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Indoor air quality (IAQ) in subway stations are dynamic due to various time-dependent factors, such as subway schedule, passenger load variance in short time scale, and outdoor air quality variation cause from climate change. To remove the indoor pollutants, a conventional mechanical ventilation system is typically utilized in subway stations; however, the working mechanism does not consider the real-time variation of the factors that may cause energy waste or deficiency. Therefore, for a quick response in controlling the time-varying indoor PM10 concentration and to design a well-adapted control system for various outdoor air quality (OAQ), a flexible optimal real-time ventilation control strategy was developed. Moreover, the optimal set-points of the PM10 concentration in the platform are determined by the multi-objective genetic algorithm (MOGA) at different time intervals to keep a balance of the energy consumption while maintaining healthy levels of IAQ. Experimentally, three cases were then specified and analyzed based on various outdoor PM10 health levels (i.e. clean, moderate, and contaminated). The results show that a real-time ventilation system can keep the platform at a healthy IAQ level in all cases. Under clean outdoor air conditions, the controlled system can reduce the energy consumption of the ventilation system by 10.3%. On the other hand, for the contaminated outdoor air condition, the peak value of the platform PM10 concentration was reduced by approximately 14 mu g/m(3) and the indoor air level in an underground station was changed from unhealthy to a moderate level.