Meteorological dependence, source identification, and carcinogenic risk assessment of PM2.5-bound Polycyclic Aromatic Hydrocarbons (PAHs) in high-traffic roadside, urban background, and remote suburban area.

The Polycyclic Aromatic Hydrocarbons (PAHs) bound to ambient fine Particular Matter (PM2.5) are currently drawing a lot of attention due to their adverse health effects increasing lung cancer risk in humans. In this study, The PM2.5 samples were collected by high volume air samplers simultaneously from three different sites (high-traffic roadside, urban background, and remote suburban) in Tehran, Iran during warm and cold seasons (from July 2018 to March 2019), and 16 PAHs were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS). Unlike previous studies, a remote suburban area was chosen so as to observe the spatial differentiation in PM2.5-bound PAH characteristics. In high-traffic roadside site, the average concentration of total PM2.5-bound PAHs (Æ©PAHs) was 3.7 times the concentration value in remote suburban area. Average (Æ©PAHs) ranged from 5.54 ng/m3 for remote suburban area to 20.67 ng/m3 for high-traffic roadside site. In all sites, seasonal trends of PAH concentrations elucidated high concentrations in the cold season and low concentrations in the warm season. Correlation analysis between Æ©PAHs and atmospheric factors (meteorology parameters and criteria air pollutants) indicated the heterogeneous processes play an important role in the level of PAHs. The results of diagnostic ratio (DR) analysis disclosed that the dominant source of PM2.5-bound PAHs was the combustion of liquid fossil fuels. Despite the fact that incremental lifetime cancer risk (ILCR) via inhaling PM2.5-bound PAHs varied significantly in high-traffic roadside site and remote suburban site, its value was beyond the acceptable risk level in both sites. Our results suggested that effective regulations are needed to monitor PAHs concentrations and reduce PAHs emissions from liquid fossil fuel combustion so as to mitigate the potential carcinogenic risk of PAHs in ambient air. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00821-2.

Particulate emissions of real-world light-duty gasoline vehicle fleet in Iran.

Fine particulate matter cause profound adverse health effects in Iran. Road traffic is one of the main sources of particulate matter (PM) in urban areas, and has a large contribution in PM2.5 and organic carbon concentration, in Tehran, Iran. The composition of fine PM vehicle emission is poorly known, so this paper aims to determine the mixed fleet source profile by using the analysed data from the two internal stations and the emission factor for PM light-duty vehicles emission. Tunnels are ideal media for extraction vehicle source profile and emission factor, due to vehicles are the only source of pollutant in the urban tunnels. In this study, PM samples were collected simultaneously in two road tunnel stations and at a background site in Niyayesh tunnel in Tehran, Iran. The tunnel samples show a large contribution for some elements and ions, such as Fe (0.23 µg µg-1 OC), Al (0.02 µg µg-1 OC), Ca (0.055 µg µg-1 OC), SO4 (0.047 µg µg-1 OC), Docosane (0.0017 µg µg-1 OC), Triacontane (0.016 µg µg-1 OC), Anthracenedione (0.0003 µg µg-1 OC) and Benzo-perylene (0.0002 µg µg-1 OC). In overall, on-road gasoline vehicle fleets source profile extracted in this study is similar to composite profiles derived from roadside tunnel measurment performed in other countries during the last decades. The PM2.5 emission factor for Tehran's light-duty vehicle fleet has been extracted 16.23 mg km-1. vehicle-1and 0.09 g kg-1. The profile would be used for Chemical Mass Balance Model studies for Iran and other countries with a similar road traffic fleet mix. Also, it would be very suitable for use in emission inventories improvement. The results of this study can be used for choosing the best management strategies and provide comperhensive insight to fine PM traffic emission in Tehran.

Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran.

PURPOSE: In this study we aimed to develop an optimized prediction model to estimate a fine-resolution grid of ground-level PM2.5 levels over Tehran. Using remote sensing data to obtain fine-resolution grids of particulate levels in highly polluted environments in areas such as Middle East with the abundance of brightly reflecting deserts is challenging. METHODS: Different prediction models implementing 3 km AOD products from the MODIS collection 6 and various effective parameters were used to obtain a reliable model to estimate ground-level PM2.5 concentrations. In this regards, the linear mixed effect model (LME), multi-variable linear regression model (MLR), Gaussian process model (GPM), artificial neural network (ANN) and support vector regression (SVR) were developed and their performance were compared. Since the LME and GPM outperformed other models, they were further optimized based on meteorological and topographical variables. These models were used to estimate PM2.5 values over the highly polluted megacity, Tehran, Iran. Moreover, the influence of site effect term on the performance of different shapes of LME models was evaluated by considering the random intercept for sites. RESULTS: Results showed LME models without the site effect term were able to explain ground-level variabilities of PM2.5 concentrations in ranges of 60-66% (RMSE = 9.6 to 10.3 µg/m3) and 35-41% (RMSE = 12.7 to 13.3 µg/m3) during the model-fitting and cross-validation, respectively. By considering the site effect term, the performance of LME models during calibrations and validations improved by 20% and 50% on average, respectively (18.5% and 17% decrease in the RSME) as compared to LME models without the site effect term. The optimized shape of LME models had a good agreement during both model-fitting (R2 of 0.76) and cross-validation (R2 of 0.6). Site-specific and seasonal performances of all types of models revealed that LME models had highest R2 values over all monitoring stations and all seasons during the cross-validation. LME models had the best performance in May and March compared to other months during the model-fitting and cross-validation. However, LME models had a significant weakness in predicting extreme values of PM2.5 during the cross-validation. Among all other types of models, GPM with the R2 value of 0.59 and the RMSE of 10.2 µg/m3 had the best performance during the cross-validation. CONCLUSIONS: While the best shape of LME and GPM had similar and reliable performances in predicting ground-level PM2.5 values during the cross-validation, GPM was able to predict extreme values of ground-level PM2.5 concentrations, which was the weakness of LME models and was an important issue in urban polluted environments. In this respect, GPM could be a good alternative for LME models for high levels of PM2.5 concentrations. The spatial distribution of estimated PM2.5 values represented that central parts of Tehran were the most polluted area over the studied region which was consistent with the ground-level recording PM2.5 data over monitoring stations.

Source apportionment of fine particulate matter in a Middle Eastern Metropolis, Tehran-Iran, using PMF with organic and inorganic markers.

With over 8 million inhabitants and 4 million motor vehicles on the streets, Tehran is one of the most crowded and polluted cities in the Middle East. Frequent exceedances of national daily PM2.5 limit have been reported in this city during the last decade, yet, the chemical composition and sources of fine particles are poorly determined. In the present study, 24-hour PM2.5 samples were collected at two urban sites during two separate campaigns, a one-year period from 2014 to 2015 and another three-month period at the beginning of 2017. Concentrations of organic carbon (OC), elemental carbon (EC), inorganic ions, trace metals and specific organic molecular markers were measured by chemical analysis of filter samples. The dominant mass components were organic matter (OM), sulfate and EC. With a 20% water-soluble organic carbon (WSOC) fraction, the predominance of primary anthropogenic sources (i.e. fossil fuel combustion) was anticipated. A positive matrix factorization (PMF) analysis using the ME-2 (Multilinear Engine-2) solver was then applied to this dataset. 5 factors were identified by Marker-PMF, named as traffic exhaust (TE), biomass burning (BB), industries (Ind.), nitrate-rich and sulfate-rich. Another 4 factors were identified by Metal-PMF, including, dust, vehicles (traffic non-exhaust, TNE), industries (Ind.) and heavy fuel combustion (HFC). Traffic exhaust was the dominant source with 44.5% contribution to total quantified PM2.5 mass. Sulfate-rich (24.2%) and nitrate-rich (18.4%) factors were the next major contributing sources. Dust (4.4%) and biomass burning (6.7%) also had small contributions while the total share of all other factors was < 2%. Investigating the correlations of different factors between the two sampling sites showed that traffic emissions and biomass burning were local, whereas dust, heavy fuel combustion and industrial sources were regional. Results of this study indicate that gas- and particle-phase pollutants emitted from fossil fuel combustion (mobile and stationary) are the principal origin of both primary and secondary fine aerosols in Tehran.

Iran in transition.

Being the second-largest country in the Middle East, Iran has a long history of civilisation during which several dynasties have been overthrown and established and health-related structures have been reorganised. Iran has had the replacement of traditional practices with modern medical treatments, emergence of multiple pioneer scientists and physicians with great contributions to the advancement of science, environmental and ecological changes in addition to large-scale natural disasters, epidemics of multiple communicable diseases, and the shift towards non-communicable diseases in recent decades. Given the lessons learnt from political instabilities in the past centuries and the approaches undertaken to overcome health challenges at the time, Iran has emerged as it is today. Iran is now a country with a population exceeding 80 million, mainly inhabiting urban regions, and has an increasing burden of non-communicable diseases, including cardiovascular diseases, hypertension, diabetes, malignancies, mental disorders, substance abuse, and road injuries.

Seasonal variations in the oxidative stress and inflammatory potential of PM2.5 in Tehran using an alveolar macrophage model; The role of chemical composition and sources.

The current study was designed to assess the association between temporal variations in urban PM2.5 chemical composition, sources, and the oxidative stress and inflammatory response in an alveolar macrophage (AM) model. A year-long sampling campaign collected PM2.5 samples at the Sharif University in Tehran, Iran. PM-induced reactive oxygen species (ROS) production was measured both with an acellular dithiothreitol consumption assay (DTT-ROS; ranged from 2.1 to 9.3 nmoles min-1 m-3) and an in vitro macrophage-mediated ROS production assay (AM-ROS; ranged from 125 to 1213 µg Zymosan equivalents m-3). The production of tumor necrosis factor alpha (TNF-α; ranged from ~60 to 518 pg TNF-α m-3) was quantified as a marker of the inflammatory potential of the PM. PM-induced DTT-ROS and AM-ROS were substantially higher for the colder months' PM (1.5-fold & 3-fold, respectively) compared with warm season. Vehicular emission tracers, aliphatic diacids, and hopanes exhibited moderate correlation with ROS measures. TNF-α secretion exhibited a markedly different pattern than ROS activity with a 2-fold increase in the warm months compared to the rest of the year. Gasoline vehicles and residual oil combustion were moderately associated with both ROS measures (R ≥ 0.67, p < 0.05), while diesel vehicles exhibited a strong correlation with secreted TNF-α in the cold season (R = 0.89, p < 0.05). mRNA expression of fourteen genes including antioxidant response and pro-inflammatory markers were found to be differentially modulated in our AM model. HMOX1, an antioxidant response gene, was up-regulated throughout the year. Pro-inflammatory genes (e.g. TNF-α and IL1ß) were down-regulated in the cold season and displayed moderate to weak correlation with crustal elements (R > 0.5, p < 0.05). AM-ROS activity showed an inverse relationship with genes including SOD2, TNF, IL1ß and IL6 (R ≥ -0.66, p < 0.01). Our findings indicate that Tehran's PM2.5 has the potential to induce oxidative stress and inflammation responses in vitro. In the current study, these responses included NRF2, NF-κB and MAPK pathways.

Developing air exchange rate models by evaluating vehicle in-cabin air pollutant exposures in a highway and tunnel setting: case study of Tehran, Iran.

The passengers inside vehicles could be exposed to high levels of air pollutants particularly while driving on highly polluted and congested traffic roadways. In order to study such exposure levels and its relation to the cabin ventilation condition, a monitoring campaign was conducted to measure the levels inside the three most common types of vehicles in Tehran, Iran (a highly air polluted megacity). In this regard, carbon monoxide (CO) and particulate matter (PM) were measured for various ventilation settings, window positions, and vehicle speeds while driving on the Resalat Highway and through the Resalat Tunnel. Results showed on average in-cabin exposure to particle number and PM10 for the open windows condition was seven times greater when compared to closed windows and air conditioning on. When the vehicle was passing through the tunnel, in-cabin CO and particle number increased 100 and 30%, respectively, compared to driving on highway. Air exchange rate (AER) is a significant factor when evaluating in-cabin air pollutants level. AER was measured and simulated by a model developed through a Monte Carlo analysis of uncertainty and considering two main affecting variables, vehicle speed and fan speed. The lowest AER was 7 h-1 for the closed window and AC on conditions, whereas the highest AER was measured 70 h-1 for an open window condition and speed of 90 km h-1. The results of our study can assist policy makers in controlling in-cabin pollutant exposure and in planning effective strategies for the protection of public health.

Seasonal trends in the composition and sources of PM2.5 and carbonaceous aerosol in Tehran, Iran.

Currently PM2.5 is a major air pollution concern in Tehran, Iran due to frequent high levels and possible adverse impacts. In this study, which is the first of its kind to take place in Tehran, composition and sources of PM2.5 and carbonaceous aerosol were determined, and their seasonal trends were studied. In this regard, fine PM samples were collected every six days at a residential station for one year and the chemical constituents including organic marker species, metals, and ions were analyzed by chemical analysis. The source apportionment was performed using organic molecular marker-based CMB receptor modeling. Carbonaceous compounds were the major contributors to fine particulate mass in Tehran, as OC and EC together comprised on average 29% of PM2.5 mass. Major portions of OC in Tehran were water insoluble and are mainly attributed to primary sources. Higher levels of several PAHs, which are organic tracers of incomplete combustion, and hopanes and steranes as organic tracers of mobile sources were obtained in cold months and compared to the warm months. The major contributing source to particulate OC was identified as vehicles, which contributed about 72% of measured OC. Among mobile sources, gasoline-fueled vehicles had the highest impact with a mean contribution of 48% to the measured OC. Mobile sources also were the largest contributor to total PM2.5 (40%), followed by dust (24%) and sulfate (11%). In addition to primary emissions, mobile sources also directly and indirectly played an important role in another 27% of fine particulate mass (secondary organics and ions), which highlights the impact of vehicles in Tehran. Our results highlighted and quantified the role of motor vehicles in fine PM production, particularly during winter time. The results of this study could be used to set more effective regulations and control strategies particularly upon mobile sources.

Short-term effects of particle size fractions on circulating biomarkers of inflammation in a panel of elderly subjects and healthy young adults.

Systemic inflammation biomarkers have been associated with risk of cardiovascular morbidity and mortality. We aimed to clarify associations of acute exposure to particulate matter (PM10 (PM < 10 µm), PM2.5-10 (PM 2.5-10 µm), PM2.5 (PM < 2.5 µm), PM1-2.5 (PM 1-2.5 µm), and PM1 (PM < 1 µm)) with systemic inflammation using panels of elderly subjects and healthy young adults. We followed a panel of 44 nonsmoking elderly subjects living in a retirement home and a panel of 40 healthy young adults living in a school dormitory in Tehran city, Iran from May 2012 to May 2013. Blood biomarkers were measured one every 7-8 weeks and included white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF). We measured hourly indoor and outdoor exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1 mass concentration to derive weighted averages of personal exposure based on simultaneously collected time-activity data. The random intercept linear mixed effects model was used for data analysis. We observed significant positive associations for WBC and IL-6 with exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1; sTNF-RII with PM2.5, PM1-2.5, and PM1; hsCRP with PM2.5 and PM1; and vWF with PM10 and PM2.5-10, PM2.5, and PM1-2.5 mass concentration in elderly subjects from the current-day and multiday averages. For healthy young adults, we found significant positive associations for WBC and IL-6 with exposure to PM10, PM2.5-10, PM2.5, and PM1-2.5, but no with PM1. The results showed that increase of hsCRP, sTNF-RII, and vWF were not significantly associated with any of the PM sizes investigated in the healthy young subjects. Our results provided some evidence that short-term exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1 was associated with inflammation and coagulation blood markers, but associations were depended on PM size and also differed across the various time lag.

Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran.

A field sampling campaign was implemented to evaluate the variation in air pollutants levels near a highway in Tehran, Iran (Hemmat highway). The field measurements were used to estimate road link-based emission factors for average vehicle fleet. These factors were compared with results of an in tunnel measurement campaign (in Resalat tunnel). Roadside and in-tunnel measurements of carbon monoxide (CO) and size-fractionated particulate matter (PM) were conducted during the field campaign. The concentration gradient diagrams showed exponential decay, which represented a substantial decay, more than 50-80%, in air pollutants level in a distance between 100 and 150meters (m) of the highway. The changes in particle size distribution by distancing from highway were also captured and evaluated. The results showed particle size distribution shifted to larger size particles by distancing from highway. The empirical emission factors were obtained by using the roadside and in tunnel measurements with a hypothetical box model, floating machine model, CALINE4, CT-EMFAC or COPERT. Average CO emission factors were estimated to be in a range of 4 to 12g/km, and those of PM10 were 0.1 to 0.2g/km, depending on traffic conditions. Variations of these emission factors under real working condition with speeds were determined.

Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory.

In the present work, we investigated the characteristics of polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s in indoor/outdoor PM10, PM2.5, and PM1 in a retirement home and a school dormitory in Tehran from May 2012 to May 2013. The results indicated that the annual levels of indoor and outdoor PM10 and PM2.5 were much higher than the guidelines issued by the World Health Organization (WHO). The most abundant detected metal(loid)s in PM were Si, Fe, Zn, Al, and Pb. We found higher percentages of metal(loid)s in smaller size fractions of PM. Additionally, the results showed that the total PAHs (Æ©PAHs) bound to PM were predominantly (83-88%) found in PM2.5, which can penetrate deep into the alveolar regions of the lungs. In general, carcinogenic PAHs accounted for 40-47% of the total PAHs concentrations; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. The percentages of trace metal(loid)s and carcinogenic PAHs in PM2.5 mass were almost twice as high as those in PM10. This can most likely be responsible for the fact that PM2.5 can cause more adverse health effects than PM10 can. The average BaP-equivalent carcinogenic (BaP-TEQ) levels both indoors and outdoors considerably exceeded the maximum permissible risk level of 1 ng/m(3) of BaP. The enrichment factors and diagnostic ratios indicated that combustion-related anthropogenic sources, such as gasoline- and diesel-fueled vehicles as well as natural gas combustion, were the major sources of PAHs and trace metal(loid)s bound to PM.

Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran.

BACKGROUND AND METHODOLOGY: Measurements by satellite remote sensing were combined with ground-based meteorological measurements to estimate ground-level PM10. Aerosol optical depth (AOD) by both MODIS and MISR were utilized to develop several statistical models including linear and non-linear multi-regression models. These models were examined for estimating PM10 measured at the air quality stations in Tehran, Iran, during 2009-2010. Significant issues are associated with airborne particulate matter in this city. Moreover, the performances of the constructed models during the Middle Eastern dust intrusions were examined. RESULTS: In general, non-linear multi-regression models outperformed the linear models. The developed models using MISR AOD generally resulted in better estimate of ground-level PM10 compared to models using MODIS AOD. Consequently, among all the constructed models, results of non-linear multi-regression models utilizing MISR AOD acquired the highest correlation with ground level measurements (R(2) of up to 0.55). The possibility of developing a single model over all the stations was examined. As expected, the results were depreciated, while nonlinear MISR model repeatedly showed the best performance being able to explain up to 38% of the PM10 variability. CONCLUSIONS: Generally, the models didn't competently reflect wide temporal concentration variations, particularly due to the elevated levels during the dust episodes. Overall, using non-linear multi-regression model incorporating both remote sensing and ground-based meteorological measurements showed a rather optimistic prospective in estimating ground-level PM for the studied area. However, more studies by applying other statistical models and utilizing more parameters are required to increase the model accuracies.

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 µm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

Electrocardiographic ST-segment depression and exposure to traffic-related aerosols in elderly subjects with coronary artery disease.

BACKGROUND: Air pollutants have not been associated with ambulatory electrocardiographic evidence of ST-segment depression ≥ 1 mm (probable cardiac ischemia). We previously found that markers of primary (combustion-related) organic aerosols and gases were positively associated with circulating biomarkers of inflammation and ambulatory blood pressure in the present cohort panel study of elderly subjects with coronary artery disease. OBJECTIVES: We specifically aimed to evaluate whether exposure markers of primary organic aerosols and ultrafine particles were more strongly associated with ST-segment depression of ≥ 1 mm than were secondary organic aerosols or PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) mass. METHODS: We evaluated relations of air pollutants to ambulatory electrocardiographic evidence of cardiac ischemia over 10 days in 38 subjects without ST depression on baseline electrocardiographs. Exposures were measured outdoors in retirement communities in the Los Angeles basin, including daily size-fractionated particle mass and hourly markers of primary and secondary organic aerosols and gases. Generalized estimating equations were used to estimate odds of hourly ST-segment depression (≥ 1 mm) from hourly air pollution exposures and to estimate relative rates of daily counts of ST-segment depression from daily average exposures, controlling for potential confounders. RESULTS: We found significant positive associations of hourly ST-segment depression with markers of combustion-related aerosols and gases averaged 1-hr through 3-4 days, but not secondary (photochemically aged) organic aerosols or ozone. The odds ratio per interquartile increase in 2-day average primary organic carbon (5.2 µg/m3) was 15.4 (95% confidence interval, 3.5-68.2). Daily counts of ST-segment depression were consistently associated with primary combustion markers and 2-day average quasi-ultrafine particles < 0.25 µm. CONCLUSIONS: Results suggest that exposure to quasi-ultrafine particles and combustion-related pollutants (predominantly from traffic) increase the risk of myocardial ischemia, coherent with our previous findings for systemic inflammation and blood pressure.

Associations of primary and secondary organic aerosols with airway and systemic inflammation in an elderly panel cohort.

BACKGROUND: Exposure-response information about particulate air-pollution constituents is needed to protect sensitive populations. Particulate matter <2.5 mm (PM2.5) components may induce oxidative stress through reactive-oxygen-species generation, including primary organics from combustion sources and secondary organics from photochemically oxidized volatile organic compounds. We evaluated differences in airway versus systemic inflammatory responses to primary versus secondary organic particle components, particle size fractions, and the potential of particles to induce cellular production of reactive oxygen species. METHODS: A total of 60 elderly subjects contributed up to 12 weekly measurements of fractional exhaled nitric oxide (NO; airway inflammation biomarker), and plasma interleukin-6 (IL-6; systemic inflammation biomarker). PM2.5 mass fractions were PM0.25 (<0.25 µm) and PM0.25-2.5 (0.25-2.5 µm). Primary organic markers included PM2.5 primary organic carbon, and PM0.25 polycyclic aromatic hydrocarbons and hopanes. Secondary organic markers included PM2.5 secondary organic carbon, and PM0.25 water soluble organic carbon and n-alkanoic acids. Gaseous pollutants included carbon monoxide (CO) and nitrogen oxides (NOx; combustion emissions markers), and ozone (O3; photochemistry marker). To assess PM oxidative potential, we exposed rat alveolar macrophages in vitro to aqueous extracts of PM0.25 filters and measured reactive-oxygen-species production. Biomarker associations with exposures were evaluated with mixed-effects models. RESULTS: Secondary organic markers, PM0.25-2.5, and O3 were positively associated with exhaled NO. Primary organic markers, PM0.25, CO, and NOx were positively associated with IL-6. Reactive oxygen species were associated with both outcomes. CONCLUSIONS: Particle effects on airway versus systemic inflammation differ by composition, but overall particle potential to induce generation of cellular reactive oxygen species is related to both outcomes.

Traffic-related air pollution and blood pressure in elderly subjects with coronary artery disease.

BACKGROUND: Associations between blood pressure (BP) and ambient air pollution have been inconsistent. No studies have used ambulatory BP monitoring and outdoor home air-pollutant measurements with time-activity-location data. We address these gaps in a study of 64 elderly subjects with coronary artery disease, living in retirement communities in the Los Angeles basin. METHODS: Subjects were followed up for 10 days with hourly waking ambulatory BP monitoring (n = 6539 total measurements), hourly electronic diaries for perceived exertion and location, and real-time activity monitors (actigraphs). We measured hourly outdoor home pollutant gases, particle number, PM2.5, organic carbon, and black carbon. Data were analyzed with mixed models controlling for temperature, posture, actigraph activity, hour, community, and season. RESULTS: We found positive associations of systolic and diastolic BP with air pollutants. The strongest associations were with organic carbon (especially its estimated fossil-fuel- combustion fraction), multiday average exposures, and time periods when subjects were at home. An interquartile increase in 5-day average organic carbon (5.2 microg/m) was associated with 8.2 mm Hg higher mean systolic BP (95% confidence interval = 3.0-13.4) and 5.8 mm Hg higher mean diastolic BP (3.0-8.6). Associations of BP with 1-8 hour average air pollution were stronger with reports of moderate to strenuous physical exertion but not with higher actigraph motion. Associations were also stronger among 12 obese subjects. CONCLUSIONS: Exposure to primary organic components of fossil fuel combustion near the home were strongly associated with increased ambulatory BP in a population at potential risk of heart attack. Low fitness or obesity may increase the effects of pollutants.

Association of biomarkers of systemic inflammation with organic components and source tracers in quasi-ultrafine particles.

BACKGROUND: Evidence is needed regarding the air pollutant components and their sources responsible for associations between particle mass concentrations and human cardiovascular outcomes. We previously found associations between circulating biomarkers of inflammation and mass concentrations of quasi-ultrafine particles

Air pollution exposures and circulating biomarkers of effect in a susceptible population: clues to potential causal component mixtures and mechanisms.

BACKGROUND: Mechanisms involving oxidative stress and inflammation have been proposed to explain associations of ambient air pollution with cardiovascular morbidity and mortality. Experimental evidence suggests that organic components and ultrafine particles (UFP) are important. METHODS: We conducted a panel study of 60 elderly subjects with coronary artery disease living in retirement communities within the Los Angeles, California, air basin. Weekly biomarkers of inflammation included plasma interleukin-6, tumor necrosis factor-alpha soluble receptor II (sTNF-RII), soluble platelet selectin (sP-selectin), and C-reactive protein (CRP). Biomarkers of erythrocyte antioxidant activity included glutathione peroxidase-1 and superoxide dismutase. Exposures included outdoor home daily particle mass [particulate matter < 0.25, 0.25-2.5, and 2.5-10 microm in aerodynamic diameter (PM(0.25), PM(0.25-2.5), PM(2.5-10))], and hourly elemental and black carbon (EC-BC), estimated primary and secondary organic carbon (OC(pri), SOC), particle number (PN), carbon monoxide (CO), and nitrogen oxides-nitrogen dioxide (NO(x)-NO(2)). We analyzed the relation of biomarkers to exposures with mixed effects models adjusted for potential confounders. RESULTS: Primary combustion markers (EC-BC, OC(pri), CO, NO(x)-NO(2)), but not SOC, were positively associated with inflammatory biomarkers and inversely associated with erythrocyte anti-oxidant enzymes (n = 578). PN and PM(0.25) were more strongly associated with biomarkers than PM(0.25-2.5). Associations for all exposures were stronger during cooler periods when only OC(pri), PN, and NO(x) were higher. We found weaker associations with statin (sTNF-RII, CRP) and clopidogrel use (sP-selectin). CONCLUSIONS: Traffic-related air pollutants are associated with increased systemic inflammation, increased platelet activation, and decreased erythrocyte antioxidant enzyme activity, which may be partly behind air pollutant-related increases in systemic inflammation. Differences in association by particle size, OC fraction, and seasonal period suggest components carried by UFP are important.

Associations between personal, indoor, and residential outdoor pollutant concentrations: implications for exposure assessment to size-fractionated particulate matter.

The physical and chemical characteristics of indoor, outdoor, and personal quasi-ultrafine (<0.25 microm)-, accumulation (0.25-2.5 microm)-, and coarse (2.5-10 microm)-mode particles were studied at four different retirement communities in southern California between 2005 and 2007. Linear mixed-effects models and Spearman's correlation coefficients were then used to elucidate the relationships among size-segregated particulate matter (PM) levels, their particle components, and gaseous co-pollutants. Seasonal and spatial differences in the concentrations of all measured species were evaluated at all sites on the basis of P values for product terms. Outdoor quasi-ultrafine (UF) and, to a lesser extent, accumulation-mode particles were the two fractions that best correlated with outdoor concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxides (NOx; during both phases of the study), and ozone (O3; only during the warmer months). Outdoor and indoor concentrations of CO, NO2, and NOx were more positively correlated to personal quasi-UF particles than larger size fractions. Despite these findings, it seems unlikely that these gaseous co-pollutants could confound epidemiologic associations between quasi-UF particles and adverse health effects. Overall, measured gaseous co-pollutants were weak surrogates of personal exposure to accumulation-mode PM, at least for subjects with similar exposure profiles and living in similar urban locations. Indoor sources were not significant contributors to personal exposure of accumulation and quasi-UF PM, which is predominantly influenced by primary emitted pollutants of outdoor origin. Correlations between personal coarse-mode PM and both outdoor and indoor gaseous co-pollutant concentrations were weak at all sites and during all seasons.