A Real-time Gradient Aware Multi-Variable Handheld Urban Scale Air Quality Mapping IoT System

Abstract

In outdoor urban scale air quality mapping, electrochemical sensors warm-up time, cross-sensitivity, geo-location typography, and energy efficiency are major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and prolonged lags in air quality mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter, ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University.