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Comparative analysis of ambient, in-home, and personal exposures reveals associations between breathing zone pollutant levels and asthma exacerbations in high-risk children

Respiratory Research volume 26, Article number: 40 (2025) Cite this article

Abstract

Background

Air pollution is associated with poor asthma outcomes in children. However, most studies focus on ambient or indoor monitor pollution levels. Few studies evaluate breathing zone exposures, which may be more consequential for asthma outcomes.

Methods

We measured personal exposures to NO2, O3, PM10 and PM10 constituents, including black carbon (BC), brown carbon (BrC), environmental tobacco smoke (ETS), endotoxins, and 𝛽-glucan, in a cohort of children with exacerbation-prone asthma for 72 h using wearable monitors. Personal exposures were compared to concentrations from in-home monitors in the child’s bedroom and ambient EPA air quality monitoring using correlation analyses. Personal exposures were tested for association with lung function and compared between participants with and without well-controlled asthma and signs of exacerbation in the prior 60 days using censored regression with robust standard errors.

Results

81 children completed 219 monitoring sessions. Personal NO2, O3, and PM10 exposures ranged from < 2 to 99.1 parts per billion (ppb), < 1.5 to 23.3 ppb, and < 1 to 141.9 𝜇g/m3, respectively. Personal endotoxin ranged from 0.04 to 101.3 EU/m3, 𝛽-glucan from 18.5 to 1,162 pg/m3, BC from < 0.3 to 46.9 𝜇g/m3, BrC from < 0.3 to 6.1 𝜇g/m3, and ETS from < 0.3 to 56.6 𝜇g/m3. Correlations between personal and ambient PM10, NO2, and O3 concentrations were poor, whereas personal PM10 and NO2 correlated with in-home concentrations. In-home monitoring less frequently detected BrC (Personal:79% > lower limit of detection, Home:36.8%) and ETS (Personal:83.7%, Home:4.1%) than personal exposures, and detected BC in participants without personal exposure (Personal: 26.5%, Home: 96%). Personal exposures were not significantly associated with lung function or daily asthma control. Children requiring corticosteroid treatment for asthma exacerbation within 60 days of exposure monitoring had 1.98, 2.21 and 2.04 times higher personal exposures to BrC (p < 0.001; 95% CI: 1.43–2.37), ETS (p = 0.007; 95% CI: 1.25–3.91), and endotoxin (p = 0.012; 95% CI: 1.14–3.68), respectively.

Conclusions

Although in-home monitoring was correlated with personal exposure to PM10 and NO2, in-home detection of ETS and BrC was not associated with personal exposures. Personal PM10 exposures in general, as well as BrC, ETS, and endotoxin levels were associated with recent childhood asthma exacerbations.

Clinical trial number

Not applicable.

Background

Asthma is one of the most prevalent diseases of childhood, impacting an estimated 6-million children in the United States [1]. For many, asthma can be effectively controlled using standard therapies; however, up to 18% of children with asthma have severe disease, characterized by frequent exacerbations that require emergency department visits, courses of systemic corticosteroids, and/or hospitalization [2]. These exacerbation-prone (EP) children experience the majority of the patient burden and economic costs associated with childhood asthma [3]. In addition, asthma exacerbations are associated with progressive loss of lung function, which puts children with EP-asthma at risk for chronic lung disease in adulthood [4, 5].

While viruses, particularly human rhinoviruses A and C, are implicated in the majority of childhood asthma exacerbations [6], non-viral exacerbations may be triggered by air pollution [7]. Additionally, air pollution may impair mucosal barrier function and phagocyte airway clearance, as well as alter adaptive immune responses, suggesting that air pollution could indirectly influence risk for exacerbations by increasing susceptibility to and harm from viral infections [8].

Several epidemiological studies found that peaks in emergency department and hospital admission for asthma coincide with fluctuations in ambient air pollution and that increased ambient pollutant levels are associated with exacerbation [9]. However, results are mixed in terms of which pollutants are involved [10,11,12,13,14,15]. Studies of the impact of ambient pollution on asthma symptoms and lung function have also been inconsistent, with some finding air pollution results in poor asthma outcomes [16,17,18], some finding no impact [19, 20], and others finding associations only for specific subgroups of children [21, 22].

One explanation for these conflicting results is measurement error from using community air quality monitoring to assess an individual’s exposure level [23]. Children spend a majority of their time indoors [24], and there can be substantial differences between indoor and ambient outdoor pollutant levels [25,26,27,28]. Therefore, there is growing interest in assessing the impact of indoor air pollution on asthma outcomes [19, 29,30,31]. However, indoor stationary monitors placed in a single location (usually a child’s bedroom) suffer from many of the same problems as ambient monitors [32,33,34], since children spend a significant portion of their time in school and physical activity can generate a “personal cloud” of increased particulate matter (PM) exposure that may not be captured by a stationary monitor [35]. Inaccurate measurements can bias results and mask associations between environmental exposures and asthma outcomes [23].

In this study, we investigate the role of personal exposures in asthma outcomes in an urban cohort of children with EP-asthma. Personal environmental exposures, including ozone (O3), nitrogen dioxide (NO2), and PM10 (particles less than 10 microns in diameter) were collected using wearable monitors to accurately characterize individual children’s breathing zone exposures. Further, we performed speciation analyses to measure PM10 constituents that have been associated with poor asthma outcomes, including environmental tobacco smoke (ETS) and endotoxins [36], fungal allergens as measured by \(\beta\)-glucans [37], and black carbon (BC) [38]. In addition, analyses for BC and ETS also provide information on brown carbon (BrC), a component of wildfire smoke. We describe factors associated with personal exposures, assess the agreement between personal exposure measurements and community ambient and in-home measures, and evaluate the relationship between personal exposures and asthma outcomes, including lung function, asthma severity, and exacerbation.

Methods

Cohort

ENIGMA was an observational study of children with EP-asthma conducted from June 2018 to September 2022 in the Denver metro area in Colorado. We recruited 100 children 8 to 16 years old with clinician-diagnosed asthma. Potential participants were identified through existing or recent research and/or clinical relationships at Children’s Hospital Colorado. We used a telephone script and recruitment form to determine their eligibility and willingness to participate. Initially, eligible children were required to have had at least one asthma exacerbation in the prior 12 months; however, eligibility criteria were modified in December 2020 to include children with at least one exacerbation in the prior 18 months to aid in recruitment during the COVID-19 pandemic. Exacerbation was defined as an asthma-related unscheduled visit to an emergency department, clinic, or urgent care facility; overnight hospitalization; or course of systemic corticosteroids. Individuals were excluded if they were self-reported active smokers, homeschooled, or had conditions that would interfere with the safety or performance of the study. Initially, the study included longitudinal seasonal assessments of environmental exposures, immediately followed by a clinical assessment of lung function and asthma status (Fig. 1). Due to COVID-19, we switched to a cross-sectional design with modifications to the exposure and clinical assessments in February 2021. The Colorado Multiple Institutional Review Board approved the protocol for the study. The participant and at least one legal guardian provided informed written consent and, if age-appropriate, assent.

Fig. 1
figure 1

Overview of the ENIGMA study design

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Environmental exposure assessments

Personal exposure monitoring was performed for approximately 72 hours prior to all scheduled study visits through May 2022. RTI MicroPEMs™ (Research Triangle Park, NC) collected filter and real-time PM10, temperature, humidity, and accelerometry measurements. Ogawa passive dosimeters (Ocala, FL) measured NO2 and O3 (summer only) exposure. Participants wore all devices in a belt bag placed diagonally across the chest such that the monitor inlet was within 10 inches of the nose and mouth [39, 40]. PM10 filters underwent gravimetric analysis and were then analyzed for BC, BrC, ETS, endotoxin and \(\beta\)-glucan (Supplement). Pre-pandemic participants (n = 49) also had in-home environmental monitors placed in their bedroom concurrent with their first personal monitoring session. While MicroPEMs™ collected PM10 at 0.4 Lpm on a 25 mm PTFE filter, in-home monitors collected PM10 at 4 L/min on 37 mm filters. Matching hourly air quality data (O3, NO2, and PM10) from the Denver Colorado Air Monitoring Program monitoring site were obtained from the Environmental Protection Agency’s Air Quality System API.

Clinical assessments

Clinical assessments included lung function measured by spirometry, asthma control, asthma severity, and questions on exacerbation signs in the previous 60 days, such as short-term systemic corticosteroid use and unscheduled health care visits for asthma problems, including emergency, urgent care, doctor and clinic visits. For children receiving asthma care at Children’s Hospital Colorado, dates of care were confirmed with the electronic medical record. Spirometry was performed per American Thoracic Society/European Respiratory Society standards [41] with a clinical spirometer (KoKo® Legend, nSpire Health™, Longmont, CO, USA). FEV1 and FVC measurements were taken before and after four 90 µg puffs of albuterol to assess bronchodilator response. Global Lung Initiative reference equations were used for spirometry values [42]. Asthma control was assessed with the validated Asthma Control Test (ACT) [43] for participants ages 12 years and older (5–25 scale), and the Childhood ACT (cACT) [44, 45] for participants ages 6–11 years (0–27 scale). Scores from both tests have been combined in asthma research in children across the school age range (6 to 17 years), with scores > 19 corresponding to well-controlled asthma [46]. Asthma severity was scored with the validated Composite Asthma Severity Index (CASI) on a scale of 0 (best) to 20 (worst); this index combines day and night symptoms and albuterol use, daily controller therapy, lung function, and exacerbations, with scores < 4 indicating mild asthma [47, 48].

Statistical methods

We compared log10(personal exposures) between participants with and without asthma control (cACT or ACT \(\:\ge\:\) 20 vs. <20), hospitalization in the 18 months prior to enrollment, and signs of exacerbation in the 60 days prior to their exposure assessment (systemic corticosteroid use or unscheduled health care visits for asthma problems), using censored regression models to account for observations below the limit of detection (LOD) and robust standard errors for repeated measures. As over 50% of BC measurements were below the LOD, we dichotomized BC concentrations into “Detected” vs. “Not Detected” and used generalized estimating equation (GEE) logistic regression models. Similar models were used to compare personal exposures between seasons (Summer: June-August; Fall: September-November; Winter: December-February; Spring: March-May) and to test for associations between subject characteristics and personal exposures. Pre-bronchodilator percent predicted FEV1, FVC, FEV1/FVC and the percent change in spirometry after bronchodilation were evaluated for association with the log10(exposures) using GEE models. All models controlled for age, sex, race-ethnicity, season (except O3) and monitor wearing compliance (percent of awake time spent wearing the monitor). Post-bronchodilation change models adjusted for the corresponding baseline lung function measure. A Benjamini-Hochberg correction was applied to the p-values across the eight exposures to control the false discovery rate (FDR) at 5%. Bland-Altman analyses were used to assess agreement between personal, in-home, and community exposure measurements. Linear mixed models were used to estimate the proportion of variability in personal exposures that could be attributed to seasonal variation, differences in wearing compliance, between participant differences, and the remaining within participant variability. Full details of all statistical analyses are available in the Supplement.

Results

ENIGMA cohort demographic and clinical characteristics

81 children with EP-asthma participated in personal environmental monitoring. Participants ranged from 8.2 to 16.7 years in age and 64% were male (Table 1). The majority reported Hispanic ethnicity (56.8%). 88.9% of participants had one or more unscheduled healthcare or emergency department visits, 86.4% had been prescribed oral corticosteroids, and 24.7% required an asthma-related hospitalization in the 18 months prior to enrollment. During the course of the study, 29.6% of participants reported one or more unscheduled asthma-related health care visits and 25.9% reported one or more courses of systemic corticosteroids for asthma in the 60 days prior to a study visit.

Table 1 Participant characteristics (N = 81). N(%) or median [IQR]
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Personal exposure to environmental pollutants is widespread and highly variable in children with EP-asthma in the ENIGMA cohort

219 personal monitoring sessions were conducted across the 81 participants. As study visits were paused during the first year of the COVID-19 pandemic (March 2020-February 2021), after which the study continued with a cross-sectional design, 26 participants had single time-point data. For those with longitudinal data, there was a median of 3 monitoring sessions per participant, with an average of 111 days between monitoring sessions (Median: 95 days; IQR: 85–112 days). Participants wore their personal monitors for a median of 72.1% (IQR: 53.2-88.4%) of their time awake.

99.5%, 98.5%, and 94.8% of samples had detectable PM10, NO2, and O3, respectively (Fig. 2A; Table 2). There was wide variation in exposure concentrations, with PM10 ranging from < 1 to 141.9 \(\mu\)g/m3, NO2, from < 2 to 99.1 parts per billion (ppb), and O3 from < 1.5 to 23.3 ppb (Fig. 2B).

Regarding PM10 constituents, all samples had detectable endotoxin and \(\beta\)-glucan, with endotoxin ranging from 0.04 to 101.3 EU/m3 and \(\beta\)-glucan concentrations ranging from 18.5 to 1,162 pg/m3. Detection rates were lower for other PM10 constituents with only 32.3%, 63.2%, and 66.7% of samples having detectable BC, BrC, and ETS respectively. BC, BrC and ETS ranged from < 0.3 to 46.9, < 0.3 to 6.1, and < 0.3 to 56.6 \(\mu\)g/m [3], respectively.

Table 2 Description of personal exposures by season. N(%) or median [IQR]
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Fig. 2
figure 2

Personal exposure concentrations in the ENIGMA cohort. A) Detection of exposures using personal monitoring. B) Box and whisker plots of personal exposures. Boxes indicate the first, second, and third quartiles of the distribution. Whiskers extend to 1.5 times the inter-quartile range. Dashed lines indicate lower limits of detection

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Seasonal variation in personal exposures

To understand if seasonal fluctuations could explain the wide variation in personal exposures, we compared concentrations of each pollutant between seasons (STable 1). Personal PM10 did not significantly vary by season (Fig. 3). In contrast, NO2 was lower in the summer compared to winter (20.1% lower, CI: 32.3–5.8% lower; FDR = 0.019); however, there was substantial overlap in the distribution of NO2 between seasons.

While total PM10 did not significantly differ between seasons, there were differences in PM10 constituents. ETS and BrC were lower in the summer compared to all other seasons. The largest difference was between summer and fall, with ETS and BrC concentrations 77.3% (CI: 87.2–59.6% lower; FDR < 0.0001) and 61.5% (CI: 73.9–43.1% lower; FDR < 0.0001) lower in the summer, respectively. BC followed a different pattern, with higher odds of detection in the summer compared to all other seasons (Odds Ratios: Summer v Spring 7.48 (2.45 to 22.82; FDR = 0.0009); Summer v Winter 2.71 (1.26 to 5.82; FDR = 0.019); Summer v Fall 2.99 (1.36 to 6.56; FDR = 0.015)). Endotoxin and \(\beta\)-glucan did not significantly differ between seasons.

Fig. 3
figure 3

Distributions of personal exposures by season. Boxes indicate the first, second, and third quartiles of the distribution. Whiskers extend to 1.5 times the inter-quartile range. Stars indicate significant differences between seasons (FDR adjusted p-value < 0.05)

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Personal exposures vary between children and within children over time

Since there was overlap in the distribution of exposures between seasons, we considered whether the wide range in exposure concentrations was more reflective of differences in environments encountered by different participants (between participant variation) or changes in the environment encountered by the same participants (within participant variation). We observed large differences in average PM10 concentrations between participants, with some participants having average exposures less than 10 \(\mu\)g/m3 and others having average exposures nearly 10-fold higher (Fig. 4A). However, there was also wide variation in PM10 concentrations within individual participants, with an average 2.8-fold difference between a participant’s highest and lowest PM10 measurement. Similar patterns can be seen for NO2 (Fig. 4B) and other exposures (SFigure 1).

To formally quantify this, we used linear mixed models to estimate the proportion of variability in exposures that could be attributed to seasonal variation, differences in wearing compliance, between participant differences, and the remaining within participant variability (Fig. 4C). Season explained a relatively small proportion of the variability, ranging from 1% for PM10 to 14% for ETS, as did wearing compliance (range: 1% for BC to 9% for PM10).

Between participant differences accounted for a more substantial proportion of the variability in personal exposures, ranging from 17% for BC to 59% for NO2, suggesting that despite living in the same geographic location, participants experience a range of exposure concentrations. To understand drivers of between participant variability in exposures, we tested for association between participant characteristics and personal exposures. Neither age, sex, race-ethnicity, nor BMI were associated with the exposures; however, there were associations between socio-economic factors and personal PM10 (STable 1). Children with parents with a high school education or less had 1.46-times higher exposure to PM10 (CI:1.12–1.92, FDR = 0.046), and children from households with annual incomes less than $20,000 had 1.45-times higher exposure to PM10 (95% CI:1.13–1.88, FDR = 0.036).

Lastly, we found within-participant variability was a substantial contributor to variation in exposures, ranging from 38% for NO2 to 73% for BC (Fig. 4C). This within participant variability could reflect longitudinal variation in microenvironments encountered by individual participants, changes in activity levels or other behavioral changes, as well as technical variation.

Fig. 4
figure 4

Sources of variation in personal exposures in the ENIGMA cohort. A) Dot plot of personal PM10 measures for subjects with at least 2 PM10 observations. Each column in the plot represents a subject. Dots indicate individual PM10 measures and lines indicate the range of personal PM10 for a subject. B) Dot plot of personal NO2 measures for subjects with at least 2 NO2 observations. C) Sources of variability in personal exposure data. Variation that can be explained by season, percent of time the participant wore the monitor, between subject differences, and the remaining within subject variability are shown in purple, blue, green and pink, respectively

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Agreement between personal, community air quality, and in-home exposure measurements is limited

Personal vs. In-home monitoring

49 pre-pandemic participants had in-home monitoring for NO2, PM10 and PM speciation from stationary monitors placed in the participant’s bedroom during the personal monitoring session, allowing us to compare personal and in-home monitoring. In-home measurements of PM10 and NO2 were strongly associated with personal exposures, particularly for NO2 (Pearson correlation: r = 0.7, 0.96 for PM10 and NO2 respectively, Fig. 5A). Despite this high correlation, PM10 was an average of 47.6% higher when measured with the personal monitor, with wide limits of agreement (95% LOA: 45.6% lower to 300.7% higher, STable 2). For NO2, exposures were an average of 8.1% higher when measured with the personal monitor compared to the in-home monitor (95% LOA: 33.4% lower to 75.3% higher).

Examining PM constituents by detection (yes/no, Fig. 5B), BrC and ETS were more often detected with the personal monitor, despite in-home monitors having a lower LOD (0.1 ug/m3) than personal monitors (0.3 ug/m3). BrC and ETS were detected in 79.6% and 83.7% of personal monitoring sessions, compared to 36.8% and 4.1% of in-home sessions, respectively. BC was more often detected with the in-home monitor (94.9% vs. 26.5%), though this may be driven in part by the lower LOD of the in-home monitor. These data suggest that while there is some level of correlation between in-home and personal pollutant concentrations, the magnitude of exposures and even exposure occurrence can be underestimated using home monitoring.

Fig. 5
figure 5

Agreement between personal, in-home and community ambient monitors. (A) Scatterplots and Spearman correlations comparing personal to community ambient exposure measurements, colored by season. (B) Detection of PM10 constituents by personal and in-home monitors. Green bars indicate the exposure was detected by both monitors, blue bars indicate the exposure was detected by neither monitor, red bars indicate the exposure was detected by the in-home monitor only, and orange bars indicate the exposure was detected with the personal monitor only. (C) Scatterplots and Spearman correlations comparing personal to in-home exposure measurements, colored by season

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Personal vs. community monitoring

We similarly examined the correspondence between community and personal PM10, NO2, and O3. Personal PM10 and NO2 were weakly correlated with ambient concentrations (r = 0.20, 0.24 for PM10 and NO2, respectively, Fig. 5C). On average, personal exposures to PM10 were 10% higher than community ambient levels (95% LOA: -68.5% lower to 285.1% higher), and personal NO2 was 0.5% higher than ambient measurements (95% LOA: -76.6% lower, 323.5% higher). There was no association between personal and community O3 measurements (r=-0.01). On average, personal O3 measurements were 84.7% lower than ambient measurements (95% LOA: 96.8% lower to 26.2% lower). These data suggest personal exposures to PM10, NO2, and O3 are poorly estimated by community monitors.

Personal exposures are associated with asthma exacerbation

Examining the relationship between personal exposure levels and asthma exacerbation (Fig. 6, STable 3), participants that reported unscheduled healthcare visits for asthma in the 60 days prior to their exposure assessment had 1.35, 1.57 and 2.15-times higher levels of PM10 (CI: 1.08 to 1.69; FDR = 0.037), BrC (CI: 1.09 to 2.26; FDR = 0.040) and ETS (CI: 1.36 to 3.42; FDR = 0.009), respectively (STable 3). Similarly, those requiring a course of systemic corticosteroids for asthma in the 60 days prior to their assessment had 1.98 and 2.21-times higher levels of BrC (CI: 1.43 to 2.37; FDR = 0.0003) and ETS (CI: 1.25 to 3.91; FDR = 0.026), as well as 2.04-times higher endotoxin (CI: 1.14 to 3.68; FDR = 0.045). Additionally, participants that were hospitalized for asthma in the 18 months prior to enrollment had 2.6-times higher summer O3 exposure (CI: 1.69-4.00; FDR = 0.0001).

Exposure levels did not significantly differ between participants with and without well-controlled asthma, based on cACT and ACT scores (STable 3) and were not significantly associated with pre-bronchodilator spirometry or change post-bronchodilator (STable 4). Community air quality and indoor stationary monitor exposure levels were not significantly associated with exacerbation, lung function or asthma control, although indoor measurements were only available on 49 subjects (SFigures 2–4).

Fig. 6
figure 6

Box and whisker plots of personal exposures by asthma exacerbation characteristics. Circles represent observations above the LOD, while triangles indicate observations below the LOD

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Discussion

Although epidemiologic studies have associated spikes in ambient pollutant levels with increases in acute respiratory events, the precise relationships are likely obscured by variation in personal exposure levels. We leveraged wearable monitors, capable of measuring breathing zone pollutant levels, to investigate the uniqueness of these measurements and their value in exploring exposure-outcome relationships. Our findings suggest that community air quality monitoring of ambient PM10, NO2, and O3 is a poor proxy for exposures experienced by children with EP-asthma. Although in-home monitoring is correlated with personal exposure to PM10 and NO2, in-home detection of some of the most important, asthma-provoking PM constituents, such as tobacco smoke, was not significantly associated with children’s personal exposures in our study. We leveraged these unique breathing zone exposure measurements to show PM10 levels in general, as well as BrC, ETS, and endotoxin levels, are associated with childhood asthma exacerbations.

A key assumption when using community air quality monitoring data in studies investigating the impact of air pollution on asthma outcomes is that all participants in a geographical region have similar exposures that are accurately characterized by ambient pollution levels. Similar to other studies [49], community ambient PM10, NO2 and O3 levels did not accurately reflect an individual child’s exposures. In addition, though participants were from a relatively small geographical region, we observed substantial variation in personal exposures, with an approximately 10-fold difference in PM10 and NO2, and 16-fold difference in O3 levels, between participants with the highest and lowest exposures. While PM10 was associated with socioeconomic factors, differences in exposures between participants are also likely driven by behavioral factors, such as the amount of time spent outdoors, their proximity to major roads and other strong ambient air pollution sources, and differences in indoor microenvironment concentrations at home and in school [50].

While indoor monitors placed in participants’ bedrooms were associated with personal measures, there were important differences. On average, personal PM10 measurements were approximately 50% higher than in-home measurements, suggesting that indoor stationary monitors substantially underestimate PM10 exposure. In particular, ETS and BrC were more likely to be detected with personal monitors, suggesting tobacco smoke exposure occurred in other areas of the home (outside the child’s bedroom) or outside of the home, limiting the utility of in-home monitors in assessing these exposures. Interestingly, BC was more often detected with in-home monitors. A potential source of indoor BC is infiltration of ambient pollution from diesel combustion, which is more pronounced in homes that rely on open windows for cooling and ventilation [51]. Many of Denver’s residential neighborhoods flank interstates and highways, which could potentially cause elevated levels of BC in some households, particularly during the summer months [52].

While personal monitoring provides a 72-hour snapshot of a participant’s exposures, personal PM10 and several PM constituents were associated with exacerbation outcomes over the prior 60-days, suggesting that short-term monitoring may provide information about typical exposures experienced by a participant over a longer period. Despite this, when examining repeated exposure measurements on participants (average of 111 days between measurements), we observed within participant variability in exposures beyond what could be explained by seasonal fluctuations. This variation in exposures within a participant suggests that more dense repeated or longitudinal personal monitoring may be valuable to accurately capture a participant’s exposures over time. While 52% of corticosteroid bursts for asthma exacerbation were within 28 days of exposure monitoring in the ENIGMA study, using a shorter gap between exposure assessments and exacerbation events in future studies may strengthen evidence linking PM constituents to asthma exacerbations.

Similar to other recent studies using personal measurements, we did not find associations between personal exposures and short-term changes in lung function or daily asthma control (ACT) [53,54,55]. While Delfino et al. [56] observed associations between PM2.5, NO2, and FEV1 in children with asthma, these findings were only significant among those who did not use bronchodilators. The broad use of asthma control medications among children with EP-asthma may explain, in part, why lung function was not associated with personal exposures in ENIGMA. However, personal PM10 and PM10 constituents, including ETS, BrC and endotoxin, were associated with exacerbation outcomes, including systemic corticosteroid courses and unscheduled healthcare visits. While previous studies have found associations between PM and asthma exacerbations [57,58,59,60,61], our results provide insights into specific PM constituents impacting asthma. While numerous studies have confirmed the deleterious effect of ETS on asthma outcomes [62,63,64,65], the role of BrC is less understood. BrC is emitted from burning biomass, including forest fires, residential heating and cooking, from “biogenic release of fungi, plant debris, and humic matter,” and from secondary atmospheric reactions [66]. Few studies have investigated the role of BrC in respiratory outcomes, although ambient BrC levels have been associated with increased risk of acute respiratory infection in children [67]. Wildfires, a source of BrC, have been associated with increased rates of emergency department visits and hospitalization for asthma, with some evidence suggesting that children are particularly vulnerable to wildfire associated exacerbations [68,69,70,71]. While wildfires are typically associated with the summer months, we found that personal exposures to BrC were lower in the summer than other seasons in the ENIGMA study. Wildfires usually occur in the mountains to the west of Denver; the impact of these events on Denver air quality depends on meteorology and seasonal wind patterns and can be mitigated by northeasterly and southerly winds during the summer [72]. In addition, higher levels of ultraviolet radiation and longer daylight hours during the summer can lead to photooxidation/photochemical degradation of BrC from mountain wildfires before it is transported to the Denver area [73, 74]. The use of wood burning stoves, pellet stoves, and fireplaces for heating during the fall, winter and spring, as well as the occurrence of several fall and winter wildfires in Colorado during the ENIGMA study period, may also play a role in these findings. Endotoxins, lipopolysaccharides and lipo-oligosaccharides from the outer cell wall of Gram-negative bacteria, induce innate immune responses and inflammation in mouse studies [75], as well as in controlled human experiments [76]. Personal and classroom endotoxin levels have been associated with increases in asthma symptoms and lung function among school age children with asthma [36, 77] and endotoxin exposure was associated with doctor and emergency room visits for wheeze in the NHANES study [78].

Our study includes several strengths, including the assessment of a wide variety of pollutants at the personal, residential and ambient levels from a cohort of well-characterized children with exacerbation-prone asthma, longitudinal monitoring on pre-COVID-19 participants, and the evaluation of the association of personal exposures and clinical outcomes. Despite these strengths, some study limitations should be noted. Although our investigation of personal exposures in children with EP-asthma, specifically from the Denver metro area, allowed us to assess the level of variability in exposures among asthmatic children in a single geographic region, it may limit the generalizability to other geographic areas or to children with less severe disease. In addition, since participants were from a small geographic region (within 25 miles of Children’s Hospital Colorado), we used the central Denver EPA air quality monitoring site to measure ambient air pollution exposures. However, finer scale ambient exposure measurements, from individual monitors placed outside each participant’s home or estimated from land use regression models, could possibly have better agreement with personal exposure monitoring. In addition, there were differences in the airflow and filter sizes used to collect in-home and personal PM10, which could explain some of the variability between in-home and personal exposure measurements. While we observed significant between and within subject variability in personal exposures, we did not attempt to attribute exposures to particular sources to explain the underlying reasons for this variability. Since longitudinal follow up was not available on all participants due COVID-19 related study disruptions, power to detect associations between exposures and asthma outcomes was reduced, particularly for exposures with high proportions of measurements below the LOD. We relied on self-report of corticosteroid use and healthcare utilization during the 60 days prior to visits, which may be subject to recall bias. Lastly, assessment of PM was limited to PM10. While we acknowledge a likely role for PM2.5 in asthma outcomes [60], we focused this investigation on PM10, as we were interested in understanding the relationship between asthma outcomes and PM constituents. PM2.5 is estimated to comprise only 28–49% of the PM in the Denver airshed [79]; therefore, limiting measurements to PM2.5 would potentially miss a substantial portion of particulate exposures. In particular, endotoxins and glucans, present in resuspended house dust and soil, are largely concentrated in coarse particles (PM10-PM2.5) [80].

Conclusions

Personal PM10 exposures in general, as well as BrC, ETS, and endotoxin levels were associated with recent childhood asthma exacerbations in the ENIGMA cohort study. This study highlights how personal exposure monitoring can be used to characterize breathing zone pollutant exposure levels, providing a more nuanced understanding of the PM constituents contributing to asthma exacerbation.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACT:

Asthma Control Test

BC:

Black carbon

BrC:

Brown carbon

cACT:

Childhood ACT

EP:

Exacerbation-prone

ETS:

Environmental tobacco smoke

FDR:

False discovery rate

IQR:

Interquartile range

LOD:

Limit of detection

NO2 :

Nitrogen dioxide

O3 :

Ozone

PM10 :

Particulate matter less than 10 microns in diameter

PM2.5 :

Particulate matter less than 2.5 microns in diameter

ppb:

Parts per billion

PM10 :

Particulate matter less than 10 microns in diameter

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Acknowledgements

Not applicable.

Funding

ENIGMA was funded by the National Heart, Lung and Blood Institute, National Institutes of Health, grant number 5P01HL132821.

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Authors and Affiliations

  1. Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA

    Camille M. Moore, Elizabeth A. Secor, Jamie L. Everman, Tasha E. Fingerlin & Max A. Seibold

  2. Department of Biostatistics and Informatics, University of Colorado-AMC, Aurora, CO, USA

    Camille M. Moore

  3. RTI International, Research Triangle Park, NC, USA

    Jonathan Thornburg

  4. Section of Pediatric Pulmonary and Sleep Medicine, Children’s Hospital Colorado, Aurora, CO, USA

    Katharine L. Hamlington, Allison M. Schiltz, Kristy L. Freeman & Andrew H. Liu

  5. Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA

    Katharine L. Hamlington, Allison M. Schiltz, Kristy L. Freeman & Andrew H. Liu

  6. Department of Pediatrics, National Jewish Health, Denver, CO, 80206, USA

    Max A. Seibold

  7. Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado-AMC, Aurora, CO, USA

    Max A. Seibold

  8. National Jewish Health, 1400 Jackson St, Denver, CO, 80206, USA

    Camille M. Moore

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Contributions

MAS, AHL, TEF, and JT conceived of the ENIGMA cohort study; CMM, MAS, AHL, KLH and JT contributed to the study design for analyses presented in this manuscript. AHL, AMS, KLF, KLH recruited the cohort, collected clinical and lung function data, and deployed the environmental monitors. JT was responsible for the laboratory analysis of personal and in-home environmental monitors. TEF, CMM, EAS, KLH, and JT were responsible for data management and quality control. CMM and EAS performed all statistical analyses. MAS, CMM, EAS, AHL, KLH, JLE and JT interpreted the results. CMM and MAS drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Camille M. Moore.

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Ethics approval and consent to participate

The ENIGMA study was performed in accordance with the Declaration of Helsinki. The Colorado Multiple Institutional Review Board approved the protocol for the ENIGMA study. The participant and at least one legal guardian provided informed written consent and, if age-appropriate, assent.

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Not applicable.

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The authors declare no competing interests.

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Moore, C.M., Thornburg, J., Secor, E.A. et al. Comparative analysis of ambient, in-home, and personal exposures reveals associations between breathing zone pollutant levels and asthma exacerbations in high-risk children. Respir Res 26, 40 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12931-025-03114-y

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Keywords

Respiratory Research

ISSN: 1465-993X

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