Indoor air pollution has become a major concern of the general publics in recent years. Indoor exposure may likely cause more harmful health effects, as the indoor concentrations of many pollutants are often higher than those typically encountered outside. The factors affecting the indoor environment mainly include temperature, relative humidity, ventilation, particle pollutants, biological pollutants, and gaseous pollutants. Ventilation and source control are the two basic engineering tools for improving indoor air quality. Source control is the most effective strategy for maintaining clean indoor air, however, it is not always possible or practical. Ventilation, either natural or mechanical, is the second most effective approach to provide acceptable indoor air quality.
Exhaust emissions in the closed vehicle garage onboard ships not sufficiently ventilated can cause damage on the human health and lead even to death and pollution. In this paper, an intelligent artificial neural network model is designed to simulate the carbon monoxide emissions to the permissible limit. The proposed model is based on data collected from real measurements at different load conditions and positions on a Roll-on/roll-off (RO-RO) vehicle garage onboard a ship vessel. The neural network model is designed, identified and verified with different ways at different loads and positions.