International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Recent research work on air quality checker technology highlight significant advancements driven by Internet of Things (IoT) and Artificial Intelligence (AI), the proliferation of low-cost sensors, and the exploration of hybrid monitoring approaches. Air pollution is a most unavoidable serious issue which risks to human health and ecosystems. Continuous monitoring of air quality is essential for assessing pollution levels and for taking timely preventive measures. The standard indicator use for air quality measurement is Air Quality Index (AQI). It includes concentration of major pollutants such as particulate matter (PM₂.₅ and PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). This paper reviews various air quality monitoring approaches, including conventional monitoring stations, Internet of Things (IoT)-based systems, and data-driven techniques. Traditional monitoring methods provide high accuracy but suffer from high cost and limited coverage. Recent advancements in low-cost sensors, wireless communication, and cloud platforms have enabled real-time and scalable AQI monitoring. Furthermore, machine learning techniques have improved AQI prediction and pollution trend analysis. The review highlights existing challenges such as sensor calibration, data reliability, and environmental interference, and discusses future research directions for smart and sustainable air quality monitoring systems.

Keywords: Air Quality Index (AQI), Air Pollution Monitoring, IoT, Low-Cost Sensors, Machine Learning, Environmental Monitoring, Smart Cities

Downloads: | DOI: 10.17148/IJARCCE.2026.15147