Fast and accurate identification of the pollutant source location and release rate is important for improving indoor air quality. From the perspective of public health, identification of the airborne pathogen source in public buildings is particularly important for ensuring people's safety and health. The existing adjoint probability method has difficulty in distinguishing the temporal source, and the optimization algorithm can only analyze a few potential sources in space. This study proposed an algorithm combining the adjoint-pulse and regularization methods to identify the spatiotemporal information of the point pollutant source in an entire room space. We first obtained a series of source-receptor response matrices using the adjoint-pulse method in the room based on the validated CFD model, and then used the regularization method and composite Bayesian inference to identify the release rate and location of the dynamic pollutant source. The results showed that the MAPEs (mean absolute percentage errors) of estimated source intensities were almost less than 15%, and the source localization success rates were above 25/30 in this study. This method has the potential to be used to identify the airborne pathogen source in public buildings combined with sensors for disease-specific biomarkers.

Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.

The multi-zone model is widely used to predict airflow and contaminant transport in large buildings under natural or mechanical ventilation. Selecting appropriate mixing factors and particle deposition velocities for the multi-zone model can compensate for the errors resulting from the model’s well-mixing assumption. However, different room types, air change rates and ventilation modes can result in different mixing factors and particle deposition velocities. This study selected three typical room types: Z-type, L-type, and rectangle type (R-type). For each room type, the mixing factors and particle deposition velocities were investigated by the CFD model under different natural ventilation rates (0.5 h^-1, 1 h^-1, 3 h^-1, 6 h^-1, 12 h^-1 and 20 h^-1) and modes (door-inlet, window-inlet). The results showed that the mixing factor of the Z-type room was the highest, and the mixing factors of these rooms were 1.32, 1.28 and 1.13, respectively. In addition, the mixing factors presented a V-shaped distribution as a function of the air exchange rate under the window-inlet mode. The particle deposition velocity increased as the air change rate increased, and also demonstrated that the V-shaped curves as a function of particle size (0.05 μm, 0.1 μm, 0.5 μm, 1 μm, 2.5 μm, 5 μm) varied under different air change rates and room types. The results of mixing factors and particle deposition velocities for different room types, air change rates and ventilation modes can be used to improve the accuracy of the multi-zone model.