Formaldehyde Exposure and Associated Health Burdens Apportioned to Residential and Public Places Based on Personal and Environmental Measurements

Abstract

Formaldehyde poses a critical indoor environmental health hazard, particularly in rapidly urbanizing settings. Residential and public buildings serve as the most significant exposure sites; however, the extent of urban populations’ formaldehyde exposure in these two types of environments remains unclear, posing challenges for precise prevention and control strategies. This study employed a comprehensive exposure assessment by combining personal exposure monitoring with environmental sampling to characterize formaldehyde exposure profiles and contributions apportioned to residential and public microenvironments. The mean personal exposure concentration of formaldehyde of working adults was 36.0 μg/m³ (SD: 30.7 μg/m³). The mean chronic daily intake derived from personal data was 5.1 μg/kg/day. Residential environments were identified as the predominant contributors to overall exposure (>50% of total exposure in working adults, and >80% in children/elderly), followed by public places (contributing to 40% among employed adults). For children under 5 years and the elderly, residential settings accounted for >80% of the contribution of total intake. The home and school environments contributed to approximately 60% and 30% of exposure for children and adolescents aged 5–18 years, respectively. Other microenvironments (such as vehicular and outdoor settings) contributed to less than 10%. Simulation scenarios further suggested that reducing indoor formaldehyde concentrations by 15–30% in both residential and public buildings could avert 10–20% of associated health burdens for targeted populations. These findings underscore the continuous need for formaldehyde exposure control in both residential and public environments as well as indoor health interventions in modern urban areas.

1. Introduction

Formaldehyde is a ubiquitous indoor air pollutant and a Group 1 human carcinogen as classified by the International Agency for Research on Cancer (IARC) [1]. Exposure to formaldehyde has been linked to a range of adverse health effects, including respiratory irritation [2,3], asthma exacerbation [4,5], central nervous system disorders [6], and leukemia [7]. In modern built environments, primary emission sources include building materials (e.g., plywood, particleboard containing urea–formaldehyde resins) and household products (e.g., paints, coatings and adhesives) [8,9]. In urban settings, ambient formaldehyde levels are further influenced by industrial or vehicular emissions as well as secondary atmospheric formation [10,11,12].

The World Health Organization (WHO) has recommended a 30-minute indoor formaldehyde exposure limit of 0.1 mg/m³ [13]; however, this threshold is frequently exceeded in newly renovated interiors, particularly in rapidly developing regions [14,15]. In 2023, China accounted for more than 10.2 billion square meters of new construction floor area [16]—approximately 20% of global construction output [17]. The extensive use of construction materials for finishing these new buildings has led to significantly higher indoor formaldehyde concentrations in China compared to other countries and regions [18], which once sparked widespread public concern. Du et al. [19] identified formaldehyde as the dominant contributor to inhalation-related cancer risks among working adults in urban China. Although national data indicate a declining trend in indoor formaldehyde levels over the past two decades [20], it remains the second leading indoor organic pollutant (after benzene) contributing to the national disease burden [21], underscoring the continued need for rigorous exposure monitoring and policy intervention.

Inhalation presents the primary route of human exposure to formaldehyde, with the upper respiratory tract serving as the site of absorption and irritation [22]. Given that indoor environments (including vehicle cabins) are the predominant exposure [23,24], accurate population-level exposure estimation is essential for effective indoor formaldehyde pollution control. The literature regarding formaldehyde exposure estimates largely focus on single microenvironment types (e.g., residential or occupational settings) [25,26,27,28], thereby limiting the understanding of cumulative exposures across diverse microenvironments and corresponding contributions. Du et al. [19] provided a framework for evaluating microenvironmental contributions to formaldehyde exposure; however, their model was based on pre-2010 data and focused solely on working adults. The recent revision of China’s indoor air quality standard (GB/T 18883-2022) introduced more stringent levels on pollutants such as formaldehyde and benzene [29], necessitating updated, population-representative assessments of exposure distribution and microenvironment-specific attribution.

To date, most formaldehyde-related health risk assessments have concentrated on cancer outcomes [8,30,31,32]. However, our systematic review and meta-analysis of global data over four decades revealed that among targeted health endpoints assessed, only asthma exhibited a statistically significant association with indoor formaldehyde exposure, while evidence linking exposure to cancer was inconsistent [9]. This finding suggests that focusing solely on cancer endpoints would lead to a deviation from the actual population health burden of indoor formaldehyde. In contrast, disease burden metrics (particularly disability-adjusted life years, DALYs) offer a more comprehensive and quantifiable measure of population-level health impacts [21,33,34]. Despite their demonstrated advantages, DALY remain underutilized in formaldehyde-related health burden assessments [21,35], contributing to a systematic underestimation of its public health significance and limiting the capacity to inform evidence-based policymaking.

Kunshan, located in Jiangsu Province within the Yangtze River Delta, a key manufacturing region in China, has a permanent population of 1.29 million over a land area 930 km². The city is characterized by high-density residential areas and concentrated industrial activity, representing typical urban settings in modern China. By taking Kunshan as an example, this study employed a dual approach, combining personal and environmental exposure monitoring, to conduct an integrated assessment of urban formaldehyde exposure, the contribution of microenvironments, and the associated health burdens. The primary objectives are to (1) monitor personal formaldehyde exposure in urban residents and map the corresponding environmental concentrations across the spectrum of their daily environments, including both their homes and public spaces; (2) quantify the contribution of residential and public microenvironments to overall formaldehyde exposure across demographic groups; and (3) quantify associated health burdens and assess the benefits of potential control strategies in built environment.

2. Materials and Methods

2.1. Sampling Site and Study Design

This field-based investigation was conducted between 17 June and 21 August 2024, in the urban districts of Kunshan, China. A total of 112 adult participants were recruited to undergo personal formaldehyde exposure monitoring. To minimize the influence of recently introduced emission sources around their personal environment, the inclusion criteria stipulated that all participants must have resided in a home with no renovation or refurbishment activities within the last year. In conjunction with personal sampling, each participant completed a structured questionnaire to capture demographic information, daily working hours, and self-reported symptoms related to ocular and respiratory irritation. A subset of participants (n = 84) consented to the assessment of residential and occupational environmental quality, allowing for environmental measurements of temperature, relative humidity, and formaldehyde levels. General characteristics of study population are summarized in

Table 1. General information of participants.

Category	Number (n)
Sex
Male	30
Female	82
Age
<30 years	15
30~45 years	60
>45 years	37
Working places
Hotel	46
Mall	29
Natatorium	19
Salon	8
Bus station	10
Annual household income (RMB)
<40 thousand	25
40~60 thousand	43
60~100 thousand	27
>100 thousand	17
Ocular irritation during the last month
Yes	32
No	80
Respiratory irritation during the last month
Yes	42
No	70

. In addition, formaldehyde concentrations from vehicular (n = 16) and outdoor environment (n = 16) were also measured. These measurements supported the apportionment analysis of exposure contributions across microenvironments.

2.2. Sampling Method and Chemical Analysis

To assess personal exposure to formaldehyde, passive sampling badges (UME^x100, Cat. No. 500-100, SKC Inc., Eighty Four, PA, USA) were deployed over a 24 h monitoring. These samplers offer high level of accuracy for a full-day sampling period (lower detection limit: 2 mg/m³ for 24 hr sampling) [36]. Participants were instructed to wear the badge on their collar during waking hours and place it near their sleeping area at night. Participants recorded the start and end times of sampler use, after which the badges were refrigerated (below 4 °C), until collection by the research team. All collected samples were analyzed within three weeks.

Chemical analysis was performed using high-performance liquid chromatography (HPLC) system (Ultimate 3000, Thermo Scientific, Waltham, MA, USA) equipped with an ultraviolet detector at 360 nm. Formaldehyde absorbed in each sampler was extracted using 5 mL acetonitrile (HPLC grade, Sinopharm Chemical Reagent, Co., Ltd., Shanghai, China) as the solvent, followed by filtration through a 0.22 mm membrane. The HPLC system utilized a mobile phase of acetonitrile and water at a flowrate of 1.0 mL/min (column: Acclaim 120 C18, Thermo Scientific). The acetonitrile content was programmed to increase linearly from 50% to 70% over 20 min, reaching 100% at 30 min, then decreased to 50% at 32 min, and remained constant for 4 min (i.e., totally 36 min). The column temperature was maintained at 30 °C.

Environmental monitoring of formaldehyde concentrations was conducted using the 3-methyl-2-benzothiazolinone hydrazine (MBTH) spectrophotometric method, in accordance with the Chinese national standard GB/T 18204.2-2014 [37]. In this approach, ambient formaldehyde was actively sampled into a 5.0 mL MBTH solution (50 mg/mL, Fluka 65875, Buchs, Switzerland) via a glass impinger connected to a calibrated air sampling pump (GilAir PLUS, Petersburg, FL, USA) operating at 500 mL/min for 45 min. Following sampling, 0.4 mL of 10 mg/mL ferric ammonium sulfate solution was added to the sampling tube to catalyze chromogenic reaction, and the absorbance of the resulting blue cationic solution was measured by a spectrophotometer (TU-1901, Beijing Puxi General Instrument Co., Ltd., Beijing, China) at 630 nm.

2.3. QA/QC and Statistical Analysis

Calibration curves for passive sampling were prepared using a formaldehyde-2,4-dinitrophenylhydrazine (DNPH) solution (1000 mg/mL of formaldehyde; TMRM Quality Inspection Technology Co., Ltd., Changzhou, China), diluted with pure acetonitrile to create solutions with 10 mg/mL. A six-point calibration curve was constructed using 20 μL injections of standard mixture with concentrations of 0, 0.02, 0.2, 0.5, 1.0, and 2.0 g/mL (of formaldehyde). One field blank was included per sampling group (approximately 15% selected) using internal blanks provided with the SKC samplers. For the MBTH method, standard solutions (0–2.0 mg of formaldehyde) were prepared from a 1000 mg/mL formaldehyde solution (TMRM Quality Inspection Technology Co., Ltd.), diluted using deionized water, and a nine-point calibration curve was generated. At least one blank sample was analyzed per data collection group. Duplicate measurements were obtained at each sampling point, and the reported concentration values were the arithmetic mean of the two duplicates. Detection limits for active sampling were 2 mg/m³. Calibration curves with R² (regression coefficient) greater than 0.99 were considered acceptable for both approaches.

Statistical analyses were performed using SPSS software (V.27.0 for Windows), with significance defined as p = 0.05 (two-tailed). Between-group differences in personal exposure concentrations (by sex, gender, occupation, subjective perception) were evaluated using the Mann–Whitney U test. The correlation between exposure levels and annual household income were explored using Spearman’s rank correlation analysis.

2.4. Exposure and Contribution Apportionment

In the present study, the total daily intake of formaldehyde (DI_tot, mg/kg/day) was calculated using

$$D{I}_{tot}={\displaystyle \frac{C_{per}\times IR}{BW}}$$

(1)

where C_per (mg/m³) is the personal exposure concentration; IR (m³/day) is the age-specific inhalation rate; BW (kg) is the body weight.

For the environmental assessments, corresponding intake (DI_env,i, mg/kg/day) in specific microenvironment was derived as follows:

$$D{I}_{env,i}={\displaystyle \frac{C_{env,i}{t}_i\times IR}{BW}}$$

(2)

where C_env,i is the formaldehyde concentration in microenvironment i, g/m³; t_i denotes corresponding time fraction spent in the microenvironment, unitless.

The contribution (x, %) of each microenvironment was further estimated by

$$x={\displaystyle \frac{D{I}_{env,i}}{D{I}_{tot}}}\times 100\%$$

(3)

2.5. Health Burden Estimation

For cancer risk assessment, the Kunshan population was divided into six age groups, i.e., <1 year, 1–5 years old, 5–12 years old, 12–18 years old, working adults (18–60 years old for male, and 18–55 years old for female), and the elderly (>60 years old for male, and >55 years old for female). The exposure factors for different age groups are listed in Supplementary Materials Tables S2 and S3. For each age interval, cancer risk was assessed based on the estimation of lifetime cancer risk (LCR), as follows:

$$LCR=CPF\times CDI\times ADAF$$

(4)

where CPF is the cancer potency factor, (g/kg/day)⁻¹, set as 2.1 × 10⁻⁵ (g/kg/day)⁻¹ for inhalation exposure to formaldehyde [38]; ADAF is the age-dependent adjustment factor, which was introduced to take into account the susceptibility of exposure during the early life stage. U.S. EPA recommended an ADAF of 10 for 0–2-year-old, 3 for 2–16-year-old, and 1 for 16–70-year-old [39]. Therefore, ADAF was set as 10 for the “<1 year” group, 3 for the “1–18 years” groups, and 1 for adults in this study. CDI (g/kg/day) is the chronic daily intake, which is expressed as

$$CDI={\displaystyle \frac{DI\times F\times ED}{AT}}$$

(5)

where F is the exposure frequency, day/year, given as 365 days/year in our calculations; ED is the exposure duration, year, which is equal to the yearly duration of each age interval [40]; AT is the average lifetime (70 years). Based on the EPA guidelines, a value of less than 10⁻⁶ was considered a negligible cancer risk [41].

Asthma was selected as the target endpoint based on prior meta-analytic findings, establishing its association with indoor formaldehyde exposure in civil buildings [9]. The disease burden was expressed in DALYs, calculated as follows:

$$DALY=PAF\times DAL{Y}_{total}$$

(6)

where PAF is the population attributable fraction corresponding to indoor formaldehyde exposure for asthma; DALY_total is the total DALY value caused by asthma for each age group of Kunshan population. DALY values for the Chinese were extracted from the Global Burden of Disease (GBD) 2021 database (ghdx.healthdata.org/gbd-results-tool) [42] and subsequently scaled to Kunshan population using the population size as the adjustment factor (see Supplementary Materials Table S1). Based on the GBD 2021 data, Kunshan population was divided into four age groups, i.e., <5 years old, 5–19 years old, 20–59 years old, and ≥60 years old. In calculating DALYs, we did not include gender-specific differences since our estimates revealed nearly identical formaldehyde exposure levels between genders when applying time-weighted environmental concentrations (mean difference < 5%).

The PAF can be calculated as follows:

$$PAF={\displaystyle \frac{RR-1}{RR}}$$

(7)

$$RR=\left\{\begin{array}{l}{R{R}_0}^{{\displaystyle \frac{C}{\Delta C}}},C\le C_{max}\\ {R{R}_0}^{{\displaystyle \frac{C_{max}}{\Delta C}}}, C>C_{max}\end{array}\right.$$

(8)

where RR is the relative risk for asthma; RR₀ is the relative risk based on our previous meta-analysis, i.e., 1.20 (95%CI: 1.11–1.31) and 1.09 (95%CI: 1.03–1.15) for children’s and adults’ asthma, respectively; C (g/m³) is the time-weighted exposure levels (integrated by environmental concentrations and time fractions spent in specific environments); C_max is the upper limit of exposure level, i.e., 214 g/m³ and 97.4 g/m³ for children and adults, respectively; and ΔC is the unit gradient of formaldehyde concentration increase (10 g/m³) [9].

We also assumed that one year of life lost due to disability or death is equivalent to the loss of the annual economic production value of one person, which is equal to the gross domestic product (GDP) per capita in one year [21,43]. The total costs associated with corresponding DALY are estimated as follows:

$$\mathrm{Costs}=\Sigma DALY\times \mathrm{GDP} \mathrm{per} \mathrm{capita}$$

(9)

with Kunshan’s GDP per capita reported at 240,000 CNY in 2024 [44].

2.6. Uncertainty and Sensitivity Analysis

Monte-Carlo simulations (20,000 iterations) were performed using Oracle Crystal Ball software (V.11.1.3.0, 64-bit), to quantify uncertainty and variability in model input parameters. Sensitivity data were also extracted from the output results when each simulation was completed. Lognormal distributions were assumed for formaldehyde concentrations and time–activity patterns. Exposure factors (including time fractions) were assumed to be log-normally distributed except for body weight (set as normally distributed). A triangular distribution was used for CPF values [45], with the OEHHA upper-bound [38] set as the most likely value and the maximum, and zero as the minimum. Relative risk (RR₀) values were also modeled as log-normally distributed.

3. Results and Discussion

3.1. Personal Exposure Levels

Figure 1 displays the distribution of personal formaldehyde exposure concentrations across occupational groups, with data missing for five samples (three for hotel, one for mall, and one for natatorium). The average formaldehyde exposure level across all subjects was 36.0 μg/m³ (SD: 30.7 μg/m³). Notably, natatorium employees exhibited significantly lower mean exposure levels (21.2 μg/m³, p < 0.05) compared to individuals in other settings, suggesting that indoor air in natatoriums may inherently contain lower concentrations of formaldehyde relative to other public spaces. No statistically significant differences in formaldehyde exposure levels were observed across gender or age categories. However, Spearman’s rank correlation analysis showed a modest yet statistically significant association between personal exposure levels and annual household income (r_s = 0.273, p = 0.005). Participants in the highest-income group (>100 thousand CNY) experienced a mean exposure level of 53.3 μg/m³, in contrast to 30.4 μg/m³ among those in the lowest-income group (<40 thousand). This trend may reflect the increased use of high-end furnishings and decorative materials in wealthier households, which are known sources of formaldehyde emissions. No significant correlation was found between personal levels and self-reported symptoms of ocular or respiratory irritation.

Table 2. Comparison of personal exposure levels of formaldehyde.

	Country	Time	Subjects	Sampling Method	Sampling Duration	Personal Level (μg/m3)
Shinohara et al. (2019) [46]	Japan	May, 2015; February, 2016	Gas station workers	Personal pumps	2 h	5.1 (spring) 11.6 (winter)
Uchiyama et al. (2024) [47]	Japan	March, 2017–March, 2022	Residents	Passive sampler	1 week	24
Lazenby et al. (2012) [48]	Australia	November–December, 2006; June–August, 2007	Children (9–12 years old)	Passive sampling badge	24 h	13.6
Yang et al. (2023) [28]	Switzerland	November, 2020	Office workers	Passive sampling badge	Daily working hours	74.7
Choi et al. (2023) [49]	Korea	November–December, 2021	Salon technicians	Passive sampling badge	8 h	49.7
Yen et al. (2023) [50]	China	May–August, 2021; February–March, 2022	University students	Passive sampler	3 days	16.4~21.8
Abelmann et al. (2020) [51]	USA	October, 2017	Office workers	Passive sampling badge	24 h	12
This study	China	June–August, 2024	Working adults	Passive sampling badge	24 h	36.0

provides a comparative summary of the personal formaldehyde exposure levels across multiple international studies. The values reported in this study fall within a moderate range. For instance, Japanese gas station workers experienced exposure levels as low as 5.1 μg/m³, while Swiss office workers exposed to significantly higher concentrations (averaging 74.7 μg/m³). This wide variability highlights the influence of differing occupational environments, regulatory standards, and building materials across countries.

3.2. Formaldehyde Concentration in Microenvironments

Table 3. Measured environmental data of various microenvironments.

	Public Places (n = 84)	Residences (n = 84)	Vehicles (n = 16)	Outdoor (n = 16)
Formaldehyde (μg/m3)	46.1 ± 16.1	41.3 ± 61.7	28.2 ± 19.7	16.1 ± 6.8
Temperature (°C)	25.4 ± 0.9	27.8 ± 2.7	27.9 ± 2.3	29.8 ± 2.9
Relative humidity	75.0 ± 10.5%	71.9 ± 10.5%	51.0 ± 13.7%	71.4 ± 16.6%

summarizes the environmental measurements of air quality across different microenvironments in Kunshan. Formaldehyde levels were higher in residential environments and public spaces, followed by vehicular environments and outdoor air. Our historical data from 2010 to 2015 indicated mean indoor formaldehyde concentrations of 45–68 μg/m³ in public places (n = 564) [52], slightly exceeding the present average of 46 μg/m³, suggesting a temporal downward trend.

As shown in

Table 4. Comparison of formaldehyde concentrations (mg/m³) in various microenvironments.

Microenvironment	Country	Sampling Site	Concentration
Public/office
Shinohara et al. (2019) [46]	Japan	Office	10.1 (spring) 24.1 (winter)
Feng et al. (2010) [53]	China	Subway station	21.2–31.7
Yang et al. (2023) [28]	Switzerland	Office	58
Evtyugina et al. (2021) [26]	Spain	Salon	11.5
Lamplugh et al. (2019) [27]	USA	Salon	5.3–20.6
Hwang et al. (2018) [54]	South Korea	Hospital, personal care center	24.4
This study	China	Hotel, salon	46.1
Commuting
Feng et al. (2010) [53]	China	Metro carriage	12.3
Gong et al. (2017) [25]	China	Metro carriage	4.5–23.3
Pang et al. (2007) [55]	China	Vehicle	16–40
Shinohara et al. (2005) [56]	Mexico	Vehicle, metro	19.4–40.2
This study	China	Vehicle	28.2
Outdoor
Shinohara et al. (2019) [46]	Japan	Gas station	4.3 (spring) 6.0 (winter)
Feng et al. (2010) [53]	China	Roadside	10.8
Wang et al. (2024) [57]	China	Urban area	14.1
Alves et al. (2025) [58]	Angola	School	2.3
Kanjanasiranont et al. (2015) [59]	Thailand	Roadside	20.1
Norbäck et al. (2017) [60]	Malaysia	School	5.5
Jia et al. (2013) [61]	USA	Residential region	2.97
Baez et al. (2003) [62]	Mexico	Metropolitan zone	4–32
This study	China	Urban area	16.1

, we compared formaldehyde levels in indoor microenvironments across international studies. Formaldehyde concentrations in Kunshan’s public office environments were generally higher than those reported in similar settings in Asia, Europe, and North America. Within commuting microenvironments, vehicular formaldehyde concentration surpassed those in subways, likely due to differences in cabin volume and materials composition. It should be noted that we did not quantify the mechanical ventilation rate, which is typically a significant factor influencing formaldehyde concentrations in confined spaces. For outdoor environments, atmospheric formaldehyde concentrations in China remained elevated relative to international measurements, underscoring ongoing national challenges in air quality monitoring and control [20].

3.3. Exposure Estimates and Contribution Apportionment

Figure 2 presents modeled distributions of total daily intake of formaldehyde based on personal monitoring. Overall, the estimates yielded similar results between genders (with mean intake dose of 5.1 μg/kg/day). Based on environmental exposure estimates, we further analyzed the contributions of different microenvironments to total formaldehyde exposure. As shown in Figure 3, residential and office environments contributed most significantly to overall exposure (50% and 40%, respectively) for working adults. Outdoor and commuting microenvironments together accounted for less than 10% of total intake. Gender-specific variations were minimal, with slightly higher residential exposures for females. The comparison with Du et al. [19] reveals that while aggregate contributions of formaldehyde exposure from the two primary microenvironments (residential and occupational settings) were similar, our study observed a higher occupational contribution. Specifically, the contribution from office environments in Du et al.’s study [19] (<10%) was substantially lower than our estimates, which was a result of differences in time–activity parameter selection. The discrepancy is attributed to longer occupational durations recorded in our field survey (~490 min/day), compared to the assumptions made by Du et al. [19] (290 min/day for male and 217 min/day females). Similarly, a previous study by Loh et al. [63] also demonstrated high residential contribution to formaldehyde exposure (median: 70%) across the US population.

Environmental 5 years, this study did not include formaldehyde measurements in school settings. To enable reasonable estimation of children’s environmental exposure, we referenced findings from previous studies. Fang et al.’s systematic review of indoor formaldehyde concentrations in China revealed comparable concentration ranges between office and school environments in Jiangsu Province [20]. As such, formaldehyde levels in schools were approximated using public space databased on the literature indicating similar concentration ranges. The modeled chronic exposure doses ranged from 0.3 μg/kg/day (<1 year) to 1.6 μg/kg/day (5–12 years) across different age groups of children. For the elderly, mean exposures ranged from 1.8 μg/kg/day (males) to 3.0 μg/kg/day (females). As shown in Figure 3, residential environments dominate with formaldehyde exposure accounted for over >90% of total intakes in Kunshan children under 5 years old, decreasing to ~60% in older children (5–18 years), with school environments contributing 30%. For elderly populations, residential environments contributed an average of 80% versus 12% from public spaces. Outdoor exposures contributed < 10% across all groups, peaking at ~8% for children above 5 years old, which is consistent with their greater outdoor activity time. For all age groups, exposures in commuting microenvironment were minimal (<5%). Collectively, built environments accounted for >90% of total formaldehyde exposure for Kunshan’s population, demonstrating their predominant roles in various microenvironments.

3.4. Cancer Risk and Burden of Disease

Figure 4 illustrates the cumulative distributions of lifetime cancer risk (LCR) from formaldehyde exposure. Working adults exhibited the highest LCR values based on personal assessments (mean: 7.1–7.3 × 10⁻⁵), with slightly higher risks among males. Children showed mean lifetime cancer risks from 3.8 × 10⁻⁵ to 6.4 × 10⁻⁵, with the highest risk observed in the 5–12 age group, followed by children under 5 years; adolescents (12–18 years) showed the lowest risks, while the elderly exhibited lower risks (2.5 × 10⁻⁵ (males > 60 years) to 4.1 × 10⁻⁵ (females > 55 years)). Comparative analysis across age groups revealed that Kunshan working adults exhibited the highest cancer risk, followed by children, with elderly populations demonstrating the lowest risk levels. Notably, these values far exceeded the US EPA’s acceptable threshold (1.0 × 10⁻⁶), representing 24–85-fold elevation in risk.

We compared the lifetime cancer risk for Kunshan residents calculated in this study with findings from previous studies. Since most existing studies focus on a single indoor microenvironment, whereas our calculated risk represents the integrated exposure across multiple microenvironments, we selected a subset of studies specifically assessing formaldehyde exposure risk in residential settings to enhance comparability, given that residential environments constitute the major contribution of formaldehyde exposure. As illustrated in Supplementary Materials Figure S1, the lifetime cancer risk associated with formaldehyde exposure among Kunshan residents was found to be relatively low, generally consistent with the estimates reported by Du et al. for Chinese urban population [19] and slightly lower than that of the U.S. population across various microenvironments [63]. However, cross-country comparisons revealed that the formaldehyde-induced cancer risk for Chinese residents remains at a relatively elevated level, with some studies reporting risks exceeding the safety threshold (10⁻⁶) by up to 1000-fold [32].

Using asthma as a representative health endpoint, the estimated total health burden from formaldehyde exposure was 571 DALYs (as in

Table 5. Disease burdens and economic losses (mean, 95% CI) are attributable to formaldehyde exposure of Kunshan population.

	Disability-Adjusted Life Years (DALYs)					Economic Losses (Million CNY)	Change in Losses a
	<5 Years	5–19 Years	20–59 Years	≥60 Years	Total
Baseline	60 (12–146)	168 (63–344)	217 (64–502)	125 (23–338)	571 (258–1055)	136 (62–245)	/
15% reduction	55 (11–143)	153 (56–329)	194 (55–475)	113 (20–327)	515 (229–995)	123 (55–232)	−9.6%
30% reduction	49 (9–137)	137 (49–308)	169 (47–433)	99 (18–311)	454 (200–926)	108 (48–215)	−20.6%

^a Change in mean economic losses from baseline value.

), equating to an annual economic cost of 136 million CNY. Working adults and school-aged children were the primary contributors (Supplementary Materials Figure S2), accounting for 38% and 29% of the total burden, respectively, followed by the elderly (22%) and children less than 5 years old (10%). DALY rates were highest among children aged 5~19 (up to 59 per 100,000), followed by children less than 5 years old (55 per 100,000) and the elderly population (48 per 100,000). DALY rates for Kunshan working adults were at a lower level (15 per 100,000). Liu et al. [9] reported a DALY rate attributable to indoor formaldehyde exposure of 53.8 per 100,000 for the Chinese population in 2017, which was consistent with our findings. However, DALY rates in Kunshan are significantly higher than those observed in the US (46 per 100,000) [35] and European countries (<0.2 per 100,000) [33], underscoring the urgency for enhanced indoor air quality regulations in China. In terms of DALY rates, the majority of the disease burden in Kunshan originated from lost life years in children and older individuals. This distribution diverged from that of carcinogenic risks, which were predominantly observed in the working adults. The disparity suggests that non-cancer health outcomes (e.g., respiratory diseases in vulnerable groups) may contribute significantly to formaldehyde-related burdens. Therefore, current risk management policies focusing solely on carcinogenicity may underestimate the total public health burdens, necessitating integrated evaluation and intervention frameworks.

Given that over 90% of exposure originated from residential and public/school environments, targeted control interventions in these domains were modeled. As shown in Table 5, a 15% reduction in formaldehyde levels in both built environments would reduce DALYs to 515 and prevent 9.6% in economic losses (13 million CNY) annually. A 30% concentration reduction would yield 454 DALYs and avert 20.6% in economic losses (28 million CNY), illustrating substantial health and economic co-benefits from relatively modest mitigation efforts.

3.5. Sensitivity Analysis

A sensitivity analysis was conducted to identify which parameters most significantly influence health burden estimates, thereby informing priorities for refining these estimates when more detailed data become available. In the context of cancer risk, although parameter contributions varied across age groups, the overall ranking of influential parameters remained consistent. Analysis data showed that variability in residential formaldehyde concentrations and CPF were the dominant contributors to the total uncertainty, jointly accounting for >80% of the variance across all age groups. For children, residential concentrations alone contributed between 46 and 76% to the total variance, followed by CPF, which contributed 13~33%. Secondary contributors included body weight and inhalation rate, which together accounted contribution for 10~20% of total variance. Among working adults, the predominant contributors were residential concentration (45%), CPF (42%) and occupational exposure level (8%). For elderly population, residential concentration and CPF, contributed 72% and 23% of the total variance, respectively. With respect to the overall burden of disease, expressed in DALYs, the primary contributors were residential formaldehyde concentrations (60%), the relative risk (RR₀) associated with adult asthma (19%), and concentration in public/school environments (14%). These findings underscore the importance of improving the accuracy of environmental monitoring data, particularly in residential settings and refining health effect estimates such as (RR₀) and CPF to enhance the precision of health burden assessments.

3.6. Limitations

Several limitations of this study must be acknowledged. First, the sample size, particularly for certain occupational categories, was relatively small, which may compromise statistical power and generalizability of the findings. Most recruited participants were employed in public venues (excluding hotels and salons), but insufficient sample sizes from locations such as shopping malls and natatoriums introduced uncertainty into personal exposure estimates. For environmental monitoring, workplace measurements were similarly skewed, with workplace assessments predominating, while vehicular and outdoor sampling remained underrepresented. In the absence of direct measurements in schools, workplace data were used as proxy to estimate children’s exposure levels, introducing potential misclassification. Furthermore, ventilation conditions within the indoor environments were not incorporated into this study. Future research could further explore the relationship between ventilation and formaldehyde concentrations in building environments. Second, all data collection occurred during the summer season, limiting the ability to account for seasonal variations in formaldehyde exposure. However, as higher temperatures in summer typically correspond to peak emission levels from indoor sources, the exposure to formaldehyde and health burden estimates reported are likely to represent conservative (worst-case scenario) calculations. The development of long-term monitoring technologies for environmental exposure remains an urgent research priority [57,64,65]. Third, due to local data limitations, time–activity patterns for certain subgroups (e.g., time spent in public spaces for the elderly and preschool-aged children), were adapted from the U.S. EPA’s Exposure Factors Handbook [66]. Consequently, this introduces potential discrepancies between modeled and actual behaviors in the local context. Finally, the asthma-attributable disease burdens for Kunshan were extrapolated through demographic scaling from national-level data, assuming uniform disease prevalence across geographical regions. Such assumptions may overlook local epidemiological variations. The economic valuation of DALYs was based on human capital method, which equates one DALY with per capita GDP. While it is widely used for quantifying associated economic losses [43], this method may overestimate the economic impact of children, given that GDP estimates primarily reflect adult productivity. Despite these limitations, this study provides critical empirical evidence and actionable recommendations for mitigating formaldehyde exposure and its associated health impacts in urban China.

4. Conclusions

This study conducted an integrated assessment of formaldehyde exposure among typical urban residents, combining personal and environmental sampling to provide microenvironmental contributions. Among working adults, the mean personal exposure concentration was 36.0 μg/m³, with estimated total chronic intake dose of 5.1 μg/kg/day. Contribution apportionment analysis revealed that residential (50%) and public (40%) environments were the primary sources of exposure for working adults. For children under 5 years and the elderly, residential settings accounted for >80% contribution of the total intake. The home and school environments contributed to approximately 60% and 30% of exposure for children and adolescents aged 5–18 years, respectively. Vehicular and outdoor environments contributed less than 10% of total exposure.

Estimated lifetime cancer risks due to formaldehyde exposure ranged from 2.5 × 10⁻⁵–8.6 × 10⁻⁵. These risks corresponded to an estimated disease burden of 571 DALYs and an economic loss of 136 million CNY annually. Scenario analyses demonstrated that reducing formaldehyde concentrations in buildings by 15–30% could yield around 10–20% reduction in health burden (13–28 million CNY economic benefit). These findings support the development of regulatory standards development and built environment interventions aimed at minimizing formaldehyde-related health burdens, particularly for vulnerable populations such as children and the elderly.