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Original Article

Next Generation Smart Air Purifier Leveraging IOT for Predictive Air Quality Control

Open Access Peer Reviewed Crossref DOI

Authors

  • Prapulla Nayaka K V1
  • Sheethal H E2
  • Charan G N3
  • Prakruthi T B4
  • Deepak R5

1,2,3,4 Students, Department of ECE, BGS Institute of Technology, Adichunchanagiri University, Karnataka, India. 5 Assistant Professor, Department of ECE, BGS Institute of Technology, Adichunchanagiri University, Karnataka, India,

Article Information

Published Online
May-June 2025
Pages
54-62
Article Type
Original Article
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Abstract

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This paper describes the about the air quality in recent years, the deterioration of air quality has emerged as a critical global concern, significantly impacting public health and environmental sustainability. The increase in urbanization, industrial activities, and vehicular emissions has contributed to the prevalence of particulate matter (PM2.5) and harmful gases, posing severe risks to human health. This project proposes the development of an Intelligent Air Purifier, integrating advanced sensor technology with Arduino-based microcontroller systems to monitor and enhance air quality in indoor environments The Intelligent Air Purifier is designed to measure PM2.5 levels, detect smoke, and assess overall air quality using sophisticated gas sensors. When air quality falls below acceptable thresholds, the system activates a fan that circulates air through a HEPA filter, effectively removing pollutants and allergens. Additionally, a Bluetooth module facilitates control via a voice recognition Android application, allowing users to operate the device remotely, enhancing user convenience.The project also includes an LPG sensor that monitors for gas leaks, triggering an alarm with a buzzer to alert occupants of potential dangers. The integration of these technologies aims to create a user- friendly, efficient air purification system that not only ensures cleaner air but also prioritizes safety in domestic settings. The data is also stored in sd card, the data then is used to train ml model which uses logistic regression to predict the future air quality

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