# Seasonal variation in exposure to particulate matter among children attending different levels of education: Comparison of two dosimetry models

Isabella Charres, Yago Cipoli, Leonardo C Furst, Estela D Vicente, Ismael Casotti Rienda, Mihalis Lazaridis, Manuel Feliciano, Célia Alves

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## To cite this version:

Isabella Charres, Yago Cipoli, Leonardo C Furst, Estela D Vicente, Ismael Casotti Rienda, et al.. Seasonal variation in exposure to particulate matter among children attending different levels of education: Comparison of two dosimetry models. Atmospheric Pollution Research, 2024, 15 (9), pp.102229. 10.1016/j.apr.2024.102229. hal-04638594

HAL Id: hal-04638594

https://hal.science/hal-04638594v1

Submitted on 8 Jul 2024

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Distributed under a Creative Commons Attribution 4.0 International License
1 This is an author generated post-print of the article:

2

3 Charres, I., Cipoli, Y., Furst, L. C., Vicente, E. D., Rienda, I. C., Lazaridis, M., ... &

4 Alves, C. (2024). Seasonal variation in exposure to particulate matter among children

5 attending different levels of education: Comparison of two dosimetry

6 models. *Atmospheric Pollution Research*, 15(9), 102229.

7

8 The final publication is available at: https://doi.org/10.1016/j.apr.2024.102229

9
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11 Seasonal variation in exposure to particulate matter among children attending different levels of education: comparison of two dosimetry models

12
13 Isabella Charresª, Yago Cipoliª, Leonardo C. Furstª, Estela D. Vicenteª, Ismael Casotti

14 Riendaª, Mihalis Lazaridisᵇ, Manuel Felicianoᶜ,ᵈ, Célia Alvesª
15 a Centre for Environmental and Marine Studies (CESAM), Department of Environment,
16 University of Aveiro, 3810-193, Aveiro, Portugal

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18 b School of Chemical and Environmental Engineering, Technical University of Crete,
19 73100, Chania, Greece

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21 c Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, 5300-
22 253, Bragança, Portugal

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24 d Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

25
26 Corresponding author:
27 Isabella Charres Fandino
28 isabellacharres@ua.pt

29 Centre for Environmental and Marine Studies (CESAM), Department of Environment,
30 University of Aveiro, 3810-193, Aveiro, Portugal

31
32 Abstract

33 Exposure to particulate matter (PM) has been associated with several adverse health
34 outcomes. Studies indicate that children may be exposed to much higher concentrations
35 of PM at school than in other environments. There exists very little data on the deposited
36 dose of PM while children attend classes. This study was carried out in a school located
37 near an industrial complex in Portugal and attended by children aged 3–12 years. Indoor
38 PM₁₀, PM₂.₅ and PM₁ were measured over two seasons in classrooms representing
39 different school year groups. Particle deposition fractions in the respiratory tract, as well
40 as the deposited doses, were calculated using the Multiple-Path Particle Dosimetry
41 (MPPD) and the Exposure Dose Model (ExDoM2). Both models were implemented
42 assuming an 8-hour exposure scenario to represent the school day. In general, differences
43 in PM concentrations were observed depending on room occupancy periods and season.

44 The highest mean PM₂.₅ concentration was recorded in winter when the classroom was
45 vacant (23.7 ± 20.5 µg m⁻³), while the highest mean PM₁₀ level was observed in spring
46 during school hours (61.7 ± 24.2 µg m⁻³). Regardless of the dosimetry model, the highest
47 deposition of PM₁₀ and PM₂.₅ was in the upper region, while the lowest was in the
48 tracheobronchial (TB) region. The results indicate that deposited dose and deposition
49 fraction in spring may be more harmful to pupils' health than in winter. PM₁₀ presented
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the highest doses, ranging from 54.2 to 128 µg and from 83.9 to 185 µg, according to MPPD and ExDoM2 estimates, respectively.

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**Keywords**

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Schoolchildren; Particulate matter; Dose; PM deposition; Dosimetry models.

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**1. Introduction**

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According to a recent report on air pollution and child health, nearly 600,000 children aged 5–15 years died from exposure to unhealthy levels of ambient and household air pollution in 2016 (WHO, 2018). Only in Europe, air pollution causes more than 1,200 deaths in people under the age of 18 every year (EEA, 2023). PM is the principal component of indoor and outdoor air pollution and includes a range of particle sizes (Lee et al., 2021). Several studies have suggested that the primary exposure mechanism of PM is inhalation, which exacerbates respiratory symptoms in patients with chronic airway diseases (Leikauf et al., 2020). PM is mostly absorbed through the respiratory tract, where it can infiltrate the lung alveoli and reach the bloodstream. In the respiratory system, PM induces the activation of alveolar macrophages and neutrophils that release reactive oxygen or nitrogen species. Oxidative stress stimulates the production of mediators of pulmonary inflammation and begin or foster numerous illnesses (Thangavel et al., 2022).

Short-term exposure to particulate matter with aerodynamic diameter lower than 2.5 µm (PM<sub>2.5</sub>) and its respiratory tract depositions are dose-responsive and related to higher blood pressure, prevalence of prehypertension and hypertension among children (Liu et al., 2021). There is also evidence that prenatal exposure to PM<sub>2.5</sub> and its components increases the risk of preterm birth (Shi et al., 2024). In addition, poor air quality in school buildings also contributes to health problems and can affect children's concentration and cognitive development (Sunyer et al., 2015). According to the study by Martins et al. (2020), concentrations of PM were often higher inside than outside the microenvironments evaluated in Lisbon (Portugal), with children being exposed to much higher concentrations of PM at school than at home. Faria et al. (2020) showed that although children spend more time at home than at school during weekdays, the classroom was the microenvironment that most contributed to the daily inhaled dose of PM<sub>2.5</sub> and PM<sub>10</sub> in Lisbon. Madureira et al. (2015) found that indoor air pollutants in primary schools located in Porto (Portugal) were related to greater odds of wheezing in children. Branco et al. (2020) also reported that high exposure to PM<sub>2.5</sub> and O<sub>3</sub> in nurseries and primary schools in urban and rural areas in northern Portugal was associated with a reduction in lung function among children.

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In order to evaluate pupils' exposure to air pollutants in the school environment, many studies have been carried out on the assessment of indoor and outdoor air in primary schools or nurseries in both urban and rural areas, focusing mainly on investigating the
effects related to ventilation in classrooms, evaluating comfort parameters, and carrying out the chemical characterisation of indoor and outdoor atmospheric pollutants (e.g., Abhijith et al., 2022; Almeida Sousa et al., 2021; Mainka et al., 2015; Mumovic et al., 2009). Especially in Portugal in the last four years, some studies have investigated potential sources of particulate matter in classrooms (Madureira et al., 2016), the relationship between allergy symptoms and indoor air quality in schools (Branco et al., 2020; Szabados et al., 2022), and inhaled doses of particulate matter (Faria et al., 2020), but only a few aimed to calculate PM deposition among children (Chalvatzaki et al., 2020a; Madureira et al., 2020). Although the regional deposition and dose of particles deposited in the respiratory tract are an important factor in understanding the health effects of aerosol particles (Goel et al., 2018; Linell et al., 2023), it is still unknown how size segregated PM is deposited in regions of the respiratory system and the dose deposited by children while attending classes, even when previous studies reported adverse effects related to particle deposition in the airways. For example, it was documented that the deposition of PM<sub>2.5</sub> increases the risk of severity of pulmonary tuberculosis in the upper and middle lobes (Makrufardi et al., 2023). In the study carried out by Kesavachandran et al. (2015), the deposition of fine particles in the airways resulted in a decline in forced expiratory volume and peak expiratory flow among outdoor exercisers. Therefore, the development of dosimetry models is an important step in understanding exposure-dose-response relationships for PM and can help in evaluating the human health effects of inhaling toxic substances in different environments (Chalvatzaki and Lazaridis, 2015). Different dosimetry modelling approaches have been broadly used to predict particle deposition and dose in human airways, such as the MPPD and the ExDoM2, for which estimates can be made for monodisperse and polydisperse aerosols, in a user-interactive environment. For example, Chalvatzaki et al. (2021) carried out simulations with ExDoM2 considering three study cases with seasonal and diurnal variations in Greek cities. Overall, a higher daily deposited dose was obtained in the cold period compared to the warm periods for all sites. This finding was associated with increased deposition rate in the cold period during the afternoon/evening, because of significant heating emissions. Recently, Khan et al. (2022) carried out MPPD simulations for a city in India and found that the mass deposited in winter was significantly greater than in monsoons for PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>, mainly due to the higher PM levels in winter.
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## 2. Material and methods

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### 2.1. Study area

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Estarreja is a small town of about 7,000 inhabitants located in northern Portugal. The town has an important chemical complex, comprising 30 companies in an area of 290 ha, which is home to various economic sectors, such as heavy industry, retail, warehousing, and services (Alves et al., 2023). This study was carried out at a school in the north side of the town, close to the industrial complex. In addition to kindergarten, this school integrates the first two cycles of basic education. In Portugal, basic education comprises 3 stages: 1st cycle (1st to 4th grades, ages 6-9 years), 2nd cycle (5th and 6th grades, 10-11 years) and 3rd cycle (7th to 9th grades, 12-14 years).

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The school of this study is surrounded by low-rise buildings, a road with little traffic, and a railway line to the west. It consists of 3 main buildings, each one dedicated to a different level of education: 1) preschool, 3 - 5 years, 2) 1st cycle, 6 - 10 years, and 3) 2nd cycle, 10 - 12 years. Buildings 1, 2 and 3 are composed of 6, 12 and 7 classrooms, respectively. To cover the different age groups and the various types of school activities, a classroom was selected in each of the buildings. By imposition of the school principal, classrooms with children with special educational needs or disabilities were considered ineligible. The general characteristics of the classrooms were: concrete walls, wooden windows, and floor, white painted and whiteboard with markers. All of them depend only on natural ventilation with windows and exterior doors.

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The school is open from 7:30 to 18.30. However, the school day starts at 8:30 and finishes at 15:30. Every child is entitled to a 60-minute school lunch. In general, cleaning takes place between 16:00 and 18:00. In this study, class time corresponds to the school day considering the lunch break.

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### 2.2. Experimental set-up and instrumentation

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Measurements took place over two seasons. The winter campaign was performed between November and December 2022, and the spring campaign between April and May 2023. However, for this study, fifteen days of monitoring during each season were considered after checking the amount of complete data for each working day and in every classroom. The monitoring equipment was installed at a height of around 1 m above the floor, and at least 1 m away from any doors, windows, and walls. The indoor instruments were removed from the room and transferred to another in the next building at the end of every Friday afternoon (Table 1). PM₁₀ simultaneous concentrations were rectified against in-situ gravimetric PM₁₀ measurements.
<table><caption>**Table 1**</caption><caption>Sampling campaign characteristics.</caption><thead><tr><th>Location</th><th>Description</th><th>Measurement periods</th></tr></thead><tbody><tr><td>Indoor, ground floor</td><td>Kindergarten; room attended by the same class (20 pupils); age group between 3 and 5 years.</td><td>Nov. 14 – 18, 2022<br>April 13 – 21, 2023</td></tr><tr><td>Indoor, ground floor</td><td>1<sup>st</sup> cycle, 1<sup>st</sup> grade; room attended by the same class (17 kids); age group between 6 and 7 years.</td><td>Nov. 18 – 25, 2022<br>April 21 – 28, 2023</td></tr><tr><td>Indoor, 1<sup>st</sup> floor</td><td>2<sup>nd</sup> cycle, 5<sup>th</sup> and 6<sup>th</sup> grades; room attended by different classes (on average 22 children per class); age group between 10 and 11 years.</td><td>Nov. 25 – Dec. 02, 2022<br>April 28 – May 05, 2023</td></tr></tbody></table>

Simultaneous measurements of PM₁₀, PM₂.₅, and PM₁ were obtained using an aerosol spectrometer (Grimm Model EDM 164) operated at a 1-minute time resolution. In this spectrometer, each particle is detected in the optical measuring cell and based on the intensity of the scattered light signal it is assigned a size. This model measures particle size distributions within the range from 0.25 to 32.0 µm. In addition, gravimetric PM₁₀ samples were collected on 15 cm quartz microfibre filters using a high-volume sampler at an airflow rate of 500 l/min (HVS, Model CAV-A/MSb, MCV S.A).

The high-volume sampler was intercompared with real-time measurements. The regression slopes for the results obtained from parallel measurements during the two monitoring campaigns were 0.95 and 0.75, with correlation coefficients (R²) of 0.91 and 0.95 for winter and spring, respectively. However, as there was no gravimetric equipment for fine particles, PM₁ and PM₂.₅ data were corrected from the ratios obtained between the initial concentrations of PM₁/PM₁₀ and PM₂.₅/PM₁₀ and multiplied by the concentrations of corrected PM₁₀, in accordance with the method applied by Cipoli et al. (2022).

2.3. Estimation of particle deposition and deposition dose in the respiratory system of pupils using two models

* The MPPD model (V3.04, ARA Inc.)

The MPPD model calculates breathing from transport, deposition, and clearance in the respiratory tract of rats and humans based upon a multiple-path method (Asgharian and Anjilvel, 1998; Asgharian et al., 2001). In general, this model can predict both deposition in a typical path per airway generation (single-path) and particle deposition in all ways of the lung (multiple-path). The model provides specific human lung geometry for 10 different ages. It has three major applications: risk assessment, drug delivery and threat
assessment. In this study, MPPD was used for risk assessment. Lung geometries for ages 3, 8 and 9 were selected because of the age of children attending the school. The model input parameters are presented in four categories: inhalant properties (aerosol), airway morphometry, exposure condition and deposition/clearance settings. For aerosol properties, the density and distribution were assumed to be 1 g cm⁻³ and single, respectively, while equations 1 and 2 (Hinds and Zhu, 2022) were employed to calculate the Mass Median Aerodynamic Diameter (MMAD) and the Geometric Standard Deviation (GSD) using the mass size distribution of PM₁, PM₂.₅ and PM₁₀ obtained in both seasons to derive the hourly values for all classrooms (Table S1, Supplementary Material):

$$MMAD = e^{\frac{\sum c_i(\ln(d_i))}{\sum c_i}} \qquad (1)$$

$$GSD = e^{\sqrt{\frac{\sum c_i(\ln(d_i)-\ln(MMAD))^2}{\sum c_i}}} \qquad (2)$$

where $c_i$ is the mass fraction in GRIMM channel i and $d_i$ is the cut-off diameter of GRIMM channel i.

For airway morphometry, the age-specific 5-lobe was chosen and the default values for Upper Respiratory Tract (URT) and Functional Residual Capacity (FRC) were obtained from the model. Exposure was considered constant and the parameters for this category included the inspiratory fraction (value of 0.5), the Breathing Frequency (BF) and Tidal Volume (TV) that were also provided by the model, while concentrations were calculated for each of the three selected classrooms and each season considering weekdays only. In addition, since the aim of the study was to evaluate particle deposition while children were in their classrooms, only the sitting activity pattern was contemplated, and the associated breathing pattern was nasal (Patterson et al., 2014). The selection of the nasal route assumed that the study population (pupils) was sitting most of the time (low-intensity activity) and breathing spontaneously through the nose. The input parameters for each age are shown in Table 2, and seasonal averages of PM concentration, MMADs and GSDs are presented in Table S1, Supplementary Material.

MPPD calculates the deposition fraction based on mathematical equations described in detail by Asgharian et al. (2001). Based on the estimated concentration of DF and PM, the dose rate is calculated for each region of the respiratory tract and a report with the results is provided to the user. In this study, the period between 8 and 16 h represents the school day, therefore, dose rates were calculated for 1-hour exposure intervals and the sum of hourly doses is equivalent to the school day.

• The ExDoM2 model
ExDoM2 was also used for the calculation of deposition fraction and dose profile for the school day. The model calculates the deposition dose, clearance, and retention of aerosol particles in the human respiratory tract based on the International Commission on Radiological Protection (ICRP) human respiratory tract model. ExDoM2 allows the user to set variable or static exposure conditions, such as exposure concentration, physical exertion levels, and different environments (Chalvatzaki and Lazaridis, 2015). Furthermore, the model works with monodisperse or polydisperse aerosol size distributions where the user has the flexibility to introduce the aerosol size parameters as median aerodynamic diameter and geometric standard deviation or to quantify these values directly from the model using as input the measurement data. For a detailed description of the model see Chalvatzaki and Lazaridis (2015) and Lazaridis (2023). **Table 2** displays the values of the physiological variables used in the deposition calculations in the models.

<caption>**Table 2**</caption>

Input parameters according to the study group and model, applicable at normal conditions of sitting breathing.

<table><thead><tr><th rowspan="2">Age (years)</th><th colspan="2">URT (ml)</th><th colspan="2">FRC (ml)</th><th colspan="2">BF (min<sup>-1</sup>)</th><th colspan="2">TV (ml)</th></tr><tr><th>MPPD</th><th>ExDoM2</th><th>MPPD</th><th>ExDoM2</th><th>MPPD</th><th>ExDoM2</th><th>MPPD</th><th>ExDoM2</th></tr></thead><tbody><tr><td>3</td><td>9.47</td><td>nd</td><td>48.2</td><td>nd</td><td>24</td><td>nd</td><td>121.3</td><td>nd</td></tr><tr><td>5</td><td>nd</td><td>13.3</td><td>nd</td><td>767</td><td>nd</td><td>25</td><td>nd</td><td>213</td></tr><tr><td>8</td><td>21.0</td><td>nd</td><td>501.3</td><td>nd</td><td>17</td><td>nd</td><td>278.2</td><td>nd</td></tr><tr><td>9</td><td>22.4</td><td>nd</td><td>683.0</td><td>nd</td><td>17</td><td>nd</td><td>295.8</td><td>nd</td></tr><tr><td>10</td><td>nd</td><td>25</td><td>nd</td><td>1484</td><td>nd</td><td>19</td><td>nd</td><td>333</td></tr></tbody></table>

nd indicates missing values when that age is not considered in the model.

In general, the input parameters required in each model are similar. However, while ExDoM2 considers wind speed and automatically selects physiological parameters based on the reference values provided in the ICRP66 model, MPPD offers the user the option of directly insert these values or select the model's default values that change according to the morphology of the lung and the person's age (Table 3). Both models consider age as an input parameter, but the selection options differ from one model to another. In ExDoM2 the user can choose between 5 options that consider gender (1 year, 5 years, 10 years, 15 years, or adults), while with MPPD, the user has the option to choose one of the following ages: 3 months, 21 months, 23 months, 28 months, 3 years, 8 years, 9 years, 14 years, 18 years, or 21 years. Additionally, different from MPPD, in ExDoM2 the user must specify the gender of the individual. The respective ages used in this study are presented according to the model in Table S2, Supplementary material.

<caption>**Table 3**</caption>

Model specifications according to input options.

<table><thead><tr><th>Parameter</th><th>Entered by user</th><th>Select from a list of options</th><th>Automatically set by the model</th></tr></thead></table>
<table><tr><td>PM concentration</td><td>Both models</td><td></td></tr><tr><td>Density</td><td>Both models</td><td></td></tr><tr><td>MMAD</td><td>Both models</td><td></td></tr><tr><td>GSD</td><td>Both models</td><td></td></tr><tr><td>Wind speed</td><td>ExDoM2 only</td><td></td></tr><tr><td>Exposure scenario</td><td></td><td>Both models</td></tr><tr><td>Activity level</td><td></td><td>Both models</td></tr><tr><td>URT</td><td>MPPD only</td><td>ExDoM2 only</td></tr><tr><td>FRC</td><td>MPPD only</td><td>ExDoM2 only</td></tr><tr><td>BF</td><td>MPPD only</td><td>ExDoM2 only</td></tr><tr><td>TV</td><td>MPPD only</td><td>ExDoM2 only</td></tr><tr><td>Total lung capacity</td><td></td><td>Both models</td></tr><tr><td>Age</td><td></td><td>Both models</td></tr><tr><td>Gender</td><td></td><td>ExDoM2 only</td></tr></table>

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The same input data for the implementation of MPPD for each group was used as input in the ExDoM2: (1) mean hourly PM concentration, (2) breathing pattern, (3) particle density, (4) hourly MMADs and GSDs, and (5) shape factor. The hourly wind speed was only considered in ExDoM2, and it was calculated for each classroom from data obtained during the winter monitoring campaign (Table S3, Supplementary material). In addition, in ExDoM2 model, the simulations were also implemented for each gender, female and male.

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In addition to using a very different approach, another difference between the models is related to the presentation of the results. While MPPD provides an output report, the ExDoM2 model estimates the dose by formula (3), and generates an output file that includes hourly doses, making it necessary to isolate the DF in equation 3:

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$$Dose (\mu g h^{-1}) = PM_{i} * DF_{i,j} * VT \quad (3)$$

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where PMᵢ is the mean exposure concentration of PM in the size fraction i (µg m⁻³), DFᵢⱼ is the deposition fraction in the region j of the respiratory tract for PM in the size fraction i, and VT is the ventilation per hour (m³ h⁻¹) of the exposed pupil, depending on age. To represent the school day, dose rates were calculated for a total of 8 h exposure duration in 1 h exposure time intervals.

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## 2.4. Data analysis

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For the treatment and analysis of the PM concentrations, a minimum data availability of 75% per school day was considered. After checking the normality of the data, PM concentrations were examined with typical descriptive statistics including measures of central tendency and dispersion, diurnal cycles, highlighting the school period and significant differences between seasons and between the time of occupancy versus non-

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occupancy with the non-parametric Wilcoxon test, where a p-value <0.05 was considered statistically significant.

Subsequently, the data from the dosimetry models were analysed separately and then the results of the two models were combined, considering the size of the PM and the groups evaluated to extract the general behaviour of the deposited fractions and doses. Association, agreement, and comparison analyses were performed using linear regression, Bland-Altman method, and t-test for paired samples.

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## 3. Results and discussion

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3.1. General overview of PM concentrations

Median concentrations of PM₁₀ varied from 6.7 to 24.4 µg m⁻³ in winter, and between 13 and 18 µg m⁻³ in spring. As shown in **Table 4**, the median concentrations of PM₁₀, PM₂.₅ and PM₁ in the room attended by 2nd cycle students were 1.3, 1.2 and 1.4 times higher in winter. PM levels were higher in winter in the 2nd cycle room, while PM concentrations were higher in spring in kindergarten and 1st cycle room. In general, PM concentrations were statistically different between the two seasons in all classrooms.

<caption>**Table 4**</caption>

Median of PM concentrations (µg m⁻³) for weekdays in each classroom in winter and spring.

<table><thead><tr><th rowspan="2">Classroom</th><th rowspan="2">PM<sub>10</sub></th><th colspan="3">Winter</th><th colspan="3">Spring</th></tr><tr><th>PM<sub>2.5</sub></th><th>PM<sub>1</sub></th><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>PM<sub>1</sub></th></tr></thead><tbody><tr><td>Kindergarten</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>6.67</td><td>6.05</td><td>4.42</td><td>14.0</td><td>12.5</td><td>10.0</td><td></td></tr><tr><td>IQR<sub>a</sub></td><td>15.6</td><td>3.07</td><td>1.10</td><td>15.8</td><td>1.93</td><td>5.15</td><td></td></tr><tr><td>1<sup>st</sup> cycle</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>18.1</td><td>9.96</td><td>6.31</td><td>18.1</td><td>15.0</td><td>11.4</td><td></td></tr><tr><td>IQR</td><td>21.6</td><td>5.76</td><td>3.06</td><td>39.6</td><td>3.99</td><td>2.62</td><td></td></tr><tr><td>2<sup>nd</sup> cycle</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>24.5</td><td>15.7</td><td>13.8</td><td>18.7</td><td>13.0</td><td>9.21</td><td></td></tr><tr><td>IQR</td><td>24.9</td><td>14.8</td><td>16.1</td><td>8.79</td><td>4.17</td><td>5.82</td><td></td></tr></tbody></table>

<sup>a</sup>Interquartile range.

To compare PM concentrations in the school environment, the hourly average concentration for PM is shown in **Fig.1**. The highest and lowest PM₁₀ hourly concentration was recorded in two different seasons. While the maximum value was observed in the 1st cycle room (128 µg m⁻³; 16:00) in spring, the lowest concentration was recorded in the kindergarten (4.9 µg m⁻³; 7:00) in winter. PM₁₀ concentrations in both seasons showed the main peaks during class time and periodic cleaning hour, which can be explained because the rooms are normally swept, in addition to the resuspension of soil material brought in on the soles of shoes, emissions associated with student
activities (e.g., paper and clothing fibres, skin peeling) and infiltration from outside to the classroom environment, as reported in other studies (Alves et al., 2013; Madureira et al., 2016). PM<sub>2.5</sub> represented, on average, 35% and 31% of the PM<sub>10</sub> concentrations during class time compared to 91% and 90% during unoccupied periods in winter and spring, respectively.

The Fig. 1 also revealed that PM<sub>2.5</sub> and PM<sub>1</sub> concentrations in the 1<sup>st</sup> and 2<sup>nd</sup> cycle rooms in winter began to increase almost at the end of the school day and, depending on the classroom, reached their peak between 18:00 and 22:00, which may suggest the penetration of particles from residential biomass combustion. A different pattern was observed in the kindergarten room, where the concentration of fine particles did not increase at night. It is believed that this behaviour is because the door always remains closed after cleaning hours, making it difficult for particles to penetrate the environment, as reported by other authors (Long et al., 2001). In a sampling campaign carried out in a nearby school between September and November, it was concluded, based on the chemical speciation of PM<sub>2.5</sub>, that biomass combustion represented 9% of the mass concentrations (Alves et al., 2023). In the present study, it is expected that the contribution from this source will be greater, since it was carried out in winter, with lower temperatures. In spring, concentrations for both particle sizes remained almost constant throughout the day, with mean values below 15 and 11 µg m<sup>-3</sup> for PM<sub>2.5</sub> and PM<sub>1</sub>, respectively.

Although the daily mean concentrations were not higher than the values recommended by the World Health Organisation (PM<sub>10</sub>: 45 and PM<sub>2.5</sub>:15 µgm<sup>-3</sup>), in winter, hourly PM<sub>10</sub> concentrations above 45 µg m<sup>-3</sup> were observed in the kindergarten and the 1<sup>st</sup> cycle room, with a maximum of 62.7 µg m<sup>-3</sup> during the school day. In spring, the peaks were registered in the kindergarten and the 1<sup>st</sup> cycle room between 11:00 and 15:00, while the 2<sup>nd</sup> cycle room did not record concentrations above 45 µg m<sup>-3</sup> at any time of the day. Regarding PM<sub>2.5</sub>, hourly concentrations above 15 µg m<sup>-3</sup> were recorded in both the 1<sup>st</sup> and 2<sup>nd</sup> cycle rooms, with levels in the 1<sup>st</sup> cycle room varying from 15.5 to 23.5 µg m<sup>-3</sup> between 6:00 and 16:00 in spring.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Variation in hourly average PM concentrations during weekdays in a) winter and b) spring. The highlighted band represents the class time.</figcaption></figure>

According to the difference between the 5th and 95th percentiles (Fig. 2), there was greater variability in PM<sub>2.5</sub> and PM<sub>1</sub> concentrations in winter than in spring, mainly when the classrooms were not occupied. On the other hand, PM<sub>10</sub> concentrations varied more in class time than unoccupancy periods in the two seasons. In general, much higher PM<sub>10</sub> concentrations were observed during the school day compared to concentrations measured during the period when classrooms were vacant. The maximum difference was recorded in spring when the average concentration in the kindergarten classroom was 17.2 µg m<sup>-3</sup> during school hours and rose up to 51.7 µg m<sup>-3</sup> when the classroom was vacant. Unlike PM<sub>10</sub>, PM<sub>1</sub> concentrations were higher during free periods than class hours in both winter and spring, except in the 1st cycle room. PM<sub>2.5</sub> concentrations between class time and unoccupancy periods in the kindergarten were similar in winter and spring, while in the 1st cycle room, class time levels were higher in spring.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Box plots showing the concentrations of particulate matter in class time and rest of the day in each room for a) winter and b) spring. Lower and upper boundaries of box plots represent the 25th and 75th percentiles, respectively; the line represents median values. The highlighted orange frames indicate when concentrations were not statistically different between school day and unoccupancy periods.</figcaption></figure>

In this study, the average concentrations of PM₁₀ and PM₂.₅ considering the two seasons and all classrooms were 3.82 and 3.16 times lower than those reported by Branco et al. (2019) in nurseries and primary schools in urban and rural locations in the north of Portugal. Some studies have shown that student activities increase PM generation indoors. Martins et al. (2020) reported that children in Lisbon were exposed to significantly higher concentrations of PM at school than at home, and that indoor concentrations of PM₁₀ and PM₂.₅ were often higher than outdoors. According to researchers, human activity and external infiltration are the main sources associated with internal PM. However, the results of the Lazović et al. (2022) study indicate that the average concentrations of PM₁₀ and PM₂.₅ in the outdoor environment in primary schools in Serbia were 20% and 32% higher than the concentrations observed in classrooms. Therefore, PM concentrations can be affected by several factors, such as classroom
activities, ventilation, occupancy rate and external sources (Alameddine et al., 2022; Tippayawong et al., 2009).

## 3.2. Deposition fraction of PM in two seasons and using two deposition models

Deposition fractions were obtained for the school day using two models (Table 5). In general, for all groups and in both seasons, the deposition fractions of PM<sub>2.5</sub> and PM<sub>10</sub> in the various regions of the respiratory tract showed the following trend: tracheobronchial < pulmonary < head airway. This is in line with the trends reported in previous studies (Jia et al., 2021; Khan et al., 2022).

Overall, there were small differences between spring and winter DFs, as DFs do not depend on PM concentrations (Sánchez-Soberón et al., 2015), as previously reported in studies using MPPD (Li et al., 2015). In this study, the total DF of PM<sub>10</sub> calculated with MPPD and ExDoM2 was up to 4% and 7% higher in spring than in winter, respectively. This behaviour was slightly different for the finer particles (PM<sub>1</sub> and PM<sub>2.5</sub>), being mainly larger in winter, except for children from the 2<sup>nd</sup> cycle, for whom DF were 4% and 10% higher in spring according to MPPD and ExDoM2, respectively (Table 5). The seasonal DF of PM<sub>10</sub> and PM<sub>2.5</sub> was identified to be associated with the mass mean aerodynamic diameter, as reported by Manojkumar and Srimuruganandam (2022) in the city of Vellore, India. Based on the data from this study and using both models, it was possible to observe that larger MMADs led to greater DF, regardless of the children's age. This indicates that the DF varies with the particle properties and, therefore, is normally not directly proportional to the mass concentration, as reported in the results of the study by Kumar et al. (2017).

<table><caption>**Table 5.** Average deposition fractions for the different PM sizes during a school day.</caption><thead><tr><th rowspan="2">Winter</th><th colspan="4">MPPD</th><th colspan="4">ExDoM2</th></tr><tr><th>Head</th><th>TB</th><th>P</th><th>Total</th><th>Head</th><th>TB</th><th>P</th><th>Total</th></tr></thead><tbody><tr><td><strong>PM<sub>1</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.22</td><td>0.03</td><td>0.13</td><td>0.39</td><td>0.12</td><td>0.02</td><td>0.09</td><td>0.23</td></tr><tr><td>1<sup>st</sup> cycle</td><td>0.23</td><td>0.04</td><td>0.17</td><td>0.44</td><td>0.07</td><td>0.02</td><td>0.08</td><td>0.17</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.21</td><td>0.04</td><td>0.18</td><td>0.43</td><td>0.04</td><td>0.03</td><td>0.08</td><td>0.15</td></tr><tr><td><strong>PM<sub>2.5</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.26</td><td>0.03</td><td>0.16</td><td>0.45</td><td>0.37</td><td>0.02</td><td>0.13</td><td>0.52</td></tr><tr><td>1<sup>st</sup> cycle</td><td>0.26</td><td>0.04</td><td>0.23</td><td>0.52</td><td>0.25</td><td>0.02</td><td>0.14</td><td>0.41</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.24</td><td>0.04</td><td>0.17</td><td>0.44</td><td>0.09</td><td>0.02</td><td>0.09</td><td>0.21</td></tr><tr><td><strong>PM<sub>10</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.33</td><td>0.09</td><td>0.24</td><td>0.66</td><td>0.73</td><td>0.03</td><td>0.08</td><td>0.84</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>1<sup>st</sup> cycle</td><td>0.32</td><td>0.12</td><td>0.30</td><td>0.74</td><td>0.63</td><td>0.04</td><td>0.11</td><td>0.78</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.34</td><td>0.16</td><td>0.18</td><td>0.68</td><td>0.56</td><td>0.03</td><td>0.10</td><td>0.69</td></tr><tr><td><strong>Spring</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>PM<sub>1</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.21</td><td>0.04</td><td>0.14</td><td>0.39</td><td>0.07</td><td>0.03</td><td>0.09</td><td>0.18</td></tr><tr><td>1<sup>st</sup> cycle</td><td>0.21</td><td>0.04</td><td>0.19</td><td>0.43</td><td>0.04</td><td>0.03</td><td>0.08</td><td>0.15</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.21</td><td>0.04</td><td>0.17</td><td>0.43</td><td>0.05</td><td>0.03</td><td>0.08</td><td>0.15</td></tr><tr><td><strong>PM<sub>2.5</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.25</td><td>0.03</td><td>0.15</td><td>0.43</td><td>0.30</td><td>0.02</td><td>0.12</td><td>0.44</td></tr><tr><td>1<sup>st</sup> cycle</td><td>0.25</td><td>0.03</td><td>0.19</td><td>0.47</td><td>0.15</td><td>0.02</td><td>0.11</td><td>0.28</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.25</td><td>0.04</td><td>0.19</td><td>0.48</td><td>0.17</td><td>0.02</td><td>0.12</td><td>0.31</td></tr><tr><td><strong>PM<sub>10</sub></strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kindergarten</td><td>0.35</td><td>0.10</td><td>0.24</td><td>0.69</td><td>0.79</td><td>0.03</td><td>0.07</td><td>0.89</td></tr><tr><td>1<sup>st</sup> cycle</td><td>0.35</td><td>0.14</td><td>0.26</td><td>0.75</td><td>0.69</td><td>0.04</td><td>0.10</td><td>0.82</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>0.34</td><td>0.14</td><td>0.24</td><td>0.72</td><td>0.63</td><td>0.03</td><td>0.10</td><td>0.77</td></tr></tbody></table>

TB: tracheobronchial; P: Pulmonary.

Still according to **Table 5**, the fraction in the head was the highest of the total airway deposition, representing up to 35% with MPPD and 79% with ExDoM2. However, this pattern was not observed for PM₁, when using the ExDoM2 model, especially in spring, since in all ages the highest value was observed in the lung region and not in the head. This can be because particles with a larger mass median aerodynamic diameter deposit more in the head region, while smaller aerodynamic diameters deposit preferably in the alveolar region, considering that deposition by Brownian diffusion occurs mainly in the acinar region of the lung (Darquenne, 2020), Although with MPPD the greatest contribution was not from the head, the fraction of PM₁ deposition in the alveolar region was higher when compared to the fractions of PM₁₀ and PM₂.₅.

In general, the main difference between the models occurs for smaller particles. ExDoM2 calculates higher total DF of PM₁₀ but lower values for PM₂.₅ and PM₁. MPPD calculates higher DF in TB and pulmonary regions, while ExDoM2 estimates higher values for the head airway region. This is mainly due to discrepancies in the morphometry data such as airway lengths, diameters, and branching angles, which in turn depend on the choice of the lung model and the input values of the respiratory variables, such as URT and FRC. For example, ExDoM2 uses average values from different models (Weibel, 1963; Yeh and Schum, 1980; Phalen et al., 1985; Hansen and Ampaya, 1975) that vary depending on the respiratory region (Aleksandropoulou and Lazaridis, 2013), but MPPD considers 8 geometries of the human lung, with the user being responsible for choosing one of the options. In addition, the models use different approaches for deposition calculations. While ExDoM2 is mainly based on the empirical equations proposed in ICRP, MPPD is
based on that proposed by Asgharian and Anjilvel. (1998), also using a Monte Carlo approach.

The smallest variation between the total deposition results of the models was observed for the 2nd cycle, whereas the largest difference between the models was identified in kindergarten. The total DF of PM₁₀ estimated with the ExDoM2 was up to 22% higher than the results from MPPD. However, the total DF of PM₂.₅ and PM₁ derived from MPPD was higher (up to 66% for PM₁) than the value estimated with ExDoM2 in both seasons (Table S4, Supplementary material). By way of example, **Fig. 3** displays the diurnal cycles of deposition fraction of particulate matter in the group of 9- and 10-year-old children, split into sizes and model. It is possible to observe little difference from one model to another in the total DF of PM₁₀, although there are large differences in the DF by regions.

It is noticeable in **Fig. 3** that a larger fraction of PM₁ and PM₂.₅ is deposited in the lung region, while PM₁₀ is deposited in the head region. ExDoM2 predictions show DFs of PM₂.₅ of 0.09 in the head and lung, while the fraction of PM₁₀ deposited in the upper airway and pulmonary region averaged 0.56 and 0.10, respectively. MPPD follows the same trend but with different values. While the fraction of PM₂.₅ deposited in the head and P was, on average, 0.24 and 0.17, respectively, the fraction of PM₁₀ deposited in head and P was, on average, 0.34 and 0.19, respectively. This trend was also observed by Gao et al. (2022) in a study carried out in China with 10-year-old children during commuting trips to school, which showed that for the three transport modes evaluated and the three particle sizes, the proportion of DFs in the head was the highest, especially for PM₁₀, representing up to more than 85% of the total fraction deposited. For PM₁ and PM₂.₅, the DFs of the P significantly exceeded those of the TB part. This is especially important since children with asthma are more sensitive to air pollution because they breathe at higher tidal volumes, which can increase the efficiency of PM₂.₅ deposition in the lung (Afshar-Mohajer et al., 2022).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Hourly deposition fraction for different particle sizes in children aged 10 years (ExDoM2) and 9 years (MPPD) during the school day in winter. DFs were calculated from average values during a week of measurements in December.</figcaption></figure>

After the comparison of both models, it was observed that the deposition of particles larger than 1.17 µm was greater than that of smaller particles. Location wise, it was greater in the head region due to their higher sedimentation and impaction rates, while the deposition of particles with smaller sizes was greater in the P region due to the diffusion effect (Guo et al., 2019). Overall, this trend was most noticeable with the ExDoM2 results, which can be explained because the respiratory tract model (RTM) used in ExDoM2 is an updated version of the RTM (ICRP, 2012), in which particles deposited in the extrathoracic region (ET) are partitioned 65% to ET1 and 35% to ET2 (Chalvatzaki and Lazaridis, 2015).
472 3.3. Deposition dose during the school day

473 **Fig. 4** shows the deposited dose during the school day of the three sizes of PM in different
474 parts of the respiratory system for all children in the two periods evaluated. Important
475 differences can be observed between the two seasons and the two models, with values
476 being higher in spring. While seasonal variations showed only a slight influence on the
477 total deposition fraction, they had a large effect on the school day deposited dose,
478 particularly during spring as a direct consequence of higher PM concentrations (Table S5,
479 Supplementary material). On the other hand, after calculating the deposited dose and the
480 deposition fraction considering the gender of the children, it was observed that regardless
481 of their age, the results were the same for both girls and 5-year-old boys, and likewise for
482 girls and boys aged 10 years. This may be because ExDoM2 uses as input the same values
483 for physiological parameters without differentiating by gender. For this reason, the results
484 in this study were always reported and discussed for children according to age, and not
485 by gender.

486 The highest accumulated dose of PM<sub>2.5</sub> and PM<sub>10</sub> in the two seasons occurred for children
487 attending the 1st cycle and coincides with the highest environmental concentration of PM<sub>1</sub>
488 and PM<sub>2.5</sub> observed in this room. The overall results show that while the total PM<sub>10</sub>
489 deposition dose estimated with ExDoM2 was up to 58% higher than the values derived
490 from the MPPD model, the dose for PM<sub>1</sub> obtained with the MPPD was between 15% and
491 69% higher than the value calculated with ExDoM2. The differences can be attributed to
492 the specific values used in the two models for the tidal volume and respiratory frequency
493 parameters and, consequently, the value of the ventilation rate. Furthermore, the increase
494 and decrease in dose estimated in any of the models coincides with the observed pattern
495 in the deposition fraction (Fig. 4).

496
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Regional deposited dose of a) PM₁, b) PM₂.₅ e c) PM₁₀ for each group during the school day in winter and spring. Comparison between the two models. Head: upper airway; P: pulmonary; TB: tracheobronchial. Different scales on the y-axis were purposely chosen to highlight the values and aid visualisation.</figcaption></figure>

The PM₁₀ doses for the school day calculated in the present study were lower than the results reported in previous studies. Chalvatzaki et al. (2020) applied ExDoM2 to predict the PM₁₀ dose received by students at five primary schools in Lisbon, Portugal, assuming that the exposed children were 10-year-old nasal breathers. The researchers found that the total deposited dose ranged from 72 to 239 µg in indoor school environments, while in the current study it was, on average, 84.4 µg with the same model (10 years old) and 66 µg with MPPD (9 years old). Faria et al. (2020) quantified the exposure of children between 5 and 10 years old to PM and the respective daily inhaled dose considering various microenvironments in Lisbon. The researchers observed that during the week children inhale, on average, 96 and 177 µg of PM₂.₅ and PM₁₀, respectively, with the classroom contributing more than the residential environment. In their study, the inhaled doses were higher than the doses calculated with both models in this study, as the inhaled dose does not consider the deposition fraction and, therefore, the estimated value will always be higher. In any case, these results showed that children inhale more PM in classrooms than in residential environments, despite spending more time at home.
Regarding the amount of PM deposited per region, higher cumulative doses occurred mainly in the head region and lower ones in the TB region, except for PM₁ calculated with ExDoM2 (Fig. 4), coinciding with the pattern of deposition fractions. However, unlike the DFs of PM₁ (ExDoM2) from kindergarten in winter, the DFs of PM₁ with ExDoM2 were always greater in the lung region than in the upper airways. This can be associated with the aerodynamic diameter of the particles, since when compared by classrooms and seasons, the largest MMADs of PM₁ obtained was in kindergarten in winter (Table S1, Supplementary Material). However, even though the DFs of PM₁ (ExDoM2) were higher in the lung region, they were lower than the values estimated with the MPPD.

In general, a comparison between the two models revealed that higher deposition doses in the lung and TB region were always estimated with MPPD for 8- and 9-year-old children in both seasons. Nevertheless, the same did not happen in kindergarten due to the ventilation rate in ExDoM2 being 0.095 m³ h⁻¹ higher than the value used by MPPD, despite the DFs calculated with MMPD for these two regions being always higher than those estimated with ExDoM2. In both seasons, the majority of the PM₁₀ dose was deposited in the head region, while PM₁ and PM₂.₅ were predominantly deposited in the lung region. It is noteworthy that different mechanisms govern PM deposition in the TB and lung regions, such as diffusion, impaction, and sedimentation (Deng et al., 2018). Regardless of the model, special attention should be paid to these results, particularly those related to finer particles, since PM₂.₅ depositions in the respiratory tract, including P and TB regions, are associated with elevated blood pressure and greater risk of prehypertension and hypertension in children aged 4 to 12 years (Liu et al., 2021).

**Fig. 5** shows a statistical summary of the hourly deposited doses of PM₁, PM₂.₅ and PM₁₀ for all groups between 8 h and 16 h. With MPPD, a 3-year-old child has a lower mean dose deposited per hour compared to children aged 8 and 9 years for the three PM sizes evaluated in both seasons, while with ExDoM2, the lowest values were observed in children aged 10 years of the 2nd cycle. The differences observed in deposited doses can be explained by the DFs predicted by the two models. In the case of MPPD, deposition efficiency is lower in the respiratory tract of 3-year-old children compared to 8- and 9-year-old children, whatever the particle size and season, while ExDoM2 predictions showed the highest DF values for 5-year-old children. As shown by Poorbahrami et al. (2021), the differences in total and regional deposition calculations are probably due to the use of different lung models, which in turn suggest volumetric and structural differences in lung morphologies according to the ages of the individuals.
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Boxplots with hourly doses of different sizes of PM for the three groups evaluated, split into model and a) winter and b) spring.</figcaption></figure>

Although both models describe the same hourly profile pattern, with peaks at different times depending on classroom activities and particle size, the deposited dose values show clear differences from model to model. In the 1st cycle and 2nd cycle classrooms, the PM<sub>2.5</sub> and PM<sub>10</sub> peaks coincided at the same time, different from the PM<sub>1</sub> pattern, with more stable values being observed throughout the day. The hourly profile for winter reveals that both particle doses calculated with ExDoM2 and those estimated from MPPD showed variations linked to PM sources (Fig. 6). Normally, the deposited dose increased between 8 h and 10 h (which is related to the start of the school day) and decreased during rest and lunch times. On the other hand, high doses, generally from 14 h onwards, are associated with students leaving, as well as emissions from cleaning classrooms.

The difference between the PM<sub>10</sub> doses predicted by ExDoM2 were 58%, 31% and 24% higher than those calculated with the MPPD for kindergarten, 1st cycle and 2nd cycle, respectively, while for PM<sub>1</sub> the MPPD calculated doses 46% and 57% higher for children
in the 1st cycle and 2nd cycle, respectively. This is in accordance with the pattern of the total deposition fraction of these PM sizes and their respective concentrations. However, despite the MPPD calculating higher DFs for PM₁ and PM₂.₅ in kindergarten and 1st cycle compared to ExDoM2, the doses did not follow this behaviour. This is due to the ventilation rate used by ExDoM2 being higher, compared to the values used by MPPD for children aged 3 and 8 years. Similar relationships were also observed in spring.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Hourly cycle of PM deposition doses during school hours in winter according to group and the dosimetry model.</figcaption></figure>

Furthermore, high R values (R> 0.89) were observed between the MPPD and ExDoM2 hourly doses in both seasons (Table S6, Supplementary Material). However, there were some exceptions, as in the case of the 2nd cycle classroom. The low correlations occur because some dose peaks calculated with MPPD were not present in the ExDoM2 dataset or because one of the models showed lower doses compared to the other. Coincidentally, weak correlations between the hourly doses of the two models were observed only for R values below 0.63 between hourly deposited dose and hourly exposure (Table S5, Supplementary Material).

In addition to linear regression, the difference and agreement between the hourly doses of the two models were also evaluated. While the paired t-test applied at a significance level of 5% revealed that there are statistically significant differences in the hourly
589 deposited dose of all PM sizes in the two seasons, the Bland-Altman test confirmed that even though there were differences in the hourly values, good agreement was found between the predictions of the two models for deposited particle doses (Table S7 and Figure S1, Supplementary Material.
590
591
592

593 Overall, this study highlights the importance of evaluating the personal exposure to PM
594 of children of different ages who attend a large school that integrates several levels of
595 education (~700 students), and which is located less than 1 km away from the largest
596 industrial complex in the country. In Portugal, schools offering pre-school, 1st cycle and
597 2nd cycle education are abundant (10,991 establishments) and represent 71% of the
598 population of children attending these levels of education (~1,189,353 students from pre-
599 school up to the 3rd cycle of basic education). Thus, the results of this study may be valid
600 in other establishments with these levels of education, and serve to encourage
601 measurements in school environments considering a larger group of classes to calculate
602 in more detail the exposure of schoolchildren to PM. However, the results should be
603 interpreted with caution because they are based on a short data set that may have been
604 affected by specific conditions of the sampling period or by sporadic events. The
605 impossibility of monitoring and collecting samples simultaneously in different
606 classrooms and other school environments constitutes another limitation of the study. In
607 addition, variability in the activities carried out in the classrooms was not considered.
608 Therefore, the children's respiratory patterns were constant, and the same exposure
609 scenario was evaluated (nasal breathing under sitting activity level). Finally, whenever
610 possible, monitoring in classrooms for longer periods (more than 1 week) should be taken
611 into account, as well as carrying out at least one sampling campaign in each season with
612 the aim of improving the representation of variations in weather conditions and school
613 activities throughout the academic year.
614

615

4. Conclusions
---

616 This study suggests that PM concentrations are statistically different from one season to
617 another, and that the variability of concentrations is also influenced by room occupancy
618 periods, with higher levels of PM₁₀ during the school day compared to periods when the
619 classrooms are empty. Although PM levels were low compared to other studies,
620 dosimetry models indicate that a significant amount of PM was deposited in pupils'
621 respiratory tracts during school hours. The patterns indicate larger deposited doses in
622 spring compared to winter, with children aged 9 and 10 attending the 1st cycle being the
623 most affected. Furthermore, the variations observed in the deposition fractions and
624 deposited doses for the models are the result of differences in the physiological
625 parameters of the respiratory system (e.g., URT, FRC, BF and TV) used as input, which
626 are age dependent. None of the models covers all ages of the children targeted in the
present study. In general, a good agreement was obtained between both models, although some differences, especially for regional deposition, could be observed. The dosimetry models applied in this research may be useful in other educational settings to identify and mitigate the impact of local and regional sources of air pollution in the school environment.

632

633 **Declaration of Competing Interest**

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work documented in this manuscript.

636 **Acknowledgements**

The authors would especially like to thank the school principal, teachers, parents, and children who have willingly participated in this study. The help of Prof. Teresa Nunes, from the Department of Environment at the University of Aveiro, in interpreting the data is also appreciated. Special thanks to Owen Price from Applied Research Associates, Inc. for his support and expertise in answering questions related to MPPD. The authors acknowledge the financial support of the Portuguese Foundation for Science and Technology (FCT) to CESAM (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020), through national funds, and to the PhD fellows I. Charres (SFRH/BD/2022.12142), Y. Cipoli (SFRH/BD/04992/2021), L. Furst (SRFH/BD/08461/2020) and I. Rienda (SFRH/BD/144550/2019). FCT is also acknowledged for the research contract under Scientific Employment Stimulus to Estela D. Vicente (DOI:10.54499/2022.00399.CEECIND/CP1720/CT0012). This work was performed within the STEP project - Schools Tackling Estarreja's Air Pollution - funded by the LabEx-DRIIHM-OHM programme (CNRS - INEE, France).

651

652 **Appendix A. Supplementary material.** Additional material related to this paper is available online at:

654
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840

841

842
SUPPLEMENTARY MATERIAL

<table><caption>**Table S6.** Mean concentration (µg m⁻³), MMADs (µm) and GSDs (µm) of PM₁₀ used as input data in the two models.</caption><thead><tr><th colspan="2">Kindergarten</th><th colspan="3">Winter</th><th colspan="3">Spring</th></tr><tr><th>Time</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th></tr></thead><tbody><tr><td>8:00</td><td>5.53</td><td>1.24</td><td>2.95</td><td>14.6</td><td>4.05</td><td>2.18</td></tr><tr><td>9:00</td><td>30.7</td><td>3.80</td><td>1.97</td><td>49.7</td><td>3.74</td><td>2.31</td></tr><tr><td>10:00</td><td>41.2</td><td>2.84</td><td>2.04</td><td>47.0</td><td>4.05</td><td>1.79</td></tr><tr><td>11:00</td><td>48.4</td><td>3.73</td><td>1.76</td><td>71.3</td><td>3.28</td><td>1.96</td></tr><tr><td>12:00</td><td>18.4</td><td>2.55</td><td>2.09</td><td>45.7</td><td>3.61</td><td>2.15</td></tr><tr><td>13:00</td><td>28.7</td><td>3.60</td><td>2.00</td><td>44.6</td><td>4.31</td><td>1.70</td></tr><tr><td>14:00</td><td>61.7</td><td>4.45</td><td>1.56</td><td>71.9</td><td>4.39</td><td>1.65</td></tr><tr><td>15:00</td><td>44.4</td><td>4.08</td><td>1.62</td><td>67.4</td><td>3.62</td><td>1.98</td></tr><tr><td>16:00</td><td>17.5</td><td>2.51</td><td>2.21</td><td>34.3</td><td>2.89</td><td>2.45</td></tr><tr><th colspan="2">1<sup>st</sup> cycle</th><th colspan="3">Winter</th><th colspan="3">Spring</th></tr><tr><th>Time</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th></tr><tr><td>8:00</td><td>9.50</td><td>1.01</td><td>2.21</td><td>19.1</td><td>3.22</td><td>3.03</td></tr><tr><td>9:00</td><td>27.3</td><td>1.32</td><td>2.47</td><td>54.2</td><td>3.54</td><td>2.65</td></tr><tr><td>10:00</td><td>52.9</td><td>3.18</td><td>2.41</td><td>69.1</td><td>3.66</td><td>2.26</td></tr><tr><td>11:00</td><td>62.7</td><td>4.17</td><td>1.80</td><td>68.0</td><td>3.91</td><td>1.97</td></tr><tr><td>12:00</td><td>34.9</td><td>4.21</td><td>1.63</td><td>66.6</td><td>3.10</td><td>2.82</td></tr><tr><td>13:00</td><td>48.2</td><td>3.48</td><td>1.93</td><td>44.7</td><td>3.86</td><td>2.13</td></tr><tr><td>14:00</td><td>32.4</td><td>3.70</td><td>1.80</td><td>71.0</td><td>4.15</td><td>1.82</td></tr><tr><td>15:00</td><td>56.1</td><td>3.15</td><td>2.05</td><td>81.4</td><td>4.51</td><td>1.57</td></tr><tr><td>16:00</td><td>41.6</td><td>4.02</td><td>1.76</td><td>128.7</td><td>2.42</td><td>3.32</td></tr><tr><th colspan="2">2<sup>nd</sup> cycle</th><th colspan="3">Winter</th><th colspan="3">Spring</th></tr><tr><th>Time</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th><th>PM<sub>10</sub></th><th>MMAD</th><th>GSD</th></tr><tr><td>8:00</td><td>19.2</td><td>0.98</td><td>5.41</td><td>24.9</td><td>3.19</td><td>2.81</td></tr><tr><td>9:00</td><td>27.7</td><td>1.70</td><td>5.82</td><td>38.1</td><td>3.49</td><td>2.37</td></tr><tr><td>10:00</td><td>44.6</td><td>2.78</td><td>3.86</td><td>40.2</td><td>3.28</td><td>2.47</td></tr><tr><td>11:00</td><td>46.0</td><td>3.08</td><td>3.25</td><td>33.4</td><td>3.53</td><td>2.34</td></tr><tr><td>12:00</td><td>48.2</td><td>3.40</td><td>2.73</td><td>37.3</td><td>3.29</td><td>2.32</td></tr><tr><td>13:00</td><td>31.1</td><td>2.81</td><td>3.20</td><td>29.9</td><td>2.53</td><td>2.45</td></tr><tr><td>14:00</td><td>19.1</td><td>2.00</td><td>3.99</td><td>21.5</td><td>3.64</td><td>2.13</td></tr><tr><td>15:00</td><td>46.4</td><td>3.90</td><td>2.25</td><td>36.5</td><td>2.64</td><td>2.89</td></tr><tr><td>16:00</td><td>27.5</td><td>2.85</td><td>3.06</td><td>25.1</td><td>2.17</td><td>3.22</td></tr></tbody></table>

The equations used by ExDoM2 to calculate the particle deposition for the upper respiratory tract region (DF<sub>H</sub>), which includes the extrathoracic airways ET1 (anterior nose) and ET2 (posterior nasal passages, larynx, pharynx and mouth); the tracheobronchial region (DF<sub>TB</sub>) which includes the bronchial (BB) and the bronchiolar (bb); the alveolar region (DF<sub>AL</sub>) and the total deposition (DF) are those formulated by the International Commission on Radiological Protection (ICRP) and are presented below. In the equations, d<sub>p</sub> is the particle diameter in µm, and IF is the inhalable fraction (in equations 1 and 4) and is calculated according to equation 5.

$$DF_H = IF \left[ \frac{1}{1 + e^{(6.84+1.183 \ln d_p)}} + \frac{1}{1 + e^{(0.924-1.885 \ln d_p)}} \right] \quad (1)$$
$$859 \qquad DF_{TB} = \left(\frac{0.00352}{d_p}\right) \left[ e^{(-0.234(\ln d_p + 3.40)^2)} + 63.9e^{(-0.819(\ln d_p - 1.61)^2)} \right] \qquad (2)$$

$$860 \qquad DF_{AL} = \left(\frac{0.0155}{d_p}\right) \left[ e^{(-0.416(\ln d_p + 2.84)^2)} + 19.11e^{(-0.482(\ln d_p - 1.362)^2)} \right] \qquad (3)$$

$$862 \qquad DF = IF \left[ 0.0587 + \frac{0.911}{1 + e^{(4.77+1.485 \ln d_p)}} + \frac{0.943}{1 + e^{(0.508-2.58 \ln d_p)}} \right] \qquad (4)$$

$$864 \qquad IF = 1 - 0.5 \left( 1 - \frac{1}{7.6 \times 10^{-4} d_p^2 + 1} \right) + 10^{-5} U^{2.75} e^{(0.055 d_p)} \qquad (5)$$

$$866 \qquad \textbf{Table S7.} Age considered according to the model. $$

<table><thead><tr><th></th><th>MPPD</th><th>ExDoM2</th></tr></thead><tbody><tr><td>Kindergarten</td><td>3 years</td><td>5 years</td></tr><tr><td>1<sup>st</sup> cycle</td><td>8 years</td><td>10 years</td></tr><tr><td>2<sup>nd</sup> cycle</td><td>9 years</td><td>10 years</td></tr></tbody></table>

868 Thermal conditions, including wind direction, were measured with a portable monitoring equipment (Delta Ohm 32.1 and 32.3), which included the following sensors: AP3203 - Omnidirectional Hot Wire Probe, TP3275 - Globe Temperature Probe, and HP3217R - Combined Temperature and RH Probe. The equipment was installed at a height of 1 m above the floor and operated at a 1-minute time resolution. Each classroom was monitored for a week in winter 2022 (Table S3).

<table><caption>875 876 **Table S8.** Average wind speed (m s⁻¹).</caption><thead><tr><th>Time</th><th>Kindergarten</th><th>1<sup>st</sup> cycle</th><th>2<sup>nd</sup> cycle</th></tr></thead><tbody><tr><td>8:00 – 8:59</td><td>0.000</td><td>0.001</td><td>0.001</td></tr><tr><td>9:00 – 9:59</td><td>0.000</td><td>0.002</td><td>0.001</td></tr><tr><td>10:00 – 10:59</td><td>0.005</td><td>0.005</td><td>0.002</td></tr><tr><td>11:00 – 11:59</td><td>0.003</td><td>0.002</td><td>0.002</td></tr><tr><td>12:00 – 12:59</td><td>0.000</td><td>0.002</td><td>0.002</td></tr><tr><td>13:00 – 13:59</td><td>0.000</td><td>0.003</td><td>0.001</td></tr><tr><td>14:00 – 14:59</td><td>0.001</td><td>0.005</td><td>0.001</td></tr><tr><td>15:00 – 15:59</td><td>0.008</td><td>0.002</td><td>0.001</td></tr><tr><td>16:00 – 16:59</td><td>0.002</td><td>0.002</td><td>0.002</td></tr></tbody></table>

877 The discrepancies between the dosimetry models for different particle sizes in the two seasons are presented in Table S4. For PM₁₀, the total DF is up to 22%, showing that the MPPD
879 underestimates the total DF compared to the ExDoM2 counterparts. However, ExDoM2 appears
to underestimate the total DF for finer particles compared to the other model.

881

882 **Table S9.** Discrepancy of total DFs derived from the models. Bold indicates that ExDoM2 gave
883
values higher than MPPD.

<table>
  <thead>
    <tr>
      <th rowspan="2"></th>
      <th colspan="3">Winter</th>
      <th colspan="3">Spring</th>
    </tr>
    <tr>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Kindergarten</th>
      <td><strong>22%</strong></td>
      <td><strong>13%</strong></td>
      <td>41%</td>
      <td><strong>22%</strong></td>
      <td><strong>3%</strong></td>
      <td>53%</td>
    </tr>
    <tr>
      <th>1<sup>st</sup> cycle</th>
      <td><strong>5%</strong></td>
      <td>20%</td>
      <td>60%</td>
      <td><strong>8%</strong></td>
      <td>40%</td>
      <td>66%</td>
    </tr>
    <tr>
      <th>2<sup>nd</sup> cycle</th>
      <td><strong>2%</strong></td>
      <td>53%</td>
      <td>66%</td>
      <td><strong>6%</strong></td>
      <td>36%</td>
      <td>64%</td>
    </tr>
  </tbody>
</table>

884

885 **Table S10.** Linear correlation coefficients between hourly exposure and hourly deposited dose
886
according to the model (R).

<table>
  <thead>
    <tr>
      <th rowspan="2"></th>
      <th colspan="3">Winter</th>
      <th colspan="3">ExDoM2</th>
      <th colspan="3">Spring</th>
      <th colspan="3">ExDoM2</th>
    </tr>
    <tr>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
      <th>PM<sub>10</sub></th>
      <th>PM<sub>2.5</sub></th>
      <th>PM<sub>1</sub></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Kindergarte</th>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <th>n</th>
      <td>0.99</td>
      <td>0.99</td>
      <td>0.99</td>
      <td>0.99</td>
      <td>0.96</td>
      <td>0.99</td>
      <td>0.99</td>
      <td>0.98</td>
      <td>1</td>
      <td>0.99</td>
      <td>0.78</td>
      <td>0.99</td>
    </tr>
    <tr>
      <th>1<sup>st</sup> cycle</th>
      <td>0.98</td>
      <td>0.99</td>
      <td>0.99</td>
      <td>0.95</td>
      <td>0.96</td>
      <td>0.90</td>
      <td>0.95</td>
      <td>0.87</td>
      <td>0.99</td>
      <td>0.91</td>
      <td><strong>0.09*</strong></td>
      <td>0.99</td>
    </tr>
    <tr>
      <th>2<sup>nd</sup> cycle</th>
      <td>0.98</td>
      <td>0.98</td>
      <td>1</td>
      <td>0.97</td>
      <td><strong>0.09*</strong></td>
      <td>0.99</td>
      <td><strong>0.63*</strong></td>
      <td><strong>0.12*</strong></td>
      <td><strong>0.41*</strong></td>
      <td>0.92</td>
      <td><strong>0.06*</strong></td>
      <td>0.99</td>
    </tr>
  </tbody>
</table>

887
*Values indicate weak correlations.

888
<table><caption>Table S11. Linear correlation coefficients between the hourly deposited doses by the two models (R).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Winter</th><th colspan="3">Spring</th></tr><tr><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>PM<sub>1</sub></th><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>PM<sub>1</sub></th></tr></thead><tbody><tr><th>Kindergarten</th><td>0.99</td><td>0.98</td><td>0.98</td><td>0.99</td><td>0.89</td><td>0.99</td></tr><tr><th>1<sup>st</sup> cycle</th><td>0.99</td><td>0.98</td><td>0.92</td><td>0.99</td><td><strong>0.40*</strong></td><td>0.99</td></tr><tr><th>2<sup>nd</sup> cycle</th><td>0.99</td><td><strong>0.20*</strong></td><td>0.99</td><td><strong>0.58*</strong></td><td><strong>0.45*</strong></td><td><strong>0.40*</strong></td></tr></tbody></table>

*Values indicate weak correlation.

892

<table><caption>Table S12. Average hourly deposited dose of PM in each monitoring campaign and p-values from the paired sample t-test. Dose values are presented in µg h⁻¹ for both models.</caption><thead><tr><th rowspan="2">Campaign</th><th colspan="3">PM<sub>10</sub></th><th colspan="3">PM<sub>2.5</sub></th><th colspan="3">PM<sub>1</sub></th></tr><tr><th>MPPD</th><th>ExDoM2</th><th>p-value</th><th>MPPD</th><th>ExDoM2</th><th>p-value</th><th>MPPD</th><th>ExDoM2</th><th>p-value</th></tr></thead><tbody><tr><th>Kindergarten - wr</th><td>3.96</td><td>9.40</td><td>1.0E-03</td><td>0.76</td><td>1.64</td><td>2.8E-04</td><td>0.28</td><td>0.31</td><td>1.11e-02</td></tr><tr><th>1<sup>st</sup> cycle - wr</th><td>8.77</td><td>12.6</td><td>7.2E-04</td><td>1.61</td><td>1.75</td><td>0.046</td><td>0.60</td><td>0.32</td><td>5.8E-01</td></tr><tr><th>2<sup>nd</sup> cycle - wr</th><td>7.20</td><td>9.44</td><td>1.2E-03</td><td>1.81</td><td>1.05</td><td>1.0E-05</td><td>1.29</td><td>0.55</td><td>2.0E-06</td></tr><tr><th>Kindergarten - sg</th><td>6.20</td><td>14.2</td><td>6.1E-05</td><td>1.01</td><td>1.92</td><td>1.6E-05</td><td>0.49</td><td>0.42</td><td>2.0E-04</td></tr><tr><th>1<sup>st</sup> cycle - sg</th><td>14.3</td><td>20.7</td><td>4.6E-05</td><td>2.40</td><td>1.93</td><td>2.8E-03</td><td>1.26</td><td>0.57</td><td>1.8E-07</td></tr><tr><th>2<sup>nd</sup> cycle - sg</th><td>7.49</td><td>9.32</td><td>1.3E-03</td><td>1.95</td><td>1.36</td><td>9.4E-05</td><td>1.29</td><td>0.40</td><td>7.3E-06</td></tr></tbody></table>

895 Wr: winter; sp: spring.

896 The Bland-Altman graph was generated to assess the agreement between the two models. The differences between doses are displayed on the y axis; on the x-axis, dose averages are plotted.

898 This method has two limits of agreement at ±1.96 times the standard deviation. Ideally, all points

899 should fall between these two limits. After making the graphs for all rooms and the three particle sizes of the two seasons, the 1<sup>st</sup> cycle room presented a point outside the limits of agreement for

901 PM<sub>10</sub> and PM<sub>1</sub> and, the 2<sup>nd</sup> cycle room for PM<sub>10</sub> in spring. The other cases were always within this

902 limit, as shown in Figure S1.
<figure><img src="image_9.png" /><figcaption>**Figure S7.** Bland-Altman plot generated for the hourly deposited doses of PM₁ calculated for 1st cycle in winter.</figcaption></figure>

## References

908 International Commission on Radiological Protection (ICRP) (2002). Guide for the Practical

909 Application of the ICRP Human Respiratory Tract Model. Annals of the ICRP 32 (1-2),

910 Pergamon, Oxford, 1 - 312