Dataset Supporting the t/Q-Based Evaluation of Ventilation Performance in a Hospital Isolation Room

This study tests the hypothesis that airborne microbial concentration in a hospital isolation room is better explained by the exposure normalized ventilation metric (t/Q ratio) and local airflow velocity fields than by air change rate (ACH) alone. Specifically, we hypothesize that lower t/Q values and higher local airflow velocities correspond to reduced microbial loads.

The isolation room geometry.csv file contains measurements of room dimensions, ventilation layout, air velocities, and pressure differentials. These values represent both the boundary conditions and validation points for the CFD model.
The microbial data.csv file contains colony forming unit (CFU/m³) counts obtained from settle plate sampling at the same spatial coordinates used for airflow measurements. The dataset includes t/Q calculations and allows correlation between microbial load, local velocity, and ventilation performance.

The data show that sampling points with low t/Q values consistently have lower microbial loads, supporting the hypothesized inverse relationship. In contrast, areas with very low airflow velocities (<0.025 m/s) show elevated CFU levels even when the overall room ventilation meets or exceeds 18 ACH. This indicates that aggregate ACH can mask localized ventilation inefficiencies.

The geometry and ventilation dataset should be used to understand airflow distribution and identify regions of stagnant air. The microbial dataset enables direct comparison of CFD predicted airflow behaviour with real microbial deposition. Researchers can use these files to reproduce the CFD model, evaluate t/Q ratios, or compare alternative ventilation configurations. Together, the datasets provide a basis for analysing how local airflow patterns influence airborne contamination risk in isolation room environments.