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<p>In recent times, the air pollution rates are radically expanding over all the nations and requires a financially effective solution. This paper leverages Internet of Things (IoT) technology to develop a cutting-edge air quality monitoring system which can lead to a concise monitoring device manufacturing process. The system features a network of low-cost, portable gas sensors used to measure pollutants like carbon monoxide (CO) in addition to carbon dioxide (CO<sub>2</sub>) levels in order to check their concentrations within the safe range in the surrounding air. A display is connected to the setup, which makes the system more convenient in terms of live data visualization and air quality tracking. The IoT module which consists of ESP32 microcontroller enables real-time data transmission to a cloud-based platform, which is used by advanced analytics and machine learning algorithms to provide predictions based on the dataset acquired. Our set-up offers high-resolution data for targeted interventions in a scalable and cost-effective deployment which can be integrated with smart city infrastructures. By demonstrating the effectiveness of IoT-based air quality monitoring, the proposed set-up will be able to optimize pollution mitigation strategies in metropolitan cities.</p>

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Design and Implementation of a Low-Cost Device to Monitor the Real-Time Presence of CO and CO2 in Ambient Air and Prediction of its Pollution Level using TinyML

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Abir Lal Dutta,
  • Tapajit Mukherjee,
  • Jayee Sinha

摘要

In recent times, the air pollution rates are radically expanding over all the nations and requires a financially effective solution. This paper leverages Internet of Things (IoT) technology to develop a cutting-edge air quality monitoring system which can lead to a concise monitoring device manufacturing process. The system features a network of low-cost, portable gas sensors used to measure pollutants like carbon monoxide (CO) in addition to carbon dioxide (CO2) levels in order to check their concentrations within the safe range in the surrounding air. A display is connected to the setup, which makes the system more convenient in terms of live data visualization and air quality tracking. The IoT module which consists of ESP32 microcontroller enables real-time data transmission to a cloud-based platform, which is used by advanced analytics and machine learning algorithms to provide predictions based on the dataset acquired. Our set-up offers high-resolution data for targeted interventions in a scalable and cost-effective deployment which can be integrated with smart city infrastructures. By demonstrating the effectiveness of IoT-based air quality monitoring, the proposed set-up will be able to optimize pollution mitigation strategies in metropolitan cities.