办公楼内生物气溶胶的预测模型:实地研究调查

IF 1.5 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
Dong Jiang, Xiaoqiang Gong, Zhengsong Xu, Kai Yuan, Zengwen Bu
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引用次数: 0

摘要

空气中微生物形成的生物气溶胶直接影响人们的健康。对中国深圳某办公大楼进行了36次空气质量调查和污染物水平测量;六个室内空间各六次。采用线性回归分析和泊松回归分析确定了室内生物气溶胶与环境因子的关系。我们的结果和分析表明,基于单一指标的线性回归是一个很差的生物气溶胶浓度预测器。泊松回归可以较好地预测生物气溶胶的浓度,PM10可能是对生物气溶胶影响最大的指标。因此,开发并报道了一种简单、快速、低成本的室内生物气溶胶在线监测方法。本研究为预测室内生物气溶胶浓度提供了第一手的基础数据,有助于制定适当的监测指南。与现有的研究相比,该方法具有更大的实用价值,因为我们的预测模型有助于以低成本估计生物气溶胶的浓度。此外,由于目前室内环境传感器的成熟和低成本,该方法适用于大多数建筑物的大规模部署。本研究以实际办公建筑的测量数据为基础,采用概率分析与实际测量相结合的方法,探讨室内微生物与建筑环境指标之间的关系。利用泊松回归模型建立了一种新的室内微生物预测模型。我们的工作提出了一种有效、低成本的生物气溶胶浓度估算方法,并讨论了在建筑物内大规模部署微生物监测设备的可能性,这可能会支持室内微生物浓度的实时监测,为人员提供健康的室内环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction models of bioaerosols inside office buildings: A field study investigation
Bioaerosols formed by microorganisms in the air directly affect people’s health. The air quality in an office building in Shenzhen, China, is investigated and pollutant levels measured on 36 occasions; six times for each of six indoor spaces. A relationship between indoor bioaerosols and environmental factors was determined using both linear regression analysis and Poisson regression analysis. Our results and analysis indicate that linear regression is a poor predictor for the concentration of bioaerosols based on a single indicator. In contrast, Poisson regression can better predict the concentration of bioaerosols, and PM10 may be the indicator with the greatest impact on bioaerosols. As a result, a simple, fast, and low-cost online monitoring method for monitoring indoor bioaerosols is developed and reported. Our paper provides first-hand basic data to predict the indoor bioaerosol concentration and helps to formulate appropriate monitoring guidelines. The proposed method offers more practical values compared to existing studies as our prediction model facilitates estimation of the concentration of bioaerosols at low cost. Additionally, due to the current maturity and low cost of indoor environmental sensors, the proposed method is suitable for large-scale deployment for most buildings. Practical application Based on measurement data from a real office building, our investigation explores the relationship between indoor microorganisms and building environmental indicators through a combination of probability analysis and actual measurement. We establish a novel indoor microbial prediction model using the Poisson regression model. Our work presents an effective, low-cost, method for estimating the concentration of bioaerosols and discusses the possibility for large-scale deployment of microbial monitoring equipment inside buildings which may then support real-time monitoring of indoor microbial concentration to provide healthy indoor environments for personnel.
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来源期刊
Building Services Engineering Research & Technology
Building Services Engineering Research & Technology 工程技术-结构与建筑技术
CiteScore
4.30
自引率
5.90%
发文量
38
审稿时长
>12 weeks
期刊介绍: Building Services Engineering Research & Technology is one of the foremost, international peer reviewed journals that publishes the highest quality original research relevant to today’s Built Environment. Published in conjunction with CIBSE, this impressive journal reports on the latest research providing you with an invaluable guide to recent developments in the field.
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