Office rooms · Natural ventilation
The estimator of COVID-19, IAQ and thermal comfort
Evaluate natural ventilation strategies at the earliest design stage — pick a ventilation type, orientation, window-to-wall ratio and operable percentage, and read the whole year at once.
Three indices, one set of inputs: airborne transmission risk (R0), CO2 concentration, and thermal comfort.
This website is created
- To produce a tool with high speed, accuracy, and minimal inputs by utilizing artificial machine learning algorithms to evaluate different natural ventilation strategies in the early stages of design and investigate their impact on the transmission of COVID-19, CO2 concentration, and thermal comfort.
- It is assumed that hands are washed and that individuals are far apart from each other — that is, the risk of short-range transmission by droplets/aerosol is not included and might be significant.
- To the fullest extent permitted by law, the authors of the website and app shall have no liability for any loss or damage suffered as a result of users’ use of or reliance on CITe.
- Refer to the document below for more information about the calculation methods and tool
performance:
Impact of Natural Ventilation on Mitigating Airborne Disease Transmission Risks in Office Rooms: Tool Development
Guide
- This web app includes three sections, and each of the output indexes — CO2 concentration, thermal comfort, and the reproduction number (R0) — is checked in a separate tab. The results of each situation are in the form of daily tables, an annual index, and a pie chart showing the frequency of different percentages throughout the year.
- In this study, rather than showing the value of various indexes, the percentage of hours each index met the standard daily was reported. The input parameters were streamlined for simplicity. When displaying outcomes, a colour gradient from green to red differentiated the results, enabling users to swiftly assess the performance of different modes throughout the year. Additionally, an annual index for each mode offered a comparative overview of their effectiveness across multiple output indices.
Produced by the Department of Construction, Shahid Beheshti University, Tehran, Iran, since 2024