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作者机构:School of Mechanical EngineeringBeijing Institute of TechnologyBeijing100081China State Key Laboratory of Green BuildingXi’an University of Architecture and TechnologyXi’an710055China
出 版 物:《Building Simulation》 (建筑模拟(英文))
年 卷 期:2025年第18卷第3期
页 面:569-579页
核心收录:
学科分类:082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程]
基 金:supported by the National Natural Science Foundation of China(Grant No.52178062) the Opening Fund of State Key Laboratory of Green Building(Grant No.LSKF202311)
主 题:interior material formaldehyde volatile organic compounds(VOCs) multi-source emission vehicle cabin source control
摘 要:Air pollution in vehicle cabin environment has gained increasing concern *** study addresses the necessity to predict and regulate the emissions of volatile organic compounds(VOCs)from interior materials to improve in-cabin air *** focus on how the selection of interior materials influences formaldehyde concentration levels under multi-source emission *** experiments were conducted to determine the three key parameters of formaldehyde emissions from five typical interior materials(carpet,car door,sealing strip,and two adhesives)using the C-history *** applying a multi-source emission model with the measured key parameters,various cabin emission scenarios are predicted and *** of formaldehyde concentration levels between experimental data and simulated results demonstrates the effectiveness of the *** based on the model indicates that adhesives contribute significantly to in-cabin air pollution,and the impact of different key parameters on the emission behaviors is *** with higher ratio of initial emittable concentration to partition coefficient will prolong the emission period and increase the health *** ventilation requirements for different multi-source emission scenarios are also *** results underscore the critical role of material selection in controlling formaldehyde emissions and the necessity of developing low-emitting materials to improve air quality and occupant safety in vehicle cabins.