Particulate emission factors for mobile fossil fuel and biomass combustion sources

Science of the Total Environment 409 (2011) 2384–2396

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Science of the Total Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v

Particulate emission factors for mobile fossil fuel and biomass combustion sources John G. Watson a,⁎, Judith C. Chow a, L.-W. Antony Chen a, Douglas H. Lowenthal a, Eric M. Fujita a, Hampden D. Kuhns a, David A. Sodeman a,1, David E. Campbell a, Hans Moosmüller a, Dongzi Zhu a, Nehzat Motallebi b a b

Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, United States California Air Resources Board, Research Division, 1001 I Street, Sacramento, CA 95812, United States

a r t i c l e

i n f o

Article history: Received 12 July 2010 Received in revised form 23 February 2011 Accepted 25 February 2011 Available online 1 April 2011 Keywords: Emission factors Motor vehicle Biomass burning

a b s t r a c t PM emission factors (EFs) for gasoline- and diesel-fueled vehicles and biomass combustion were measured in several recent studies. In the Gas/Diesel Split Study (GD-Split), PM2.5 EFs for heavy-duty diesel vehicles (HDDV) ranged from 0.2 to ~ 2 g/mile and increased with vehicle age. EFs for HDDV estimated with the U.S. EPA MOBILE 6.2 and California Air Resources Board (ARB) EMFAC2007 models correlated well with measured values. PM2.5 EFs measured for gasoline vehicles were ~ two orders of magnitude lower than those for HDDV and did not correlate with model estimates. In the Kansas City Study, PM2.5 EFs for gasoline-powered vehicles (e.g., passenger cars and light trucks) were generally b 0.03 g/mile and were higher in winter than summer. EMFAC2007 reported higher PM2.5 EFs than MOBILE 6.2 during winter, but not during summer, and neither model captured the variability of the measured EFs. Total PM EFs for heavy-duty diesel military vehicles ranged from 0.18 ± 0.03 and 1.20 ± 0.12 g/kg fuel, corresponding to 0.3 and 2 g/mile, respectively. These values are comparable to those of on-road HDDV. EFs for biomass burning measured during the Fire Laboratory at Missoula Experiment (FLAME) were compared with EFs from the ARB Emission Estimation System (EES) model. The highest PM2.5 EFs (76.8 ± 37.5 g/kg) were measured for wet (N50% moisture content) Ponderosa Pine needles. EFs were generally b 20 g/kg when moisture content was b 20%. The EES model agreed with measured EFs for fuels with low moisture content but underestimated measured EFs for fuel with moisture content N 40%. Average EFs for dry chamise, rice straw, and dry grass were within a factor of three of values adopted by ARB in California's San Joaquin Valley (SJV). Discrepancies between measured and modeled emission factors suggest that there may be important uncertainties in current PM2.5 emission inventories. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Particulate matter (PM) emissions affect the Earth's climate (MacCracken, 2008a, 2008b), visibility (Chow et al., 2002; Watson, 2002), surface soiling (Sabbioni and Brimblecombe, 2003; Sabbioni et al., 2003), crop productivity (Grantz et al., 2003), and human health (Chow et al., 2006; Mauderly and Chow, 2008; Pope, III and Dockery, 2006). Annual emission rates are compiled by states, provinces, and countries (CARB, 2009a; Environment Canada, 2008; EPD, 2008; U.S. EPA, 2008a) in a bottom-up approach to estimate primary PM2.5 and PM10 (PM mass with aerodynamic diameters less than 2.5 and 10 μm,

⁎ Corresponding author at: Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, United States. Tel.: +1 775 674 7046; fax: +1 775 674 7009. E-mail address: [email protected] (J.G. Watson). 1 Present Address: San Diego Air Pollution Control District, San Diego, CA, USA. 0048-9697/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2011.02.041

respectively), carbon monoxide (CO), reactive organic gasses (ROG, sometimes termed total non-methane hydrocarbons [NMHC] or volatile organic compounds [VOCs]), sulfur dioxide (SO2), oxides of nitrogen (NOx), and sometimes ammonia (NH3). These inventories are usually expressed as tons/year or tonnes/year and are derived as the products of emission factors (EFs) and activities for different source categories (Mobley et al., 2005). PM2.5 and PM10 mass emissions can be sub-divided into chemical components by applying source profiles (Watson, 1984; Watson et al., 2008a), or the mass fraction of each measured chemical component in primary emissions for each source category (CARB, 2009b; U.S. EPA, 2007). The majority of PM mass from combustion sources such as engine exhaust and biomass burning is in the PM2.5 fraction (Lighty et al., 2000; Lloyd and Cackette, 2001), and emission rates and compositions have changed as new fuels and combustion technologies have been adopted (Chow, 2001). In 2006, mobile fossil fuel and biomass combustion sources accounted for 16 and 47% of PM2.5 emissions, respectively, and 43 and 53% of black carbon (BC) emissions,

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respectively, in California (Chow et al., 2010). PM2.5 EFs were measured in the Gas/Diesel Split Study (GD-Split; Fujita et al., 2007a, 2007b), the Kansas City Study (Kishan et al., 2006; Nam et al., 2008; U.S. EPA, 2008b, 2008c), the Strategic Environmental Research and Development Program (SERDP; Watson et al., 2008b), and the Fire Laboratory at Missoula Experiment (FLAME; McMeeking et al., 2008). Measurements from these studies are summarized, evaluated, and compared with those from California's emission inventory. Because PM2.5 from engine exhaust and biomass burning are primarily composed of organic and elemental carbon (OC and EC), accurate emission estimates are needed to evaluate future climaterelated emission control strategies (Bond and Sun, 2005; Jacobson, 2002) as well as to attain National Ambient Air Quality Standards (Bachmann, 2007; Chow et al., 2007a) for PM2.5. 2. Emission characterization studies 2.1. Diesel and gasoline engine emission factors The GD-Split Study measured exhaust from 53 light-duty vehicles (52 gasoline- and 1 diesel-fueled) and 34 light-, medium-, and heavyheavy-duty diesel-fueled vehicles (HDDV). Dynamometer emission tests were conducted at the Ralphs Grocery distribution center in Riverside, California, during the summer of 2001 (June 2–23 for lightduty gasoline- and diesel-fueled vehicles and from July 20 to September 19 for HDDV). Emissions were sampled into a constantvolume sampler with continuous monitoring for CO, CO2, NMHC, and NOx, and integrated filter sampling for PM2.5 mass, elements, ions, OC, EC, and organic compounds. PM2.5 emission rates were estimated using the MOBILE 6.2 (Cook et al., 2007; U.S. EPA, 2008d) and EMFAC2007 (CARB, 2007) emission models under conditions corresponding to those in the GD-Split Study tests (Fujita et al., 2007a, 2007b). The EMFAC2007 model considers technology group and odometer mileage in addition to vehicle model year. The MOBILE 6.2 model accounts for vehicle type and age but omits the influence of fuel type, mileage, driving mode, and vehicle maintenance (Rakha et al., 2003; McCarthy et al., 2006). The MOtor Vehicle Emission Simulator (MOVES) model (http://www.epa.gov/otaq/models/ moves/index.htm) improves on MOBILE 6.2, but was not available at the time of this analysis. MOBILE 6.2 calculates EFs in grams per vehicle mile traveled (g/VMT) for PM2.5 mass, lead (Pb), sulfate (SO4 =), OC, and EC from gasoline- and diesel-engine exhaust, as well as for brake and tire wear. EMFAC2007 estimates non-speciated PM2.5 and PM10 EFs (in g/VMT). To facilitate comparisons of model estimates with dynamometer measurements, which only account for tailpipe emissions, MOBILE 6.2 PM2.5 EFs were calculated from the sum of Pb, SO4=, OC, and EC emissions for gasoline- and diesel-fueled vehicles. Gasoline-fueled vehicles were operated according to a modified California Unified Driving Cycle Schedule (UDC; DieselNet, 2008). The UDC is more aggressive in terms of acceleration and maximum speeds than the Federal Test Procedure (FTP), especially during the hotstabilized portion of the cycle. The gasoline-fueled vehicles were tested under “Warm Start” (WS) and “Cold Start” (CS) cycles. The HotCity-Suburban (HCS) Heavy Vehicle Route and Highway Cycle (HW) were used in HDDV tests. Additional HDDV test cycles included the Cold-City-Suburban (CCS) Heavy Vehicle Route, hot idle (ID) and cold idle (CID) periods, a City-Suburban Heavy Vehicle Route with Jacobs Brake (CSJ), and a Heavy-Duty Urban Dynamometer Driving Schedule (UDDS). Busses were tested on the HCS and Manhattan Cycle (MC) for Transit Busses cycles. Observed and model-estimated PM2.5 EFs for diesel-fueled vehicles are presented in Fig. 1. Because some EFs from the GD-Split Study represented composites of exhaust from more than one vehicle, the corresponding MOBILE 6.2 and EMFAC2007 model estimates are presented as ranges. Table 1 includes 23 gasoline-fueled vehicle

Fig. 1. Comparisons of measured diesel-fueled vehicle PM2.5 emission factors (EFs) for the Hot City-Suburban route (HCS) driving cycle during the Gas/Diesel Split Study with MOBILE 6.2 and EMFAC 2007 model estimates for the Federal Test Procedure (FTP) cycle for each diesel group. See Table 1 for vehicle identification codes and composite information. Composites in each diesel group (light/medium heavy-duty, heavy heavyduty, and urban bus) are ordered by the average vehicle model year. Error bars associated with the Gas/Diesel Split Study data indicate measurement uncertainties.

sample composites tested under WS and CS cycles, and 17 dieselfueled vehicle sample composites tested under various cycles. Because the HCS cycle is common for all heavy-duty diesel vehicles in this study (Table 1), HCS PM2.5 EFs are compared with MOBILE 6.2 and EMFAC2007 model estimates (for the FTP cycle) in Fig. 1 with the understanding that EFs for other cycles may differ (Fujita et al., 2007b). Fig. 1 shows that the EMFAC2007 model slightly overestimated diesel-fueled vehicle emissions for GD-Split Study tests, especially for low emitters, but the overall agreement was good (r2 = 0.8) considering the variability among individual vehicles. MOBILE 6.2 underestimated measured diesel vehicle EFs (within an order of magnitude), and correlation with measurements was moderate (r2 = 0.63). Differences between minimum and maximum EF estimates by MOBILE 6.2 were small. Both MOBILE 6.2 and EMFAC2007 estimated an increase in diesel vehicle PM2.5 EFs with vehicle age (i.e., the difference between calendar year [2001] and vehicle model year), as shown in Fig. 2. Inter-cycle comparisons of measured EFs for typical EMFAC2007 medium heavy-duty vehicles (14,001–33,000 lbs) and heavy-heavyduty vehicles (N33,000 lbs) are presented in Fig. 3. Both CCS and HCS cycles produced similar EFs which were about double those of HW cycle EFs. Sample composite CI-9e (Table 1) on the UDDS (i.e., FTP) cycle produced an EF ~20% lower than those measured on the HCS or CCS cycles. The GD-Split Study gasoline-fueled vehicles were either passenger cars (LDA) or light-duty trucks (LDT). Their emissions were often mixed in a composite sample (Table 1). MOBILE 6.2 and EMFAC2007 reported distinct EFs for LDA and LDT vehicles, resulting in a wider range of EFs. Information on vehicle maintenance was not available and is not reflected in MOBILE 6.2 and EMFAC2007 EF estimates. The comparisons in Fig. 4 show that measured EFs for the WS and CS cycles were more variable than the modeled EFs, especially for vehicles manufactured before 1989 (Fig. 4a). A few high-emitting vehicles (often referred to as smokers) produced clear outliers (see footnote to Fig. 4), and all vehicles manufactured after 1995 displayed lower measured than modeled EFs. Fig. 5 compares the GD-Split Study PM2.5 EFs for gasoline-fueled vehicles under WS and CS cycles. EFs for CS (Fig. 5b) were higher than those for WS (Fig. 5a) with a few exceptions. Modeled and measured diesel-fueled vehicle EFs from Fig. 2 are superimposed in

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Table 1 Descriptions of vehicle sample composites during the Gas/Diesel Split Studya. Vehicle test cycleb

MOBILE model vehicle categoryc

EMFAC model vehicle categoryd

Number of vehicles tested

Range of vehicle model year

Odometer range (miles)

SI_1_1 SI_2_1 SI_3_1 SI_4_1 SI_5_1 SI_5_2 SI_6_1 SI_6_2 SI_6_3 SI_7_1 SI_7_2 SI_7_3 SI_8_1 SI_8_2 SI_8_3e SI_9_1 SI_9_2 SI_9_3 SI_9_4e SI_10_1 SI_10_2 SI_10_3 SI_10_4e LCI-11_1 CI-10 CI-11 CI-11e CI-11n CI-12 CI-13.1 CI-13.2 CI-4r CI-5 CI-8r CI-9e CI-9n CI-Ia CI-Ib CI-II CI-IIb

CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS; WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CS, WS CCS, HCS, HW HCS, HW HCS, HW, CID, ID CCS, HCS, HW, ID CCS, HCS, HW HCS, MC HCS, MC HCS HCS, HW HCS CCS, HCS, CSJ, HW, CID, UDDS CCS, HCS, HW, ID CCS, HCS, HW CCS, HCS, HW HCS, HW,ID HCS, HW

LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDGV/LDGT1 LDDT12 HDDV8 HDDV8 HDDV8 HDDV8 HDDV8 HDDBT HDDBT HDDV3 HDDV7 HDDV5 HDDV8 HDDV7 HDDV3 HDDV2/HDDV3 HDDV6 HDDV6

LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDA/LDT1 LDT1 HHDV HHDV HHDV HHDV HHDV UB UB LHDT2 MHDT MHDT HHDT MHDT LHDT2 LHDT1/LHDT2 MHDT MHDT

4 4 4 4 2 6 6 2 1 4 1 1 2 1 1 2 1 1 1 1 1 1 1 1 3 5 1 1 4 1 1 1 1 1 1 1 2 5 5 1

1995–1997 1995 1995–1999 1991–1992 1984–1995 1992–1995 1991–1995 1990–1991 1992 1986–1989 1987 1989 1983–1984 1985 1984 1979–1980 1977 1979 1980 1989 1990 1978 1988 1982 1992–1993 1994–1997 1995 1994 1998–2001 1992 1982 2000 1988 1999 1985 1985 1989–1990 1997–2000 1995–1999 1995

23 K–59 K 32 K–83 K 95 K–125 K 52 K–134 K 84 K–154 K 103 K–216 K 120 K–172 K 149 K–160 K 160 K 92 K–418 K 162 K 174 K 197 K–248 K 212 K 167 K 159 K–182 K 158 K 121 K 98 K 421 K 259 K 128 K 149 K 162 K 109 K–842 K 109 K–602 K 241 K NA 145 K–327 K 519 K 103 K 45 K 170 K 15 K 36 K 501 K NA NA 15 K–162 K 151 K

a

Fujita et al., 2007a; 2007b. Driving cycles: CCS (Cold City-Suburban Route); CID (Cold Idle); CS (Cold Start Unified Driving Cycle [UDC]); CSJ (Hot-City-Suburban with Jacobs Brake); HCS (Hot City-Suburban Route); HW (Highway Cycle); ID (Idle); MC (Manhattan Cycle for Transit busses); UDDS (Urban Dynamometer Driving Schedule); WS (Warm Start UDC). c MOBILE model category: LDGV (Light-Duty Gasoline Vehicle); LDGT1 (Light-Duty Gasoline Truck; weight class 1 [0–3000 lbs]); LDDT12 (Light-Duty Diesel Truck; combined weight class 1 and 2 [0–6000 lbs]); HDDV1 (Heavy-Duty Diesel Vehicle; weight class 1 [0–8500 lbs]); HDDV2 (Heavy-Duty Diesel Vehicle; weight class 2 [8501–10,000 lbs]); HDDV3 (Heavy-Duty Diesel Vehicle; weight class 3 [10,001–14,000 lbs]) HDDV5 (Heavy-Duty Diesel Vehicle; weight class 4 [14,001–16,000 lbs]); HDDV5 (Heavy-Duty Diesel Vehicle; weight class 5 [16,001–19,500 lbs]); HDDV6 (Heavy-Duty Diesel Vehicle; weight class 6 [19,501–26,000 lbs]); HDDV7 (Heavy-Duty Diesel Vehicle; weight class 7 [26,001– 33,000 lbs]); HDDV8 (Heavy-Duty Diesel Vehicle; weight class 8 [N 33,000 lbs]); HDDBT (Heavy-Duty Diesel Bus Transit). d EMFAC Model Category: LDA (Light-Duty Passenger Vehicle); LDT1 (Light-Duty Truck; weight class 1 [0–5750 lbs]); LHDT1 (Light Heavy-Duty Truck; weight class 1 [8501–10,000 lbs]); LHDT2 (Light Heavy-Duty Truck; weight class 2 [10,001–14,000 lbs]); MHDT (Medium Heavy-Duty Truck; 14,001–33,000 lbs); HHDT (Heavy Heavy-Duty Truck; 33,001–60,000 lbs); UB (Urban Bus). e High emitting vehicles; smokers. b

J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396

Sample composite code

J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396

Fig. 2. Comparisons between modeled (MOBILE 6.2 and EMFAC2007) and measured diesel-fueled vehicle PM2.5 emission factors (EFs) for the Hot-City-Suburban route (HCS) driving cycle during the Gas/Diesel Split Study. Bubble diameter represents average vehicle age from smallest (0 = model year 2001) to largest (19 = model year 1982).

Fig. 5 for comparison. Measured PM2.5 gasoline-fueled vehicle EFs were one to two orders of magnitude lower than those measured for diesel-fueled vehicles, but neither MOBILE 6.2 nor EMFAC2007 models reflect the large variability in measured gasoline-fueled vehicle EFs. Gasoline-fueled vehicle EFs may be more sensitive to factors such as engine design and vehicle maintenance compared with diesel vehicles. Prior studies (Gertler, 2005) suggest that gasoline-fueled vehicles are responsible for a substantial portion of PM2.5 emissions from onroad mobile sources, because they outnumber diesel-fueled vehicles and include high-emitting vehicles and operating cycles not captured by certification tests. The Kansas City Study (Kishan et al., 2006; Nam et al., 2008; U.S. EPA, 2008b, 2008c) during summer of 2004 and winter of 2005 intended to better characterize these high-emitting vehicles and cycles. PM2.5 emissions were measured from ~100 vehicles, divided into four age groups by model year (i.e., pre–1981; 1981–1990; 1991–1995; and post–1995) for LDA and LDT. Emissions measurements were made for each of the three phases of the UDC (i.e., WS, CS, and Hot-Stabilized), but composite EFs are reported for comparison with the emission models.

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Table 2 summarizes the vehicle sample composites, model years, and categories (e.g., passenger vehicle or truck) assigned in the MOBILE 6.2 and EMFAC2007 models. Measured and modeled PM2.5 EFs during summer and winter are compared in Fig. 6. There was a general decrease in PM2.5 EFs for later model year vehicles, although it is not clear whether this is related to improved technology or reflects “…varying levels of vehicle deterioration” (U.S. EPA, 2008b, 2008c). The highest EFs were associated with the three oldest vehicles (i.e., 1980 passenger vehicle [S5-5]; and 1985 and 1989 trucks [S2-4 and S2-1, respectively],) tested during summer and three pre–1989 vehicles (i.e., W6-1, W6-4, and W2-1) tested during winter. In general, gasoline-fueled LDT had slightly higher PM2.5 EFs than did LDA. Fig. 7 compares modeled and measured PM2.5 EFs for summer 2004 and winter 2005. Measured EFs were higher in winter than summer. EMFAC2007 estimated higher EFs than MOBILE 6.2 during winter (Fig. 7b), but there was no clear distinction in summer. Similar to the results for the GDSplit Study (Fig. 5), neither model captured the variability of the measured PM2.5 EFs. Non-road diesel vehicles were tested at a Marine Corps Training Facility in southern California during April 2007 as part of the SERDP Study (Watson et al., 2008b, Zhu et al., 2011). These heavyduty vehicles serve as surrogates for off-road equipment used in construction and agriculture (e.g., graders, front loaders, tractors, and harvesters). Two Medium Tactical Vehicle Replacement vehicles (MTVR; Vehicles 1 and 2) and one Logistics Vehicle System vehicle (LVS; Vehicle 12) were tested to measure total PM (with no size cut) EFs under loop and extended driving conditions (defined in Table 3) using in-plume measurement systems (Nussbaum et al., 2009, Zhu et al., 2009). Both driving cycles represented relatively smooth operation with little hard acceleration and deceleration. CO2 was measured with an LI-840 CO2/H2O Gas Analyzer (Licor Biosciences, Lincoln, NE; Watson et al., 2008b). Fuel-based EFs in grams per kilogram of fuel (g/kg fuel) were estimated from PM mass and CO2 emissions assuming a diesel fuel carbon content of 85.6%, following the approach of Moosmüller et al. (2003). Discounting the first MTVR sample (0.63 g/kg) as an outlier in Table 4, the average MTVR and LVS PM EFs were 0.18 ± 0.03 and 1.20 ± 0.12 g/kg, respectively. The LVS emitted ~ 7 times more PM than the MTVR. Higher LVS emissions are expected from its twostroke engine. Military vehicle fuel consumption data are needed to convert MTVR and LVS PM EFs (g/kg) into units of g/mile for crosscomparison among studies. However, fuel consumption was not measured during the 2007 SERDP Study. Fuel efficiency for the MTVR is rated at 3.8 miles per gallon (mpg) (http://www.oshkoshdefense. com/products/6/mtvr). Fuel efficiency for the baseline LVS is rated at 2 mpg (http://www.globalsecurity.org/military/systems/ground/ lvsr.htm). Applying a fuel efficiency of 2 mpg and assuming a diesel fuel density of 3.3 kg/gallon, the average equivalent MTVR and LVS PM EFs are 0.3 and 2 g/mile, respectively, similar to the average PM2.5 EF for HDDV (~ 1.8 g/mile) obtained during the GD-Split Study (Fig. 3). While there is considerable uncertainty in this comparison, the military vehicle PM EFs seem relatively low compared to those of on-road HDDV. 2.2. Biomass burning emission factors

Fig. 3. Comparison of measured PM2.5 emission factors (EFs) during the Gas/Diesel Split Study between a medium heavy-duty diesel truck (CI-9n) and heavy heavy-duty diesel truck (CI-9e) under different driving cycles. Vehicle Driving Cycle: CCS (Cold CitySuburban Route); CCSF (Cold City-Suburban Route operated with Federal fuel); HCS (Hot City-Suburban Route); HW (Highway Cycle); ID (Idle); CSJ (Hot-City-Suburban with Jacobs Brake); UDDS (Urban Dynamometer Driving Schedule); CID (Cold Idle). See Table 1 for vehicle classification. Units are in g/mile except for ID and CID (g/min).

The Emission Estimation System Model (EES) (http://www.arb.ca. gov/ei/see/see.htm) estimates gaseous and PM emissions for wildfires, prescribed burns, and wildland fires. The core of EES is the First Order Fire Effects Model (FOFEM 4.0) that determines the fuel loading characteristics for fuel components by vegetation type. EFs in EES are functions of fuel moisture (i.e., dry, moderate, and wet) and fuel components, including: 1) litter; 2) small wood; 3) large wood; 4)

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0.3

1988

a

MOBILE 6.2 Min EMFAC2007 Average CS EF

1985

1983-1984

1986-1989

1980 1979-1980

1979

0.1

1978

0.15

0.05

1984

WS EF

1987

0.2

1977

PM2.5 Emission Factor (g/mile)

MOBILE 6.2 Max 0.25

SI_7_1

SI_10_4

1995

1995-1997

1995-1999

SI_2_1

SI_1_1

SI_3_1

SI_7_2

1992-1995 SI_5_2

SI_8_2

1991-1995 SI_6_1

SI_8_3

1992

SI_8_1 SI_6_3

SI_9_4

SI_9_1

SI_9_3

SI_10_3

SI_9_2

0

Vehicle Sample Composite

b

0.3

MOBILE 6.2 Min EMFAC2007 Average

0.2

CS EF WS EF

1990-1991

1991-1992 SI_4_1

1990 SI_10_2

SI_6_2

1984-1995 SI_5_1

0

1989

0.05

1989

0.1

SI_10_1

0.15

SI_7_3

PM2.5 Emission Factor (g/mile)

MOBILE 6.2 Max 0.25

Vehicle Sample Composite Fig. 4. Comparisons of measured gasoline-fueled vehicle PM2.5 emission factors (EFs) under cold start (CS) and warm start (WS) cycles during the Gas/Diesel Split Study with MOBILE 6.2 and EMFAC2007 estimates for model years: a) 1977–1988; and b) 1989–1999. See Table 1 for vehicle composite information. Composites are ordered by average vehicle model year. Model year(s) are shown above the bar in chronological order. Error bars indicate measurement uncertainties. The high-emitting vehicles are: SI_9_1 (model years 1979-1980, 159,000-182,000 miles); SI_9_4 (model year 1980, 98,000 miles); SI_8_3 (model year 1984, 167,000 miles); SI_7_1 (model years 1986-1989, 92,000-418,000 miles); and SI_10_4 (model year 1988, 149,000 miles).

herb and shrub; 5) duff; and 6) canopy fuels. However, EFs from dry and wet fuels do not differ significantly for all fuel components. A separate set of EFs are used to estimate emissions from agricultural and other management burns (http://www.arb.ca.gov/ei/see/ mngdburnemissionfactors.xls), where EFs are provided for CO, VOCs, SO2, NOx, PM2.5, and PM10, but do not depend on fuel moisture, and are based on U.S. EPA (2006) and Jenkins et al. (1996). Hereafter, these EFs are noted as “San Joaquin Valley (SJV) EFs” because they have been used to develop the biomass burning emission inventory for California's SJV which is affected by different forms of biomass burning throughout the year (Chen et al., 2007b; Chow et al., 1992, 2007b; Rinehart et al., 2006). FLAME (Chakrabarty et al., 2006; Chen et al., 2006, 2007a; McMeeking et al., 2008; http://chem.atmos.colostate.edu/FLAME/) measurements were taken at the U.S. Forest Service Fire Science Laboratory (FSL) in Missoula, MT during November, 2003 (Pilot

Study), May, 2006 (Phase I), and June, 2007 (Phase II). Fresh fuels were tested within one week of collection, while dried fuels were prepared by long-term indoor storage. Before the experiment, fuel moisture was determined by the weight difference prior to and after heating the fuel to 80 °C for 24–48 h, and reported as the percentage of water with respect to dry fuel mass. Table 5 documents the 17 biofuel types tested. Fuel-based EFs were quantified from in-plume PM2.5 CO and CO2 concentrations (Moosmüller et al., 2003). Carbon mass fractions for most biomes were ~0.4–0.5 (see carbon content in Table 5). The FLAME PM2.5 EFs are classified into six EES categories, separated by moisture content (dry or wet), and compared with EES EFs in Fig. 8. The FLAME Study contains more detailed breakdowns of fuel properties such as shrubs (e.g., chamise [Che] (Adenostoma sp.) and manzanita [Maz] (Arctostaphylos sp.)), leaves, and branches than does EES. Burning leaves produced more particles per unit dry mass of

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(2007a). The corresponding data from SJV overestimated EFs from burning chamise (a species found in chaparral) but underestimated EFs from burning rice straw and grass (grassland-type species) by a factor of three (Fig. 10). The effects of fuel moisture and combustion phase are being incorporated into emission models such as the Fire Emission Production Simulator (FEPS; Anderson et al., 2004; http:// www.fs.fed.us/pnw/fera/feps/). 3. Discussion 3.1. Comparison of measured and modeled emission factors

Fig. 5. Comparison between modeled (MOBILE 6.2 and EMFAC2007) and measured gasoline-fueled vehicle PM2.5 emission factors (EFs) during the Gas/Diesel Split Study under Federal Test Procedure (FTP) for: a) Warm Start (WS); and b) Cold Start (CS) cycles. Bubble diameter represents average vehicle age. Modeled and measured EFs for diesel-fueled vehicle from Fig. 2 are plotted in the dashed circle for comparison.

fuel burned from both dry and wet (e.g., newly harvested or fresh) plants. The highest EFs resulted from burning fresh Ponderosa Pine (PP) (Pinus ponderosa) needles (76.8 ± 37.5 g/kg), followed by fresh manzanita leaves (62.3 g/kg; single test) and fresh lodgepole pine (LP) (Pinus contorta) needles (56.2 g/kg, single test). PM2.5 EFs from plant branch burning were b15 g/kg, even for fresh wood with a moisture content of ~ 70%. Considering the variability in burning different fuels and fuel components, Fig. 8 shows that EES provides reasonable estimates for dry litter source (EES 1), dry small wood (EES 2), wet large wood (EES 3), dry herb and shrub (EES 4a (dry)), and dry duff burn (EES 5) EFs. However, EES underestimates PM2.5 EFs from wet herb and shrub (EES 4b (wet)), as well as from wet needles from Ponderosa and lodgepole pine trees. Fresh pine needles should not burn well during prescribed burns, which focus on fuels from the forest floor, but are vulnerable to wild fires. The PM2.5 EFs in EES should be updated with these recent measurements. Fig. 9 shows that PM2.5 EFs varied from 2.38 ± 1.38 g/kg (dry Dambo grass) to 12.8 g/kg dry fuel (fresh tundra; single test). The EFs represent a wide variety of fuels with relatively low moisture content (b35%). As shown in Table 5, two pairs of samples were taken specifically for the flaming and smoldering phases of dry chamise and rice straw burns. Each phase was distinguished visually according to whether flames were present. Smoldering combustion produced higher EFs than did flaming conditions, as shown in Fig. 10 (3.2 vs. 2.8 g/kg for chamise and 15.4 g/kg compared with 2.3 g/kg for rice straw, respectively), consistent with the findings of Chen et al.

PM2.5 EFs for on-road gasoline and diesel vehicles and biomass burning are compared with modeled and/or empirical EFs from California's PM2.5 emission inventory in Table 6. Comparisons are first evaluated using the average of the absolute differences (AAD), i.e., the average of the percent differences between the non-default and default EFs divided by the default factors for specific source categories. For the 16 sets of on-road diesel-fueled vehicle EFs obtained under the HCS cycle, the AAD, with respect to the EMFAC2007 model, is 37.6%. The Kansas City Study was conducted three to four years after the GD-Split Study. Gasoline-fueled vehicle EFs in the GD-Split Study were classified into WS and CS cycles, while the Kansas City EFs were classified according to seasons (summer versus winter) based on the three phases of the UDC (i.e., WS, CS, and Hot-Stabilized). The AAD for gasoline-fueled vehicle EFs ranged from 65.9% (Kansas City [winter]) to 163.9% (Kansas City [summer]), two to four times the diesel vehicle AAD (37.6%) from the GD-Split Study. Removing two extreme values (i.e., SI_9_4 and SI_10_4; see Fig. 4 and footnotes in Table 1) from the GD-Split Study reduced the AAD from 136.1% to 66.3%, and from 150.3% to 77.7% for the GD-Split WS and CS categories, respectively. Similarly, removing extreme values (i.e., S2_1, S2_2, W6_1, and W6_4; see Fig. 6 and footnotes in Table 2) from the Kansas City Study reduced the AAD from 163.9% to 107.1% and from 103.5% to 65.9% during summer and winter, respectively. Even with extreme values excluded, the AAD are much higher for the gasoline- than for the diesel-fueled vehicles. The best agreement with the model for gasoline-fueled vehicles is found for the Kansas City Study during winter, which represents the more recent experimental data (February–March, 2005). Both the California biomass burning EFs calculated from the EES model and the previous SJV measurements were based on fewer measurements than were the on-road mobile source EFs, so comparisons of these with the FLAME biomass burning EFs may be less statistically significant. The largest AAD is 524.3% for wet fresh canopy fuels, followed by 245% for rice straw (Table 6). Combustion and fuel conditions, such as moisture content, were more variable for biomass burning than for motor vehicle operating conditions. Emission models do not appear to accurately simulate the full complexity of biomass burning. Considering the large variability associated with biomass burning, modeled average EFs were compared with the measured average for each subcategory. This was achieved by calculating the regression slope of measured (y) against modeled (x) EFs with the intercept constrained to zero. Both ordinary least-squares (OLS) regressions and robust regressions (RR) are presented in Table 6. RR reduces the influence of extreme values using an iterative feedback algorithm developed by Huber (2004). The standard error of the slope is reported for both cases. The OLS and RR slopes of 103 ± 9% and 87 ± 7%, respectively, for the GD-Split Study diesel EFs in Table 6 suggest that EMFAC2007 overestimates diesel emissions for vehicles that are not high-emitters. The error is small (within 10%) in terms of category averages. EMFAC2007 also overestimates gasoline-fueled vehicle emissions, as indicated by RR slopes of less than 100%. When high-emitting gasoline

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Table 2 Descriptions of vehicle sample composites during the Kansas City Studya. Sample composite code d

a b c d

MOBILE model vehicle categoryc

EMFAC model vehicle categoryd

Number of vehicles tested

Range of vehicle model year

Odometer range (miles)

FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Summer) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter) FTP (Winter)

LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGV LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1 LDGT1

LDA LDA LDA LDA LDA LDA LDA LDA LDA LDA LDA LDA LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDA LDA LDA LDA LDA LDA LDA LDA LDA LDA LDA LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDT1 LDT1

1 1 1 1 1 2 3 1 1 5 5 5 1 1 1 1 3 3 3 5 1 1 2 1 2 3 2 2 4 2 3 1 1 1 3 3 1 5 3 3

1980 1989 1989 1985 1986 1991–1994 1991–1994 1994 1991 1996–1998 1996–2000 1996–2003 1989 1985 1989 1985 1995 1990–1995 1998–2003 1999–2004 1988 1988 1989–1990 1989 1995 1991–1995 1994–1995 1993–1995 1996–2002 1997–1998 1998–2001 1989 1987 1988 1992–1995 1993–1995 1992 1996–2004 1998–2002 1996–1997

– 116 K 209 K 236 K 36 K 169 K–214 K 32 K–185 K 101 K 226 K 45 K–131 K 40 K–148 K 24 K–146 K 161 K 30 K 132 K 47 K 74 K–113 K 73 K–171 K 19 K–131 K 11 K–75 K 207 K 287 K 168 K–176 K 62 K 146 K–163 K 80 K–145 K 78 K–112 K 140 K–168 K 26 K–68 K 29 K–63 K 56 K–65 K 145 K 232 K 162 K 85 K–136 K 47 K–113 K 154 K 14 K–66 K 0 K–56 K 125 K–146 K

U.S. EPA, 2008b, 2008c. FTP: Federal Test Procedure, includes the average of three cycles (Cold Start, Warm Start, and Hot Start); tests conducted during summer of 2004 and winter of 2005. MOBILE model category: LDGV (Light-Duty Gasoline Vehicle); LDGT1 (Light-Duty Gasoline Truck; weight class 1 [0–3000 lbs]). EMFAC model category: LDA (Light-Duty Passenger Vehicle); LDT1 (Light-Duty Truck; weight class 1 [0–5750 lbs]).

J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396

S5-5 S6-1 S6-2 S6-3 S6-4 S7-1 S7-2 S7-3 S7-4 S8-1 S8-2 S8-3 S2-1 S2-2 S2-3 S2-4 S3-1 S3-2 S4-1 S4-2 W6-1 W6-2 W6-3 W6-4 W7-1 W7-2 W7-3 W7-4 W8-1 W8-2 W8-3 W2-1 W2-2 W2-3 W3-1 W3-2 W3-3 W4-1 W4-2 W4-3

Vehicle test cycleb

J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396

MOBILE 6.2 Max

MOBILE 6.2 Min

EMFAC2007 Average

FTP Emission Factor

1996-2003

1985

S8-3

S2-2

1999-2004

1996-2000 S8-2

1989 1995

1996-1998

1991-1994 1994 S7-2

S8-1

1991 S7-1

1989 1991

1986 S6-4

1989

1985

0.05

1989

Trucks

1985

Passenger Vehicles 0.1

1990-1995 1989-2003

1980

0.15

S6-3

PM2.5 Emission Factor (g/mile)

a

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S4-2

S4-1

S3-2

S3-1

S2-3

S2-4

S2-1

S7-3

S7-4

S6-2

S6-1

S5-5

0

Vehicle Sample Composite

0.15 MOBILE 6.2 Max

MOBILE 6.2 Min

EMFAC2007 Average

FTP Emission Factor

1998-2002

1996-2004

W4-2

W4-1

1996-1997

1993-1995

1992-1995

1992

1987 W2-2

1988

1996-2002 W8-1

1989

Trucks

1998-2001

1997-1998

1993-1995

1994-1995

1991-1995

0.05

Passenger Vehicles

1989-1990 1995

1988

1989

0.1

1988

PM2.5 Emission Factor (g/mile)

b

W4-3

W3-2

W3-1

W3-3

W2-1

W2-3

W8-3

W8-2

W7-4

W7-3

W7-2

W7-1

W6-3

W6-4

W6-2

W6-1

0

Vehicle Sample Composite Fig. 6. Comparisons of measured gasoline-fueled vehicle PM2.5 emission factors (EFs) from the Kansas City Study with MOBILE 6.2 and EMFAC2007 estimates for: a) summer 2004 and b) winter 2005. See Table 2 for vehicle composite information. Composites are ordered by average vehicle model year. Model year(s) are shown above the bars in chronological order. Error bars indicate measurement uncertainties.

vehicles are not weighted less in the regression analysis, the measured EFs equal or exceed EMFAC2007 estimates (i.e., OLS slopes are ≥100%). The only exception is for GD-Split WS vehicles (with an OLS slope of 80 ± 40%), in which EFs were smaller than EMFAC2007

estimates. EMFAC2007 estimates are based on the FTP cycle which includes CS conditions. This study shows that gasoline-fueled vehicles under WS conditions emit only 36 ± 11% (RR slope) of the EMFAC2007 estimates for the category average.

Table 3 Summary of military diesel-fueled vehicles tested during the April 2007 Strategic Environmental Research and Development Program (SERDP) Study. Vehicle typea/ID number

Vehicle numberb

Engine specification

Gross vehicle weight (lbs)

Vehicle model year/month

Odometer range (miles)

Vehicle test cyclec

Number of vehicles tested

Total number of tests

MTVR/593901 MTVR/592995 LVS/550978 LVS/550978

1,2 1,2 12 12

Caterpillar C-12 (four stroke) Caterpillar C-12 (four stroke) Detroit Diesel 8V92TA (two stroke) Detroit Diesel 8V92TA (two stroke)

62,200 62,200 32,000 32,000

2002/10 2002/4 2006/8 2006/8

6637 5306 377 377

Loop driving Extended driving Loop driving Extended driving

2 2 1 1

8 2 3 1

a The MTVR (Medium Tactical Vehicle Replacement) is a six-wheel drive, all-terrain vehicle utilizing the Caterpillar C-12 turbo-charged, four-stroke, 12 l, 6-cylinder engine. The LVS (Logistics Vehicle System) is an eight-wheel drive all-terrain vehicle powered by a Detroit Diesel 8V92TA turbo-charged, two-stroke, 12 l, 8-cylinder engine. b Vehicles 1 and 2 were Medium Tactical Vehicle Replacement vehicles (MTVR); Vehicle 12 was a Logistics Vehicle System vehicle (LVS; Vehicle 12); these three vehicles were tested to measure total PM (no size cut) EFs under loop and extended driving conditions using in-plume measurement systems. c Loop Driving Cycle: An 1.8 km round-trip loop with driving time of 100–130 s over paved and concrete surfaces was followed in each of the tests as documented in Watson et al. (2008b). This test loop has an approximately uniform slope resulting in equal time on 1.1° uphill and downhill slopes. Extended Driving Cycle (10–21 min): More high speed cruising and less frequent acceleration and deceleration.

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J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396 Table 4 PMa Emission Factors (EF) for military vehicles measured via on-board sampling for the Strategic Environmental Research and Development Program (SERDP) Study during April 2007b. Vehicle Typec

Vehicle Number

Vehicle Test Cycled

g/kg Fuele PM EF

MTVR MTVR MTVR MTVR MTVR MTVR MTVR MTVR MTVR MTVR LVS LVS LVS LVS

1 1 1 1 1 1 2 2 2 2 12 12 12 12

Loop driving Loop driving Loop driving Loop driving Loop driving Extended driving Loop driving Loop driving Loop driving Extended driving Loop driving Loop driving Loop driving Extended driving

0.63 0.20 0.23 0.17 0.20 0.19 0.19 0.15 0.16 0.16 1.38 1.11 1.18 1.13

a b c d e f

Fig. 7. Comparison between modeled and measured gasoline-fueled vehicle PM2.5 emission factors (EFs) during the Kansas City Study under the Unified Driving Cycle (UDC) with MOBILE 6.2 and EMFAC2007 model estimates for: a) summer 2004; and b) winter 2005. Bubble diameter represents average vehicle age from lowest (0 = model year 2004) to largest (24 = model year 1980).

For biomass burning, the RR slopes indicate that the modeled EES and SJV EFs were lower than measured EFs for dry litter (229 ± 91%), wet herb and shrub (253 ± 129%), wet canopy (624 ± 97%), rice straw (339 ± 152%), and grass (222 ± 99%; Table 6) fuels. The results are not statistically significant because the standard errors of the RR slopes are large compared to those associated with mobile source emissions. However, measured and model-estimated SJV EFs for dry herb, dry duff, and wet wood burning are similar, with RR slopes of 81–97%. For biomass burning, the OLS and RR slopes are similar, but are based on a limited number of data points (N = 2 to 5 data pairs).

4. Conclusions PM emission factors (EFs) for gasoline- and diesel-fueled vehicles and biomass combustion measured in several recent studies are reported and compared with modeled and previously used values. The 2001 Gas/Diesel Split Study (GD-Split) measured PM2.5 emissions on light-duty gasoline and heavy-duty diesel vehicles using mobile dynamometers. PM2.5 EFs were grouped according to vehicle model year. Measured PM2.5 EFs for diesel-fueled vehicles increased with vehicle age from ~ 0.2 to over 2 g/mile. The correlations between measured and modeled PM2.5 EFs were 0.79 and 0.89 for the U.S. EPA MOBILE 6.2 and ARB EMFAC2007 models, respectively. PM2.5 EFs for diesel vehicles operated under suburban driving cycles

f

Total PM (no size cut). Watson et al. (2008b). MTVR = Medium Tactical Vehicle Replacement. LVS = Logistics Vehicles System. See Table 3 for description of test cycle. Assumed a carbon content of 85.6% for diesel fuel. Outlier, excluded from average.

were nearly double those of vehicles operated under highway conditions. Measured PM2.5 EFs for gasoline vehicles were considerably lower than PM2.5 EFs for diesel vehicles. Gasoline vehicle PM2.5 EFs were b0.05 g/mile for vehicle model year later than 1988. PM2.5 EFs were somewhat higher (~ 0.1 g/mile) and more variable for vehicle model years between 1977 and 1988. Neither MOBILE 6.2 nor EMFAC2007 captured the variability in measured PM2.5 EFs for gasoline vehicles. This may reflect the lack of data on vehicle maintenance, which is available to be used in EMFAC2007, but not MOBILE 6.2, modeling. In the Kansas City Study, PM2.5 EFs were measured on 100 gasoline-fueled passenger vehicles and light trucks during summer 2004 and winter 2005. Measured PM2.5 EFs generally increased with vehicle age but, aside from several high emitters, most EFs measured were less than 0.03 g/mile. Measured EFs were also higher in winter than in summer. EMFAC2007 estimated significantly higher EFs than MOBILE 6.2 during winter but not during summer. As in the GD-Split Study, neither MOBILE 6.2 nor EMFAC2007 completely simulated the range of measured and modeled EFs. The PM EFs for non-road-related activities are not well-represented in real-world measurements or in emission models and are not easily translated to the g/mile units used for on-road activities. The SERDP non-road diesel emissions correspond to values of 0.3 and 2 g/ mile, assuming a fuel consumption efficiency of 2 mpg and a fuel density of 3.3 kg/gal, and are comparable in magnitude to the average heavy-duty diesel PM2.5 EF of ~1.8 g/mile measured during the GDSplit Study. The highest biomass burning PM2.5 EFs (76.8 ± 37.5 g/kg) were measured for wet (N50% moisture content) Ponderosa Pine needles. When moisture content was less than 20% EFs were b20 g/kg. EES model EFs agreed well with measured EFs for fuels with low moisture content but underestimated measured EFs when moisture content was higher than 40%. Rice straw burning in the smoldering phase produced a much higher PM2.5 EF (15.4 g/kg) than when burning in the flaming phase (2.3 g/kg). Average EFs for dry chamise, rice straw, and dry grass were within a factor of three of the values adopted by ARB in the San Joaquin Valley (SJV). The discrepancies between measured and modeled PM2.5 EFs for gasoline vehicles and burning of biomass with high moisture content

J.G. Watson et al. / Science of the Total Environment 409 (2011) 2384–2396

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Table 5 Summary of biomass burned during the Fire Laboratory at Missoula Experiment (FLAME) Study. Biofuel types Ceanothus Chamise

Dambo grass Excelsior (Shredded Aspen Wood Product) Lignin Lodgepole Pine

Manzanita

Montana Grass Palmetto Ponderosa Pine

Puerto Rico fern Rice Sagebrush Tundra Juniper Wax Myrtle White Pine a

Biofuel components Branches and leaves, dried Branches, dried Branches, fresh Leaves, dried Leaves, fresh Leaves, dried Dried

Branches, dried Needles, fresh Needles, litter Needles, duff Branches, fresh Leaves, dried Leaves, fresh Leaves, dried Leaves, fresh Leaves, fresh Branches (Large), dried Branches (Large), fresh Branches (Small), dried Branches (Small), fresh Needles, dried Needles, fresh Needles, litter Needles and branche litter Needles, duff Straw Branches and leaves, dried Foliage and sticks Core, fresh Foliage and sticks, fresh Branches and foliage Needles, dried

Fuel moisture (%)

C(%)/N(%)a

1 5 2 3 3 2 2 2

24.3 11.8–19.4 30.7–35.0 23.9–50.0 8.7–19.6 52.2–60.4 6.3 5.9

48/1.3 49/1

49/0.5 48/0.07

Mixed-phase Mixed-phase

1 3 1 2 2 3 2 1 1 5 3 3 3 2 3 2 3 13 1 4 1 5

17 9.0–9.3 76.4–90.6 13.7–15.6 20.1–24.1 62.9–70.5 52.5–59.8 75.4–107.0 5.0–13.0 17.5–94.0 5.0–7.1 9.1–9.3 63.0–72.0 9.0–9.6 43.4–50.5 7.3 57.5–60.7 9.2–10.5 10.3 13.9–14.7 12.8 8.1–10.1

– 42–50/0.3–1.2

Mixed-phase Mixed-phase

48/0.8

Mixed-phase

44/0.17

Mixed-phase

51/1.0 46–49/0.04–1.3

Mixed-phase Mixed-phase

46/0.4 39–46/0.6–0.9

8.3 9.1 113 8.7 13.6 8.2

47–51/1.5–2.1

Mixed-phase Separate burns for flaming and smoldering Mixed-phase

31/0.5 49–0.9 48–53/1.1–1.4 49–0.5

Mixed-phase Mixed-phase Mixed-phase Mixed-phase

Number of samples

2 1 1 1 1 2

Phase Separate burns for flaming and smoldering

Fuel carbon (C) and nitrogen (N) content, with respect to dry fuel mass.

implies that there may be large uncertainties in current PM2.5 emission inventories when there are substantial contributions from these sources. The results presented here provide guidance for placing bounds on PM2.5 emission estimates. To achieve more realistic emission estimates, emission models such as the MOtor Vehicle Emission Simulator (MOVES) and the Fire Emission Production Simulator (FEPS) should incorporate information from this and other recent source measurement studies.

Acknowledgements This work was primarily supported by the California Air Resources Board (ARB) under contract 04–307. The statements and conclusions in this paper are those of the contractor and not necessarily those of ARB. Other sources of support include the Department of Energy Office of Heavy Vehicles Technologies and FreedomCAR Vehicles Technologies through the National Renewable Energy Laboratory (NREL) for the Gas/Diesel Split Study, U.S. EPA's Kansas City Study under Contract #GS 10F-0036K, the U.S. DOD's Strategic Environmental Research and Development Program Project WP-1336, and the Joint Fire Science Project through NPS Task J8R07060005. The mention of commercial products, their source, or their use in connection with material reported herein is not to be construed as actual or implied endorsement of such products. Additional support was provided by the EPA STAR Grant RD-83108601-0. The authors

wish to thank Ms. Jo Gerrard of DRI for assisting with manuscript editing and preparation. References Anderson G, Sandberg D, Norheim R. Fire emission production simulator (FEPS) user's guide. 2004. U.S. Forest Service. Bachmann JD. Will the circle be unbroken: a history of the US national ambient air quality standards — 2007 Critical Review. J Air Waste Manage Assoc 2007;57: 652–97. Bond TC, Sun HL. Can reducing black carbon emissions counteract global warming? Environ Sci Technol 2005;39:5921–6. CARB. EMFAC2007, version 2.3. Calculating emission inventories for vehicles in California. Sacramento, CA: California Air Resources Board; 2007. CARB. ARB's emissions inventory. Sacramento, CA: California Air Resources Board; 2009a. CARB. Speciation profiles used in ARB modeling. Sacramento, CA: California Air Resources Board; 2009b. Chakrabarty RK, Moosmüller H, Garro MA, Arnott WP, Walker J, Susott RA, et al. Emissions from the laboratory combustion of wildland fuels: particle morphology and size. J Geophys Res -Atmospheres 2006;111:D07204. Chen L-WA, Moosmüller H, Arnott WP, Chow JC, Watson JG, Susott RA, et al. Particle emissions from laboratory combustion of wildland fuels: in situ optical and mass measurements. Geophys Res Lett 2006;33:1–4. Chen L-WA, Moosmüller H, Arnott WP, Chow JC, Watson JG, Susott RA, et al. Emissions from laboratory combustion of wildland fuels: emission factors and source profiles. Environ Sci Technol 2007a;41:4317–25. Chen L-WA, Watson JG, Chow JC, Magliano KL. Quantifying PM2.5 source contributions for the San Joaquin Valley with multivariate receptor models. Environ Sci Technol 2007b;41(8):2818–26. Chow JC. 2001 Critical review discussion — diesel engines: environmental impact and control. J Air Waste Manage Assoc 2001;51:1258–70.

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Fig. 8. Comparison of FLAME PM2.5 emission factors (EFs) with those in the California Air Resource Board's Emission Estimation System (EES). FLAME EFs are classified into the six EES categories and separated by dry and wet moisture content indicated by the boxes in the figure. EES EFs are shown by the black bar. The EES Categories are: 1) EES 1: Dry Litter; 2) EES 2: Small Dry Wood; 3) EES 3: Large Wet Wood; 4) EES 4a: Dry Herb and Shrub; 5) EES 4b: Wet Herb and Shrub; 6) EES 5: Dry Duff; and 7) EES 6 Wet Canopy Fuels. The biofuel types are: 1) Che: Chamise; 2) Maz: Manzanita; 3) MTg: Montana Grass; 4) PP: Ponderosa Pine; and 5) LP: Lodgepole Pine.

Chow JC, Watson JG, Lowenthal DH, Solomon PA, Magliano KL, Ziman SD, et al. PM10 source apportionment in California's San Joaquin Valley. Atmos Environ 1992;26A(18):3335–54. Chow JC, Bachmann JD, Wierman SSG, Mathai CV, Malm WC, White WH, et al. 2002 Critical review discussion — visibility: science and regulation. J Air Waste Manage Assoc 2002;52:973–99. Chow JC, Watson JG, Chen L-WA, Ho SSH, Koracin D, Zielinska B, et al. Exposure to PM2.5 and PAHs from the Tong Liang, China, epidemiological study. J Env Sci Health Part A 2006;A41:517–42. Chow JC, Watson JG, Feldman HJ, Nolan J, Wallerstein BR, Bachmann JD. Critical review discussion — will the circle be unbroken: a history of the U.S. National Ambient Air Quality Standards. J Air Waste Manage Assoc 2007a;57:1151–63. Chow JC, Watson JG, Lowenthal DH, Chen L-WA, Zielinska B, Mazzoleni LR, et al. Evaluation of organic markers for chemical mass balance source apportionment at the Fresno supersite. Atmos. Chem. Phys. 2007b;7(7):1741–2754. Chow JC, Watson JG, Lowenthal DH, Chen L-WA, Motallebi N. Black and organic carbon emission inventories: review and application to California. J Air Waste Manage Assoc 2010;60:497–507. Cook R, Touma JS, Fernandez A, Brzezinski D, Bailey C, Scarbro C, et al. Impact of underestimating the effects of cold temperature on motor vehicle start emissions of air toxics in the United States. J Air Waste Manage Assoc 2007;57:1469–79.

Fig. 9. FLAME PM2.5 emission factors (EFs) and corresponding fuel moisture (fuel moisture of fresh tundra cores was not determined).

DieselNet. Emission test cycles: summary of worldwide engine and vehicle test cycles. Mississauga, ON, Canada: Ecopoint, Inc; 2008. Environment Canada. Air pollutant emissions; 2008. EPD. Hong Kong air pollutant emission inventory. 2008. Hong Kong, China SAR, Hong Kong Environmental Protection Dept. Fujita EM, Campbell DE, Arnott WP, Chow JC, Zielinska B. Evaluations of the chemical mass balance method for determining contributions of gasoline and diesel exhaust to ambient carbonaceous aerosols. J Air Waste Manage Assoc 2007a;57:721–40. Fujita EM, Zielinska B, Campbell DE, Arnott WP, Sagebiel JC, Mazzoleni LR, et al. Variations in speciated emissions from spark–ignition and compression–ignition motor vehicles in California's south coast air basin. J Air Waste Manage Assoc 2007b;57:705–20. Gertler AW. Diesel vs. gasoline emissions: does PM from diesel or gasoline vehicles dominate in the US? Atmos Environ 2005;39:2349–55. Grantz DA, Garner JHB, Johnson DW. Ecological effects of particulate matter. Environ Int 2003;29:213–39. Huber PJ. Robust statistics. Hoboken, NJ: John Wiley & Sons; 2004. Jacobson MZ. Control of fossil-fuel particulate black carbon plus organic matter, possibly the most effective method of slowing global warming. J Geophys Res 2002;107:ACH 16-1–ACH 16–22. Jenkins BM, Turn SQ, Williams RB, Goronea M, Abd-el-Fattah H. Atmospheric pollutant emission factors from open burning of agricultural and forest biomass by wind tunnel simulations. Volume 1: Final Report. PB–97-133037/XAB. 1996. University of California, Davis, Davis, CA. Kishan S, Burnette AD, Fincher.S., Sabisch MA, Crews W, Snow R, Zmud M, Santos RC, Bricka S, Fujita EM, Campbell DE, Arnott WP. Kansas City PM characterization study final report. ERG No. 0133.18.007.001. 2006. Austin, TX, Eastern Research Group, Inc. Lighty JS, Veranth JM, Sarofim AF. Critical review: combustion aerosols: factors governing their size and composition and implications to human health. J Air Waste Manage Assoc 2000;50:1565–618. Lloyd AC, Cackette TA. Critical review — diesel engines: environmental impact and control. J Air Waste Manage Assoc 2001;51:809–47. MacCracken MC. A summary of the 2008 critical review prospects for future climate change and the reasons for early action. EM; 2008a. p. 40–5. MacCracken MC. Critical review: prospects for future climate change and the reasons for early action. J Air Waste Manage Assoc 2008b;58:735–86. Mauderly JL, Chow JC. Health effects of organic aerosols. Inhal Toxicol 2008;20:257–88. McCarthy MC, Eisinger DS, Hafner HR, Chinkin LR, Roberts PT, Black KN, et al. Particulate matter: a strategic vision for transportation-related research. Environ Sci Technol 2006;40:5593–9. McMeeking GR, Kreidenweis SM, Baker S, Carrico CM, Chow JC, Collett Jr JL, Hao WM, Holden AS, Kirchstetter TW, Malm WC, Moosmüller H, Sullivan AP, Wold CE. Emissions of Trace Gases and Aerosols during the Open Combustion of Biomass in the Laboratory. J Geophys Res 2009;(114):D19210. doi:10.1029/2009JD011836. Mobley JD, Deslauriers M, Rojas-Brachos L. Improving emission inventories for effective air-quality management across North America — A NARSTO assessment. NARSTO05-001. Pasco, WA: NARSTO; 2005.

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35

FLAME Study SJV Study

30 25

21.5

20 15.4 15 8.5 2.8

3.2

3.0

Dry Chamise (Overall)

5

Dry Chamise (Smoldering)

10

Dry Chamise (Flaming)

8.2

7.9

6.7

2.5

2.3

Grassland (SJV)

Fresh Motana Grass

Dry Motana Grass

Rice Straw (SJV)

Rice Straw (Overall)

Rice Straw (Smoldering)

Rice Straw (Flaming)

0

Chaparral (SJV)

PM2.5 Emission Factor (g/kg Dry Fuel)

40

Type of Biofuel and Burn Phase

Fig. 10. FLAME PM2.5 emission factors (EFs) (shown above each bar in g/kg dry fuel) from different burning phases measured during the FLAME Study and comparisons with emission factors from California's San Joaquin Valley (SJV).

Table 6 Comparability of measured (y) and modeled (x) PM2.5 emission factors. Study and source category for measured emission factors (y)

Number of data pairs

Average Absolute Difference (AAD)%a

100 × Ordinary Least Squares (OLS) Regression Slope ± σ (%)b

100 × Robust Regression Slope (RR) ± σ (%)c

Gas/Diesel Split Diesel (Hot City-Suburban route [HCS] mode) Gas/Diesel Split Gasoline (Warm Start [WS]) Gas/Diesel Split Gasoline (Warm Start [WS]) Gas/Diesel Split Gasoline (Cold Start [CS]) Gas/Diesel Split Gasoline (Cold Start [CS]) Kansas City Gasoline (FTP Summer) Kansas City Gasoline (FTP Summer) Kansas City Gasoline (FTP Winter) Kansas City Gasoline (FTP Winter) FLAME Dry Herb, Shrub FLAME Dry Litter FLAME Dry Wood FLAME Dry Duff FLAME Wet Herb, Shrub FLAME Wet Canopy Fuels FLAME Wet Wood FLAME Chaparral (Chamise) FLAME Rice Straw FLAME Grass

16 23 21d 23 21e 19 17f 20 18g 5 4 3 2 5 2 2 3h 3h 2i

37.6 136.1 66.3 150.3 77.7 163.9 107.1 103.5 65.9 149 151 33.3 18.9 186 524.3 16.8 64.6 245 122

103 ± 9 80.1 ± 39.7

87.2 ± 7.2 36.2 ± 10.5

128 ± 49

80.1 ± 20.3

111 ± 34

86.8 ± 25.6

92.9 ± 24.7

72.7 ± 16.5

185 ± 114 229 ± 91 66.7 ± 17.0 81.1 ± 13.9 255 ± 100 624 ± 97 83.2 ± 10.0 35.4 ± 1.5 339 ± 152 222 ± 99

97.1 ± 67.1 229 ± 91 63.7 ± 24.6 81.1 ± 13.9 253 ± 129 624 ± 97 83.2 ± 10.0 35.4 ± 1.5 339 ± 152 222 ± 99.4

a AAD = (100 × jY−Xj=X), where Y and X are the measured and modeled (2006 California Emission Inventory) emission factors, respectively. Emission factors for mobile sources were estimated with EMFAC2007. Emission factors for biomass burning were based on the Emission Estimation System (EES) model except for chaparral, rice straw and grass, which were based on SJV emission factors. b Based on ordinary least square regression with zero intercept; σ is the standard error of the slope. c Based on robust regression with zero intercept to reduce the influence of outliers; σ is the standard error of the slope. d Two outliers, SI_9_4 and SI_10_4 for warm start, were removed from the comparison (see Fig. 4). e Two outliers, SI_9_4 and SI_10_4 for cold start, were removed from the comparison (see Fig. 4). f Two outliers, S2_1 and S2_4, were removed from the comparison (see Fig. 6). g Two outliers, W6_1 and W6_4, were removed from the comparison (see Fig. 6). h Emission factors represent both flaming and smoldering phases. i Emission factors represent dry and wet fuels.

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