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Article

The Association Between Indoor Air Pollutants and Brain Structure Indicators Using eTIV-Adjusted and Unadjusted Models: A Study in Seoul and Incheon

by
Sun-Min An
1 and
Ho-Hyun Kim
1,2,*
1
Department of Research, Institute for Living and Industrial Environment, Seokyeong University, Seoul 02173, Republic of Korea
2
Department of Environmental and Chemical Engineering, Seokyeong University, Seoul 02173, Republic of Korea
*
Author to whom correspondence should be addressed.
Brain Sci. 2025, 15(8), 868; https://doi.org/10.3390/brainsci15080868
Submission received: 14 July 2025 / Revised: 4 August 2025 / Accepted: 7 August 2025 / Published: 14 August 2025
(This article belongs to the Section Environmental Neuroscience)
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Abstract

Background/Objectives: As older adults spend increasing amounts of time indoors, concerns are rising about the neurological effects of indoor air pollution. This study examined associations between indoor air pollutants and structural brain changes in community-dwelling older adults in Seoul and Incheon, South Korea. A purposive sample of 23 individuals aged ≥65 years was recruited. Internet of Things (IoT)-based devices were installed in participants’ homes to continuously monitor indoor concentrations of PM10, PM2.5, and CO2 for over two months. All participants underwent 3T brain magnetic resonance imaging (MRI), and brain structure metrics were analyzed using multiple linear regression models with and without adjustment for estimated total intracranial volume (eTIV). Hierarchical clustering was also performed based on exposure and neuroanatomical characteristics. Brain MRI indicators included cortical surface area, cortical thickness in six regions, and volumes of seven subcortical structures including the hippocampus and amygdala. Higher CO2 concentrations were significantly associated with lower hippocampal volumes in both hemispheres (left: −2.83, −0.88, −1.02 mm3; right: −3.29, −0.86, −0.99 mm3; p ≤ 0.05). Elevated PM2.5 levels were associated with reduced bilateral amygdala volume (−283.24 mm3 left; −292.37 mm3 right) and right hippocampal volume (−544.55 mm3; p ≤ 0.05). Cluster analysis showed that, before eTIV adjustment, Group C exhibited the lowest subcortical volumes. After adjustment, Group A showed the smallest cortical surface area, and Group D had the lowest subcortical volumes. These findings suggest that indoor air pollutants, including PM10, PM2.5, and CO2, may be associated with structural brain alterations in older adults, supporting the need for age-specific indoor air quality standards and residential monitoring systems.

Graphical Abstract

1. Introduction

According to the World Health Organization (WHO), 90% of the world’s population reside in areas with air quality parameters exceeding WHO Air Quality Guidelines, and indoor air pollutants, including particulate matter (PM10), fine particulate matter (PM2.5), carbon dioxide (CO2), formaldehyde, and volatile organic compounds (VOCs), account for approximately 3.8 million deaths annually [1,2,3,4]. The indoor concentrations of some air pollutants are two to five times higher than their outdoor concentrations [5,6,7].
As the tendency to spend more time indoors increases due to changes in lifestyle, the health effects of indoor air pollutants are becoming more obvious [8,9]. Several studies have been conducted to investigate the effects of PM10 and PM2.5 on the occurrence of cerebropathies in Korea and other countries [10,11,12]. In addition, a cohort study in Taiwan indicated that a 4.34 µg/m3 increase in PM2.5 concentration was associated with a 138% increase in the incidence of Alzheimer’s disease [13]. Another study in the UK showed that a 2.0 µg/m3 increase in PM2.5 concentrations resulted in a 0.32% decrease in brain volume and increased the risk of cerebral infarction in individuals aged 60 years or older [14]. Nano-scale particles such as PM10 can easily be transported to the brain via the olfactory epithelium without crossing the blood–brain barrier, eventually triggering cardiovascular and cerebrovascular diseases [15,16]. According to the Lawrence Berkeley National Laboratory, an increase in indoor CO2 concentrations from 600 to 2500 ppm results in a decline in brain structure [17,18]. Studies on exposure to formaldehyde, which have been conducted predominantly using animals, have also shown that formaldehyde exposure negatively affects brain volume and white matter [19,20]. Exposure to VOCs has also been shown to induce neurological disorders, including cognitive changes, hallucinations, dizziness, and dementia, in humans [21]. Additionally, highly toxic BTEX compounds (benzene, toluene, ethylbenzene, and xylene) can cause neuropsychiatric changes, such as dementia, headaches, learning disabilities, and memory loss, and toluene exposure negatively affects white matter in the brain [22].
Vulnerable populations, particularly infants, older adults, and those with underlying medical conditions, spend considerably more time indoors than the general population [23]. They are therefore at a greater risk of exposure to indoor air pollutants and are more likely to suffer more severely from indoor air pollutant-related health issues, even when exposed to the same pollutant concentrations as the general population [24]. According to the National Institute of Environmental Research, Koreans spend an average of 20.66 h indoors daily, with 15.86 h of this time spent at home [25]. Thus, there is an urgent need to investigate the effects of indoor air quality on brain health.
Recent neuroimaging studies have highlighted the importance of adjusting for intracranial volume (ICV) as a covariate, given that individual differences in skull size can influence the outcomes of brain structure analyses [26,27,28]. For example, Canning et al. [29] explicitly controlled for ICV and age at the time of the scan in their MRI-based analysis of environmental exposure and cognitive function, demonstrating that ICV adjustment is systematically incorporated into statistical modeling in neuroimaging cohorts. Similarly, Thompson et al. [30] investigated whether MRI-derived brain volumes mediated the association between air pollution and dementia risk, implying that volumetric normalization for cranial size was embedded in their analytical framework. Following these approaches, the present study compared results before and after eTIV correction to enhance analytical precision and improve comparability with prior research. Although ICV and estimated total intracranial volume (eTIV) differ in calculation methods, eTIV derived from FreeSurfer software version 6.0 was used as a reliable proxy for ICV in this study [27,31]. This approach is expected to contribute to a more accurate understanding of the relationship between indoor air quality exposure and brain structure in older adults.
In this study, we examined the association between indoor air pollutant concentrations and brain-related indicators from the perspective of brain volume using indoor air quality data from residential spaces obtained using IoT devices. The findings of this study may serve as a basis for policy recommendations aimed at facilitating the prevention and management of brain diseases.

2. Materials and Methods

2.1. Study Design and Population

This pilot cross-sectional study was conducted as part of the Environmental Pollution-Induced Neurological Effects (EPINEF) project to explore the potential impact of environmental pollution on brain structure among community-dwelling older adults aged 65 years or older. Participants were initially recruited from two metropolitan areas in Korea (Seoul and Incheon) through voluntary participation in a brain magnetic resonance imaging (MRI) examination [32,33]. Among 262 individuals who underwent MRI (205 in Seoul and 57 in Incheon), 94 agreed to participate in additional indoor air quality (IAQ) monitoring [34,35] in accordance with the Ministry of Environment’s official test method for indoor air quality [36]. Of these, 23 participants (14 from Seoul and 9 from Incheon) who were able to install IoT-based air quality monitoring equipment in their homes for at least two months were included in the final analysis (Figure 1). As this was a pilot study, the final sample size was primarily determined based on feasibility. This study was approved by the Institutional Review Board of the Yonsei University Health System (Approval No. 4-2022-0418).

2.2. Measurement of Concentrations of Indoor Air Pollutants

In this study, we monitored three air pollutants, PM10, PM2.5, and CO2, using IoT measurement equipment (Smart Aircok; Aircok, Seoul, Republic of Korea) installed in the kitchens of the participants. The device used was certified as Grade 1 under the Ministry of Environment’s performance certification system for simplified measurement devices, pursuant to relevant regulations on indoor air quality management [37]. For PM, certification is based on comparison with reference instruments in terms of the coefficient of determination (R2), accuracy, precision, and repeatability. For CO2, Grade 1 certification requires a repeatability ≤ 10%, linearity ≤ 15%, and relative accuracy ≤ 30%, as defined in the official notification. In this study, only Grade-1-certified sensors that fully complied with these criteria were used, and thus, the measurements were considered valid and reliable according to national regulatory standards [37].
The kitchen was selected as the primary monitoring site based on prior studies identifying it as a major source of indoor air pollutants during cooking activities [38,39,40]. Although no official guidelines exist in Korea regarding indoor monitoring locations in residential settings, this approach is consistent with standard practice in similar studies. When installation in the kitchen was not feasible due to safety or structural constraints, the monitoring device was placed in the nearest adjacent room. The observation period varied by region: in Seoul, it was 4 April to 16 July 2023, and in Incheon, it was from 31 August 2023 to 1 February 2024. All measured data were collected in real time at 5 min intervals, and were averaged into 24 h (1 day) values before analysis.

2.3. Selection of Brain MRI Indicators

Brain MRI examinations of the 23 participants were performed using a Philips 3T Achieva MRI scanner (Philips Healthcare, Amsterdam, The Netherlands) at one of two University Hospitals: Severance Hospital, Yonsei University (Seoul) and Gil Medical Center, Gachon University (Incheon). The brain MRI was performed according to a standardized protocol, and to estimate cortical thickness, subcortical gray matter volume, and total brain volume, 3D T1 magnetization-prepared rapid acquisition gradient echo images (3D T1-weighted MPRAGE) were analyzed using FreeSurfer software version 6.0, which automatically analyzes brain MRI images. Finally, cortical surface area, cerebral cortex thickness (frontal lobe, parietal lobe, temporal lobe, occipital lobe, cingulate gyrus, and insular lobe), and subcortical structures (nucleus accumbens, amygdala, hippocampus, globus pallidus, putamen, caudate nucleus, and thalamus) were selected as brain MRI indicators.

2.4. Covariates

The covariates in this study included height, weight, total intracranial volume, sex, education level, past medical history (e.g., angina, hypertension, hyperlipidemia, and diabetes), age, and body mass index (BMI), and these were selected according to previous studies [41,42,43,44]. Data on these covariates were collected using questionnaires and physical examinations. Education level was categorized as follows: never attended school, elementary school dropout, elementary school graduate, middle school dropout, middle school graduate, high school dropout, high school graduate, technical/vocational school graduate, university dropout, university graduate, and postgraduate degree or higher. With respect to age, the participants were categorized as older adults aged ≥75 years and those aged <75 years, and according to BMI, as individuals with BMI ≥ 25 and those with BMI < 25. Height, weight, and total intracranial volume were not categorized given that they are continuous variables.

2.5. Multiple Regression Model

Statistical analyses were performed using indoor air quality data collected via the IoT equipment and brain MRI indicators. Before analysis, indoor air quality data were refined into four variables (minimum, average, maximum, and geometric mean), which were then used alongside MRI survey data to quantify the impact of environmental factors on brain structure indicators using multiple regression models. Basically, a multiple regression model is a statistical model that indicates the relationship between a dependent and an independent variable, assuming that the dependent variable is linearly influenced by more than one independent variable [45]. The multiple regression model used was:
yi = β0 + β1x1i + β2x2i + … + βpxpi + εi → [i = 1, 2, …, n]
where β0, …, βp represent regression coefficients; x1i, …, xpi represent explanatory variables (independent variables); yi represents response variables (dependent variables); and εi represents the error term.
In the multiple regression models (Models 1–4), we corrected for the effects of environmental factors, as well as those of personal characteristics (e.g., age, gender, BMI, and education level) that could affect MRI variables. Specifically, the following models were applied:
  • Model 1 assessed the association between each brain MRI feature and a single continuous environmental variable, adjusting for personal characteristics.
  • Model 2 extended Model 1 by additionally adjusting for total intracranial volume.
  • Model 3 was identical to Model 1, except that the continuous environmental variable was replaced with a dichotomous variable derived using its median as a cut-off.
  • Model 4 extended Model 3 by also adjusting for total intracranial volume.
Statistical analyses were further performed using two-tailed tests, with the level of significance (alpha) set at p = 0.05.

2.6. Hierarchical Clustering

To investigate the effects of environmental variables on brain structural features derived from MRI data, we applied hierarchical clustering after adjusting for individual-level covariates. First, we corrected for the effects of individual characteristics (such as age, sex, and education) on these MRI-derived features using multiple linear regression models. The residuals from these models, representing variance unexplained by the covariates, were used as input for the clustering analysis to minimize the influence of individual-level confounders.
In addition, given the importance of total intracranial volume adjustment in neuroimaging analyses, we further accounted for total intracranial volume by including it as an additional covariate in the regression models. Residuals were then re-derived from models including both individual characteristics and total intracranial volume, allowing for clustering based on brain features independent of these effects.
After this correction and standardization process (to adjust for unit differences across features), we conducted hierarchical clustering using Euclidean distance and Ward’s linkage method. The optimal number of clusters was determined based on silhouette scores. All statistical analyses were performed using R software (version 4.3.1) [46].

3. Results

3.1. Characteristics of Study Participants

The mean age, height, and weight of the 23 study participants were 75 years, 156.2 cm, and 61.2 kg, respectively. The sample included 16 females and 7 males; 13 (56.5%) had a BMI below 25 and 18 (78.3%) were educated above high school level. A total of 19 participants had no history of angina (82.6%) and 18 had no history of diabetes (78.3%). However, 17 (73.9%) and 14 (60.9%) had a history of hypertension and hyperlipidemia (60.9%), respectively, indicating that more than half of the research participants had an underlying disease (Table 1).

3.2. Indoor Air Pollutants and MRI Results

Combining the Seoul and Incheon cohorts, the average indoor concentrations of PM2.5 (17.99 µg/m3), PM10 (24.07 µg/m3), and CO2 (791.59 ppm) were all below the respective thresholds for facilities for vulnerable populations specified in the Enforcement Decree of the Indoor Air Quality Control Act [47], which are 35 µg/m3 for PM2.5, 75 µg/m3 for PM10, and 1000 ppm for CO2 (Table 2 and Table S1).
Compared to the WHO’s 24 h mean air quality guidelines, the PM2.5 concentration exceeded the limit of 15 µg/m3, while the PM10 concentration remained below the 45 µg/m3 threshold [2]. WHO does not provide a health-based guideline for CO2, noting instead that it is often used as a surrogate marker for ventilation adequacy. According to Mendell et al. [48], the most commonly used indoor CO2 threshold is 1000 ppm, which serves as a practical indicator of acceptable indoor air quality.
These values were derived from indoor measurements conducted in participant’s homes during the designated monitoring period. Of the MRI variables (combining the Seoul and Incheon cohorts), the mean total cerebral volume was 1,530,571 mm3 and the mean cerebral surface areas of the left and right hemispheres were 75,784 mm2 and 75,876 mm2, respectively. Additionally, the mean thickness of the entire cerebral cortex was 2.35 mm. The average volumes of the subcortical structures, including the nucleus accumbens, amygdala, hippocampus, globus pallidus, putamen, caudate nucleus, and thalamus, were 365 mm3, 1360 mm3, 3593 mm3, 1776 mm3, 4107 mm3, 2999 mm3, and 6151 mm3, respectively (Table S2).

3.3. Multiple Regression Analysis Results

Model 1, with the new environmental variables as independent variables, showed that left hemisphere hippocampal volume (min est: −2.83 mm3, p = 0.05; mean est: −0.88 mm3, p = 0.05; gmean est: −1.02 mm3, p < 0.05) and right hemisphere hippocampal volume (min est: −3.29 mm3, p < 0.05; mean est: −0.86 mm3, p < 0.05; gmean est: −0.99 mm3, p < 0.05) were negatively correlated with higher CO2 concentrations (Table 3 and Table S3). Model 2 showed results similar to those obtained for Model 1, despite the addition of individual cerebral volumes (Table 3 and Table S4). For Model 3, in which categorical variables were used based on the median values of the new environmental variables and were included as independent variables, left hemisphere amygdala volume (mean est: −283.24 mm3, p < 0.05), right hemisphere amygdala volume (mean est: −292.37 mm3, p < 0.05), and right hemisphere hippocampal volume (mean est: −544.55 mm3, p = 0.05) showed negative correlations with higher PM2.5 concentrations (Table 4 and Table S5). Model 4 showed results similar to those obtained for Model 3 despite the consideration of individual cerebral volumes (Table 4 and Table S6).

3.4. Hierarchical Clustering Results

Hierarchical clustering was performed after removing the effects of 10 individual characteristics (e.g., height, weight, gender, and education level). This analysis identified three distinct clusters—Groups A, B, and C—which were characterized by different combinations of neuroanatomical features, including cortical thickness, cortical surface area, and subcortical volume (Figure 2). Of the 23 participants, Groups A, B, and C comprised 9 (39.1%), 10 (43.5%), and 4 (17.4%) individuals, respectively.
When comparing MRI-derived metrics across groups, Group A exhibited the lowest mean cortical thickness (mean z-score: Group A = −0.93, Group B = 0.46, Group C = 0.94), whereas Group C showed the lowest cortical surface area (left hemisphere: Group A = −0.08, Group B = 0.64, Group C = −1.43; right hemisphere: Group A = −0.04, Group B = 0.62, Group C = −1.45). Subcortical volumes also varied by region. Group C generally exhibited lower volumes across multiple subcortical structures. Regarding environmental exposure, Group C was characterized by the highest average concentrations of indoor air pollutants, including PM2.5, PM10, and CO2 (Table S7).
A second clustering analysis was conducted after additionally adjusting for total intracranial volume (eTIV), in combination with the original 10 individual characteristics. This resulted in four groups—Groups A, B, C, and D—each reflecting distinct profiles driven primarily by variations in cortical thickness, subcortical volume, cortical surface area, and a combination of surface area and volume, respectively (Figure 3). The number of participants in Groups A, B, C, and D were 3 (13.0%), 11 (47.8%), 5 (21.7%), and 4 (17.4%), respectively.
Group C showed the lowest mean cerebral cortex thickness (Group A = 0.57, Group B = 0.66, Group C = −1.37, Group D = −0.52), whereas Group A presented the lowest cortical surface area (left hemisphere: Group A = −1.24, Group B = 0.28, Group C = −0.41, Group D = 0.69; right hemisphere: Group A = −1.47, Group B = 0.25, Group C = −0.25, Group D = 0.73). Regarding subcortical structures, the volumes of the nucleus accumbens, hippocampus, and caudate nucleus were lowest in Groups D, A, and C, respectively. The volumes of the amygdala, globus pallidus, and putamen showed varied trends depending on environmental exposure levels. Notably, Groups A and D exhibited higher average exposures to PM10 and CO2 compared to the other groups (Table S7).
Overall, distinct characteristics were observed across clusters when considering both environmental exposure levels and neuroanatomical patterns. Before ICV correction, Group C exhibited the highest concentrations of indoor air pollutants and generally lower subcortical volumes. After ICV correction, Groups A and D showed relatively higher levels of PM10 and CO2, which were associated with reduced cortical surface area in Group A and decreased subcortical volumes in Group D.
These trends are summarized descriptively in the main text, and detailed values for pollutant concentrations and brain structure metrics are provided in Supplementary Tables S7 and S8.

4. Discussion

In this study, we monitored the concentrations of indoor air pollutants in the residential spaces of 23 individuals aged 65 years and older, and thereafter investigated the effects of these indoor air pollutants on brain structure before and after brain volume correction using a multiple regression model and hierarchical clustering.
Higher PM2.5 and PM10 concentrations were negatively associated with brain-related indicators. Specifically, PM2.5 had an effect on left and right hemisphere hippocampal volume (p < 0.05), and CO2 was found to affect left hemisphere amygdala volume, right hemisphere amygdala volume, and right hemisphere hippocampal volume (p < 0.05).
These relationships between indoor air pollutant levels and brain structure are consistent with those reported in previous studies. Balboni et al. [49] found that exposure to nitrogen dioxide, PM10, and PM2.5 decreased the hippocampal volume in adults, and particularly, under high PM2.5 concentrations, hippocampal volume decreased further. Similarly, Cho et al. [33] observed that a 10 µg/m3 increase in PM10 concentration was associated with a 55.4 mm3 decrease in hippocampal volume. The hippocampus is one of the areas of the brain responsible for learning and remembering new information, and a decrease in its volume causes a deterioration in cognitive function, leading to neurological disorders, such as dementia [50]. Alzheimer’s disease, a neurodegenerative condition, has also been associated with exposure to air pollutants, such as nitrogen dioxide and PM10, and the hippocampus is the first part of the brain that reportedly shrinks as symptoms of Alzheimer’s disease progress [11,51].
Previous studies on the correlation between CO2 concentration and brain structure have shown that high CO2 concentrations affect the functioning of brain structures, such as brain blood vessels and the amygdala [52,53,54,55]. In a study involving rats [56], long-term exposure to CO2 not only influenced brain structures (e.g., hippocampus, prefrontal cortex, and amygdala), but it also lowered cognitive and decision-making functions [57,58]. In a cohort study involving 24 participants, Allen et al. [59] reported an association between higher indoor CO2 concentrations and a lower cognitive function. The mean concentration of CO2 reported in Allen’s study was 500~1400 ppm, similar to that obtained in the current study.
Recent advances in automated brain imaging techniques have contributed considerably to the growing body of knowledge about the brain’s structure. Brain structure varies according to demographic variables, such as sex, region, age, height, and weight [60,61,62]. In the current study, a comparison of individual cerebral volumes pre- and post-correction (estimated total intracranial volume, eTIV) in line with brain imaging techniques indicated no statistical differences with respect to these variables. Barnes et al. [60] reported a negative association between cortical thickness and intracranial volume; however, this association disappeared after correcting for age and sex [61]. Individual cerebral volumes and eTIVs are affected by demographic variables [27,62,63,64,65].
In the current study, hierarchical clustering was conducted to examine the relationship between brain structure and environmental factors by clustering research targets based on MRI variables. Hierarchical clustering is a maximum similarity data grouping method, and its strength lies in its ability to facilitate the identification of associations between groups [66]. Based on our results without correcting for cerebral volume, three groups relating to cerebral cortex thickness (Group A), cerebral surface area (Group B), and cerebral volume (Group C), were identified. However, after cerebral volume was corrected for, hierarchical clustering resulted in four groups, Groups A, B, C, and D, relating to cerebral cortex thickness, cerebral surface area, cerebral volume, and cerebral surface area and cerebral volume, respectively. In the clustering results that were not corrected for cerebral volume, Group C had the lowest cerebral surface area and was exposed to higher CO2 concentrations than the other groups. This observation implies that environmental factors can markedly influence brain surface area. In the clustering results that were corrected for cerebral volume, Groups C and A had the lowest cerebral cortex thickness and cerebral surface area, respectively. The smallest cerebral surface area and the higher CO2 concentration observed in Group A relative to the other groups implies that CO2 may affect brain structure. Consistent with this conclusion, Gellhorn [67] suggested that CO2 destroys neurons in the cortex and hypothalamus, and noted that long-term exposure to high concentrations of CO2 can temporarily pause cerebral cortex function.
This study suggests that indoor air pollutants such as PM2.5, PM10, and CO2 may negatively affect brain structure in older adults, consistent with previous epidemiological and experimental findings [17,18,68,69]. While this study focused on indoor air quality, the potential infiltration of outdoor air pollutants into indoor environments should also be considered, particularly in densely populated urban areas such as Seoul and Incheon, where various environmental factors coexist [70,71]. Older adults may be especially vulnerable to the adverse health effects of such exposures [72]. Among the key biological mechanisms involved, neuroinflammation refers to an inflammatory response triggered by the disruption of central nervous system homeostasis due to external stimuli. This process involves microglial activation and the release of pro-inflammatory cytokines, which can impair brain structure [73,74]. Block and Kim [75] found that exposure to air pollutants induced neuroinflammation, ultimately leading to neuronal damage and brain atrophy. In addition, oxidative stress and disruption of the blood–brain barrier (BBB) may serve as important interconnected mechanisms. Although these are distinct biological processes, they are closely linked in the context of air-pollution-induced neurotoxicity [76]. Oxidative stress has been shown to disrupt the integrity of the BBB by impairing endothelial tight junctions and increasing permeability, thereby allowing peripheral toxins to infiltrate the brain. These effects were demonstrated in both 2D and 3D in vitro BBB models [77], supporting a potential mechanism linking air pollution to neuroinflammation and structural brain changes.
This study had certain limitations. First, only individuals who participated in the EPINEP study were included; hence the small sample size and short research period limit the representativeness of the study in considering the entire population. To compensate for this limitation, we installed IoT equipment in each participant’s home to monitor and collect data on indoor air pollutants and to identify patterns in the activities of these individuals. We noted that the participants stayed indoors for more than 80% of the day and were also involved in various activities, such as household chores, social gatherings, workouts, and religious activities, similar to observations made in previous studies [78,79,80]. Nevertheless, the small sample size also limited the statistical power of the regression models, resulting in only a few statistically significant associations between indoor CO2 levels and selected brain MRI indicators. Second, given the cross-sectional nature of this study, only participants without neurological disorders were included, which limited the ability to capture changes over time. Therefore, future follow-up studies with an expanded scope will be necessary to examine long-term trends.
Third, although indoor exposure was estimated using IoT devices, the non-overlapping monitoring periods in the Incheon and Seoul cohorts limited between-cohort comparisons and seasonal adjustment. Hierarchical clustering was used to explore within-cohort heterogeneity, but this was not a substitute for seasonal confounding control. However, both cohorts had measurement durations exceeding three months, pollutant concentrations remained within regulatory limits, and brain structural measures were not expected to be highly sensitive to short-term fluctuations, so these intervals were considered reasonably stable exposure estimates. Future studies should aim for overlapping measurement periods and incorporate explicit seasonal adjustment and sensitivity analyses.
Given that the primary aim of this study was to analyze the correlation between indoor air pollutants and brain structure before and after brain volume correction, this difference likely had a negligible effect on the results obtained. Fourth, in this study, we analyzed the association between indoor air pollutants and brain-related indicators while making brain volume corrections. Thus, the results before and after correction showed similar trends, with no statistically significant differences detected in our analyses.
Some studies have indicated that variables reflecting head size, such as eTIV, may influence research outcomes, but evidence in this regard is still lacking [27,60,65,81,82]. Therefore, further research with expanded research targets is necessary.
Taken together, the findings of this study indicate that indoor air pollutants markedly affect brain structure (surface area, cortex thickness, and volume), and environmental factors are therefore associated with neurological disorders. Considering that environmental factors are modifiable, follow-up studies supplementing the limitations of this study may yield valuable results.
Given that older adults spend most of their time indoors and are physiologically more vulnerable to environmental stressors [83,84], the findings of this study highlight the urgent need to establish IAQ standards specifically tailored to elderly residential settings. Although many countries have implemented IAQ management guidelines for elderly-concentrated facilities such as nursing homes, regulatory frameworks for general residential environments remain limited [85,86,87]. Therefore, establishing regular IAQ monitoring systems in real-life residential spaces and implementing continuous education programs to raise awareness of indoor air quality are necessary [88,89]. These efforts could help preserve cognitive health in older adults and promote health equity from an environmental public health perspective.

5. Conclusions

In conclusion, our findings suggest that exposure to indoor air pollutants, including PM2.5, PM10, and CO2, may contribute to structural alterations in the brain regions involved in cognition and emotional regulation among older adults. These results highlight the need to develop targeted IAQ standards and implement continuous monitoring systems in elderly residential environments. Future longitudinal studies are warranted to validate these associations and to support public health strategies aimed at mitigating environmental risks to brain health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci15080868/s1, Table S1. Comprehensive Summary Statistics of New Environmental Variables; Table S2. Comprehensive Summary statistics of magnetic resonance imaging variables; Table S3. Association between indoor air pollutants and brain-related indicators without adjustment for cerebral volume (Model 1); Table S4. Association between indoor air pollutants and brain-related indicators after correction for cerebral volume (Model 2); Table S5. Association between indoor air pollutants and brain-related indicators without adjustment for cerebral volume (Model 3); Table S6. Association between indoor air pollutants and brain-related indicators after correction for cerebral volume (Model 4); Table S7. Summary Statistics of Magnetic Resonance Imaging Variables Using Hierarchical Clustering Groups; Table S8. Summary Statistics of Indoor Air Pollutants Using Hierarchical Clustering Groups.

Author Contributions

S.-M.A.: Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing—original draft and review and editing. H.-H.K.: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing—original draft and review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Korea Environmental Industry & Technology Institute (KEITI) through Core Technology Development Project for Environmental Diseases Prevention and Management, funded by Korea Ministry of Environment (MOE) (RS-2022-KE002233).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Yonsei University Health System (Approval No. 4-2022-0418; 25 May 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The data are not publicly available due to ethical and privacy restrictions, but are available from the corresponding author upon reasonable request.

Acknowledgments

This research was supported by Seokyeong University in 2023.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BMIBody Mass Index
BBBBlood–Brain Barrier
CO2Carbon Dioxide
EPINEFEnvironmental Pollution-Induced Neurological Effects
IAQIndoor Air Quality
IoTInternet of Things
MRIMagnetic Resonance Imaging
PM10Particulate Matter With Aerodynamic Diameter ≤ 10 µm
PM2.5Fine Particulate Matter With Aerodynamic Diameter ≤ 2.5 µm
VOCsVolatile Organic Compounds
WHOWorld Health Organization

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Brainsci 15 00868 g001
Figure 1. Flow diagram showing the process used to select study participants.
Figure 1. Flow diagram showing the process used to select study participants.
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Figure 2. Heatmap showing hierarchical clustering results without correction for intracranial volume.
Figure 2. Heatmap showing hierarchical clustering results without correction for intracranial volume.
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Figure 3. Heatmap showing hierarchical clustering with correction for intracranial volume.
Figure 3. Heatmap showing hierarchical clustering with correction for intracranial volume.
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Table 1. Characteristics of study participants according to study design.
Table 1. Characteristics of study participants according to study design.
VariablesTotal (Seoul + Incheon)SeoulIncheon
N = 23N = 14N = 9
Age, mean ± SD (years)
<7511 (47.8)8 (57.1)3 (33.3)
≥7512 (52.2)6 (42.9)6 (66.7)
Height, mean ± SD (cm)156.2 (7.7)159.5 (7.6)151.2 (4.8)
Weight, mean ± SD (kg)61.2 (7.4)62.9 (7.7)58.6 (6.4)
Gender, N (%)
Male7 (30.4)7 (50)0 (0)
Female16 (69.6)7 (50)9 (100)
Education, N (%)
High school graduate or below5 (21.7)3 (21.4)2 (22.2)
High school graduate or above18 (78.3)11 (78.6)7 (77.8)
Body mass index (BMI), mean ± SD (kg/m2)
<2513 (56.5)8 (57.1)5 (55.6)
≥2510 (43.5)6 (42.9)4 (44.4)
Angina pectoris, N (%)
Without diabetes19 (82.6)11 (78.6)8 (88.9)
With diabetes4 (17.4)3 (21.4)1 (11.1)
Hypertension, N (%)
Without diabetes6 (26.1)4 (28.6)2 (22.2)
With diabetes17 (73.9)10 (71.4)7 (77.8)
Hyperlipidemia, N (%)
Without diabetes9 (39.1)3 (21.4)6 (66.7)
With diabetes14 (60.9)11 (78.6)3 (33.3)
Diabetes, N (%)
Without diabetes18 (78.3)9 (64.3)9 (100)
With diabetes5 (21.7)5 (35.7)0 (0.00)
SD, standard deviation. Values for continuous variables are presented as mean (SD); values for categorical variables are presented as sample size and percentages. Data are presented as mean ± standard deviation or N (%). High school graduate or below includes high school graduates and those who dropped out before completing primary or secondary education. Age categories were defined using a 75-year cutoff for subgroup analysis purposes. All participants were aged 65 years or older at enrollment.
Table 2. Summary statistics of new environmental variables.
Table 2. Summary statistics of new environmental variables.
VariableMeanMinMaxMedianIQR
PM2.5 (µg/m3)17.991.00196.8013.4813.52
PM10 (µg/m3)24.071.07324.1518.8818.40
CO2 (ppm)791.59295.292554.54688.44427.42
Table 3. Association between indoor air pollutants and brain-related indicators before and after correction for cerebral volume (Models 1 and 2).
Table 3. Association between indoor air pollutants and brain-related indicators before and after correction for cerebral volume (Models 1 and 2).
VariablesModel lModel 2
Hippocampal Volume by Hemisphere
LeftRightLeftRight
Estimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-Value
CO2min−2.83(−5.73, 0.06)0.05−3.29(−5.55, −1.03)0.01−2.29(−4.85, 0.26)0.07−2.88(−4.90, −0.86)0.01
mean−0.88(−1.74, −0.02)0.05−0.86(−1.62, −0.10)0.03−0.77(−1.49, −0.04)0.04−0.77(−1.43, −0.10)0.03
max−0.32(−0.76, 0.13)0.15−0.33(−0.72, 0.05)0.08−0.25(−0.64, 0.14)0.19−0.28(−0.63, 0.07)0.11
gmean−1.02(−1.94, −0.10)0.03−0.99(−1.80, −0.19)0.02−0.88(−1.66, −0.11)0.03−0.88(−1.59, −0.17)0.02
Model 1 shows unadjusted results, while Model 2 presents results adjusted for cerebral volume based on Model 1; Bold values indicate statistical significance (p < 0.05).
Table 4. Association between indoor air pollutants and brain-related indicators before and after correction for cerebral volume (Models 3 and 4).
Table 4. Association between indoor air pollutants and brain-related indicators before and after correction for cerebral volume (Models 3 and 4).
VariablesModel 3Model 4
Hippocampal Volume by AmygdaloidHippocampal Volume by HippocampusHippocampal Volume by AmygdaloidHippocampal Volume by Hippocampus
LeftRightRightLeftRightRight
Estimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-ValueEstimate95%CIp-Value
PM2.5mean−283.24(−496.78, −69.71)0.01−292.37(−549.47, −35.27)0.03−544.55(−1088.22, −0.88)0.05−272.09(−449.34, −94.85)0.01−276.69(−463.52, −89.86)0.01−516.53(−971.50, −61.56)0.03
gmean−79.35(−345.08, 186.39)0.52 −67.80(−371.21, 235.60)0.63−21.78(−643.27, 599.71)0.94−57.85(−304.20, 188.50)0.61−37.67(−294.54, 219.20)0.75 33.36 (−523.34, 590.07)0.90
Model 3 shows unadjusted results, while Model 4 presents results adjusted for cerebral volume based on Model 3; Bold values indicate statistical significance (p < 0.05).
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An, S.-M.; Kim, H.-H. The Association Between Indoor Air Pollutants and Brain Structure Indicators Using eTIV-Adjusted and Unadjusted Models: A Study in Seoul and Incheon. Brain Sci. 2025, 15, 868. https://doi.org/10.3390/brainsci15080868

AMA Style

An S-M, Kim H-H. The Association Between Indoor Air Pollutants and Brain Structure Indicators Using eTIV-Adjusted and Unadjusted Models: A Study in Seoul and Incheon. Brain Sciences. 2025; 15(8):868. https://doi.org/10.3390/brainsci15080868

Chicago/Turabian Style

An, Sun-Min, and Ho-Hyun Kim. 2025. "The Association Between Indoor Air Pollutants and Brain Structure Indicators Using eTIV-Adjusted and Unadjusted Models: A Study in Seoul and Incheon" Brain Sciences 15, no. 8: 868. https://doi.org/10.3390/brainsci15080868

APA Style

An, S.-M., & Kim, H.-H. (2025). The Association Between Indoor Air Pollutants and Brain Structure Indicators Using eTIV-Adjusted and Unadjusted Models: A Study in Seoul and Incheon. Brain Sciences, 15(8), 868. https://doi.org/10.3390/brainsci15080868

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