1 Introduction

Industrialization is one of the most essential aspects of our progress. Specifically, the garment sector, which is one of the primary generators of total GDP, has a significant influence on our economy while generating substantial environmental damage. Water is one of the most vital components of the ecosystem. Most of the garbage generated by textile manufacturing is discharged into nearby rivers or canals. Industrial wastewater is water waste produced by compounds that have been dissolved or suspended in water, usually during the use of water in an industrial production process or the cleaning operations that occur in conjunction with that process. As a result, the quality of the water in these bodies has degraded to the point that it is incompatible with living organisms, posing a significant threat to the environment and human health. It also harms aquatic life. To safeguard fish, other aquatic species, and the ecosystem, we must monitor water quality and identify the source of the contamination. Recognizing the urgent need for effective water quality management, our study endeavors to address this pressing issue through the lens of technological innovation. The garment industry’s integration with IoT systems marks a pivotal step towards sustainable manufacturing practices. By employing IoT technology, garment factories can precisely monitor water usage, detect anomalies in real time, and optimize resource management processes. This synergy enables proactive intervention to minimize environmental impact, reduce water wastage, and ensure regulatory compliance. Moreover, IoT-enabled solutions offer scalability and adaptability, empowering industries to streamline operations while enhancing environmental sustainability. Real-time monitoring of water quality is critical for reducing pollution. We present an IoT-based real-time 3-level water quality monitoring system that is coupled with a mobile application. It will be able to measure some of the most significant water-related indicators such as hydrogen potential (pH), total dissolved solids (TDS), turbidity, and water temperature. The system’s findings will be extremely beneficial in safeguarding the environment and living things on Earth. Chowdhury et al. investigated a real-time river water quality monitoring system based on the IoT [1]. They discovered that the current water quality monitoring system, which is manual and time-consuming, can be effectively addressed by implementing a sensor-based monitoring system that incorporates wireless sensor network (WSN) components and IoT technology [2, 3]. The study proposes an IoT-based water quality measurement system that uses sensors for parameters such as pH, conductivity, salt, and LDR to allow real-time monitoring and analysis of water quality. Data is transmitted via ZigBee and GSM modules, allowing for immediate alerts and cloud data storage. Hamid et al. [4] investigated and evaluated an IOT-based water quality monitoring system. They discovered that the SWQMS can automatically monitor water quality, and the pH value remains constant independent of time, pool size, or their combination, although the water temperature changes with time.

Konde et al. [5] investigated an IoT-based system for monitoring water quality. They discovered that implementing a Smart Water Quality Monitoring System (SWQM) with an adjustable sensor interface device in an IoT environment, using FPGA design boards, Zigbee-based wireless communication modules, and various sensors, allows for real-time monitoring of critical water parameters, presenting an effective solution to the growing challenge of global water pollution. Lakshmikantha et al. [6] discovered that addressing the growing threat of water pollution is critical, and that early detection through a cost-effective IoT-based smart water quality monitoring system is essential for preventing contamination, protecting human and animal health, and maintaining ecosystem balance by taking timely measures. Budiarti et al. [7] ‘Development of IoT for automated water quality monitoring system’ highlighted the development of an integrated 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. [8] investigated the creation of a real-time water quality monitoring system based on the IoT. The study presents the development of a real-time 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 practicability.

Haque et al. [9] designed an 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 is 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. [10] researched an 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 tender crabs. Ajith et al. [11] conducted research on a smart water quality monitoring system based on the 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. [12] developed a cost-effective smart water quality monitoring system using the 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. [13] developed an 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. [14] developed an 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. [15] 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. [16] 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. [17] implemented a real-time water quality monitoring system using the IoT. The findings highlight the system's capability for self-decision 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 et al. [18] 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. Al Metwally et al. [19] present a real-time 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. [20] 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. [21] explore the monitoring of water quality using the IoT and propose a system integrating RFID, WSN and IP-based communication, specifically designed for vegetation areas and using a 920 MHz 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. [22] 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 °C, the accuracy at ± 0.12 °C, dissolved oxygen control within < 0.55 mg/L, and the accuracy within ± 0.55 mg/L, and pH control at ± 0.09, providing essential data and technical support for effective regulation of water quality and management of farming production.

Vijayakumar et al. [23] 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. [22] 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 16 × 2 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-V 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.

Integrating insights from prior research on IoT-based water quality monitoring systems, we position our study within the broader landscape of environmental monitoring technologies. Existing literature has highlighted the potential of IoT systems to revolutionize water quality management through real-time data collection and analysis. However, the novel contribution of our research lies in developing a multi-level monitoring system that offers enhanced spatial resolution and comprehensive coverage of industrial wastewater environments. By extending the capabilities of traditional IoT frameworks, our system promises to provide more granular insights into water quality dynamics, facilitating more effective pollution control measures and environmental conservation efforts.

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 is 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. Our research intends to create an IoT-based water quality monitoring system to combat water pollution, particularly in industrial regions like garment manufacturing zones. In this project, we hope to create a multi-level monitoring system that can measure crucial factors such as pH, TDS, turbidity, and temperature. By integrating real-time data collection and analysis, we want to deliver accurate and timely information on water quality indicators, allowing for proactive pollution mitigation interventions. Furthermore, we intend to examine the environmental impact of industrial wastewater discharge on neighboring water bodies and ecosystems. We hope that these initiatives will encourage sustainable manufacturing methods and help to preserve and conserve water resources for future generations.

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 monitors and a mobile application that shows the digital data conversion performed by the microcontroller.

2.1.1 Block diagram

Figure 1 depicts a block schematic of the three-level water monitoring system. The system comprises of three components: an Arduino UNO microcontroller board, input, and output. The Arduino board, which is also attached to the output units, the serial monitors on the Arduino board, and a mobile app made using MIT App Inventor and coupled to a Bluetooth module are used to show the viewer's digitally changed data.

Fig. 1
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Block diagram of the three-level water monitoring system

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2.1.2 Working process flow chart

The Arduino software integrates the capabilities of several sensors and Bluetooth modules, which connect the Arduino UNO to the mobile application. The CPU gets analog data from temperature, pH, TDS, and turbidity sensors that detect the analog output from the water supply. These analog impulses are sent into the Arduino UNO, which converts them into digital signals that the Arduino IDE monitor displays as real-time data. The two devices are linked by Bluetooth, and the Bluetooth device sends this information to the mobile application, which displays changes in water quality measures in real time. Figure 2 shows the system flow chart.

Fig. 2
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Flow chart of the three-level water monitoring system

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2.2 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 total dissolved solids (TDS) 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 shows the locations we visited and collected data. 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.

Table 1 Visited locations
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3.2 Modeled device and 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.

Fig. 3
figure 3

Prototype of the project of a three-level water monitoring assessment system

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3.3 Data analysis of different measurements

3.3.1 Temperature analysis

Analyzing industrial wastewater temperature data through IoT devices necessitates a comprehensive assessment encompassing various critical factors. While collecting the data we ensured the accuracy and precision of the IoT devices to ensure reliable measurements. The collected data compared against baseline values or regulatory standards helps ascertain compliance, while correlating temperature with other parameters reveals underlying process dynamics. Table 2 shows the temperature data collected from those locations. The wastewater temperature varies from factory to factory. Although the temperature measurements in two cases were in the range of 25–29 °C, which is very near to normal water temperature, there were two samples in the experiment where the wastewater temperature was rather high, reaching about 37 °C. The study of temperature data shows that the water from the first and second locations has higher temperatures than the water samples gathered from the other two sites.

Table 2 Temperature data
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Figure 4 depicts a line chart of the water temperatures for numerous samples obtained from different locales. Sites 1 and 2 are industrial zones in Narayanganj, near Dhaka. The temperature of wastewater at these sites is greater than that of wastewater samples taken at the other two locations. This analysis offers predictive insights and root cause identification. These elevated wastewater temperatures, which are frequently caused by industrial operations or waste disposal, provide a complex set of difficulties with substantial environmental, regulatory, and operational repercussions. Exceeding permissible temperature limits can disrupt aquatic ecosystems, attract regulatory penalties, and impair the efficiency of treatment processes, while accelerating corrosion and energy consumption.

Fig. 4
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Temperature graph chart

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3.3.2 TDS analysis

Total Dissolved Solids (TDS) analysis in industrial wastewater management is critical for assessing water quality and regulatory compliance. Effective TDS monitoring and management strategies, including treatment processes like reverse osmosis or ion exchange, are imperative to mitigate these adverse effects and ensure sustainable water resource utilization within industrial operations. Table 3 shows that the TDS value varies substantially from one location to the next. In every situation, elevated TDS levels endanger aquatic life. TDS levels vary from 170 to 360 ppm, which is alarmingly high. The investigation indicated that the lowest TDS value was 204.46 ppm at level 1, which is still quite high and can significantly impact water quality. Figure 5 depicts the TDS readings for all levels in a bar graph for easier viewing. In this study, it was discovered that the parameters influencing water quality in industrial locations are often greater than the values for normal water. It is critical to handle this dangerous scenario. These elevated TDS levels, often indicative of high concentrations of dissolved salts, minerals, and organic compounds, can arise from various industrial activities and waste discharge. Such elevated TDS levels can impair water usability for industrial processes, agricultural irrigation, and potable water sources. Additionally, TDS can exacerbate corrosion in infrastructure, decrease soil permeability, and affect aquatic ecosystems by altering water chemistry and osmotic balance, ultimately impacting aquatic organisms' health and biodiversity. This data and analysis will be extremely useful to government officials in monitoring and taking the required actions for proper trash management. Specifically, for the Buriganga river. Otherwise, it becomes more harmful to humans, water-based creatures, and the environment. We must safeguard our rivers and the ecosystem for the sake of everyone.

Table 3 TDS data
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Fig. 5
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TDS graph chart

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3.3.3 pH analysis

In the context of industrial wastewater management, pH analysis holds paramount importance as it directly influences various chemical and biological processes, as well as regulatory compliance. Implementing robust pH monitoring and control measures, coupled with appropriate treatment technologies like pH adjustment systems or neutralization processes, is essential for maintaining wastewater pH within acceptable ranges and ensuring environmental sustainability and regulatory compliance within industrial operations. Figure 6 shows the pH analysis of the collected data. The pH value in our experiment varied by industry. According to Table 4, most of the garment industry’s light dying takes place at sites 1 and 2. The pH of the wastewater produced by this industry ranges from 10.00 to 10.90. At site-3, most enterprises create winter clothes, and the pH values of the wastewater are in the range of 9.60. Finally, at site-4, Buriganga river, there are so many knitting industries in Old Dhaka City that all of their waste is poured into 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.

Fig. 6
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pH graph chart

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Table 4 pH data
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Table 4 shows that most of the samples had high pH values at all levels. All samples exceed 7.0 pH, indicating significant concentration of molecules in the base solution. The Buriganga river has a lower pH than the others at all water levels. These higher pH levels in wastewater can arise from industrial activities such as chemical processing or metal plating, posing significant environmental and operational challenges. Acidic or alkaline wastewater can impair treatment processes, leading to decreased efficiency and increased operational costs. Moreover, extreme pH levels can cause corrosion of infrastructure, detrimentally impact aquatic ecosystems, and violate regulatory discharge limits, potentially resulting in fines or legal repercussions.

3.3.4 Turbidity analysis

Turbidity analysis is indispensable in industrial wastewater management as it provides crucial insights into water clarity and suspended particle concentrations, directly impacting treatment efficiency and environmental impact. Effective turbidity monitoring and control measures, including sedimentation basins, filtration systems, or chemical coagulation, are imperative for mitigating these adverse effects, ensuring regulatory compliance, and safeguarding water quality within industrial operations. Table 5 displays the turbidity values of several samples obtained at the same four sites. Turbidity readings ranged from 1.30 to 1.60 throughout the three levels. This range is pretty similar to that of standard tap water. However, one sample, from Site 4, had a significantly higher turbidity value than the other locations.

Table 5 Turbidity data
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Table 5 shows that the Buriganga river has the greatest turbidity value (12.89 NTU). Figure 7 depicts the turbidity values from the levels for all sites as a bar graph. Turbidity levels for various locales are displayed in a variety of hues. 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. Higher turbidity levels in wastewater, often resulting from industrial activities such as sediment runoff or discharge of suspended solids, can impair treatment processes, reduce disinfection efficacy, and obstruct light penetration essential for aquatic life. Additionally, high turbidity can exacerbate sedimentation in water bodies, smother benthic habitats, and disrupt aquatic ecosystems.

Fig. 7
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Turbidity graph chart

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3.3.5 Statistical analysis

The statistical analyses conducted for pH, TDS, temperature, and turbidity sensors reveal significant variations in water quality indicators across the four sites. Descriptive statistics showcase distinct characteristics for temperature, TDS, pH, and turbidity levels across the four sites. Site 2 exhibits the highest mean temperature (33.730 °C), while Site 4 has the lowest (25.670 °C). TDS levels vary significantly, with Site 1 having the highest mean value (484.805 ppm) and Site 3 the lowest (224.953 ppm). pH levels also differ notably, with Site 2 having the highest mean value (9.827) and Site 4 having the lowest (7.313). Turbidity levels highlight Site 4 as having the highest mean value (12.593 NTU), while Sites 1, 2, and 3 exhibit comparatively lower levels. Statistical analyses confirm significant differences among sites for each parameter, emphasizing the importance of monitoring and managing these water quality indicators for environmental health and management. The One-way ANOVA test for pH levels indicates substantial differences among locations (F(3, 12) = 332.11, p < 0.001), with Site 2 exhibiting significantly higher pH levels compared to all others. Similarly, for TDS levels, significant differences are observed (F(3, 12) = 296.53, p < 0.001), with Sites 1 and 2 showing notably higher concentrations compared to Sites 3 and 4. Regarding temperature, significant variability is detected (F(3, 12) = 33.64, p < 0.001), with Site 2 recording higher temperatures than the others. Moreover, turbidity levels exhibit marked distinctions (F(3, 12) = 104.27, p < 0.001), with Site 4 displaying significantly higher turbidity compared to all others. These findings underscore the necessity of continuously monitoring water quality parameters to safeguard aquatic ecosystems and human health, highlighting the importance of proactive management strategies in regions experiencing elevated contaminants.

3.3.6 Data comparison

The comparison between the project data presented and baseline or traditional methods reveals both adherences to expected ranges and notable deviations, providing valuable insights into water quality dynamics in industrial wastewater in Table 6. While pH levels generally fall within optimal and acceptable ranges as indicated, occasional spikes suggest potential sources of contamination or alterations in physico-chemical conditions, emphasizing the need for rigorous monitoring and investigation. Temperature measurements, consistent with suitable ranges for fish culture, demonstrate no significant deviations, reassuring the compatibility of environmental conditions with aquatic life. However, the elevated TDS levels observed pose concerns regarding potential risks to marine ecosystems and water quality degradation, necessitating comprehensive investigations into contamination sources and appropriate remedial actions. Furthermore, the varying turbidity levels, some of which exceed baseline values, underscore the imperative for effective management strategies like applying alum and ferric sulfate to mitigate adverse impacts on aquatic habitats. While the project data generally aligns with expected trends, deviations underscore the importance of ongoing monitoring and management efforts to ensure environmental sustainability and the preservation of aquatic habitats.

Table 6 Comparison of water quality parameters with baseline/traditional methods [24]
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3.3.7 Limitations

The device is applicable to be used in industrial sites to monitor wastewater. This proposed system’s application is extensible. In the future, more sensors can be added to detect different water properties. By adding more particular sensors, the system can monitor different environmental pollutants. Our system consists mostly of electronic devices, and we know that water can damage sensors and electronic devices easily. So, we need to be careful while taking water samples from the source. We also must regulate the essential voltage for each component separately. High voltage can damage our devices. Our system is mainly a hardware-based device. We are using some wires, batteries, and plastic-covered components. These can damage the environment if we do not take the necessary steps to recycle them.

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. This study underscores the critical significance of integrating IoT technologies into water quality monitoring, particularly in industrial regions like Bangladesh's garment industry hubs. Through our comprehensive analysis of key parameters including temperature, TDS, pH, and turbidity, we have unveiled significant variations among different sites, with alarming pollution levels detected along the Buriganga river. Our investigation revealed that TDS levels varied substantially, ranging from 170 to 360 ppm, posing a significant risk to aquatic life. Notably, even the lowest TDS value recorded at 204.46 ppm remains concerning, highlighting the pervasive impact on water quality. Furthermore, our analysis of pH levels revealed distinct patterns across different industrial zones, with the garment industry’s wastewater exhibiting pH values ranging from 10.00 to 10.90 at sites 1 and 2, primarily due to light dying processes. Conversely, site-3, where winter clothing production predominates, showcased pH values around 9.60. Interestingly, the pH of wastewater in the Buriganga river, influenced by numerous knitting industries, was relatively lower, possibly attributed to the continuous flow of water. These findings emphasize the intricate interplay between industrial activities and water quality parameters, necessitating tailored monitoring and intervention strategies. As we navigate towards the future, it becomes imperative to not only refine existing sensor technologies for enhanced accuracy but also to explore novel approaches for system scalability and data processing. Leveraging insights from our results, future research endeavors should prioritize the development of advanced sensor prototypes capable of delivering real-time, high-resolution data streams. Additionally, efforts should be directed towards optimizing data analysis algorithms to enable more efficient pollution detection and mitigation strategies. By addressing these challenges head-on, IoT-based water quality monitoring systems can emerge as indispensable tools in safeguarding our precious water resources and fostering sustainable environmental practices.