Modelling exercise with Calpuff. Real case studies.

Activity 4. Tools for response management Action 4.2. Airborne Pollution Propagation from water incidents

ARCOPOL

The Atlantic Regions’ Coastal Pollution Response

Version: 1.0

Last updated on: 30/12/201 1

Author: Patricia Simal Campos, Vicente Pérez-Muñuzuri, Breogán Gómez Hombre

Responsible partner: MeteoGalicia. Consellería de Medio Ambiente, Territorio e Infraestructuras.

Involved partners: CETMAR, MeteoGalicia- Consellería de Medio Ambiente, Territorio e Infraestructuras, INTECMAR, IST, CIIMAR, Agencia de Medio Ambiente y Agua de Andalucía, Irish Marine Institute, C.R. de Bretagne, C.R. d’Aquitaine

Table of contents Page 1. Introduction ...... 7 2. The CALPUFF Modelling System ...... 7 3. Model Selection ...... 9 4. Emergency episodes ...... 10 4.1. Kerosene spill in O Burgo estuary - A Coruña (September 2nd 2011) ...... 10 4.1.1. Description of the emergency ...... 10 4.1.2. Meteorological conditions ...... 12 4.1.3. Surface weather station near the incident ...... 14 4.1.4. Air quality monitoring station near the incident ...... 15 4.2. Fire in frozen fish factory -Vigo (September 26th 2011) ...... 16 4.2.1. Description of the emergency ...... 16 4.2.2. Meteorological conditions ...... 17 4.2.3. Surface weather station near the incident ...... 19 4.2.4. Air quality monitoring station near the incident ...... 20 4.3. Wildfire in Poio - Pontevedra (October 15th 2011) ...... 20 4.3.1. Description of the emergency ...... 20 4.3.2. Meteorological conditions ...... 21 4.3.3. Surface weather station near the incident ...... 23 4.3.4. Air quality monitoring station near the incident ...... 24 5. WRF atmospheric modeling system ...... 24 6. CALMET Modelling ...... 27 6.1. Model Description ...... 27 6.2. Geophysical Input Data ...... 30 6.2.1. Terrain Data ...... 31 6.2.2. Land Use Data ...... 31 6.3. Meteorological input data ...... 32 6.4. CALMET Parameter Settings ...... 35 6.5. WRF and CALMET Output Analysis ...... 38 6.5.1. Winds near the emergency locations ...... 38 6.5.2. Wind Vector Diagrams ...... 43 6.5.3. Mixing Heights at the emergency location ...... 47 6.5.4. Stability at the emergency location ...... 51 7. CALPUFF Modelling ...... 59 7.1. Model Description and Options ...... 59 7.2. Model Initialization ...... 63 7.2.1. Dispersion model options ...... 63 7.2.2. Emissions and Source Characteristics ...... 65 8. Ground Level Concentration Predictions: RESULTS ...... 66 8.1. Ambient Standards ...... 66 8.2. Concentrations predicted for kerosene spill incident ...... 67 8.3. Concentrations predicted for fire in frozen fish factory (Vigo) ...... 69 8.4. Concentrations predicted for forest fire in Poio (Pontevedra) ...... 71 9. Integration of results in Arcopol web tool ...... 73 10. Limitations and Uncertainty ...... 74 11. References ...... 76

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List of Figures Page Figure 1.- Overview of the program elements in the CALPUFF modeling system ...... 9 Figure 2.- Two 3D terrain elevation images of the Galician coast. Source: Nasa Shuttle Radar Topography Mission ...... 10 Figure 3.- Emergency personnel deploying counter-pollution measures ...... 11 Figure 4.- O Burgo estuary bridge with counter-pollution barriers ...... 11 Figure 5.- Location of Kerosene spill at O Burgo estuary ...... 12 Figure 6.- Synoptic situation obtained from GFS model for September 2nd, 2011 ...... 12 Figure 7.- Wind speed and direction forecasted from WRF model (6UTC and 12UTC) ...... 13 Figure 8.- Wind speed and direction forecasted from WRF model (18UTC and 00UTC) ...... 13 Figure 9.- Beaufort Wind scale ...... 14 Figure 10.-Location of A Coruña –Dique met. station in relation to kerosene spill ...... 15 Figure 11.- Pollutant plume from fire in frozen fish factory (Vigo) ...... 16 Figure 12.- Fire services in the facilities of “Frigoríficos Berbés” ...... 17 Figure 13.- Location of Fire in frozen fish factory (Vigo) ...... 17 Figure 14.- Synoptic situation obtained from GFS model for September 26th , 2011 ...... 18 Figure 15.- Wind speed and direction forecasted from WRF model (6UTC and 12UTC) ...... 18 Figure 16.- Wind speed and direction forecasted from WRF model (18UTC and 00UTC) ...... 18 Figure 17.-Location of Vigo – Illas Marinas met. station in relation to fire in frozen fish factory . 19 Figure 18.- The cloud of smoke caused by forest fire clearly perceived from different points of the Pontevedra area...... 21 Figure 19.- Location of the forest fire (surroundings of Pontevedra)...... 21 Figure 20.- Synoptic situation obtained from GFS model for October 15th , 2011 ...... 22 Figure 21.- Wind speed and direction forecasted from WRF model (1UTC and 6UTC) ...... 22 Figure 22.- Wind speed and direction forecasted from WRF model (12UTC and 18UTC) ...... 22 Figure 23.-Location of Mount Castrove met. station in relation to forest fire in Poio (Pontevedra) ...... 23 Figure 24.- Operational WRF domains at MeteoGalicia (d01@36km, d02@12km, d03@4km) 25 Figure 25.- WRF Domain 3 along with the CALMET domains 1 (O Burgo- A Coruña), 2 (Wildfire Pontevedra), 3 (Vigo-Berbés) ...... 27 Figure 26.- Flow Diagram of Diagnostic Wind Module in CALMET...... 28 Figure 27.- Flowchart of meteorological modeling for Calmet ...... 32 Figure 28.- Gridded receptors from WRF model with Calmet domain (Kerosene spill in O Burgo estuary – A Coruña, September 2nd 2011) ...... 33 Figure 29.- Gridded receptors from WRF model with Calmet domain (Fire in frozen fish factory – Vigo, September 26th 2011) ...... 34 Figure 30.- Gridded receptors from WRF model with Calmet domain (Wildfire in Poio- Pontevedra, October 15th 2011) ...... 34 Figure 31.- CALMET and CALPUFF Modelling Domain (Kerosene spill in O Burgo estuary – A Coruña, September 2nd 2011) ...... 37 Figure 32.- CALMET and CALPUFF Modelling Domain (Fire in frozen fish factory – Vigo, September 26th 2011) ...... 37 Figure 33.- CALMET and CALPUFF Modelling Domain (Wildfire in Poio-Pontevedra, October 15th 2011) ...... 37 Figure 34.- Wind roses plotted at A Coruña – dique met. station during kerosene-spill incident.39 Figure 35.- Wind roses plotted at Vigo- Illas Marinas met. station during the fire in a frozen fish factory (Vigo)...... 40 Figure 36.- Wind roses plotted at Mount Castrove met. station during forest fire in Poio (Pontevedra)...... 41 Figure 37.- Wind speed as a function of wind direction at A Coruña-Dique met. station vs Calmet during kerosene spill...... 42 Figure 38.- Wind speed as a function of wind direction at Vigo-Illas Marinas met. station during fire in frozen fish factory (Vigo)...... 42

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Figure 39.- Wind speed as a function of wind direction at Mount Castrove met. station during forest fire in Poio (Pontevedra)...... 43 Figure 40.- Illustrations of the Wind Vectors during Kerosene spill incident (A Coruña)...... 44 Figure 41.- Illustrations of the Wind Vectors during fire in frozen fish factory (Vigo)...... 45 Figure 42.- Illustrations of the Wind Vectors during forest fire in Poio (Pontevedra)...... 46 Figure 43.- Right: CALMET-Predicted diurnal mixing heights near the kerosene spill incident. Left: Radiosonde launched at A Coruña site (September 2nd, 2011) ...... 48 Figure 44.- Right: CALMET-Predicted diurnal mixing heights near the fire in frozen fish factory (Vigo). Left: Radiosonde launched at A Coruña site (September 26th, 2011) ...... 49 Figure 45.- Right: CALMET-Predicted diurnal mixing heights near the forest fire in Poio (Pontevedra). Left: Radiosonde launched at A Coruña site (October 15th, 2011) ...... 49 Figure 46.- Mixing Heights (m) in the CALMET modeling domain during kerosene spill in O Burgo estuary (A Coruña) ...... 50 Figure 47.- Mixing Heights (m) in the CALMET modeling domain during fire in frozen fish factory (Vigo) ...... 50 Figure 48.- Mixing Heights (m) in the CALMET modeling domain during forest fire in Poio (Pontevedra) ...... 50 Figure 49.- Stability class and mixing of a pollutant cloud...... 51 Figure 50.- Fanning plume type under stable conditions...... 52 Figure 51.- Lofting plume type with stable-neutral inversion layer...... 52 Figure 52.- Looping plume type under unstable conditions...... 52 Figure 53.- Coning plume type under neutral conditions...... 53 Figure 54.- Fumigation plume type with neutral-stable inversion layer...... 53 Figure 55.- CALMET-Predicted PG stability classes near the kerosene spill incident ...... 56 Figure 56.- Predicted SO2 concentrations at each receptor (ug/m3) related to stability during kerosene spill ...... 56 Figure 57.- CALMET-Predicted diurnal PG stability classes near the fire in frozen fish factory (Vigo) ...... 57 Figure 58.- Schematic diagram of plume rise in light winds with neutral stability ...... 57 Figure 59.- Predicted NOx concentrations at each receptor (ug/m3) related to stability during fire in frozen fish factory (Vigo) ...... 58 Figure 60.- CALMET-Predicted diurnal PG stability classes near the forest fire in Poio (Pontevedra) ...... 58 Figure 61.- Schematic diagram of plume rise in strong winds with neutral stability ...... 59 Figure 62.- Predicted PM10 concentrations at each receptor (ug/m3) related to stability during forest fire in Poio (Pontevedra) ...... 59 Figure 63.- Observed SO2 concentration at A Coruña Centro air quality monitoring station ..... 68 Figure 64.- Observed SO2 concentration at A Coruña Torre de Hercules air quality monitoring station ...... 68 Figure 65.- Predicted highest 1-hour average SO2 concentrations at each receptor (ug/m3) for kerosene spill incident...... 69 Figure 66.- Predicted highest 1-hour average SO2 concentrations at each receptor (ug/m3) for kerosene spill incident and air quality stations value...... 69 Figure 67.- Observed SO2 concentration at Vigo Coia air quality monitoring station ...... 70 Figure 68.- Predicted highest 1-hour average NOx concentrations at each receptor (ug/m3) for fire in frozen fish factory (Vigo)...... 71 Figure 69.- Predicted highest 1-hour average NOx concentrations at each receptor (ug/m3) for fire in frozen fish factory (Vigo) and air quality stations value...... 71 Figure 70.- Observed PM10 concentration at Marin air quality monitoring station ...... 72 Figure 71.- Observed CO concentration at Marin air quality monitoring station ...... 72 Figure 72 and 73.- Predicted highest 1-hour average CO concentrations at each receptor (ug/m3) for forest fire (Pontevedra)...... 73 Figure 74 and 75.- Predicted highest 1-hour average PM10 concentrations at each receptor (ug/m3) for forest fire (Pontevedra)...... 73 Figure 76.- Integration of CALPUFF model output graphs into Arcopol web tool ...... 74

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List of Tables Page

Table 1. Surface meteorological station near kerosene spill in O Burgo estuary (A Coruña)..... 15 Table 2. Air quality monitoring stations operated by Galician Government near A Coruña...... 15 Table 3. Surface meteorological station near fire in frozen fish factory (Vigo) ...... 19 Table 4. Air quality monitoring station operated by Galician Government near Vigo ...... 20 Table 5. Surface meteorological station near forest fire in Poio (Pontevedra) ...... 23 Table 6. Air quality monitoring station near Pontevedra operative during the emergency episode ...... 24 Table 7. WRF-ARW model features implemented by MeteoGalicia ...... 26 Table 8. Default CALMET Land Use Categories and Associated Geophysical Parameters Based on the U.S. Geological Survey Land Use Classification System (14-Category System) ...... 31 Table 9. Data Records repeated for each grid cell in extraction subdomain in 3D.DAT...... 32 Table 10. CALMET Model Parameter Values or Options common for the three emergency episodes ...... 35 Table 11. Grid Parameters for CALMET ...... 36 Table 12. The Pasquill stability classes ...... 54 Table 13. Meteorological conditions that define the Pasquill stability classes ...... 55 Table 14. CALMET-Predicted Atmospheric Stability at the emergency locations ...... 55 Table 15. Technical Specifications for Calpuff ...... 61 Table 16. Summary of CALPUFF input parameters common for the three emergency episodes ...... 64 Table 17. Area Source Parameters and Emission Rates for the Kerosene spill ...... 65 Table 18. Point Source Parameters and Emission Rates for Fire in frozen fish factory (Vigo) .. 65 Table 19. Area Source Parameters and Emission Rates for the forest fire (Pontevedra) ...... 65 Table 20. Summary of the air quality directive limit, target, assessment threshold, long term objective, information threshold and alert threshold values for the protection of human health...... 66 Table 21. Summary of CALPUFF Modeling Results for Kerosene spill incident ...... 67 Table 22. Summary of CALPUFF Modeling Results for Fire in frozen fish factory (Vigo ...... 70 Table 23. Summary of CALPUFF Modeling Results for forest fire in Poio (Pontevedra) ...... 72

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List of Appendices

APPENDIX A: SO2 predicted concentrations from kerosene spill in O Burgo estuary (A Coruña) ...... 79 APPENDIX B:Toluene predicted concentrations from kerosene spill in O Burgo estuary (A Coruña) ...... 83 APPENDIX C:Xylene predicted concentrations from kerosene spill in O Burgo estuary (A Coruña) ...... 87 APPENDIX D:NOx predicted concentrations from fire in frozen fish factory (Vigo) ...... 91 APPENDIX E:PM10 predicted concentrations from forest fire in Poio (Pontevedra) ...... 95 APPENDIX F:CO predicted concentrations from forest fire in Poio (Pontevedra) ...... 99 APPENDIX G:CH4 predicted concentrations from forest fire in Poio (Pontevedra) ...... 103 APPENDIX H:FEPS model ...... 107

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1. Introduction

During an emergency due to an episode of air pollution is necessary to have different tools that support the operation of emergency services in a short space of time. In order to achieve this goal, MeteoGalicia has tested and implemented a dispersion model to predict maximum ground-level concentrations during last emergencies in Galicia, in the northwest of Spain, where MeteoGalicia is responsible for the meteorological forecast. The California PUFF (CALPUFF) modelling system was selected to conduct the predictive modelling of Hazardous and Noxious substances (HNS) emissions.

This report provides the technical details and assumptions regarding the dispersion modelling conducted for air pollutants assessment. The following is a technical description of the CALPUFF model, and an overview of the initialization and parameterization of this model for this application. The results of the dispersion modelling can be found in the main body of this report.

2. The CALPUFF Modelling System

The CALPUFF modeling system can be downloaded free of charge from the US EPA from www.epa.gov/scram001/dispersion_prefrec.htm#calpuff, which provides a link to www.src.com/calpuff/calpuff1.htm to obtain the models themselves. The CALPUFF components are provided as executables and sample input files for the Microsoft Windows operating system. The models are also provided in source code format with versions for HP UNIX and Sun UNIX. PC-based GUIs for the major components are not available for the UNIX versions. All versions may be executed from text-based control files.

Codes used: . CALPUFF - Version 6.42 - Level 110325 . CALMET - Version 6.334 - Level 110421 . CALPOST - Version 6.292 - Level 110406

The core of the CALPUFF modelling system consists of a meteorological model CALMET, a transport and dispersion model CALPUFF, and a postprocessing tool named CALPOST.

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The CALMET meteorological model is used to provide the meteorological data necessary to initialize the CALPUFF dispersion model. This model is initialized with terrain and land use data describing the region of interest, as well as meteorological input from potentially numerous sources. Various user-defined parameters control both how the input meteorological data is interpolated to the grid, as well as which internal algorithms are applied to these input fields. More details regarding these options are provided in later sections. Output from the CALMET model includes hourly temperature and wind fields on a user-specified three-dimensional domain as well as additional two- dimensional variables used by the CALPUFF dispersion model. PRTMET is a postprocessing program that displays user-selected portions of the meteorological data file produced by the CALMET meteorological model.

CALPUFF is a non-steady-state Gaussian puff dispersion model capable of simulating the effects of time and space-varying meteorological conditions on pollutant transport, transformation, and removal. This model requires time-variant two-dimensional and three-dimensional meteorological data output from a model such as CALMET, as well as information regarding the relative location and nature of the sources to be modeled for the application. Output from the CALPUFF model includes ground-level concentrations of the species considered, as well as dry and wet deposition fluxes.

CALPOST is used to process these files, producing tabulations that summarize the results of the simulation, identifying the highest and second highest 1-hour and 3-hour average concentrations at each receptor, for example. When performing visibility- related modeling, CALPOST uses concentrations from CALPUFF to compute extinction coefficients and related measures of visibility, reporting these for selected averaging times and locations.

In addition to CALMET, CALPUFF, CALPOST, there are numerous other processors that may be used to prepare geophysical (land use and terrain) data in many standard formats, meteorological data (surface, upper air, precipitation, and buoy data), and interfaces to other models such as the Penn State/NCAR Mesoscale Model (MM5), the National Centers for Environmental Prediction (NCEP) Eta/NAM and RUC models, the Weather Research and Forecasting (WRF) model and the RAMS model.

Figure 1 displays the overall modeling system configuration in the framework of Arcopol project.

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Figure 1.- Overview of the program elements in the CALPUFF modeling system

The mesoscale meteorological model WRF is not included in the CALPUFF system, but it can be interfaced with CALPUFF modules through CALWRF program. CALWRF is a processor that extracts and interprets data in selected output files from WRF, and creates a file of meteorological data compatible with CALMET in 3D.DAT format. The CALWRF processor runs on a UNIX platform or UNIX-like environment for Microsoft Windows as Cygwin.

3. Model Selection

The Guideline on Air Quality Models (US EPA 2009a) recommends the use of CALPUFF over other regulatory dispersion models for applications where the terrain contains complex topography, the ground cover is not uniform, where wind circulation may driven by lake or sea breezes, flow along coastlines, under stagnation, inversion, recirculation, and fumigation conditions, light wind speed and calm wind conditions, such that the assumption of steady-state straight line transport is not appropriate.

Many conventional plume dispersion models, such as Industrial Source Complex Short Term (ISCST) and AERMOD, are unable to model such dispersion with the precision that is necessary to accurately predict ground-level concentrations in coastal areas (Fisher et al. 2003).

Therefore, CALPUFF model fulfills the particular necessities to model a complex terrain like the Galicia region and its local scale meteorological phenomena.

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Specifically, the CALMET/CALPUFF modeling system for the Arcopol geographic area was selected for the following reasons:

a) the presence of complex terrain in the immediate vicinity of the coast, especially in Galicia, and its importance in producing spatially varying wind fields; b) the presence of a body of water (Atlantic Ocean) introducing spatial inhomogeneities in the meteorological fields and the importance of sea breeze circulations; c) the potential importance of light wind speed and calm wind effects at this area; and d) the potential importance of recirculation and fumigation on the plume dispersion forecast.

Figure 2.- Two 3D terrain elevation images of the Galician coast. Source: Nasa Shuttle Radar Topography Mission1

4. Emergency episodes

4.1. Kerosene spill in O Burgo estuary - A Coruña (September 2nd 2011)

4.1.1. Description of the emergency

September 2nd 2011, a kerosene spill reached the estuary of O Burgo, A Coruña, and forced to close this area to shellfishing. Galician regional government activated the contingency emergency plan for territorial marine pollution and proceeded to the deployment of barriers to surround the spill and prevent pollutants from spreading in the estuary of O Burgo.

1 http://www2.jpl.nasa.gov/srtm/dataprod.htm

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The spill reached the river through sewage system of Culleredo township, although a large amount remained within the sewage system. Kerosene spill came from a truck that was parked in a service area of the AP-9 highway and occurred early in the morning.

Around 08:00 am, police contacted the emergency services in order to request technical means for a "large spill of kerosene." The truck driver raised the alarm when he found a leak and reported that the loss could be due to "sabotage" in the tank valve, containing 36,000 liters of kerosene. In the emergency place, means from Civil Protection, Police, Coast Guard and Arteixo firefighters were working for several days.

Figure 3.- Emergency personnel deploying counter-pollution measures

Figure 4.- O Burgo estuary bridge with counter-pollution barriers

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Figure 5.- Location of Kerosene spill at O Burgo estuary

4.1.2. Meteorological conditions

Figure 6 shows the graphical output from NCEP’s global model GFS (General Forecast System) for September 2nd 2011. The presence of a high pressure area affecting the Iberian Peninsula, and the absence of a high pressure gradient, determine the presence of a barometric swamp, which produced sunny weather with nearly calm winds and local breezes on the coast. This situation usually lasts several days, in that case the meteorological regime is dominated by the local phenomena. For example, the pollution in large cities increases, fogs or mists are generated in closed basins, it is sunny on the mountains and the humidity and breezes on the coast increase.

Figure 6.- Synoptic situation obtained from GFS model for September 2nd, 2011

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As we can see in the next figures, graphical outputs from WRF model reproduces the situation explained before, with winds oscillating between calm and gentle breeze according Beaufort wind scale (Figure 9).

Figure 7.- Wind speed and direction forecasted from WRF model (6UTC and 12UTC)

Figure 8.- Wind speed and direction forecasted from WRF model (18UTC and 00UTC)

Results of the simulation with WRF for the meteorological situation will be described in section 6.4. In order to validate the WRF model behaviour, comparisons with surface measurements of wind and with the radiosonde launched at Coruña site have been done, and will be presented. Calpuff results from the kerosene spill incident will be shown in section 8.2 and Appendix A, B and C. Graphical outputs display model simulations and observations from air monitoring quality stations near the incident.

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Figure 9.- Beaufort Wind scale

4.1.3. Surface weather station near the incident

In order to compare the WRF and CALMET model outputs, observed meteorological data from surface weather station was used. As shown in Figure 10, surface meteorological station is close to the emergency site and inside the CALMET domain.

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Figure 10.-Location of A Coruña –Dique met. station in relation to kerosene spill

A summary of regional surface weather stations used and the meteorological data available from each station is shown in Table 1.

Table 1. Surface meteorological station near kerosene spill in O Burgo estuary (A Coruña) Station Source UTM-29 X UTM-29 Y Latitude Longitude Elevation Parameters name coord coord (ºN) (ºW) (m) available ED50 (km) ED50 (km) A Port of A 550.783 4801.765 43.3662 8.3733 7 W, T, P, Rh, Coruña- Coruña Prc Dique W=wind speed + wind direction; T=air temperature; Rh= relative humidity; P=pressure; Prc=hourly precipitation rate.

4.1.4. Air quality monitoring station near the incident

Environmental Department of Galician Government has two air quality monitoring stations working 24 hours/day and 7 days/week near the kerosene spill incident. These two stations are detailed in the next table.

Table 2. Air quality monitoring stations operated by Galician Government near A Coruña Station Source Latitude Longitude UTM-29 UTM-29 Y Averagi Pollutants name (ºN) (ºW) X coord coord ng measured (km) (km) period ED50 ED50 A Coruña Galician 43.367091 -8.420599 546.946 4801.831 Hourly SO2, NO, NO2, Torre de Government NOX, CO, O3, PM10 Hércules A Coruña Galician 43.382786 -8.409211 547.856 4803.580 Hourly SO2, NO, NO2, Centro Government NOX, CO, O3, PM10

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4.2. Fire in frozen fish factory -Vigo (September 26th 2011)

4.2.1. Description of the emergency

September 26th 2011, a fire broke out on the frozen fish factory “Frigoríficos Berbés”, Vigo. The fire originated just after 10.00 a.m. hours and around 13.00 hours firefighters gave it controlled. During the work of extinguishing the fire, self-contained breathing equipments were used as a precaution measure because it was believed that tanks located in the facility contained ammonia, but later inspectors did not find large amounts, however they found "small amounts" of this substance in packaging machines, although in no case supposed danger according to the emergency services.

Initially, the worst case scenario was modelled, ie a large amount of ammonia evaporated quickly. Ammonia could not be modeled with Calpuff model, so nitrogen oxides were used instead.

However they found "small amounts" of this substance in packaging machines, although in no case supposed danger according to the emergency services.

The fire started in one of the higher plants, where machinery for processing fish is stored and only eight administrative staff was working at the time of the fire which delayed the fire alarm. Consequently the facility was practically fallen into disuse, which generated a large plume of smoke. These workers were evacuated without personal injuries.

Figure 11.- Pollutant plume from fire in frozen fish factory (Vigo)

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Figure 12.- Fire services in the facilities of “Frigoríficos Berbés”

Figure 13.- Location of Fire in frozen fish factory (Vigo)

4.2.2. Meteorological conditions

Figure 14 shows the graphical output from NCEP’s global model GFS (General Forecast System) for the 26th September 2011. That day was characterized by the influence of a strong anticyclone located in Center Europe over Galicia, with the presence of weak winds from the west or southwest over the area of Rias Baixas.

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Figure 14.- Synoptic situation obtained from GFS model for September 26th , 2011

Like the previous emergency episode, graphical outputs from WRF model over Galicia region show winds oscillating between calm and light breeze in Vigo area according Beaufort wind scale (Figure 9), blowing from Southwest.

Figure 15.- Wind speed and direction forecasted from WRF model (6UTC and 12UTC)

Figure 16.- Wind speed and direction forecasted from WRF model (18UTC and 00UTC)

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Results of the simulation with WRF for the meteorological situation will be described in section 6.4. In order to validate the WRF model behaviour, comparisons with surface measurements of wind and with the radiosonde launched at Coruña site have been done, and will be presented. Calpuff results from the fire in frozen fish factory will be shown in section 8.3 and Appendix D. Graphical outputs display model simulations and observations from air monitoring quality stations near the incident.

4.2.3. Surface weather station near the incident

In order to compare the WRF and CALMET model outputs, observed meteorological data from surface weather station was used. As shown in Figure 17, surface meteorological station is very close to the emergency site and inside the CALMET domain.

Figure 17.-Location of Vigo – Illas Marinas met. station in relation to fire in frozen fish factory

A summary of regional surface weather stations used and the meteorological data available from each station is shown in Table 3.

Table 3. Surface meteorological station near fire in frozen fish factory (Vigo) Station Source UTM-29 UTM-29 Y Latitude Longitude Elevation Parameters name X coord coord (ºN) (ºW) (m) available ED50 ED50 (km) (km) Vigo - MeteoGalicia 520.553 4675.090 42.2270 8.7510 22 W, T, P, Rh, IIMarinas Prc, SR W=wind speed + wind direction; T=air temperature; Rh= relative humidity; P=pressure; Prc=hourly precipitation rate; SR: Solar radiation

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4.2.4. Air quality monitoring station near the incident

As in the previous emergency episode, air quality monitoring station near Vigo was used to validate the CALPUFF model results. Its location and other features of the station are shown in the next table.

Table 4. Air quality monitoring station operated by Galician Government near Vigo Station Source Latitude Longitude UTM-29 UTM-29 Y Averaging Pollutants name (ºN) (ºW) X coord coord period measured (km) (km) ED50 ED50 Vigo Coia Galician 42.219002 -8.742058 521.290 4674.208 Hourly SO2, NO, NO2, Government NOX, CO, O3, PM10

4.3. Wildfire in Poio - Pontevedra (October 15th 2011)

4.3.1. Description of the emergency

Several forest fires were reported in the region of Pontevedra during the early hours of Saturday 15th of October, 2011. Up to three fires were recorded in the area, and more specifically, between the municipalities of Poio and Pontevedra,

The fire occurred in three different locations between 00.30 and 01.49 am, according to villagers. The quick intervention of the extinction personnel and the proximity of the fire to the AP-9 highway, which acted as a firewall, prevented the flames to propagate toward Mount Castrove (in Poio), as happened previously in 2006. Although wind gusts were not very strong during the morning, the wind spread the fire around different areas in Poio. The flames consumed more than 72.5 hectares, according to Regional Ministry of Rural Affairs of Galicia. Institutional sources confirmed that the fire was completely extinguished past seven-thirty in the afternoon.

This forest area has undergone several unsuccessful attempts to fire the last two years. The last attempt was in 2010. A fire of this nature does not arise spontaneously and less at night, when temperatures decrease considerably so the fireworkers assumed it was arson.

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Figure 18.- The cloud of smoke caused by forest fire clearly perceived from different points of the Pontevedra area.

Figure 19.- Location of the forest fire (surroundings of Pontevedra).

4.3.2. Meteorological conditions

Figure 20 shows the graphical output from the NCEP’s global model GFS (General Forecasting System) for October 15th 2011. During this day the Iberian Peninsula is mainly influenced by two anticyclones, the European anticyclone located over Germany, and the Azores anticyclone. The low pressure gradient on the peninsula indicates light winds from the west.

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Figure 20.- Synoptic situation obtained from GFS model for October 15th , 2011

As we can see in the next figures, graphical outputs of wind at 10 meters from WRF model show the situation explained before, with winds oscillating between calm and moderate breeze according Beaufort wind scale (Figure 9).

Figure 21.- Wind speed and direction forecasted from WRF model (1UTC and 6UTC)

Figure 22.- Wind speed and direction forecasted from WRF model (12UTC and 18UTC)

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Results of the simulation with WRF for the meteorological situation will be described in section 6.4. In order to validate the WRF model behaviour, comparisons with surface measurements of wind and with the radiosonde launched at Coruña site have been done, and will be presented. Calpuff results will be shown in section 8.4 and Appendix E, F and G. Graphical outputs display model simulations and observations from air monitoring quality stations near the incident.

4.3.3. Surface weather station near the incident

In order to compare the WRF and CALMET model outputs, observed meteorological data from surface weather station was used. As shown in Figure 23, surface meteorological station is very close to the emergency site and inside the CALMET domain.

Figure 23.-Location of Mount Castrove met. station in relation to forest fire in Poio (Pontevedra)

A summary of regional surface weather stations used and the meteorological data available from each station is shown in Table 5.

Table 5. Surface meteorological station near forest fire in Poio (Pontevedra) Station Source UTM-29 UTM-29 Y Latitude Longitude Elevation Parameters name X coord coord (ºN) (ºW) (m) available ED50 ED50 (km) (km) Castrove MeteoGalicia 524.431 4701.030 42.46047 8.70286 424 m. W, T, P, Rh, Prc, SR W=wind speed + wind direction; T=air temperature; Rh= relative humidity; P=pressure; Prc=hourly precipitation rate; SR: Solar radiation

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4.3.4. Air quality monitoring station near the incident

In this case, two air quality monitoring stations were located near the emergency site, Marin – San Narciso and Lourizán Pontevedra, but only Marin San Narciso station provided valid data during emergency episode. Some features of Marin air quality monitoring station are detailed in the next table.

Table 6. Air quality monitoring station near Pontevedra operative during the emergency episode Station Source Latitude Longitude UTM-29 UTM-29 Y Averaging Pollutants name (ºN) (ºW) X coord coord period measured (km) (km) ED50 ED50 Marin Galician 42.388738 -8.709742 523.893 4693.063 Hourly SO2, NO, NO2, Government NOX, CO, O3, PM10, PM2.5

5. WRF atmospheric modeling system

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs.

The effort to develop WRF has been a collaborative partnership, principally among the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (the National Centers for Environmental Prediction (NCEP) and the Forecast Systems Laboratory (FSL), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration (FAA). WRF allows researchers the ability to conduct simulations reflecting either real data or idealized configurations. WRF provides operational forecasting a model that is flexible and efficient computationally, while offering the advances in physics, numerics, and data assimilation contributed by the research community.

The model is designed to be a flexible, state-of-the-art, portable code that is efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modelling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for

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use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g. boundary-layer eddies, convection, baroclinic waves).

The WRF System consists of these major components:  WRF Preprocessing System (WPS)  Dynamic solver (ARW or NMM)  WRF Postprocessor and graphics tools

The operational scheme at MeteoGalicia implements three different resolutions, covering Southwestern Europe at 36 km of resolution, Iberian Peninsula at 12 km, and Galicia at 4 km, as it can be seen in the next figure:

Domain 1 (36km)

Domain 2 (12km)

Domain 3 (4km)

Figure 24.- Operational WRF domains at MeteoGalicia (d01@36km, d02@12km, d03@4km)

Model Physics Model physics parameterizations are quite similar in both dynamic solvers. Main parameterizations are:  Microphysics: Bulk schemes ranging from simplified physics suitable for mesoscale modelling to sophisticated mixed-phase physics suitable for cloud- resolving modelling.

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 Cumulus parameterizations: Adjustment and mass-flux schemes for mesoscale modelling including NWP.  Surface physics: Multi-layer land surface models ranging from a simple thermal model to full vegetation and soil moisture models, including snow cover and sea ice.  Planetary boundary layer physics: Turbulent kinetic energy prediction or non-local K schemes.  Atmospheric radiation physics: Longwave and shortwave schemes with multiple spectral bands and a simple shortwave scheme. Cloud effects and surface fluxes are included.

Table 7. WRF-ARW model features implemented by MeteoGalicia Model name WRF-ARW Use of model Operational forecast for Galicia Region Ocean model forcing Research Prognostic Variables 3D fields: Wind, temperature, relative humidity, pressure Computed ceiling height and cloud cover Frequency of run Twice a day (at 0000UTC and 1200UTC) Horizontal 36 – 12 – 4 km Resolution(s) Vertical Discretization 27 vertical levels up to 5hPa Length of forecast At 0000UTC (72 hours in 4k resolution domain, 96 hours in coarser domains) At 1200UTC (84 hours for all the operational domains) Additional WRF model is running in a non-hydrostatic way. It is running description of model in 3 one-way nested domains. Computer used WRF is running in 32 processors of Finisterrae High Performance Computer machine located in CESGA (Centre of Supercomputing of Galicia). WRF system needs 80 minutes of CPU time Initial and boundary GFS model (0.5º resolution) conditions Validation method WRF results are compared against surface meteorological stations. In Galicia there are 85 meteorological stations and hourly results are used for this validation process. Output file Formats NetCDF and GRIB1 Output Time Interval Hourly Output Files Web Thredds Server. OpenDap, WMS and NetCDF Subseting Publishing Methods

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WRF Domain 3 (4km)

Calmet 1

Calmet 2

Calmet 3

Figure 25.- WRF Domain 3 along with the CALMET domains 1 (O Burgo- A Coruña), 2 (Wildfire Pontevedra), 3 (Vigo-Berbés)

6. CALMET Modelling

6.1. Model Description

The CALMET meteorological model consists of a diagnostic wind field module and micrometeorological modules for overwater and overland boundary layers. The diagnostic wind field module uses a two-step approach to the computation of the wind fields, as illustrated in the next figure.

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Figure 26.- Flow Diagram of Diagnostic Wind Module in CALMET. Source: CALPUFF Modeling System Version 6 User Instructions April 2011

In the first step, an initial guess wind field is adjusted for kinematic effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field.

The second step consists of an objective analysis procedure to introduce observational data into the Step 1 wind field to produce a final wind field. An option is provided to allow gridded prognostic wind fields to be used by CALMET, which may better represent regional flows and certain aspects of sea breeze circulations and slope/valley circulations. Wind fields generated by the CSUMM prognostic wind field model can be input to CALMET as either the initial guess field (pathway A in Figure 26) or the Step 1 wind field (pathway B in Figure 26). MM4/MM5, NAM(Eta), WRF, RUC, RAMS and TAPM model output fields can be introduced into CALMET in one of three different ways:

 as a replacement for the initial guess wind field (pathway A in Figure 26).  as a replacement for the Step 1 field (pathway B); or  as "observations" in the objective analysis procedure (pathway C).

The techniques used in the CALMET model are briefly described below. The recommended approach is pathway A.

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a) Diagnostic Wind Field Module

a.1) Initial Guess Field

Options existing with CALMET to create an initial guess field either by interpolating observation data or by using output from a prognostic meteorological model, such as the Mesoscale Modelling System WRF. The prognostic model data is usually run over a very large domain with much coarser resolution than that applied with CALMET. CALMET will interpolate the prognostic data to develop a 3-D (three-dimensional) fine scale first guess field of wind speeds and directions.

a.2) Step 1 Wind Field

The step one wind field is adjusted for kinematic effects of terrain, slope flows, and blocking effects as follows:

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate kinematic terrain effects. The domain-scale winds are used to compute a terrain-forced vertical velocity, subject to an exponential, stability-dependent decay function. The kinematic effects of terrain on the horizontal wind components are evaluated by applying a divergence-minimization scheme to the initial guess wind field. The divergence minimization scheme is applied iteratively until the three-dimensional divergence is less than a threshold value.

Slope Flows: An empirical scheme based on Allwine and Whiteman (1985) is used to estimate the magnitude of slope flows in complex terrain. The slope flow is parameterised in terms of the terrain slope, terrain height, domain-scale lapse rate, and time of day. The slope flow wind components are added to the wind field adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are parameterised in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude number at a particular grid point is less than a critical value and the wind has an uphill component, the wind direction is adjusted to be tangent to the terrain.

a.3) Step 2 Wind Field

The wind field resulting from the adjustments of the initial-guess wind described above is the Step 1 wind field. The second step of the procedure involves the introduction of observational data into the Step 1 wind field through an objective analysis procedure. An inverse-distance squared interpolation scheme is used which weighs observational data heavily in the vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind field in regions with no observational data.

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The resulting wind field is subject to smoothing, an optional adjustment of vertical velocities based on the O'Brien (1970) method, and divergence minimization to produce a final Step 2 wind field. b) Micrometeorology Modules

The CALMET model contains two boundary layer models for application to overland and overwater grid cells:

b.1) Overland Boundary Layer Model

Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observed temperature soundings using a modified Carson (1973) method based on Maul (1980). The model also determines gridded fields of PGT stability class and optional hourly precipitation rates.

b.2) Overwater Boundary Layer Model

The aerodynamic and thermal properties of water surfaces suggest that a different method is best suited for calculating the boundary layer parameters in the marine environment. A profile technique (Garratt, 1977; Hanna et al., 1985), using air-sea temperature differences, is used in CALMET to compute the micrometeorological parameters in the marine boundary layer.

6.2. Geophysical Input Data

To initialize the CALMET model, terrain elevation and land use data depicting the geophysical conditions in the selected modelling domain are required. Terrain elevation data are used in CALMET in various model algorithms to characterize meteorological phenomena such as up- and down-slope flows terrain steering of winds. In addition to the terrain elevation data, the CALMET model uses surface parameters such as surface roughness length, albedo, Bowen ratio, leaf area index, soil heat flux, and anthropogenic heat flux to estimate meteorological parameters such as surface heat flux and mechanical turbulence. In the model’s geophysical pre-processor MAKEGEO, values for each of these surface parameters are specified based on input land use categories.

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6.2.1. Terrain Data

Terrain elevations in CALMET were initialized with data from the Shuttle Radar Topography Mission (SRTM). This data, a product from a joint project between the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Geospatial-Intelligence Agency (NGA), is available at 3 arc-second (approximately 90 m) horizontal resolution for all over the whole planet. The SRTM data was processed by the CALPUFF pre-processor TERREL over the domain of interest to approximate terrain elevations at 1000 m resolution.

6.2.2. Land Use Data

In addition to terrain elevation data, the CALMET model utilizes surface parameters such as surface roughness length, albedo, Bowen ratio, leaf area index, soil heat flux, and anthropogenic heat flux to provide input to subroutines estimate quantities such as surface heat flux and mechanical turbulence. In the model’s geophysical pre-processor MAKEGEO, values for each of these surface parameters are specified based on input land use categories. For this application, a 900 m resolution land use dataset was used to determine the dominant land

Table 8. Default CALMET Land Use Categories and Associated Geophysical Parameters Based on the U.S. Geological Survey Land Use Classification System (14- Category System) I Land Use Description Surface Albedo Bowen Soil Heat Anthropogenic Leaf Category Roughness Ratio Flux Heat Flux Area (m) Parameter (W/m2) Index 10 Urban or Built-up 1.0 0.18 1.5 0.25 0.0 0.2 Land 20 Agricultural Land- 0.25 0.15 1.0 .15 0.0 3.0 Unirrigated -20* Agricultural Land- 0.25 0.15 0.5 .15 0.0 3.0 Irrigated 30 Rangeland 0.05 0.25 1.0 .15 0.0 0.5 40 Forest Land 1.0 0.10 1.0 .15 0.0 7.0 50 Water 0.001 0.10 0.0 1.0 0.0 0.0 54 Small Water Body 0.001 0.10 0.0 1.0 0.0 0.0 55 Large Water Body 0.001 0.10 0.0 1.0 0.0 0.0 60 Wetland 1.0 0.10 0.5 .25 0.0 2.0 61 Forested Wetland 1.0 0.1 0.5 0.25 0.0 2.0 62 Nonforested 0.2 0.1 0.1 0.25 0.0 1.0 Wetland 70 Barren Land 0.05 0.30 1.0 .15 0.0 0.05 80 Tundra 0.20 0.30 0.5 .15 0.0 0.0 90 Perennial Snow or 0.05 0.70 0.5 .15 0.0 0.0 Ice *Negative values indicate “irrigated” land use

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6.3. Meteorological input data

Meteorological data from the Weather Research and Forecasting (WRF), implemented operationally by MeteoGalicia, were used to initialize surface winds and upper level meteorological fields in CALMET, instead of surface and upper air stations. The preparation of these meteorological inputs involved two steps:

1) The WRF model was initialized and run over the modelling period, this task is already performed as part of the operational setup executed twice a day by MeteoGalicia; 2) The fields required for CALMET were extracted from the WRF model standard output NETCDF files into CALMET readable files using the CALWRF preprocessor (version 1.1);

Figure 27.- Flowchart of meteorological modeling for Calmet

The resultant 3D meteorological dataset covering the period of interest was used to input the prognostic meteorological data into the CALMET model. A 3D.DAT file is described in Table 9.

Table 9. Data Records repeated for each grid cell in extraction subdomain in 3D.DAT MYR integer Year of MM5/WRF wind data (YYYY) MMO integer Month of MM5/WRF wind data (MM) MDAY integer Day of MM5/WRF wind data (DD) MHR integer Hour (GMT) of MM5/WRF wind data (HH) IX integer I-index (X direction) of grid cell JX integer J-index (Y direction) of grid cell PRES real sea level pressure (hPa) RAIN real total rainfall accumulated on the ground for the past hour (cm) SC integer snow cover indicator (0 or 1, where 1 = snow cover was determined to be present for the MM5 simulation) RADSW * real Short wave radiation at the surface (W/m**2) RADLW * real long wave radiation at the top (W/m**2) T2 * real Air temperature at 2 m (K), zero or blank if not exist Q2 * real Specific humidity at 2 m (g/kg), zero or blank if not exist WD10 * real Wind direction of 10-m wind (m/s), zero or blank if not exist WS10 * Real Wind speed of 10-m wind (m/s), zero or blank if not exist SST * real Sea surface temperature (K), zero or blank if not exist format (i4,3i2,2i3,f7.1,f5.2,i2,3f8.1,f8.2,3f8.1) PRES integer Pressure (in millibars) Z integer Elevation (meters above m.s.l.)

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TEMPK integer Temperature (K) WD integer Wind direction (degrees) WS real Wind speed (m/s) W real Vertical velocity (m/s) RH integer Relative humidity (%) VAPMR real Vapor mixing ratio (g/kg) CLDMR + real Cloud mixing ratio (g/kg) RAINMR + real Rain mixing ratio (g/kg) ICEMR + real Ice mixing ratio (g/kg) SNOWMR real Snow mixing ratio (g/kg) + GRPMR + real Graupel mixing ratio (g/kg) * Set to all zero if not existing in output of WRF or other models + Output for variables CLDMR, RAINMR, ICEMR, SNOWMR, GRPMR will be compressed using a negative number if ALL are zero. -5.0 represents all five variables are zero. (CALPUFF Modeling System Version 6 User Instructions April 2011)

The following three figures show the gridded receptors of WRF model (black crosses are the WRF grid points with resolution of 4 km) used on Calmet modeling domain for the three emergency episodes. As we can see, 4km-WRF Domain provides meteorological information for the whole Calmet model domain in the three cases. It might even be possible to increase number of km around the Calmet current domain.

Figure 28.- Gridded receptors from WRF model with Calmet domain (Kerosene spill in O Burgo estuary – A Coruña, September 2nd 2011)

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Figure 29.- Gridded receptors from WRF model with Calmet domain (Fire in frozen fish factory – Vigo, September 26th 2011)

Figure 30.- Gridded receptors from WRF model with Calmet domain (Wildfire in Poio- Pontevedra, October 15th 2011)

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6.4. CALMET Parameter Settings

There are numerous operating parameters that must be established for CALMET to help define how the meteorological data will be treated in the model. While CALMET provides default values for most parameters, there are several key parameters that require selection by the user. Below, we provide a brief description of several parameters and the value selected that was used during the three emergency episodes, these key CALMET parameters are provided in Table 10.

Table 10. CALMET Model Parameter Values or Options common for the three emergency episodes Parameters Values Parameter description PMAP Map Projection UTM IUTMZN UTM Zone 29 DATUM Datum code WGS-84 UTMHEM Hemisphere N NX Number of X 99 Grid cells NY Number of Y 99 Grid cells NZ Number of 9 vertical layers ZFACE Cell face heights 0,20,100,300,500,750,1000,2000,3000,4000 (m) DGRIDKM Grid Spacing 1 (km) TERRAD 10 km Radius of influence of terrain features like hills or valleys on local winds NSSTA 0 Number of surface met stations NUSTA 0 Number of upper-air met stations NOWSTA 0 Number of overwater met stations NPSTA 0 Number of precipitation stations NM3D 1 Number of prognostic data files ISTEPPGS 3600 Timestep in prognostic data files (s) IPROG 14 Use gridded prognostic winds as Initial Guess Field ISURFT -1 Surface temperature is 2D interpolated field IUPT -1 Lapse rate is 2D interpolated field IUPWND -1 Initial guess wind from 3D interpolated field IWFCOD 1 (Default) Use Diagnostic Wind Model IFRADJ 1 (Default) Compute Froude number adjustment IKINE 0 (Default) Do Not compute kinematic effects IOBR 0 (Default) Do Not use OBrien adjustment to w-velocities ISLOPE 1 (Default) Compute slope flow effects ZIMIN 50 (Default) MIN over-land mixing height (m) ZIMAX 3000 (Default) MAX over-land mixing height (m) ZIMAXW 3000 (Default) MAX overwater mixing height (m)

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DZZI 200 Depth of layer above current conv. mixing height for (Default) computing lapse rate (m) DPTMIN 0.001 (Default) MIN potential temp lapse rate above the current conv. mixing height (K/m) HAFANG 30 (Default) Half-angle of upwind-looking cone averaging (deg) ILEVZI 1 (Default) Layer for wind used in upwind averaging IAVEZI Calculated Spatial averaging of mixing heights MNMDAV 5 Search radius for mixing ht averaging (cells) TGDEFB -0.0098 Default lapse rate below current conv. mixing ht over water (K/m) TGDEFA -0.0045 Default lapse rate above current conv. mixing ht over water (K/m) IRAD 1/R Type of temperature interpolation IAVET Calculated Spatial averaging of temperatures

The specifications of the modeled CALMET grid are summarized below in Table 11. Graphical representations of the modelling domain are provided in Figures 31 to 33. The black points are the CALMET grid points with 1 km of horizontal resolution, and pink triangles mark the source emission location.

Table 11. Grid Parameters for CALMET Grid cell Kerosene spill in Fire in frozen fish Wildfire in Poio- O Burgo estuary – factory – Vigo, Pontevedra, A Coruña, Sept 2nd September 26th October 15th 2011 2011 2011 XORIGKM* 500 471 477 YORIGKM* 4745 4624 4649 *XORIGKM: Southwest grid cell X Easting coordinate (km) *YORIGKM: Southwest grid cell Y Northing coordinate (km)

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Figure 31.- CALMET and CALPUFF Modelling Domain (Kerosene spill in O Burgo estuary – A Coruña, September 2nd 2011)

Figure 32.- CALMET and CALPUFF Modelling Domain (Fire in frozen fish factory – Vigo, September 26th 2011)

Figure 33.- CALMET and CALPUFF Modelling Domain (Wildfire in Poio-Pontevedra, October 15th 2011)

The CALMET computational domain for the three emergency episodes consists of a uniform horizontal grid with a grid cell size of 1.0 km. This resolution was used for this application to better depict the variance in meteorological conditions created by the landsea interface and the coastal marine environment. It extends out to approximately 45 km from source emission point. In the vertical, a stretched grid is used with fine resolution in the lower layers in order to resolve the mixed layer and a somewhat

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coarser resolution aloft. Nine vertical layers are used that are centered at 10, 60, 200, 400, 625, 875, 2050, 2500, and 3500 meters. This horizontal and vertical grid structure was chosen to provide a detailed fine-scale representation of terrain effects. There are significant topographical features in the area that affect the wind flow and offer the potential for plume-terrain interaction. Peak terrain heights are over 300-500 meters in the area surrounding the emergency episodes. The base elevation of the three emergency episodes is 1, 2 and 100 meters MSL. Therefore, complex terrain effects, in terms of both low-level wind flow channeling as well as terrain-plume interaction effects, are potentially important.

6.5. WRF and CALMET Output Analysis

Meteorological data such as mixing heights, stability and winds determine the transport and dispersion of pollutants within the CALPUFF model. Hourly three dimensional meteorological fields for the emergency day were prepared using the CALMET model based on WRF forecasts, and this information was provided to the CALPUFF model.

The following subsections present a summary of the WRF run output, and CALMET modelling predictions at both near the three emergency locations as well as over the CALMET model domain.

6.5.1. Winds near the emergency locations

In the next figures, wind rose diagrams are plotted at three meteorological stations near the locations of emergency (read subsections 4.1.3, 4.2.3 and 4.3.3 for more information about these surface weather stations), with WRF run output and hourly- CALMET-output winds at the model grid cell nearest to the meteorological stations. The wind data presented has been extracted from 3D.DAT (WRF model) and CALMET model level which correspond to meteorological station height, and spans all hours in the modelling period (1 day of emergency).

For ease of comparison, the wind roses have been plotted with sixteen wind directions, with each barb on the rose representing winds blowing from ±22.5º from the barb direction.

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A Coruña- dique met station Wrf model output at the met. station Calmet model output at the met. station

Figure 34.- Wind roses plotted at A Coruña – dique met. station during kerosene-spill incident. From left to right, plots are for observations, WRF and CALMET.

Figure 34 shows plots for A Coruña-Dique meteorological station, the closest station to the kerosene spill with wind data information situated less than 6 km northwest of the emergency location, used to compare the performance of the CALMET run and WRF run output. At the meteorological station of Figure 34, the predicted WRF winds reproduce the observed winds coming from the northwest very well, while the south-easterly winds are shifted slightly south. These small differences in wind direction observed in WRF are slightly corrected in the CALMET runs. The modelling tends to under predict the frequency of winds from North-West and over predict the southerly winds.

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Vigo – Illas Marinas met. station Wrf model output at the met. station Calmet model output at the met. station

Figure 35.- Wind roses plotted at Vigo- Illas Marinas met. station during the fire in a frozen fish factory (Vigo). From left to right, plots are for observations, WRF and CALMET.

Figure 35 shows plots for Vigo – Illas Marinas meteorological station, the closest station to the fire in a frozen fish factory (Vigo) with wind data information situated less than 1 km west of the emergency location. At this meteorological station, the predicted WRF winds don’t reproduce the observed winds coming from the southwest well, the south-westerly winds are splitted in two directions: west and south-east. These differences in wind module and direction observed in WRF are corrected in the CALMET runs, increasing the wind direction frequency distribution to the west, and decreasing the wind speed in the south-easterly direction. The WRF is not able to reproduce the local wind regime probably due to its 4Km resolution however CALMET is able to reproduce the local wind regime based on the topographic effects. The modelling tends to under predict the frequency of winds from south–west and over predict south-easterly winds.

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Mount Castrove met. station Wrf model output at the met. station Calmet model output at the met. station

Figure 36.- Wind roses plotted at Mount Castrove met. station during forest fire in Poio (Pontevedra). From left to right, plots are for observations, WRF and CALMET.

Figure 36 shows plots for Mount Castrove meteorological station, the closest station to the forest fire in Poio (Pontevedra) with wind data information situated less than 4 km northwest of the emergency location. At this meteorological station, the predicted WRF winds reproduce the observed winds coming from the northeast very well, but weak winds from North-north-east and East-north-east were not well represented. These weak winds not observed in WRF are corrected in the CALMET runs, where the complex topography and sea breeze influences have been included. The modelling tends to under predict wind speed from the north-east and over predict the frequency of winds from the west and south-west.

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The comparison shows that the WRF/CALMET modelling does a reasonable job in capturing the dominant wind directions, as influenced by both synoptic and boundary layer (lake breeze) influences, over the emergency period.

Next, wind speed is plotted as a function of wind direction at three meteorological stations compared with the CALMET output for the meteorological stations locations, shown on Figures 37 to 39. This graphical representation can help to better understand the wind roses that were displayed before.

Figure 37.- Wind speed as a function of wind direction at A Coruña-Dique met. station vs. Calmet during kerosene spill.

The average wind speed at A Coruña – Dique met. station on September 2nd 2011 is 2.13 m/s, which corresponds to light breeze according Beaufort scale. For the whole day, northwesterly wind direction dominates and is the strongest on average.

Figure 38.- Wind speed as a function of wind direction at Vigo-Illas Marinas met. station during fire in frozen fish factory (Vigo).

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The average wind speed at Vigo- Illas Marinas met. station on September 26th 2011 is 1.52 m/s, which corresponds to light air according Beaufort scale. For the whole day, two wind direction dominate, southwesterly and northeasterly, but southwesterly wind is the strongest on average.

Figure 39.- Wind speed as a function of wind direction at Mount Castrove met. station during forest fire in Poio (Pontevedra).

The average wind speed at Mount Castrove met. station on October 15th 2011 is 5.75 m/s, which corresponds to moderate breeze according Beaufort scale. For the whole day, northeasterly wind direction dominates with high values of wind speeds.

6.5.2. Wind Vector Diagrams

Wind vector plots are displayed to provide an overview of how the wind fields predicted by CALMET vary across the model domain vertically and horizontally. The vector plots were selected to demonstrate the wind variation that can occur across the study domain during a given hour. In these diagrams, an arrow is shown to represent the direction and velocity of the wind for each meteorological grid cell. The direction of the arrow indicates the direction that the wind is blowing towards and the length of the arrow indicates the relative wind speed.

As we can see in the next figures, wind vectors at 10 meters above the surface are strongly influenced by terrain features, wind vectors at 600-800 meters are lightly influenced by topography and wind vectors at 3500 meters are not almost affected by the surface terrain, showing wind direction of synoptic conditions.

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Kerosene spill incident

An example of the wind vector plots on September 2nd, 2011 at 1300 LST is provided in Figure 40 for the surface (10 m), 875 m and 3500 m winds, respectively.

Right-picture of this figure shows surface winds on a warm summer afternoon. Winds are moderate in magnitude during this period, less than 5 m/s across the model domain. This combination of lower wind speeds and the differential in surface heating between the land and water can cause the formation of a local pressure gradient along the coastline which can give rise to a lake breeze onto the shore. During this particular time, the wind orientation is predominantly from the west over the sea, becoming more north-westerly as it gets closer to the coastline.

Wind vector at 10 meters (first layer) Wind vector at 875 meters (6th layer) Wind vector at 3500 meters (9th layer)

Figure 40.- Illustrations of the Wind Vectors during Kerosene spill incident (A Coruña).

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Fire in frozen fish factory (Vigo)

Wind vector plots for this case are provided in Figure 41. Several graphs at 14 LST are displayed for the surface (10 m), 625 m and 3500 m winds, respectively.

In this emergency episode, surface winds were highly variable showing a lot of phenomena related with topographic effects such as mountain/hill blocking, channeling, and valley flows. In the Miño river valley and the estuaries called “Rias Baixas”, the winds showed the channeling effects. In addition, the plot shows how the wind field converges in valley locations and diverges as it meets mountains and hills.

Wind vector at 10 meters (first layer) Wind vector at 625 meters (5th layer) Wind vector at 3500 meters (9th layer)

Figure 41.- Illustrations of the Wind Vectors during fire in frozen fish factory (Vigo).

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Forest fire in Poio (Pontevedra)

The last example of wind vector plots is provided in Figure 42. A forest fire occurred on October 15th, 2011. Several graphs at 13 LST are displayed for the surface (10 m), 625 m and 3500 m winds, respectively.

Wind vector at 10 meters (first layer) Wind vector at 625 meters (5th layer) Wind vector at 3500 meters (9th layer)

Figure 42.- Illustrations of the Wind Vectors during forest fire in Poio (Pontevedra).

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These plots demonstrate the complexity of the winds within the modeling domain and verify the reasonableness of the selection of CALMET/CALPUFF model.

6.5.3. Mixing Heights at the emergency location

Mixing height (also called mixing depth) is the height above ground level through which relatively vigorous vertical mixing occurs. Low mixing heights mean that the air is generally stagnant with very little vertical motion; pollutants usually are trapped near the ground surface. High mixing heights allow vertical mixing within a deep layer of the atmosphere and good dispersion of pollutants. Vertical Pollutant Dispersion is largely a function of mixing height. The CALMET model calculates a maximum mixing height, as determined by either convective or mechanical forces. The convective mixing height is the height to which an air package will rise under the buoyant forces created by the heating of the Earth’s surface. The convective mixing height is dependent on solar radiation amount, wind speed, as well as the vertical temperature structure of the atmosphere. Mechanical mixing heights are, similarly, the height to which an air package will rise under the influence of mechanical-invoked turbulence. The mechanical mixing height is proportional to low-level wind speeds and surface roughness.

The height of the mixing layer is an extremely important factor in determining the dispersion of pollution in the atmosphere. The mixing heights under different stability conditions are estimated through different methods based on either surface heat flux (i.e., thermal turbulence) and vertical temperature profile, or friction velocity (i.e., mechanical turbulence). Diurnal variations of mean mixing height, as estimated by the CALMET model near the emergency location are shown on the right-pictures of Figures 43, 44 and 45.

Model mixing heights can vary from several meters to several thousand meters, depending on the intensity of solar radiation and wind speed. Daytime mixing heights are generally greater during the summer and fall, than during the winter and spring due to different surface radiation budgets. Also overwater conditions can influence the mixing height, that effect is showed clearly on the right-pictures of Figures 44 and 45, moderating the mixing heights during fire in frozen fish factory (Vigo) and a forest fire in Pontevedra.

There are important differences in the structures of the marine and continental boundary layers which can have significant effects on plume dispersion in the overwater and coastal environments. These differences arise for three basic reasons:

 Water has a high heat capacity and is partially transparent to solar radiation, resulting in a relatively small diurnal temperature range (~ 0.5 deg. C).

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 The sea surface is generally more uniform and less aerodynamically rough than typical land surfaces.  There is a constant source of moisture in the marine boundary layer.

As a result of these differences, the sensible heat flux over the open water is typically more than an order of magnitude less than over land. The absence of a strong sensible heat flux to drive the marine mixed-layer and the small surface roughness result in relatively low mixing heights that offer the potential for significant plume trapping effects. LeMone (1978) indicates that the typical marine mixing depth is only about 500 m. Data from three offshore and coastal experiments reported by Hanna et al. (1985) (two of which were conducted in California) show many hours with mixing heights less than 100 m. Another result is that the diurnal and annual variations of stability over water are completely unrelated to the typical overland behavior.

The techniques used in the CALMET meteorological model for determining overwater mixing height, stability, and surface layer parameters are based on the air-sea temperature difference, wind speed, and the specific humidity. These methods are applied by CALMET to the portions of the modeling domain over water. At the land-sea interface, rapid changes in the dispersion characteristics may occur which can significantly affect the ground-level concentrations from coastal sources. The puff model formulation is well-suited to accommodate these spatial changes in the coastal transition zone.

In order to validate the model behaviour, comparisons with the radiosonde launched at A Coruña site2, owned by AEMET, have been done and will be presented on the left- pictures of Figures 43, 44 and 45.

Extracted Period: 02 Sep-2011, 12:00:00 AM To 03 Sep-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: MIXHGT ( 0.000 m) MIXHGT hourly values

800

700

600

500

400

300

MIXHGT (METERS) MIXHGT 200

100

0

02/09/2011, 00:00:00 02/09/2011,

02/09/2011, 01:00:00 02/09/2011,

02/09/2011, 02:00:00 02/09/2011,

02/09/2011, 03:00:00 02/09/2011,

02/09/2011, 04:00:00 02/09/2011,

02/09/2011, 05:00:00 02/09/2011,

02/09/2011, 06:00:00 02/09/2011,

02/09/2011, 07:00:00 02/09/2011,

02/09/2011, 08:00:00 02/09/2011,

02/09/2011, 09:00:00 02/09/2011,

02/09/2011, 10:00:00 02/09/2011,

02/09/2011, 11:00:00 02/09/2011,

02/09/2011, 12:00:00 02/09/2011,

02/09/2011, 13:00:00 02/09/2011,

02/09/2011, 14:00:00 02/09/2011,

02/09/2011, 15:00:00 02/09/2011,

02/09/2011, 16:00:00 02/09/2011,

02/09/2011, 17:00:00 02/09/2011,

02/09/2011, 18:00:00 02/09/2011,

02/09/2011, 19:00:00 02/09/2011,

02/09/2011, 20:00:00 02/09/2011,

02/09/2011, 21:00:00 02/09/2011,

02/09/2011, 22:00:00 02/09/2011,

02/09/2011, 23:00:00 02/09/2011, Hours Figure 43.- Right: CALMET-Predicted diurnal mixing heights near the kerosene spill incident. Left: Radiosonde launched at A Coruña site (September 2nd, 2011)

2 http://weather.uwyo.edu/upperair/sounding.html

48

Extracted Period: 26 Sep-2011, 12:00:00 AM To 27 Sep-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: MIXHGT ( 0.000 m) MIXHGT hourly values

250

200

150

100

MIXHGT (METERS) MIXHGT

50

0

26/09/2011, 00:00:00 26/09/2011,

26/09/2011, 01:00:00 26/09/2011,

26/09/2011, 02:00:00 26/09/2011,

26/09/2011, 03:00:00 26/09/2011,

26/09/2011, 04:00:00 26/09/2011,

26/09/2011, 05:00:00 26/09/2011,

26/09/2011, 06:00:00 26/09/2011,

26/09/2011, 07:00:00 26/09/2011,

26/09/2011, 08:00:00 26/09/2011,

26/09/2011, 09:00:00 26/09/2011,

26/09/2011, 10:00:00 26/09/2011,

26/09/2011, 11:00:00 26/09/2011,

26/09/2011, 12:00:00 26/09/2011,

26/09/2011, 13:00:00 26/09/2011,

26/09/2011, 14:00:00 26/09/2011,

26/09/2011, 15:00:00 26/09/2011,

26/09/2011, 16:00:00 26/09/2011,

26/09/2011, 17:00:00 26/09/2011,

26/09/2011, 18:00:00 26/09/2011,

26/09/2011, 19:00:00 26/09/2011,

26/09/2011, 20:00:00 26/09/2011,

26/09/2011, 21:00:00 26/09/2011,

26/09/2011, 22:00:00 26/09/2011,

26/09/2011, 23:00:00 26/09/2011, Hours Figure 44.- Right: CALMET-Predicted diurnal mixing heights near the fire in frozen fish factory (Vigo). Left: Radiosonde launched at A Coruña site (September 26th, 2011)

Extracted Period: 15 Oct-2011, 12:00:00 AM To 16 Oct-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: MIXHGT ( 0.000 m) MIXHGT hourly values

350

300

250

200

150

MIXHGT (METERS) MIXHGT 100

50

0

15/10/2011, 00:00:00 15/10/2011,

15/10/2011, 01:00:00 15/10/2011,

15/10/2011, 02:00:00 15/10/2011,

15/10/2011, 03:00:00 15/10/2011,

15/10/2011, 04:00:00 15/10/2011,

15/10/2011, 05:00:00 15/10/2011,

15/10/2011, 06:00:00 15/10/2011,

15/10/2011, 07:00:00 15/10/2011,

15/10/2011, 08:00:00 15/10/2011,

15/10/2011, 09:00:00 15/10/2011,

15/10/2011, 10:00:00 15/10/2011,

15/10/2011, 11:00:00 15/10/2011,

15/10/2011, 12:00:00 15/10/2011,

15/10/2011, 13:00:00 15/10/2011,

15/10/2011, 14:00:00 15/10/2011,

15/10/2011, 15:00:00 15/10/2011,

15/10/2011, 16:00:00 15/10/2011,

15/10/2011, 17:00:00 15/10/2011,

15/10/2011, 18:00:00 15/10/2011,

15/10/2011, 19:00:00 15/10/2011,

15/10/2011, 20:00:00 15/10/2011,

15/10/2011, 21:00:00 15/10/2011,

15/10/2011, 22:00:00 15/10/2011,

15/10/2011, 23:00:00 15/10/2011, Hours Figure 45.- Right: CALMET-Predicted diurnal mixing heights near the forest fire in Poio (Pontevedra). Left: Radiosonde launched at A Coruña site (October 15th, 2011)

As we can see, the radiosonde shows an inversion layer in the three cases: around 780 meters above ground during kerosene spill incident, at 200 meters above ground during fire in fish frozen factory, and at 300 meters above ground during forest fire in Poio. These observations are consistent with the predicted peaks by the CALMET model as we can see on the right-pictures. As expected, maximum mixing heights are seen to occur during mid-afternoon hours when the effects of solar heating are greatest; while minimum mixing heights occur most frequently at night.

Extracting mixing heights from the CALMET outputs for the emergency sites is another means to evaluate the reasonableness of the wind fields generated by CALMET. For this comparison, the predicted mixing heights for the same hour (13:00 local standard time) within the domain are plotted as the contours as shown in Figures 46 through 48. Pink triangles mark the source emission location.

49

Figure 46.- Mixing Heights (m) in the CALMET modeling domain during kerosene spill in O Burgo estuary (A Coruña)

Figure 47.- Mixing Heights (m) in the CALMET modeling domain during fire in frozen fish factory (Vigo)

Figure 48.- Mixing Heights (m) in the CALMET modeling domain during forest fire in Poio (Pontevedra)

The CALMET predicted mixing heights range from about 100 m to 1000 m above the ground.

50

On an overall basis, the model-predicted mixing heights are relatively low due to the moderating influence of Atlantic Ocean. Lower mixing heights tend to inhibit dispersion of air pollutants and higher predicted ground-level concentrations (conservative).

6.5.4. Stability at the emergency location

Atmospheric turbulence near the earth’s surface is often described in terms of atmospheric stability, which is governed by both thermal and mechanical factors. Atmospheric stability can, very broadly, be classified as stable (suppress turbulent motions), neutral (tolerate, neither enhance nor suppress), or unstable (support turbulent motions). Under unstable conditions, a dispersing gas mixes rapidly with the air around it and Calpuff expects that the cloud will not extend as far downwind as it would under more stable conditions, because the pollutant is soon diluted.

Figure 49.- Stability class and mixing of a pollutant cloud.

Stability has a stronger influence in the vertical than in the horizontal, and we can see this effect with 5 diagrams of how plumes spread under different stabilities.

The first is the fanning plume, and on the left you can see that the dashed white line is the adiabatic lapse rate and the dark line shows that the actual lapse rate is very stable. A fanning plume tends to be very narrow in the vertical. Over a short period of time, it's also narrow in the horizontal, but as the wind direction fluctuates, it tends to spread out widely in the horizontal while staying very confined in the vertical.

51

Figure 50.- Fanning plume type under stable conditions.

The second is the lofting plume, where you have a stable layer underneath a neutral or unstable layer so the plume is lofted upward. It can't disperse downwards because of the inversion and stable layer, so you get this look with a flat bottom and a rising plume on the top.

Figure 51.- Lofting plume type with stable-neutral inversion layer.

The third plume type is called looping. Here you can see there is a super-adiabatic lapse rate from the ground up to plume height, and the plume goes rapidly up and down as it goes through thermals. The drawing is a snapshot, but if you looked at this over time, you would see the plume spread very widely over the vertical.

Figure 52.- Looping plume type under unstable conditions.

52

The coning plume occurs when there is roughly a neutral lapse rate from the surface well past plume height. Here the plumes grow gradually both upwards and downwards, resulting is this cone shape.

Figure 53.- Coning plume type under neutral conditions.

The last plume type is the fumigating plume. It's a special case of the fanning plume that goes through a transition. Imagine that a fanning plume, which is very stable and very confined, extends a significant distance out over the countryside. And then as mid- morning comes, the stable layer begins to erode and as it gets to the plume level, it mixes the plume down to the ground in a fairly concentrated amount. The important thing is to realize that this process can extend a concentrated plume a significant distance from the source and then rapidly mix it to the ground, which can be kind of a surprise.

Figure 54.- Fumigation plume type with neutral-stable inversion layer.

Stable atmospheric conditions occur when vertical motion in the atmosphere is suppressed. With respect to air quality, this means contaminants released near ground-level are not well-dispersed and are believed to have a larger incremental effect on local ambient levels. This type of situation frequently occurs at night, when the Earth’s surface emits thermal radiation and cools. Air in contact with the ground thus becomes cooler and denser than the air aloft. This phenomenon is referred to as a ground-based temperature inversion and is often associated with poor air quality conditions.

53

Unstable atmospheric conditions are also highly dependent on radiation at the earth’s surface, and most frequently occur during daytime hours. During such times, as short- wave energy from the sun heats the ground, air in contact with the ground becomes warmer and less dense than the air aloft. Subsequently, vertical motion in the atmosphere is enhanced and the atmosphere is said to be unstable.

When a balance exists between incoming and outgoing radiation, there is no net heating or cooling of the air in contact with the ground, and vertical motions of the atmosphere are neither enhanced nor suppressed. Such an atmosphere is described as neutral and exists during overcast skies or during transition from unstable to stable conditions.

Mechanical mixing, which is mostly a function of lower level wind speeds (and surface roughness), can also influence atmospheric stability. Higher wind speeds (and a greater surface roughness) promote higher levels of turbulence in the region of discussion. This, in turn, leads to more mechanical mixing, which means that the atmosphere becomes more unstable. Mechanical mixing plays a more important role in determining stability when wind speeds are very high and at night, when convective vertical motion is suppressed.

The relative stability of the Earth’s boundary layer is often expressed in terms of the Pasquill-Gifford- Turner (PGT) Stability Classes (Pasquill, 1961). These classes are shown in the table below:

Table 12. The Pasquill stability classes Stability Definition Stability class Definition class A very unstable D neutral B unstable E slightly stable slightly C F stable unstable

Normally, unstable conditions are associated with daytime ground-level heating, which produces thermal turbulence in the boundary layer. Stable conditions are primarily associated with night time cooling which suppresses the turbulence levels and produces temperature inversions at lower levels. Neutral conditions are mostly associated with high wind speeds and overcast sky conditions.

54

Table 13. Meteorological conditions that define the Pasquill stability classes Surface wind Daytime incoming solar Night time conditions speed radiation Thin overcast or <3/8 m/s Strong Moderate Slight >4/8 low clouds Cloudiness < 2 A A – B B E F 2 – 3 A – B B C E F 3 – 5 B B – C C D E 5 – 6 C C – D D D D > 6 C D D D D

NOTES: Strong insolation corresponds to sunny, midday, midsummer conditions in England; slight insolation corresponds to similar conditions in midwinter. Night refers to the period from one hour after sunrise. The neutral category, D, should be used regardless of wind speed, for overcast conditions during day or night.

The CALMET predictions of atmospheric stability at the grid cell closest to the site location are shown in Table 14. Atmospheric conditions near the emergency location are shown to be unstable, neutral and stable 50%, 4.17% and 45.8% of the time, respectively, for the kerosene spill incident, and neutral for the other emergency episodes. The high proportion of neutral conditions can be attributed to the moderating influence of Atlantic Ocean.

Table 14. CALMET-Predicted Atmospheric Stability at the emergency locations PG Class Kerosene spill in Fire in frozen fish Wildfire in Poio- O Burgo estuary – factory – Vigo, Pontevedra, A Coruña, Sept 2nd September 26th October 15th 2011 2011 2011 Frequency (%) Frequency (%) Frequency (%) A (Very Unstable) 4.17% B (Moderately 16.7% Unstable) C (Slightly 29.17% Unstable) D Neutral 4.17% 100 % 100% E (Slightly Stable) 0% F (Moderately 45.83% Stable)

55

Different graphical outputs are displayed below. These graphs show the hourly PG stability class determined by CALMET for the emergency locations. Levels 1,2,3,4,5 and 6, correspond to A,B,C,D,E,F respectively.

During kerosene spill incident, peaks of predicted concentration were produced when stability changes from stable to unstable and vice versa took place, as we can see in figures 55 and 56.

Extracted Period: 02 Sep-2011, 12:00:00 AM To 03 Sep-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: STAB_CLASS ( 0.000 m) STAB_CLASS hourly values

7

6

5

4

3

2

STAB_CLASS (CLASS) STAB_CLASS

1

0

02/09/2011, 00:00:00 02/09/2011,

02/09/2011, 01:00:00 02/09/2011,

02/09/2011, 02:00:00 02/09/2011,

02/09/2011, 03:00:00 02/09/2011,

02/09/2011, 04:00:00 02/09/2011,

02/09/2011, 05:00:00 02/09/2011,

02/09/2011, 06:00:00 02/09/2011,

02/09/2011, 07:00:00 02/09/2011,

02/09/2011, 08:00:00 02/09/2011,

02/09/2011, 09:00:00 02/09/2011,

02/09/2011, 10:00:00 02/09/2011,

02/09/2011, 11:00:00 02/09/2011,

02/09/2011, 12:00:00 02/09/2011,

02/09/2011, 13:00:00 02/09/2011,

02/09/2011, 14:00:00 02/09/2011,

02/09/2011, 15:00:00 02/09/2011,

02/09/2011, 16:00:00 02/09/2011,

02/09/2011, 17:00:00 02/09/2011,

02/09/2011, 18:00:00 02/09/2011,

02/09/2011, 19:00:00 02/09/2011,

02/09/2011, 20:00:00 02/09/2011,

02/09/2011, 21:00:00 02/09/2011,

02/09/2011, 22:00:00 02/09/2011,

02/09/2011, 23:00:00 02/09/2011, Hours

Figure 55.- CALMET-Predicted PG stability classes near the kerosene spill incident

Figure 56.- Predicted SO2 concentrations at each receptor (ug/m3) related to stability during kerosene spill

The fire in the frozen fish factory of Vigo started in the morning when the atmosphere was neutral and the winds were light. This meteorological situation continued all day, and that was the reason why hot plume rose and passed over the city of Vigo. Figure

56

58 shows a schematic diagram of this explanation, and Figure 59 shows the predicted concentration during two different hours of the emergency episode

Extracted Period: 26 Sep-2011, 12:00:00 AM To 27 Sep-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: STAB_CLASS ( 0.000 m) STAB_CLASS hourly values

4.5

4

3.5

3

2.5

2

1.5

STAB_CLASS (CLASS) STAB_CLASS 1

0.5

0

26/09/2011, 00:00:00 26/09/2011,

26/09/2011, 01:00:00 26/09/2011,

26/09/2011, 02:00:00 26/09/2011,

26/09/2011, 03:00:00 26/09/2011,

26/09/2011, 04:00:00 26/09/2011,

26/09/2011, 05:00:00 26/09/2011,

26/09/2011, 06:00:00 26/09/2011,

26/09/2011, 07:00:00 26/09/2011,

26/09/2011, 08:00:00 26/09/2011,

26/09/2011, 09:00:00 26/09/2011,

26/09/2011, 10:00:00 26/09/2011,

26/09/2011, 11:00:00 26/09/2011,

26/09/2011, 12:00:00 26/09/2011,

26/09/2011, 13:00:00 26/09/2011,

26/09/2011, 14:00:00 26/09/2011,

26/09/2011, 15:00:00 26/09/2011,

26/09/2011, 16:00:00 26/09/2011,

26/09/2011, 17:00:00 26/09/2011,

26/09/2011, 18:00:00 26/09/2011,

26/09/2011, 19:00:00 26/09/2011,

26/09/2011, 20:00:00 26/09/2011,

26/09/2011, 21:00:00 26/09/2011,

26/09/2011, 22:00:00 26/09/2011,

26/09/2011, 23:00:00 26/09/2011, Hours

Figure 57.- CALMET-Predicted diurnal PG stability classes near the fire in frozen fish factory (Vigo)

Figure 58.- Schematic diagram of plume rise in light winds with neutral stability

57

Figure 59.- Predicted NOx concentrations at each receptor (ug/m3) related to stability during fire in frozen fish factory (Vigo)

Finally, the forest fire in Poio started at night with light winds and neutral stability class, but during the morning winds increased in intensity slightly, so the plume laid down much closer to the ground because the plume rise was lower, and the ground-level concentration predicted was high the whole day.

Extracted Period: 15 Oct-2011, 12:00:00 AM To 16 Oct-2011, 12:00:00 AM Percentage of missing values allowed by averaging period: 0 % Variables extraction height: STAB_CLASS ( 0.000 m) STAB_CLASS hourly values

4.5

4

3.5

3

2.5

2

1.5

STAB_CLASS (CLASS) STAB_CLASS 1

0.5

0

15/10/2011, 00:00:00 15/10/2011, 01:00:00 15/10/2011, 02:00:00 15/10/2011, 03:00:00 15/10/2011, 04:00:00 15/10/2011, 05:00:00 15/10/2011, 06:00:00 15/10/2011, 07:00:00 15/10/2011, 08:00:00 15/10/2011, 09:00:00 15/10/2011, 10:00:00 15/10/2011, 11:00:00 15/10/2011, 12:00:00 15/10/2011, 13:00:00 15/10/2011, 14:00:00 15/10/2011, 15:00:00 15/10/2011, 16:00:00 15/10/2011, 17:00:00 15/10/2011, 18:00:00 15/10/2011, 19:00:00 15/10/2011, 20:00:00 15/10/2011, 21:00:00 15/10/2011, 22:00:00 15/10/2011, 23:00:00 15/10/2011, Hours

Figure 60.- CALMET-Predicted diurnal PG stability classes near the forest fire in Poio (Pontevedra)

58

Figure 61.- Schematic diagram of plume rise in strong winds with neutral stability

Figure 62.- Predicted PM10 concentrations at each receptor (ug/m3) related to stability during forest fire in Poio (Pontevedra)

7. CALPUFF Modelling

7.1. Model Description and Options

The following description of the CALPUFF model’s major algorithms and options are all excerpts from the CALPUFF model’s user manual.

The CALPUFF model is a multi-layer, multi-species, non-steady-state Gaussian puff dispersion model used for a wide variety of air quality modeling studies, including:

1. Near-field impacts in complex flow or dispersion situations a. complex terrain b. stagnation, inversion, recirculation, and fumigation conditions c. overwater transport and coastal conditions d. light wind speed and calm wind conditions 2. Long range transport 3. Visibility assessments and Class I area impact studies

59

4. Criteria pollutant modeling, including application to State Implementation Plan (SIP) development 5. Secondary pollutant formation and particulate matter modeling 6. Buoyant area and line sources (e.g., forest fires and aluminum reduction facilities) 7. Toxic pollutant deposition,

CALPUFF has been recommended for use by the U.S. EPA (U.S. EPA 1999) in its Guideline on Air Quality Models specifically for long-range transport (i.e., greater than 50 kilometers [km]) of air pollutants and associated effects. The CALPUFF model was developed with the following objectives:  consideration of time varying point, line, area and volume sources;  suitability for modelling domains ranging from tens of meters to hundreds of kilometers from a source;  prediction of averages ranging from 1 hour to 1 year;  incorporation of building downwash effects, modeled by BPIP or BPIP-PRIME;  incorporation of horizontal and vertical wind shear effects  applicability to inert pollutants and those subject to linear removal and chemical conversion mechanisms;  applicability to complex terrain scenarios.

See below the technical specifications for the CALPUFF modeling system:

60

Table 15. Technical Specifications for Calpuff Parameter Description Model Name CALPUFF Model Type Non-steady state Gaussian Puff model Consultants, industries, authorities, research and educational User community establishments. Control file inputs: -Geophysical and hourly meteorological data, created by the Input data CALMET meteorological model -Emissions data for point, line, area or volume sources with time- varying emission parameters. CALPUFF produces files of hourly concentrations of ambient concentration s for each modeled species, wet deposition fluxes, Output data dry deposition fluxes, and for visibility applications, extinction coefficients. Postprocessing programs (PRTMET and CALPOST) provide options for analysis and display of the modeling results. Time Step 1 hour (version 5.8), variable down to the second (version 6.0) Terrain Elevated, processed by TERREL Source Types Point, area, volume, line Hourly surface, upper air and precipitation data and/or prognostic Meteorology data (e.g. MM5 or WRF) Wind Field Three dimensional Release Types Buoyant or neutrally buoyant plumes Emission Types Constant or time-varying, planned or fugitive Atmospheric MESOPUFF II, RIVAD/ARM3, SOA, ISORROPIA and user Chemistry specified transformation rates Heavier-than-air releases are not considered. The model does not Model limitations compute the area of impact above ERPG (Emergency Response Planning Guideline) level. Preferred US EPA regulatory model for long-range and visibility Regulatory Status applications. Also used for complex wind field scenarios

The major features and options of the CALPUFF model are briefly described below.

 Chemical Transformation CALPUFF includes options for parameterizing chemical transformation effects using the five species scheme (SO2, SO, NOx, HNO3, and NO) employed in the MESOPUFF II model, the six species RIVAD/ARM3 scheme, RIVAD+ISORROPIA, or a set of user-specified,

61

diurnally-varying transformation rates. Calculations of chemical transformations require, among other information, a knowledge of background concentrations of ozone and ammonia.  Subgrid Scale Complex Terrain The complex terrain module in CALPUFF is based on the approach used in the Complex Terrain Dispersion Model (CTDMPLUS) (Perry et al,. 1989). Plume impingement on subgrid scale hills is evaluated using a dividing streamline (Hd) to determine which pollutant material is deflected around the sides of a hill (below Hd) and which material is advected over the hill (above Hd). Individual puffs are split into up to three sections for these calculations.  Puff Sampling Functions A set of accurate and computationally efficient puff sampling routines are included in CALPUFF which solve many of the computational difficulties with applying a puff model to near-field releases. For near-field applications during rapidly varying meteorological conditions, an elongated puff (slug) sampling function can be used. An integrated puff approached is used during less demanding conditions. Both techniques reproduce continuous plume results exactly under the appropriate steady state conditions.  Wind Shear Effects CALPUFF contains an optional puff splitting algorithm that allows vertical wind shear effects across individual puffs to be simulated. Differential rates of dispersion and transport occur on the puffs generated from the original puff, which under some conditions can substantially increase the effective rate of horizontal growth of the plume.  Building Downwash The Huber-Snyder and Schulman-Scire downwash models are both incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been implemented in such a way as to allow the use of wind direction specific building dimensions.  Over water and Coastal Interaction Effects Because the CALMET meteorological model contains both over water and over land boundary layer algorithms, the effects of water bodies on plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion conditions, including the abrupt changes that occur at the coastline of a major body of water.  Dispersion Coefficients Several options are provided in CALPUFF for the computation of dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion equations based on the Complex Terrain Dispersion Model (CTDM). Options are provided to apply an averaging time correction or surface roughness length adjustment to the PG coefficients.

62

 Dry Deposition A full resistance model is provided in CALPUFF for the computation of dry deposition rates of gases and particulate matter as a function of geophysical parameters, meteorological conditions, and pollutant species. Options are provided to allow user-specified, diurnally varying deposition velocities to be used for one or more pollutants instead of the resistance model (e.g., for sensitivity testing) or to by-pass the dry deposition model completely.  Wet Deposition An empirical scavenging coefficient approach is used in CALPUFF to compute the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. liquid precipitation).  Interface to the Fire Emissions Production Model (FEPS) Time-varying heat flux and emissions from controlled burns and wildfires

7.2. Model Initialization

7.2.1. Dispersion model options

The CALPUFF computational domain is the area in which the transport and dispersion of puffs are considered for the calculation of ground level concentrations. Dispersion modelling was conducted using CALPUFF over a computational domain equal to the CALMET meteorological grid defined before.

All the CALPUFF input and output files, showing the model options selected for this study, are provided in attached files. Note that the parameterization provided in these files represents a specific emissions emergency scenario used to model specific air contaminants over a particular receptor grid. Therefore, case-specific model parameters (i.e., the number and type of sources modeled and species considered) would have different values for different model runs.

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Table 16. Summary of CALPUFF input parameters common for the three emergency episodes Parameters Values Parameter Description ICON 1 (yes, create CONC.DAT) Creation of an output disk file (CONC.DAT) containing concentration fields METRUN Running all periods in met. 1 (yes) file? METFM CALMET.DAT Meteorological data format MGAUSS 1 (Always use) Vertical distribution used in the near field MCHEM 0 (Not modeled) Chemical Transformation MWET 0 (Not modeled) Wet removal modeled MDRY 0 (Not modeled) Dry Deposition MSPLIT 0 (No puff splitting, Puff Splitting allowed recommended in short range modelling) MBDW 1 (ISC/BLP downwash Method to simulate building downwash method) MDISP 3 (PG rural ISC curves and Dispersion coefficients MP coeff in urban areas) MTRANS 1 (yes, computed) Transitional plume rise modeled? MTIP 1 (yes) Stack tip downwash modeled? MPARTL 1 (yes) Partial plume penetration of elevated inversion? MSLUG 0 (No) Near-field puffs are modeled as elongated slugs? MCTSG 0 (not modeled) Calpuff subgrid scale complex terrain module (CTSG) MCATDJ 3 (partial plume path Terrain adjustment method adjustment) XMXLEN Maximum length of emitted 1 (to allow the wind channeling effects to puffs (in met. grid units) be accounted for in the puff trajectory calculations) XSAMLEN Maximum travel distance of 1 (to allow the wind channeling effects to a puff (in met. grid units) be accounted for in the puff trajectory calculations) WSCALM 0.5 Minimum wind speed (m/s) for non-calm conditions XMAXZI 3000 Maximum mixing height (m) XMINZI 50 Minimum mixing height (m) NESPEC Number of chemical Depends on the emergency case species CSPEC Species modeled SO2,PM10,CO,CH4,Toluene, Xylene, NOx

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7.2.2. Emissions and Source Characteristics

CALPUFF was used to model the dispersion of emissions from three emergency scenarios to assess potential changes in air quality. It should be noted that for the modelling done in this study, emission rates were estimated based on a combination of emission factors, engineering estimates and emission models. Actual emissions can vary from hour to hour and day to day. Emissions from the emergency scenarios employed a conservative maximum hourly emissions approach, which is expected to over-estimate longer-term averaging periods. Because of the nature of this approach, there is a high degree of confidence that emissions are over-estimated and include considerable conservatism.

Source emission information for these three cases is listed in the following tables.

Table 17. Area Source Parameters and Emission Rates for the Kerosene spill Source UTM-29 UTM-29 UTM-29 UTM-29 Release Base SO2 TOLUENE XYLENE Description Beg. X Beg. Y End X End Y Height Elevation Emission Emission Emission coord coord coord coord (m) (m) Rate Rate Rate (km) (km) (km) (km) (kg/h/m2) (kg/h/m2) (kg/h/m2) Area 551.888 4795.892 551.908 4795.902 0 1 1.70E-02 3.44E-03 4.05E-03

Table 18. Point Source Parameters and Emission Rates for Fire in frozen fish factory (Vigo) Source UTM-29 X UTM-29 Y Stack Base Stack Exit veloc. Exit Temp NOx Description coord coord (km) Height Elevation Diameter (m/s) (K) Emission (km) (m) (m) (m) Rate (g/s) Point time- 521.528 4674.87 20 2 100 2.10 453.15 332 variant (PTEMARB.DAT)

During the forest fire in Poio, the characterization of the emission source to model the cloud of smoke was obtained from the emission model FEPS (Fire Emission Production Simulator Model). More information about this model is on Appendix H.

Once the emissions report was obtained from FEPS model, Calpuff model is able to use this information to define the source by means of FEPS2BAEM, this is a conversion utility which creates a time-varying emissions file for buoyant forest fire area sources based on the output from the FEPS model. Arithmetic mean of the emission rate is shown in the next table.

Table 19. Area Source Parameters and Emission Rates for the forest fire (Pontevedra) Source UTM-29 UTM-29 UTM-29 UTM-29 Releas Base CO PM10 CH4 Description Beg. X Beg. Y End X End Y e Elevatio Emissio Emissio Emissio coord coord coord coord Height n (m) n Rate n Rate n Rate (km) (km) (km) (km) (m) (g/s) (g/s) (g/s) Area time- 526.963 4698.206 527.437 4699.108 10 100 From 0 to From 0 to From 0 to variant 3 3 5 9 96261 7713 4504 (BAEMARB.DAT )

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8. Ground Level Concentration Predictions: RESULTS

8.1. Ambient Standards

CALPUFF was used to predict maximum short-term ground-level concentrations at receptor locations for input into the human health assessment.

The various health related limits and levels for the legislated pollutants are provided in Table 17. These limits and guidelines are from European Union directives. These include the limit values, target values, the assessment threshold values, the long term objectives and the information and alert thresholds. Both short-term (1h and 24h) and long-term (annual) averages are covered.

Table 20. Summary of the air quality directive limit, target, assessment threshold, long term objective, information threshold and alert threshold values for the protection of human health. Source: European Directive 2008/50/EC ambient air quality and cleaner air for Europe, and European Directive 2004/107/CE on heavy metals and PAH.

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Note also, that some limits are allowed to be exceeded a specified number of times per year.

8.2. Concentrations predicted for kerosene spill incident

The air quality analysis was performed in two steps. First, the maximum predicted concentrations resulting from the emergency location emissions are evaluated and compared to the corresponding guideline concentrations, if that limit exists. If the predicted maximum concentrations exceed the guideline limits, then a second step is taken in order to determine the number of averaging periods for which the guideline limit values are exceeded. This number is then compared with the number of exceedances allowed.

Table 21 summarizes the predicted number of exceedances near the kerosene spill for all modeled species. Runs using estimated emission rates evaluate the impacts of SO2, Xylene and Toluene for all regulated averaging time periods. The model results show that the predicted impacts from the kerosene spill will be in compliance with the applicable standards and guidelines.

Table 21. Summary of CALPUFF Modeling Results for Kerosene spill incident Parameter Averaging Limit Number of Number of Maximum period value Exceedances Exceedances peak Allowed predicted predicted on a grid point (ug/m3) SO2 Hourly mean 350 24 hours/year 0 278.69 ug/m3 Daily mean 125 3 days/year 0 15.70 ug/m3 Toluene Hourly mean - - - 56.57 Daily mean - - - 3.18 Xylene Hourly mean - - - 66.65 Daily mean - - - 3.76

Model predicted concentrations are consistent with observed data from air quality monitoring stations. Two graphical pictures of air quality monitoring stations near the incident are shown in Figures 63 and 64. As shown in the two images, a peak concentration of SO2 is produced during the September 2nd , but the peak value is below the legal standards (around 18 ug/m3), and we are not sure to attribute this peak to the kerosene spill incident, since other peaks in A Coruña monitoring station are shown on different days of the emergency day.

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Figure 63.- Observed SO2 concentration at A Coruña Centro air quality monitoring station.

Figure 64.- Observed SO2 concentration at A Coruña Torre de Hercules air quality monitoring station.

Below graphical results from CALPUFF outputs for kerosene spill incident are shown in Figures 65 and 66, the second one was done in order to compare graphically observations with CALPUFF predicted values. Graphs for the whole emergency episode are displayed at Appendix A, B and C.

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Figure 65.- Predicted highest 1-hour average SO2 concentrations at each receptor (ug/m3) for kerosene spill incident.

Figure 66.- Predicted highest 1-hour average SO2 concentrations at each receptor (ug/m3) for kerosene spill incident and air quality stations value. White point is the emission source location. Logarithmic scale

8.3. Concentrations predicted for fire in frozen fish factory (Vigo)

Table 22 summarizes the predicted number of exceedances near the fire in frozen fish factory for NOx. Runs using estimated emission rates evaluate the impacts of this compound for all averaging time periods. In this case, NOx compound does not have applicable standards.

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Table 22. Summary of CALPUFF Modeling Results for Fire in frozen fish factory (Vigo Parameter Averaging Limit Number of Number of Maximum period value Exceedances Exceedances peak Allowed predicted predicted (ug/m3) NOx Hourly mean - - - 2.95 Daily mean - - - 0.16

Graph of Vigo – Coia air quality monitoring station near the incident is shown in Figure 67. As displayed in this image, a peak concentration of NOx is produced during the September 26th , but we cannot attribute this peak to the fire in frozen fish factory because other peaks were produced in the city without the emergency episode causing this pollution.

Figure 67.- Observed SO2 concentration at Vigo Coia air quality monitoring station.

Now, two graphical results for the fire in fish frozen factory are shown in Figures 68 and 69, the second one was done in order to compare graphically observations with CALPUFF. Graphs for the whole emergency episode are displayed at Appendix D.

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Figure 68.- Predicted highest 1-hour average NOx concentrations at each receptor (ug/m3) for fire in frozen fish factory (Vigo).

Figure 69.- Predicted highest 1-hour average NOx concentrations at each receptor (ug/m3) for fire in frozen fish factory (Vigo) and air quality stations value. Logarithmic scale

8.4. Concentrations predicted for forest fire in Poio (Pontevedra)

Table 23 summarizes the predicted number of exceedances near the forest fire in Poio (Pontevedra) for all modeled species. Runs using estimated emission rates evaluate the impacts of PM10, CO and CH4 for all regulated averaging time periods. These results show that the predicted impacts from this emergency episode are not in compliance with the applicable standards and guidelines for PM10 and CO, but the number of exceedances for PM10 is under the legal standards.

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Table 23. Summary of CALPUFF Modeling Results for forest fire in Poio (Pontevedra) Parameter Averaging Limit Number of Number of Maximum peak period value Exceedances Exceedances predicted (ug/m3) Allowed predicted PM10 Hourly mean - - - 102670 Daily mean 50 35 days/year 2 14233 ug/m3 CO Hourly mean - - - 1315311 Max daily 8- 10000 - 6 252588 hour mean ug/m3 CH4 Hourly mean - - - 60870 Daily mean - - - 8453

Two graphical results of air quality monitoring stations near the forest fire are shown in Figures 70 and 71. As shown in the two images, a peak concentration of PM10 and CO is detected by the air quality monitoring station during the emergency episode, and this peak is above the legal standards without any other reason causing the peak than emergency episode, so we can attribute this peak to the forest fire.

Figure 70.- Observed PM10 concentration at Marin air quality monitoring station.

Figure 71.- Observed CO concentration at Marin air quality monitoring station. Units in ug/m3.

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Finally, several graphical results for forest fire in Poio (Pontevedra) are shown in Figures 72 to 75. Graphs for the whole emergency episode are displayed at Appendix E, F and G.

Figure 72 and 73.- Predicted highest 1-hour average CO concentrations at each receptor (ug/m3) for forest fire (Pontevedra).

Figure 74 and 75.- Predicted highest 1-hour average PM10 concentrations at each receptor (ug/m3) for forest fire (Pontevedra).

9. Integration of results in Arcopol web tool

The CALPUFF model output graphs were transformed into other formats suited to the Arcopol web tool developed by Intecmar in the framework of this project.

A picture of how data is displayed in the tool is shown in the figure below.

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Figure 76.- Integration of CALPUFF model output graphs into Arcopol web tool.

10. Limitations and Uncertainty

In the US Guideline on Air Quality Models (U.S. EPA, 2005), the need to address the uncertainties associated with dispersion modelling is acknowledged as an important issue that should be considered.

The US Guideline divides the uncertainty associated with dispersion model predictions into two main types, as follows:

 Reducible uncertainty, which results from uncertainties associated with the input values and with the limitations of the model physics and formulations. Reducible uncertainty can be minimized by improved (i.e., more accurate and representative) measurements and improved model physics.  Inherent uncertainty is associated with the stochastic (turbulent) nature of the atmosphere and its representation (approximation) by numerical models. Models predict concentrations that represent an ensemble average of numerous repetitions for the same nominal event. An individual observed value can deviate significantly from the ensemble value. This uncertainty may be responsible for a ± 50% deviation from the measured value.

There is no disputing that models are inherently uncertain. Both reducible and inherent uncertainties mean that dispersion modelling results may over- or under-estimate measured ground-level concentrations at a specific time or place. However, the US

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Guideline on Air Quality Models also states the following, which summarizes quite well what is accepted by the dispersion modelling community for regulatory applications:

“Models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of +/- 10 to 40 percent are found to be typical, i.e., certainly well within the often-quoted factor of two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable.”

Thus, although model uncertainty is important to consider, when dispersion models such as CALPUFF are used to assess maximum ground-level concentration and when a sufficiently large number of meteorological conditions are considered, the modelling results should fall well within the often quoted “factor of two” accuracy for these modeled..

The US Guideline on Air Quality Models also provides pragmatic advice regarding what is to be done about the uncertainties associated with dispersion modelling in a regulatory context:

“ … (W)hile (information regarding uncertainty) can be provided by the modeler to the decision-maker, it is unclear how this information should be used to make an air pollution control decision. Given a range of possible outcomes, it is easiest and tends to ensure consistency if the decision-maker confines his judgement to the use of the “best guess” estimate provided by the modeler … This is an indication of the practical limitations imposed by current abilities of the technical community.”

Thus, from a regulatory perspective, and given that there remains uncertainty surrounding model accuracy, it is standard practice to accept the modeler’s best estimate as to what the maximum change in air quality will be as the main piece of evidence in the final decision. Thus, although the existence of model uncertainty is well-accepted, it does not mean that important environmental decisions cannot be made based on dispersion modelling results – it should simply be acknowledged and understood that given their inherent uncertainty, models are a best case approximation of what are otherwise very complex physical processes in the atmosphere, and should be used as one of many tools in the regulatory decision-making “toolbox”.

It should be noted that for the modelling done in this study, emission rates were estimated based on a combination of emission factors and engineering estimates. Because of the nature of this approach, there is a high degree of confidence that emissions are over-estimated and include considerable conservatism. In addition, air quality dispersion models such as CALPUFF also employ assumptions to simplify the random behaviour of the atmosphere into short periods of average

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behaviour. These assumptions limit the capability of the model to replicate every individual meteorological event. Furthermore, regulatory models, such as CALPUFF, are designed to have a bias toward over estimation of contaminant concentrations (i.e., to be conservative under most conditions). Therefore, on an overall basis, it is expected that the CALPUFF modelling provides conservative estimates of maximum ground-level concentrations during adverse meteorological conditions.

11. References

1. Scire, J.S.; Strimaitis, D.G.; Yamartino, R.J. (2000). “A User's guide for the CALPUFF dispersion model (Version 5)”. EarthTech, Inc., Concord, MA.

2. U.S. EPA. (2008). “Technical Issues Related to use of the CALPUFF Modeling System for Near-field Applications”. Research Triangle Park, NC 27711.

3. David L. MacIntosh, James H. Stewart, Theodore A. Myatt, Joseph E. Sabato, George C. Flowers, Kirk W. Brown, Dennis J. Hlinka , David A. Sullivan. “Use of CALPUFF for exposure assessment in a near-field, complex terrain setting”. Atmospheric Environment 44 (2010) 262-270.

4. Patricia Moreno Simoes Veiga, “Evaluation of the Atmospheric pollution dispersion’s scenarios for the Paraiba river valley”. Posgrade Dissertation, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, 2009. Brazil.

5. Huiling cui, Rentai Yao, Xiangjun Xu, Cuntian Xin, jinming Yang. “A tracer experiment study to evaluate the CALPUFF real time application in a near-field complex terrain setting”. Atmospheric Environment 45 (2011) 7525-7532.

6. Cengiz Deniz and Alper Kilic, “Estimation and Assessment of Shipping Emissions in the Region of Ambarli Port, Turkey”, Department of Marine Engineering, Maritime Faculty, Istanbul Technical University, Turkey. Environmental Progress & Sustainable Energy (Vol.29, No.1) 2009.

7. Performance of aermod vs. calpuff on fugitive emission sources in the nearfield. Trinity Consultants, June 2008, USA.

8. Elizabeth Carper and Eri Ottersburg, “Significance of a CALPUFF Near-Field Analysis”, A&WMA's 96th Annual Conference and Exhibition June 2003. http://www.environmental- expert.com/Files/3783/articles/5160/tp_calpuff_signif.pdf

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9. Assessment of Air Quality Impacts of Emissions from the Alcoa Aluminum Plant in Husavik, Iceland. Submitted by TRC (USA), March 2010. http://www.alcoa.com/iceland/ic/pdf/vidauki_01_loftdreifing.pdf

10. James Bay Air Quality Study: Phase II, Report on the Results of CALPUFF, Air Quality Dispersion Modelling, Prepared for the Vancouver Island Health Authority, Victoria, Canada, February 2009. http://www.viha.ca/NR/rdonlyres/3E1E4738-C457-4E64-84CA- 8B3132723814/0/JBAQS_PhaseII_Feb25.pdf

11. Karla Poplawski et al, “Impact of cruise ship emissions in Victoria, BC, Canada”, Atmospheric Environment 45 (2011) 824-833

12. Luiz Claudio Gomes Pimentel et al, “Performance Assessment of Regulatory Air Quality Models Aermod and Calpuff – A near field case study in metropolitan region of Rio de Janeiro, Brazil”, HARMO13 - 1-4 June 2010, Paris, France - 13th Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. http://www.harmo.org/Conferences/Proceedings/_Paris/publishedSections/H13- 126-abst.pdf

13. Regulation for Fuel Sulfur and Other Operational Requirements for Ocean-Going Vessels within California Waters and 24 Nautical Miles of the California Baseline (July 24, 2008). California Environmental Protection Agency, Air Resources Board, USA. http://www.arb.ca.gov/regact/2008/fuelogv08/fuelogv08.htm

14. Larkin et al “The BlueSky smoke modeling framework” International Journal of Wildland Fire 2009, 18, 906–920.

15. N. S. Panchal and E. Chandrasekharan “Terrain roughness and atmospheric stability at a typical coastal site” Boundary-Layer Meteorology, Volume 27, Number 1, 89-96.

16. “Modeling Sulfur Oxides (SOx) Emissions Transport from Ships at Sea”, Assessment and Standards Division Office of Transportation and Air Quality, U.S. Environmental Protection Agency, July 2007 http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r07009.pdf

17. Volume 2 EIA: JACOS Hangingstone Expansion Project, Appendix 5D: CALPUFF Dispersion Model, April 2010. http://www.jacos.com/Documents/eia/volume-2a/Hangingstone- Expansion_Volume-2-Part-A_EIA_Section-5-Appendix-5D.pdf

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18. Sabah Abdul-Wahab, Ali Sappurd. “Application of California Puff (CALPUFF) model: a case study for Oman. Clean Techn Environ Policy (2011) 13:177–189.

19. An Analysis of the atmospheric dispersion of radionuclides released from the Idaho Chemical Processing plant. http://www.cdc.gov/nceh/radiation/ineel/CALPUFFReportFinal.pdf

20. Ping K. Wan, “Atmospheric dispersion analysis preparing permit applications for the new nuclear power plants in the United States”, Bechtel Power Corporation, Proceedings of 15th International Conference on Nuclear Engineering April 22- 26, 2007, Nagoya, Japan http://www.bechtel.com/assets/files/TechPapers/atmospheric-dispersion- analysis.pdf

21. Hulda Winnes, “Air Pollution from Ships, Emission Measurements and Impact Assessments”. Thesis for the degree of doctor, Department of Shipping and Marine Technology, Chalmers University of Technology, Gothenburg, Sweden 2010 http://www.lighthouse.nu/CommonResources/Files/www.lighthouse.nu/Other/ Hulda%20Winnes%20-%20Air%20Pollution%20From%20Ships.pdf

22. Zannetti, P. (1990). Air Pollution Modelling. Van Nostrand Reinhold. New York.

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APPENDIX A:

SO2 predicted concentrations from kerosene spill in O Burgo estuary (A Coruña)

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APPENDIX B:

Toluene predicted concentrations from kerosene spill in O Burgo estuary (A Coruña)

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APPENDIX C:

Xylene predicted concentrations from kerosene spill in O Burgo estuary (A Coruña)

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APPENDIX D:

NOx predicted concentrations from fire in frozen fish factory (Vigo)

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APPENDIX E:

PM10 predicted concentrations from forest fire in Poio (Pontevedra)

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APPENDIX F:

CO predicted concentrations from forest fire in Poio (Pontevedra)

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APPENDIX G:

CH4 predicted concentrations from forest fire in Poio (Pontevedra)

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APPENDIX H:

FEPS model

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The emission model FEPS (Fire Emission Production Simulator Model), was developed by Fire and Environmental Research Applications Team, USDA Forest Service Pacific Wildland Fire Sciences Lab (Oregon, USA).

FEPS is a user-friendly computer program designed for scientists and resource managers with some working knowledge of Microsoft Windows applications. The software manages data concerning consumption, emissions and heat release characteristics of prescribed burns and wildland fires.

FEPS can be used for most forest, shrub and grassland types in North America and the world. The program allows users to produce reasonable results with very little information by providing default values and calculations; advanced users can customize the data they provide to produce very refined results.

Total burn consumption values are distributed over the life of the burn to generate hourly emission and release information. Data managed includes the amount and fuel moisture of various fuel strata, hourly weather, and a number of other factors.

The basic steps are: 1. User describes an event. This description includes the name, location, start date, end date, and other miscellaneous properties. 2. For the event, user may specify up to five unique fuel profiles. Each profile includes fuel loading and fuel moisture information. 3. FEPS will calculate total fuel consumption for each profile. 4. FEPS determines flaming, short-term smoldering and long-term smoldering involvement and consumption. 5. User indicates how the Event behaves over time. 6. FEPS calculates emissions and heat release parameters on an hourly basis. Fuel characteristics for each hour are managed by distributing the fire across the five user-specified fuel profiles.

FEPS software is in the public domain, application updates and additional information may be found at http://www.fs.fed.us/pnw/fera/feps/

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Figure H.1.- Event Management Screen – Load, Create, Delete and Export Events

FEPS calculates emissions of CO, CO2, CH4, and PM2.5 based on combustion efficiency of the burn.

Figure H.2.- Wildfire in Poio (Pontevedra) – Consumption / Emissions Report

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