IoT-Driven Pollution Detection System for Indoor and Outdoor Environments

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.