Workgroup report: workshop on source apportionment of particulate matter health effects--intercomparison of results and implications

The effects of fine particulate matter on the blood-testis barrier and its potential mechanisms

With the rapid expansion of industrial scale, an increasing number of fine particulate matter (PM2.5) has bringing health concerns. Although exposure to PM2.5 has been clearly associated with male reproductive toxicity, the exact mechanisms are still unclear. Recent studies demonstrated that exposure to PM2.5 can disturb spermatogenesis through destroying the blood-testis barrier (BTB), consisting of different junction types, containing tight junctions (TJs), gap junctions (GJs), ectoplasmic specialization (ES) and desmosomes. The BTB is one of the tightest blood-tissue barriers among mammals, which isolating germ cells from hazardous substances and immune cell infiltration during spermatogenesis. Therefore, once the BTB is destroyed, hazardous substances and immune cells will enter seminiferous tubule and cause adversely reproductive effects. In addition, PM2.5 also has shown to cause cells and tissues injury via inducing autophagy, inflammation, sex hormones disorder, and oxidative stress. However, the exact mechanisms of the disruption of the BTB, induced by PM2.5, are still unclear. It is suggested that more research is required to identify the potential mechanisms. In this review, we aim to understand the adverse effects on the BTB after exposure to PM2.5 and explore its potential mechanisms, which provides novel insight into accounting for PM2.5-induced BTB injury.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Characteristics of Commercial and Raw Pellets Available on the Italian Market: Study of Organic and Inorganic Fraction and Related Chemometric Approach

Air pollution and the increasing production of greenhouse gases has prompted greater use of renewable energy sources; the EU has set a target that the use of green energy should be at 32 percent by 2030. With this in mind, in the last 10 years, the demand for pellets in Italy has more than doubled, making Italy the second largest consumer in Europe. The quality of the pellets burned in stoves is crucial to indoor and outdoor pollution. Among other parameters, moisture and ash are used to classify pellets according to EN ISO 17225:2014. This work involved the analysis of the organic and inorganic fraction of both some finished products on the Italian market and some raw materials (e.g., wood chips) sampled according to the technical standard EN 14778:2011. The analytical results showed the presence of some substances potentially harmful to human health such as formaldehyde, acetone, toluene and styrene for the organic fraction and nickel, lead and vanadium for the inorganic fraction. The chemometric approach showed that it is the inorganic fraction which is most responsible for the diversification of the samples under study. The detection of some substances may be a warning bell about the impact of such materials, both for the environment and for human health.

Air pollution-induced health impacts and health economic losses in China driven by US demand exports

Understanding how trade between regions or countries drives the transfer of air pollution has attracted considerable interest recently, but few studies have explored the various transfer pathways or evaluated economic losses due to the health impact of such air pollution. Here, we assess the air pollutant emissions and related health impacts and economic losses in China caused by export trade due to US demand by combining the linked multi-regional input-output (MRIO) model, GEOS-Chem model, integrated exposure-response model, and the willingness to pay method. We show that the air pollutant emissions embedded in China's export due to the US demand reached 5792.38 Kt in 2012 (2.48% of the total), which includes direct exports of intermediate (40.27%) and final (33.61%) products and indirect exports of intermediate products via domestic provinces (16.43%, domestic spillover) and other countries (9.69%, foreign spillover). The resulting increase in PM_2.5 (<2.8 μg m^-3) leads to additional 27,963 deaths in 30 provinces, with a higher death toll in coastal areas and the corresponding economic loss was higher in more developed regions and reached USD 2.08 billion. This study highlights the region-different air pollution and health impacts in China embedded in the US-demand trade, and provides a framework for the analysis of health and economic losses hidden in global trade, particularly between developing and developed countries.

Methods for assessing the impact of PM2.5 concentration on mortality while controlling for socio-economic factors

Even though industrial development has brought vast improvements to our daily lives, it carries with it negative effects such as adverse health outcomes caused by PM_2.5 and other pollutants. The negative externalities and external costs might occur when property rights are not properly defined, which means that if no one holds a property right on the atmosphere and the quality of air, there is no appropriate mechanism to prevent a further expansion of negative effects. An economic burden of pollution related to premature morbidity and mortality in individual countries can account for 5–14% of GDP (World Bank, 2021). In 2019, the worldwide health cost of mortality and morbidity caused by exposure to PM_2.5 concentration was $8.1 trillion, which is equivalent to 6.1 percent of the global gross domestic product (GDP) (World Bank estimate). Policymakers require evidence-based results that clearly show the impact that air pollution has on the economy and society, in order to be able to establish the proper regulations and ensure their successful implementation. The purpose of this long term study is to provide methods for assessing the negative effects of PM_2.5 concentration on PM_2.5-related mortality using panel data structure and demonstrate how socio-economic factors affect this relation. This study employed advanced econometric techniques to analyse the long-term impact of PM_2.5 on human health, while controlling for socio economic indicators. This study has demonstrated significant effects of socio-economic, health risk and system and governance variables on the relation between PM_2.5 ​concentration and PM_2.5-related mortality.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project

We assessed mortality risks associated with source-specific fine particles (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m³ increase) across five identified sources. On a 1 μg/m³ basis, the residual oil-related PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

Associations of Exposure to Fine Particulate Matter Mass and Constituents with Systemic Inflammation: A Cross-Sectional Study of Urban Older Adults in China

Systemic inflammation is a key mechanism in the development of cardiovascular diseases induced by exposure to fine particles (particles with aerodynamic diameter ≤2.5 μm [PM_2.5]). However, little is known about the effects of chemical constituents of PM_2.5 on systemic inflammation. In this cross-sectional study, filter samples of personal exposure to PM_2.5 were collected from community-dwelling older adults in Tianjin, China, and the chemical constituents of PM_2.5 were analyzed. Blood samples were collected immediately after the PM_2.5 sample collection. Seventeen cytokines were measured as targets. A linear regression model was applied to estimate the relative effects of PM_2.5 and its chemical constituents on the measured cytokines. A positive matrix factorization model was employed to distinguish the sources of PM_2.5. The calculated source contributions were used to estimate their effects on cytokines. After adjusting for other covariates, higher PM_2.5-bound copper was significantly associated with increased levels of interleukin (IL)1β, IL6, IL10, and IL17 levels. Source analysis showed that an increase in PM_2.5 concentration that originated from tire/brake wear and cooking emissions was significantly associated with enhanced levels of IL1β, IL6, tumor necrosis factor alpha (TNFα), and IL17. In summary, personal exposure to some PM_2.5 constituents and specific sources could increase systemic inflammation in older adults. These findings may explain the cardiopulmonary effects of specific particulate chemical constituents of urban air pollution.

The Role of Fossil Fuel Combustion Metals in PM2.5 Air Pollution Health Associations

In this review, we elucidate the central role played by fossil fuel combustion in the health-related effects that have been associated with inhalation of ambient fine particulate matter (PM2.5). We especially focus on individual properties and concentrations of metals commonly found in PM air pollution, as well as their sources and their adverse health effects, based on both epidemiologic and toxicological evidence. It is known that transition metals, such as Ni, V, Fe, and Cu, are highly capable of participating in redox reactions that produce oxidative stress. Therefore, particles that are enriched, per unit mass, in these metals, such as those from fossil fuel combustion, can have greater potential to produce health effects than other ambient particulate matter. Moreover, fossil fuel combustion particles also contain varying amounts of sulfur, and the acidic nature of the resulting sulfur compounds in particulate matter (e.g., as ammonium sulfate, ammonium bisulfate, or sulfuric acid) makes transition metals in particles more bioavailable, greatly enhancing the potential of fossil fuel combustion PM2.5 to cause oxidative stress and systemic health effects in the human body. In general, there is a need to further recognize particulate matter air pollution mass as a complex source-driven mixture, in order to more effectively quantify and regulate particle air pollution exposure health risks.

Neurodegenerative hospital admissions and long-term exposure to ambient fine particle air pollution

PURPOSE

Long-term exposure to ambient fine particle (PM_2.5) concentrations has been associated with an increased rate or risk of neurodegenerative conditions, but individual PM sources have not been previously examined in relation to neurodegenerative diseases.

METHODS

Using the Statewide Planning and Research Cooperative System database, we studied 63,287 hospital admissions with a primary diagnosis of either Alzheimer's disease, dementia, or Parkinson's disease for New York State residents living within 15 miles from six PM_2.5 monitoring sites. In addition to PM_2.5 concentrations, we studied seven specific PM_2.5 sources: secondary sulfate, secondary nitrate, biomass burning, diesel, spark-ignition emissions, pyrolyzed organic rich, and road dust. We estimated the rate of neurodegenerative hospital admissions associated with increased concentration of PM_2.5 and individual PM_2.5 sources average concentrations in the previous 0-29, 0-179, and 0-364 days.

RESULTS

Increases in ambient PM_2.5 concentrations were not consistently associated with increased hospital admissions rates. Increased source-specific PM2.5 concentrations were associated with both increased (e.g., secondary sulfates and diesel emissions) and decreased rates (e.g., secondary nitrate and spark-ignition vehicular emissions) of neurodegenerative admissions.

CONCLUSIONS

We did not observe clear associations between overall ambient PM_2.5 concentrations or source-apportioned ambient PM_2.5 contributions and rates of neurologic disease hospitalizations.

Caloric restriction attenuates C57BL/6 J mouse lung injury and extra-pulmonary toxicity induced by real ambient particulate matter exposure

Background

Caloric restriction (CR) is known to improve health and extend lifespan in human beings. The effects of CR on adverse health outcomes in response to particulate matter (PM) exposure and the underlying mechanisms have yet to be defined.

Results

Male C57BL/6 J mice were fed with a CR diet or ad libitum (AL) and exposed to PM for 4 weeks in a real-ambient PM exposure system located at Shijiazhuang, China, with a daily mean concentration (95.77 μg/m³) of PM_2.5. Compared to AL-fed mice, CR-fed mice showed attenuated PM-induced pulmonary injury and extra-pulmonary toxicity characterized by reduction in oxidative stress, DNA damage and inflammation. RNA sequence analysis revealed that several pulmonary pathways that were involved in production of reactive oxygen species (ROS), cytokine production, and inflammatory cell activation were inactivated, while those mediating antioxidant generation and DNA repair were activated in CR-fed mice upon PM exposure. In addition, transcriptome analysis of murine livers revealed that CR led to induction of xenobiotic metabolism and detoxification pathways, corroborated by increased levels of urinary metabolites of polycyclic aromatic hydrocarbons (PAHs) and decreased cytotoxicity measured in an ex vivo assay.

Conclusion

These novel results demonstrate, for the first time, that CR in mice confers resistance against pulmonary injuries and extra-pulmonary toxicity induced by PM exposure. CR led to activation of xenobiotic metabolism and enhanced detoxification of PM-bound chemicals. These findings provide evidence that dietary intervention may afford therapeutic means to reduce the health risk associated with PM exposure.

Identification of genetic features associated with fine particulate matter (PM2.5) modulated DNA damage using improved random forest analysis

Recent studies have found multiple single nucleotide variants (SNVs) associated with DNA damage. However, previous association analysis may ignore the potential interaction effects between SNVs. Therefore, we used an improved random forest (RF) analysis to identify the SNVs related to personal DNA damage in exon-focused genome-wide association study (GWAS). A total of 301 subjects from three independent centers (Zhuhai, Wuhan, and Tianjin) were retained for analysis. An improved RF procedure was used to systematically screen key SNVs associated with DNA damage. Furthermore, we used genetic risk score (GRS) and mediation analysis to investigate the integrative effect and potential mechanism of these genetic variants on DNA damage. Besides, gene set enrichment analysis was conducted to identify the pathways enriched by key SNVs using the Data-driven Expression Prioritized Integration for Complex Traits (DEPICT). Finally, a set of 24 SNVs with the lowest mean square errors (MSE) were identified by improved RF analysis. Both weighted and unweighted GRSs were associated with increased DNA damage levels (P_weight < 0.001 and P_unweight < 0.001). Gene set enrichment analysis indicated that these loci were significantly enriched in several biological features associated with DNA damage. These findings suggested the role of SNVs in modifying DNA damage levels. It may be convincing that this improved RF analysis can effectively identify SNVs associated with DNA damage levels.

A chemical dynamic model for the infiltration of outdoor size-resolved ammonium nitrate aerosols to indoor environments

In the present study, we developed a chemical dynamic model to describe the infiltration of size-resolved ammonium nitrate aerosols from outdoor to indoor environments. This model considered the penetration factor, deposition rate, and the reversible reaction process, which was quantified by the diffusive molar flux on the surface of ammonium nitrate aerosols depending on indoor temperature, humidity, and concentrations of nitric acid (HNO₃ ) and ammonia (NH₃ ). To verify the model, we employed a single-particle aerosol mass spectrometer with an automated switching system to simultaneously measure size-resolved outdoor and indoor ammonium nitrate aerosols. Comparisons between the predicted and measured concentrations of these aerosols showed a mean relative error of 4.8 ± 18.3%. To analyze the sensitivity of model parameters, several parameters were perturbed. This analysis indicated that parameters related to HNO₃ were more sensitive than those related to NH₃ because the indoor gas phase concentration of NH₃ was much higher than that of HNO₃ .

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.

Association of emergency room visits for respiratory diseases with sources of ambient PM2.5

Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter (PM_2.5) and increased mortality or hospitalizations for respiratory diseases. Few studies, however, have focused on the short-term effects of source-specific PM_2.5 on emergency room visits (ERVs) of respiratory diseases. Source apportionment for PM_2.5 was performed with Positive Matrix Factorization (PMF) and generalized additive model was applied to estimate associations between source-specific PM_2.5 and respiratory disease ERVs. The association of PM_2.5 and total respiratory ERVs was found on lag4 (RR = 1.011, 95%CI: 1.002, 1.020) per interquartile range (76 μg/m³) increase. We found PM_2.5 to be significantly associated with asthma, bronchitis and chronic obstructive pulmonary disease (COPD) ERVs, with the strongest effects on lag5 (RR = 1.072, 95%CI: 1.024, 1.119), lag4 (RR = 1.104, 95%CI: 1.032, 1.176) and lag3 (RR = 1.091, 95%CI: 1.047, 1.135), respectively. The estimated effects of PM_2.5 changed little after adjusting for different air pollutants. Six primary PM_2.5 sources were identified using PMF analysis, including dust/soil (6.7%), industry emission (4.5%), secondary aerosols (30.3%), metal processing (3.2%), coal combustion (37.5%) and traffic-related source (17.8%). Some of the sources were identified to have effects on ERVs of total respiratory diseases (dust/soil, secondary aerosols, metal processing, coal combustion and traffic-related source), bronchitis ERVs (dust/soil) and COPD ERVs (traffic-related source, industry emission and secondary aerosols). Different sources of PM_2.5 contribute to increased risk of respiratory ERVs to different extents, which may provide potential implications for the decision making of air quality related policies, rational emission control and public health welfare.

The Protective Effect of a Topical Mucin Secretagogue on Ocular Surface Damage Induced by Airborne Carbon Black Exposure

Purpose

Exposure to airborne particulate matter can induce ocular surface damage and inflammation. We evaluated the effects of a topical mucin secretagogue on the mitigation of ocular surface damage induced by exposure to airborne carbon black (CB).

Methods

Sprague-Dawley rats were exposed to ambient CB for 2 hours twice daily for 5 days. Corneal staining score and tear lactic dehydrogenase (LDH) activity were measured to evaluate ocular surface damage. Serum immunoglobulin (Ig) G and IgE levels and the sizes of cervical lymph nodes were also measured. The expressions of interleukin (IL)-4, IL-17, and interferon (IFN)-γ were measured by Western blot analysis. Diquafosol tetrasodium was instilled six times a day for 5 days, and the extent of ocular surface damage was evaluated.

Results

After exposure to airborne CB, the median corneal staining score and LDH activity were significantly increased. Serum IgG and IgE levels and the sizes of cervical lymph nodes were also significantly increased. Additionally, the expression of IL-4 and IFN-γ was elevated in the anterior segment of the eyeball. Furthermore, the expression of IL-4, IL-17, and IFN-γ was elevated in the cervical lymph nodes. When exposed to airborne black carbon, topical diquafosol tetrasodium significantly increased tear MUC5AC concentration and decreased tear LDH activity.

Conclusions

Exposure to airborne CB induced ocular surface damage and increased proinflammatory cytokines in the eyes and cervical lymph nodes. Topical mucin secretagogues seem to have a protective effect on the ocular surface against exposure to airborne particulate matters.

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.

High level of source-specific particulate matter air pollution associated with cardiac arrhythmias

Epidemiological evidence linking source-specific ambient particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) and cardiac arrhythmias is limited. In this study, we investigated the impact of source-specific PM_2.5 on cardiac arrhythmias in a panel of forty-five healthy adults living in Beijing, China, between 2015 and 2016. Repeated measures of 24-hour electrocardiograms were conducted during clinical visits, and daily counts of four arrhythmia events including supraventricular premature beat (SVPB), atrial tachycardia (AT), premature ventricular contraction (PVC) and ventricular tachycardia (VT) were recorded. One hundred forty-seven constituents in PM_2.5 were analyzed for collected particulate samples, in which fifty-six of them above laboratory detection limits were selected for source apportionment analysis using positive matrix factorization. The average contributions of identified five major sources to PM_2.5 were 45.9% from secondary nitrate/sulfate, 18.0% from coal combustion, 16.9% from crustal soil, 13.8% from biomass burning, and 5.4% from cooking. Generalized estimating equation models were used to estimate relative risks (RR) of arrhythmias in association with interquartile-range (IQR) increases in PM_2.5 constituents and specific sources. Total PM_2.5 mass as well as several combustion related constituents were found of significant impacts on increased risks of arrhythmia events. Among the identified sources of PM_2.5, coal burning has been found the major source that associated with increased risks of SVPB, PVC and VT with RR of 1.19 [95% confidence intervals (CI): 1.04, 1.36] to 1.64 (95% CI: 1.35, 2.00). PM_2.5 from combustion related secondary nitrate/sulfate was also found of significant impact on SVPB and AT, followed by PM_2.5 from biomass burning and crustal soil. Our results indicated that PM_2.5 from anthropogenic activity related sources were most responsible for increased risks of arrhythmia events. Our findings enhance the understanding of increased risks of arrhythmias from exposure to PM_2.5, and provide evidence on source-specific PM control priorities.

Integrating farm and air pollution studies in search for immunoregulatory mechanisms operating in protective and high-risk environments

BACKGROUND

Studies conducted in farm environments suggest that diverse microbial exposure promotes children's lung health. The underlying mechanisms are unclear, and the development of asthma-preventive strategies has been delayed. More comprehensive investigation of the environment-induced immunoregulation is required for better understanding of asthma pathogenesis and prevention. Exposure to air pollution, including particulate matter (PM), is a risk factor for asthma, thus providing an excellent counterpoint for the farm-effect research. Lack of comparable data, however, complicates interpretation of the existing information. We aimed to explore the immunoregulatory effects of cattle farm dust (protective, Finland) and urban air PM (high-risk, China) for the first time using identical research methods.

METHODS

We stimulated PBMCs of 4-year-old children (N = 18) with farm dust and size-segregated PM and assessed the expression of immune receptors CD80 and ILT4 on dendritic cells and monocytes as well as cytokine production of PBMCs. Environmental samples were analysed for their composition.

RESULTS

Farm dust increased the percentage of cells expressing CD80 and the cytokine production of children's immune cells, whereas PM inhibited the expression of important receptors and the production of soluble mediators. Although PM samples induced parallel immune reactions, the size-fraction determined the strength of the effects.

CONCLUSIONS

Our study demonstrates the significance of using the same research framework when disentangling shared and distinctive immune pathways operating in different environments. Observed stimulatory effects of farm dust and inhibitory effects of PM could shape responses towards respiratory pathogens and allergens, and partly explain differences in asthma prevalence between studied environments.

Chemical composition of outdoor and indoor PM2.5 collected during haze events: Transformations and modified source contributions resulting from outdoor-to-indoor transport

Changes in the chemical constitution and sources of ambient PM_2.5 following the infiltration of air into indoor environments were investigated. We collected PM_2.5 samples from air inside and outside 31 rooms in Beijing residences during hazy episodes. We calculated the indoor-to-outdoor ratios and the correction (kⁱ ) of each infiltration factor for each chemical component of PM_2.5 to determine the effects of infiltrative behavior. The outdoor and indoor mass concentrations of PM_2.5 during the sampling period were 70-460 and 10-315 μg/m³ , respectively. Differences in the average indoor-to-outdoor ratios of PM_2.5 mass and each component (mean value ± standard deviation: PM_2.5 mass = 0.53 ± 0.26, organic matter = 0.75 ± 0.34, elemental carbon = 0.62 ± 0.31, trace elements = 0.62 ± 0.26, SO 4 2 - = 0.67 ± 0.32 , NH 4 + = 0.53 ± 0.54 , NO 3 - = 0.45 ± 0.36 , Cl^- = 0.37 ± 0.35, and crustal dust = 0.30 ± 0.19) may be attributed to size distribution, chemical properties, temperature, and humidity. The positive matrix factorization model was applied to calculate the source contributions to equivalent population exposure (Indoor concentration·Indoor time fraction + Outdoor concentration·Outdoor time fraction). The contributions of fossil fuel combustion, secondary source, vehicle exhaust, and mixed dust to the equivalent PM_2.5 population source exposure were 37%, 24%, 22%, and 17%, respectively.

Differential health effects of short-term exposure to source-specific particles in London, U.K

BACKGROUND

There is ample evidence of adverse associations between short-term exposure to ambient particle mass concentrations and health but little is known about the relative contribution from various sources.

METHODS

We used air particle composition and number networks in London between 2011 and 2012 to derive six source-related factors for PM₁₀ and four factors for size distributions of ultrafine particles (NSD). We assessed the associations of these factors, at pre-specified lags, with daily total, cardiovascular (CVD) and respiratory mortality and hospitalizations using Poisson regression. Relative risks and 95% confidence intervals (CI) were expressed as percentage change per interquartile range increment in source-factor mass or number concentration. We evaluated the sensitivity of associations to adjustment for multiple other factors and by season.

RESULTS

We found no evidence of associations between PM₁₀ or NSD source-related factors and daily mortality, as the direction of the estimates were variable with 95% CI spanning 0%. Traffic-related PM₁₀ and NSD displayed consistent associations with CVD admissions aged 15-64years (1.01% (95%CI: 0.03%, 2.00%) and 1.04% (95%CI: -0.62%, 2.72%) respectively) as did particles from background urban sources (0.36% for PM₁₀ and 0.81% for NSD). Most sources were positively associated with pediatric (0-14years) respiratory hospitalizations, with stronger evidence for fuel oil PM₁₀ (3.43%, 95%CI: 1.26%, 5.65%). Our results did not suggest associations with cardiovascular admissions in 65+ or respiratory admissions in 15+ age groups. Effect estimates were generally robust to adjustment for other factors and by season.

CONCLUSIONS

Our findings are broadly consistent with the growing evidence of the toxicity of traffic and combustion particles, particularly in relation to respiratory morbidity in children and cardiovascular morbidity in younger adults.

Effect of Titanium Dioxide Nanoparticle Exposure on the Ocular Surface: An Animal Study

PURPOSE

To evaluate the effect of titanium dioxide (TiO2) nanoparticle exposure on the ocular surface.

METHODS

Eighty eyes of 40 rabbits were used. The TiO2-1D group (n = 20) received a single instillation of TiO2 in the right eye. The TiO2-4D group (n = 20) received a TiO2 instillation in the right eye once a day for four days. The 40 untreated left eyes were used as controls. Ocular surface staining (n = 5 for each group) was performed with rose bengal dye, tear secretion (n = 5) was measured using the phenol red thread test, lactic dehydrogenase (LDH) activity (n = 5) and MUC5AC levels (n = 5) were measured in tears, and the area of the conjunctival goblet cells (n = 5) was measured through impression cytology and scanning electron microscopy 24 hours after the last TiO2 instillation.

RESULTS

Ocular surface staining was increased but the tear secretion was not changed after TiO2 exposure. The TiO2-1D (1.39 OD) and TiO2-4D groups (0.58 OD) had higher median tear LDH activity than the control groups (0.57 OD and 0.29 OD, respectively). Although the median tear MUC5AC level in the TiO2-1D group (92.7 ng/ml) was higher than that of control 1 group (37.4 ng/ml), there was no significant difference in MUC5AC levels between the TiO2-4D and control 2 groups. Conjunctival goblet cell area decreased after TiO2 exposure.

CONCLUSIONS

Exposure to TiO2 nanoparticles induced ocular surface damage. Although the tear MUC5AC level increased after a single exposure, it decreased to normal levels after repeated exposures. The area of conjunctival goblet cells decreased after TiO2 exposure.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995-1998: A multipollutant approach to acute, time-series air pollution epidemiology: I

The objective of this paper is to estimate the increase in risk of daily cardiovascular mortality due to an increase in the daily ambient concentration of the individual particulate pollutants sulfur (S), arsenic (As), selenium (Se), and mercury (Hg) using single-pollutant models (SPMs) and to compare this risk to the combined increase in risk due to an increase in all four pollutants by including all four pollutants in the same model (multipollutant model, MPM) and to the risks from source contributions from power plants and smelters. Individual betas in a multipollutant model (MPM) were summed to give a combined beta. Interaction was investigated with a pollutant product term. SPMs (controlling for time trends, temperature, and relative humidity), for an interquartile range (IQR) increase in the pollutant concentration on lag day 0, gave these percent excess risks (±95% confidence levels): S, 6.9% (1.3-12%); As, 2.9% (0.4-5.5%); Se, 1.4% (-1.7 to 4.6); Hg, 9.6% (4.8-14.6%). The SPM beta for S (as sulfate) was higher than found in other studies. The SPM beta for Hg gave the largest t-statistic and beta per unit mass of any pollutant studied. An (IQR) increase in all four pollutants gave an excess risk of 15.4% (7.5-23.8%), slightly smaller than the combination of S and Hg, 16.7% (9.1-24.9%). The combined beta was 71% of the sum of the four individual SPM betas, indicating a reduction in confounding among pollutants in the combined model. As and Se were shown to be noncausal; their SPM betas could be explained as confounding by S.

IMPLICATIONS

The combined effect of several pollutants can be estimated by including the appropriate pollutants in the same statistical model, summing their individual betas to give a combined beta, and using a variance-covariance matrix to obtain the standard error. This approach identifies and reduces confounding among the species in the multipollutant model and can be used to identify confounded species that have no independent relationship with mortality. The effect of several pollutants acting together may be higher than that of one pollutant. Further work is needed to understand the strong relationship of mortality with particulate mercury and sulfate.

Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma

Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17).

Exploration of the composition and sources of urban fine particulate matter associated with same-day cardiovascular health effects in Dearborn, Michigan

The objective was to explore associations of chemical components and source factors of ambient fine particulate matter (aerodynamic diameter ≤ 2.5 μm; PM2.5) with cardiovascular (CV) changes following same-day exposure to ambient PM2.5. Twenty-five healthy adults living in rural Michigan were exposed to ambient air in an urban/industrial community for 4 to 5 h daily for five consecutive days. CV health outcomes were measured 1-2 h post exposure. Contributing emission sources were identified via positive matrix factorization. We examined associations between PM2.5 mass, composition and source factors, and same-day changes in CV outcomes using mixed-model analyses. PM2.5 mass (10.8 ± 6.8 μg/m(3)), even at low ambient levels, was significantly associated with increased heart rate (HR). Trace elements as well as secondary aerosol, diesel/urban dust and iron/steel manufacturing factors potentially explained the HR changes. However, trace element analysis demonstrated additional associations with other CV responses including changes in blood pressure (BP), arterial compliance, autonomic balance and trends toward reductions in endothelial function. Two factors were related to BP changes (diesel/urban dust, motor vehicle) and trends toward impaired endothelial function (diesel/urban dust). This study indicates composition of PM2.5 and its sources may contribute to CV health effects independently of PM2.5 mass.

Comparing multipollutant emissions-based mobile source indicators to other single pollutant and multipollutant indicators in different urban areas

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NO_x) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NO_x correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

A Bayesian Multivariate Receptor Model for Estimating Source Contributions to Particulate Matter Pollution using National Databases

Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects.

Health effects of multi-pollutant profiles

BACKGROUND

The association between exposure to particle mass and mortality is well established; however, there are still uncertainties as to whether certain chemical components are more harmful than others. Moreover, understanding the health effects associated with exposure to pollutant mixtures may lead to new regulatory strategies.

OBJECTIVES

Recently we have introduced a new approach that uses cluster analysis to identify distinct air pollutant mixtures by classifying days into groups based on their pollutant concentration profiles. In Boston during the years 1999-2009, we examined whether the effect of PM2.5 on total mortality differed by distinct pollution mixtures.

METHODS

We applied a time series analysis to examine the association of PM2.5 with daily deaths. Subsequently, we included an interaction term between PM2.5 and the pollution mixture clusters.

RESULTS

We found a 1.1% increase (95% CI: 0.0, 2.2) and 2.3% increase (95% CI: 0.9-3.7) in total mortality for a 10 μg/m(3) increase in the same day and the two-day average of PM2.5 respectively. The association is larger in a cluster characterized by high concentrations of the elements related to primary traffic pollution and oil combustion emissions with a 3.7% increase (95% CI: 0.4, 7.1) in total mortality, per 10 μg/m(3) increase in the same day average of PM2.5.

CONCLUSIONS

Our study shows a higher association of PM2.5 on total mortality during days with a strong contribution of traffic emissions, and fuel oil combustion. Our proposed method to create multi-pollutant profiles is robust, and provides a promising tool to identify multi-pollutant mixtures which can be linked to the health effects.

Evaluating the application of multipollutant exposure metrics in air pollution health studies

BACKGROUND

Health effects associated with air pollution are typically evaluated using a single pollutant approach, yet people are exposed to mixtures consisting of multiple pollutants that may have independent or combined effects on human health. Development of exposure metrics that represent the multipollutant environment is important to understand the impact of ambient air pollution on human health.

OBJECTIVES

We reviewed existing multipollutant exposure metrics to evaluate how they can be applied to understand associations between air pollution and health effects.

METHODS

We conducted a literature search using both targeted search terms and a relational search in Web of Science and PubMed in April and December 2013. We focused on exposure metrics that are constructed from ambient pollutant concentrations and can be broadly applied to evaluate air pollution health effects.

RESULTS

Multipollutant exposure metrics were identified in 57 eligible studies. Metrics reviewed can be categorized into broad pollutant grouping paradigms based on: 1) source emissions and atmospheric processes or 2) common health outcomes.

DISCUSSION

When comparing metrics, it is apparent that no universal exposure metric exists; each type of metric addresses different research questions and provides unique information on human health effects. Key limitations of these metrics include the balance between complexity and simplicity as well as the lack of an existing "gold standard" for multipollutant health effects and exposure.

CONCLUSIONS

Future work on characterizing multipollutant exposure error and joint effects will inform development of improved multipollutant metrics to advance air pollution health effects research and human health risk assessment.

The impact of source contribution uncertainty on the effects of source-specific PM2.5 on hospital admissions: a case study in Boston, MA

Epidemiologic studies of particulate sources and adverse health do not account for the uncertainty in the source contribution estimates. Our goal was to assess the impact of uncertainty on the effect estimates of particulate sources on emergency cardiovascular (CVD) admissions. We examined the effects of PM2.5 sources, identified by positive matrix factorization (PMF) and absolute principle component analysis (APCA), on emergency CVD hospital admissions among Medicare enrollees in Boston, MA, during 2003-2010, given stronger associations for this period. We propagated uncertainty in source contributions using a block bootstrap procedure. We further estimated average across-methods source-specific effect estimates using bootstrap samples. We estimated contributions for regional, mobile, crustal, residual oil combustion, road dust, and sea salt sources. Accounting for uncertainty, same-day exposures to regional pollution were associated with an across-methods average effect of 2.00% (0.18, 3.78%) increase in the rate of CVD admissions. Weekly residual oil exposures resulted in an average 2.12% (0.19, 4.22%) increase. Same-day and 2-day exposures to mobile-related PM2.5 were also associated with increased admissions. Confidence intervals when accounting for the uncertainty were wider than otherwise. Agreement in PMF and APCA results was stronger when uncertainty was considered in health models. Accounting for uncertainty in source contributions leads to more stable effect estimates across methods and potentially to fewer spurious significant associations.

Association of cardiopulmonary health effects with source-appointed ambient fine particulate in Beijing, China: a combined analysis from the Healthy Volunteer Natural Relocation (HVNR) study

Previous studies have associated ambient particulate chemical constituents with adverse cardiopulmonary health effects. However, specific pollution sources behind the cardiopulmonary health effects of ambient particles are uncertain. We examined the cardiopulmonary health effects of fine particles (PM2.5) from different pollution sources in Beijing, China, among a panel of 40 healthy university students. Study subjects were repeatedly examined for a series of cardiopulmonary health indicators during three 2-month-long study periods (suburban period, urban period 1, and urban period 2) in 2010-2011 before and after relocating from a suburban campus to an urban campus with changing air pollution levels and contents. Daily ambient PM2.5 mass samples were collected over the study and measured for 29 chemical constituents in the laboratory. Source appointment for ambient PM2.5 was performed using Positive Matrix Factorization, and mixed-effects models were used to estimate the cardiopulmonary effects associated with source-specific PM2.5 concentrations. Seven PM2.5 sources were identified as traffic emissions (12.0%), coal combustion (22.0%), secondary sulfate/nitrate (30.2%), metallurgical emission (0.4%), dust/soil (12.4%), industry (6.9%), and secondary organic aerosol (9.9%). Ambient PM2.5 in the suburban campus had larger contributions from secondary sulfate/nitrate (41.8% vs. 22.9%-26.0%) and metallurgical emission (0.7% vs. 0.3%) as compared to that in the urban campus), whereas PM2.5 in the urban campus had larger contributions from traffic emissions (13.0%-16.3% vs. 5.1%), coal combustion (21.0%-30.7% vs. 10.7%), and secondary organic aerosol (9.7%-12.0% vs. 8.7%) as compared to that in the suburban campus. Potential key sources were identified for PM2.5 effects on inflammatory biomarkers (secondary sulfate/nitrate and dust/soil), blood pressure (coal combustion and metallurgical emission), and pulmonary function (dust/soil and industry). Analyses using another source appointment tool Unmix yielded a similar pattern of source contributions and associated health effects. In conclusion, ambient PM2.5 in Beijing suburban and urban areas has two distinct patterns of source contributions, and PM2.5 from different sources may play important roles on different aspects of PM2.5-related cardiopulmonary health effects.

Ambient Woodsmoke and Associated Respiratory Emergency Department Visits in Spokane, Washington

Three multivariate receptor algorithms were applied to seven years of chemical speciation data to apportion fine particulate matter to various sources in Spokane, Washington. Source marker compounds were used to assess the associations between atmospheric concentration of these compounds and daily cardiac hospital admissions and/or respiratory emergency department visits. Total carbon and arsenic had high correlations with two different vegetative burning sources and were selected as vegetative burning markers, while zinc and silicon were selected as markers for the motor vehicle and airborne soil sources, respectively. The rate of respiratory emergency department visits increased 2% for a 3.0 microg/m3 interquartile range change in a vegetative burning source marker (1.023, 95% CI 1.009-1.038) at a lag of one day. The other source markers studied were not associated with the health outcomes investigated. Results suggest vegetative burning is associated with acute respiratory events.

Bayesian-based ensemble source apportionment of PM2.5

A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties. Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM2.5). The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model. The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM2.5 data set. For each day in a long-term PM2.5 data set, 10 source profiles are sampled from these distributions and used in a CMB application, resulting in 10 SA results for each day. This formulation results in a distribution of daily source impacts rather than a single value. The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty, respectively. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R(2) = 0.66) and water-soluble potassium (R(2) = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S. EPA). In addition to providing results that are more consistent with independent observations and known emission sources being present, the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results.

Structural equation modeling of parasympathetic and sympathetic response to traffic air pollution in a repeated measures study

BACKGROUND

Traffic-related air pollution has been associated to a range of adverse health impacts, including decreased heart rate variability (HRV). The association between traffic-related pollution and HRV, however, has varied by traffic-related or HRV marker as well as by study, suggesting the need for a more comprehensive and integrative approach to examining air pollution-mediated biological impacts on these outcomes. In a Bayesian framework, we examined the effect of traffic pollution on HRV using structural equation models (SEMs) and looked at effect modification by participant characteristics.

METHODS

We studied measurements of 5 HRV markers [high frequency (HF), low frequency (LF), 5-min standard deviation of normal-to-normal intervals (SDNN), square root of the mean squared differences of successive normal-to-normal intervals (rMSSD), and LF/HF ratio (LF/HF)] for 700 elderly men from the Normative Aging Study. Using SEMs, we fit a latent variable for traffic pollution that is reflected by levels of carbon monoxide, nitrogen monoxide, nitrogen dioxide, and black carbon (BC) to estimate its effect on latent variable for parasympathetic tone that included HF, SDNN and rMSSD, and the sympathetic tone marker, LF/HF. Exposure periods were assessed using 4-, 24-, 48-, 72-hour moving average pre-visit. We compared our main effect findings using SEMs with those obtained using linear mixed models.

RESULTS

Traffic pollution was not associated with mean parasympathetic tone and LF/HF for all examined moving averages. In Bayesian linear mixed models, however, BC was related to increased LF/HF, an inter quartile range (IQR) increase in BC was associated with a 6.5% (95% posterior interval (PI): -0.7%, 14.2%) increase in mean LF/HF 24-hours later. The strongest association observed was for the 4-hour moving average (10.1%; 95% PI: 3.0%, 17.6%). The effect of traffic on parasympathetic tone was stronger among diabetic as compared to non-diabetic participants. Specifically, an IQR increase in traffic pollution in the 48-hr prior to the clinic visit was associated with a 44.3% (95% PI: -67.7%, -4.2%) lower mean parasympathetic tone among diabetics, and a 7.7% (95% PI: -18.0%, 41.4%) higher mean parasympathetic tone among non-diabetics.

CONCLUSIONS

BC was associated with adverse changes LF/HF in the elderly. Traffic pollution may decrease parasympathetic tone among diabetic elderly.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

BACKGROUND

Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies.

OBJECTIVES

Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative.

METHODS

109 monitoring sites with 75% reported speciation data during the period 2003-2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources.

RESULTS

Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location.

CONCLUSIONS

The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research.

Monitoring intraurban spatial patterns of multiple combustion air pollutants in New York City: design and implementation

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM(2.5) and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2-8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management.

Exploring prenatal outdoor air pollution, birth outcomes and neonatal health care utilization in a nationally representative sample

The impact of air pollution on fetal growth remains controversial, in part, because studies have been limited to sub-regions of the United States with limited variability. No study has examined air pollution impacts on neonatal health care utilization. We performed descriptive, univariate and multivariable analyses on administrative hospital record data from 222,359 births in the 2000, 2003 and 2006 Kids Inpatient Database linked to air pollution data drawn from the US Environmental Protection Agency's Aerometric Information Retrieval System. In this study, air pollution exposure during the birth month was estimated based on birth hospital address. Although air pollutants were not individually associated with mean birth weight, a three-pollutant model controlling for hospital characteristics, demographics, and birth month identified 9.3% and 7.2% increases in odds of low birth weight and very low birth weight for each μg/m(3) increase in PM(2.5) (both P<0.0001). PM(2.5) and NO(2) were associated with -3.0% odds/p.p.m. and +2.5% odds/p.p.b. of preterm birth, respectively (both P<0.0001). A four-pollutant multivariable model indicated a 0.05 days/p.p.m. NO(2) decrease in length of the birth hospitalization (P=0.0061) and a 0.13 days increase/p.p.m. CO (P=0.0416). A $1166 increase in per child costs was estimated for the birth hospitalization per p.p.m. CO (P=0.0002) and $964 per unit increase in O(3) (P=0.0448). A reduction from the 75th to the 25th percentile in the highest CO quartile for births predicts annual savings of $134.7 million in direct health care costs. In a national, predominantly urban, sample, air pollutant exposures during the month of birth are associated with increased low birth weight and neonatal health care utilization. Further study of this database, with enhanced control for confounding, improved exposure assessment, examination of exposures across multiple time windows in pregnancy, and in the entire national sample, is supported by these initial investigations.

Modeling the association between particle constituents of air pollution and health outcomes

There is increasing interest in evaluating the association between specific fine-particle (particles with aerodynamic diameters less than 2.5 µm; PM2.5) constituents and adverse health outcomes rather than focusing solely on the impact of total PM2.5. Because PM2.5 may be related to both constituent concentration and health outcomes, constituents that are more strongly correlated with PM2.5 may appear more closely related to adverse health outcomes than other constituents even if they are not inherently more toxic. Therefore, it is important to properly account for potential confounding by PM2.5 in these analyses. Usually, confounding is due to a factor that is distinct from the exposure and outcome. However, because constituents are a component of PM2.5, standard covariate adjustment is not appropriate. Similar considerations apply to source-apportioned concentrations and studies assessing either short-term or long-term impacts of constituents. Using data on 18 constituents and data from 1,060 patients admitted to a Boston medical center with ischemic stroke in 2003-2008, the authors illustrate several options for modeling the association between constituents and health outcomes that account for the impact of PM2.5. Although the different methods yield results with different interpretations, the relative rankings of the association between constituents and ischemic stroke were fairly consistent across models.

Assessment of heterogeneity of metal composition of fine particulate matter collected from eight U.S. counties using principal component analysis

The main objectives of this study are to (1) characterize chemical constituents of particulate matter (PM) and (2) compare overall differences in PM collected from eight US. counties. This project was undertaken as a part of a larger research program conducted by the Johns Hopkins Particulate Matter Research Center (JHPMRC). The goal of the JHPMRC is to explore the relationship between health effects and exposure to ambient PM of differing composition. The JHPMRC collected weekly filter-based ambient fine particle samples from eight US. counties between January 2008 and January 2010. Each sampling effort consisted of a 5-6-week sampling period. Filters were analyzed for 25 metals using inductively coupled plasma mass spectrometry (ICP-MS). Overall compositional differences were ranked by principal component analysis (PCA). The results showed that weekly concentrations of each element varied 3-40 times between the eight counties. PCA showed that the first five principal components explained 85% of the total variance. The authors found significant overall compositional differences in PM as the average of standardized principal component scores differed between the counties. These findings demonstrate PCA is a useful tool to identify the differences in PM compositional mixtures by county. These differences will be helpful for epidemiological and toxicological studies to help explain why health risks associated with PM exposure are different in locations with similar mass concentrations of PM.

Source Apportionment and Elemental Composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia

This paper presents the first comprehensive investigation of PM2.5 and PM10 composition and sources in Saudi Arabia. We conducted a multi-week multiple sites sampling campaign in Jeddah between June and September, 2011, and analyzed samples by XRF. The overall mean mass concentration was 28.4 ± 25.4 μg/m³ for PM2.5 and 87.3 ± 47.3 μg/m³ for PM10, with significant temporal and spatial variability. The average ratio of PM2.5/PM10 was 0.33. Chemical composition data were modeled using factor analysis with varimax orthogonal rotation to determine five and four particle source categories contributing significant amount of for PM2.5 and PM10 mass, respectively. In both PM2.5 and PM10 sources were (1) heavy oil combustion characterized by high Ni and V; (2) resuspended soil characterized by high concentrations of Ca, Fe, Al, and Si; and (3) marine aerosol. The two other sources in PM2.5 were (4) Cu/Zn source; (5) traffic source identified by presence of Pb, Br, and Se; while in PM10 it was a mixed industrial source. To estimate the mass contributions of each individual source category, the CAPs mass concentration was regressed against the factor scores. Cumulatively, resuspended soil and oil combustion contributed 77 and 82% mass of PM2.5 and PM10, respectively.

Association of particulate air pollution with daily mortality: the China Air Pollution and Health Effects Study

China is one of the few countries with some of the highest particulate matter levels in the world. However, only a small number of particulate matter health studies have been conducted in China. The study objective was to examine the association of particulate matter with an aerodynamic diameter of less than 10 μm (PM(10)) with daily mortality in 16 Chinese cities between 1996 and 2008. Two-stage Bayesian hierarchical models were applied to obtain city-specific and national average estimates. Poisson regression models incorporating natural spline smoothing functions were used to adjust for long-term and seasonal trends of mortality, as well as other time-varying covariates. The averaged daily concentrations of PM(10) in the 16 Chinese cities ranged from 52 μg/m(3) to 156 μg/m(3). The 16-city combined analysis showed significant associations of PM(10) with mortality: A 10-μg/m(3) increase in 2-day moving-average PM(10) was associated with a 0.35% (95% posterior interval (PI): 0.18, 0.52) increase of total mortality, 0.44% (95% PI: 0.23, 0.64) increase of cardiovascular mortality, and 0.56% (95% PI: 0.31, 0.81) increase of respiratory mortality. Females, older people, and residents with low educational attainment appeared to be more vulnerable to PM(10) exposure. Conclusively, this largest epidemiologic study of particulate air pollution in China suggests that short-term exposure to PM(10) is associated with increased mortality risk.

Estimating the health effects of exposure to multi-pollutant mixture

PURPOSE

Air pollution constitutes a major public health concern because of its ubiquity and of its potential health impact. Because individuals are exposed to many air pollutants at once that are highly correlated with each other, there is a need to consider the multi-pollutant exposure phenomenon. The characteristics of multiple pollutants that make statistical analysis of health-related effects of air pollution complex include the high correlation between pollutants prevents the use of standard statistical methods, the potential existence of interaction between pollutants, the common measurement errors, the importance of the number of pollutants to consider, and the potential nonlinear relationship between exposure and health.

METHODS

We made a review of statistical methods either used in the literature to study the effect of multiple pollutants or identified as potentially applicable to this problem. We reported the results of investigations that applied such methods.

RESULTS

Eighteen publications have investigated the multi-pollutant effects, 5 on indoor pollution, 10 on outdoor pollution, and 3 on statistical methodology with application on outdoor pollution. Some other publications have only addressed statistical methodology.

CONCLUSIONS

The use of Hierarchical Bayesian approach, dimension reduction methods, clustering, recursive partitioning, and logic regression are some potential methods described. Methods that provide figures for risk assessments should be put forward in public health decisions.

Development of outcome-based, multipollutant mobile source indicators

Multipollutant indicators of mobile source impacts are developed from readily available CO, NOx, and elemental carbon (EC) data for use in air quality and epidemiologic analysis. Two types of outcome-based Integrated Mobile Source Indicators (IMSI) are assessed. The first is derived from analysis of emissions of EC, CO, and NOx such that pollutant concentrations are mixed and weighted based on emission ratios for both gasoline and diesel vehicles. The emission-based indicators (IMSI(EB)) capture the impact of mobile sources on air quality estimated from receptor models and their uncertainty is comparable to measurement and source apportionment uncertainties. The IMSI(EB) have larger correlation between two different receptor sites impacted by traffic than single pollutants, suggesting they are better indicators of the local impact ofmobile sources. A sensitivity analysis of fractions of pollutants in a two-pollutant mixture and the inclusion in an epidemiologic model is conducted to develop a second set of indicators based on health outcomes. The health-based indicators (IMSI(HB)) are weighted combinations of CO, NOx, and EC pairs that have the lowest P value in their association with cardiovascular disease emergency department visits, possibly due to their better spatial representativeness. These outcome-based, multipollutant indicators can provide support for the setting of multipollutant air quality standards and other air quality management activities.

Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city

BACKGROUND

Although ambient fine particulate matter (PM(2.5); particulate matter ≤ 2.5 µm in aerodynamic diameter) has been linked to adverse human health effects, the chemical constituents that cause harm are unknown. To our knowledge, the health effects of PM(2.5) constituents have not been reported for a developing country.

OBJECTIVES

We examined the short-term association between PM(2.5) constituents and daily mortality in Xi'an, a heavily polluted Chinese city.

METHODS

We obtained daily mortality data and daily concentrations of PM(2.5), organic carbon (OC), elemental carbon (EC), and 10 water-soluble ions for 1 January 2004 through 31 December 2008. We also measured concentrations of fifteen elements 1 January 2006 through 31 December 2008. We analyzed the data using overdispersed generalized linear Poisson models.

RESULTS

During the study period, the mean daily average concentration of PM(2.5) in Xi'an was 182.2 µg/m³. Major contributors to PM(2.5) mass included OC, EC, sulfate, nitrate, and ammonium. After adjustment for PM(2.5) mass, we found significant positive associations of total, cardiovascular, or respiratory mortality with OC, EC, ammonium, nitrate, chlorine ion, chlorine, and nickel for at least one lag period. Nitrate demonstrated stronger associations with total and cardiovascular mortality than PM(2.5) mass. For a 1-day lag, interquartile range increases in PM(2.5) mass and nitrate (114.9 and 15.4 µg/m³, respectively) were associated with 1.8% [95% confidence interval (CI): 0.8%, 2.8%] and 3.8% (95% CI: 1.7%, 5.9%) increases in total mortality.

CONCLUSIONS

Our findings suggest that PM(2.5) constituents from the combustion of fossil fuel may have an appreciable influence on the health effects attributable to PM(2.5) in Xi'an.

The effects of particulate matter sources on daily mortality: a case-crossover study of Barcelona, Spain

BACKGROUND

Dozens of studies link acute exposure to particulate matter (PM) air pollution with premature mortality and morbidity, but questions remain about which species and sources in the vast PM mixture are responsible for the observed health effects. Although a few studies exist on the effects of species and sources in U.S. cities, European cities-which have a higher proportion of diesel engines and denser urban populations-have not been well characterized. Information on the effects of specific sources could aid in targeting pollution control and in articulating the biological mechanisms of PM.

OBJECTIVES

Our study examined the effects of various PM sources on daily mortality for 2003 through 2007 in Barcelona, a densely populated city in the northeast corner of Spain.

METHODS

Source apportionment for PM ≤ 2.5 μm and ≤ 10 µm in aerodynamic diameter (PM2.5 and PM10) using positive matrix factorization identified eight different factors. Case-crossover regression analysis was used to estimate the effects of each factor.

RESULTS

Several sources of PM2.5, including vehicle exhaust, fuel oil combustion, secondary nitrate/organics, minerals, secondary sulfate/organics, and road dust, had statistically significant associations (p < 0.05) with all-cause and cardiovascular mortality. Also, in some cases relative risks for a respective interquartile range increase in concentration were higher for specific sources than for total PM2.5 mass.

CONCLUSIONS

These results along with those from our multisource models suggest that traffic, sulfate from shipping and long-range transport, and construction dust are important contributors to the adverse health effects linked to PM.

Chemical properties of air pollutants and cause-specific hospital admissions among the elderly in Atlanta, Georgia

BACKGROUND

Health risks differ by fine particle (aerodynamic diameter ≤ 2.5 μm) component, although with substantial variability. Traditional methods to assess component-specific risks are limited, suggesting the need for alternative methods.

OBJECTIVES

We examined whether the odds of daily hospital admissions differ by pollutant chemical properties.

METHODS

We categorized pollutants by chemical properties and examined their impacts on the odds of daily hospital admissions among Medicare recipients > 64 years of age in counties in Atlanta, Georgia, for 1998-2006. We analyzed data in two stages. In the first stage we applied a case-crossover analysis to simultaneously estimate effects of 65 pollutants measured in the Aerosol Research and Inhalation Epidemiology Study on cause-specific hospital admissions, controlling for temperature and ozone. In the second stage, we regressed pollutant-specific slopes from the first stage on pollutant properties. We calculated uncertainty estimates using a bootstrap procedure. We repeated the two-stage analyses using coefficients from first-stage models that included single pollutants plus ozone and meteorological variables only. We based our primary analyses on exposures on day of admission.

RESULTS

We found that 24-hr transition metals and alkanes were associated with increased odds [0.26%; 95% confidence interval (CI), 0.02-0.48; and 0.37%; 95% CI, 0.04-0.72, respectively] of hospital admissions for cardiovascular disease (CVD). Transition metals were significantly associated with increased hospital admissions for ischemic heart disease, congestive heart failure, and atrial fibrillation. Increased respiratory-related hospital admissions were significantly associated with alkanes. Aromatics and microcrystalline oxides were significantly associated with decreased CVD- and respiratory-related hospital admissions.

CONCLUSIONS

The two-stage approach showed transition metals to be consistently associated with increased odds of CVD-related hospital admissions.

Effects of thoracic and fine PM and their components on heart rate and pulmonary function in COPD patients

Population-based personal exposures to particulate matter (PM) and personal-ambient relationships of PM and component concentrations for outpatients with COPD and/or asthma were investigated in New York City (NYC) and Seattle for thoracic PM (PM(10)) and fine PM (PM(2.5)). Measurements of outdoor, indoor, and personal PM(10) and PM(2.5) concentrations were made concurrently for 12-consecutive days at 24 patients' residences. Filters were analyzed for elemental components, using XRF and black carbon (BC), by reflectance. Daily morning and evening measurements of heart rate (HR) and blood oxygen saturation (SpO(2)) by pulse oximeter, and forced expiratory volume in 1 s (FEV(1)) and peak expiratory flowrate (PEF) by spirometry were also measured, and symptom data were collected. Central monitoring site, outdoor, indoor, and personal concentration-response relationships of PM(2.5), PM(10-2.5), and their components were examined using mixed-effect models. The relatively small sample size of the study limited the interpretation of results, but of the PM chemical components examined, only nickel concentrations showed consistent associations, and only with HR in the NYC COPD patients.