Advances Low Cost IOT Based Air Pollution Monitoring System for Urban Environments

Advances Low Cost IOT Based Air Pollution Monitoring System for Urban Environments

A Minor Research Project under Research Promotion Scheme (RPS)

Dr. Manish T. Thakker (Principal Investigator)

Professor, HOD, Instrumentation and Control Engineering Department,

L.D. College of Engineering

Dr. Shriji V. Gandhi (Co Investigator) Lecturer, Government Polytechnic Ahmedabad Gujarat Technological University

PREFACE

Air pollution has become a critical concern in urban environments, directly affecting public health, ecological stability, and overall quality of life. With rapid industrialization, urbanization, and vehicular emissions, the demand for real-time air quality monitoring and proactive solutions has never been greater.

IoT Based Air Pollution Monitoring System for Urban Environments delves into the application of the Internet of Things (IoT) technology as a transformative tool for addressing the multifaceted challenges posed by air pollution. By integrating smart sensors, data analytics, and communication networks, IoT solutions promise precise, scalable, and cost-effective monitoring systems that can inform urban planning and empower citizens.

The work presented here reflects a culmination of interdisciplinary efforts spanning environmental science, data engineering, and IoT technologies. It not only explores the theoretical aspects of air quality monitoring but also offers practical insights into the design, development, and deployment of IoTbased systems tailored to the unique needs of urban environments.

This project aims to serve as a comprehensive guide for students, researchers, urban planners, and technologists seeking to advance sustainable cities and healthier communities through innovative technological solutions. We hope that this work inspires continued exploration and collaboration to mitigate the adverse effects of air pollution and contribute to a cleaner, greener world.

ACKNOWLEDGEMENT

We express our deepest gratitude to Gujarat Technological University (GTU) for generously funding our research project, Advances in Low Cost IoT Based Air Pollution Monitoring System for Urban Environments. This project would not have been possible without their financial support, vision, and commitment to fostering innovative research aimed at addressing real world challenges.

We are immensely thankful to L.D. College of Engineering, Ahmedabad, and Government Polytechnic, Ahmedabad, for their institutional support and encouragement throughout the project. The resources, infrastructure, and academic environment provided by these esteemed institutions significantly contributed to the successful execution of our research.

We also thank the faculty, staff, and students from both institutions who provided assistance and collaborated during various stages of the project. Their technical expertise, constructive feedback, and handson efforts were instrumental in bringing this vision to reality.

Finally, we acknowledge the unwavering support of our families, colleagues, and peers, whose encouragement has been a source of strength and motivation throughout this journey. This project serves as a collective achievement that highlights the importance of collaboration, innovation, and academic partnership in addressing pressing societal and environmental challenges.

1. INTRODUCTION & LITERATURE REVIEW
   1. Background and Motivation

      Air pollution is a significant environmental and public health issue, especially in urban environments where population density, industrial activities, and vehicular traffic are high. According to the World Health Organization (WHO), air pollution contributes to millions of deaths annually, causing respiratory and cardiovascular diseases. The rapid urbanization and industrialization experienced in many parts of the world exacerbate this issue, leading to increased emissions of pollutants such as particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and sulfur dioxide (SO2)[1].

      Traditional air quality monitoring relies on centralized, largescale stations that are expensive to install and maintain. While these systems provide accurate data, their limited coverage makes it challenging to detect localized air quality variations in real-time. Moreover, the lack of real-time access to air quality data hampers timely decisionmaking, which is crucial for mitigating health risks and improving urban air quality[2].

      Motivation:

      1. Need for Real-time Monitoring: Urban areas exhibit high variability in air quality due to localized pollution sources such as traffic congestion, construction activities, and industrial processes. A real-time air pollution monitoring system can help identify these hotspots and provide actionable insights for policymakers, environmental agencies, and urban planners[3].
      2. Internet of Things (IoT) for Scalability: IoT offers a scalable and costeffective solution to air quality monitoring. By deploying a network of IoTenabled sensors, it is possible to achieve finegrained spatial and temporal monitoring of air pollutants. These systems can complement traditional monitoring stations and expand coverage.
      3. Improved Public Awareness and Engagement: Providing real-time data to the public through userfriendly interfaces such as mobile applications or dashboards can empower individuals to make informed decisions about their health and activities. For example, people can avoid high pollution areas or adjust their outdoor plans.
      4. Data Driven Urban Management: Insights from an IoT based air pollution monitoring system can inform urban planning and traffic management, helping reduce emissions. These systems can also provide early warnings in case of hazardous pollution events, supporting emergency response efforts.
      5. Technological Advancements and Cost Reduction: Advances in sensor technologies, wireless communication, and cloud computing have made it feasible to deploy IoT systems for air pollution monitoring at a significantly lower cost. This opens up opportunities for widescale adoption, even in developing countries with limited budgets.
   2. Overview of Urban Air Pollution Issues

      Urban air pollution is a major environmental and public health challenge, driven by rapid urbanization, industrialization, and increasing energy consumption. Common sources include emissions from vehicles, industrial processes, construction activities, and residential heating. These contribute to pollutants such as particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), and groundlevel ozone. Urban air pollution poses serious health risks, including respiratory diseases, cardiovascular problems, and premature mortality, while also damaging ecosystems and infrastructure. Additionally, it exacerbates climate change, creating a cycle of environmental degradation. Addressing this issue requires comprehensive strategies, including clean energy adoption, stricter emissions standards, and public awareness campaigns[4].

   3. Objectives of the Project
      • Design and develop an IoTbased system capable of monitoring various air quality parameters such as particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and temperature/humidity.
      • Deploy a network of sensor nodes across strategic locations in urban areas to gather real-time data on air quality.
      • Implement data analytics techniques to process the collected data and generate actionable insights into air pollution trends and patterns.
      • Develop a userfriendly interface, accessible through web or mobile platforms, to visualize air quality data for both the general public and relevant authorities.
      • Integrate the system with notification capabilities to alert users about unhealthy air quality levels and recommend appropriate actions.
2. SYSTEM DESIGN
   1. Block diagram of our system

      Figure 2.1 Block diagram of advanced, low-cost IoTbased air pollution monitoring system

      This project presents an advanced, low-cost IoT based air pollution monitoring system designed to address these challenges. The system integrates affordable air quality sensors, a microcontroller (e.g., ESP32/ESP8266), and wireless communication technologies to collect and transmit real-time data on key pollutants such as particulate matter (PM2.5 and PM10), carbon dioxide (CO), and volatile organic compounds (VOCs).

      Data is sent to a cloud platform for storage, visualization, and analysis, enabling users and authorities to monitor trends and identify pollution hotspots via dashboards and mobile applications. The system employs efficient power management to operate on rechargeable batteries, ensuring reliability in urban outdoor conditions. It is enclosed in a compact, weatherresistant housing for longterm deployment.

      This innovative solution not only reduces the cost of air quality monitoring but also enhances accessibility and scalability. By leveraging IoT technology, this system can be deployed across cities to provide actionable insights, promote awareness, and support policy decisions for improving air quality in urban environments.

      1. Architecture of IoT Based Monitoring System
         • Sensor Nodes: Deploy low-cost IoT sensor nodes equipped with sensors for measuring various air quality parameters. These nodes will communicate wirelessly with a central hub.
         • Central Hub: A central gateway device responsible for collecting data from sensor nodes, processing the data, and transmitting it to a cloud server.
         • Cloud Server: Store and manage the collected data securely in a cloud based database. Perform data analytics and generate insights using machine learning algorithms.
         • User Interface: Develop a web based dashboard and/or a mobile application to visualize real-time air quality data, historical trends, and predictive analytics.

           The architecture of an IoT based monitoring system is typically composed of several layers and components that work together to collect, process, transmit, and analyze data. Below is an overview of the key elements:

         • Perception Layer

           This is the sensing layer, responsible for collecting data from the environment. Components: Sensors, actuators, RFID tags.

           Role: Measures physical parameters like temperature, humidity, air quality, or motion.

         • Network Layer

           This layer ensures data transmission between sensors/devices and storage or processing systems. Components: Wireless communication technologies (WiFi, Bluetooth, Zigbee, LoRaWAN, 4G/5G). Role: Transfers data securely and reliably from edge devices to higherlevel systems.

         • Edge Layer

           This layer performs preliminary data processing near the source of data collection. Components: Microcontrollers, singleboard computers (e.g., Raspberry Pi), and local gateways.

           Role: Filters, processes, or compresses raw sensor data to reduce the load on centralized systems.

         • Cloud Layer

           This is the backbone of an IoT system for data storage and advanced processing. Components: Cloud servers, IoT platforms (e.g., AWS IoT, Azure IoT).

           Role: Stores large volumes of data, runs analytics, and supports machine learning for insights and predictions.

         • Application Layer

           This is the interface between the system and users. Components: Web and mobile applications, dashboards, APIs.

           Role: Displays processed data in a userfriendly format for monitoring, decisionmaking, and control.

         • Security and Management

           IoT systems require robust security protocols to protect against threats.

           Components: Encryption tools, authentication protocols, device management platforms. Role: Ensures data integrity, privacy, and the proper functioning of connected devices.

           End to End Flow

           1. Sensors capture data (Perception Layer).
           2. Data is transmitted to gateways (Network Layer).
           3. Preliminary processing occurs at the edge (Edge Layer).
           4. Data is stored and analyzed in the cloud (Cloud Layer).
           5. Insights and control commands are delivered via user applications (Application Layer).

           This layered approach ensures scalability, flexibility, and efficiency in IoT based monitoring systems.

   2. Components and Their Selection

      The components of an IoT based monitoring system and their selection are critical to ensuring the system functions efficiently, reliably, and costeffectively. Here’s an outline of the primary components and factors influencing their selection:

      1. Sensors

         Role: Capture physical parameters like temperature, humidity, pressure, light, motion, or air quality.

         Examples: Temperature sensors (DHT22, LM35), Gas sensors (MQ series), Proximity sensors (HCSR04). Selection Criteria:

         • Parameter sensitivity and accuracy.
         • Measurement range and resolution.
         • Power consumption and operational lifespan.
         • Environmental compatibility (e.g., ruggedness, waterproofing).
      2. Microcontrollers and Microprocessors

         Role: Act as the brains of the system, processing data from sensors and controlling communication modules. Examples: Arduino, ESP8266, ESP32, Raspberry Pi.

         Selection Criteria:

         • Processing power (CPU performance).
         • Number of GPIO pins and supported peripherals.
         • Memory (RAM and storage).
         • Support for communication protocols (e.g., SPI, UART, I2C).
         • Power efficiency and size.
      3. Communication Modules

         Role: Facilitate data transmission to the cloud or local systems. Examples:

         Shortrange: Bluetooth (HC05), Zigbee, WiFi (ESP8266/ESP32). Longrange: LoRa, NBIoT, 4G/5G modules.

         Selection Criteria:

         • Range and data rate requirements.
         • Power consumption.
         • Network coverage and reliability.
         • Cost and ease of integration.
      4. Gateways

         Role: Bridge sensors with cloud systems, managing data aggregation and protocol translation. Examples: Dedicated IoT gateways or multipurpose devices (Raspberry Pi, Intel NUC).

         Selection Criteria:

         • Compatibility with communication protocols.
         • Scalability and support for multiple devices.
         • Computational power for edge processing.
         • Network interface options.
      5. Cloud Platforms

         Role: Provide storage, analytics, and remote access for IoT data. Examples: AWS IoT Core, Microsoft Azure IoT, Google Cloud IoT. Selection Criteria:

         • Scalability for large data volumes.
         • Availability of analytics and visualization tools.
         • Security features for data integrity.
         • Cost model and developer support.
      6. Power Supply

         Role: Provides reliable energy to sensors and other devices. Examples: Batteries, solar panels, or gridconnected sources. Selection Criteria:

         • Power requirements of the components.
         • Battery life and recharging options.
         • Portability and environmental constraints.
      7. Software Components

         Role: Control, manage, and process data within the system.

         Examples: Embedded firmware, IoT platforms, custom analytics software. Selection Criteria:

         • Compatibility with chosen hardware.
         • Scalability for future requirements.
         • Availability of APIs for integration and customization.
         • Security and compliance standards.
      8. Security Modules

         Role: Protect data and devices from unauthorized access.

         Examples: Cryptographic chips (e.g., ATECC608A), SSL/TLS protocols. Selection Criteria:

         • Type and sensitivity of data handled.
         • Encryption and authentication mechanisms.
         • Compliance with data protection regulations.
   3. Data Acquisition and Communication Protocols

Efficient data acquisition and communication protocols are fundamental to the performance of an IoTbased air pollution monitoring system in urban environments. These systems rely on a network of lowcost sensors to measure pollutants such as PM2.5, PM10, CO2, CO, NO2, O3, and VOCs in real time. The accuracy and reliability of air quality data depend on precise data collection mechanisms, transmission protocols, and seamless cloud integration[5].

The data acquisition process begins with environmental sensors embedded in IoT edge devices, such as ESP32, Raspberry Pi, or Arduinobased microcontrollers. These sensors continuously measure air quality parameters and transmit the data at regular intervals. Advanced signal processing techniques help filter out noise, ensuring clean and reliable data collection. Furthermore, calibration techniques, including AIdriven correction models, enhance sensor accuracy and longevity [6].

For seamless data transmission, efficient communication protocols are required to ensure reliable, lowlatency, and energyefficient data exchange. WiFi, Bluetooth Low Energy (BLE), LoRa (Long Range), Zigbee, NBIoT (Narrowband IoT), and LTEM are some of the key protocols used based on system design requirements. MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol) are commonly used lightweight messaging protocols that enable efficient machinetomachine (M2M) communication with minimal

bandwidth consumption. For systems requiring high data security, HTTPS with RESTful APIs and WebSockets are implemented for real-time twoway communication.

The collected data is sent to cloudbased storage solutions like Firebase, AWS IoT Core, or Google Cloud IoT for processing and visualization. To ensure scalability, edge computing can be integrated to filter and process data locally before transmission, reducing cloud dependency and improving response times. These advanced data acquisition and communication protocols make IoTbased pollution monitoring systems highly scalable, real-time, and reliable for smart city environmental management [7].

Protocol Selection Considerations

• Bandwidth Requirements: Applications requiring highspeed data, such as video streaming, demand high bandwidth protocols like 4G/5G.
• Power Consumption: Low power protocols like LoRa and Zigbee are essential for batteryoperated devices.
• Latency: Time critical applications, such as industrial automation, favor low latency options like MQTT.
• Range and Scalability: LoRa or Sigfox works well for largescale, remote deployments.
• Data Security: Protocols like HTTPS and AMQP provide robust encryption and authentication mechanisms.

1. SYSTEM IMPLEMENTATION
   1. Hardware Assembly

      Creating an advanced, lowcost IoTbased air pollution monitoring system involves integrating sensors, microcontrollers, communication modules, and power management hardware that shows in figure 3.1. Heres an outline of the hardware assembly process for such a system:

      Figure 3.1 Hardware of the system

      Figure 3.2 Hardware of the system

      Microcontroller/Development Board:

      ESP32/ESP8266 (preferred for WiFi capability). Arduino Uno (if external WiFi modules are added).

      MQ series sensors (e.g., MQ135 for general air quality). PMS5003 or SDS011 (PM2.5 and PM10 detection).

      DHT22/DHT11 (temperature and humidity monitoring, optional). CO2 sensor (e.g., MG811 or Senseair S8).

      Builtin WiFi (for ESP32/ESP8266).

      GSM module (SIM800L or similar) for remote connectivity. 5V DC adapter or Lithiumion batteries.

      TP4056 charger module for battery management. Stepup/Stepdown regulators for voltage control (e.g., LM2596). Customdesigned PCB or breadboard for circuit testing.

      3Dprinted or weatherresistant box to house components. Resistors, capacitors, jumper wires, headers.

      OLED/LED display (optional for local output).

      1. Assembly Process
         1. • 1. Microcontroller Setup
                 1. Program the microcontroller to process sensor data and send it to the IoT platform.
                 2. Load necessary libraries for sensors and communication protocols.
                 3. Test individual sensors with the microcontroller using jumper wires.
              2. Sensor Integration
                 1. Connect sensors to analog/digital pins of the microcontroller.
                 2. Ensure proper voltage levels for each sensor (use resistors or voltage dividers as needed).
                 3. Test sensors using basic scripts to validate functionality.
              3. Power Management
                 1. Wire the power supply to the microcontroller and sensors.
                 2. Use the TP4056 for rechargeable power setups.
                 3. Test voltage regulation using a multimeter.
              4. Communication Module Configuration
                 1. Configure the microcontroller for WiFi (for cloudbased data transmission).
                 2. Test the GSM module (if applicable) by sending test messages.
                 3. Secure and insulate communication wires.
              5. Enclosure Preparation
                 1. Place all components in the enclosure securely.
                 2. Drill or 3D print openings for air exchange for accurate sensor readings.
                 3. Seal the enclosure to prevent dust or water ingress.
              6. Final Testing
                 1. Run a systemwide test, logging real-time data.
                 2. Ensure accurate sensor calibration.
                 3. Test data transmission to the cloud/server.
   2. Software Development

      The Android application for an Advanced LowCost IoTBased Air Pollution Monitoring System is designed to provide real-time air quality data for urban environments. This smart monitoring system integrates IoTenabled sensors with a mobile application, allowing users to track pollution levels such as PM2.5, PM10, CO2, CO, NO2, O3, temperature, and humidity. The application collects and visualizes data from distributed sensor nodes using WiFi, LoRa, or MQTT protocols, displaying air quality metrics on an interactive dashboard with real-time graphs and maps. Users can monitor historical trends, receive alerts for hazardous pollution levels, and access geolocationbased pollution insights via Google Maps integration. With a focus on affordability and efficiency, the system leverages Firebase, MySQL, or cloud databases to store pollution records, enabling datadriven decisionmaking for urban planners, environmentalists, and the general public. Through push notifications, detailed reports, and an intuitive UI, this Android app enhances environmental awareness and helps mitigate the impact of air pollution in smart cities. We have incorporated flutter platform to develop android application shown on Fig. 2 for the system

      Flutter, an opensource UI software development toolkit by Google, is an ideal platform for developing Android applications for an advanced, lowcost IoTbased air pollution monitoring system. With its single codebase approach, Flutter enables efficient development, seamless UI rendering, and robust performance, making it a perfect choice for real-time air quality monitoring applications.

      One of Flutters key advantages is its fast development cycle, supported by features like hot reload, which allows developers to see immediate updates without restarting the app. This is particularly useful in IoT applications where real-time sensor data needs to be processed and displayed dynamically. Additionally, Flutters widgetbased architecture enables developers to design highly responsive and customizable interfaces that provide real-time air pollution data, including parameters such as PM2.5, PM10, CO2, NO2, and temperature.

      Flutters integration with Firebase and cloud services enhances the systems capability by enabling remote data storage, push notifications, and real-time updates. It also supports REST APIs and MQTT protocols, allowing seamless communication with IoT devices that collect air pollution data. This ensures that the application can receive and display data from multiple sensor nodes deployed across urban environments.

      Moreover, Flutters support for crossplatform development allows the application to be easily deployed on both Android and iOS devices, increasing accessibility for users. Its optimized performance, powered by the Dart programming language, ensures smooth execution even when handling large datasets from IoT sensors.

      In conclusion, Flutter provides an efficient and costeffective solution for developing an Android application that enables real-time monitoring of air pollution. Its cross platform capabilities, real-time data handling, and seamless UI design make it an excellent choice for building a smart, lowcost IoTbased air quality monitoring system for urban environments.

      1. Key Features of this application[3]:

1. Real-time Air Quality Monitoring

   Fetch air pollution data from IoT sensors (e.g., using MQTT, Firebase, or REST API). Display metrics like PM2.5, PM10, CO2, CO, NO2, O3, Humidity, and Temperature.

2. Data Visualization

   Live charts & graphs (e.g., Fl_chart package in Flutter). Colorcoded air quality index (AQI) display.

3. GPSbased Location Mapping

   Show pollution levels on a Google Maps widget. Geotagging support for different sensor locations.

4. Alerts & Notifications

   Push notifications for high pollution levels. Integration with Firebase Cloud Messaging (FCM).

5. User Authentication & Dashboard Login/signup system (Google, emailbased). User roles: Admin, General User, Researcher.
6. Historical Data & Reports

   Store past pollution data (SQLite or Firebase). Generate & export reports in CSV or PDF format.

7. Cloud & Database Connectivity

Store real-time sensor data in a database (Firebase, PostgreSQL, MySQL). Integration with cloud services like AWS, Google Cloud, or ThingsBoard IoT.

Figure 3.3 Screenshot of Android Application prepared for this system

Figure 3.3 shows the screenshot of android application fir this system. it shows different parameters can be monitored with this system

1. • 1. 1. DATA ANALYSIS AND VISUALIZATION
           1. Real-time Monitoring and Data Logging
              1. Aspects and Factors Affecting Data Analysis and Visualization for this system
                 1. Data Collection Aspects
                    • Sensor Selection & Accuracy: The precision, resolution, and reliability of lowcost sensors impact data quality.
                    • Environmental Factors: Temperature, humidity, and interference from other pollutants affect sensor readings.
                    • Data Sampling Rate: Higher frequency sampling improves accuracy but increases data storage and processing needs.
                    • Network Connectivity: Stability of IoT communication (WiFi, LoRa, 5G) affects real-time data transmission.
                 2. Data Processing and Preprocessing Factors
                    • Data Cleaning & Noise Reduction: Outlier detection, missing value handlin, and calibration methods improve data reliability.
                    • Data Fusion: Combining multiple sensor data streams enhances accuracy and reduces redundancy.
                    • Edge vs Cloud Processing: Edge computing reduces latency, while cloud computing enables largescale analysis.
                 3. Data Analysis Factors
                    • Real-time vs Batch Processing: Real-time monitoring requires fast analytics, while batch processing allows deeper insights.
                    • Machine Learning & Predictive Analytics: AI models detect pollution trends, anomalies, and forecast future air quality.
                    • Statistical Methods: Regression, clustering, and timeseries analysis help in pattern identification.
                 4. Data Visualization Aspects
                    • Graphical Representation: Heatmaps, line charts, and bar graphs provide intuitive air pollution insights.
                    • GIS & Geospatial Mapping: Integration with GPS and GIS helps visualize pollution hotspots.
                    • User Dashboard & UI Design: Responsive, interactive dashboards improve accessibility for stakeholders.
                    • Real-time Alerts & Notifications: SMS, email, or appbased alerts for pollution threshold breaches.

                    Figure 4.1 Screenshot of PM2.5 chart prepared for this system

                    Fig. 4.1 shows The PM2.5 chart prepared for the Advanced Low-Cost IoT-Based Air Pollution Monitoring System for Urban Environments provides a real-time visualization of fine particulate matter (PM2.5) concentrations, crucial for assessing air quality in densely populated areas. Utilizing IoT-enabled sensors, the system continuously collects and transmits PM2.5 data to a centralized platform, where it is processed and displayed in an easy-to-interpret graphical format. The chart highlights variations in pollution levels over time, allowing users to identify peak exposure periods, track trends, and correlate data with environmental factors such as traffic flow and meteorological conditions. This real-time monitoring approach enhances urban air quality management by enabling timely interventions, raising public awareness, and supporting data-driven policymaking for healthier urban living.

                    Figure 4.2 Screenshot of CO2 chart prepared for this system

                    The CO2 developed for the Advanced Low-Cost IoT-Based Air Pollution Monitoring System for Urban Environments provides a comprehensive visualization of carbon dioxide concentration levels in real time. Integrated with IoT-enabled sensors, the system collects CO2 data from various urban locations and transmits it to a cloud- based platform, where the software processes and displays it in an interactive chart. The chart helps in identifying trends, peak emission periods, and potential sources of elevated CO2levels, such as traffic congestion and industrial activities. By leveraging real-time analytics and historical data comparisons, the software enhances urban air quality monitoring, supports environmental policymaking, and enables timely interventions to mitigate pollution. This intelligent visualization tool empowers researchers, city planners, and policymakers to make data-driven decisions for sustainable urban environments.

        2. EXTERNAL FACTORS AFFECTING SYSTEM PERFORMANCE
   • Urban Infrastructure & Placement: Sensor locations impact data accuracy (e.g., near traffic vs residential areas).
   • Regulatory Compliance: Meeting environmental monitoring standards (EPA, WHO, CPCB).
   • Community Engagement & Accessibility: Open datasharing platforms improve awareness and decisionmaking.

Figure 4.3 Screenshot of Application developed for this system

Figure 4.3 shows the GUI of the entire system. Real-time monitoring and data logging are crucial components of an IoT based air pollution monitoring system designed for urban environments[8]. With the rising levels of particulate matter (PM2.5, PM10), CO2, CO, NO2, O3, and volatile organic compounds (VOCs), continuous air quality assessment is necessary to mitigate health risks and enhance environmental sustainability. This system integrates IoT enabled sensors deployed across the city to measure pollution levels in real-time, transmitting data wirelessly via WiFi, LoRa, Zigbee, or NBIoT protocols to a cloud based server or an edge computing device [9].

Figure 4.4 Screenshot of Application with data log feature developed for this system

Figure 4.4 displays The data logging facility in the Advanced Low-Cost IoT-Based Air Pollution Monitoring System for Urban Environments enables seamless storage and management of real-time air quality data in formats such as Excel, CSV, or cloud databases. The IoT-enabled system continuously records key environmental parameters, including PM2.5, CO2, temperature, and humidity, and logs them into structured datasets for analysis. The software automatically timestamps and organizes the data, ensuring easy retrieval, filtering, and visualization. With Excel compatibility, users can perform statistical analysis, generate reports, and create dynamic charts for trend identification. Additionally, cloud-based storage options enhance data accessibility and remote monitoring capabilities, supporting researchers, city planners, and policymakers in making data-driven decisions for urban air quality management

The real-time monitoring framework enables instant air quality visualization through an Android mobile application or web dashboard, where users can observe live pollution metrics on interactive graphs, charts, and geographical heat maps. Leveraging APIs like Google Maps and Open Weather, the application provides GPSbased air pollution indexing, allowing individuals to make informed decisions about o[10]utdoor activities based on real-time air quality data. Additionally, threshold based alert notifications warn users and city authorities of dangerously high pollution levels, promoting immediate corrective actions [11].

In addition to real-time monitoring, data logging and storage play a critical role in analyzing long-term air quality trends. The system archives pollution data in a cloud database, a local SQL database, or a decentralized ledger for security and transparency. This historical data enables researchers, environmental agencies, and urban planners to conduct predictive analytics identify pollution sources, and design targeted mitigation strategies. Through AI and machine learning algorithms, the system can generate trend analysis, pollution forecasts, and automated anomaly detection, improving decision making in smart cities.

By integrating real-time monitoring with robust data logging, this IoTbased solution fosters a proactive approach to air pollution management, ensuring healthier and smarter urban environments [12].

1. SUSTAINABILITY AND SCALABILITY
   1. Environmental Impact

      The environmental impact of advanced low-cost IoT based air pollution monitoring systems for urban environments is significant, promoting healthier ecosystems and aiding in sustainable urban planning. These systems enable real time tracking of air quality parameters like particulate matter, greenhouse gases, and volatile organic compounds, empowering authorities to identify pollution hotspots and implement targeted mitigation strategies[13]. By facilitating the adoption of clean energy sources, optimized traffic flow, and pollution control measures, they help reduce emissions and improve urban air quality. Furthermore, their affordability and scalability ensure widespread deployment, even in resource constrained settings, enhancing global environmental monitoring efforts. These systems ultimately contibute to reducing ecological degradation and mitigating climate change by fostering data driven, environmentally conscious decision-making [14].

   2. Advantages of this system

      Advanced low-cost IoT based air pollution monitoring systems offer several advantages for urban environments, addressing both technological and socioeconomic challenges. These systems provide real-time and granular data on air quality, enabling swift identification of pollution sources and efficient intervention strategies. Their affordability ensures widespread accessibility, making it feasible for municipalities, organizations, and even individuals to deploy them. Scalability is another key benefit, allowing these systems to be integrated into smart city frameworks and expanded as needed. Moreover, they support remote monitoring and centralized data analytics through cloud integration, reducing the need for costly, traditional monitoring infrastructure. By empowering authorities and citizens with actionable insights, these systems foster improved public health, awareness, and collaboration in addressing air pollution challenges [15].

2. CONCLUSION AND FUTURE SCOPE
   1. Summary of Findings

      Advanced low-cost IoT based air pollution monitoring systems are transformative solutions designed to tackle the challenges of urban air pollution. By integrating affordable sensors, microcontrollers, and communication technologies, these systems provide real-time, high-resolution monitoring of air quality parameters such as particulate matter, greenhouse gases, and other pollutants. They utilize cloud platforms for data storage, analysis, and visualization, enabling remote monitoring through mobile or web applications. The systems are highly scalable

      and cost-effective, making them accessible for deployment in resource constrained settings and adaptable for largescale applications like smart cities. Their ability to deliver actionable insights promotes proactive measures to reduce pollution, improve public health, and foster sustainable urban development. These systems are pivotal in enhancing environmental awareness and supporting data driven strategies to address the pressing issue of air pollution in modern urban environments.

   2. Outcome of this project Publication:

      Manish Thakker , Gandhi S.V. IoT Enabled low cost Air Pollution Monitoring System for Urban Environments International Journal of Engineering Research & Technology (IJERT) (ISSN: 2278-0181),March 2025.

   3. Future Scope

      The future scope of advanced low-cost IoT based air pollution monitoring systems for urban environments is vast and transformative, driven by technological advancements and growing awareness of environmental issues. These systems are poised to become integral components of smart city initiatives, with enhanced integration into urban infrastructure for real-time adaptive responses, such as dynamic traffic management to reduce vehicle emissions. Advances in machine learning and artificial intelligence will enable predictive analytics, allowing for early warnings of pollution spikes and long-term trend analysis. The adoption of renewable energy sources and energy efficient designs will further increase their sustainability and deployment in remote or underserved areas. Collaboration with global environmental agencies can standardize data collection, enabling worldwide air quality comparison and coordinated pollution control strategies. Additionally, the increasing use of citizen science initiatives, powered by personal monitoring devices, will democratize data collection, fostering greater public engagement in air quality improvement efforts. These systems hold the potential to revolutionize urban environmental management, significantly improving quality of life and promoting sustainable urban development.

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