Statistical Optimization of a Novel Rice-Based CO₂ Purification Medium Using Response Surface Methodology

Indoor environments have a significant impact on human health, productivity, and learning outcomes. Elevated CO₂ levels in indoor spaces often surpass 1000 ppm, impairing cognitive function and well-being. Classrooms are particularly concerning because they contain many students exposed to indoor air pollutants for extended periods. Given the importance of indoor air quality (IAQ) in educational settings, there is a pressing need for practical solutions to improve air quality. This study introduces a novel bio-based purification medium made from puffed rice waste and assesses its effectiveness using Response Surface Methodology (RSM). Three operational parameters, temperature, relative humidity, and material surface coverage, were statistically analyzed to optimize CO₂ absorption. Experiments conducted in a controlled chamber demonstrated that the RSM model exhibited strong predictive accuracy (R² = 97.38%, adjusted R² = 94.42%), with surface coverage identified as the most influential factor. Under optimal conditions (25 °C, 55% RH, 45% coverage), CO₂ levels decreased from 1001 ppm to 684 ppm, averaging a reduction of 79.25 ppm per hour. The findings highlight the potential of waste-derived biomaterials to sustainably enhance indoor air quality and reduce CO₂ concentrations, supporting global efforts toward sustainability and resource efficiency in the construction sector.