Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of

Dec 16, 2015 - This paper presents ultrafine-particle (UFP) emission factors (EFs) as a function of vehicle mode of operation (free flow and congestio...
0 downloads 20 Views 3MB Size
Download PDF
Article pubs.acs.org/est

Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of Operation for LDVs Based on Near-Roadway Monitoring Wenjuan Zhai, Dongqi Wen, Sheng Xiang, Zhice Hu, and Kenneth E. Noll* Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States ABSTRACT: This paper presents ultrafine-particle (UFP) emission factors (EFs) as a function of vehicle mode of operation (free flow and congestion) using (1) concurrent 5 min measurements of UFPs and carbon monoxide (CO) concentration, wind speed and direction, traffic volume and speed near a roadway that is restricted to light-duty vehicles (LDVs) and (2) inverse dispersion model calculations. Shortterm measurements are required to characterize the highly variable and rapidly changing UFP concentration generated by vehicles. Under congestion conditions, the UFP vehicle EFs increased from 0.5 × 1013 to 2 × 1013 (particles km−1 vehicle−1) when vehicle flow increased from 5500 to 7500 vehicles/h. For free-flow conditions, the EF is constant at 1.5 × 1013 (particles km−1 vehicle−1). The analysis is based on the assumption that air-quality models adequately describe the dilution process due to both traffic and atmospheric turbulence. The approach used to verify this assumption was to use an emission factor model to determine EFs for CO and then estimate dilution factors using measured CO concentrations. This procedure eliminates the need to rely only on air quality models to generate dilution factors. The EFs are suitable for fleet emissions under real-world traffic conditions.

■

INTRODUCTION Motor vehicles are a major source of ultrafine particles (UFPs) in urban air. UFPs are defined as particles with diameters less than 0.1 μm.1 Because of their small size, they may penetrate deep into the lungs causing negative impacts on health.2 Recent health studies indicate that fine particles may be responsible for some of the adverse health effects attributed to ambient particle matter, including asthma and increased illness, hospitalizations, and mortality rates.1−4 Although an UFP number emission standard has been established in Europe, the National Ambient Air Quality Standards (NAAQS) in the United States do not currently include a separate standard for UFP.5 To determine if a standard for fine particles is required, the collection and evaluation of fine-particle concentrations near highways are needed to aid in determination of harmful effects due to the exposure and response of populations near highways. Numerous investigators have measured air quality near roadways and applied models to quantify relevant processes related to changes in traffic conditions.6−13 They have identified the key factors governing changes in air quality near roadways as (1) the rate of emissions from individual vehicle as a function of vehicle mode of operation (free flow and congestion), (2) the total number of vehicles, (3) the rate of mixing due to traffic flow and atmospheric diffusion, and (4) the distance from the roadway. It is difficult to relate air-quality levels near roadways to these parameters because of the inability to relate short-term changes in traffic flow (minutes) to long-term measurements of air quality (hours). This study © XXXX American Chemical Society

overcomes this difficulty by using fast-response instruments to measure air quality levels every second and log the average in 5 min intervals that can be related to short-term changes in the vehicle mode of operation. Combining short-term air pollution measurements with model calculations of dilution of tail-pipe emissions allows in situ estimates of vehicle emission factors as a function of the vehicle mode of operation and traffic conditions. The advantage of this method is that it provides EFs for the actual vehicle fleet under varying real-world conditions. However, determining the most appropriate EFs for a given highway is not trivial. In addition to inaccuracies in the model, the necessary input data contains a high degree of uncertainty, and therefore, the development of EFs based on traffic conditions are generated from data with a high error factor.14 UFPs are generally measured in terms of particle number per unit volume of air and occur in large numbers close to traffic sources.15−18 Currently available particle EFs (particle km−1 vehicle−1) are for average or steady driving conditions. Keogh et al.19 derived average EFs based on a statistical analysis of data from more than 600 studies. Fine-particle EFs were determined as 3.63 × 1014 (particles km−1 vehicle−1) for UFPs emitted by gasoline light-duty vehicles (LDVs). Statistical significant differences were found between LDVs and diesel heavy-duty Received: August 11, 2015 Revised: December 12, 2015 Accepted: December 16, 2015

A

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 1. Map of sampling site near Lake Shore Drive with the north- and southbound lanes and sampling locations identified.

depending on traffic and meteorological conditions. Measurements were made at the edge of the roadway (site A, 3 m west and site B, 3 m east) and at downwind distances of 30, 60, 90, 150, and 200 m sequentially and 100 m upwind from the edge of the highway as background. Background samples were collected at the beginning and the end of each sampling day. A total of 5 min of wind speed and traffic were measured simultaneously with background samples. At each sampling location, concentrations of carbon monoxide (CO) and UFPs were measured simultaneously with meteorological conditions including wind speed and direction, temperature, and relative humidity. Traffic volume and speed were obtained from video recordings taken simultaneously with the other measurements for a 135 m long road segment. Total particle number concentration with size 0.02 to 1 μm was measured at 1 m above the ground by a condensation particle counter (CPC) (TSI 8525).29 A daily zero check of the CPC was performed. CO concentration (0 to 200 ppm with resolution of 0.1 ppm) was measured by a handheld gas sampler (E-inst AQ-PRO3).30 Both instruments were calibrated in July 2014 before the sampling program started. CPC measurements were periodically compared to those of two other CPCs (TSI 3007) and a SMPS. Wind speed and direction, and temperature were measured and logged with a handheld weather station (Kestrel 4500). A video recorder (Sony HDR-CX330) was used to record traffic information including traffic volume and speed with 2 min samples taken for each 5 min time period. Each 5 min is a separate sampling period. A total of 5 min averages were used to record and reflect the short-term variation in traffic flow. Selection of an appropriate sampling site is important to allow the results to be universally applicable and not dependent on features associated with local conditions. LSD is a limitedaccess roadway with eight lanes and north- and southbound traffic directions. This site is ideal for this study for a number of reasons. The roadway is level with no grade, restricted to LDVs only and without highway barriers and separation to restrict air flow. The site has varying traffic flow conditions. The surrounding terrain is level in both the east and west directions, and there are no other major highways or emission sources and no obstacles present for at least 350 m. A pedestrian walkway is located above the center of the roadway at the site, where video recording of traffic from all lanes is possible. Determination of Fleet EFs. In this study, traffic-fleet EFs are determined by two methods (1) using ensemble means of air quality measurements and (2) using individual 5 min air quality measurements. The ensemble mean is the arithmetic

vehicles (HDVs), with HDV emissions an order of magnitude larger than LDVs.20−22 The available data on UFP emissions near highways generally include measurements of particle emissions from both LDVs and HDVs. Typically, diesel vehicles are under 5% of the total vehicles on highways but contribute more than half of the UFPs,11,20 and it is difficult to accurately determine the contribution due only to gasoline powered vehicles. The national average percentage of diesel LDVs is 2.88%.23 The potential impacts of the diesel LDV on roadways are considered small compared to those of the gasoline LDVs. Therefore, this study was conducted near a roadway that was restricted to LDVs and focused on petrol fueled LDVs. Driving conditions appear to be an indicator of UFP emissions from vehicles with 2 to 3 times higher emissions during periods of congestion.6,24−27 Fruinet al.28 used real-time mobile monitoring to assess on-road association between UFP concentrations and traffic conditions and determined that emissions from gasoline-powered vehicles were highest for hard acceleration. This indicates that average EFs are not entirely suited for the estimation of emissions from roadways with different driving conditions. EFs that account for vehicle operating mode are needed for modeling vehicle emissions at lower speeds and under congested conditions and for the establishment of ambient and vehicle-emission standards. This paper provides UFP EFs that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. Congestion represents hindered, unstable vehicle-flow conditions. The EFs are based on near roadway meteorological and traffic measurements (5 min averages) and application of air-quality dispersion models. Because changes in ambient air quality near roadways are very episodic due to rapid changes in traffic patterns and meteorological conditions, near-roadway monitoring programs need to measure in time periods as short as 5 min to respond to changes in traffic conditions.

■

METHODS AND MATERIALS Sampling Program. A sampling program was conducted between August 4 and October 30, 2014 (summer and fall) near a roadway of Lake Shore Drive (LSD) at North Avenue (41°54′56.40″ N, 87°37′40.80″ W) in Chicago, IL using handheld, fast-response instruments. Figure 1 is a map of the sampling site, in which the sampling locations and the surrounding area are presented. Samples were collected at 5 min intervals for 11 sampling days in the afternoon and evening between 15:00 and 20:00 for time periods of 2 to 5 h, B

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology average of a variable obtained from repeating an experiment many times under the same general conditions and is often used for turbulent flow conditions.31,32 Applying either method requires the calculation of the dilution (DL) that describes the dilution due to roadway mixing and atmospheric dispersion of tail-pipe emissions. Many studies have used CO as a tracer gas to determine the dilution.6,9 The DL is determined from California line-source dispersion model version 4 (CALINE4),33 based on measured meteorological and traffic-flow conditions.34 Method 1. Fleet EFs from vehicles are determined as follows. EFfleet = (C − C b) × DL/N

(1)

EFfleet is the vehicle-fleet emission factor for UFPs (particles km−1 vehicle−1), C is measured ambient UFP concentration (particles/cm3), Cb is background concentration of UFPs (particles/cm3), DL is a model calculated dilution that describes the dilution due to roadway mixing and atmospheric dispersion (cm3), and N is traffic flow (vehicles/km). All of the parameters in this equation are for the unit time period, which is 5 min in this study. The equation can be applied to a particular 5 min sample and also to ensemble means. Ensemble mean dilutions were determined for 1000 vehicles/h intervals and were used to calculate fleet EFs using eq 1. Categorizing the data by traffic flow rate (N) allows the determination of the vehicle fleet emission factor as a function of traffic volume and mode of operation on the roadway (free-flow and congestion).8,9,35 Method 2. Palmgren et al.36 used a different method to calculate fleet EFs. On the basis of the Palmgren et al. study,36 the fleet EFs are calculated as the slope of the best-fit line to the relationship between the individual 5 min UFP measurement and the meteorological factor with traffic flow rate. EFfleet = (C − C b)/(F × N )

Figure 2. Relationship between measured traffic vehicle flow and speed for north- and southbound lanes separated into free flow (speed < 65 km/h) and congestion (speed > 65 km/h).

typical of traffic conditions on roadways.37 The figure shows that vehicle flow is at maximum capacity with the corresponding speed near 65 km/h and decreases for higher and lower speeds. The traffic speed of 65 km/h separates traffic into free flow and congestion conditions. The traffic is considered free-flow on the upper part of the speed−flow curve with speed above 65 km/h; when the speed is below 65 km/h, traffic is hindered and in a highly congested and unstable condition.37 Free flow represents conditions when the traffic flow is unhindered by other vehicles. The traffic patterns were different for the north- and south-bound lanes due to different traffic conditions. There were no time periods when both directions were congested. The figure indicates that there was significant variation in traffic conditions that can be related to vehicle EFs. Figure 3 provides information on near-roadway measurements. There were a total of 225 measurements made during the afternoon and evening time on 11 different days. Temperature varied between 50 and 90 °F. Wind speed ranged from 0.5 to 3 m/s and was from the east during nine of the sampling days so that sampling was conducted on the west side of the roadway at site A. Sampling days 4 and 11 were conducted on the east side of the road at site B, where the wind was from the west. The UFP concentration varied between 7000 and 37 000 particles/cm3 with 2000 to 25 000 particles/ cm3 emitted from traffic (background subtracted). A large variation between days occurred due to differences in background concentration, which varied from 1700 to 20 000 particles/cm3 (see Figure 3). Low background concentrations(less than 3000 particles/cm3) occurred with northeast wind, representing Lake Michigan background conditions. The large background concentrations of UFPs and the large variation with time indicate the importance of measuring background concentrations and accounting for the impact that background concentrations have on the near roadway environment. The minimum difference between background and the total measurement is 2000 particles/cm3, well above the detectable limit of the CPC. The northbound vehicle flow varied from 4500 to 9000 vehicles/h, which is about 60% of the total vehicle flow, while the southbound vehicle flow ranged from 3000 to 6000 vehicles/h.

(2)

F is a meteorological factor that is the inverse of the dilution (1/DL) (cm−3). The fleet EF is determined as the ratio of the UFP concentration (C−Cb) and the combined meteorological factor with flow rate (F × N). The method uses the slope of the best-fit line based on individual 5 min measurements of air quality instead of ensemble means. Determination of Free Flow and Congestion EFs. Because we have measurements of the free-flow and congestion traffic volumes (traffic mix), we are able to evaluate the EFs for free flow and congestion using eq 3: EFfleet × Nfleet = EFFF × NFF + EFC × NC

(3)

EFfleet, EFFF, and EFC are the emission rates for the fleet, freeflow and congestion vehicles (particles km−1 vehicle−1) and Nfleet, NFF, and NC are the total traffic flow for each condition (vehicles/km). Zhang et al. used a similar approach to evaluate the EFs for a mix of trucks and nontrucks.9 When there is no congestion, the EFFF × NFF values are equal to the measured (EFfleet × Nfleet) values . When there is a mix of free-flow and congestion-traffic conditions, we can subtract the free-flow conditions from the fleet conditions to determine the congestion EFs.

■

RESULTS Characterization of Near-Roadway Measurements. Figure 2 provides information on the speed−flow relationship during the sampling program. The speed−flow relationship is C

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 3. Time series plot over 11 sampling days of 5 min average measurements near roadway (site A and B), including wind speed, vehicle flow and speed, and ultrafine particle and carbon monoxide (CO) concentrations with background concentrations.

turbulent and chaotic. The concentration of pollutants in the atmosphere near a roadway is influenced by interaction between the source of the pollutant (traffic flow and individual vehicle emission varying with traffic conditions) and atmospheric dispersion of the pollutants due to turbulent mixing. It is important to note that because atmospheric turbulence is chaotic, pollutant concentrations measured over short time periods near sources are not constant, even when the source and meteorological conditions are constant. In turbulent flow, variables vary irregularly in time and space around their mean values. Therefore, it is common practice to consider variables as the sum of their ensemble mean and fluctuation compo-

Figure 4 presents information on the decrease of UFP with distance from the roadway. The average UFP concentration decreased exponentially with downwind distance from the roadway. The calculated decrease in concentration was determined from CALINE4 and indicates that atmospheric dispersion contributes to the rapid decrease. The decrease was nearly the same for CO, indicating that inert pollutants and UFPs behave similarly when emitted from vehicles.8,10 Analysis of the Ensemble Mean and Fluctuation Component. Measuring pollutants in the atmosphere in short time intervals such as 5 min introduces special problems of data evaluation because air flow in the atmosphere is D

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

short-term measurements consist of two components: namely, material transported by the mean flow and a turbulent flux component. To evaluate changes in concentration with traffic conditions, we presented ensemble means for vehicle flow (interval of 1000 vehicles/h). The figures provide the best-fit line obtained by linear least-squares regression and the correlation coefficients for the averaged data. In Figure 5a, for one-direction congestion conditions, the UFP concentrations increased from 5000 to 15 000 particles/cm3 when the average vehicle flow increased from 9500 to 14 000 vehicles/h with the correlation coefficient of 0.88. In Figure 5b, for free-flow conditions, there was an increase from 5000 to 10 000 particles/cm3 when the flow increased from 9500 and 14 000 vehicles/h with correlation coefficient of 0.52. The overall results indicate that there is an increase in UFPs with increases in flow for both one-direction congestion conditions and freeflow conditions. The increase for one-direction congestion conditions (10 000 particles/cm3) was larger than the increase for free-flow conditions (5000 particles/cm3). The results indicate that traffic flow is an indicator of UFP emissions. The variations of UFPs are larger in congestion conditions with relatively unstable traffic mode compared to free-flow conditions. Determination of UFP Dilution. There are two ways available for estimation of emissions from vehicles, a direct procedure that employs an emission model (MOVES, 2014)38 that is based on the measurement of pollutant emissions from dynamometer tests using a certain driving cycle and an indirect procedure employing ambient pollutant measurements and applying dilution determined from air quality modeling.39 The advantage of the direct procedure is that it eliminates the need to use air-quality-model generated dilution. The EFs for UFPs are not available from the MOVES database. However, CO EFs have been the subject of many investigations and are wellknown and therefore can be used to evaluate the EFs for other concurrently measured pollutants,9 for which there are no known EFs such as UFPs. In procedure 1, DLs are first calculated using eqs 1 and 2 with CO EFs determined from MOVES and measured CO concentrations. The UFP EFs are then estimated using eqs 1 and 2 with calculated DLs for CO and concurrent measurements of UFP concentrations and traffic flow, based on the assumption that DLs are the same for CO and UFP. In procedure 2, EFs for UFPs are determined by the indirect procedure using air-quality dispersion modeling.35 First, the DL

Figure 4. Decrease in measured average vehicle ultrafine particle (UFP) and carbon monoxide (CO) concentrations (background subtracted) and CALINE4 calculated variation in concentration for UFP with distance from the roadway. The error bar indicates one standard deviation.

nent.30,31 The deviations of short-term values from the ensemble mean are defined as turbulent fluctuations. The ensemble mean has been used to evaluate the short-term (5 min) measurements made in this study (see Figure 5). The data shown in Figure 5 varies by ±50% of the ensemble means, and this variation is mainly due to turbulent fluctuations. By definition, averages of fluctuations must be zero so that there are compensating positive fluctuations for all negative fluctuations. Because atmospheric turbulent flow varies both in time and space, it is not possible to apply equations to determine instantaneous quantities measured over short time periods.30,31 Therefore, most efficient and practical models are based on time-averaged equations (i.e., Gaussian plume concept used in CALINE 4), and values predicted by these models are usually close to the ensemble mean values. Therefore, statistical tools such as determining ensemble means to describe short-term measurements made under turbulent flow conditions are needed for comparisons to model results. The ensemble mean allows the evaluation of variation in concentration due to atmospheric turbulence. Variations in the near-roadway UFP measured concentrations as a function of changes in traffic flow are shown in Figure 5. The data are divided into (a) one-direction congestion and (b) both-direction free-flow data sets. The

Figure 5. Variation in vehicle ultrafine particle concentration and ensemble means (site A) with vehicle flow for (a) one-direction congestion and (b) both-direction free-flow. The regression line is for the ensemble means with one standard deviation. E

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 6. Variation in emission factors (EF) with vehicle flow for (a) free flow and (b) congestion using eq 3. The regression lines are for method 1(eq 1) using ensemble averages and method 2(eq 2) using individual UFP measurements.

comparisons with results from other studies; however, the average EFs can be compared to other studies. Fruin et al.28 indicated that fine-particle emissions were highest during hard acceleration, which is similar to this study’s results. Zhang et al.9 determined that the average EFs from different studies including dynamometer studies agreed with each other and were in the range of 1013−1014 particles km−1 vehicle−1. The lower limit was similar to that in this study and also was comparable to emission estimates from gasoline engines derived by Kristensson et al.24 from tunnel measurements. The average emission factor reported by Keogh et al.,19 based on a statistical analysis of data from various studies, was 3.6 × 1014 (particles km−1 vehicle−1) for gasoline LDVs, which is higher than the EFs reported in this study. The analysis also indicated that fine-particle emissions from heavy-duty diesel vehicles were 20 times higher than for gasoline-fueled passenger vehicles, and most studies are unable to effectively separate realworld EFs for gasoline and diesel vehicles, which is why a roadway with only LDVs was selected in this study. Lack of standardization of measuring instruments between studies can also results in significant differences in the estimated EFs.14,21 Considering the dynamic nature of UFP vehicle emissions and the large variation in background concentrations, it is significant that similar EFs were obtained using two different methods that are based on different assumptions and measured and modeled data with a high degree of variability. The good agreement indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs under actual driving conditions. The UFP vehicle EFs for congestion conditions are 0.5 to 2 × 1013 (particles km−1 vehicle−1) and free flow conditions are 1.5 × 1013 (particles km−1 vehicle−1). The changing UFP vehicle emission factor for congestion condition could be due to the highly unstable traffic under congestion condition. The overall results indicate that there is an increase in UFP concentrations with increases in flow for both congestion and free-flow conditions. The added impact from vehicles under congestion conditions can be seen by the fact that the ensemble mean UFP concentration was 3 times higher at 13 500 vehicles/h compared to 9500 vehicles/h. This increase is due to both an increase in traffic flow and an increase in the vehicle emission factor. The smaller UFP concentration increase (2 times) with increasing flow for free flow is only due to increasing traffic flow with a constant vehicle-emission factor. Because of the similar

due to roadway mixing and atmospheric dispersion of tail pipe emissions was determined from CALINE 4 based on measured meteorological and traffic flow conditions, and then this calculation was combined with measured UFP concentrations and eqs 1 and 2 to calculate EFs. The advantage of the indirect procedure is that it provides EFs for the total vehicle fleet under varying real-world conditions. The ensemble average dilution was calculated based on the use of the vehicle emissions model (MOVES) and the dispersion model (CALINE4). These models were developed specifically to categorize emissions from vehicles and roadways. The ensemble mean dilution was 2.3 × 1012 cm3 using CALINE 4 and 2 × 1012 cm3 using MOVES, which was slightly lower than for CALINE4. There was very little difference with vehicle flow between the two models for congestion (8%) or free flow (1%). Determination of UFP Vehicle EFs. As discussed in a previous section, two methods were used to calculate fleet EFs. Method 1 considers ensemble means of the measured variables using eq 1, and method 2 includes the variation in the individually measured variables due to turbulent fluctuations using eq 2. For method 1, ensemble mean dilutions were determined for 1000 vehicles/h intervals and 5 km/h intervals, and these average values were used to calculate EFs using CALINE4. For method 2, the fleet EFs were determined as the slope of the best fit-line to the relationship between the individual UFP measurements and calculated meteorological factors. The data were divided into the same intervals on the basis of traffic flow and speed, as in method 1. Figure 6 provides the estimated UFP vehicle emission factor for LDVs as a function of mode of operation (free-flow and congestion) for method 1 and 2 using eq 3. Figure 6a presents the EFs for free-flow conditions, and Figure 6b presents the EFs for congestion conditions. In Figure 6a, the vehicle emission factor for free-flow conditions did not change with vehicle flow and was 1.5 × 1013 (particles km−1 vehicle−1) for vehicle flow from 9500 vehicles/h to 13500 vehicles/h. In Figure 6b, the vehicle EFs for congestion condition increased from 0.5 × 1013 (particles km−1 vehicle −1) to 2 × 1013 (particles km−1 vehicle−1) when the flow increased from 5500 to 7500 vehicles/h.

■

DISCUSSION The lack of published UFP EFs as a function of vehicle operating mode for near-road analysis makes it difficult to make F

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology UFP vehicle EFs for free flow and congestion from this study, it shows that the differences due to driving conditions are not major.

■

(12) Zhu, Y.; Hinds, W.; Krudysz, M.; Kuhn, T.; Froines, J.; Sioutas, C. Penetration of freeway ultrafine particles into indoor environments. J. Aerosol Sci. 2005, 36, 303−322. (13) Zhu, Y.; Fung, D. C.; Kennedy, N.; Hinds, W. C.; EigurenFernandez, A. Measurements of ultrafine particles and other vehicular pollutants inside a mobile exposure system on Los Angeles freeways. J. Air Waste Manage. Assoc. 2008, 58 (3), 424−434. (14) Kumar, P.; Robins, A.; Vardoulakis, S.; Britter, R. A review of the characteristics of nanoparticles in the urban atmosphere and the prospects for developing regulatory controls. Atmos. Environ. 2010, 44 (39), 5035−5052. (15) Bukowiecki, N.; Dommen, J.; Prevô t, A.; Richter, R.; Weingartner, E.; Baltensperger, U. A mobile pollutant measurement laboratory measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution. Atmos. Environ. 2002, 36, 5569−5579. (16) Charron, S.; Harrison, M. Primary particle formation from vehicle emission during exhaust dilution in the roadside atmosphere. Atmos. Environ. 2003, 37, 4109−4119. (17) Jamriska, M.; Morawska, L. A model for determination of motor vehicle emission factors from on-road measurements with a focus on submicrometer particles. Sci. Total Environ. 2001, 264 (3), 241−255. (18) Keogh, D. U.; Sonntag, D. Challenges and Approaches for Developing Ultrafine Particle Emission Inventories for Motor Vehicle and Bus Fleets. Atmosphere 2011, 2, 36−56. (19) Keogh, D. U.; Kelly, J.; Mengersen, K.; Jayaratne, R.; Ferreira, L.; Morawska, M. Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments. Environ. Sci. Pollut. Res. 2010, 17, 724−739. (20) Kirchstetter, T. W.; Harley, R.; Kreisberg, N.; Stolzenburg, M.; Hering, S. On-road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles. Atmos. Environ. 1999, 33 (18), 2955−2968. (21) Morawska, L.; Jamriska, M.; Thomas, S.; Ferreira, L.; Mengersen, K.; Wraith, D.; McGregor, F. Quantification of particle number emission factors for motor vehicles from on-road measurements. Environ. Sci. Technol. 2005, 39, 9130−9139. (22) Ntziachristos, L.; Ning, Z.; Geller, M. D.; Sioutas, C. Particle concentration and characteristics near a major freeway with heavy-duty diesel traffic. Environ. Sci. Technol. 2007, 41 (7), 2223−2230. (23) Diesel technology forum web site. http://www.dieselforum.org/ news/california-texas-and-florida-continue-to-lead-u-s-in-fuel-efficientclean-diesel-and-hybrid-vehicle-registrations (accessed Dec. 6, 2015). (24) Kristensson, A.; Johansson, C.; Westerholm, R.; Swietlicki, E.; Gidhagen, L.; Wideqvist, U.; Vesely, V. Real-world traffic emission factors of gases and particles measured in a road tunnel in Stockholm, Sweden. Atmos. Environ. 2004, 38 (5), 657−673. (25) Kittelson, D. B.; Watts, W. F.; Johnson, J. P. On-road and laboratory evaluation of combustion aerosolsPart 1: Summary of diesel engine results. J. Aerosol Sci. 2006, 37, 913−930. (26) Kittelson, D. B.; Watts, W. F.; Johnson, J. P. Nanoparticle emissions on Minnesota highways. Atmos. Environ. 2004, 38 (1), 9−19. (27) Kittelson, D. B.; Watts, W. F.; Johnson, J. P.; Remerowki, M. L.; Ische, E. E.; Oberdőrster, G.; Gelein, R. M.; Elder, A.; Hopke, P. K.; Kim, E.; Zhao, W.; Zhou, L.; Jeong, C. H. On-road exposure to highway aerosols. 1. Aerosol and gas measurements. Inhalation Toxicol. 2004, 16 (S1), 31−39. (28) Fruin, S.; Westerdahl, D.; Sax, T.; Sioutas, C.; Fine, P. M. Measurements and predictors of on-road ultrafine particle concentrations and associated pollutants in Los Angeles. Atmos. Environ. 2008, 42, 207−219. (29) P-TRAK Ultrafine Particle Counter Model 8525 Operation Service Manual. http://www.tsi.com/uploadedFiles/_Site_Root/ Products/Literature/Manuals/Model-8525-P-Trak-1980380.pdf (accessed Dec. 6, 2015). (30) E Instruments Products AQ Pro Website. http://www.e-inst. com/environmental-iaq/products-AQ-Pro (accessed Dec. 6, 2015). (31) Georgopoulos, P. G.; Seinfeld, J. H. Instantaneous concentration fluctuations in point-source plumes. AIChE J. 1986, 32, 1642−1654.

AUTHOR INFORMATION

Corresponding Author

*Phone: 312-545-8343; fax: 312-567-8874; e-mail: noll@iit. edu. Notes

The authors declare no competing financial interest.

■

ABBREVIATIONS ultrafine particle carbon monoxide dilution condensation particle counter emission factors light-duty vehicle heavy-duty vehicle California line source dispersion model version 4 motor vehicle emission simulator scanning mobility particle sizer

UFP CO DL CPC EFs LDV HDV CALINE4 MOVES SMPS

■

REFERENCES

(1) Health Effects Institute. Understanding the Health Effects of Ambient Ultrafine Particles; HEI Review Panel on Ultrafine Particles: Health Effects Institute: Boston, MA, 2013. (2) Brook, R. D.; Rajagopalan, S.; Pope, C. A., 3rd; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A.; Peters, A.; Siscovick, D.; Smith, S. C., Jr.; Whitsel, L.; Kaufman, J. D. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010, 121 (21), 2331−78. (3) Pope, C. A.; Dockery, D. W. Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manage. Assoc. 2006, 56, 709−732. (4) Pope, C.; Burnett, R.; Thun, M.; Calle, E.; Krewski, D.; Ito, K.; Thurston, G. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 2002, 287 (9), 1132−1141. (5) National Ambient Air Quality Standards (NAAQS). http:// www3.epa.gov/ttn/naaqs/criteria.html (accessed Dec 6, 2015). (6) Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles. Atmos. Environ. 2005, 39 (20), 3597−3610. (7) Morawska, L.; Ristovski, Z.; Jayaratne, E.; Keogh, D.; Ling, X. Ambient nano and ultrafine particles from motor vehicle emissions: characteristics, ambient processing and implications on human exposure. Atmos. Environ. 2008, 42 (35), 8113−8138. (8) Zhang, K. M.; Wexler, A. S. Evolution of particle number distribution near roadwaysPart I: analysis of aerosol dynamics and its implications for engine emission measurement. Atmos. Environ. 2004, 38 (38), 6643−6653. (9) Zhang, K. M.; Wexler, A. S.; Niemeier, D. A.; Zhu, Y.; Hinds, W. C.; Sioutas, C. Evolution of particle number distribution near roadways. Part III: Traffic analysis and on-road size resolved particulate emission factors. Atmos. Environ. 2005, 39 (22), 4155− 4166. (10) Zhu, Y.; Hinds, W. C.; Kim, S.; Sioutas, C. Concentration and size distribution of ultrafine particles near a major highway. J. Air Waste Manage. Assoc. 2002, 52 (9), 1032−1042. (11) Zhu, Y.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmos. Environ. 2002, 36 (27), 4323−4335. G

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology (32) Arya, S. P. Atmospheric systems and pollutant transport. In Air Pollution Meteorology and Dispersion; Arya, S. P.; Oxford University Press: New York, 1999; pp 51−86. (33) Benson, P. E. A review of the development and application of the CALINE3 and CALINE4 models. Atmos. Environ., Part B 1992, 26 (3), 379−390. (34) Mulholland, M.; Seinfeld, J. H. Inverse air pollution modelling of urban-scale carbon monoxide emissions. Atmos. Environ. 1995, 29 (4), 497−516. (35) Zhang, K. M.; Wexler, A. S.; Zhu, Y.; Hinds, W. C.; Sioutas, C. Evolution of particle number distribution near roadways. Part II: the ‘Road-to-Ambient’ process. Atmos. Environ. 2004, 38 (38), 6655− 6665. (36) Palmgren, F.; Berkowicz, R.; Ziv, A.; Hertel, O. Actual car fleet emissions estimated from urban air quality measurements and street pollution models. Sci. Total Environ. 1999, 235 (1), 101−109. (37) Mannering, F. L., Washburn, S. S. Principles of Highway Engineering and Traffic Analysis, 5th ed.; John Wiley and Sons: Hoboken, 2013. (38) Motor Vehicle Emission Simulator (MOVES): User Guide for MOVES2014; Report EPA-420-B-14−055; U.S. Environmental Protection Agency, Assessment and Standards Division, Office of Transportation and Air Quality: Washington, DC, 2014; www.epa. gov/oms/models/moves/documents/420b14055.pdf. (39) Kittelson, D. B. Engines and nanoparticles: a review. J. Aerosol Sci. 1998, 29, 575−588.

H

DOI: 10.1021/acs.est.5b03885 Environ. Sci. Technol. XXXX, XXX, XXX−XXX