Smogpharos- Integrable Air Quality Monitoring and Recommendation System

Abstract

Urban Air Pollution has become one of the serious global concerns affecting human health as well as environmental sustainability. However, existing air quality monitoring systems are quite costly, complicated to design and lack real-time accessibility capabilities. To address this issue, this paper introduces SmogPharos, a low-cost and intelligent air quality monitoring system based on IoT and AI technologies. The system uses an ESP32 microcontroller along with PM2.5, PM10, and MQ7 carbon monoxide sensors to continuously collect realtime environmental data. The collected data is transmitted to the ThingSpeak cloud platform through Wi-Fi for remote monitoring and visualization. All the data gathered by the hardware is processed and sent to the cloud computing platform in real-time. Additionally, an AI-based analysis model has been implemented to perform analysis on collected data and forecast future air pollution levels. The performance of the designed system has been evaluated experimentally. The results show that the proposed system is able to efficiently acquire the data, transmit it to the cloud computing infrastructure and perform meaningful analysis.