Water Monitoring System Research Articles

Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems

Predicting essential water quality parameters, such as discharge, pressure, turbidity, temperature, conductivity, residual chlorine, and pH, is crucial for ensuring the safety and efficiency of water supply systems. This study employs long short-term memory (LSTM) networks to address the challenge of capturing temporal dependencies in these complex processes. Our approach, using a robust LSTM-based model, has demonstrated significant predictive accuracy, as evidenced by substantial R-squared values (e.g., 0.86 for discharge and 0.97 for conductivity). These models have proven particularly effective in handling non-linear patterns and time-series data, which are prevalent in water quality metrics. The results indicate the potential for LSTMs not only to enhance the real-time monitoring of water systems but also to aid in the strategic planning and management of water supply systems. This study’s findings can serve as a basis for further research into the integration of AI in environmental engineering, particularly for predictive tasks in complex, dynamic systems.

Data-Driven Water Monitoring System with Descriptive Analytics for Aquaculture Prawn Farms

This study aimed to develop a data-driven water monitoring system with descriptive analytics for aquaculture prawn farms. This system was designed to automate the monitoring of the water quality of the prawn farm so as to minimize labor and time spent for manually testing the water. Utilizing a rapid prototyping methodology, which is a rapid or quick creation of a prototype or a physical model of a device used in the development of a project, the said monitoring system was evaluated through a comparative analysis between the data gathered manually and the data collected by the monitoring system. To ensure reliability and validity of this monitoring system, it was used various times over several consecutive days. Also, a t-test was used to determine the significant differences between manual and system readings. Findings of the study showed that the designed water monitoring system provided accurate results, and the objectives of the study were met, particularly in terms of integrating a Raspberry Pi 3 microprocessor to automate the system in monitoring the water quality. It was also found out that the use of different sensors to test the water condition in terms of its temperature, pH level, salinity, dissolved oxygen, and turbidity was accurate. Furthermore, the system also met its objective of developing an Android mobile application to monitor the status remotely. With this, it is recommended for prawn farmers to utilize the system (with internet connectivity) for their efficient water quality management and to improve their remote monitoring capabilities.

IMPLEMENTASI SISTEM MONITORING AIR DENGANSENSOR ULTRASONIK HC-SR04 DAN WATER FLOW DI ASONE HIDROPONIK BERBASIS INTERNET OF THINGS (IoT) MENGGUNAKAN ENERGI PANEL SURYA(PLTS)

The cultivation system using hydroponics is now widely popular among the community because it does not require large areas of land. Using the hydroponic system results in faster and optimal harvests. However, the busy activities of urban communities often cause them to fail in implementing it due to constraints in controlling and monitoring hydroponic plants. Additionally, PLN electricity is essential for operating the water pump, and the electricity load and tariff increases, along with sudden blackouts, significantly impact business actors. To address this problem, this research creates an Internet of Things (IoT)-based water monitoring system for ACONE Hydroponics partners with solar panel (PLTS) energy sources as an alternative energy in the event of a sudden PLN power outage. The sensors used in this system include the HCR-04 ultrasonic sensor to monitor water availability and the water flow sensor to measure water flow (discharge). Sensor data is processed by the ESP32 microcontroller and sent to the Blynk cloud platform via an internet connection and displayed on an LCD. System testing shows that the system can send sensor data to the cloud platform stably and in real-time. Data visualization on the cloud platform is easy to understand and helps hydroponic owners monitor water conditions in hydroponic systems. This water monitoring system can help users optimize water use in hydroponic systems.

Perceptions and knowledge about the use of biological indicators in freshwater ecosystem monitoring in Rwanda

The use of biological monitoring (biomonitoring) to assess water quality is recognised alongside the use of chemical and physicochemical parameters due to its ability and efficiency in providing information about both current and long-term changes. Indeed, biomonitoring is applied in several developed and developing countries. Nevertheless, some developing countries, such as Rwanda, are yet to adopt such a monitoring system. This paper presents results of an assessment of the knowledge and perceptions by water resource managers about the use of biomonitoring and bioindicators, and challenges to their integration into existing routine water quality monitoring systems in Rwanda. Qualitative research, using semistructured interviews, was conducted with nine water resource practitioners from six water governance institutions between August and November 2021. The results show that participants are aware that macroinvertebrates, algae and fish are bioindicators that have potential applications in Rwanda to complement the chemical and physicochemical parameters already being collected in the country’s water monitoring system. The main challenges for integration of biological indicators, as indicated by participants, include the need for equipment, funding, technical skills and taxonomic knowledge. Training in the use of bioindicators and taxonomy are needed to raise the awareness and skills of staff from the institutions involved in freshwater ecosystem management in Rwanda, and to encourage integration of biomonitoring results into national water monitoring frameworks.

A Comprehensive Review on : Aquaponic Farming Water Quality Prediction

Aquaponic farming, which combines aquaculture and hydroponics, depends strongly on maintaining optimal water quality to guarantee the health and productivity of both fish and plants. This review paper explores the latest developments in IoT-based automated water monitoring systems, focusing on their role in predicting and managing water quality in aquaponic systems. Regardless of significant progress there are several research gaps. Recent studies highlight challenges such as inconsistent sensor selection, calibration issues, insufficient publicly available data, and inadequate data cleaning and preprocessing. Also, the issues of imbalanced datasets, limited long-term data, and underdeveloped IoT and AI integration prevent the development of accurate predictive models. The scalability and maintenance of systems, understanding microbial dynamics, and nutrient management are also critical areas needing further exploration. This review also identifies the need for deeper case studies and advanced feature extraction methods to enhance prediction accuracy. By addressing these gaps, including system scalability and nutrient management, future research can improve data availability and quality, enabling more robust predictions and contributing to more efficient and sustainable aquaponic systems.

Internet of Things (IoT) Driven Water Management System for Efficient Level Control

Water is an essential and valuable resource in daily life, making its conservation crucial to prevent adverse effects. Storing water for domestic, industrial, agricultural, and other purposes is particularly important. Safe drinking water is increasingly becoming polluted due to the growing population and their demands for urbanization and industrialization. At the household level, some people leave electric water pumps running and either go to work or sleep, forgetting to turn off the pumps when the water container is full. This highlights the need for a reliable and continuous water supply. IoT plays a significant role in environmental monitoring, particularly in disaster management, early warning systems, and environmental data analytics. One major challenge in urban cities is water management, especially with the rapid growth of urbanization, necessitating sustainable urban development plans. To address these issues, we propose a "Water Level Monitoring System" solution. This paper presents an IoT-based water monitoring system for real-time applications. The system's sensors measure the water level in the tank, and the data is sent to a cloud server, allowing users to view it on a remote dashboard. This system can be used efficiently by both homeowners and industrial users, as well as other water utilities.

Water Contaminant Detection and Water Purification of Household Drinking Water Using a New Stacking Ensemble Model

ABSTRACTContaminated drinking water sources pose a significant health risk worldwide. Monitoring programs for drinking water quality aims to ensure safe water supply by informing management practices. Improved online monitoring of water systems is necessary as current lab‐based methods are slow and do not offer real‐time public health protection. Rapid detection and response to potential contamination events are crucial to mitigate health risks. Mark of‐purpose water treatment strategies offer a reasonable method for upgrading drinking water quality at the family level and forestalling waterborne illnesses. This study focuses on collecting household drinking water and utilizing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity. A consistent water quality noticing system using the stacking outfit model, which solidifies Bayesian association and decision tree techniques, is proposed in this article. Bayesian network analyzes the input data attained from sensors collecting real‐time data and concludes whether the data represents the contamination event. DTs are utilized to demonstrate the connections between multivariate water boundaries utilized in the review. Afterward, a multiobjective, such as a biobjective optimization model and a nondominated genetic algorithm (NGA) are used in this work of optimization to minimize the volume of contaminated water. After the pollution in the water is identified, water decontamination processes are done given point of purpose medicines like ceramic channels and solar water disinfection (SODIS). The method outlined is executed through Python software. The findings indicate that the estimated values for PH, temperature, and turbidity are 7.3, 31.8, and 0.77, respectively. However, the proposed method is compared with the existing C‐NSGA‐II, while compared to this method, the proposed system produces improved cost functions. Consequently, suitable water treatment and supply should be considered to reduce the effects on people's health as well as to improve living conditions, respectively.

Automatic Monitoring System Iot (Internet Of Things) Based Water Tanks

Water tanks are widely used to store water in the community. However, the filling process still uses a manual method so it cannot monitor/monitor the water level in the tank, so when the water is full in the tank until it overflows it is then turned off. Water tanks are often placed at a height to take advantage of gravity so that water can flow optimally. The high position of the water tank is difficult for users to reach to determine the quality and volume of water. The performance of this tool has 2 conditions. The first condition will discuss the water level monitoring tool in the tank. Using the tool developed, the water pump can be turned on and off automatically which is controlled using a monitoring tool using a smartphone and the Blynk application with a water level indicator. , water flow, and watervolume and Notification of water conditions via Telegram. Technology for distributing and monitoring water in tanks is still limited to conventional means by turning on and opening the water tap. For this reason, a control system is needed that can monitor and control the water volume in several tanks through one application display on a smartphone such as Telegram. By using the NodeMCU ESP8266 as a microcontroller base, an ultrasonic sensor as a water volume measuring sensor, and a Mini Water Pump, the condition of the water volume and all changes can always be monitored via notifications on Telegram. Apart from that, the tool can also be controlled automatically or manually through the commands provided on Telegram. In this way, the concept of using Telegram as a water tank volume control and monitoring system can be the right alternative choice in designing a water tank control system. The research method used is a prototyping system. The expected result is the realization of automatic floodgates to make it easier for humans to carry out their tasks and make the system run more efficiently. Apart from being automatic, this tool has the advantage that it can be controlled remotely using the internet of things. The design of this automatic sluice gate uses an Arduino Uno microcontroller as a processing tool, and is equipped with an ultrasonic sensor, water level float switch sensor, servo motor.

A Design and implementation of a sustainable microcontroller based solar power automatic water irrigation control and monitoring system

The major cause of the shortage of land reserve water owing to the lack of rainfall makes the proper operation of irrigation systems extremely important. A major concern for farmers these days is water scarcity. Pakistan is an important agricultural nation and major part of economy depend on agriculture. This study analyses the soil moisture monitoring and automatic plant watering system since it is really helpful in all kinds of weather. In this study a prototype is design and implement using Arduino-based automation sensors are employed for the control and observation of a solar powered smart irrigation system. An Arduino based remote irrigation system developed for the agricultural plantation, which is placed at a remote location and requires water, provides for plantation when the moisture of the earth drives under the set-point rate which turns on the pump on autopilot. An estimation was carried for solar power energy potential agriculture system condition using soil and humidity sensors. The measurements and mathematical formulation were observed to monitor effectivity of solar based power generation. This analysis is conducted for 20 Watt power panel integrated with 5-watt electrical drive with battery capacity 1200 m-Ah. A modified energy model was used to determine the agricultural load for a 5-watt dc motor. The results showed a cumulative analysis to implement this system inspired by smart sustainable clean renewable energy technologies which meet the farmer’s demands significantly increase the productivity of farming and reduce wastage of water and energy.

Biosensors in environmental analysis of microplastics and heavy metal compounds – A review on current status and challenges

Biosensors (biological sensors) are a promising alternative to standard analytical techniques for testing and monitoring in the environment, among others, heavy metals. Biological materials that detect heavy metals include enzymes, bacteria, fungi, and plant and animal cells. The paper aimed to evaluate the possibilities of biosensor usage in environmental analysis, considering an easy and environmentally friendly analysis of individual compounds (e.g., heavy metals) or their mixtures in various samples directly in the environment, as well as continuous monitoring of these compounds. The work considers biosensors' characteristics, classification, and operation principles. Attention is paid to the possibility of using bioluminescent bacteria as biological material in a biosensor. Examples of the study of heavy metals using various biological sensors were given. It also indicates the possibility of using biosensors to determine microplastics by applying specific hormones (estrogens) receptor use and peptides as a solution or incorporating them into living microbial cells. Additionally, support was given to the use of biosensors to monitor plastics' degradation in the water environment using recombinant whole-cell microbial biosensors as detectors for plastic monomers such as acrylic acid monomers. The application of the biosensors highlighted in the paper is to shorten the time of toxicity studies by using new indicators or acceptors. However, several challenges must be overcome, such as cell viability and activity. A continuous water-monitoring system that can handle a severely toxic sample has yet to be developed in toxicity monitoring.

Development of electrochemical sensors based on silver nanoparticles electrodeposited on gold screen-printed electrodes: application to nitrate trace analysis in water

Abstract. A simple, reusable and sensitive electrochemical sensor based on a gold screen-printed electrode modified with silver nanoparticles has been developed for the detection of nitrate in water. Scanning electron microscopy, square wave voltammetry and electrochemical impedance spectroscopy were used to characterize the modification of the electrode surface. The modified electrode with different silver nanoparticle loadings was also tested, as well as the influence of scan rate on the reduction of nitrate. The sensor exhibited a wide linear response to nitrate from 100 to 1500 µM and a detection limit of 7.7 µM, which is significantly less than the maximum contaminant level admitted in drinking water (800 µM). The reproducibility, repeatability and selectivity of the sensor have also been examined. The suitability of the proposed sensor for real sample detection was successfully demonstrated via recovery studies performed in spiked tap water samples. The proposed approach was used to determine nitrate in freshwater, and the results were in good agreement with those obtained from a commercial nitrate sensor. These advantages make the developed sensor a promising alternative approach for integration into an online monitoring system for water monitoring.

Modeling- and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases.

PSVI-5 Extruded blend of Nannochloropsis oculata microalgae and flaxseed and its effects on ruminal fermentation and nutrient digestion

Abstract Omega-3 fatty acids are essential nutrients for mammals, and are required for cellular growth and synthesis of numerous cellular signaling molecules. NBO3 Technologies, LLC (Manhattan, KS) has developed an omega-3 supplement consisting of an extruded blend of flaxseed and Nannochloropsis oculata microalgae (GreatOplus; GOP). Our objective was to evaluate effects of GOP on dry matter intake (DMI), water intake (WI), ruminal metabolism, and apparent total tract nutrient disappearance in cannulated Holstein steers (n = 11; initial body weight 586.4 ± 49.7 kg). Treatments consisted of backgrounding diets (40% roughage, 60% concentrate) containing 0% (CON) or 10% GOP (dry basis). Feed and water intake were recorded using an Insentec Roughage and Water monitoring system (Hokofarm Group; Emmeloord, The Netherlands). Steers were utilized in a cross-over design with two 19-d periods. Each period consisted of a 15-d adaptation phase and a 4-d collection phase. Ruminal contents, duodenal digesta, and feces were collected at 8-h intervals for 4 d starting at 1000 h on d 16. The sampling schedule was advanced by 2 h with each subsequent sampling day, thus generating a 24-h profile at 2-h intervals post-feeding over the 4-d sampling phase. Titanium dioxide (15 g) was dispensed directly into the rumen of each steer 15 min before feeding and to estimate apparent ruminal flow and fecal excretion of nutrients. There was a Treatment X Day interaction for DMI (P < 0.001); DMI was greater for steers fed GOP some of the days during the adaptation phase compared with steers fed the control diet, but intakes were similar during the collection phase. Ruminal fluid pH, ruminal ammonia concentration, and WI were not affected by treatment (P > 0.10). As expected, ruminal outflows of total fatty acids (195.6 vs 338.1 g/d for CON and GOP, respectively) and α-linolenic acid (ALA; 1.6 vs 6.3 g/d, respectively) were greater for steers fed GOP compared with steers fed CON (P < 0.05); however, ruminal disappearance (i.e., biohydrogenation) of ALA was extensive, with only 8% and 4.7% of ALA intake exiting the rumen for CON and GOP, respectively. Apparent total tract digestion of organic matter was not impacted by diet (P > 0.20; 78.1 and 76.8% for CON and GOP, respectively). Supplementing an extruded blend of flaxseed and microalgae can effectively increase the amount of α-linolenic acid available for absorption in the small intestine, but overall recovery of dietary α-linolenic acid is relatively low due to extensive biohydrogenation by microbes within the rumen.

Drinking water monitoring system with detection of ph level conditions in laying hens farms based on internet of things

The increase in egg-laying chicken population must be balanced with the increase in productivity. The productivity (quantity of eggs produced), which affects profitability, can be obtained with sufficient and high-quality drinking water (determined by its pH levels). One of the poultry farms in Jember, Rizqie Farm Partners, experienced a problem with the management of water and feed provision for the egg-laying chicken especially in rainy seasons. The farm is located in an open space and is approximately ±25 meters from the owner’s house, making monitoring and controlling of the drinking water irregular. It is pivotal to be disciplined in controlling the drinking water so that the chickens do not become stressed. The objective of the study is to develop a smart automated system for monitoring drinking water to increase the quality and quantity of egg production. Sensor data connected to the Arduino Uno was converted to an Android smartphone as a controlling and monitoring medium. The stages carried out included problem identification, literature study, data collection, system creation using the design sprint software development method, testing, and data analysis. The pH sensor test results on bottled mineral water, tap water, orange juice, water mixed with soil, and water mixed with vitamins for chickens were 3-8.27 for each type of water. The pH indicator paper as a comparison of the results with the sensor has an accuracy level of 85.06% and an error value of 14.94%.

Eco-Flow: Water Management System Using UV Sterilization

Abstract: In our contemporary world, the accessibility of clean and potable water is a pervasive issue, particularly in residential complexes and apartment buildings where shared waterstorage facilities pose a significant challenge. The lack of awareness and diligence in maintaining these communal tanks exposes residents to waterborne diseases. The current hurdle in water monitoring and filtering systems is the creation of a cost- effective, scalable solution that can be easily implemented across diverse environments. This project introduces EcoFlow, an innovative and budget- friendly solution for water level monitoring and purification. The system utilizes UV lights and activated carbon to efficiently filter water, eliminating harmful microorganisms and removing impurities, chemicals, and odors. By addressing the unique challenges of different regions, EcoFlow aims to contribute to the broader adoption of UV and activated carbon-based water treatment systems. Beyond its filtration capabilities, EcoFlow incorporates a real-time water level monitoring system, empowering users with vital information for sustainable and equitable access to clean water

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Evaluation of soil water content and bulk electrical conductivity across the U.S. Climate Reference Network using two electromagnetic sensors

AbstractSoil bulk electrical conductivity (BEC) was evaluated alongside soil volumetric water content (VWC) and soil temperature measurements using the HydraProbe (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP) with accuracy range of BEC ≤ 0.3 S m−1, and the time domain reflectometry (TDR)‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP) suitable for BEC up to 0.6 S m−1, at 23 stations of the U.S. Climate Reference Network. Previous evaluations revealed inconsistent performance of both sensors in some clay soils using manufacturer‐recommended calibrations in converting dielectric permittivity measurements to VWC. Here, we found that hourly values of BEC reached 0.6 S m−1 in high clay content soils and exceeded 2 S m−1 in high saline soils, and these high values of BEC were associated with poor performance and failures of both HP and AP sensors. Large values of BEC occurred in predominantly saturated soils where VWC values reached about 0.5 m3 m−3 for saline soils and about 0.7 m3 m−3 for clay soils, while low magnitudes of BEC were associated with low soil water content and seldomly saturated soils. Low hourly BEC values of less than 0.1 S m−1 were observed in wide variety of soil types, where sensor performance was typically excellent. The most influential factor on BEC was high soil water content conditions. Although dielectric permittivity measurements in estimating the soil water content were sensitive to BEC as some high clay content and high salinity soils increased BEC, the impact of large BEC on dielectric permittivity measurements was smaller in the well‐drained top soil layers than in deep soil layers that remained near saturation. Soil temperature had only a small impact on BEC. With high clay content and high salinity, the specific area of clay minerals was also associated with the magnitude of BEC.

Automated Water Monitoring & Managing System for Aquaculture Using IOT

In recent years, the Internet of Things (IoT) has emerged as a transformative technology with profound implications for various sectors, including environmental monitoring and management. This paper presents an innovative IoT-based automated water quality monitoring and management system (AWQMS) designed to revolutionize the way we monitor and safeguard water resources.The AWQMS leverages a network of IoT-enabled sensors deployed at strategic locations throughout water bodies, continuously measuring crucial parameters such as pH, dissolved oxygen, turbidity, temperature, and conductivity. These sensors are equipped with wireless communication capabilities, enabling real-time data transmission to a centralized data acquisition and processing platform. At the heart of the AWQMS lies advanced data analytics and machine learning algorithms, which analyze incoming data streams to detect anomalies and deviations from predefined water quality thresholds. Key Word: Automated Water Monitoring and Managing system, IoT based solution, sensor-based monitoring, Real time monitoring system, cost-effectiveness.

Estimation of Suspended Particulate Matter Using Landsat 9 Imagery: Generating Algorithms and Spatio-Temporal Distributions

The fluctuation of suspended particulate matter (SPM) is essential to the biogeochemical cycle and ecological health of coastal waters. Anthropogenic activities potentially trigger an increase in SPM, so it needs to be monitored continuously. Spatial and temporal monitoring of SPM can be carried out cost-effectively with broad coverage using a remote sensing application. This study aims to build the SPM algorithm and estimate its temporal variability. The algorithm model in this study is based on an empirical formula between field data and reflectance data with the same acquisition. Water samples were taken from 100 stations in July 2022. Half were used for model building and the other for model validation. Suspended Particulate Matter was determined gravimetrically and estimated their temporal variability was based on Landsat 9 image records from December 2021 - November 2022. The results of the analysis show that the best algorithm for SPM estimation can be built based on coastal aerosol canals (B1), blue canals (B2), and green canals (B3) with the accuracy test result of (R2 = 0.68; RMSE = 5.551 mg.L-1; MAPE= 7.07%; Bias= 0.28). The SPM temporal fluctuations were generally higher in the west monsoon and lowered in the east monsoon, ranging from 30 to 70 mg.L-1. On the other hand, the spatial distribution shows a higher magnitude in the estuary than in the offshore waters, with a deviation of about 30 mg.L-1. Regional authorities can use the results obtained to improve the management of coastal water quality and monitoring systems.

Analysis and Design of Natural Spring Water Preservation and Monitoring System Using Rapid Application Development

Analysis and Design of Natural Spring Water Preservation and Monitoring System Using Rapid Application Development