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. 2025 Sep 5;4(1):65–73. doi: 10.1021/envhealth.5c00228

Ambient Ultrafine Particulate Air Pollution and Incidence of Cardiovascular Diseases

Huihuan Luo †, Yixuan Jiang †, Lu Zhou †, Jiaxin Chen †, Kexin Yu †, Anni Li †, Xia Meng †, Haidong Kan †,‡,*, Renjie Chen †,*
PMCID: PMC12813698  PMID: 41562035

Abstract

Fine particulate matter (PM2.5) pollution is causally linked to cardiovascular diseases (CVDs), but the long-term effects of potentially more toxic ultrafine particles (UFP) are unclear. We evaluated the associations between long-term exposure to UFP concentrations and the incidence of overall and cause-specific CVDs in 453,692 middle-aged and older UK Biobank participants living >100 m from major roads, followed from 2006–2010 baseline to 2023. A Cox proportional hazard model was applied to estimate the hazard ratio (HR) and 95% confidence interval per interquartile range increment in UFP concentrations. Long-term exposure to ambient UFP was associated with a 3% higher incidence risk of overall CVD (95% CI: 1–4%). Significant positive associations were observed for several specific CVDs, including chronic rheumatic heart diseases, hypertension, ischemic heart diseases, conduction disorders, heart failure, and cerebrovascular diseases (HRs: 1.03–1.09), while others showed no association. These associations were generally robust to adjustment of other air pollutants including PM2.5. The exposure–response relationship curves were generally linear without thresholds. This large prospective study suggests that UFP is an independent, modifiable CVD risk factor, distinct from PM2.5, relevant even for populations distant from major emission sources. This highlights the need to consider UFP in air quality management and public health strategies.

Keywords: ultrafine particulate matter, air pollution, cardiovascular disease, cohort, incidence

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Environmental Implication

Our study reveals that ultrafine particles (UFP) pose independent cardiovascular risks beyond PM2.5, with no safe threshold and effects extending beyond roadside areas. This highlights the inadequacy of PM2.5 mass alone for protection, the need for routine UFP monitoring, and the need for revising air quality standards to consider UFP-specific metrics for better cardiovascular health protection.

Introduction

Long-term exposure to fine particulate matter (PM2.5, aerodynamic diameter ≤2.5 μm) pollution has been linked to multiple diseases. − Notably, there is a growing body of evidence that suggests that ultrafine particulate matter (UFP) that has an aerodynamic diameter ≤0.1 μm may pose higher health hazards than PM2.5, as it could more easily enter the circulatory system, deposit in extrapulmonary organs, and carry more toxic components. , Cardiovascular disease (CVD) is the leading cause of death globally, and thus, identification of risk factors of CVD is a priority. Numerous epidemiological studies have linked PM2.5 to incidence and mortality of CVDs. , Mechanistic researches suggest that PM2.5 may promote various CVDs via biological pathways such as autonomic imbalance, endothelial dysfunction, hypothalamic–pituitary axis activation, systemic inflammation, and thrombotic pathways. The Integrated Science Assessment has concluded a causal relationship between PM2.5 and CVD. The Global Burden Disease study further lists ambient PM2.5 as the most important environmental risk factor contributing to global CVD mortality and disability, accounting for approximately 13% of cardiovascular deaths. , However, the associations between UFP exposure and CVDs remain largely unclear.

While previous studies have demonstrated the associations between short-term UFP exposure and CVD mortality and hospitalizations, − the existing evidence linking long-term UFP exposure and CVDs was very limited and inconclusive. − The lack of evidence from prospective cohort studies impedes efforts to establish a causal link between UFP exposure and CVDs, as well as to determine Air Quality Guidelines for long-term UFP exposure. More importantly, UFP concentrations decay exponentially with increasing distance from their sources, typically reaching background levels within approximately 100 m. This suggests that a substantial proportion of the population is predominantly exposed to the background UFP concentrations. However, the association between long-term exposure to background UFP concentrations and CVD risk remains unclear. While previous studies have largely focused on assessing the health effects of UFP in traffic-prone areas, ,, no study has specifically examined the impact of background UFP concentrations on CVD risk. Additionally, prior studies have predominantly focused on overall CVD or ischemic heart disease (IHD), with relatively less attention being given to other major CVDs that may also be linked to air pollution. Furthermore, it remains unclear whether long-term exposure to UFP contributes to cardiovascular risks independent of other air pollutants. This uncertainty hampers the integration of UFP into current cardiovascular prevention and control strategies. Finally, it has yet to be determined whether a safe threshold exists below which long-term UFP exposure does not exert significant adverse effects on the cardiovascular system.

By virtue of a large-scale, prospective cohort study, we aimed to investigate the associations of long-term exposure to background concentrations of UFP with the incidence of overall and a wide spectrum of specific CVDs among 453,692 individuals from the UK Biobank. We also assessed the independence of the observed associations against other air pollutants (especially PM2.5), characterized the shape of the exposure–response (E–R) curves, and examined whether lifestyles modified the chronic impacts of UFP exposure on CVDs.

Materials and Methods

Study Population

From 2006 to 2010, the UK Biobank cohort recruited half a million participants aged 40–69 years old across the United Kingdom. Information on demographics, socioeconomic status, lifestyle habits, and physical measurements were collected at enrollment. Morbidity data have been regularly updated since recruitment according to the medical records from self-reported medical conditions, primary care, hospitals, and Death Registers. Details of study design and data collection have been previously published. A total of 502,359 participants were enrolled initially. Traffic sources are the primary contributors to UFP in Western countries. Therefore, to better characterize background concentrations of UFP, we first excluded 38,909 participants residing within 100 m of the major road (i.e., close to road) or with missing data on distance to the major road. Then, we excluded 9758 people with missing data on covariates. For categorical covariates with a relatively high proportion of missing values, including education, income, and physical activity, we did not exclude participants but instead treated missing values as separate categories in the model. Of the remaining 453,692 individuals, we further excluded those with prevalent overall or cause-specific CVDs in corresponding analysis (Figure S1). Each participant was followed up from their enrollment up to date of disease diagnosis, February 1, 2023, loss to follow-up, or censored at death.

The UK Biobank was approved by the North West Multicenter Research Ethics Committee, and all participants in the UK Biobank provided informed consent. This research was conducted in accordance with the Declaration of Helsinki.

Assessment of Outcomes

The diagnoses in UK Biobank are coded using the International Classification of Diseases, 10th Revision (ICD-10). Specifically, the incidence of overall CVD was defined as ICD10 I00–I99. The specific CVDs included chronic rheumatic heart diseases (ICD10: I05–I09), hypertension (I10–I15), IHD (I20–I25), heart valve disorder (I33–I39), cardiomyopathy (I42–I43), conduction disorders (I44–I49), heart failure (I50), cerebrovascular diseases (I60–I69), and aortic aneurysm and dissection (I71–I72).

Environmental Data

The UFP exposure estimates used in our study were derived from the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. In brief, ambient UFP concentrations were inferred by particle size distributions (PSDs) and particle number concentrations (PNCs), with PSDs serving as the primary source of information when available. The observational daily PSDs and PNCs from both rural and urban monitoring stations across Europe were obtained from the European Atmosphere Satellite database. The initial simulation was conducted at a spectral resolution of 1.875° × 1.875°. Subsequently, an observation-guided downscaling was employed to capture concentration gradients and generate UFP concentrations with an unprecedented fine resolution of 10 km × 10 km at the Earth’s surface. The correlation coefficient between the observed and simulated UFP concentrations was found to be 0.95, indicating a good agreement. While this 10 × 10 km spatial resolution is relatively coarser than that of land use regression models, which have commonly been used for traffic-related UFP studies at ∼100 m resolution, it is well-suited for capturing broader-scale background exposure patterns. This aligns with the objective of our study, which is to assess the long-term health effects of ambient UFP exposure at the population level rather than localized near-roadway concentrations.

Acknowledging the constraint that modeled UFP data were available only for 2015–2017, whereas the cohort follow-up spanned 2006–2023, we utilized the average UFP concentrations from this central period (2015–2017) as a proxy for participants’ long-term exposure throughout the follow-up. This approach, necessitated by the scarcity of historical UFP monitoring data, is consistent with methodologies adopted in almost all prior epidemiological studies investigating long-term UFP health effects. ,,− While potential limitations exist due to temporal variations, this proxy method holds merit. First, anthropogenic emission inventories for Europe suggest relative stability in the emissions of key traffic-related pollutants, major contributors to UFP, over recent decades. Second, the approach relies on the assumption of relative stability in the spatial patterns of UFP pollution over time, even if absolute levels fluctuate. Supporting this assumption, Montagne et al. found that contemporary UFP models retained moderate ability to predict spatial variations observed a decade earlier, suggesting that the relative geographic distribution of UFP sources and concentrations exhibits considerable persistence.

The ambient concentration of PM2.5, particulate matter with an aerodynamic diameter ≤10 μm (PM10), and ozone (2006–2010) was predicted based on random forest models by integrating multiple-source predictors. Nitrogen oxide (NO x ) levels for the year 2010 were estimated using the land use regression model developed for the European Study of Cohorts for Air Pollution Effects project. The exposure models of PM2.5, PM10, ozone, and NO x had a spatial resolution of 1 km × 1 km and could capture 85%, 77%, 85%, and 88% of spatial variability in their levels, respectively. The residential air pollution exposure for study participants was subsequently obtained by linking the modeled concentrations to participant’s geocoded residential address.

Statistical Analysis

The associations of UFP with the incidence of overall and cause-specific CVDs were estimated by using Cox proportional hazards models. We examined the assumption of proportional hazards by the Schoenfeld residuals plot and found no violations. UFP was analyzed as a continuous variable. Age, sex, body mass index, race, education, income, physical activity, smoking, alcohol drinking, diet, and recruitment centers were adjusted in the main model. The detailed information on these covariates is presented in the Supporting Information. We also visualized the E–R relationships using restricted cubic spline with 4 knots.

Stratified analyses were conducted to explore whether lifestyle factors may modify the effects of UFP on outcomes. Potential grouping factors included current smoking (yes vs no), frequency of alcohol consumption (over three times a week vs ≤ once or twice a week), physical activity (high level vs low to moderate level), and diet score (dichotomized into favorable and unfavorable diet based on the median). The statistical significance of between-stratum difference was tested using Cochran’s Q-test. P values less than 0.05 indicated significant modification. More details are provided in the Supporting Information.

Several sensitivity analyses were performed. First, we adjusted for the concomitant exposures to PM2.5, PM10, NO x , or ozone to examine the independent effects of UFP in the two-pollutant model. Second, we redefined “close to the road” as residing within 50 m or 200 m of a major road to examine the impact of different proximity thresholds. Third, to eliminate potential influence of delayed diagnosis and reverse causation, we excluded cases occurring before January 1, 2015. Fourth, to mitigate potential bias due to temporal misalignment between exposure and outcome, we censored follow-up at the end of 2017. Finally, to account for the potential impact of the COVID-19 pandemic, we redefined the administrative end of follow-up as Dec 31, 2019.

Analyses were conducted using R (version 4.2.0). Associations were presented as HRs and 95% confidence intervals (CIs) per interquartile range (IQR, particles/cm3) increments in UFP concentrations. The Benjamini–Hochberg false discovery rate procedure was applied to adjust the P values across the nine specific CVDs, and an adjusted P value of less than 0.05 was considered statistically significant after correction for multiple comparisons.

Role of the Funding Source

The funders of this study had no role in the study design, in the collection, analysis, or interpretation of the data, or in drafting the manuscript.

Results

The mean age of the participants enrolled in the study was 56.6 (standard deviation: 8.1) years at baseline. The median concentration of UFP was 3421 (IQR: 943) particles/cm3 (Table S1), and the spatial distribution of UFP among participants is presented in Figure S2.

During a median follow-up of 13.7 (IQR:13.0, 14.4) years, 87,028 developed CVD. The number of incident cause-specific CVD cases ranged from 2716 (cardiomyopathy) to 60,844 (hypertension). Table presents the associations of ambient UFP exposure with CVDs. We found that per IQR increment of UFP was significantly associated with a 3% higher incidence risk of overall CVD (HR = 1.03, 95% CI: 1.01, 1.04). Among all specific CVDs, UFP had the strongest association with chronic rheumatic heart diseases (HR = 1.08, 95% CI: 1.05, 1.12), followed by heart failure (HR = 1.06, 95% CI: 1.04, 1.09), cerebrovascular diseases (HR = 1.04, 95% CI: 1.01, 1.06), conduction disorders (HR = 1.03, 95% CI: 1.01, 1.04), hypertension (HR = 1.03, 95% CI: 1.01, 1.04), and IHD (HR = 1.03, 95% CI: 1.01, 1.04). No significant associations were found between UFP exposure and the incidence of heart valve disorder, cardiomyopathy, and aortic aneurysm and dissection (Table ).

1. Associations of Incidence of Cardiovascular Diseases with per IQR Increment in Ambient UFP Concentrations .

diseases cases hazard ratio (95% CI) P value
cardiovascular disease 87,028 1.03 (1.01, 1.04) <0.001
chronic rheumatic heart diseases 9451 1.08 (1.05, 1.12) <0.001
hypertension 60,844 1.03 (1.01, 1.04) <0.001
ischemic heart diseases 35,305 1.03 (1.01, 1.04) 0.006
heart valve disorder 13,747 1.02 (0.99, 1.05) 0.255
cardiomyopathy 2716 1.04 (0.98, 1.11) 0.226
conduction disorders 45,570 1.03 (1.01, 1.04) <0.001
heart failure 16,340 1.06 (1.04, 1.09) <0.001
cerebrovascular diseases 19,332 1.04 (1.01, 1.06) 0.006
aortic aneurysm and dissection 5198 1.00 (0.96, 1.05) 0.923
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Abbreviation: UFP, Ultrafine particulate matter; CI, confidence interval; IQR, interquartile range. Notes: The interquartile range of UFP was 943 particles/cm3; models were adjusted for age, sex, income, race, education, assessment center, smoking, physical activity, and diet; the Benjamini–Hochberg procedure was applied to adjust the P values across the nine specific cardiovascular diseases; an adjusted P value of less than 0.05 was considered statistically significant after multiple comparisons.

The E–R curves for the associations of UFP with the incidence of overall and cause-specific CVDs are illustrated in Figures and . We observed no safety thresholds below which UFP exposure had no detrimental effects, and there was an apparent plateauing trend at high levels of UFP.

1.

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Exposure–response curve of the effects of ambient UFP on the incidence of cardiovascular disease. Abbreviation: UFP, Ultrafine particulate matter. Notes: The relationships were presented as the hazard ratios (dark red line) and 95% confidence intervals (light pink shading) for the incidence of cardiovascular disease per unit change of UFP using the model in Table .

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Exposure–response curves of the effects of ambient UFP on the incidence of (A) Chronic rheumatic heart diseases; (B) Hypertension; (C) Ischemic heart diseases; (D) Conduction disorders; (E) Heart failure; and (F) Cerebrovascular diseases. Abbreviation: UFP, Ultrafine particulate matter. Notes: The relationships were presented as the hazard ratios (dark red line) and 95% confidence intervals (light pink shading) for the incidence of cardiovascular disease per unit change of UFP using the model in Table .

The effects of UFP on CVD and its specific categories were mostly similar across different smoking statuses, alcohol drinking frequency, physical activity levels, and diet habits, with only one exception (Figures and S3). We found smokers were more vulnerable to chronic rheumatic heart diseases in relation to UFP exposure than their counterparts (Figure S3).

3.

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Associations of ambient UFP with the incidence of cardiovascular disease, stratified by potential modifiers. Abbreviation: UFP, Ultrafine particulate matter; HR, hazard ratio; CI, confidence interval. Notes: Associations were presented as HR (95% CI) per interquartile range (943 particles/cm3) increment in UFP; models were adjusted for age, sex, income, race, education, assessment center, smoking, physical activity, and diet (except for corresponding modifier); P values < 0.1 indicated significant modification.

In sensitivity analysis, our results were mostly robust to further adjustment of PM2.5 and other air pollutants including PM10 and ozone in the main model (Table ). Nevertheless, the associations between UFP and CVD outcomes were slightly attenuated after adjustment of NOx. Our results also remained stable when redefining the “close to the road” as residing within 50 m or 200 m of a major road, further excluding events that occurred before 2015.01.01, censoring follow-up at the end of 2017, or redefining the cutoff date as Dec 31, 2019 (Table S2).

2. Associations of Incidence of Cardiovascular Diseases with per IQR Increment in Ambient UFP Concentrations in Two-Pollutant Models .

  + PM2.5 + PM10 +NO x +ozone
cardiovascular disease 1.02 (1.01, 1.03) 1.03 (1.02, 1.04) 1.01 (1.00, 1.03) 1.02 (1.01, 1.03)
chronic rheumatic heart diseases 1.09 (1.05, 1.12) 1.08 (1.05, 1.11) 1.07 (1.04, 1.11) 1.08 (1.05, 1.12)
hypertension 1.02 (1.01, 1.04) 1.03 (1.01, 1.04) 1.01 (1.00, 1.03) 1.02 (1.01, 1.03)
ischemic heart diseases 1.03 (1.01, 1.04) 1.03 (1.01, 1.05) 1.01 (0.99, 1.03) 1.02 (1.00, 1.04)
conduction disorders 1.02 (1.01, 1.04) 1.03 (1.01, 1.05) 1.01 (1.00, 1.03) 1.02 (1.01, 1.04)
heart failure 1.05 (1.02, 1.08) 1.06 (1.04, 1.09) 1.03 (1.01, 1.06) 1.04 (1.02, 1.07)
cerebrovascular diseases 1.03 (1.01, 1.06) 1.04 (1.01, 1.06) 1.02 (0.99, 1.04) 1.03 (1.00, 1.05)
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a

Abbreviation: UFP, Ultrafine particulate matter; IQR, interquartile range; PM2.5, particulate matter with an aerodynamic diameter ≤2.5 μm; PM10, particulate matter with an aerodynamic diameter ≤10 μm; NOx, nitrogen oxides. Notes: The interquartile range of UFP was 943 particles/cm3; associations were presented as hazard ratios (95% confidence intervals); models were adjusted for age, sex, income, race, education, assessment center, smoking, physical activity, and diet; associations in bold were statistically significant.

Discussion

Despite growing attention to UFP exposure, existing evidence remains insufficient to assess its long-term effects, especially in areas with background UFP concentrations. In this nationwide, prospective cohort study, we found linear and independent associations between long-term exposure to background UFP concentrations and the incidence of overall and several specific CVDs. Additionally, we found that smokers were more vulnerable, potentially due to compromised respiratory mucosa and generally poorer health among smokers.

Although UFP concentrations are much higher among individuals residing closer to sources, a vast majority of the population is exposed to background concentrations. This study is the first to explore the associations between long-term exposure to the background levels of UFP and CVDs. As expected, the UFP levels reported in our study were much lower than UFP levels in studies based on short-term mobile monitoring campaigns on road segments ,, but were comparable to the background concentrations reported previously. We found that even populations exposed to the background levels of UFP may still face elevated risks of incident CVDs in association with UFP exposure. Although the effect sizes were modest (e.g., HR = 1.03 for overall CVD), their public health relevance should not be underestimated as even small risks from a ubiquitous exposure can lead to a substantial disease burden at the population level. Notably, the association between long-term UFP exposure and CVD incidence appears stronger in our study than in previous studies examining health effects at nonbackground concentrations. , This underscores the need to recognize UFP as a health hazard beyond high-exposure scenarios (i.e., traffic-dense areas) and to incorporate it into broader air quality regulations and CVD prevention strategies.

We further identified significant associations of UFP exposure with several specific CVDs, including IHD, cerebrovascular diseases, hypertension, and heart failure. These associations have not been fully elucidated in previous researches. Specifically, although several prior studies of small sample size or limited spatial coverage have reported a null association between long-term UFP exposure and IHD, , our study aligns with recent research by Poulsen et al., which found that each IQR increase in UFP exposure heightened the risk of IHD by 4% (95% CI: 2.5–5.5%) in a national cohort study from Denmark. Previous studies of cerebrovascular diseases were limited in scope and consistency. In contrast to our research, a cohort study focusing on airport workers reported no association between long-term occupational exposure to UFP and stroke. Nevertheless, a prospective study conducted in The Netherlands, involving 33,831 participants, suggested a positive link between long-term UFP exposure and cerebrovascular diseases, albeit not statistically significant probably due to the limited sample size (HR per 10,000 particles/cm3 in UFP concentrations: 1.11; 95% CI: 0.88, 1.41). Our study confirms the association between long-term exposure to UFP and cerebrovascular diseases by using the most extensive investigation thus far. Previous studies have consistently revealed increased risk of incident heart failure in association with long-term exposure to UFP in a few selected cities. , As for hypertension, only two single-city studies of small sample size have explored the association between UFP and hypertension previously, reporting inconsistent findings. , Our nationwide prospective study provides further support for the associations between long-term UFP exposure and both heart failure and hypertension.

Furthermore, we reported significant harmful effects of UFP that have never been reported, including conduction disorders and chronic rheumatic heart diseases. While this is the first to unveiling the impacts of chronic UFP exposure on conduction disorders, previous studies focusing on PM2.5 and conduction disorders have provided support for our findings. , Additionally, we are the first to link UFP exposure to the development of chronic rheumatic heart disease. Chronic rheumatic heart disease, the long-term consequence of acute rheumatic fever resulting from infections with Lancefield group A, β-hemolytic streptococci, and Streptococcus pyogenes, persists as one of the most prevalent heart diseases in regions with suboptimal living conditions. Prior to our current research, only one study had previously examined 83 UK urban areas and proposed that early life exposure to air pollution, denoted by coal consumption, may be associated with increased risk of adult mortality from chronic rheumatic heart disease. Despite the scarcity of evidence, our finding is not unexpected and can be biologically plausible. First, UFP may enhance the susceptibility to infections, episodes of rheumatic fever, and subsequent chronic rheumatic heart disease by facilitating the entry of microorganisms into the circulatory system and body fluids. Additionally, the destruction of the respiratory function by UFP may further accelerate this process. Second, air pollution has been increasingly linked to autoimmune diseases, potentially through the promotion of T-cell imbalance, the production of proinflammatory cytokines, local pulmonary inflammation, oxidative stress, and methylation changes. Consequently, UFP may exacerbate chronic rheumatic heart disease by amplifying the autoimmune response within the heart. Third, individuals with CVD are inherently more vulnerable to developing chronic rheumatic heart disease. Therefore, exposure to UFP may also elevate the risk of developing chronic rheumatic heart disease by augmenting the risk of other chronic CVDs. Despite these plausible biological explanations, this result should be interpreted with caution, considering it hypothesis-generating. Future studies are warranted to replicate the association and clarify the underlying mechanisms.

The independency of the cardiovascular effects of UFP and the shape for the E–R curves are critical considerations in UFP management. UFP accounts for the largest percentage of the total particulate number concentrations, yet constitutes the smallest portion of total PM mass concentrations. Consequently, epidemiological studies based on PM mass may not adequately capture the health effects of UFP. However, prior studies investigating the independent cardiovascular effects of UFP have produced inconsistent conclusions. For example, although some studies reported robust associations between UFP and cardiovascular outcomes after controlling for PM2.5, , others have found a significant attenuation in these associations. , Our study observed independent detrimental effects of UFP on CVDs from those of PM mass exposures. This, combined with previous findings indicating that regulating PM2.5 does little to reduce UFP, reinforces the integration of UFP into cardiovascular prevention strategies. Regarding the E–R curves, several short-term exposure studies have reported that the E–R curves for UFP did not have apparent thresholds, , but the shape of E–R curves of prolonged UFP exposure has not been well characterized. To date, only one cohort study in The Netherlands has reported a slightly nonlinear E–R curve, with a slope observed between 7000 and 20,000 particles/cm3, followed by a leveling off with much wider confidence intervals at concentrations exceeding 20,000 particles/cm3. Our E–R curves showed that harmful effects on CVDs persist even at concentrations as low as 1000 particles/cm3, the low level preliminarily proposed by the Air Quality Guidelines for short-term exposure to UFP in 2021, based on the concentrations typically observed in environments not affected by anthropogenic emissions and urban background areas. This again underscores the importance of strengthening efforts toward UFP mitigation, even in areas distant from sources, where UFP concentrations are relatively low.

This study has several limitations. First, limited by the data availability, we only used the predictions of UFP exposures in years 2015 to 2017 to represent the long-term exposure during follow-up; thus, exposure measurement errors cannot be excluded. However, the resulting influences may not be substantial as the spatial contrasts of air pollution levels remain relatively stable over time in the UK these years. Even if misclassification occurred, it would likely be nondifferential, biasing the associations toward the null and rendering our results conservative. In addition, our results remained stable in sensitivity analyses excluding CVD events occurring before 2015 and censoring follow-up at 2017 to align with the exposure assessment period. Second, in the two-pollutant models, copollutants were derived from time periods that did not precisely align with the UFP exposure window, due to limitations in data availability. This “mixed-time” adjustment strategy may introduce uncertainty in estimating the independent effect of UFP. Although spatial contrasts in air pollution levels in the UK have remained relatively stable over time, the potential for residual confounding due to temporal misalignment cannot be entirely ruled out and should be taken into account when interpreting the findings. Third, the spatial resolution of our UFP model was 10 km × 10 km. Although our model, compared to the short-term mobile monitoring campaigns in previous studies, could not effectively capture the high variability of UFP concentrations near sources and measure traffic-related UFP, it does reflect long-term background UFP concentrations. Thus, our study could effectively evaluate the health impacts of ambient UFP on the vast majority of the population exposed to background UFP concentrations (>90% in our study). Fourth, although we operationally defined “background exposure” as residing more than 100 m away from major roads, we acknowledge that this criterion may not fully account for other localized, nontraffic sources of UFP, such as industrial emissions and urban point sources. Given the 10 km × 10 km resolution of our exposure model, these small-scale hotspots may be spatially averaged and thus not be fully captured in grid-level exposure estimates. Fifth, although we adjusted for a wide range of baseline covariates, these variables may change over time. However, repeated measures from participants with available follow-up data suggest that these factors remained relatively stable (Table S3), supporting the robustness of our adjustment. Finally, our analyses were primarily restricted to Caucasian from the UK with low UFP levels, thereby limiting the generalizability of our results to populations from other ethnic backgrounds or with higher exposure levels. In addition, UK Biobank participants tend to be healthier and of higher socioeconomic status than the general population, which may reduce their vulnerability to UFP and lead to an underestimation of the true effect sizes observed in our study.

In conclusion, this national, prospective cohort study demonstrates that long-term exposure to ambient UFP was independently and linearly associated with higher incidence risks of a wide range of CVDs, including IHD, cerebrovascular diseases, hypertension, heart failure, conduction disorders, and chronic rheumatic heart diseases among populations exposed to the background levels of UFP. UFP may be considered a significant, independent, and modifiable environmental risk factor for CVDs even in areas distant from sources.

Supplementary Material

eh5c00228_si_001.pdf (688.1KB, pdf)

Acknowledgments

We thank the team members and participants of UK Biobank. We also acknowledge team of Prof. Matthias Kohl and Prof. Andrea Pozzer.

The data that support the findings of this study are available from the UK Biobank but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00228.

  • Supplementary method; baseline characteristics of UK Biobank participants included in this study; sensitivity analyses of the associations between UFP and the incidence of cardiovascular diseases; changes in covariates during follow-up compared to baseline; flow diagram of the analytic sample; the distribution of UFPs; and associations of ambient UFP with the incidence of various cardiovascular outcomes, stratified by potential modifiers (PDF)

H.L.: data verification, formal analysis, and writingoriginal draft & review & editing. Y.J.: writingreview & editing. L.Z.: writingreview & editing. J.C.: writingreview & editing. K.Y.: writingreview & editing. A.L.: writingreview & editing. X.M.: writingreview & editing. H.K.: conceptualization, data verification, supervision, and writingreview & editing. R.C.: conceptualization, data verification, funding acquisition, supervision, and writingreview & editing. All authors have read and approved the final version of the manuscript. All authors have final responsibility for the decision to submit for publication.

This work was supported by Shanghai Pilot Program for Basic ResearchFudan University 21TQ1400100 (21TQ015), the National Natural Science Foundation of China (82373532), the National Key Research and Development Program (2022YFC3702701), and Key Program for Public Health High-Quality Development of Jiading District in Shanghai (GWGZLXK-2023–03).

The authors declare no competing financial interest.

References

  1. Luo H., Zhang Q., Niu Y., Kan H., Chen R.. Fine Particulate Matter and Cardiorespiratory Health in China: A Systematic Review and Meta-Analysis of Epidemiological Studies. J. Environ. Sci. (China) 2023;123:306–316. doi: 10.1016/j.jes.2022.04.026. [DOI] [PubMed] [Google Scholar]
  2. Sun H., Chen X., Huang W., Wei J., Yang X., Shan A., Zhang L., Zhang H., He J., Pan C.. et al. Association between Long-Term Exposure to PM2.5 Inorganic Chemical Compositions and Cardiopulmonary Mortality: A 22-Year Cohort Study in Northern China. Environ. Health. 2024;2(8):530–540. doi: 10.1021/envhealth.4c00020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ma J., Yao Y., Xie Y., Yin H., Yang S., Shang B., You X., Guo Y., Chen W.. Omega-3 Modify the Adverse Effects of Long-Term Exposure to Ambient Air Pollution on the Incidence of Chronic Obstructive Pulmonary Disease: Evidence from a Nationwide Prospective Cohort Study. Environ. Health. 2025;3(7):787–794. doi: 10.1021/envhealth.4c00198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Hu J., Tang M., Zhang X., Ma Y., Li Y., Chen R., Kan H., Cui Z., Ge J.. Size-Fractionated Particulate Air Pollution and Myocardial Infarction Emergency Hospitalization in Shanghai, China. Sci. Total Environ. 2020;737:140100. doi: 10.1016/j.scitotenv.2020.140100. [DOI] [PubMed] [Google Scholar]
  5. Nemmar A., Hoet P. H. M., Vanquickenborne B., Dinsdale D., Thomeer M., Hoylaerts M. F., Vanbilloen H., Mortelmans L., Nemery B.. Passage of Inhaled Particles Into the Blood Circulation in Humans. Circulation. 2002;105(4):411–414. doi: 10.1161/hc0402.104118. [DOI] [PubMed] [Google Scholar]
  6. Naghavi M., Ong K. L., Aali A., Ababneh H. S., Abate Y. H., Abbafati C., Abbasgholizadeh R., Abbasian M., Abbasi-Kangevari M., Abbastabar H.. et al. Global Burden of 288 Causes of Death and Life Expectancy Decomposition 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(10440):2100–2132. doi: 10.1016/s0140-6736(24)00367-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen J., Hoek G.. Long-Term Exposure to PM and All-Cause and Cause-Specific Mortality: A Systematic Review and Meta-Analysis. Environ. Int. 2020;143:105974. doi: 10.1016/j.envint.2020.105974. [DOI] [PubMed] [Google Scholar]
  8. Rajagopalan S., Al-Kindi S. G., Brook R. D.. Air Pollution and Cardiovascular Disease: JACC State-of-the-Art Review. J. Am. Coll Cardiol. 2018;72(17):2054–2070. doi: 10.1016/j.jacc.2018.07.099. [DOI] [PubMed] [Google Scholar]
  9. EPA, U. S . Integrated Science Assessment (ISA) for Particulate Matter (Final Report, Dec 2019).; U.S. Environmental Protection Agency. EPA/600/R-19/188: Washington, DC, 2019. [Google Scholar]
  10. Health Effects Institute . State of Global Air 2019. Special Report; Health Effects Institute: Boston, MA, 2019. [Google Scholar]
  11. Sang S., Chu C., Zhang T., Chen H., Yang X.. The Global Burden of Disease Attributable to Ambient Fine Particulate Matter in 204 Countries and Territories, 1990–2019: A Systematic Analysis of the Global Burden of Disease Study 2019. Ecotoxicol. Environ. Saf. 2022;238:113588. doi: 10.1016/j.ecoenv.2022.113588. [DOI] [PubMed] [Google Scholar]
  12. Bergmann M. L., Andersen Z. J., Massling A., Kindler P. A., Loft S., Amini H., Cole-Hunter T., Guo Y., Maric M., Nordstrøm C.. et al. Short-Term Exposure to Ultrafine Particles and Mortality and Hospital Admissions Due to Respiratory and Cardiovascular Diseases in Copenhagen, Denmark. Environ. Pollut. 2023;336:122396. doi: 10.1016/j.envpol.2023.122396. [DOI] [PubMed] [Google Scholar]
  13. Jiang Y., Chen R., Peng W., Luo Y., Chen X., Jiang Q., Han B., Su G., Duan Y., Huo J.. et al. Hourly Ultrafine Particle Exposure and Acute Myocardial Infarction Onset: An Individual-Level Case-Crossover Study in Shanghai, China, 2015–2020. Environ. Sci. Technol. 2023;57(4):1701–1711. doi: 10.1021/acs.est.2c06651. [DOI] [PubMed] [Google Scholar]
  14. Gardner B., Ling F., Hopke P. K., Frampton M. W., Utell M. J., Zareba W., Cameron S. J., Chalupa D., Kane C., Kulandhaisamy S.. et al. Ambient Fine Particulate Air Pollution Triggers ST-Elevation Myocardial Infarction, but Not Non-ST Elevation Myocardial Infarction: A Case-Crossover Study. Part Fibre Toxicol. 2014;11:1. doi: 10.1186/1743-8977-11-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hennig F., Quass U., Hellack B., Küpper M., Kuhlbusch T. A. J., Stafoggia M., Hoffmann B.. Ultrafine and Fine Particle Number and Surface Area Concentrations and Daily Cause-Specific Mortality in the Ruhr Area, Germany, 2009–2014. Environ. Health Perspect. 2018;126(2):027008. doi: 10.1289/EHP2054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chen K., Wolf K., Breitner S., Gasparrini A., Stafoggia M., Samoli E., Andersen Z. J., Bero-Bedada G., Bellander T., Hennig F.. et al. Two-Way Effect Modifications of Air Pollution and Air Temperature on Total Natural and Cardiovascular Mortality in Eight European Urban Areas. Environ. Int. 2018;116:186–196. doi: 10.1016/j.envint.2018.04.021. [DOI] [PubMed] [Google Scholar]
  17. Bouma F., Janssen N. A., Wesseling J., van Ratingen S., Strak M., Kerckhoffs J., Gehring U., Hendricx W., de Hoogh K., Vermeulen R.. et al. Long-Term Exposure to Ultrafine Particles and Natural and Cause-Specific Mortality. Environ. Int. 2023;175:107960. doi: 10.1016/j.envint.2023.107960. [DOI] [PubMed] [Google Scholar]
  18. Li Y., Lane K. J., Corlin L., Patton A. P., Durant J. L., Thanikachalam M., Woodin M., Wang M., Brugge D.. Association of Long-Term Near-Highway Exposure to Ultrafine Particles with Cardiovascular Diseases, Diabetes and Hypertension. Int. J. Environ. Res. Public Health. 2017;14(5):461. doi: 10.3390/ijerph14050461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Møller K. L., Brauer C., Mikkelsen S., Bonde J. P., Loft S., Helweg-Larsen K., Thygesen L. C.. Cardiovascular Disease and Long-Term Occupational Exposure to Ultrafine Particles: A Cohort Study of Airport Workers. Int. J. Hyg Environ. Health. 2020;223(1):214–219. doi: 10.1016/j.ijheh.2019.08.010. [DOI] [PubMed] [Google Scholar]
  20. Zhu Y., Hinds W. C., Kim S., Sioutas C.. Concentration and Size Distribution of Ultrafine Particles Near a Major Highway. J. Air Waste Manag Assoc. 2002;52(9):1032–1042. doi: 10.1080/10473289.2002.10470842. [DOI] [PubMed] [Google Scholar]
  21. Downward G. S., van Nunen E., Kerckhoffs J., Vineis P., Brunekreef B., Boer J. M. A., Messier K. P., Roy A., Verschuren W. M. M., van der Schouw Y. T.. et al. Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort. Environ. Health Perspect. 2018;126(12):127007. doi: 10.1289/EHP3047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., Downey P., Elliott P., Green J., Landray M.. et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kohl M., Lelieveld J., Chowdhury S., Ehrhart S., Sharma D., Cheng Y., Tripathi S. N., Sebastian M., Pandithurai G., Wang H.. et al. Numerical Simulation and Evaluation of Global Ultrafine Particle Concentrations at the Earth’s Surface. Atmos. Chem. Phys. 2023;23(20):13191–13215. doi: 10.5194/acp-23-13191-2023. [DOI] [Google Scholar]
  24. Bai L., Weichenthal S., Kwong J. C., Burnett R. T., Hatzopoulou M., Jerrett M., van Donkelaar A., Martin R. V., Van Ryswyk K., Lu H.. et al. Associations of Long-Term Exposure to Ultrafine Particles and Nitrogen Dioxide With Increased Incidence of Congestive Heart Failure and Acute Myocardial Infarction. Am. J. Epidemiol. 2019;188(1):151–159. doi: 10.1093/aje/kwy194. [DOI] [PubMed] [Google Scholar]
  25. Ostro B., Hu J., Goldberg D., Reynolds P., Hertz A., Bernstein L., Kleeman M. J.. Associations of Mortality With Long-Term Exposures to Fine and Ultrafine Particles, Species and Sources: Results From the California Teachers Study Cohort. Environ. Health Perspect. 2015;123(6):549–556. doi: 10.1289/ehp.1408565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Poulsen A. H., Sørensen M., Hvidtfeldt U. A., Christensen J. H., Brandt J., Frohn L. M., Ketzel M., Andersen C., Raaschou-Nielsen O.. Source-Specific Air Pollution Including Ultrafine Particles and Risk of Myocardial Infarction: A Nationwide Cohort Study from Denmark. Environ. Health Perspect. 2023;131(5):57010. doi: 10.1289/EHP10556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. McDuffie E. E., Smith S. J., O’Rourke P., Tibrewal K., Venkataraman C., Marais E. A., Zheng B.-f., Crippa M., Brauer M., Martin R. V.. A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS) Earth Syst. Sci. Data. 2020;12(4):3413–3442. [Google Scholar]
  28. Montagne D. R., Hoek G., Klompmaker J. O., Wang M., Meliefste K., Brunekreef B.. Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation. Environ. Sci. Technol. 2015;49(14):8712–8720. doi: 10.1021/es505791g. [DOI] [PubMed] [Google Scholar]
  29. Chen J., Zhu S., Wang P., Zheng Z., Shi S., Li X., Xu C., Yu K., Chen R., Kan H.. et al. Predicting Particulate Matter, Nitrogen Dioxide, and Ozone Across Great Britain With High Spatiotemporal Resolution Based on Random Forest Models. Sci. Total Environ. 2024;926:171831. doi: 10.1016/j.scitotenv.2024.171831. [DOI] [PubMed] [Google Scholar]
  30. Beelen R., Hoek G., Vienneau D., Eeftens M., Dimakopoulou K., Pedeli X., Tsai M.-Y., Künzli N., Schikowski T., Marcon A.. et al. Development of NO2 and NOx Land Use Regression Models for Estimating Air Pollution Exposure in 36 Study Areas in Europe – The ESCAPE Project. Atmos. Environ. 2013;72:10–23. doi: 10.1016/j.atmosenv.2013.02.037. [DOI] [Google Scholar]
  31. Altman D. G., Bland J. M.. Interaction Revisited: The Difference Between Two Estimates. BMJ. 2003;326(7382):219. doi: 10.1136/bmj.326.7382.219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Drozd M., Pujades-Rodriguez M., Lillie P. J., Straw S., Morgan A. W., Kearney M. T., Witte K. K., Cubbon R. M.. Non-Communicable Disease, Sociodemographic Factors, and Risk of Death From Infection: A UK Biobank Observational Cohort Study. Lancet Infect Dis. 2021;21(8):1184–1191. doi: 10.1016/S1473-3099(20)30978-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kerckhoffs J., Hoek G., Gehring U., Vermeulen R.. Modelling Nationwide Spatial Variation of Ultrafine Particles Based on Mobile Monitoring. Environ. Int. 2021;154:106569. doi: 10.1016/j.envint.2021.106569. [DOI] [PubMed] [Google Scholar]
  34. Bai L., Chen H., Hatzopoulou M., Jerrett M., Kwong J. C., Burnett R. T., van Donkelaar A., Copes R., Martin R. V., Van Ryswyk K.. et al. Exposure to Ambient Ultrafine Particles and Nitrogen Dioxide and Incident Hypertension and Diabetes. Epidemiology. 2018;29(3):323–332. doi: 10.1097/EDE.0000000000000798. [DOI] [PubMed] [Google Scholar]
  35. Zhang Y., Liu D., Liu Z.. Fine Particulate Matter (PM2.5) and Chronic Kidney Disease. Rev. Environ. Contam Toxicol. 2021;254:183–215. doi: 10.1007/398_2020_62. [DOI] [PubMed] [Google Scholar]
  36. Ou Z., Yu D., Liang Y., Wu J., He H., Li Y., He W., Gao Y., Wu F., Chen Q.. Global Burden of Rheumatic Heart Disease: Trends from 1990 to 2019. Arthritis Res. Ther. 2022;24(1):138. doi: 10.1186/s13075-022-02829-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Phillips D. I. W., Osmond C., Williams M. L., Jones A.. Air Pollution in Early Life and Adult Mortality From Chronic Rheumatic Heart Disease. Int. J. Epidemiol. 2016;46(4):dyw249. doi: 10.1093/ije/dyw249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zhao C.-N., Xu Z., Wu G.-C., Mao Y.-M., Liu L.-N., Qian W., Dan Y.-L., Tao S.-S., Zhang Q., Sam N. B.. et al. Emerging Role of Air Pollution in Autoimmune Diseases. Autoimmun Rev. 2019;18(6):607–614. doi: 10.1016/j.autrev.2018.12.010. [DOI] [PubMed] [Google Scholar]
  39. Zhu Y., Sulaymon I. D., Xie X., Mao J., Guo S., Hu M., Hu J.. Airborne Particle Number Concentrations in China: A Critical Review. Environ. Pollut. 2022;307:119470. doi: 10.1016/j.envpol.2022.119470. [DOI] [PubMed] [Google Scholar]
  40. Poulsen A. H., Sørensen M., Hvidtfeldt U. A., Ketzel M., Christensen J. H., Brandt J., Frohn L. M., Massling A., Khan J., Münzel T.. et al. Concomitant Exposure to Air Pollution, Green Space and Noise, and Risk of Myocardial Infarction: A Cohort Study From Denmark. Eur. J. Prev Cardiol. 2024;31(1):131–141. doi: 10.1093/eurjpc/zwad306. [DOI] [PubMed] [Google Scholar]
  41. de Jesus A. L., Rahman M. M., Mazaheri M., Thompson H., Knibbs L. D., Jeong C., Evans G., Nei W., Ding A., Qiao L.. et al. Ultrafine Particles and PM2.5 in the Air of Cities Around the World: Are They Representative of Each Other? Environ. Int. 2019;129:118–135. doi: 10.1016/j.envint.2019.05.021. [DOI] [PubMed] [Google Scholar]
  42. Li H., Li X., Zheng H., Liu L., Wu Y., Zhou Y., Meng X., Hong J., Cao L., Lu Y.. et al. Ultrafine Particulate Air Pollution and Pediatric Emergency-Department Visits for Main Respiratory Diseases in Shanghai, China. Sci. Total Environ. 2021;775:145777. doi: 10.1016/j.scitotenv.2021.145777. [DOI] [PubMed] [Google Scholar]
  43. World Health Organization . WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. 2021. https://www.who.int/publications/i/item/9789240034228 (accessed Aug 20, 2025). [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

eh5c00228_si_001.pdf (688.1KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the UK Biobank but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).

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