VOC transport in an occupied residence: Measurements and predictions via deep learning

Zhang, Rui; He, Xinglei; Liu, Jialong; Xiong, Jianyin

doi:10.1016/j.scitotenv.2023.164559

Cited by 5 publications

(4 citation statements)

References 51 publications

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“…This study encompassed diverse weather scenarios, including sunny, cloudy, and rainy conditions. AER ranks the lowest of all the influencing factors, contrary to the conclusion of exploring the influencing factors of VOC concentrations in residence (where AER was the most important influencing factor) ( 45 ). This is understandable, since during the experiment, both the car doors and windows were closed, and the engine was turned off, thus only the infiltration could influence the AER.…”

Section: Resultsmentioning

confidence: 73%

“…During the experiment, there were obvious variations in the concentration of acrolein in the residence, particularly at 12:50 AM on September 3 and at 5:50 PM on September 7. Previous investigation ( 45 ) indicated that the significant peaks in concentration at these two moments were attributed to human activities. Figure S2 indicates that both models closely match experimental trends, affirming their ability to capture acrolein emission patterns.…”

Section: Resultsmentioning

confidence: 91%

“…Furthermore, we employed data spanning 10 days, from August 31 to September 9, as a validation dataset to evaluate the model's performance. The predictive results of the LSTM model were reported in a prior publication ( 45 ).…”

Section: Resultsmentioning

confidence: 99%

“…Yuan and Yang ( 42 ) introduced a hybrid model comprising a self-attention mechanism, empirical mode decomposition algorithm, and LSTM, and successfully achieved prediction of PM 2.5 concentrations in classrooms. Zhang et al made multiple attempts to apply artificial neural network models ( 43 ), single-feature LSTM model ( 44 ), and multifeature LSTM model ( 45 ) to forecast VOC concentrations emitted from furniture, classroom, and actual residence, with acceptable outcomes. Deep learning models, including RNN and LSTM models, have gained recognition for their efficacy in predicting indoor pollutant concentrations, and are also playing an important role in controlling the internal environment of smart farming facilities ( 46 , 47 ).…”

Section: Introductionmentioning

confidence: 99%

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Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles

Zhang,

Zhao,

Wang

et al. 2024

PNAS Nexus

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Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction–observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment.

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Section: Resultsmentioning

confidence: 73%

Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%