Low Level Road Project Air Quality Assessment Summary

Transcription

1 Low Level Road Project Air Quality Assessment Summary Overview As part of the Environmental Assessment for the proposed Low Level Road Project (the Project ), Port Metro Vancouver retained Levelton Consultants Ltd. (Levelton) of Richmond B.C. to conduct an air quality assessment. The air quality assessment was undertaken in two phases: Phase 1: A screening level air quality impact assessment, following BC dispersion modelling guidelines, of road traffic taking into account proposed Project. Specifically, three scenarios were assessed - Current road location with current traffic levels, elevated road location with current traffic levels (portconstruction) and elevated road location with future (2031) traffic levels Phase 2: An emissions inventory of other relevant air emission sources in the North Shore Trade Area today as well as in 2025 as a result of Low Level Road Project infrastructure changes and increased activities, primarily rail, marine and fugitive dust emissions. The air quality assessment indicates a marginal decrease in air quality over the short term as the traffic and rail emissions are shifted north due to the Project; however, over the longer term with projected improvements in road and rail vehicle technology, no net change to air quality is expected. Port Metro Vancouver commissions a detailed air emission inventory every five years in conjunction with other regional and local regulators, in order to assess and track the impacts of port activities and guide development of mitigation programs. The next inventory undertaken for 2015 will provide an opportunity to verify anticipated air quality impacts resulting from the Project. Additionally, Port Metro Vancouver works closely with all terminal operators and the railways, and encourages them to make improvements that reduce impacts on the community. We know from our work with port stakeholders that they are committed to this and they are all looking for ways to do this. Conclusions: Phase 1 Road Traffic Emissions Conditions that influence vehicle emissions include vehicle volumes and distance travelled, vehicle speed, vehicle type, fuel efficiency, fuel cleanliness and government regulation. In general, there may be short term increases in vehicle pollutant concentrations when the road is elevated (scenario 2) as compared with existing conditions (scenario 1); however, by 2031, despite a forecasted 10% increase in traffic, vehicle pollutant concentrations will decrease as a result of improvements in vehicle and fuel efficiency. The largest short-term increases occur to the south of the road, in the nonresidential port area. Concentrations decrease rapidly north of the new roadway. This analysis does not take into consideration the potential beneficial effects of terrain blocking due to the topography of the area and as such, represents a worst case over-estimation of pollutant concentrations. 1 Low Level Road Project Summary of Air Quality Assessment Study

2 Phase 2 Emissions from Other Sources: Marine Emissions Marine emissions are generated from diesel fuel-fired auxiliary engines and boilers used to meet shipboard power and hot water requirements while in dock. By 2025, despite an estimated 29% increase in marine traffic, most marine emissions will decrease, primarily due to improved emissions standards, fuel quality regulations and more efficient engines. Carbon Monoxide (CO), Volatile Organic Compounds (hydrocarbons) and Ammonia (NH 3 ) levels will increase by about 14%. Rail Locomotive Emissions The Railway Association of Canada has committed to increase the number of locomotives that are compliant with US EPA emission standards, by retiring old locomotives with new ones. As a result, by 2025, despite an increase in rail activity (estimated at between 30% and 50%), overall locomotive emissions will remain stable at current levels. Fugitive Dust from Coal Stockpiles Fugitive dust is created when material is transferred as well as from surface erosion when stockpiles are exposed to wind. By 2025, coal dust particulates are expected to increase by 25-27%, since there will be increased coal handling activity. The dust from coal handling, in particular the fine dust, makes up a small percentage of the local contribution, less than 3%. Study Methodology The Phase 1 assessment considered vehicle tailpipe emissions from current and future traffic levels. Future traffic levels also considered the effect of elevating the road. Levelton used current and forecast traffic levels from the Stantec s Low Level Road Improvements Traffic Analysis and the USEPA Motor Vehicle Emissions Simulator to model vehicle emissions for the various vehicle types in the three traffic scenarios. Buses were excluded, as current plans assume the Low Level Road will not be a designated transit route. Levelton then applied the CALINE3 dispersion model to provide a comparative analysis of the three modelled scenarios. Finally, to assess potential worst-case conditions, Levelton used a screening meteorological data set to simulate the effects of wind conditions. Four zones were selected, loosely based on residential neighbourhoods nearest the road. The Phase 2 assessment considered current emissions from existing rail and marine traffic and fugitive dust as well as future (2025) emissions based on forecast traffic growth and anticipated changes in technology designed to improve fuel efficiency. Levelton obtained estimated average marine engine and boiler characteristics per vessel from the B.C. Chamber of Shipping, and used the Environment Canada (EC) 2010 National Marine Emissions Inventory to estimate emission levels for key emission factors, and IMO EEDI energy efficiency standards (which will become mandatory for new ships in 2015) and IMO SEEP ship operations improvement standards to estimate average ship efficiency. Based on these standards, a conservative 9% reduction in overall emissions from improved efficiency of marine engines and boilers was assumed. Actual 2010 vessel activity and EC 2010 growth estimates were used to determine total emissions of all factors. Fuel emission factors for rail line haul and switch engines was based on the Railway Association of Canada publications, and estimates of current and future rail activity were provided by MMM. For fugitive coal dust, Levelton used the US EPA AP42 databases for emission factors and the AWAMA Air Pollution Engineering Manual to determine fugitive dust from wind erosion. Stockpile sizes were estimated based on satellite imagery available on Google Earth. 2 Low Level Road Project Summary of Air Quality Assessment Study

3 Low Level Road Project Air Quality Assessment Phase 1: Screening Level Dispersion Modeling Assessment of Road Traffic from the Relocation of Low Level Road Submitted by: Levelton Consultants Ltd Clarke Place Richmond, BC V6V 2H9 T: F: Authors: Tyler Abel, M.Sc. Kyle Howe, M.Sc. Date: May 30, 2012 Levelton File # EE

4 May 30, 2012 Richard Lyell, MSc (Eng.) Senior Project Manager, Associate MMM GROUP LIMITED Suite Howe Street Vancouver, BC, V6Z 2A9 Dear Mr. Lyell: Regarding: Phase 1: Screening Level Dispersion Modeling Assessment of Road Traffic from the Relocation of Low Level Road Levelton is pleased to submit the final report for Phase 1 of the project. If you have any questions, please do not hesitate to contact the undersigned. Sincerely, Levelton Consultants Ltd. Tyler Abel, M.Sc. Manager, Air Quality Modelling and Assessment tabel@levelton.com

5 Table of Contents Letter of Transmittal page 1. INTRODUCTION METHODOLOGY Road Traffic Assessment Emission Factors Traffic Volumes Screening Level Dispersion Modelling Meteorology Modelling Domain and Receptors RESULTS NO x Results PM 10 Results PM 2.5 Results CO Results VOC Results CONCLUSIONS List of Figures Figure 2-1 Figure 3-1 Figure 3-2 Figure 3-3 Figure 3-4 Figure 3-5 Figure 3-6 Figure 3-7 Figure 3-8 Figure 3-9 Figure 3-10 Figure 3-11 Figure 3-12 Modelling Domain and Receptors... 5 Neighbourhood Zones for Results Interpretation... 6 Location of maximum concentrations for each "Zone" Maximum 1-hour NO x Concentrations for Scenario Maximum 1-hour NO x Concentrations for Scenario Maximum 1-hour NO x Concentrations for Scenario Comparison Plot of Scenario 2 Maximum NO x Concentrations and Scenario 1 Maximum NO x Concentrations Comparison Plot of Scenario 3 Maximum NO x Concentrations and Scenario 1 Maximum NO x Concentrations Maximum 24-hour PM 10 Concentrations for Scenario Maximum 24-hour PM 10 Concentrations for Scenario Maximum 24-hour PM 10 Concentrations for Scenario Comparison Plot of Scenario 2 Maximum PM 10 Concentrations and Scenario 1 Maximum PM 10 Concentrations Comparison Plot of Scenario 2 Maximum PM 10 Concentrations and Scenario 1 Maximum PM 10 Concentrations... 19

6 Figure 3-13 Figure 3-14 Figure 3-15 Figure 3-16 Figure 3-17 Figure 3-18 Figure 3-19 Figure 3-20 Figure 3-21 Figure 3-22 Figure 3-23 Figure 3-24 Figure 3-25 Figure 3-26 Figure 3-27 Maximum 24-hour PM 2.5 Concentrations for Scenario Maximum 24-hour PM 2.5 Concentrations for Scenario Maximum 24-hour PM 2.5 Concentrations for Scenario Comparison Plot of Scenario 2 Maximum PM 2.5 Concentrations and Scenario 1 Maximum PM 2.5 Concentrations Comparison Plot of Scenario 3 Maximum PM 2.5 Concentrations and Scenario 1 Maximum PM 2.5 Concentrations Maximum 1-hour CO Concentrations for Scenario Maximum 1-hour CO Concentrations for Scenario Maximum 1-hour CO Concentrations for Scenario Comparison Plot of Scenario 2 Maximum CO Concentrations and Scenario 1 Maximum CO Concentrations Comparison Plot of Scenario 3 Maximum CO Concentrations and Scenario 1 Maximum CO Concentrations Maximum 1-hour VOC Concentrations for Scenario Maximum 1-hour VOC Concentrations for Scenario Maximum 1-hour VOC Concentrations for Scenario Comparison Plot of Scenario 2 Maximum VOC Concentrations and Scenario 1 Maximum VOC Concentrations Comparison Plot of Scenario 3 Maximum VOC Concentrations and Scenario 1 Maximum VOC Concentrations List of Tables Table 2-1 Table 2-2 Table 3-1 Table 3-2 Emission Factors... 3 Screening Meteorology Wind... 4 Maximum 1-hour Predicted Pollutant Concentrations... 7 Maximum 1-hour Predicted Pollutant Concentrations By Neighbourhood Zone for each Scenario... 7

7 Page 1 1. Introduction On March 27, 2009, the Government of Canada, the Province of British Columbia, the Vancouver Fraser Port Authority (VFPA), TransLink, local municipalities, and the private sector jointly announced an investment of over $225 million in five infrastructure improvements in the North Shore Trade Area (NSTA) under the Federal Government s Asia-Pacific Gateway & Corridor Initiative ( APGCI ) to enhance rail and port operations and resolve road/rail issues on the North Shore. Further to these infrastructure improvements, VFPA is currently undergoing an internal environmental assessment of the effects of the Low Level Road Project proposed for the North Shore Trade Area. To identify potential air quality impacts of the proposed Low Level Road project to the adjacent community, VFPA retained Levelton Consultants Ltd. (Levelton) to undertake an air quality assessment of the Project. This report for the air quality assessment summarizes the methodology and results from Phase 1 of the Project which focuses on the air quality impacts from road traffic changes along Low Level Road. 2. Methodology This section describes the methodology used to assess the air quality impacts of the road traffic changes proposed for the Low Level Road Project. Levelton conducted screening level air dispersion modelling of three comparative scenarios for the road relocation, increased traffic activity expected in the future, and forecasted improvements in vehicle emissions. The modelling scenarios were: Scenario 1: Current road location with current traffic levels Scenario 2: Elevated road location with current traffic levels Scenario 3: Elevated road location with future traffic levels (2031) Vehicle tailpipe emissions yield air contaminants that could potentially impact local and regional air quality. The air quality assessment evaluated potential air quality impacts, from vehicle emissions, for all three scenarios of interest, using available information and screening level modelling. The air contaminants of concern considered in the study were: Carbon monoxide (CO) Oxides of nitrogen (NO x ) Volatile organic compounds (VOCs) Particulate matter as PM 2.5, PM Road Traffic Assessment This section outlines conditions that influence vehicle emission rates and describes the road traffic emission rates used for the Low Level Road air quality assessment. A variety of conditions influence vehicle emissions. These conditions are listed and described below: Vehicle volume and distance travelled The number of vehicles using the road, and the distance they travel, directly influences the quantity of pollutants emitted to the air. More vehicles mean more pollutant emissions, and the greater the distance travelled, the greater the volume of pollutants that are emitted.

8 Page 2 Vehicle speed At different speeds, a vehicle will emit each of the pollutants of concern at varying rates. There is an optimum speed at which a vehicle will have the least pollutant emissions, while this speed is different for each vehicle and vehicle type; there is generally a range within which most vehicles are operating at their optimum for minimum pollutant emissions. Idling and stop and go traffic conditions result in higher emissions per kilometre traveled. Fleet profile Vehicle types differ in the emissions they produce (Table 8-7), therefore the proportion of vehicles of each type in the fleet can change the emissions inventory. For example, a road with a greater proportion of heavy truck and/or bus traffic will have greater emissions of certain pollutants and less of others, compared to a road with a lower proportion of heavy vehicles. Vehicle fuel efficiency Newer vehicles tend to have better fuel efficiency and lower emissions than older vehicles. As older vehicles are replaced with new ones in the fleet, the emission inventory changes to reflect the improved fuel efficiency and lower emissions for the same distance travelled. Fuel cleanliness Fuels have different performance characteristics depending on their composition (the oil fraction from which they were derived) and the additives contained. Increasingly, fuels are being manipulated to reduce pollutant emissions; harmful additives are being removed, and additives that decrease emissions are being included. The composition of fuels utilized by vehicles can therefore alter the resulting emissions. Legislation Government regulations such as vehicle fuel efficiency requirements (AirCare and catalytic converters) and fuel cleanliness (lower sulphur content) can change vehicle emissions Emission Factors The emission factors (EFs) applied to the current year road traffic scenarios were provided by Metro Vancouver and calculated using the latest USEPA model Motor Vehicle Emissions Simulator (MOVES). These EFs were generated for the 2010 year for inclusion in Metro Vancouver s 2010 Emission Inventory. These factors were deemed relevant to the Project as they are the most up-to-date emission values and provide a consistent approach to the entire Metro Vancouver region. The vehicle classes from MOVES were distributed according to Metro Vancouver s 2010 distribution of actual vehicle population. The buses were omitted from the distribution as it was indicated they do not travel on the Lower Level Road. The Metro Vancouver vehicular distributions were separated into two classes: light duty vehicles (LDV) and heavy duty vehicles (HDV), which were assumed to constitute 95% and 5% of the total vehicular population, respectively, based on values provided by the. The future scenario, set in 2031, was calculated using projected EFs. The projected EFs were estimated by multiplying appropriate reduction percentages to the EFs generated for the current year. The reduction rates were based on results generated by MOBILE6.2c in a previous Levelton project. The percentage reductions in emissions of various pollutants from 2010 to 2031 were calculated based on the previous MOBILE6.2c results and applied to Metro Vancouver s 2010 emissions inventory. Although this accounts for fleet replacement and new vehicular and emissions reduction technology, this does not consider advancements in alternative fuels and changes in engines technology, (e.g. fuel cells and electric vehicles) which may have a greater impact on emissions. Table 1 below illustrates the EFs for 2010 and 2031 for CO, NO x, VOCs, PM 10, and PM 2.5.

9 Page 3 Table 2-1 Emission Factors Pollutant CO NO x VOCs PM 10 PM Emission Factors (g/vkmt) Emission Factors (g/vkmt) Traffic Volumes Traffic Volumes were determined from the Stantec Report entitled, Low Level Road Improvements Traffic Analysis. The forecasted traffic volumes from Scenario 1 within the Stantec Report were used as future traffic volumes. The current traffic volumes were determined by applying a 10% reduction in the forecasted traffic volumes. To determine worst-case impacts, each of the modelling scenarios was modelled with the PM traffic peak volume. 2.2 Screening Level Dispersion Modelling Levelton used the CALINE3 dispersion model 1 in the screening level air quality assessment to provide a comparative analysis of the three modelling scenarios. The CALINE3 model is a Gaussian based, line-source, dispersion model used to predict ambient air quality concentrations near arterial streets and highways from vehicle emissions. Predictions of ambient air quality concentrations resulting from vehicle exhaust from roadways in previous assessments in BC have commonly used the CALINE 3 model. Other screening models mentioned above can handle a variety of emission source types, but do not have explicit algorithms to address road emission sources Meteorology To assess worst-case conditions from each of the road alignments and traffic scenarios, Levelton used a screening meteorological data set. The screening meteorological file is used to provide a wide range of possible combinations of wind speed, wind direction and stability class. A DOS utility program, BCMETISC.EXE, provided by the BC Ministry of Environment 2 was used to develop the screening meteorological data file. In the file the combinations of P-G stability and wind speed as given in Table 2-2 were applied through a sweep to all compass directions at 2.5 increments. 1 Office of Transportation Laboratory, 1979, CALINE 3 A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highway and Arterial Streets 2 British Columbia Ministry of Environment, 2008, Guidelines for Air Quality Dispersion Modelling in British Columbia

10 Page 4 Table 2-2 Screening Meteorology Wind

11 Page Modelling Domain and Receptors Receptors are specific points where the dispersion model calculates pollutant concentrations. CALINE3 is limited in its abilities to predict accurate pollutant concentrations at distances further than 500 metres from the road link modelled. Therefore, a uniform grid of receptors at 50 metre spacing was applied to the area shown in Figure 2-1. The Universal Transverse Mercator (UTM, NAD83) coordinate system was used for this model application. The modelling domain was a 2.5 (east-west) km 1.1 (south-north) km grid, with the road located just south of the center of the domain. The off center placement of the roadway was done to encompass more of the residential area and eliminate receptors to the South that would be located over English Bay. Digital topographic data for this area were used in the modelling to determine the height of the receptors. The CALINE3 model always assumes that the road level is at the 0 metre level 3. Therefore, in order to account for the elevation of the road links, in scenario 1 the receptors to the North of the road were adjusted by the average height above sea level of the existing road. For scenarios 2 and 3, the receptors were adjusted by the increased elevation of the road link from the existing road alignment to the elevated road alignment. To account for worst-case conditions, each receptor was adjusted by 14 metres, which represents the highest elevated point along the elevated road alignment in comparison to the existing road alignment. Although the receptors were adjusted to account for the topography in the North Shore Area, the CALINE3 model does not take into account the effects of terrain blocking (i.e. deposition of pollutant mass on hillside, blocking by vegetation), but instead calculates a straight-line concentration at each receptor. While CALINE3 may lack some complexity, it provides a worst-case estimate of concentrations and thus is appropriate for this study, which is focused on the comparison of potential local air quality impacts. Figure 2-1 Modelling Domain and Receptors 3 Office of Transportation Laboratory, 1979, CALINE 3 A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highway and Arterial Streets

12 Page 6 3. Results The results of each scenario are presented below. Table 3-1 and Table 3-2 summarize both the overall results for each scenario as well as results for various zones. These zones are depicted in Figure 3-1 and outline the residential neighbourhoods nearest the Low Level Road. These zones were also chosen to line up with the zonal analysis presented in the noise impact study conducted by BKL Consultants 4. Figure 3-2 identifies the receptor which recorded the overall maximum for the three scenarios (represented by a blue star) as well as the location of the maximum within each zone (represented by yellow stars). The maximum predicted concentrations occur at receptors located directly next to the low level road. Figure 3-1 Neighbourhood Zones for Results Interpretation 4 Noise Modelling of Low Level Road Redevelopment Project, April 13, 2012

13 Page 7 Table 3-1 Maximum 1-hour Predicted Pollutant Concentrations Maximum 1 hour Concentration (µg/m 3 ) Overall Scenario 1 Scenario 2 Scenario 3 NO x CO PM PM VOC Table 3-2 Maximum 1-hour Predicted Pollutant Concentrations By Neighbourhood Zone for each Scenario Maximum 1 hour Concentration (µg/m 3 ) Zone 1 Zone 2 Zone 3 Zone 4 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 NO x CO PM PM VOC

14 Page 8 The following figures present the results for the five species modeled: NO x, PM 10, PM 2.5, CO, and VOCs. In addition to the contour plots representing the maximum predicted concentrations for each scenario, comparative plots are provided. These comparative plots show the difference between the existing scenario and the other two scenarios with the cool colours (green to blue) showing a decreases in predicted maximum concentrations and warm colours (yellow to pink) showing increases in predicted maximum concentrations. In general, the comparative analysis shows an increase in pollutant concentrations between scenario 1 (existing alignment, current traffic) and scenario 2 (elevated alignment, current traffic) and a decrease in pollutant concentrations from scenario 1 to scenario 3 (elevated alignment, future traffic).

15 Page 9 Figure 3-2 Location of maximum concentrations for each "Zone".

16 Page NO x Results Figure 3-3 Maximum 1-hour NO x Concentrations for Scenario 1

17 Page 11 Figure 3-4 Maximum 1-hour NO x Concentrations for Scenario 2

18 Page 12 Figure 3-5 Maximum 1-hour NO x Concentrations for Scenario 3

19 Page 13 Figure 3-6 Comparison Plot of Scenario 2 Maximum NO x Concentrations and Scenario 1 Maximum NO x Concentrations * Scenario 2 = elevated alignment, current traffic levels Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 2 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 2 compared to Scenario 1.

20 Page 14 Figure 3-7 Comparison Plot of Scenario 3 Maximum NO x Concentrations and Scenario 1 Maximum NO x Concentrations * Scenario 3 = elevated alignment, future traffic levels (2031) Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 3 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 3 compared to Scenario 1.

21 Page PM 10 Results Figure 3-8 Maximum 24-hour PM 10 Concentrations for Scenario 1

22 Page 16 Figure 3-9 Maximum 24-hour PM 10 Concentrations for Scenario 2

23 Page 17 Figure 3-10 Maximum 24-hour PM 10 Concentrations for Scenario 3

24 Page 18 Figure 3-11 Comparison Plot of Scenario 2 Maximum PM 10 Concentrations and Scenario 1 Maximum PM 10 Concentrations * Scenario 2 = elevated alignment, current traffic levels Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 2 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 2 compared to Scenario 1.

25 Page 19 Figure 3-12 Comparison Plot of Scenario 2 Maximum PM 10 Concentrations and Scenario 1 Maximum PM 10 Concentrations * Scenario 3 = elevated alignment, future traffic levels (2031) Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 3 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 3 compared to Scenario 1.

26 Page PM 2.5 Results Figure 3-13 Maximum 24-hour PM 2.5 Concentrations for Scenario 1

27 Page 21 Figure 3-14 Maximum 24-hour PM 2.5 Concentrations for Scenario 2

28 Page 22 Figure 3-15 Maximum 24-hour PM 2.5 Concentrations for Scenario 3

29 Page 23 Figure 3-16 Comparison Plot of Scenario 2 Maximum PM 2.5 Concentrations and Scenario 1 Maximum PM 2.5 Concentrations * Scenario 2 = elevated alignment, current traffic levels Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 2 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 2 compared to Scenario 1.

30 Page 24 Figure 3-17 Comparison Plot of Scenario 3 Maximum PM 2.5 Concentrations and Scenario 1 Maximum PM 2.5 Concentrations * Scenario 3 = elevated alignment, future traffic levels (2031) Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 3 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 3 compared to Scenario 1.

31 Page CO Results Figure 3-18 Maximum 1-hour CO Concentrations for Scenario 1

32 Page 26 Figure 3-19 Maximum 1-hour CO Concentrations for Scenario 2

33 Page 27 Figure 3-20 Maximum 1-hour CO Concentrations for Scenario 3

34 Page 28 Figure 3-21 Comparison Plot of Scenario 2 Maximum CO Concentrations and Scenario 1 Maximum CO Concentrations * Scenario 2 = elevated alignment, current traffic levels Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 2 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 2 compared to Scenario 1.

35 Page 29 Figure 3-22 Comparison Plot of Scenario 3 Maximum CO Concentrations and Scenario 1 Maximum CO Concentrations * Scenario 3 = elevated alignment, future traffic levels (2031) Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 3 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 3 compared to Scenario 1.

36 Page VOC Results Figure 3-23 Maximum 1-hour VOC Concentrations for Scenario 1

37 Page 31 Figure 3-24 Maximum 1-hour VOC Concentrations for Scenario 2

38 Page 32 Figure 3-25 Maximum 1-hour VOC Concentrations for Scenario 3

39 Page 33 Figure 3-26 Comparison Plot of Scenario 2 Maximum VOC Concentrations and Scenario 1 Maximum VOC Concentrations * Scenario 2 = elevated alignment, current traffic levels Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 2 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 2 compared to Scenario 1.

40 Page 34 Figure 3-27 Comparison Plot of Scenario 3 Maximum VOC Concentrations and Scenario 1 Maximum VOC Concentrations * Scenario 3 = elevated alignment, future traffic levels (2031) Scenario 1 = current alignment, current traffic levels ** Positive values represent an increased concentration in Scenario 3 compared to Scenario 1. Negative values represent a decreased concentration in Scenario 3 compared to Scenario 1.

41 Page Conclusions The comparative analysis shows that in the short term, there is the potential that local pollutant concentrations may increase due to the realignment of Low Level Road. This is expected as the realignment of the road brings the traffic closer to the residential areas. The largest increase in pollutant concentrations in the short-term occur to the south of the Low Level Road in the non-residential port area. In the residential zones defined, the maximum predicted concentrations occur along the southern boundary of each zone, closest to the road alignment. Predicted pollutant concentrations decrease rapidly further north of the new Low Level road alignment. While the predicted maximum pollutant concentrations may increase with the realignment of Low Level Road, the increases are expected to be mitigated in the future by improvements in vehicle and fuel efficiencies. The 2031 analysis shows that pollutant concentrations are predicted to be lower than those from the existing Low Level Road alignment. Although traffic is expected to increase by 10 percent over the next 20 years, this increase, along with the increase shown as a result of the new road positioning, is offset in the analysis by the reduction in road traffic emissions resulting from vehicle and fuel efficiencies. Therefore, the screening level modelling predicts that the improvements proposed to Low Level Road could result in a short-term increase in pollutant concentrations from the roadway. However, these increases will be mitigated as vehicle technology and fuel efficiencies are realized over the next 20 years. Although the receptors in the modelling were adjusted to account for the topography in the North Shore Area, the screening model methodology used in this study does not take into account the effects of terrain blocking (i.e. deposition of pollutant mass on hillside, blocking by vegetation), but instead calculates a straight-line concentration at each receptor. This screening approach provides a worst-case estimate of concentrations and was appropriately used in a comparative approach to evaluate the improvements proposed for Low Level Road. This approach, however, likely overestimates the air quality impacts of the road in both its current and future alignments and caution should be used in the interpretation of the predicted pollutant concentrations from individual scenarios.

42 Low Level Road Project Air Quality Assessment Phase 2: Current & Forecast Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area Submitted by: Levelton Consultants Ltd Clarke Place Richmond, BC V6V 2H9 T: F: Date: May 31, 2012 Levelton File # EE

43 May 31, 2012 Richard Lyell, MSc (Eng.) Senior Project Manager, Associate MMM GROUP LIMITED Suite Howe Street Vancouver, BC, V6Z 2A9 Dear Mr. Lyell: Re: Phase 2: Current & Forecast Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area Levelton is pleased to submit this final emissions inventory report for Phase 2 of the project. This report presents the methodologies and air emission estimates for rail, marine and fugitive sources within the North Shore Trade Area. Air releases from current operations as well as projected changes resulting from the proposed Lower Level Road infrastructure improvement project are also presented in this report. Should you have any questions, please do not hesitate to contact the undersigned. Thank you very much for the opportunity in working with you on this exciting project and we look forward to future collaborations with the MMM Group Limited and the Port of Metro Vancouver. Sincerely, Levelton Consultants Ltd. Ana Booth, Ph.D. Division Manager Environment & Energy Division abooth@levelton.com

44 Table of Contents Letter of Transmittal page EXECUTIVE SUMMARY INTRODUCTION METHODOLOGY Marine Emissions Emission Factors Activity Data Forecast Data Rail Emissions Emission Factors Activity Data Forecast Data Fugitive Dust Emissions Material Transfers Stockpile Erosion Emissions Forecast INVENTORY RESULTS Marine Emissions Locomotive Emissions Fugitive Dust Emissions CONCLUSIONS List of Figures Figure 1-1 North Shore Trade Area... 5 List of Tables Table 2-1 SOX and PM Emission Factors... 7 Table 2-2 Auxiliary Engine CAC Emission Factors for Current Scenario... 7 Table 2-3 Boiler CAC Emission Factors for Current Scenario... 8 Table 2-4 Current and Future Engine and Fuel Standards... 9 Table 2-5 Vessel Age Distribution... 9 Table 2-6 Weighted-Average Fuel Sulphur Content, NOx Emission Limits and Energy Efficiency Reductions for Bulk Vessel Fleet Table 2-7 Engine Duty Cycle and Power Distribution for Line Haul Locomotives Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

45 Table 2-8 Table 2-9 Table 2-10 Table 2-11 Table 2-12 Table 2-13 Table 2-14 Table 2-15 Table 3-1 Table 3-2 Table 3-3 Table 3-4 Table 3-5 Table 3-6 Table 3-7 Table 3-8 Engine Duty Cycle and Power Distribution for Switch Engines Locomotive Emission Factors for Current Scenario Current Line Haul Train Traffic & Engine Count Estimates Current Switch & Other Train Traffic & Engine Count Estimates Line Haul Train Traffic Data for Other Train Traffic Data for Line Haul Engine Population Average 2025 Emission Factors for Line Haul & Switch Locomotives Emissions of Marine Vessel Auxiliary Engines & Boilers Comparison of 2010 and 2025 Emissions from Total Marine Emissions Emissions from Locomotives in the Current Fleet Emissions from Locomotives in the 2025 Fleet Comparison of 2010 and 2025 Emissions from Total Rail Emissions Base Year Coal Stockpile Emissions Coal Stockpile Emissions Comparison of 2010 and 2025 Emissions from Total Stockpile Emissions Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

46 Page 1 Executive Summary Under the Federal Government s Asia-Pacific Gateway & Corridor Initiative ( APGCI ) to enhance rail and port operations and resolve road/rail issues on the North Shore, an infrastructure improvement investment of $225 million was announced in 2009 for projects in the North Shore Trade Area (NSTA), including the Low Level Road (LLR) Expansion Project. The (VFPA) is currently undertaking an internal environmental assessment of the effects of the proposed LLR Project for the North Shore Trade Area. This Phase 2 report is an emissions inventory of relevant air emission sources in the NSTA resulting from LLR infrastructure changes and increased port commodity traffic. The primary focus of this report is on two scenarios: current and projected 2025 marine, rail and fugitive dust emissions from the NSTA. Common air contaminants (CACs) of interest include carbon monoxide (CO), oxides of nitrogen (NO x ), sulphur oxides (SO X ), volatile organic compounds (VOCs), Particulate matter (PM, PM 10, PM 2.5 ) and ammonia (NH 3 ). Marine Emissions The major bulk commodities handled by port tenants in the study area include grain, coal and potash. Marine emission sources of interest in this study include diesel fuel-fired auxiliary engines and boilers which are used to meet shipboard power and hot water requirements when a bulk vessel is at berth. Emissions were estimated using an emission factor approach in which published contaminant- and source-specific factors were used in conjunction with source activity data, including auxiliary engine characteristics (engine count, rating and load factor), boiler fuel consumption estimates and source operating schedule. Since SO X and PM emissions are highly dependent on the fuel sulphur content, their releases were determined based on published correlations. For the current scenario, vessel activity data, in the form of marine calls to the study area, was provided by VFPA for 2010 in order to determine the amount of time vessels spent at berth when the auxiliary engines and boilers were used. Emissions factors were obtained from recent marine vessel emission inventories, including the Canadian 2010 National Marine Emissions Inventory from Environment Canada and the B.C. Chamber of Shipping BC Ocean-Going Vessel Emissions Inventory. By 2025, several international regulations and programs, mandated by the International Maritime Organization (IMO) under its MARPOL 73/78 Annex VI, will affect emissions from marine vessels in the study area, which has been designated as an Emission Control Area (ECA). These include the IMO fuel sulphur limit of 0.1% beginning in 2015, the IMO engine NOx multi-tiered emission standards for marine vessel, the IMO ship Energy Efficiency Index (EEDI) requirements and the IMO Ship Energy Efficiency Management Plan (SEEMP). The 2025 marine emission estimates reflect the impacts of above emission requirements as well as the projected growth in marine vessel activities for the West Coast. For the merchant bulk fleet in the study area, a growth rate of 29% by 2025 (relative to 2010 level) was adopted based on the Environment Canada national marine vessel emissions inventory for This growth rate was applied in this study to estimate the increase in engine and boiler operating hours in Table ES-1 compares the emissions from marine auxiliary engines and boilers under the current (2010) and future (2025) scenarios. Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

47 Page 2 Table ES-1: Marine Emissions for 2010 and 2025 Inventory Year Emission Source Auxiliary Engines NOx SOx CO HC* PM PM 10 PM 2.5 NH Boilers Total Auxiliary Engines Boilers Total Reduction in auxiliary engine NOx is primarily attributable to the lowering of engine emission standards while the reductions in SOx and PM species are due to the reduction in fuel S content despite projected increases in marine vessel activities. For CO, HC/VOCs and NH 3, the percent increase in emissions are due to the increase in marine activities since their corresponding emission factors were largely unchanged from the 2010 inventory year. Locomotive Emissions Emissions from rail traffic in the study area arise from the combustion of diesel fuel by the line haul grain, coal and potash trains arriving/departing the study area, rail yard switch engine operations, manifest transfers, sulphur trains and other facility trains within the study area boundary. Similar to the emission methodology for marine auxiliary engines, the emission factor approach was used to calculate emissions from locomotive engines. Current and projected rail traffic data for the study area was made available for the inventory. Pertinent assumptions on train engine counts, engine characteristics (power rating, duty cycle and load factor), and operating schedules were discussed prior to their adoption to ensure they are appropriate, to the extent possible, for use for the rail fleet in the study area. The emission factors for line haul and switch engines were derived from published US Environmental Protection Agency (EPA) tiered locomotive emission standards and the respective population distribution data for the line haul and switch engines for the Canadian fleet which is published in the Railway Association of Canada (RAC) Locomotive Emissions Monitoring Program 2009 report. Using these population distributions and their corresponding emission limits, weighted emission factor averages were derived for the fleet operating in the study area. In forecasting emissions from the rail fleet, the approach from the recent Port of Metro Vancouver 2010 Landside Emission Inventory (LEI) was adopted, including the assumption of a 2% fleet rollover per year for line haul locomotives starting in 2012 and for locomotive replacement units to meet US EPA Tier 3 emission limits during the period and Tier 4 limits s during For switch engines, the projected Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

48 Page 3 number of units is expected to decline from current level due to a reduction in switching events for bulk commodities as a result of new track configuration. Consequently some of the old switch engines are expected to be retired and would not be replaced with new models. Therefore, the 2% rollover rate was not applied. For line haul locomotives, units in addition to the current population will be in operation on port due to the projected increase in train traffic. These additional units were assumed to have the same population distribution profile by tier level. Tables ES-2 and ES-3 show the respective emissions by locomotive type in the current and future scenarios. Table ES-2 Current Rail Emissions by Locomotive Type Scenario Current CAC Emissions (tonnes/y) Locomotive Type NOx SOx CO VOC PM PM 10 PM 2.5 NH 3 Line Haul Switch Manifest Sulphur Facility Genset Facility Diesel Total Table ES-3 Future Rail Emissions by Locomotive Type (2025) Scenario 2025 CAC Emissions (tonnes/y) Locomotive Type NOx SOx CO VOC PM PM 10 PM 2.5 NH 3 Line Haul Switch Manifest Sulphur Facility Genset Facility Diesel Total Line haul locomotives are a significant emission source of CAC emissions in the current as well as 2025 scenario. Emissions from the single genset are generally low for all CACs, as expected. The changes in CAC emissions shown can be attributable to the increase in rail traffic in the study area in 2025 and, to a less extent, the impact of more stringent emission standards as old locomotives retire and new units are purchased. For CO and NH 3, the increases in emission are due to the increase in rail activities since their corresponding emission factors were largely unchanged from the current inventory year. The reductions in SOx emissions are primarily due to the significant lower fuel S content in diesel in 2025 (from 110 ppm in the current case to 15 ppm in 2025). Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

49 Page 4 Cargo Handling Equipment To provide additional context, emissions from cargo handling equipment from the 2010 LEI for terminals in the study area are presented in Table ES-4. ES-4: 2010 Cargo Handling Equipment Emissions Inventory Year CAC Emissions (tonnes/y) NOx SOx CO VOC PM 10 PM 2.5 NH Fugitive Emissions Fugitive emissions originating from the coal stockpiles at the coal terminal can be separated into two major sources, fugitive material transfer emissions and wind erosion emissions. Whenever material is transferred (e.g. to stockpiles, hoppers, conveyors or trucks) fugitive dust emissions are generated. Fugitive dust is also generated by the surface erosion of active storage piles exposed to the wind. Using published correlations from EPA and the Air & Waste Management Association for these fugitive dust sources as well as the current and projected increase in commodity volumes, Tables ES-5 and ES-6 show the estimated emissions from bulk material handling and stockpiles. Particulate emanating from these sources are shown to be relatively small in magnitude. ES-5: Current Fugitive Dust Emission Estimates Sources Emissions (tonnes/yr) PM PM 10 PM 2.5 Material Transfers Stockpile Erosion Total ES-5: Future Fugitive Dust Emission Estimates (2025) Sources Emissions (tonnes/yr) PM PM 10 PM 2.5 Material Transfers Stockpile Erosion Total Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

50 Page 5 1. Introduction On March 27, 2009, the Government of Canada, the Province of British Columbia, the Vancouver Fraser Port Authority (VFPA), TransLink, local municipalities, and the private sector jointly announced an investment of over $225 million in five infrastructure improvements in the North Shore Trade Area (NSTA) under the Federal Government s Asia-Pacific Gateway & Corridor Initiative ( APGCI ) to enhance rail and port operations and resolve road/rail issues on the North Shore. Further to these infrastructure improvements, VFPA is currently undertaking an internal environmental assessment of the effects of the Low Level Road (LLR) Project proposed for the North Shore Trade Area. Figure 1 below shows the NSTA. Figure 1-1 North Shore Trade Area To identify potential air quality impacts of the proposed Low Level Road project to the local community, VFPA retained Levelton Consultants Ltd. (Levelton) to undertake an air quality assessment of the Project. This study consists of the following two phases. Phase 1: Screening level dispersion modelling of road traffic taking into account the increased activity from the relocation of the Low Level Road; and Phase 2: Emissions inventory of other relevant air emission sources in the NSTA resulting from LLR infrastructure changes and increased traffic. Primary focus is to be placed on rail, marine and fugitive dust emissions. This report for the air quality assessment summarizes the emissions inventory methodologies used and results obtained for Phase 2 of the Project. Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area

51 Page 6 2. Methodology This section describes the methodologies for estimating the current and future emissions from rail and marine operations as well as fugitive dust releases from the study area. Emissions were compiled for the following sources under two comparative scenarios below. Scenario 1: Current emissions from existing rail and marine traffic and fugitive dust; and Scenario 2: Future rail, marine and fugitive dust emissions for 2025 The common air contaminants (CACs) considered in the study included the following: Carbon monoxide (CO) Oxides of nitrogen (NO x ) Sulphur oxides (SO X ) Volatile organic compounds (VOCs) Particulate matter (PM, PM 10, PM 2.5 ) Ammonia (NH 3 ) 2.1 Marine Emissions The major bulk commodities handled by port tenants in the study area include grain, coal and potash. Marine emission sources of interest in this study include diesel fuel-fired auxiliary engines and boilers which are used to meet shipboard power and hot water requirements when a bulk vessel is at berth. CAC emissions, except SO X and PM, were estimated based on the following respective equations for an auxiliary engine and a boiler. where: E ae = (AE * LF * T * EF act ) / 10 6 E b = (BO * T *EF fuel ) / 10 3 E ae E b AE LF T EF act BO EF fuel = emissions of a given pollutant from an auxiliary engine (tonnes/y) = emissions of a given pollutant from a boiler (tonnes/y) = auxiliary engine capacity (kw or HP) = engine load factor (fraction) = operating time (h/y) = activity-based emission factors for a given pollutant (g/kwh or g/hph) = boiler fuel consumption (tonnes/h) = fuel-based emission factors (kg/tonne fuel) Emissions from each engine were then multiplied by the total number of engines onboard the vessel to arrive at an annual emissions estimate. Based on the ship survey conducted by the BC Chamber of Shipping (COS), Phase 2: Current & Forecast of Emissions from Rail, Marine and Fugitive Sources in the North Shore Trade Area