Indoor Air Quality in Cob Buildings: In Situ Studies and Artificial Neural Network Modeling

Abstract

Knowledge of indoor air quality (IAQ) in cob buildings during the first few months following their delivery is of vital importance in preventing occupants’ health problems. The present research focuses on evaluating IAQ in cob buildings through a prototype built in Normandy, France. To achieve this, the prototype was equipped with a set of sensors to monitor various parameters that determine indoor and outdoor air quality. These parameters include relative humidity (RH), carbon dioxide (CO2), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM1 and PM10), and volatile organic compounds (VOCs). The obtained experimental results indicate that, overall, there is good indoor air quality in the prototype building. However, there are some noteworthy findings, including high indoor RH and occasional spikes in CO2, PM1, PM10, and VOCs concentrations. The high RH is believed to be a result of the ongoing drying process of the cob walls, while the peaks in pollutants are likely to be attributed to human presence and the earthen floor deterioration. To ensure consistent good air quality, this study recommends the use of a properly sized Controlled Mechanical Ventilation system. Additionally, this study explored IAQ in the cob building from a numerical perspective. A Long Short-Term Memory (LSTM) model was developed and trained to predict pollutant concentrations inside the building. A validation test was conducted on the CO2 concentration data collected on-site, and the results indicated that the LSTM model has accurately predicted the evolution of CO2 concentration within the prototype building over an extended period.

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

“…The monitoring system accommodates a temperature spectrum ranging from −55 to 125 • C, with a precision rate of ±2 • C. Data were recovered during 27 days from 16 September to 13 October 2022. The period of interest of the present study was the one following the building delivery in which the building walls were not completely dry [18].…”

“…Data were recovered during 27 days from 16 September to 13 October 2022. The period of interest of the present study was the one following the building delivery in which the building walls were not completely dry [18].…”