2012 Air Emissions Inventory

Transcription

1 SECTION 6 HEAVY-DUTY VEHICLES This section presents emissions estimates for the heavy-duty vehicles (HDV) source category, including source description (6.1), geographical delineation (6.2), data and information acquisition (6.3), operational profiles (6.4), the emissions estimation methodology (6.5), and the emission estimates (6.6). 6.1 Source Description Heavy-duty vehicles, or trucks, are used extensively to move cargo, particularly containerized cargo, to and from the marine terminals that serve as the bridge between land and sea transportation. Trucks deliver cargo to both local and national destinations, and they also transfer containers between terminals and off-port railcar loading facilities, an activity known as drayage. In the course of their daily operations, trucks are driven onto and through the terminals, where they deliver and/or pick up cargo. They are also driven on the public roads within the Port boundaries, and on the public roads outside the Port. While most of the trucks that service the Port s terminals are diesel-fueled vehicles, alternative fuel trucks, primarily those fueled by liquefied natural gas (LNG), made approximately 10% of the terminal calls in 2012, according to the Port s Clean Trucks Program activity records and the Port Drayage Truck Registry. Diesel particulate matter is not emitted by trucks that are not burning diesel fuel, so the diesel particulate emission estimates presented in this inventory have been adjusted to take the alternative-fueled trucks into account. The most common configuration of HDV is the articulated tractor-trailer (truck and semi-trailer) having five axles, including the trailer axles. The most common type of trailer in the study area is the container trailer, built to accommodate standard-sized cargo containers. Additional trailer types include tankers, boxes, and flatbeds. A tractor traveling without an attached trailer is called a bobtail. A tractor pulling an unloaded container trailer chassis is known simply as a chassis. These vehicles are all classified as heavy HDVs regardless of their actual weight because the classification is based on gross vehicle weight rating (GVWR), which is a rating of the vehicle s total carrying capacity. Therefore, the emission estimates do not distinguish among the different configurations. As an example of typical HDVs, Figure 6.1 shows typical container trucks operating on a terminal. Port of Long Beach 103 July 2013

2 Figure 6.1: Typical Container Trucks 2012 Air Emissions Inventory 6.2 Geographical Domain The geographical domain for trucks is discussed in section To develop emission estimates, truck activities were evaluated as having two components: On-terminal operations, which include waiting for terminal entry, transiting the terminal to drop off and/or pick up cargo, and departing the terminals. On-road operations, consisting of travel on public roads within the SoCAB. This also includes travel on public roads within the Port boundaries and those of the adjacent POLA. Port of Long Beach 104 July 2013

3 6.3 Data and Information Acquisition For on-terminal truck activity, the Port collected information regarding on-terminal truck activity during in-person and/or telephone interviews with terminal personnel. This information includes terminal gate operating schedules, on-terminal speeds, time and distance traveled on terminal while dropping off and/or picking up loads, and time spent idling at the entry and exit gates. Most terminals were able to provide estimates of these activity parameters, although few keep detailed records of information such as gate wait times and on-terminal turn-around time. However, the reported values appear to be reasonable and have been used in estimating on-terminal emissions, except where noted otherwise. The Port retained Cambridge Systematics, Inc. to develop estimates of truck activity on the public roads inside and outside the Port using trip generation and travel demand models that have been developed to estimate the volumes (number of trucks) and average speeds on roadway segments between defined intersections. Output from the trip generation model (number of truck trips) is also used as a component of the on-terminal emission estimates. 6.4 Operational Profiles Operational profiles were developed for on-terminal truck activity using data and information collected from terminal operators. These profiles are summarized in section The on-road truck activity profiles were developed using trip generation and travel demand models summarized in section On-Terminal Truck Activity Table 6.1 shows the range and average of reported operating characteristics of on-terminal truck activities at Port container terminals, while Table 6.2 shows the same summary data for noncontainer terminals and facilities. Table 6.1: 2012 Summary of Reported Container Terminal Operating Characteristics Speed Distance Gate In Unload/Load Gate Out (mph) (miles) (hours) (hours) (hours) Maximum Minimum Average Port of Long Beach 105 July 2013

4 Table 6.2: 2012 Summary of Reported Non-Container Facility Operating Characteristics Speed Distance Gate In Unload/Load Gate Out (mph) (miles) (hours) (hours) (hours) Maximum Minimum Average The total numbers of truck calls in 2012 were 3,149,886 associated with container terminals and 163,025 associated with non-container facilities. The total number of truck calls associated with container terminals is based on the trip generation model described above, while non-container terminal truck calls were obtained from the terminal operators. Table 6.3 provides the on-terminal operating parameters; listing total estimated vehicle miles traveled (VMT) and hours of idling on-terminal and waiting at entry gates. The table lists 0 miles traveled and 0 total hours idling for temporarily closed terminals in Table 6.3: 2012 Estimated On-Terminal VMT and Idling Hours by Terminal Vehicle Total Terminal Miles Hours Idling Type Traveled (all calls) Container 873, ,480 Container 421, ,556 Container 392, ,416 Container 257, ,661 Container 243, ,822 Container 202, ,271 Auto 3,275 5,629 Break Bulk 5,296 4,449 Break Bulk 2, Break Bulk 2, Break Bulk 1,500 0 Break Bulk 31 0 Dry Bulk 10, Dry Bulk Dry Bulk 0 0 Dry Bulk 0 0 Liquid 5,700 4,560 Liquid 2, Liquid 1,350 0 Total 2,427,404 1,562,511 Port of Long Beach 106 July 2013

5 6.4.2 On-Road Truck Activity The trip generation model is derived from a computer model originally designed to forecast truck volumes for the 2001 Port Transportation Study. 48 The model has recently been updated to more closely reflect current conditions and incorporates each container terminal s average daily container throughput in The results of the trip generation model were used as input to a regional travel demand model used for transportation planning by the Southern California Association of Governments (SCAG), the federally designated metropolitan planning organization for the SoCAB area. The terminalspecific truck travel information from the trip generation model, as well as the results of an origin/destination survey of approximately 3,300 Port-area truck drivers, were incorporated into the Port-area travel demand model to predict truck travel patterns and estimate the number of trucks traveling over roadways in the region. The model estimates the movements of Port-related trucks on their way from the Port until they make their first stop, whether for delivery of a container to a customer, a transloading facility, or an off-port rail yard, or when the trucks reaches the boundary of the SoCAB. Transloading is the process of unloading freight from its overseas shipping container and re-packing it for shipment to its destination over land. 48 POLB, Ports of Long Beach/Los Angeles Transportation Study, Meyer, Mohaddes Associates. June, Port of Long Beach 107 July 2013

6 Figure 6.2 shows the roadways in and around the Port that trucks use in daily operations. Figure 6.2: Port and Near-Port Roadways Port of Long Beach 108 July 2013

7 Figure 6.3 provides a regional map of the major area roadways. The daily traffic estimates are based on average weekday activity during an average month over these roads and on the regional network of smaller, local roads. The daily activities have been annualized for the emission estimates presented in this inventory on the basis of 300 days of terminal operation per year. Figure 6.3: Regional Map The travel demand model produces estimates of the number of trucks and their average speed in each direction over defined roadway segments, along with the length of each roadway segment. A brief example illustrating the data is provided in Table 6.4. The number of trucks and distances traveled are multiplied for each segment and summed to produce estimates of vehicle miles of travel. In addition, a VMT-weighted average speed was calculated taking into account how many miles were driven at each speed; these VMT and speed estimates were used with the speed-specific EMFAC emission factors (discussed in section 6.5) to estimate on-road driving emissions. Port of Long Beach 109 July 2013

8 Table 6.4: On-road HDV Activity Modeling Results Example Volume Volume Speed Speed Distance Direction 1 Direction 2 Direction 1 Direction 2 (miles) (# trucks) (# trucks) (mph) (mph) Emissions Estimation Methodology The general equation for estimating the emissions inventory for a fleet of on-road vehicles can be expressed as: Equation 6.1 Where: E = Emissions (e.g. tons/year) Pop = Population (number of vehicles of a particular model year in the fleet) Act = Activity (average number of miles driven per truck, hours of idle operation) BER = Basic Emission Rate (amount of pollutants emitted per unit of activity for vehicles of that model year), g/mile CorF = Correction Factor (adjustment to BER for specific assumptions of activity and/or atmospheric conditions), dimensionless The emissions from all truck model years in the fleet are summed to complete the emission estimates. In practice, the fleet estimates are prepared by combining the base emission rates and correction factors for all model years in such a way as to develop a single set of emission factors that represent the fleet s distribution of model years. Population and activity are also combined to estimate total fleet activity, and the activity and emission factors are combined to estimate fleet emissions. Port of Long Beach 110 July 2013

9 6.5.1 Model Year Distribution Because vehicle emissions vary according to the vehicle's model year and age, the activity level of trucks within each model year is an important part of developing emission estimates. The 2012 model year distribution for the current emissions inventory is based on call data originating from radio frequency identification (RFID) data, which tracked over 5.5 million truck calls made to the Port of Long Beach and Port of Los Angeles in 2012, as well as engine model year data drawn from the Port Drayage Truck Registry (PDTR). The PDTR contains model year information on all registered drayage trucks serving the Port and the fuel type used by each truck, from which an adjustment factor was developed for non-diesel fueled vehicles. The RFID data provided the number of calls made by each engine model year. The distribution of the truck fleet s engine model year by calls, used to develop the composite emission factors, is presented in Figure 6.4. The call weighted average engine age of the Portrelated fleet is 3 years. Figure 6.4: 2012 Engine Model Year Distribution of the Heavy-Duty Truck Fleet Port of Long Beach 111 July 2013

10 6.5.2 The EMFAC Model The EMFAC model was developed by CARB to calculate the fleet emissions of various vehicle classes in the California fleet. The output from the most recent version of the model released in 2011, EMFAC2011, was used to estimate the emissions from trucks that called on the Port in EMFAC2011 is comprised of databases that provide emission rates in grams per mile by speed and by model year for various subcategories of heavy-duty vehicles including drayage trucks; these databases can be accessed on CARBs website. 49 This data, coupled with Port-specific engine model year distribution and vehicle miles traveled by speed are used to calculate emissions from Port-related heavy-duty trucks in tons per year Overview of the EMFAC2011 Emissions Calculation Methodology EMFAC2011 models the basic emission rate in units of grams per mile as a constant value over time with a zero mile rate representing the emissions of the vehicle when it is new or like-new assuming that the truck is well maintained and un-tampered, plus a deterioration rate representing the gradual increase in the emission rate over time as the engine accumulates mileage. Equation 6.2 Where: /, BER = Basic Emission Rate (amount of pollutants emitted per unit of activity for vehicles of that model year), g/mile ZMR = Zero Mile Rate (emissions of the vehicle when new or like-new), g/mile DR = Deterioration Rate (slope representing the gradual increase in the emission rate over time as a functions of use), g/mi/10,000 miles CM = Cumulative Mileage (total miles on the vehicle since new), miles Emission rates for each engine model year and speed that are obtained from EMFAC2011 reflect the correction factors included in equation 6.1 to reflect vehicle specific activity such as speed, type and quality of fuel burned, and specific ambient conditions such as temperature and relative humidity. CARB has published idle emission factors expressed in grams per hour (g/hr) to estimate idle emissions associated with drayage operations. The idle emission factors are multiplied by the total hours of idle operation to derive the emission estimates. 49 CARB, Port of Long Beach 112 July 2013

11 CARB has developed low idle and high idle emission rates to represent emissions from different types of truck idling. The low idle rates are "indicative of a truck in queue to either pick up or drop off a shipment," whereas the high idle rates "reflect activity associated with truck stops, rest areas, and distribution centers" rather than normal port operations. 50 The low idle emission factors are presented in Table Table 6.5: Idle Emission Rates (g/hr) Model Years HC CO NO x PM CO 2 Pre , , , , , , , , Speed-Specific Emission Factors Composite model year and speed specific gram-per-mile emission rates reflect the distribution of truck calls by engine model year within the fleet of trucks calling at Port terminals, with a single emission factor for each 5-mile-per-hour speed increment representing the distribution of model years using the call-weighted model year distribution discussed in the previous subsection. A single set of pollutant specific gram-per-hour idle emission rates has also been derived using the distribution of truck calls by engine model year. Because the EMFAC model does not produce emission factors for N 2 O or speed-specific emission factors for SO x, emission factors for these pollutants were developed using a mass balance approach for SO x and a gram-per-gallon emission factor from CARB for N 2 O. The following equation was used to derive the SO x emission factor: Equation 6.3 / /,,, / /. / 50 CARB, 51 CARB, for CO 2 and NO x (Table 11-6); for the remaining pollutants (Table 11); and for fuel correction factors (Table 9). Port of Long Beach 113 July 2013

12 The emission calculations are based on the use of 15 ppm ULSD diesel fuel. The weight of a gallon of diesel fuel is assumed to be 7.1 pounds or 3,220 grams (7.1 lbs x g/lb). Based on the EMFAC2011 model, the 2012 fleet average fuel economy of the heavy-heavy duty diesel fleet was calculated to be 5.64 miles per gallon. The N 2 O emission factor was calculated using the following equation: Equation 6.4 /. /. / Idling emission rates of SO 2 and N 2 O have been based on an average fuel consumption rate of 0.48 gallons of diesel per hour during idling derived from an analysis of the idling CO 2 emission factor established by CARB). Tables 6.6 and 6.7 summarize the speed-specific emission factors used to estimate emissions. The emission factors were multiplied by the on-road and on-terminal VMT and on-terminal idling hours to develop the overall on-road and on-terminal emission estimates. Table 6.6: Speed-Specific Composite Emission Factors Speed Range PM 10 PM 2.5 DPM NO x SO x CO HC Units (mph) 0 (Idle) g/hr g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi g/mi Port of Long Beach 114 July 2013

13 Table 6.7: Speed-Specific GHG Emission Factors 2012 Air Emissions Inventory Speed Range CO 2 N 2 O CH 4 Units (mph) 0 (Idle) 4, g/hr 1-5 4, g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi , g/mi Improvements to Methodology from Previous Years The following improvements to the data and methodology underlying the emission calculations were made in this inventory compared to the previous EI. Refer to Section 8 for a comparison of 2012 emissions with 2005 emissions. Activity and truck characteristics data including terminal calls, fuel type, and engine model year developed under the Port s Clean Trucks Program was used to develop the model year distribution, an improvement over the use of optical character recognition records and body model year data from the California Department of Motor Vehicles. Data from the Port s Clean Trucks Program was also used to enhance the trip generation model and the travel demand model that estimate the number of truck trips and vehicle activity on public roads. The improvements noted above do not represent a change of methodology, but an improvement in estimating methods that better reflect current port and terminal operations. Port of Long Beach 115 July 2013

14 6.6 Emission Estimates Tables 6.8 and 6.9 summarize the vehicle miles traveled and emissions associated with overall HDV activity. Table 6.8: 2012 HDV Emissions, tons Vehicle Activity Location Miles PM 10 PM 2.5 DPM NO x SO x CO HC Traveled On-Terminal 2,427, On-Road 145,754, Total 148,181, Table 6.9: 2012 HDV GHG Emissions, metric tons Vehicle Activity Location Miles CO 2 e CO 2 N 2 O CH 4 Traveled On-Terminal 2,427,404 17,256 17, On-Road 145,754, , , Total 148,181, , , Tables 6.10 through 6.13 show the vehicle miles traveled and emissions associated with container terminal activity separately from emissions associated with other Port terminals and facilities. Table 6.10: 2012 HDV Emissions Associated with Container Terminals, tons Vehicle Activity Location Miles PM 10 PM 2.5 DPM NO x SO x CO HC Traveled On-Terminal 2,391, On-Road 138,549, Total 140,941, Port of Long Beach 116 July 2013

15 Table 6.11: 2012 HDV GHG Emissions Associated with Container Terminals, metric tons Vehicle Activity Location Miles CO 2 e CO 2 N 2 O CH 4 Traveled On-Terminal 2,391,959 17,031 16, On-Road 138,549, , , Total 140,941, , , Table 6.12: 2012 HDV Emissions Associated with Other Port Terminals, tons Vehicle Activity Location Miles PM 10 PM 2.5 DPM NO x SO x CO HC Traveled On-Terminal 35, On-Road 7,204, Total 7,240, Table 6.13: 2012 HDV GHG Emissions Associated with Other Port Terminals, metric tons Vehicle Activity Location Miles CO 2 e CO 2 N 2 O CH 4 Traveled On-Terminal 35, On-Road 7,204,594 12,112 11, Total 7,240,039 12,337 12, Port of Long Beach 117 July 2013