Fuzzy risk assessment system for indoor air quality and respiratory disease prevention

Abstract

This study addresses the evaluation of indoor air quality, with a focus on mitigating respiratory diseases and sick building syndrome (SBS). Recognizing that different pollutants exhibit variable behavior depending on environmental factors and human activity, the objective was to develop a fuzzy logic-based classification system that integrates environmental variables such as temperature, relative humidity, and pollutant concentrations‒particulate matter (PM10, PM2.5), carbon dioxide (CO₂), and total volatile organic compound (TVOC)‒into a unified model. The method involved defining risk levels as low, moderate, high, and very high, and implementing 54 fuzzy rules to dynamically and accurately categorize these risks, based on measurements taken between 2022 and 2024 in the states of Morelos and Puebla under various relative humidity and temperature scenarios. The analysis of the results demonstrated robust system performance, with an overall accuracy of 94.08%, but also revealed challenges in distinguishing between adjacent risk classes. This research contributes to a better understanding of the complex impacts of air quality on health and reinforces efforts to mitigate respiratory problems and SBS in densely populated indoor environments