Optimizing Air Quality in Military Vehicles: A Sensor Fusion and Machine Learning Approach Focusing on Low-Ventilation Scenarios

Abstract

Urban military vehicles frequently operate in environments with limited ventilation, a condition that can lead to the dangerous accumulation of airborne contaminants such as particulate matter (PM) and volatile organic compounds (VOCs). This ongoing study highlights the critical need for continuous air quality monitoring to safeguard the health and operational readiness of military personnel. Preliminary observations suggest that low-ventilation scenarios may significantly increase exposure to harmful substances, potentially resulting in adverse health effects ranging from respiratory irritation to long-term cardiovascular issues. Although final results are not yet available, our work emphasizes the urgency of developing robust monitoring strategies and raising awareness among military decision-makers and vehicle designers about the risks posed by inadequate air circulation.Within the context of the Brazilian military and its diverse fleet of vehicles, this research examines the specific challenges posed by PM and VOCs in military vehicles with low ventilation. This study aims to support the development and implementation of comprehensive strategies to protect the health of Brazilian military personnel and ensure operational readiness across the full spectrum of military operations. The findings will be valuable to military decision-makers, vehicle designers, and health and safety professionals responsible for safeguarding personnel in challenging operational environments.

Keywords: Air Quality Monitoring, Data-Driven Decision Making, Sensor Fusion, IoT, Environmental Sensing

DOI: 10.54941/ahfe1006587