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Real-world PM, NOx, CO, and ultrafine particle emission factors for military non-road heavy duty diesel vehicles
Atmospheric Environment 45 (2011) 2603e2609
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Short communication
Real-world PM, NOx, CO, and ultrafine particle emission factors for military non-road heavy duty diesel vehicles Dongzi Zhu a, *, Nicholas J. Nussbaum a, Hampden D. Kuhns a, M.-C. Oliver Chang b, David Sodeman a, Hans Moosmüller a, John G. Watson a a b
Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA California Air Resource Board, 9480 Telstar Avenue, El Monte, CA 91731, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 May 2010 Received in revised form 6 December 2010 Accepted 14 February 2011
Training on US military bases involves nonroad diesel vehicles with emissions that can affect base personnel, nearby communities, and attainment of air quality standards. Nonroad diesel engines contribute 44% of diesel PM and 12% of total NOx emissions from mobile sources nationwide. Although military sector fuel use accounts for only z0.4% of distillate fuel use in US, emissions factors measured for these engines improve the representation of the relatively small (as compared to onroad sources) database of nonroad emission factors. Heavy-duty multi-axle, all-wheel drive military trucks are not compatible with regular single-axle dynamometers and their emissions cannot be measured under standard laboratory conditions. We have developed a novel in-plume technique to measure in-use emissions from vehicles with elevated stack. Real-world gaseous and particulate matter (PM) emission factors (EFs) from ten 7-ton 6-wheel drive trucks and two 8-wheel drive heavy tactical Logistics Vehicle System (LVS) vehicles were measured using in-plume sampling. The EFs of these trucks are comparable to those of onroad trucks while the PM EFs of 2-stroke LVS are z10 times higher than those of onroad vehicles. Lower EC/PM ratio was observed for LVS compared with MTVR. PM number emission factors were 5.9 1014 particles km1 for the trucks and 2.5 1016 particles km1 for the LVSs, three orders of magnitude higher than the proposed European Union standard of 6 1011 particles km1. The EFs sampled can be extended to engines used in the broader nonroad sector including agriculture and mining and used as inputs to the NONROAD model. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Nonroad Diesel Emission factor Military Real-world emissions
1. Introduction Advances in fuels, engine technology, and emission regulations have reduced emissions from onroad diesel engines (Chow, 2001; Lloyd and Cackette, 2001). These technologies will soon be applied to some nonroad engines (EPA, 2004), but military heavyduty diesel vehicles (HDDVs) are not subject to these regulations. Military diesel engines are of older design to minimize complexity, simplify repair, make use of a wide variety of fuels, and to assure reliability under taxing conditions. Although military engines have been subjected to engine certification tests, few emission data are available for real-world fuels and operating conditions (Durbin et al., 2007; Zhu et al., 2009). Nonroad diesel engines contribute 44% of diesel PM and 12% of total NOx emissions from mobile sources nationwide (EPA, 2003). Although military sector fuel use
* Corresponding author. Tel.: þ1 775 674 7086; fax: þ1 775 674 7007. E-mail address: [email protected] (D. Zhu). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.02.032
accounts for only w0.4% of distillate fuel use in US (DOE, 2009), emissions factors measured for these nonroad engines provide needed additional detail to the database of nonroad emission factors. Commonly used techniques for measuring in-use fleet emissions include tunnel (Kirchstetter et al., 1999), chassis dynamometers (Yanowitz et al., 2000), and onroad studies. Onroad studies include cross-road remote sensing (Moosmüller et al., 2003; Bishop et al., 2008) and other techniques that capture exhaust plumes from tailpipes into analytical instruments. Plume-capture sampling methods include mobile laboratories (vehicles) chasing target vehicles (Kittelson et al., 2004; Yli-Tuomi et al., 2005; Canagaratna et al., 2004), or towed by the target vehicles (Shah et al., 2006). Other studies have used sampling inlets fixed close to plume (in this study) or lying on the ground (Hak et al., 2009). Another method uses on-board measurements such as commercial Portable Emissions Measurement System (PEMS) with sampling inlet inside the tailpipe (Frey et al., 2008). Military multi-axle, allwheel drive nonroad HDDVs are incompatible with the commonly used HDDV single-axle dynamometers, thus very few researches
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have been concentrated on in-use emissions from these vehicles with elevated stack. Using a novel in-plume measurement technique, this study reports real-world fuel-based emission factors from JP-8-powered nonroad vehicles operating on a southern California Marine Corps base. 2. Instrumentation and methods Emissions were characterized for ten 26,200 kg Medium Tactical Vehicle Replacement (MTVR) vehicles with 3.8 miles per gallon (mpg) fuel economy (Oshkosh Defense, 2009) and two 39,100 kg Logistics Vehicle Systems (LVS) (GlobalSecurity.org, 2010) with 2 mpg fuel economy. The MTVR is a six-wheel drive all-terrain truck powered by a Caterpillar C-12, 425 hp turbo-charged, four-stroke, 6-cylinder, 12 L displacement diesel engine. MTVRs are the Marine Corps’ prime movers for artillery, fuel, water, troops, and equipment. The LVS is a modular assortment of eight-wheel drive all-terrain trucks powered by a Detroit Diesel 8V92TA, 455 hp turbo-charged, two-stroke, 8-cylinder, 12 L displacement diesel engine. JP-8 jet fuel with an average sulfur content of 600 ppmw (U.S. DOD, 2008) was used in all tests reported here. The 8V92 TA engine consumed 12% of Marine Corps and Army nonroad fuel during 2003 (Kemme et al., 2006); fuel consumption for the C-12 engine was less than 0.8% and was not listed in the top 20 fuel consumption engines. The vehicles passed through the sampling system illustrated in Fig. 1 at cruise, acceleration, and deceleration modes with target speeds of 5e25 mph. All the test vehicles were stopped at z25 m before the sampling line and the driver was asked to accelerate the vehicle hard or to cruise it through the test section. Drivers were not always able to attain these conditions, as documented by an observer for each vehicle pass. Exhaust plumes were sampled with the in-plume experiment test stand (IPETS, Nussbaum et al., 2009) as detailed in Fig. 1. The 4 m above ground level (AGL) sensing height was selected to accommodate the 3e3.5 m AGL exhaust outlets of these vehicles. Ambient air concentrations were characterized with the IPETS at the beginning, midpoint, and end of each sampling day. CO2 mixing ratios and PM2.5 mass concentrations were measured
simultaneously with a LICOR LI-840 H2O/CO2 monitor and a PM2.5 filter sampler located z20 m upwind of the vehicle path. These background levels were subtracted from the diluted plume concentrations. Ambient temperature and relative humidity (RH) were recorded every 2 h. Average and standard deviations of ambient temperature and RH during the sampling period were 19.7 3.0 C and 17.0 7.4%, respectively. The IPETS uses a Fourier transform infrared spectrometer (FTIR) (Midac, Costa Mesa, CA) to quantify gas concentrations and an electrical low pressure impactor (ELPI) (Dekati, Finland) to measure particle size distributions (Fig. 1) with 1e2 s time resolution. Integrated PM2.5 samples were collected on Teflon and quartz-fiber filters for chemical laboratory analysis, including elemental carbon (EC), organic carbon (OC) and total carbon (TC) analysis using the Thermal/Optical Reflectance (TOR) method (Chow et al., 2005). The FTIR’s wave number scan resolution is 0.5 cm1 with a sample flow of 50 L min1 through a 2 L volume optical cell with a 10 m length folded light path and a data sampling rate of 0.66 Hz. The sampling line and cell were not heated and water was not removed from the exhaust samples. For the species of interest, particularly NO and NO2, the interference due to water vapor was minimized by the least squares algorithm within Midac’s Autoquant software (Nussbaum et al., 2009). In this configuration, most gaseous detection limits of the FTIR are z1 ppm. With roadside sampling, rapid increases in CO2 concentrations over background indicate the presence of exhaust plumes (Singer and Harley, 1996). Heywood (1988) measured undiluted exhaust CO2 concentrations of z2e3% at low loads and z10% at high loads. Bergmann et al. (2009) measured exhaust CO2 levels of 1e11%, and Kim et al. (2001) found tailpipe CO2 levels of 6% in undiluted diesel exhaust. In this study, excess CO2 concentrations sampled from the diluted exhaust through the overhead inlets ranged from 0.01% to 0.12%. Assuming an undiluted CO2 concentration range of 2%e11%, dilution ratios were in the range of 90e200. Fuel-based emission factors (EFi, g of pollutant i emitted per kg of fuel burned) are calculated from the ratio of each pollutant to the sum of excess CO2 and CO. These ratios are related to the fuel carbon content and the amount of fuel consumed (Moosmüller et al., 2003) as
Fig. 1. Experimental configuration for sampling vertical exhaust from nonroad military vehicles. A portion of the exhaust plume was drawn through the sampling ports at w180 L min1 a 4 m high sampling inlet pipe was set up across a paved tank trail and suspended on each end using two scissor-jacks. A z 9.5 m long rigid conductive sampling pipe (1.91 cm ID) with inlets mounted at z1 m intervals was connected to the IPETS system via flexible conductive tubing 4.7 m long (2.54 cm ID) with the IPETS system located in a cargo van on the side of the road. FTIR measures gas concentrations (CO, CO2, NO, NO2, N2O, NH3, SO2, propane, ethylene, and hexane). ELPI measures real-time particle number size distributions. Grimm Optical Particle Counter (OPC) measures particle size resolved number concentration. IPETS included two DustTrak Model 8520 (TSI, Shoreline, MN) light scattering monitors (O’Shaughnessy and Slagley, 2002) measuring PM2.5 and PM10 mass concentrations. Filters collected PM2.5 using a 113 L min1 flow rate Bendix cyclone. Transit time from plume extraction to the measurement instruments was z3 s. Curves in the flexible conductive sampling line exceeded a 30 cm radius to minimize particle deposition.
D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
EFi ¼ CMFdiesel
ri rCO2
3. Results and discussion
CMFCO2 þ CMFCO rrCO þ CMFHC rrHC CO2
(1)
CO2
where ri, rCO2, rCO, rHC are the excess (above ambient) concentrations (mg m3) of pollutant i, CO2, CO, and HC, respectively, CMF is the carbon mass fraction of each pollutant with CMFCO ¼ 42.9%, CMFCO2 ¼ 27.3%, and CMFdiesel ¼ 86.6% (assuming CH1.85 for dieselfuel), CMFjp-8 ¼ 86.3% with average formula C11H21 for JP-8 (Martel, 2000). CO and CO2 typically account for more than 99% of the carbon emitted in engine exhaust (Yanowitz et al., 2000). Fig. 2 shows measured CO2 mixing ratios and fine particle number concentrations as a function of time. Data within the z25 s width peak width can be isolated to estimate EFs for each vehicle. Background-subtracted peak CO2 mixing ratios averaged 239 ppm with a range of 100e1200 ppm. Particle number concentrations from the first six ELPI stages (D50 < 322 nm) were used following the Maricq et al. (2006) algorithm to estimate PM mass concentrations (mg m3) from the ELPI measurements. Diesel particles are fractal-like agglomerates of nucleation particles (approximately spherical 10e30 nm diameter primary particles) and their density decreases with particle size (Maricq and Xu, 2004), but standard ELPI number-to-mass conversions assume a spherical shape and unit density. Some of the numerous smaller particles may also deposit on the larger particle impactor stages, thereby positively biasing mass concentration estimates. ELPI PM mass was shown to be as much as 115% higher than gravimetric filter mass for diesel exhaust (Maricq et al., 2006).
a
MTVR plume peaks 5.0E+06
770
ELPI PM0.322 720 670 620
3.0E+06
570
2.0E+06
520
CO2 (ppm)
PM number concentration 3 (Particles/cm )
CO2
4.0E+06
470
1.0E+06
420
1.0E+04 14:18
370
14:19
14:20
14:21
14:21
14:22
14:23
14:24
14:24
Time (hr:min) on 2007-04-19
LVS plume peaks
5.0E+07
770
ELPI PM0.322
PM number concentration 3 (Particles/cm )
CO2
720
4.0E+07
670 620
3.0E+07
570
2.0E+07
520
CO2 (ppm)
b
470
1.0E+07 420
1.0E+04 15:15
370
15:16
15:16
15:17
15:18
2605
15:19
15:19
15:20
15:21
Time (hr:min) on 2007-04-19 Fig. 2. Exhaust CO2 mixing ratio peaks detected by FTIR and PM number concentration peaks recorded by ELPI for MTVR (a) and LVS (b) passes. The LVS PM concentration peaks are 10 times higher than those of MTVR. The PM peaks lagging CO2 peaks is due to the different residence time (flow rate, size of the tubing) in sampling lines before the plume enters the instruments.
Fuel-based EFs vary less than time- and distance-based EFs as vehicle instantaneous speed changes (Tong et al., 2000). EFs from a single vehicle are expected to vary based on the mode of operation (i.e., slight and hard acceleration and cruise). Time-based NOx emissions (in g s1) (Cernuschi et al., 1995) and PM number concentration emissions (in particles s1) (Wang et al., 2006) from diesel engines were found to be higher in acceleration mode than in cruise and idle modes. Fuel-based EF variation could not be calculated from these measurements since CO2 mass concentration (indicator of fuel use) was not concurrently measured. Shah et al. (2006) reported that the distance-based EFNOx (g mi1) for creep mode (simulates vehicle operation in heavily congested conditions) were three times higher than those for the transient (simulates vehicle operation on arterial roads) and cruise (simulates freeway driving) modes. These three modes plus start/idle mode constitute the ARB fouremode cycle (Table 1). Fuel-based EFCO, EFPM were lowest for cruise mode, EFPM was highest for transient mode, and EFCO was highest for creep mode, consistent with more efficient steady-state engine conditions during cruise mode. EFNOx was highest for cruise and lowest for transient mode, consistent with the lower engine temperatures at the lower speeds encountered during transient mode operation. Vehicle operating modes in this study, with a typical vehicle speed of z15 mph, correspond most closely to the transient mode. Creep and cruise mode EFs might be extrapolated from the results of Shah et al. (2006); the cruise mode EFNOx is 180% of the EFNOx in transient mode measured from this study (Table 1). For the MTVRs, five tests approximated a steady-state cruise, five tests approximated a slight acceleration, and twenty tests approximated a hard acceleration. For the LVSs, two tests corresponded to a steady-state cruise and four tests corresponded to a hard acceleration. Five MTVRs and one LVS passed through the test section multiple times. Average NOx EFs during cruise were higher than EFs during acceleration for both LVSs and MTVRs. Average PM EFs during acceleration were higher than those during cruise. The relationships between fuel-based emission factors and operating mode (e.g., accelerating, cruise) are not significant at p < 0.05. This might be due to the relatively low vehicle speeds (<25 mph) and the qualitative recording of operating modes by a field assistant. An example of CO2 mixing ratios in the exhaust plume as function of time and of time-integrated CO2 mixing ratio peak areas from the LICOR and FTIR are shown in Fig. 3. Fig. 4 shows that EFs were most comparable among tests when CO2 peak areas exceeded 400 ppm-s, which appears to be the effective detection limit for the test methodology, and tests with CO2 peak areas <400 ppm-s were excluded from further analyses. Of the 258 vehicle passes, 36 met this criterion owing to variable meteorological conditions; most of the plumes did not encounter the sampling inlet. Table 2 summarizes the NOx, CO, and PM EFs for these valid tests. Consistency among the tests is evident for average MTVR EFNOx, ranging from 24 to 33 g NOx kg fuel1. However, MTVR EFCO were not as consistent among tests, ranging from 2.7 to 24 g CO kg fuel1. Five MTVRs were measured with only one pass. These measurements were within the range of EFNOx of those MTVRs with multiple passes (Table 1). Variations in EFPM were greatest between vehicle types (4-stroke MTVR vs. 2-stroke LVS) as opposed to operating mode (acceleration vs. cruise). For EFCO, the variability of repeated emission measurements for the same vehicles (i.e. MTVRs #593901 and #543901 with coefficients of variation (COV) of 94% and 113%) are higher than the 72% COV for fleet average EFCO (Table 2). The MTVR #543901 tests included three cruise and two hard acceleration modes, consistent
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Table 1 Distance-based fleet average emission factors for CO, NOx and PM emitted by 11 onroad heavy-duty diesel engines from Shah et al. (2006) running on ARB HDDT 4-mode cycle (Gautam et al., 2002). Only distance-based EFs were reported in that study, while converted fuel-based EF trends can better explain the EF variation with operating modes. Based on average speed, vehicle operating mode is in this study more closely associated with the transient mode, the EFs of nonroad military vehicles operating at creep and cruise modes can be adjusted.
EF EF EF EF EF EF
Creepa (g mi1) Transientb (g mi1) Cruisec (g mi1) Creep (g kg1) Transient (g kg1) Cruise (g kg1)
Average Speed (mph)
CO2
CO
1.77 15.4 39.9 1.77 15.4 39.9
5024 682 2933 221 1808 190
19.9 7.90 3.38 7.95 5.41 3.75
Fuel-based EF Ratio to Transient mode Creep Cruise
NOx
9.33 4.16 1.72 3.73 2.85 1.91
PM
75.2 25.5 28.1 32.18 18.69 33.42
1.47 0.69
31.7 8.59 11.3 13.57 6.30 13.44
1.02 0.656 0.215 0.355 0.391 0.208
1.72 1.79
0.426 0.153 0.201 0.148 0.091 0.195
0.91 0.53
a Creep mode represents very low speed truck operation with a maximum speed of 8.24 mph. The EFCO would be 1.47 of, EFNOx would be 1.72 of, and EFPM would be 0.91 of EFs at Transient mode, respectively. b Transient mode of HDDT is a 10 min drive that simulates the vehicle stopping and going at an average speed of 20 mph, it involves sharp acceleration and deceleration with a peak speed of <50 mph. c Cruise mode of HDDT cycle, which is representative of truck driving on the interstate highway, is a 2000 s cycle with a speed z60 mph continuously for about 1400 s. The EFCO would be 0.69 of, EFNOx would be 1.79 of, and EFPM would be 0.53 of EFs at the Transient mode, respectively.
a
FTIR CO2 LICOR CO2
1850
160 140 120
CO EF (g/kg fuel)
with the operating mode having a larger influence on EFCO than the specific engine. The LVS EFPM was z10 times the MTVR EFPM, consistent with prior observations for two-stroke vs. four-stroke diesel engines (Ålander et al., 2005). Higher EFPM result from the oil-fuel mixture
40 20
0
1000
2000
1350
3000
4000
5000
6000
7000
8000
9000
7000
8000
9000
Peak CO2 Area (ppm-s) 100
1100
NOx (as NO2) EF (g/kg fuel)
CO2 (ppm)
60
-20
850 600 350 14:29:46
14:31:55
14:34:05
14:36:14
14:38:24
Time on 2007-04-19
50
0
-50
-100
8000 -150
Licor Area = 0.98 FTIR Area + 78.55 R = 0.66
7000
0
1000
2000
3000
4000
5000
6000
Peak CO2 Area (ppm-s)
6000
14
5000
12
4000
PM EF (g/kg fuel)
Licor-CO2 peak-area (ppm-s)
80
0
1600
b
100
3000 2000 1000 0 0
1000
2000
3000
4000
5000
6000
7000
8000
FTIR CO2 peak-area (ppm-s) Fig. 3. (a) CO2 mixing ratio peaks detected by FTIR and LICOR. LICOR response time is 1 s and sampling flow rate is 1 L min1, FTIR has response time of 1.5 s and sampling flow rate of 50 L min1, the LICOR has shorter and higher peaks, FTIR has a longer and lower peaks. (b) Correlation between CO2 mixing ratio peak area (ppm-s) by FTIR and LICOR. The integrated exhaust CO2 mixing ratio peak areas from the LICOR and FTIR showed negligible systematic bias.
LVS
10
LVS
8 6
LVS
4 LVS
2
LVS
LVS
0 -2
0
1000
2000
3000
4000
5000
Peak CO2 Area (ppm-s) Fig. 4. NOx, CO, and PM EF distribution vs. peak CO2 mixing ratio area for each vehicle pass. Error bars represent the propagated analytical uncertainty of the EF.
D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
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Table 2 Fuel-based average CO, NOx and PM Emission Factors for each vehicle tested. NOx EF (g kg1) Vehicle ID
Number of tests
Average
LVS 557098 LVS 550978 MTVR 593901 MTVR 543904 MTVR 594191 MTVR 542995 MTVR 543901 MTVR 593947 MTVR 593987 MTVR 596373 MTVR 595930 MTVR 596393
1 5 10 6 2 2 5 1 1 1 1 1
32 33 31 26 30 33 24 18 36 2.1 13 14
CO EF (g kg1) SDa
COVa
4.6 20 2.9 19 8 24
Average 2.6 5.3 14 2.7 5.8 17 24 2.4 7.8 11 11 2.2
14% 64% 11% 64% 24% 104%
PM EF (g kg1) SD
COV
3.2 13 1.4 4.2 9.2 27
Average 2.5 6.1 0.68 0.37 0.32 0.39 0.36 0.06 0.17 0.55 0.08 0.57
60% 94% 51% 73% 55% 113%
SD
COV
4.4 0.36 0.19 0.17 0.18 0.16
73% 52% 51% 52% 47% 44%
a SD ¼ standard deviation and COV ¼ Coefficient of variation of average emission factors for the series of tests on a given vehicle. These are not reported for vehicles that were measured one time only.
lubrication employed in two-stroke engines, as the unburned or partially burned lubricating oil exits the engine with the exhaust (Nuti, 1998). As the PM2.5 chemical speciation, higher EC/PM2.5 ratio was observed for MTVR group (55 14%) than LVS group (42 15%) (at 95% confidence), this might due to higher OC emissions from LVS’s unburned lubricating oil. Elevated n-alkanes abundances (4.6 4.1% of PM2.5) were found for MTVR, with ∼0.4% found for LVS. Branched n-alkanes were 0.5 0.59% of PM2.5 for MTVR and 0.05 0.03%for LVS. PAHs were low, in the range of 0.03e0.08% of PM2.5. Table 3 compares average EFs from this experiment with values from emission models and tests of similar vehicles. Power-based and distance-based EFs are often reported in addition to fuel-based EFs and conversions are needed for comparison. Heavy-duty diesel engines are certified using power-based EFs (g of pollutant kW h1). Units of g kW h1 are converted to g kg fuel1 (EPA, 1996) assuming an average brake-specific fuel consumption (BSFC) of 9900 kJ kW h1 and a diesel-fuel heating value of 44900 kJ kg1, yielding BSFC ¼ 0.22 kg kW h1. A 2000 model year (MY) Caterpillar C-12 450 HP engine (same model engine used in MTVR) was measured with a BSFC of 0.22 kg kW h1 with DECSE (Diesel Emission Control Sulfur Effects program) 3 ppmw sulfur fuel
(McGill et al., 2003). Vehicle-sales-weighted BSFC for class 8A (GVW range 15000e27220 kg) HDDV diesel trucks for MY 1995 were reported as 0.24 kg kW h1 for input to MOBILE6 (Browning, 1998). JP-8 has a heating value z43400 kJ kg1 (Fernandes et al., 2007). As tested on an MY2002 engine, the BSFC for JP-8 in a 6.5 L GM heavy-duty diesel engine was 8% lower than that for low sulfur diesel (Frame and Blanks, 2003). A BSFC of 0.22 kg kW h1 is used here to relate fuel-based to power-based EFs. Fuel-based EFs are related to distance-based (g mi1) EFs by assuming a fuel-density of 0.85 kg L1 (7.1 lb gal1) and the 3.8 mpg MTVR and 2 mpg LVS fuel economies cited above. Table 3 summarizes and compares fuel-, power-, and distance-based average CO, NOx and PM EFs for the MTVR and LVS engines. Average MTVR EFCO was more than twice that of the LVS. Average LVS EFNOx was 44% higher than that for the MTVR. The average LVS EFPM was more than 10 times that of the MTVR. The NONROAD emission model (EPA, 2005a,b) provides EFs for state and local inventories based on the proportions of different engine types in the fleet. The NONROAD equipment code 2270002051 is closest to the engines tested here, corresponding to a Tier 1 (MY 2000e2005) off-highway truck with power range 300e600 hp and power-based EFs presented in Table 3. The MTVR EFPM is z25% of the
Table 3 Fleet average CO, NOx and PM Emission Factors in fuel-based, distance-based and power-output based units, respectively. Uncertainty terms are standard deviations of all measurements of each vehicle type. Other modeling and studies EF result are also compared. Vehicle Type (Number of vehicles)
Average EF CO
Average EF NOx
PM
CO
(g kg fuel1) MTVR (10) LVS (2) NONROAD Off-highway trucksa NONROAD construction equipmentb Nonroad excavatorsc Nonroad gradersd Onroad HDDV transient modee Diesel trucks (5)f Tunnel HDDVg HDDV chassis dynamometer testh a
23 11 33 0.7
0.36 0.21 4.3 2.5
17.9 3.2 4.6
34.1 34.6 33.7
3 0.25 0.26
18 70
PM
CO
(g mile1)
9.8 7.1 3.9 1.9
26.4
Average EF NOx
NOx
PM
(g kW h1)
8.3 6.0 6.3 3.0
19 9.0 53 1.1
0.30 0.18 6.9 4.0
7.9
25.5 8 e 22
0.66 0.22 e 0.52
2.1 1.6 0.8 0.4 2.7 1.6 e 12
5.0 2.4 7.2 0.1 7.6 6.2 e 10
0.08 0.04 0.95 0.55 0.34 0.31 e 1.81
0.44 3.58
EPA NONROAD EF for Tier 1 off-highway trucks with 300e600 hp engines, Source Classification Code 2270002051. EPA NONROAD model EF range for Tier 1 diesel-powered construction equipment (including off-highway trucks and tractors, loaders and backhoes, excavators) with 3e3000 hp engines, Source Classification Code 2270002066. c EF of a 2001 254 hp Komatsu excavator measured by an on-board emissions monitor. PM is an opacity-based measurement (Abolhasani et al., 2008). d EF of a 2001 195 hp Cummins motor grader measured by PEMS, PM is opacity-based measurement (Frey et al., 2008). e Fleet average EF of 11 trucks under transient mode with an average speed of 15.4 mph (Shah et al., 2006). f EF for 5 diesel trucks under normal speed range from 16 to 34 mph (Durbin et al., 2008). g Tunnel study with early morning HDDV dominant sampling (Grieshop et al., 2006). h Chassis dynamometer tests of Class 8b HDDV trucks (Yanowitz et al., 2000). b
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Table 4 Fleet average PM Number Emission Factors and comparison with other studies. Uncertainties are one standard deviation. Ultrafine particles calculation using PM data from stage 1e4 of ELPI. The impactor stage 4 has a D50 of 100 nm. Vehicle
Number of vehicles
Fine (<322 nm) PM EF (particles kg fuel1)
Fine (<322 nm) PM EF (particles km1)
Ultra fine (<100 nm) PM EF (particles kg fuel1)
Ultra fine (<100 nm) PM EF (particles km1)
MTVR (7-ton Truck) LVS HDVa HDVb On-road vehiclesc On-Road Measurementd
10 2
(1.1 1.1) 1015 (2.5 1.2) 1016 (6.3 1.9) 1015 (8.2 2.5) 1015 (1.0e3.1) 1015 8.3 1015
(5.9 6.0) 1014 (2.5 1.2) 1016
(1.1 1.1) 1015 (2.4 1.1) 1016
(5.6 5.8) 1014 (2.4 1.1) 1016
a
Tunnel study of HDV using CNC, particles > 10 nm, (Kirchstetter et al., 1999, 2002). Tunnel study of HDV using SMPS-CPC, particle range 7e270 nm (Geller et al., 2005). c On-road measurement with SMPS particle sampling range 8e300 nm, on Minnesota highways with diesel vehicles to gasoline vehicles ratio of w0.09 (Kittelson et al., 2004). d On-road measurement with ELPI particle range 7e960 nm, Helsinki metropolitan areas, Finland. (Yli-Tuomi et al., 2005). b
NONROAD value while the LVS PMEF is 184% of the NONROAD estimate. NONROAD EFs are based on an 8-mode steady-state cycle implemented on an engine dynamometer that differs from the realworld test reported here. The LVS EFPM is more similar to those for NONROAD construction equipment category (equipment code 22700020666), with a range of 0.31e1.81 g PM kW h1. Table 3 also compares results from this study with those from other real-world emission experiments. Durbin et al. (2008) reported NOx and PM EFs for five diesel trucks (MY 1996e2005) during “normal” traffic in a Stockton, CA business district, with speed ranging from 16 to 34 mph. NOx EFs ranged from 8 to 22 g mi1 and PM EFs ranged from 0.22 to 0.52 g mi1. These results are comparable to the EFNOx of 19.3 g mi1 and the EFPM of 0.3 g mi1 for the 7 ton truck MTVRs in this study. The MTVR and LVS EFs are not appreciably different from similar size and model year onroad truck EFs operating under similar transient modes. Although not currently regulated, ultrafine particles (UP) are becoming of greater concern for public health and EFUP from MTVR and LVS are compared with results from similar studies in Table 4. EFUP from MTVR and LVS span the range of values found in other studies. These EFPM are three orders of magnitude higher than the proposed European Union UP standards of 6 1011 particles km1 (EU, 2009). 4. Summary and conclusions Emission factors from in-use military heavy-duty nonroad vehicles with high stacks were measured with a novel in-plume technique. Fleet average EFs for MTVR trucks were 9.8 g CO kg fuel1, 23 g NOx kg fuel1 and 0.36 g PM kg fuel1. Fleet average EFs for LVSs are 3.9 g CO kg fuel1, 33 g NOx kg-fuel1, and 4.3 g PM kg fuel1 (Table 3), driving conditions varied between slight and hard accelerations and cruise at a target speed of 5e25 mph passing the elevated sampling inlet. Two-stroke LVS had 10 times higher PM EFs than the average PM EFs of 4-stroke MTVRs, lower EC/PM2.5 ratio was observed for LVS (42%) compared with MTVR (55%) (at 95% confidence). With MTVR’s and LVS’s fuel economy of 3.8 mpg and 2 mpg, respectively, the fuel-based EFs were also converted into distance-based emission factors; with 0.22 kg kW h1 BSFC value assumed, fuel-based EF were converted into power-output based EFs, to compare with modeling and other studies reporting EF in different units. Test results for the four-stroke MTVR are comparable with emission factors used in EPA’s NONROAD model and results from tunnel, chassis dynamometer, and in-use HDDV emissions measurements. EFs for military nonroad trucks are not significantly different from those for onroad trucks with similar engine size, model year, and operating under similar transient modes. LVS’s 10 times higher PM EF still falls in the NONROAD offroad construction equipments EF range. The particle number
emission factors from this study were comparable with results reported by other researchers for their tunnel or onroad chasing experiments, but 1000 times higher than EU proposed PM number emission standards. Although for both LVS and MTVR, the average NOx EFs in cruise mode are higher than those in the acceleration mode and average PM EFs in the acceleration mode are higher than those in the cruise mode, these differences are not statistically significant at the p < 0.05 level. This might be due to the fact that in this study all vehicles were driving at relatively low speed (<25 mph) and the operating modes were only qualitatively recorded by a field assistant. Application of these EFs for emission inventory purposes should consider the operating modes of the modeled fleet and adjust the EFs based on the relative trends observed by Shah et al. (2006). The EFs sampled can be extended to engines used in the broader nonroad sector including agriculture and mining and can also be used to improve the representativeness of the EFs in EPA’s NONROAD model. The variation of EFs from different vehicles in this study indicates the need of measurements from multiple vehicles to accurately quantify fleet averages for use in emission inventories. Acknowledgements This work was partially sponsored by the Federal Transit Administration (Contract 26-70030) and the Strategic Environmental Research and Development Program (Contract CP-1336). The U.S. Marine Corps provided extraordinary cooperation and assistance in providing access to vehicles and assisting with tests. References Abolhasani, S., Frey, H.C., Kim, K., Rasdorf, W., Lewis, P., Pang, S.H., 2008. Real-world in-use activity, fuel use, and emissions for nonroad construction vehicles: a case study for excavators. Journal of the Air & Waste Management Association 58, 1033e1046. Ålander, T., Antikainen, E., Raunemaa, T., Elonen, E., Rautiola, A., Torkkell, K., 2005. Particle emissions from a small two-stroke engine: effects of fuel, lubricating oil, and exhaust aftertreatment on particle characteristics. Aerosol Science & Technology 39, 151e161. Bergmann, M., Kirchner, U., Vogt, R., Benter, T., 2009. On-road and laboratory investigation of low-level PM emissions of a modern diesel particulate filter equipped diesel passenger car. Atmospheric Environment 43, 1908e1916. Bishop, G.A., Stedman, D.H., 2008. A decade of on-road emissions measurements. Environmental Science & Technology 42, 1651e1656. Browning, L., 1998. Update Heavy-Duty Engine Emission Conversion Factors for MOBILE6: Analysis of BSFC and Calculation of Heavy-Duty Engine Emission Conversion Factors. Arcadis Geraghty & Miller, Inc., Mountain View, CA. EPA Report No. 420-P-98-015. Canagaratna, M.R., Jayne, J.T., Ghertner, D.A., Herndon, S., Shi, Q., Jimenez, J.L., Silva, P.J., Williams, P., Lanni, T., Drewnick, F., Demerjian, K.L., Kolb, C.E., Worsnop, D.R., 2004. Chase studies of particulate emissions from in-use New York city vehicles. Aerosol Science & Technology 38, 555e573. Cernuschi, S., Giugliano, M., Cemin, A., Giovannini, I., 1995. Modal analysis of vehicle emission factors. Science of the Total Environment 169, 175e183.
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Kirchstetter, T.W., Harley, R.A., Kreisberg, N.M., Stolzenburg, M.R., Hering, S.V., 1999. On road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles. Atmospheric Environment 33, 2955e2968. Kirchstetter, T.W., Harley, R.A., Kreisberg, N.M., Stolzenburg, M.R., Hering, S.V., 2002. Corrigendum to “On-road measurement of fine particle and nitrogen oxide emissions from light and heavy-duty motor vehicles”. Atmospheric Environment 36, 6059. Kittelson, D.B., Watts, W.F., John, J.P., 2004. Nanoparticles emissions on Minnesota highways. Atmospheric Environment 38, 9e19. Lloyd, A.C., Cackette, T.A., 2001. Critical review - diesel engines: environmental impact and control. Journal of the Air & Waste Management Association 51, 809e847. Maricq, M.M., Xu, N., 2004. The effective density and fractal dimension of soot particles from premixed flames and motor vehicle exhaust. Journal of Aerosol Science 35, 1251e1274. Maricq, M.M., Xu, N., Chase, R.E., 2006. Measuring particulate mass emissions with the electrical low pressure impactor. Aerosol Science & Technology 40, 68e79. Martel, C.R., 2000. Molecular Weight and Average Composition of JP4, JP5, JP8 and Jet A. Chemical Propulsion Information Agency Airbreathing Propulsion Manual, Columbia, MD. McGill, R., Khair, M.K., Sharp, C.A., 2003. Demonstration of Potential for Selective Catalytic Reduction and Diesel Particulate Filters. Proceedings of the 9th Diesel Engine Emissions Reduction Conference, 2003. DOE Office of Scientific and Technical Information. http://www.osti.gov/bridge/servlets/purl/ 828944-fl81pR/native/828944.pdf. Moosmüller, H., Mazzoleni, C., Barber, P.W., Kuhns, H.D., Keislar, R.E., Watson, J.G., 2003. On-Road measurement of automotive particle emissions by ultraviolet lidar and transmissometer: instrument. Environmental Science & Technology 37, 4971e4978. Nussbaum, N.J., Zhu, D., Kuhns, H.D., Mazzoleni, C., Chang, M.-C.O., Moosmüller, H., Watson, J.G., 2009. The in-plume emission test-stand: an instrument platform for the real-time characterization of fuel based combustion emissions. Journal of the Air & Waste Management Association 59, 1437e1445. Nuti, M.P., 1998. Emissions from Two-Stroke Engines. Society of Automotive Engineers, Warrendale, MI. 283. O’Shaughnessy, P.T., Slagley, J.M., 2002. Photometer response determination based on 436 aerosol physical characteristics. American Industrial Hygiene Association Journal 63, 578e585. Oshkosh Defense, 2009. MTVR: Medium Tactical Vehicle Replacement. Oshkosh Defense, Oshkosh, WI, USA. http://www.oshkoshdefense.com/pdf/Oshkosh_ MTVR_brochure_08.pdf. Shah, S.D., Johnson, K.C., Miller, J.W., Cocker III, D.R., 2006. Emission rates of regulated pollutants from on-road heavy-duty diesel vehicles. Atmospheric Environment 40, 147e153. Singer, B.C., Harley, R.A., 1996. A fuel-based motor vehicle emission inventory. Journal of the Air & Waste Management Association 46, 581e593. Tong, H.Y., Hung, W.T., Cheung, C.S., 2000. On-road motor vehicle emissions and fuel consumption in urban driving conditions. Journal of the Air & Waste Management Association 50, 543e554. U.S. DOD, 2008. Performance Quality Information System (PQIS) 2007 Annual Report. Department of Defense Defense Logistics Agency, Fort Belvoir, VA. http://www.desc.dla.mil/DCM/Files/2007PQISreport.pdf. Wang, J., Storey, J., Domingo, N., Huff, S., Thomas, J., West, B., 2006. Studies of diesel engine particle emissions during transient operations using an engine exhaust particle sizer. Aerosol Science & Technology 40, 1002e1015. Yanowitz, J., McCormick, R.L., Graboski, M.S., 2000. In-use emissions from heavyduty diesel vehicles. Environmental Science & Technology 34, 729e740. Yli-Tuomi, T., Aarnio, P., Pirjola, L., Mäkelä, T., Hillamo, R., Jantunen, M., 2005. Emissions of fine particles, NOx, and CO from on-road vehicles in Finland. Atmospheric Environment 39, 6696e6706. Zhu, D., Nussbaum, N.J., Kuhns, H.D., Chang, M.-C.O., Sodeman, D., Uppapalli, S., Moosmüller, H., Chow, J.C., Watson, J.G., 2009. In-Plume emission test stand 2: emission factors for 10 kWe100 kW U.S. military generators. Journal of the Air & Waste Management Association 59, 1446e1457.
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Short communication
Real-world PM, NOx, CO, and ultrafine particle emission factors for military non-road heavy duty diesel vehicles Dongzi Zhu a, *, Nicholas J. Nussbaum a, Hampden D. Kuhns a, M.-C. Oliver Chang b, David Sodeman a, Hans Moosmüller a, John G. Watson a a b
Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA California Air Resource Board, 9480 Telstar Avenue, El Monte, CA 91731, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 May 2010 Received in revised form 6 December 2010 Accepted 14 February 2011
Training on US military bases involves nonroad diesel vehicles with emissions that can affect base personnel, nearby communities, and attainment of air quality standards. Nonroad diesel engines contribute 44% of diesel PM and 12% of total NOx emissions from mobile sources nationwide. Although military sector fuel use accounts for only z0.4% of distillate fuel use in US, emissions factors measured for these engines improve the representation of the relatively small (as compared to onroad sources) database of nonroad emission factors. Heavy-duty multi-axle, all-wheel drive military trucks are not compatible with regular single-axle dynamometers and their emissions cannot be measured under standard laboratory conditions. We have developed a novel in-plume technique to measure in-use emissions from vehicles with elevated stack. Real-world gaseous and particulate matter (PM) emission factors (EFs) from ten 7-ton 6-wheel drive trucks and two 8-wheel drive heavy tactical Logistics Vehicle System (LVS) vehicles were measured using in-plume sampling. The EFs of these trucks are comparable to those of onroad trucks while the PM EFs of 2-stroke LVS are z10 times higher than those of onroad vehicles. Lower EC/PM ratio was observed for LVS compared with MTVR. PM number emission factors were 5.9 1014 particles km1 for the trucks and 2.5 1016 particles km1 for the LVSs, three orders of magnitude higher than the proposed European Union standard of 6 1011 particles km1. The EFs sampled can be extended to engines used in the broader nonroad sector including agriculture and mining and used as inputs to the NONROAD model. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Nonroad Diesel Emission factor Military Real-world emissions
1. Introduction Advances in fuels, engine technology, and emission regulations have reduced emissions from onroad diesel engines (Chow, 2001; Lloyd and Cackette, 2001). These technologies will soon be applied to some nonroad engines (EPA, 2004), but military heavyduty diesel vehicles (HDDVs) are not subject to these regulations. Military diesel engines are of older design to minimize complexity, simplify repair, make use of a wide variety of fuels, and to assure reliability under taxing conditions. Although military engines have been subjected to engine certification tests, few emission data are available for real-world fuels and operating conditions (Durbin et al., 2007; Zhu et al., 2009). Nonroad diesel engines contribute 44% of diesel PM and 12% of total NOx emissions from mobile sources nationwide (EPA, 2003). Although military sector fuel use
* Corresponding author. Tel.: þ1 775 674 7086; fax: þ1 775 674 7007. E-mail address: [email protected] (D. Zhu). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.02.032
accounts for only w0.4% of distillate fuel use in US (DOE, 2009), emissions factors measured for these nonroad engines provide needed additional detail to the database of nonroad emission factors. Commonly used techniques for measuring in-use fleet emissions include tunnel (Kirchstetter et al., 1999), chassis dynamometers (Yanowitz et al., 2000), and onroad studies. Onroad studies include cross-road remote sensing (Moosmüller et al., 2003; Bishop et al., 2008) and other techniques that capture exhaust plumes from tailpipes into analytical instruments. Plume-capture sampling methods include mobile laboratories (vehicles) chasing target vehicles (Kittelson et al., 2004; Yli-Tuomi et al., 2005; Canagaratna et al., 2004), or towed by the target vehicles (Shah et al., 2006). Other studies have used sampling inlets fixed close to plume (in this study) or lying on the ground (Hak et al., 2009). Another method uses on-board measurements such as commercial Portable Emissions Measurement System (PEMS) with sampling inlet inside the tailpipe (Frey et al., 2008). Military multi-axle, allwheel drive nonroad HDDVs are incompatible with the commonly used HDDV single-axle dynamometers, thus very few researches
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have been concentrated on in-use emissions from these vehicles with elevated stack. Using a novel in-plume measurement technique, this study reports real-world fuel-based emission factors from JP-8-powered nonroad vehicles operating on a southern California Marine Corps base. 2. Instrumentation and methods Emissions were characterized for ten 26,200 kg Medium Tactical Vehicle Replacement (MTVR) vehicles with 3.8 miles per gallon (mpg) fuel economy (Oshkosh Defense, 2009) and two 39,100 kg Logistics Vehicle Systems (LVS) (GlobalSecurity.org, 2010) with 2 mpg fuel economy. The MTVR is a six-wheel drive all-terrain truck powered by a Caterpillar C-12, 425 hp turbo-charged, four-stroke, 6-cylinder, 12 L displacement diesel engine. MTVRs are the Marine Corps’ prime movers for artillery, fuel, water, troops, and equipment. The LVS is a modular assortment of eight-wheel drive all-terrain trucks powered by a Detroit Diesel 8V92TA, 455 hp turbo-charged, two-stroke, 8-cylinder, 12 L displacement diesel engine. JP-8 jet fuel with an average sulfur content of 600 ppmw (U.S. DOD, 2008) was used in all tests reported here. The 8V92 TA engine consumed 12% of Marine Corps and Army nonroad fuel during 2003 (Kemme et al., 2006); fuel consumption for the C-12 engine was less than 0.8% and was not listed in the top 20 fuel consumption engines. The vehicles passed through the sampling system illustrated in Fig. 1 at cruise, acceleration, and deceleration modes with target speeds of 5e25 mph. All the test vehicles were stopped at z25 m before the sampling line and the driver was asked to accelerate the vehicle hard or to cruise it through the test section. Drivers were not always able to attain these conditions, as documented by an observer for each vehicle pass. Exhaust plumes were sampled with the in-plume experiment test stand (IPETS, Nussbaum et al., 2009) as detailed in Fig. 1. The 4 m above ground level (AGL) sensing height was selected to accommodate the 3e3.5 m AGL exhaust outlets of these vehicles. Ambient air concentrations were characterized with the IPETS at the beginning, midpoint, and end of each sampling day. CO2 mixing ratios and PM2.5 mass concentrations were measured
simultaneously with a LICOR LI-840 H2O/CO2 monitor and a PM2.5 filter sampler located z20 m upwind of the vehicle path. These background levels were subtracted from the diluted plume concentrations. Ambient temperature and relative humidity (RH) were recorded every 2 h. Average and standard deviations of ambient temperature and RH during the sampling period were 19.7 3.0 C and 17.0 7.4%, respectively. The IPETS uses a Fourier transform infrared spectrometer (FTIR) (Midac, Costa Mesa, CA) to quantify gas concentrations and an electrical low pressure impactor (ELPI) (Dekati, Finland) to measure particle size distributions (Fig. 1) with 1e2 s time resolution. Integrated PM2.5 samples were collected on Teflon and quartz-fiber filters for chemical laboratory analysis, including elemental carbon (EC), organic carbon (OC) and total carbon (TC) analysis using the Thermal/Optical Reflectance (TOR) method (Chow et al., 2005). The FTIR’s wave number scan resolution is 0.5 cm1 with a sample flow of 50 L min1 through a 2 L volume optical cell with a 10 m length folded light path and a data sampling rate of 0.66 Hz. The sampling line and cell were not heated and water was not removed from the exhaust samples. For the species of interest, particularly NO and NO2, the interference due to water vapor was minimized by the least squares algorithm within Midac’s Autoquant software (Nussbaum et al., 2009). In this configuration, most gaseous detection limits of the FTIR are z1 ppm. With roadside sampling, rapid increases in CO2 concentrations over background indicate the presence of exhaust plumes (Singer and Harley, 1996). Heywood (1988) measured undiluted exhaust CO2 concentrations of z2e3% at low loads and z10% at high loads. Bergmann et al. (2009) measured exhaust CO2 levels of 1e11%, and Kim et al. (2001) found tailpipe CO2 levels of 6% in undiluted diesel exhaust. In this study, excess CO2 concentrations sampled from the diluted exhaust through the overhead inlets ranged from 0.01% to 0.12%. Assuming an undiluted CO2 concentration range of 2%e11%, dilution ratios were in the range of 90e200. Fuel-based emission factors (EFi, g of pollutant i emitted per kg of fuel burned) are calculated from the ratio of each pollutant to the sum of excess CO2 and CO. These ratios are related to the fuel carbon content and the amount of fuel consumed (Moosmüller et al., 2003) as
Fig. 1. Experimental configuration for sampling vertical exhaust from nonroad military vehicles. A portion of the exhaust plume was drawn through the sampling ports at w180 L min1 a 4 m high sampling inlet pipe was set up across a paved tank trail and suspended on each end using two scissor-jacks. A z 9.5 m long rigid conductive sampling pipe (1.91 cm ID) with inlets mounted at z1 m intervals was connected to the IPETS system via flexible conductive tubing 4.7 m long (2.54 cm ID) with the IPETS system located in a cargo van on the side of the road. FTIR measures gas concentrations (CO, CO2, NO, NO2, N2O, NH3, SO2, propane, ethylene, and hexane). ELPI measures real-time particle number size distributions. Grimm Optical Particle Counter (OPC) measures particle size resolved number concentration. IPETS included two DustTrak Model 8520 (TSI, Shoreline, MN) light scattering monitors (O’Shaughnessy and Slagley, 2002) measuring PM2.5 and PM10 mass concentrations. Filters collected PM2.5 using a 113 L min1 flow rate Bendix cyclone. Transit time from plume extraction to the measurement instruments was z3 s. Curves in the flexible conductive sampling line exceeded a 30 cm radius to minimize particle deposition.
D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
EFi ¼ CMFdiesel
ri rCO2
3. Results and discussion
CMFCO2 þ CMFCO rrCO þ CMFHC rrHC CO2
(1)
CO2
where ri, rCO2, rCO, rHC are the excess (above ambient) concentrations (mg m3) of pollutant i, CO2, CO, and HC, respectively, CMF is the carbon mass fraction of each pollutant with CMFCO ¼ 42.9%, CMFCO2 ¼ 27.3%, and CMFdiesel ¼ 86.6% (assuming CH1.85 for dieselfuel), CMFjp-8 ¼ 86.3% with average formula C11H21 for JP-8 (Martel, 2000). CO and CO2 typically account for more than 99% of the carbon emitted in engine exhaust (Yanowitz et al., 2000). Fig. 2 shows measured CO2 mixing ratios and fine particle number concentrations as a function of time. Data within the z25 s width peak width can be isolated to estimate EFs for each vehicle. Background-subtracted peak CO2 mixing ratios averaged 239 ppm with a range of 100e1200 ppm. Particle number concentrations from the first six ELPI stages (D50 < 322 nm) were used following the Maricq et al. (2006) algorithm to estimate PM mass concentrations (mg m3) from the ELPI measurements. Diesel particles are fractal-like agglomerates of nucleation particles (approximately spherical 10e30 nm diameter primary particles) and their density decreases with particle size (Maricq and Xu, 2004), but standard ELPI number-to-mass conversions assume a spherical shape and unit density. Some of the numerous smaller particles may also deposit on the larger particle impactor stages, thereby positively biasing mass concentration estimates. ELPI PM mass was shown to be as much as 115% higher than gravimetric filter mass for diesel exhaust (Maricq et al., 2006).
a
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15:21
Time (hr:min) on 2007-04-19 Fig. 2. Exhaust CO2 mixing ratio peaks detected by FTIR and PM number concentration peaks recorded by ELPI for MTVR (a) and LVS (b) passes. The LVS PM concentration peaks are 10 times higher than those of MTVR. The PM peaks lagging CO2 peaks is due to the different residence time (flow rate, size of the tubing) in sampling lines before the plume enters the instruments.
Fuel-based EFs vary less than time- and distance-based EFs as vehicle instantaneous speed changes (Tong et al., 2000). EFs from a single vehicle are expected to vary based on the mode of operation (i.e., slight and hard acceleration and cruise). Time-based NOx emissions (in g s1) (Cernuschi et al., 1995) and PM number concentration emissions (in particles s1) (Wang et al., 2006) from diesel engines were found to be higher in acceleration mode than in cruise and idle modes. Fuel-based EF variation could not be calculated from these measurements since CO2 mass concentration (indicator of fuel use) was not concurrently measured. Shah et al. (2006) reported that the distance-based EFNOx (g mi1) for creep mode (simulates vehicle operation in heavily congested conditions) were three times higher than those for the transient (simulates vehicle operation on arterial roads) and cruise (simulates freeway driving) modes. These three modes plus start/idle mode constitute the ARB fouremode cycle (Table 1). Fuel-based EFCO, EFPM were lowest for cruise mode, EFPM was highest for transient mode, and EFCO was highest for creep mode, consistent with more efficient steady-state engine conditions during cruise mode. EFNOx was highest for cruise and lowest for transient mode, consistent with the lower engine temperatures at the lower speeds encountered during transient mode operation. Vehicle operating modes in this study, with a typical vehicle speed of z15 mph, correspond most closely to the transient mode. Creep and cruise mode EFs might be extrapolated from the results of Shah et al. (2006); the cruise mode EFNOx is 180% of the EFNOx in transient mode measured from this study (Table 1). For the MTVRs, five tests approximated a steady-state cruise, five tests approximated a slight acceleration, and twenty tests approximated a hard acceleration. For the LVSs, two tests corresponded to a steady-state cruise and four tests corresponded to a hard acceleration. Five MTVRs and one LVS passed through the test section multiple times. Average NOx EFs during cruise were higher than EFs during acceleration for both LVSs and MTVRs. Average PM EFs during acceleration were higher than those during cruise. The relationships between fuel-based emission factors and operating mode (e.g., accelerating, cruise) are not significant at p < 0.05. This might be due to the relatively low vehicle speeds (<25 mph) and the qualitative recording of operating modes by a field assistant. An example of CO2 mixing ratios in the exhaust plume as function of time and of time-integrated CO2 mixing ratio peak areas from the LICOR and FTIR are shown in Fig. 3. Fig. 4 shows that EFs were most comparable among tests when CO2 peak areas exceeded 400 ppm-s, which appears to be the effective detection limit for the test methodology, and tests with CO2 peak areas <400 ppm-s were excluded from further analyses. Of the 258 vehicle passes, 36 met this criterion owing to variable meteorological conditions; most of the plumes did not encounter the sampling inlet. Table 2 summarizes the NOx, CO, and PM EFs for these valid tests. Consistency among the tests is evident for average MTVR EFNOx, ranging from 24 to 33 g NOx kg fuel1. However, MTVR EFCO were not as consistent among tests, ranging from 2.7 to 24 g CO kg fuel1. Five MTVRs were measured with only one pass. These measurements were within the range of EFNOx of those MTVRs with multiple passes (Table 1). Variations in EFPM were greatest between vehicle types (4-stroke MTVR vs. 2-stroke LVS) as opposed to operating mode (acceleration vs. cruise). For EFCO, the variability of repeated emission measurements for the same vehicles (i.e. MTVRs #593901 and #543901 with coefficients of variation (COV) of 94% and 113%) are higher than the 72% COV for fleet average EFCO (Table 2). The MTVR #543901 tests included three cruise and two hard acceleration modes, consistent
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D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
Table 1 Distance-based fleet average emission factors for CO, NOx and PM emitted by 11 onroad heavy-duty diesel engines from Shah et al. (2006) running on ARB HDDT 4-mode cycle (Gautam et al., 2002). Only distance-based EFs were reported in that study, while converted fuel-based EF trends can better explain the EF variation with operating modes. Based on average speed, vehicle operating mode is in this study more closely associated with the transient mode, the EFs of nonroad military vehicles operating at creep and cruise modes can be adjusted.
EF EF EF EF EF EF
Creepa (g mi1) Transientb (g mi1) Cruisec (g mi1) Creep (g kg1) Transient (g kg1) Cruise (g kg1)
Average Speed (mph)
CO2
CO
1.77 15.4 39.9 1.77 15.4 39.9
5024 682 2933 221 1808 190
19.9 7.90 3.38 7.95 5.41 3.75
Fuel-based EF Ratio to Transient mode Creep Cruise
NOx
9.33 4.16 1.72 3.73 2.85 1.91
PM
75.2 25.5 28.1 32.18 18.69 33.42
1.47 0.69
31.7 8.59 11.3 13.57 6.30 13.44
1.02 0.656 0.215 0.355 0.391 0.208
1.72 1.79
0.426 0.153 0.201 0.148 0.091 0.195
0.91 0.53
a Creep mode represents very low speed truck operation with a maximum speed of 8.24 mph. The EFCO would be 1.47 of, EFNOx would be 1.72 of, and EFPM would be 0.91 of EFs at Transient mode, respectively. b Transient mode of HDDT is a 10 min drive that simulates the vehicle stopping and going at an average speed of 20 mph, it involves sharp acceleration and deceleration with a peak speed of <50 mph. c Cruise mode of HDDT cycle, which is representative of truck driving on the interstate highway, is a 2000 s cycle with a speed z60 mph continuously for about 1400 s. The EFCO would be 0.69 of, EFNOx would be 1.79 of, and EFPM would be 0.53 of EFs at the Transient mode, respectively.
a
FTIR CO2 LICOR CO2
1850
160 140 120
CO EF (g/kg fuel)
with the operating mode having a larger influence on EFCO than the specific engine. The LVS EFPM was z10 times the MTVR EFPM, consistent with prior observations for two-stroke vs. four-stroke diesel engines (Ålander et al., 2005). Higher EFPM result from the oil-fuel mixture
40 20
0
1000
2000
1350
3000
4000
5000
6000
7000
8000
9000
7000
8000
9000
Peak CO2 Area (ppm-s) 100
1100
NOx (as NO2) EF (g/kg fuel)
CO2 (ppm)
60
-20
850 600 350 14:29:46
14:31:55
14:34:05
14:36:14
14:38:24
Time on 2007-04-19
50
0
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Licor Area = 0.98 FTIR Area + 78.55 R = 0.66
7000
0
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6000
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PM EF (g/kg fuel)
Licor-CO2 peak-area (ppm-s)
80
0
1600
b
100
3000 2000 1000 0 0
1000
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4000
5000
6000
7000
8000
FTIR CO2 peak-area (ppm-s) Fig. 3. (a) CO2 mixing ratio peaks detected by FTIR and LICOR. LICOR response time is 1 s and sampling flow rate is 1 L min1, FTIR has response time of 1.5 s and sampling flow rate of 50 L min1, the LICOR has shorter and higher peaks, FTIR has a longer and lower peaks. (b) Correlation between CO2 mixing ratio peak area (ppm-s) by FTIR and LICOR. The integrated exhaust CO2 mixing ratio peak areas from the LICOR and FTIR showed negligible systematic bias.
LVS
10
LVS
8 6
LVS
4 LVS
2
LVS
LVS
0 -2
0
1000
2000
3000
4000
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Peak CO2 Area (ppm-s) Fig. 4. NOx, CO, and PM EF distribution vs. peak CO2 mixing ratio area for each vehicle pass. Error bars represent the propagated analytical uncertainty of the EF.
D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
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Table 2 Fuel-based average CO, NOx and PM Emission Factors for each vehicle tested. NOx EF (g kg1) Vehicle ID
Number of tests
Average
LVS 557098 LVS 550978 MTVR 593901 MTVR 543904 MTVR 594191 MTVR 542995 MTVR 543901 MTVR 593947 MTVR 593987 MTVR 596373 MTVR 595930 MTVR 596393
1 5 10 6 2 2 5 1 1 1 1 1
32 33 31 26 30 33 24 18 36 2.1 13 14
CO EF (g kg1) SDa
COVa
4.6 20 2.9 19 8 24
Average 2.6 5.3 14 2.7 5.8 17 24 2.4 7.8 11 11 2.2
14% 64% 11% 64% 24% 104%
PM EF (g kg1) SD
COV
3.2 13 1.4 4.2 9.2 27
Average 2.5 6.1 0.68 0.37 0.32 0.39 0.36 0.06 0.17 0.55 0.08 0.57
60% 94% 51% 73% 55% 113%
SD
COV
4.4 0.36 0.19 0.17 0.18 0.16
73% 52% 51% 52% 47% 44%
a SD ¼ standard deviation and COV ¼ Coefficient of variation of average emission factors for the series of tests on a given vehicle. These are not reported for vehicles that were measured one time only.
lubrication employed in two-stroke engines, as the unburned or partially burned lubricating oil exits the engine with the exhaust (Nuti, 1998). As the PM2.5 chemical speciation, higher EC/PM2.5 ratio was observed for MTVR group (55 14%) than LVS group (42 15%) (at 95% confidence), this might due to higher OC emissions from LVS’s unburned lubricating oil. Elevated n-alkanes abundances (4.6 4.1% of PM2.5) were found for MTVR, with ∼0.4% found for LVS. Branched n-alkanes were 0.5 0.59% of PM2.5 for MTVR and 0.05 0.03%for LVS. PAHs were low, in the range of 0.03e0.08% of PM2.5. Table 3 compares average EFs from this experiment with values from emission models and tests of similar vehicles. Power-based and distance-based EFs are often reported in addition to fuel-based EFs and conversions are needed for comparison. Heavy-duty diesel engines are certified using power-based EFs (g of pollutant kW h1). Units of g kW h1 are converted to g kg fuel1 (EPA, 1996) assuming an average brake-specific fuel consumption (BSFC) of 9900 kJ kW h1 and a diesel-fuel heating value of 44900 kJ kg1, yielding BSFC ¼ 0.22 kg kW h1. A 2000 model year (MY) Caterpillar C-12 450 HP engine (same model engine used in MTVR) was measured with a BSFC of 0.22 kg kW h1 with DECSE (Diesel Emission Control Sulfur Effects program) 3 ppmw sulfur fuel
(McGill et al., 2003). Vehicle-sales-weighted BSFC for class 8A (GVW range 15000e27220 kg) HDDV diesel trucks for MY 1995 were reported as 0.24 kg kW h1 for input to MOBILE6 (Browning, 1998). JP-8 has a heating value z43400 kJ kg1 (Fernandes et al., 2007). As tested on an MY2002 engine, the BSFC for JP-8 in a 6.5 L GM heavy-duty diesel engine was 8% lower than that for low sulfur diesel (Frame and Blanks, 2003). A BSFC of 0.22 kg kW h1 is used here to relate fuel-based to power-based EFs. Fuel-based EFs are related to distance-based (g mi1) EFs by assuming a fuel-density of 0.85 kg L1 (7.1 lb gal1) and the 3.8 mpg MTVR and 2 mpg LVS fuel economies cited above. Table 3 summarizes and compares fuel-, power-, and distance-based average CO, NOx and PM EFs for the MTVR and LVS engines. Average MTVR EFCO was more than twice that of the LVS. Average LVS EFNOx was 44% higher than that for the MTVR. The average LVS EFPM was more than 10 times that of the MTVR. The NONROAD emission model (EPA, 2005a,b) provides EFs for state and local inventories based on the proportions of different engine types in the fleet. The NONROAD equipment code 2270002051 is closest to the engines tested here, corresponding to a Tier 1 (MY 2000e2005) off-highway truck with power range 300e600 hp and power-based EFs presented in Table 3. The MTVR EFPM is z25% of the
Table 3 Fleet average CO, NOx and PM Emission Factors in fuel-based, distance-based and power-output based units, respectively. Uncertainty terms are standard deviations of all measurements of each vehicle type. Other modeling and studies EF result are also compared. Vehicle Type (Number of vehicles)
Average EF CO
Average EF NOx
PM
CO
(g kg fuel1) MTVR (10) LVS (2) NONROAD Off-highway trucksa NONROAD construction equipmentb Nonroad excavatorsc Nonroad gradersd Onroad HDDV transient modee Diesel trucks (5)f Tunnel HDDVg HDDV chassis dynamometer testh a
23 11 33 0.7
0.36 0.21 4.3 2.5
17.9 3.2 4.6
34.1 34.6 33.7
3 0.25 0.26
18 70
PM
CO
(g mile1)
9.8 7.1 3.9 1.9
26.4
Average EF NOx
NOx
PM
(g kW h1)
8.3 6.0 6.3 3.0
19 9.0 53 1.1
0.30 0.18 6.9 4.0
7.9
25.5 8 e 22
0.66 0.22 e 0.52
2.1 1.6 0.8 0.4 2.7 1.6 e 12
5.0 2.4 7.2 0.1 7.6 6.2 e 10
0.08 0.04 0.95 0.55 0.34 0.31 e 1.81
0.44 3.58
EPA NONROAD EF for Tier 1 off-highway trucks with 300e600 hp engines, Source Classification Code 2270002051. EPA NONROAD model EF range for Tier 1 diesel-powered construction equipment (including off-highway trucks and tractors, loaders and backhoes, excavators) with 3e3000 hp engines, Source Classification Code 2270002066. c EF of a 2001 254 hp Komatsu excavator measured by an on-board emissions monitor. PM is an opacity-based measurement (Abolhasani et al., 2008). d EF of a 2001 195 hp Cummins motor grader measured by PEMS, PM is opacity-based measurement (Frey et al., 2008). e Fleet average EF of 11 trucks under transient mode with an average speed of 15.4 mph (Shah et al., 2006). f EF for 5 diesel trucks under normal speed range from 16 to 34 mph (Durbin et al., 2008). g Tunnel study with early morning HDDV dominant sampling (Grieshop et al., 2006). h Chassis dynamometer tests of Class 8b HDDV trucks (Yanowitz et al., 2000). b
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D. Zhu et al. / Atmospheric Environment 45 (2011) 2603e2609
Table 4 Fleet average PM Number Emission Factors and comparison with other studies. Uncertainties are one standard deviation. Ultrafine particles calculation using PM data from stage 1e4 of ELPI. The impactor stage 4 has a D50 of 100 nm. Vehicle
Number of vehicles
Fine (<322 nm) PM EF (particles kg fuel1)
Fine (<322 nm) PM EF (particles km1)
Ultra fine (<100 nm) PM EF (particles kg fuel1)
Ultra fine (<100 nm) PM EF (particles km1)
MTVR (7-ton Truck) LVS HDVa HDVb On-road vehiclesc On-Road Measurementd
10 2
(1.1 1.1) 1015 (2.5 1.2) 1016 (6.3 1.9) 1015 (8.2 2.5) 1015 (1.0e3.1) 1015 8.3 1015
(5.9 6.0) 1014 (2.5 1.2) 1016
(1.1 1.1) 1015 (2.4 1.1) 1016
(5.6 5.8) 1014 (2.4 1.1) 1016
a
Tunnel study of HDV using CNC, particles > 10 nm, (Kirchstetter et al., 1999, 2002). Tunnel study of HDV using SMPS-CPC, particle range 7e270 nm (Geller et al., 2005). c On-road measurement with SMPS particle sampling range 8e300 nm, on Minnesota highways with diesel vehicles to gasoline vehicles ratio of w0.09 (Kittelson et al., 2004). d On-road measurement with ELPI particle range 7e960 nm, Helsinki metropolitan areas, Finland. (Yli-Tuomi et al., 2005). b
NONROAD value while the LVS PMEF is 184% of the NONROAD estimate. NONROAD EFs are based on an 8-mode steady-state cycle implemented on an engine dynamometer that differs from the realworld test reported here. The LVS EFPM is more similar to those for NONROAD construction equipment category (equipment code 22700020666), with a range of 0.31e1.81 g PM kW h1. Table 3 also compares results from this study with those from other real-world emission experiments. Durbin et al. (2008) reported NOx and PM EFs for five diesel trucks (MY 1996e2005) during “normal” traffic in a Stockton, CA business district, with speed ranging from 16 to 34 mph. NOx EFs ranged from 8 to 22 g mi1 and PM EFs ranged from 0.22 to 0.52 g mi1. These results are comparable to the EFNOx of 19.3 g mi1 and the EFPM of 0.3 g mi1 for the 7 ton truck MTVRs in this study. The MTVR and LVS EFs are not appreciably different from similar size and model year onroad truck EFs operating under similar transient modes. Although not currently regulated, ultrafine particles (UP) are becoming of greater concern for public health and EFUP from MTVR and LVS are compared with results from similar studies in Table 4. EFUP from MTVR and LVS span the range of values found in other studies. These EFPM are three orders of magnitude higher than the proposed European Union UP standards of 6 1011 particles km1 (EU, 2009). 4. Summary and conclusions Emission factors from in-use military heavy-duty nonroad vehicles with high stacks were measured with a novel in-plume technique. Fleet average EFs for MTVR trucks were 9.8 g CO kg fuel1, 23 g NOx kg fuel1 and 0.36 g PM kg fuel1. Fleet average EFs for LVSs are 3.9 g CO kg fuel1, 33 g NOx kg-fuel1, and 4.3 g PM kg fuel1 (Table 3), driving conditions varied between slight and hard accelerations and cruise at a target speed of 5e25 mph passing the elevated sampling inlet. Two-stroke LVS had 10 times higher PM EFs than the average PM EFs of 4-stroke MTVRs, lower EC/PM2.5 ratio was observed for LVS (42%) compared with MTVR (55%) (at 95% confidence). With MTVR’s and LVS’s fuel economy of 3.8 mpg and 2 mpg, respectively, the fuel-based EFs were also converted into distance-based emission factors; with 0.22 kg kW h1 BSFC value assumed, fuel-based EF were converted into power-output based EFs, to compare with modeling and other studies reporting EF in different units. Test results for the four-stroke MTVR are comparable with emission factors used in EPA’s NONROAD model and results from tunnel, chassis dynamometer, and in-use HDDV emissions measurements. EFs for military nonroad trucks are not significantly different from those for onroad trucks with similar engine size, model year, and operating under similar transient modes. LVS’s 10 times higher PM EF still falls in the NONROAD offroad construction equipments EF range. The particle number
emission factors from this study were comparable with results reported by other researchers for their tunnel or onroad chasing experiments, but 1000 times higher than EU proposed PM number emission standards. Although for both LVS and MTVR, the average NOx EFs in cruise mode are higher than those in the acceleration mode and average PM EFs in the acceleration mode are higher than those in the cruise mode, these differences are not statistically significant at the p < 0.05 level. This might be due to the fact that in this study all vehicles were driving at relatively low speed (<25 mph) and the operating modes were only qualitatively recorded by a field assistant. Application of these EFs for emission inventory purposes should consider the operating modes of the modeled fleet and adjust the EFs based on the relative trends observed by Shah et al. (2006). The EFs sampled can be extended to engines used in the broader nonroad sector including agriculture and mining and can also be used to improve the representativeness of the EFs in EPA’s NONROAD model. The variation of EFs from different vehicles in this study indicates the need of measurements from multiple vehicles to accurately quantify fleet averages for use in emission inventories. Acknowledgements This work was partially sponsored by the Federal Transit Administration (Contract 26-70030) and the Strategic Environmental Research and Development Program (Contract CP-1336). The U.S. Marine Corps provided extraordinary cooperation and assistance in providing access to vehicles and assisting with tests. References Abolhasani, S., Frey, H.C., Kim, K., Rasdorf, W., Lewis, P., Pang, S.H., 2008. Real-world in-use activity, fuel use, and emissions for nonroad construction vehicles: a case study for excavators. Journal of the Air & Waste Management Association 58, 1033e1046. Ålander, T., Antikainen, E., Raunemaa, T., Elonen, E., Rautiola, A., Torkkell, K., 2005. Particle emissions from a small two-stroke engine: effects of fuel, lubricating oil, and exhaust aftertreatment on particle characteristics. Aerosol Science & Technology 39, 151e161. Bergmann, M., Kirchner, U., Vogt, R., Benter, T., 2009. On-road and laboratory investigation of low-level PM emissions of a modern diesel particulate filter equipped diesel passenger car. Atmospheric Environment 43, 1908e1916. Bishop, G.A., Stedman, D.H., 2008. A decade of on-road emissions measurements. Environmental Science & Technology 42, 1651e1656. Browning, L., 1998. Update Heavy-Duty Engine Emission Conversion Factors for MOBILE6: Analysis of BSFC and Calculation of Heavy-Duty Engine Emission Conversion Factors. Arcadis Geraghty & Miller, Inc., Mountain View, CA. EPA Report No. 420-P-98-015. Canagaratna, M.R., Jayne, J.T., Ghertner, D.A., Herndon, S., Shi, Q., Jimenez, J.L., Silva, P.J., Williams, P., Lanni, T., Drewnick, F., Demerjian, K.L., Kolb, C.E., Worsnop, D.R., 2004. Chase studies of particulate emissions from in-use New York city vehicles. Aerosol Science & Technology 38, 555e573. Cernuschi, S., Giugliano, M., Cemin, A., Giovannini, I., 1995. Modal analysis of vehicle emission factors. Science of the Total Environment 169, 175e183.
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