Real-world particulate matter and gaseous emissions from motor vehicles in a highway tunnel.

Gertler AW ，Gillies JA ，Pierson WR ，Rogers CF ，Sagebiel JC ，Abu-Allaban M ，Coulombe W ，Tarnay L ，Cahill TA ... -  《-》

Airborne carbonyls from motor vehicle emissions in two highway tunnels.

Grosjean D ，Grosjean E 《-》

Real-World Vehicle Emissions Characterization for the Shing Mun Tunnel in Hong Kong and Fort McHenry Tunnel in the United States.

Motor vehicle exhaust is an important source of air pollutants and greenhouse gases. Concerns over the health and climate effects of mobile-source emissions have prompted worldwide efforts to reduce vehicle emissions. Implementation of more stringent emission standards have driven advances in vehicle, engine, and exhaust after-treatment technologies as well as fuel formulations. On the other hand, vehicle numbers and travel distances have been increasing because of population and economic growth and changes in land use. These factors have resulted in changes to the amount and chemical composition of vehicle emissions. Roadway tunnel studies are a practical way to characterize real-world emissions from the on-road vehicle fleet in an environment isolated from other combustion pollution sources. Measurements in the same tunnel over time allow evaluation of vehicle emission changes and the effectiveness of emission reduction measures. Tunnel studies estimate the impacts of vehicle emissions on air quality and traffic-related exposures, generate source profile inputs for receptor-oriented source apportionment models, provide data to evaluate emission models, and serve as a baseline for future comparisons. The present study characterized motor vehicle emission factors and compositions in two roadway tunnels that were first studied over a decade ago. The specific aims were to (1) quantify current fleet air pollutant emission factors, (2) evaluate emission change over time, (3) establish source profiles for volatile organic compounds (VOCs) and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5), (4) estimate contributions of fleet components and non-tailpipe emissions to VOCs and PM2.5, and (5) evaluate the performance of the latest versions of mobile-source emission models (i.e., the EMission FACtors vehicle emission model used in Hong Kong [EMFAC-HK] and the MOtor Vehicle Emission Simulator used in the United States [MOVES]). Measurements were conducted in the Shing Mun Tunnel (SMT) in Hong Kong and the Fort McHenry Tunnel (FMT) in Baltimore, Maryland, in the United States, representing the different fleet compositions, emission controls, fuels, and near-road exposure levels found in Hong Kong and the United States. These tunnels have extensive databases acquired in 2003-2004 for the SMT and 1992 for the FMT. The SMT sampling was conducted during the period from 1/19/2015 to 3/31/2015, and the FMT sampling occurred during the periods from 2/8/2015 to 2/15/2015 (winter) and 7/31/2015 to 8/7/2015 (summer). Concentrations of criteria pollutants (e.g., carbon monoxide [CO], nitrogen oxides [NOx], and particulate matter [PM]) were measured in real time, and integrated samples of VOCs, carbonyls, polycyclic aromatic hydrocarbons (PAHs), and PM2.5 were collected in canisters and sampling media for off-line analyses. Emission factors were calculated from the tunnel measurements and compared with previous studies to evaluate emission changes over time. Emission contributions by different vehicle types were assessed by source apportionment modeling or linear regression. Vehicle emissions were modeled by EMFAC-HK version 3.3 and MOVES version 2014a for the SMT and the FMT, respectively, and compared with measured values. The influences of vehicle fleet composition and environmental parameters (i.e., temperature and relative humidity) on emissions were evaluated. In the SMT, emissions of PM2.5, sulfur dioxide (SO2), and total non-methane hydrocarbons (NMHCs) markedly decreased from 2003-2004 to 2015: SO2 and PM2.5 were reduced by ~80%, and total NMHCs was reduced by ~44%. Emission factors of ethene and propene, key tracers for diesel vehicle (DV) emissions, decreased by ~65%. These reductions demonstrate the effectiveness of control measures, such as the implementation of low-sulfur fuel regulations and the phasing out of older DVs. However, the emission factors of isobutane and n-butane, markers for liquefied petroleum gas (LPG), increased by 32% and 17% between 2003-2004 and 2015, respectively, because the number of LPG vehicles increased. Nitrogen dioxide (NO2) to NOx volume ratios increased between 2003-2004 and 2015, indicating an increased NO2 fraction in primary exhaust emissions. Although geological mineral concentrations were similar between the 2003-2004 and 2015 studies, the contribution of geological materials to PM2.5 increased from 2% in 2003-2004 to 5% in 2015, signifying the continuing importance of non-tailpipe PM emissions as tailpipe emissions decrease. Emissions of CO, ammonia (NH3), nitric oxide (NO), NO2, and NOx, as well as carbonyls and PAHs in the SMT did not show statistically significant (at P < 0.05 based on Student's t-test) decreases from 2003-2004 to 2015. The reason for this is not clear and requires further investigation. A steady decrease in emissions of all measured pollutants during the past 23 years has been observed from tunnel studies in the United States, reflecting the effect of emission standards and new technologies that were introduced during this period. Emission reductions were more pronounced for the light-duty (LD) fleet than for the heavy-duty (HD) fleet. In comparison with the 1992 FMT study, the 2015 FMT study demonstrated marked reductions in LD emissions for all pollutants: emission factors for naphthalene were reduced the most, by 98%; benzene, toluene, ethylbenzene, and xylene (BTEX), by 94%; CO, NMHCs, and NOx, by 87%; and aldehydes by about 71%. Smaller reductions were observed for HD emission factors: naphthalene emissions were reduced by 95%, carbonyl emissions decreased by about 75%, BTEX by 60%, and NOx 58%. The 2015 fleet-average emission factors were higher in the SMT for CO, NOx, and summer PM2.5 than those in the FMT. The higher CO emissions in the SMT were possibly attributable to a larger fraction of motorcycles and LPG vehicles in the Hong Kong fleet. DVs in Hong Kong and the United States had similar emission factors for NOx. However, the non-diesel vehicles (NDVs), particularly LPG vehicles, had higher emission factors than those of gasoline cars, contributing to higher NOx emissions in the SMT. The higher PM2.5 emission factors in the SMT were probably attributable to there being more double-deck buses in Hong Kong. In both tunnels, PAHs were predominantly in the gas phase, with larger (four and more aromatic rings) PAHs mostly in the particulate phase. Formaldehyde, acetaldehyde, crotonaldehyde, and acetone were the most abundant carbonyl compounds in the SMT. In the FMT, the most abundant carbonyls were formaldehyde, acetone, acetaldehyde, and propionaldehyde. HD vehicles emitted about threefold more carbonyl compounds than LD vehicles did. In the SMT, the NMHC species were enriched with marker species for LPG (e.g., n-butane, isobutane, and propane) and gasoline fuel vapor (e.g., toluene, isopentane, and m/p-xylene), indicating evaporative losses. Source contributions to SMT PM2.5 mass were diesel exhaust (51.5 ± 1.8%), gasoline exhaust (10.0 ± 0.8%), LPG exhaust (5.0 ± 0.5%), secondary sulfate (19.9 ± 1.0%), secondary nitrate (6.3 ± 0.9%), and road dust (7.3 ± 1.3%). In the FMT, total NMHC emissions were 14% and 8% higher in winter than in summer for LD and HD vehicles, respectively. Elemental carbon (EC) and organic carbon (OC) were the major constituents of tunnel PM2.5. De-icing salt contributions to PM2.5 were observed in the FMT in winter. Emission estimates by the EMFAC-HK agreed with SMT measurements for CO2; the modeled emission factors for CO, NOx, and NMHCs were 1.5, 1.6, and 2.2 times the measurements, respectively; and the modeled emission factor for PM2.5 was 61% of the measured value in 2003. The EMFAC-HK estimates and SMT measurements for 2015 differed by less than 35%. The MOVES2014a model generally overestimated emissions of most of the pollutants measured in the FMT. No pollutants were significantly underestimated. The largest overestimation was observed for emissions measured during HD-rich driving conditions in winter. Significant reductions in SO2 and PM2.5 emissions between 2003 and 2015 were observed in the SMT, indicating the effectiveness of control measures on these two pollutants. The total NMHC emissions in the SMT were reduced by 44%, although isobutane and n-butane emissions increased because of the increase in the size of the LPG fleet. No significant reductions were observed for CO and NOx, results that differed from those for roadside ambient concentrations, emission inventory estimates, and EMFAC-HK estimates. In contrast, there was a steady decrease in emissions of most pollutants in the tunnels in the United States.

Wang X ，Khlystov A ，Ho KF ，Campbell D ，Chow JC ，Kohl SD ，Watson JG ，Lee SF ，Chen LA ，Lu M ，Ho SSH ... -  《-》

The London low emission zone baseline study.

On February 4, 2008, the world's largest low emission zone (LEZ) was established. At 2644 km2, the zone encompasses most of Greater London. It restricts the entry of the oldest and most polluting diesel vehicles, including heavy-goods vehicles (haulage trucks), buses and coaches, larger vans, and minibuses. It does not apply to cars or motorcycles. The LEZ scheme will introduce increasingly stringent Euro emissions standards over time. The creation of this zone presented a unique opportunity to estimate the effects of a stepwise reduction in vehicle emissions on air quality and health. Before undertaking such an investigation, robust baseline data were gathered on air quality and the oxidative activity and metal content of particulate matter (PM) from air pollution monitors located in Greater London. In addition, methods were developed for using databases of electronic primary-care records in order to evaluate the zone's health effects. Our study began in 2007, using information about the planned restrictions in an agreed-upon LEZ scenario and year-on-year changes in the vehicle fleet in models to predict air pollution concentrations in London for the years 2005, 2008, and 2010. Based on this detailed emissions and air pollution modeling, the areas in London were then identified that were expected to show the greatest changes in air pollution concentrations and population exposures after the implementation of the LEZ. Using these predictions, the best placement of a pollution monitoring network was determined and the feasibility of evaluating the health effects using electronic primary-care records was assessed. To measure baseline pollutant concentrations before the implementation of the LEZ, a comprehensive monitoring network was established close to major roadways and intersections. Output-difference plots from statistical modeling for 2010 indicated seven key areas likely to experience the greatest change in concentrations of nitrogen dioxide (NO2) (at least 3 microg/m3) and of PM with an aerodynamic diameter < or = 10 microm (PM10) (at least 0.75 microg/m3) as a result of the LEZ; these suggested that the clearest signals of change were most likely to be measured near roadsides. The seven key areas were also likely to be of importance in carrying out a study to assess the health outcomes of an air quality intervention like the LEZ. Of the seven key areas, two already had monitoring sites with a full complement of equipment, four had monitoring sites that required upgrades of existing equipment, and one required a completely new installation. With the upgrades and new installations in place, fully ratified (verified) pollutant data (for PM10, PM with an aerodynamic diameter < or = 2.5 microm [PM2.5], nitrogen oxides [NOx], and ozone [O3] at all sites as well as for particle number, black smoke [BS], carbon monoxide [CO], and sulfur dioxide [SO2] at selected sites) were then collected for analysis. In addition, the seven key monitoring sites were supported by other sites in the London Air Quality Network (LAQN). From these, a robust set of baseline air quality data was produced. Data from automatic and manual traffic counters as well as automatic license-plate recognition cameras were used to compile detailed vehicle profiles. This enabled us to establish more precise associations between ambient pollutant concentrations and vehicle emissions. An additional goal of the study was to collect baseline PM data in order to test the hypothesis that changes in traffic densities and vehicle mixes caused by the LEZ would affect the oxidative potential and metal content of ambient PM10 and PM2.5. The resulting baseline PM data set was the first to describe, in detail, the oxidative potential and metal content of the PM10 and PM2.5 of a major city's airshed. PM in London has considerable oxidative potential; clear differences in this measure were found from site to site, with evidence that the oxidative potential of both PM10 and PM2.5 at roadside monitoring sites was higher than at urban background locations. In the PM10 samples this increased oxidative activity appeared to be associated with increased concentrations of copper (Cu), barium (Ba), and bathophenanthroline disulfonate-mobilized iron (BPS Fe) in the roadside samples. In the PM2.5 samples, no simple association could be seen, suggesting that other unmeasured components were driving the increased oxidative potential in this fraction of the roadside samples. These data suggest that two components were contributing to the oxidative potential of roadside PM, namely Cu and BPS Fe in the coarse fraction of PM (PM with an aerodynamic diameter of 2.5 microm to 10 microm; PM(2.5-10)) and an unidentified redox catalyst in PM2.5. The data derived for this baseline study confirmed key observations from a more limited spatial mapping exercise published in our earlier HEI report on the introduction of the London's Congestion Charging Scheme (CCS) in 2003 (Kelly et al. 2011a,b). In addition, the data set in the current report provided robust baseline information on the oxidative potential and metal content of PM found in the London airshed in the period before implementation of the LEZ; the finding that a proportion of the oxidative potential appears in the PM coarse mode and is apparently related to brake wear raises important issues regarding the nature of traffic management schemes. The final goal of this baseline study was to establish the feasibility, in ethical and operational terms, of using the U.K.'s electronic primary-care records to evaluate the effects of the LEZ on human health outcomes. Data on consultations and prescriptions were compiled from a pilot group of general practices (13 distributed across London, with 100,000 patients; 29 situated in the inner London Borough of Lambeth, with 200,000 patients). Ethics approvals were obtained to link individual primary-care records to modeled NOx concentrations by means of post-codes. (To preserve anonymity, the postcodes were removed before delivery to the research team.) A wide range of NOx exposures was found across London as well as within and between the practices examined. Although we observed little association between NOx exposure and smoking status, a positive relationship was found between exposure and increased socioeconomic deprivation. The health outcomes we chose to study were asthma, chronic obstructive pulmonary disease, wheeze, hay fever, upper and lower respiratory tract infections, ischemic heart disease, heart failure, and atrial fibrillation. These outcomes were measured as prevalence or incidence. Their distributions by age, sex, socioeconomic deprivation, ethnicity, and smoking were found to accord with those reported in the epidemiology literature. No cross-sectional positive associations were found between exposure to NOx and any of the studied health outcomes; some associations were significantly negative. After the pilot study, a suitable primary-care database of London patients was identified, the General Practice Research Database responsible for giving us access to these data agreed to collaborate in the evaluation of the LEZ, and an acceptable method of ensuring privacy of the records was agreed upon. The database included about 350,000 patients who had remained at the same address over the four-year period of the study. Power calculations for a controlled longitudinal analysis were then performed, indicating that for outcomes such as consultations for respiratory illnesses or prescriptions for asthma there was sufficient power to identify a 5% to 10% reduction in consultations for patients most exposed to the intervention compared with patients presumed to not be exposed to it. In conclusion, the work undertaken in this study provides a good foundation for future LEZ evaluations. Our extensive monitoring network, measuring a comprehensive set of pollutants (and a range of particle metrics), will continue to provide a valuable tool both for assessing the impact of LEZ regulations on air quality in London and for furthering understanding of the link between PM's composition and toxicity. Finally, we believe that in combination with our modeling of the predicted population-based changes in pollution exposure in London, the use of primary-care databases forms a sound basis and has sufficient statistical power for the evaluation of the potential impact of the LEZ on human health.

Kelly F ，Armstrong B ，Atkinson R ，Anderson HR ，Barratt B ，Beevers S ，Cook D ，Green D ，Derwent D ，Mudway I ，Wilkinson P ，HEI Health Review Committee ... -  《-》

Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.

Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived multiple indicators of traffic using Massachusetts Highway Department (MHD) data and traffic counts collected outside the residences where the air monitoring was conducted. We used a standardized questionnaire to collect data on home characteristics and occupant behaviors. Additional housing information was collected through property tax records. Ambient concentrations of pollutants as well as meteorological data were collected from centrally located ambient monitors. We used GIS-based LUR models to explain spatial and temporal variability in residential outdoor concentrations of PM2.5, EC, and NO2. We subsequently derived latent-source factors for residential outdoor concentrations using confirmatory factor analysis constrained to nonnegative loadings. We developed LUR models to determine whether GIS covariates and other predictors explain factor variability and thereby support initial factor interpretations. To evaluate indoor concentrations, we developed physically interpretable regression models that explored the relationship between measured indoor and outdoor concentrations, relying on questionnaire data to characterize indoor sources and activities. Because outdoor pollutant concentrations measured directly outside of homes are unlikely to be available for most large epidemiologic studies, we developed regression models to explain indoor concentrations of PM2.5, EC, and NO2 as a function of other, more readily available data: GIS covariates, questionnaire data reflecting both sources and ventilation, and central site monitoring data. As we did for outdoor concentrations, we then derived latent-source factors for residential indoor concentrations and developed regression models explaining variability in these indoor latent-source factors. Finally, to provide insight about the effects of improved characterization of exposures for the results of subsequent epidemiologic investigations, we developed a simulation framework to quantitatively compare the implications of using exposure models derived from validation studies with the use of other surrogate models with varying amounts of measurement error. The concentrations of outdoor PM2.5 were strongly associated with the central site monitor data, whereas EC concentrations showed greater spatial variability, especially during colder months, and were predicted by the length of roadway within 200 m of the home. Outdoor NO2 also showed significant spatial variability, predicted in part by population density and roadway length within 50 m of the home. Our constrained factor analysis of outdoor concentrations produced loadings indicating long-range transport, brake wear and traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust as sources; corresponding LUR models largely corroborated these factor interpretations through covariate significance. For example, long-range transport was predicted by central site PM2.5, and season, brake wear and traffic exhaust and resuspended road dust by traffic and residential density, diesel exhaust by the percentage of diesel traffic on the nearest major road, and fuel oil combustion by population density. Our modeling of the concentrations of indoor pollutants demonstrated substantial variability in indoor-outdoor relationships across constituents, helping to separate constituents dominated by outdoor sources (e.g., S, Se, and V) from those dominated by indoor sources (e.g., Ca and Si). Regression models indicated that indoor PM2.5 was not influenced substantially by local traffic but had significant indoor sources (cooking activity and occupant density), while EC was associated with distance to the nearest designated truck route, and NO2 was associated with both traffic density within 50 m of the home and gas stove usage. Our constrained factor analysis of indoor concentrations helped to separate outdoor-dominated factors from indoor-dominated factors, though some factors appeared to be influenced by both indoor and outdoor sources. Subsequent factor analyses of the indoor-attributable fractions from indoor-outdoor regression models provided generally consistent interpretations of indoor-dominated factors. The use of regression models on indoor factors demonstrated the limited predictive power of questionnaire data related to indoor sources, but reinforced the viability of modeling indoor concentrations of pollutants of ambient origin. In spite of the relatively weak predictive power of some of the indoor-concentration regression models, our epidemiologic simulations illustrated that exposure models with fairly modest R2 values (in the range of 0.3 through 0.4, corresponding with the regression models for PM2.5 and NO2) yielded substantial improvements in epidemiologic study performance relative to the use of exposure proxies that could be applied in the absence of validation studies. In spite of limitations related to sample size and available covariate data, our study demonstrated significant outdoor spatial variability within an urban area in NO2 and in several constituents of airborne particles. LUR techniques combined with constrained factor analysis helped to disentangle the contributions to temporal variability of local, long-range transport, and other sources, ultimately allowing exposures from defined source categories to be investigated in epidemiologic studies. For the indoor residential environment, we demonstrated substantial variability in indoor-outdoor relationships among particle constituents; then, using information from public databases and focused questionnaire data, we were able to predict indoor concentrations for a subset of key pollutants. Constrained factor analysis methods applied to the indoor environment helped to separate indoor sources from outdoor sources. The corresponding indoor regression models had limited predictive power, reinforcing the complexity of characterizing the indoor environment when only limited information about key predictors is available. This finding also underscores the likelihood that these regression models might characterize indoor concentrations of pollutants with ambient origins better than they can the indoor concentrations from all sources. Our findings provide direction for future studies characterizing indoor exposure sources and patterns, and our epidemiologic simulation reinforced the importance of reducing measurement error in a context where many traffic-related air pollutants are influenced by both indoor and outdoor sources. The combination of analytical techniques used in our study could ultimately allow for more refined exposure characterization and evaluation of the relative contributions of various sources to health outcomes in epidemiologic studies.

Levy JI ，Clougherty JE ，Baxter LK ，Houseman EA ，Paciorek CJ ，HEI Health Review Committee ... -  《-》