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 ... -  《-》

Personal and ambient exposures to air toxics in Camden, New Jersey.

Lioy PJ ，Fan Z ，Zhang J ，Georgopoulos P ，Wang SW ，Ohman-Strickland P ，Wu X ，Zhu X ，Harrington J ，Tang X ，Meng Q ，Jung KH ，Kwon J ，Hernandez M ，Bonnano L ，Held J ，Neal J ，HEI Health Review Committee ... -  《-》

Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site.

Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development. The key pollutants that are the key focus of this work include nitrogen oxides (NO), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O). NO, CO, and BC are tracers of vehicle emissions and dispersion. PM is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM and UFP concentrations. O concentrations are influenced by interaction with NO near the roadway. Nitrogen dioxide (NO), CO, PM, and O are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work. The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection. The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites. We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system. At the freeway site, we measured the spatial gradient of NO, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants. At the urban site, we measured daily average concentrations of nitric oxide (NO), NO, O, and PM at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O, PM, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution. Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R) was 0.61 for hourly NO concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NO at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NO at the freeway site and for UFPs and O at the urban site pedestrian transect. Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.

Frey HC ，Grieshop AP ，Khlystov A ，Bang JJ ，Rouphail N ，Guinness J ，Rodriguez D ，Fuentes M ，Saha P ，Brantley H ，Snyder M ，Tanvir S ，Ko K ，Noussi T ，Delavarrafiee M ，Singh S ... -  《-》

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 ... -  《-》

Airborne carbonyls from motor vehicle emissions in two highway tunnels.

Grosjean D ，Grosjean E 《-》