Twelve healthy male college-age students were recruited to investigate the effects of different noise exposure conditions on complex task performance and vigilance. During each noise exposure, the Multi-Attribute Task Battery (MATB) with low, medium, and high mental workloads were conducted in the order designated by the Latin square method. Meanwhile, a psychomotor vigilance test (PVT) was used to evaluate human vigilance. Heart rate variability (HRV) signals were also collected while participants performed the tasks. The generalized additive mixed-effect model (GAMM) results showed that the increased mental workload had an inverted U-shaped effect on MATB task performance. Noise exposure had no significant impact on the overall performance of MATB tasks. However, when exposed to increased noise sharpness at low mental workloads, Tracking Task (TRA) performance significantly decreased, whereas the System Monitoring Task (SYS) performance was significantly improved. In addition, higher noise sound pressure level and sharpness would impair human vigilance, which was reflected in a lower mean sample entropy of HRV and worse performance on the PVT. The results indicated that noise control in the workplace should consider both sound pressure level and sharpness.

In this study, the indoor air quality (IAQ) was investigated in a subway station with fully enclosed platform screen doors in Beijing, China. Eight indoor air pollutants, including PM2.5, PM10, SO2 (sulfur dioxide), NO2 (nitrogen dioxide), NH3 (ammonia), CO (carbon monoxide), CH2O (formaldehyde) and TVOC (total volatile organic compound), were measured for six consecutive days in October 2019. The results indicated that the IAQ in the subway station was basically stable at good levels for most times during the whole measurement period. All eight indoor air pollutants were far below their corresponding maximum allowable concentrations, except for the PM2.5 concentrations, which occasionally exceeded the concentration limits. The concentrations of indoor air pollutants in the subway station were basically within the corresponding standards. The correlation analyses showed that outdoor air pollutants have important influences on indoor air pollutants. The concentrations of PM10, PM2.5, SO2, NO2 and CO in the subway station were positively correlated with their corresponding outdoor concentrations. PM10 was statistically significantly correlated with the passenger flow and train frequency, but the other air pollutants were less impacted by the passenger flow and train frequency.

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