A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities

A fingerprint of source-specific health risk of PM2.5-bound components over a coastal industrial city

The influence of specific local land-use activities (continuously redistributing elements across environments) and environmental conditions (altering the chemical composition of airborne particulate matter) on the intrinsic health risk of PM2.5 exposure is sparsely reported. To fill this gap, we employed a novel integrated approach to address the influence of short-term changes in source-specific PM2.5 composition on the exposure-response risk, while controlling for weather conditions. We combine receptor-based source apportionment with conditional logistic regression in a space-time-stratified case-crossover design. This approach is different from previous studies as it: i) controls the impact of spatiotemporal variations in air pollution and human mobility using multilocation-specific fixed and disjointed space-time strata ii) addresses the spatial heterogeneity of personal exposure separating its variable effect from other predictors by allowing different baseline hazards for each space-time stratum; iii) aligns case/control periods with strong/regular episodes of source-specific PM-multipollutant fingerprint contributions rather than health outcomes. This enabled comprehensive examination of the association between source-specific PM2.5-bound species and cardiorespiratory disease hospitalizations. The epidemiological findings were that primary anthropogenic emissions [industrial (ORs 2.5 - 4.8)] were associated with higher 1-day moving average PM-induced risks. Natural-related sources [fresh / aged sea salt aerosol, dust, soil resuspension] and secondary sulfate formation were consistently associated with higher health risks (ORs 1.0 - 1.54) after 1 to 5-days since exposure. The results emphasize the importance of source-specific air quality management in complex areas and our research provides an adaptable universal tool to support targeted place-based policy interventions to mitigate air pollution impacts on health.

Controlled human exposures: a review and comparison of the health effects of diesel exhaust and wood smoke

One of the most pressing issues in global health is air pollution. Emissions from traffic-related air pollution and biomass burning are two of the most common sources of air pollution. Diesel exhaust (DE) and wood smoke (WS) have been used as models of these pollutant sources in controlled human exposure (CHE) experiments. The aim of this review was to compare the health effects of DE and WS using results obtained from CHE studies. A total of 119 CHE-DE publications and 25 CHE-WS publications were identified for review. CHE studies of DE generally involved shorter exposure durations and lower particulate matter concentrations, and demonstrated more potent dysfunctional outcomes than CHE studies of WS. In the airways, DE induces neutrophilic inflammation and increases airway hyperresponsiveness, but the effects of WS are unclear. There is strong evidence that DE provokes systemic oxidative stress and inflammation, but less evidence exists for WS. Exposure to DE was more prothrombotic than WS. DE generally increased cardiovascular dysfunction, but limited evidence is available for WS. Substantial heterogeneity in experimental methodology limited the comparison between studies. In many areas, outcomes of WS exposures tended to trend in similar directions to those of DE, suggesting that the effects of DE exposure may be useful for inferring possible responses to WS. However, several gaps in the literature were identified, predominantly pertaining to elucidating the effects of WS exposure. Future studies should strongly consider performing head-to-head comparisons between DE and WS using a CHE design to determine the differential effects of these exposures.

Effect of Air Pollution on Heart Failure: Systematic Review and Meta-Analysis

BACKGROUND

Heart failure (HF) poses a significant global disease burden. The current evidence on the impact of air pollution on HF remains inconsistent.

OBJECTIVES

We aimed to conduct a systematic review of the literature and meta-analysis to provide a more comprehensive and multiperspective assessment of the associations between short- and long-term air pollution exposure and HF from epidemiological evidences.

METHODS

Three databases were searched up to 31 August 2022 for studies investigating the association between air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO, O 3 ) and HF hospitalization, incidence, or mortality. A random effects model was used to derive the risk estimations. Subgroup analysis was conducted by geographical location, age of participants, outcome, study design, covered area, the methods of exposure assessment, and the length of exposure window. Sensitivity analysis and adjustment for publication bias were performed to test the robustness of the results.

RESULTS

Of 100 studies covering 20 countries worldwide, 81 were for short-term and 19 were for long-term exposure. Almost all air pollutants were adversely associated with the risk of HF in both short- and long-term exposure studies. For short-term exposures, we found the risk of HF increased by 1.8% [relative risk ( RR ) = 1.018 , 95% confidence interval (CI): 1.011, 1.025] and 1.6% (RR = 1.016 , 95% CI: 1.011, 1.020) per 10 - μ g / m 3 increment of PM 2.5 and PM 10 , respectively. HF was also significantly associated with NO 2 , SO 2 , and CO, but not O 3 . Positive associations were stronger when exposure was considered over the previous 2 d (lag 0-1) rather than on the day of exposure only (lag 0). For long-term exposures, there were significant associations between several air pollutants and HF with RR (95% CI) of 1.748 (1.112, 2.747) per 10 - μ g / m 3 increment in PM 2.5 , 1.212 (1.010, 1.454) per 10 - μ g / m 3 increment in PM 10 , and 1.204 (1.069, 1.356) per 10 -ppb increment in NO 2 , respectively. The adverse associations of most pollutants with HF were greater in low- and middle-income countries than in high-income countries. Sensitivity analysis demonstrated the robustness of our results.

DISCUSSION

Available evidence highlighted adverse associations between air pollution and HF regardless of short- and long-term exposure. Air pollution is still a prevalent public health issue globally and sustained policies and actions are called for to reduce the burden of HF. https://doi.org/10.1289/EHP11506.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM₁₀, NO₂, and SO₂ concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM₁₀ and NO₂ concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

Evaluation of real-time monitored ozone concentration from Abuja, Nigeria

Real-time ozone (O₃) concentration is vital for accurate analysis of O₃ to inform the public about O₃ concentrations that may have an adverse effect on health. Few studies have analysed air pollution in Abuja, Nigeria and non on real-time ozone concentrations. As a result, there is a scarcity of data and information on real-time ozone pollution, pointing to a gap that needs to be urgently closed to enable a better understanding of ozone pollution and the causes and consequences in terms of the associated health risks.In this study, -time concentrations of ground-level ozone were measured in a busy urban pollution monitoring station. Using a real-time ozone monitor to enable real-time monitoring of O₃ concentration of ozone for the first time in Abuja. The ozone concentrations followed a clear pattern with high concentrations being recorded during the dry (harmattan) season. Concentrations higher than the WHO standard of (eight-hour averaged) 100 µg/m³, occurred on 53 days over the 5-month dry season. Of those 53 days, 18 had ozone concentrations greater than 200 µg/m³. Daily patterns showed a rise throughout the day, reaching a peak in the evening. Weekday/weekend differences were less pronounced than those found in other studies. High temperatures and local climatic conditions in Abuja encourage the formation of ozone. In this study, we confirm the concentration of ozone, and the pattern can be episodic and potentially damaging to health. There is a need for better regulation and measures to reduce ozone, particularly when local climatic conditions, such as harmattan, favour the development of photochemical smog in such settings.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Impacts of Environmental Insults on Cardiovascular Aging

PURPOSE OF REVIEW

With cardiovascular disease (CVD) being the top cause of deaths worldwide, it is important to ensure healthy cardiovascular aging through enhanced understanding and prevention of adverse health effects exerted by external factors. This review aims to provide an updated understanding of environmental influences on cardiovascular aging, by summarizing epidemiological and mechanistic evidence for the cardiovascular health impact of major environmental stressors, including air pollution, endocrine-disrupting chemicals (EDCs), metals, and climate change.

RECENT FINDINGS

Recent studies generally support positive associations of exposure to multiple chemical environmental stressors (air pollution, EDCs, toxic metals) and extreme temperatures with increased risks of cardiovascular mortality and morbidity in the population. Environmental stressors have also been associated with a number of cardiovascular aging-related subclinical changes including biomarkers in the population, which are supported by evidence from relevant experimental studies. The elderly and patients are the most vulnerable demographic groups to majority environmental stressors. Future studies should account for the totality of individuals' exposome in addition to single chemical pollutants or environmental factors. Specific factors most responsible for the observed health effects related to cardiovascular aging remain to be elucidated.

Adipose to serum ratio and mixtures of persistent organic pollutants in relation to endometriosis: Findings from the ENDO Study

BACKGROUND

Endometriosis is an estrogen-dependent disease. Endocrine disrupting chemicals (EDCs) and their mixtures may play an etiologic role.

OBJECTIVES

We evaluated an adipose-to-serum ratio (ASR) of lipophilic EDCs and their mixtures associated with incident endometriosis.

METHODS

We quantified 13 polychlorinated biphenyl (PCB) congeners, 6 polybrominated diphenyl ether (PBDE) congeners, and 11 organochlorine pesticides (OCPs) in serum and omental fat among women from the ENDO Study (2007-2009) aged 18-44 years diagnosed with (n=190) or without (n=283) surgically-visualized incident endometriosis. Odds ratios (OR) and 95% confidence intervals (CI) between ASR and endometriosis were estimated using logistic regression models adjusted for age (years), body mass index (kg/m²), serum cotinine (ng/ml), and breastfeeding conditional on parity. Bayesian hierarchical models (BHM) compared estimated associations for adipose and ASR to serum. Bayesian kernel machine regression (BKMR) estimated change in latent health and 95% posterior intervals (PI) between chemical mixtures and endometriosis.

RESULTS

Select ASR for estrogenic PCBs and OCPs were associated with an increased odds of an endometriosis diagnosis, but not for anti-estrogenic PCBs or PBDEs. Across all chemicals, BHMs generated ORs that were on average 14% (95% PI: 6%, 22%) higher for adipose and 20% (95% PI: 12%, 29%) higher for ASR in comparison to serum. ORs from BHMs were greater for estrogenic PCBs and OCPs, with no differences for PBDEs. BKMR models comparing the 75th to 25th percentile were moderately associated with endometriosis for estrogenic PCBs [adipose 0.27 (95% PI: 0.18, 0.72) and ASR 0.37 (95% PI: 0.06, 0.80)] and OCPs [adipose 0.17 (95% PI: 0.21, 0.56) and ASR 0.26 (95% PI: 0.05, 0.57)], but not for antiestrogenic PCBs and PBDEs.

DISCUSSION

ASR added little insight beyond adipose for lipophilic chemicals. BKMR results supported associations between ASR and adipose estrogenic PCB and OCP mixtures and incident endometriosis. These findings underscore the importance of choice of biospecimen and considering mixtures when assessing exposure-disease relationships.

Short-term effect of particular matter and sulfur dioxide exposure on asthma and/or chronic obstructive pulmonary disease hospital admissions in Center of Anatolia

We investigated the associations between the daily variations of coarse particulate matter (PM₁₀) and/or sulfur dioxide (SO₂) and hospital admissions for asthma and/or chronic obstructive pulmonary disease (COPD) diseases in Kirsehir, Center of Anatolia of Turkey. We analyzed the poison generalized linear model (GLM) to analyze the association between ambient air pollutants such as PM₁₀ and SO₂ and asthma and/or COPD admissions. We investigated single-lag days and multi-lag days for the risk increase in asthma, COPD, asthma, and/or COPD hospital admissions PM₁₀, SO₂, and PM₁₀ with SO₂ per 10 μg/m³. In single-lag day model a 10 μg/m³ increase in the current day (lag 0) concentrations of PM₁₀ and SO₂ corresponded to increase of 1.027 (95% CI:1.022-1.033) and 1.069 (95% CI:1.062, 1.077) for asthma. A 10 μg/m³ increase in the current day (lag 0) concentrations of PM₁₀ and SO₂ corresponded to increase of 1.029 (95% CI:1.022-1.035) and 1.065 (95% CI:1.056, 1.075) for COPD. A 10 μg/m³ increase in the current day (lag 0) concentrations of PM₁₀ and SO₂ corresponded to increase of 1.028 (95% CI:1.024-1.032) and 1.068 (95% CI:1.062, 1.074) for asthma and/or COPD. It was found that some lag structures were related with PM₁₀ and SO₂. Significant lags were detected in some lag structures from the previous first day until the previous eighth day (lag 1 to lag 7) in the asthma, COPD, and asthma and/or COPD hospital admissions in the model created with PM₁₀ with SO₂ both in the single-lag day model and in the multi-lag day model. Our study that used GLM in time series analysis showed that PM₁₀ and/or SO₂ short-term exposure in single-lag day and multi-lag day models was related with increased asthma, COPD, and asthma and/or COPD hospital admissions in the city between 2016 and 2019 until the previous-eighth day.

Particulate matter pollution and hospital outpatient visits for endocrine, digestive, urological, and dermatological diseases in Nanjing, China

Clinical or pathological evidence demonstrated that air pollution could undermine other organ systems of human body besides respiratory and circulation systems. Investigations that directly relate hospital outpatient visits for endocrine (ENDO), digestive (DIGE), urological (UROL), and dermatological (DERM) diseases categories with ambient particulate matter (PM) are still lacking, particularly in heavily polluted cities. Here, we conducted a time-series analysis using 812,624, 1,111,342, 539,803, and 741,662 hospital visits for ENDO, DIGE, UROL, and DERM, respectively, in Nanjing, China from 2013 to 2019. A generalized additive model was applied to estimate the exposure-response associations. Results showed that a 10 μg/m³ increase in PM_2.5 concentration on lag 0 day was significantly associated with 0.59% (95% CI: 0.30%, 0.88%), 0.43% (0.15%, 0.70%), 0.36% (0.06%, 0.66%), and 0.65% (0.42%, 0.87%) increase for ENDO, DIGE, UROL, and DERM hospital visits, respectively. The estimated effects of PM₁₀ were slightly smaller but still statistically significant. The magnitude and significance of the associations between PM and four health outcomes were sensitive to additional adjustment for co-pollutants. Exposure-response relationships were linear for PM concentrations lower than 100 μg/m³ but the curves became nonlinear across the full range of exposures due to a flatten slope at higher concentrations. We also explored the effect modifications by season (cold or warm), age (5-18, 18-64, 65-74, or 75+ years), and sex (male or female). Results showed that the DERM-related population aged 65 years or older was more vulnerable to PM exposure, compared with the 5 to 17-year age group; the DERM-related population aged 75 years or older and 65 years or older was more vulnerable to PM_2.5 and PM₁₀ exposure, respectively, compared with the 18 to 64-year age group. Our study provided suggestive evidence that ambient PM pollution was associated with ENDO, DIGE, UROL, and DERM outpatient hospital visits in Nanjing, China.

Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity

BACKGROUND

Nitrogen dioxide (NO₂) is a pervasive urban pollutant originating primarily from vehicle emissions. Ischemic heart disease (IHD) is associated with a considerable public health burden worldwide, but whether NO₂ exposure is causally related to IHD morbidity remains in question. Our objective was to determine whether short term exposure to outdoor NO₂ is causally associated with IHD-related morbidity based on a synthesis of findings from case-crossover and time-series studies.

METHODS

MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts between reviewers were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran's Q and I² measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined using Leave One Out analysis and publication bias was evaluated using Funnel plots, Begg's and Egger's tests, and trim and fill.

RESULTS

Thirty-eight case-crossover studies and 48 time-series studies were included in our analysis. NO₂ was significantly associated with IHD morbidity (pooled odds ratio from case-crossover studies: 1.074 95% CI 1.052-1.097; pooled relative risk from time-series studies: 1.022 95% CI 1.016-1.029 per 10 ppb). Pooled estimates for case-crossover studies from Europe and North America were significantly lower than for studies conducted elsewhere. The high degree of heterogeneity among studies was only partially accounted for in meta-regression. There was evidence of publication bias, particularly for case-crossover studies. For both case-crossover and time-series studies, pooled estimates based on multi-pollutant models were smaller than those from single pollutant models, and those based on older populations were larger than those based on younger populations, but these differences were not statistically significant.

CONCLUSIONS

We concluded that there is a likely causal relationship between short term NO₂ exposure and IHD-related morbidity, but important uncertainties remain, particularly related to the contribution of co-pollutants or other concomitant exposures, and the lack of supporting evidence from toxicological and controlled human studies.

Associations between Source-Specific Particulate Matter and Respiratory Infections in New York State Adults

The response of respiratory infections to source-specific particulate matter (PM) is an area of active research. Using source-specific PM_2.5 concentrations at six urban sites in New York State, a case-crossover design, and conditional logistic regression, we examined the association between source-specific PM and the rate of hospitalizations and emergency department (ED) visits for influenza or culture-negative pneumonia from 2005 to 2016. There were at most N = 14 764 influenza hospitalizations, N = 57 522 influenza ED visits, N = 274 226 culture-negative pneumonia hospitalizations, and N = 113 997 culture-negative pneumonia ED visits included in our analyses. We separately estimated the rate of respiratory infection associated with increased concentrations of source-specific PM_2.5, including secondary sulfate (SS), secondary nitrate (SN), biomass burning (BB), pyrolyzed organic carbon (OP), road dust (RD), residual oil (RO), diesel (DIE), and spark ignition vehicle emissions (GAS). Increased rates of ED visits for influenza were associated with interquartile range increases in concentrations of GAS (excess rate [ER] = 9.2%; 95% CI: 4.3%, 14.3%) and DIE (ER = 3.9%; 95% CI: 1.1%, 6.8%) for lag days 0-3. There were similar associations between BB, SS, OP, and RO, and ED visits or hospitalizations for influenza, but not culture-negative pneumonia hospitalizations or ED visits. Short-term increases in PM_2.5 from traffic and other combustion sources appear to be a potential risk factor for increased rates of influenza hospitalizations and ED visits.

Data Science in Environmental Health Research

PURPOSE OF REVIEW

Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research.

RECENT FINDINGS

We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research.

SUMMARY

In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.

Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York State

BACKGROUND

Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference.

OBJECTIVES

To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations.

METHODS

Using source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days.

RESULTS

Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources.

CONCLUSIONS

These findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events.