This air quality assessment evaluates environmental conditions inside an industrial vacant warehouse from February 11-17, 2025, focusing on particulate matter (PM), carbon dioxide (CO2), volatile organic compounds (VOCs), and nitrogen oxides (NOX).
Conducted by SensusAir BioDefense Platforms, the study utilised three AirVue sensors to collect comprehensive data, revealing a mix of compliance and concern. Key findings include PM2.5 levels approaching regulatory thresholds, moderate VOC spikes, stable CO2 with a brief exceedance, and significant NOX elevations. Recommendations emphasise filtration, ventilation improvements, and source investigation to mitigate health risks and ensure occupant safety.
Beyond the immediate findings, this paper illustrates how AI-driven learning models can strengthen environmental analysis by detecting subtle patterns, highlighting potential airborne risks, and supporting early interventions. By moving beyond static measurements to adaptive, pattern-based interpretation, these models provide a valuable framework for anticipating hazards and guiding proactive health and safety decisions.
Access the full research paper on https://dx.doi.org/10.2139/ssrn.5288524