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Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments.

Laura García1, Antonio-Javier Garcia-Sanchez2, Rafael Asorey-Cacheda2

  • 1Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, 46730 Valencia, Spain.

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Summary

This study introduces an IoT-based air quality monitoring system for industries, using smart devices and machine learning to predict pollutant levels and ensure safety. The system accurately identifies Gaussian Process Regression for real-time industrial air quality analysis.

Keywords:
air quality monitoringmachine-learningparticulate matterpolluting gas

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Area of Science:

  • Environmental Science
  • Industrial Engineering
  • Computer Science

Background:

  • Industrial environments face significant air quality challenges impacting human health and ecosystems.
  • Existing air quality monitoring systems often lack industry-specific adaptability and real-time capabilities.

Purpose of the Study:

  • To develop an integrated, IoT-based air quality monitoring infrastructure tailored for industrial applications.
  • To create precise, compact devices for real-time monitoring of particulate matter, gases, temperature, humidity, and GPS location.
  • To apply machine learning to analyze collected Big Data for predictive insights.

Main Methods:

  • Development of two compact, high-precision IoT devices for environmental sensing.
  • Integration of these devices into existing industrial systems.
  • Application of machine learning techniques, specifically Gaussian Process Regression, to analyze Big Data.

Main Results:

  • The developed IoT system provides real-time air quality data, including pollutant concentrations, temperature, humidity, and GPS location.
  • Gaussian Process Regression demonstrated the highest accuracy in analyzing the collected air quality datasets.
  • The system enables intelligent prediction of potential safety level exceedances.

Conclusions:

  • The proposed IoT infrastructure offers a robust and adaptable solution for industrial air quality monitoring.
  • Machine learning, particularly Gaussian Process Regression, enhances the system's predictive capabilities for industrial safety.
  • This technology facilitates proactive environmental management and worker protection in industrial settings.