Industry released Water pollution alarming system

1. Sir M. Visvesvaraya Institute of Technology
GUIDED BY: MR NATARAJA R
Literature survey Presentation (21RMI56)
on
Industry Released Water pollution alarming system
Presented by:
1MV21EC034 - Divyanshu Gupta
1MV21EC020 - Ayush Kumar
1MV21EC062 - P Pranav
1MV21EC037 - Harsh Gupta

2. Abstract
• Water is essential for human survival, sourced from various bodies like lakes and streams.
• Ensuring water quality for consumption is crucial, often monitored in traditional labs, which
can be time-consuming and prone to inaccuracies.
• This paper explores the feasibility of using an Arduino - based sensor system for water
quality monitoring.
• A simple prototype with a microcontroller and multiple sensors conducted weekly onsite
tests at various intervals.
• The system proved reliable but depended on human assistance and had potential data
inaccuracies.
• Despite this, it lays a foundation for future expansion, aiming to make the system IoT-
friendly.

3. Introduction
•Water pollution is the contamination of water bodies due to human activities, negatively impacting its uses.
•Industrial activities contribute to water pollution, necessitating efficient water pollution alarming systems for
environmental management.
•This literature survey provides an overview of research on industry-released water pollution alarming systems.
•The research focuses on identifying harmful particles in wastewater from the textile industry, detecting solids, gases, and
other molecules.
•The proposed system helps industry authorities reduce harmful elements in chemicals, with fixed parameters for
measuring water quality: pH, TDS, turbidity, and temperature.
•The goal is to amalgamate insights from diverse studies on sensor technologies, data analytics, regulatory frameworks,
and case studies in water pollution alarming systems.
•The study's structure includes methods, system outline, hardware details, sensors, and a flowchart in Section 2. Section 3
presents results based on real-time sensor data analysis. Section 4 summarizes the research, emphasizing the
importance of safe water.
•The literature survey explores electronics-enabled industry-released water pollution alarming systems, with a focus on
contributions within the electronics department.

4. Literature Survey
•The text emphasizes the critical role of water for life on Earth, constituting 75% of the planet.
However, human activities, particularly industrialization, have led to severe water pollution.
Industrial discharges contain harmful chemicals and heavy metals, affecting aquatic life, human
health, and ecosystems. The Ganga River in India is highlighted as a case study, with various
industries contributing to its contamination.
•The paper discusses the impact of industrial pollutants, such as heavy metals, on water bodies
and outlines the adverse effects on the environment, including changes in pH, temperature, and
turbidity. Different treatment methods, including adsorption, coagulation-flocculation, and
membrane techniques, are presented as solutions to mitigate industrial water pollution.
•In conclusion, the text stresses the need for awareness campaigns, strict regulations, and proper
waste treatment to safeguard water quality. The importance of implementing preventive
measures, locating industries away from residential areas, and adhering to environmental laws is
emphasized for maintaining a healthy and sustainable water environment.

5. Literature survey
Detecting water pollution involves various methods, ranging from traditional field tests to advanced technologies. Here are some new and
emerging methods to monitor water pollution:
Remote Sensing:
• Satellite Imagery: Satellites equipped with sensors can monitor large water bodies for changes in color, temperature, and other indicators
of pollution. Unmanned
• Aerial Vehicles (UAVs): Drones equipped with sensors can be used to collect high-resolution images and data from specific locations,
providing detailed insights into water quality.
Sensor Technologies:
• Smart Sensors: Miniaturized, wireless sensors can be deployed in water bodies to continuously monitor parameters like pH, dissolved
oxygen, and pollutants. These sensors can transmit real-time data for analysis.
• Internet of Things (IoT): IoT devices can be integrated with water quality sensors to create a network for monitoring and managing water
pollution in real-time.
Biosensors:
• Biological Indicators: Using living organisms like algae or bacteria as indicators of water quality. Changes in their behavior or health can
signal the presence of pollutants. Enzymatic Biosensors: These sensors use enzymes to detect specific pollutants, providing a rapid and
sensitive method for water quality assessment.
• Machine Learning and AI: Data Analytics: Applying machine learning algorithms to analyze large datasets from water quality sensors,
satellites, and other sources to identify patterns and trends related to pollution. Predictive Modeling: AI can be used to create models
predicting water quality changes based on historical data, weather conditions, and other relevant factors.

6. Literature Survey
Machine Learning and AI:
Data Analytics: Applying machine learning algorithms to analyze large datasets from water quality sensors, satellites, and other sources to
identify patterns and trends related to pollution. Predictive Modeling: AI can be used to create models predicting water quality changes
based on historical data, weather conditions, and other relevant factors.
DNA-based Techniques:
• Metagenomics: Analyzing the DNA of microorganisms in water to identify potential pollutants and assess overall microbial diversity. This
method can provide insights into the biological impact of pollution.
• Polymerase Chain Reaction (PCR): A molecular biology technique that amplifies specific DNA sequences, allowing for the detection of
harmful microorganisms and pathogens in water.
Emerging Technologies:
• Nanotechnology: Using nanomaterial for sensing and remediation purposes. Nano scale sensors can be highly sensitive to specific
pollutants.
• Block chain Technology: Providing a secure and transparent way to record and share water quality data, ensuring data integrity and
traceability.
Citizen Science:
• Mobile Apps: Engaging citizens in water quality monitoring through mobile applications that allow them to report and collect data on
local water conditions

7. Sensors
Set of sensors in the Arduino:
A. Temperature Sensors[1]:
• Temperature of water is one of most important property because of other parameter depend
on temperature for accuracy. The main function of the DS18S20 is to provide direct digital values
of temperature. The resolution of the temperature sensors (DS18S20) is configure to 9, 10, 11
and 12 bits, corresponding to increment of 0.5C, 0.25C, 0.125C and 0.625C correspondingly.
B. pH sensor[1]:
•pH is a very important parameter for water. It is used to measure the acidity or alkalinity of
water [7]. These acidity or alkalinity is determined by relative hydrogen ions h+ or relative
hydroxyl ions OH- present in the water. Higher number of hydrogen ions signifies acidic solution
while the alkaline solutions have higher number of hydroxyl ions present in the water.

8. Sensors
C. Turbidity sensors[1]:
• Turbidity is measure clarity of water. Clear water has the low turbidity value on the other hand
muddy water has high turbidity. Cloudiness is caused by suspended soils and plankton that
suspended in the water column. Effect of turbidity is that it diminishes transparency of water
and decreases photosynthesis rate and also increases water temperature. As per USGS standard,
surface water is likely to have turbidity between 1 NTU and 50 NTU. Water is considered safe to
drink if its turbidity is 1.0 NTU. It should not be more than 5.0 NTU and should ideally be below
1.0 NTU

9. Arduino
D. About the Arduino[2]:
•The board based on ATmege328 with 14 digital input/output and 6 analog input pins having 16
MHz crystal oscillator. The added more features are ICSP heater, a USB connection, in circuit
serial programmer, and reset button. The Serial out (TX) and Serial in (RX), the external power
supply taken AC to DC Adapter. The Arduino UNO board is operated on an external supply 6 to
20 volts

10. References
•[1]Proceedings of the IEEE 2017 International Conference on Computing Methodologies and
Communication (ICCMC) Yogesh K. Taru Electronics & Telecommunication Engineering
Department, Government College of Engineering, Aurangabad, India. taruyogesh05@gmail.com.
•[2]Proceedings of the IEEE 2017 International Conference on Computing Methodologies and
Communication (ICCMC) Anil Karwankar Electronics & Telecommunication Engineering
Department, Government College of Engineering, Aurangabad, India. karwankar@yahoo.com
•[3]A. R. Dhruba[3], K. N. Alam, M. S. Khan et al., “IoT-Based Water Quality Assessment System
for Industrial Waste WaterHealthcare Perspective,” Journal of Healthcare Engineering, vol. 2022,
Article ID 3769965, 13 pages, 2022.

11. Conclusion
•Water pollution from industrialization is a growing concern.
•The literature survey explores electronics-enabled water pollution alarming systems, showcasing
significant contributions.
•Results from an IoT-based water quality assessment system for industrial wastewater show real-time
monitoring of pH, temperature, turbidity, and TDS in a cost-effective and user-friendly manner.
•Sensor technologies enhance water pollution detection, ensuring a swift and accurate response to
industrial contaminants.
•The survey emphasizes a harmonized approach with regulations guiding effective monitoring, aligning
with environmental guidelines.
•The literature survey stands as a testament to progress, guiding future research.
•The research aids industries in protecting the environment, offering real-time water quality
evaluation to improve the health of living creatures by preventing pollution.

12. Thank you for your
attention