Optimising Ventilation Strategies Based on Predicted Mean Vote and Indoor CO2 Concentration: A Case Study of University Teaching Spaces in Cold Region of China

Background: Educational spaces, which represent a vital setting for students’ learning activities, significantly influence both learning efficiency and physiological health. As university teaching spaces are characterised by high occupancy density and prolonged occupancy, it is vital to address the problem of elevated CO2 concentrations in these spaces. Although effective ventilation design can improve indoor air quality, the relatively short ventilation periods in cold regions of China, driven by winter insulation requirements, often lead to substandard indoor air quality. Purpose: Based on predicted mean vote (PMV), this study explored the effect of natural ventilation on indoor CO2 concentration during the transition season in cold regions and proposed reasonable optimization strategies. Method: Through the method of combination of measurement and simulation, an effective design method of doors and Windows and an intermittent ventilation strategy for improving the indoor environment quality of teaching space in universities in cold regions of China are put forward. Result: The results revealed that indoor CO2 concentrations exceeded the standard limit of 1000 ppm for the majority of the periods studied. Peak indoor CO2 concentrations reached 1970 ppm, 2751 ppm, and 3200 ppm in large-, medium-, and small-sized classrooms, respectively. The duration of exceeding CO2 concentration accounted for the highest proportion of class time, which were 89%, 93%, and 88%, respectively. Furthermore, optimisation of door and window design and ventilation methods was carried out for a representative medium-sized classroom. The simulation results indicated that while maintaining indoor comfort (−0.5 < PMV < 0.5), peak indoor CO2 concentrations during class periods decreased from 2551 ppm to 1149 ppm, and the proportion of time for which CO2 concentrations exceeded standards decreased from 87% to 47%. Conclusions: This study suggests that relevant codes and standards should be refined for the relationship between indoor CO2 concentration and thermal comfort, and that designers should provide suggestions for suitable door and window design and ventilation strategies to improve indoor air quality in educational spaces in cold regions of China.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2024-11-28
DOI
https://doi.org/10.3390/buildings14123822
Primary Topic
Indoor Air Quality and Microbial Exposure
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimising Ventilation Strategies Based on Predicted Mean Vote and Indoor CO2 Concentration: A Case Study of University Teaching Spaces in Cold Region of China

Yitong Xu, Jiahui Yu, Chen Yang, Hongnan Wang et al.
Buildings
Indoor Air Quality and Microbial Exposure
article

Optimising Ventilation Strategies Based on Predicted Mean Vote and Indoor CO2 Concentration: A Case Study of University Teaching Spaces in Cold Region of China

Yitong Xu, Jiahui Yu, Chen Yang, Hongnan Wang, Yi Sui
article en

Abstract

Background: Educational spaces, which represent a vital setting for students’ learning activities, significantly influence both learning efficiency and physiological health. As university teaching spaces are characterised by high occupancy density and prolonged occupancy, it is vital to address the problem of elevated CO2 concentrations in these spaces. Although effective ventilation design can improve indoor air quality, the relatively short ventilation periods in cold regions of China, driven by winter insulation requirements, often lead to substandard indoor air quality. Purpose: Based on predicted mean vote (PMV), this study explored the effect of natural ventilation on indoor CO2 concentration during the transition season in cold regions and proposed reasonable optimization strategies. Method: Through the method of combination of measurement and simulation, an effective design method of doors and Windows and an intermittent ventilation strategy for improving the indoor environment quality of teaching space in universities in cold regions of China are put forward. Result: The results revealed that indoor CO2 concentrations exceeded the standard limit of 1000 ppm for the majority of the periods studied. Peak indoor CO2 concentrations reached 1970 ppm, 2751 ppm, and 3200 ppm in large-, medium-, and small-sized classrooms, respectively. The duration of exceeding CO2 concentration accounted for the highest proportion of class time, which were 89%, 93%, and 88%, respectively. Furthermore, optimisation of door and window design and ventilation methods was carried out for a representative medium-sized classroom. The simulation results indicated that while maintaining indoor comfort (−0.5 < PMV < 0.5), peak indoor CO2 concentrations during class periods decreased from 2551 ppm to 1149 ppm, and the proportion of time for which CO2 concentrations exceeded standards decreased from 87% to 47%. Conclusions: This study suggests that relevant codes and standards should be refined for the relationship between indoor CO2 concentration and thermal comfort, and that designers should provide suggestions for suitable door and window design and ventilation strategies to improve indoor air quality in educational spaces in cold regions of China.

BuildingsVol. 14(12)
Harbin Institute of Technology (CN), Ministry of Industry and Information Technology (CN)
Openalex Percentile: Top 69%
Indoor Air Quality and Microbial Exposure
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.