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.