| Résumé | Chamber testing is a common method to evaluate volatile organic compound (VOC) emissions from building materials. Empirical models based on short-term testing (typically less than 28 days) are frequently used to estimate long-term emissions (up to years). However, the applicability of the empirical models for long-term prediction remains unclear in practice. Four empirical models, i.e., two constant models with and without a prerequisite (M1 and M2), a power-law model (M3), and an exponential model (M4), were used to test the applicability of predicting year-long emissions using emission data that were less than one month. The diffusion-based mass-transfer model was used to generate reference emission data with random variations involved to represent measurement errors, etc. For M1 and M2, the discrepancy ratios between the constant emissions and the characteristic average emissions are quantified. For M3 and M4, an additional measure, i.e., normalized mean square error (NMSE), was adopted to statistically study the applicability of using empirical models to predict long-term emissions. The results shown that, first, the NMSE values indicate that M3 prefers slow emissions and generally performs better than M4. However, M4 performs better for predicting year-long emissions for cases with characteristic emission time of one year. Second, both M3 and M4 predict the average life-long emissions reasonably well for most scenarios. Third, while the effects of test duration are less significant for M3 than M4, the early-stage sampling points are more important for better long-term predictions. Additionally, experimental data by National Research Council Canada (NRC) were used to validate the applicability of the empirical models in year-long emission predictions, with the results similar to those from the simulated data. This paper can be used as a reference to select appropriate empirical model(s), as well as the testing duration, to simulate long-term VOC emissions from building materials using short-term testing data. |
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