PPs in the water environment.After ascertaining the predicted no-effect concentration (PNEC) of PPs for selected groups of aquatic organisms, the risk quotient (RQ) is estimated based on their measured environmental concentration (MEC). When MEC > PNEC and RQ > 1 for any of the biotic indicator, ecologically it is ‘high risk’ condition. The determination of PNEC uses a minimum assessment factor (AF) of 10 due to uncertainty in data over the no observed effect level (NOEL) or lowest observed effect level (LOEL). Accordingly, MEC 10 times higher than PNEC, (RQ = 10) represents a threshold risk concentration (RCT) beyond which adverse effects may start showing observable manifestations. In the present study, a new classification system of ‘high risk’ conditions for RQ = 1–10 has been proposed, starting from ‘moderately high’ to ‘severely high’. For RQ > 10, the ecological condition of the river is considered ‘impaired’.For river health assessment, in the present study, the whole range of physico-chemical characteristics of river water quality has been divided into three groups based on their ease of measurement and frequency of monitoring. Dissolved oxygen related parameters (DORPs), nutrients (NTs), and EPs. PPs represent EPs in this study. A framework for calculating separate indicator group score (IGS) and the overall river health index (RHI) has been developed to predict indicator group condition (IGC) and river health condition (RHC), respectively. Color-coded hexagonal pictorial forms representing IGC and RHC provide a direct visible perception of the existing aquatic environment and a scientific basis for prioritization of corrective measures in terms of treatment technology selection for river health improvements.The analyses indicate that many rivers across the world are under ‘high risk’ conditions due to PPs having MEC > PNEC and RQ > 1. Up to RCT, (where RQ = 10), the ‘high risk’ condition varies from ‘moderately high’ to ‘severely high’. In many instances, RQ is found much more higher than 10, indicating that the ecological condition of river may be considered as ‘impaired’. Algae is the most frequently affected group of biotic indicators, followed by MI and fish.A review of treatment methods for selection of appropriate technology to reduce the pollution load, especially PPs from the wastewater streams has been summarized. It appears that constructed wetlands (CWs) are at present the most suitable nature-based solutions, particularly for the developing economies of the world, to reduce the concentrations of PPs within limits to minimize the ecological impacts of pharmaceutical compounds on biotic indicators and restore the river health condition. Some suggestive design guidelines for the CWs have also been presented to initiate the process.

A new method for accurate inversion of Forel-Ule index using MODIS images - revealing the water color evolution in China's large lakes and reservoirs over the past two decades

The Forel Ule water color index (FUI) based on satellite inversion can characterize the comprehensive characteristics of water quality on a large spatiotemporal scale. The high-frequency observations and rich historical data of the MODIS surface reflectance product (MODIS-500 m) provide important data support for monitoring the FUI of inland lakes. However, MODIS-500 m has only three bands in the visible light range, resulting in significant uncertainty in FUI inversion. To address this problem, this study developed an improved FUI inversion model using 500 synthetic spectra covering natural waters. The model, with a performance threshold set at 170° (FUI = 8), used a segmented algorithm across the entire color space. Validated with on-site measurement datasets (3500 samples), the model exhibited excellent performance, with mean relative error (MRE) and root mean square error (RMSE) of 1.71 % and 3.63°, respectively. Compared to existing models, it was more suitable for long-term FUI inversion in various types of lakes, particularly in eutrophic regions. Subsequently, the model was applied to MODIS-500 m observations from 2000 to 2022, revealing the spatiotemporal dynamics of FUI in 180 large lakes and reservoirs (hereinafter referred to as lakes) in China. The results indicated that the long-term mean FUI in the study area was 9, with 7 and 12 in the western and eastern regions, respectively, showing a distinct spatial distribution of "blue in the west and green in the east." The mean change rate of hue angle for all lakes was -0.085°/yr, showing an overall decreasing trend. Environmental factors' relative contributions to long-term water color changes in each lake region were quantified using the multiple general linear model (GLM). Although each lake region exhibited different driving forces, they were primarily influenced by vegetation, lake surface area, and anthropogenic factors. Additionally, the seasonal types of lake water color were analyzed, with the west and east showing opposite patterns, reflecting the significant influence of topographic features and seasonal changes in climate on water color. The research results provide techniques for accurate inversion of FUI using MODIS-500 m data, while deepening the understanding of long-term water color changes in inland lakes in China.

WATER QUALITY OF YAMUNA RIVER AT PRAYAGRAJ, U.P., INDIA

Rivers are an essential part of our biosphere which is essential for the overall development of the present civilization. The present study was carried out to analyze the water quality at selected sites of river Yamuna in Prayagraj (Allahabad) city during January 2019 to May 2019. Samples were collected and analyzed for assessing the water quality characteristics as pH, turbidity, alkalinity, Ca 2+ hardness, Mg 2+ hardness, Cl- and Sulpate. The obtained results were compared to the standards limits of BIS. Analysis results showed that pH, turbidity, alkalinity, sulphate, Chloride of water was found within the permissible limits while Calcium and magnesium hardness values are higher than the permissible limits of BIS standards. The possible causes of deteriorating of Yamuna water due to discharged of untreated water and other human activity.

Water Quality of Lake Erhai in Southwest China and Its Projected Status in the near Future

The water quality of Lake Erhai has deteriorated in recent decades due to socioeconomic development in the lake basin. After the massive implementation of water environmental protection measures in Lake Erhai in 2016, the trend of water quality deterioration has been curbed and the intensity and frequency of algal blooms has decreased. However, water quality monitoring data show that pollutant concentrations in Lake Erhai still exceed acceptable values, and there is a risk of water quality standard limits being further exceeded in the future. Therefore, it is urgent to systematically study the variability characteristics of water quality in Lake Erhai to provide practical methods to predict the future evolution of water quality. Based on water quality monitoring data from 2009 to 2019, the current water quality characteristics of Lake Erhai were analyzed, and a two-dimensional hydrodynamic and water quality mathematical model was built to predict the water quality in 2025. The results showed that the total phosphorus (TP) concentration declined after 2016, mainly due to the significant reduction of TP entering the lake due to pollution interception. However, the concentrations of the potassium permanganate index (CODMn) and total nitrogen (TN) increased after 2016, demonstrating that the pollution control measures have had little effect on the improvement of CODMn and TN. The spatial and temporal distribution of pollutants showed that the water quality in winter and spring was better than in summer and autumn, and the water quality in the southern lake was better than in the northern lake. This analysis indicates that non-point source pollution remains the main source of pollution in Lake Erhai, and that rainfall is the main driving force of pollutants exceeding the water quality standard. According to the water quality predictions, without additional pollution control measures, pollutant concentrations in Lake Erhai will exceed the Class II water quality standard by 2025. This study analyzes the water quality characteristics, predicts the direction of future water quality changes, and provides a theoretical basis for the future water quality protection of Lake Erhai.

Current Status of Zero Liquid Discharge Technology for Desulfurization Wastewater

Desulfurization wastewater is industrial wastewater with a high salt content, high metal ions, and high hardness produced by flue gas desulfurization of the limestone-gypsum method in coal-fired power plants. This paper summarizes the source of desulfurization wastewater, water quality characteristics, water quality impacts, and other factors, combined with the current status of research worldwide to introduce the advantages and shortcomings of the existing desulfurization wastewater treatment technology. In addition, zero liquid discharge technology as a novel method to treat desulfurization wastewater is also summarized. It mainly includes evaporation and crystallization, flue gas evaporation, membrane distillation removal, etc. Finally, this manuscript looks forward to the future development direction of desulfurization wastewater based on its existing technology and emission standards.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Karakteristik Biologi dan Kelimpahan Rajungan (Portunus pelagicus) di Sekitar Perairan Sumenep

The high demand for the crab export market has resulted in the crab commodity experiencing a decline and leading to exploitation. Prediction of crab stocks in Sumenep Regency is minimal due to a need for further information regarding crab data. This study aims to determine the biological characteristics of the blue swimming crab (Portunus pelagicus), limiting blue swimming crab (Portunus pelagicus), and water quality characteristics around Sumenep. Research methods is observation methods or directly contact fishermen. Data collection included blue swimming crab biology, including morphometric aspects, which consisted of measurements of carapace width, carapace length, weight, and level of gonadal maturity. Data collection for water quality parameters used the insitu method at each research location. The results of this study were variations in the morphometric characters of the crabs in the distribution of the carapace width of the male crabs found in class 121-138 mm, as many as 137 individuals, while the female crabs were found in class 121-138 mm as many as 136 individuals, the distribution of size class based on the length of the female crab in class 60 .42-68.42mm as many as 112 people. The size class distribution of male crabs was mainly found in the 51.42-59.42mm class of 96 individuals; the distribution of size classes based on the weight in male crabs in the 126-157 gram class was 65 individuals. The size class of female swimming crabs was found in the 158-189 gram class; as many as 67 individuals applied for crabs around Sumenep. Based on the crab data collection at the first location, Talango was 289 ind/700 cm2, the second location was Tanjung Saronggi, 65 and/700 cm2, and in the third location, Gili Genting, there was as much as 104 ind/700 cm2. The waters of Tanjung Saronggi are a location that has more male crabs than female crabs. The air quality characteristics around Sumenep waters are categorized as optimal for blue swimming crab habitat.

Analysis on Operation and Water Quality Characteristics of Centralized Wastewater Treatment Plants of Industrial Parks in Yellow River Basin, China

The Yellow River basin serves as an important economic belt and industrial base in China, featuring numerous industrial parks. However, alongside its economic significance, the basin struggles with significant water environmental challenges. This study analyzed the operational status, influent water quality, and energy consumption of 63 centralized wastewater treatment plants (WWTPs) from 54 major industrial parks in the Yellow River basin. The scale of these WWTPs was primarily within the range of 1 × 104~5 × 104 m3/d, with an average hydraulic loading rate of 53.8%. Aerobic treatment processes are predominant. The influent concentrations of chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) in the WWTPs exhibited a right-skewed distribution. The BOD/COD ratio of the WWTPs fluctuated between 0.1 and 1.6, and 75% of the WWTPs showed a COD/TN ratio lower than eight. The average BOD5/TN was 2.7, and the probability of influent BOD5/TP > 20 was 84.6%. A significant linear correlation exists between the influent BOD and COD concentrations, while moderate linear relationships are also observed among NH3-N, TN and TP, emphasizing the importance of maintaining appropriate nitrogen and phosphorus levels for efficient pollutant removal. The average electricity consumption of WWTPs in the Yellow River basin in 2023 was 1.1 kWh/m3. It is important to upgrade these WWTPs and reduce their energy consumption. Further strengthening the construction of industrial wastewater collection and treatment facilities based on regional characteristics is recommended to promote the high-quality development of industrial wastewater treatment in the Yellow River basin.

Seasonal subsurface water quality variation of physiochemical and bacteriological characteristics in Kamutwa-Kigali, Rwanda

Abstract The study was conducted to assess subsurface water quality on a seasonal basis in dry (January–February and June–August) and rainy (March and October–December) seasons. Samples were collected in the rainy and dry seasons of the year 2022. The results of this study were compared with global drinking water guidelines of the World Health Organization (WHO) to understand its status in terms of threshold levels of pollution and protect public health. Total hardness (TH), pH, alkalinity, electrical conductivity (EC), total dissolved solids (TDS), nitrate, sulfate, heavy metals (Zn, Mn, and Fe), fecal, and total coliforms were examined, and the water quality index (WQI) was calculated to assess the level of water contamination. The results indicated that during the rainy season, all physiochemical parameters gradually increased and the values were well within the permissible limit as prescribed by the WHO. The bacteriological test showed that there were no fecal coliform (FC) and total coliform (TC) in all trials performed, and the calculated WQI showed excellent water quality characteristics, thus proving fit for human consumption.

Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers

A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming techniques employed and environmental variables involved. The aim of this research is to enhance the competitiveness of Greek fish farming through the development of an intelligent system that is able to diagnose fish diseases in farms. This system concurrently addresses medication and dosage issues. To achieve this, a comprehensive dataset derived from various aquaculture sources was used, including various factors such as the geographic locations, farming techniques, and indicative parameters such as the water quality, climatic conditions, and fish biological characteristics. The main objective of the research was to categorize fish mortality cases through predictive models. Advanced data mining classification methods, specifically decision trees (DTs), were used for the comparison, aiming to recognize the most appropriate method with high precision and recall rates in predicting fish death rates. To ensure the reliability of the results, a methodical evaluation process was adopted, including cross-validation and a classification performance assessment. In addition, a statistical analysis was performed to gain insights into the factors that identify the correlations between the various factors affecting fish mortality. This analysis contributes to the development of targeted conservation and restoration action strategies. The research results have important implications for sustainable management actions, enabling stakeholders to proactively address issues and monitor aquaculture practices. This proactive approach ensures the protection of farmed fish quantities while meeting global seafood requirements. The data mining using a classification approach coincides with the general context of the UN sustainability goals, reducing the losses in seafood management and production when dealing with the consequences of climate change.

ISSUES OF WATER USE IN THERMAL POWER PLANTS. PROBLEMS AND SOLUTIONS

The article considers the most effective methods of water treatment for thermal power plants. Water treatment is one of the most important processes in the operation of thermal power plants (TPP). The quality of water treatment for use in boiler units of thermal power plants is of fundamental importance to ensure the efficiency and durability of process equipment. This article reviews the main aspects of water treatment in TPP plants, including treatment methods, water quality characteristics and standards, and the impact of water treatment on the overall energy efficiency and safety of power generation in TPP plants.

Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)

In this study, the spatial distributions of organic contamination stressors in water of fluvial habitats in the Çanakkale Strait (ÇS) watershed were investigated and the data were assessed in terms of human health and mucilage threat. Seven significant riverine ecosystems flowing into the ÇS were defined in the basin. Water samples were taken in the spring season (2023), when the phytoplankton communities reach their highest densities. Then they were tested for a total of 8 limnological parameters.The Nutrient Pollution Index (NPI) and Water Quality Index (WQI) were applied to assess the comprehensive quality characteristics of waters. The Hazard Quotient (HQ) and Hazard Index (HI) were applied to indicate the prospective non-carcinogenic human health risks of organic stressors. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to categorize the investigated habitats and define the sources of investigated contamination parameters. Also, Geographic Information System (GIS) was used to make an effective assessment through visualization.The determined spatial mean values of the measured variables in ÇS watershed as follows: 18.21 °C for temperature, 8.51 mg/L for DO, 4.57 NTU for turbidity, 3.95 mg/L for suspended solids, 1.11 mg/L for NO3–N, 0.012 mg/L for NO2–N; 0.173 mg/L for PO4–P and 2.32 mg/L for BOD. It has been determined that the organic pollution loads and water temperature values of the investigated sub-basins increase from the upstream to the downstream locations and Çanakkale Stream was recorded as the riskiest fluvial habitat for the ÇS watershed. According to the results of health risk assessment indices, non-carcinogenic risks of organic pollutants would not be expected for all age groups.

Estimating the water quality index based on interpretable machine learning models.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from -9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.

Fabrication of impregnated activated carbon promoted by iron nitrate and its application for adsorptive removal of organic matters from actual reverse osmosis concentrate

In this study, the potential of a highly efficient composite of activated carbon with iron oxide active substances was explored for removing organic matters from reverse osmosis concentrate. The water quality characteristics indicated that the organic matters were mainly small molecular with molecular weight less than 1 kDa, and composed of hydrophilic-alkali, hydrophilic-neutral and hydrophobic-neutral substances. The specific surface area of the activated carbon composite was 482.96 m2/g, with 91.97% of the total specific surface area being microporous. Iron oxide active substances was loaded on activated carbon skeletons, mainly forming Fe2O3 and Fe3O4. The adsorption process was fitted with pseudo-second-order and Langmuir isotherm models, with the active site playing an important role in the adsorption capacity and the adsorption was spontaneous and endothermic. Combined with the refractory characteristics of the wastewater quality, electrostatic interactions, hydrogen bonding and functional group complexation were the main forces involved in the adsorption process, taking into account the refractory nature of the wastewater.

Geochemical evolution along regional groundwater flow in a semi-arid closed basin using a multi-tracing approach

Groundwater is the main source for domestic drinking water supply, irrigation, and industrial activities in Zacatecas in central Mexico, a good example for numerous semiarid regions worldwide, however, there is insufficient information about characterization and evolution of the baseline water quality to create hydrogeological conceptual models, fundamentals for the future planning of new sources of groundwater supply for the population, especially under ongoing global climate change conditions. By a multi-method analysis that included geological, geophysical, hydrochemical and isotopic tools this investigation allowed the identification of: a) local flows, represented by shallow groundwater and shallow wells, hosted over basin fill sediments with low average temperatures (≈ 23.3 °C), variation in water types related to anthropogenic activities, low trace elements concentrations, δ18O and δ2H signatures indicating surface water and tritium being present in this system, b) intermediate flows (25 °C), identified in wells with depths of 100 to 200 m, water circulating through the basin fill sediments and the sequence of volcanic rocks characterized by intermediate concentrations of trace elements, and groundwater ages calculated for this system vary from 1,000 to 6,000 years, c) regional flows in volcanic rocks, characterized by groundwater from wells with depths of more than 200 m, high temperatures (up to 31.5 °C) and elevated concentrations of trace elements. The δ18O and δ2H signals are more homogenous and the 14C residence times for this flow system are greater than 8,000 years, and d) regional flows in Cretaceous rocks, with depths greater than 400 m, the highest temperatures (≈ 43.7 °C) and intermediate concentrations of trace elements. The δ18O and δ2H signals are homogenous and δ13C can widely be associated with carbonate shales. The hydrogeochemical evolution has been documented along a 62 km flow line to develop a robust conceptual model, reflecting the water–rock interaction in the system. Physicochemical parameters, as well as major and trace element concentrations, reflect a distinct evolution according to the flow paths and their increase is generally proportional to the residence time. Likewise, processes of dissolution of feldspars and some clay minerals from initial weathering in the basin fill sediments, as well as devitrification of the glassy matrix in the volcanic rocks are taking place in the evolution of the system.The results obtained through the analysis of water–rock interaction in this research can be applied to other semi-arid regions with geogenically impacted aquifers, due to the geological and climatic similarity with numerous volcano-sedimentary basins around the world.

Spatial-temporal Variation in Water Quality of Rain-source Rivers in Shenzhen from 2015 to 2021 and Its Response to Rainfall

Rain-source urban rivers have the characteristics of small water capacity， lack of dynamic water supply， and being easily polluted. This study analyzed the spatial and temporal distribution characteristics of river water quality and the response of characteristic pollutants to rainfall based on daily rainfall data and 21 water quality indicators of nine major river basins in Shenzhen （excluding Shenzhen-Shantou） from 2015 to 2021 by using the single-factor assessment method， comprehensive pollution index method， hierarchical cluster analysis， and Pearson correlation. The results showed that： ① in 2015， the water quality of most sections in the whole region was inferior Class V water. After October 2018， the overall water quality of rivers was greatly improved， which was consistent with the background of Shenzhen's special water control activities in 2018. By 2021， the water quality of approximately 62% of sections reached Class Ⅰ-Ⅲ water standards. ② The water pollution in the densely populated western part of Shenzhen was more serious than that in the eastern part， and the water pollution in the lower reaches of the estuaries and tributaries was more serious than that in the upper reaches. ③ The water quality of the Pingshan River， Guanlan River， Longgang River， and Maozhou River was significantly affected by rainfall. ④ The main characteristic pollution indexes of the Shenzhen River were DO， permanganate index， COD， BOD5， NH4+-N， TP， petroleum， and anionic surfactant. For the Pingshan River and Longgang River， rainfall increased the concentrations of TP and NH4+-N. For the Maozhou River， rainfall increased the concentrations of TP and COD. For the Shenzhen River， rainfall increased the concentrations of COD， TP， and NH4+-N. The above results reveal the spatio-temporal variation in rain-source river water quality in Shenzhen and its response to non-point source pollution caused by rainfall events and provide a scientific reference for building a higher quality water environment in Shenzhen.

Characterization of water quality in the river Lobo and tributaries Center-West of Côte d’Ivoire

Characterization of water quality in the river Lobo and tributaries Center-West of Côte d’Ivoire

Precipitation impacts the physicochemical water quality and abundance of microbial source tracking markers in urban Texas watersheds

This study investigated the sources of fecal pollution in surface and groundwaters collected from three urban watersheds in Texas during dry and wet weather and identified the impact of precipitation on water quality. Water samples (n = 316 and 107 for dry and wet events, respectively) were collected biweekly from eight sampling sites (6 sites from creeks and ponds, and 2 well sites) during two-year monitoring and analyzed for six physico-chemical parameters and ten qPCR-based assays targeting general (E. coli, Enterococcus, and universal Bacteroidales), human (BacHum and HF183), animal (Rum2Bac, BacCow, BacCan), and avian (Chicken/Duck-Bac and GFD) fecal markers. Elevated concentrations of NO3-N and NO2-N were observed in ponds and creeks sites during wet weather. Fecal markers analysis indicated higher concentrations of Rum2Bac, BacCow, and BacCan markers in most of pond and creek sites under wet weather, suggesting stormwater runoff contributed to non-point sources of fecal contamination by animal sources. Furthermore, sporadically higher concentrations of these markers were detected at groundwater sampling sites, demonstrating the significant human health risk. Multivariate statistical analysis such as cluster analysis (CA) and principal coordinate analysis (PCoA) was performed to identify relationship between sampling sites; while CA majorly classified ponds, creeks, and well sites separately, PCoA identified similarities in water quality characteristics between waters of wells with ponds and creeks. Overall, results indicate ruminant and dog fecal contamination is a major concern during storm events, consequently impacting surface and groundwater quality of the study.

Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil.

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple wateruses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed thatthearea occupied by fish farms has increased in the reservoir from 2016 to 2022and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.