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26 May 2026

19 Pages

Short-Term Effects of Indoor Infiltration Exposure to Particulate Matter and Ozone on Mortality Risk

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1
Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
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Department of Otolaryngology Head and Neck Surgery, Peking University Third Hospital, Beijing 100191, China
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Non-Communicable Chronic Disease Control and Prevention Institute, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
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School of Public Health, Shanxi Medical University, Taiyuan 030001, China
Buildings2026, 16(11), 2129;https://doi.org/10.3390/buildings16112129
This article belongs to the Section Building Energy, Physics, Environment, and Systems
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Abstract

People spend the majority of their time indoors; however, most previous studies on the health effects of particulate matter (PM) and ozone (O3) have used outdoor concentrations as a proxy for personal exposure, which may introduce misclassification bias. Since indoor PM and O3 originate primarily from outdoors, estimating their indoor infiltration levels provides a closer approximation of true personal exposure. This study used data on approximately four million deaths occurring over an eight-year period in Jiangsu Province, China. The infiltration factor method and time-series analysis were employed to assess the linear and nonlinear associations of short-term indoor exposure to outdoor-origin PM1, PM2.5, PM10, and O3 with all-cause, cardiovascular, and respiratory mortality. In addition, the interactions between indoor PM and O3 were investigated. The results indicate that indoor exposure to outdoor-origin PM and O3 was positively associated with mortality, and these associations were stronger than those observed for direct outdoor exposure. Each 10 μg/m3 increase in the 2-day moving average concentration of indoor PM1, PM2.5, PM10, and O3 was associated with a 1.82% (95% confidence interval [CI]: 1.64, 2.01), 1.02% (95% CI: 0.91, 1.13), 0.69% (95% CI: 0.62, 0.77), and 1.79% (95% CI: 1.60, 1.99) increase in all-cause mortality, respectively. No threshold was observed in the exposure-response associations. Furthermore, significant multiplicative and additive interactions were identified between infiltrated PM and O3. Consequently, greater attention should be directed toward indoor air quality, particularly the coordinated management of combined exposure to indoor PM and O3, in order to better protect public health.

1. Introduction

The detrimental health effects of outdoor air pollution have been extensively documented [1]. In 2021, ambient air pollution became the leading risk factor for the global burden of disease [2]. Among various air pollutants, particulate matter (PM) and ozone (O3) have attracted significant attention due to their widespread prevalence and substantial adverse impacts. For example, in 2017, approximately 1.24 million deaths in China were attributable to air pollution, of which 851,660 were caused by PM2.5 and 178,187 by O3 [3]. Numerous studies have demonstrated that short-term exposure to ambient PM and O3 is positively associated with adverse health outcomes [4,5,6,7,8]. As complex pollution involving ambient PM and O3 becomes increasingly prominent [9], and their synergistic effects exacerbate adverse health impacts [10], control strategies targeting these two air pollutants are receiving growing attention.
People spend 90% of their time indoors [11]; however, most previous studies on the health effects of PM and O3 have used outdoor pollutant concentrations as a proxy for personal exposure [4,5,6]. This approach may lead to misclassification bias, thereby distorting the estimated exposure-health associations. For example, a large-scale study in China developed and validated a prediction model for indoor PM2.5 concentration by incorporating ambient PM2.5 level and time-activity patterns to estimate personal exposure [12]. The reconstructed personal exposure to PM2.5 was associated with a 128% greater effect on all-cause mortality compared with exposure based on outdoor concentration alone [12]. Although sensors can accurately monitor indoor PM and O3 concentrations, their high labor and material costs hinder large-scale population studies [13]. It is noteworthy that 95% of indoor PM1, 65% of PM2.5, 50% of PM10, and 74% of O3 originate outdoors [14,15]. Ambient pollutants can infiltrate buildings, and the infiltration factor (Finf) quantifies the proportion of outdoor pollutants that enter and remain suspended indoors [16]. Finf depends on the air exchange rate (AER), the penetration factor (Fp), and the first-order rate constant (k) [17]. Estimating indoor infiltration concentrations from outdoor levels and Finf may better approximate actual personal exposure than using outdoor concentrations alone, and this approach is also more feasible.
At present, the application of the infiltration factor method in epidemiological research remains very limited [16,18,19,20,21]. One study aggregated data on building infiltration factors and effect estimates of outdoor PM on mortality risk from different countries and regions worldwide [16]. Using a log-linear exposure-response model, it estimated the impact of indoor exposure to outdoor-origin PM on all-cause and cause-specific mortality [16]. The results indicated that for every 10 μg/m3 increase in indoor concentration of outdoor-origin PM10, all-cause, cardiovascular, and respiratory mortality among European populations increased by 1.20%, 1.80%, and 2.60%, respectively [16]. Another study, applying a burden of disease approach, estimated that indoor exposure to PM2.5 of outdoor origin was responsible for approximately 734,696 premature deaths in mainland China in 2018, accounting for 63.1% of total deaths attributable to ambient PM2.5 [21]. Regarding the health effects of indoor exposure to ambient O3, one study reported that indoor exposure to O3 originating outdoors caused 70,800 premature deaths in urban China in 2015 [20].These studies, however, focused only on the linear and independent impact of indoor exposure to outdoor-origin PM or O3 on mortality risk. They did not explore nonlinear effect, potential synergistic interactions between these pollutants, or susceptible populations.
Therefore, this study aimed to examine the linear and nonlinear associations of indoor exposure to outdoor-origin PM and O3 with the risk of non-accidental all-cause, cardiovascular, and respiratory mortality, and to investigate the potential interaction between indoor PM and O3. The findings may provide scientific evidence for the coordinated control of indoor PM and O3 pollution and the formulation of relevant policies.

2. Materials and Methods

2.1. Study Population

Our study included individuals from Jiangsu Province, China, who died of non-accidental all-cause between 1 August 2014, and 31 December 2021. Jiangsu is an economically developed province on China’s eastern coast, covering 107,200 km2 and encompassing13 prefecture-level cities and 96 counties. At the end of 2021, its population was 85.1 million. We obtained data from the Jiangsu Province Center for Disease Control and Prevention, including the underlying cause of death, date of death, residential address, and sociodemographic characteristics (gender, age, marital status, and educational level). In total, this study included 3,670,770 deaths, of which 1,528,567 were attributed to cardiovascular diseases and 451,258 to respiratory diseases.

2.2. Health Outcomes

The underlying cause of death was identified using codes from the 10th revision of the International Classification of Diseases (ICD-10). Non-accidental all-cause mortality was defined as A00-R99, which also included intentional self-harm (ICD-10 codes: X60-X84), since this outcome may be influenced by short-term air pollution [22]. Cardiovascular and respiratory mortality were coded as I00-I99 and J00-J98, respectively. These outcomes were determined by the local death registration system of Jiangsu Province.

2.3. Exposure Assessment of Indoor Exposure to Outdoor-Origin PM and O3

The infiltration factor (Finf) complies with the principle of mass balance and can be expressed as:
C in = C out   ×   F inf
where Cin and Cout represent the indoor and outdoor concentrations (μg/m3) of pollutants originating outdoors, respectively.
Based on the steady-state assumption, Finf can be estimated using Formula (2).
F inf = AER   ×   F P AER + k
where AER (h−1) is the air exchange rate; Fp is the penetration factor, representing the fraction of ambient pollutants that infiltrate through the building envelope; and k (h−1) is the first-order rate constant, accounting for pollutant reaction with surfaces and direct deposition.
Specific data on air exchange rates (AER) for individual counties or cities are currently unavailable. However, Jiangsu Province lies south of the Qin Mountain-Huai River (Q-H) demarcation line, which runs between 32° N and 34.5° N and corresponds to the January 0 °C isotherm in China. The Chinese thermal design code for civil buildings (promulgated in 1993) uses this isotherm to separate the hot summer/cold winter and hot summer/warm winter zones in the south from the cold and severe cold zones in the north [23]. Buildings in southern China are designed to be less airtight than those in the north, to ensure natural ventilation and thermal comfort. With reference to measurements conducted in Shanghai [24], which is also located in southern China, the AER with windows closed was set at 0.31 ± 0.26 h−1; while with windows open it was set at 4.38 ± 2.45 h−1. Based on the review by Diapouli et al. [25], under closed-window conditions, Fp for PM1, PM2.5, and PM10 were selected as 0.98 ± 0.02, 0.85 ± 0.02, and 0.76 ± 0.04, respectively; the corresponding k values were 0.10 ± 0.01 h−1, 0.17 ± 0.02 h−1, and 0.29 ± 0.04 h−1. The k value is independent of window state. For O3 under closed-window conditions, Fp and k were set to 0.54 ± 0.10 and 3.40 ± 1.30 h−1, respectively [20]. Under open-window conditions, Fp for all pollutants was set to 1 [24].
A field study was conducted in several naturally ventilated residential buildings in Jiangsu, with on-site monitoring carried out throughout each season. A regression equation was established to describe the association of window-opening probability with outdoor air temperature, relative humidity, wind speed, and PM2.5 concentration [26].
log P 1 − P = − 1.129 + 0.102   ×   T emp − 0.012   ×   RH − 0.065   ×   V − 0.0007   ×   C  
where P is the hourly probability of opening the window; Temp (°C), RH (%), and V (m/s) denote outdoor air temperature, relative humidity, and wind speed, respectively. These meteorological variables were obtained from the fifth generation of the European ReAnalysis (ERA-5)-Land dataset, with a spatial resolution of 9 km × 9 km (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download) (accessed on 8 November 2025) [27]. C represents the hourly average concentration of ambient PM2.5, which in this study was substituted with the daily average concentration. A previous study in China indicated that the probability of residents opening or closing windows between 1:00 and 5:00 is below 5% [28]. Therefore, window status (open or closed) was assumed to remain unchanged during this period. For each hour from 5:00 to 1:00 (next day), we calculated the hourly P based on the corresponding hourly values of Temp, RH, V, and C. A random number between 0 and 1 was generated each hour and compared to the calculated p value [20]. If P exceeded the random number, the window was considered open; otherwise, it was considered closed.
Indoor hourly concentrations of PM1, PM2.5, PM10, and O3 were then calculated using Formula (1). Because ambient PM concentrations are relatively stable within a day, hourly variations in PM were not considered. Daily ambient concentrations of PM1, PM2.5, and PM10 were obtained from the China High Air Pollutants (CHAP) dataset, with a spatial resolution of 1 km × 1 km (https://weijing-rs.github.io/product.html) (accessed on 8 November 2025) [29,30]. Given the significant hourly variability of ambient O3 and the lack of hourly O3 data in the CHAP dataset, hourly ambient O3 concentrations were sourced from the National Urban Air Quality Real-Time Publishing Platform of the China Environmental Monitoring Station (https://air.cnemc.cn:18007/) (accessed on 8 November 2025). The geographical locations of all monitoring stations in Jiangsu Province in 2021 are shown in Figure S1. Finally, the hourly concentrations of indoor PM and O3 were averaged for each day to obtain the daily mean indoor concentrations.
Differences in building characteristics, climatic conditions, and ventilation habits across Jiangsu Province mean that using fixed parameters may introduce systematic bias in the estimation of Finf and P. Following previous literature [20,24], the parameters (AER, Fp, and k) related to Finf and the daily window-opening time proportion derived from P were assumed to follow lognormal distributions. A Monte Carlo simulation approach was adopted, with 5000 samples drawn from the distribution of each parameter. The weighted Finf for window-opening time in each season was calculated to conduct relevant sensitivity analyses. The distribution characteristics of these parameters are presented in Table S1.

2.4. Statistical Analysis

2.4.1. Association of Indoor Exposure to PM and O3 Originating Outdoors with Mortality

Linear Association
We employed a two-stage analytical framework to evaluate the short-term effects of indoor exposure to outdoor-origin PM or O3 on non-accidental all-cause, cardiovascular, and respiratory mortality. In the first stage, a generalized additive model with quasi-Poisson regression was used to estimate the linear association between exposure and mortality in each county (“mgcv” package). The models included the following covariates: (1) long-term trend, modeled using a natural spline function with seven degrees of freedom (df) per year [4]; (2) day of the week (Monday to Sunday) [4]; (3) season, categorized as warm (March to November) and cold (January, February, and December) [31]; (4) average temperature for lag 0–3 days, using a natural spline function with 6 df [4,5,32,33,34]; (5) average relative humidity for lag 0–3 days, modeled with a natural spline function with 3 df [4,5,32,33,34]. In the second stage, random-effects meta-analysis was used to estimate the pooled association across Jiangsu Province (“metafor” package). Random-effects meta-analysis accounts for both within-group statistical error and between-group variability, and it is commonly applied in multi-regional studies to aggregate effect estimates [35]. We reported the percent change in mortality per 10 μg/m3 increase in pollutant concentration, along with 95% confidence interval (CI). We explored associations between indoor exposure and mortality across different exposure windows and select the window with the lowest generalized cross-validation (GCV) score for subsequent analyses. GCV is widely used for optimal model selection, subset selection in regression, and singular value truncation [36], and has been employed in previous studies for lag window selection [4,5].
Nonlinear Association
We employed a distributed lag nonlinear model combined with a natural spline function to investigate the nonlinear exposure-response association (“dlnm”, “splines”, and “mvmeta” packages). The nodes of the natural spline function were placed at the 25th, 50th, and 75th percentiles of the target pollutant concentration. We plotted exposure-response curves to examine the trend in mortality risk with indoor pollutant infiltration concentrations, focusing particularly on whether there was a significant increase in mortality risk when these concentrations were below the World Health Organization Air Quality Guidelines (WHO AQG) 2021 and the Chinese national standards (GB/T 18883-2022) [37,38].

2.4.2. Interaction of Short-Term Indoor Exposure to PM and O3 Originating Outdoors on Daily Mortality

Participants were stratified by the median daily indoor O3 concentration to examine its influence on the association between indoor PM and daily mortality. A two-stage analysis framework was employed to pool estimates of the multiplicative interaction between indoor exposure to PM of outdoor origin and O3 on daily mortality across counties. Additionally, we analyzed the additive interaction between short-term indoor exposure to outdoor-origin PM and O3. To ensure the representativeness of each subgroup, stratification was performed according to the median daily concentrations of indoor PM1, PM2.5, PM10, and O3. A generalized linear model (with county as a covariate) and quasi-Poisson regression were used to examine the additive interaction on daily mortality.

2.4.3. Subgroup Analysis

Participants were divided into subgroups according to gender (male or female), age (≤75 years or >75 years), marital status (married or other), educational level (junior school and below, or high school and above), season of death (warm or cold), and COVID-19 pandemic (yes or no). A two-stage analytical framework was also employed to estimate the short-term effects of indoor exposure to PM and O3 originating outdoors on daily mortality in each subgroup. Because the number of deaths in some subgroups was relatively small, a generalized additive model with zero-inflated Poisson regression was used in the first stage to avoid overfitting [39]. Then, a Z-test was used to compare the effect estimates across subgroups.

2.4.4. Sensitivity Analysis

In order to examine the robustness of our findings, we conducted the following sensitivity analyses. (1) To verify the reliability of the estimated indoor infiltration concentrations, we compared the window-opening-time-weighted Finf from this study with previous field measurements. Fan et al. measured indoor and outdoor PM2.5 concentrations in a small number of residential units without indoor sources in Nanjing, Jiangsu Province, in October 2015 (raw data are provided in Table S2) [40], from which we calculated the real-world Finf for PM2.5. Yang et al. monitored indoor and outdoor O3 concentrations in several student dormitories without indoor sources in Nanjing during July–August 2015 and reported the Finf for O3 [41]. We compared these field-measured Finf values with the corresponding parameters from our study and tested whether the differences were statistically significant. (2) To evaluate how variability in Finf and P-related parameters affects exposure estimates, we performed Monte Carlo simulation following previous studies to determine the bounds of indoor infiltration concentrations and the associated mortality risk [20,24]. Specifically, we generated 5000 samples from the distributions of AER, Fp, k, and window-opening time proportion. For each season in Jiangsu, we computed the 2.5th and 97.5th percentiles of indoor infiltration concentrations and then calculated the corresponding percent changes in mortality. (3) To avoid the potential influence of the inherent correlation between indoor infiltration concentrations and outdoor concentrations on effect comparisons, we further estimated the impact on daily mortality per interquartile range (IQR) increase in outdoor and indoor infiltration exposure, instead of using a fixed increment of 10 μg/m3. (4) To mitigate the possible impact of extreme values, we excluded records where indoor PM and O3 concentrations fell above the 99th percentile or below the 1st percentile within each county. (5) To compare the influence of different statistical approaches, we incorporated county as a covariate in the model instead of employing the two-stage analytical framework. (6) To avoid potential bias from the pollutant data source, we restricted the analysis to counties with ground monitoring stations and recalculated indoor infiltration exposure using the actual monitored PM concentrations rather than the CHAP dataset.
Statistical analyses were performed using R software, version 4.2.0 (R Development Core Team). All p values were two-sided, and statistical significance was defined as p < 0.05.

3. Results

3.1. Statistical Characteristics of Study Participants and Indoor Exposure

Among all-cause, cardiovascular, and respiratory mortality cases, the highest proportions were observed in individuals over 75 years of age, those with an education level of junior school and below, and those who were married. Additionally, a greater number of deaths occurred during the warm season. Males accounted for a higher proportion of all-cause and respiratory disease deaths, whereas females slightly outnumbered males in cardiovascular deaths. The sociodemographic characteristics of the study participants are detailed in Table 1. The cumulative and average daily deaths for all-cause, cardiovascular, and respiratory mortality in each county are presented in Table S3. Table S4 shows the daily average indoor exposure levels to outdoor-origin PM and O3, as well as outdoor temperature and relative humidity.
Table 1. Demographic characteristics of study participants by mortality category.

3.2. Association of Indoor Exposure to PM and O3 Originating Outdoors with Mortality

3.2.1. Linear Associations

We examined the linear associations of indoor exposure to outdoor-origin PM and O3 with non-accidental all-cause, cardiovascular, and respiratory mortality across various exposure windows. Among the windows tested, the 2-day moving average concentrations (covering the day of death and the preceding day, lag 0–1 days) of indoor PM1, PM2.5, PM10, and O3 yielded the lowest GCV scores (Table S5). Similarly, the 4-day moving average level (spanning the day of death and the three preceding days, lag 0–3 days) of ambient temperature and humidity showed lower GCV scores and larger effect estimates (Tables S6 and S7). Based on these findings and consistent with previous literature [4,32,33,34], we selected the 2-day moving average concentrations of indoor pollutants and the 4-day moving average levels of temperature and humidity for subsequent analyses.
We observed significant positive associations between the 2-day moving average concentrations of indoor PM and O3 and non-accidental all-cause, cardiovascular, and respiratory mortality (Figure 1). Detailed estimates are provided in Table S8. For example, each 10 μg/m3 increase in indoor concentrations of outdoor-origin PM1, PM2.5, PM10, and O3 was associated with increases in all-cause mortality of 1.82% (95% CI: 1.64, 2.01), 1.02% (95% CI: 0.91, 1.13), 0.69% (95% CI: 0.62, 0.77), and 1.79% (95% CI: 1.60, 1.99), respectively. Notably, the effect size of indoor PM exposure on daily mortality increased with decreasing particle diameter. Moreover, the effects of indoor exposure to outdoor-origin PM and O3 on all-cause, cardiovascular, and respiratory mortality risks were stronger than those of direct outdoor exposure.
Figure 1. Percentage changes in mortality for every 10 μg/m3 increase in the 2-day moving average concentrations of indoor and outdoor PM and O3.

3.2.2. Nonlinear Associations

The exposure-response associations between indoor exposure to PM1, PM2.5. PM10, and O3 originating outdoors and all-cause mortality were positive and showed no clear threshold (Figure 2A). The slopes of the curves for indoor PM1, PM2.5, and PM10 were steeper at lower concentrations. At higher concentration ranges, the exposure-response curve for indoor PM2.5 tended to flatten; while the curves for indoor PM1 and PM10 exhibited a decline, which disappeared after excluding extreme values (Figure S2). The curve for indoor O3 exposure and all-cause mortality continued to rise, with the slope becoming steeper as concentrations increased. The exposure-response curves for cardiovascular mortality were similar to those for all-cause mortality (Figure 2B).
Figure 2. (A) Pooled exposure-response curves for all-cause mortality. (B) Pooled exposure-response curves for cardiovascular mortality. (C) Pooled exposure-response curves for respiratory mortality. These figures display the pooled exposure-response curves for the 2-day moving average concentrations of indoor PM1, PM2.5, PM10, and O3 and daily all-cause, cardiovascular, and respiratory mortality. The red solid and dashed lines represent the pooled mortality estimates and their 95% CIs (left Y-axis); the gray histogram in the background shows the distribution of deaths across pollutant concentrations (right Y-axis). The blue dashed line indicates the indoor pollutant concentration limit from the World Health Organization Air Quality Guidelines (WHO AQG), and the purple dashed line indicates the limit from the Chinese national standards (GB/T 18883-2022). The WHO AQG limits for PM1, PM2.5, PM10, and O3 are 15, 15, 45, and 100 μg/m3, respectively; the GB/T 18883-2022 limits are 50, 50, 100, and 160 μg/m3 (the PM2.5 limit is used for PM1 due to the current lack of a specific standard).
Positive exposure-response associations were also observed between indoor exposure to outdoor-origin PM1, PM2.5, PM10, and O3 and respiratory mortality (Figure 2C). The curves showed a continuously increasing trend without a discernible threshold. At relatively low indoor PM concentrations, the curves exhibited steeper slopes. At higher concentrations, the exposure-response curves for indoor PM tended to flatten. In contrast, the curve for indoor O3 exposure and respiratory mortality continued to rise, with a steeper slope observed at higher O3 concentrations. In addition, positive associations of indoor exposure to outdoor-origin PM and O3 with all-cause, cardiovascular, and respiratory mortality were detectable even at levels well below the indoor air quality standards set by both the WHO and China.

3.3. Interaction Effects of Indoor Exposure to Outdoor-Origin PM and O3 on Mortality Risk

When participants were stratified by the median indoor O3 concentration, the association between indoor PM exposure and mortality was stronger among participants with higher indoor O3 exposure (Table 2). Short-term exposure to indoor PM and O3 exhibited a significant multiplicative interaction on daily all-cause, cardiovascular, and respiratory mortality (Table 2). Furthermore, combined exposure to indoor PM and O3 showed a significant additive interaction on these mortality outcomes (Tables S9–S11). Specifically, the risk of mortality was significantly higher among individuals simultaneously exposed to high concentrations of both PM and O3 indoors compared to those exposed to low concentrations of both pollutants.
Table 2. Multiplicative interaction of indoor PM and O3 exposure on different mortality categories.

3.4. Subgroup Analysis

Subgroup analyses indicated that sex, age, marital status, educational level, season, and the COVID-19 pandemic modified the associations of indoor exposure to PM and O3 originating outdoors with daily mortality (Figure 3 and Table S12). The effects of indoor PM exposure on all-cause, cardiovascular, and respiratory mortality were more pronounced in the warm season and during the COVID-19 pandemic. Stronger associations of indoor PM and O3 exposure with cardiovascular mortality were observed among females, individuals older than 75 years, and those with other marital status.
Figure 3. (A) Subgroup analysis of percentage changes in all-cause mortality for every 10 μg/m3 increase in indoor PM and O3 exposure. (B) Subgroup analysis of percentage changes in cardiovascular mortality for every 10 μg/m3 increase in indoor PM and O3 exposure. (C) Subgroup analysis of percentage changes in respiratory mortality for every 10 μg/m3 increase in indoor PM and O3 exposure. Footnotes: The p value represents the result of a Z-test comparing estimated effect values across subgroups. Other marital status includes unmarried, divorced, widowed, and unspecified. The warm season is defined as March through November; the cold season includes January, February and December. The COVID-19 pandemic period refers to 1 January 2020 onwards; the non-pandemic period refers to before 31 December 2019. Solid squares represent point estimates, and horizontal lines represent 95% confidence intervals. Arrows indicate that the limits of the confidence interval extend beyond the range of the x-axis.

3.5. Sensitivity Analysis

To verify the reliability of our method, we compared the window-opening time-weighted infiltration factors obtained in this study with previous field measurements. Fan et al. monitored indoor and outdoor PM2.5 concentrations in several residences without indoor sources in Nanjing, Jiangsu, in October 2015. Although the temporal and spatial coverage was limited, the measurement period and location fall within the scope of the present study, allowing a meaningful comparison. From their raw data, we calculated a PM2.5 infiltration factor (95% CI) of 0.63 (0.12, 1.14). The autumn PM2.5 infiltration factor (95% CI) in our study was 0.76 (0.54, 0.96) (Table S1). The overlapping confidence intervals indicate no statistically significant difference, supporting the reliability of our PM infiltration factor. Similarly, Yang et al. measured indoor and outdoor O3 concentrations in several student dormitories without indoor sources in Nanjing during July-August 2015, reporting an O3 infiltration factor (95% CI) of 0.42 (0.21, 1.00). This value is very close to the summer O3 infiltration factor of 0.43 (95% CI: 0.20, 0.70) in our study (Table S1), further confirming the accuracy of our O3 infiltration factor. We also evaluated the impact of variability in infiltration factor and window-opening probability parameters. The mortality risks associated with the 2.5th and 97.5th percentiles of indoor PM and O3 concentrations (Table S13) displayed patterns consistent with the main analysis: PM-related mortality risk increased with decreasing particle size, and indoor exposure risks remained higher than outdoor exposure risks. The percentage increases in all-cause, cardiovascular, and respiratory mortality per IQR increase in indoor concentrations of outdoor-origin PM and O3 remained higher than those for outdoor exposure (Table S14). Excluding the top and bottom 1% of indoor pollutant concentrations did not materially alter the results (Table S15). When indoor pollutant concentrations and daily mortality from all counties were incorporated into a generalized additive model with quasi-Poisson regression, adjusted for county as a covariate, similar association persisted (Table S16). Furthermore, recalculating indoor PM2.5 and PM10 concentrations with monitoring station data rather than the CHAP dataset did not substantially change the findings (Table S17).

4. Discussion

This study estimated indoor concentrations of outdoor-origin PM and O3 using the infiltration factor method. For the first time, we applied time-series analysis to examine the associations of short-term indoor infiltration exposure to PM1, PM2.5, PM10, and O3 with all-cause, cardiovascular, and respiratory mortality. Interactions between indoor PM and O3 were also explored. The results indicate that short-term indoor exposure to infiltrated PM and O3 significantly increases mortality risk, and that this effect is greater than that caused by direct outdoor exposure. The exposure-response curves demonstrate that the adverse effects of indoor infiltrated PM and O3 on mortality risk exhibit no threshold; positive associations with mortality were still observed even when indoor concentrations of PM and O3 fell below the indoor air quality standards set by the WHO and China. Furthermore, significant multiplicative and additive interactions were found between indoor PM and O3 on mortality risk.
We found that indoor exposure to PM and O3 of outdoor origin was significantly and positively associated with mortality risk, which is consistent with previous findings [12,16,18,19,20]. In this study, each 10 μg/m3 increase in indoor concentrations of outdoor-origin PM1, PM2.5, and PM10 was associated with increases in all-cause mortality of 1.82%, 1.02%, and 0.69%, respectively; indicating that the health hazards caused by indoor PM exposure increase as particle size decreases. A previous study reviewed the existing literature and integrated outdoor PM-related mortality estimates, building infiltration factors, and individual time-activity patterns into a model [16]. It reported that each 10 μg/m3 increase in indoor concentrations of outdoor-origin PM2.5 and PM10 corresponded to a 1.41% and a 0.81% increase in all-cause mortality in the U.S. population, which supports our results [16]. Smaller particles can penetrate deeper into lung tissue and persist longer; moreover, their larger relative surface area facilitates the adsorption of toxic heavy metals, acidic oxides, organic pollutants, or pathogenic microorganisms, thereby increasing their toxicity [42]. It is worth noting that the building envelope exerts a filtration effect on PM. Larger particles are more readily removed by impaction and gravitational settling when infiltrating through cracks and openings, whereas smaller particles, owing to their lower inertia, infiltrate more effectively [43,44]. Consequently, the fraction of PM infiltrating from outdoors to indoors is largely size-dependent, and the stronger impact of smaller particles on mortality reflects their higher infiltration efficiency. This study also found that the association between indoor exposure to PM and O3 and respiratory mortality was stronger than that for all-cause and cardiovascular mortality. For instance, each 10 μg/m3 increase in indoor PM2.5 concentration was associated with increases in all-cause, cardiovascular, and respiratory mortality of 1.02%, 1.08%, and 1.15%, respectively. Similarly, a study conducted in China reported that each 10 μg/m3 increase in personal PM2.5 exposure led to increases of 0.44%, 0.50%, and 0.59% in all-cause, cardiovascular, and respiratory mortality, respectively [12]. PM and O3 primarily enter the human body through the respiratory tract, initially causing airway damage and inflammatory responses, during which some pollutants may be cleared [45]. This may explain the relatively larger increase in respiratory mortality.
In this study, owing to the lack of data on ambient PM components, we differentiated particles only by size. Jiangsu Province is located in the Yangtze River Delta region of China, where the industrial sector is diverse, encompassing textiles, electronics manufacturing, petrochemicals, and other fields, resulting in varying chemical compositions of atmospheric PM across different areas, such as black carbon, nitrate, sulfate, and ammonium [46]. Previous studies have indicated that emissions from power plants, industry, and fugitive dust are the main sources of PM in Jiangsu [47], and black carbon is generally considered a particularly harmful component [48]. Although we conducted additional sensitivity analyses adjusting for county and the results did not change significantly, this adjustment may not fully reveal the impact of compositional differences in PM across various regions of Jiangsu on mortality risk. Further investigation is needed in future studies.
This study demonstrates that indoor exposure to PM and O3 originating outdoors had stronger effects on all-cause, cardiovascular, and respiratory mortality than direct outdoor exposure. One study applied exposure-response functions combined with adjusted mortality effect estimates to calculate the mortality burden attributable to PM2.5 exposure in the U.S. population [18]. The results indicated that indoor exposure to outdoor-origin PM2.5 accounted for the largest proportion of total exposure and was responsible for approximately 40–60% of total mortality [18]. Another study conducted in a Chinese population used the Global Burden of Disease approach and the infiltration factor method [21]. It estimated that indoor exposure to outdoor-origin PM2.5 caused 734,696 premature deaths, accounting for approximately 63.1% of total mortality [21]. The health impact of indoor exposure to outdoor-origin PM, estimated by accounting for infiltration and other factors, is approximately 10% higher than that estimated using only outdoor PM concentrations [21]. A further study in China found that the mortality risk associated with average exposure to both indoor and outdoor PM2.5 was higher than that associated with direct outdoor exposure alone, with the effect estimate approximately doubling [12]. These findings suggest that caution should be exercised when using outdoor concentration directly as a proxy for personal exposure, as this approach may underestimate the adverse health effects of air pollution. People spend the majority of their time indoors each day [11], which results in a higher indoor exposure dose of pollutants. Additionally, in indoor environments, PM can adsorb various semi-volatile organic compounds (SVOCs, such as polycyclic aromatic hydrocarbons, organophosphate flame retardants, and polybrominated diphenyl ethers) [49], while O3 can react with volatile organic compounds (VOCs) to form secondary pollutants [50], thereby increasing toxicity. However, based on the theoretical assumptions of this study, indoor pollutant concentrations are correlated with outdoor concentrations. The difference in their effect estimates for mortality risk may partly arise from differences in the value ranges caused by numerical transformation, rather than true epidemiological differences. Although we further calculated and compared the percentage changes in mortality associated with each IQR increase in indoor and outdoor exposure, caution is still warranted when interpreting these findings.
Although the non-linear association of outdoor exposure to PM and O3 with mortality has been extensively reported [4,5,51,52], evidence on the exposure–response association between indoor exposure to PM and O3 originating outdoors and mortality risk remains limited. Our study is the first to demonstrate that indoor infiltration exposure to PM and O3 is positively associated with all-cause, cardiovascular, and respiratory mortality without a discernible threshold. At higher concentration ranges, the exposure–response curves for indoor PM and all-cause and cardiovascular mortality tended to flatten or even decline. However, after excluding observations with indoor PM concentrations above the 99th or below the 1st percentile, the declining trend disappeared. This indicates that the abnormal fluctuations are partly attributable to data sparsity at extreme concentrations, which leads to unstable curve fitting. Additionally, a decrease in population sensitivity to pollutant exposure at high concentrations, manifesting as a saturation effect, may also play a role [5]. Interestingly, the risks of all-cause, cardiovascular, and respiratory mortality increased exponentially with rising indoor O3 concentrations. This may be attributed to the strong oxidizing property of O3, which promotes the formation of secondary pollutants indoors [53]. Even when indoor concentrations of PM and O3 are below the standards set by the WHO and China’s indoor air quality guidelines, they still pose health risks. This suggests that further tightening the limit standards for indoor PM and O3, and improving the infiltration resistance of buildings, would help protect public health.
This study is also the first to observe both multiplicative and additive interactions of co-exposure to indoor PM and O3 of outdoor origin on all-cause, cardiovascular, and respiratory mortality. Compared with a time-series analysis also conducted in Jiangsu, China, the multiplicative interaction effect of infiltrated PM and O3 on mortality was approximately 3 to 8 times greater than that of direct outdoor exposure [4]. Exposure to PM and O3 primarily induces health damage by triggering inflammatory responses, oxidative stress, and cytokine production [54]. O3 can enhance the reactivity of PM, promoting its aggregation and retention in the body [55]. The two pollutants may also interact synergistically, leading to increased mucin production and altered interferon-related gene expression [56]. The adverse effects of co-exposure to PM and O3 on the cardiovascular and respiratory systems have been confirmed in multiple toxicological studies [57]. For example, compared with exposure to O3 alone, co-exposure to PM and O3 increased total cell count, inflammatory cytokines, lactate dehydrogenase, and total protein levels in rat bronchoalveolar lavage fluid [57]. This suggested exacerbation of lung injury [57]. It also led to increases in epicardial and perirenal adipose tissue and elevated expression of nitric oxide synthase, indicating cardiovascular dysfunction [57]. Furthermore, pollutants commonly present in indoor environments, such as VOCs and SVOCs, may amplify the toxicity of PM and O3 through physical or chemical processes [49,50]; thereby enhancing the effects of their co-exposure.
The subgroup analyses indicated that the association between indoor PM exposure and mortality was stronger during the warm season and the COVID-19 pandemic period. Numerous studies have shown that low temperatures attenuate the association between PM exposure and mortality [4,5]. High temperatures in the warm season may increase respiratory rate and accelerate metabolism [58], leading to a relatively higher dose of pollution exposure. During the COVID-19 pandemic, people tended to pay more attention to opening windows for ventilation, which increased the infiltration of outdoor PM, and they also stayed indoors for longer periods, thereby increasing pollutant exposure. We also found that the effects of infiltrated PM and O3 on cardiovascular mortality were more pronounced among females, older adults (>75 years), and those without a partner, which is consistent with previous studies [4,12,54]. Females have smaller tracheal diameters and higher airway reactivity, making them more susceptible to indoor air pollution [59]. Older adults are more vulnerable because of reduced physiological resilience and the presence of multiple chronic comorbidities [60]. Additionally, the elderly population may experience declines in cognitive function and a reduced awareness and capacity to prevent indoor air pollution [61]. These factors may collectively render them more susceptible to the effects of indoor infiltration exposure to PM and O3. For individuals without a partner, increased social isolation and psychological stress may also contribute to greater susceptibility [62]. Therefore, in settings with limited intervention resources, priority should be given to improving indoor air quality for these vulnerable groups in order to reduce the mortality burden.
The strengths of this study include the analysis of linear and nonlinear associations between indoor exposure to outdoor-origin PM and O3 and daily all-cause, cardiovascular, and respiratory mortality, based on nearly four million deaths over an eight-year period, using the infiltration factor method and time-series analytical approaches. Furthermore, this study thoroughly investigated the interaction effects of indoor infiltrated PM and O3 exposure, identified vulnerable populations, and thereby addressed important gaps in previous research.
This study also has several limitations. First, the contributions of indoor sources to indoor PM and O3 concentrations were not considered. In addition to outdoor infiltration, indoor sources of PM include cooking and smoking, while indoor sources of O3 include photocopiers and laser printers. Neglecting these indoor sources may lead to an underestimation of total indoor pollutant concentrations and consequently an overestimation of the impact of indoor infiltration concentrations on mortality risk. However, in recent years, the widespread use of range hoods, the promotion of indoor smoking bans, and the relative rarity of equipment such as copiers in households have kept the contribution of indoor sources relatively low. Furthermore, a previous study simulating indoor PM10 concentration compared the predictive performance of original and adjusted exposure variables on daily mortality and found that considering only outdoor-origin PM10 still significantly improved the effect estimates [19]. Second, data are limited for parameters such as AER, Fp, k, and P, which may be influenced by building characteristics, meteorological conditions, and ventilation habits. We made every effort to select appropriate parameters, and the calculated season-specific infiltration factors showed close agreement with previous field measurements [40,41], with no statistically significant differences. Moreover, we used Monte Carlo simulation to assess the impact of parameter uncertainties and found that the results remained robust. Nevertheless, more detailed measurements of these parameters in future studies would be of considerable value. Third, as the study population was primarily from Jiangsu Province, China, extrapolation of our findings to other regions or populations should be approached with caution.

5. Conclusions

This study reveals that short-term indoor exposure to outdoor-origin PM and O3 has a greater impact on mortality risk than direct outdoor exposure, lacks a threshold, and exhibits a significant synergistic effect between the two pollutants. These findings indicate that indoor air quality standards should be more stringent than outdoor standards and that indoor pollutant concentration limits need to be further tightened. There is no safe lower limit for indoor PM and O3 concentrations; the closer the concentration approaches zero, the greater the associated health benefits. Outdoor pollutants can infiltrate indoors; improving building airtightness while enhancing indoor air filtration and ventilation will help protect public health. Furthermore, the synergistic control of PM and O3 has so far been addressed mainly in outdoor environments; the dual pollution of indoor PM and O3 urgently requires attention and management. The findings of this study thus provide scientific evidence for the revision of indoor air quality and healthy building standards, as well as for the formulation of relevant policies and measures for indoor pollution control.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/buildings16112129/s1, Figure S1: Regional distribution of environmental monitoring stations in Jiangsu Province in 2021; Figure S2: Pooled exposure-response curves for all-cause mortality excluding extreme data; Table S1: The distribution characteristics of infiltration factor-related parameters and the proportion of window-opening time for different pollutants in each season in Jiangsu Province; Table S2: Indoor and outdoor PM2.5 concentrations in several residences without indoor sources in Nanjing, Jiangsu Province, in October 2015; Table S3: Distribution of deaths from different mortality in each county during the study period; Table S4: The average daily levels of indoor pollutant concentrations and outdoor weather conditions in each county during the study period; Table S5: Percentage changes and generalized cross validations scores in all-cause mortality for indoor PM and O3 exposure at different lag days; Table S6: Estimated effects of indoor PM and O3 exposure at different temperature windows on all-cause mortality and generalized cross validation scores; Table S7: Estimated effects of indoor PM and O3 exposure at different humidity windows on all-cause mortality and generalized cross validation scores; Table S8: Percentage changes in different mortality for every 10 μg/m3 increase in the 2-day moving average concentrations of indoor and outdoor exposure to PM and O3; Table S9: Additive interaction of indoor exposure to PM1 and O3 on different mortality; Table S10: Additive interaction of indoor exposure to PM2.5 and O3 on different mortality; Table S11: Additive interaction of indoor exposure to PM10 and O3 on different mortality; Table S12: Subgroup analysis of percentage changes in different mortality for every 10 μg/m3 increase in indoor PM and O3 exposure; Table S13: Sensitivity analysis of the effect on mortality risk of each 10 μg/m3 increase in the interval bounds of indoor infiltration concentration, calculated by accounting for variations in infiltration factor-related parameters and window opening time; Table S14: Sensitivity analysis of percentage changes in all-cause, cardiovascular, and respiratory mortality for each interquartile range (IQR) increase in indoor and outdoor PM and O3 exposure; Table S15: Sensitivity analysis of percentage changes in all-cause, cardiovascular, and respiratory mortality for every 10 μg/m3 increase in indoor exposure to PM and O3, using dataset excluding extreme values; Table S16: Sensitivity analysis of percentage changes in all-cause, cardiovascular, and respiratory mortality for every 10 μg/m3 increase in indoor exposure to PM and O3 after adjustment of county; Table S17: Sensitivity analysis of percentage changes in all-cause, cardiovascular, and respiratory mortality for every 10 μg/m3 increase in indoor and outdoor exposure of PM using station data.

Author Contributions

Conceptualization and supervision, J.N. and S.H.; funding acquisition and investigation, S.H.; formal analysis, software, visualization and writing—original draft, H.W.; methodology, H.W., B.F. and S.H.; data curation, F.Z., R.H. and H.Y.; writing—review and editing, B.F., F.Z., R.H., H.Y., J.N. and S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Nos. 52478073, 52208092, 721990083).

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Acknowledgments

The authors thank the staff of the Cause of Death Surveillance Points of the Jiangsu Provincial Center for Disease Control and Prevention in China for their data support for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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