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Assessing vehicle emissions is crucial for understanding their environmental impact and developing effective emission reduction strategies. This article discusses modern research tools that combine traditional laboratory measurements on chassis dynamometers with advanced theoretical models. Probabilistic methods, including stochastic processes based on Markov and semi-Markov chains, are important tools for modelling driving cycles, considering the variability of road conditions and driver behaviour. The article also presents mathematical approaches to emission data analysis, considering both the states of the technical system and the transitions between them, which allows for precise modelling of real vehicle operating conditions. Ultimately, the synergy of experimental measurements with computational modelling offers a more complete and accurate tool for assessing pollutant emissions, which is crucial in global efforts to improve air quality.
Zimakowska-Laskowska et al. (Fri,) studied this question.