Operation Of Treatment Plants Research Articles

Evaluation of the Performance of Wastewater Treatment Plant at Steel Mill Karachi, Pakistan

The purpose of this study is to evaluate how well Steel Mill Karachi's wastewater treatment system works. Wastewater treatment facilities employ the activated sludge technique. Several influent and effluent locations allowed for the collection of influent and effluent samples. A number of physicochemical parameters, such as total dissolved solids (TDS), biochemical oxygen demand (BOD5), total suspended solids (TSS), chemical oxygen demand (COD), electric conductivity (EC), pH, and other metals, such as potassium (K), iron (Fe), sulphate (SO4-2), calcium (Ca), sodium (Na), zinc (Zn), and magnesium (Mg), were measured in order to evaluate the effectiveness of wastewater treatment plants. In a lab environment, the sample's analysis findings were compared to Sindh Environmental Quality Standards (SEQS) for Pakistan. EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, TDS, TSS, EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, Fe, and Zn were removed to different degrees: 42.93%, 60.33%, 43.63%, 51.53%, 40.5%, 35.56%, 26.33%, 22.12%, 34.09%, 20.75%, 55.87%, 22.12%, and 76.10%. Only two metrics, the COD and Zn concentrations, were over the SEQS limits, keeping other effluent results parameters within the limits. Additionally, it is advised that proper maintenance be carried out to increase the effectiveness of wastewater treatment plants.

EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Modeling the effects of sidestreams recycling on wastewater treatment plant performance operated by anaerobic-anoxic-oxic (A2/O) processes using GPS-X8 simulation

The water separated by sludge treatment processes is known as the sidestreams, such as the centrate, underflow belt thickener, and supernatant gravity thickener. The sidestreams have significant amounts of contaminants, particularly nutrients, and are returned to the wastewater treatment plant head. Returning the sidestreams to the head of the plant causes both technical and financial problems. This paper models the effect of sidestreams on the treatment of mainstream in full-scale conventional activated sludge systems operated with anaerobic/anoxic/oxic (A2/O). The effects of sidestreams nutrients and inert materials, the pollutants fate in the A2/O system, oxygen consumed, and energy required by sidestreams are modeled using GPS-X8 simulation. The results showed that the discharge ratio of the sidestreams to the mainstream is 4 %. The proportion of the pollutants present in the sidestream to the mainstream were 4.3 %, 4.7 %, 8.8 %, 21 %, 40 %, and 11 % for BOD, COD, TSS, NH4, PO4, and H2S, respectively. The GPS-X model showed that high orthophosphate and hydrogen sulfide concentrations were seen in the anaerobic zone. While all other pollutants begin to decay, phosphates and hydrogen sulfide gas also begin to decrease gradually in anoxic and oxic zones, noting the high concentrations of nitrates and sulfates in the oxic zone. An added 10 % of oxygen is needed for the sidestreams ammonia oxidation, which is more than the amount needed for the oxidation of BOD. The percentage of alkali used to improve the nitrification process due to the ammonia in the sidestreams was 30 % of the specified amount of ammonia in the mainstream. This study proved that the plant's sidestreams adversely increased oxygen, energy, and alkali consumption.

Life Cycle Assessment Methodology Applied to a Wastewater Treatment Plant

Wastewater treatment plants are highly energy-intensive systems. This research uses Life Cycle Assessment (LCA) to determine the impacts generated during the operation of a wastewater treatment plant. Three different scenarios are analyzed: a baseline scenario that considers a conventional activated sludge treatment technology exploiting data from an existing plant located in central Italy, a second scenario that involves the implementation of MBR technology applied to the baseline scenario, and finally a third scenario that consists of the addition of an anaerobic digester that allows energy recovery from biogas production, followed by a photovoltaic plant capable of supplying the plant energy demand. Global warming potential, eutrophication, and acidification are the environmental categories considered most relevant to emissions. The results showed that the effluent had the highest impact in terms of CO2 equivalent in all three situations due to the presence of N2O. Since emissions from biological processes, transportation, and wastewater are almost similar in all three scenarios, it is preferable to focus on the environmental impacts associated with energy consumption. The third scenario involves careful resource management and the use of treatment technologies that allow for a reduction in the use of nonrenewable energy sources in favor of renewable ones.

The potential for water recovery from urban waste water - The perspective of urban waste water treatment plant operators in Poland

Water recovery from waste water has become an essential element of the circular economy in the Baltic Sea region. However, there is little data on the possibility of using water recovered from urban waste water. A survey was conducted to learn the opinions of Poland waste water treatment plant operators. They were asked whether they recovered water for internal or external needs. Respondents indicated opportunities and barriers in this activity. The opinions of 107 operators show that work is underway on closing internal circuits in urban WWTPs. These solutions are technically relatively easy to implement and show measurable benefits (i.e., saving drinking water). However, water recovery for external purposes is rare and is at a very early stage. Despite this, the potential is significant, although many financial, organizational, technical, and mental barriers exist. The most critical challenge is the safe use of reclaimed water and the cost-effectiveness of the solutions. The survey also shows a need for education and involvement of the public.

Detection and characterisation of microplastics in tap water from Gauteng, South Africa

This study reports the presence, concentration, and characteristics of microplastics (MPs) in tap water in three suburbs in Gauteng Province in South Africa. Physical characterisation was conducted using stereomicroscopy and scanning electron microscopy following staining of MPs with the Rose Bengal dye. The concentrations of MPs in all samples ranged from 4.7 to 31 particles/L, with a mean of 14 ± 5.6 particles/L. Small-sized (<1 mm) and fibrous-shaped MPs were most abundant in all samples. Fibers accounted for 83.1% of MPs in samples from all the three areas, followed by fragments (12.4%), pellets/beads (3.1%), and films (1.5%), with a minor variation in the distribution of shapes and sizes in samples from each area. Raman microspectroscopy was used for chemical analysis, and five polymers were identified, namely: high-density polyethylene, polyurethane, polyethylene terephthalate, poly(hexamethylene terephtalamide), and poly(acrylamide-co-acrylic acid). C.I Pigment Red 1, C.I. Solvent Yellow 4, Potassium indigotetrasulphonate, and C.I Pigment Black 7 were the colourants detected. These colourants are carcinogenic and mutagenic and are potentially toxic to humans. The prevalence of MPs in tap water implies their inadequate removal during water treatment. For instance, the presence of poly(AM-co-AA) suggests that drinking water treatment plants may be a potential source of MPs in tap water. Other polymers, e.g., high-density polyethylene may be released from pipes during the transportation of drinking water. The estimated daily consumption of MPs from tap water was 1.2, 0.71, and 0.50 particles/kg.day for children, men, and women, respectively. The findings of this study provide evidence of the presence of MPs in drinking water in South Africa, thus giving some insights into the performance of treatment plants in removing these contaminants and a benchmark for the formulation of standard limits for the amount of MPs in drinking water.

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Factors affecting the performance of a pharmaceutical wastewater treatment plant: Characterization of effluent and environmental risk

Pharmaceutical industries produce a huge volume of emerging pollutants (EPs) that pose a threat to the aqueous environment. Biological processes have shown their inefficacy in treating many pharmaceutical products. The study assessed physicochemical parameters, EPs, heavy metals in pharmaceutical industrial wastewater, and the removal efficiency (RE) of an aerobic biological treatment plant. The study also assessed the contamination levels and risk using several indices, such as the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and risk quotients index (RQs). The study found that the treated water quality was poor, having antibiotics, nonsteroidal anti-inflammatory drugs, and others, along with several transformation products (TPs) and heavy metals, which were unsafe for consumption with high environmental risk. The analysis results showed that the RE for TSS, BOD5, COD, TDS, and EC were found to be 91.80%, 86.81%, 72.29%, 72.20%, and 65.60%, respectively, where the values of BOD5, COD, NO3−, and PO43− in the effluent were still higher than the permissible limits of the ECR (2023). However, the RE for heavy metals was in the order of Cu (84.62%) > Fe (65.04%) > Mn (63.3%) > Zn (60.58%) > Cd (53.85%) > Ni (54.12%) > Pb (42.42%) > Cr (38%), where Cr and Cd concentrations were still higher than the permissible limit of DoE (2019). The Pearson correlation and PCA suggested that EC, TDS, TSS, DO, BOD5, and COD were the most correlating and contributing variables. This study argued that metal-ligand behaviors mainly affect the removal efficiency of the treatment plant by lowering the removal rate of heavy metals and pharmaceutical products.

Simulation and Modeling of Water Treatment Plant with GPS-X Software for Health and Water Quality

Background: Waterborne diseases are infectious in nature, significantly impacting global health related to water quality and disease prevalence globally. In this study, GPS-X was used to design and simulate water treatment plants to determine the effectiveness of different mechanisms in water treatment. Method: The GPS-X water treatment software was utilized to evaluate raw and treated water performance at the Al-Kadhmaya Water Treatment Plant. Three scenarios were designed to assess the impact of varying flow rates and water parameters such as pH, alkalinity, turbidity, total dissolved solids (TDS), and total suspended solids (TSS) on the plant's performance. Results: The evaluation of the operational performance of the water treatment plant revealed that the concentrations of all contaminants in the effluent water consistently met the established Iraqi and WHO standards across all scenarios. This indicated the effectiveness of the plant's treatment processes in producing safe drinking water. The findings of this study held significance for water authorities, decision-makers in Iraq, and national and international environmental agencies, providing valuable information for improving water treatment practices and ensuring compliance with water quality standards. Conclusion: The study demonstrated the effectiveness of the GPS-X water treatment software in assessing the performance of the Al-Kadhmaya Water Treatment Plant. The results highlighted the plant's ability to consistently meet water quality standards, underscoring the importance of robust water treatment processes. These findings contributed to the body of knowledge regarding water treatment plant design and operation, offering insights that can inform decision-making processes to enhance water quality and ensure public health.

Should wastewater treatment plants' operational mode radically change to minimize GHG emissions?

The operation of municipal wastewater treatment plants (WWTPs) invariably results in significant emission of greenhouse gases (i.e., CH4, N2O, and CO2) into the atmosphere. We propose to consider a radical change in the way municipal WWTPs are operated, with the aim of minimizing GHG emissions while recycling most of the nutrient mass. The means to this end are to reduce the WWTP energy demand while maximizing the recovery of resources (phosphorus, ammonia, methane). The suggested concept involves operating the activated sludge process at a low sludge retention time (SRT < 2 d), i.e., under conditions that maximize the heterotrophic mass yield and eliminate nitrification. The ammonia concentration that remains in the water (considering N in the excess sludge and struvite production in the sludge-dewatering supernatant line) would be separated from the WWTP effluents using a unique ion-exchange material (ZnHCF), which would be regenerated using a low-volume 4 M NaCl solution. The ammonia would be then stripped at high pH and re-adsorbed by an acidic solution for reuse as fertilizer. The high bacterial yield and lack of nitrification in the aerobic step are expected to boost methane yield 3–4-fold, induce lower oxygen consumption, and most importantly, yield much lower N2O release. An approximate energy mass balance shows the concept to merit further consideration, owing to the potential significant reduction in N2O(g) emissions and recovery of resources. Empirical work followed by LCA is required to corroborate the hypothesis presented herein.

The use of energy management ISO 50001 to increase the effectiveness of water treatment plants: An application study on the Zai water treatment plant

This study sought to determine the impact of implementing the energy management system ISO 50001 on the Zai Water Treatment Plant's energy efficiency performance and demonstrate how this implementation affected the cost and rate of energy consumption. The proposed study model contained three dependent variables—energy consumption, energy efficiency, and the cost of energy consumption. It also contained an independent variable—the energy management system ISO 50001. All these variables were used to develop various questions to help accomplish the study's goals. Planning was done by selecting pumping stations, selecting the most energy-consuming type of pump, and finally, choosing a pump maintenance project to improve energy performance. The researcher used the case of the Zai water pumping station as an example where the ISO 50001 energy management system was applied along with the stages of the Deming Cycle of management. Four pumping units from the Zai water pumping station served as the research sample for the study.•Find the impact of implementing the ISO 50001 energy management system on the energy efficiency performance of the Zai water treatment plant.•The effects of implementing ISO 50001 energy management system on cost and energy consumption at the Zai water treatment plant.•What effect does the ISO 50001 energy management system affect the Zai Water Treatment Plant's energy efficiency?After applying the ISO 50001 energy management system, several conclusions were drawn. Energy costs and consumption rates in the pumping units dropped while the energy efficiency in the chosen pumping units increased.

Evaluating the environmental and economic performance of biological and advanced biological wastewater treatment plants by life cycle assessment and life cycle costing.

The primary objective of this study is to assess and establish benchmarks for environmental and economic sustainability of biological and advanced biological wastewater treatment plants (WWTPs) with different treatment technologies and characteristics. Furthermore, the study aims to determine the beneficial role of WWTPs to reduction of eutrophication potential. Environmental and economic sustainability of ten municipal WWTPs was assessed using life cycle assessment (LCA) and life cycle costing (LCC). In the first section of the study, LCA was performed to determine the environmental performance of the WWTPs. Furthermore, net environmental benefit (NEB) approach was implemented to reveal the beneficial role of WWTPs to eutrophication potential. In the subsequent section, LCA-based LCC was conducted by integrating the results of LCA. The most significant environmental impact was determined as marine aquatic ecotoxicity, which is highly affected from the generation and transmission of electricity consumed in the WWTPs. Wastewater recovery and co-incineration of sewage sludge in cement kiln ensure significant environmental savings on ozone layer depletion, human toxicity, acidification, photochemical oxidation, and abiotic depletion (fossil fuel) potential. Considering NEB approach, the highest NEB values were found for the WWTPs with the higher organic load and nutrient concentration in the influent. The results of LCC in WWTPs varied between 0.21 and 0.53 €/m3. External (environmental) costs were evaluated higher than internal (operational) costs for all selected WWTPs. While eutrophication was the highest among environmental costs, electricity cost was the highest among operational costs for almost all WWTPs.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Is Africa Ready to Use Phycoremediation to Treat Domestic Wastewater as an Alternative Natural Base Solution? A Case Study

This review outlines the potential of phycoremediation as a natural, cost-effective solution for domestic wastewater treatment in Africa, particularly focusing on its application in less densely populated and rural areas. The urgency of improving sanitation access, a key objective in both the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030), is underscored by the fact that half of Africa’s population suffers from diseases linked to inadequate water and sanitation facilities. South Africa, a focal point of this study, faces significant challenges in wastewater management. These include the limited capacity of wastewater treatment plants to handle the burgeoning wastewater volumes due to population growth, unregulated discharges causing fluctuating pollution levels, and high operational costs leading to improper sludge disposal and odor issues. Compounding these problems are frequent power outages, financial constraints impacting wastewater treatment plant operations and maintenance across Africa, and a lack of skilled personnel to manage these facilities.

Unraveling the characteristics of dissolved organic matter removed by aluminum species based on FT-ICR MS analysis

Given the complexity of dissolved organic matter (DOM) and its interactions with coagulant chemicals, the mechanisms of DOM removal by aluminum (Al) coagulants remains a significant unknown. In this study, six test waters containing DOM with molecular weight (MW, <1 kDa, 1–10 kDa and >10 kDa) and hydrophobicity (hydrophilic, transphilic and hydrophobic) were prepared and coagulated with Al0, Al13 and Al30. The molecular-level characteristics of DOM molecules that were removed or resistant to removal by Al species were analyzed using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The results showed that at the molecular level, saturated and reduced tannins and lignin-like compounds containing abundant carboxyl groups exhibited higher coagulation efficiency. Unsaturated and oxidized lipids, protein-like, and carbohydrates compounds were relatively resistant to Al coagulation due to their higher polarity and lower content of carboxyl groups. Al13 removed molecules across a wider range of molecular weights than Al0 and Al30, thus the DOC removal efficiency of Al13 was the highest. This study furthers the understanding of interactions between Al species and DOM, and provides scientific insights on the operation of water treatment plants to improve control of DOM.

Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model

Abstract As Karbala City is a religious tourism destination, millions of tourists visit the city annually, so there is a high fluctuation of flow in the wastewater between the plants during the year. The aim of this study is to evaluate the performance of the wastewater treatment plant (WWTP) in Karbala, Iraq, in removing pollutants for different flow rates in five scenarios using the GPS-X model. The most important phase in modeling, which greatly impacts simulation accuracy, is characterizing the influent composition to meet the mass balance. As a result, the influent wastewater was initially described and thoroughly examined. The model has been calibrated, followed by the collected data’s validation. The sensitivity of different stoichiometric and kinetic factors in the GPS-X was examined and screened to calibrate the model. To demonstrate the consistency between the simulated and measured data, the route mean square error was used in this instance. The result showed that the Karbala WWTP complies with Iraqi environmental regulations for water discharged to surface water or water for other uses and has an appropriate efficiency of wastewater treatment even if the flow entering the plant reaches the peak flow rate of 180,000/day. An improvement in orthophosphate removal efficiency was observed as the flow rate increased because of the contact time in the anaerobic basins, which gradually decreased as the flow increased until it reached the appropriate time for PO 4 3 − {\text{PO}}_{4}^{3-} removal. The outcomes of the present study provide an impression to the operators of the treatment plant of the impact of fluctuating flow on the treatment plant. The developed model can also be used for future studies.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

Performance of full-scale rural wastewater treatment plants in the reduction of antibiotic-resistant bacteria and antibiotic resistance genes from small-city effluents

The main objective of this study was to evaluate the performance of full-scale rural wastewater treatment plants (WWTPs) in the reduction of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) from small-city effluents. Twenty full-scale WWTPs in rural Chile with different biological treatment technologies (vermifiltration (VF), activated sludge (AS) and biodisc (BD)) and disinfection treatments (chlorination (Cl) and UV irradiation (UV)) were monitored and studied in two campaigns: Campaign 1 (20 WWTPs) and Campaign 2 (6 WWTPs). In both campaigns, the rural WWTPs improved the water quality of the effluents very significantly (90%, 70%, and 40% reductions in TU, COD, and NH4+-N, respectively) and reduced ARB and ARG loads by 2–4 log units. All three biological treatments contributed to the final quality of the effluents, especially in terms of microbiological parameters, with statistically indistinguishable efficiencies between them. These results show the importance of rural WWTPs in improving the water quality of urban effluents while reducing microbiological risk and the spread of antibiotic resistance into the environment. The study demonstrates the utility of a non-centralized, self-managed wastewater treatment scheme that can be implemented in many sparsely populated areas around the world.

Performance monitoring of the wastewater system of Bharati Station, Antarctica

The untouched condition of Antarctica's environment is a result of its inherent absence of human activity. Nevertheless, with the introduction of scientific research stations and human presence, a considerable volume of wastewater has been produced by these research facilities. The wastewater needs to be treated before discharge and has strict rules and regulations set by the Antarctic Treaty (Annexure III). However, the performance of the wastewater treatment plant and the data on the quality of the effluents being discharged into the sea by different stations are limited. In this study, the performance of the wastewater treatment plant at Bharati research station located at Larsemann Hills, East Antarctica was investigated from 2015 to 2022. Physical and chemical parameters such as pH, EC, NH4–N and COD were determined in effluents from the drinking water plant, blackwater and greywater plant from Bharati station. The pH and EC of the effluents were measured using portable probes, ammonia was measured spectrophotometrically, and COD was measured using the closed reflux digestion method. The monthly data of the effluents from 2015 to 2022 for pH, EC, NH4–N and COD indicate values within the permissible limits except for a few parameters at a certain time frame. It was observed that the pH of black, grey and drinking water varied between pH 6.5 and 9.1. Both grey and blackwater showed a decreasing conductivity trend, suggesting decreased ionic content. The blackwater exhibited an increase in ammonia concentration and COD trend, indicating higher organic pollution levels, while the grey water displayed a decreasing trend in COD, indicating a reduction in organic matter content. The study also investigated the correlation between NH4–N concentration in wastewater and greywater with levels of COD and compared these levels with standard values to assess effluent water quality. The data will provide baseline values to assess any malfunctioning of the wastewater system in treating the contaminants. Effluent data from other Antarctic stations show a high value compared with the effluents from Bharati station. Such variability largely depends on the station size, water usage and number of expedition members during the summer and winter seasons.