IoT-Based Air Quality Monitoring System with Machine Learning for Accurate and Real-time Data Analysis

TL;DR

This paper presents a portable IoT-based air quality monitoring system that uses sensors and machine learning to provide real-time, location-specific air pollution data accessible via a cloud web app.

Contribution

It introduces a novel portable device with sensors and machine learning for accurate, real-time air quality analysis and visualization through a cloud platform.

Findings

Effective detection of harmful gases using MQ135 and MQ3 sensors

Real-time data visualization enabled via ThinkSpeak platform

Machine learning analysis improves data interpretation

Abstract

Air pollution in urban areas has severe consequences for both human health and the environment, predominantly caused by exhaust emissions from vehicles. To address the issue of air pollution awareness, Air Pollution Monitoring systems are used to measure the concentration of gases like CO2, smoke, alcohol, benzene, and NH3 present in the air. However, current mobile applications are unable to provide users with real-time data specific to their location. In this paper, we propose the development of a portable air quality detection device that can be used anywhere. The data collected will be stored and visualized using the cloud-based web app ThinkSpeak. The device utilizes two sensors, MQ135 and MQ3, to detect harmful gases and measure air quality in parts per million (PPM). Additionally, machine learning analysis will be employed on the collected data.