Water Quality Parameters Research Articles

Delineation and mapping of groundwater quality in Salem district: A comprehensive assessment of contaminant sources

A comprehensive study was conducted at block level in Salem District, Tamil Nadu, India, to evaluate the major ionic chemistry and to assess the suitability of ground water for irrigation purpose. A total of 200 groundwater samples were systematically collected through grid survey and analyzed for major ionic constituents and irrigation water quality parameters. The resulting water quality parameters were interpreted based on established hydrochemical guidelines. The classification of groundwater samples under various quality categories based on pH, electrical conductivity (EC), residual sodium carbonate (RSC) and sodium adsorption ratio (SAR) indicates that many blocks (70 %) in the study area exhibit good-quality groundwater suitable for irrigation. However, exceptions (30 %) were observed in the blocks of Omalur, Mecheri, Kolathur and Pethanaickenpalayam having been observed with saline and alkali water quality. This might be due to the underlying metamorphic and crystalline rocks carrying the element calcium and sodium rich minerals are responsible for the development of saline/alkali groundwater. It was observed that mushrooming of industrial complexes in these blocks might also be the reasons for the development of saline or alkali groundwater. The need for this work is to evaluate groundwater quality for irrigation in Salem District and identify areas affected by saline and alkali contamination due to natural and industrial influences.

Diversity and distribution of aquatic insects and their relation to water quality parameters at the selected stations of the Chalakudy River, Kerala, India

Diversity and distribution of aquatic insects and their relation to water quality parameters at the selected stations of the Chalakudy River, Kerala, India

Effects of water level changes on the hydrological connectivity and water quality of a lake-type wetland

IntroductionUnderstanding the effects of water level fluctuations on wetland ecosystems is crucial for water resource management and ecological conservation. While there have been some studies on this topic, comprehensive analyses of how water level changes affect the hydrological connectivity and water quality of complex lake-type wetlands remain relatively limited. This work aims to explore the effects of water level changes on the connectivity and water quality of lake-type water bodies in the Huixian Karst Wetland, Guilin.MethodsWater quality data for Mudong Lake from January 2021 to December 2022 were obtained by collecting monthly water samples and conducting laboratory tests for six common water quality parameters. Through a combination of remote sensing image data, drone image data and actual measured data on the Mudong Lake water level, the comprehensive ecological-hydrological connectivity index and comprehensive water quality index were used to evaluate the water level changes in Mudong Lake, water body connectivity, and water quality. The correlations between them were then analysed.ResultsThe results show that as the water level increases, the degree of landscape fragmentation decreases, and the water connectivity index increases by 262%. In addition, as the water level increases, the comprehensive water quality pollution index shows an overall downward trend.DiscussionHowever, the factors affecting lake water quality are complex and changeable. Future research could consider increasing the monitoring frequency during critical periods of rapid hydrological change, such as rainfall-runoff events, to capture more precise and detailed hydrological and water quality data. This study provides an important example of the effects of changes in water level and wetland connectivity on the water quality of a lake ecosystem.

GIS based ground water assessment of Nilakkottai Taluk, Tamil Nadu, India: hydrogeochemistry and statistical perspective

Water quality is imperative for drinking and agriculture purposes in order to meet the increasing requirements for water. The systematic assessment of groundwater quality in Nilakkottai Taluk, Dindigul District, Tamil Nadu, was performed. In order to ascertain the quality of the study area’s groundwater, various water quality indices, spatial distribution maps, multivariate statistical analysis, and hydrofacies diagrams have been contemplated. 40 samples were collected and analysed for 20 water quality parameters, using the standard techniques. The quality results of the irrigation analysis showed that the groundwater samples were satisfactory for agricultural use. The deduction of four principal components denotes that hydrogeochemical processes and anthropogenic inputs were the main controlling factors. The durov plot demonstrated the dominance of Ca-HCO3 type groundwater, indicating a weathering process through fresh water recharge. This study insisted that majority of the samples satisfactory for crop yield and need to be protected from further contamination.

Effect of the use of ovaprim® on the spawning of kissing gourami (Helostoma temminckii) at UPR Doa Mandeh, Ogan Ilir

The kissing gourami (Helostoma temminckii) is a freshwater species increasingly cultivated in controlled aquaculture systems. However, its production is hindered by the limited availability of broodstock and fry, which still heavily depend on natural catches that fluctuate seasonally. To address this challenge, controlled environment aquaculture offers a sustainable solution to boost the kissing gourami population. One effective method is semi-natural spawning through gonadotropin hormone induction using Ovaprim®, which promotes year-round reproductive readiness in broodstock. This study aims to apply and evaluate the semi-natural spawning technique of kissing gourami using Ovaprim® at UPR Doa Mandeh, Ogan Ilir. Conducted between August and September 2024, the research compared two treatments: natural spawning (P0) and semi-natural spawning (P1). The findings demonstrated promising results, with a fecundity of 9,619 eggs, a fertilization rate of 90.4%, a hatching rate of 94%, and a survival rate of 95%. Water quality parameters remained within optimal ranges, with broodstock pond temperatures between 30–30.6°C and pH levels of 7.27–7.53, while the rearing aquarium maintained a temperature of 31°C and a pH of 7.62–7.66. These results highlight the potential of semi-natural spawning using Ovaprim® as a viable approach for enhancing kissing gourami reproduction, reducing dependency on wild populations, and supporting sustainable aquaculture practices.

Aerobic Stress Detection in Aquatic Environments with Water Quality Data Using Hybrid Deep Learning Based ConvRec Model

Depletion of dissolved oxygen in the water is a serious threat to fish and other aquatic organisms, it causes aerobic stress disease in fish. Detection of aerobic stress is crucial to maintain better growth and spawning in the fishes. Recently many studies proposed deep learning-based water quality analysis techniques, but these techniques inadequate in handling the complex water quality data. Because water quality has both spatial and temporal characteristics, this makes most of the deep learning models inadequate. To handle such complex and multifaceted data we proposed ConvRec, a deep learning architecture that incorporates CNN (Convolution neural network) and LSTM (Long-short term network) structures. CNN component extracts feature in the spatial domain from the water quality data from different locations while LSTM captures temporal features hence the model can learn both spatial and temporal correlations between the movement of water quality parameters to classify the aerobic stress in aqua ponds. In this work we use the two dataset both are unlabelled collected using IoT (Internet of things) devices. To handle this data using ConvRec model, usus the fine-grained annotation of data points that have the effect of empowering the model to detect relevant traits associated with oxygen stress in fish. It can be therefore ascertained that ConvRec yields high degrees of accuracy of 99.2% and 99.65%, on the “ponds” and “waterx” datasets respectively while the past models can only 98.2% and 98.1% respectively on the same datasets. These results demonstrate that ConvRec is not only promising for estimating the health of fish during oxygen deficiency but also it can take part in reducing the negative impact of low oxygen levels in the water on fish.

Development of spatial trends of physicochemical parameters and microbial load in drinking water to assess the risk zone in District Kasur

ABSTRACT Water resources contamination is damaging human health in most of the industrial cities in Pakistan. The present study was planned to assess the risk zone in District Kasur. Drinking water quality in rural and urban areas of four tehsils of Kasur was found to be poor for human health when analyzed for physicochemical parameters and microbial load (E. coli, Klebsiella and Shigella species), in comparison to World Health Organization (WHO) permissible limits. Extreme ranges in water quality parameters were observed, with biochemical oxygen demand (BOD) levels between 1.6 ppm to 3 ppm exceeding WHO limits, the highest chloride ion content was in Tehsil Chunian, total dissolved solids (TDS) reaching 2520 ppm, and pH values ranging from 7.5 to 8.9. E. coli and Shigella were observed in water samples but no Klebsiella species were observed. The study area’s drinking water quality is poor, with high physicochemical parameters and pathogens like E. coli and Shigella, posing a direct risk to human health.

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.

Seasonal and Inter‐Annual Dynamics in Water Quality and Stream Metabolism in a Beaver‐Impacted Drought‐Sensitive Lowland Catchment

ABSTRACTIncreasing drought frequency and severity from climate change are causing streamflow to become increasingly intermittent in many areas. This has implications for the spatio‐temporal characteristics of water quality regimes which need to be understood in terms of risks to the provision of clean water for public supplies and instream habitats. Recent advances in sensor technology allow reliable and accurate high‐resolution monitoring of a growing number of water quality parameters. Here, we continuously monitored a suite of water quality parameters over 3 years in an intermittent stream network in the eutrophic, lowland Demnitzer Millcreek catchment, Germany. We focused on the effects of wetland systems impacted by beaver dams on the diurnal, seasonal and inter‐annual variation in water quality dynamics at two sites, upstream and downstream of these wetlands. We then used the data to model stream metabolism. Dissolved oxygen and pH were higher upstream of the wetlands, while conductivity, turbidity, chlorophyll a and phosphorous concentrations were higher downstream. We found clear diurnal cycling of dissolved oxygen and pH at both sites. These dynamics were correlated with seasonal hydroclimatic changes and stream metabolism, becoming increasingly pronounced as temperatures increased and flows decreased in spring and summer. Upstream of the wetlands this corresponded to the stream rapidly becoming increasingly heterotrophic as modelled Gross Primary Production (GPP) was exceeded by Ecosystem Respiration (ER). Downstream, where GPP was lower, the stream was usually strongly heterotrophic and prone to increasingly hypoxic conditions (i.e., insufficient oxygen) before streamflow ceased in summer. This coincided with lower velocities and deeper channels in beaver impacted areas. Seasonal and inter‐annual variations in water quality were found to mainly correlate with hydroclimatic factors (particularly temperature) and their influence on streamflow. This study highlights that heterotrophy and hypoxia in lowland rivers in central Europe is an important seasonal feature of intermittent streams where agricultural landscapes continue leaching nutrients. These insights contribute to an evidence base for understanding how climate change will affect the quantity and quality of rural water resources in intermittent lowland streams with wetlands where the presence of beavers requires management responses.

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.

Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022). The parameters include water temperature, pH, specific conductivity (Sp_Con), dissolved oxygen (DO), nitrate-N, total phosphate (TP), turbidity, biochemical oxygen demand (BOD), silicate, total dissolved solids (TDS), and rainfall. The present study evaluated the predictive performance of five models-Multiple Polynomial Regression (MPR), Generalized Additive Models (GAMs), Decision Tree Regression, Random Forest (RF), and XGBoost (Extreme Gradient Boosting)-using RMSE, MAE, MAPE, NSE and R2 metrics. XGBoost emerged as the top performer, with an RMSE of 0.024, MAE of 0.018, MAPE of 51.805, NSE of 0.855 and R2 of 0.85, explaining 85% of the variance in nitrate-N concentrations. Random Forest also demonstrated strong predictive capability, with an RMSE of 0.028, MAE of 0.021, MAPE of 57.272, NSE of 0.804 and R2 of 0.80. MPR effectively modelled non-linear relationships, explaining 75% of the variance, while Decision Tree Regression and GAMs were less effective, with R2 values of 0.60 and 0.48, respectively. Variables (BOD, pH, Rainfall, water temperature, and total phosphate) were the best predictors of nitrate-N dynamics. Comparative analysis with previous studies confirmed the robustness of XGBoost and Random Forest in environmental data modelling. The findings highlight the importance of advanced machine learning models in accurately predicting water quality parameters and facilitating proactive management strategies.

Long-term monitoring of constructed wetlands in distilleries in Scotland - Evaluating treatment performance and seasonal microbial dynamics.

Constructed wetlands (CWs) have been used to treat industrial wastewater in Scotland, including within the whisky industry to treat the second distillation byproduct known as "spent lees". However, despite the notable advantages of CWs (including simplicity, cost-effectiveness, and energy efficiency) their adoption by distilleries still remains relatively limited. Concerns regarding unstable performance, clogging, and sensitivity to cold temperatures have tended to limit their widespread utilisation. This paper considers these concerns through a comprehensive analysis of water treatment performance and microbial community variation across different seasons in three horizontal subsurface flow constructed wetlands (HSF CWs) situated within several whisky distilleries in Scotland. One of these wetlands operates year-round and the other two are generally only active in autumn and spring. Across these CWs, consistent success was observed in the removal of dissolved copper (dissCu) throughout the seasons. However, significant differences in performance were observed both within the same CW (across different seasons) and between different CWs (during the same season) for various water quality parameters (including suspended solids, turbidity, dissolved organic carbon, chemical oxygen demand, nutrients, and pH). Microbial diversity within each CW showed minimal variation between seasons at the class and order levels. However, substantial differences in bacterial community were detected between different CWs during the same season. Gammaproteobacteria, Alphaproteobacteria, and Bacteroidia consistently ranked among the top 5 most abundant classes in each CW, though their specific abundance rankings varied. At the bacterial class level, redundancy analysis revealed strong correlations between treatment performance metrics and specific taxa, including Planctomycetacia with TON removal, Acidobacteriia with COD and DOC removal and pH buffering, Bacilli with SRP removal, and Alphaproteobacteria with NH₄⁺ removal. At the fungal class level, Rozellomycota_cls_Incertae_sedis demonstrated significant associations with multi-pollutant removal and pH buffering, highlighting its ecological role in treatment processes. This research provides insights regarding the seasonal dynamics of CWs, offering recommendations that could be used to optimise their application within the whisky industry and beyond.

Analysis of water quality and cadmium content around Awarange beach, Barru Regency, Indonesia in relation cultivation of milkfish (Chanos chanos Forskal)

Objective: The study aims to determine the condition of the water quality parameters and cadmium content around Awarange Beach, Barru Regency, Indonesia, in relation cultivation of milkfish (Chanos chanos Forskal). Material and method: This research was conducted from July to August 2024 at Awarange Beach, Barru Regency, Indonesia. The research location is divided into 3 stations, namely Station 1 (ST.1), namely the beach near the La Sonrai recreation area/mangrove forest; Station 2 (ST.2), namely around Awarange Port, Barru Regency; and Station 3 (ST.3), namely around the Awarange Shipyard. The parameters measured were temperature, dissolved oxygen, pH, salinity, and cadmium content of seawater. The research data obtained were then processed and analyzed using descriptive analysis. Results: Based on the research results, it shows that the water quality at the research location, consisting of temperature, dissolved oxygen, pH, salinity, and cadmium content, only salinity has a fairly high value. Kualitas perairan yang lain masih sesuai dengan kebutuhan hidup dan pertumbuhan ikan bandeng, termasuk kandungan cadmium dengan nilai <0.003 ppm. Conclusion:Overall, the water quality and cadmium heavy metal content at the research location are still suitable and feasible for the development of milkfish (Chanos chanos Forskal) cultivation.

System- and fish-size-specific reference intervals for plasma analytes in Atlantic salmon (Salmo salar L.) reared in commercial fresh- and saltwater recirculating aquaculture systems: a descriptive study

IntroductionIn aquaculture, clinical chemistry can be used pre-emptively to manage fish health and for early disease intervention. While less established in farmed fish compared to farmed terrestrial animals, it has been used for salmonid cage farming but is almost non-existent in recirculation aquaculture systems (RAS) for Atlantic salmon (Salmo salar L.). In this descriptive study, the level and distribution of selected biochemical plasma analytes from different weight classes of Atlantic salmon reared in commercial RAS facilities maintained in either freshwater or saltwater were determined.MethodsFacility 1 focused on the production of fry to smolt at a salinity of 0.5-2 ppt, and Facility 2 produced fish from smolt to market size. Four fish groups (FG) were included: FG 1-2 in Facility 1 and FG 3-4 in Facility 2. Fish in Facility 1 were sampled monthly, while fish in Facility 2 were sampled at varying intervals (between 4 and 19 weeks) to determine selected biochemical analytes. The study spanned 100-250 days, depending on the fish group. Biochemical analyses included sodium, chloride, potassium, osmolality, calcium, magnesium, total protein, albumin, and globulin. In addition, water quality parameters measured included temperature, salinity, NH4-N, NO2, NO3, color, turbidity, alkalinity, CO2, H2S, and gas pressure.ResultsResults indicated that selected blood analytes of Atlantic salmon were influenced by the production environment (freshwater or saltwater). In addition, there was an effect of fish weight, but variable for the different analytes. Water quality parameters varied with time and system; however, were within acceptable ranges in both production environments. No clear impact biochemical values were found from variations in water quality parameters.DiscussionThese results underscore the need for future research into system/environment-specific reference intervals and a better understanding of how fish weight impacts clinical chemical parameters. Factors that encompass water quality, management practices, fish strain/batch (i.e., stress tolerance), and the physiological status of the fish need to be considered and can influence obtained reference intervals. A threshold for acceptable prevalence and degree of exterior deviations should be defined. Reference values should then be compared to analyte levels in fish with known diseases to assess the diagnostic value of blood sampling.

Evaluation of spatio-temporal water quality status of Jeera river, Odisha, India

Jeera River of Bargarh District, Odisha faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The river was formerly a flourishing tributary of the massive Mahanadi River that possessed excellent navigability, an array of aquatic ecosystems, and a well-established basin with an expanding agricultural sector. The current condition of the Jeera River is deplorable, leaving behind only minimal economic and ecological values. The study emphasizes analyzing the seasonal variation of the water quality rating of Jeera River in terms of the Water Quality Index (WQI). WAWQI (Weighed Arithmetic Water Quality Index) values show that almost all sampling sites have poor or unsuitable quality. During the monsoon season, the water quality deteriorated the most, with an average WQI score of 516.430 compared to pre- and post-monsoon with average WQI values of 154.558 and 276.014 respectively. CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) values indicate that water quality ranges from marginal, and poor to fair. This study concludes that out of the eight sampling sites, station 5 (Dumerpali) is observed to be the most polluted site. Many water quality parameters including iron, turbidity, nitrate, phosphate, E. coli, and Total coliform are found to exceed the permissible limits prescribed by WHO and BIS. Reducing sewage outflow, blocking direct stormwater discharge, and avoiding continuous solid garbage disposal by neighbouring populations are ways to improve river water quality.Graphical abstract

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.