Carbonaceous aerosol sampling of gasoline direct injection engine exhaust with an integrated organic gas and particle sampler.

Positive and negative artifacts of particle-phase organic carbon (p-OC) and the polycyclic aromatic hydrocarbons (PAHs) in gasoline direct injection (GDI) engine exhaust particulate matter (PM) were assessed using an integrated organic gas and particle sampler (IOGAPS). Three configurations (denuder + sorbent impregnated filters (SIFs), upstream Zefluor filter + denuder + SIFs, and standard filter pack + SIFs) were used to collect GDI exhaust samples at cold start and highway cruise operating conditions with no aftertreatment. Approximately 35% of the measured GDI p-OC was attributed to positive artifacts; negative artifacts were not detectable due to low overall SVOC concentrations. GDI engine exhaust PAH concentrations were approximately 10 times higher during cold start than highway cruise. At highway cruise, pyrene and fluoranthene were the dominant PAHs in the undenuded filter pack; downstream of the denuder benzo(a)anthracene was the dominant PAH. From a comparison of our findings to published PAH emission factors we estimate that three-way catalyst conversion efficiencies of PAHs were approximately 80% for 3 of the 15 PAHs measured during highway cruise operation. These conversion efficiencies may be considerably lower during cold start operation when the three-way catalyst has not reached its operating temperature. Our previous work showed that adverse biological responses to GDI engine exhaust exposure may be dominated by the particle phase when measured downstream of a Teflon filter. Understanding the partitioning characteristics of PAHs may help elucidate specific PAHs contributing to this effect.

Near-road modeling and measurement of cerium-containing particles generated by nanoparticle diesel fuel additive use.

Cerium oxide nanoparticles (nCe) are used as a fuel-borne catalyst in diesel engines to reduce particulate emissions, yet the environmental and human health impacts of the exhaust particles are not well understood. To bridge the gap between emission measurements and ambient impacts, size-resolved measurements of particle composition and mass concentration have been performed in Newcastle-upon-Tyne, United Kingdom, where buses have used an nCe additive since 2005. These observations show that the noncrustal cerium fraction thought to be associated with the use of nCe has a mass concentration ∼ 0.3 ng m(-3) with a size distribution peaking at 100-320 nm in aerodynamic diameter. Simulations with a near-roadway multicomponent sectional aerosol dynamic model predict that the use of nCe additives increases the number concentration of nuclei mode particles (<50 nm in diameter) while decreasing the total mass concentration. The near-road model predicts a downwind mass size distribution of cerium-containing particles peaking at 150 nm in aerodynamic diameter, a value similar to that measured for noncrustal cerium in Newcastle. This work shows that both the emission and atmospheric transformation of cerium-containing particles needs to be taken into account by regional modelers, exposure scientists, and policymakers when determining potential environmental and human health impacts.

Ultrafine particle concentrations in the surroundings of an urban area: comparing downwind to upwind conditions using Generalized Additive Models (GAMs).

The aim of this study was to investigate the influence of an urban area on ultrafine particle (UFP) concentration in nearby surrounding areas. We assessed how downwind and upwind conditions affect the UFP concentration at a site placed a few kilometres from the city border. Secondarily, we investigated the relationship among other meteorological factors, temporal variables and UFP. Data were collected for 44 days during 2008 and 2009 at a rural site placed about 3 kilometres from Bologna, in northern Italy. Measurements were performed using a spectrometer (FMPS TSI 3091). The average UFP number concentration was 11 776 (±7836) particles per cm(3). We analysed the effect of wind direction in a multivariate Generalized Additive Model (GAM) adjusted for the principal meteorological parameters and temporal trends. An increase of about 25% in UFP levels was observed when the site was downwind of the urban area, compared with the levels observed when wind blew from rural areas. The size distribution of particles was also affected by the wind direction, showing higher concentration of small size particles when the wind blew from the urban area. The GAM showed a good fit to the data (R(2) = 0.81). Model choice was via Akaike Information Criteria (AIC). The analysis also revealed that an approach based on meteorological data plus temporal trends improved the goodness of the fit of the model. In addition, the findings contribute to evidence on effects of exposure to ultrafine particles on a population living in city surroundings.

Comparative modeling approaches for personal exposure to particle-associated PAH.

Several models for simulation of personal exposure (PE) to particle-associated polycyclic aromatic hydrocarbons (PAH) have been developed and tested. The modeling approaches include linear regression models (Model 1), time activity weighted models (Models 2 and 3), a hybrid model (Model 4), a univariate linear model (Model 5), and machine learning technique models (Model 6 and 7). The hybrid model (Model 4), which utilizes microenvironment data derived from time-activity diaries (TAD) with the implementation of add-on variables to account for external factors that might affect PE, proved to be the best regression model (R(2) for B(a)P = 0.346, p < 0.01; N = 68). This model was compared with results from two machine learning techniques, namely decision trees (Model 6) and neural networks (Model 7), which represent an innovative approach to PE modeling. The neural network model was promising in giving higher correlation coefficient results for all PAH (R(2) for B(a)P = 0.567, p < 0.01; N = 68) and good performance with the smaller test data set (R(2) for B(a)P = 0.640, p < 0.01; N = 23). Decision tree accuracies (Model 6) which assess how precisely the algorithm can determine the correct classification of a PE concentration range indicate good performance, but this is not comparable to the other models through R(2) values. Using neural networks (Model 7) showed significant improvements over the performance of hybrid Model 4 and the univariate general linear Model 5 for test samples (not used in developing the models). The worst performance was given by linear regression Models 1 to 3 based solely on home and workplace concentrations and time-activity data.

Environmental and biological monitoring of exposures to PAHs and ETS in the general population.

The objective of this study was to analyse environmental tobacco smoke (ETS) and PAH metabolites in urine samples of non-occupationally exposed non-smoker adult subjects and to establish relationships between airborne exposures and urinary concentrations in order to (a) assess the suitability of the studied metabolites as biomarkers of PAH and ETS, (b) study the use of 3-ethenypyridine as ETS tracer and (c) link ETS scenarios with exposures to carcinogenic PAH and VOC. Urine samples from 100 subjects were collected and concentrations of monophenolic metabolites of naphthalene, fluorene, phenanthrene, and pyrene and the nicotine metabolites cotinine and trans-3'-hydroxycotinine were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess PAH and ETS exposures. Airborne exposures were measured using personal exposure samplers and analysed using GC-MS. These included 1,3-butadiene (BUT), 3-ethenylpyridine (3-EP) (a tobacco-specific tracer derived from nicotine pyrolysis) and PAHs. ETS was reported by the subjects in 30-min time-activity questionnaires and specific comments were collected in an ETS questionnaire each time ETS exposure occurred. The values of 3-EP (>0.25 microg/m(3) for ETS) were used to confirm the ETS exposure status of the subject. Concentrations as geometric mean, GM, and standard deviation (GSD) of personal exposures were 0.16 (5.50)microg/m(3) for 3-EP, 0.22 (4.28)microg/m(3) for BUT and 0.09 (3.03)ng/m(3) for benzo(a)pyrene. Concentrations of urinary metabolites were 0.44 (1.70)ng/mL for 1-hydroxypyrene and 0.88 (5.28)ng/mL for cotinine. Concentrations of urinary metabolites of nicotine were lower than in most previous studies, suggesting very low exposures in the ETS-exposed group. Nonetheless, concentrations were higher in the ETS population for cotinine, trans-3'hydroxycotinine, 3-EP, BUT and most high molecular weight PAH, whilst 2-hydroxyphenanthrene, 3+4-hydroxyphenanthrene and 1-hydroxyphenanthrene were only higher in the high-ETS subpopulation. There were not many significant correlations between either personal exposures to PAH and their urinary metabolites, or of the latter with ETS markers. However, it was found that the urinary log cotinine concentration showed significant correlation with log concentrations of 3-EP (R=0.75), BUT (R=0.47), and high molecular weight PAHs (MW>200), especially chrysene (R=0.55) at the p=0.01 level. On the other hand, low correlation was observed between the PAH metabolite 2-naphthol and the parent PAH, gas-phase naphthalene. These results suggest that (1) ETS is a significant source of inhalation exposure to the carcinogen 1,3-butadiene and high molecular weight PAHs, many of which are carcinogenic, and (2) that for lower molecular weight PAHs such as naphthalene, exposure by routes other than inhalation predominate, since metabolite levels correlated poorly with personal exposure air sampling.

Model development and validation of personal exposure to volatile organic compound concentrations.

BACKGROUND: Direct measurement of exposure to volatile organic compounds (VOCs) via personal monitoring is the most accurate exposure assessment method available. However, its wide-scale application to evaluating exposures at the population level is prohibitive in terms of both cost and time. Consequently, indirect measurements via a combination of microenvironment concentrations and personal activity diaries represent a potentially useful alternative. OBJECTIVE: The aim of this study was to optimize a model of personal exposures (PEs) based on microenvironment concentrations and time/activity diaries and to compare modeled with measured exposures in an independent data set. MATERIALS: VOC PEs and a range of microenvironment concentrations were collected with active samplers and sorbent tubes. Data were supplemented with information collected through questionnaires. Seven models were tested to predict PE to VOCs in 75% (n = 370) of the measured PE data set, whereas the other 25% (n = 120) was used for validation purposes. RESULTS: The best model able to predict PE with independence of measurements was based upon stratified microenvironment concentrations, lifestyle factors, and individual-level activities. The proposed model accounts for 40-85% of the variance for individual VOCs and was validated for almost all VOCs, showing normalized mean bias and mean fractional bias below 25% and predicting 60% of the values within a factor of 2. CONCLUSIONS: The models proposed identify the most important non-weather-related variables for VOC exposures; highlight the effect of personal activities, use of solvents, and exposure to environmental tobacco smoke on PE levels; and may assist in the development of specific models for other locations.