Water Quality Parameters Research Articles

Navigating AAMI ST108 Water Quality Requirements for Safe Processing of Medical Devices.

Water quality plays a critical role in health care, particularly in the processing of medical devices. This article highlights the infection control risks associated with water sources and the regulatory requirements for water management plans in health care facilities. The guidance on water quality has evolved from the initial technical information report provided by the Association for the Advancement of Medical Instrumentation in 2014 to the more recent 2023 publication of the American National Standards Institute and Association for the Advancement of Medical Instrumentation standard for water used in medical device processing. The new standard emphasizes the importance of consistent water and steam quality in instrument processing and outlines the chemical and microbiological parameters for water quality. This article also addresses the challenges of implementing these standards, discusses the implications for patient safety and instrument integrity, and provides strategies for health care teams to ensure compliance.

Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors

Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors

Control of an aquaponic system to improve the yield of gray tilapia and lettuce cultivation

Water quality assessment presents challenges, primarily the paucity of available data and ongoing system maintenance. This research develops an automated monitoring and control of water quality parameters in aquaponic systems with internet of thing (IoT) technology. Proper fish feeding management is important, which is why the fish were fed at 12:00, 16:00 and 07:00. The most significant relative error recorded during the validation of the DS18B20, PH-4502C, SEN0244, SEN0237-A, SEN0189 and DFR0300 sensors is 5.0%. The maximum standard deviation between the mentioned sensors was 1.96, and the highest coefficient of variation reached 7.24%. Before the installation of the aquaponic system, the specific growth rate (SGR) of fish was 4.89±0.17% and after implementing the automated aquaponics system, the SGR of fish increased to 6.21±0.24%. The feed conversion ratio values of the fish, both before and after the installation of the control system, were 1.98±0.14% and 1.53±0.09%, respectively. In addition, an improvement in plant growth was observed, evidenced by the difference in the values of height, number of leaves, leaf length, and weight of the plants before and after the installation of the control system, which was 7.74 cm, 5 leaves, 5.6 cm, and 41.6 g respectively.

Impact of shipbreaking industries on the Sitakunda coastal environment, Chattogram by analyzing water quality parameters.

Shipbreaking is an extremely profitable business; however, it simultaneously destroys the surrounding environment. The discharge of toxic chemicals and materials containing wastes is contaminating surrounding water. However, there is still no sufficient published information particularly focusing on shipbreaking yard (SBY) water quality. Therefore, this research was carried out by questionnaire survey among the workers following simple random sampling and purposive simple random sampling for water samples collection from SBY. Results showed that young energetic, experienced and courageous workers (20-35years) generally work in SBY. Most of the workers are illiterate except for a few graduate officials. The workers are provided in-yard small-scale treatment facilities for injuries which is not sufficient. The tested water parameters were as turbidity (276-640 JTU), pH (6.3-6.7) and EC (1850-3636μs/cm), while TSS (1925-4005), TDS (921-2150), chloride (543-1023), ferrous (1.4-34), DO (5.3-6.5), BOD (4.3-7.3), oil (30-7375), NH3 (0.75-2.27), lead (55-107), copper (29-58), cadmium (0.2-0.7), mercury (0.01-0.12), zinc (71-128), chromium (18-107) and arsenic (0.02-5.3) in mg/l unit in SBY. These findings indicate the potential water contamination by shipbreaking activities. Statistical analysis showed big F-value with small p-value in all studied water parameters, indicating significantly different. In addition, the water parameters in most of the sampling points in SBY crossed the Department of Environment (DoE) standards. Therefore, regular monitoring of DoE and strengthened government regulations with sufficient technical support to shipbreaking industries are recommended for mitigating water pollution and protecting the surrounding ecosystem.

AQuA-P: A machine learning-based tool for water quality assessment.

This study addresses the critical challenge of assessing the quality of groundwater and surface water, which are essential resources for various societal needs. The main contribution of this study is the application of machine learning models for evaluating water quality, using a national database from Mexico that includes groundwater, lotic (flowing), lentic (stagnant), and coastal water quality parameters. Notably, no comparable water quality classification system currently exists. Five advanced machine learning techniques were employed: extreme gradient boosting (XGB), support vector machines, K-nearest neighbors, decision trees, and multinomial logistic regression. The performance of the models was evaluated using the accuracy, precision, and F1 score metrics. The decision tree models emerged as the most effective across all water body types, closely followed by XGB. Therefore, the decision tree models were integrated into the AQuA-P software, which is currently the only software of its kind. It is recommended that these innovative water classification models be used through the AQuA-P software to facilitate informed decision-making in water quality management. This software provides a probability-based classification system that contributes to a deeper understanding of water quality dynamics. Lastly, an open-access repository containing all the datasets and Python notebooks used in our analysis is provided, allowing for easy adaptation and implementation of our methodology for other datasets worldwide.

A Study on Water Quality Characteristics and Classification Scheme for Improving Major Tributaries in Sejong Special Self-Governing City: Development and Application of a Monthly Water Quality Index

This study performed a comprehensive assessment of water quality in four major tributaries - Yeongicheon, Samsungcheon, Bangchukcheon, and Jecheon - in Sejong Special Self-Governing City. The aim was to guide the development of effective river management policies and improve water quality for the first time. Monthly data from the city's water quality monitoring system, from January 2022 to June 2024, were analyzed. The study evaluated 11 water quality parameters. Additionally, we compared BOD and TOC, analyzed correlations between nutrient levels and organic pollutants, performed principal component analysis, and calculated a water quality index, and applied the Monthly Water Quality Index methodology. The results showed that the average water quality index ratings for all four rivers were classified as “Excellent,” aligning with trends observed in living environment standards. However, ratings derived from living environment standards showed greater variability than those from the water quality index, indicating the necessity for a combined water quality index to enhance river management strategies. Seasonal analysis revealed a decline in water quality during the rainy season, emphasizing the need for targeted management during this period. Among the tributaries, Yeongicheon, a rural stream, showed a greater need for nutrient management than the other urban streams, highlighting the importance of controlling non-point source pollution in its vicinity for comprehensive water quality improvement. The water quality demonstrated a tendency to decline during the rainy season, underscoring the necessity for enhanced water quality management during this period. Among the tributaries, Yeongicheon, a rural stream, exhibited a heightened requirement for nutrient management compared to the remaining three urban streams, indicating that effective control of non-point source pollution in the surrounding areas is essential for the comprehensive water quality management of these tributaries. In addition, the use of Monthly Water Quality Index developed in this study is recommended to calculate the water quality index with monthly water quality data.

Microbial community structure and functional characteristics in a membrane bioreactor used for real rural wastewater treatment.

Membrane bioreactors (MBRs) have been widely used in the field of wastewater treatment because of their small footprint and high treatment efficiency. In this research, 10 rural wastewater treatment sites in China that employ the MBR process were systematically studied. Specifically, treatment of actual domestic wastewater using MBRs was examined by high-throughput 16S rRNA gene sequencing to explore the microbial community composition and perform function prediction. The data of water quality parameters revealed high removal rates of chemical oxygen demand and NH4+-N in all the sites. Proteobacteria were absolutely dominant in all the sites. Thauera, Nitrospira, Ferribacterium, and Dechloromonas were the main functional genera responsible for nitrogen and phosphorus removal at the tested sites. Nitrospira includes conventional nitrite-oxidizing bacteria and complete ammonia-oxidizing bacteria. Among them, 26 genes related to nitrogen metabolism were retrieved according to gene prediction, which verified the good NH4+-N removal efficiency at the tested sites. This study focuses on the analysis of microbial community structure and functional characteristics of MBR-based treatment systems for rural wastewater treatment, thereby providing a microbial basis for improving rural wastewater treatment processes.

Spatio-temporal pollution assessment of Kulfo River using CCME-WQI and pollution tolerance index by benthic macroinvertebrates, Arba Minch, Ethiopia.

In developing nations, the biggest threat to public health is the quality of the water. The Kulfo River provides the majority demand of the domestic water and irrigation along its course; however, it is observed that wastes from anthropogenic and natural activities enter the river. Therefore, this study aimed to examine the pollution status by integrating conventional methods with benthic macroinvertebrates. One hundred twenty samples were collected for 15 water quality parameters from five sampling sites during dry and rainy events. The mean concentration of DO, TSS, BOD5, COD, ortho-phosphate, and turbidity did not meet the standard limits in five sampling sites during the rainy season. Benthic macroinvertebrates were collected from the left, middle, and right sides of the river for each sampling site and examined using the Benthos Biomonitoring Protocol Network Manual. CCME index and PTI index of benthic macroinvertebrates were used to examine the pollution status of the river. According to CCME index result, S2 (39.79), S3 (38.90), and S4 (37.71) are classified under poor pollution status during the rainy season and S1 (92.13) and S5 (95.70) are categorized as good and excellent pollution status in the dry season, respectively. On the contrary, based on the PTI index result, S2 (7.0), S3 (9.0), S4 (9.0), and S5 (9.0) are classified under poor pollution status during the rainy season, whereas S2 (14.0), S3 (10.0), and S4 (15.0) are categorized under fair, and sampling sites S1 (21.0) and S5 (20.0) have good pollution status in the dry season. Based on study findings, the Kulfo River is contaminated by pollutant intrusion, resulting in a decrease in its quality. Therefore, management of solid, liquid, runoff, and domestic activities is critical in upstream tributaries and Arba Minch town to prevent river water quality deterioration.

Unveiling the impact of the COVID-19 pandemic on water quality: a case study on Ain-Silan and Ain-Karma sources

ABSTRACT The water quality may change over time due to a variety of physical, chemical, and biological conditions. The objectives of this study were to use statistical methods to compare the composition of cations and anions in water before and after the COVID-19 pandemic. The statistical method (ordinary least square regression) employed to assess the relationships between water quality parameters involved calculating the coefficient of determination (R2). Two sampling sites were selected for the study due to their importance to the local population: The Ain-Karma site represented a source in an urban environment located in Khenchela city, and the Ain-Silan site represented a source in a rural environment. Samples were collected frequently over 7 years (from 2015 to 2022). Parameters analyzed include electrical conductivity (EC), pH, sodium (Na), magnesium (Mg), calcium (Ca), ammonium (NH4), nitrate (NO3), chloride (Cl), sulfate (SO4), and nitrite (NO2). At Ain-Silan, the regression models show R² values of 0.8708 for Mg, 0.850 for SO4, and 0.7495 for NO3, highlighting their significant changes over time and importance as water quality predictors. At Ain-Karma, NO2, NH4, and Mg exhibit high R2 values of 0.8418, 0.7947, and 0.8347, respectively, underscoring their critical roles in water quality prediction.

Machine learning-driven intelligent water quality assessment for enhanced drinking safety and real-time consumer awareness

ABSTRACT As to the sphere of smart water management and managing water Internet of Things (IoT) systems, water condition safety for drinking is very important. The proposed methodology, known as the Smart Water Consumption Monitoring System (SWCMS), is based on the WaterNet dataset acquired from a standard data repository for training the selected machine learning (ML) models. For water quality parameters such as temperature, turbidity, pH, and some chemical concentrations, the system uses real-time sensors. At the testing phase, information received from the sensors is time-stamped, and with the utilization of applicable ML approaches, potential challenges; assessment of water quality is processed. This encompasses the employment of advanced instruments for the detection of water quality with concentration on pH and other chemical values through a detection accuracy rate of over 95% on any other signs of abnormalities. This processed information is further availed with the timestamps to the consumers' mobile phones through a user interface application for real-time awareness and timely response. With the aid of timely information about their drinking water, the SWCMS increases the water safety parameter by 90% and the overall consumer awareness by 92.5%, thereby creating an effective health parameter among the public.

UNRAVELLING SENSITIVE PHYSICOCHEMICAL DYNAMICS IN COMMERCIAL AQUACULTURE EARTHEN PONDS ACROSS SOUTHERN SARAWAK

Aquaculture activities in Sarawak is currently undergoing great expansion and advancement by the government to provide sustainable food source for the local people. With the changing environment, climate change and increases in nutrients this could disrupt the water quality for the fish production. Hence, this study was conducted to assess the water quality in commercialize aquaculture ponds in Southern Sarawak. This study was carried out between June 2023 to November 2023. The temperature, dissolved oxygen, turbidity, pH, conductivity, total dissolved solids, ammonia, phosphate, nitrate, and nitrite which are deemed as the most sensitive and pertinent for the fish production were evaluated. The YSI multiparameter was used to measure the physical variables and the nutrients variables were measured using the HACH spectrophotometer. The results obtained for the temperature (28±0.00-32±0.00 °C), dissolved oxygen (1.10±0.01-12.99±0.01 mg/L), pH (3.72±0.03-8.39±0.06 mg/L), conductivity (0.03±0.00- 215.10±0.35 μS/cm), total dissolved solids (10.45±0.01-10137.17±0.02 mg/L), turbidity (6.77±0.16-483.22±40.41 NTU), ammonia (0-3.5 mg/L), phosphate (0.06±0.03-4.52±0.04 mg/L), nitrate (0.01±0.00-3.5 mg/L) and nitrite (0.003±0.002-3.5 mg/L) denoted that the physicochemical parameters of water quality in the aquaculture ponds, if found within normal levels considered tolerable. Some of the physicochemical parameters examined this study did not fall within the recommended range for optimal fish production, suggesting that some physicochemical parameters in these aquaculture ponds does not provide conducive conditions for the fish survival and growth. Thus, vigilant monitoring and stricter management of water quality in aquaculture ponds is needed to protect the health of aquaculture industry as well as the public health that consume them.

Assessment of Water Quality in a Public Supply Source: Combined With the General Index of Human Contamination

Objective: The aim of this study was to evaluate the water quality of the Miringuava River watershed through monitoring of chemical water quality parameters, assess the presence of Emerging Contaminants (EC) such as hormones and caffeine, and determine the General Index of Human Contamination (GIHC). Theoretical Framework: Watersheds located near urbanized areas play a crucial role in water resource management, especially when used for public supply. Due to land use and occupation, the natural dynamics of rivers, and reurbanization processes, monitoring water quality within a watershed is necessary (Pereira et al., 2021). Consequently, the social impact and effects on aquatic environments must be considered (Sekamane et al., 2023). Method: Two collections were conducted (C1 with 7 points and C2 with 16 points) upstream of the water intake station of SANEPAR to obtain water samples from the Miringuava River watershed. The methodology was defined in four stages: Definition of sampling points, sending water samples to the LEAQUA Laboratory – UTFPR, analysis of the data processed by the laboratory, and construction of the GIHC graphs. Results and Discussion: The results obtained revealed the presence of Emerging Contaminants in some water bodies in the region. Considering the hormone Estradiol (E2), which is an emerging contaminant, Ide et al., (2017) found a maximum value of 1420 ng L-1 in the Iguaçu River basin. In comparison, the values found in the Miringuava River basin were 23.14 ng L-1, indicating that the level of estradiol is considered low. Research Implications: The practical and theoretical implications of this research indicate that there are already signs of minor contamination in the water bodies of the Miringuava River basin. Such implications demonstrate the need for monitoring in the region. Originality/Value: This study contributes to the understanding and confirmation of the presence of emerging contaminants in the Miringuava River basin, which will be used for public supply. By evaluating chemical parameters and the General Index of Human Contamination, it provides important information for water quality management in the region.

Machine learning analysis for the Hilla River water quality index- Iraq

<p>Since industrial and human activities have been developed in different ways in Iraq, water quality has been declining along ‎the Hilla River, the only water resource for drinking water in the Hilla City, Iraq. In this research, The Weighted Arithmetic Water Quality Index (WQI) along the river was analysis using linear regression machine learning algorithm. Water quality parameters including Turbidity ‎‎(Turb), Electric Conductivity (EC), Hydrogen Ions (pH), Total ‎Suspended Solid (TSS), Chloride ‎Ions (Cl), Sulfate Ions (SO4), Alkaline (ALK), Total ‎Hardness (TH), Calcium Ions (Ca), ‎Potassium Ions (k), Sodium Ions (Na), Magnesium ‎Ions (Mg), and Total Dissolved Solid (TDS) were utilized to determine WQI from January 2016 to June 2021 depending on datasets from five sampling stations located along the river at the main city. It was noticed that the river WQI ‎has a significant relationship with ‎Turb only (positive proportion). ‎ This relationship between WQI and Turbidity in ‎the river is limited to ‎a WQI value of 220, Thus, two ‎linear regression models were developed and validated: One for WQI values greater than ‎‎220 and another for the values less than 220.‎ In addition, ‎the results of this study showed that the Hilla River is severely polluted since WQI values are high. The ‎best WQI and turbidity value were in 2018. However, ‎in 2020 and 2021, ‎there were some improvement in WQI and turbidity compared to 2019.      </p>

Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images

Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still in the initial and exploratory stage, and the research results are relatively few. Especially for mid-to-high latitude lakes, the research is even more blank. Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. Remote sensing models were developed to estimate the biomass abundance of three major algal groups: Cyanophyta, Chlorophyta, and Bacillariophyta. This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). The results indicated that XGBoost exhibited the highest accuracy (R2 = 0.92, RMSE = 1.78%, MAPE = 9.96%) in estimating Cyanophyta’s biomass abundance. The RF model demonstrated the highest accuracy for estimating Chlorophyta’s biomass abundance (R2 = 0.72, RMSE = 6.57%, MAPE = 50.8%), while the GBDT model exhibited the highest accuracy for estimating Bacillariophyta’s biomass abundance (R2 = 0.9, RMSE = 4.66%, MAPE = 47.87%). The models were subsequently applied to all cloud-free OLCI images from Hulun Lake during the ice-free periods from 2016 to 2023, producing spatiotemporal distribution maps of the different phytoplankton biomass abundance. Cyanophyta dominated the biomass abundance (44.62 ± 3.47%), followed by Bacillariophyta (36.35 ± 2.68%), and Chlorophyta had the lowest proportion (10.42 ± 1.08%). Together, these three algae groups constituted 91.4 ± 1.55% of all phytoplankton in Hulun Lake. Significant annual variations in the biomass abundance of Cyanophyta and Bacillariophyta were observed, whereas those of Chlorophyta remained stable. Additionally, this study examined the effects of climatic factors and water quality parameters on the biomass abundance of algae. The findings suggest that temperature, wind speed, and atmospheric pressure are critical factors influencing the biomass abundance of the different algae groups. This study not only fills the gaps in the related field, but also provides a new method for monitoring algae, as well as a strong support for realizing the goals of sustainable management of water resources and ecological protection.

Assessing water quality restoration measures in Lake Pampulha (Brazil) through remote sensing imagery.

Urban reservoirs are frequently exposed to impacts from high population density, polluting activities, and the absence of environmental control measures and monitoring. In this study, we investigated the use of satellite imagery to assess restoration measures and support decision-making in a hypereutrophic urban reservoir. Since 2016, Lake Pampulha (Brazil) has undergone restoration measures, including the application of Phoslock®, to mitigate its poor water quality conditions. Satellite images from Landsat-8 (L8) and Sentinel-2 (S2) and historical monitoring data were used to estimate total suspended matter (TSM), chlorophyll-a (Chla), and Secchi disk depth (SDD). We explored both established models from existing literature and novel alternatives, the latter employing machine learning approaches. Model performance was assessed through the coefficient of determination (R ) during calibration and using the root mean square error (RMSE) and its normalization by the observed mean (nRMSE) during validation. Random forest regressor presented superior performance for the three water quality parameters and both satellites. The performance metrics for Landsat-8 were TSM (n=44) R of 0.62; Chla (n=48) R of 0.74; and SDD (n=23) R of 0.67. For Sentinel-2, TSM (n=53) R of 0.72; Chla (n=54) R of 0.64; SDD (n=33) R of 0.89. The selected models were then applied to cloud-free images from 2013 to 2022 to provide modeled TSM, Chla, and SDD values. These were used for spatiotemporal analysis, encompassing cluster analysis, sample comparisons, and trend analysis. Three distinct regions within Lake Pampulha were identified, with the upstream region displaying poorer conditions for TSM, Chla, and SDD. Restoration measures have contributed positively to improving water quality in Lake Pampulha; however, rainy periods pose a challenge due to water quality deterioration associated with urban runoff. Remote sensing has proven to be a robust tool for assisting managers in assessing recovery measures in urban lakes.

Dynamics of inland saline ground water and associated growth potential of common carp Cyprinus carpio for a sustainable aquaculture

Natural and anthropogenic causes have led to soil degradation, salinisation of inland areas, and groundwater. The degraded soils are unsuitable for primary agriculture, but can be utilised for saline aquaculture. ICAR-CIFE has initiated a selective breeding program for faster growth in common carp cultured at low salinity (6–8 ppt). The present study demonstrates the dynamic nature of physico-chemical parameters of Inland Saline Ground Water (ISGW) and the growth potential of selectively bred F1 generation of common carp using ISGW. About 1560 common carp fingerlings were stocked in two ponds, each representing a unique size class (> 100 g and < 100 g). "The water quality parameters, viz., temperature, salinity, pH, dissolved oxygen, total hardness, total alkalinity, calcium, and magnesium recorded during the culture period had mean values of 30.27 ℃, 6.73 ppt, 8.48, 5.02 mg L−1, 1544.83 mg L−1, 217.29 mg L−1, 177.22 mg L−1 and 332.33 mg L−1, respectively". The average body weight of common carp at stocking in Pond 1 and Pond 2 was 42.43 g and 180.86 g, respectively. The average standard length at stocking for Pond 1 and Pond 2 was 10.57 cm and 16.57 cm, respectively. The overall means for harvest body weight and standard length at 200 days of culture in summer months were 432.48 ± 4.14 g and 22.72 ± 3.04 cm, respectively. The effects of standard length, sex of fish, and sex by pond interaction were found to be significant on the harvest body weight. The Feed Conversion Ratio (FCR), Feed Conversion Efficiency (FCE), and Protein Efficiency Ratio (PER) obtained for Pond 1 and Pond 2 were 2.19, 45.8, 1.53; 2.25, 44.44, and 1.48, respectively. The daily weight gain for Pond 1 and Pond 2 was 1.58 g and 1.61 g, respectively. In conclusion, the common carp reared in an inland saline environment showed a similar growth pattern in both size classes as reported in freshwater culture. Thus, the present study recommends common carp as a potential candidate species for inland saline aquaculture in degraded soils and advocates its scientific propagation.

Comparative relationship between the water quality parameters and standard for drinking in Jibia Dam, Katsina State

A study was conducted on the water quality of Jibia Dam to evaluate its suitability for human consumption. The research aimed to compare the water quality parameters of the dam with international drinking water standards. Poor monitoring of water quality could result in pollution and pose significant health risks. Water samples were collected from three locations during the wet and dry seasons of 2019/2020, and 13 parameters were analyzed using standard laboratory methods. These parameters included temperature, pH, electrical conductivity, total dissolved solids, turbidity, total hardness, chloride, nitrate, dissolved oxygen, biological oxygen demand, ammonia, nickel, and lead. The study found that the mean values of the parameters were as follows: temperature (26.81°C), pH (6.99), electrical conductivity (116.95 µS/cm), total dissolved solids (96.52 mg/L), turbidity (30.60 NTU), total hardness (196.83 mg/L), chloride (96.15 mg/L), nitrate (13.41 mg/L), dissolved oxygen (62.04 mg/L), biological oxygen demand (45.93 mg/L), ammonia (11.93 mg/L), nickel (0.24 mg/L), and lead (0.13 mg/L). Among these, total hardness had the highest mean value (196.83 mg/L), while lead had the lowest (0.13 mg/L). The study also revealed that 46.15% of the parameters, including temperature, pH, dissolved oxygen, chloride, turbidity, and nitrate, met international drinking water standards. The findings highlight the need for further research on additional physicochemical parameters, particularly heavy metals, that were not included in this study. This will provide a more comprehensive assessment of the water quality in Jibia Dam.

Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir.

Danjiangkou Reservoir has been widely concerned as the water source of the world's longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC) were significantly higher in wet season than in dry season, while no significant differences were found for dissolved inorganic carbon (DIC) and dissolved silica (DSi). Spatially, the concentrations of dissolved carbon, DIC, TN, and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO2, seasonal factor, and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95%, and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge, and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin.

Effects of chronic exposure of specific water quality parameters in poultry drinking water on dietary amino acid digestibility and early broiler performance.

Poultry drinking water quality parameters influence physiological functions and bird health. This study aimed to explore the impacts of poultry drinking water pH, TDS (total dissolved solids), and elevated levels of certain minerals on the early phase of broiler performance. Four treatment groups were created based on water source and water quality types: i) municipal water source (MW); ii) well/underground water source with moderately higher levels of Ca, SO4-, and Mn (WW); iii) high-pH (>8) water (HPH); and iv) high-TDS water (>3000 ppm) (HTDS). All treatment water samples were profiled for mineral content. Treatment water was fed ad libitum throughout the experimental period. A total of 288 birds (Cobb 500 males), fed a standard diet (day(d) 0-21), were utilized for the study, creating six replicates/treatment with 12 birds/replicate cage unit. Birds were weighed on days 0, 7, 14, and 21. Water consumption, body weight gain (BWG), feed consumption, feed conversion ratio (FCR), water: feed, and amino acid digestibility were measured. Statistical analysis was performed using 1-way ANOVA with Tukey's HSD test for significance (P < 0.05). Water consumption was higher for MW and HTDS until d10, but there was no difference in overall water consumption by d21 (P > 0.05). The overall feed intake and BWG were lower for HTDS birds compared to other treatment groups (P < 0.05), however, there was no difference in FCR (P > 0.05). Meanwhile, the apparent ileal digestibility of dietary amino acids for essential and non-essential amino acids did not differ among treatment groups (P > 0.05) and ranged from 0.40 to 0.94 across all treatments. The overall results showed that high-TDS water above 3000 ppm with elevated calcium and chloride levels could negatively impact early bird performance on feed consumption and BWG.

Synergistic activation of peroxydisulfate by photothermal and FeS2-loaded air-laid cloth in a shallow continuous flow reactor.

Synergistic activation of persulfate by photothermal and solid catalysts is an effective method for degrading organic pollutants. However, the batch reaction mode not only fails to fully utilize photothermal, but also makes it hard to separate the solid catalysts from the reaction solution. In this work, a shallow continuous flow way of effluent was set up for synergistic activation of peroxydisulfate (PDS) by photothermal and FeS2-loaded air-laid cloth (FeS2-ALC). The degradation efficiency of carbamazepine (CBZ) was significantly enhanced with a pseudo-first-order kinetic constant of 0.0359 min-1, which is 180, 40 and 10 times higher than that of the photothermal/ALC, photothermal/FeS2-ALC and photothermal/PDS processes, respectively. The enhanced efficiency was partly due to the absorption of sunlight by FeS2-ALC to heat up the reaction solution which in turn can use photothermal to activate PDS, and partly due to the activation of the PDS by Fe2+ from FeS2-ALC and the promotion of Fe3+/Fe2+ cycling via S2-. Parameters such as reaction conditions, hydraulic conditions and water quality parameters affecting degradation efficiency were investigated. A possible degradation pathway was proposed and the toxicities of byproducts were tested. Finally, cycling experiments and outdoor experiments demonstrated that the photothermal/FeS2-ALC/PDS process has potential for practical wastewater treatment.