Vulnerable populations, including elderly individuals and patients with respiratory or rheumatic conditions, are highly sensitive to rapid changes in temperature, humidity, and atmospheric pressure, necessitating continuous environmental monitoring. This study presents the experimental validation of a distributed environmental monitoring and decision-support system deployed in Qasr Bin Ghashir, specifically designed to detect thermal, hygrometric, and barometric shocks affecting these populations. Two ESP32-based sensing nodes were installed in indoor and outdoor environments to measure temperature, relative humidity, and atmospheric pressure at one-minute intervals, communicating via MQTT with a central Raspberry Pi B+4 unit for real-time processing. A total of 2951 raw readings were collected over approximately 98 hours, yielding 1421 synchronized paired samples for analysis. Environmental gradients (ΔT, ΔH, ΔP) revealed frequent short-term shocks, with outdoor air generally drier and pressure differences remaining moderate. The fuzzy inference system, compared with a conventional rule-based mechanism, achieved 91.4% agreement with an AUC of 0.967, demonstrating enhanced contextual interpretation near threshold boundaries without compromising safety. Correlation analysis identified humidity transitions as the primary factor influencing respiratory-related recommendations, while pressure gradients showed moderate association with rheumatism and elderly-related decisions. The system's fuzzy suitability index closely aligned with aggregated health recommendations, confirming its ability to translate environmental transitions into actionable guidance. These findings demonstrate the practical feasibility, robustness, and health-oriented relevance of a distributed fuzzy-based monitoring framework, providing a scientifically grounded tool for assessing real-time environmental stressors and supporting adaptive health-aware interventions for vulnerable populations.
mousbah (Sun,) studied this question.