Water Quality Management Research Articles

Rancang Bangun Prototype Sistem Kontrol Suhu dan Monitoring PH Air pada Aquarium Benih Ikan Lele Dumbo Menggunakan Arduino UNO dan NodeMcu Berbasis Internet Of Things (IoT)

Cultivation of Dumbo Catfish requires optimal water quality management to ensure healthy fry growth. Key parameters to monitor are water temperature and pH, as imbalances can negatively affect the health and growth of the fish. This study aims to design and develop a prototype water quality monitoring system based on the Internet of Things (IoT) using Arduino UNO and NodeMCU. In ideal cultivation conditions, the optimal water acidity (pH) ranges between 6–9, and the ideal water temperature is 26–30 °C. Using Arduino UNO and NodeMCU IoT technology, fish farmers can more easily control temperature and monitor water pH in the fry nursery aquariums. Arduino is the microcontroller, while the NodeMCU Wi-Fi module connects the microcontroller to the Blynk application as the user interface. Temperature regulation is achieved through water heaters and Peltier elements. Test results indicate that the system can maintain an average temperature of 26 °C and an average pH of 6, consistent with manual measurements using a water thermometer and litmus paper. The heating and cooling systems function optimally, with an average delay time of 13.5884298 milliseconds, significantly lower than the 150-millisecond threshold. Thus, the system operates effectively, facilitating fish farmers in real-time monitoring and controlling aquarium water quality.

Determination of optimum dose of using Cao (quick lime) for acid mine water management on pH parameters in settling pond 81W pt. Antareja Mahada Makmur Site Perkasa Inakakerta Bengalon district, East Kutai Regency, East Kalimantan

Background: Efforts to manage AAT can be done in several ways, namely by using active and passive methods. For active management is by adding certain chemicals such as lime or alum so that a reaction occurs to control pH and TSS levels. This is related to the actual problems of the research area due to the pH that does not meet the quality standards for coal mining wastewater. The low pH parameters in the research area are a problem in the mining environment, if the content of mining wastewater that has an acidic pH is not neutralized and then directly discharged into the river body causes environmental pollution such as, disruption of aquatic biota that cannot live with an acidic pH and also has an impact on river water which will later be used by the local community for daily activities. Method: The research methodology is divided into 3 stages, namely: pre-field stage, field stage, and studio or post-field stage. Titration testing on each water sample with quicklime Titration testing to determine the optimum dose using initial measurement data obtained from the field. The dose used starts from 0.1 gr/L to neutral pH according to Environmental Quality Standards. Dosage determination is carried out in the Laboratory owned by PT Antareja Mahada Makmur site Perkasa Inakakerta using a jar test tool. Furthermore, the first data analysis carried out was to analyze rainfall data. From the results of actual flow rate measurements carried out at the inlet of the sedimentation pond and pH measurements in each compartment, the process of determining the amount of efficient quicklime dose for water quality management in the sedimentation pond was carried out. Results: The initial pH test results averaged 3.86. After the titration test, the final pH averaged 7.45. The results of testing several dose ranges obtained for the initial pH of 3.86 were 0.05 gr/L. Why did the researchers only use one initial pH value because it was the average result of daily measurements carried out for 10 days. To change the optimum dose from a laboratory scale to a field scale is to multiply the optimum dose by the hourly discharge entering the settling pond 81W. Conclusion : Several efforts have been made by PT Antareja Mahada Makmur, one of which is stabilization using the lime addition method on coal mining wastewater.

Exploring the Potential of Candida sp. SW4-6 as a Probiotic for Enhancing Water Quality in Aquaculture.

Aquaculture, a vital industry supplying a significant portion of the world's seafood, faces challenges such as the deterioration of the aquaculture environment. The objective of this study was to isolate and identify microorganisms with the capacity to eliminate nitrite in water from shrimp ponds and evaluate their potential as probiotics to improve water quality. Additionally, the study also determines the ideal conditions for the probiotic to effectively reduce nitrite-N and ammonia-N. Water samples were collected from four shrimp ponds (SW1, SW2, SW3, SW4) and isolates were obtained. Among all the samples, SW4 was the most effective in reducing the concentration of nitrite-N. Upon further isolation of SW4, the strain SW4-W6 showed significant nitrite-N reduction capabilities compared to the 19 other isolates tested. Through morphological, genetic (ITS sequence), and phylogenetic analyses, strain SW4-6 was identified as Candida sp. The isolation of Candida sp. SW4-6 showed superior nitrite-N and ammonia-N reduction capabilities, with sucrose as the carbon source and complete reduction observed at a C/N ratio of 15-20. Gene expression analysis revealed the up-regulation of nitrite reductase in SW4-6 after inoculation, with significantly higher expression observed with sucrose as the carbon source. Salinity and temperature significantly influenced nitrite-N and ammonia-N reduction by SW4-6, with higher temperatures (30 °C) and 0% NaCl favoring faster reduction rates. Candida sp. SW4-6 emerges as a promising probiotic candidate for aquaculture water quality management due to its efficient nitrite-N and ammonia-N reduction capabilities under optimal conditions. Its virulence profile and ability to thrive across various salinity and temperature conditions further support its potential applicability in aquaculture.

Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model

With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters. This work pays particular attention to their impact on key indicators such as Chemical Oxygen Demand (COD), NH4+-N, Total Phosphorus (TP), and Total Nitrogen (TN), and the application of predictive models. The work first establishes a Random Forest Regression (RFR) model. The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model’s prediction accuracy and stability. Using bootstrap sampling, the RFR model generates multiple training subsets from the original data and randomly selects subsets of variables to construct regression trees. Its performance in predicting various water quality indicators is then evaluated. The results show that the RFR model exhibits excellent performance, achieving high levels of prediction accuracy and stability for all indicators. For example, the R2 for COD prediction is 0.99954, while the R2 values for NH4+-N, TP, and TN predictions reach 0.99989. Compared to five other models, the RFR model demonstrates the best performance across all water quality indicator predictions. This work provides critical support for optimizing wastewater treatment technologies and developing water resource management policies. These findings also offer essential theoretical and empirical insights for the future improvement of urban wastewater treatment technologies and water resource management decision-making.

Spatiotemporal distribution and human health risks of potential toxic elements in the groundwater of the lower Tista Floodplain, Rangpur, Bangladesh

This study assessed the extent of potential toxic element (PTE) contamination and its associated health risks in groundwater from the Tista Floodplain, Rangpur, northern Bangladesh. A total of 130 groundwater samples (65 from the wet season and 65 from the dry season) were analyzed for six PTEs: As, Al, Cu, Mn, Cr, and B using Atomic Absorption Spectrophotometry (AAS) and VIS Spectrophotometry. The mean concentrations of PTEs followed a decreasing order of B > Mn > Cu > Al > As > Cr in the wet season and Mn > B > Al > Cu > As > Cr in the dry season. Metal evaluation index and degree of contamination indicated minor contamination, while the Nemerow pollution index showed moderate to severe contamination, especially in Nilphamari district. Contamination levels were higher in the dry season compared to the wet season. No individual PTEs posed non-carcinogenic health risks, but combined PTEs presented potential health threats, particularly to infants, with higher severity during the dry season. No dermal health risks were identified. Despite the relatively low contamination levels, monitoring policies for new and existing tubewells should be implemented to ensure sustainable groundwater management and long-term water quality.

Water Quality by Spectral Proper Orthogonal Decomposition and Deep Learning Algorithms

Water quality plays a pivotal role in human health and environmental sustainability. However, traditional water quality prediction models are limited by high model complexity and long computation time, whereas AI models often struggle with high-dimensional time series and lack physical interpretability. This paper proposes a two-dimensional water quality surrogate model that couples physical numerical models and AI. The model employs physical simulation results as input, applies spectral proper orthogonal decomposition to reduce the dimensionality of the simulation results, utilizes a long short-term memory neural network for matrix forecasting, and reconstructs the two-dimensional concentration field. The simulation and predictive performance of the surrogate model were systematically evaluated through four design scenarios and three sampling dataset lengths, with a particular focus on the convection–diffusion zone and high-concentration zone. The results indicated that the model achieves high prediction accuracy for up to 7 h into the future, with sampling dataset lengths ranging from 20 to 80 h. Specifically, the model achieved an average R2 of 0.92, a MAE of 0.38, and a MAPE of 1.77%, demonstrating its suitability for short-term water quality predictions. The methodology and findings of this study demonstrate the significant potential of integrating spectral proper orthogonal decomposition and deep learning for water quality prediction. By overcoming the limitations of traditional models, the proposed surrogate model provides high-accuracy predictions with enhanced physical interpretability, even in complex, dynamic environments. This work offers a practical tool for rapid responses to water pollution incidents and supports improved watershed water quality management by effectively capturing pollutant diffusion dynamics. Furthermore, the model’s scalability and adaptability make it a valuable resource for addressing intelligent management in environmental science.

Evaluation of Water Quality in a Highly Impacted Urban Stream Using Water Quality Index (Ankara Stream, Türkiye)

Population growth along with other factors such as industrial, agricultural, and urban development, threaten freshwater resources in urban areas. Protecting urban water quality for ecological balance, water security, and energy production is crucial. The water quality index (WQI) provides an effective tool for assessing and managing water quality, and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) is one of the extensively used method. In this study, the pollution status of the Ankara Stream which flows through the densely populated Ankara was examined using physico-chemical parameters collected from five stations (S1-S5), and the water quality status was estimated via CCME-WQI. The results revealed varying water quality across different points on the stream. S2, located in a protected area, exhibited the best quality; in contrast, S4 and S5, located downstream of a wastewater treatment plant, exhibited the poorest quality. The consistency of these findings with the literature and the historical records of Ankara Stream emphasize that the CCME-WQI can be used for the management of water resources with high levels of pollution. This study contributes to sustainable water management practices and highlights the need for advanced treatment techniques to control pollution in urban freshwater resources.

EVALUATION OF TIGER SHRIMP (Penaeus monodon Fabr.) HATCHERY MANAGEMENT AT THE BRACKISHWATER AQUACULTURE DEVELOPMENT CENTER, JEPARA, CENTRAL JAVA

The success of tiger shrimp production is influenced by the availability of quality postlarvae. The objective of this study was to evaluate biotechnical aspects of tiger shrimp hatcheries to produce quality postlarvae and analyse the economic aspects of tiger shrimp hatchery business in BBPBAP Jepara. The method used in this research was a case study method. Analysis of data used include analysis of biotechnical aspects of tiger shrimp hatchery maintenance and spawning broodstock, broodstock feed management, larval and post larvae rearing management, and water quality and health management. Analysis of economic aspects that include, cost analysis, revenue, income, and business feasibility analysis which includes, Payback Period (PP), and Net Present Value (NPV), Internal Rate of Return (IRR), Benefit Cost Ratio (B/C Ratio). The results showed that tiger shrimp broodstock tiger shrimp comes from nature, spawning using the ablation method, the feed used is fresh feed, the type of feed used, and the type of feed used. feed used in the form of fresh feed, the type of feed for larvae and tiger shrimp larval and post larvae consisted of phytoplankton, artemia, and artificial feed. The economic aspects analysed include business capital of IDR 1,063,000,000.00/year, fixed costs of IDR 216,300,000.00/year, production costs of IDR 368,670,000.00/year, income of IDR 960,000,000.00/year and profit IDR 375,030,000.00/year. The results of business feasibility analysis including the PP value of 1,9 years, PP (discount factor 6,16%) of 3,04 years, the NPV value of IDR 156,237,555.00, the B/C ratio of 1.64 and the IRR value 19.64%.

Heterotis niloticus (African Bonytongue) Domestication: Potentials, Efforts, and Challenges

Heterotis niloticus is a fascinating freshwater fish species popularly referred to as the African bonytongue. It offers great potential for aquaculture due to its rapid growth, flesh quality, adaptability to low oxygen environments, and high market value. Domestication efforts for this species have been initiated to optimize its production and meet the increasing global demand for sustainable protein sources. This review explores the current state of H. niloticus domestication and highlights the key challenges and opportunities. Despite their promising potential, several obstacles remain, including the development of efficient hatchery techniques, optimization of feed formulations, and disease control. Overcoming these challenges is essential to promote the full potential of H. niloticus aquaculture and contribute to improved food security. Future research should focus on refining breeding programs, improving water quality management, and exploring innovative aquaculture systems. Policy support is key to driving investments, innovation, conservation efforts, and the sustainable development of H. niloticus aquaculture.

Application Software for Water Quality Data Management (MajiBora-DM) in Tanzania

In Tanzania, water quality laboratories face the difficult task of managing all of the processes involved in handling water samples. These tasks include registering samples, evaluating their quality, documenting critical parameters, analyzing data, making professional recommendations on water treatment solutions to achieve superior results, and creating comprehensive reports for clients. In this paper, the authors explain the development of the Majibora-DM program, a comprehensive tool for managing water quality data. The authors developed MajiBora-DM using the Python Integrated Development Environment (IDE), pyinstaller, and the Inno compiler, then tested it on a Windows operating system computer. It demonstrated the ability to register samples, allow data sharing among computers connected to the internet, record water quality parameters, analyze water quality, interpret data, and generate reports with water treatment approach recommendations. The software plays a crucial role by calculating the impact of chemical dosages on water quality parameters in water treatment plants, thereby recommending the most effective dosage to achieve the desired quality. Also, it can simplify water quality data analysis, allow real-time data sharing, generate water quality reports, and suggest the appropriate water treatment method using artificial intelligence to achieve clean and safe water for the intended use.

Spatiotemporal Analysis and Risk Prediction of Water Quality Using Copula Bayesian Networks: A Case in Qilu Lake, China

Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and CODCr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators.

Prospects and challenges of effluent reuse and management of stream water quality: a review

Prospects and challenges of effluent reuse and management of stream water quality: a review

Improved Detection of Great Lakes Water Quality Anomalies Using Remote Sensing

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies.

Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country

Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country

Incidence and effects of anomalies and hybridization on Alabama freshwater fish index of biotic integrity results.

The index of biotic integrity (IBI) is an effective multi-metric tool for assessing biological integrity of aquatic ecosystems and regulating water quality based on fish community surveys. Percentages of individuals with deformity, eroded fin, lesion, and tumor (DELT) anomalies are used in IBIs as a measure of fish health, while the percentage of hybrids reflects changes in reproductive isolation. When rare, these are combined into a joint '% DELT + hybrids' metric. We investigated the distribution and effects of this metric on overall fish IBI scores and biological condition ratings by analyzing data from 646 fish surveys from across Alabama. Following exploratory data analyses, we assessed the impacts of % DELT + hybrids by comparing IBI scores for sites before and after setting this metric to zero to simulate systematic error (no detection). While DELTs and other anomalies were infrequent (~ 11% of sites), the two most commonly diseased fish species were Campostoma oligolepis (Leuciscidae) and Lepomis cyanellus (Centrarchidae). Hybrids were less frequent (~ 7% of sites) than anomalies and were scored only for Lepomis sunfishes. Overall, we found no statistically significant differences in final IBI scores or biological condition ratings before and after anomaly and hybrid removal; however, removal led to increased IBI scores and biological condition ratings in 13.2% and ~ 6% of surveys, respectively. These findings demonstrate that, while IBI outcomes appear relatively robust to systematic error in % DELT + hybrids, anomaly and hybrid observations can alter IBI outcomes, with important implications for fish-based bioassessments and water quality management.

Probiotics and paraprobiotics in aquaculture: a sustainable strategy for enhancing fish growth, health and disease prevention-a review

This review delves into the increasing interest in probiotics and paraprobiotics as a viable alternative to antibiotics in aquaculture, highlighting their potential to enhance fish health and prevent diseases. As the aquaculture industry continues its global expansion, addressing the challenges associated with disease outbreaks in high-density fish populations becomes imperative. The review underscores the promising role of probiotics and paraprobiotics as a sustainable strategy to mitigate these challenges. The diverse positive impacts of various probiotic strains such as Arthrobacter, Bacillus, Lactobacillus, Bifidobacterium, Clostridium, and others emphasize their roles in enhancing growth, resistance to diseases (including bacterial, viral, and parasitic infections), stress reduction, water quality management, and environmental sustainability. Challenges such as stability, host specificity, and regulatory considerations must be addressed to optimize the use of probiotics in aquaculture. Additionally, paraprobiotics, or non-viable microbial cells, present a safer alternative to the criticized antibiotics and even live probiotics in environments where microbial viability poses a risk. These inactivated cells retain the ability to modulate the immune system and improve gut health, offering a promising complementary approach to fish disease prevention. The review advocates for a systematic approach combining research, innovation, and collaboration to effectively integrate probiotics and paraprobiotics into fish farming practices. Furthermore, the mechanisms by which probiotics and paraprobiotics modulate gut microbiota, produce antimicrobial compounds, and strengthen fish’s immune system have been elucidated. Moreover, the practical applications of probiotics in fish farming, including optimal administration methods and the challenges and limitations faced by the industry, have been discussed. Emphasis on the importance of continued research to explore new probiotic and paraprobiotic strains and develop innovative delivery systems to ensure the sustainability of aquaculture has been discussed. By enhancing fish health, reducing the need for antibiotics, and improving water quality, probiotics, and paraprobiotics contribute to more sustainable and environmentally responsible aquaculture operations.

Reproductive performance of Channa striata in wetland ecosystems: a fuzzy logic approach to water quality and eco-climatic factors for long-term sustainable management and aquaculture advancement.

The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro-climatic changes using a fuzzy logic approach for long-term management in the wetland ecosystem (Gajner beel), Bangladesh. A total of 1200 C. striata individuals were collected monthly during January to December 2019 using various fishing methods. Measurements of total length (TL), body weight (BW), and gonad weight (GW) were taken with a precision of 0.01cm and 0.01g, respectively. The GSI (gonadosomatic index in %), MGSI (modified gonadosomatic index in %), and DI (Dobriyal index) were used to determine size at sexual maturity (Lm) and the spawning season. Results revealed a wide range of TL, ranging from 12.7 to 46cm. The Lm was estimated between 21.7 and 25.33cm based on various model as TL vs. GSI, MGSI, and DI; TL-fork length (FL) regressions; and maximum length (Lmax) for this species. The spawning period was observed from April to July, peaking in May, indicated by higher GSI, MGSI, and DI values. Eco-climatic factors showed a significant relationship with GSI, except for total dissolved solids (TDS). Rising temperatures and declining rainfall could delay spawning of C. striata. A fuzzy logic approach was employed to predict future GSI changes over extended periods (e.g., 50 or 60years), enabling anticipation of shifts in the timing and duration of the spawning season. Finally, the findings of our research might be effective to carry out specific conservation efforts of C. striata in response to climate variability in sub-tropical water.

Remote Water Quality Monitoring System for Use in Fairway Applications

In the context of climate change, there is a growing need for accurate, real-time data on water quality in river waterways. This results in the development of advanced monitoring systems. This article presents a remote water quality monitoring system designed specifically for use in inland waterways, the basic elements of which are placed in a buoy with an IoT unit. The proposed system uses a network of sensors strategically placed along the waterway to continuously measure critical parameters: temperature, pH, dissolved oxygen, and conductivity. Various compatibility, efficiency, and ease-of-use tests have been conducted to verify each aspect of the monitoring system. It has been shown that the sensors operate within the intended accuracy ranges. The central unit equipped with a GSM (Global System for Mobile Communications) module can wirelessly transmit data to a main server, enabling remote access and analysis via a user-friendly interface of the developed application. The paper details the technical architecture of the system, the integration of GSM technology to ensure reliable data transmission, and the results of the monitoring studies of the proposed parameters. The remote monitoring system offers significant benefits in terms of early detection of pollution events, ensuring the safety of aquatic life, and supporting sustainable navigation practices. The research results highlight the potential of GSM-based remote monitoring systems to revolutionize water quality management in waterways in various regions.

Review of Climate Change Impacts on Water Quantity and Quality in the Murray–Darling Basin, Australia

Climate change is a global phenomenon that significantly affects water quality and quantity, with implications observed across various regions worldwide. In the Murray–Darling Basin (MDB), Australia’s largest and most vital river system, climate change is exacerbating environmental and public health challenges, particularly through its impact on water resources. This review assesses the historical, current, and potential future impacts of climate change on both water quantity and quality in the MDB. The study involved a systematic review of 126 reputable sources, including peer-reviewed journals, government reports, and relevant books. A particular focus was given to the increasing frequency of blue-green algae (BGA) and blackwater events, which are key indicators of climate change’s impact on the basin’s water systems. The findings underscore the critical importance of integrating climate adaptation measures into existing water quality management policies to mitigate these adverse effects. The review concludes that proactive adaptation measures are essential for enhancing the resilience of the MDB’s water resources against the ongoing and future impacts of climate change, with lessons that may be applicable to other regions facing similar challenges.

Analysis of manganese (Mn) and copper (Cu) content in leachate water of Putri Cempo landfill, Surakarta

Abstract Leachate can contain hazardous components that can pollute the environment and health if not managed. This study aims to determine the content of heavy metals Mn and Cu in leachate from Putri Cempo Solo TPA and provide recommendations if the Mn and Cu values exceed the threshold. The method used is laboratory analysis. Analysis of Mn and Cu content was carried out using Atomic Absorption Spectroscopy by comparing the results of Mn and Cu content with quality standards according to PP 82/2001 Class IV concerning Water Quality Management and Water Pollution Control and PerMenKes 492/2010 concerning Mn content and PP 82/2001 Class IV concerning Water Quality Management and Water Pollution Control against Cu content. Laboratory test results showed that the Mn content was 33.7060 mg/l and Cu was 0.9294 mg/l. It is known that the maximum limit of heavy metal contamination Mn ranges from 0.1-0.4 mg/l and Cu is 0.2 mg/l. It was concluded that the heavy metal content of Putri Cempo landfill leachate has exceeded the quality standard. In order not to exceed the quality standard, it is recommended to treat leachate with coagulation-flocculation, adsorbent, and bioremediation processes.