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Source Characterization and Exposure Modeling of Gas-Phase Polycyclic Aromatic Hydrocarbon (PAH) Concentrations in Southern California
Abstract
Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39–40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R² of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs.
PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.
... Polycyclic aromatic hydrocarbons (PAHs) are by-products of incomplete combustion reactions (Masri et al. 2018). Air pollution and fuel emissions are sources of human exposure to PAHs (Kim et al. 2021). ...
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants that pose a potentially serious threat to maternal and infant health, yet there are few studies on low-dose maternal and infant health. Therefore, this study investigated 2566 mother–infant pairs in Zunyi rural area, southwest China, and used multiple linear regression (MLR) and Bayesian kernel machine regression (BKMR) to reveal the relationship between pre-pregnancy PAHs exposures and neonatal birth outcomes. The results showed that more than 75% of the PAHs metabolite concentrations were above the limit of detection, and the main exposure was to 2-OHNAP (2. 9379 µg/L).The results of the MLR showed that, in both the original and adjusted models, 9-OHFLU and 4-OHPH were associated with lower birth weight, corresponding to β (95% CI) of − 36.684 (− 7.580, − 65.788), − 76.931 (− 22.524, − 131. 338), − 33.254 (− 4.628, − 61.879) and − 65.490 (− 12.073, − 118.906) (95%). 9-OHFLU was associated with loss of birth length, corresponding to β (95% CI) of − 0.094 (− 0.013, − 0.176) and − 0.084 (− 0.003, − 0.164). 9-OHFLU and 4-OHPH were associated with decreased neonatal BMI, corresponding to β (95%) CI) of − 0.104 (− 0.006, − 0.202) and − 0.323 (− 0.139, − 0.507), respectively. The results of the BKMR model showed that in the original model, 1-OHNAP, 1-OHPYR and total PAHs were negatively correlated with birth weight, with the corresponding PIP value of 0.0008, and that, after adjusting the model, total PAHs and 2-0HPH were negatively correlated with birth weight and birth BMI, corresponding to PIP values of 0.0232 and 0.0316, and total PAHs levels were negatively correlated with birth length. In conclusion, exposure of pregnant women to PAHs during pregnancy can have a negative impact on the birth outcomes of their newborns. Therefore, it is recommended that pregnant women with potentially adverse pregnancy outcomes, especially those suffering from gestational malnutrition, pre-eclampsia, or who are substance abusers, should reduce or avoid exposure to PAHs.
... This might be because of regionalization and uncontrolled additional emission contributions (e. g., wildfire). In the compiled dataset, the industrial sampling sites in the USA were located in the states of California, Ohio, and Missouri, which would lead to the bias on spatial distribution (Masri et al., 2018;Noth et al., 2016;USEPA, 2022). The occurrence of wildfires in the USA should be realized which could be a novel and vital source contributed to the atmospheric PAHs (Kramer et al., 2020;Wentworth et al., 2018), and which might be difficult to reduce the related errors. ...
Polycyclic aromatic hydrocarbons (PAHs) are common air pollutants worldwide, associated with industrial processes. In the general population, both modeling and field studies revealed a positive correlation between air PAH concentrations and urinary PAH metabolite levels. Many countries lack population urinary data that correspond to local PAH air concentrations. Thus, we proposed a scoring-based approximate approach to investigating that correlation in selected countries, hypothesizing that PAH air concentrations in selected regions could represent the national air quality influenced by industrial emission and further correlate to PAH internal exposure in the general population. This research compiled 85 peer-reviewed journal articles and 9 official monitoring datasets/reports covering 34 countries, 16 of which with both atmospheric PAH data and human biomonitoring data. For the air pollution score (AirS), Egypt had the highest AirS at 0.94 and Pakistan was at the bottom of the score ranking at -1.95, as well as the median in the UK (AirS: 0.50). For the population exposure score (ExpS), China gained the top ExpS at 0.44 and Spain was with the lowest ExpS of -1.52, with the median value in Italy (ExpS: 0.43). Through the correlation analysis, atmospheric PAHs and their corresponding urinary metabolites provided a positive relationship to a diverse extent, indicating that the related urinary metabolites could reflect the population's exposure to specific atmospheric PAHs. The findings also revealed that in the 16 selected countries, AirS indexes were positively correlated with ExpS indexes, implying that higher PAH levels in the air may lead to elevated metabolite urinary levels in general populations. Furthermore, lowering PAH air concentrations could reduce population internal PAH exposure, implying that strict PAH air regulation or emission would reduce health risks for general populations. Notably, this study was an ideal theoretical research based on proposed assumptions to some extent. Further research should focus on understanding exposure pathways, protecting vulnerable populations, and improving the PAH database to optimize PAH pollution control.
... Additionally, several city-scale LUR models have also been studied [13,15,16]. Most previous research has focused on particle-bound PAHs, and less attention has been paid to gaseous-phase PAHs [13,17,18]. Second, only a few studies have reported seasonally dependent models. ...
Polycyclic aromatic hydrocarbons (PAHs) are an important class of pollutants in China. The land use regression (LUR) model has been used to predict the selected PAH concentrations and screen the key influencing factors. However, most previous studies have focused on particle-associated PAHs, and research on gaseous PAHs was limited. This study measured representative PAHs in both gaseous phases and particle-associated during the windy, non-heating and heating seasons from 25 sampling sites in different areas of Taiyuan City. We established separate prediction models of 15 PAHs. Acenaphthene (Ace), Fluorene (Flo), and benzo [g,h,i] perylene (BghiP) were selected to analyze the relationship between PAH concentration and influencing factors. The stability and accuracy of the LUR models were quantitatively evaluated using leave-one-out cross-validation. We found that Ace and Flo models show good performance in the gaseous phase (Ace: adj. R2 = 0.14–0.82; Flo: adj. R2 = 0.21–0.85), and the model performance of BghiP is better in the particle phase (adj. R2 = 0.20–0.42). Additionally, better model performance was observed in the heating season (adj R2 = 0.68–0.83) than in the non-heating (adj R2 = 0.23–0.76) and windy seasons (adj R2 = 0.37–0.59). Those gaseous PAHs were highly affected by traffic emissions, elevation, and latitude, whereas BghiP was affected by point sources. This study reveals the strong seasonal and phase dependence of PAH concentrations. Building separate LUR models in different phases and seasons improves the prediction accuracy of PAHs.
... Various forms of cancer (among others lung, bladder, liver, skin, or stomach) are attributed to the PAHs exposure (Xue et al. 2019). Because PAHs originate, to a large extent, from the incomplete combustion of fossil fuels (Masri et al. 2018), roads or coal-fired power plants are potential sources of these hazardous substances. ...
Detailed individual particle characterization of PM10, in terms of particle size, morphology, and elemental composition, was done using scanning electron microscopy combined with energy-dispersive X-ray spectroscopy. The samples were collected in four localities in the Czech Republic (Central Europe), three of which are medium-sized cities, and one is a natural locality in the mountains. More than 1600 particles obtained from each locality were evaluated. During the sampling period (1.9.–8.9.2019), the atmospheric conditions were similar in the localities, which enabled the identification of PM10 characteristics common to all the sampling sites. Some differences in the particles’ morphology and composition, arising from site-specific conditions, were observed too. The most abundant elements in the PM10 were C, O, Si, Fe, Al, Ca, Na, K, Mg, and S, but some toxic elements (Cr, Cu, and Ni) were also detected. The main component of the PM10 is carbon, whose multimodal distribution indicates that the particles contain different carbonaceous chemical compounds. The distribution of carbon in the natural locality was different compared to the other sites, suggesting a specific character of the sources of carbonaceous compounds in this region. Last but not least, a relationship between Al, Si, and O concentrations was found, which implies the presence of aluminosilicates and silicon dioxide (possibly sand) of crustal origin in the particles.
... The combustion products identified through our case studies included PM 2.5 , PM 10 , PAHs, and mixed metals. Combustion products are known to contain other products as well; however, these products are among the most important as it relates to health effects at ambient levels [56,57]. ...
Background
Despite 15–36% of the U.S. population reporting Chemical Intolerances (CI) or sensitivity, the condition has been overlooked in medicine and public health. CI is characterized by multisystem symptoms and new-onset intolerances that develop in a subset of individuals following a major chemical exposure event or repeated low-level exposures. While Toxicant-Induced Loss of Tolerance (TILT) is a two-stage disease mechanism proposed to explain CI, less is known about the exposures that initiate the disease, than about the intolerances that have been documented.
Methods
We reviewed eight major exposure events that preceded onset of chemical intolerance in groups of individuals sharing the same exposure. Our goal was to identify the chemicals and/or groups of chemicals that were most pervasive during each exposure event as well as identify the concentrations of key chemicals involved in each exposure event and the proportions of exposed individuals who ultimately developed TILT following exposure. Case studies we selected for review included (1) workers at U.S. Environmental Protection Agency (EPA) headquarters during renovations; (2) Gulf War veterans; (3) pesticide exposure among casino workers; (4) exposure to aircraft oil fumes; (5) the World Trade Center tragedy; (6) surgical implants; (7) moldy environments; and (8) tunnel workers exposed to solvents.
Results
Mixed volatile and semi-volatile organic compounds (VOCs and SVOCs), followed by pesticides and combustion products were most prevalent across TILT initiation events. As a broader category, synthetic organic chemicals and their combustion products were the primary exposures associated with chemical intolerance. Such chemicals included pesticides, peroxides, nerve agents, anti-nerve agent drugs, lubricants and additives, xylene, benzene, and acetone.
Conclusion
A select group of exposures were predominant in several major initiating events, suggesting their potential role in TILT initiation. Such insights are useful to public health scientists, physicians, and policymakers seeking to minimize harmful exposures and prevent future disease.
... PAHs can be persistent in environments with multiple media. Due to the low vapor pressure and low aqueous solubility, PAHs can move from the middle or lower latitudes to remote high latitude areas via air or ocean currents (Balmer et al. 2019;Luo et al. 2020;Masri et al. 2018;Shimada et al. 2020). ...
Atmospheric, soil, and feces samples were collected in Ny-Ålesund during July 2015. The concentrations, distributions, congener profiles, and contaminant migration levels were analyzed for 16 polycyclic aromatic hydrocarbons (PAHs) identified by the United States Environmental Protection Agency (US EPA) as priority contaminants (16 PAHs). Mean concentrations in the gas and particle phases were 37.8 ng m⁻³ and 2.9 ng m⁻³, respectively, and mean concentrations in soil and reindeer/bird feces were 329.1 ng g⁻¹ and 720.7 ng g⁻¹, respectively, on a dry weight (d.w.) basis. In more than three phases, naphthalene and phenanthrene dominated the concentrations of the 16 total PAHs (Σ16PAH) and the concentration of PAHs in the gas phase was much higher than in the particle phase. The main sources of local PAHs may be coal combustion and air-surface exchange. There was a volatilization tendency from soil to air for 2–4 ring PAHs, and exchange fluxes were ~ 10⁵ times greater than the deposition fluxes of 5–6 ring PAHs. The underground migration of PAHs was investigated in Ny-Ålesund; the results showed flux values of ~ 0.07% from the initial PAH concentrations.
... Passive samplers typically contain an air receiving unit and a holder that collects the target constituents that rely on the molecular diffusion mechanism and the affinity of the absorbent to PAHs. Several adequate absorbent materials, such as silicone products and PUF and GC capillary integration units, were selected as passive samplers [90][91][92][93][94][95][96]. Silicone wristbands are light and pose no inconvenience to subjects who are in vulnerable or occupational groups, such as pregnant females and firefighters [93,95,96]. ...
Polycyclic aromatic hydrocarbons (PAHs) are a class of important organic pollutants widely emitted from anthropogenic activities, with a general distribution in the gas and particulate phases. Some PAHs are carcinogenic, teratogenic, and mutagenic. Inhalation exposure to PAHs is correlated with adverse health outcomes in the respiratory and cardiovascular systems. Thus, it is significant to determine the exposure level of the general population. This study summarizes the evaluation methods for PAH exposure, focusing on different exposure parameters. External exposure can be determined via the collection of the environmental pollution concentration through active samplers or passive samplers during environmental monitoring or personal sampling. Time-activity patterns give critical exposure information that captures the exposure period, origin, and behaviors. Modeling is a labor-less approach for human exposure estimation, and microenvironmental exposure requires specific research. It is important to select appropriate methods to quantify the exposure level to provide accurate data to establish the exposure–risk relationship and make scientific suggestions for the protection of public health.
Despite the success of passive sampler-based monitoring networks in capturing global atmospheric distributions of semivolatile organic compounds (SVOCs), their limited spatial resolution remains a challenge. Adequate spatial coverage is necessary to better characterize concentration gradients, identify point sources, estimate human exposure, and evaluate the effectiveness of chemical regulations such as the Stockholm Convention on Persistent Organic Pollutants. Land use regression (LUR) modelling can be used to integrate land use characteristics and other predictor variables (industrial emissions, traffic intensity, demographics, etc.) to describe or predict the distribution of air concentrations at unmeasured locations across a region or country. While LUR models are frequently applied to data-rich conventional air pollutants such as particulate matter, ozone, and nitrogen oxides, they are rarely applied to SVOCs.
The MONET passive air sampling network (RECETOX, Masaryk University) continuously measures atmospheric SVOC levels across Czechia in monthly intervals. Using monitoring data from 29 MONET sites over a two-year period (2015–2017) and a variety of predictor variables, we developed LUR models to describe atmospheric levels and identify sources of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and DDT across the country. Strong and statistically significant (R² > 0.6; p < 0.05) models were derived for PAH and PCB levels on a national scale. The PAH model retained three predictor variables – heating emissions represented by domestic fuel consumption, industrial PAH point sources, and the hill:valley index, a measure of site topography. The PCB model retained two predictor variables – site elevation, and secondary sources of PCBs represented by soil concentrations. These models were then applied to Czechia as a whole, highlighting the spatial variability of atmospheric SVOC levels, and providing a tool that can be used for further optimization of sampling network design, as well as evaluating potential human and environmental chemical exposures.
Polycyclic aromatic hydrocarbons (PAHs) have gained attention because of their environmental persistence and effects on ecosystems, animals, and human health. They are mutagenic, carcinogenic, and teratogenic. The review provides background knowledge about their sources, metabolism, temporal variations, and size distribution in atmospheric particulate matter. The review article briefly discusses the analytical methods suitable for the extraction, characterization, and quantification of nonpolar and polar PAHs, addressing the challenges. Herein, we discussed the molecular diagnostic ratios (DRs), stable carbon isotopic analysis (SCIA), and receptor models, with much emphasis on the positive matrix factorization (PMF) model, for apportioning PAH sources. Among which, DRs and PCA identified as the most widely employed method, but their accuracy for PAH source identification has received global criticism. Therefore, the review recommends compound-specific isotopic analysis (CSIA) and PMF as the best alternative methods to provide detailed qualitative and quantitative source analysis. The compound-specific isotopic signatures are not affected by environmental degradation and are considered promising for apportioning PAH sources. However, isotopic fractions of co-eluted compounds like polar PAHs and aliphatic hydrocarbons make the PAHs isotopic fractions interpretation difficult. The interference of unresolved complex mixtures is a limitation to the application of CSIA for PAH source apportionment. Hence, for CSIA to further support PAH source apportionment, fast and cost-effective purification techniques with no isotopic fractionation effects are highly desirable. The present review explains the concept of stable carbon isotopic analysis (SCIA) relevant to PAH source analysis, identifying the techniques suitable for sample extract purification. We demonstrate how the source apportioned PAHs can be applied in assessing the health risk of PAHs using the incremental lifetime cancer risk (ILCR) model, and in doing so, we identify the key factors that could undermine the accuracy of the ILCR and research gaps that need further investigation.
To comparatively analyze source-specific risks of atmospheric particulate matter (PM), PM10-bound polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) were synchronously detected in a megacity (Chengdu, China) from 2009 to 2016. Non-cancer risk (assessed by hazard quotient, HQ) of PAHs and HMs was within the acceptable level, while cancer risk (assessed by incremental life cancer risk (ILCR), R) of PAHs and HMs were 1.01 × 10-4 and 9.40 × 10-5 in DP and WP, which showed low risk. HMs dominated cancer (92.12%) and non-cancer (99.99%) risks. An advanced method named as joint source-specific risk assessment of HMs and PAHs (HP-SRA model) was developed to assess comprehensive source-specific risks. Gasoline combustion (contributed 9.6% of PM10, 0.3% of HQ and 10.0% of R), diesel combustion (6.2% of PM10, 0.2% of HQ and 10.7% of R), coal combustion (17.5% of PM10, 1.8% of HQ and 13.4% of R), industrial source (9.1% of PM10, 80.7% of HQ and 35.0% of R), crustal dust (28.1% of PM10, 9.0% of HQ and 1.6% of R), nitrate (7.5% of PM10, 1.1% of HQ and 6.2% of R) and sulphate & secondary organic carbon & adsorption (SSA, 19.6% of PM10, 6.9% of HQ and 23.1% of R) were identified as main sources. For cancer risk, industrial sources and SSA posed the highest proportion. Higher levels of Co and Ni generated from industrial sources and Cr (Ⅵ), Cd and Ni absorbed in the SSA can result in high-risk contributions. Thus, controlling HMs levels in industrial emissions is essential to protecting human health.
Thermal desorption (TD) is a universal solvent-free pre-concentration technique. It is often used to pre-concentrate semi-volatile and volatile organic compounds in various sample types. Polycyclic aromatic hydrocarbons (PAHs) are widespread contaminants from incomplete combustion of organic matter and fossil fuel, which have carcinogenic effects on human health. Conventional methods for determining PAHs, represented by solvent extraction, are gradually being replaced by solvent-free methods, typically the TD technique, because of TD's many advantages, including time savings and environmentally friendly treatment. This work presents an extensive review of the universal methods used to determine PAHs in the atmosphere based on the TD technique. The methods currently used for collection and detection of both gas- and particle-phase PAHs in the air are critically reviewed. In addition, the operating parameters of the TD unit are summarized and discussed. The design shortcomings of existing studies and the problems that researchers should address are presented, and promising alternatives are suggested. This paper also discusses important parameters, such as reproducibility and limit of detection, that form a crucial part of quality assurance. Finally, the limitations and the future prospects of the TD technique for use in airborne PAH analyses are addressed. This is the first review of the latest developments of the TD technique for analysis of PAHs and their derivatives in the atmosphere.
Introduction: There is growing epidemiologic evidence of associations between maternal exposure to ambient air pollution and adverse birth outcomes, such as preterm birth (PTB). Recently, a few studies have also reported that exposure to ambient air pollution may also increase the risk of some common pregnancy complications, such as preeclampsia and gestational diabetes mellitus (GDM). Research findings, however, have been mixed. These inconsistent results could reflect genuine differences in the study populations, the study locations, the specific pollutants considered, the designs of the study, its methods of analysis, or random variation. Dr. Jun Wu of the University of California– Irvine, a recipient of HEI’s Walter A. Rosenblith New Investigator Award, and colleagues have examined the association between air pollution and adverse birth and pregnancy outcomes in California women. In addition, they examined the effect modification by socioeconomic status (SES) and other factors.
Despite increasing evidence that airborne polycyclic aromatic hydrocarbon (PAH) exposures contribute to adverse health outcomes for sensitive populations, limited data are available on short-term intraurban spatial distributions for use in epidemiologic research. Exposure assessments for airborne PAHs are uncommon because air sampling for PAHs is a labor-, equipment-, and time-intensive task. To address this gap, we measured wintertime PAH concentrations during 2010–2011 in Bakersfield, California, USA, a major city in the Southern San Joaquin Valley. Specifically, 58 96-hour integrated PAH samples were collected during four time periods at 14 locations from November 2010 to January 2011; duplicates were collected at two sites. We also collected elemental carbon (EC) at the same 14 sites and analyzed the two time periods with the highest ambient PAH pollution. We used linear regression models to quantify the relationship between potential spatial and temporal predictors of PAH concentrations. We found that wintertime PAH concentrations in Bakersfield, CA, are best predicted by meteorological variables and traffic proximity. Our model explains a moderate amount of the variability in the data (R
2 = 0.58), likely reflecting the major sources of PAHs in Bakersfield. We also observed that PAH concentrations were more spatially variable than EC concentrations. Comparing our data to historical monitoring data at one location in Bakersfield showed that the relatively low PAH concentrations during the 2010–2011 winter in Bakersfield is part of a long-term trend in decreasing PAH concentrations.
Thirteen particle-phase PAHs, including nine >4-ring congeners [Benz[a]anthracene (BaAn), Chrysene (Chry), Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthene (BkF), Benzo[e]pyrene (BeP), Benzo[a]pyrene (BaP), Dibenzo[a,h]anthracene (dBaAn), Benzo[g,h,i]perylene (BghiPe), Indeno(1,2,3-c,d)pyrene (IP)], listed by IARC (International Agency for Research on Cancer) as class 1, class 2A, and 2B carcinogens, plus four ≤4-ring congeners [Phenanthrene (Ph), Anthracene (An), Fluoranthene (Fl), Pyrene (Py)], were concurrently measured in inhalable and respirable particle fractions (PM10 and PM2.5) at a heavy-traffic and an urban background site in Thessaloniki, northern Greece, during the warm and the cold period of the year. Carcinogenic and mutagenic potencies of the PAH-bearing particles were calculated, and the inhalation cancer risk (ICR) for local population was estimated. Finally, Chemical Mass Balance (CMB) modeling was employed for the source apportionment of ambient PAH levels and the estimated lung cancer risk. Resulted inhalation cancer risk during winter was found to be equivalent in the city center and the urban background area suggesting that residential wood burning may offset the benefits from minor traffic emissions.
Unlabelled:
Identifying the sources, composition, and temporal variability of fine (PM2.5) and coarse (PM2.5-10) particles is a crucial component in understanding particulate matter (PM) toxicity and establishing proper PM regulations. In this study, a Harvard Impactor was used to collect daily integrated fine and coarse particle samples every third day for 9 years at a single site in Boston, MA. In total, 1,960 filters were analyzed for elements, black carbon (BC), and total PM mass. Positive Matrix Factorization (PMF) was used to identify source types and quantify their contributions to ambient PM2.5 and PM2.5-10. BC and 17 elements were identified as the main constituents in our samples. Results showed that BC, S, and Pb were associated exclusively with the fine particle mode, while 84% of V and 79% of Ni were associated with this mode. Elements mostly found in the coarse mode, over 80%, included Ca, Mn (road dust), and Cl (sea salt). PMF identified six source types for PM2.5 and three source types for PM2.5-10. Source types for PM2.5 included regional pollution, motor vehicles, sea salt, crustal/road dust, oil combustion, and wood burning. Regional pollution contributed the most, accounting for 48% of total PM2.5 mass, followed by motor vehicles (21%) and wood burning (19%). Source types for PM2.5-10 included crustal/road dust (62%), motor vehicles (22%), and sea salt (16%). A linear decrease in PM concentrations with time was observed for both fine (-5.2%/yr) and coarse (-3.6%/yr) particles. The fine-mode trend was mostly related to oil combustion and regional pollution contributions. Average PM2.5 concentrations peaked in summer (10.4 µg/m3), while PM2.5-10 concentrations were lower and demonstrated little seasonal variability. The findings of this study show that PM2.5 is decreasing more sharply than PM2.5-10 over time. This suggests the increasing importance of PM2.5-10 and traffic-related sources for PM exposure and future policies.
Implications:
Although many studies have examined fine and coarse particle composition and sources, few studies have used concurrent measurements of these two fractions. Our analysis suggests that fine and coarse particles exhibit distinct compositions and sources. With better knowledge of the compositional and source differences between these two PM fractions, better decisions can be made about PM regulations. Further, such information is valuable in enabling epidemiologists to understand the ensuing health implications of PM exposure.
The Polycyclic Aromatic Hydrocarbons (PAHs or polyaromatic hydrocarbons) have been extensively studied to understand their distribution, fate and effects in the environment (Haftka 2009; Laane et al. 1999, 2006, 2013; Okuda et al. 2002; Page et al. 1999; Pavlova and Ivanova 2003; Stout et al. 2001a; Zhang et al. 2005). They are organic compounds consisting of conjoined aromatic rings without heteroatoms (Schwarzenbach et al. 2003). Sander and Wise (1997) list 660 parent PAH compounds (i.e., aromatic substances without alkyl groups and consisting solely of fused rings connected to each other), ranging from the monocyclic molecule of benzene (molecular weight = 78) up to nine-ringed structures (MW up to 478). PAHs containing one or more alkyl groups are called alkyl PAHs. Our study deals with the parent compounds (without alkyl groups and/or heteroatoms), the alkyl PAHs (denoted as PAHn, with n referring to the number of methyl groups; see footnotes in Table 1), and certain heterocyclic sulfur PAHs (dibenzothiophenes). The term PAHs includes all the above, unless explicitly specified. In Table 1, we present the nomenclature of PAHs used in this paper.
Assessment of the health risks resulting from exposure to ambient polycyclic aromatic hydrocarbons (PAH) is limited by a lack of environmental exposure data among the general population. This study characterized personal exposure and ambient concentrations of PAH in the Village of Waterfront South (WFS), an urban community with many mixed sources of air toxics in Camden, New Jersey, and CopeWood/Davis Streets (CDS), an urban reference area located ∼1 mile east of WFS. A total of 54 and 53 participants were recruited from non-smoking households in WFS and CDS, respectively. In all, 24-h personal and ambient air samples were collected simultaneously in both areas on weekdays and weekends during summer and winter. The ambient PAH concentrations in WFS were either significantly higher than or comparable to those in CDS, indicating the significant impact of local sources on PAH pollution in WFS. Analysis of diagnostic ratios and correlation suggested that diesel truck traffic, municipal waste combustion and industrial combustion were the major sources in WFS. In such an area, ambient air pollution contributed significantly to personal PAH exposure, explaining 44-96% of variability in personal concentrations. This study provides valuable data for examining the impact of local ambient PAH pollution on personal exposure and therefore potential health risks associated with environmental PAH pollution.
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 <or= 0.25 microm in aerodynamic diameter (PM0.25) in a panel cohort study of 60 elderly subjects with coronary artery disease living in the Los Angeles Basin.
We reassessed biomarker associations with PM0.25 using new particle composition data.
Weekly biomarkers of inflammation were plasma interleukin-6 (IL-6) and soluble tumor necrosis factor-alpha receptor II (sTNF-RII) (n = 578). Exposures included indoor and outdoor community organic PM0.25 constituents [polycyclic aromatic hydrocarbons (PAHs), hopanes, n-alkanes, organic acids, water-soluble organic carbon, and transition metals]. We analyzed the relation between biomarkers and exposures with mixed-effects models adjusted for potential confounders.
Indoor and outdoor PAHs (low-, medium-, and high-molecular-weight PAHs), followed by hopanes (vehicle emissions tracer), were positively associated with biomarkers, but other organic components and transition metals were not. sTNF-RII increased by 135 pg/mL [95% confidence interval (CI), 45-225 pg/mL], and IL-6 increased by 0.27 pg/mL (95% CI, 0.10-0.44 pg/mL) per interquartile range increase of 0.56 ng/m3 outdoor total PAHs. Two-pollutant models of PM0.25 with PAHs showed that nominal associations of IL-6 and sTNF-RII with PM0.25 mass were completely confounded by PAHs. Vehicular emission sources estimated from chemical mass balance models were strongly correlated with PAHs (R = 0.71).
Traffic emission sources of organic chemicals represented by PAHs are associated with increased systemic inflammation and explain associations with quasi-ultrafine particle mass.
The authors briefly review the current potential and limitations of molecular epidemiology. This approach uses biomarkers to measure the internal and bioeffective dose of toxicants, early biologic effects likely to be predictive of cancer, and variations in individual susceptibility. The most frequent application of biomarkers has been in assessment of exposure/dose and susceptibility due to genetic and nongenetic factors. More research is needed to establish the predictive significance of biomarkers in terms of disease risk. To illustrate that molecular epidemiology has potential in identifying etiologic factors in disease, this article presents data from a recent study of the developmental effects of fetal exposure to polycyclic aromatic hydrocarbons (PAH) via ambient pollution. The study was carried out in an industrialized area of Poland with relatively high levels of PAH pollution from coal burning. PAH-DNA adducts in leukocytes and plasma cotinine were measured in umbilical cord blood as dosimeters of transplacental PAH and cigarette smoke, respectively. The study subjects were 70 newborns from the industrialized city of Krakow and 90 newborns from Limanowa, a rural town with far greater use of coal for home heating. Neworns whose levels of PAH-DNA adducts were above the median (3. 85/108 nucleotides) had a significantly decreased birth length, weight, and head circumference. Cotinine was significantly inversely associated with birth weight and length. Although preliminary, these results provide a new molecular link between PAH exposure and developmental effects, generating initial data and hypotheses for further study. Am J Epidemiol 1998; 147: 309–14.
We examined the effect of proximity to specific mobile, area, and point sources on the residential outdoor concentrations of fine particulate matter PM (PM(2.5)) and several of its particle components. Integrated (48-h) PM(2.5) samples were collected outside non-smoking residences in Elizabeth, NJ, between summer 1999 and spring 2001. Samples were analyzed for PM(2.5) mass, organic and elemental carbon (OC and EC, respectively), trace elements, particle-phase polycyclic aromatic hydrocarbons (p-PAHs), and other important particle species. Information about the proximity of the study homes to potential mobile and area sources of OC, EC, p-PAHs, sulfur (S), and selenium (Se) (including urban interstate highways, local roadways, the Newark International Airport, the Elizabeth seaport, and a nearby refinery in Linden, NJ) were retrieved from a database that included detailed emissions, meteorological, and geographical data for the study area. The dependence of residential outdoor concentrations on source proximity and on various meteorological parameters was then examined for each species by multiple linear regression analysis. As expected, the predicted ambient air concentrations of all particle species (except S, Se) decreased with increasing distance from the sources. Although the enhancement in PM(2.5) and OC levels outside the study homes closest to primary PM sources was modest (e.g., 1.6 and 2.5 times the background levels 37 m from interstate highways), the elevation of EC and p-PAH concentrations was substantial outside the closest study homes (i.e., about 20 times for p-PAHs 37 m from interstate highways and about 14 times for EC 192 m from the refinery in Linden, NJ). The predicted EC concentrations 192 and 500 m from the oil refinery were 22.8 and 3.0 microgC/m(3), compared with an urban background of 1 microgC/m(3). Thus, emissions from this source might dramatically affect EC exposure for residents living in its close proximity.
The concentrations of 21 individual polycyclic aromatic hydrocarbons (PAHs) and total suspended particles (TSPs) were measured using high-volume air samplers at Tzuo-Yin and Hsiung-Kong sites in Kaohsiung City, Taiwan, for four seasons from 2005 to 2006. The gaseous and particulate phases of individual PAHs were identified using a gas chromatograph with a flame-ionization detector (GC/FID). Measurements show that the concentrations of total (gas+particulate) PAHs generally followed the seasonal variations of the concentrations of TSP (107.2-117.1 microg m(-3)), being the highest in winter (143.9-182.9 ng m(-3)) and lowest in summer (81.4-95.2 ng m(-3)) at both sites. Most PAH species were low-weight PAHs (approximately 80.8-82.0%), followed by high-weight PAHs (10.5-14.6%) and medium-weight PAHs (6.5-6.8%). The fractions of gaseous PAHs decreased with molecular weight or ring number. The particle phase (60.2(73.5%) dominated the high-weight PAHs. Results of receptor model show that industrial combustions (49.1-63.7%) contributed most to ambient PAHs, followed by restaurant emissions (18.4-39.7%) and mobile sources (11.3-22.8%) at the Tzuo-Yin site. At the Hsiung-Kong, mobile sources (49.5-63.3%) contributed most to ambient PAHs, followed by restaurant emissions (19.8-36.6%) and industrial combustions (13.7-27.1%). The differences in the results at the two sites are mainly attributed to the different industries at each site.
Prior studies reported an association between ambient air concentrations of total suspended particles and SO2 during pregnancy and adverse pregnancy outcomes. We examined the possible impact of particulate matter up to 10 microm (PM10) and up to 2.5 microm (PM2. 5) in size on intrauterine growth retardation (IUGR) risk in a highly polluted area of Northern Bohemia (Teplice District). The study group includes all singleton full-term births of European origin over a 2-year period in the Teplice District. Information on reproductive history, health, and lifestyle was obtained from maternal questionnaires. The mean concentrations of pollutants for each month of gestation were calculated using continuous monitoring data. Three intervals (low, medium, and high) were constructed for each pollutant (tertiles). Odds ratios (ORs) for IUGR for PM10 and PM2.5 levels were generated using logistic regression for each month of gestation after adjustment for potential confounding factors. Adjusted ORs for IUGR related to ambient PM10 levels in the first gestational month increased along the concentration intervals: medium 1.62 [95% confidence interval (CI), 1.07-2.46], high 2.64 (CI, 1.48-4.71). ORs for PM2.5 were 1.26 (CI, 0.81-1.95) and 2.11 (CI, 1. 20-3.70), respectively. No other associations of IUGR risk with particulate matter were found. Influence of particles or other associated air pollutants on fetal growth in early gestation is one of several possible explanations of these results. Timing of this effect is compatible with a current hypothesis of IUGR pathogenesis. Seasonal factors, one of the other possible explanations, is less probable. More investigation is required to examine these findings and alternative explanations.
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Evidence shows that fetuses and infants are more affected than adults by a variety of environmental toxicants because of differential exposure, physiologic immaturity, and a longer lifetime over which disease initiated in early life can develop. In this article we review data on the effects of in utero exposure to common environmental contaminants, including polycyclic aromatic hydrocarbons (PAH), particulate matter and environmental tobacco smoke (ETS). We then summarize results from our molecular epidemiologic study to assess risks from in utero exposures to ambient air pollution and ETS. This research study, conducted in Poland, used biomarkers to measure the internal and bioeffective dose of toxicants and individual susceptibility factors. The study included 160 mothers and 160 newborns. Ambient air pollution was significantly associated (p= 0.05) with the amount of PAH bound to DNA (PAH-DNA adducts) in both maternal and infant cord white blood cells (WBC). Newborns with elevated PAH-DNA adducts (greater than the median) had significantly decreased birth weight (p= 0.05), birth length (p= 0.02), and head circumference (p= 0.0005) compared to the newborns with lower adducts (n= 135). Maternal and infant cotinine levels were increased by active and passive cigarette smoke exposure of the mother (p= 0.01). An inverse correlation was seen between newborn plasma cotinine (nanograms per milliliter) and birth weight (p= 0.0001) and length (p= 0.003). Adducts were elevated in placental tissue and WBC of newborns who were heterozygous or homozygous for the cytochrome P4501A1 MspI restriction fragment length polymorphism (RFLP) compared to newborns without the RFLP. Levels of PAH-DNA and cotinine were higher in newborns than mothers. These results document that there is significant transplacental transfer of PAH and ETS constituents from mother to fetus; that PAH-DNA adduct levels in maternal and newborn WBC were increased with environmental exposure to PAH from ambient pollution; and that the fetus is more sensitive to genetic damage than the mother. The study also provided the first molecular evidence that transplacental PAH exposure to the fetus is compromising fetal development. If confirmed, these findings could have significant public health implications since a number of studies have found that reduction of head circumference at birth correlates with lower intelligence quotient as well as poorer cognitive functioning and school performance in childhood.
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The relationship between intrauterine growth retardation (IUGR) and exposure to particulate matter ≤ 10 μm (PM10) and particulate matter ≤ 2.5 μm (PM2.5) in early pregnancy was recently studied in the highly polluted district of Teplice (Northern Bohemia). From this observation rose the question about the possible role of the carcinogenic fraction of polycyclic aromatic hydrocarbons (c-PAHs), which are usually bound to fine particles. The impact of c-PAHs and fine particles on IUGR was analyzed in Teplice and in Prachatice, a region with similarly high c-PAH but low particle levels. All European, single live births occurring in a 4-year period in Teplice (n = 3,378) and Prachatice (n = 1,505) were included. Detailed personal data were obtained via questionnaires and medical records. Mean PM10, PM2.5, and c-PAHs levels during the 9 gestational months (GM) were estimated for each mother. Adjusted odds ratios (AORs) of IUGR for three levels of c-PAHs (low, medium, and high) and for continuous data were estimated after adjustment for a range of covariates using logistic regression models. In the present 4-year sample from Teplice, previously published results about increasing IUGR risk after exposure to particles in the first GM were fully confirmed, but no such effects were found in Prachatice. The AOR of IUGR for fetuses from Teplice exposed to medium levels of c-PAHs in the first GM was 1.60 [confidence interval (CI), 1.06-2.15], and to high levels 2.15 (CI, 27-3.63). An exposure-response relationship was established by analyzing the continuous data. For each 10 ng increase of c-PAHs in the first GM, the AOR was 1.22 (CI, 1.07-1.39). About the same relationship was observed in Prachatice in spite of the low particle levels. The results prove that exposure to c-PAHs in early gestation may influence fetal growth. The particulate matter-IUGR association observed earlier may be at least partly explained by the presence of c-PAHs on particle surfaces.
Epidemiologic studies of air pollution require accurate exposure assessments at unmonitored locations in order to minimize exposure misclassification. One approach gaining considerable interest is the land-use regression (LUR) model. Generally, the LUR model has been utilized to characterize air pollution exposure and health effects for individuals residing within urban areas. The objective of this article is to briefly summarize the history and application of LUR models to date outlining similarities and differences of the variables included in the model, model development, and model validation. There were 6 studies available for a total of 12 LUR models. Our findings indicated that among these studies, the four primary classes of variables used were road type, traffic count, elevation, and land cover. Of these four, traffic count was generally the most important. The model R2 explaining the variability in the exposure estimates for these LUR models ranged from .54 to .81. The number of air sampling sites generating the exposure estimates, however, was not correlated with the model R2 suggesting that the locations of the sampling sites may be of greater importance than the total number of sites. The primary conclusion of this study is that LUR models are an important tool for integrating traffic and geographic information to characterize variability in exposures.
Relatively little is known about long term effects of wood smoke on population health. A wood combustion marker - levoglucosan - was measured using a standardized sampling and measurement method in four European study areas (Oslo, The Netherlands, Munich/Augsburg, Catalonia) to assess within and between study area spatial variation. Levoglucosan was analyzed in addition to: PM2.5, PM2.5 absorbance, PM10, polycyclic aromatic hydrocarbons (PAH), nitrogen oxides (NOx), elemental and organic carbon (EC/OC), hopanes, steranes and elemental composition. Measurements were conducted at street, urban and regional background sites. Three two-week samples were taken per site and the annual average concentrations of pollutants were calculated using continuous measurements at one background reference site. Land use regression (LUR) models were developed to explain the spatial variation of levoglucosan. Much larger within than between study area contrast in levoglucosan concentration was found. Spatial variation patterns differed from other measured pollutants: PM2.5, NOx and EC. Levoglucosan had the highest spatial correlation with ΣPAH (r = 0.65) and the lowest with traffic markers — NOx, Σhopanes/steranes (r = − 0.22). Levoglucosan concentrations in the cold (heating) period were between 3 and 20 times higher compared to the warm period. The contribution of wood-smoke calculated based on levoglucosan measurements and previous European emission data to OC and PM2.5 mass was 13 to 28% and 3 to 9% respectively in the full year. Larger contributions were calculated for the cold period.
Land use regression (LUR) models have been used to model concentrations of mainly traffic related air pollutants (nitrogen oxides (NOx), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes and elemental and organic carbon (EC/OC) content of PM2.5. In 10 European study areas PAH, hopanes/steranes and EC/OC concentrations were measured at 16-40 sites per study area. LUR models for each study area were developed based on annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R2) was found for EC - 84%. The median R2 was 51% for OC, 67% for benzo[a]pyrene and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies.
Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modelling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005-2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1km to 10km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables and we compare two available land cover datasets (global; European). Model performance (adjusted R2) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R2) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100m grid across Western Europe; to support future research, these datasets are publicly available.
Air pollution in New Delhi, India is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC) and ultrafine particle number concentrations (UFPN). We used 136 hours (39 sites), 112 hours (26 sites), 147 hours (39 sites) of PM2.5, BC and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200- 1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC and UFPN at LUR sites were 133 (96-232) μg m-3, 11 (6-21) µg m-3 and 40 (27-72) × 103 cm-3 respectively. In addition a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; b) the magnitude and spatial-variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R2 values were higher for morning (for PM2.5, BC and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69 and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; d) spatial patterns were similar for PM2.5 and BC, but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and, (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.
High concentrations of air pollutants on roadways, relative to ambient concentrations, contribute significantly to total personal exposure. Estimation of these exposures requires measurements or prediction of roadway concentrations. Our study develops, compares and evaluates linear regression and non-linear generalized additive models (GAMs) to estimate on-road concentrations of four key air pollutants, particle-bound polycyclic aromatic hydrocarbons (PB-PAH), particle number count (PNC), nitrogen oxides (NOx), and particulate matter with diameter <2.5 µm (PM2.5) using traffic, meteorology, and elevation variables. Critical predictors included wind speed and direction for all the pollutants, traffic-related variables for PB-PAH, PNC, and NOx, and air temperatures and relative humidity for PM2.5. GAMs explained 50%, 55%, 46%, and 71% of the variance for log or square-root transformed concentrations of PB-PAH, PNC, NOx, and PM2.5 respectively, an improvement of 5 to over 15% over the linear models. Accounting for temporal autocorrelation in the GAMs further improved the prediction, explaining 57-89% of the variance. We concluded that traffic and meteorological data are good predictors in estimating on-road traffic-related air pollutant concentrations and GAMs perform better for non-linear variables, such as meteorological parameters.
Land Use Regression (LUR) models have been used to describe/model spatial variability of annual mean concentrations of traffic related pollutants like nitrogen dioxide (NO2), nitrogen oxides (NOx/) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements; copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulphur (S), silicon (Si), vanadium (V) and zinc (Zn) were developed. Good models were developed for Cu, Fe and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting non-tailpipe emissions. Models for V and S in the PM10 and PM2.5) fractions and Si, Ni and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI and K models for PM2.5) performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.
Land-use regression (LUR) models have been developed to estimate spatial distributions of traffic-related pollutants. Several studies have examined spatial autocorrelation among residuals in LUR models, but few utilized spatial residual information in model prediction, or examined the impact of modeling methods, monitoring site selection, or traffic data quality on LUR performance. This study aims to improve spatial models for traffic-related pollutants using generalized additive models (GAM) combined with cokriging of spatial residuals. Specifically, we developed spatial models for nitrogen dioxide (NO(2)) and nitrogen oxides (NO(x)) concentrations in Southern California separately for two seasons (summer and winter) based on over 240 sampling locations. Pollutant concentrations were disaggregated into three components: local means, spatial residuals, and normal random residuals. Local means were modeled by GAM. Spatial residuals were cokriged with global residuals at nearby sampling locations that were spatially auto-correlated. We compared this two-stage approach with four commonly-used spatial models: universal kriging, multiple linear LUR and GAM with and without a spatial smoothing term. Leave-one-out cross validation was conducted for model validation and comparison purposes. The results show that our GAM plus cokriging models predicted summer and winter NO(2) and NO(x) concentration surfaces well, with cross validation R(2) values ranging from 0.88 to 0.92. While local covariates accounted for partial variance of the measured NO(2) and NO(x) concentrations, spatial autocorrelation accounted for about 20% of the variance. Our spatial GAM model improved R(2) considerably compared to the other four approaches. Conclusively, our two-stage model captured summer and winter differences in NO(2) and NO(x) spatial distributions in Southern California well. When sampling location selection cannot be optimized for the intended model and fewer covariates are available as predictors for the model, the two-stage model is more robust compared to multiple linear regression models.
A previous study (Ahmed, A. et al., Anal. Chem. 84, 1146-1151( 2012) reported that toluene used as a solvent was the proton source for polyaromatic hydrocarbon compounds (PAHs) that were subjected to (+)-mode atmospheric-pressure photoionization. In the current study, the exact position of the hydrogen atom in the toluene molecule (either a methyl hydrogen or an aromatic ring hydrogen) involved in the formation of protonated PAH ions was investigated. Experimental analyses of benzene and anisole demonstrated that although the aromatic hydrogen atom of toluene did not contribute to the formation of protonated anthracene, it did contribute to the formation of protonated acridine. Thermochemical data and quantum mechanical calculations showed that the protonation of anthracene by an aromatic ring hydrogen atom of toluene is endothermic, while protonation by a methyl hydrogen atom is exothermic. However, protonation of acridine by either an aromatic ring hydrogen or a methyl hydrogen atom of toluene is exothermic. The different behavior of acridine and anthracene was attributed to differences in gas-phase basicity. It was concluded that both types of hydrogen in toluene can be used for protonation of PAH compounds, but a methyl hydrogen atom is preferred, especially for non-basic compounds.
We have observed a wide area of air pollutant impact downwind of a freeway during pre-sunrise hours in both winter and summer seasons. In contrast, previous studies have shown much sharper air pollutant gradients downwind of freeways, with levels above background concentrations extending only 300 m downwind of roadways during the day and up to 500 m at night. In this study, real-time air pollutant concentrations were measured along a 3600 m transect normal to an elevated freeway 1–2 h before sunrise using an electric vehicle mobile platform equipped with fast-response instruments. In winter pre-sunrise hours, the peak ultrafine particle (UFP) concentration (∼95 000 cm−3) occurred immediately downwind of the freeway. However, downwind UFP concentrations as high as ∼40 000 cm−3 extended at least 1200 m from the freeway, and did not reach background levels (∼15 000 cm−3) until a distance of about 2600 m. UFP concentrations were also elevated over background levels up to 600 m upwind of the freeway. Other pollutants, such as NO and particle-bound polycyclic aromatic hydrocarbons, exhibited similar long-distance downwind concentration gradients. In contrast, air pollutant concentrations measured on the same route after sunrise, in the morning and afternoon, exhibited the typical daytime downwind decrease to background levels within ∼300 m as found in earlier studies. Although pre-sunrise traffic volumes on the freeway were much lower than daytime congestion peaks, downwind UFP concentrations were significantly higher during pre-sunrise hours than during the daytime. UFP and NO concentrations were also strongly correlated with traffic counts on the freeway. We associate these elevated pre-sunrise concentrations over a wide area with a nocturnal surface temperature inversion, low wind speeds, and high relative humidity. Observation of such wide air pollutant impact area downwind of a major roadway prior to sunrise has important exposure assessment implications since it demonstrates extensive roadway impacts on residential areas during pre-sunrise hours, when most people are at home.
BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.
Exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with cardiopulmonary mortality and cardiovascular events. This study investigated the association between a biological marker of PAH exposure, assessed by urinary 1-hydroxypyrene (1-OHP), and heart-rate variability in an occupational cohort of boilermakers.
Continuous 24 h monitoring of the ambulatory electrocardiogram (ECG) and pre- and postshift urinary 1-OHP were repeated over extended periods of the work week. Mixed-effects models were fitted for the 5 min SD of normal-to-normal intervals (SDNN) in relation to urinary 1-OHP levels pre- and postworkshift on the day they wore the monitor, controlling for potential confounders.
The authors found a significant decrease in 5 min SDNN during work of -13.6% (95% CI -17.2% to -9.8%) per SD (0.53 μg/g creatinine) increase in the next-morning preshift 1-OHP levels. The magnitude of reduction in 5 min SDNN was largest during the late night period after work and increased with each SD (0.46 μg/g creatinine) increase in postshift 1-OHP levels.
This is the first report providing evidence that occupational exposure to PAHs is associated with altered cardiac autonomic function. Acute exposure to PAHs may be an important predictor of cardiovascular disease risk in the work environment.
Polycyclic aromatic hydrocarbons (PAHs) are formed during incomplete burning of fossil fuels, wood, and tobacco products. High PAH exposure has been associated with low birth weight, intrauterine growth restriction, and preterm birth, but little is known about its impact on adverse outcomes in early pregnancy such as in-utero fetal death.
To examine associations between exposure to PAHs and missed abortion in which the embryo has died but a miscarriage has not yet occurred during early pregnancy in a Chinese population in Tianjin.
A case-control study was conducted from April to November, 2007 in Tianjin, China. Cases experienced a missed abortion while controls underwent elective abortions before 14weeks of pregnancy. Eighty-one cases were recruited from four hospitals, with the same number of controls matched on hospital, maternal age (+/-8years), gravidity (1 or >1), and gestational age (+/-30days). Two maternal measures of PAH exposures were obtained based on benzo[a]pyrene (BaP) DNA adducts in 1) aborted tissues and 2) maternal blood (for a subset of subjects). In addition, proxy measures for PAH exposures from different sources were derived from maternal interviews.
In conditional logistic regression analyses, we estimated more than 4-fold increase in risk of having experienced a missed abortion in women with above the median levels of blood BaP-DNA adducts (adjusted OR=4.27; 95% CI, 1.41-12.99); but no increase with adduct levels in aborted tissues (adjusted OR=0.76; 95% CI, 0.37-1.54). BaP-DNA adduct levels in maternal blood and aborted tissues were poorly correlated (r=-0.12; n=102). Missed abortion risk also was higher among women reporting traffic congestion near the residence, commuting by walking, and performing regular cooking activities during pregnancy.
High levels of maternal PAH exposures may contribute to an increased risk of experiencing a missed abortion during early pregnancy.
A text detailing critical aspects of geographic information systems for land resources assessment. Nine chapters cover themes on: 1. Overview of GIS components - computer mapping, data bases, future directions. 2. Data structures for thematic maps, including files, spatial data definitions, vector and raster structures, database facilities. 3. Digital elevation models. 4. Data input, verification, storage and output. 5. Methods of spatial data analysis and modelling, ranging from basic map overlay, to natural language processing. 6. Data quality, errors, and the nature of spatial data on maps. 7. Classification techniques - multivariate, and using expert systems. 8. Spatial interpolation methods. 9. Selecting a GIS. Each chapter has a list of references, and two appendices give a glossary of terms, and a list of selected information sources.-after Author
There are large geographical variations of cancer mortality rates in the United States. In a series of ecological studies in the U.S., a number of risk-modifying factors including alcohol, diet, ethnic background, poverty, smoking, solar ultraviolet-B (UVB), and urban/rural residence have been linked to many types of cancer. Air pollution also plays a role in cancer risk.
Cancer mortality rates averaged by state for two periods, 1950-1969 and 1970-1994, were used in multiple-linear regression analyses with respect to many of the risk-modifying factors mentioned with the addition of an air pollution index in the form of a map of acid deposition in 1985. This index is correlated with emissions from coal-fired power plants. In addition, lung cancer mortality rates for five-year periods from 1970-74 to 1990-94 were used in multiple linear regression analyses including air pollution and cigarette smoking.
The air pollution index correlated with respiratory, digestive tract, urogenital, female, blood and skin cancer. Air pollution was estimated to account for 5% of male cancer deaths and 3% of female cancer deaths between 1970-1994. Solar UVB was inversely correlated with all these types of cancer except the respiratory, skin and cervical cancer. Cigarette smoking was directly linked to lung cancer but not to other types of cancer in this study.
Combustion of coal, diesel fuel and wood is the likely source of air pollution that affects cancer risk on a large scale, through production of black carbon aerosols with adsorbed polycyclic aromatic hydrocarbons.
The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
A sensitive, simple, and cost-effective passive sampling methodology was developed to quantify personal exposure to gaseous polycyclic aromatic hydrocarbons (PAHs). A Fan-Lioy passive PAH sampler (FL-PPS) is constructed from 320 sections of 1-cm long SPB-5 GC columns (0.75-mm i.d. and 7-microm film thickness), similar to a mini-honeycomb denuder. Given the unique feature of the GC column stationary phase, gaseous PAHs are collected on the inner surfaces of the columns by molecular diffusion and thermally desorbed to GC/MS for analysis. The sampling rates of FL-PPS were determined in the laboratory using a controlled test atmosphere containing eight PAHs for a range of face velocity, temperature, relative humidity, PAH concentration, and sampling duration. The sampling rate (mean, %RSD, cm3/min) was 26.7 (21%) for acenaphthylene, 37.6 (25%) for acenaphthene, 56.2 (13%) for fluorene, 49.1 (25%) for phenanthrene, 62.7 (22%) for anthracene, 65.4 (24%) for fluoranthene, and 64.4 (18%) for pyrene over a sampling duration of 8-48 h. The sampling rate for naphthalene was approximately 14.1 (12%) cm3/min over a sampling period of 8 h but decreased along with an increase of sampling time. The effects of temperature, humidity, face velocity, and PAH concentration on the sampling rate were not significant for all the compounds tested. A reasonable agreement (<30%) was obtained for most compounds measured by FL-PPS and a conventional active PAH sampling method colocated side-by-side in the field, but a sampling time of 24 h or longer was required for detection of less abundant PAHs in community settings.
Combustion emissions account for over half of the fine particle (PM2.5) air pollution and most of the primary particulate organic matter. Human exposure to combustion emissions including the associated airborne fine particles and mutagenic and carcinogenic constituents (e.g., polycyclic aromatic compounds (PAC), nitro-PAC) have been studied in populations in Europe, America, Asia, and increasingly in third-world counties. Bioassay-directed fractionation studies of particulate organic air pollution have identified mutagenic and carcinogenic polycyclic aromatic hydrocarbons (PAH), nitrated PAH, nitro-lactones, and lower molecular weight compounds from cooking. A number of these components are significant sources of human exposure to mutagenic and carcinogenic chemicals that may also cause oxidative and DNA damage that can lead to reproductive and cardiovascular effects. Chemical and physical tracers have been used to apportion outdoor and indoor and personal exposures to airborne particles between various combustion emissions and other sources. These sources include vehicles (e.g., diesel and gasoline vehicles), heating and power sources (e.g., including coal, oil, and biomass), indoor sources (e.g., cooking, heating, and tobacco smoke), as well as secondary organic aerosols and pollutants derived from long-range transport.
Land use regression (LUR) has been successfully used to assess the intraurban variability of air pollution. In the INMA (Environment and Childhood) Study, ambient nitrogen oxides (NO(x) and NO(2)) and aromatic hydrocarbons (BTEX) were measured at 57 sampling sites in Sabadell (northeast Spain). Multiple regression models were developed to predict residential outdoor concentrations in a cohortof pregnantwomen (n = 657), using geographic data as predictor variables. The models accounted for 68 and 69% of the variance in NO(x) and NO(2) levels, respectively, with four predictor variables (altitude, land coverage, and two road length indicators). These percentages of explained variability could be further improved by replacing the two road length indicators with an ordinal indicator (road type). To our knowledge, this is the first study using LUR to assess the intraurban variability of BTEX in Europe, with a model including altitude and source-proximity variables that explained 74% of the variance in BTEX levels. These models will be used to study the association between prenatal exposure to air pollution and adverse pregnancy outcomes and early childhhod effects in the cohort.