IoT and GSM-based Vehicle Carbon Dioxide Emission Prediction System using ESP32

Abstract

The rapid escalation of vehicular density in urban centers has led to a critical increase in greenhouse gas emissions, primarily carbon dioxide (CO2), necessitating advanced real-time monitoring solutions. This research presents the design and implementation of an IoT and GSM-based Vehicle Emission Prediction and Control System utilizing the high-performance ESP32 microcontroller. The proposed architecture integrates an MQ-135 electrochemical sensor for gas detection and a DHT11 sensor for environmental correlation. Unlike traditional passive logging systems, this device employs an active mitigation strategy: utilizing the ESP32’s 12-bit ADC resolution, the system precisely monitors CO2 levels and executes an automated engine immobilization protocol via a relay-driven ignition cut-off when concentrations exceed a critical safety threshold (>500 ppm). The methodology leverages the ESP32’s dual-core capability to manage localized sensing and wireless data transmission simultaneously. Data telemetry is handled via Wi-Fi for cloud-based historical analysis, while a SIM800L GSM module provides a redundant, long-range emergency SMS alert system for immediate user notification. Experimental results indicate that the system achieves high sensitivity in gas detection and maintains a 100% success rate in automated motor intervention. This dual-layer communication and control framework offers a scalable, low-cost solution for enforcing environmental compliance and enhancing air quality management in smart city infrastructures.