检索结果-南通市图书馆

Development of a Bayesian inference model for assessing ventilation condition based on CO_(2)meters in primary schools

引用

Building Simulation 2023年第1期16卷 133-149页

作者： Danlin Hou Liangzhu(Leon)Wang Ali Katal Shujie Yan Liang(Grace)Zhou Vicky Wang mark vuotari Ethan Li Zihan Xie Centre for Zero Energy Building Studies Department of BuildingCivil and Environmental EngineeringConcordia University1455 de Maisonneuve Blvd.WestMontrealQuebecH3G 1M8Canada Construction Research Centre Engineering DivisionNational Research Council of CanadaM-241200 Montreal RoadOttawaOntario K1A 0R6Canada

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor *** classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education,untimely and incompleted vaccinations,high occupancy density,and uncertain ventilation *** schools started to use CO_(2)meters to indicate air quality,but how to interpret the data remains *** uncertainties are also involved,including manual readings,student numbers and schedules,uncertain CO_(2)generation rates,and variable indoor and ambient *** study proposed a Bayesian inference approach with sensitivity analysis to understand CO_(2)readings in four primary schools by identifying uncertainties and calibrating key *** outdoor ventilation rate,CO_(2)generation rate,and occupancy level were identified as the top sensitive parameters for indoor CO_(2)*** occupancy schedule becomes critical when the CO_(2)data are limited,whereas a 15-min measurement interval could capture dynamic CO_(2)profiles well even without the occupancy *** CO_(2)recording should be avoided because it failed to capture peak values and overestimated the ventilation *** the four primary school rooms,the calibrated ventilation rate with a 95%confidence level for fall condition is 1.96±0.31 ACH for Room#1(165 m^(3)and 20 occupancies)with mechanical ventilation,and for the rest of the naturally ventilated rooms,it is 0.40±0.08 ACH for Room#2(236 m^(3)and 21 occupancies),0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room#3(236 m^(3)and 19 occupancies),0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room#4(231 m^(3)and 8–9 occupancies)for three consecutive days.

关键词： COVID-19 Bayesian calibration markov Chain Monte Carlo ventilation rate school CO_(2)

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：