Background: There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than ) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). Objectives: We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. Methods: Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter ( ), PM with aerodynamic diameter ( ), and . Results: Long-term UFP exposure was associated with an increased risk for all incident CVD [ per ; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) ( ; 95% CI: 1.00, 1.79), and heart failure ( ; 95% CI: 1.17, 2.66). Positive associations were also estimated for ( ; 95% CI: 1.01, 1.48 per ) and coarse PM ( ; HR for all ; 95% CI: 1.01, 1.45 per ). CVD was not positively associated with (HR for all ; 95% CI: 0.75, 1.28 per ). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models ( and ), positive associations tended to remain for UFP, while HRs for and generally attenuated towards the null. Conclusions: These findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics. https://doi.org/10.1289/EHP3047

Background:Results from studies on residential health effects of livestock farming are inconsistent, potentially due to simple exposure proxies used (e.g., livestock density). Accuracy of these proxies compared with measured exposure concentrations is unknown.Objectives:We aimed to assess spatial variation of endotoxin in PM10 (particulate matter ≤10μm) at residential level in a livestock-dense area, compare simple livestock exposure proxies to measured endotoxin concentrations, and evaluate whether land-use regression (LUR) can be used to explain spatial variation of endotoxin.Methods:The study area (3,000 km2) was located in Netherlands. Ambient PM10 was collected at 61 residential sites representing a variety of surrounding livestock-related characteristics. Three to four 2-wk averaged samples were collected at each site. A local reference site was used for temporal variation adjustment. Samples were analyzed for PM10 mass by weighing and for endotoxin by using the limulus amebocyte lysate assay. Three LUR models were developed, first a model based on general livestock-related GIS predictors only, followed by models that also considered species-specific predictors and farm type–specific predictors.Results:Variation in concentrations measured between sites was substantial for endotoxin and more limited for PM10 (coefficient of variation: 43%, 8%, respectively); spatial patterns differed considerably. Simple exposure proxies were associated with endotoxin concentrations although spatial variation explained was modest (R2<26%). LUR models using a combination of animal-specific livestock-related characteristics performed markedly better, with up to 64% explained spatial variation.Conclusion:The considerable spatial variation of ambient endotoxin concentrations measured in a livestock-dense area can largely be explained by LUR modeling based on livestock-related characteristics. Application of endotoxin LUR models seems promising for residential exposure estimation within health studies. https://doi.org/10.1289/EHP2252

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