IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater

IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater Rejoan Kobir Nishan, Shapla Akter, Rayhanul Islam Sony, Md Mozammal Hoque, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3992923/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Industrialization and urbanization both lead to pollution of the environment, particularly of the water. Industrialization and urbanization-related pollution have detrimental consequences both for the environment and for life on Earth. Food poisoning, diarrhea, short-term gastrointestinal problems, respiratory illnesses, skin conditions, and other major health problems can all result from this contaminated water. Most of the waste generated from garment factories is dumped into the closest rivers or canals in a developing nation such as Bangladesh, where the ready-made garment sector is one of the key contributors to the overall gross domestic product (GDP). The quality of water in these bodies has deteriorated to the point that it is no longer suitable for supporting life, making it one of the biggest hazards to both the environment and public health. Furthermore, water pollution in Bangladesh causes a daily decline in the number of fish in Bangladesh's rivers and canals. We must therefore keep an eye on the water quality and identify the causes of pollution if we want to conserve fish, other aquatic life, and the ecosystem. To reduce water pollution, real-time monitoring of water quality is essential. Most methods to reduce water pollution are primarily biological and laboratory-based and require a significant amount of time and resources. To solve this issue, we developed a real-time, three-level water quality monitoring system that is based on the Internet of Things (IoT) and integrated with mobile applications. The system suggested in this study collects data from three layers of wastewater and measures some of the most significant indicators of water, including temperature, turbidity, total dissolved solids (TDS) and hydrogen potential (pH). The outcomes of the proposed system will significantly improve the health living organisms on Earth by reusing wastewater and preserving the environment. IoT Arduino Uno pH Sensor TDS Turbidity LM-35 Water Temperature Sensor HC-05 Bluetooth Module MIT inventor 2 IDE Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Industrialization is one of the most important parts of our development. especially the garments sector, which is one of the main sources of total gross domestic product (GDP), making a great impact on our economy, but causing serious damage to our environment. Water is one of the most important elements of the environment. Most of the waste released from the garment factories is dumped into the nearest rivers or canals. Industrial wastewater is aqueous waste that results from substances that have been dissolved or suspended in water, typically during the use of water in an industrial manufacturing process or the cleaning activities that take place along with that process. Hence, the quality of the water in these bodies has become very incompatible with living beings and has become one of the major threats to the environment and human health. It is also harmful to the lives in the water. To save fish and other aquatic animals and the environment, we need to monitor the quality of water and find the reason for the pollution. Real-time monitoring of the quality of water is vital to control water pollution. We propose an Internet of Things (IoT)-based real-time 3-level water quality monitoring system integrated with a mobile application. It will be able to measure some of the most important water-related indexes, including the potential of hydrogen (pH), total dissolved solids (TDS), turbidity, and temperature of water. The system's results will be very helpful in saving the environment and living creatures on Earth. Chowdhury et al. conducted a study on a real-time river water quality monitoring system based on the internet of things (IoT) [1]. They found that the current water quality monitoring system, characterized by a manual and time-consuming process, can be effectively addressed through the implementation of a sensor-based monitoring system utilizing wireless sensor network (WSN) components and internet of things (IoT) technology [2]. The study proposes an IoT-based water quality measurement system, which uses sensors for parameters such as pH, conductivity, salt, and LDR, allowing real-time monitoring and analysis of water quality, with data transmitted through ZigBee and GSM modules, allowing immediate alerts and cloud data storage. S.A. Hamid et al. [3] conducted a study on an IoT-based water quality monitoring system and its evaluation. They found that The SWQMS can monitor the water quality automatically and the pH value is stable regardless of time, pool size, or their combination, but the water temperature varies with time. Konde et al. [4] conducted research on an Internet of Things (IoT)-based system to monitor water quality. They found that implementing a Smart Water Quality Monitoring System (SWQM) with a reconfigurable sensor interface device in an IoT environment, utilizing FPGA design boards, Zigbee-based wireless communication modules, and various sensors, allows real-time monitoring of critical water parameters, presenting an effective solution to address the increasing challenge of global water pollution. Lakshmikantha et al. [5] found that addressing the growing threat of water pollution is crucial, and early detection through a cost-effective IoT-based smart water quality monitoring system is essential to prevent contamination, protect human and animal health, and maintain ecosystem balance by taking timely measures. Budiarti et al. [6] ‘Development of IoT for automated water quality monitoring system' highlighted the development of an integrated Internet of Things (IoT) water quality monitoring system using Raspberry Pi and environmental sensors, demonstrating its effectiveness as an online automated real-time monitoring solution to manage water quality and ensure the sustainability of water resources. Hawari et al. [7] conducted research on the development of a real-time water quality monitoring system based on the Internet of Things (IoT). The research presents the development of a real-time Internet of Things (IoT) based water quality monitoring system, utilizing temperature, turbidity, and pH sensors, enabling immediate water quality assessment, data analysis based on the Water Quality Index (WQI), cloud computing for remote data storage, efficient power management for extended battery life, deployment at multiple locations, and a user-friendly mobile application, demonstrating high reliability and practicality in real-time water quality monitoring. Haque et al. [8] designed an Internet of Things (IoT)--based water quality monitoring system using the Zigbee protocol. The system incorporates specific sensors to measure parameters such as conductivity, dissolved oxygen, turbidity, pH, and temperature. The data collected are processed by microcontrollers and transmitted to a central controller, Raspberry Pi, through the Zigbee protocol. The findings highlight the efficacy of this system in providing continuous water quality assessment, ensuring suitability for general use, and enabling convenient data access through cloud computing via any browser, on request. Muhammad et al. [9] researched an Internet of Things (IoT)-based system designed to monitor water quality in soft shell crab farming, with the aim of improving awareness among farmers and improving the survival rates and yields of soft-shell crabs. Ajith et al. [10] conducted research on a smart water quality monitoring system based on the Internet of Things (IoT) utilizing cloud technology. The research proposes an IoT-based smart water quality monitoring system employing cloud and deep learning, highlighting the need for continuous, real-time monitoring of water quality to address water pollution issues in India, utilizing NodeMCU devices and multiple sensors to measure parameters, with results stored in the cloud and deep learning techniques predicting water suitability. Pasika et al. [11] developed a cost-effective smart water quality monitoring system using the Internet of Things (IoT). The research proposes a cost-effective Water Quality Monitoring (WQM) system utilizing IoT technology, employing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity, with data processed by a Microcontroller Unit (MCU) and sent to the cloud through ThinkSpeak application for real-time monitoring and ensuring the supply of purified drinking water. Chen et al. [12] developed an Internet of Things (IoT)-based system to monitor water quality in fish farms. The study presents an innovative smart water quality monitoring system that utilizes wireless transmission technology, various sensors, and a robotic arm for automatic measurements in fish farms, effectively addressing the challenges of aquaculture losses caused by typhoons and cold snaps in Taiwan, while reducing the dependence on human resources and mitigating data errors. Simitha et al. [13] developed an Internet of Things (IoT) and Wireless Sensor Network (WSN)-based water quality monitoring system. The findings highlight the effectiveness of the proposed system in providing real-time water quality data, allowing efficient management and conservation of water resources through the integration of low-cost, low-power, and long-range communication using LoRaWAN technology. Shanmugam et al. [14] developed an IoT-based smart water quality monitoring system for Malaysia. The proposed system aims to address the challenges of water pollution caused by rapid development, ensuring improved water quality management, and preventing disruptions in water supply systems. Raji et al. [15] developed an IoT-based water quality monitoring system with an Android application. The findings highlight the successful implementation of a cost-effective system that uses various sensors for real-time monitoring, providing a user-friendly interface to detect impurities and ensure pollution-free water resources. Das et al. [16] implemented a real-time water quality monitoring system using the Internet of Things (IoT). The findings highlight the system's capability for autonomous decision making, real-time data acquisition, and remote access through the Internet, aimed at improving public health, reducing costs, and eliminating the need for offline laboratory analysis of water samples. Prasad, A. N. et al. [17] implemented a smart water quality monitoring system for Fiji, integrating IoT and remote sensing technology. The system addresses concerns related to the deterioration of water quality caused by industrial production and agricultural practices, emphasizing the need for frequent data collection to monitor and improve water quality in the region. AlMetwally et al. [18] present a real-time Internet of Things (IoT) based water quality management system, offering autonomous control of water quality factors through sensors measuring pH, temperature, and turbidity for home applications. The system aims to enhance public health and reduce costs by eliminating offline lab analyzes, providing real-time data acquisition, and user-friendly interfaces for configuration and monitoring. Ramesh et al. [19] present a study on water quality monitoring and waste management using IoT, focusing on the design and implementation of a system in Pettipalam Colony, Thalassery. The findings highlight the importance of real-time data on environmental parameters for effective waste management, with potential implications for land restoration initiatives in various regions of India. Kamaludin et al. [20] explore the monitoring of water quality using the Internet of Things (IoT) and propose a system integrating RFID, WSN and IP-based communication, specifically designed for vegetation areas and using a 920MHz Digi Mesh protocol. The study includes a real environment evaluation on the lake in the campus area of University Sains Malaysia, incorporating analyses of energy consumption and the read range of communication to assess the overall performance of the proposed system in measuring pH levels. Huan et al. [21] designed a water quality monitoring system for aquaculture ponds based on NB-IoT technology, achieving accurate and real-time data transmission of environmental parameters (temperature, pH, dissolved oxygen) with high control precision. The system demonstrated stable overall operation in Changzhou, Jiangsu Province, China, maintaining the accuracy of the temperature control at <0.12 ℃, the accuracy at ±0.12℃, dissolved oxygen control within <0.55 mg / L, and the accuracy within ±0.55mg/L, and pH control at ±0.09, providing essential data and technical support for effective regulation of water quality and management of aquaculture production. Vijayakumar et al. [22] conducted a study on real-time monitoring of water quality in an IoT environment, presenting a low-cost system with sensors that measure various parameters such as temperature, pH, turbidity, conductivity, and dissolved oxygen. The core controller, the Raspberry Pi B+ model, processes sensor data, and the findings highlight the capability of this system to ensure safe drinking water through cloud-based data visualization. Hong et al. [23] conducted a study on water quality monitoring using Arduino-based sensors, revealing that while the implemented system proved reliable, it currently relies on human assistance and is prone to data inaccuracies. The research suggests the potential for future expansion into an IoT-friendly system. In our IoT-based three-level water quality assessment system, we have used the Arduino UNO as our main controller. For data collection, we have taken four sensors: TDS, temperature, pH, and turbidity. We have used a 16x2 LCD display to show real-time data in our system. We have also built an Android mobile application using MIT App Inventor 2 to show real-time data on our mobile. A Bluetooth module is used to send the data from the Arduino board to our mobile. We have used a 3.7-volt lithium battery to power our system. We have used Arduino IDE software to write hardware code for our system and upload it to Arduino UNO. After integrating the whole system, we checked it and fixed the bugs. Then we visited an industrial site for data collection. We have visited sites at Narayanganj, Buriganga river, and Savar and collected water data. Then we analyzed the data and made conclusions. In previous projects, researchers collected data from one level of water in their studies. However, we gathered water data from three different levels. Each level's distance is 1 inch. We also collected water data from the Buriganga River. We know that there are so many industries on the banks of the Buriganga river, and the waste waters of these industries are thrown directly into the river. That is why we marked the Buriganga river as our fourth site. By comparing all these data, we could see how industries harm our rivers and the environment. This is a big step forward in understanding the impact of industries on our natural resources. 2 Methods and Materials 2.1 Methodology This section discusses the techniques, elements, and steps used to achieve the goal. To reduce the pollution caused by industrial wastewater, the system measures the water quality of the waste. Three separate layers make up the proposed microcontroller-based industrial water quality monitoring system. The core layer, the microcontroller unit, connects the input and output layers. Four different sensors make up the input layer, which the Arduino uses to measure various indicators of water quality with an analog signal. The output layer is made up of two components: the microcontroller monitor and a mobile application that shows the digital data conversion performed by the microcontroller. 2.2.1 Block Diagram Figure 1 shows the block diagram of the three-level water monitoring system. The system consists of an Arduino UNO microcontroller board, input, and output. The Arduino board, which is also connected to the output units, the serial monitors on the Arduino board, and a mobile app created using MIT App Inventor and linked to a Bluetooth module are used to display the viewer's digitally altered data. 2.2.2. Working Process Flow Chart The Arduino software combines the functionality for multiple sensors and Bluetooth modules that link the Arduino UNO to the mobile application. The microprocessor receives analog data from the temperature, pH, TDS, and turbidity sensors, which sense the analog output from the water supply. These analog impulses are inputted into the Arduino UNO, which transforms them into digital signals that the Arduino IDE monitor displays as real-time values. The two devices are connected by Bluetooth, and the Bluetooth device transmits this data to the mobile application that displays the changes in water quality metrics in real-time. Figure 2 shows the system flow chart. 2.3 Materials and Tools The system is made up of several parts of various kinds that serve various purposes. Some are used to connect the input and output, while others are for the input and output. We have used Arduino UNO as our main controller. It collects data from sensors and sends it to mobile devices. We have used a water temperature sensor lm35. Measures the temperature of the water and sends it to Arduino UNO. We have used a pH sensor. This device measures the pH value of water. We have used TDS (total dissolved solids) sensors. It collects the TDS value of water and sends it to Arduino UNO. We have used a turbidity sensor. This device measures the turbidity value of water. We have used the Bluetooth Module HC-05. This module receives data from Arduino UNO and sends it to mobile devices. We used MIT App Inventor 2. We have developed an android application using this website. We have used Arduino IDE. It helped us program our entire system and Arduino UNO. 3 Result and Analysis 3.1 List of Sites Visited for Sample Collection Table 1. Visited Locations Site No Location 1 Araihazar 2 Rupganj 3 Savar 4 Buriganga We tested the water in three levels (level 1, level 2, level 3). Each level contains a 1-inch distance. We visited four sites. Three sites are industrial areas located in Narayanganj and Savar, another site is the Buriganga River. From our analysis, we have found that the water of the Buriganga river has more harmful particles than the water of the industrial area. The temperature of the Buriganga river water was normal, but the temperature of the other three sites was very high. 3.2 Design prototype and view of real-time data Figure 3 shows the prototype of the entire system. In our IoT-based three-level water quality assessment system, we have used Arduino UNO as our controller, which receives analog data from the sensors and converts it to digital data. We have used four sensors in our system to measure various parameters of water. 3.3 Data Sheet and Analytical Charts for Different Measurements 3.3.1 Temperature Analysis The value of the wastewater temperature differs from factory to factory. Although in two cases the temperature values were in the range of 25–29°C, which is quite close to the normal water temperature, there were two samples in the experiment where the wastewater temperature was quite high, reaching around 37 °C. From the analysis of temperature data, it appears that the water from the first and second sites has high-temperature values compared to the water sample collected from the other two sites. Table 2. Temperature data Sites Level 1 Level 2 Level 3 Site 1 34.36 35.39 35.35 Site 2 36.65 35.97 35.57 Site 3 28.5 28.3 28.9 Site 4 25.69 25.66 25.66 The water temperatures for the different samples collected from the various sites are represented in a line chart in Figure 4. Sites 1 and 2 are the industrial areas located in Narayanganj, which is very near Dhaka. The temperature of wastewater at these sites is higher than that of the wastewater sample collected from the other two site. 3.3.2 TDS Analysis From Table 3, the TDS value varies greatly from one site to the next. In all cases, high TDS values are very hazardous to aquatic life. The value of TDS varies from an alarming range of 170 to 360 ppm. In the experiment, the lowest TDS value was found to be 204.46 ppm at level 1, which is still very high and can cause a lot of damage to the quality of the water. Table 3. TDS data Sites Level 1 Level 2 Level 3 Site 1 483.93 480.83 487.81 Site 2 371.06 371.02 368.92 Site 3 204.46 208.38 206.02 Site 4 278.00 277.87 278.01 Figure 5 presents the TDS values for all levels using a bar graph for a clearer view. In this study, it was observed that the factors determining the water quality for industrial areas are higher in most cases compared to the values for normal water. It is vital to control this alarming situation. This data and study will be very helpful for government authorities in monitoring and taking the necessary action for proper waste management. Especially for the Buriganga River. Otherwise, it becomes more hazardous for people, aquatic species, and the environment. We must protect our rivers and the environment for the benefit of all of us. 3.3.3 pH Analysis In Table 4, it is noted that, in all cases, most of the samples have high pH values at every level. All samples have a pH value of more than 7.0, which tells us that all samples contain high-concentration molecules in the base solution. The Buriganga River has a lower pH value than the others at every level of water. Table 4. pH data Sites Level 1 Level 2 Level 3 Site 1 9.5 9.61 9.59 Site 2 10.13 10.03 10.32 Site 3 10.31 10.35 10.33 Site 4 7.35 7.28 7.31 The pH value in our experiment varied from industry to industry. Sites 1 and 2 are the places where most of the garment industries use light dyeing. The pH value of the wastewater released from this kind of industry is in the range of 10.00–10.90. At site-3, most industries produce winter clothing where the pH values of the wastewater are in the range of around 9.60, and lastly, at site-4, Buriganga River, there are so many knitting industries in Old Dhaka City that all the wastage of those industries is thrown in the river. However, the pH value is not as high as the other sites. It may be because of the large amount of water and because the water is always flowing. 3.3.4 Turbidity Analysis Table 5 shows the turbidity values of different samples collected from the same four locations. Turbidity values varied in a range of 1.30 to 1.60 for the three levels. This range is quite like that of normal tap water. But for one sample, which is from Site 4, the turbidity value is much higher than at the other sites. In Table 4, it is observed that the highest turbidity value is observed in the Buriganga River, which is 12.89 NTU. Table 5. Turbidity data Sites Level 1 Level 2 Level 3 Site 1 2.01 2.05 1.99 Site 2 2.88 2.49 2.79 Site 3 1.43 1.46 1.51 Site 4 12.51 12.89 12.38 Figure 7 presents the turbidity values from the levels for all sites using a bar graph. Here, the turbidity values for different locations have been presented in various colors. The lowest turbidity, which was 1.43, was observed at level 1 at Site 3. The highest turbidity is observed at level 2 at site 4 (the Buriganga River), which is 12.89, and the lowest turbidity is 12.38 at level 3. 4 Conclusions In the context of Bangladesh, an IoT-based water quality measuring system can be extremely important. Because we are aware of Bangladesh's dire situation, we are specifically speaking to Bangladesh here. Bangladesh is home to numerous clothing industries. Most of them are located along the rivers of the country. Because of this, many rivers are contaminated. The appropriate authorities should take the necessary steps to purify the water. But it is past time for us to act practically. People need to first be informed. We believe that many companies will benefit from our research to keep the environment safe. For the benefit of the country, we also hope that individuals will use the suggested system. Before providing water to the public, the quality can be assessed using this approach. The proposed system, the findings of the study, and the analysis will contribute to protecting our water from contamination and improving the well-being of all living things. IoT is a crucial developing technology in healthcare that will help extend the lives of humans and other living things. After developing the system, we tested our device. We tested it with two sensors, a TDS sensor and a temperature sensor. The developed system has TDS, and temperature measuring units that are interfaced with sensors successfully. Results and analysis show that our device and the sensors used are working properly. Now our device is ready to be used in industrial sites to monitor wastewater. The application of this proposed system can be further extended. In the future, more sensors can be added to measure other parameters of the water. The system can monitor other factors that pollute the environment by adding more specific sensors to the system. Declarations Data Availability Statement: Data was not used to support the findings of this study. Conflicts of Interest: The authors would like to confirm that there are no conflicts of interest regarding the study. References Chowdury, Mohammad Salah Uddin, et al. "IoT based real-time river water quality monitoring system." 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2024 Reviews received at journal 13 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviews received at journal 09 Apr, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviewers invited by journal 04 Apr, 2024 Editor assigned by journal 02 Apr, 2024 Submission checks completed at journal 02 Apr, 2024 First submitted to journal 26 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3992923","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275104596,"identity":"2896ab27-1bda-42db-af4f-ff2ac962c767","order_by":0,"name":"Rejoan Kobir Nishan","email":"","orcid":"","institution":"North South University","correspondingAuthor":false,"prefix":"","firstName":"Rejoan","middleName":"Kobir","lastName":"Nishan","suffix":""},{"id":275104597,"identity":"9305bbc2-063e-47da-b72c-46dc54966ec4","order_by":1,"name":"Shapla Akter","email":"","orcid":"","institution":"North South 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University","correspondingAuthor":false,"prefix":"","firstName":"Meratun","middleName":"Junnut","lastName":"Anee","suffix":""},{"id":275104601,"identity":"f5f11cca-1e60-418d-9143-e2362b846b06","order_by":5,"name":"Amzad Hossain","email":"data:image/png;base64,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","orcid":"","institution":"North South University","correspondingAuthor":true,"prefix":"","firstName":"Amzad","middleName":"","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2024-02-27 05:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3992923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3992923/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52083651,"identity":"8d0ca4c9-8a09-4ac8-9b16-781c43c814c3","added_by":"auto","created_at":"2024-03-06 11:43:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":825707,"visible":true,"origin":"","legend":"\u003cp\u003eBlock diagram of the three-level water monitoring system\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/d89cf2b5fc6b64246bf1f3e3.png"},{"id":52083289,"identity":"3d5daa23-cfb5-4b9f-b6d3-e9c9f074615e","added_by":"auto","created_at":"2024-03-06 11:35:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196949,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the three-level water monitoring system\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/52a9dfedb116be7b1a57ed87.png"},{"id":52083649,"identity":"48b85a3f-898c-4807-b897-20acd9cc1eed","added_by":"auto","created_at":"2024-03-06 11:43:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":374973,"visible":true,"origin":"","legend":"\u003cp\u003ePrototype of the project of a three-level water monitoring assessment system\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/c12745f31946970a02a2e4c8.png"},{"id":52083288,"identity":"3859e997-d3f9-4b26-9162-c8eac5af1817","added_by":"auto","created_at":"2024-03-06 11:35:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24363,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature Graph Chart\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/438dab231a8a6d3434d93f3a.png"},{"id":52083294,"identity":"3d3b24b2-d944-4aea-a8c5-d4082fbffbcf","added_by":"auto","created_at":"2024-03-06 11:35:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28994,"visible":true,"origin":"","legend":"\u003cp\u003eTDS Graph Chart\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/12e620c60d2c616016df5506.png"},{"id":52083292,"identity":"e5579298-1b38-4613-a346-4dd607bc68f2","added_by":"auto","created_at":"2024-03-06 11:35:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":26632,"visible":true,"origin":"","legend":"\u003cp\u003epH Graph Chart\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/ba059877461e14a5ac7e578c.png"},{"id":52084818,"identity":"3bc2c681-bcb9-4630-881d-bbfcacff4446","added_by":"auto","created_at":"2024-03-06 11:51:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":23989,"visible":true,"origin":"","legend":"\u003cp\u003eTurbidity Graph Chart\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/0af7b26d3663bbf4c875ff2e.png"},{"id":52085256,"identity":"bcca59b7-1cfd-4eec-a305-ea2ab1b47e1a","added_by":"auto","created_at":"2024-03-06 11:59:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1615952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3992923/v1/d70efb47-ad79-4951-8cd7-e24578cbcc72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIndustrialization is one of the most important parts of our development. especially the garments sector, which is one of the main sources of total gross domestic product (GDP), making a great impact on our economy, but causing serious damage to our environment. Water is one of the most important elements of the environment. Most of the waste released from the garment factories is dumped into the nearest rivers or canals. Industrial wastewater is aqueous waste that results from substances that have been dissolved or suspended in water, typically during the use of water in an industrial manufacturing process or the cleaning activities that take place along with that process. Hence, the quality of the water in these bodies has become very incompatible with living beings and has become one of the major threats to the environment and human health. It is also harmful to the lives in the water. To save fish and other aquatic animals and the environment, we need to monitor the quality of water and find the reason for the pollution. Real-time monitoring of the quality of water is vital to control water pollution. We propose an Internet of Things (IoT)-based real-time 3-level water quality monitoring system integrated with a mobile application. It will be able to measure some of the most important water-related indexes, including the potential of hydrogen (pH), total dissolved solids (TDS), turbidity, and temperature of water. The system\u0026apos;s results will be very helpful in saving the environment and living creatures on Earth. Chowdhury et al. conducted a study on a real-time river water quality monitoring system based on the internet of things (IoT) [1]. They found that the current water quality monitoring system, characterized by a manual and time-consuming process, can be effectively addressed through the implementation of a sensor-based monitoring system utilizing wireless sensor network (WSN) components and internet of things (IoT) technology [2]. The study proposes an IoT-based water quality measurement system, which uses sensors for parameters such as pH, conductivity, salt, and LDR, allowing real-time monitoring and analysis of water quality, with data transmitted through ZigBee and GSM modules, allowing immediate alerts and cloud data storage. S.A. Hamid et al. [3] conducted a study on an IoT-based water quality monitoring system and its evaluation. They found that The SWQMS can monitor the water quality automatically and the pH value is stable regardless of time, pool size, or their combination, but the water temperature varies with time.\u003c/p\u003e\n\u003cp\u003eKonde et al. [4] conducted research on an Internet of Things (IoT)-based system to monitor water quality. They found that implementing a Smart Water Quality Monitoring System (SWQM) with a reconfigurable sensor interface device in an IoT environment, utilizing FPGA design boards, Zigbee-based wireless communication modules, and various sensors, allows real-time monitoring of critical water parameters, presenting an effective solution to address the increasing challenge of global water pollution. Lakshmikantha et al. [5] found that addressing the growing threat of water pollution is crucial, and early detection through a cost-effective IoT-based smart water quality monitoring system is essential to prevent contamination, protect human and animal health, and maintain ecosystem balance by taking timely measures. Budiarti et al. [6] \u0026lsquo;Development of IoT for automated water quality monitoring system\u0026apos; highlighted the development of an integrated Internet of Things (IoT) water quality monitoring system using Raspberry Pi and environmental sensors, demonstrating its effectiveness as an online automated real-time monitoring solution to manage water quality and ensure the sustainability of water resources. Hawari et al. [7] conducted research on the development of a real-time water quality monitoring system based on the Internet of Things (IoT). The research presents the development of a real-time Internet of Things (IoT) based water quality monitoring system, utilizing temperature, turbidity, and pH sensors, enabling immediate water quality assessment, data analysis based on the Water Quality Index (WQI), cloud computing for remote data storage, efficient power management for extended battery life, deployment at multiple locations, and a user-friendly mobile application, demonstrating high reliability and practicality in real-time water quality monitoring.\u003c/p\u003e\n\u003cp\u003eHaque et al. [8] designed an Internet of Things (IoT)--based water quality monitoring system using the Zigbee protocol. The system incorporates specific sensors to measure parameters such as conductivity, dissolved oxygen, turbidity, pH, and temperature. The data collected are processed by microcontrollers and transmitted to a central controller, Raspberry Pi, through the Zigbee protocol. The findings highlight the efficacy of this system in providing continuous water quality assessment, ensuring suitability for general use, and enabling convenient data access through cloud computing via any browser, on request. Muhammad et al. [9] researched an Internet of Things (IoT)-based system designed to monitor water quality in soft shell crab farming, with the aim of improving awareness among farmers and improving the survival rates and yields of soft-shell crabs. Ajith et al. [10] conducted research on a smart water quality monitoring system based on the Internet of Things (IoT) utilizing cloud technology. The research proposes an IoT-based smart water quality monitoring system employing cloud and deep learning, highlighting the need for continuous, real-time monitoring of water quality to address water pollution issues in India, utilizing NodeMCU devices and multiple sensors to measure parameters, with results stored in the cloud and deep learning techniques predicting water suitability.\u003c/p\u003e\n\u003cp\u003ePasika et al. [11] developed a cost-effective smart water quality monitoring system using the Internet of Things (IoT). The research proposes a cost-effective Water Quality Monitoring (WQM) system utilizing IoT technology, employing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity, with data processed by a Microcontroller Unit (MCU) and sent to the cloud through ThinkSpeak application for real-time monitoring and ensuring the supply of purified drinking water. Chen et al. [12] developed an Internet of Things (IoT)-based system to monitor water quality in fish farms. The study presents an innovative smart water quality monitoring system that utilizes wireless transmission technology, various sensors, and a robotic arm for automatic measurements in fish farms, effectively addressing the challenges of aquaculture losses caused by typhoons and cold snaps in Taiwan, while reducing the dependence on human resources and mitigating data errors. Simitha et al. [13] developed an Internet of Things (IoT) and Wireless Sensor Network (WSN)-based water quality monitoring system. The findings highlight the effectiveness of the proposed system in providing real-time water quality data, allowing efficient management and conservation of water resources through the integration of low-cost, low-power, and long-range communication using LoRaWAN technology.\u003c/p\u003e\n\u003cp\u003eShanmugam et al. [14] developed an IoT-based smart water quality monitoring system for Malaysia. The proposed system aims to address the challenges of water pollution caused by rapid development, ensuring improved water quality management, and preventing disruptions in water supply systems.\u003c/p\u003e\n\u003cp\u003eRaji et al. [15] developed an IoT-based water quality monitoring system with an Android application. The findings highlight the successful implementation of a cost-effective system that uses various sensors for real-time monitoring, providing a user-friendly interface to detect impurities and ensure pollution-free water resources.\u003c/p\u003e\n\u003cp\u003eDas et al. [16] implemented a real-time water quality monitoring system using the Internet of Things (IoT). The findings highlight the system\u0026apos;s capability for autonomous decision making, real-time data acquisition, and remote access through the Internet, aimed at improving public health, reducing costs, and eliminating the need for offline laboratory analysis of water samples. Prasad, A. N. et al. [17] implemented a smart water quality monitoring system for Fiji, integrating IoT and remote sensing technology. The system addresses concerns related to the deterioration of water quality caused by industrial production and agricultural practices, emphasizing the need for frequent data collection to monitor and improve water quality in the region. AlMetwally et al. [18] present a real-time Internet of Things (IoT) based water quality management system, offering autonomous control of water quality factors through sensors measuring pH, temperature, and turbidity for home applications. The system aims to enhance public health and reduce costs by eliminating offline lab analyzes, providing real-time data acquisition, and user-friendly interfaces for configuration and monitoring.\u003c/p\u003e\n\u003cp\u003eRamesh et al. [19] present a study on water quality monitoring and waste management using IoT, focusing on the design and implementation of a system in Pettipalam Colony, Thalassery. The findings highlight the importance of real-time data on environmental parameters for effective waste management, with potential implications for land restoration initiatives in various regions of India. Kamaludin et al. [20] explore the monitoring of water quality using the Internet of Things (IoT) and propose a system integrating RFID, WSN and IP-based communication, specifically designed for vegetation areas and using a 920MHz Digi Mesh protocol. The study includes a real environment evaluation on the lake in the campus area of University Sains Malaysia, incorporating analyses of energy consumption and the read range of communication to assess the overall performance of the proposed system in measuring pH levels. Huan et al. [21] designed a water quality monitoring system for aquaculture ponds based on NB-IoT technology, achieving accurate and real-time data transmission of environmental parameters (temperature, pH, dissolved oxygen) with high control precision. The system demonstrated stable overall operation in Changzhou, Jiangsu Province, China, maintaining the accuracy of the temperature control at \u0026lt;0.12 ℃, the accuracy at \u0026plusmn;0.12℃, dissolved oxygen control within \u0026lt;0.55 mg / L, and the accuracy within \u0026plusmn;0.55mg/L, and pH control at \u0026plusmn;0.09, providing essential data and technical support for effective regulation of water quality and management of aquaculture production.\u003c/p\u003e\n\u003cp\u003eVijayakumar et al. [22] conducted a study on real-time monitoring of water quality in an IoT environment, presenting a low-cost system with sensors that measure various parameters such as temperature, pH, turbidity, conductivity, and dissolved oxygen. The core controller, the Raspberry Pi B+ model, processes sensor data, and the findings highlight the capability of this system to ensure safe drinking water through cloud-based data visualization. Hong et al. [23] conducted a study on water quality monitoring using Arduino-based sensors, revealing that while the implemented system proved reliable, it currently relies on human assistance and is prone to data inaccuracies. The research suggests the potential for future expansion into an IoT-friendly system.\u003c/p\u003e\n\u003cp\u003eIn our IoT-based three-level water quality assessment system, we have used the Arduino UNO as our main controller. For data collection, we have taken four sensors: TDS, temperature, pH, and turbidity. We have used a 16x2 LCD display to show real-time data in our system. We have also built an Android mobile application using MIT App Inventor 2 to show real-time data on our mobile. A Bluetooth module is used to send the data from the Arduino board to our mobile. We have used a 3.7-volt lithium battery to power our system. We have used Arduino IDE software to write hardware code for our system and upload it to Arduino UNO. After integrating the whole system, we checked it and fixed the bugs. Then we visited an industrial site for data collection. We have visited sites at Narayanganj, Buriganga river, and Savar and collected water data. Then we analyzed the data and made conclusions.\u003c/p\u003e\n\u003cp\u003eIn previous projects, researchers collected data from one level of water in their studies. However, we gathered water data from three different levels. Each level\u0026apos;s distance is 1 inch. We also collected water data from the Buriganga River. We know that there are so many industries on the banks of the Buriganga river, and the waste waters of these industries are thrown directly into the river. That is why we marked the Buriganga river as our fourth site. By comparing all these data, we could see how industries harm our rivers and the environment. This is a big step forward in understanding the impact of industries on our natural resources.\u003c/p\u003e"},{"header":"2 Methods and Materials","content":"\u003ch2\u003e\u003cstrong\u003e2.1\u0026nbsp;\u003c/strong\u003eMethodology\u003c/h2\u003e\n\u003cp\u003eThis section discusses the techniques, elements, and steps used to achieve the goal. To reduce the pollution caused by industrial wastewater, the system measures the water quality of the waste. Three separate layers make up the proposed microcontroller-based industrial water quality monitoring system. The core layer, the microcontroller unit, connects the input and output layers. Four different sensors make up the input layer, which the Arduino uses to measure various indicators of water quality with an analog signal. The output layer is made up of two components: the microcontroller monitor and a mobile application that shows the digital data conversion performed by the microcontroller.\u003c/p\u003e\n\u003ch2\u003e2.2.1 Block Diagram\u003c/h2\u003e\n\u003cp\u003eFigure 1 shows the block diagram of the three-level water monitoring system. The system consists of an Arduino UNO microcontroller board, input, and output. The Arduino board, which is also connected to the output units, the serial monitors on the Arduino board, and a mobile app created using MIT App Inventor and linked to a Bluetooth module are used to display the viewer\u0026apos;s digitally altered data.\u003c/p\u003e\n\u003ch2\u003e2.2.2. Working Process Flow Chart\u003c/h2\u003e\n\u003cp\u003eThe Arduino software combines the functionality for multiple sensors and Bluetooth modules that link the Arduino UNO to the mobile application. The microprocessor receives analog data from the temperature, pH, TDS, and turbidity sensors, which sense the analog output from the water supply. These analog impulses are inputted into the Arduino UNO, which transforms them into digital signals that the Arduino IDE monitor displays as real-time values. The two devices are connected by Bluetooth, and the Bluetooth device transmits this data to the mobile application that displays the changes in water quality metrics in real-time. Figure 2 shows the system flow chart.\u003c/p\u003e\n\u003ch2\u003e2.3 Materials and Tools\u003c/h2\u003e\n\u003cp\u003eThe system is made up of several parts of various kinds that serve various purposes. Some are used to connect the input and output, while others are for the input and output.\u003c/p\u003e\n\u003cp\u003eWe have used Arduino UNO as our main controller. It collects data from sensors and sends it to mobile devices. We have used a water temperature sensor lm35. Measures the temperature of the water and sends it to Arduino UNO. We have used a pH sensor. This device measures the pH value of water. We have used TDS (total dissolved solids) sensors. It collects the TDS value of water and sends it to Arduino UNO. We have used a turbidity sensor. This device measures the turbidity value of water. We have used the Bluetooth Module HC-05. This module receives data from Arduino UNO and sends it to mobile devices. We used MIT App Inventor 2. We have developed an android application using this website. We have used Arduino IDE. It helped us program our entire system and Arduino UNO.\u003c/p\u003e"},{"header":"3 Result and Analysis","content":"\u003ch2\u003e3.1 List of Sites Visited for Sample Collection\u003c/h2\u003e\n\u003cp\u003eTable 1. Visited Locations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u0026nbsp;No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"68.87417218543047%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"68.87417218543047%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAraihazar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"68.87417218543047%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRupganj\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"68.87417218543047%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSavar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"68.87417218543047%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBuriganga\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe tested the water in three levels (level 1, level 2, level 3). Each level contains a 1-inch distance. We visited four sites. Three sites are industrial areas located in Narayanganj and Savar, another site is the Buriganga River. From our analysis, we have found that the water of the Buriganga river has more harmful particles than the water of the industrial area. The temperature of the Buriganga river water was normal, but the temperature of the other three sites was very high.\u003c/p\u003e\n\u003ch2\u003e3.2 Design prototype and view of real-time data\u003c/h2\u003e\n\u003cp\u003eFigure 3 shows the prototype of the entire system. In our IoT-based three-level water quality assessment system, we have used Arduino UNO as our controller, which receives analog data from the sensors and converts it to digital data. We have used four sensors in our system to measure various parameters of water.\u003c/p\u003e\n\u003ch2\u003e3.3 Data Sheet and Analytical Charts for Different Measurements\u003c/h2\u003e\n\u003ch2\u003e3.3.1 Temperature Analysis\u003c/h2\u003e\n\u003cp\u003eThe value of the wastewater temperature differs from factory to factory. Although in two cases the temperature values were in the range of 25\u0026ndash;29\u0026deg;C, which is quite close to the normal water temperature, there were two samples in the experiment where the wastewater temperature was quite high, reaching around 37 \u0026deg;C. From the analysis of temperature data, it appears that the water from the first and second sites has high-temperature values compared to the water sample collected from the other two sites.\u003c/p\u003e\n\u003cp\u003eTable 2. Temperature data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.76780185758514%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e34.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.76780185758514%\" valign=\"top\"\u003e\n \u003cp\u003e35.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e35.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e36.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.76780185758514%\" valign=\"top\"\u003e\n \u003cp\u003e35.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.76780185758514%\" valign=\"top\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.76780185758514%\" valign=\"top\"\u003e\n \u003cp\u003e25.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077399380804952%\" valign=\"top\"\u003e\n \u003cp\u003e25.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe water temperatures for the different samples collected from the various sites are represented in a line chart in Figure 4. Sites 1 and 2 are the industrial areas located in Narayanganj, which is very near Dhaka. The temperature of wastewater at these sites is higher than that of the wastewater sample collected from the other two site.\u003c/p\u003e\n\u003ch2\u003e3.3.2 TDS Analysis\u003c/h2\u003e\n\u003cp\u003eFrom Table 3, the TDS value varies greatly from one site to the next. In all cases, high TDS values are very hazardous to aquatic life. The value of TDS varies from an alarming range of 170 to 360 ppm. In the experiment, the lowest TDS value was found to be 204.46\u0026thinsp;ppm at level 1, which is still very high and can cause a lot of damage to the quality of the water.\u003c/p\u003e\n\u003cp\u003eTable 3. TDS data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.79108635097493%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.79108635097493%\" valign=\"top\"\u003e\n \u003cp\u003e483.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e480.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e487.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.79108635097493%\" valign=\"top\"\u003e\n \u003cp\u003e371.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e371.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e368.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.79108635097493%\" valign=\"top\"\u003e\n \u003cp\u003e204.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e208.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e206.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.79108635097493%\" valign=\"top\"\u003e\n \u003cp\u003e278.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e277.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.069637883008358%\" valign=\"top\"\u003e\n \u003cp\u003e278.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 5 presents the TDS values for all levels using a bar graph for a clearer view. In this study, it was observed that the factors determining the water quality for industrial areas are higher in most cases compared to the values for normal water. It is vital to control this alarming situation. This data and study will be very helpful for government authorities in monitoring and taking the necessary action for proper waste management. Especially for the Buriganga River. Otherwise, it becomes more hazardous for people, aquatic species, and the environment. We must protect our rivers and the environment for the benefit of all of us.\u003c/p\u003e\n\u003ch2\u003e3.3.3 pH Analysis\u003c/h2\u003e\n\u003cp\u003eIn Table 4, it is noted that, in all cases, most of the samples have high pH values at every level. All samples have a pH value of more than 7.0, which tells us that all samples contain high-concentration molecules in the base solution. The Buriganga River has a lower pH value than the others at every level of water.\u003c/p\u003e\n\u003cp\u003eTable 4. pH data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe pH value in our experiment varied from industry to industry. Sites 1 and 2 are the places where most of the garment industries use light dyeing. The pH value of the wastewater released from this kind of industry is in the range of 10.00\u0026ndash;10.90. At site-3, most industries produce winter clothing where the pH values of the wastewater are in the range of around 9.60, and lastly, at site-4, Buriganga River, there are so many knitting industries in Old Dhaka City that all the wastage of those industries is thrown in the river. However, the pH value is not as high as the other sites. It may be because of the large amount of water and because the water is always flowing.\u003c/p\u003e\n\u003ch2\u003e3.3.4 Turbidity Analysis\u003c/h2\u003e\n\u003cp\u003eTable 5 shows the turbidity values of different samples collected from the same four locations. Turbidity values varied in a range of 1.30 to 1.60 for the three levels. This range is quite like that of normal tap water. But for one sample, which is from Site 4, the turbidity value is much higher than at the other sites. In Table 4, it is observed that the highest turbidity value is observed in the Buriganga River, which is 12.89 NTU.\u003c/p\u003e\n\u003cp\u003eTable 5. Turbidity data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLevel\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSite\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e12.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 7 presents the turbidity values from the levels for all sites using a bar graph. Here, the turbidity values for different locations have been presented in various colors. The lowest turbidity, which was 1.43, was observed at level 1 at Site 3. The highest turbidity is observed at level 2 at site 4 (the Buriganga River), which is 12.89, and the lowest turbidity is 12.38 at level 3.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eIn the context of Bangladesh, an IoT-based water quality measuring system can be extremely important. Because we are aware of Bangladesh\u0026apos;s dire situation, we are specifically speaking to Bangladesh here. Bangladesh is home to numerous clothing industries. Most of them are located along the rivers of the country. Because of this, many rivers are contaminated. The appropriate authorities should take the necessary steps to purify the water. But it is past time for us to act practically. People need to first be informed. We believe that many companies will benefit from our research to keep the environment safe. For the benefit of the country, we also hope that individuals will use the suggested system. Before providing water to the public, the quality can be assessed using this approach. The proposed system, the findings of the study, and the analysis will contribute to protecting our water from contamination and improving the well-being of all living things. IoT is a crucial developing technology in healthcare that will help extend the lives of humans and other living things.\u003c/p\u003e\n\u003cp\u003eAfter developing the system, we tested our device. We tested it with two sensors, a TDS sensor and a temperature sensor. The developed system has TDS, and temperature measuring units that are interfaced with sensors successfully. Results and analysis show that our device and the sensors used are working properly. Now our device is ready to be used in industrial sites to monitor wastewater.\u003c/p\u003e\n\u003cp\u003eThe application of this proposed system can be further extended. In the future, more sensors can be added to measure other parameters of the water. The system can monitor other factors that pollute the environment by adding more specific sensors to the system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData was not used to support the findings of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors would like to confirm that there are no conflicts of interest regarding the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChowdury, Mohammad Salah Uddin, et al. \u0026quot;IoT based real-time river water quality monitoring system.\u0026quot; Procedia computer science 155 (2019): 161-168. https://doi.org/10.1016/j.procs.2019.08.025.\u003c/li\u003e\n\u003cli\u003eA. Hossain, M. J. Anee, R. Faruqui, S. Bushra, P. Rahman, and R. Khan, \u0026quot;A GPS Based Unmanned Drone Technology for Detecting and Analyzing Air Pollutants,\u0026quot; in IEEE Instrumentation \u0026amp; Measurement Magazine, vol. 25, no. 9, pp. 53-60, 2022. \u003ca data-fr-linked=\"true\" href=\"https://10.1109/MIM.2022.9955468\"\u003ehttps://10.1109/MIM.2022.9955468\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eS. A. Hamid, A. M. A. Rahim, S. Y. Fadhlullah, S. Abdullah, Z. Muhammad and N. A. M. 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Mourad. \u0026quot;Real time internet of things (IoT) based water quality management system.\u0026quot; Procedia CIRP 91 (2020): 478-485.https://doi.org/10.1016/j.procir.2020.03.107.\u003c/li\u003e\n\u003cli\u003eKamaludin, Kamarul Hafiz, and Widad Ismail. \u0026quot;Water quality monitoring with internet of things (IoT).\u0026quot; 2017 IEEE Conference on Systems, Process and Control (ICSPC). IEEE, 2017.https://doi.org/10.1109/SPC.2017.8313015.\u003c/li\u003e\n\u003cli\u003eHuan, Juan, et al. \u0026quot;Design of water quality monitoring system for aquaculture ponds based on NB-IoT.\u0026quot; Aquacultural Engineering 90 (2020): 102088.https://doi.org/10.1016/j.aquaeng.2020.102088.\u003c/li\u003e\n\u003cli\u003eVijayakumar, N., and and R. Ramya. \u0026quot;The real time monitoring of water quality in IoT environment.\u0026quot; 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2015.https://doi.org/10.1109/ICIIECS.2015.7193080.\u003c/li\u003e\n\u003cli\u003eHong, Wong Jun, et al. \u0026quot;Water quality monitoring with arduino based sensors.\u0026quot; Environments 8.1 (2021): 6.https://doi.org/10.1109/ICIIECS.2015.7193080.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IoT, Arduino Uno, pH Sensor, TDS, Turbidity, LM-35 Water Temperature Sensor, HC-05 Bluetooth Module; MIT inventor 2, IDE","lastPublishedDoi":"10.21203/rs.3.rs-3992923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3992923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Industrialization and urbanization both lead to pollution of the environment, particularly of the water. Industrialization and urbanization-related pollution have detrimental consequences both for the environment and for life on Earth. Food poisoning, diarrhea, short-term gastrointestinal problems, respiratory illnesses, skin conditions, and other major health problems can all result from this contaminated water. Most of the waste generated from garment factories is dumped into the closest rivers or canals in a developing nation such as Bangladesh, where the ready-made garment sector is one of the key contributors to the overall gross domestic product (GDP). The quality of water in these bodies has deteriorated to the point that it is no longer suitable for supporting life, making it one of the biggest hazards to both the environment and public health. Furthermore, water pollution in Bangladesh causes a daily decline in the number of fish in Bangladesh's rivers and canals. We must therefore keep an eye on the water quality and identify the causes of pollution if we want to conserve fish, other aquatic life, and the ecosystem. To reduce water pollution, real-time monitoring of water quality is essential. Most methods to reduce water pollution are primarily biological and laboratory-based and require a significant amount of time and resources. To solve this issue, we developed a real-time, three-level water quality monitoring system that is based on the Internet of Things (IoT) and integrated with mobile applications. The system suggested in this study collects data from three layers of wastewater and measures some of the most significant indicators of water, including temperature, turbidity, total dissolved solids (TDS) and hydrogen potential (pH). The outcomes of the proposed system will significantly improve the health living organisms on Earth by reusing wastewater and preserving the environment.","manuscriptTitle":"IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 11:35:47","doi":"10.21203/rs.3.rs-3992923/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-22T16:31:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-13T16:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0aaceaab-efca-45d5-bcbf-0a7228739fa0","date":"2024-04-10T06:11:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0ce39a2c-bfd5-4fcd-8a0f-0609ee25d6c4","date":"2024-04-09T16:29:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-09T07:30:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0f320118-98c2-4d1d-866b-218ee9beaba0","date":"2024-04-09T04:38:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-04T15:56:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-02T06:48:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-02T06:47:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Water","date":"2024-02-27T04:41:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62e03e38-262d-4d56-a925-ec86f613335f","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-02T16:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 11:35:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3992923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3992923","identity":"rs-3992923","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}