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DOI: 10.1039/D5EM00252D (Paper) Environ. Sci.: Processes Impacts, 2025, Advance Article

Dust-borne emerging contaminants: an underrecognized risk factor for non-communicable diseases in older Chinese adults†

Luhan Yang‡ ab, Yu Wang‡c, Le Heab, Lei Xiangd, Lei Wangc, Yiming Yaoc, Hongwen Sunc and Tao Zhang*ab
aSchool of Agriculture and Biotechnology, Sun Yat-Sen University, Shenzhen 518107, China. E-mail: zhangt47@mail.sysu.edu.cn
bSchool of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
cMOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
dCollege of Life Science and Technology, Jinan University, Guangzhou 510632, China

Received 2nd April 2025 , Accepted 19th June 2025

First published on 7th July 2025

Abstract

Given global aging and the growing burden of non-communicable diseases (NCDs), identifying environmental risk factors in older adults is critical. This study investigated the associations between dust-borne emerging contaminant (EC) exposures and several high-incidence NCDs in older adults. 137 province-level dust measurements of microplastics, liquid crystal monomers, organophosphate esters, and per- and polyfluoroalkyl substances across 23 provinces were used to estimate exposure levels for 14[thin space (1/6-em)]466 Chinese participants (mean age 84.7). Propensity score-adjusted logistic regression showed significantly higher odds of NCDs at the third vs. first tertile of specific exposure, including hypertension [polyethylene terephthalates: odds ratio (OR), 1.28; 95% confidence interval (CI), (1.16–1.41), etc.], heart disease [perfluoroalkyl carboxylic acids (PFCAs) (C4–C12): 1.25 (1.04–1.49), etc.], cataract [organophosphate triesters: 1.61 (1.32–1.96), etc.], stroke [polycarbonates: 1.30 (1.06–1.58), etc.], respiratory diseases [organophosphate triesters: 1.57 (1.27–1.94), etc.], and arthritis [PFCAs (C4–C12): 1.73 (1.34–2.24), etc.]. G-computation estimated 6–13% absolute increases in disease probability (∼60–130 additional cases per 1000) from low- to high-exposure scenarios. Subgroup analyses suggested stronger associations among females, the oldest participants, and eastern residents. These pilot findings demonstrate dust-borne ECs as an underrecognized health risk for older adults and warrant further investigation. Adopting precautionary principles prevents studies and regulations from remaining confined to criteria air pollutants. An interactive online dashboard provides full results.


Environmental significance

Dust is an underrecognized exposure pathway for emerging contaminants (ECs). Recent findings suggest that dust-borne ECs can approach or even exceed dietary intakes. Given that the global population aged 60 years and older is projected to double by 2050, and that non-communicable diseases (NCDs) now account for 74% of global deaths, traditional air quality regulations focusing on a few “criteria pollutants” may overlook some critical environmental risks. This work demonstrates that higher dust-borne ECs are associated with certain NCDs (e.g., hypertension and heart disease) in older Chinese adults. By adopting precautionary principles and conducting exploratory investigations, we may help break the cycle in which only well-established pollutants receive systematic monitoring and prevent regulations from remaining confined to them.

Introduction

Rapid industrialization in the Anthropocene has driven an unprecedented surge in global synthetic chemical production, leading to waste streams that transgress planetary boundaries.1–3 Among the many emerging contaminants (ECs), microplastics (MPs), liquid crystal monomers (LCMs), organophosphate esters (OPEs), and per- and polyfluoroalkyl substances (PFAS) have captured attention due to their extensive use, environmental persistence, and toxicity.4–7 MPs are byproducts of plastic degradation; LCMs come from electronics; OPEs serve as flame retardants and plasticizers; and PFAS are ubiquitous in waterproof products and food packaging. Weak covalent bonds with host materials enable them to readily leach or migrate under heat or mechanical stress, ultimately remaining airborne or settling onto surfaces.8–10 Consequently, dust becomes a significant reservoir for these ECs, posing human exposure risks via inhalation and ingestion.11–13

Despite efforts to quantify dust-borne ECs, limited attention was paid to their health implications.14–19 This is concerning given the growing burden of non-communicable diseases (NCDs), which cause 41 million deaths annually (74% of global fatalities).20–22 Although air pollution is recognized as the single biggest environmental risk factor for NCDs, the contribution of dust-borne ECs to such pollution-and thus to NCD onset-remains underexplored. Unlike water or food, where EC intakes can be reduced by source selection or cooking practices, dust exposure is far more insidious, occurring continuously both indoors and outdoors. Recent studies suggest that, for some ECs, dust intake may approach or exceed that from water or food.23–25 Nevertheless, there are no strict regulations on dust contamination. This blind spot may inadvertently enable EC accumulation in our surroundings, posing underrecognized health risks.

The stakes are particularly high for older adults. On the one hand, they frequently spend a higher proportion of time indoors, either at home or in institutional settings like nursing homes. On the other hand, aging is accompanied by weakened immune function and diminished physiological resilience, which may amplify the negative impacts of low-level environmental contaminants. Declining health in this vulnerable group not only drives up personal healthcare costs but also places heavier demands on social support networks and national health insurance systems compared with younger populations. Given that the global population aged 60 years and older is expected to nearly double in the next two decades, ensuring the health of older adults has becomes an urgent and high-impact priority.26,27

In this pilot study, we investigate associations of dust-borne MPs, LCMs, OPEs, and PFAS with NCDs among elderly individuals in China. By integrating dust measurements into data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), we provide preliminary evidence that these underrecognized contaminants may contribute to age-related chronic diseases, highlighting the pressing need for more exploratory investigations into unestablished air pollutants.

Materials and methods

Study design and participants

This study used demographic data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) conducted between 2017 and 2018. A detailed description of the prospective population-based study has been previously published.28–30 Our analysis included 14[thin space (1/6-em)]466 participants from a total of 15[thin space (1/6-em)]874 individuals aged 65 years or older across 23 provinces or municipalities in China (Fig. 1). All participants or their legal representatives provided written informed consent and the study was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-13074). We analyzed seven NCDs with a prevalence greater than 5% in the study population. The presence of each NCD was ascertained from self-reported information and clinical diagnoses.
image file: d5em00252d-f1.tif
Fig. 1 Flowchart of study population enrollment and analysis.

Measurement of ECs in dust

Indoor and outdoor dust samples were collected from the corresponding 23 provinces in China between March and August 2017. To ensure representativeness, sampling sites were selected away from potential pollution sources, such as major highways and industrial areas, with final sampling sites chosen in close proximity to residential neighborhoods. Indoor dust samples were specifically collected from typical middle-income households, each consisting of a couple and one child (Text S1†). Details of the sampling sites are provided in Table S1.† Dust concentrations of 2 kinds of MPs (i.e., polyethylene terephthalates [PETs] and polycarbonates [PCs]), 60 kinds of LCMs (i.e., 46 fluorinated and 14 unfluorinated LCMs), 30 kinds of OPEs (i.e., 19 OP triesters and their 11 diester degradation products), and 30 kinds of PFAS (i.e., 2 PFCAs (C2–C3), 9 PFCAs (C4–C12), 4 PFSAs, 12 emerging PFAS alternatives, and 3 FTOHs) were quantified using high-performance liquid chromatography (HPLC) coupled with electrospray triple quadrupole mass spectrometry (ESI-MS) and gas chromatography (GC) coupled with mass spectrometry (MS) (Table S2†). For notational convenience, we used the “Σ” symbol before EC names to represent the sum of the dust concentrations of each category of contaminants. Further details on sample collection, preparation, and analysis methods, as well as quality assurance and control protocols, are provided in previous publications.18,31–33

Estimated daily intakes of dust-borne ECs

The elderly population is primarily exposed to ECs in dust through ingestion and inhalation. Thus, we calculated the estimated daily intake (EDI; ng per kg bw per day) of ECs from dust using eqn (1)–(3):
 
EDI = EDIing + EDIinh (1)
 
image file: d5em00252d-t1.tif(2)
 
image file: d5em00252d-t2.tif(3)
where EDIing and EDIinh represent the daily intake of ECs from dust through ingestion and inhalation, respectively; C (ng g−1) is the concentration of ECs in dust samples; IngR (mg per day) and InhR (m3 per day) are the ingestion and inhalation rates for the elderly; EF (minutes per day) is the daily exposure frequency; CF (kg mg−1) is the conversion factor; PEF (m3 kg−1) is the particle emission factor; and BW (kg) is the body weight of the individual. Details of the parameters are listed in Table S3.† 18,31–34

Statistical analyses

Covariates. We recorded each participant's age, sex, region, body mass index (BMI), taste preferences, smoking status, alcohol consumption, physical activity, education level, economic status, and marital status. Missing values for these covariates were imputed by multiple imputation. Both age and BMI were standardized with z-transformations before inclusion in the analyses.
Propensity score (PS) matching. To reduce confounding and approximate a randomized trial, we used a PS matching approach. Specifically, we employed a Gradient Boosting Decision Tree (GBDT) algorithm to estimate each participant's likelihood of being a case (having the disease) rather than a control (disease-free). Compared with traditional logistic regression, GBDT excels at modeling non-linear and higher-order interactions among covariates. By iteratively fitting decision trees that focus on residual errors from previous steps, GBDT can more accurately capture complex relationships between participant characteristics and disease odds.35 For each NCD cohort, the GBDT-based PSs were computed, and a 1[thin space (1/6-em)]:[thin space (1/6-em)]1 nearest–neighbor matching procedure was then applied. The quality of matching was assessed using three methods. First, standardized mean differences (SMDs) were calculated for all covariates before and after matching; an SMD below 0.1 generally indicates adequate balance. Second, we compared the distribution of PSs between cases and controls to ensure substantial overlap, thereby confirming that individuals in the cases and controls shared similar covariate profiles. Third, we performed statistical tests on included covariates and applied Benjamini–Hochberg (BH) adjustments to account for multiple comparisons; BH-adjusted P values above 0.05 were interpreted as indicating no significant residual imbalance. With this approach, we sought to create a pseudo-population in which the distribution of measured covariates no longer differed substantially between cases and controls, making dust exposure independent of observed confounders. Consequently, any remaining differences in disease odds between cases and controls could be more plausibly attributed to dust exposure rather than pre-existing imbalances.
Main analyses. We used logistic regression to examine the associations between exposure to dust-borne ECs and the odds of each NCD. First, each exposure variable of interest was divided into three categories (tertiles), using the lowest tertile as the reference group. The models were fitted with certain NCDs as the binary outcome, treating the second and third tertiles of each exposure as indicator variables. All models were adjusted for the prespecified covariates described above. For each exposure, we estimated odds ratios (ORs) and thw corresponding 95% confidence intervals (CIs), as well as the Wald test P values.
Trend analyses. To evaluate a possible linear trend across tertiles, we incorporated a numeric variable (i.e., 0, 1, and 2) corresponding to each tertile into the logistic model. This model was otherwise identical to the primary model but replaced the indicator variables with the numeric term. We extracted the P for trends by examining the significance of this numeric term. This allowed us to determine whether a linear dose–response relationship might exist between increasing exposure levels and the odds of certain NCDs.
Sensitivity and subgroup analyses. Several additional analyses were conducted to assess the robustness of our findings. First, we adjusted for exposure variables, which included additional dermal contact with dust-borne ECs (Text S2†). Second, we created a new cohort by repeating PS matching with logistic regression. Third, participants who used air purifiers or activated carbon devices were excluded to account for potential differences in dust composition and removal. We also performed subgroup analyses based on sex, age, and region to verify whether the observed associations were consistent across different groups. For each subgroup, logistic regression models were re-fitted using the same exposure categorizations and covariate adjustments as in the main analysis. In addition, we compared a reduced logistic model (containing main effects for EC exposure, sex/age/region, and other covariates) with a full model (adding an interaction term for exposure × sex/age/region). We used a likelihood ratio test to compare these nested models by calculating the difference in log-likelihood values, which was then compared with a chi-square distribution. The resulting P value was denoted as the interaction P for sex, age, or region.

All P values for trends and for interactions were adjusted for multiple comparisons using the BH method. A 2-sided BH-adjusted P < 0.05 was considered statistically significant.

Parametric g-computation analyses. We used the parametric g-computation approach to estimate how varying levels of a given exposure may influence a binary outcome in the presence of measured confounders. Parametric g-computation, first introduced by Robins et al., involves several key steps: (1) fitting a suitable parametric model to describe the relationship between the exposure, confounders, and outcome, (2) generating predicted probabilities in hypothetical exposure scenarios, and (3) averaging these predictions over the empirical distribution of confounders in the study population.

In this study, we used a logistic regression model for binary outcomes. Let Y be the outcome variable (i.e., disease status), X be the exposure, and C be a set of measured confounders. We fit the model using eqn (4):

 
log[thin space (1/6-em)]it[Pr(Y = 1|X,C)] = α + βX + γC (4)
where image file: d5em00252d-t3.tif, α is the intercept, β represents the effect of the exposure on the log–odds scale, and γ is a vector of coefficients corresponding to the confounders in C.

After estimating these parameters via maximum likelihood, we used the fitted model to compute predicted probabilities in hypothetical scenarios in which all participants were assigned one of three exposure levels (low for 0, moderate for 1, or high for 2). Specifically, for each individual i in the dataset, we substituted X = 0, X = 1, or X = 2 into the model while keeping the individual's observed confounder values Ci unchanged. This yielded three predicted outcome probabilities using eqn (5):

 
[p with combining circumflex]i(X = 0), [p with combining circumflex]i(X = 1), [p with combining circumflex]i(X = 2) (5)

We then averaged these predicted probabilities across all individuals to obtain mean outcome probabilities in each hypothetical exposure scenario using eqn (6):

 
image file: d5em00252d-t4.tif(6)

These mean probabilities represent counterfactual estimates of the population-level risk for Y = 1 if everyone in the study had been exposed at level 0, 1, or 2, respectively. Differences in these mean probabilities (e.g., [p with combining macron](1) − [p with combining macron](0)) can be interpreted as the estimated absolute effect of shifting the entire population from one exposure level to another, with conditional on measured confounders.

To quantify uncertainty around these estimates, we incorporated a nonparametric bootstrap procedure. We resampled the original dataset with replacement multiple times (i.e., 1000 iterations), repeated the g-computation steps for each resampled dataset, and then derived the distribution of the effect estimates (e.g., [p with combining macron](1), [p with combining macron](2)) across all bootstrap samples. From these distributions, we calculated 95% confidence intervals by identifying the 2.5th and 97.5th percentiles. This approach accounts for random sampling variability, providing more robust inferences regarding the magnitude and precision of the estimated effects.

Finally, because potential sex-, age-, and region-specific factors may modify the associations between exposure and the outcome, we performed subgroup analyses by stratifying participants according to sex, age, and region. We repeated all g-computation steps within each subgroup, thereby allowing for the estimation of group-specific disease probabilities.

All analyses were performed using Python (version 3.9). The study flow is illustrated in Fig. 2.


image file: d5em00252d-f2.tif
Fig. 2 Analysis flowchart.

Results

Study population and exposure characteristics

All results can be viewed interactively through online data visualization at https://public.tableau.com/app/profile/luhan.yang/viz/Dust-BorneEmergingContaminants/SunYat-senUniversity (Video).†

The study included 14[thin space (1/6-em)]466 participants, with a balanced gender distribution of 45% male. The mean (SD) age was 84.7 (11.6) years, and the mean (SD) BMI was 22.4 (4.2) kg m−2. Participants resided in eastern (49.3%), central (37.5%) and western (13.2%) regions of China. Over 70% were nonsmokers and nondrinkers, reducing potential confounding from lifestyle factors. The prevalence of NCDs was as follows: hypertension (38.7%), heart disease (15.1%), cataract (11.3%), stroke or cerebrovascular disease (CBD) (9.8%), respiratory diseases (9.0%), diabetes (8.9%), and arthritis (7.6%). Baseline characteristics are presented in Fig. S1 and Tables S4–S6.† The EDIs of dust-borne ECs were calculated for each participant based on dust measurements and individualized exposure factors. Among the assessed ECs, ΣPETs exhibited the highest mean (SD) EDI at 15[thin space (1/6-em)]453.9 (14[thin space (1/6-em)]627.2) ng per kg bw per day, indicating significant exposure to PETs via dust. The mean (SD) EDIs for other ECs were substantially lower, with ΣPCs, ΣOP triesters, and ΣOP diesters at 4.75 (7.37), 1.29 (0.65), and 0.14 (0.08) ng per kg bw per day, respectively. For ΣPFCAs (C2–C3), ΣPFCAs (C4–C12), and ΣEmerging PFAS alternatives, mean (SD) EDIs ranged from 0.03 (0.03) to 0.14 (0.14) ng per kg bw per day. ΣLCMs, ΣPFSAs, and ΣFTOHs exhibited negligible EDIs (<0.03 ng per kg bw per day), suggesting limited dust-borne ECs in this elderly cohort.

Associations between dust-borne ECs and NCDs

Several NCDs showed progressive increases in odds ratios (ORs) across tertiles of specific dust-borne EC exposure (BH-adjusted P for trend <0.05) (Fig. 3a). Hypertension was positively associated with ΣPETs [third vs. first tertile: OR, 1.28; 95% confidence interval (CI), 1.16 to 1.41] and ΣOP diesters (OR, 1.15; 95% CI, 1.05 to 1.26); heart disease with ΣPCs (OR, 1.22; 95% CI, 1.04 to 1.43), ΣPFCAs (C4–C12) (OR, 1.25; 95% CI, 1.04 to 1.49), and ΣPFSAs (OR, 1.24; 95% CI, 1.07 to 1.45); cataract with ΣPCs (OR, 1.39; 95% CI, 1.16 to 1.67), ΣPETs (OR, 1.30; 95% CI, 1.08 to 1.56), ΣOP triesters (OR, 1.61; 95% CI, 1.32 to 1.96), and ΣPFSAs (OR, 1.35; 95% CI, 1.13 to 1.6); stroke or CBD with ΣPCs (OR, 1.30; 95% CI, 1.06 to 1.58), ΣOP triesters (OR, 1.29; 95% CI, 1.05 to 1.58), and ΣPFSAs (OR, 1.30; 95% CI, 1.08 to 1.57); respiratory diseases with ΣPCs (OR, 1.43; 95% CI, 1.16 to 1.77) and ΣOP triesters (OR, 1.57; 95% CI, 1.27 to 1.94); and arthritis with ΣPCs (OR, 1.47; 95% CI, 1.17 to 1.84), ΣPFCAs (C4–C12) (OR, 1.73; 95% CI, 1.34 to 2.24), and ΣPFSAs (OR, 1.30; 95% CI, 1.05 to 1.61). Of note, some dust-borne EC exposures exhibited inverted U-shaped associations with certain NCDs (i.e., significantly increased ORs in the second but not the third tertile). Since nonlinear relationships require more advanced modeling methods and often involve complex mechanisms, this study focused exclusively on associations exhibiting linear trends across the full exposure range to ensure clearer and more reliable conclusions.
image file: d5em00252d-f3.tif
Fig. 3 (a) A partial forest plot for associations between dust-borne emerging pollutants and non-communicable diseases. (b) Probability differences of all investigated non-communicable diseases in hypothetical scenarios for all participants. (c) Probability differences of all investigated non-communicable diseases in hypothetical scenarios for specific demographic groups. Note: Color and circle size together indicate the estimated probability difference from the baseline. Larger, bluer circles suggest higher disease probability, whereas smaller, redder circles suggest lower disease probability. Only partial results are presented here. Full results can be viewed interactively in the online data visualization. https://public.tableau.com/app/profile/luhan.yang/viz/Dust-BorneEmergingContaminants/SunYat-senUniversity.

Sensitivity and subgroup analyses

Sensitivity analyses that accounted for additional skin exposure, applied logistic regression-based PS matching, and excluded participants who used air purifiers or activated carbon devices consistently supported the main findings (Table S7†). Despite variation in effect sizes, the directions of associations remained stable, suggesting that residual confounding was unlikely to fully explain the observed associations.

Subgroup analyses based on sex, age, and region (excluding subgroups with insufficient data) identified several significant interactions (Pinteraction < 0.05). For hypertension, older adults demonstrated a stronger association with high ΣPETs (OR, 1.68; 95% CI, 1.30 to 2.16) compared with younger individuals (OR range, 1.13 to 1.31; 95% CI range, 0.97 to 1.51). Regional variation was also noted in ORs for ΣPETs (OR range, 0.85 to 1.74; 95% CI range, 0.71 to 1.98) and ΣOP diesters (OR range, 0.88 to 1.78; 95% CI range, 0.70 to 2.04), with stronger associations observed in eastern regions. For heart disease, exposures to ΣPCs and ΣPFSAs were more strongly associated among female participants (OR range, 1.26 to 1.48; 95% CI range, 1.03 to 1.81) compared to males (OR range, 1.01 to 1.11; 95% CI range, 0.79 to 1.42). Regionally, ΣPCs, ΣPFCAs (C4–C12), and ΣPFSAs showed significant heterogeneity, with stronger associations observed in urbanized and industrialized eastern regions. Cataract showed sex-based and regional heterogeneity, with stronger effects among female participants (ΣOP triesters: OR, 1.71; 95% CI, 1.34 to 2.18 vs. OR, 1.21; 95% CI, 0.84 to 1.74) and in eastern areas (i.e., ΣPCs, ΣPETs, and ΣPFSAs). Stroke or CBD also exhibited sex-specific differences for ΣOP triesters (female: OR, 1.76; 95% CI, 1.33 to 2.32 vs. male: OR, 0.86; 95% CI, 0.63 to 1.18) and stronger associations with high ΣPCs exposure in the east (OR, 1.54; 95% CI, 1.18 to 2.00 vs. OR range, 0.93 to 2.16; 95% CI range, 0.26 to 10.48). Similarly, respiratory diseases displayed a markedly higher OR with ΣPCs in eastern regions (OR, 1.93; 95% CI, 1.41 to 2.64 vs. OR range, 0.65 to 1.03; 95% CI range, 0.23 to 1.90). Lastly, arthritis showed notable regional variation for high ΣPFSAs exposure (OR range, 0.65 to 2.02; 95% CI, 0.35 to 2.77).

Probability differences of NCDs in hypothetical scenarios

Using g-computation, we estimated population-level probabilities for seven NCDs in two hypothetical scenarios (Fig. 3b). One was the worsening of dust pollution, in which the target dust-borne EC was set to its third-tertile level for all participants, and the other was the mitigation of dust pollution, in which the target dust-borne EC was set to its first-tertile level for all participants. Complete results were available for online visualization at the website. Below we only presented the hypothetical findings that were previously identified as exhibiting linear dose–response relationships (BH-adjusted P for trend >0.05) and achieved statistical significance in both hypothetical scenarios.

For hypertension, dust-borne ΣPETs were associated with a 2.18% (95% CI, 0.85% to 3.08%) probability increase in the more-polluted scenario, corresponding to approximately 22 additional cases per 1000 individuals. Conversely, effective control of ΣPETs (in the less-polluted scenario) was linked to a 3.56% (95% CI, −4.52% to −2.42%) decrease in probability, indicating about 36 fewer cases per 1000 individuals. The absolute probability difference between these two hypothetical scenarios was 5.74%, corresponding to a potential difference of about 58 cases per 1000 individuals. For cataract, absolute probability differences in ΣPCs, ΣOP triesters, and ΣPFSAs ranged from 7.55% to 11.33%, equating to a potential difference of about 76 to 113 cases per 1000 individuals. For stroke or CBD, ΣPCs and ΣOP triesters exhibited probability differences of 6.22% (about 62 cases per 1000) and 5.98% (about 60 cases per 1000), respectively. For respiratory diseases, ΣOP triesters conferred a 9.80% probability difference (about 98 cases per 1000). For arthritis, changes in ΣPCs and ΣPFCAs (C4–C12) yielded differences of 8.57% and 13.19%, about 86 to 131 cases per 1000, respectively.

As shown in Fig. 3c, interventions targeting dust-borne ΣPCs and ΣOP triesters were associated with lower probabilities of all the investigated NCDs, whereas those targeting ΣPETs, ΣPFCAs (C4–C12), and ΣPFSAs were associated with lower probabilities of some of the investigated NCDs. In contrast, other interventions offered fewer benefits across the NCDs. Subgroup analyses suggested that women might derive greater benefits from interventions targeting dust-borne ΣPCs and ΣPFCAs (C4–C12), whereas men could benefit more from interventions targeting ΣPFSAs. Older and the oldest adults appeared to gain more from controlling ΣPCs, ΣOP diesters, ΣPFCAs (C2–C3), and ΣPFCAs (C4–C12). Participants in central regions might experience more benefits from interventions targeting ΣOP triesters, ΣPFCAs (C2–C3), and ΣPFCAs (C4–C12), while those in eastern regions might benefit more from interventions focused on ΣPCs and ΣPFSAs.

Discussion

This pilot study provides the first population-level evidence associating dust-borne ECs with NCDs in older adults. Propensity score-adjusted logistic regression showed significantly higher odds of NCDs at the third vs. first tertile of specific exposure, including hypertension, heart disease, cataract, stroke, respiratory diseases, and arthritis. G-computation estimated 6–13% absolute increases in disease probability (∼60–130 additional cases per 1000) from low- to high-exposure scenarios. Subgroup analyses suggested stronger associations among females, the oldest participants, and eastern residents. These findings, while requiring further validation, offer important insights into the understudied yet potentially critical dust exposure pathway.

Dust has long been overlooked in environmental health regulations, despite functioning as an important reservoir for synthetic chemicals released from consumer products, building materials, and industrial emissions. Unlike food and water, which can be filtered or substituted to reduce contamination, dust exposures occur persistently through inhalation, dermal absorption, and inadvertent ingestion. Older adults, who often spend more time indoors due to decreased mobility or chronic conditions, can experience particularly high cumulative exposures. Over the years, even minimal daily doses may substantially elevate risks of NCDs. In this study, higher dust-borne EC exposures in older populations were associated with increased ORs for several NCDs, with some estimates reaching 1.61 (cataracts) and 1.73 (arthritis). Although these effect sizes may appear modest, they become highly consequential given the already high prevalence of these conditions. G-computation simulations further demonstrated that moving from lower to higher exposure scenarios could increase disease probabilities by several percentage points, and in some cases by 10% or more, potentially translating into tens or hundreds of thousands of additional cases at the population level.

By 2050, the global population aged 60 years or older is expected to double, and chronic conditions such as cardiovascular disease, diabetes, and respiratory disorders continue to account for millions of deaths yearly.27,36 Age-related physiologic changes, including compromised immune defenses and impaired mucociliary clearance, likely increase vulnerability to low-level environmental exposures that might be harmless to younger, healthier populations. Besides, the effects of chronic inflammation or stress caused by decades of incremental chemical accumulations may only become evident later in life. Therefore, studying older cohorts can help identify health effects not yet apparent in younger groups, flagging potential hazards that are currently underestimated in population-level assessments. Beyond health implications, identifying modifiable risk factors holds critical socioeconomic relevance. High medical expenditures for chronic disease management in older adults place considerable pressure on healthcare systems and caregiving networks, particularly in rapidly aging societies undergoing shifts in family structures. Even small reductions in dust contamination could lead to meaningful decreases in NCD prevalence and associated healthcare costs. Although this study focused on older adults, these findings underscore the need to investigate other at-risk groups, such as pregnant women and young children, whose developmental and hormonal factors may increase susceptibility to environmental contaminants.37,38

The associations identified in our study between dust-borne ECs and NCDs corroborate and extend the growing evidence of their adverse health effects, including those observed from other exposure sources or internal exposures. Specifically, MPs were associated with increased risks of several NCDs (e.g., hypertension, heart disease, and cataracts). For example, mice with hypertension had higher concentrations of MPs in their fecal samples compared with controls.39 In addition, Natalia et al. reported that a dose of 100 mg kg−1 MPs contributed to cataract formation in OXYS rats.40 Additionally, we observed that OPEs, including ΣOP diesters and ΣOP triesters, were associated with NCDs like hypertension, diabetes, cataract, and so on. These results are consistent with prior epidemiological research, such as those of Luo et al., who demonstrated that the OPE mixture was positively associated with metabolic syndrome (MetS) (OR, 1.65; 95% CI, 1.21 to 2.24), hyperglycemia (OR, 1.47; 95% CI, 1.09 to 2.00), and central obesity (OR, 1.36; 95% CI, 1.01 to 1.83).41 Furthermore, our analysis of PFAS, particularly PFCAs and PFSAs, supports existing literature associating PFAS exposure with elevated risks of cardiovascular disease, arthritis, and metabolic dysregulation. For example, Wu et al. reported associations between higher serum PFAS levels and dyslipidemia,42 while Park et al. identified potential associations with type 2 diabetes.43 Conversely, our study did not find significant associations between LCMs and any NCDs. Several explanations could underlie these null findings. First, limited exposure through settled dust might reduce the likelihood of measurable health impacts. Many LCMs are used in closed systems, such as electronic displays, potentially reducing their propensity for environmental dispersion.44 Previous studies suggest that the environmental persistence of LCMs varies with their chemical structure, affecting their accumulation in dust.45 Second, the toxicokinetic profiles of LCMs are less characterized compared to other ECs (e.g., PFAS and OPEs), and LCMs may undergo rapid degradation or clearance in human tissues, minimizing long-term biological effects.46 Last, a smaller sample size or narrower range of LCM concentrations in our cohort might mask subtle dose–response relationships. While these results are encouraging, enhanced analytical methods, longitudinal data, and research into LCM-specific biological pathways are necessary to determine whether the observed lack of association reflects truly benign exposures or merely current measurement and understanding limitations.

Subgroup analyses revealed differential susceptibilities consistent with existing literature on demographic and regional variations in pollutant exposure and health outcomes.47 For instance, females exhibited stronger associations between PCs and heart disease compared to males, aligning with research indicating that hormonal and physiological differences influence susceptibility to environmental toxins.48,49 Additionally, high exposures to PETs and OP triesters were more strongly associated with hypertension in the oldest adults, suggesting that age-related factors such as reduced metabolic clearance and increased physiological stress may exacerbate the health impacts of ECs.50,51 Geographic variations, particularly stronger associations observed in industrial eastern regions, are consistent with studies like that of Wang et al. which emphasized the impact of localized industrial emissions on population health.52 These strong associations likely reflect higher dust-borne EC concentrations due to intensive manufacturing activities and potential co-exposures to other industrial pollutants. This regional heterogeneity underscores the importance of considering environmental context in epidemiological assessments and implementing region-specific public health interventions.

Toxicological research provides plausible mechanisms associating dust-borne EC exposures with NCDs. MPs contribute to NCDs via both physical and chemical interactions within the body.53 Inhaled MPs can penetrate deep into the respiratory tract, triggering chronic inflammation and oxidative stress. MPs disrupt cellular homeostasis by generating reactive oxygen species (ROS), leading to oxidative DNA damage and the activation of pro-inflammatory cytokines. This oxidative stress is critical in the development of hypertension and heart disease, as it impairs endothelial function, promotes atherosclerosis, and disrupts vascular tone regulation.54 Additionally, MPs can translocate to systemic circulation, accumulating in various organs, exacerbating inflammatory responses, and contributing to insulin resistance, a precursor to diabetes.55 OPEs are potent inhibitors of acetylcholinesterase, an enzyme essential for the termination of neurotransmitter signaling at synapses.56 Chronic inhibition of acetylcholinesterase results in sustained cholinergic stimulation, leading to autonomic nervous system dysfunction. This dysregulation manifests as elevated blood pressure and increased vascular resistance, thereby heightening the risk of hypertension and heart disease.57 Additionally, OPEs induce systemic inflammation and oxidative stress, further damaging the vascular endothelium and promoting atherosclerotic plaque formation.58 PFCAs and PFSAs are well-documented for disrupting lipid metabolism and promoting inflammatory pathways. These substances activate peroxisome proliferator-activated receptors alpha and gamma (PPARα and PPARγ), nuclear receptors involved in fatty acid oxidation and adipogenesis.59 Chronic activation by PFAS can lead to dyslipidemia, characterized by elevated low-density lipoprotein cholesterol and triglycerides, which are risk factors for cardiovascular diseases.60 Furthermore, PFCAs and PFSAs can cause immunosuppression,61 reducing the body's ability to combat infections and increasing susceptibility to autoimmune conditions such as arthritis.62 Emerging PFAS alternatives and FTOHs exhibit similar toxicological profiles to legacy PFAS, including endocrine disruption and immune system modulation.63 These compounds interfere with thyroid hormone regulation, essential for metabolic homeostasis and cognitive function, thereby increasing the risk of metabolic disorders and neurodegenerative diseases. Notably, the inverted U-shaped associations observed for some outcomes suggest more complex toxicodynamics than traditionally expected. It was suggested that low-to-moderate contaminant levels may trigger pronounced physiological and immunological stress responses, whereas very high exposures might activate adaptive pathways that mitigate additional harm.64 Further research is needed to elucidate the mechanisms underlying these complex dose–response associations. Although causality remains unverified, these mechanistic insights align with observed associations, underscoring both the biological plausibility of dust-borne EC-NCD associations and the need for further research.

Air and dust contain a myriad of harmful chemical and biological components, yet environmental regulations often focus on a few known “criteria pollutants” (e.g., particulate matter, sulfur dioxide, nitrogen oxides, and ozone).26,65 This creates an evidential circularity problem: only pollutants with compelling scientific evidence of harm are regulated, but gathering such evidence requires long-term monitoring data. Since data collection is expensive, these data are typically collected only for already regulated pollutants, leaving less-studied pollutants (such as dust-borne ECs) without sufficient evidence to warrant regulation. This may result in continued exposure and escalating public health burdens, especially among vulnerable populations such as the elderly. This study breaks this cycle by providing epidemiological evidence associating dust-borne MPs, LCMs, OPEs, and PFAS with increased odds of NCDs in the elderly. Although the cross-sectional design restricts definitive causal inference, such evidence often serves as a critical early warning, prompting precautionary measures and more rigorous longitudinal investigations. As analytical technologies advance, the identification and assessment of specific harmful components within complex air pollutant mixtures becomes more feasible.66,67 Targeted interventions on specific pollutants may be more cost-effective in delivering health benefits than strategies that focus solely on reducing overall particulate mass.68,69 Given the global burden of NCDs, even partial contributions from underexplored factors may have significant health implications. By adopting the precautionary principle and conducting exploratory investigations into dust-borne ECs, we can prevent regulations from remaining confined to well-established pollutants.65

Strengths and limitations

This study used data from a large, well-designed cohort of older adults to demonstrate that dust-borne ECs may be underappreciated risk factors for chronic disease in this population. Multiple hypothesis testing and PS matching further enhanced the robustness of our findings. By combining empirical dust measurements instead of relying on self-reported data or modeling alone, we obtained relatively more accurate exposure estimates. G-computation analyses allowed estimation of absolute differences in NCD probabilities in hypothetical scenarios, providing practical policy implications. An interactive online dashboard not only enables users to visualize dust contamination hotspots and local health trends but also allows them to filter data by NCDs, focus on specific ECs, or compare multiple outcomes side by side, thereby preventing the information overload often encountered with static tables or figures.

Nevertheless, several limitations must be considered. First, the cross-sectional design precludes definitive causal inferences. However, these findings suggest at least two possibilities. One is the independent effect of dust-borne ECs on NCDs, and the other is the contributory effect through interactions with other risk factors. Prospective cohort studies with repeated exposure assessments are needed to clarify these associations. Second, despite relatively rigorous estimation of daily dust intake, exposure misclassification may persist due to unmeasured factors (e.g., handwashing frequency and ventilation systems). In addition, the EDI assessment was based on provincial-level dust measurements, which cannot fully capture individual-level variation. National studies of dust-borne ECs typically use only 2 to 3 samples per province or city owing to high costs.70–72 Although prior research found no significant regional differences in dust pollutant concentrations within the same province,33,34 risk estimates may be systematically underestimated. Such non-differential measurement error typically biases associations toward the null rather than creates false positives. Consequently, our risk estimates should be viewed as conservative. Future efforts should incorporate personal air samplers, wearable sensors, repeated dust sampling, and biomarkers to improve exposure assessment and enhance EDI estimations. Third, residual confounding remains possible. Although PS matching and multivariable adjustments were applied, factors (e.g., dietary supplements) might still influence the observed associations. Collecting more complete information could strengthen causal inference. Fourth, our findings may not generalize to regions with different regulatory environments or consumer product profiles. Multi-country cohort studies could elucidate regional variations in exposure and risk. Finally, older adults are typically exposed to multiple chemicals, yet we assessed dust-borne ECs individually.46 Interactions among these contaminants or with other environmental stressors may shape health risks in ways single-chemical models cannot capture, warranting further investigation into complex mixtures.73

Conclusion

This cross-sectional study in China provides a prima facie insight into the associations between dust exposure to ECs and NCDs in the elderly. These associations persisted across multiple sensitivity analyses and appeared to be more pronounced among the oldest participants, women, and individuals residing in eastern provinces. Although effect sizes were modest at the individual level, g-computation estimates suggested measurable absolute increases in disease probabilities in higher exposure scenarios, potentially translating into substantial population burdens given the high baseline prevalence of NCDs in older populations. Further research is required to validate and extend these preliminary findings. As the global community continues to focus on sustainable cities and society, ECs in dust should be treated not as secondary challenges but rather as integral elements of comprehensive air quality management.

Data availability

This study was carried out using publicly available data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) at https://opendata.pku.edu.cn/dataverse/CHADS.

Author contributions

Luhan Yang (first author): conceptualization, methodology, software, visualization, writing-original draft; Yu Wang (first author): resources, data curation, investigation, formal analysis; Le He: resources, formal analysis; Lei Xiang: writing-review & editing; Lei Wang: resources, data curation, investigation, formal analysis; Yiming Yao: resources, data curation, investigation, formal analysis; Hongwen Sun: resources, data curation, investigation, formal analysis; Tao Zhang: funding acquisition, project administration, supervision, writing-review & editing.

Conflicts of interest

The authors declare no competing financial interest.

Acknowledgements

The National Natural Science Foundation of China (42477295, 22306209, and 22036004), the National Key Research and Development Program of China (No. 2023YFC3706800), and the Science and Technology Program of Guangzhou, China (2024A04J6442) are acknowledged for their partial research support.

References

  1. W. Steffen, K. Richardson, J. Rockström, S. E. Cornell, I. Fetzer, E. M. Bennett, R. Biggs, S. R. Carpenter, W. De Vries and C. A. De Wit, Planetary boundaries: guiding human development on a changing planet, Science, 2015, 347, 1259855 CrossRef PubMed .
  2. L. Persson, B. M. Carney Almroth, C. D. Collins, S. Cornell, C. A. De Wit, M. L. Diamond, P. Fantke, M. Hassellöv, M. MacLeod and M. W. Ryberg, Outside the safe operating space of the planetary boundary for novel entities, Environ. Sci. Technol., 2022, 56, 1510 CrossRef CAS PubMed .
  3. F. Wang, L. Xiang, K. S. Y. Leung, M. Elsner, Y. Zhang, Y. Guo, B. Pan, H. Sun, T. An and G. Ying, Emerging contaminants: a one health perspective, Innovation, 2024, 5, 100612 CAS .
  4. Official Journal of the European Union, Commission Implementing Decision (EU) 2015/495 of 20 March 2015 Establishing a Watch List of Substances for Union-wide Monitoring in the Field of Water Policy Pursuant to Directive 2008/105/EC of the European Parliament and of the Council, 2015, vol. 78, p. , p. 40 Search PubMed.
  5. M. MacLeod, H. P. H. Arp, M. B. Tekman and A. Jahnke, The global threat from plastic pollution, Science, 2021, 373, 61 CrossRef CAS PubMed .
  6. I. T. Cousins, J. H. Johansson, M. E. Salter, B. Sha and M. Scheringer, Outside the safe operating space of a new planetary boundary for per-and polyfluoroalkyl substances (PFAS), Environ. Sci. Technol., 2022, 56, 11172 CrossRef CAS PubMed .
  7. China's Ministry of Ecology and Environment, List of New Pollutants under Key Control, 2023 Search PubMed.
  8. J. Li, L. Zhao, R. J. Letcher, Y. Zhang, K. Jian, J. Zhang and G. Su, A review on organophosphate ester (OPE) flame retardants and plasticizers in foodstuffs: levels, distribution, human dietary exposure, and future directions, Environ. Int., 2019, 127, 35 CrossRef CAS PubMed .
  9. J. H. Bridson, E. C. Gaugler, D. A. Smith, G. L. Northcott and S. Gaw, Leaching and extraction of additives from plastic pollution to inform environmental risk: a multidisciplinary review of analytical approaches, J. Hazard. Mater., 2021, 414, 125571 CrossRef CAS PubMed .
  10. J. J. Feng, X. F. Sun and E. Y. Zeng, Emissions of liquid crystal monomers from obsolete smartphone screens in indoor settings: characteristics and human exposure risk, Environ. Sci. Technol., 2022, 56, 8053 CrossRef CAS PubMed .
  11. Y. Yao, Y. Zhao, H. Sun, S. Chang, L. Zhu, A. C. Alder and K. Kannan, Per-and polyfluoroalkyl substances (PFASs) in indoor air and dust from homes and various microenvironments in China: implications for human exposure, Environ. Sci. Technol., 2018, 52, 3156 CrossRef CAS PubMed .
  12. H. Su, S. Shi, M. Zhu, D. Crump, R. J. Letcher, J. P. Giesy and G. Su, Persistent, bioaccumulative, and toxic properties of liquid crystal monomers and their detection in indoor residential dust, Proc. Natl. Acad. Sci. U. S. A., 2019, 116, 26450 CrossRef CAS PubMed .
  13. S. Sridharan, M. Kumar, L. Singh, N. S. Bolan and M. Saha, Microplastics as an emerging source of particulate air pollution: a critical review, J. Hazard. Mater., 2021, 418, 126245 CrossRef CAS PubMed .
  14. C. M. Makey, T. F. Webster, J. W. Martin, M. Shoeib, T. Harner, L. Dix Cooper and G. M. Webster, Airborne precursors predict maternal serum perfluoroalkyl acid concentrations, Environ. Sci. Technol., 2017, 51, 7667 CrossRef CAS PubMed .
  15. S. G. Al Kindi, R. D. Brook, S. Biswal and S. Rajagopalan, Environmental determinants of cardiovascular disease: lessons learned from air pollution, Nat. Rev. Cardiol., 2020, 17, 656 CrossRef PubMed .
  16. M. E. Morales McDevitt, J. Becanova, A. Blum, T. A. Bruton, S. Vojta, M. Woodward and R. Lohmann, The air that we breathe: neutral and volatile PFAS in indoor air, Environ. Sci. Technol. Lett., 2021, 8, 897 CrossRef CAS PubMed .
  17. M. Wang, Q. Li, M. Hou, L. L. Chan, M. Liu, S. K. Ter, T. Dong, Y. Xia, S. H. Chotirmall and M. Fang, Inactivation of common airborne antigens by perfluoroalkyl chemicals modulates early life allergic asthma, Proc. Natl. Acad. Sci. U. S. A., 2021, 118, e2011957118 CrossRef CAS PubMed .
  18. S. Zhang, M. Yang, Y. Li, Y. Wang, Y. Lu, Z. Cheng and H. Sun, Occurrence, distribution, and human exposure of emerging liquid crystal monomers (LCMs) in indoor and outdoor dust: a nationwide study, Environ. Int., 2022, 164, 107295 CrossRef CAS PubMed .
  19. P. Miramontes Gonzalez and L. Li, Evaluating the environmental persistence of liquid crystal monomers indoors and outdoors, Environ. Sci. Technol. Lett., 2023, 11, 216 CrossRef .
  20. J. E. Bennett, G. A. Stevens, C. D. Mathers, R. Bonita, J. Rehm, M. E. Kruk, L. M. Riley, K. Dain, A. P. Kengne and K. Chalkidou, NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4, Lancet, 2018, 392, 1072 CrossRef PubMed .
  21. R. Fuller, P. J. Landrigan, K. Balakrishnan, G. Bathan, S. Bose O'Reilly, M. Brauer, J. Caravanos, T. Chiles, A. Cohen and L. Corra, Pollution and health: a progress update, Lancet Planet Health, 2022, 6, e535 CrossRef PubMed .
  22. World Health Organization, Assessing National Capacity for the Prevention and Control of Noncommunicable Diseases: Report of the 2021 Global Survey, 2023 Search PubMed.
  23. M. Hou, J. Fang, Y. Shi, S. Tang, H. Dong, Y. Liu, F. Deng, J. P. Giesy, K. J. G. Pollitt and Y. Cai, Exposure to organophosphate esters in elderly people: relationships of OPE body burdens with indoor air and dust concentrations and food consumption, Environ. Int., 2021, 157, 106803 CrossRef CAS PubMed .
  24. G. Zuri, A. Karanasiou and S. Lacorte, Microplastics: human exposure assessment through air, water, and food, Environ. Int., 2023, 179, 108150 CrossRef CAS PubMed .
  25. D. J. Wallis, K. E. Miller, N. M. DeLuca, K. Thomas, C. Fuller, J. McCord, E. A. C. Hubal and J. M. Minucci, Understanding prenatal household exposures to per-and polyfluorylalkyl substances using paired Biological and dust measurements with sociodemographic and housing variables, Environ. Int., 2024, 194, 109157 CrossRef CAS PubMed .
  26. W. Huang, H. Xu, J. Wu, M. Ren, Y. Ke and J. Qiao, Toward cleaner air and better health: current state, challenges, and priorities, Science, 2024, 385, 386 CrossRef CAS PubMed .
  27. X. Chen, J. Giles, Y. Yao, W. Yip, Q. Meng, L. Berkman, H. Chen, X. Chen, J. Feng and Z. J. T. L. Feng, The path to healthy ageing in China: a Peking University-Lancet Commission, Lancet, 2022, 400, 1967 CrossRef PubMed .
  28. Y. Zeng, Q. Feng, T. Hesketh, K. Christensen and J. W. Vaupel, Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study, Lancet, 2017, 389, 1619 CrossRef PubMed .
  29. D. Gu, Q. Feng, H. Chen and Y. Zeng, Chinese longitudinal healthy longevity survey (CLHLS), in Encyclopedia of Gerontology and Population Aging, Springer, 2022, pp. 957–970 Search PubMed .
  30. J. Wang, C. Chen, J. Zhou, L. Ye, Y. Li, L. Xu, Z. Xu, X. Li, Y. Wei and J. Liu, Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study, Lancet Healthy Longev., 2023, 4, e535 CrossRef PubMed .
  31. C. Liu, J. Li, Y. Zhang, L. Wang, J. Deng, Y. Gao, L. Yu, J. Zhang and H. Sun, Widespread distribution of PET and PC microplastics in dust in urban China and their estimated human exposure, Environ. Int., 2019, 128, 116 CrossRef CAS PubMed .
  32. Y. Wang, Y. Yao, X. Han, W. Li, H. Zhu, L. Wang, H. Sun and K. Kannan, Organophosphate di-and tri-esters in indoor and outdoor dust from China and its implications for human exposure, Sci. Total Environ., 2020, 700, 134502 CrossRef CAS PubMed .
  33. B. Wang, Y. Yao, Y. Wang, H. Chen and H. Sun, Per-and polyfluoroalkyl substances in outdoor and indoor dust from mainland China: contributions of unknown precursors and implications for human exposure, Environ. Sci. Technol., 2021, 56, 6036 CrossRef PubMed .
  34. United States Environmental Protection Agency, Exposure Factors Handbook: 2011 Edition, 2011 Search PubMed.
  35. D. Westreich, J. Lessler and M. J. Funk, Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression, J. Chronic Dis., 2010, 63, 826 Search PubMed .
  36. A. J. Ferrari, D. F. Santomauro, A. Aali, Y. H. Abate, C. Abbafati, H. Abbastabar, S. Abd ElHafeez, M. Abdelmasseh, S. Abd Elsalam and A. J. T. L. Abdollahi, Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021, Lancet, 2024, 403, 2133 CrossRef PubMed .
  37. C. E. Draper, A. K. Yousafzai, D. C. McCoy, J. Cuartas, J. Obradović, S. Bhopal, J. Fisher, J. Jeong, S. Klingberg and K. J. T. L. Milner, The next 1000 days: building on early investments for the health and development of young children, Lancet, 2024, 404, 2094 CrossRef PubMed .
  38. X. Li, M. Hou, F. Zhang, Z. Ji, Y. Cai and Y. Shi, Per-and polyfluoroalkyl substances and female health concern: gender-based accumulation differences, adverse outcomes, and mechanisms, Environ. Sci. Technol., 2025, 59, 1469 Search PubMed .
  39. S. Wang, K. Yan, Y. Dong, Y. Chen, J. Song, Y. Chen and J. Li, The influence of microplastics on hypertension-associated cardiovascular injury via the modulation of gut microbiota, Environ. Pollut., 2025, 368, 125760 Search PubMed .
  40. N. A. Stefanova, Y. S. Sotnikova, A. E. Osechkova, E. V. Karpova, D. N. Polovyanenko, A. Z. Fursova and E. G. Bagryanskaya, Invisible but insidious effects of microplastics, Molecules, 2024, 29, 5776 CrossRef CAS PubMed .
  41. K. Luo, R. Zhang, R. Aimuzi, Y. Wang, M. Nian and J. Zhang, Exposure to organophosphate esters and metabolic syndrome in adults, Environ. Int., 2020, 143, 105941 CrossRef CAS PubMed .
  42. B. Wu, Y. Pan, Z. Li, J. Wang, S. Ji, F. Zhao and X. Shi, Serum per-and polyfluoroalkyl substances and abnormal lipid metabolism: a nationally representative cross-sectional study, Environ. Int., 2023, 172, 107779 CrossRef CAS PubMed .
  43. S. K. Park, X. Wang, N. Ding, C. A. K. Gutierrez, A. M. Calafat, W. H. Herman and S. D. Harlow, Per-and polyfluoroalkyl substances and incident diabetes in midlife women: the Study of Women’s Health Across the Nation (SWAN), Diabetologia, 2022, 65, 1157 CrossRef CAS PubMed .
  44. Q. Jin, J. Yu, Y. Fan, Y. Zhan, D. Tao, J. Tang and Y. He, Release behavior of liquid crystal monomers from waste smartphone screens: occurrence, distribution, and mechanistic modeling, Environ. Sci. Technol., 2023, 57, 10319 CrossRef CAS PubMed .
  45. H. Su, S. Shi, M. Zhu, D. Crump, R. J. Letcher, J. P. Giesy and G. Su, Persistent, bioaccumulative, and toxic properties of liquid crystal monomers and their detection in indoor residential dust, Proc. Natl. Acad. Sci. U. S. A., 2019, 116, 26450 CrossRef CAS PubMed .
  46. Y. Kong, Y. Wen, G. Su, Y. Peng and X. Cui, Tissue-specific uptake and distribution of liquid crystal monomers (LCMs) in mice, Environ. Int., 2023, 174, 107894 CrossRef CAS PubMed .
  47. Q. Zhang, X. Meng, S. Shi, L. Kan, R. Chen and H. Kan, Overview of particulate air pollution and human health in China: evidence, challenges, and opportunities, Innovation, 2022, 3, 100312 CAS .
  48. S. Lymperi and A. Giwercman, Endocrine disruptors and testicular function, Metabolism, 2018, 86, 79 CrossRef CAS PubMed .
  49. P. Palanza, S. Paterlini, M. M. Brambilla, G. Ramundo, G. Caviola, L. Gioiosa, S. Parmigiani, F. S. Vom Saal and D. Ponzi, Sex-biased impact of endocrine disrupting chemicals on behavioral development and vulnerability to disease: of mice and children, Neurosci. Biobehav. Rev., 2021, 121, 29 CrossRef PubMed .
  50. A. Peters, T. S. Nawrot and A. A. Baccarelli, Hallmarks of environmental insults, Cell, 2021, 184, 1455 CrossRef CAS PubMed .
  51. J. Ren, J. R. Sowers and Y. Zhang, Metabolic stress, autophagy, and cardiovascular aging: from pathophysiology to therapeutics, Trends Endocrinol. Metab., 2018, 29, 699 CrossRef CAS PubMed .
  52. W. Wang and W. Su, Impacts of urban block form on carbon and pollutant emissions from urban life in China from the perspective of regional differences, Sustain. Cities Soc., 2024, 115, 105849 CrossRef .
  53. R. Marfella, F. Prattichizzo, C. Sardu, G. Fulgenzi, L. Graciotti, T. Spadoni and G. Paolisso, Microplastics and nanoplastics in atheromas and cardiovascular events, N. Engl. J. Med., 2024, 390, 900 CrossRef CAS PubMed .
  54. X. Zhu, C. Wang, X. Duan, B. Liang, E. G. Xu and Z. Huang, Micro-and nanoplastics: a new cardiovascular risk factor?, Environ. Int., 2023, 171, 107662 CrossRef CAS PubMed .
  55. S. Krause, V. Ouellet, D. Allen, S. Allen, K. Moss, H. A. Nel, H. S. Manaseki and I. Lynch, The potential of micro-and nanoplastics to exacerbate the health impacts and global burden of non-communicable diseases, Cell Rep. Med., 2024, 5, 101581 CrossRef CAS PubMed .
  56. H. B. Patisaul, M. Behl, L. S. Birnbaum, A. Blum, M. L. Diamond, S. Rojello Fernández, H. T. Hogberg, C. F. Kwiatkowski, J. D. Page and A. Soehl, Beyond cholinesterase inhibition: developmental neurotoxicity of organophosphate ester flame retardants and plasticizers, Environ. Health Perspect., 2021, 129, 105001 CrossRef PubMed .
  57. J. Cong, J. Wu, Y. Fang, J. Wang, X. Kong, L. Wang and Z. Duan, Application of organoid technology in the human health risk assessment of microplastics: a review of progresses and challenges, Environ. Int., 2024, 188, 108744 CrossRef CAS PubMed .
  58. B. H. Giles, N. Kukolj, K. K. Mann and B. Robaire, Phenotypic and functional outcomes in macrophages exposed to an environmentally relevant mixture of organophosphate esters in vitro, Environ. Health Perspect., 2024, 132, 087002 CrossRef CAS PubMed .
  59. Q. Sun, G. Zong, D. Valvi, F. Nielsen, B. Coull and P. Grandjean, Plasma concentrations of perfluoroalkyl substances and risk of type 2 diabetes: a prospective investigation among US women, Environ. Health Perspect., 2018, 126, 037001 CrossRef PubMed .
  60. J. J. Schlezinger and N. Gokce, Perfluoroalkyl/polyfluoroalkyl substances: links to cardiovascular disease risk, Circ. Res., 2024, 134, 1136 CrossRef CAS PubMed .
  61. X. Li, M. Hou, F. Zhang, Z. Ji, Y. Cai and Y. Shi, Per-and polyfluoroalkyl substances and female health concern: gender-based accumulation differences, adverse outcomes, and mechanisms, Environ. Sci. Technol., 2025, 59, 1469 CrossRef CAS PubMed .
  62. T. Hyötyläinen, T. Ghaffarzadegan, B. S. Karthikeyan, E. Triplett, M. Oresic and J. Ludvigsson, Impact of environmental exposures on human breast milk lipidome in future immune-mediated diseases, Environ. Sci. Technol., 2024, 58, 2214 CrossRef PubMed .
  63. N. J. Herkert, C. D. Kassotis, S. Zhang, Y. Han, V. F. Pulikkal, M. Sun, P. L. Ferguson and H. M. Stapleton, Characterization of per-and polyfluorinated alkyl substances present in commercial anti-fog products and their in vitro adipogenic activity, Environ. Sci. Technol., 2022, 56, 1162 CrossRef CAS PubMed .
  64. Y. Guéguen, A. Bontemps and T. G. Ebrahimian, Adaptive responses to low doses of radiation or chemicals: their cellular and molecular mechanisms, Cell. Mol. Life Sci., 2019, 76, 1255 CrossRef PubMed .
  65. A. Lewis, D. Misonne and E. Scotford, Harnessing science, policy, and law to deliver clean air, Science, 2024, 385, 362 CrossRef CAS PubMed .
  66. P. Gao, Chasing “emerging” contaminants: an endless journey toward environmental health, Environ. Sci. Technol., 2024, 58, 1790 CrossRef CAS PubMed .
  67. J. Lelieveld and U. Pöschl, Chemists can help to solve the air-pollution health crisis, Nature, 2017, 551, 291 CrossRef PubMed .
  68. Q. Zhang, X. Meng, S. Shi, L. Kan, R. Chen and H. Kan, Overview of particulate air pollution and human health in China: evidence, challenges, and opportunities, Innovation, 2022, 3, 100312 CAS .
  69. H. Yin, E. E. McDuffie, R. V. Martin and M. Brauer, Global health costs of ambient PM2.5 from combustion sources: a modelling study supporting air pollution control strategies, Lancet Planet Health, 2024, 8, 476 CrossRef PubMed .
  70. Y. Liu and K. Kannan, Concentrations, profiles, and potential sources of liquid crystal monomers in residential indoor dust from the United States, Environ. Sci. Technol., 2024, 58, 12400 CrossRef CAS PubMed .
  71. J. Zhang, L. Wang and K. Kannan, Microplastics in house dust from 12 countries and associated human exposure, Environ. Int., 2020, 134, 105314 CrossRef CAS PubMed .
  72. K. Kannan, Q. Wu and Z. M. Li, Sources and pathways of human exposure to perchlorate in the United States: a comparison of environmental monitoring and biomonitoring approaches, Environ. Chem. Ecotoxicol., 2024, 6, 363 CrossRef CAS .
  73. B. I. Escher, H. M. Stapleton and E. L. Schymanski, Tracking complex mixtures of chemicals in our changing environment, Science, 2020, 367, 388 CrossRef CAS PubMed .

Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5em00252d
‡ These authors contributed to this work equally and are co-first authors.
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