## IMPACT OF OFFSHORE SHIP EMISSIONS
ON PUERTO RICO AND THE
U.S. VIRGIN ISLANDS

FINAL REPORT
STI-909201-3729-FR

By Neil J. M. Wheeler Stephen B. Reid
Erin K. Pollard Kenneth R. Craig

Yuan Du Sonoma Technology, Inc. 1455 N. McDowell Blvd., Suite D Petaluma, CA 94954-6503

Prepared for U.S. Environmental Protection Agency Office of Transportation and Air Quality
2000 Traverwood Drive Ann Arbor, MI 48105

October 27, 2009iii TABLE OF CONTENTS

Section Page

LIST OF FIGURES ....................................

................................................... ................................ iv

LIST OF TABLES.....................................

................................................... ................................. iv

1. INTRODUCTION....................................

................................................... ....................... 1-1 1.1 Background.....................................

................................................... ....................... 1-1 1.2 Overview of Technical Approach.................

................................................... ......... 1-2 1.3 Summary of Findings ............................

................................................... ................ 1-3

2. TECHNICAL APPROACH..............................

................................................... .............. 2-1 2.1 Data Selection and Processing..................

................................................... ............. 2-1 2.2 Creation of Back Trajectories..................

................................................... .............. 2-2 2.3 Analysis of Back Trajectories..................

................................................... .............. 2-3

3. FINDINGS AND RECOMMENDATIONS.................... .................................................. 3 -1 3.1 Transport Potential............................

................................................... ..................... 3-1
3.2 Discussion.....................................

................................................... ......................... 3-9
3.3 Conclusions and Recommendations................

................................................... .... 3-10

4. REFERENCES......................................

................................................... .......................... 4-1

APPENDIX A: FREQUENCY PLOTS BY RECEPTOR SITE ...... ......................................... A-1iv LIST OF FIGURES Figure Page

1-1. Ship activity patterns based on the ICOADS/AMV ER data sets. ................................... 1-1

1-2. Map of receptor sites for back trajectory anal yses............................................... ............ 1-2

1-3. Overall spatial probability density map for al l receptor sites. .................................. ....... 1-3

3-1. Spatial probability density map showing maximu m densities for all receptor sites, with a 50-m start height and meteorological data fo r the full year.................................. 3- 2

3-2. Spatial probability density map showing maximu m densities for all receptor sites, with a 500-m start height and meteorological data f or the full year................................ 3-3

3-3. Spatial probability density map showing maximu m densities for all receptor sites, with a 50-m start height and meteorological data fo r August through November
excluded. ..........................................

................................................... ............................. 3-4

3-4. Spatial probability density map showing maximu m densities for all receptor sites, with a 500-m start height and meteorological data f or August through November
excluded. ..........................................

................................................... ............................. 3-5

3-5. Spatial probability density map for the Aguadi lla site, with a 50-m start height and meteorological data for the full year..............

................................................... ............... 3-6

3-6. Spatial probability density map for the Aguadi lla site, with a 500-m start height and meteorological data for the full year..............

................................................... ............... 3-7

3-7. Spatial probability density map for the Aguadi lla site, with a 50-m start height and meteorological data for August through November exc luded......................................... 3-8

3-8. Spatial probability density map for the Aguadi lla site, with a 500-m start height and meteorological data for August through November exc luded......................................... 3-9

LIST OF TABLES

Table Page

2-1. HYSPLIT model inputs. .........................

................................................... ...................... 2-31-1 1. INTRODUCTION

1.1 BACKGROUND

The purpose of this project is to evaluate the prob ability that offshore ship emissions impact selected onshore sites in Puerto Rico and th e U.S. Virgin Islands. To conduct this evaluation, Sonoma Technology, Inc. (STI) performed back trajectory analyses, which use interpolated meteorological fields to track a parce l of air backward in time from a specified location. STI calculated back trajectories from se ven locations in Puerto Rico and the Virgin Islands and aggregated that data to evaluate the pr obability that emissions from certain offshore locations have an impact on selected onshore sites. STI also generated plots showing frequencies of potential impact onshore from ship e missions source regions in the area. As shown in Figure 1-1, there is shipping activity, an d therefore emission sources, surrounding these
islands.

Figure 1-1. Ship activity patterns based on the IC OADS/AMVER data sets. 1
(Puerto Rico is highlighted in red.)

1 The ship traffic patterns shown in Figure 1-1 are b ased on two combined data sets: the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) ma naged by the National Oceanic and Atmospheric Administration (NOAA) and the Automated Mutual-Assi stance Vessel Rescue System (AMVER) data set manage d by the U.S. Coast Guard.1-2

1.2 OVERVIEW OF TECHNICAL APPROACH

STI performed back trajectory analyses for four pop ulated areas in Puerto Rico (San Juan, Pole Ojea, Ponce, and Aguadilla), two populat ed areas in the Virgin Islands (Charlotte
Amalie on the island of St. Thomas and Christianst ed on the island of St. Croix), and an additional location on the island of St. John (see Figure 1-2 ).

Figure 1-2. Map of receptor sites for back traject ory analyses.

The tasks required to complete the back trajectory analyses are listed below and
described in detail in Section 2.

1. Data selection and processing – STI reviewed available meteorological data for t he region of interest, acquired the best available dat a for the years 2001-2005, and reformatted the data for use with the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Air Resources Laborator y, 2008).

2. Creation of back trajectories – STI used the HYSPLIT model to calculate back trajectories for the seven receptor sites in Puerto Rico and the Virgin Islands.

3. Analyses of back trajectories – STI aggregated the back trajectories to provide data and plots detailing the probability that trajectories o riginated in various source regions around each receptor site. STI also compared these plots to ship activity and emissions data provided by the EPA.1-3

1.3 SUMMARY OF FINDINGS

The analysis methods used for aggregating an ensemb le of trajectories are sufficient to provide an overall picture of transport in the regi on around Puerto Rico and the U.S. Virgin
Islands and to inform an initial assessment of the potential impact of offshore ship emissions on populated areas in the region. Winds are predomina ntly from the east in this region, and the analyses confirm this observation, with the highest percentage of trajectories originating from locations east of Puerto Rico and the Virgin Island s. Figure 1-3 , which displays the maximum probability that trajectories originating in a give n location would impact any one of seven receptor sites, shows that the highest probability values are clustered around each of the receptor sites. Analyses indicate that transport from areas west of Puerto Rico is limited compared to transport from the east, north, and south. Dominan t easterly flows may transport emissions from shipping hundreds of kilometers to populated areas of Puerto Rico and the Virgin Islands. This is especially true for shipping to the east, northe ast, and southeast of these islands.

Figure 1-3. Overall spatial probability density ma p for all receptor sites.2-1 2. TECHNICAL APPROACH

The objective of this analysis is to evaluate the p robability that offshore ship emissions impact populated areas in Puerto Rico and the Virgi n Islands. To perform this evaluation, STI identified the sources of meteorological data for t he region of interest, selected the most appropriate data sets, validated and processed the data, and used the HYSPLIT model to create back trajectories that could be used to generate fr equency plots showing potential emissions impacts on selected sites. Descriptions of data so urces and analysis steps for these tasks are provided in the sections below.

2.1 DATA SELECTION AND PROCESSING

After a review of available meteorological data for the region of interest, the National Center for Environmental Prediction’s (NCEP) North American Regional Reanalysis (NARR) data set (Mesinger et al., 2006) was chosen as the source of three-dimensional meteorological data for the HYSPLIT trajectory calculations. The NARR is a regional long-term, dynamically consistent, high-resolution weather and climate dat a set for North America. The data set was created by running NCEP’s Eta regional prediction m odel at 32-km resolution with a land surface physics parameterization package and the Et a three-dimensional variational (3D-VAR) Data Assimilation System (EDAS). EDAS assimilates a full complement of quality-controlled surface and upper air meteorological observations, plus additional precipitation analyses, satellite data, and other special observational data, to mini mize the error between the observed and modeled atmospheric state. The NARR is an improvem ent in both resolution and accuracy (Mesinger et al., 2006) over the NCEP/National Cent er for Atmospheric Research (NCAR) Global Reanalysis data (Kalnay et al., 1996).

The NARR was selected for this analysis because it is currently the only multi-year, medium-resolution, three-dimensional data set with sufficient coverage of the eastern Caribbean Sea region. Global-scale data sets, such as the NC EP/NCAR Global Reanalysis, were not used in this analysis because those data sets were devel oped at a spatial and temporal resolution coarser than that of the NARR, and they do not inco rporate recent advances in modeling and data assimilation systems.

NARR data products are distributed in gridded binar y (GRIB) format on the NCEP Advanced Weather Interactive Processing System (AWI PS) 221 Lambert Conformal Conic grid at 32-km horizontal resolution on 29 vertical press ure levels at 3-hr intervals. The GRIB data are available through the NOAA National Operational Mod el Archive and Distribution System (NOMADS) and through the NCAR mass data store. STI had obtained 27 years of NARR data during previous work; thus, no additional data acqu isition effort was required for this project.

The NARR domain covers all of North America and ext ends into the Atlantic Ocean
several hundred kilometers east of Puerto Rico and the U.S. Virgin Islands. A 2144-km by 2335-km (67 by 73 NARR grid cell) analysis subgrid, roughly centered on Puerto Rico, was chosen for the HYSPLIT analysis. The eastern bound ary of the analysis subgrid was intentionally set several grid cells from the easte rn boundary of the NARR domain to avoid2-2

missing data near the NARR domain edge due to misma tch between the native Eta model grid and the AWIPS 221 grid.

NARR data from the analysis domain were extracted f rom the GRIB data files and translated into the NOAA Air Resources Laboratory ( ARL) packed binary file format required by HYSPLIT. The code developed to perform this tas k was based on codes developed by ARL to perform similar translations of other model data sets. This code also performed all unit conversions necessary to conform to HYSPLIT input r equirements. Vertical velocity was converted from Pascals per second (Pa/s) to milliba rs per second (mb/s), while mean sea level pressure was converted from hectoPascals (hPa) to m illibars (mb). Also, NARR wind fields were rotated to grid-relative coordinates. Unlike most modeled data sets, wind fields in the NARR have been rotated relative to true geographic north and must be re-rotated prior to use by a modeling system.

Several quality assurance steps were taken to ensur e that the NARR data were properly translated to ARL format. First, ARL-formatted dat a were processed through the chk_arl_data file check program developed by ARL to verify the d ata encoding. Vertical profiles at selected geographic locations from the ARL-formatted NARR da ta files were then examined to ensure the reasonableness of the vertical temperature, win d, and moisture profiles and to verify unit conversions. These vertical profiles were also qua litatively compared for consistency against vertical profiles extracted at the same locations f rom the operational 40-km North American Model (NAM) Data Assimilation System, a gridded dat a set produced by NCEP and translated to ARL format by ARL for use in HYSPLIT. Finally, sel ected HYSPLIT trajectories were calculated, using the ARL-formatted NARR and NAM da ta sets, and compared for consistency.

2.2 CREATION OF BACK TRAJECTORIES

The HYSPLIT model was developed by the NOAA ARL (Ai r Resources Laboratory, 2008). HYSPLIT uses archived three-dimensional met eorological fields to compute air parcel trajectories, and the model can either be run inter actively on the web or be run using executable code downloaded to a personal computer.

On the basis of discussions with the EPA Work Assig nment Manager (WAM), STI ran HYSPLIT using a back trajectory length of 72 hours and trajectory start heights of 50 and 500 m above ground level. The 50-m start height was sele cted to match the effective stack height for ocean-going marine vessels (Mason et al., 2008), wh ile the 500-m start height represents the middle of the typical mixed layer, which generally has a top that is from 800 to 1,000 m high in the region of interest. Running HYSPLIT with these two start heights provides some insight into potential differences between near-surface and elev ated winds and helps to bound the potential impacts of off-shore ship emissions. In addition, HYSPLIT was run using the most recent five years of NARR data (2001–2005). Meteorological dat a from a five-year period was used in order to minimize the influence of any invalid data or unusual meteorological events such as
hurricanes. Table 2-1 provides a summary of HYSPLIT model inputs.2-3 Table 2-1. HYSPLIT model inputs.

Parameter Inputs Meteorological Data NARR; 32-km resolution; 2001-20 05 Trajectory Length 72 hours (backward) Receptor Locations 7 sites: San Juan, Puerto Rico; Pole Ojea, Puerto Rico; Ponce, Puerto Rico; Aguadilla, Puerto Rico; Charlotte Amalie, St. Thomas; Christiansted, St. Croix; and St. John Island Trajectory Start Heights 50 m and 500 m above ground level Vertical Motion Constant sigma a

a With the constant sigma vertical motion option in H YSPLIT, a trajectory remains on its original terrai n-following sigma surface. STI also tested the isobaric option , which derived vertical motion from isobaric data, but there was no impact on the resulting aggregated trajectories.

2.3 ANALYSIS OF BACK TRAJECTORIES

After generating back trajectories for 2001-2005 fo r each of the seven locations of interest, STI used geographical information system (GIS) software to aggregate the trajectories and calculate the percentage of time that the traje ctories originated in various offshore regions around Puerto Rico and the Virgin Islands. STI’s a nalyses extended at least 500 km in all directions from the receptors, and the analyses wer e performed for two periods: (1) the entire year and (2) the year with the rainy season (August -November) excluded. Once these analyses were complete, STI prepared frequency plots showing the percentage of time that trajectories originated in various regions around the site. Plo ts were prepared for each receptor site individually and for the seven sites overall. Thes e overall plots represent the maximum probability that trajectories originating in a give n location would impact any of the receptor sites. These plots were developed by combining spatial pro bability values for all seven sites in a GIS environment and using GIS to select the maximum pro bability value among the seven data sets for a given location. In addition, plots were prep ared for trajectory start heights of 50 m and
500 m.3-1 3. FINDINGS

3.1 TRANSPORT POTENTIAL

Figures 3-1 through 3-4 show overall spatial probability density plots for combined trajectories from all seven receptor sites based on 2001-2005 meteorological data (note that the black outline around each of the frequency plots sh ows the extent of the NARR data used for these analyses 2 ). These plots show the maximum probability that t rajectories originating in a given location would impact any of the receptor loc ations. Note that these plots do not consider additive impacts. For instance, if emissions from a given location have a 50% chance of reaching Aguadilla and a 20% chance of reaching San Juan, a value of 50% is recorded on the plot, rather than a higher value approaching 70%. In addition, emissions reaching shore between receptor site areas are not considered (receptor si te areas are 32-km grids). As such, these overall spatial probability density plots may under state the true impact on land.

The overall spatial probability density plots are s hown with trajectory start heights of 50 and 500 m for all meteorological data (Figures 3-1 and 3-2) and for meteorological data excluding data from the rainy season (Figures 3-3 a nd 3-4). Multiple trajectory start heights were used to represent the effective stack height f or ocean-going marine vessels (50 m) and the middle of the typical mixed layer in the region (50 0 m). Running HYSPLIT with these two start heights provides some insight into potential differ ences between near-surface and elevated winds and helps to bound the potential impacts of off-sho re ship emissions. Data from the rainy season were excluded because the meteorological characteri stics of these periods may bias long-term averages, and the increased mixing and deposition t hat is likely to occur during stormy weather would alter the impact of off-shore emissions on re ceptor locations. However, the similarity of Figures 3-1 through 3-4 indicates that the selectio n of trajectory start heights and the exclusion of rainy season (August-November) meteorological data do not significantly alter transport
patterns.

In general, these figures show that easterly flows dominate the region, as the highest percentage of trajectories originates in locations east of Puerto Rico and the Virgin Islands. As expected, the highest probability values for emissi ons reaching any one of the seven receptor sites are clustered around each of the receptor sit es. Analyses indicate that transport from areas west of Puerto Rico is limited compared to transpor t from the east, north, and south. Dominant easterly flows may transport emissions from shippin g hundreds of kilometers to populated areas of Puerto Rico and the Virgin Islands. This is esp ecially true for shipping to the east, northeast,
and southeast of these islands.

Similar patterns are observed when back trajectorie s for individual receptor sites are considered. For instance, Figures 3-5 through 3-8 show spatial probability density values calculated from back trajectories from the Aguadill a site. Plots for all seven receptor sites are provided in Appendix A.

2 The polygon around the analysis domain appears ske wed because the NARR data are provided in a Lambert Conformal projection, which becomes distorted in re gions distant from the projection’s two reference p arallels.3-2

Figure 3-1. Spatial probability density map showin g maximum densities for all receptor sites, with a 50-m start height and meteor ological data for the full year.3-3

Figure 3-2. Spatial probability density map showin g maximum densities for all receptor sites, with a 500-m start height and meteo rological data for the full year.3-4

Figure 3-3. Spatial probability density map showin g maximum densities for all receptor sites, with a 50-m start height and meteor ological data for August through November excluded.3-5

Figure 3-4. Spatial probability density map showin g maximum densities for all receptor sites, with a 500-m start height and meteo rological data for August through November excluded.3-6

Figure 3-5. Spatial probability density map for th e Aguadilla site, with a 50-m start height and meteorological data for the full y ear.3-7

Figure 3-6. Spatial probability density map for th e Aguadilla site, with a 500-m start height and meteorological data for the full y ear.3-8

Figure 3-7. Spatial probability density map for th e Aguadilla site, with a 50-m start height and meteorological data for August thr ough November excluded.3-9

Figure 3-8. Spatial probability density map for th e Aguadilla site, with a 500-m start height and meteorological data for August thr ough November excluded.

3.2 DISCUSSION

Overall, the spatial probability plots shown in Sec tion 3.1 and in Appendix A offer a reasonable picture, obtained by using the best avai lable meteorological data, of transport in the region of interest. However, some limitations are associated with these analyses. First, the 32-km resolution of the NARR data may not capture l ocal circulation patterns (e.g., land-sea breezes that may have a more westerly component). In addition, caution should be exercised when interpreting probability values within about 1 00 km of the eastern boundary of the analysis domain. This is because, for each data point, a se arch radius of 50 km was used to calculate the fraction of trajectories that passed through a give n grid cell; near the eastern boundary of the analysis domain, however, the 50-km search radius e xtends into regions where no meteorological
data are available.3-10

3.3 CONCLUSIONS

The analysis methods used for aggregating an ensemb le of trajectories are sufficient to provide an overall picture of transport in the regi on around Puerto Rico and the U.S. Virgin
Islands and to inform an initial assessment of the potential impact of offshore ship emissions on populated areas in the region. Analyses indicate t hat transport from areas west of Puerto Rico is limited compared to transport from the east, north, and south. Dominant easterly flows may transport emissions from shipping hundreds of kilom eters to populated areas of Puerto Rico and the Virgin Islands. This is especially true for sh ipping to the east, northeast, and southeast of
these islands.4-1 4. REFERENCES

Air Resources Laboratory (2008) HYSPLIT model. Web page of the National Oceanic and Atmospheric Association, Air Resources Laboratory, Silver Spring, MD. Available on
the Internet at http://www.arl.noaa.gov/ready/hysplit4.html

.

Kalnay E., Kanamitsu M., Kistler R., Collins W., De aven D., Gandin L., Iredell M., Saha S., White G., Woollen J., Zhu Y., Leetmaa A., Reynolds B., Chelliah M., Ebisuzaki W., Higgins W., Janowiak J., Mo K.C., Ropelewski C., Wa ng J., Jenne R., and Joseph D. (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. 77 (3),
437-471.

Mason R., Dolwick P., Carey P., Kinnee E., and Wils on M. (2008) Emissions processing and sensitivity air quality modeling of category 3 comm ercial marine vessel emissions. Paper presented at the 17th International Emissions Inventory Conference, Portland, OR, June

5 , by the U.S. Environmental Protection Agency, Rese arch Triangle Park, NC.

Mesinger F., DiMego G., Kalnay E., Mitchell K., Sha fran P.C., Ebisuzaki W., Jovic D., Woollen J., Rogers E., Berbery E.H., Ek M.B., Fan Y., Grumb ine R., Higgins W., Li H., Lin Y., Manikin G., Parrish D., and Shi W. (2006) North Ame rican Regional Reanalysis. Bull.

Amer. Meteor. Soc. 87 (3), 343-360.A-1 

## APPENDIX A

FREQUENCY PLOTS BY RECEPTOR SITEA-3

Spatial probability density map for the Aguadilla s ite, with a 50-m start height and meteorological data for the full year.A-4

Spatial probability density map for the Aguadilla s ite, with a 500-m start height and meteorological data for the full year.A-5

Spatial probability density map for the Aguadilla s ite, with a 50-m start height and meteorological data for August through November exc luded.A-6

Spatial probability density map for the Aguadilla s ite, with a 500-m start height and meteorological data for August through November excluded.A-7

Spatial probability density map for the Pole Ojea s ite, with a 50-m start height and meteorological data for the full year.A-8

Spatial probability density map for the Pole Ojea s ite, with a 500-m start height and meteorological data for the full year.A-9

Spatial probability density map for the Pole Ojea s ite, with a 50-m start height and meteorological data for August through November exc luded.A-10

Spatial probability density map for the Pole Ojea s ite, with a 500-m start height and meteorological data for August through November excluded.A-11

Spatial probability density map for the Ponce site, with a 50-m start height and meteorological data for the full year.A-12

Spatial probability density map for the Ponce site, with a 500-m start height and meteorological data for the full year.A-13

Spatial probability density map for the Ponce site, with a 50-m start height and meteorological data for August through November exc luded.A-14

Spatial probability density map for the Ponce site, with a 500-m start height and meteorological data for August through November exc luded.A-15

Spatial probability density map for the San Juan si te, with a 50-m start height and meteorological data for the full year.A-16

Spatial probability density map for the San Juan si te, with a 500-m start height and meteorological data for the full year.A-17

Spatial probability density map for the San Juan si te, with a 50-m start height and meteorological data for August through November exc luded.A-18

Spatial probability density map for the San Juan si te, with a 500-m start height and meteorological data for August through November excluded.A-19

Spatial probability density map for the Charlotte A malie site, with a 50-m start height and meteorological data for the full year.A-20

Spatial probability density map for the Charlotte A malie site, with a 500-m start height and meteorological data for the full year.A-21

Spatial probability density map for the Charlotte A malie site, with a 50-m start height and meteorological data for August through N ovember excluded.A-22

Spatial probability density map for the Charlotte A malie site, with a 500-m start height and meteorological data for August through N ovember excluded.A-23

Spatial probability density map for the Christianst ed site, with a 50-m start height and meteorological data for the full year.A-24

Spatial probability density map for the Christianst ed site, with a 500-m start height and meteorological data for the full year.A-25

Spatial probability density map for the Christianst ed site, with a 50-m start height and meteorological data for August through November excluded.A-26

Spatial probability density map for the Christianst ed site, with a 500-m start height and meteorological data for August through N ovember excluded.A-27

Spatial probability density map for the St. John si te, with a 50-m start height and meteorological data for the full year.A-28

Spatial probability density map for the St. John si te, with a 500-m start height and meteorological data for the full year.A-29

Spatial probability density map for the St. John si te, with a 50-m start height and meteorological data for August through November exc luded.A-30

Spatial probability density map for the St. John si te, with a 500-m start height and meteorological data for August through November exc luded.