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Multivariate time series models for prediction of air quality inside a public transportation bus using available software



Indoor air pollution predictions, if reliable and accurate, could play an important role in managing indoor air quality (IAQ). Accurate predictions of the air contaminants inside a transit microenvironment could assist vehicle manufacturers in the design of optimal ventilation systems by facilitating adequate air exchange rate that can prevent the buildup of in-vehicle contaminants beyond recommended IAQ guidelines. The predictions can also be of particular interest to the public in understanding the possible levels of exposure when commuting during different time periods of a day. Due to the simple structure and the robustness in prediction, the use of time series models is greatly encouraged. This study demonstrates the methodology to develop and validate the multivariate time series transfer function models (ARMAX/ARIMAX) for the in-bus contaminant concentrations of carbon dioxide and carbon monoxide using available software. © 2014 American Institute of Chemical Engineers Environ Prog, 33: 337–341, 2014

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