The document presents the results of a 2013 mobile source emission inventory for the Port of Houston Authority. It includes emissions estimates from marine vessels, heavy duty diesel vehicles, cargo handling equipment, and locomotives. Emissions were estimated using data on vessel traffic patterns, truck counts and movements, equipment surveys, and locomotive tier levels. Preliminary results found the largest contributors to emissions were cargo handling equipment, heavy duty diesel vehicles accessing terminals, and marine vessels. The inventory will help quantify the local air quality impacts of Port of Houston mobile sources.
Weigh-in-motion systems are used to determine the weight of vehicle while it is in motion. It is used for vehicle overweight enforcement. Various classifications and types of WIM systems - pavement-based, bridge-based, low-speed and high-speed WIM etc are included. Status of WIM implementation in India is also stated in the presentation
Risk based, multi objective vehicle routing problem for hazardous materials: ...Valerio Cuneo
The paper analyses a practical case of study related to the distribution of fuels for the Total Erg Oil Company to the service stations located in the Province of Rome (Italy).
The problem is formulated as a capacitated vehicle routing problem with time windows, where several heuristic procedures have been tested, considering both static and dynamic travel times. With respect to the standard operational costs used typically, a multivariable objective function has been proposed which takes into account also a new risk index.
Regulatory Outlook for real-world emissions - ICCTAutomotive IQ
Earlier this year, Franco Vicente, Researcher at the ICCT, presented at our "Engine Optimisation for RDE Conference" in Frankfurt, Germany. His presentation gave the audience an overview of current challenges for exhaust emissions from modern diesel cars, advanced systems and trends for emission measurement technologies to meet 2020 targets.
GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing ...Naoki Shibata
Jiaxing Xu, Weihua Sun, Naoki Shibata and Minoru Ito : "GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion," in Proc. of IEEE Vehicular Networking Conference 2014 (IEEE VNC 2014), pp. 179-186.
Serious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. GreenWave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. GreenWave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with GreenWave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system.
Weigh-in-motion systems are used to determine the weight of vehicle while it is in motion. It is used for vehicle overweight enforcement. Various classifications and types of WIM systems - pavement-based, bridge-based, low-speed and high-speed WIM etc are included. Status of WIM implementation in India is also stated in the presentation
Risk based, multi objective vehicle routing problem for hazardous materials: ...Valerio Cuneo
The paper analyses a practical case of study related to the distribution of fuels for the Total Erg Oil Company to the service stations located in the Province of Rome (Italy).
The problem is formulated as a capacitated vehicle routing problem with time windows, where several heuristic procedures have been tested, considering both static and dynamic travel times. With respect to the standard operational costs used typically, a multivariable objective function has been proposed which takes into account also a new risk index.
Regulatory Outlook for real-world emissions - ICCTAutomotive IQ
Earlier this year, Franco Vicente, Researcher at the ICCT, presented at our "Engine Optimisation for RDE Conference" in Frankfurt, Germany. His presentation gave the audience an overview of current challenges for exhaust emissions from modern diesel cars, advanced systems and trends for emission measurement technologies to meet 2020 targets.
GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing ...Naoki Shibata
Jiaxing Xu, Weihua Sun, Naoki Shibata and Minoru Ito : "GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion," in Proc. of IEEE Vehicular Networking Conference 2014 (IEEE VNC 2014), pp. 179-186.
Serious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. GreenWave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. GreenWave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with GreenWave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system.
Совместная работа ведущего специалиста ООО «Морстройтехнология» Ольги Гопкало и специалиста голландской компании Ecorys профессора Симме Велдмана на конференции IFSPA 2010 – International Forum on Shipping, Ports and Airports (Международный форум по вопросам судоходства, портов и аэропортов)
Presentation on Spot Speed Study Analysis for the course CE 454nazifa tabassum
This presentation describes the process of Spot Speed Study Analysis, how it can be performed and how the findings from such studies can help to improve road design in urban areas.
Spot speed studies are used to determine the speed
distribution of a traffic stream at a specific location. I The data gathered in spot speed studies are used to determine vehicle speed percentiles, which are useful in making many speed-related decisions
Traffic volume study-opresentation by ahmed ferdous - 1004137-buetAhmed Ferdous Ankon
Traffic volume study-opresentation by Ahmed ferdous.......Please remind this is not a unique effort..My Classmates and specially Ahasanullah Un iversity Students were a major help...We have tried DATA ANALYSIS part to be a solo doing ..But other parts are nearly copy past from net especially from AUST ian...Hope you can do the whole on your own.....
Совместная работа ведущего специалиста ООО «Морстройтехнология» Ольги Гопкало и специалиста голландской компании Ecorys профессора Симме Велдмана на конференции IFSPA 2010 – International Forum on Shipping, Ports and Airports (Международный форум по вопросам судоходства, портов и аэропортов)
Presentation on Spot Speed Study Analysis for the course CE 454nazifa tabassum
This presentation describes the process of Spot Speed Study Analysis, how it can be performed and how the findings from such studies can help to improve road design in urban areas.
Spot speed studies are used to determine the speed
distribution of a traffic stream at a specific location. I The data gathered in spot speed studies are used to determine vehicle speed percentiles, which are useful in making many speed-related decisions
Traffic volume study-opresentation by ahmed ferdous - 1004137-buetAhmed Ferdous Ankon
Traffic volume study-opresentation by Ahmed ferdous.......Please remind this is not a unique effort..My Classmates and specially Ahasanullah Un iversity Students were a major help...We have tried DATA ANALYSIS part to be a solo doing ..But other parts are nearly copy past from net especially from AUST ian...Hope you can do the whole on your own.....
Apartmanski smestaj u Beogradu, izdavanje apartmana Beograd, rentiranje apartmana Beograd, rent a stan Beograd, smestaj Beograd apartmani,i sve to na dan, kraći ili duži vremenski period.
Nudimo Vam najbolji apartmanski smestaj u Beogradu
Apartmani Beograd izdaju se na kraći ili duži rok a možete dobiti i apartman u Beogradu na dan.
Hoteli u Beogradu vise nisu opcija jer cete u našim apartmanima dobiti sve što vam je potrebno za udobno odsedanje.
Jeftini apartmani Beograd, namenjeni su za iznajmljivanje poslovnim ljudima i turistima, korisnicima zdravstvenih usluga i svima onima kojim treba jeftin smeštaj za višednevni ugodan boravak u Beogradu kao i travel agencijama u Beogradu.
Naša delatnost je: privatno izdavanje – prenoćišta,smeštaj na dan.
Apartmani u centru Beograda provereni su izbor za Vas.
Svi nasi apartmani u Beogradu imaju nov, funkcionalan nameštaj modernog dizajna, kablovsku TV, wireless internet. u .
Stanovi za izdavanje Rentalsbelgrade predstavljaju idealno rešenje za sve ljude kojima treba jeftini apartmani i jeftino prenoćište u Beogradu.
Kratkoročno izdavanje apartmana je nasa delatnost
Kliknite ovde da bi ste pogledali nas izbor apartmana...
http://www.rentalsbelgrade.com
Dr James Tate - Better estimation of vehicle emissions for modelling - DMUG17IES / IAQM
An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
Heavy-Duty Natural Gas Vehicle Roadmap September 2014CALSTART
Heavy-Duty Natural Gas Vehicle Roadmap September 2014 created by the California High-Efficiency Advanced Truck Research Center (CalHEAT) found NG a Significant Enabler for California and the SoCalGas region to enable a reduction in the use of petroleum as well as reduce criteria emissions in heavy duty vehicles
A presentation by Ibrahim Djama, commercial director, port of Djibouti, delivered during African Ports Evolution 2015 in Durban, South Africa.
More like this on www.transportworldafrica.co.za
Real-World Activity and Fuel Use of Diesel and CNG Refuse TrucksGurdas Sandhu
See journal paper at http://dx.doi.org/10.1016/j.atmosenv.2014.04.036
According to a 2006 report, the waste collection industry in the U.S. operates over 136,000 refuse trucks, almost all diesels, that average 25,000 miles annually and with average fuel economy of less than 3 miles per gallon. There is an increasing adoption of Compressed Natural Gas (CNG) fuelled trucks in the waste collection industry due to the significantly lower cost of CNG per diesel gallon equivalent (dge). This presentation includes results of activity and fuel use from in-use real-world field measurements of eighteen diesel fuelled refuse trucks, with six each of side-load, front-load, and roll-off configurations and six CNG fuelled refuse trucks, with three each of side-load and front-load configurations. The study design included trucks from various manufacturers such as Mack, Autocar, and Freightliner and model years 2003 to 2012. Each truck was instrumented for one day of operation with a portable activity measurement system (PAMS) to log Engine Control Unit (ECU) data and Global Positioning System (GPS) receivers. Trucks were also instrumented with portable emissions measurement system (PEMS), however, emissions results are not included here.
The total quality assured data covers over 2,000 miles and 190 hours of in-use real-world driving. During the measurement period the trucks picked about 7,500 cans with a total of over 500 tons of trash. Measured 1 Hz activity data includes, but is not limited to, vehicle speed, engine speed, intake manifold pressure, intake air temperature, engine load, and elevation (leading to road grade). Duty cycles and fuel use rates are quantified in terms of operating mode bins defined by the U.S. Environmental Protection Agency for the MOVES emission factor model. Overall results are included here; detailed results by truck configuration and fuel type will be covered in the presentation. On average, 50 percent of time was spent at idle, 5 percent braking or decelerating, 28 percent at low speed (up to 25 mph), 12 percent at moderate speed (25 to 50 mph), and 5 percent at high speed (50 mph or higher). Diesel trucks spend more time in high speed mode compared to CNG. Estimated cycle average diesel fuel economy ranges were 2.0 to 3.4 mpg, 2.3 to 3.2 mpg, 3.9 to 6.0 mpg, and for side-loaders, front-loaders, and roll-offs, respectively. In comparison, CNG fuel economy ranges were 1.2 to 1.7 mpdge and 2.0 to 2.5 mpdge for side-loaders and front-loaders, respectively.
Future of Heavy Duty Vehicles CO2 Emissions Legislation and Fuel Consumption ...JMDSAE
By Dimitrios Savvidis
The talk will be covering :
Latest developments on CO2 legislation in Europe
Overview of GHG emissions in the transport sector in Europe
New simulation tool – VECTO
Future steps
Portion of Gastar Investor Presentation for August 2015 Focused on Marcellus/...Marcellus Drilling News
An extract/portion of Gastar's August 2015 investors' presentation. Marcellus Drilling News has extracted out only those slides dealing with information about their Marcellus/Utica operations. Slide #14 (page #43) shows the top 10 Utica dry gas wells as of August 2015 for all drillers. Gastar has two wells in the list.
Comparative study of emission pollutants between BIM and VSP methods.AdithCR1
In order to determine the present condition at the junction various types of surveys such as road inventory survey, turning movement survey, spot speed analysis were conducted at existing intersection of the road and necessary data were collected for completing the project. The method used for calculating the emission rates of vehicle is VSP which is done for vehicle (passenger cars) manually. Modelling of roundabout is done which is based on the BIM system (VISSIM). Here initially the existing condition of the intersection is analysed for peak hour traffic flow, so based on the traffic simulation carried out in the software, emission rates are calculated and compared with the manually calculated emission rates. So the basic idea of this case study is to check the emission rates at the junction especially during peak hours and to check if the rate exists within n the standard emission rates so that the surrounding area isnt affected due to pollution caused by the moving vehicles.
A presentation by Mr Oliver Naidoo (JC Auditors) at the Transport Forum SIG 14 July 2016 hosted by Standard Bank in Cape Town, South Africa.
The theme for the event was: "RTMS - Industry Best Practice and Standards". The topic of the presentation was: "Certification requirements"
Designing of sprinkler irrigation systemEngr Mehmood
Sprinkler irrigation is a method of applying irrigation water that is similar to natural rainfall. Water is distributed through a system of pipes usually by pumping. It is then sprayed into the air through sprinklers so that it breaks up into small water drops that fall to the ground.
International Experience with Electric and Zero Emission Buses
AWMA PHA 3
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.