Modeled exposure to phthalates via inhalation and dermal pathway in children's sleeping environment: A preliminary study and its implications

In this study,exposure to phthalates during children’s sleeping time was evaluated based on a mechanistic model.Airborne phase-specific concentrations in this model were estimated taking into account kinetic partitioning,particle dynamics and time-varying ventilation.Exposure pathways considered were inhalation exposure to airborne phases,dermal absorption from "air-to-skin" transport and direct contact.By taking di(n-butyl)phthalate(DnBP)and bis(2-ethylhexyl)phthalate(DEHP)as two representative pollutants,inhalation exposure accounted for>90%when children wore clean pajamas before going to bed.Dermal uptake significantly increased when children's pajamas had absorbed DnBP before putting them on,whereas dermal uptake of DEHP was relatively lower and insensitive to the condition of clothing.Increasing air exchange rate at night and keeping clothes cleaner were effective for reducing children's exposures during sleeping period.Further studies are necessary taking into account source-proximity effect,particle emissions and refined dermal exposure routes.

A sleep staging model for the sleep environment control based on machine learning

To date,dynamic sleep environment has been attracted the focus of researchers.Owing to the individual difference on sleep phase and thermal comfort,changes in sleep environment should be occupant-centered,and precise regulation of the environment required current sleep stages.However,few studies connected occupants and the environment through physiological signal-based model of sleep staging.Therefore,this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information.Raw database was processed and selected efficiently according to the characteristics of physiological signals.Finally,the sleep staging model with an average accuracy of 93.9%was built,and other mean indicators(precision:82.5%,recall:83.1%,F1 score:82.8%)performed well.The features adopted by model were found to come from different brain regions,and the global brain signals were suggested to play an important role in the construction of sleep staging model.Moreover,the computational processing of physiology signals should consider their characteristics,i.e.,time domain,frequency domain,time-frequency domain and nonlinear characteristics.The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.

Parametric study on the sleep thermal environment

Sleep thermal environment has a great effect on occupants7 sleep quality.The purpose of this study was to explore the relationships between sleep thermal environment parameters and sleep quality in winter and summer.The whole experiments involved indoor ambient temperatures(winter:17℃,20℃,23℃;summer:23℃,26℃,29℃)and relative humidity(RH;winter:40%,55%,70%;summer:40%,60%,80%).A total of 18 young subjects(23±2 years old)were divided into the two different season groups to experience the corresponding environmental conditions.Their subjective environmental perception and objective physiological signals were monitored.The results showed that thermal sensations at 20℃in winter,as well as 26℃in summer,were considered as the thermal neutral states.Humid sensation in winter before sleep was susceptible to SET*,while the middle value of SET*(32℃)related to the middle indoor air temperature(26℃)and air humidity(60%)before and after sleep could be the summer combination of neutral thermal environment.Furthermore,cold and wet environment in winter was found to seriously affect the deep sleep.In summer,mild temperature contributing to good sleep quality might weaken the effect of humidity on sleep.Through combining the sleep environmental perception result with contour line and regression analysis on objective sleep quality,the suggested values of thermal environment parameters for young adults(21-25 years old)were as follows:20.3℃and 56%RH in winter,as well as 26.1℃and 52%RH in summer.

An effective method to determine bedding system insulation based on measured data

The thermal environment is an essential factor that affects sleep quality.In many circumstances,the bed microenvironment is more important than the ambient environment because of the large covered area of the human body and the close contact between the bedding system and the human body.The main objective of this research is to establish an effective method to determine bedding system insulation.A thermal manikin was used in the measurement of bedding system insulation.Three different types of quilts,which were filled with cotton,polyester and duvet respectively,were chosen to be tested.In total ten different quilts with different materials and weights were involved in the test.Four regular arrangements of covers were chosen with coverage rates of 94.1%,85.9%,70.6%,and 54.4%to test.A total of 64 bedding systems were tested to build an effective method to determine the bedding system insulation.On the basis of test data,the change of bedding system insulation with coverage was found to be nonlinear.Exponential fitting was applied to establish an insulation evaluation method for bedding system insulation.In addition,the effects of quilt cover and sleepwear on bedding system insulation were discussed and thermal insulation increment caused by quilt cover and sleepwear were estimated.The relationships between neutral indoor temperature and weight per unit area of the quilt for different coverage rates have been quantified based on existing subject experiments.This research provides an effective method to determine bedding system insulation,which can be widely used in thermal comfort research and HVAC system design.