AIR POLLUTION DETECTION SYSTEM USING CLOUD COMPUTING TECHNOLOGY

Abstract

This research presents the development of a real-time air quality monitoring and PM2.5 notification system aimed at enhancing health surveillance efficiency and providing users with timely air quality updates. The system was developed using an ESP32 microcontroller interfaced with a PMS7003 sensor to measure fine particulate matter. The collected data is transmitted for processing and updated to the Firebase Realtime Database for live display on a Web Dashboard, alongside recording hourly historical data. Additionally, the system integrates the LINE Messaging API to deliver notifications directly to users' smartphones. Alert thresholds are set according to air quality standards across two levels: Warning and Danger. An Anti-Spam messaging status control mechanism was implemented to prevent unnecessary duplicate alerts. Test results demonstrate that the system reliably transmits real-time data to Firebase for Web Dashboard display and accurately sends urgent LINE notifications every time dust concentrations cross risk thresholds. This effectively resolves the issue of notification fatigue caused by excessive alerts while optimizing system resource utilization.

Keywords: PM2.5, ESP32, PMS7003 Sensor, Firebase Realtime Database, LINE Messaging API, Internet of Things (IoT)