Autonomous AQI Mapping IoT WSN for Urban Scale Applications

In outdoor urban scale air quality mapping, electrochemical sensors warm-up time, cross-sensitivity, geo location typography, and energy efficiency are major challenges. 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 (VOCs), particulate matter, Ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principal variables. Results have shown that the proposed system optimized the real-time air quality mapping for the chosen geospatial cluster, i.e. Qatar University. This work focuses on a comprehensive study, design, fabrication, testing, calibration, and deployment of industry-grade AQM sensor nodes with extremely high-resolution sensing to ensure trustable warnings against exceeding permissible site limits and early warning. The major demands in disaster monitoring consist of flexible deployment, system scalability, and fast retrieval of data to quickly locate hazardous areas.