An Integrated Framework for Predictive Indoor Air Quality and Ventilation Assessment in Hospital Pathology Laboratories

Hospital pathology laboratories represent challenging healthcare environments for Indoor Air Quality (IAQ) management due to the coexistence of hazardous chemical emissions, transient pollutant peaks, and stringent ventilation requirements. This study proposes an integrated data-driven framework for IAQ assessment and predictive ventilation management based on a high-resolution monitoring campaign conducted over 17 calendar days in a pathology grossing room (V = 113.90 m3) and an adjacent chemical storage room (V = 56.03 m3). The monitoring system generated 4356 synchronized 1-min observations, of which 4232 complete multivariate records were retained after data-quality screening. The proposed methodology combines three complementary analytical layers: (i) predictive modelling of pollution episodes using supervised machine learning architectures; (ii) multivariate anomaly detection to identify atypical environmental states; and (iii) temporal dependency analysis based on Granger causality and Bayesian networks to investigate predictive relationships between occupancy-related indicators, ventilation behaviour, and pollutant evolution. This integrated framework enables the transition from descriptive IAQ assessment toward predictive environmental management in healthcare facilities. Baseline statistical diagnostics demonstrated the limited capability of conventional linear approaches, with an Ordinary Least Squares (OLS) model explaining only 7.4% of TVOC variability (R2 = 0.074). Ventilation assessment identified an approximately 38% deficit relative to the selected ASHRAE 170 ventilation requirement in the monitored grossing room. Among the evaluated predictive models, Random Forest achieved the highest test-set performance (R2 = 0.78; MAE = 10.5 ppb), enabling short-term forecasting of TVOC evolution. Isolation Forest identified 212 atypical environmental states, corresponding to 5.01% of the valid analytical observations, with substantially higher TVOC concentrations than under normal operating conditions. The proposed framework establishes a transferable methodology for predictive IAQ assessment and ventilation management in chemically intensive healthcare facilities, providing decision-support information for risk-informed HVAC operation within existing regulatory and ventilation requirements.