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Open Access

ARTICLE

IoT-Driven Pollution Detection System for Indoor and Outdoor Environments

Fatima Khan1, Amna Khan1, Tariq Ali2, Tariq Shahzad3, Tehseen Mazhar4,*, Sunawar Khan5, Muhammad Adnan Khan6,*, Habib Hamam7,8,9,10
1 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
2 Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
3 Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
4 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
5 Department of Software Engineering, Islamia University of Bahawalpur, Bahawalnagar, 62300, Pakistan
6 Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea
7 Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
8 School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa
9 International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville, BP 1989, Gabon
10 Bridges for Academic Excellence-Spectrum, Tunis, 60102, Tunisia
* Corresponding Author: Tehseen Mazhar. Email: email; Muhammad Adnan Khan. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.068228

Received 23 May 2025; Accepted 05 September 2025; Published online 20 November 2025

Abstract

The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO2, SO2, CO, CH4, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution. Sensor data is processed using decision tree algorithms for classification, enabling threshold-based detection with environmental context. A Progressive Web Application (PWA) interface provides users with accessible, cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings, with real-time alerts triggered when thresholds were exceeded. The collected data showed consistent classification of normal, warning, and critical states for methane, CO2, temperature, humidity, and noise levels. These results confirm the system’s reliability in dynamic environmental conditions.The proposed framework offers a scalable, energy-efficient, and user-friendly solution for pollution detection and public awareness. Future enhancements will focus on extending the sensor suite, improving machine learning accuracy, and integrating meteorological data for predictive pollution modeling.

Keywords

Noise sensor; harmful air pollutants; PWA; gases and noise

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