Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands

Ultrafine particles and late-life cognitive function: Influence of stationary mobile monitoring design on health inferences

Growing evidence links ultrafine particles (UFP) to neurotoxicity, but human studies remain limited. Various mobile monitoring approaches have been used to develop air pollution exposure models. However, whether design choices impact epidemiology, including for UFP and cognitive function, remains unclear. We evaluated the adjusted association between 5-year average UFP number concentration (PNC) and late-life cognitive function (Cognitive Abilities Screening Instrument - Item Response Theory [CASI-IRT]) in the Adult Changes in Thought cohort (N = 5283) by leveraging an extensive roadside mobile monitoring campaign specifically designed for epidemiology. To assess the impact of reduced monitoring approaches on this association, we repeatedly subsampled UFP measures from the campaign, developed exposure models, and evaluated the degree to which associations were impacted. In the primary analysis, each 1900 pt/cm3 increment in PNC was associated with an adjusted mean baseline CASI-IRT score that was 0.002 (95 % CI: -0.016, 0.020) higher, which was not statistically significant. Point estimates were consistent across sampling designs with fewer visits per site (≤12), fewer seasons (1-3), and unbalanced visit frequency across sites. Sampling designs restricted to rush hours were more similar (median point estimate 0.002, IQR of point estimates: 0.000, 0.003) than business hour designs (0.006, IQR: 0.005, 0.007), but the opposite was true when temporal adjustments were applied (rush: -0.003, IQR: -0.005, -0.001; business: 0.002, IQR: 0.001, 0.004). We observed similar results in sensitivity and secondary analyses. We did not find evidence of an association between UFP and cognitive function in fully adjusted models. Monitoring design had minimal impact on the inferential results in this setting, which may have been caused by the lack of association. Secondary analyses in a reduced model that is potentially confounded suggest that monitoring design might have a greater impact in other datasets. Further research is needed, particularly in contexts with robust statistically significant health associations.

Comparison of air pollution mortality effect estimates using different long-term exposure assessment modelling methods

INTRODUCTION

Epidemiological studies have used different approaches to assess long-term exposure to ambient air pollution. Little is known about how different exposure models affect health effect estimates in these studies. The aim of this study was to compare air pollution mortality effect estimates in an administrative cohort in the Netherlands based on different exposure assessment methods for black carbon (BC), nitrogen dioxide (NO2), ultrafine particles (UFP), and particulate matter <2.5 μm (PM2.5).

METHODS

Annual average air pollution exposure estimates using eight methods, differing in modelling and monitoring strategy, were applied to a Dutch national cohort of 10.7 million adults aged ≥30 years. Dispersion and land-use regression models based on mobile and fixed-site monitoring were evaluated. Follow-up was from 2013 to 2019. Hazard ratios (HR) for natural and cause-specific mortality were estimated using Cox proportional hazards models.

RESULTS

Exposure estimates from different models were highly correlated. Even though the direction of mortality effect estimates was similar between methods, their magnitude differed substantially, e.g. the HR for BC with natural mortality ranged from 1.01 to 1.09 per increment of 1 μg/m3. No consistent differences in effect estimates were found between deterministic and empirical fixed-site and mobile models. Model predictions over a 10-year period correlated highly and resulted in similar HRs.

DISCUSSION

Different exposure models resulted in similar conclusions about the presence of associations with mortality, but HRs differed up to a ratio of 1.27. Differences in exposure assessment may therefore contribute to the observed heterogeneity of mortality estimates in systematic reviews of epidemiological studies.

Transition metals and chemical compositions determine the oxidation capacity of atmospheric particulate matters

The knowledge of the causal relationship between exposure to airborne particulate matter (PM) and respiratory-related health issues remains unsatisfactory, owing to the complexities of physical and chemical characteristics in PM. One measure that greatly lifts the complexity is oxidative potential (OP), the overall production capacity of reactive oxygen species. We analyzed PM at different size fractions from three localities, exhibiting different source emission properties and photochemical aging states. We also investigated possible causes for their OPs, which were assessed using cellular and acellular assays. We found that higher PM mass did not always yield higher OP. Instead, chemical composition, modified by photochemical alteration (particle oxidation), played a critical role in the PM's reactivity. From a pollution hot spot to a downwind country town, the PM2.5 levels (mean ± SD) were 9.3 ± 4.5, 9.7 ± 4.9, and 6.6 ± 4.7 μg/m3, respectively. In contrast, the PM mass-normalized OP values in the downwind region were approximately 20 % higher than those in the upwind region based on the cellular assay and about three times higher from the acellular assay. Enhanced PM OP is associated with atmospheric oxidation, approximated by sulfur and nitrogen oxidation ratios. We further identified transition metals, particularly copper, a single most important species group, the primary determinant to the values of OP measured, contributing directly to OP and indirectly through metal-oxides enhanced photochemical alterations to PM.

Inhibition of Lactate Accumulation via USP38-Mediated MCT1 Deubiquitination Activates AKT/mTOR Signaling to Mitigate PM2.5-Induced Lung Injury

BACKGROUND

Lactate, traditionally viewed as a glycolysis byproduct, has emerged as an important mediator influencing immunity, inflammation, and tissue damage. While PM2.5 exposure is known to cause various metabolic disturbances, the role of lactate metabolism in PM2.5-induced lung injury remains unclear. This study aims to elucidate the mechanisms underlying PM2.5-induced lung injury from a metabolic perspective.

METHODS

Lactate and pyruvate assays were performed to assess metabolic changes following PM2.5 exposure. Protein expression and tissue damage were assessed using Western blot, IHC, ELISA, and TUNEL staining. The biological role of USP38 in PM2.5-induced injury was identified using gain- and loss-of-function experiments. Co-immunoprecipitation and ubiquitination assays were conducted to analyze the interaction between USP38 and MCT1, as well as the regulation of MCT1 deubiquitination. The role of MCT1 in lactate metabolism and PM2.5-induced apoptosis was validated through gene editing. Proteomics revealed the potential mechanisms involved in USP38 regulation of apoptosis.

RESULTS

Our results demonstrated that PM2.5 exposure induced lactate accumulation, leading to cell apoptosis and lung injury. USP38 stabilized MCT1 expression by deubiquitination, facilitating lactate export and reducing apoptosis and lung injury caused by lactate accumulation. Mechanistically, PM2.5 increased lactate production, suppressed AKT/mTOR pathway activation, and promoted apoptosis and lung injury. USP38 promoted lactate export through MCT1, activated the AKT/mTOR pathway, and mitigated PM2.5-induced lung injury.

CONCLUSION

USP38 reduces lactate accumulation by promoting AKT/mTOR pathway activation through MCT1-mediated lactate export, thereby alleviating PM2.5-induced lung injury. These findings reveal a novel mechanism of PM2.5-related lung injury and highlight potential therapeutic targets.

Comparison of air pollution exposure assessment methods and the association with children's respiratory health

INTRODUCTION

Epidemiological studies of the associations of long-term exposure to outdoor air pollution with asthma onset and lung function in children have used different exposure assessment methods. Little is known about how these different methods affect the magnitude of the effect estimates. The aim of this study was to compare associations of long-term air pollution exposures, estimated with different exposure assessment methods, with asthma incidence and lung function.

METHODS

Eight exposure assessment methods, differing in modelling (dispersion, empirical) and monitoring strategy (fixed site, mobile), were applied to estimate annual average air pollution levels at the residential addresses of 3,687 participants of the Dutch PIAMA birth cohort. Associations of air pollution exposure with asthma and lung function were assessed and compared between methods. Heterogeneity in the associations was assessed with meta-analyses.

RESULTS

Estimated exposure levels and contrasts differed substantially between methods. Exposure estimates from the different methods were moderately to highly correlated, with Pearson correlations ranging from 0.5 to 0.9. Higher air pollution levels were consistently associated with higher asthma incidence and lower FEV1. However, the magnitude of the association differed between methods (e.g. the ORs (95 % CI) for asthma incidence ranged from 1.09 (0.99; 1.21) to 2.56 (1.50; 4.36) for BC per 1 µg/m3 increment).

CONCLUSION

Different air pollution exposure assessment methods resulted in consistent conclusions about the presence and direction of associations with asthma incidence and lung function in children, but associations differed in magnitude. Differences in exposure assessment methods may partially drive heterogeneity in associations between different studies.

Air Pollution and Temperature: a Systematic Review of Ubiquitous Environmental Exposures and Sudden Cardiac Death

PURPOSE OF REVIEW

Environmental exposures have been associated with increased risk of cardiovascular mortality and acute coronary events, but their relationship with out-of-hospital cardiac arrest (OHCA) and sudden cardiac death (SCD) remains unclear. SCD is an important contributor to the global burden of cardiovascular disease worldwide.

RECENT FINDINGS

Current literature suggests a relationship between environmental exposures and cardiovascular disease, but their relationship with OHCA/SCD remains unclear. A literature search was conducted in PubMed, Embase, Web of Science, and Global Health. Of 5138 studies identified by our literature search, this review included 30 studies on air pollution, 42 studies on temperature, 6 studies on both air pollution and temperature, and 1 study on altitude exposure and OHCA/SCD. Particulate matter air pollution, ozone, and both hot and cold temperatures are associated with increased risk of OHCA/SCD. Pollution and other exposures related to climate change play an important role in OHCA/SCD incidence.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs

BACKGROUND

Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.

OBJECTIVES

We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.

METHODS

We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.

RESULTS

Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.

DISCUSSION

Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Ambient ultrafine particle (PM0.1): Sources, characteristics, measurements and exposure implications on human health

The problem of ultrafine particles (UFPs; PM_0.1) has been prevalent since the past decades. In addition to become easily inhaled by human respiratory system due to their ultrafine diameter (<100 nm), ambient UFPs possess various physicochemical properties which make it more toxic. These properties vary based on the emission source profile. The current development of UFPs studies is hindered by the problem of expensive instruments and the inexistence of standardized measurement method. This review provides detailed insights on ambient UFPs sources, physicochemical properties, measurements, and estimation models development. Implications on health impacts due to short-term and long-term exposure of ambient UFPs are also presented alongside the development progress of potentially low-cost UFPs sensors which can be used for future UFPs studies references. Current challenge and future outlook of ambient UFPs research are also discussed in this review. Based on the review results, ambient UFPs may originate from primary and secondary sources which include anthropogenic and natural activities. In addition to that, it is confirmed from various chemical content analysis that UFPs carry heavy metals, PAHs, BCs which are toxic in its nature. Measurement of ambient UFPs may be performed through stationary and mobile methods for environmental profiling and exposure assessment purposes. UFPs PNC estimation model (LUR) developed from measurement data could be deployed to support future epidemiological study of ambient UFPs. Low-cost sensors such as bipolar ion and ionization sensor from common smoke detector device may be further developed as affordable instrument to monitor ambient UFPs. Recent studies indicate that short-term exposure of UFPs can be associated with HRV change and increased cardiopulmonary effects. On the other hand, long-term UFPs exposure have positive association with COPD, CVD, CHF, pre-term birth, asthma, and also acute myocardial infarction cases.

A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements

Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO2) and ultrafine particles (UFP) collected by Google Street View cars with long-term near-road measurements from regular monitoring networks in Amsterdam. We found that transfer-learning LUR models performed 55.2% better in predicting long-term near-road concentrations than the LUR model trained only with mobile measurements for NO2 and 26.9% for UFP, evaluated by normalized mean absolute errors. This improvement in model accuracy suggests that transfer-learning models provide a solution for narrowing the knowledge gaps and can improve the accuracy of mapping long-term near-road air pollution concentrations using short-term on-road mobile monitoring data.

Non-linear models for black carbon exposure modelling using air pollution datasets

Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018-2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R² = 0.828) with a relatively low error (RMSE = 0.48 μg/m³). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R² = 0.633; RMSE = 1.19 μg/m³) due to the lack of N data and PM_2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas.

Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights

(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.

Air Pollution, Ultrafine Particles, and Your Brain: Are Combustion Nanoparticle Emissions and Engineered Nanoparticles Causing Preventable Fatal Neurodegenerative Diseases and Common Neuropsychiatric Outcomes?

Exposure to particulate matter (PM) pollution damages the human brain. Fossil fuel burning for transportation energy accounts for a significant fraction of urban air and climate pollution. While current United States (US) standards limit PM ambient concentrations and emissions, they do not regulate explicitly ultrafine particles (UFP ≤ 100 nm in diameter). There is a growing body of evidence suggesting UFP may play a bigger role inflicting adverse health impacts than has been recognized, and in this perspective, we highlight effects on the brain, particularly of young individuals. UFP penetrate the body through nasal/olfactory, respiratory, gastrointestinal, placenta, and brain-blood barriers, translocating in the bloodstream and reaching the glymphatic and central nervous systems. We discuss one case study. The 21.8 million residents in the Metropolitan Mexico City (MMC) are regularly exposed to fine PM (PM_2.5) above the US 12 μg/m³ annual average standards. Alzheimer's disease (AD), Parkinson's disease (PD), and TAR DNA-binding protein (TDP-43) pathologies and nanoparticles (NP ≤ 50 nm in diameter) in critical brain organelles have been documented in MMC children and young adult autopsies. MMC young residents have cognitive and olfaction deficits, altered gait and equilibrium, brainstem auditory evoked potentials, and sleep disorders. Higher risk of AD and vascular dementia associated with residency close to high traffic roadways have been documented. The US is not ready or prepared to adopt ambient air quality or emission standards for UFP and will continue to focus regulations only on the total mass of PM_2.5 and PM₁₀. Thus, this approach raises the question: are we dropping the ball? As research continues to answer the remaining questions about UFP sources, exposures, impacts, and controls, the precautionary principle should call us to accelerate and expand policy interventions to abate or eliminate UFP emissions and to mitigate UFP exposures. For residents of highly polluted cities, particularly in the developing world where there is likely older and dirtier vehicles, equipment, and fuels in use and less regulatory oversight, we should embark in a strong campaign to raise public awareness of the associations between high PM pollution, heavy traffic, UFP, NP, and neuropsychiatric outcomes, including dementia. Neurodegenerative diseases evolving from childhood in polluted, anthropogenic, and industrial environments ought to be preventable.

Quantile regression to examine the association of air pollution with subclinical atherosclerosis in an adolescent population

BACKGROUND

Air pollution has been associated with carotid intima-media thickness test (CIMT), a marker of subclinical atherosclerosis. To our knowledge, this is the first study to report an association between ambient air pollution and CIMT in a younger adolescent population.

OBJECTIVE

To investigate the associations beyond standard mean regression by using quantile regression to explore if associations occur at different percentiles of the CIMT distribution.

METHODS

We measured CIMT cross-sectionally at the age of 16 years in 363 adolescents participating in the Dutch PIAMA birth cohort. We fit separate quantile regressions to examine whether the associations of annual averages of nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), PM_2.5 absorbance (a marker for black carbon), PM_coarse and ultrafine particles up to age 14 assigned at residential addresses with CIMT varied across deciles of CIMT. False discovery rate corrections (FDR, p < 0.05 for statistical significance) were applied for multiple comparisons. We report quantile regression coefficients that correspond to an average change in CIMT (µm) associated with an interquartile range increase in the exposure.

RESULTS

PM_2.5 absorbance exposure at birth was statistically significantly (FDR < 0.05) associated with a 6.23 µm (95% CI: 0.15, 12.3) higher CIMT per IQR increment in PM_2.5 absorbance in the 10th quantile of CIMT but was not significantly related to other deciles within the CIMT distribution. For NO₂ exposure we found similar effect sizes to PM_2.5 absorbance, but with much wider confidence intervals. PM_2.5 exposure was weakly positively associated with CIMT while PM_coarse and ultrafine did not display any consistent patterns.

CONCLUSIONS

Early childhood exposure to ambient air pollution was suggestively associated with the CIMT distribution during adolescence. Since CIMT increases with age, mitigation strategies to reduce traffic-related air pollution early in life could possibly delay atherosclerosis and subsequently CVD development later in life.

Ultrafine particles, particle components and lung function at age 16 years: The PIAMA birth cohort study

BACKGROUND

Particulate matter (PM) air pollution exposure has been linked to lung function in adolescents, but little is known about the relevance of specific PM components and ultrafine particles (UFP).

OBJECTIVES

To investigate the associations of long-term exposure to PM elemental composition and UFP with lung function at age 16 years.

METHODS

For 706 participants of a prospective Dutch birth cohort, we assessed associations of forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) at age 16 with average exposure to eight elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc) in PM_2.5 and PM₁₀, as well as UFP during the preceding years (age 13-16 years) estimated by land-use regression models. After assessing associations for each pollutant individually using linear regression models with adjustment for potential confounders, independence of associations with different pollutants was assessed in two-pollutant models with PM mass and NO₂, for which associations with lung function have been reported previously.

RESULTS

We observed that for most PM elemental components higher exposure was associated with lower FEV₁, especially PM₁₀ sulfur [e.g. adjusted difference -2.23% (95% confidence interval (CI) -3.70 to -0.74%) per interquartile range (IQR) increase in PM₁₀ sulfur]. The association with PM₁₀ sulfur remained after adjusting for PM₁₀ mass. Negative associations of exposure to UFP with both FEV₁ and FVC were observed [-1.06% (95% CI: -2.08 to -0.03%) and -0.65% (95% CI: -1.53 to 0.23%), respectively per IQR increase in UFP], but did not persist in two-pollutant models with NO₂ or PM_2.5.

CONCLUSIONS

Long-term exposure to sulfur in PM₁₀ may result in lower FEV₁ at age 16. There is no evidence for an independent effect of UFP exposure.