# Carbon dioxide sorption in earthen plasters and its impact on Indoor Air Quality

Sofia Arris-Roucan, Lyna Oulhaci, Nathalie Costarramone, Céline Perlot, Fionn McGregor, Antonin Fabbri, Valérie Desauziers, Hervé Plaisance

► To cite this version:

Sofia Arris-Roucan, Lyna Oulhaci, Nathalie Costarramone, Céline Perlot, Fionn McGregor, et al. Carbon dioxide sorption in earthen plasters and its impact on Indoor Air Quality. Building and Environment, 2025, 284, pp.113347. 10.1016/j.buildenv.2025.113347. hal-05167572

HAL Id: hal-05167572

https://imt-mines-ales.hal.science/hal-05167572v1

Submitted on 21 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Carbon dioxide sorption in earthen plasters and its impact on Indoor Air Quality

Sofia Arris-Roucan<sup>a,b,D*</sup>, Lyna Oulhaci<sup>a,c</sup>, Nathalie Costarramone<sup>c,D</sup>, Céline Perlot<sup>a,d</sup>, Fionn McGregor<sup>a,D</sup>, Antonin Fabbri<sup>b,D</sup>, Valérie Desauziers<sup>c,D</sup>, Hervé Plaisance<sup>c,D</sup>

## ARTICLE INFO

### Keywords:

* Indoor air quality

* Raw earth

* Emission chamber

* Partition coefficient

* Sorption rate constants

## ABSTRACT

Raw earth-based materials play a significant role in creating comfortable indoor environments, particularly due to the presence of clay minerals, which can interact with indoor air constituents such as moisture and provide passive regulation. This study explores the interactions between raw earth plasters and carbon dioxide (CO₂) in indoor air, with a focus on their potential role in passive CO₂ regulation, particularly in densely occupied or poorly ventilated spaces. Experiments were conducted using an emission chamber with continuous CO₂ monitoring to assess: (i) the influence of humidity on sorption processes, (ii) sorption differences between two types of earth materials and a plasterboard, and (iii) the effect of plaster thickness. Results show that raw earth plasters effectively adsorb CO₂, with higher adsorption under high humidity. Furthermore, CO₂ diffusion occurs within the plaster, leading to greater adsorption with increased plaster thickness. Key coefficients, adsorption and desorption rate constants (kₐ, kₑ) and the material/air partition coefficient (kₔ), were determined. Using these coefficients, simulations were performed to predict the impact of earthen plasters on CO₂ concentrations in a typical bedroom. Simulations predict that earth plasters can reduce CO₂ levels, with a modest, yet measurable, 8% reduction overnight compared to a room without plasters.

## 1. Introduction

The quality of indoor air is a critical aspect of building performance, directly affecting the health and comfort of occupants [1–3]. Over the past decades, modern construction practices have prioritised energy efficiency, leading to increasingly airtight building envelopes [4]. While this approach has significantly reduced energy consumption for heating and cooling [5], it has also created a new set of challenges by limiting natural ventilation and exacerbating the accumulation of indoor air pollutants [6–8]. Among these, carbon dioxide (CO₂) is of particular concern due to its ubiquitous presence mainly as a consequence of human respiration. Elevated indoor CO₂ concentrations, even within ranges traditionally considered safe, have been shown to impair cognitive function, hinder decision-making processes, and reduce overall well-being [9–12]. The proposed limit of CO₂ concentration for indoor air is 1000 ppm [13]. However, in inadequately ventilated space its concentration can often rise above this limit. Ramalho et al. [14] measured CO₂ levels in 310 schools and day-care centres across France, finding average concentrations between 440 and 2160 ppm in 80% of

cases, with a median of 1200 ppm in winter and 960 ppm in summer. In 10% of classrooms, levels exceeded 4000 ppm. Similar findings were reported by the SINPHONIE project across the European Union [15] and other studies [16,17]. High CO₂ concentrations can also occur in bedrooms. Guyot et al. [7] measured CO₂ levels in 21 low-energy homes in France and found that elevated concentrations in bedrooms were linked to improperly functioning ventilation systems, such as insufficient airflows or incorrectly undercut doors. Notably, even when overall airflow rates met standards, poorly undercut doors were associated with high CO₂ levels, particularly in main bedrooms. Additionally, according to the U.S. Institute of Medicine, human exposure to indoor CO₂ is expected to increase as a result of climate change, due to a higher ambient CO₂ concentrations and the energy-efficient building practices that reduce ventilation [18]. Then, regulating the concentration of CO₂ in indoor air could have multiple benefits, improving learning conditions in educational environments, sleep quality in bedrooms and working conditions in offices [19] as well as improve the resilience of indoor space towards climate change.

<footnote><sup>a</sup> Université de Pau et des Pays de l'Adour, SIAME, Anglet, France</footnote>

<footnote><sup>b</sup> LTDS, UMR 5513 CNRS, ENTPE, Université de Lyon, 69120, Vaulx-en-Velin, France</footnote>

<footnote><sup>c</sup> IPREM, IMT Mines Ales, Université de Pau et des Pays de l'Adour, CNRS, Pau, France</footnote>

<footnote><sup>d</sup> Institut Universitaire de France, Paris, France</footnote>

<footnote>* Corresponding author at: Université de Pau et des Pays de l'Adour, SIAME, Anglet, France.
E-mail address: sofia.roucan@univ-pau.fr (S. Arris-Roucan).</footnote>
Recent research in indoor air quality has explored a variety of solutions for regulating CO₂ concentrations. Ventilation strategies [20], including mechanical ventilation systems equipped with CO₂ sensors, are commonly implemented but can be energy-intensive and costly. Alternatively, natural ventilation offers a low-energy solution but is often limited by climatic constraints, building design, and occupant behaviour. On the materials side, various coatings and absorptive surfaces have been investigated for their ability to interact with indoor pollutants, including ceiling tiles [21], gypsum board [22,23], gypsum board enhanced with sorptive materials [24,25], and sorptive paints [23]. Most studies conduct uptake experiments under representative indoor conditions, considering pollutant concentrations, temperature, and relative humidity, using small-scale chambers (6.5–400 L) or larger test environments (up to 40 m³). CO₂ removal has been demonstrated, for instance, by Rajan et al. [26], who reported an 8% CO₂ reduction using silica-supported amines in an 8 m³ chamber. Similarly, Brun et al. [24] observed decay rates of 0.2 to 0.3 h⁻¹ using lime-cement plasters with and without activated carbon. The reliance on synthetic materials and complex manufacturing processes raises concerns about environmental impact and economic cost [27,28]. Improving indoor air quality without depending solely on mechanical or high-tech solutions, which can increase energy use and greenhouse gas emissions, calls for passive, low-tech strategies aligned with both health and sustainability goals [29,30].

For that purpose materials such as raw earth have been considered [31,32]. Earthen construction materials present a promising solution due to their physical properties and significant environmental benefits [33,34]. Renowned for their hygroscopic nature, earthen materials regulate indoor humidity through the adsorption of moisture on clay minerals [35,36]. Furthermore, raw earth is a sustainable construction material, as its production and use, when properly implemented, result in minimal or no CO₂ emissions compared to conventional building materials [37–39].

When applied as wall plasters, raw earth could offer the potential for passive regulation of indoor CO₂ concentrations, complementing its well-established moisture-buffering capabilities. Our previous research at the bulk material scale has demonstrated that a 80 µm sieved raw earth interacts with CO₂ under both dry (0% RH) conditions and in the presence of humidity (50% and 70% RH), through additional interactions with adsorbed water [40,41]. However, the potential for these mechanisms to reduce indoor CO₂ concentrations has not yet been evaluated at the scale of a plaster, and the influence of diffusion mechanisms within the material, must be considered. Consequently, it remains unclear whether these adsorption and desorption processes observed at the bulk material scale can effectively translate into measurable reduction in CO₂ concentrations in occupied indoor spaces.

This study addresses this gap by investigating the potential of earthen plasters to passively regulate indoor CO₂ levels. An experimental setup is developed to measure the CO₂ sorption capacity of earthen plasters with a methodology based on the development of Plaisance et al. [42,43]. The aim of the CO₂ buffering experiment is to determine the CO₂ sorption potential of the earthen plasters. This is done by evaluating whether the plasters can reduce the concentration of CO₂ in a closed volume of air (an airtight emission chamber equipped with a sensor to monitor the concentration of CO₂ within the chamber). Adsorption and desorption rate constants of the material with respect to the concentration of CO₂ can be calculated from the output data. These parameters are used in a simplified model designed to predict the impact of earthen plasters on CO₂ concentrations in a bedroom under realistic conditions of occupancy and ventilation. This approach is in line with the overarching objectives of environmental health (or One Health), namely to minimise the environmental impact of construction practices through the use of natural resources, while promoting human health and comfort through improved indoor air quality.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Particle size distribution of DAG, NAG and the sand used in this study.</figcaption></figure>

## 2. Materials and methods

### 2.1. Materials characterisation and preparation

#### 2.1.1. Materials characterisation

Two earths were considered for this study to observe the influence of earth nature on the interactions with CO₂. The first, designated DAG, was obtained from the ruins of an old rammed earth house in the Rhône-Alpes region (Est of France). The second, designated NAG, was sourced from a brickworks in the Occitanie region (South of France). These two earths were selected for their availability, their existing use in construction, which indicates their suitability for construction and because they have been the subject of prior research [44–47]. A siliceous 0/1 mm sand was used as well for plaster fabrication.

The particle size distribution of the earths and sand was characterised in accordance with French standard NF EN ISO 17892-4 [48]. For the earths, analysis was performed on the <2 mm fraction, the portion used in plaster fabrication. Both earth samples were crushed and sieved at 2 mm prior to analysis. The classification proposed in standard NF EN ISO 14688-1 [49], which considers clay particles (<0.002 mm), silts (from 0.002 to 0.063 mm) and sands (from 0.063 to 2 mm) was adopted. The results, shown in Fig. 1, indicate that the DAG earth is fine and rich in clay and silt, while NAG exhibits a well-graded granulometric distribution. The sand also displays a well-graded granulometry, though within a narrower range.

Then, methylene blue value was used as an indicator of the activity of the clayey fraction of the earths, since methylene blue is preferentially absorbed by clays and organic materials. It represents the quantity of methylene blue that can absorb on the outer and inner surfaces of earth particles. The methylene blue tests were carried out on the 2 mm fraction of the two earths following the French Standard NF P94-068 [50]. The results, in Table 1 show that NAG has the highest methylene blue value compared to DAG. According to the classification [51], DAG, with a value between 0.2 and 1.5, corresponds to sandy and silty earths and NAG, with a value between 2.5 and 6, corresponds to a clay to high clay sand and gravel earth. Then, while DAG and NAG have almost the same quantity of clay (<0.002 mm), the clay minerals present in NAG have a higher activity than those in DAG.

Further, the plasticity properties of the fine fraction (i.e. of the earth fraction smaller than 400 µm) have been measured in agreement with the norm NF EN ISO 17892-12 [52]. The results, presented in Table 1, indicate that the plasticity index increases with the rise of clay content for the two studied earths.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Earthen plasters manufacturing process: adding earth/sand/water ratio, mixing the paste, casting in moulds and drying.</figcaption></figure>

<table><caption>**Table 1**
Summary of earths characteristics.</caption><thead><tr><th></th><th>DAG</th><th>NAG</th></tr></thead><tbody><tr><td>Methylene blue value</td><td>1.32</td><td>2.94</td></tr><tr><td>Plastic limit (W<sub>p</sub>) [%]</td><td>17.9</td><td>20.1</td></tr><tr><td>Liquid limit (W<sub>l</sub>) [%]</td><td>31.2</td><td>33.0</td></tr><tr><td>Plasticity index (I<sub>p</sub>) [%]</td><td>18.4</td><td>12.9</td></tr></tbody></table>

### 2.1.2. Plasters manufacturing for experiments

The two earths were prepared into plasters using a single formulation: 40% earth and 60% sand in mass, following Mauffré's work [46, 53]. This mix balances a high clay content, favourable for CO₂ sorption, with sufficient sand to reduce shrinkage. Dry components were mixed first, followed by water. The water content of the mortars was adjusted during mixing to ensure consistent workability, with 15% water added for DAG plasters and 17% for NAG plasters (ratio expressed in mass). The mixture was cast into bamboo hoop moulds (14.4 cm diameter, 1–3 cm height) on perforated wooden supports for drainage (Fig. 2). Samples were dried at 20 °C and 50% RH for at least two weeks. Prior to testing, the density of each sample was determined by weight- ing and measuring its volume with a caliper. The formulations and specifications are provided in Table 2.

For the tests, three 1 cm-thick samples were made from each earth, along with two thicker DAG samples (2 and 3 cm). The different

<table><caption>**Table 2**
Sample formulations and specifications for CO₂ buffering test, ratio expressed in mass.</caption><thead><tr><th>Sample</th><th>Materials</th><th>Earth (%)</th><th>Sand (%)</th><th>Water (%)</th><th>Mean density (kg/m³)</th></tr></thead><tbody><tr><td>DAG</td><td>2 mm DAG + 0/1 mm sand</td><td>40</td><td>60</td><td>15</td><td>1817 ± 86</td></tr><tr><td>NAG</td><td>2 mm NAG + 0/1 mm sand</td><td>40</td><td>60</td><td>17</td><td>1854 ± 2</td></tr></tbody></table>

thicknesses will allow for the observation of the influence of the active layer thickness, i.e., the depth over which CO₂ diffuses and can thus contribute to the retention phenomenon. To compare the results of the earth plasters with conventional building materials, a gypsum plasterboard disc (BA 13) of similar size was prepared and painted with a multi-support acrylic paint (wall, woodwork, radiator), to replicate a conventional wall finish.

### 2.2. Experimental methods

#### 2.2.1. Set-up and protocol

The experimental setup is based on a 10 L glass chamber, equipped with a Kimo AQ200 and SCO2TH multifunction probe for temperature (range from -20 to +80 °C with a resolution of 0.1 °C), hygrometry (range from 5 to 95% RH with a resolution of 0.1% RH), and CO₂
<figure><img src="image_5.png" /><figcaption>Fig. 3. Schematic view of the emission chamber for the measurement of the CO₂ buffering capacity of plasters: in open mode connected to the gas circuit (a) and in closed mode (b).</figcaption></figure>

measurement (range from 0 to 5000 ppm with a resolution of 1 ppm and accuracy of ±3% of reading or ±50 ppm), enabling real-time monitoring of CO₂ concentration in the air. CO₂ measurement is carried out with a non-dispersive infrared sensor. This emission chamber can either be completely sealed with side plugs (closed mode) or left open and the inlet connected to a gas circuit on one side and the outlet left open for gas evacuation (open mode). The gas circuit is designed to generate air with a desired humidity and CO₂ concentration, as illustrated in Fig. 3. It consists of a CO₂-free air generator (Claind), a pure CO₂ cylinder, a water bubbler humidifier, and three Mass Flow Controllers (Bronkhorst).

In this assembly, the pure CO₂ cylinder is connected to Mass Flow Controller 3 (MFC3) to precisely adjust the CO₂ flow rate. Simultaneously, the zero-air generator is connected to two MFCs: MFC2 for dry air flow and MFC1 for wet air flow. MFC1 is further connected to the humidifier. The dry and wet air flows are then mixed, and by adjusting their respective flow rates, air with a specific humidity level is generated. This humidified air is subsequently mixed with the CO₂ flow and injected into the chamber.

At the start of each test, the specimen(s) of the studied material are placed in the chamber the day before. The chamber is conditioned and flushed with CO₂-free air (0 ppm) at the desired humidity overnight (Fig. 3a). This step allows the materials to equilibrate to the target humidity and ensures the desorption of any CO₂ previously present in the materials before the test begins. The experiment consists of two phases: sorption and desorption.

In the sorption phase, air with a high CO₂ concentration (targeting 5000 ppm) is introduced into the chamber by mixing humid and dry air flows with CO₂ using the MFCs. For an air at 50% RH: the flow rates are set to 5 L/min for MFC1, 3 L/min for MFC2 and 52 mL/min for MFC3, resulting in a total flow rate of approximately 8 L/min, allowing the chamber to rapidly reach the target concentration in approximately 3–4 min. Once the concentration reaches 4500–5000 ppm, the chamber is switched to “closed mode” with all inlets and outlets sealed (Fig. 3b). The decrease in CO₂ concentration is then recorded to evaluate the material’s sorption capacity. This decrease corresponds to the amount of CO₂ sorbed by the material.

In the desorption phase, the chamber is reopened and swept with CO₂-free air, reducing the concentration to 50–100 ppm in 4–5 min. The chamber is then sealed again to monitor any re-emission of CO₂ from the material. The subsequent increase in CO₂ concentration corresponds to the amount of previously absorbed CO₂ being released back into the air.

### 2.2.2. Output data

The experiment’s output data are delivered by the sensors: temperature, RH and CO₂ concentration in the chamber. Fig. 4 presents an example test conducted with three DAG plasters at 43% relative humidity. It is shown here to illustrate how the data are processed, as described in the results section. A blank test was also performed, and its results are presented first in the results section. Fig. 4(a) shows the CO₂ concentration in the chamber with the different phases of the experiment: at the start, the open chamber is flushed with a high concentration of CO₂, indicated by the dotted vertical lines. The chamber is then closed and the experiment begins, with the initial concentration in chamber $C_0^a$. A decrease in CO₂ concentration is observed up to $C_{eq}^a$, as the materials interact with the CO₂ in the air. Then for desorption, the chamber is reopened and flushed with CO₂-free air, indicated by the vertical dotted lines. A re-increase in concentration is observed corresponding to the previously adsorbed CO₂ that is released.

To facilitate the comparison of the experiments (in Section 3), the concentration of CO₂ in the chamber (C) is not given in absolute terms (in ppm) but is given as a concentration normalised ($C_{norm}$) with respect to its maximum value ($C_0^a$) reached in the chamber after filling with CO₂, as follow:

$$C_{norm} = \frac{C}{C_0^a} \qquad (1)$$

Additionally, to harmonise all experiments, results are presented with a standard 4-h sorption phase and a 1-h desorption phase, even if, for some tests, the sorption and/or desorption phases lasted more. These durations have been chosen as the greatest common denominator of all the experiments.

The temperature and RH data are used to verify the stability of the environment during the test. This ensures that the material does not release moisture or absorb it during the experiment. It is observed, for the example in Fig. 4, that the temperature and RH remains constant throughout the test, except when the chamber is open, indicating that the sample is well stabilised at the correct humidity (it neither releases nor adsorbs moisture).

### 2.2.3. Test series

The glass emission chamber was used in four types of tests: (i) ‘Repeatability’ and ‘Reversibility’, (ii) ‘Humidity’, (iii) ‘Materials’, (iv) ‘Thickness’.

The ‘Repeatability’ series involved conducting the same test six times using three DAG plaster samples placed in the chamber at the same time, with all faces exposed. In the “Reversibility” series, several
<figure><img src="image_6.png" /><figcaption>Fig. 4. Output data obtained with the set-up.</figcaption></figure>

desorption phases were performed to confirm that all CO₂ absorbed by the samples was fully released back into the air. Like the repeatability tests, the reversibility experiments also used three DAG samples with all faces exposed. The decision to utilise three samples with all surfaces exposed was made with the objective of maximising the exposed surface area.

The second series focused on the influence of humidity on the sorption of CO₂ at the scale of the plaster ('Humidity'). This was to ascertain whether the same results would be obtained as on the bulk material scale [40], namely a decrease in sorption capacity when RH increased initially (from 0% to 50% RH), followed by a re-augmentation of CO₂ capacity at high RH levels (70% RH). To achieve this, three tests were carried out under different atmospheres: 9%, 43% and 72% RH. They were done with three samples of DAG plasters placed all faces exposed in the chamber.

The third series examined the differences in the sorption of CO₂ between different materials ('Materials'), including two earths (DAG and NAG) and the painted plasterboard. It was done with three DAG samples all faces exposed, the three NAG samples all faces exposed and three plasterboards samples all faces exposed in the chamber.

The fourth series investigated the influence of the thickness of the plaster ('Thickness'), with a particular focus on the potential for CO₂ to diffuse through the thickness of the plaster. For these tests, counterfaces and edges of the 1 cm, 2 cm and 3 cm thick samples were taped with aluminium adhesive paper. Thus, only one sample with only one exposed face was placed in the chamber to be exposed to CO₂. The choice of one sample taped was made to ensure unidirectional penetration of the CO₂ within each sample, as would be the case when the plaster is applied to a wall. For each test, the results tables will display the number of samples present in the chamber (3 DAG or 1 DAG) and the faces exposed.

## 2.3. IAQ model and simulations scenario

The experimental findings are subsequently analysed through the utilisation of a simple sorption model, a method that relies on the calculation of adsorption rate constant ($k_a$), desorption rate constant ($k_d$), and a partition coefficient ($k_e$). These coefficients characterise the exchanges that occur between the material and the compound in question. The coefficients, which are intrinsic to the material/compound couple, are then employed to predict the contribution of a plaster in a given environment (a bedroom).

### 2.3.1. General theory

The Tichenor model [54] used considers only sorption on the exposed surface, with the total adsorption and desorption rates depending linearly on the concentrations of the gas phase and the adsorbed phase. This model, derived from the Langmuir theory, is based on the

<figure><img src="image_7.png" /><figcaption>Fig. 5. Schematic view of closed emission chamber and associated mass balance equation.</figcaption></figure>

assumption that the concentration of species in bulk air (C) is homogeneous and proportional to the surface concentration (M). Therefore, the concentration variations ($dC/dt$ and $dM/dt$) observed in a chamber depends on the adsorption ($k_a$) and desorption ($k_d$) rate constants. This model is summarised in (2) and Fig. 5 for CO₂ gas-phase concentrations C (ppm) and CO₂ sorbed-phase concentrations M (ppm m) normalised to projected surface area. Other parameters are the adsorption rate coefficient $k_a$ (m h⁻¹), the desorption coefficient $k_d$ (h⁻¹), and exposed surface area of the material to volume ratio of the chamber $S/V$ (m⁻³):

$$ \frac{dC}{dt} = -k_a C \frac{S}{V} + k_d M \frac{S}{V} \quad (2) $$

In the case of the emission chamber, three assumptions are made:

* the CO₂ concentration is assumed to be homogeneous in the volume inside the chamber (C homogeneous);

* no interaction of the emission chamber with CO₂ is assumed, so the effect of the experimental setup is not included in the general equation. To validate this assumption, a blank test is performed with no material in the chamber (results presented in Section 3);

* the diffusion of CO₂ inside the material is not explicitly modelled, but its effect is indirectly taken into account through the dependence of the results on the thickness of the plaster samples (1 cm, 2 cm, 3 cm), as discussed in Section 3.4.

For the sorption phase, the initial conditions ($t_{ads} = 0$) considered here are a CO₂ concentration of $C_0^a$ within the chamber and no CO₂ initially adsorbed on the material surface:

$$ \text{Sorption phase: } C(t_{ads} = 0) = C_0^a \quad \text{and} \quad M(t_{ads} = 0) = M_0^a = 0 \quad (3) $$

For the desorption phase, the initial conditions ($t_{des} = 0$) considered a CO₂ concentration within the chamber equal to $C_0^d$ (it was not possible, experimentally, to reach an initial condition of $C_0^d = 0$), while CO₂ initially adsorbed at the plaster surface is equal to the one obtained at the end of the sorption phase, which is denoted by $M_0^d$:

$$ \text{Desorption phase: } C(t_{des} = 0) = C_0^d \quad \text{and} \quad M(t_{des} = 0) = M_0^d = (C_0^a - C_{eq}^a) \frac{V}{S} \quad (4) $$
The aim is to determine $k_a$ and $k_d$ on the basis of the tests carried out in the emission chamber and use them then to solve the general equation for a bedroom with 4 plastered earth walls.

### 2.3.2. Determination of the adsorption and desorption rate constants

In the case of a closed system, the mass balance of the CO₂ (both in the chamber and sorbed at the material surface) writes in the form:

$$VdC + SdM = 0$$

By integrating between $t_0$ and $t$ using the initial conditions (3)-(4) it gives:

$$M = M_0^i + \frac{V}{S}(C_0^i - C)$$

where $i = a$ for the sorption phase and $i = d$ for the desorption phase. By replacing the expression of $M$ in (2):

$$\frac{dC}{dt} + C(k_d + k_a \frac{S}{V}) = k_d (C_0^i + \frac{S}{V} M_0^i) \quad \text{or,} \quad \frac{dC}{dt} + L.C = P^i \quad (5)$$

with $L$ (in h⁻¹) and $P$ (in ppm h⁻¹) two parameters, defined as:

$$L = k_d + k_a \frac{S}{V} \quad \text{and} \quad P^i = k_d \left(C_0^i + \frac{S}{V} M_0^i\right) \quad \text{with } i = a, d \quad (6)$$

The solution of the equation, with the initial condition $C = C_0^i$ at $t = 0$ gives:

$$C = \left(C_0^i - \frac{P^i}{L}\right) e^{-L.t} + \frac{P^i}{L} \quad (7)$$

$P^i/L$ is the value reach by $C$ at equilibrium, which is theoretically for $t \to \infty$. Practically, equilibrium is assumed to be reached when the variation of CO₂ within the chamber becomes negligible. At that time, equal to $t_{eq}$, the CO₂ concentration with the chamber is equal to $C_{eq}^a$ for the sorption phase and $C_{eq}^d$ for the desorption phase. Using these notations, (7) can be written in the form:

$$C = \left(C_0^i - C_{eq}^i\right) e^{-L.t} + C_{eq}^i \quad (8)$$

The $L$ parameter of this relation can be obtained by drawing the experimental results of $ln(C_0^i - C_{eq}^i)/C_0^i - C_{eq}^i$ over time, for both sorption $i = a$ and desorption $i = d$ phases. Theoretically, the parameters $L$, which are obtained either during the sorption and desorption phases, should be the same. This is because the coefficients $k_a$ and $k_d$ are considered to be intrinsic to the surface and should not depend on the test phase under consideration. Practically, a small difference is generally observed. Then, instead of considering the average value of $L$ between the two phases, $k_a$ is commonly determined with the value obtained from the sorption phase (denoted by $L^a$), while $k_d$ is determined with the one obtained from the desorption phase (denoted by $L^d$). This approach is justified by the fact that the term involving $k_a$ in (2) is predominant during the adsorption phase, while it is the term involving $k_d$ which is predominant during the desorption phase. Then a new $L$ is determined as (6) and is considered equal is the adsorption and desorption phase.

Under these considerations, the determination procedure of $k_a$, consists in rewriting (2) at time $t_{ads} = 0$ for the sorption phase (thus using the initial conditions (3)). It leads to:

$$\frac{dC}{dt}|_{t_{ads}=0} = -k_a C_0^a \frac{S}{V} \quad (9)$$

The comparison between the temporal derivation of (8) for $i = a$ at $t_{ads} = 0$ and (9) eventually gives:

$$k_a = L^a \frac{C_0^a - C_{eq}^a}{C_0^a} \frac{V}{S} \quad (10)$$

Let us underline that here, $S$ represents the exposed surface area of the plaster, and $V$ represents the volume of the chamber (e.g. 10 L) minus the volume of the plaster.

The determination of $k_d$ is done by considering only the desorption data. For $i = d$ at $t_{des} = 0$ and considering that $C(t_{des} = 0) = C_0^d = 0$:

$$\frac{dC}{dt}|_{t_{des}=0} = -k_d M_0^d \frac{S}{V} \quad (11)$$

The value of $M_0^d$ is assessed considering that the CO₂ in the material at the beginning of the desorption phase correspond to the CO₂ adsorbed at the end of the adsorption phase. Meaning that $M_0^d = (C_0^a - C_{eq}^a) \frac{V}{S}$. The comparison between the temporal derivation of (8) for $i = d$ at $t_{des} = 0$ and (11) with the value of $M_0^d$ eventually gives:

$$k_d = -L^d \frac{C_0^d - C_{eq}^d}{C_0^a - C_{eq}^a} \quad (12)$$

Finally, the equilibrium constant or partition coefficient, $k_e$ in m, can be deduced by:

$$k_e = \frac{k_d}{k_d} \quad (13)$$

It represents the ratio of the material-phase concentration to the gas-phase concentration adjacent to the material surface when the system reaches steady state. The higher the value of $k_e$, the greater the potential sink of material for the compound.

### 2.3.3. IAQ model development

A room in real-life conditions can be modelled as shown in Eqs. (14) and Fig. 6. This model is summarised for a CO₂ gas-phase concentrations $C$ (ppm) and a CO₂ sorbed-phase concentrations $M$ (ppm m) normalised to projected surface area. In addition to the adsorption rate coefficient $k_a$ (m h⁻¹), the desorption coefficient $k_d$ (h⁻¹) and the exposed surface area to volume ratio $S/V$ (m⁻¹), this model requires the air-change rate $N$ (h⁻¹), the CO₂ concentration of the outside air $C_{ext}$ (ppm) and the source of CO₂ Source (ppm m³ h⁻¹) as additional parameters compared to the closed system model of the previous section.

$$\frac{dC}{dt} = N(C_{ext} - C) - k_a C \frac{S}{V} + k_d M \frac{S}{V} + \frac{Source}{V} \quad (14) \\ \frac{dM}{dt} = k_a C - k_d M$$

The room configuration selected for analysis is a bedroom. It was chosen because it represents the type of situation where a “CO₂ buffering plaster” would be relevant. The bedroom is a space that can be poorly ventilated at night, and the CO₂ exhaled during the night can accumulate there, leading to fairly high levels [7].

<figure><img src="image_8.png" /><figcaption>Fig. 6. Schematic view of open room and associated mass balance equation.</figcaption></figure>

For the simulation, parameters of (14) are chosen as:

* The bedroom dimensions are 4 × 3 × 2.5 m, yielding a total volume ($V$) of 30 m³. The resulting surface area ($S$) of the four walls, excluding one door and one window, is 31 m².

* The air change rate ($N$), defined as the rate at which indoor air is replaced by a combination of recirculated and outdoor air, was set at 0.1 h⁻¹. This is a low value for the air change rate, but it is found in rooms with no mechanical ventilation and closed doors and windows, where only the infiltration of air through the room envelope is involved [55,56].

* For the CO₂ emission source (Source): the CO₂ emissions from a person can vary significantly based on age, activity level, and body position. For an average adult at rest, the emission rate is
approximately 18 L h⁻¹ (5 ppm m³ s⁻¹). During sleep, metabolic activity decreases, leading to lower CO₂ emissions. To represent this, a single adult occupant was assumed to emit 3 ppm m³ s⁻¹ of CO₂ over an 8-h sleep period, which aligns with typical nighttime sleep durations. Then, the Source term, representing the emission of CO₂ by a person was set at 10 800 ppm m³ h⁻¹.

* The CO₂ concentration outside the room (Cext) is set at 500 ppm.

* The initial CO₂ gas-phase concentration in the room (C₀), set at 500 ppm.

* The initial CO₂ sorbed-phase concentration at the material's surface (M₀), set at 1.38 ppm m (corresponding to the equilibrium concentration of the material exposed to 500 ppm in air).

The parameters ka and kd, which are determined in the emission chamber, are assumed to be intrinsic parameters of the plaster, and independent of external influences. Thus, the values obtained in the chamber can be used to predict the behaviour of a plaster under real open conditions of a ventilated room containing a source of CO₂ (e.g. human breathing). The Eqs. (14) are solved explicitly using an Excel spreadsheet with a time increment of 0.001 h and the values of ka and kd calculated for the chamber, with the methodology explained in the previous paragraph.

# 3. Results

## 3.1. Blank, repeatability and reversibility of CO₂ sorption measurements

Before starting the experiments on earthen plasters, a blank test was performed. The test consisted of a usual test with no material inside the chamber at 43% RH and 24 °C. It aims at quantifying the impact of the emission chamber alone on the CO₂ concentration, to determine the exchange of CO₂ between air and the glass surfaces. Results are given in Fig. 7 (blank) and the CO₂ concentration is normalised by its maximum values, as proposed in Eq. (1).

In this figure, it is visible that the CO₂ concentration remains quite constant even if a small decrease is noticeable. This decrease in concentration can be attributed either to the influence of the chamber glass or to a residual leak in the chamber seal. This decrease is quantified to 0.5% h⁻¹ and is taken into account when analysing the results in the following section.

The repeatability of the experiments was investigated through 6 tests involving the same three 1 cm-thick DAG plasters (all faces exposed). The tests were repeated under similar conditions of around 40% RH and 24 °C. The results are shown in Fig. 7 and Table 3.

The figure illustrates that the concentration of CO₂ in the chamber after 4 h is globally identical for the six plasters, reaching a normalised CO₂ concentration between 0.878 and 0.919 (meaning 8% to 12% reduction) in 4 h. The CO₂ re-emitted by the six plasters reaches a normalised CO₂ concentration between 0.074 and 0.098 during the desorption phase (7.4% to 9.8% of CO₂ re-emitted). In comparison, the blank shows normalised CO₂ concentration of 0.980 at adsorption (2% reduction) and 0.009 at desorption, significantly lower than the results obtained with the plasters. The kinetic behaviour is almost identical and repeatable, with a pronounced drop in concentration at the start that stabilised fairly quickly. The remaining differences can be attributed to the non-perfect reproducibility of the experimental conditions, in particular the control of RH and temperature. All the experimental conditions and associated results are given in Table 3.

From those tests, the mean value of normalised CO₂ concentration can be calculated as:

$$ \overline{C}_{norm} = \frac{1}{n} \sum_{i=1}^{n} C_{norm,i} \qquad (15) $$

with Cnorm,i being the normalised CO₂ concentration of the six experiments. At the end of the 4-h adsorption phase, it results in

<figure><img src="image_9.png" /><figcaption>Fig. 7. Normalised CO₂ concentration in the chamber for the repeatability series with 3 DAG plasters in the emission chamber, all faces exposed at ≈40% RH and 24 °C.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Normalised CO₂ concentration in the chamber for the reversibility series: 3 desorption cycles visible between the fourth and eighth hours, at 48% RH and 24 °C.</figcaption></figure>

$$ \begin{gathered} \overline{fC}_{norm,f}^{a} = 0.889 \text{ and at the end of the 1-h desorption phase it results} \\ \text{in } \overline{C}_{norm,f}^{d} = 0.089. \end{gathered} $$

The standard error of the mean is estimated based on the assumption that the measured variables follow a normal distribution. To account for the small sample size (six repetitions of the DAG 43% RH measurement), a Student's t distribution was used to establish a two-sided 95% confidence interval (C.I.). The t coefficient is took equal to 2.57 for the six independent repetitions of the measurement.

The standard deviation s can be calculated as:

$$ s = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (C_{norm,i} - \overline{C}_{norm})^2} \qquad (16) $$

and the standard error on the mean results in:

$$ \overline{C}_{norm,f}^{a} \pm \frac{st}{\sqrt{n}} = 0.889 \pm 0.007 \text{ at adsorption and } \overline{C}_{norm,f}^{d} \pm \frac{st}{\sqrt{n}} = 0.089 \pm 0.005 \text{ at desorption. In the forthcoming result section for the "Humidity" series and the "Materials" series, the mean value of DAG 43% RH experiment will be displayed as well as the 95% confidence interval.} $$

During these initial experiments, the question arose as to whether all the CO₂ retained could be released during desorption. Indeed, it is observed for all tests that the normalised CO₂ concentration after 4 h is around 0.889 (11.1% reduction), while the normalised CO₂ concentration at desorption is around 0.089 (8.9% re-emission). The discrepancy in values may be due to the way desorption is observed. Actually, let us recall that, to observe desorption, the air of the emission chamber is renewed with an air flush without CO₂ during a short period
of time (4 to 5 min), and then, this latter is closed again. Consequently, as the plaster releases CO₂, the concentration in the chamber will increase, thereby altering the equilibrium state. To observe directly the reversibility on one cycle, it would be necessary to maintain a zero CO₂ concentration in the chamber while measuring the amount of CO₂ released by the plaster, which is not possible with the setup used here. To address this experimental limit, several desorption cycles were performed on three DAG samples. Instead of renewing the air only once and observing the released CO₂, here the air was renewed three times: first to observe desorption for about 1 h, then renewed again for a second desorption observation for another hour, and finally renewed a third time to observe a third desorption for a third hour. By gradually reaching equilibrium, all CO₂ could be released and measured. The results are visible in Fig. 8 where the three desorption cycles are shown.

For these three cycles, CO₂ re-emission is observed. By calculating the cumulative quantity of CO₂ desorbed during the three cycles, a normalised CO₂ concentration of 0.12 is obtained at desorption for 0.894 normalised concentration at adsorption. It indicates the complete release of sorbed CO₂. Therefore, the process is indeed reversible even at the scale of the plaster. The 0.014 difference may be attributed to measurement error on the CO₂ probe.

## 3.2. Influence of RH on CO₂ sorption

The second experimental test series focused on the influence of RH. At the bulk material scale, previous research conducted at various RH levels (0%, 51%, and 71% RH) revealed a strong competition between CO₂ and water sorption in the material [40]. Particularly, the main results showed that the highest sorption capacity occurred at 0% RH for which CO₂ retention was attributed to physisorption processes on available adsorption sites. Between 0% and 51% RH, the sorption capacity decreased significantly due to competition between CO₂ and water molecules at the adsorption site. However, at 71% RH, there was a re-increase in sorption capacity, supporting the hypothesis of CO₂ dissolution in the adsorbed water. The objective of this experimental test series at the plaster scale was to determine if the same trends were observed. To achieve this, three tests at different RHs (9%, 43%, and 72% RH) were conducted using the 3 DAG samples in the chamber. The dry state at 0% RH was not investigated since the resolution of the RH sensor was minimum 5% RH. The results are visible in Fig. 9 and detailed in Table 3. For the 43% RH test, the mean of the six repeatability experiments is displayed.

At 9% RH, the three DAG plasters in the chamber reached a normalised CO₂ concentration of 0.857 (14.3% reduction), at 43% RH the same three plasters reached a mean normalised CO₂ concentration of 0.889 (11.1% reduction) and at 72% RH they reached one of 0.795 (20.5% reduction). Then, results indicate that the greatest reduction in CO₂ concentration within the chamber is at 72% RH, which corresponds to the wettest sample. This observation is consistent with the previous results since no dry state at 0% RH was investigated. The two tests at 9% and 43% RH provide comparable results. This smaller CO₂ sorption capacity for these two RHs is also quite consistent with the results obtained at the bulk material scale. Indeed, it has been demonstrated that even at 9% RH, a large proportion of the adsorption sites are already occupied by water molecules, resulting in a reduced CO₂ adsorption capacity. On the other side, the increase in adsorption capacity with water is quite gradual, which explains the relatively low values obtained at 43% RH compared to those obtained at 72% RH.

## 3.3. Influence of the composition of the material on CO₂ sorption

The third experimental test series compared DAG and NAG earths, as well as comparing these earths with a conventional construction system: a plasterboard. Tests were conducted at 43% RH and 24 °C with three specimens of each material (first test with the 3 DAG, second one with the 3 NAG and the last one with the 3 plasterboards), all having

<figure><img src="image_11.png" /><figcaption>Fig. 9. Normalised CO₂ concentration in the chamber with 3 DAG samples, all faces exposed, at 9% RH, 43% RH (mean value and 95% confidence interval) and 72% RH at 24 °C.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 10. Normalised CO₂ concentration in the chamber with 3 DAG samples (mean value and 95% confidence interval), 3 NAG samples and 3 plasterboards samples, all faces exposed, ≈43% RH and 24 °C.</figcaption></figure>

approximately the same exposed surface area and thickness. For the DAG test, the mean of the six repeatability experiments is displayed. The results are visible in Fig. 10, with the values provided in Table 3.

Results showed that, after 4 h, three DAG samples has managed to lower the CO₂ content down to a mean normalised concentration of 0.889 (11.1% reduction). Three NAG samples could reduce the normalised CO₂ concentration to 0.818 (18.2% reduction) and three plasterboard sample could reduced it to 0.95 (5% reduction). Then, the results show quite different sorption behaviours and capacities among the materials. The material with the least CO₂ retention is the plasterboard, exhibiting a very weak effect on CO₂ concentrations in the chamber during sorption and desorption. Next is the DAG earth, where a clear decrease in concentration is observed initially, followed by a rapid stabilisation. The NAG earth shows a fairly pronounced decrease in concentration, and it remained unstable for over four hours, indicating that NAG plasters still have significant sorption capacity at the end of the test. Regarding the RH values in Table 3, it is worth noting that while DAG and plasterboard are under similar conditions, in the case of NAG, the RH is slightly higher, thus making the comparison conditions not entirely perfect. Anyway, this higher sorption capacity for NAG earth appears to be in line with the observation that NAG earth has a higher clay activity compared to DAG earth.
<table><caption>**Table 3**
Summary of results for the “Thickness” test series.</caption><thead><tr><th></th><th>Material(s) in the chamber</th><th>T<br>[°C]</th><th>RH<br>[%]</th><th>C<sub>0</sub><sup>a</sup><br>[ppm]</th><th>C<sub>norm,f</sub><sup>a</sup><br>[-]</th><th>C<sub>norm,f</sub><sup>d</sup><br>[-]</th></tr></thead><tbody><tr><td rowspan="3">Blank</td><td>None</td><td>23</td><td>44</td><td>4520</td><td>0.980</td><td>0.009</td></tr><tr><td>3 DAG all faces exposed</td><td>24</td><td>40</td><td>4220</td><td>0.887</td><td>0.074</td></tr><tr><td>3 DAG all faces exposed</td><td>24</td><td>43</td><td>4264</td><td>0.878</td><td>0.082</td></tr><tr><td rowspan="3">Repeatability</td><td>3 DAG all faces exposed</td><td>24</td><td>43</td><td>4228</td><td>0.879</td><td>0.098</td></tr><tr><td>3 DAG all faces exposed</td><td>24.5</td><td>40</td><td>4122</td><td>0.911</td><td>0.097</td></tr><tr><td>3 DAG all faces exposed</td><td>24.5</td><td>41</td><td>4292</td><td>0.919</td><td>0.095</td></tr><tr><td rowspan="3">Reversibility</td><td>3 DAG all faces exposed</td><td>24</td><td>41</td><td>4464</td><td>0.891</td><td>0.075</td></tr><tr><td>3 DAG all faces exposed</td><td>24.4</td><td>48</td><td>4372</td><td>0.894</td><td>0.12</td></tr><tr><td>9%</td><td>24.5</td><td>9.3</td><td>4753</td><td>0.857</td><td>0.085</td></tr><tr><td rowspan="2">Mean 43%</td><td>3 DAG all faces exposed</td><td>24</td><td>43</td><td>-</td><td>0.889 ± 0.007</td><td>0.089 ± 0.005</td></tr><tr><td>3 DAG all faces exposed</td><td>24.3</td><td>72.0</td><td>4213</td><td>0.795</td><td>0.114</td></tr><tr><td rowspan="3">Mean DAG</td><td>3 DAG all faces exposed</td><td>24</td><td>43</td><td>-</td><td>0.889 ± 0.007</td><td>0.089 ± 0.005</td></tr><tr><td>3 DAG all faces exposed</td><td>24.5</td><td>45</td><td>4245</td><td>0.818</td><td>0.071</td></tr><tr><td>3 plas. bds. all faces exposed</td><td>24.4</td><td>42.5</td><td>4484</td><td>0.950</td><td>0.058</td></tr><tr><td>1 cm</td><td>1 DAG 1 cm one face exposed</td><td>21.5</td><td>47</td><td>4746</td><td>0.925</td><td>0.072</td></tr><tr><td>2 cm</td><td>1 DAG 2 cm one face exposed</td><td>21.5</td><td>46</td><td>4658</td><td>0.905</td><td>0.072</td></tr><tr><td>3 cm</td><td>1 DAG 3 cm one face exposed</td><td>21.6</td><td>50</td><td>4608</td><td>0.875</td><td>0.089</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Normalised CO₂ concentration in the chamber for 1 cm, 2 cm and 3 cm thick DAG specimen, single-face exposed at 24 °C.</figcaption></figure>

### 3.4. Influence of plaster thickness on CO₂ sorption

The fourth and last experimental test series focused on the influence of the plaster thickness. As the preceding tests were all conducted with 1 cm thick plasters, the aim was to compare the results with a 2 cm and a 3 cm thick DAG plaster to determine if, within the experimental time frame, CO₂ had sufficient time to diffuse throughout the entire thickness of the material. To ensure that this diffusion occurred in a single direction according to the thickness of the plaster, aluminium adhesive paper was used to tape the back and sides of plasters. The tests were conducted sequentially, with each of the three plasters being tested separately in the emission chamber. The results are presented in Fig. 11 and in Table 3.

The results show that the normalised CO₂ concentration in the chamber decreases to 0.875 with the 3 cm-thick plaster (12.5% reduction), 0.905 with the 2 cm-thick plaster (9.5% reduction), and 0.925 with the 1 cm-thick plaster (7.5% reduction) in the chamber. This suggests that the thickness of the plaster directly correlates with its capacity to retain CO₂, implying that CO₂ can diffuse through the material's thickness. This result with regard to CO₂ is consistent with the behaviour of this type of material with regard to water: numerous studies have demonstrated that earth materials allow water to diffuse through their thickness [57–61].

<table><caption>**Table 4**
Determination of constants and L parameter for the 1 cm, 2 cm and 3 cm-thick DAG plasters.</caption><thead><tr><th></th><th></th><th>C<sub>0</sub><br>[ppm]</th><th>C<sub>eq</sub><br>[ppm]</th><th>L<sup>i</sup><br>[h<sup>-1</sup>]</th><th>R<sup>2</sup></th><th>k<sub>a</sub><br>[m h<sup>-1</sup>]</th><th>k<sub>d</sub><br>[h<sup>-1</sup>]</th><th>k<sub>e</sub><br>[m]</th><th>L<br>[h<sup>-1</sup>]</th></tr></thead><tbody><tr><td rowspan="2">1 cm</td><td>Sorption</td><td>4746</td><td>4391</td><td>3.48</td><td>0.93</td><td rowspan="2">0.17</td><td rowspan="2">1.70</td><td rowspan="2">0.10</td><td rowspan="2">1.96</td></tr><tr><td>Desorption</td><td>124</td><td>387</td><td>2.30</td><td>0.95</td></tr><tr><td rowspan="2">2 cm</td><td>Sorption</td><td>4658</td><td>4200</td><td>3.12</td><td>0.98</td><td rowspan="2">0.20</td><td rowspan="2">1.43</td><td rowspan="2">0.14</td><td rowspan="2">1.74</td></tr><tr><td>Desorption</td><td>130</td><td>383</td><td>2.59</td><td>0.96</td></tr><tr><td rowspan="2">3 cm</td><td>Sorption</td><td>4608</td><td>4015</td><td>2.44</td><td>0.97</td><td rowspan="2">0.20</td><td rowspan="2">1.32</td><td rowspan="2">0.15</td><td rowspan="2">1.63</td></tr><tr><td>Desorption</td><td>125</td><td>470</td><td>2.27</td><td>0.94</td></tr></tbody></table>

### 4. Discussion

#### 4.1. Determination of adsorption and desorption rate constants

In order to model as closely as possible what can happen in a room, tests in which sorption takes place only in 1D, i.e. with the back and edges of the disc taped (1 cm, 2 cm and 3 cm DAG samples), are preferred to others in which the processes take place in 3D, and which thus exhibit much more complex pattern of CO₂ ingress within the material. The determination of $k_a$, $k_d$ and $k_e$ is done with the 1 cm, 2 cm and 3 cm DAG experiments, following the methodology explained in Section 2.3.2.

The $k_a$ and $k_d$ coefficients were determined for the 1 cm DAG plaster test. For the sorption phase, the values of $C_0$ and $C_{eq}$ are given in Table 4 and are respectively 4746 ppm and 4391 ppm (sorption of 355 ppm). The graphical determination of $L^a$ with a linear regression gives an $L^a$ of 3.48 $h^{-1}$ with an $R^2$ of 0.93. Using these values, a $k_a$ of 0.17 m $h^{-1}$ is obtained. For the desorption phase, the graphical determination leads to $L^d$ of 2.593 $h^{-1}$ and $k_d$ of 1.70 $h^{-1}$. With the $k_a$ and $k_d$ values a new L can be determined and is found equal to 1.96 $h^{-1}$.

The plot of the experimental data with the theoretical C calculated using Eq. (8) is shown in Fig. 12. To assess the accuracy of the model compared to the experimental data, the coefficient of determination $R^2$ is calculated and reported in the figure. The $R^2$ value is computed as:

$$R^2 = 1 - \frac{\sum(y_i - \hat{y}_i)^2}{\sum(y_i - \bar{y})^2} \qquad (17)$$

where $y_i$ are the experimental values, $\hat{y}_i$ are the model predictions, and $\bar{y}$ is the mean of the experimental values.

For the 1 cm-thick experiment, at adsorption the $R^2$ is 0.85 and 0.94 at desorption. This highlights a satisfactory correlation between the model and the experimental data, although it appears that the model does not fully capture the underlying physics of the process,
<figure><img src="image_14.png" /><figcaption>Fig. 12. Experimental/model comparison for DAG 1 cm thickness.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 13. Experimental/model comparison for DAG 2 cm thickness.</figcaption></figure>

particularly concerning the mechanisms happening in the volume of the material.

For the 2 cm thickness test, the same procedure is repeated. The determination of $L^a$ for the sorption phase gives $L^a = 3.12 \text{ h}^{-1}$ for an $R^2$ of 0.98, giving a $k_a$ of 0.20 m h$^{-1}$. For the desorption phase, $L^d = 2.59 \text{ h}^{-1}$, with an $R^2$ of 0.96, resulting in a $k_d$ value of 1.43 h$^{-1}$. The fit between the model and the experimental curves, shown in Fig. 13, is satisfactory, with an $R^2$ of 0.83 for adsorption and 0.95 for desorption. As with the 1 cm thick experiment, the fit for the 2 cm thick model is also satisfactory, but could be improved by incorporating more detailed representations of the underlying physics, particularly those governing adsorption and diffusion.

At this stage we see that the adsorption coefficient $k_a$ increases from 0.17 m h$^{-1}$ for the 1 cm DAG test to 0.20 m h$^{-1}$ for the 2 cm DAG test, which indicates an adsorption rate greater for the 2 cm plaster. We can also see that the desorption coefficient decreases from 1.70 h$^{-1}$ for 1 cm to 1.43 h$^{-1}$ for the 2 cm test, which indicates the increase of adsorption rate with thickness. These two phenomena are also reflected in the partition coefficient ($k_c$), which increases from 0.10 m for 1 cm to 0.14 m for 2 cm.

For the 3 cm thickness test, the same operation is repeated. The determination of $L^a$ for the sorption phase gives $L^a = 2.44 \text{ h}^{-1}$ for an $R^2$ of 0.97, giving a $k_a$ of 0.20 m h$^{-1}$. For the desorption phase, $L^d = 2.27 \text{ h}^{-1}$, with an $R^2$ of 0.94, resulting in a $k_d$ value of 1.32 h$^{-1}$. The fit between the model and the experimental curves (Fig. 14) appears satisfactory, with an $R^2$ of 0.91 for adsorption and 0.84 for

desorption. As for the 1 cm and 2 cm models, the accuracy for the 3 cm could be improved by accounting for volume-related phenomena which might become more pronounced in thicker materials. In addition, the desorption experiment for the 3 cm sample was stopped after 2.5 h, likely before equilibrium was reached, preventing the model from fully converging to the experimental data.

Interestingly, while the partition coefficient ($k_c$) increases between the 2 cm and 3 cm plaster thicknesses, indicating greater interaction between CO$_2$ and the 3 cm plaster, this increase is primarily driven by the decrease in the desorption coefficient ($k_d$) rather than an increase in the adsorption coefficient ($k_a$). Specifically, between the 2 cm and 3 cm experiments, $k_a$ remains constant at 0.2 m h$^{-1}$, while $k_d$ decreases from 1.43 h$^{-1}$ to 1.32 h$^{-1}$. This suggests that the initial surface adsorption remains nearly identical, and the dominant factor is the effect of CO$_2$ diffusion in the plaster thickness. This observation supports the hypothesis that CO$_2$ effectively diffuses through the plaster thickness. However, it is important to note that the $k_c$ does not double between 1 cm and 2 cm, nor does it triple between 1 cm and 3 cm thick plasters. This may be due to a gradient of moisture content in the material, differences in compaction or density between samples.

## 4.2. Contribution of earthen plasters to IAQ of a bedroom

Now, it is possible to simulate the impact of a 1 cm, 2 cm, or 3 cm thick plaster in a bedroom by applying the model described
<figure><img src="image_16.png" /><figcaption>Fig. 14. Experimental/model comparison for DAG 3 cm thickness.</figcaption></figure>

in Section 2.3.3 using the calculated values of $k_a$ and $k_d$ previously determined.

The simulations for the bedroom are presented in Fig. 15. The results for the CO₂ concentration in the chamber are given over 8 h with a configuration comprising 1 cm, 2 cm and 3 cm of DAG plaster and a configuration without adsorbent material ($k_a$ and $k_d$ equals to zero, 'no sink material' in Fig. 15).

<figure><img src="image_17.png" /><figcaption>Fig. 15. CO₂ concentration in the modelled bedroom over time, results of the simulation for the bedroom with 1 cm, 2 cm and 3 cm earthen plasters and comparison with the same bedroom without earthen plasters 'no sink material'.</figcaption></figure>

It can be observed that after 8 h, the CO₂ concentration in the bedroom without any plaster reaches 2482 ppm, with 1 cm DAG plaster it reaches 2336 ppm, with 2 cm plasters it reaches 2282 ppm and with a 3 cm thick plaster over all walls it reaches 2262 ppm. It represents a diminution of 220 ppm achieved with the contribution of a 3 cm thick plaster. The impact of the plaster thus appears to be relatively low (8% of reduction). Then, the interactions, although visible at plaster scale, may be too weak to allow passive CO₂ regulation in a real room environment.

However this reduction is achieved without any addition of mechanised system, it is simply done by the choice of materials for indoor finishes. Then, this result should not be over interpreted in relation to the incapacity of earth plasters to buffer CO₂. As no optimisation of the plaster is proposed here, and the DAG earth used as a reference in this study did not appear to be the most reactive to CO₂, compared to NAG for example. As the interactions are still visible, this suggests that optimising these plasters in terms of formulation, surface topography and composition could improve the CO₂ regulation potential, making it relevant in certain critical situations (with no or almost no ventilation).

## 5. Conclusion

The main aim of this study was to investigate whether a plaster made from raw earth could interact with indoor CO₂, capture a fraction of it, and subsequently release it, thereby contributing to passive regulation. While this phenomenon has been previously observed at the bulk material scale [40,41], its behaviour at the scale of a plaster remained unexamined. To address this knowledge gap, an experimental setup was developed, comprising a 10 L glass chamber equipped with a CO₂, temperature, and humidity sensor. The experimental approach involved introducing earth-based plasters into the chamber and monitoring the decrease in CO₂ concentration in the air, attributed to sorption by the material. The main findings of the experiments are:

* Raw earth plasters can effectively interact with CO₂, capturing and releasing it when the concentration increase or decrease respectively.

* Higher humidity levels (72% RH) enhanced CO₂ sorption, consistent with findings at the bulk material scale.

* NAG earth showed superior adsorption capacity compared to DAG earth, attributed to higher clay activity. Both earth plasters outperformed gypsum board in CO₂ sorption.

* Thicker plasters (3 cm) adsorbed more CO₂ than thinner ones (2 cm and 1 cm), suggesting CO₂ diffusion occurs within the plaster and that thickness is key to optimising sorption capacity.

Subsequently, the study aimed to extrapolate these findings to the scale of a room with walls coated with raw earth plaster. To this end, a numerical model consistent with the experimental results was identified and selected for application. This was then employed in a typical bedroom scenario, with a single person sleeping for a period of eight hours. The main findings of the numerical simulations are:

* A simple sorption model was selected.

* The sorption constants, $k_a$, $k_d$ and $k_e$ governing the material–CO₂ interactions, were determined for 1 cm, 2 cm, and 3 cm DAG plasters. With those constants, the comparison between the numerical model and experimental results showed good agreement.

* The model predicted that a 3 cm-thick DAG plaster in a 30 m³ bedroom could reduce CO₂ concentration by 200 ppm (8% reduction) over an 8-h period with an occupant sleeping. Showing that the contribution remains limited.

However, these findings do not preclude the potential for performance enhancement through the selection of more effective earth materials or optimisation of plaster formulations. Overall, this study demonstrates that CO₂ interactions observed at the bulk material scale
are also measurable at the scale of plasters, both within an airtight
10 L volume and under conditions approximating real-world indoor
environments.

While this study adopts a simplified modelling approach, a possible extension of this work would be to develop a more detailed model based on porous media theory, explicitly accounting for CO₂ diffusion within the plaster. Such a model would enable a more precise description of the underlying mechanisms of CO₂ retention, distinguishing the respective contributions of adsorption, diffusion, and other transport processes.

CRediT authorship contribution statement

**Sofia Arris-Roucan:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Lyna Oulhaci:** Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation. **Nathalie Costarramone:** Supervision, Methodology, Investigation, Data curation, Conceptualization. **Céline Perlot:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Fionn McGregor:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. **Antonin Fabbri:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Valérie Desauziers:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Hervé Plaisance:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal rela-
tionships which may be considered as potential competing interests:
Celine Perlot reports financial support was provided by French Uni-
versities Institute. Sofia Arris- Roucan reports financial support was
provided by European Commission Marie Sklodowska-Curie Actions.
If there are other authors, they declare that they have no known
competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.

Acknowledgements

This project has received funding from both the European Union's
Horizon 2020 research and innovation program under the Marie
Sklodowska-Curie grant agreement No 945416 and the Institut Univer-
sitaire de France.

Data availability

Data will be made available on request.

References

[1] M.Y.Z. Abouleish, Indoor air quality and COVID-19, Public Health 191 (2021) 1–2.

[2] Andy Peter Jones, Indoor air quality and health, Atmos. Environ. 33 (28) (1999) 4535-4564.

[3] Mohamad Awada, Burçin Becerik-Gerber, Elizabeth White, Simi Hoque, Zheng O'Neill, Giulia Pedrielli, Jin Wen, Teresa Wu, Occupant health in buildings: Impact of the COVID-19 pandemic on the opinions of building professionals and implications on research, Build. Environ. 207 (2022) 108440.

[4] Wanyu R. Chan, William W. Nazaroff, Phillip N. Price, Michael D. Sohn, Ashok J. Gadgil, Analyzing a database of residential air leakage in the United States, Atmos. Environ. 39 (19) (2005) 3445-3455.

[5] G. Habert, S.A. Miller, V.M. John, J.L. Provis, A. Favier, A. Horvath, K.L. Scrivener, Environmental impacts and decarbonization strategies in the cement and concrete industries, Nat. Rev. Earth Environ. 1 (11) (2020) 559–573.

[6] Mickaël Derbez, Bruno Berthineau, Valérie Cochet, Cécile Pignon, Jacques Ribéron, Guillaume Wyart, Corinne Mandin, Séverine Kirchner, A 3-year follow-up of indoor air quality and comfort in two energy-efficient houses, Build. Environ. 82 (2014) 288–299.

[7] Gaëlle Guyot, Adeline Melois, Anne-Marie Bernard, Claire-Sophie Coeudevez, Suzanne Déoux, Sandra Berlin, Enora Parent, Alexis Huet, Sylvain Berthault, Romuald Jobert, Damien Labaume, Ventilation performance and indoor air pollutants diagnosis in 21 French low energy homes, Int. J. Vent. 17 (3) (2018) 187–195.

[8] Gaëlle Guyot, Romuald Jobert, Detailed analysis of regulatory compliance controls of 1287 dwellings ventilation systems, in: Proceedings of the 34th AIVC-3rd TightVent-2nd Cool Roofs'-1st Venticool, 2013.

[9] Tyler A. Jacobson, Jasdeep S. Kler, Michael T. Hernke, Rudolf K. Braun, Keith C. Meyer, William E. Funk, Direct human health risks of increased atmospheric carbon dioxide, Nat. Sustain. 2 (8) (2019) 691–701.

[10] Kenichi Azuma, Naoki Kagi, U. Yanagi, Haruki Osawa, Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance, Environ. Int. 121 (2018) 51–56.

[11] Jie Zhang, Xiaodong Cao, Xin Wang, Liping Pang, Jin Liang, Liang Zhang, Physiological responses to elevated carbon dioxide concentration and mental workload during performing MATB tasks, Build. Environ. 195 (2021) 107752.

[12] Jie Zhang, Liping Pang, Xiaodong Cao, Xiaoru Wanyan, Xin Wang, Jin Liang, Liang Zhang, The effects of elevated carbon dioxide concentration and mental workload on task performance in an enclosed environmental chamber, Build. Environ. 178 (2020) 106938.

[13] ASHRAE, ANSI/ASHRAE Standard 62.1-2013: The standards For Ventilation And Indoor Air Quality, Technical Report, American Society for Heating, Refrigeration and Air Conditioning Engineering, Atlanta, 2013.

[14] O. Ramalho, C. Mandin, J. Ribéron, G. Wyart, Air Stuffiness and Air Exchange Rate in French Schools and Day-Care Centres, Int. J. Vent. 12 (2) (2013) 175-180, Publisher: Taylor & Francis eprint: https://doi.org/10.1080/14733315.2013.11684013.

[15] Directorate General for Health and Consumers and Regional Environmental Centre for Central and Eastern Europe, SINPHONIE: Schools Indoor Pollution & Health Observatory Network in Europe : final report, European Commission. Joint Research Centre. Institute for Health and Consumer Protection. and European Commission, Publications Office, LU, 2014.

[16] Máté Szabados, Réka Kakucs, Anna Páldy, Bohumil Kotlík, Helena Kazmarová, Arianna Dongiovanni, Andrea Di Maggio, Anna Kozajda, Anja Jutraz, Andreja Kukec, Peter Otopepec, Tamás Szigeti, Association of parent-reported health symptoms with indoor air quality in primary school buildings – The InAirQ study, Build. Environ. 221 (2022) 109339.

[17] Zsolt Bakó-Biró, N. Kochhar, Derek Clements-Croome, H.B Awbi, M.J. Williams, Ventilation Rates in Schools and Learning Performance, 2007, Journal Abbreviation: Proceedings of CLIMA 2007 - WellBeing Indoors, The 9th REHVA World Congress Publication Title: Proceedings of CLIMA 2007 - WellBeing Indoors, The 9th REHVA World Congress.

[18] Institute of Medecine, Climate Change, the Indoor Environment, and Health, The National Academies Press, Washington, DC, 2011.

[19] Elliott T. Gall, William W. Nazaroff, New directions: Potential climate and productivity benefits from CO 2 capture in commercial buildings, Atmos. Environ. 103 (2015) 378-380.

[20] Lukas Baus, Sascha Nehr, Potentials and limitations of direct air capturing in the built environment, Build. Environ. 208 (2022) 108629.

[21] Mitra Bahri, Hans Schleibinger, William Render, Olga Naboka, Removal performance of formaldehyde by ceiling tiles as sorptive passive panels, Build. Environ. 160 (2019) 106172.

[22] Alessandra Ranesi, Paulina Faria, Clay-based plasters for passive air pollutant removal: the case of ozone, in: International Conference on Construction, Energy, Environment and Sustainability, 2023.

[23] Patrice Blondeau, Guillaume Sérafin, François Maupetit, Mélanie Nicolas, Stéphane Delaby, Corinne Mandin, Assessment of Six Sorptive Materials for Indoor Air Quality Improvement, 2022.

[24] Raphaël Brun, Vincent Gaudion, Marie Verriele, Manolis N. Romanias, Marion Chenal, Arnaud Soisson, Frédéric Becquart, Wolfram Maier, Frédéric Thevenet, Mitigation of indoor pollution events by lime-cement plasters: Full-scale assessment, Atmos. Environ. 343 (2025) 120997.

[25] Hoon Kim, Shin-ichi Tanabe, Influence of sorption area ratio and test method on formaldehyde reduction performance for sorptive building materials, Constr. Build. Mater. 70 (2014) 175-182.

[26] P.E. Rajan, A. Krishnamurthy, G. Morrison, F. Rezaei, Advanced buffer materials for indoor air CO 2 control in commercial buildings, Indoor Air 27 (6) (2017) 1213-1223.

[27] Glenn Morrison, Pseudoscience, snake oil, and indoor air quality, Indoor Air 28 (3) (2018) 357–359.

[28] Fanhe Kong, Guanhe Rim, MinGyu Song, Cornelia Rosu, Pranjali Priyadarshini, Ryan P. Lively, Matthew J. Realff, Christopher W. Jones, Research needs targeting direct air capture of carbon dioxide: Material & process performance characteristics under realistic environmental conditions, Korean J. Chem. Eng. 39 (1) (2022) 1–19.
[29] Richard L. Corsi, Connect or stagnate: the future of indoor air sciences, Indoor Air 25 (3) (2015) 231-234.

[30] Hal Levin, Designing for people: what do building occupants really want?, Singapore, 2003.

[31] Jean-Claude Morel, Ali Mesbah, Michel Oggero, Pete Walker, Building houses with local materials: means to drastically reduce the environmental impact of construction, Build. Environ. 36 (10) (2001) 1119-1126.

[32] Pierre Estève-Bourrel, Christopher T.S. Beckett, Frédéric Bosché, Guillaume Habert, How load-bearing earth construction can meet todays's energy demands? in: 5th International Conference on Bio-Based Building Materials, Vienna, 2023.

[33] Lola Ben-Alon, Vivian Loftness, Kent A. Harries, Erica Cochran Hameen, Overcoming the Perceptual Gap: Worldwide Perceived Comfort Survey of Earthen Building Experts and Homeowners, Barcelona, Spain, 2022, pp. 521-528.

[34] Lola Ben-Alon, Alexandra R. Rempel, Thermal comfort and passive survivability in earthen buildings, Build. Environ. 238 (2023) 110339.

[35] Fionn McGregor, Andrew Heath, Andrew Shea, Mike Lawrence, The moisture buffering capacity of unfired clay masonry, Build. Environ. 82 (2014) 599-607.

[36] Monika Woloszyn, Targo Kalamees, Marc Olivier Abadie, Marijke Steeman, Angela Sasic Kalagasidis, The effect of combining a relative-humidity-sensitive ventilation system with the moisture-buffering capacity of materials on indoor climate and energy efficiency of buildings, Build. Environ. 44 (3) (2009) 515-524.

[37] Anne Ventura, Claudiane Ouellet-Plamondon, Martin Röck, Torben Hecht, Vincent Roy, Paula Higuera, Thibaut Lecompte, Paulina Faria, Erwan Hamard, Jean-Claude Morel, Guillaume Habert, Environmental Potential of Earth-Based Building Materials: Key Facts and Issues from a Life Cycle Assessment Perspective, in: Antonin Fabbri, Jean-Claude Morel, Jean-Emmanuel Aubert, Quoc-Bao Bui, Domenico Gallipoli, B.V. Venkatarama Reddy (Eds.), Testing and Characterisation of Earth-Based Building Materials and Elements: State-of-the-Art Report of the RILEM TC 274-TCE, in: RILEM State-of-the-Art Reports, Springer International Publishing, Cham, 2022, pp. 261-296.

[38] Henri Van Damme, Hugo Houben, Earth concrete. Stabilization revisited, Cem. Concr. Res. 114 (2018) 90-102.

[39] Henri Van Damme, Hugo Houben, Reply to the discussion of the paper "Earth Concrete. Stabilization Revisited" by A.T.M. Marsh, A. Heath, P. Walker, B.V. Venkatarama Reddy, and G. Habert, Cem. Concr. Res. 130 (2020) 105992.

[40] Sofia Arris-Roucan, Antonin Fabbri, Fionn McGregor, Céline Perlot, Jean-Claude Morel, Sorption mechanisms of CO2 in earthen materials under variable hygric states, Mater. Struct. 57 (4) (2024) 86.

[41] Sofia Arris-Roucan, Fionn McGregor, Antonin Fabbri, Céline Perlot, Towards the determination of carbon dioxide retention in earthen materials, Build. Environ. 239 (2023) 110415.

[42] Herve Plaisance, Pierre Mocho, Alexandre Gross, Valerie Desauziers, Potential of static sampling using solid-phase microextraction for the assessment of formaldehyde sorption on building materials, Atmos. Environ. 218 (2019) 117009.

[43] Herve Plaisance, Pierre Mocho, Valerie Desauziers, Comparative analysis of formaldehyde and toluene sorption on indoor floorings and consequence on Indoor Air Quality, Indoor Air 30 (6) (2020) 1256-1267.

[44] Noha Al Haffar, Efficient stabilization with hydraulic binders of local earth for building construction applications (Ph.D. thesis), Université de Lyon, 2021, Issue: 2021LYSET006.

[45] Bakary Traoré, Analyse physique et modélisation dynamique de l'impact du gel-dégel sur la durabilité des matériaux en terre crue compactée pour la construction (Ph.D. thesis), École Nationale des Travaux Publics de l'État [ENTPE], 2022, Issue: 2022ENTP0004.

[46] Thibaud Mauffre, Understanding and Optimisation of the Hydraulic and Hydroscopic Behaviour Between Surface and Volume in Raw Earth Plasters (Ph.D. thesis), Université de Lyon, 2022, Issue: 2022LYSET006.

[47] Agostino Walter Bruno, Hygro-Mechanical Characterisation of Hypercompacted Earth for Building Construction (Ph.D. thesis), Université de Pau et des Pays de l'Adour - Laboratoire SIAME, 2016.

[48] EN ISO 17892-4, Geotechnical Investigation and Testing - Laboratory Testing of Soil - Part 4: determination of Particle Size Distribution, Technical Report, 2018.

[49] NF EN ISO 14688-1, Geotechnical Investigation and Testing - Identification and Classification of Soil - Part 1: Identification and Description, Technical Report, 2018.

[50] NF P94-068, Soils: Investigation and Testing - Measuring of the Methylene Blue Adsorption Capacity of a Rocky Soil - Determination of the Methylene Blue of a Soil by Means of the Stain Test, Technical Report, Association Française de Normalisation, 1998.

[51] NF P11-300, Exécution Des Terrassements - Classification Des Matériaux Utilisables Dans La Construction Des Remblais Et Des Couches De Forme D'infrastuctures Routières, Technical Report, Association Française de Normalisation, 1992.

[52] EN ISO 17892-12, Geotechnical Investigation and Testing - Laboratory Testing of Soil - Part 12: Determination of Liquid and Plastic Limits, Technical Report, 2018.

[53] T. Mauffré, F. McGregor, M.S. Force, E. Keita, E. Contraires, A. Fabbri, Influence of surface topography on the surface film resistance in earth plasters, Constr. Build. Mater. 394 (2023) 132144.

[54] Bruce A. Tichenor, Zhishi Guo, James E. Dunn, Leslie E. Sparks, Mark A. Mason, The Interaction of Vapour Phase Organic Compounds with Indoor Sinks, Indoor Air 1 (1) (1991) 23-35.

[55] William W. Nazaroff, Residential air-change rates: A critical review, Indoor Air 31 (2) (2021) 282-313.

[56] Xiaojun Fan, Chenxi Liao, Mariya Petrova Bivolarova, Anna Mainka, Chandra Sekhar, Jelle Laverge, Li Lan, Mizuho Akimoto, Pawel Wargocki, Air change rates during sleep in Danish bedrooms, CLIMA 2022 Conf. (2022).

[57] Daniel Maskell, Andrew Thomson, Pete Walker, Manfred Lemke, Determination of optimal plaster thickness for moisture buffering of indoor air, Build. Environ. 130 (2018) 143-150.

[58] Fionn McGregor, Andrew Heath, Daniel Maskell, Antonin Fabbri, Jean-Claude Morel, A review on the buffering capacity of earth building materials, Proc. Inst. Civ. Eng. - Constr. Mater. 169 (5) (2016) 241-251, Publisher: ICE Publishing.

[59] Hans Janssen, Gregor Albrecht Scheffler, Rudolf Plagge, Experimental study of dynamic effects in moisture transfer in building materials, Int. J. Heat Mass Transfer 98 (2016) 141-149.

[60] Peng Ren, Chi Feng, Hans Janssen, Hygric properties of porous building materials (V): Comparison of different methods to determine moisture diffusivity, Build. Environ. 164 (2019) 106344.

[61] Lucile Soudani, Antonin Fabbri, Jean-Claude Morel, Monika Woloszyn, Pierre-Antoine Chabriac, Henry Wong, Anne-Cécile Grillet, Assessment of the validity of some common assumptions in hygrothermal modeling of earth based materials, Energy Build. 116 (2016) 498-511.