Monitoring the Concentration of Air Pollutants and Its Health Hazards Using Machine Learning Models (original) (raw)

Abstract

With the world moving rapidly towards industrialization driven by economic growth and technological advancements, there is an alarming surge of air pollution leading to significant health concerns. In response, this work introduces a research-driven approach for a continuous air quality monitoring system, designed to continuously track air quality in real-time in the proximity and proactively predict potential health hazards for the user. Central to the system’s efficacy is a state-of-the-art hybrid Machine Learning model, seamlessly amalgamating the strengths of Adaptive Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) models, renowned for their ability in handling intricate time series data. This model is securely deployed on a cloud platform, ensuring not only accessibility but also scalability to meet current and future technological standards. The system primarily concentrates on the monitoring of air pollutants, such as PM2.5, PM10, and CO, ensuring that users have access to immediate and up-to-date insights into the air quality in their surroundings. Beyond this, the system goes a step further by employing this data to assess users’ potential risk of developing lung cancer. Through the use of Internet of Things (IoT) sensors, the system can issue timely and potentially life-saving insights, providing users with valuable information for decision-making improving their well-being. In a world, where the link between air quality and health is increasingly evident, our research-based initiative serves as a beacon for a healthier future, while also fostering environmental consciousness and public well-being.

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Authors and Affiliations

  1. PES University, Bengaluru, 560085, Karnataka, India
    Aditi Jain, Aditya Shenoy, Ananya Adiga, Anirudha Anekal & Saritha Prajwal

Authors

  1. Aditi Jain
  2. Aditya Shenoy
  3. Ananya Adiga
  4. Anirudha Anekal
  5. Saritha Prajwal

Corresponding author

Correspondence toSaritha Prajwal .

Editor information

Editors and Affiliations

  1. Ganpat University, Gujarat, Gujarat, India
    Nirbhay Chaubey
  2. Taylor’s University, Subang Jaya, Malaysia
    Noor Zaman Jhanjhi
  3. Kerala University of Digital Sciences, Innovation and Technology (KUDSIT), Trivandrum, Kerala, India
    Sabu M. Thampi
  4. Ganpat University, Gujarat, Gujarat, India
    Satyen Parikh
  5. Ganpat University, Gujarat, Gujarat, India
    Kiran Amin

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© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Jain, A., Shenoy, A., Adiga, A., Anekal, A., Prajwal, S. (2025). Monitoring the Concentration of Air Pollutants and Its Health Hazards Using Machine Learning Models. In: Chaubey, N., Jhanjhi, N.Z., Thampi, S.M., Parikh, S., Amin, K. (eds) Computing Science, Communication and Security. COMS2 2024. Communications in Computer and Information Science, vol 2174. Springer, Cham. https://doi.org/10.1007/978-3-031-75170-7\_19

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