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Relationship between traffic-related air pollution and inflammation biomarkers using structural equation modeling
Abstract
Background:
Evidence suggests that exposure to traffic-related air pollution (TRAP) and social stressors can increase inflammation. Given that there are many different markers of TRAP exposure, socio-economic status (SES), and inflammation, analytical approaches can leverage multiple markers to better elucidate associations. In this study, we applied structural equation modeling (SEM) to assess the association between a TRAP construct and a SES construct with an inflammation construct.
Methods:
This analysis was conducted as part of the Community Assessment of Freeway Exposure and Health (CAFEH; N = 408) study. Air pollution was characterized using a spatiotemporal model of particle number concentration (PNC) combined with individual participant time-activity adjustment (TAA). TAA-PNC and proximity to highways were considered for a construct of TRAP exposure. Participant demographics on education and income for an SES construct were assessed via questionnaires. Blood samples were analyzed for high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α receptor II (TNFRII), which were considered for the construct for inflammation. We conducted SEM and compared our findings with those obtained using generalized linear models (GLM).
Results:
Using GLM, TAA-PNC was associated with multiple inflammation biomarkers. An IQR (10,000 particles/cm3) increase of TAA-PNC was associated with a 14 % increase in hsCRP in the GLM. Using SEM, the association between the TRAP construct and the inflammation construct was twice as large as the associations with any individual inflammation biomarker. SES had an inverse association with inflammation in all models. Using SEM to estimate the indirect effects of SES on inflammation through the TRAP construct strengthened confidence in the association of TRAP with inflammation.
Conclusion:
Our TRAP construct resulted in stronger associations with a combined construct for inflammation than with individual biomarkers, reinforcing the value of statistical approaches that combine multiple, related exposures or outcomes. Our findings are consistent with inflammatory risk from TRAP exposure.
... IL-6 and CRP are the two most commonly increased inflammatory factors, and the nerve growth factor (NGF) is the most commonly decreased factor in depression and schizophrenia [67]. On the other hand, exposure to traffic-related air pollution is associated with increased peripheral IL-6, CRP, and TNF-α receptor II (TNFRII) [68]. Inflammatory factors, such as IL-6 and CRP, may also have the potential to serve as early warning indicators for air pollutionrelated risk of mental disorders ( Figure 1). ...
... IL-6 and CRP are the two most commonly increased inflammatory factors, and the nerve growth factor (NGF) is the most commonly decreased factor in depression and schizophrenia [67]. On the other hand, exposure to traffic-related air pollution is associated with increased peripheral IL-6, CRP, and TNF-α receptor II (TNFRII) [68]. Inflammatory factors, such as IL-6 and CRP, may also have the potential to serve as early warning indicators for air pollution-related risk of mental disorders ( Figure 1). ...
Air pollution is one of the greatest environmental risks to health, with 99% of the world’s population living where the World Health Organization’s air quality guidelines were not met. In addition to the respiratory and cardiovascular systems, the brain is another potential target of air pollution. Population- and experiment-based studies have shown that air pollution may affect mental health through direct or indirect biological pathways. The evidence for mental hazards associated with air pollution has been well documented. However, previous reviews mainly focused on epidemiological associations of air pollution with some specific mental disorders or possible biological mechanisms. A systematic review is absent for early effect biomarkers for characterizing mental health hazards associated with ambient air pollution, which can be used for early warning of related mental disorders and identifying susceptible populations at high risk. This review summarizes possible biomarkers involved in oxidative stress, inflammation, and epigenetic changes linking air pollution and mental disorders, as well as genetic susceptibility biomarkers. These biomarkers may provide a better understanding of air pollution’s adverse effects on mental disorders and provide future research direction in this arena.
The fine particulate matter (PM2.5) in air pollution is a critical risk factor influencing human health. Our study included 8144 participants and showed that the risk of major adverse cardiovascular events increases by 35% (HR, 1.35; 95% CI, 1.14–1.60) for participants with the highest quartile to PM2.5 exposure as compared to those with lowest quartile. Bioaerosols, as an important environmental exposure in PM2.5, can induce systemic chronic inflammation leading to vascular aging. Thus, the effects of bioaerosols are investigated from household garbage stations in PM2.5 on vascular aging, and the underlying mechanisms are explored. In vivo, chronic exposure to bioaerosols upregulated senescence marker expression levels while causing vascular dysfunction and remodeling. In vitro, bioaerosol exposure induced decreased proliferation, G0/G1 arrest, and impaired migration of human umbilical vein endothelial cells (HUVECs). Furthermore, a single bacterium (AS22a) from the bioaerosol community was isolated and demonstrated that it upregulated inflammatory factors and accelerated cell senescence and vascular aging by activating the NF‐κB/NLRP3 signaling pathway, which may serve as a primary mechanism underlying vascular aging induced by bioaerosols in PM2.5. These findings suggest that high levels of bioaerosols in household garbage stations may adversely affect cardiovascular health.
Given the growing global emphasis on sustainable transportation systems, this research presents a comprehensive approach to achieving economic, social, and environmental efficiency in transport within the waste management sector. To address the different challenges of sustainable transportation issues, this paper presents a hybrid multi-criteria decision-making (MCDM) approach that incorporates the analytic hierarchy process (AHP) along with data envelopment analysis (DEA) for sustainable route selection. By leveraging the strengths of both methods, this approach reconciles conflicting requirements and diverse perspectives, facilitating effective decision making. This paper involves identifying relevant criteria for route evaluation, engaging waste management company experts and stakeholders in pairwise comparisons using AHP. Furthermore, DEA is used to calculate route efficiency based on the inputs and outputs of the system. These evaluations enable the identification of the most effective and sustainable routes. This proposed methodology empowers decision makers and transportation policymakers to develop an effective decision-making tool for addressing waste transportation challenges in developing countries. The study contributes to the growing body of research on sustainable waste management practices and provides insights for waste management companies and decision makers on how to optimize waste transportation routes while reducing economic, social, and environmental impacts.
Particulate matter air pollution is associated with blood inflammatory biomarkers, however, the biological pathways from exposure to periferal inflammation are not well understood. We propose that the NLRP3 inflammasome is likely stimulated by ambient particulate matter, as it is by some other particles and call for more research into this pathway.
Background:
Few longitudinal studies have examined the association between ultrafine particulate matter (UFP, particles < 0.1 μm aerodynamic diameter) exposure and cardiovascular disease (CVD) risk factors. We used data from 791 adults participating in the longitudinal Boston Puerto Rican Health Study (Massachusetts, USA) between 2004 and 2015 to assess whether UFP exposure was associated with blood pressure and high sensitivity C-reactive protein (hsCRP, a biomarker of systemic inflammation).
Methods:
Residential annual average UFP exposure (measured as particle number concentration, PNC) was assigned using a model accounting for spatial and temporal trends. We also adjusted PNC values for participants' inhalation rate to obtain the particle inhalation rate (PIR) as a secondary exposure measure. Multilevel linear models with a random intercept for each participant were used to examine the association of UFP with blood pressure and hsCRP.
Results:
Overall, in adjusted models, an inter-quartile range increase in PNC was associated with increased hsCRP (β = 6.8; 95% CI = - 0.3, 14.0%) but not with increased systolic blood pressure (β = 0.96; 95% CI = - 0.33, 2.25 mmHg), pulse pressure (β = 0.70; 95% CI = - 0.27, 1.67 mmHg), or diastolic blood pressure (β = 0.55; 95% CI = - 0.20, 1.30 mmHg). There were generally stronger positive associations among women and never smokers. Among men, there were inverse associations of PNC with systolic blood pressure and pulse pressure. In contrast to the primary findings, an inter-quartile range increase in the PIR was positively associated with systolic blood pressure (β = 1.03; 95% CI = 0.00, 2.06 mmHg) and diastolic blood pressure (β = 1.01; 95% CI = 0.36, 1.66 mmHg), but not with pulse pressure or hsCRP.
Conclusions:
We observed that exposure to PNC was associated with increases in measures of CVD risk markers, especially among certain sub-populations. The exploratory PIR exposure metric should be further developed.
Background:
Little is known about the long-term health effects of ambient ultrafine particles (<0.1 μm) (UFPs) including their association with respiratory disease incidence. In this study, we examined the relationship between long-term exposure to ambient UFPs and the incidence of lung cancer, adult-onset asthma, and chronic obstructive pulmonary disease (COPD).
Methods:
Our study cohort included approximately 1.1 million adults who resided in Toronto, Canada and who were followed for disease incidence between 1996 and 2012. UFP exposures were assigned to residential locations using a land use regression model. Random-effect Cox proportional hazard models were used to estimate hazard ratios (HRs) describing the association between ambient UFPs and respiratory disease incidence adjusting for ambient fine particulate air pollution (PM2.5), NO2, and other individual/neighbourhood-level covariates.
Results:
In total, 74,543 incident cases of COPD, 87,141 cases of asthma, and 12,908 cases of lung cancer were observed during follow-up period. In single pollutant models, each interquartile increase in ambient UFPs was associated with incident COPD (HR = 1.06, 95% CI: 1.05, 1.09) but not asthma (HR = 1.00, 95% CI: 1.00, 1.01) or lung cancer (HR = 1.00, 95% CI: 0.97, 1.03). Additional adjustment for NO2 attenuated the association between UFPs and COPD and the HR was no longer elevated (HR = 1.01, 95% CI: 0.98, 1.03). PM2.5 and NO2 were each associated with increased incidence of all three outcomes but risk estimates for lung cancer were sensitive to indirect adjustment for smoking and body mass index.
Conclusions:
In general, we did not observe clear evidence of positive associations between long-term exposure to ambient UFPs and respiratory disease incidence independent of other air pollutants. Further replication is required as few studies have evaluated these relationships.
Background:
Long-term exposure to fine particulate matter has been linked to cardiovascular disease and systemic inflammatory responses; however, evidence is limited regarding the effects of long-term exposure to ultrafine particulate matter (UFP, <100nm). We used a cross-sectional study design to examine the association of long-term exposure to near-highway UFP with measures of systemic inflammation and coagulation.
Methods:
We analyzed blood samples from 408 individuals aged 40-91years living in three near-highway and three urban background areas in and near Boston, Massachusetts. We conducted mobile monitoring of particle number concentration (PNC) in each area, and used the data to develop and validate highly resolved spatiotemporal (hourly, 20m) PNC regression models. These models were linked with participant time-activity data to determine individual time-activity adjusted (TAA) annual average PNC exposures. Multivariable regression modeling and stratification were used to assess the association between TAA-PNC and single peripheral blood measures of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), tumor-necrosis factor alpha receptor II (TNFRII) and fibrinogen.
Results:
After adjusting for age, sex, education, body mass index, smoking and race/ethnicity, an interquartile-range (10,000particles/cm(3)) increase in TAA-PNC had a positive non-significant association with a 14.0% (95% CI: -4.6%, 36.2%) positive difference in hsCRP, an 8.9% (95% CI: -0.4%, 10.9%) positive difference in IL-6, and a 5.1% (95% CI: -0.4%, 10.9%) positive difference in TNFRII. Stratification by race/ethnicity revealed that TAA-PNC had larger effect estimates for all three inflammatory markers and was significantly associated with hsCRP and TNFRII in white non-Hispanic, but not East Asian participants. Fibrinogen had a negative non-significant association with TAA-PNC.
Conclusions:
Our findings suggest an association between annual average near-highway TAA-PNC and subclinical inflammatory markers of CVD risk.
Exposures to ultrafine particles (<100 nm, estimated as particle number concentration, PNC) differ from ambient concentrations because of the spatial and temporal variability of both PNC and people. Our goal was to evaluate the influence of time-activity adjustment on exposure assignment and associations with blood biomarkers for a near-highway population. A regression model based on mobile monitoring and spatial and temporal variables was used to generate hourly ambient residential PNC for a full year for a subset of participants (n=140) in the Community Assessment of Freeway Exposure and Health study. We modified the ambient estimates for each hour using personal estimates of hourly time spent in five micro-environments (inside home, outside home, at work, commuting, other) as well as particle infiltration. Time-activity adjusted (TAA)-PNC values differed from residential ambient annual average (RAA)-PNC, with lower exposures predicted for participants who spent more time away from home. Employment status and distance to highway had a differential effect on TAA-PNC. We found associations of RAA-PNC with high sensitivity C-reactive protein and Interleukin-6, although exposure-response functions were non-monotonic. TAA-PNC associations had larger effect estimates and linear exposure-response functions. Our findings suggest that time-activity adjustment improves exposure assessment for air pollutants that vary greatly in space and time.Journal of Exposure Science and Environmental Epidemiology advance online publication, 1 April 2015; doi:10.1038/jes.2015.11.
While several cohort studies report associations between chronic exposure to fine particles (PM2.5) and mortality, few have studied the effects of chronic exposure to ultrafine (UF) particles. In addition, few studies have estimated the effects of the constituents of either PM2.5 or UF particles.
We used a statewide cohort of over 100,000 women from the California Teachers Study who were followed from 2001 through 2007. Exposure data at the residential level were provided by a chemical transport model that computed pollutant concentrations from over 900 sources in California. Besides particle mass, monthly concentrations of 11 species and 8 sources or primary particles were generated at 4 km grids. We used a Cox proportional hazards model to estimate the association between the pollutants and all-cause, cardiovascular, ischemic heart disease (IHD) and respiratory mortality.
We observed statistically significant (p < 0.05) associations of IHD with PM2.5 mass, nitrate elemental carbon (EC), copper (Cu), and secondary organics and the sources gas- and diesel-fueled vehicles, meat cooking, and high sulfur fuel combustion. The hazard ratio estimate of 1.19 (95% CI: 1.08, 1.31) for IHD in association with a 10-µg/m(3) increase in PM2.5 is consistent with findings from the American Cancer Society cohort. We also observed significant positive associations between IHD and several UF components including EC, Cu, metals, and mobile sources.
Using an emissions-based model with a 4 km spatial scale, we observed significant positive associations between IHD mortality and both fine and ultrafine particle species and sources. Our results suggest that the exposure model effectively measured local exposures and facilitated the examination of the relative toxicity of particle species.
Background
Previous work has found that first-generation immigrants to developed nations tend to have better health than individuals born in the host country. We examined the evidence for the healthy immigrant effect and convergence of health status between Chinese immigrants (n = 147) and U.S. born whites (n = 167) participating in the cross-sectional Community Assessment of Freeway Exposure and Health study and residing in the same neighborhoods.
Methods
We used bivariate and multivariate models to compare disease prevalence and clinical biomarkers.
Results
Despite an older average age and lower socioeconomic status, Chinese immigrants were less likely to have asthma (OR = 0.20, 95% CI = 0.09–0.48) or cardiovascular disease (OR = 0.44, 95% CI = 0.20–0.94), had lower body mass index (BMI), lower inflammation biomarker levels, lower average sex-adjusted low-density lipoprotein (LDL) cholesterol, and higher average sex-adjusted high-density lipoprotein (HDL) cholesterol. However, there was no significant difference in the prevalence of diabetes or hypertension. Duration of time in the U.S. was related to cardiovascular disease and asthma but was not associated with diabetes, hypertension, BMI, HDL cholesterol, LDL cholesterol, socioeconomic status, or health behaviors.
Conclusions
The lower CVD and asthma prevalence among the Chinese immigrants may be partially attributed to healthier diets, more physical activity, lower BMI, and less exposure to cigarette smoke. First generation immigrant status may be protective even after about two decades.
A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.Journal of Exposure Science and Environmental Epidemiology advance online publication, 12 March 2014; doi:10.1038/jes.2014.13.
Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA (USA) using a mobile monitoring platform driven for 234 hours on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100-200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R(2) values ranged from 0.38-0.47, with higher values achieved (0.43-0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood.
The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways.
We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups.
Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0--50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0--50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups.
These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.
Abstract Current literature is insufficient to make causal inferences or establish dose-response relationships for traffic-related ultrafine particles (UFPs) and cardiovascular (CV) health. The Community Assessment of Freeway Exposure and Health (CAFEH) is a cross-sectional study of the relationship between UFP and biomarkers of CV risk. CAFEH uses a community-based participatory research framework that partners university researchers with community groups and residents. Our central hypothesis is that chronic exposure to UFP is associated with changes in biomarkers. The study enrolled more than 700 residents from three near-highway neighborhoods in the Boston metropolitan area in Massachusetts, USA. All participants completed an in-home questionnaire and a subset (440+) completed an additional supplemental questionnaire and provided biomarkers. Air pollution monitoring was conducted by a mobile laboratory equipped with fast-response instruments, at fixed sites, and inside the homes of selected study participants. We seek to develop improved estimates of UFP exposure by combining spatiotemporal models of ambient UFP with data on participant time-activity and housing characteristics. Exposure estimates will then be compared with biomarker levels to ascertain associations. This article describes our study design and methods and presents preliminary findings from east Somerville, one of the three study communities.
Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0-4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9-36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution-cardiovascular epidemiological studies.Journal of Exposure Science and Environmental Epidemiology advance online publication, 12 December 2012; doi:10.1038/jes.2012.106.
Accurate quantification of exposures to traffic-related air pollution in near-highway neighborhoods is challenging due to the high degree of spatial and temporal variation of pollutant levels. The objective of this study was to measure air pollutant levels in a near-highway urban area over a wide range of traffic and meteorological conditions using a mobile monitoring platform. The study was performed in a 2.3-km(2) area in Somerville, Massachusetts (USA), near Interstate I-93, a highway that carries 150,000 vehicles per day. The mobile platform was equipped with rapid-response instruments and was driven repeatedly along a 15.4-km route on 55 days between September 2009 and August 2010. Monitoring was performed in 4-6-hour shifts in the morning, afternoon and evening on both weekdays and weekends in winter, spring, summer and fall. Measurements were made of particle number concentration (PNC; 4-3,000 nm), particle size distribution, fine particle mass (PM(2.5)), particle-bound polycyclic aromatic hydrocarbons (pPAH), black carbon (BC), carbon monoxide (CO), and nitrogen oxides (NO and NO(x)). The highest pollutant concentrations were measured within 0-50 m of I-93 with distance-decay gradients varying depending on traffic and meteorology. The most pronounced variations were observed for PNC. Annual median PNC 0-50 m from I-93 was two-fold higher compared to the background area (>1 km from I-93). In general, PNC levels were highest in winter and lowest in summer and fall, higher on weekdays and Saturdays compared to Sundays, and higher during morning rush hour compared to later in the day. Similar spatial and temporal trends were observed for NO, CO and BC, but not for PM(2.5). Spatial variations in PNC distance-decay gradients were non-uniform largely due to contributions from local street traffic. Hour-to-hour, day-to-day and season-to-season variations in PNC were of the same magnitude as spatial variations. Datasets containing fine-scale temporal and spatial variation of air pollution levels near highways may help to inform exposure assessment efforts.
NLRP3 (NOD-, LRR- and pyrin domain-containing protein 3) is an intracellular sensor that detects a broad range of microbial motifs, endogenous danger signals and environmental irritants, resulting in the formation and activation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent release of the pro-inflammatory cytokines IL-1β and IL-18, as well as to gasdermin D-mediated pyroptotic cell death. Recent studies have revealed new regulators of the NLRP3 inflammasome, including new interacting or regulatory proteins, metabolic pathways and a regulatory mitochondrial hub. In this Review, we present the molecular, cell biological and biochemical bases of NLRP3 activation and regulation and describe how this mechanistic understanding is leading to potential therapeutics that target the NLRP3 inflammasome. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
Objectives
Due to their small size, ultrafine particles (UFP) are believed to exert higher toxicity than larger particles. As numerous studies on health effects of UFP have been published since the last systematic review in 2013, we aim to systematically review the new literature.
Methods
We searched MEDLINE and the specialized LUDOK database for studies published between 01.01.2011 and 11.05.2017 investigating health effects of ambient air pollution-related UFP. We included epidemiologic studies containing UFP measures and quantifiable measures of associations. Relevant data were extracted on the basis of previously developed evaluation criteria.
Results
We identified 85 original studies, conducting short-term (n = 75) and long-term (n = 10) investigations. Panel (n = 32), scripted exposure with predefined settings (n = 16) or time series studies (n = 11) were most frequent. Thirty-four studies adjusted for at least one other pollutant. Most consistent associations were identified for short-term effects on pulmonary/systemic inflammation, heart rate variability and blood pressure.
Conclusions
The evidence suggests adverse short-term associations with inflammatory and cardiovascular changes, which may be at least partly independent of other pollutants. For the other studied health outcomes, the evidence on independent health effects of UFP remains inconclusive or insufficient.
Background:
There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than [Formula: see text]) 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 [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), and [Formula: see text].
Results:
Long-term UFP exposure was associated with an increased risk for all incident CVD [[Formula: see text] per [Formula: see text]; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) ([Formula: see text]; 95% CI: 1.00, 1.79), and heart failure ([Formula: see text]; 95% CI: 1.17, 2.66). Positive associations were also estimated for [Formula: see text] ([Formula: see text]; 95% CI: 1.01, 1.48 per [Formula: see text]) and coarse PM ([Formula: see text]; HR for all [Formula: see text]; 95% CI: 1.01, 1.45 per [Formula: see text]). CVD was not positively associated with [Formula: see text] (HR for all [Formula: see text]; 95% CI: 0.75, 1.28 per [Formula: see text]). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models ([Formula: see text] and [Formula: see text]), positive associations tended to remain for UFP, while HRs for [Formula: see text] and [Formula: see text] 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.
Significant spatial and temporal variation in ultrafine particle (UFP; <100 nanometers in diameter) concentrations creates challenges in developing predictive models for epidemiological investigations. We compared the performance of land-use regression models built by combining mobile and stationary measurements (hybrid model) with a regression model built using mobile measurements only (mobile model) in Chelsea and Boston, MA (USA). In each study area, particle number concentration (PNC; a proxy for UFP) was measured at a stationary reference site and with a mobile laboratory driven along a fixed route during a ~1-year monitoring period. In comparing PNC measured at 20 residences and PNC estimates from hybrid and mobile models, the hybrid model showed higher Pearson correlations of natural log-transformed PNC (r=0.73 vs. 0.51 in Chelsea; r=0.74 vs. 0.47 in Boston) and lower root-mean-square error in Chelsea (0.61 vs. 0.72) but no benefit in Boston (0.72 vs. 0.71). All models over-predicted log-transformed PNC by 3-6% at residences, yet the hybrid model reduced the standard deviation of the residuals by 15% in Chelsea and 31% in Boston with better tracking of overnight decreases in PNC. Overall, the hybrid model considerably outperformed the mobile model and could offer reduced exposure error for UFP epidemiology.
Land-use regression (LUR) models of air pollutants are frequently developed based on short-term stationary or mobile monitoring approaches, which raises the question of whether these two data collection protocols lead to similar exposure surfaces. In this study, we measured Ultrafine Particles (UFP) and Black Carbon (BC) concentrations in Toronto during summer 2016, using two short-term data collection approaches: mobile, involving 3,023 road segments sampled on bicycles, and stationary, involving 92 sidewalk locations. We developed four LUR models and exposure surfaces, for the two pollutants and measurement protocols. Coefficients of determination (R²) varied from 0.434 to 0.525. Various small-scale traffic variables were included in the mobile LUR. Pearson correlation coefficients between the mobile and stationary surfaces were 0.23 for UFP and 0.49 for BC. We also compared the two surfaces using personal exposures from a panel study in Toronto conducted during the same period. The personal exposures differed from the outdoor exposures derived from the combination of GPS information and exposure surfaces. For UFP, the median for personal outdoor exposure was 26,344 part/cm³, while the cycling and stationary surfaces predicted medians of 31,201 and 19,057 part/cm³. Similar trends were observed for BC, with median exposures of 1,764 (personal), 1,799 (cycling) and 1,469 ng/m³ (stationary).
Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km² area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.
Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal particle number concentration (PNC) LUR models for Boston-area (MA, USA) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to all four neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) <0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models. However, the coefficients of locally-calibrated transferred models were sometimes uninterpretable. Our results show that transferability of neighborhood-specific PNC LUR models is limited, but that general models perform acceptably in multiple areas, especially when calibrated with local data.
Relatively few studies have characterized differences in intra- and inter-neighborhood traffic-related air pollutant (TRAP) concentrations and distance-decay gradients in along an urban highway for the purposes of exposure assessment. The goal of this work was to determine the extent to which intra- and inter-neighborhood differences in TRAP concentrations can be explained by traffic and meteorology in three pairs of neighborhoods along Interstate 93 (I-93) in the metropolitan Boston area (USA). We measured distance-decay gradients of seven TRAPs (PNC, pPAH, NO, NOX, BC, CO, PM2.5) in near-highway (<400 m) and background areas (>1 km) in Somerville, Dorchester/South Boston, Chinatown and Malden to determine whether (1) spatial patterns in concentrations and inter-pollutant correlations differ between neighborhoods, and (2) variation within and between neighborhoods can be explained by traffic and meteorology. The neighborhoods ranged in area from 0.5 to 2.3 km(2). Mobile monitoring was performed over the course of one year in each pair of neighborhoods (one pair of neighborhoods per year in three successive years; 35-47 days of monitoring in each neighborhood). Pollutant levels generally increased with highway proximity, consistent with I-93 being a major source of TRAP; however, the slope and extent of the distance-decay gradients varied by neighborhood as well as by pollutant, season and time of day. Correlations among pollutants differed between neighborhoods (e.g., ρ = 0.35-0.80 between PNC and NOX and ρ = 0.11-0.60 between PNC and BC) and were generally lower in Dorchester/South Boston than in the other neighborhoods. We found that the generalizability of near-road gradients and near-highway/urban background contrasts was limited for near-highway neighborhoods in a metropolitan area with substantial local street traffic. Our findings illustrate the importance of measuring gradients of multiple pollutants under different ambient conditions in individual near-highway neighborhoods for health studies involving inter-neighborhood comparisons.
The link between air pollution and adverse health effects was established clearly and consistently 15 years ago. More recently, improved air quality in American cities was associated with increased life expectancy (Pope III et al., 2009). These observations spurred policy makers to devise air quality legislation and impose an increasing set of tightening emission standards aimed at reducing the anticipated health effects but at an increasing economic cost.Over the last couple of years there was a growing consensus that vehicle related air pollution, may be more toxic or detrimental to public health than the general air pollution mixture. The hypothesis that emissions from mobile sources are a main culprit is based on the observation that tailpipe PM emissions generally fall within the Ultra Fine Particle (UFP<0.1mm) and UFP may have health impacts which are additive to those attributed to PM (Knol et al., 2009). Also a number of studies have observed an association between health effects and the proximity to major roads (Gauderman et al., 2007).In addition there is continued concern over emissions of road trafficbecause suburban sprawl and increased vehicle miles traveled mayin theorycontribute to an increased exposure through an increaseinthe average intake fraction of pollutants from vehicle exhaust.A thorough review of peer reviewed literature concluded thatthere was suggestive but insufficient evidence to decide on a causallink between vehicle emissions and most of the health endpointsexcept for the exacerbation of existing asthma although therewas no unanimity (Health Effects Institute, 2009). Summarizingthe situation in its critical review the Health Effects Institute panelconcluded that most epidemiological studies lack accurate infor-mation on the true exposure of the test-persons involved (HealthEffects Institute, 2009).Nevertheless policy makers often refer to health effects tosupport specific policies, plans, projects and measures targetingthe transport sector. Given the uncertainty about the causal linkbetweenspecificvehicle emissions and health, policy makersshould carefully devise no-regret measures or risk spending budgeton measures that in retrospect may prove to be less effective.Several recent epidemiological studies have used the proximityof the home to major roads as a surrogate for exposure and sug-gested that proximity of people to motorized road traffic partlyexplains observed health effects (Beelen et al., 2007). The use ofsimple proximityas a surrogate for exposure to mobile source emis-sions has its merits, but the HEI now recommends that exposureanalysis should use moreaccurate methods such as land-use regres-sion, or hybrid models including measurements. Unfortunatelymeasuring exposure directly requires a large number of peopleand is therefore often not feasibly or prohibitively expensive.In our opinion, exposure analysis could be improved by deter-mining more accurately where people spend their time. Peopleare only exposed to concentrations occurring in the areas wherethey are active at that time, which during the day is very oftennotat their home address (Beckx et al., 2008, 2009). We thereforesuggest that exposure modelling takes advantage of the new possi-bilities offered by Activity-based models. This new class of modelsis able to predict for individuals where and when specific activities(e.g. work, leisure, shopping,.) are conducted.Both location, time of day and the microenvironment are essen-tial parameters to accurately determine exposure to mobile sourcepollution with a high spatial variability such as NO2and Ultra FineParticles. High resolution data on the temporal and spatial variationof the pollutant concentrations can be derived from measurementsor dispersion models respectively. Similar high resolution data forthe whereabouts of people can only be derived from Activity-basedmodels. Although their obvious advantages for environmentalpurposes were recognized by Shiftan almost a decade ago (Shiftan,2000), applications to exposure modelling remain scarce. Activity-based models have recently been used to provide a better totalestimate of exposure while also enabling the disaggregation of indi-vidual exposure over activities (Beckx et al., 2008, 2009). They cantherefore be used to reduce exposure misclassification and estab-lish relationships between health impacts and air quality moreprecisely. Policy makers for their part can take advantage of theActivity-based paradigm to devise strategies that reduce exposureby changing time activity patterns. This will enable policies thatreduce emissions from those sources that have the largest impacton exposure.
Exposure to high levels of traffic-generated particles may pose risks to human health; however, limited measurement has been conducted at homes near highways. The purpose of this study was to characterize differences between indoor and outdoor particle number concentration (PNC) in homes near to and distant from a highway and to identify factors that may affect infiltration. We monitored indoor and outdoor PNC (6-3000 nm) for 1-3 weeks at 18 homes located <1500 m from Interstate-93 (I-93) in Somerville, MA (USA). Median hourly indoor and outdoor PNC pooled over all homes were 5.2 × 10(3) and 5.9 × 10(3) particles/cm(3), respectively; the median ratio of indoor-to-outdoor PNC was 0.95 (5(th)/95th percentile: 0.42/1.75). Homes <100 m from I-93 (n=4) had higher indoor and outdoor PNC compared with homes >1000 m away (n=3). In regression models, a 10% increase in outdoor PNC was associated with an approximately equal (10.8%) increase in indoor PNC. Wind speed and direction, temperature, time of day and weekday were also associated with indoor PNC. Average mean indoor PNC was lower for homes with air conditioners compared with homes without air conditioning. These results may have significance for estimating indoor, personal exposures to traffic-related air pollution.Journal of Exposure Science and Environmental Epidemiology advance online publication, 16 January 2013; doi:10.1038/jes.2012.116.
Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concentration measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the observed concentrations. Local regression models were specified to the concentration-distance relationship and analysis of variance was used to determine the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more standard background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to underpredict (be lower than) actual background. Changes in pollutant concentrations with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concentration by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter number concentrations); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concentrations).
Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.
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