International Experience with Electric and Zero Emission Buses
Port of Houston 2013 Mobile Source Emission Inventory
1. Presented by:
Richard Billings*, John Koupal, Rick Baker, Heather Perez, Roger
Chang and Jennifer Sellers
Eastern Research Group, Inc. (ERG)
1600 Perimeter Park Drive, Morrisville, NC 27312
Kenneth Gathright and Leah Oberlin
Port of Houston Authority (PHA)
111 East Loop North, Houston, TX 77029
June 20-23, 2016
Port of Houston 2013 Mobile Source
Emission Inventory (Paper #870)
Presented to:
AWMA’s 109th Annual Conference & Exhibition
New Orleans, Louisiana
1
2. Abstract
Port of Houston 2013 Mobile Source Emission
Inventory. Paper # 870
Richard Billings ERG
A State-of- the-Art emission inventory was developed for the Port of Houston Authority (PHA)
that quantifies the impact that PHA mobile source operations have on local air quality. This
inventory included emission from: 1) marine vessels; 2) heavy duty diesel vehicles; 3) cargo
handling equipment; and 4) locomotives. Marine vessel estimates were developed by using
Automatic Identification System (AIS) data to identify vessels and quantify ship traffic patterns.
These vessels were linked to their individual characteristics to estimate criteria and GHG
emissions. Diesel truck emissions were estimated for multiple terminal sites using the project
scale feature of EPA's MOVES2014 model, based on field measurement of truck counts, time-in-
port, driving patterns and age. Local terminals were surveyed to compile necessary cargo
handling equipment data, ERG modeled nonroad equipment with onroad engines using
MOVES2014. And engine load factors for RTGs based on PHA in-use measurement profiles. For
locomotives, tier level engine data were provided by the Class I and local yard operators to
account for compliance with current regulatory standards; this information was used along
with railcar traffic data and fuels usage data to estimate emissions from these engines. GIS
shapefiles were used to spatially allocate activity and emissions. This paper presents
preliminary results of this study, the project is scheduled for
completion in June 2016.
2
3. Introduction
• Commercial Marine
Vessels (CMV)
• Heavy Duty Diesel
Vehicles (HDDV)
• Cargo Handling
Equipment (CHE)
• Locomotives
2013 inventory of Port of
Houston Authority (PHA) -
related emissions within the
Houston/Galveston/Brazoria
(HGB) Nonattainment area
3
4. Introduction
CMV
• Automatic Identification
System (AIS)
• Geographic Information
System (GIS) vessel movement
mapping
• Information Handling Service
(IHS) Register of Ships
CHE
• Equipment survey
• TexN
HDDV
• MOVES2014
• License Plate Recognition
(LPR) Cameras
• Gate Data
• GIS truck movement
mapping
Rail
• Locomotive Tier data
• EPA emission factors
4
6. Approach: CMV
DE = MCR x LF x A x EF
Where:
DE = Emissions from the engine(s), usually calculated
as grams or converted to Tons of emissions.
MCR = Maximum continuous rated engine power, kW.
LF = Load factor ((actual speed/maximum speed)3)
A = Activity (hours)
EF = Emission factor (g/kWh).
6
7. Approach: CMV
. Category 3 Emission Factors (g/kW-hr)
Type Engine Fuel NOX VOCa CO SO2 CO2 PM10 PM2.5
b
SSD Main 1% Sulfur 14.7 0.6318 1.4 3.62 588.86 0.45 0.42
MSD Aux 1% Sulfur 12.1 0.4212 1.1 3.91 636.6 0.47 0.43
7
Category 2 Emission Factors (g/kW-hr)
Tier PM10 NOx CO VOC PM2.5
a SO2 CO2
0 0.32 13.36 2.48 0.141102 0.3104 0.006 648.16
1 0.32 10.55 2.48 0.141102 0.3104 0.006 648.16
2 0.32 8.33 2.00 0.141102 0.3104 0.006 648.16
3 0.11 5.97 2.00 0.073710 0.1067 0.006 648.16
8. Approach:
Onroad
8
Estimated Total Yearly Visits to Each Terminal and the Source of the Estimate
Terminal Gate Estimated Yearly Entries Source
Barbours Cut
Main 463,695 PHA log
Gate 4 46,722
Extrapolated TTI LPR-logged
entries
APM/Maersk 419,451 PHA log
Bayport - 652,536 PHA log
Jacintoport - 91,601 PHA log
Bulk Material
Handling Plant
- 19,045
Extrapolated terminal-
provided six-month log
Care
- 22,100
Extrapolated TTI LPR-logged
entries
Turning Basin
Cargo Bay Rd 236,821
Extrapolated TTI LPR-logged
entries
Industrial Park
East 87,256
Extrapolated TTI LPR-logged
entries
Manchester 24,191
Terminal-provided estimates
for three client operators
Southside 18 6,500
Extrapolated TTI LPR-logged
entries
Southside Jacob
Stern 1,691 Terminal-provided estimate
Woodhouse
- 14,092
Extrapolated TTI LPR-logged
entries
Grain Elevator #2
Ardent Mills,
Louis Dreyfus
(operators) 19,068 PHA-provided estimates
Total 2,104,769
Drayage truck
In-Terminal
Connecting Roads
HGB Non-Attainment Area
Dock trucks
Limited Operation within Port Area
9. Approach:
Onroad
9
Terminal/Gate
Estimated Average Visit
Duration (minutes)
Barbours Cut 58.7
Barbours Cut/APM 54
Bayport 48.3
Jacintoport 54.2
Bulk Material Plant 77.3
Care 78.9
Turning Basin/ Cargo Bay Rd 124.4
Turning Basin/ Industrial Park East 78.1
Turning Basin/ Manchester 77.3
Turning Basin/ Southside 18 94.6
Turning Basin/ Southside Jacob Stern 77.3
Woodhouse & Grain Elevator #2 104.7
License Plate Recognition (LPR)
Cameras
10. Approach:
Onroad
10
Sa = Da / Ta
Where:
Sa = Vehicle speed from terminal a along connector to highway (mph)
Da = Distance traveled from terminal a gate to main highway (miles)
Ta = Time to travel distance D (hrs)
VMT = ∑ DBa * Ta
Where :
VMT = Vehicle miles traveled (VMT)
DBa = Distance to HGB boundary from terminal a ( miles)
Ta = Trip count from terminal a
11. Approach: Onroad
11
• Dock Trucks
– Movement of steel
from Turning Basin
to Lay Yards
– 64 trucks
– Model years 1976
to 2006
– GPS vehicle tracking
12. Approach: CHE
12
• Forklifts
• Generators
• Lifts
• Loaders
• Rubber tire gantry
(RTG) cranes
• Yard tractors
• Other support equipment
14. Approach: Rail
14
• Port Terminal Railroad
Association (PTRA)
• Burlington Northern
Santa Fe Railway
(BNSF)
• Union Pacific Railroad (UP)
• Kansas City Southern
Railway (KCS)
15. Approach: Rail
15
GTMi = GT X M
GTMi = Gross Ton Miles for Railway i
GT = Gross Tonnage of Freight Handled by Railway i
M = Distance between yard and HGB boundary
LHfi = GTMi x FRi/GTMRi
LHfi = Line Haul Fuel Usage for Railway i (gallons)
FRi = System Wide Fuel Usage Reported for Railway i in R-1 form (gallons)
GTMRi = Total Gross Ton Millage Reported for Railway i in R-1 form (GTM)
EMpi = LHfi x EFp
EMpi = Emissions of Pollutant p for Railway i (g)
EFp = Emission factor for Pollutant p (g/gallon)
16. Results: CMV
16
All Vessels Entering or Leaving
Harris County
PHA-related Vessels
Port of Houston Vessel Traffic
17. Results: HDDV
17
Summary of Diesel-Powered Heavy-Duty Vehicle Emissions Related to PHA Activities in 2013
(tons/year)
NOx VOC CO PM2.5 PM10 SO2 CO2
GHGs
(CO2eq)
In-Terminal 222.4 38.6 108.1 22.4 31.2 0.26 30,362 30,383
Connector Roads 97.1 7.6 35.8 7.6 10.6 0.15 17,267 17,272
Greater HGB Region
846.8 62.9 279.8 51.4 70.8 1.81 212,520 212,664
Dock Trucks 5.4 0.83 1.6 0.44 0.6 0.003 350 350
Total 1,172 110 425 82 113 2.2 260,499 260,669
20. Acknowledgements
20
Ken Gathright, Leah Oberlin, & Hugh Moore, PHA
Jeremy Johnson, Joe Zietsman, Christopher Fleming,
Christina Houlstead, & Chadyi Gru, TTI
Jon Haworth, Kinder Morgan
Dick Schiefelbein, Woods Harbour Consultants
Laura Fiffick, Kevin Maggay, & James Mayhar, BNSF
Michael J. Germer, Union Pacific
Jeff Norwood, General Manager of PTRA
P. Conlon, Kansas City Southern Railroad
Editor's Notes
Normally, I present papers that focus on very specific technical issues,
but this presentation has turned out to be particularly challenging
as it is a broader discussion of the different State-of-the–Art tools we used to develop the Port of Houston’s 2013 emission inventory.
The Port of Houston Authority commissioned ERG to develop a 2013 inventory of PHA-related emission sources within the HGB nonattainment area.
The emission inventory included all criteria pollutants and greenhouse gases from mobile sources included vessels, trucks, cargo handling equipment, and locomotives.
PHA implemented a similar study for 2007.
For this 2013 project, we made enhancements to the previous inventory using the latest emission estimating tools, many of which were not available in 2009 when the previous inventory was completed.
For example, the 2013 CMV component used AIS data to map individual vessel movements and quantify operating speeds.
We also estimated truck emissions using the EPA MOVES2014 model, tailored to PHA’s terminals.
And we used LPR cameras to develop accurate gate activity data.
For CHEs, we used TCEQ’s TexN model, which reflects the local equipment fleet, usage patterns, and state control programs.
For this 2013 inventory, railway companies provided data on the regulatory tier of each locomotive, allowing emissions to be adjusted for compliance with federal standards.
Lastly we mapped activity and emissions using GIS tools.
As noted earlier, the primary data source used to quantify ship movements was AIS data provided by PortVision.
This dataset captured all vessels that transmit an AIS signal, identifying the vessel, its location, speed, and direction.
These signals are transmitted every 2 to 5 seconds, providing vessel operators with real-time data on the movements of other nearby AIS vessels.
The raw AIS data compiled into a remarkably large and cumbersome dataset.
We used an alternative approach where a “snapshot” of the Port and Ship Channel was taken every 15 minutes.
Each vessel record included in each snapshot was handled as a vector with a known position and speed.
These vessel-specific records were linked to their individual engine characteristics obtained from the IHS Register of Ships
The vessel characteristics compiled included data elements you see on this slide:
In general, each vessel record represents 15 minutes of operation, but we recognized that approximately 20% of the AIS transmittal data are interrupted, such that vessels do not always appear in consecutive snapshots.
To fill missing transmittal gaps, we arranged vessel records chronologically and determined the duration by comparing time stamps.
The engine power data were multiplied by the duration to estimate kilowatt hours.
Kilowatt-hours were adjusted to account for engine load; for AIS vessels this was estimated by using the propeller law in conjunction with the AIS reported speed, relative to the vessel’s maximum design speed, as reported in the Register of Ships.
To ensure that the vessel dataset was complete and included small non-AIS vessels, we reviewed the Army Corps of Engineers Waterborne Commerce Data.
This dataset is a compilation of domestic vessel movements for tugs and barges, and smaller bulk carriers, and tankers.
Only two additional tug routes were identified in the WBC data that were not included in the AIS data..
We also compiled local data to account for smaller Category 1 and 2 vessels that operate in the area but are not included in either the AIS or WBC data.
Only local dredging operation needed to be added to the inventory. Hours of operation were estimated based on the time spent on site as documented by the Army Corps of Engineers Dredging Database.
Emissions were developed based on the EPA engine category for propulsion and auxiliary engines.
The engine Category emission factors we used were from the EPA’s 2014 National Emission Inventory, which account for use of ECA and Nonroad compliant fuels
We also made an adjustment for Category 1 and 2 engines to account for NOX reductions related to the use of Texas Low Emission Diesel (TxLED).
For onroad, we quantified emissions within the HGB nonattainment area from drayage and dock trucks associated with the PHA terminals.
Drayage trucks, are heavy-duty diesel trucks used to transport cargo between port terminals, intermodal rail yards, and other locations.
Dock trucks are retired heavy-duty trucks used for hauling steel from the Turning Basin to nearby “lay yards.”.
We used the project scale feature of the MOVES model to estimate emissions in and around each port terminal.
To do this, we developed a custom set of inputs unique to each of the PHA-owned terminals, including nearby “connector” roads.
Additionally, MOVES county scale runs were used to estimate truck emissions from port-related visits in the greater Houston area.
We worked with TTI to develop the input data used in the MOVES run,
Basically we updated the 2009 Houston Port Drayage Study with new field data, using LPR cameras installed at six different terminal gates.
The LPR data in conjunction with PHA terminal entry logs were used to validate the frequency and average duration of drayage truck visits.
These LPR data were also matched to vehicle registration data from the Tx DMV to develop the age distribution used in the MOVES model.
For connector roads, we found the distances from each terminal gate to the nearest highway using Google Maps, which also provided an estimate of the time required to travel the distance, from which average speed was estimated.
We then estimated the emissions from drayage truck operations in the greater HGB region, based on total vehicle miles traveled.
The average distance traveled to and from the terminals along the HGB road network was obtained from the 2007 inventory.
This distance was multiplied by the total number of terminal visits in 2013 to find the total truck miles traveled within the HGB area.
We compiled vehicle age and fuel consumption data for all 64 dock trucks
Model years for dock trucks ranged from 1976 to 2006.
Activity patterns for two dock trucks were monitored using GPS data,
confirmed that these trucks stay within the confines of Turning Basin.
CHEs are used to transfer cargo between vessels, trucks, and rail operations.
To characterize CHE use, we developed an electronic survey which was distributed to 38 tenants identified by the PHA.
The survey requested information on the equipment population, engine and fuel type, hp, model year, and hours of use, as well as data on temporal aspects of operation and emission control retrofits.
Data were also provided by the port for their CHEs. This included second-by-second engine load data for 20 RTGs.
The load factors developed for RTGs were adjusted to account for frictional losses at engine idle, resulting in a net average load factor of 13.9%.
This value is significantly lower than EPA’s default value of 43%.
Emissions for CHE were estimated using the TCEQ’s TexN model.
This model combines engine hp, load factors, and hours of operation, with emission factors in terms of brake hp-hrs to calculate emissions by equipment and fuel type.
With 1,200 pieces of equipment we had to group the CHEs into nine “activity bins” for each CHE equipment type shown here. Next we ran TexN for each activity bin.
The model-year emission outputs were summing across engine technologies; providing emission rates in grams of pollutant per unit per hour by model year, to account for engine deterioration over time.
We multiplied these model year emission rates, by the hours of operation for individual units to obtain total mass emissions.
Only 34 gasoline and propane powered CHEs were reported in the survey.
We used a simplified approach for these engines, applying a single annual activity estimate toTexN.
TexN cannot be used to estimate emissions from onroad certified engines, such as terminal tractors.
Therefore, we created a project-level MOVES run to model a single link representing terminal tractor in-terminal operations.
Use of TxLED fuels was assumed for all CHEs.
Yard and line haul locomotives are critical for the efficient movement of marine cargo across the Midwest region.
There are several rail lines that operate within the Port.
PTRA focuses on yard and shuttle operations and there are 3 Class I railroads that provide line-haul services.
PTRA submitted data about its yard operations including an estimate of the power rating and Tier level of each yard locomotive, hours of operation, and an estimate of fuel consumption.
For line-haul operations, BNSF and UP provided documentation of the movement and Tier level of locomotives operating in the area.
Knowing the engines’ Tier level allowed us to accurately match engines to appropriate emission factors, accounting for compliance with EPA exhaust regulations.
PTRA and PHA also provided data on the volume of PHA –related railcars handled by BNSF, UP, and KCS.
We used these data to estimate the gross tonnage of PHA related line-haul shipments.
Gross ton mileage was estimated based on the tonnage hauled and the distance between the PTRA yards and the HGB boundary via rail segments.
We obtained BNSF, UP, and KCS’s R-1 data from the Surface Transportation Board which provided system-wide fuel usage which was divided by the system wide GTM of each railway to estimate fuel usage in terms of gallons/GTM.
These fuel usage ratios were applied to the PHA GTM estimates to calculate line haul fuel usage.
To estimate emissions, each railway’s fuel usage was applied to EPA’s fuel-based locomotive emission factors which were weighted to represent the Tier level of the locomotive fleet.
We also adjusted NOX emissions to account for use of TxLED fuels.
For all vessels that enter Harris county, whether they visit a PHA terminal or a private terminal, tankers dominated; contributing approximately half of the total vessel NOx emissions.
Miscellaneous vessels and containerships accounted for 14% and 11% of the NOx emissions, respectively.
Our analysis of the AIS data revealed that roughly 45% of vessels that entered Harris county stopped at one or more PHA-owned terminal.
Collectively containerships, general cargo ships and bulk carriers accounted for 71% of PHA – related NOX emissions
These 2013 heavy duty truck estimates account for fleet turnover to cleaner vehicles - This is evident in the total NOx emissions, that are about half that of the 2007 estimates which were based on MOBILE6.
2007 and 2013 In‑terminal NOx emissions are more comparable. This is due to the use of more refined operating mode data that more accurately estimated higher activity levels which were offset by use of newer lower emission trucks.
The more refined in-terminal activity data combined with higher emissions rates in MOVES also led to a larger PM emissions, even though the fleet as a whole is considerably cleaner due to stringent PM standards introduced in 2007.
CO, SO2, and CO2 emissions are comparable between the two inventories, while VOC is about 20% higher in the 2013 inventory.
The cargo handling equipment survey identified over 1,200 pieces of equipment operated by PHA or by one of their tenants.
Fork lifts and terminal tractors constitute 79% of the equipment fleet.
With the exception of SO2 emissions, which have fallen dramatically due to the use of ultra-low sulfur diesel,
Other emissions have risen due to the increase in the number of units. For example, the rise in CO2 emissions matches the percent increase in equipment counts.
Anticipated reduction in Nonroad NOx and PM due to use Tier 3 engines was less dramatic, as equipment scrappage and replacements rates have slowed significantly.
It is interesting to note that the number of RTGs increased by 27%, but due to the use of actual engine loads for RTGs, emissions fell by 28%.
Similarly, use of the project scale feature of the MOVES model to estimate terminal tractor emissions meant NOx emissions increased only 9 percent despite an increase of 35% in the tractor count.
The objective of this study was to quantify the impact that PHA-related activities have on the HGB nonattainment area.
To this end, PHA-related emissions were compared with the TCEQ’s state implementation plan emission inventory.
Currently TCEQ is finalizing the 2014 SIP inventory, therefor the 2011 inventory was used for this comparison,
As you can see PHA contributions are relatively small.
Lastly, I would like to especially thank all the people who cooperated in this study.
Including staff from the port, terminal operators, and the railways.
