Exposure to Environmental Pollutants and the Risk of Respiratory Illnesses in 36-Month-Old Children: The Korean Children’s Environmental Health Study (Ko-CHENS)

Abstract

Purpose

This study aimed to investigate the association of exposure to environmental pollutants and surrounding residential environments with the development of respiratory illnesses in 36-month-old children using data from the Korean CHildren’s ENvironmental health Study.

Methods

Demographic information and anthropometric measurements were obtained from the newborns. At 36 months of age, indoor environmental pollutant levels (particulate matter [PM₁₀ and PM_2.5], volatile organic compounds, and house dust mite allergens) were measured during home visits. Concentrations of both heavy metals (lead, mercury, and cadmium) in blood and metabolites of environmental hormones in urine were analyzed. A structured questionnaire was used to assess the presence of 6 common respiratory illnesses (infantile asthma, current asthma, recurrent wheezing, allergic rhinitis, bronchiolitis, and pneumonia) and residential environmental characteristics. Quantile-based g-computation was employed to estimate the joint association of exposure to mixtures of physical and chemical environmental factors with the risk of respiratory illnesses.

Results

Among the total of 4,059 children, current allergic rhinitis was the most prevalent respiratory illness at 36 months. Compared with children without respiratory illnesses, those with respiratory illnesses showed significantly higher frequencies of exposure to environmental tobacco smoking and pet ownership. Dust mite concentrations were higher among children with recurrent wheezing and pneumonia. Mixture exposure analysis revealed that overall mixture exposure was positively associated with current allergic rhinitis, largely driven by lead and phthalate metabolites, and with pneumonia, mainly attributable to phthalate metabolites. Exposure to a mixture of multiple chemicals was positively associated with infantile asthma and current asthma.

Conclusions

Direct and indirect exposures to environmental pollutants are closely associated with the development of respiratory illnesses in young children, and the surrounding residential environment during early childhood also plays a critical role in determining respiratory disease risk. These findings support the need for systematic and sustainable environmental management at both individual and community levels to protect children who are particularly vulnerable to environmental hazards.

Graphical Abstract

INTRODUCTION

The prevalence of respiratory allergic diseases in early childhood has been rising globally, and increasing attention is being paid to the role of environmental exposure during critical developmental periods.¹ Children are particularly vulnerable to environmental hazards owing to their immature immune systems, developing lungs, and higher relative exposure to air, food, and dust.² Numerous studies have indicated that both indoor and outdoor environmental pollutants may contribute to the development and exacerbation of asthma, allergic rhinitis, and other respiratory illnesses in children.²,³,⁴,⁵,⁶,⁷

The Korea CHildren’s ENvironmental health Study (Ko-CHENS),⁸ a large-scale, nationwide, prospective birth cohort study, was designed to investigate the impact of environmental exposure on children’s health. This study focused on 36-month-old children to assess the association of early-life exposure to physical and chemical environmental pollutants with the development of respiratory illnesses.

Previous studies have explored the association of exposure to indoor or outdoor pollutants with respiratory or allergic diseases during infancy and early childhood. Most studies have reported that exposure to fine particulate matter (PM₁₀ and PM_2.5), volatile organic compounds (VOCs), and indoor dampness or mold is associated with an increased risk of wheezing, bronchiolitis, allergic rhinitis, and asthma.²,⁵,⁶,⁹,¹⁰ However, many of these investigations were limited due to small sample sizes, cross-sectional designs, or indirect exposure assessments, such as self-reported questionnaires and area-level monitoring data, rather than individual-level direct measurements. Although a few studies have simultaneously applied biomarker-based exposure assessments and evaluated multiple respiratory and allergic outcomes, these were primarily based on prenatal exposure and relatively small cohorts.¹¹,¹²,¹³ In contrast, our study integrated direct indoor pollutant measurements with biomarker-based evaluations at 36 months of age in a large, nationwide cohort, thereby enabling a more comprehensive assessment of early-life environmental risk factors.

We aimed to identify environmental and lifestyle risk factors associated with respiratory illnesses in early childhood using mixture exposure analysis. This approach allows evaluation of the synergistic or antagonistic effects of multiple chemicals and better reflects real-world exposure settings. This study may provide important evidence for developing public health policies and environmental guidelines which reflect the unique exposure profile of the Korean pediatric population.

MATERIALS AND METHODS

Study population

We retrieved data from the Ko-CHENS, a national, prospective birth cohort established to explore associations between environmental exposures and health outcomes in mothers and children.⁸ The cohort enrolled 5,213 pregnant women before 20 weeks of gestation between 2015 and 2018 from 12 Environmental Health Centers and one Support Center designated by the Ministry of Environment in South Korea. Maternal demographic characteristics, clinical information, and blood heavy metal levels were linked to offspring data to construct a registry of mother-offspring pairs.

Among the 4,712 term singleton live-births in the Ko-CHENS birth cohort, 4,059 children who had completed assessments of biological exposure markers, environmental pollutant measurements, and questionnaires regarding diseases and lifestyle at 36 months of age were included in the analysis (Fig. 1). The study protocol was approved by the Institutional Review Board of the Korea University Anam Hospital (approval No. 2025AN0370).

Fig. 1. Flow chart of the study population selected from the Ko-CHENS.

Ko-CHENS, Korean CHildren's ENvironmental health Study.

Measurement of serum heavy metal and urine metabolite levels

Approximately 20 mL of venous blood and urine samples were collected from participants during outpatient visits at 36 months of age. Samples were refrigerated and transferred to the laboratory for analysis of lead (Pb), mercury (Hg), and cadmium (Cd) levels using inductively coupled plasma mass spectrometry (ICP-MS; Agilent 7900 ICP-MS, Agilent Technologies^®, Santa Clara, CA, USA). The detection limits for serum Pb, Hg, and Cd levels were 0.009 μg/dL, 0.10 μg/L, and 0.05 μg/L, respectively.

Urine samples were collected during outpatient clinic visits, placed in polypropylene containers, processed, and stored at –80°C until analysis. Urinary metabolites, including bisphenols, parabens, phthalate metabolites, and cotinine, were measured to evaluate exposure to environmental hormones and environmental tobacco smoke (ETS).

Indoor air pollutant levels

Indoor air pollutant levels, including PM₁₀, PM_2.5, and VOCs (chloroform, benzene, and toluene), were measured during home visits when the children were 36 months old. The concentrations of house dust mite (HDM) allergens were quantified in the collected household dust samples.

Environmental exposure and health status questionnaires

A structured questionnaire was administered to the parents or caregivers of 36-month-old children to collect information on 6 common respiratory and allergic diseases in children (infantile asthma, current asthma, recurrent wheezing, allergic rhinitis, bronchiolitis, and pneumonia), home environmental factors, pet ownership, tobacco smoke exposure, and residential characteristics.

In total, 808 variables were derived from the questionnaire across 13 categories: baseline characteristics, residential environment, respiratory illnesses, lifestyle habits, nutrition, and developmental status. Disease definitions were as follows: infantile asthma (physician-diagnosed asthma before 12 months); current asthma (asthma medication use within the previous year); recurrent wheezing (≥ 4 wheezing episodes reported); current allergic rhinitis (physician-diagnosed allergic rhinitis with medication use within the previous year); and bronchiolitis and pneumonia (physician-diagnosed conditions). Questions regarding lifestyle factors included: “Do you live within 100 meters of a green space/park?” “How many hours a week do you spend at a green space/park?” “How many hours a week are you exposed to ETS?” “Do you live within 100 m of a 4-lane road?” and “Is either parent currently smoking?”

Statistical analysis

Descriptive characteristics of participants, including demographic data, incidence of respiratory illnesses, and environmental exposures, were summarized as frequencies (percentages) for categorical variables and means ± standard deviations (SDs) for continuous variables. To assess the association between environmental mixture exposures and respiratory illnesses, a quantile-based g-computation method was employed, which enabled the identification of the contribution of individual exposures to the overall association. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) of infantile asthma, current asthma, recurrent wheezing, allergic rhinitis, bronchiolitis, and pneumonia were calculated. Due to sparse data (prevalence ≤ 2%), maternal and paternal history of asthma and maternal smoking were excluded from the models for infantile and current asthma to minimize non-convergence. Analyses were conducted using R software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

A total of 4,059 children aged 36 months were included in this study. Of the participants, 51.8% were male. The mean (± SD) body weight, height, and head circumference were 14.60 ± 1.66 kg, 95.18 ± 3.60 cm, and 49.28 ± 1.66 cm, respectively. The average blood concentrations of environmental heavy metals were 0.73 ± 0.56 μg/dL for Pb, 1.70 ± 1.18 μg/L for Hg, and 0.25 ± 0.11 μg/L for Cd. Urinary concentrations of selected environmental biomarkers were as follows: 2.37 ± 5.50 for bisphenol A, 0.58 ± 2.22 for bisphenol F (BPF), 30.57 ± 42.67 for mono-(2-ethyl-5-hydroxyhexyl) phthalate, and 1.83 ± 1.88 μg/L for cotinine. Indoor air pollutant levels measured at the 36-month home visit were 23.84 ± 18.15 for PM₁₀, 15.73 ± 10.68 for PM_2.5, and 56.31 ± 158.08 μg/m³ for total VOCs. The average HDM allergen level was 711.73 ± 2,477.01 ng/g of house dust. At the time of the home visit, 7.9% of the households reported current pet ownership. Regarding parental characteristics, maternal smoking during pregnancy was rare (0.5%), although 12.9% of mothers reported ever smoking, and 16.3% reported exposure to ETS during pregnancy. Maternal alcohol consumption was reported in 1.6% of cases, and 13.1% of mothers owned pets during pregnancy. The prevalence of a parental allergic disease history was 1.98% for maternal asthma, 1.72% for paternal asthma, 27.1% for maternal allergic rhinitis, and 21.2% for paternal allergic rhinitis (Table 1).

Table 1. Baseline characteristics of the study participants of the Korean CHildren's ENvironmental health Study (n = 4,059).

Variables		Values*
Prenatal factors
	Maternal smoking during pregnancy	25 (0.5)
	Maternal ever smoking	777 (12.9)
	Maternal secondary smoking	977 (16.3)
	Maternal alcohol consumption during pregnancy	75 (1.6)
	Pet ownership during pregnancy	714 (13.1)
	Maternal history of asthma	119 (2.0)
	Paternal history of asthma	103 (1.7)
	Maternal history of allergic rhinitis	1,626 (27.1)
	Paternal history of allergic rhinitis	1,275 (21.2)
Growth at 36 mon
	Male (%)	2,101 (51.8)
	Body weight (kg)	14.60 ± 1.66
	Height (cm)	95.18 ± 3.60
	Head circumference (cm)	49.28 ± 1.66
Environmental exposures
	Indoor PM10 (μg/m3)	23.84 ± 18.15
	Indoor PM2.5 (μg/m3)	15.73 ± 10.68
	Volatile organic compounds (μg/m3)	56.31 ± 158.08
	House dust mite (ng/g)	711.73 ± 2,477.01
	Current pet ownership (%)	355 (7.9)
Biomarkers for environmental exposures
	Blood lead (μg/dL)	0.73 ± 0.56
	Blood mercury (μg/L)	1.70 ± 1.18
	Blood cadmium (μg/L)	0.25 ± 0.11
	Urine bisphenol A (μg/L)	2.37 ± 5.50
	Urine bisphenol F (μg/L)	0.58 ± 2.22
	Urine mono-(2-ethyl-5-hydroxyhexyl) phthalate (μg/L)	30.57 ± 42.67
	Urine cotinine (μg/L)	1.83 ± 1.88
	Total IgE (IU/L)	95.96 ± 202.70
	sIgE (Dermatophagoides farinae, IU/L)	3.59 ± 13.40
	sIgE (Dermatophagoides pteronyssinus, IU/L)	1.70 ± 7.59
	Eosinophils (n)	223.50 ± 171.20
	Vitamin D (ng/mL)	28.68 ± 9.81

Values are presented as mean ± standard deviation or number (%).

PM, particulate matter; IgE, immunoglobulin E; sIgE, specific immunoglobulin E.

^*The denominators may not be equal to the total number of participants owing to missing values.

At 36 months of age, 2,568 children had no respiratory illnesses. The numbers of children with specific outcomes were as follows: 41 (1.01%) with infantile asthma, 83 (2.04%) with current asthma, 353 (8.70%) with recurrent wheezing, 870 (21.43%) with allergic rhinitis, 368 (9.07%) with bronchiolitis, and 426 (10.50%) with pneumonia. Maternal allergic rhinitis was common across all groups, ranging from 26% in the infantile asthma group to 39% among those with allergic rhinitis, while paternal allergic rhinitis showed a comparable but slightly lower prevalence. Maternal asthma was reported in 1.94%–3.48% and paternal asthma in around 2% across all groups. Current maternal smoking and exposure to ETS were significantly higher in infantile asthma, bronchiolitis, and pneumonia. Respiratory wheezing illnesses were more common among children with direct or indirect pet exposure. The distribution of residential green space appeared similar, though living within 100 m from green space was slightly less frequent among children with infantile asthma (Table 2).

Table 2. Distribution of prenatal and environmental exposure characteristics according to children’s respiratory illnesses.

	Infantile asthma (n = 41, 1.01%)			Current asthma (n = 83, 2.04%)			Recurrent wheezing (n = 353, 8.70%)			Allergic rhinitis (n = 870, 21.43%)			Bronchiolitis (n = 368, 9.07%)			Pneumonia (n = 426, 10.50%)
	Yes	No	P	Yes	No	P	Yes	No	P	Yes	No	P	Yes	No	P	Yes	No	P
Current pet ownership	13.89	12.45	0.794	13.79	12.48	0.764	18.04	11.03	0.012	12.79	12.43	0.773	17.49	11.91	0.002	14.76	12.16	0.128
Exposure to pet (1 ≥ week)	22.50	20.54	0.760	20.97	20.34	0.904	21.90	21.31	0.846	22.62	19.73	0.048	24.81	20.09	0.026	19.91	20.66	0.709
Current maternal smoking	2.44	1.84	0.012	9.09	11.93	0.416	15.57	12.67	0.265	12.47	11.71	0.514	13.24	11.55	0.315	12.07	11.70	0.815
Exposure to ETS (1 ≥ week)	12.20	15.28	0.584	14.52	15.29	0.867	22.50	18.49	0.409	15.46	15.23	0.859	18.63	14.87	0.045	19.40	14.74	0.009
Green space within 100 m of residence	31.71	42.43	0.167	40.32	42.48	0.733	42.82	44.86	0.852	43.71	42.11	0.373	44.85	42.06	0.279	43.53	42.15	0.569
Maternal asthma	2.44	2.01	0.567	2.27	2.00	0.696	3.48	4.11	0.655	2.47	1.88	0.239	2.45	1.96	0.504	1.94	2.02	0.909
Paternal asthma	2.44	1.59	0.521	1.14	1.61	0.726	1.02	1.71	0.514	2.06	1.48	0.200	1.23	1.61	0.556	1.08	1.63	0.366
Maternal allergic rhinitis	26.83	28.83	0.778	30.68	28.17	0.604	30.12	31.85	0.613	39.18	25.21	< 0.001	35.29	28.08	0.002	33.84	28.18	0.011
Paternal allergic rhinitis	17.07	22.02	0.447	23.86	21.54	0.600	23.97	18.24	0.005	28.35	19.72	< 0.001	20.34	22.15	0.404	23.49	21.76	0.398

Values are presented as percentages. Bold letters are used to indicate statistical significance (P < 0.05).

ETS, environmental tobacco smoking.

Table 3 shows physical and chemical environmental risk factors for respiratory illnesses. Ambient PM levels (PM₁₀ and PM_2.5) and HDM allergen exposure showed minimal differences across disease categories. Blood heavy metals (Pb, Hg, and Cd) exhibited comparable mean concentrations between groups, without clear trends related to respiratory conditions. Urinary chemical exposures (bisphenols, parabens, phthalate metabolites, and cotinine) demonstrated high variability, with slightly elevated mean concentrations of some parabens and bisphenols among children with asthma or allergic rhinitis, though SDs were large and overlapping across groups.

Table 3. Physical and chemical environmental risk factors in relation to children’s respiratory illnesses.

Variables		No respiratory illnesses (n = 2,568)	Infantile asthma (n = 41, 1.01%)	Current asthma (n = 83, 2.04%)	Recurrent wheezing (n = 353, 8.70%)	Allergic rhinitis (n = 870, 21.43%)	Bronchiolitis (n = 368, 9.07%)	Pneumonia (n = 426, 10.50%)
Indoor
	PM10 (μg/m3)	22.03 ± 20.16	26.33 ± 6.89	24.56 ± 7.81	24.10 ± 14.59	22.84 ± 11.89	25.94 ± 12.86	24.07 ± 13.09
	PM2.5 (μg/m3)	15.60 ± 11.40	16.29 ± 4.19	17.56 ± 4.85	15.70 ± 8.64	15.59 ± 7.74	17.79 ± 10.93	17.11 ± 9.88
	House dust mite (ng/g)	583.51 ± 2,318.38	558.60 ± 1,697.47	400.48 ± 1,171.97	848.71 ± 2,848.79	610.29 ± 2,173.70	701.53 ± 2,438.98	863.95 ± 2,886.02
Blood
	Lead (μg/dL)	0.72 ± 0.62	0.80 ± 0.42	0.73 ± 0.39	0.73 ± 0.37	0.77 ± 0.46	0.75 ± 0.47	0.76 ± 0.43
	Mercury (μg/L)	1.71 ± 1.25	1.96 ± 1.27	1.79 ± 1.14	1.62 ± 0.92	1.77 ± 1.11	1.70 ± 1.07	1.77 ± 1.19
	Cadmium (μg/L)	0.26 ± 0.11	0.26 ± 0.09	0.25 ± 0.09	0.26 ± 0.10	0.26 ± 0.11	0.26 ± 0.10	0.24 ± 0.09
Urine
	Bisphenol A (μg/L)	2.58 ± 6.82	2.07 ± 1.85	2.70 ± 3.35	2.85 ± 4.40	2.42 ± 3.34	2.82 ± 3.77	2.50 ± 3.51
	Bisphenol F (μg/L)	0.39 ± 1.48	1.13 ± 4.62	0.79 ± 3.48	0.48 ± 1.81	0.44 ± 1.73	0.35 ± 0.75	0.46 ± 1.67
	Bisphenol S (μg/L)	0.85 ± 2.07	1.26 ± 1.97	0.95 ± 1.56	1.13 ± 3.29	0.78 ± 1.63	0.68 ± 1.04	0.82 ± 1.83
	Triclosan (μg/L)	1.63 ± 5.20	1.35 ± 1.08	1.36 ± 1.14	1.60 ± 6.02	1.50 ± 3.64	1.38 ± 3.21	1.44 ± 3.43
	Ethylparaben (μg/L)	69.23 ± 185.08	106.83 ± 344.56	96.06 ± 271.36	65.26 ± 140.62	78.93 ± 191.67	79.15 ± 199.40	69.83 ± 168.12
	Methylparaben (μg/L)	404.17 ± 1,178.48	535.83 ± 1,143.57	649.50 ± 1,449.17	780.60 ± 1,690.75	642.03 ± 1,471.13	556.06 ± 1,387.14	593.24 ± 1,386.72
	Propylparaben (μg/L)	50.88 ± 202.49	68.82 ± 225.13	77.32 ± 207.38	106.86 ± 342.96	103.25 ± 386.28	78.01 ± 252.60	91.46 ± 311.51
	MEHPP (μg/L)	30.57 ± 47.49	26.05 ± 23.20	30.95 ± 29.85	37.86 ± 43.61	29.44 ± 32.16	29.58 ± 32.53	31.57 ± 31.17
	MEOHP (μg/L)	19.60 ± 26.23	17.52 ± 15.97	20.60 ± 20.27	23.34 ± 24.47	18.71 ± 20.06	18.93 ± 20.81	20.02 ± 19.67
	MECPP (μg/L)	47.68 ± 65.69	43.48 ± 37.64	49.86 ± 48.14	56.37 ± 61.15	45.25 ± 49.44	45.74 ± 50.04	48.97 ± 48.53
	MnBP (μg/L)	47.27 ± 55.72	45.43 ± 45.81	49.83 ± 46.78	49.60 ± 76.92	46.69 ± 53.50	45.28 ± 43.30	50.27 ± 73.19
	MBzP (μg /L)	3.07 ± 8.38	2.38 ± 3.02	3.38 ± 4.96	3.93 ± 8.20	2.96 ± 5.79	2.86 ± 5.39	3.38 ± 8.66
	MCOP (μg/L)	3.25 ± 7.77	2.27 ± 2.73	13.84 ± 90.57	3.42 ± 6.22	3.23 ± 5.86	3.22 ± 6.10	3.19 ± 7.04
	MCPP (μg/L)	1.56 ± 2.26	2.04 ± 4.51	2.53 ± 6.36	1.79 ± 3.38	1.57 ± 2.25	1.86 ± 3.71	1.64 ± 2.04
	MCNP (μg/L)	0.56 ± 1.51	0.73 ± 1.64	0.85 ± 1.52	2.26 ± 17.47	0.54 ± 0.75	1.50 ± 13.12	0.53 ± 0.67
	Cotinine (μg/L)	1.94 ± 1.82	2.31 ± 3.61	2.19 ± 2.75	1.98 ± 1.65	2.09 ± 2.18	1.73 ± 1.34	2.05 ± 1.79

Values are presented as mean ± standard deviation. Bold letters are used to indicate statistical significance (P < 0.05).

PM, particulate matter; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MnBP, mono-n-butyl phthalate; MBzP, mono-benzyl phthalate; MCOP, mono-carboxyoctyl phthalate; MCPP, mono-isononyl phthalate; MCNP, mono-carboxynonyl phthalate.

Further analysis of the mixture exposure to pre- and postnatal environmental features and children’s respiratory illnesses showed the following results. For infantile asthma, the aOR for the overall exposure mixture was markedly elevated, mainly driven by chemical mixture (aOR, 13.96; 95% CI, 2.49–78.20), whereas the physical exposure mixture showed no clear association. Current asthma also showed an increased risk associated with chemical mixture (aOR, 4.30; 95% CI, 1.24–14.86), but not with physical mixture. For recurrent wheezing and bronchiolitis, no consistent or strong associations were observed across exposure categories. Current allergic rhinitis showed modest associations with the overall exposure mixture (aOR, 1.73; 95% CI, 1.10—2.72), but null effects for physical or chemical exposures. The precision of estimates varied widely, with some extremely broad CIs indicating potential model instability or sparse data in specific exposure–outcome strata. Although extreme CIs likely reflect sparse data or model non-convergence, the directionality of associations may still be informative despite limited precision (Table 4).

Table 4. Mixture exposure to pre- and postnatal environmental features and children’s respiratory illnesses.

Childhood illnesses	All mixture	Physical environmental exposure*	Chemical environmental exposure†
Infantile asthma‡	7.66 (0.03–1,692.52)	0.71 (0.13–4.02)	13.96 (2.49–78.20)
Current asthma‡	8.63 (0.12–634.47)	1.83 (0.46–7.27)	4.30 (1.24–14.86)
Recurrent wheezing	0.53 (0.13–2.20)	0.92 (0.51–1.66)	0.93 (0.63–1.37)
Current allergic rhinitis	1.73 (1.10–2.72)	1.06 (0.71–1.60)	1.19 (0.91–1.57)
Bronchiolitis	0.98 (0.32–2.97)	1.41 (0.88–2.26)	1.09 (0.72–1.64)
Pneumonia	1.57 (1.43–3.23)	1.13 (0.66–1.94)	0.94 (0.63–1.42)

Risk estimates were adjusted for the maternal and paternal history of asthma and allergic rhinitis. Extreme confidence intervals reflect sparse data or model non-convergence. Directionality may still be informative even if the precision is low. Bold letters are used to indicate statistical significance (P < 0.05).

PM, particulate matter; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MnBP, mono-n-butyl phthalate; MBzP, mono-benzyl phthalate; MCOP, mono-carboxyoctyl phthalate; MCPP, mono-isononyl phthalate; MCNP, mono-carboxynonyl phthalate.

^*PM₁₀, PM_2.5, House Dust Mite, and residential greenness.

^†Lead, mercury, cadmium, bisphenol A/F/S, triclosan, ethylparaben, methylparaben, propylparaben MEHHP, MEOHP, MECPP, MnBP, MBzP, MCOP, MCPP, MCNP.

^‡To ensure convergence of the models, parental history of asthma and maternal smoking during pregnancy was removed for infantile and current asthma.

Figs. 2 and 3 show the positive and negative contributions of individual exposures to the associations within the physical and chemical mixtures, respectively. Within the chemical mixture analyses, BPF showed the greatest positive contribution to both infantile and current asthma. In the overall mixture analysis, the largest positive weights were observed for Pb and PM_2.5 for current allergic rhinitis (Fig. S1).

Fig. 2. Proportion of directional (positive/negative) associations between chemical mixture exposures and children’s respiratory illnesses.

Pb, lead; Hg, mercury; Cd, cadmium; BPA, bisphenol A; BPF, bisphenol F; BPS, bisphenol S; TCS, triclosan; MEP, mono-ethyl phthalate; ETP, ethyl paraben; PRP, propyl paraben; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MnBP, mono-n-butyl phthalate; MBzP, mono-benzyl phthalate; MCOP, mono-carboxyoctyl phthalate; MCPP, mono-(3-carboxypropyl) phthalate; MCNP, mono-carboxynonyl phthalate.

Fig. 3. Proportion of directional (positive/negative) associations between physical mixture exposures and children’s respiratory illnesses.

PM, particulate matter; HDM, house dust mite allergen.

DISCUSSION

This study investigated the association of environmental exposures, including indoor pollutants, urinary biomarkers, and biological heavy metals, with the development of respiratory illnesses in 36-month-old children. Overall, the findings suggest that chemical environmental exposure may contribute more strongly to the risk of early asthma phenotypes than physical environmental exposure. By analyzing a well-characterized birth cohort with detailed environmental and parental data, we aimed to disentangle the joint associations of multiple exposures that more realistically reflect the complex environmental context during the first 1,000 days of life.

In the present study, indoor PM₁₀ concentrations were slightly higher in the asthma, recurrent wheezing, bronchiolitis, and pneumonia groups, supporting prior epidemiological evidence linking particulate pollution to airway morbidities.⁶,¹⁴ Fine and coarse particles can deposit in both upper and lower airways, thereby inducing oxidative stress, epithelial barrier disruption, and immune activation.¹⁵,¹⁶ Experimental studies have further demonstrated that PM enhances viral replication and airway hyperresponsiveness, explaining its observed association with bronchiolitis and asthma.¹⁷,¹⁸ Notably, these effects occur at pollution levels commonly encountered in urban environments, emphasizing the public health relevance of even modest air-quality improvements.¹⁹

We also observed the significant associations of urinary concentrations of BPF and phthalate metabolites with the occurrence of wheezing illnesses in young children. Although urinary biomarkers reflect only short-term exposure and may vary according to age, sex, and sampling time, our findings are consistent with previous epidemiological studies suggesting that these environmental hormone metabolites are associated with the development or exacerbation of respiratory allergic diseases in children. For example, a recent systematic review and meta-analysis reported a significant positive association between phthalate exposure and the risk of allergic rhinitis in children.²⁰ The authors concluded exposure to phthalates, particularly during early life, may increase susceptibility to allergic airway inflammation and the development of rhinitis through immune-modulating mechanisms. Using data from 1,905 mother-child pairs in the US Environmental influences on Child Health Outcomes consortium, Miller et al. ²¹ investigated prenatal exposure to bisphenols in relation to childhood asthma, allergic rhinitis, and atopic dermatitis.

Consistent with previous studies, parental allergic history, particularly maternal allergic rhinitis, emerged as a strong and consistent risk factor for multiple childhood respiratory outcomes.²² This likely reflects both genetic predisposition and shared environmental exposures such as allergen load, microbial transmission, and lifestyle factors.² Although genetic susceptibility is non-modifiable, our findings suggest that children with a family history of allergic diseases may be especially vulnerable to environmental insults such as environmental hormone metabolites or traffic-related air pollution. Identifying these high-risk groups may facilitate targeted preventive interventions and counseling in pediatric practice.

One of the consistent findings is the null associations with secondary tobacco smoke exposure from the directional analysis of chemical environmental features. Cotinine, a robust biomarker of nicotine exposure,²³ strengthens the validity of this association by minimizing recall bias and reflecting actual biological uptake. Tobacco smoke contains thousands of toxic compounds that impair mucociliary clearance, damage the airway epithelium, and alter innate and adaptive immune responses.²⁴ These may explain the heightened susceptibility of children to respiratory infections and airway inflammation. Although questionnaire-based measures of ETS exposure and urinary cotinine levels were significantly higher in the infantile asthma, bronchiolitis, and pneumonia groups, the harmful effect of ETS within mixture exposure models was not replicated, likely due to generally low exposure levels in this cohort.

Current pet ownership is significantly higher in children with recurrent wheezing and bronchiolitis, but not in the asthma or allergic rhinitis groups. The relationship between pet ownership and development of respiratory allergic diseases remains controversial. While early-life pet ownership has been associated with an increased risk of sensitization, paradoxically it has also been linked to a reduced asthma risk,²⁵ suggesting that the timing and intensity of exposure, as well as individual susceptibility, may differentially influence disease outcomes. A recent study demonstrated that exposure to pets may trigger allergic immune responses, leading to airway inflammation in asthmatic children.²⁶ This null association with allergic diseases may be explained by: (1) differences in the timing of exposure assessment; (2) variations in outcome definitions; and (3) the cultural and environmental context, particularly pet-keeping practices in Korean households, where most pets are small dogs kept strictly indoors in high-density apartments. This differs from Western cohorts where larger or outdoor-indoor pets may introduce greater microbial diversity (including endotoxins), which is central to the hygiene hypothesis.

The environmental physical context can also exert protective or harmful effects. Proximity to green space was negatively associated with a risk of infantile asthma, whereas residence near major roads increased the risk of respiratory illnesses. Green spaces are increasingly recognized as health-promoting environments that improve air quality, reduce urban heat islands, and enrich microbial diversity, which may promote immune tolerance.²⁷,²⁸,²⁹ A recent study further suggested that fungal microbiome diversity in urban forests reduces asthma morbidity and modulate allergic inflammation.³⁰ Conversely, pollutants, such as ultrafine particles, VOCs, and HDM allergens, are established as respiratory irritants and sensitizers.⁹,³¹,³²

The weaker association of chemical exposure with recurrent wheezing likely reflects the inclusion of “transient early wheezers”—children whose symptoms are triggered primarily by viral infections and typically resolve by age 3 without progressing to asthma. Our finding of a stronger association with asthma suggests that chemical mixture may specifically influence the persistent asthma phenotype or airway hyperresponsiveness rather than transient viral-induced wheezing.

Overall, early childhood respiratory and allergic health appears to be shaped by a complex interplay between chemical exposure (tobacco smoke, heavy metals, and PM), physical environment (green space and traffic density), and genetic predisposition. Our findings support early preventive strategies, such as improving indoor environmental quality and minimizing exposure to chemical mixture, thereby reinforcing the importance of reducing indoor sources of chemicals during early childhood. From a policy perspective, we propose several early-life interventions. First, it is essential to strengthen indoor air quality regulations in residential settings during the first 1,000 days of life, which our data suggest is a critical window for respiratory priming. Secondly, it is necessary to reinforce mandatory environmental monitoring and ventilation standards in daycare centers, given that toddlers spend substantial time in these settings. Thirdly, it is vital to incorporate environmental exposure assessments into national child health screening programs. Finally, it is crucial to implement targeted environmental management policies for socioeconomically disadvantaged families to mitigate environmental health inequities in early childhood respiratory outcomes.

Despite its strengths, this study has a few limitations. First, the observational design precludes causal inferences. Secondly, exposure measurements were obtained at a single point, which may not capture long-term variability. Thirdly, residual confounding from unmeasured factors, such as diet, physical activity, ventilation, and indoor microbial exposure, may persist. Nevertheless, the strengths of this study include a large sample size, standardized health outcome assessment, comprehensive exposure evaluation, and biomarker validation within a well-characterized national birth cohort.

In conclusion, this study highlights the pivotal role of modifiable environmental exposure in determining respiratory outcomes in early childhood in a large birth cohort. Reducing tobacco smoke exposure, improving indoor and outdoor air quality, and promoting green urban environments should be prioritized in pediatric health policies. Given the critical window of immune and lung development during the first 1,000 days of life, targeted environmental interventions may yield lasting benefits for children’s respiratory health.

SUPPLEMENTARY MATERIAL

Fig. S1

Contribution of individual exposures to the overall mixture effect.