Machine Learning-Driven System for Real-Time Air Quality Monitoring and Prediction
DOI:
https://doi.org/10.32628/CSEIT251112283Keywords:
Air Quality Monitoring, Machine Learning, Internet of Things (IoT), Air Pollution Sensors, Real-time Data Analysis, Predictive Analytics, MQ Series Sensors (MQ-135, MQ-9, MQ-6, etc.), Temperature and Humidity Measurement, MCP3008 ADC, BUK Converter, Data Processing, Air Quality Index (AQI), Environmental Impact, Health Risks Assessment, Smart City Solutions, Wireless Sensor NetworksAbstract
This project introduces a machine learning-driven system for real-time air quality monitoring and prediction, utilizing MQ-135, MQ-9, and MQ-6 sensors alongside a DHT sensor. The MQ-13 sensor measures the concentration of carbon dioxide (CO2) and ammonia (NH3), the MQ-9 sensor detects methane (CH4) and liquefied petroleum gas (LPG), while the MQ-6 sensor monitors alcohol and carbon monoxide (CO) levels. The DHT sensor provides concurrent temperature and humidity data. This multi-sensor data is collected and transmitted to a machine learning model to compute the Air Quality Index (AQI), which represents the overall air quality. The system aims to deliver accurate, real-time air quality assessments and predictive insights, enabling timely interventions and better environmental management. By combining diverse sensor data with advanced machine learning techniques, this approach enhances the precision and effectiveness of air quality monitoring and prediction.
Downloads
References
Virendra Barot,Viral Kapadia,"Air Quality Monitoring Systems using IoT: A Review",2020 International Conference on Computational Performance Evaluation (ComPE).
Helton Pierre Lucena de Medeiros,Gustavo Giru00e3o,"An IoT-based Air Quality Monitoring Platform",2020 IEEE International Smart Cities Conference (ISC2).
Tigor Hamonangan Nasution,Ainul Hizriadi,Kasmir Tanjung,Fitra Nurmayadi,"Design of Indoor Air Quality Monitoring Systems",2020 4rd International Conference on Electrical Telecommunication and Computer Engineering (ELTICOM).
Fadli Pradityo,Nico Surantha,"Indoor Air Quality Monitoring and Controlling System based on IoT and Fuzzy Logic",2019 7th International Conference on Information and Communication Technology (ICoICT).
Harsh Gupta,Dhananjay Bhardwaj,Himanshu Agrawal,Vinay Anand Tikkiwal,Arun Kumar,"An IoT Based Air Pollution Monitoring System for Smart Cities",2019 IEEE International Conference on Sustainable Energy Technologies and Systems (ICSETS).
Mohamed Tahar Bennai,"Air Quality Monitoring Using IoT: A Survey",2019 IEEE International Conference on Smart Internet of Things (SmartIoT).
Temesegan Walelign Ayele,Rutvik Mehta,"Air pollution monitoring and prediction using IoT",2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
S De Vito, E. Esposito, M. Salvato, O. Popoola, F. Formisano, R. Jones, et al., "Calibrating chemicalmultisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches", inSens. Actuators B Chem, vol. 255, pp. 1191-1210, 2018.
Marin B. Marinov,Ivan Topalov,Elitsa Gieva,Georgi Nikolov,"Air quality monitoring in urban environments",2016 39th International Spring Seminar on Electronics Technology (ISSE).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.