Method for Modeling Driving Cycles, Fuel Use, and ... - ACS Publications

Jun 19, 2014 - Over snow vehicles (OSVs), including snow coaches and snowmobiles, ...... Environmental Science & Technology 2015 49 (22), 13492-13500...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/est

Method for Modeling Driving Cycles, Fuel Use, and Emissions for Over Snow Vehicles Jiangchuan Hu,† H. Christopher Frey,*,† Gurdas S. Sandhu,† Brandon M. Graver,† Gary A. Bishop,‡ Brent G. Schuchmann,‡,§ and John D. Ray∥ †

Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, North Carolina 27695-7908, United States ‡ Department of Chemistry and Biochemistry, University of Denver, Denver, Colorado 80208, United States § SGS Environmental Testing Corporation, 2022 Helena St., Aurora, Colorado 80011, United States ∥ National Park Service, Air Resources Division, Denver, Colorado 80225, United States S Supporting Information *

ABSTRACT: As input to a winter use plan, activity, fuel use, and tailpipe exhaust emissions of over snow vehicles (OSV), including five snow coaches and one snowmobile, were measured on a designated route in Yellowstone National Park (YNP). Engine load was quantified in terms of vehicle specific power (VSP), which is a function of speed, acceleration, and road grade. Compared to highway vehicles, VSP for OSVs is more sensitive to rolling resistance and less sensitive to aerodynamic drag. Fuel use rates increased linearly (R2 > 0.96) with VSP. For gasoline-fueled OSVs, fuel-based emission rates of carbon monoxide (CO) and nitrogen oxides (NOx) typically increased with increasing fuel use rate, with some cases of very high CO emissions. For the diesel OSVs, which had selective catalytic reduction and diesel particulate filters, fuel-based NOx and particulate matter (PM) emission rates were not sensitive to fuel flow rate, and the emission controls were effective. Inter vehicle variability in cycle average fuel use and emissions rates for CO and NOx was substantial. However, there was relatively little inter-cycle variation in cycle average fuel use and emission rates when comparing driving cycles. Recommendations are made regarding how real-world OSV activity, fuel use, and emissions data can be improved.

■

INTRODUCTION Over snow vehicles (OSVs), including snow coaches and snowmobiles, are the major winter transportation mode at Yellowstone National Park (YNP). A snow coach is a multipassenger vehicle designed or modified to operate over snow or ice. Temporary YNP winter use plans were adopted by the National Park Service starting in 2003 to regulate visiting OSVs.1−6 To support development of the most recent YNP Supplemental Winter Use Plan and Environmental Impact Statement, assessments of fuel economy and emission rates of OSVs were conducted.7−9 In-use OSV emissions were measured using remote sensing in 1998, 1999, and 2005 and a portable emissions measurement system (PEMS) in 2005 and 2006.10−12 The measured vehicles actually operated in the park but were not selected to represent a fleet distribution. The lower emitting gasoline OSVs measured in 2006 were, on average, 5 years newer than those measured in 2005. Differences in results for measured vehicles from different study years are, at least in part, a result of differences in engine fuel delivery and emissions control. Differences in snow conditions, ambient temperature, and driving cycles among the field studies may also lead to © 2014 American Chemical Society

variability in the comparisons. These measurements were conducted during real-world operations which may be specific to the observed driving cycle. To enable comparisons between vehicles, there is a need to be able to estimate cycle average rates for each vehicle based on a common cycle. Fuel use and emission rates of passenger cars, trucks, and buses are found to be highly associated with instantaneous engine load, which is affected by driving cycle.13−17 A driving cycle is typically represented in terms of second-by-second speed, acceleration, and road grade. For highway vehicles, these three factors are used to estimate vehicle specific power (VSP).18,19 Key coefficients in calculating VSP are related to changes in kinetic energy, changes in potential energy, rolling resistance, and aerodynamic drag. Since OSVs operate on snow and use tracks instead of wheels, the rolling resistance term is expected to differ. Since OSVs typically operate at relatively low speeds, aerodynamic drag may not be an important factor. Received: Revised: Accepted: Published: 8258

March 19, 2014 June 12, 2014 June 19, 2014 June 19, 2014 dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

Table 1. Over Snow Vehicle Specifications and Fuel Economy year

make

model

design

engine cylinders

displacement

fuel used

track type

1956

Bombardierc

B12 “Kitty”

snow coach

8

5.3 L

gasoline

2008 2011 2011 2011 2011

Chevrolet Ford Ford Ford Arctic Cat

Express E350 F450 F550 TZ1

snow coach snow coach snow coach snow coach snowmobile

8 10 8 8 3

6.0 L 6.8 L 6.7 L 6.7 L 1.06 L

gasoline gasoline diesel diesel gasoline

twin tracks with skies mattracks mattracks mattracks griptracs tread and twin skies

emission control unita

fuel economy (mpg)b

CC

5.8

CC CC SCR, DPF SCR, DPF none

2.1 2.7 1.8 2.0 14.4

Emission control technologies: CC = catalytic converter; SCR = selective catalytic reduction; DPF = diesel particle filter. bFuel economy observed during field measurements. cThe Bombardier was powered by a 2008 Chevrolet Suburban gasoline engine with 8 cylinders and 5.3 L displacement.

a

studies.10,11 Each one way trip is a “cycle,” and roundtrip data from start to finish is a “run.” The total distance traveled during each run was approximately 32 miles. Garmin 76CSx global positioning system receivers with barometric altimeters (GPS/BA) were used to measure latitude, longitude, and elevation at 1 Hz. For each vehicle, three receivers were used to improve the precision of road grade estimates, per the method of Yazdani et al.20 Each receiver measures position to within ±3 m, and relative elevation change to within ±1 m. Road grades were estimated as the slope of a linear regression of elevation versus distance for every consecutive non-overlapping 0.1 mile segment during outbound and inbound trips. The methodological approach for road grade estimation is detailed elsewhere.20,21 For each of the five snow coaches, an on-board diagnostic (OBD) scan tool was used to record engine speed (RPM), manifold absolute pressure (MAP), intake air temperature (IAT), mass air flow (MAF), and mass fuel flow (MFF) from the engine control unit. The sum of second-by-second OBD logged MFF was compared with gas station based actual fuel use to verify reasonable data. For the snowmobile, an OBD interface was not available. Therefore, a temporary engine sensor array was installed to measure RPM, MAP, and IAT.22 MAF was estimated using the Speed-Density method.23 The OEM-2100 PEMS is comprised of two gas analyzers that measure second-by-second exhaust concentrations of carbon dioxide (CO2), carbon monoxide (CO), and hydrocarbons (HC) using non-dispersive infrared, nitric oxide (NO) and oxygen using electrochemical cells, and particulate matter (PM) using laser light scattering. Exhaust PM emission concentrations were measured only for the diesel OSVs. The term “NOx” is used to represent measured NO as equivalent mass of nitrogen dioxide (NO2). Data from the GPS, OBD or sensor array, and PEMS were synchronized and combined. Quality assurance (QA) was conducted for the combined data set to check the validity of data, correct errors if possible, and remove invalid data.24 Further details of PEMS calibration and validation, and regarding quality assurance are given elsewhere and in the Supporting Information (SI). Modeling Fuel Use and Emission Rates. OSV engine load is expected to depend on the same key factors accounted for in VSP. However, numerical values of VSP parameters for OSVs are expected to differ from those of highway vehicles, because the rolling resistance is expected to be larger and because OSVs do not operate at high speed, which mitigates the importance of aerodynamic drag. VSPOSV is a function of instantaneous vehicle speed, acceleration, and road grade:

Therefore, the VSP coefficients developed for highway vehicles are not suitable for OSVs. There is a need to evaluate the suitability of VSP to enable emission factors to be estimated for any OSV driving cycle. Therefore, a method for modeling OSV driving cycles, and cycle average fuel use and emissions rates, is explored and evaluated. The objectives of this research are to (a) quantify the relationship between OSV driving cycle and engine load; (b) quantify the relationship between engine load, fuel use, and emissions rates; and (c) evaluate inter-vehicle and inter-cycle variability in fuel use and emission rates.

■

MATERIALS AND METHODS Field Measurements. Field measurements were conducted on five snow coaches and one snowmobile in YNP during March 2012. The specifications of each OSV are summarized in Table 1. The Bombardier and the Arctic Cat are dedicated OSVs. The other four were converted highway vehicles for which wheels were previously replaced with snow tracks. The Bombardier has a replacement gasoline engine from a 2008 Chevrolet Suburban, and has a higher power-to-weight ratio than the converted snow coaches.11 All OSVs were measured for one round-trip on a designated route, shown in Figure 1(a), that was used in previous

Figure 1. (a) Aerial View of the Test Route in Yellowstone National Park. (b) Average Elevation and Estimated Road grade Map along the Route Based on Multiple Data Sets from GPS Receivers with Barometric Altimeter. 8259

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

evaluated based on whether the trend in fuel use rate versus VSPOSV was monotonically increasing. Average fuel-based emission rates were estimated for each fuel use rate bin. Uncertainty in average emission rates was quantified using 95% confidence intervals. Inter-Vehicle and Inter-Cycle Variability. Inter-vehicle and inter-cycle variability in fuel use and emissions rates were evaluated for the five measured snow coaches. Given that only one snowmobile data set was available, and it is not appropriate to compare a snowmobile to snow coaches because of differences in vehicle characteristics, the snowmobile was excluded from the evaluation of inter-vehicle and inter-cycle variability. Inter-vehicle variability is the comparison of cycle average fuel use and emission rates between different snow coaches driving on the same driving cycle. Inter-cycle variability is the comparison of cycle average fuel use and emission rates between different driving cycles for the same snow coach. To evaluate inter-vehicle and inter-cycle variability, cycle total fuel use rates and cycle average emission rates were estimated for each snow coach based on its driving cycle, as well as the driving cycles observed for each of the other four measured snow coaches. For a particular snow coach and driving cycle, second-by-second VSPOSV was estimated using vehicle specific rolling resistance (b1) and aerodynamic drag coefficients (b2), as well as driving cycle specific speed, acceleration, and road grade. Fuel use and emissions rates associated with the estimated VSPOSV were inferred for each second.

VSPOSV = (1.1 × a × v) + (9.8 × r × v) + (b1 × v) + (b2 × v 3)

(1)

Where, VSPOSV is the vehicle specific power for OSVs (kW/ ton); a is acceleration (m/s2); v is speed (m/s); and r is road grade. GPS speed was used instead of OBD speed because the latter was not accurate for the snow coaches. Acceleration was inferred as the difference in GPS speed between the current and previous second. The parameters b1 and b2 represent rolling resistance and aerodynamic drag coefficients, respectively. The terms (1.1 × a × v), (9.8 × r × v), (b1 × v), and (b2 × v3) represent engine power associated with changing kinetic energy, changing potential energy, overcoming rolling resistance, and aerodynamic drag, respectively. To compare trends in fuel use rate over VSPOSV among the measured OSVs, VSPOSV values were scaled to a range of 0−1 for each OSV. There is precedence for scaling VSP for comparison purposes.25 Fuel use rates increase monotonically with increasing positive VSP for light duty passenger cars and heavy duty trucks.15,17 Therefore, for OSVs, fuel use rates over a threshold idling fuel use rate (FR0) are hypothesized to increase monotonically with positive VSPOSV. For positive values of VSPOSV, the estimated fuel use rate is (FR − FR 0) = C × VSPOSV = C × (1.1 × a × v + 9.8 × r × v) + (C × b1 × v) + (C × b2 × v 3)

■

(2)

RESULTS AND DISCUSSION A total of five snow coaches and one snowmobile were measured, with a total of 12 h of 1 Hz data. Each OSV was measured once on the selected route. For average of the five snow coaches for which MFF was recorded, the sum of MFF was within 3% of the actual amount of fuel needed to refuel the vehicle. The vehicles traveled at average speeds of 15−20 mph for the snow coaches and 21 mph for the snowmobile. These average speeds are typical for OSVs running in the YNP. More than 96% of the raw data were valid after quality assurance. The main causes of data loss after QA were a few instances when both gas analyzer benches zeroed simultaneously or unusual airto-fuel (A/F) ratios. Road Grades. Average road grades along the route were estimated for 0.1 mile segments. There were 9 and 14 GPS data sets available for outbound and inbound trips, respectively. Road grades, as well as elevations, along the route are plotted in Figure 1b. Over 86% of the 0.1 mile segments had grades within ±2%. The portion of the route between Madison Junction and the Turn Around Point was more hilly, for which only 46% of the 0.1 mile segments had grades within ±2%. The precision of the estimated road grades for all 0.1 mile segments was less than ±0.5%, which is the desired data quality objective to enable estimation of VSP.20,21 The road grade precision for 96% of the segments was less than ±0.25%. The average precision was approximately ±0.13% for both outbound and inbound trips. Modeling Fuel Use and Emission Rates. The estimated coefficients of eq 2 related to aerodynamic drag, (C × b2), were negative for all OSVs. The OSV speeds were not high enough for aerodynamic drag to be a significant factor. Furthermore, this regression term is collinear with other terms that include speed. Negative aerodynamic drag terms were removed from the model, and the model was refit to the data, because it is

Where, FR is the fuel use rate (g/s); FR0 is the average fuel use rate at idling (g/s); and C is a proportionality coefficient between fuel use rate and VSPOSV. The term (FR−FR0) is the nonidle fuel use rate. As discussed later, OSVs rarely operate at negative VSPOSV. Therefore, a model for negative VSPOSV is not developed, but is hypothesized to simply be the idle fuel use rate typical of no engine load. Multiple linear regression was used to estimate the parameters of eq 2 based on quality assured 1 Hz data from the field measurements. The estimated coefficients include C, the quantity (C × b1), and the quantity (C × b2). From these, the values of b1 and b2 are inferred. When all input variables are 0, the vehicle is idling, and the nonidle fuel use rate is zero. Therefore, the intercept of the linear regression was set to zero. Parameters are considered statistically significant at the 95% confidence level if p-values were less than 0.05. Terms were removed for which the estimated parameters were statistically insignificant or physically implausible, such as negative for aerodynamic drag. To construct a modal model to estimate fuel use and emission rates based on VSPOSV, the values of VSPOSV were separated into 7 to 12 bins, depending on the range of absolute VSPOSV values. The range of VSPOSV values for each OSV was divided by 10. The resulting quotient was rounded to the nearest integer to be the width of each bin, except for the highest bin which is unbounded. However, cycle average results are not sensitive to these choices. During exploratory development of the approach, other bin definitions were considered and the cycle average results obtained from those were within 3% of those reported here. Average fuel use rate was estimated for each bin. The uncertainty in average fuel use rate for each bin was quantified using 95% confidence intervals. The feasibility of using VSPOSV to represent engine load was 8260

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

Because of the high rolling resistance coefficients for dedicated OSVs, rolling resistance may be the dominant factor affecting engine load. To explore this possibility, linear regression between nonidle fuel use rates and only the rolling resistance term were conducted for the Bombardier and the Arctic Cat; results are shown in Table 2. The R2 coefficients of determinations for the fuel use model based only on rolling resistance are nearly as high as those based on models that also include changes in kinetic and potential energy. Thus, rolling resistance alone is able to explain a large share of the variance of dedicated OSV nonidle fuel use rates. Rolling resistance is a property of the vehicle, not of the terrain. The effect of road grade is accounted for separately from rolling resistance. Although the kinetic and potential energy terms are statistically significant, they do not contribute much to the variance of nonidle fuel use rates. To demonstrate the relationship between fuel use and engine load, a plot of average fuel use rate versus VSPOSV bin is shown in Figure 2, using the Ford E350 snow coach as an example.

physically impossible to have negative aerodynamic drag. Thus, the negative terms were removed even if they were statistically significant. The estimated regression coefficients for fuel use versus VSPOSV without aerodynamic drag are shown in Table 2. All reported coefficients are statistically significant. The adjusted R2 coefficients of determination for all OSVs are at least 0.96, indicating a strong linear relationship. Table 2. Linear Regression Results of Non-Idling Fuel Use versus Kinetic and Potential Energy, and Rolling Resistancea,c

over snow vehicle Chevy Express Ford E350 Ford F450 Ford F550 Bombardier Arctic Cat TZ1 Bombardierb Arctic Cat TZ1b

C

(C × b1)

b1

kinetic and potential energy coefficientd

rolling resistance term coefficientd

rolling resistance

g-s2/m2

g/m

m/s2

0.138 0.279 0.271 0.554 0.008 0.011

0.764 0.541 0.960 0.816 0.325 0.100

5.5 1.9 3.5 1.5 41 9.1

0.98 0.98 0.98 0.98 0.96 0.98

N/A N/A

0.328 0.103

N/A N/A

0.96 0.97

adjusted R2

Linear regression for the first five rows is based on eq 2 excluding the aerodynamic drag term. a

FR − FR 0 = C × [(1.1 × a × v) + (9.8 × r × v)] Figure 2. Modal average fuel use rate versus scaled vehicle specific power for over snow vehicles for the Ford E350 gasoline-fueled snow coach. Error bars indicate 95% confidence intervals.

+ (C × b1 × v) Where FR = fuel use rate (g/s), FR0 = average idling fuel use rate (g/ s), v = speed (m/s), a = acceleration (m/s2), r = road grade (slope), C = regression coefficient for kinetic and potential energy (g-s2/m2), C × b1 = regression coefficient for rolling resistance (g/m), and b1 = rolling resistance coefficient (m/s2). bLinear regression for these cases is based on a simplified version of eq 2 that focuses only on rolling resistance as the dominant source of engine load:

There were no VSPOSV values less than 0, because the OSV driver typically coasts to a stop to avoid skidding, rather than applying the brakes. A monotonically increasing relationship was observed between fuel use rate and VSPOSV bins. Similar relationships are observed for highway vehicles.24 For the other snow coaches, no negative VSPOSV values were observed except for the Ford F550 snow coach, which had only 4 s of negative VSPOSV values. The relative trends between fuel use rates and VSPOSV for the other OSVs are similar to the E350, as shown in the SI. For each individual OSV, fuel use rates are observed to increase linearly with VSPOSV. Therefore, VSPOSV is a useful parameter for representing OSV engine load. However, the magnitudes of fuel use rates are different among the OSVs. The measured fuel economies for the OSVs are listed in Table 1. Among the five snow coaches, the Bombardier typically had the lowest modal incremental nonidle fuel use rates, ranging from 0 to 5 g/s. The modal incremental nonidle fuel use rates for the other four are similar, ranging from 0 to 10 g/s. The lighter chassis enabled the Bombardier to have higher fuel economy than the converted snow coaches. The Arctic Cat has the lowest fuel use rates, because of a much smaller engine and lower weight. To evaluate emissions rates trends, examples of fuel-based average CO and NOx emission rates versus fuel use rates for the Ford E350 snow coach are shown in Figure 3. Fuel-based CO emission rates increase with increasing fuel use rate initially, then decrease for fuel use bins ranging from 2 to 4 g/s, and

FR − FR 0 = (C × b1 × v) c

Average idling fuel use rate are 0.34, 0.36, 0.47, 0.37, 0.26, and 0.23 g/s for the Bombardier, Chevrolet Express, Ford E350, Ford F450, Ford F550, and Arctic Cat TZ1, respectively. Sample size ranges from 2000 s to 3240 s for each vehicle. dThe p-values for all coefficient estimates are less than 0.001.

The rolling resistance coefficient, b1, ranged from 1.9 to 5.5 for the converted snow coaches. For these OSVs, the rolling resistance coefficients are an order of magnitude larger than for a typical highway light-duty vehicle, which has a typical value of 0.132.18 For the dedicated OSVs, Bombardier, and the Arctic Cat, the values of the rolling resistance coefficients are much greater than for the converted highway vehicles, at 41, and 9.1, respectively. The tracks of the two dedicated OSVs have a larger ground contact area compared to the converted OSVs. Thus, a likely operational advantage of the dedicated OSVs compared to the converted highway vehicles is that they are less likely to slide. 8261

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

The modal average fuel-based CO emission rates for the Bombardier were typically between 20 g/gallon and 60 g/ gallon, with the exception of some data representing transients at low fuel flow. In contrast, the Chevrolet Express had CO emission rates that typically increased with fuel flow, with the highest modal rate of over 1,000 g/gallon, more than a factor of 5 higher than the next highest rate. For the two diesel OSVs, the average exhaust CO concentrations were below the gas analyzer detection limit for all bins except for one bin for the Ford F550. The low CO concentrations for the diesel OSVs were expected because diesel engines operate with excess air. For the Arctic Cat, fuel-based modal CO emission rates ranged from approximately 2000−2500 g/gallon for low to moderate fuel flow rate, and dropped to approximately 500 g/gallon at high fuel flow rate. CO emissions can be produced as a result of incomplete combustion. A gasoline A/F ratio less than 14.7 indicates fuel rich combustion.26 The A/F ratios of the Chevrolet and the Arctic Cat were less than 14 for 66% and 46% of the time, respectively, compared to less than 1% of the time for the Bombardier and the Ford E350. Thus, the high CO emissions

Figure 3. Average carbon monoxide and nitrogen oxide emission rates in each fuel use bin for the Ford E350 gasoline-fueled snow coach. Error bars indicate 95% confidence intervals.

subsequently increase again. However, less than 3% of time was spent in the bins with the decreasing emissions trend, which has two implications: (1) the decrease in emission rate at moderate fuel use rate does not have a substantial impact on cycle averages; and (2) this trend may be an artifact of transient operation. For all the bins, the exhaust HC concentrations were generally below the gas analyzer detection limit. Fuel-based NOx emission rates generally increase with fuel use rates.

Table 3. Estimated Total Fuel Use and Cycle Average Carbon Monoxide and Nitrogen Oxides Emissions Rates for Each Snow Coach for Each Driving Cycle for the Out-Bound Trip total fuel use (gallon) driving cyclea snow coach

B

Bombardier (B) Chevy Express (CE) Ford E350 (E350) Ford F450 (F450) Ford F550 (F550) inter-vehicle H/L ratio ± st. dev vehicle totalc

3.5 7.8 5.9 9.7 8.4 2.7 ± 0.001

CE

E350

F450

3.4 3.4 3.5 7.5 7.6 7.7 5.6 5.7 5.8 9.4 9.5 9.6 8.1 8.2 8.2 2.8 ± 0.001 2.8 ± 0.001 2.7 ± 0.001 cycle average CO emission rates (g/gallon)

F550 3.4 7.5 5.6 9.4 8.1 2.8 ± 0.001

inter-cycle H/L ratio ± st. dev cycle totalb 1.05 ± 0.0003 1.04 ± 0.003 1.05 ± 0.0005 1.04 ± 0.0006 1.04 ± 0.0005

driving cycle snow coach

B

Bombardier (B) Chevy Express (CE) Ford E350 (E350) Ford F450 (F450)d Ford F550 (F550)d inter-vehicle H/L ratio ± st. dev vehicle avg.e

36 900 39 1.1 0.8 25 ± 0.61

CE

E350

. F450

40 40 39 940 820 910 34 32 34 3.7 4.3 3.4 0.1 0.4 0.6 27 ± 0.29 26 ± 0.30 26 ± 0.28 cycle average NOx emission rates (g/gallon)

F550 41 840 32 4.5 0.1 26 ± 0.31

inter-cycle H/L ratio ± st. dev cycle avg 1.14 ± 0.04 1.15 ± 0.01 1.20 ± 0.01 n/a n/a

driving cycle snow coach

B

CE

E350

F450

F550

Bombardier (B) Chevy Express (CE) Ford E350 (E350) Ford F450 (F450) Ford F550 (F550) inter-vehicle H/L ratio ± st. dev vehicle avg.

24 9.6 0.8 23 12 31 ± 0.46

30 10 0.8 22 14 39 ± 0.61

30 9.0 0.7 22 14 42 ± 0.70

28 9.6 0.7 22 13 38 ± 0.60

30 9.2 0.7 22 14 41 ± 0.69

inter-cycle H/L ratio ± st. dev cycle avg 1.27 1.10 1.08 1.06 1.17

± ± ± ± ±

0.02 0.03 0.02 0.01 0.04

a

Driving cycle: B = Bombardier driving cycle; CE = Chevrolet Express driving cycle; E350 = Ford E350 driving cycle; F450 = Ford F450 driving cycle; F550 = Ford F550 driving cycle. bH/L ratio cycle total = Ratio of highest to lowest cycle total rates; For example, for Bombardier, for fuel use total, H/L ratio cycle total of 1.05 is obtained by 3.5 gallons divided by 3.4 gallons. cH/L ratio vehicle total/Avg. = Ratio of highest to lowest vehicle total or average rates. For example, for Bombardier, for fuel use total, H/L ratio vehicle total of 2.8 is obtained by 9.7 gallons divided by 3.5 gallons. d Values in italics are below the detection limit and are not included in the H/L ratio. eThe H/L ratios are calculated only using values above the detection limit. 8262

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

Inter-Vehicle and Inter-Cycle Variability. Results for inter-vehicle and inter-cycle variability for the five measured snow coaches, in terms of total fuel use and cycle average emission rates of CO, and NOx, are given in Table 3. This table includes results for the out-bound trip. The results for the inbound trip, provided in the SI, were similar to the out-bound trip. Because the exhaust HC concentrations were typically below the detection limit, cycle average HC emission rates are not included. For fuel use, the value in each cell of Table 3 is the estimated total fuel use for a given OSV on a given cycle. For example, for the Bombardier simulated on the Chevrolet Express driving cycle, the estimated total fuel use is 3.4 gallons. Likewise, the estimated cycle average CO and NOx emission rates are 40 g per gallon and 30 g per gallon, respectively. To quantify inter-vehicle variability, for a given driving cycle, the ratio of the highest to lowest rates (H/L ratio) was estimated. For example, for the Bombardier cycle, the highest total fuel use is 9.7 gallons from the Ford F450 and the lowest is 3.5 gallons from the Bombardier. Therefore, the H/L ratio is 2.8. For the other driving cycles, the H/L ratios were similar, ranging from 2.7 to 2.8. Differences in vehicle weight, track type, and engine characteristics are key reasons for the variability. There is inter-vehicle variability in cycle average CO and NOx emission rates among the five OSVs, with H/L ratios of 25−27 for CO and 31−42 for NOx. The CO emission rates for the two diesel vehicles were below the detection limit and are not included in the H/L ratio. Even though the precise CO emission rate for the diesels is not quantifiable, it is clear that the diesels have much lower CO emissions than the gasoline OSVs. Inter-cycle variability for a given OSV is quantified based on the H/L ratio among different driving cycles. Differences in total fuel use from different driving cycles are within 5%. For cycle average CO and NOx emissions, the differences from different driving cycles are within 30%, excluding the F450 and F550 CO emission rates which were below the detection limit. Therefore, the inter-cycle variability has limited effect on total fuel use. For PM emissions for the two diesel OSVs, the inter-vehicle H/L ratios are approximately 1.4, as detailed in the SI. The PM emissions were low due to the equipped DPF. The inter-cycle H/L ratios are approximately 1.1. Both inter-vehicle and intercycle variability have only moderate effect on cycle average PM emission rates. The standard deviations of the H/L ratios were estimated, as shown in Table 3. All of the H/L ratios are statistically significantly different than 1. Prior to this work, little was known as to whether differences in driving cycles might lead to substantial differences in fuel use and emission rates for OSVs. The observed driving cycles are based on different vehicles and different drivers. The similarity in the cycle average rates may be a result of the dominance of rolling resistance as a factor determining engine load. While one might posit the possibility of “alternate” driving cycles that could produce different cycle average rates, OSV operation is constrained by the preference to avoid skidding or slippage, thus limiting acceleration and deceleration rates. It is not likely, therefore, that there many alternate cycles to those observed that would also be consistent with safe operation. The finding that cycle average rates are robust to inter-cycle variability is useful in that it implies there is not as much need to consider

for the Chevrolet and the Arctic Cat were attributed to fuel rich operation. The average exhaust HC concentrations for the Bombardier, Ford F450 and F550 were below the gas analyzer detection limit for all bins. Therefore, there was typically no identifiable trend in HC emission rates for these snow coaches. For the Chevrolet Express, the average exhaust HC concentrations were below the detection limit for all except the highest fuel flow bins. For the Arctic Cat, the exhaust HC concentrations were above the detection limit, and the fuel-based HC emission rates generally decreased with fuel use rates. Thus, the HC emission rates were generally low except for the two OSVs that had substantial portions of time with fuel-rich engine operation. A generally increasing trend of fuel-based NOx emission rates versus fuel use rate was observed for the gasoline-fueled Bombardier, Chevrolet, and Arctic Cat. Based on the Zeldovich mechanism, engine-out NOx emission rates typically increase with engine load because of increasing peak flame temperatures in the engine cylinders during the power stroke.26 The NOx emission rate for the E350 was very low compared to all other vehicles. For the diesel-powered Ford F450 and Ford F550 with SCR, the fuel-based NOx emission rates were not sensitive to fuel use rate. SCR is effective at controlling NOx, since the observed emission rates are approximately 66% lower than the diesel OSV measured previously in 2006.11 In addition, the measured NOx emission rates were comparable to those of the gasoline vehicles, which would not have been the case without SCR. However, for diesel OSVs equipped with SCR and DPF, the NO to NOx ratio in the exhaust could decrease to approximately 0.7, compared to approximately 0.95 when not equipped with SCR and DPF.27 If this decreased NO/NOx ratio is used, the NOx emission rates for the diesel OSVs would increase by approximately 30%. For PM emissions from the diesel OSVs with DPF, no trend was observed with respect to fuel use rate. The opacity-based PM emission rates were below 0.1 g/gallon for all fuel use bins. These rates are very low and are consistent with DPF operation. The PM emission rates for OSVs with DPFs are comparable to highway vehicles with DPFs.22 For the measured three gasoline snow coaches, the cycle average HC and NOx emission rates were 85% and 65% lower, respectively, compared to the gasoline snow coaches measured in 2006.11 The average CO emission rate was 17% higher, but the rate would be 92% lower if excluding the high CO emitting Chevrolet Express. For the two diesel OSVs, the PM, CO, and NOx emission rates were lower by 97%, 86%, and 66%, respectively, compared to the diesel OSV measured in 2006.11 The general lower emissions for the recently measured vehicles are mainly attributed to more stringent emission controls compared to older model year vehicles measured in prior work. This is the first time that a relationship between engine load, fuel use, and emission rates have been developed for OSVs, taking into account speed, acceleration and road grade, that can be used for any real-world driving cycle. VSP can be adapted to vehicle types other than highway vehicles, thereby enabling development of VSP-based modal models that can be used to predict cycle average emissions for specific combinations of vehicles and driving cycles. Such models can support development of vehicle emissions estimates with high spatial and temporal resolution, which in turn are useful for emission inventories, air quality modeling, exposure assessment, and other applications. 8263

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

above. However, vehicle models of fuel use and emissions can be calibrated with a limited amount of PEMS data. Furthermore, a limited sample of vehicles is sufficient to quantify a fleet average with a desired degree of precision. In contrast, vehicle activity data can be collected less expensively and therefore more extensively, than emissions data. Using the modeling approach demonstrated here, it is possible to estimate OSV fuel use and emissions for a variety of road segments taking into account speed, acceleration, and road grade.

multiple cycles for OSVs, which differs from other types of vehicles such as highway vehicles. The VSP-based approach here enables comparisons of multiple vehicles for the same cycle, thereby enabling better comparison of inter-vehicle variability in fuel use and emission rates, such as because of fuel (e.g., gasoline, diesel) or emission control technologies. Furthermore, the approach here enables quantitative assessment of the extent to which variability between driving cycles leads to variability in fuel use and emission rates. In past work, it was not possible to separate the effect of driving cycles, which inherently vary from one test to another, from inter-vehicle variability. The approach demonstrated here enables more clear insight regarding the variability in fuel use and emission rates associated with driving cycles, which are depending on activity patterns, as distinct from modal fuel use and emission rates specific to a vehicle, which are depending on vehicle fuel and technology. The results obtained here can be used to estimate how many vehicles need to be sampled to achieve a target data quality objective for uncertainty in fleet average emission rates. The OSV inter-vehicle variability in fuel use and emissions was large. Assuming the same sample average and standard deviation as for the five measured snow coaches, a sample size of 15 vehicles for fuel use and 100 vehicles for NOx emissions is required to bound the 95% confidence interval to within ±20% of the mean. Data collection for this study was conducted for only 2 weeks. Therefore, it was not possible to observe variability in all factors that could affect OSV fuel use and emission rates, such as snow condition and passenger load. It is hypothesized that the snow condition, such as whether loose or packed, has a significant effect on the rolling resistance and, therefore, affects fuel use and emissions rates. There is evidence that loose snow might lead to a significant increase in CO and HC emission rates.11 Based on previous OSV studies, the effect of passenger load was inferred to be less significant than the effect of snow conditions.11 However, passenger load was found to have a significant effect on fuel use and emissions for other types of vehicles, such as transit buses.15 For the Ford E350, the curb weight is approximately 6400 lb, and the passenger capacity is 15. Assuming 200 lb per passenger, the fully loaded weight could be approximately 50% greater than for an empty van, and thus, might lead to significant increase in fuel use and emission rates. Activity data should be collected over a longer time period for a selected set of OSVs to enable assessment of the effect of snow conditions and passenger load on fuel use. The activity data needed include 1 Hz fuel use rate, speed, acceleration, and road grade. These data could be accessed using the OBD interface and GPS/BA receivers only. Using the method introduced in this study, the rolling resistance coefficients could similarly be quantified under selected combinations of snow and passenger load conditions. If fuel use rates are found to be substantially affected by these factors, a future study could additionally target emission measurements. Such data would support development of improved winter use plans. The methods demonstrated here can be applied to other OSVs. The general form of the regression models is broadly applicable to OSVs. The values of the regression coefficients may be specific to combinations of vehicles and snow conditions, which should be investigated in further work using simplified activity data collection methods described

■

ASSOCIATED CONTENT

S Supporting Information *

Additional description of the measured vehicles, additional details regarding the study route, additional details regarding methods for instrument calibration and validation, and for data synchronization, quality assurance and analysis; linear regression results for nonidling fuel use versus kinetic and potential energy, rolling resistance, and aerodynamic drag; figures of average fuel use in VSPOSV bins for selected OSVs; figures of average emissions rates in fuel use bins for selected OSVs; average emissions rates in predicted fuel use bins; inter-vehicle and inter-cycle variability for fuel use and emissions rates for selected OSVs; fuel use comparison for each snow coach; and cumulative probabilities of speed, acceleration, and road grade for selected driving cycles. This material is available free of charge via the Internet at http://pubs.acs.org/.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: 919-515-1155; fax: 919-515-7908; e-mail: frey@ncsu. edu. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The field measurements were sponsored by the National Park Service of the U.S. Department of the Interior via Louis Berger Group, Inc. in Denver, Colorado. Numerous vehicle owners and operators provided logistical support for this project, including access to and operation of the OSVs during data collection.

■

REFERENCES

(1) U.S. District Court for the District of Columbia December 16, 2003 Ruling; available at http://www.nps.gov/yell/parkmgmt/ upload/12_16opinion-2.pdf (accessed 2014). (2) U.S. District Court for Wyoming February 10, 2004 Ruling; available at http://www.nps.gov/yell/parkmgmt/upload/brimmer_ order.pdf (accessed 2014). (3) NPS. Winter Use Plans: Final Environmental Impact Statement for the Yellowstone and Grand Teton National Parks and John D. Rockefeller, Jr. Memorial Parkway; National Park Service: Lakewood, CO, 2000. (4) NPS. Temporary Winter Use Plans Environmental Assessment; U.S. Department of Interior; National Park Service: Lakewood, CO, 2004; available at http://www.nps.gov/yell/parkmgmt/upload/ tempwinteruseea8-18.pdf (accessed 2014). (5) NPS. Best available technology (BAT). Fed. Regist., November 20, 2009, 74 (223), 60159. (6) NPS. Snowmobile Best Available Technology (BAT) List; National Park Service: Lakewood, CO, 2009; available at http://www.nps.gov/ yell/parkmgmt/current_batlist.htm (accessed 2014). (7) NPS. Yellowstone in Winter: Current Management and Planning; National Park Service: Lakewood, CO, 2012; available at http://www. nps.gov/yell/parkmgmt/currentmgmt.htm (accessed 2014). 8264

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265

Environmental Science & Technology

Article

(8) Frey, H. C., Sandhu, G. S., Graver, B. M., Hu, J. Measurement of Fuel Use and Emissions of Over-Snow Vehicles at Yellowstone National Park; Final Report prepared by North Carolina State University for Louis Berger Group: Denver, CO, August, 2012. (9) Ray, J. D., Bishop, G.; Schuchmann, B.G.; Frey, H.C.; Sandhu, G.S.; , and Graver, B.M.; Yellowstone Over-snow Vehicle Emission Tests − 2012: Summary Vehicle Data and Fleet Estimates for Modeling, Natural Resource Technical Report NPS/NRPC/ARD/NRTR − 2012. Draft V6.5; National Park Service: Fort Collins, CO, 2012. (10) Bishop, G. A.; Burgard, D. A.; Dalton, T. R.; Stedman, D. H.; Ray, J. D. Winter motor-vehicle emissions in Yellowstone National Park. Environ. Sci. & Technol. 2006, 40 (8), 2505−2510. (11) Bishop, G. A.; Stadtmuller, R.; Stedman, D. H.; Ray, J. D. Portable emission measurements of snowcoaches and snowmobiles in Yellowstone National Park. J. Air Waste Manage. Assoc. 2009, 59 (8), 936−942. (12) Bishop, G. A.; Stedman, D. H.; Hektner, M.; Ray, J. D. An in-use snowmobile emission survey in Yellowstone National Park. Environ. Sci. & Technol. 1999, 33 (21), 3924−3926. (13) Unal, A.; Frey, H. C.; Rouphail, N. M. Quantification of highway vehicle emissions hot spots based upon on-board measurements. J. Air Waste Manage. Assoc. 2004, 54 (2), 130−140. (14) Frey, H. C.; Kim, K. Comparison of real-world fuel use and emissions for dump trucks fueled with B20 biodiesel versus petroleum diesel. Transp. Res. Rec. 2006, 1987, 110−117. (15) Frey, H. C.; Rouphail, N. M.; Zhai, H.; Farias, T. L.; Gonçalves, G. A. Modeling and comparing real-world fuel consumption for dieseland hydrogen-fueled transit buses and implication for emissions. Transp. Res., Part D 2007, 12, 281−291. (16) Wang, H.; Fu, L.; Zhou, Y.; Li, H. Modeling of the fuel consumption for passenger cars regarding driving characteristics. Transp. Res., Part D 2008, 13 (7), 479−482. (17) Zhai, H.; Frey, H. C.; Rouphail, N. M. A Vehicle-specific power approach to speed- and facility- specific emissions estimates for diesel transit buses. Environ. Sci. Technol. 2008, 42 (21), 7985−7991. (18) Jimenez-Palacios, J. Understanding and quantifying motor vehicle emissions and vehicle specific power with TILDAS remote sensing, Doctoral Dissertation, Massachusetts Institute of Technology, 1999. (19) Frey, H. C.; Zhang, K.; Rouphail, N. M. Fuel use and emissions comparisons for alternative routes, time of day, road grade, and vehicles based on in-use measurements. Environ. Sci. Technol. 2008, 42 (7), 2483−2489. (20) Yazdani, B.; Frey, H. C. Road grade quantification based on global positioning system data obtained from real-world vehicle fuel use and emission measurements,. Atmos. Environ. 2014, 85, 179−186. (21) Yazdani, B.; Frey, H. C.; Sandhu, G. S. In-vehicle road grade measurement using stand-alone GPS with barometric altimeter. J. Transp. Eng. 2013, 139 (6), 605−611. (22) Sandhu, G. S.; Frey, H. C. Real-world measurement and evaluation of duty cycles, fuels, and emission control technologies of heavy-duty trucks. J. Transp. Res. Board 2012, 2270, 180−187. (23) Hendricks, E. and Sorenson, S.C.; SI Engine Controls and Mean Value Engine Modelling, SAE Technical Papers; Number 910258; SAE International, 1991. (24) Sandhu, G. S.; Frey, H. C. Effects of errors on vehicle emission rates from portable emissions measurement systems. Transp. Res. Rec. 2013, 2340, 10−19. (25) USEPA. MOVES2010 Highway Vehicle: Population and Activity Data, Final Report EPA-420-R-10-026; U.S. Environmental Protection Agency, Office of Transportation and Air Quality: Ann Arbor, MI, 2010. (26) Heywood, J. B. Internal Combustion Engine Fundamentals; McGraw-Hill: New York, 1988. (27) Dallmann, T. R.; DeMartini, S. J.; Kirchstetter, T. W.; Herndon, S. C.; Onasch, T. B.; Wood, E. C.; Harley, R. A. On-road measurement of gas and particle phase pollutant emission factors for individual heavy-duty diesel trucks. Environ. Sci. Technol. 2012, 46, 8511−8518.

8265

dx.doi.org/10.1021/es501164j | Environ. Sci. Technol. 2014, 48, 8258−8265