Dispersion Modeling Analysis for the Village of Corrales,...

Final Report

Dispersion Modeling Analysis for the Village of Corrales, NM

Prepared by

Darko Koracin1 John G. Watson2

16400 Mesa Road, Reno, NV 89511

2Worldwide Environmental Corporation, 8101 Meadow Vista Dr., Reno, NV 89511

Prepared for

New Mexico Environment Department Air Quality Bureau, Santa Fe, NM

March 2003

EXECUTIVE SUMMARY An air quality dispersion modeling study was undertaken in response to odor and

health reports filed with the New Mexico Environment Department (NMED) by residents of the Village of Corrales in Sandoval County, NM. Most of the reports were filed by residents along the western benches of the Rio Grande river valley located southeast of an Intel semiconductor manufacturing facility. Thirty-six organic and inorganic gases have been identified as potential emissions from this facility at low average emission rates.

The purpose of the modeling study was to determine wind flows in the area and to estimate ambient concentrations of semiconductor manufacturing emissions near residences where reports have been filed. This was accomplished by applying the CALMET diagnostic meteorological model to wind measurements from six surface monitors in the area for the period of 1 July, 2001 through 30 June, 2002. These CALMET wind fields were coupled with emission rate estimates and locations for the semiconductor facility to estimate 1-hr. 3-hr., 8-hr, 24-hr, and annual maximum concentrations of chemical compounds at specified receptors using the CALPUFF dispersion model. Simulated wind fields and surface concentration patterns for each of the odor and health reports are also documented in this report.

Sixty odor and health reports were filed for the study period, 45 of which identified the time of day. Most of the reports were during nighttime (17) and evening hours (17); morning (7) and afternoon (4) reports were less frequent. Most reports occurred in the summer and fall (28 and 23, respectively). Winter and spring reports may have been fewer due to less time spent outdoors and lower outdoor to indoor infiltration. Although several olfactory characteristics were described in the reports, none of these could be associated at this time with identified chemicals from the semiconductor facility.

During the night local circulations develop with westerly, northwesterly, and northerly drainages from the western plateau toward the river valley. Wind speeds are low. During the day, mainly southwesterly/southerly flows develop from the cooler valley floor air toward the warmer air over the slope. Windspeeds and the mixing depth are higher during day compared to the nighttime. Flows from the western plateau toward the lower benches on the western side of the river valley correspond to the evening and nighttime odor and health reports. Some reports were made when the flow was not favorable to transport from the western plateau to the residences, and not all favorable transport situations corresponded with an odor/health report. Odor and health reports are subjective, and are limited to living (being inside or outside of residence), working (being absent or present), sleeping habits, and sensitivity thresholds. Model results are derived using sparse measurements and in some of the cases cannot fully represent atmospheric flows and thermal stability in this complex terrain. Emissions causing the complaints may be sporadic, rather than constant as assumed for the modeling.

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Table of Contents

1. Introduction.........................................................................................................................1 1.1 Background..................................................................................................................1 1.2 Project Goals and Objectives.......................................................................................1

2. Technical Approach............................................................................................................1

3. Study Area and Emissions ..................................................................................................3

4. Receptors and Odor Reports...............................................................................................5

5. Meteorology........................................................................................................................5 5.1 CALMET Input Data...................................................................................................5 5.2 CALMET Model .........................................................................................................5 5.3 Conceptual Model of the Corrales Meteorology .......................................................10

5.3.1 Comparison with Surface Measurements ........................................................10 5.3.2 CALMET Flows ..............................................................................................10 5.3.3 CALMET and Surface Measurement Wind Roses..........................................17

6. Ambient Concentration Estimates ....................................................................................19 6.1 CALPUFF Model ......................................................................................................20 6.2 CALPUFF Results.....................................................................................................21 6.3 Annual Simulations for Specific Chemical Emissions..............................................22

7. Lagrangian Random Particle Dispersion Model...............................................................23 7.1 Cases Simulated with the Lagrangian Random Particle Model ................................23

8. Summary, Conclusions, and RecommenDations..............................................................26

9. References.........................................................................................................................27

10. Appendix A.......................................................................................................................29

11. Appendix B.......................................................................................................................30

12. Appendix C.......................................................................................................................31

13. Appendix D.......................................................................................................................32

14. Appendix E .......................................................................................................................33

15. Appendix F .......................................................................................................................34

16. Appendix G.......................................................................................................................35

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1. INTRODUCTION

1.1 Background Residents in the village of Corrales, located in Sandoval County New Mexico,

have reported unpleasant odor/health events in recent years. Most of the reports are from residences on the western benches of the Rio Grande river valley that serves as the eastern border of the village. A semiconductor manufacturing facility that uses various organic chemicals in its fabrication processes is located on a plateau overlooking the river and higher than several of the residences. Several of the residents believe that emissions from this facility might be the cause of some of the problems, even though emissions are treated prior to release and permit requirements are attained. To evaluate this possibility, an air quality dispersion modeling study was commissioned by the New Mexico Environment Department (NMED) to estimate concentrations of different materials that might cause problems. The methods and results of this study are described in this report.

1.2 Project Goals and Objectives The main goal of this project was to estimate concentrations of selected chemical

compounds that correspond to times and locations of odor/health reports with an air quality dispersion model. The scope was limited to emissions from the Intel semiconductor manufacturing facility. Specific project objectives were:

• To simulate and analyze an annual cycle (1 July 2001 – 30 June 2002) of wind speeds and directions and provide a conceptual model of the atmospheric conditions relevant to pollutant transport and dispersion in the Corrales area.

• To simulate transport and dispersion of nearby emissions for the annual cycle using the CALPUFF dispersion model and episodes using a random particle dispersion model.

• To analyze how pollutant concentration fields estimated from the model correspond with odor and health reports.

• To estimate 1-hour, 3-hour, 8-hour, 24-hour, and annual maximum concentrations of selected chemical compounds at nearby residential sites.

2. TECHNICAL APPROACH Air quality dispersion models are commonly used for permitting planned sources,

determining the pollutant contributions from existing sources, and estimating human exposure (Moschandreas et al., 2002). These source-oriented models simulate how emissions from point, line, and area sources disperse and transport in the atmosphere. They are most often applied to estimate ground-level concentrations of emitted pollutants that might increase human exposure beyond acceptable levels. Source models contrast with, and are complementary to, receptor models that infer source contributions from chemical “fingerprints” of source emissions that combine into complex concentration patterns at a receptor (Watson et al., 2001, 2002). Only source-oriented dispersion models are used in this study.

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All models are simplifications of reality. Simplifying assumptions must be made because input data are usually sparse, emissions may be sporadic or poorly characterized, and the random nature of atmospheric movements can never be completely known. Exact correspondence between dispersion model estimated and measured concentrations are rare. However, when dispersion models are validly applied, the general characteristics of emitted plumes can be determined and ambient concentrations can be estimated within stated uncertainties. These estimates become more reliable with longer averaging periods as random variations over shorter periods cancel each other in the long-term averaging.

For this study, the exposure domain of interest is fairly small (less than 20 km2) and micro-effects due to small-scale obstacles, buildings, and houses may influence local meteorology, as well as transport and dispersion of atmospheric pollutants. Most dispersion models, including EPA regulatory models, are not appropriate for urban and sub-urban scales. Considering the complexity of the topography, local circulations, and urban features of the Corrales environment, even small inaccuracies in emission source locations and emission rates may cause discrepancies between model results and measured concentrations.

Most elevated concentrations occur during low-wind and stagnant conditions that minimize dilution. These conditions are most poorly simulated by dispersion models. Most EPA regulatory models assume a bell-shaped (Gaussian) dispersion with distance from the source that is not valid for near-zero wind speeds. Although there are some correction formulae for estimating concentrations in sluggish conditions, their accuracy is questionable.

EPA’s CALMET diagnostic atmospheric model is most appropriate for estimating wind fields for this application. CALMET interpolates between available meteorological measurements with user-given constraints about terrain and flow obstructions. It does not require expensive super-computers. More accurate “prognostic” models, such as Mesoscale Model 5 (MM5), solve complex physical equations rather than interpolate between measurements. These models require substantial set up and computational resources that are beyond the scope of this project, although they might be considered in future projects.

CALMET can estimate where and when low winds speeds occur. Winds from CALMET can be coupled with the CALPUFF dispersion model as well as a random particle (Monte Carlo) plume dispersion model. Random particle dispersion models simulate transport and dispersion in complex terrain for a wide range of weather conditions. Random particle models directly link dispersion with turbulence and are applicable to low-wind, stagnant conditions. These models require detailed information on wind speed and direction at closely spaced spatial and temporal intervals, such as that produced by CALMET.

Merely running modeling software on a computer and obtaining results is insufficient for a valid air quality study. The results must be examined with respect to measurements to determine model performance. The results must also be consistent with conceptual models of air quality emissions, dispersion and transport. For this reason, a conceptual model of pollutant flows is formulated as part of this effort and model-

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estimated concentrations are compared in space and time with odor and health reports and ambient concentrations when they are available. Sensitivity tests are also undertaken to determine how uncertainties in input data may affect calculated concentrations.

CALMET was applied to meteorological measurements available for one year from 1 July 2001 through 30 June 2002. CALPUFF was applied to the CALMET wind fields and emission rates provided by NMED to estimate concentrations within and around the area of odor and health reports. Six episodes of elevated concentrations were simulated in animated visualizations with the Lagrangian random particle dispersion model to better understand plume movements.

3. STUDY AREA AND EMISSIONS Figure 1 designates the area of interest, including locations associated with odor

and health reports and the outline of a nearby semiconductor manufacturing facility. Detailed reports are included in Appendix A. Figure 1 shows that the most of the reports are southeast of the semiconductor facility. However, a smaller number of reports was registered further to the east, northeast, north, and northwest of the facility. Emissions were limited to those from the semiconductor manufacturing facility for this study as summarized in Table 1.

Fig. 1. Locations (triangles) of residences that reported odor and health symptoms in the Corrales area. The semiconductor facility on the western plateau is outlined.

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Table 1. INTEL semiconductor facility point and area source emissions from NMED.

Chemical Abstract Service (CAS) Number

Compound Point Source Control Measure

Point Source Emission Rate (lb/hr) (monthly maximum)

Area Source Emission Rate (lb/hr)

2551-62-4 Sulfur Hexafluoride Scrubber 0.006 75-73-0 Tetrafluoromethane Scrubber 0.02 76-16-4 Hexafluoroethane Scrubber 0.72 1330-20-7 Xylene therm.oxidizer 0.02 23.11 64-17-5 Ethanol therm.oxidizer 0.01 67-64-1 Acetone therm.oxidizer 0.03 0.2 67-63-0 2-propanol therm.oxidizer 25.5 67-56-1 Methanol therm.oxidizer 0.5 3.2 108-65-6 PGMEA therm.oxidizer 0.42 56-23-5 Carbon Tetrachloride (CCl4) Scrubber 0.008 7647-01-0 Hydrogen Chloride (HCl) Scrubber 0.07 0.3 7664-39-3 Hydrogen Fluoride (HF) Scrubber 0.24 7664-41-7 Ammonia (NH3) Scrubber 2.5 74-90-8 Hydrogen Cyanide (HCN) Scrubber 0.01 0.55 7782-50-5 Chlorine (Cl2) Scrubber 0.55 0.39 107-21-1 Ethylene Glycol Scrubber 0.05 7664-93-9 Sulfuric Acid Scrubber 0.2 0.58 7697-37-2 Nitric Acid Scrubber 0.3 115-25-3 Octafluorocyclobutane Scrubber 1.76 78-10-4 Ethyl Silicate

(Tetraethylorthosilicate) therm.oxidizer 1.7

110-43-0 Methyl n-amyl ketone (2-heptanone)

therm.oxidizer 1.3

10035-10-6 Hydrogen Bromide Scrubber 3.2 7803-62-5 Silicon Tetrahydride (Silane) therm.oxidizer 2.2 123-86-4 n-Butyl Acetate therm.oxidizer 0.1 12125-02-9 Ammonium Chloride Scrubber 1.2 156-60-5 1,2-Dichloroethylene Scrubber 0.1 7783-54-2 Nitrogen Trifluoride Scrubber 3.7 107-13-1 Acryloniyrile 0.15 71-43-2 Benzene 19.27 74-87-3 Chloromethane 0.03 100-41-4 Ethylbenzene 0.83 Formaldehyde 3.09 Styrene 1.12 75-09-2 Methylene Chloride 0.71 108-88-3 Toluene 38.77 78-93-3 2-Butanone (TIC) 0.002

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4. RECEPTORS AND ODOR/HEALTH REPORTS Receptors points (summarized in Table 2) were assigned to locations where

residents filed odor and health reports. During the modeling period (1 July 2001 – 30 June 2002), there were 60 reports, 45 of which identified the time of day. Most of the reports were during nighttime (17) and evening hours (17); morning (7) and afternoon (4) reports were less frequent. Most reports occurred in the summer and fall (28 and 23, respectively). Winter and spring reports may have been fewer due to less time spent outdoors and lower outdoor to indoor infiltration. Table 3 summarizes reports of discomfort that sometimes accompanied the odor and health reports.

5. METEOROLOGY

5.1 CALMET Input Data

Main inputs to CALMET are:

• Surface meteorological data (hourly observations of wind speed and direction, temperature, cloud cover, ceiling height, surface pressure, relative humidity, hourly precipitation data).

• Upper-air data (vertical profiles of wind speed and direction, temperature, pressure, and elevation from radiosonde every 12 hours).

• Geophysical data (terrain elevation, land use categories, and optional parameters such as surface roughness length, albedo, Bowen ratio, soil heat flux, anthropogenic heat flux, and vegetative leaf area index).

Table 5 presents the locations of the different meteorological monitors from which input data for CALMET were derived. Figure 2 shows the modeling domain and elevation isopleths. The modeling domain is larger than the Village of Corrales so that synoptic and terrain induced flows that affect wind flows near the receptors can be simulated.

5.2 CALMET Model CALMET is a meteorological model that includes a diagnostic wind field module

with objective analysis and parameterization of slope flows, kinematic terrain effects, terrain-blocking effects, and a micrometeorological module for overland and overwater boundary layers (Scire et al., 2000a). Applications of CALMET and its comparison with other atmospheric models in complex terrain of the southwestern US are shown by Koracin et al. (2000). The diagnostic wind field module contains options that allow wind fields produced by complex atmospheric prognostic models (e.g., Mesoscale Model 5 /MM5/) to be used as virtual meteorological stations or as initial conditions for the objective analysis procedure. As previously noted, CALMET might better represent regional flows and slope/valley circulations with MM5 input, but this was not possible within project constraints. Use of MM5 for initial wind fields and the resulting improvements for CALMET wind fields are described by Koracin et al. (1999b).

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Table 2. List of locations that filed odor and health reports. Locations in bold are the ones with more frequent reports. Table provided by NMED. Receptor UTM UTM Number Easting northing

1 349851.2496 3897012.247 2 349751.3312 3898324.455 3 349877.4858 3898248.671 4 349887.3929 3898242.72 5 349625.4607 3898573.305 6 349943.5485 3898405.397 7 350171.472 3898257.145 8 350135.7746 3898459.087 9 350135.7746 3898459.087 10 350133.6868 3898457.066 11 350174.3902 3898427.997 12 349992.435 3898581.19 13 349921.5243 3898604.699 14 349714.3543 3898716.273 15 350589.3352 3898820.989 16 349807.2261 3900810.284 17 351139.9822 3898672.335 18 351191.522 3898645.303 19 350427.891 3899737.479 20 352013.5732 3898919.524 21 352059.5913 3899050.175 22 349742.4293 3899792.25 23 347780.401 3901045.96 24 353273.4994 3898733.201 25 349706.0238 3898910.515 26 352018.9626 3901303.733 27 353571.3242 3901859.64 28 354587.4109 3900354.607

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Table 3. Discomfort symptoms that sometimes accompanied odor and health reports during the period of 1 July 2001 through 30 June 2002.

Asthma, chronic sinus congestion, reduced sense of smell.Burning eyes, nose, throat.Burning eyes.Burning eyes.Cough, sinus drainage, respiratory distress.Difficulty breathing.Difficulty breathing.Eye irritation, allergic reaction.Eye irritation, sore throat, rash.Eye, nose, throat irritation.Eyes burning, difficulty breathing, dry cough.Eyes irritation, cough, lack of energy.Headache, coughing, sinus drainage, reduced sense of smell.Headache, eye irritation.Headache, respiratory distress.Headache, sore throat.Nasal irritation.Nausea, headache, upset stomach, vomiting.Nausea, vomiting, headache.Respiratory distress.Respiratory distress.Respiratory distress.Respiratory distress.Runny stuffy nose, cough, headache, nausea.Skin rash, coughing.Sore throat.

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Table 4. Description of the chemicals reported during the complaints in Corrales in the period from 1 July 2001 to 30 June 2002.

Acid. 2Acrid chemical. 1Acrid, burning. 1Acrid, burnt odor (like burnt wood). 1Bitter chemical. 2Bittersweet. 1Burning. 1Burnt coffee, acid. 1Burnt coffee, clorox. 1Burnt coffee. 5Burnt petroleum distillates. 1Burnt, caustic. 3Burnt, vinegar. 1Burnt. 3Chemical burn. 1Chemical sweet. 1Chemical, bittersweet to sweet. 1Chemical, burnt coffee. 1Chemical, musty. 1Chemical, sweet. 1Chemical. 1Coffee. 2Diesel, chemical. 1Downy, sulfur. 1Noxious. 1Paint. 1Perfume. 2Pungent chemical. 1Strange chemical. 1Strong. 2Sweet perfume. 3

Table 5. Meteorological stations used for data analysis and modeling.

ID Network Location UTM_E UTM_N zone Long_dd Lat_ddINT INTEL INTEL Co. 349300 3899500 13 -106.6558 35.22722ZJ NMED-AQB Bernalillo 359700 3907100 13 -106.5428 35.29722ZR NMED-AQB Rio Rancho 349900 3900516 13 -106.6494 35.2364TIJ RAWS Tijeras 374176 3881707 13 -106.3800 35.0703KABQ NWS Albuquerque International Airport 352719 3878658 13 -106.6147 35.0417KAEG NWS Albuquerque, Double Eagle II Airport 336480 3890400 13 -106.7950 35.1450

CALMET’s diagnostic wind field module calculates winds in two steps. In the first step, an initial estimate is adjusted for kinematic effects of terrain, slope flows, and terrain-blocking effects. For the kinematic 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 on the horizontal wind components are derived from a divergence minimization algorithm to the initial wind field estimate. Slope flows are parameterized by the balance of momentum, surface drag, and entrainment at the top of the flow layer. Specifics of the slope flows are determined from the slope angle, distance to the crest, and local sensible heat flux. The thickness of the flow layer is determined as a function of the elevation drop from the crest. The blocking effects of the terrain on the flow are parameterized in terms of the local Froude number and the wind

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direction of the flow is changed accordingly. In the second step, an objective analysis is used to produce a final wind speed.

330 340 350 360 370 380UTME(km)

Topography(10 m Data Resolution) - Corrales Domain

3870

3880

3890

3900

3910

3920

3930

UTM

N(k

m)

Legend

Met stations

Radiosonde station

Intel

Fig. 2. Topography of Corrales and the surrounding area with indicated meteorological stations used for meteorological modeling. Locations of meteorological monitors are shown. The semiconductor facility is located on the western plateau approximately 100 m above the Rio Grande river.

Main outputs from CALMET are:

• Gridded fields of east-west (U), north-south (V), and vertical (W) wind components

• Surface friction velocity, convective velocity scale

• Monin-Obukhov length, mixing height, stability classes, air temperature, and precipitation rate.

CALMET was configured as follows for this study:

• Period of simulations: 1 July 2001 – 30 June 2002

• Model horizontal grid: 51 km x 44 km centered at Corrales/Intel area

• Model horizontal resolution: 200 m x 200 m

• Model vertical grid: 8 points

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• Vertical layer heights: 0, 20, 50, 100, 500, 1000, 1500, 2000, and 3000 m

• Horizontal resolution of topography: 10 m x 10 m.

One year of hourly windfields for the specified domain resulted in 90 Gigabytes of stored data. These outputs were then used as meteorological input to the CALPUFF dispersion model and for the episodic Lagrangian random particle modeling.

5.3 Conceptual Model of the Corrales Meteorology The Village of Corrales is situated in complex terrain along the Rio Grande river

valley that runs approximately south-north with the steep Sandia mountains to the east (elevation difference of approximately 700 m) and a gradual upslope and plateau toward the west, as delineated in Fig. 2 and shown in Fig. 3. This terrain induces local complex meteorology: channeled flow (valleys, canyons), downslope winds (nighttime), and upslope convective flows (daytime, warm season). The elements that affect flows are: 1) synoptic forcing on the large scale (high and low-pressure systems, fronts, advection); 2) topographic forcing (mountains, ridges, valleys), 3) seasonal effects (seasonal variation in the intensity of solar radiation); and 4) diurnal variation (change of heating/cooling during the day/night).

Large-scale easterly and northerly flows occur during winter that are mainly channeled into the north-south valley alignment. Wind speeds and mixing depth are generally low. During summer, flows are mainly westerly/northwesterly and southwesterly/southerly. Summer flows are also channeled along the river valley. Wind speeds and mixing depths are generally high.

During the night (see Fig. 4) local circulations develop with westerly, northwesterly, and northerly drainages from the western plateau toward the river valley. Wind speeds are low. During the day (see Fig. 5), mainly southwesterly/southerly flows develop from the cooler valley floor air toward the warmer air over the slope. Windspeeds and the mixing depth are higher compared to the nighttime

5.3.1 Comparison with Surface Measurements Frequency distributions for the wind direction are shown in Fig. 6 for nighttime

and in Fig. 7 for daytime hours. Measured wind directions at the Intel meteorological station at the top of the plateau are consistent with the conceptual model. During the daytime hours, southeasterly and southerly upslope winds are mainly directed from the river valley up toward the western plateau. However, during the nighttime hours, northerly and northwesterly downslope winds are directed almost opposite to the daytime winds.

5.3.2 CALMET Flows Since a few meteorological stations cannot reproduce the spatial structure of

airflows in complex terrain, CALMET results are used to infer the temporal and spatial structure of the airflows. Simulated wind averages for nighttime hours (from midnight to 6am) for all of September 2001 are shown in Fig. 8. There are significant downslope winds that form an intense downvalley northerly drainage. These model results are also consistent with the conceptual model for a nighttime flow regime (Fig. 4). Northerly

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Fig. 3. View toward the west (upper) and east (lower panel) from the eastern edge of the Corrales area.

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flows during nighttime remain during the morning hours; however, there is an initial development of upslope flows on the eastern and western sides of the valley (Fig. 9).

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• Strong downslope winds over the eastern slopes of the valley (steep mountain).

• Weaker downslope winds over the western slopes of the valley (Corrales).

• Down-valley drainage flow at the valley floor.

330 340 350 360 370 380UTME(km)


3870

3880

3890

3900

3910

3920

3930

UTM

N(k

m)

Legend

Met stations

Radiosonde station

Intel

Nighttime

Arrow length - approximate wind speed magnitude

Arrow direction - wind direction

(with weak synoptic forcing)

Fig. 4. Schematic of local circulations in the Corrales area during nighttime and under conditions of weak synoptic forcing.

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• Strong upslope winds over the eastern slopes of the valley (steep mountain).

• Weaker upslope winds over the western slopes of the valley (Corrales).

• Up-valley drainage flow at the valley floor.

330 340 350 360 370 380UTME(km)


3870

3880

3890

3900

3910

3920

3930

UTM

N(k

m)

Legend

Met stations

Radiosonde station

Intel

Arrow length - approximate wind speed magnitude

Arrow direction - wind direction

(with weak synoptic forcing)

Daytime

Fig. 5. Schematic of local circulations in the Corrales area during daytime and under conditions of weak synoptic forcing.

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Night (21-06)

Fig. 6. Frequency distribution of wind direction measured at the Intel station for nighttime hours from 2100 to 0600 LST.

Day (11-15)

Fig. 7. Frequency distribution of wind direction measured at the Intel station for daytime hours from 1100 to 1500 LST.

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335 340 345 350 355 360 365 370 375 380

CALMET -September Mean- Night (00-06 LST)

3875

3880

3885

3890

3895

3900

3905

3910

Fig. 8. Wind vectors for the nighttime hours (from midnight to 6am) for all of September as simulated by the CALMET atmospheric model.

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335 340 345 350 355 360 365 370 375 380

CALMET -September Mean- Morning (06-12 LST)

3875

3880

3885

3890

3895

3900

3905

3910

Fig. 9. Wind vectors for the morning hours (from 6am to noon) for all of September as simulated by the CALMET atmospheric model.

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Wind averages for the afternoon hours (from noon to 6pm) for all of September 2001 are shown in Fig. 10. The figure shows that the winds reversed from nighttime northerly into a strong upvalley southerly flows with significant upslope flows on the eastern and western sides of the valley.

335 340 345 350 355 360 365 370 375 380

CALMET -September Mean- Afternoon (12-18 LST)

3875

3880

3885

3890

3895

3900

3905

3910

Fig. 10. Wind vectors for the afternoon hours (from noon to 6pm) for all of September 2001 as simulated by the CALMET atmospheric model.

CALMET simulations show that the most frequent northwesterly flow from the plateau toward the residences (Fig. 2) occurs during nighttime. During the afternoon hours there is a broad regional scale southwesterly flow that is nearly homogeneous throughout the entire area (Fig. 10). This flow is caused by prevailing southerly and southwesterly flows that channel through the valley. These flows are also enhanced by the southwesterly and westerly upslope flows induced by the Sandia mountains. Average winds for the morning hours (6am to noon) show a similar flow structure (Fig. 9). Upvalley southerly winds remain during evening hours along the valley; however; the drainage downslope flows developed on both sides of the valley (Fig. 11).

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335 340 345 350 355 360 365 370 375 380

CALMET -September Mean- Evening (18-23 LST)

3875

3880

3885

3890

3895

3900

3905

3910

Fig. 11. Wind vectors for the evening hours (from 6pm to midnight) for all of September as simulated by the CALMET atmospheric model.

As shown in Appendix A, there were 60 odor and health reports and in most of the cases (45), the time of the day is indicated. Analysis of the wind fields indicates that prior to the reports the winds were almost equally from north/northwest and also from south/southeast. Around the time of the reports the winds were usually from the north/northwest/west. According to the simulation results, most of the odor and health reports coincided with the westerly and northwesterly flows, but there are cases when the flow was not favorable to transport from the western plateau to the residences. Odor and health reports are subjective, and are limited to living (being inside or outside of residence), working (being absent or present), sleeping habits, and sensitivity thresholds. Model results are derived using sparse measurements and in some of the cases cannot fully represent atmospheric flows and thermal stability in this complex terrain.

5.3.3 CALMET and Surface Measurement Wind Roses Figure 12 shows wind roses for nighttime, morning, afternoon, and evening

periods for fall when 23 (out of total 60) odor and health reports were filed.

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WIND ROSE PLOT

Station #23078 - ,

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

Wind Speed (Knots)

> 21

17 - 21

11 - 16

7 - 10

4 - 6

1 - 3

PLOT YEAR-DATE-TIME

2001 Oct 1 - Dec 31Midnight - 6 AM

DISPLAY

Wind SpeedUNIT

Knots

CALM WINDS

2.02%AVG. WIND SPEED

4.50 Knots

COMMENTS

ORIENTATION

Direction(blowing from)

WRPLOT View 3.5 by Lakes Environmental Software - www.lakes-environmental.com

WIND ROSE PLOT

Station #23078 - ,

NORTH

SOUTH

WEST EAST

3%

6%

9%

12%

15%

Wind Speed (Knots)

> 21

17 - 21

11 - 16

7 - 10

4 - 6

1 - 3

PLOT YEAR-DATE-TIME

2001 Oct 1 - Dec 316 AM - Noon

DISPLAY

Wind SpeedUNIT

Knots

CALM WINDS


5.59 Knots

COMMENTS

ORIENTATION



WIND ROSE PLOT

Station #23078 - ,

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

Wind Speed (Knots)

> 21

17 - 21

11 - 16

7 - 10

4 - 6

1 - 3

PLOT YEAR-DATE-TIME

2001 Oct 1 - Dec 3111 AM - 6 PM

DISPLAY

Wind SpeedUNIT

Knots

CALM WINDS


6.88 Knots

COMMENTS

ORIENTATION



WIND ROSE PLOT

Station #23078 - ,

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

Wind Speed (Knots)

> 21

17 - 21

11 - 16

7 - 10

4 - 6

1 - 3

PLOT YEAR-DATE-TIME

2001 Oct 1 - Dec 316 PM - 11 PM

DISPLAY

Wind SpeedUNIT

Knots

CALM WINDS


4.86 Knots

COMMENTS

ORIENTATION



Fig. 12. Wind roses obtained from the Intel meteorological station data for nighttime (upper left), morning (upper right), afternoon (lower left), and evening hours (lower right) for fall 2001.

Wind roses are consistent with the conceptual model with dominant northerly winds during nighttime. There is a transition from downvalley to upvalley flows during morning hours when the drainages are replaced by the upslope flows. Southerly upvalley winds dominate during the afternoon hours, while the evening flows show dominant northwesterly direction.

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In summary, the measurement and simulation results indicate that the most favorable conditions for the transport from the western plateau toward the residences on the western slope of the Rio Grande river valley occur during the evening and nighttime hours. Most of the odor and health reports were filed for evening and nighttime/early morning hours (34 of total 45). Appendix B shows plots of the surface vector wind as simulated by CALMET atmospheric model for each of the odor and health reports. There are generally three plots for the each case with 6-hr averages prior, at, and after the time of the odor and health report.

6. AMBIENT CONCENTRATION ESTIMATES Ambient concentrations were estimated in two types of annual simulations:

• First annual simulation: Unit emissions were used in the standard CALPUFF for different source groupings to determine differences in transport and dispersion among these groupings.

• Second annual simulation: CALPUFF model code was modified to accommodate emission rates for all permitted chemicals (Table 1). This type of simulations provided estimates of maximum 1-hr, 3-hr, 8-hr, 24-hr, and annual concentrations at selected receptors.

Appendix C summarizes locations of point sources and their technological specifics. There are 38 emission points with scrubbers and 5 emission points with thermal oxidizers. Since it was not feasible to run all these sources separately and there were no exact emission specifications for each of the sources, sources were grouped by their locations. Figure 13 shows how the sources were arranged.

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Fig. 13. Locations of Intel’s scrubbers and thermal oxidizers as collected into line and points sources.

Scrubber sources were aggregated into two line and two point sources and oxidizers were treated as three point sources. The spatial dimensions of all sources are only about 400m x 700m. The small spatial extent of the sources compared to the model grid size (200m x 200m) supports the aggregation of emissions into point and line sources. For the first type of simulation, each of these 7 sources was associated with a different chemical to distinguish their contributions in further analysis, as described in Fig. 14. Each of the sources has its own specific chemical and as well as a common chemical (Toxics /TXS/).

Assignment of separate pollutants for each source

• Pt.-Scr 1: NO• Pt.-Scr 2: NO2

• Ln-Scr 3: B-PINENE• Ln-Scr 4: A-PINENE• Pt.-Oxi 5: SO2, PM10, • Pt.-Oxi 6: TOLUENE, PM10, • Pt.-Oxi 7: XYLENE, PM10,

TXS, ODOR, TXS, ODOR, TXS, TXS,

TXS, ODORTXS, ODOR

TXS, ODOR

Fig. 14. Allocation of chemicals to collective sources shown in Fig. 13 for the first type of annual simulations.

6.1 CALPUFF Model CALPUFF is a non-steady-state Lagrangian Gaussian puff model containing

modules for complex terrain effects, building downwash, wet and dry removal, and simple chemical transformation (Scire et al., 2000b). Applications of CALPUFF and its evaluation are shown by Strimaitis et al. (1998). Use of CALPUFF to estimate impact of sources to an urban area is reported by Koracin et al. (1999b).

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CALPUFF advects “puffs” of material emitted from modeled sources and simulates dispersion and chemical transformation processes along the transport path. All necessary meteorological inputs to CALPUFF are provided by the CALMET model. As a simpler option, CALPUFF can use single point meteorology (non-gridded data). Temporal and spatial variations of selected meteorological fields are explicitly incorporated in the resulting distribution of puffs throughout an entire simulation. CALPUFF can accommodate arbitrarily-varying sources of various geometries (point, line, area, and volume) as well as gridded area source emissions.

CALPUFF includes options for parameterizing chemical transformation effects using pseudo-first-order chemical mechanisms for SO2, SO4

=, NOx, HNO3, and NO3-.

These are not needed, nor are they used, in this application and project phase. The complex terrain module in CALPUFF parameterizes plume impingement on subgrid scale hills by dividing the streamline to determine which pollutant material is deflected around the sides of a hill and which material is advected over the hill. Individual puffs are split into up to three sections for these calculations. CALPUFF contains sampling routines for applications to near-field releases. There are also puff-splitting algorithms to account for vertical wind shear effects across individual puffs. EPA recommended CALPUFF for air quality modeling (US Government 2000).

Main inputs to CALPUFF are:

• Geophysical data from CALMET outputs (surface roughness length, land use categories, terrain elevation, and leaf area index).

• Meteorological data from the CALMET outputs (gridded fields of wind, temperature, surface friction velocity, convective velocity scale, mixing depth, Monin-Obukhov length, stability class, and the precipitation rate); and hourly values of the parameters at surface meteorological stations (air density, temperature, solar radiation, relative humidity, and precipitation type).

• Emission data, deposition velocities, ozone concentrations (for reactive chemistry), chemical transformation data, hill specifics, and receptor specifications.

Main outputs from the CALPUFF model are:

• One-hour averaged concentrations at the gridded and discrete receptors for species selected by the user in the control file.

• One hour averaged dry and wet deposition fluxes at the gridded and discrete receptors for species selected by the user in the control file.

• Tables of detailed puff/slug data.

• Hourly output of changes in mass of all modeled species.

6.2 CALPUFF Results Using the meteorological fields simulated by CALMET and the setup of the Intel

emission sources (described above), CALPUFF was run for the entire year (from 1 July

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2001 through 30 June 2002). CALPUFF concentration patterns were produced for the time of each odor and health report with a time and location, along with patterns before and after the report time (Appendix D). Figure 15 shows an example of these patterns.

348 349 350 351 352 353 354

CALPUFF - Toxics (all sources) - 14 Jul 2001 - 1:00 pm (4:30pm/14th/) AT-min3hr (#09) 512 Windov Ln.

3897

3898

3899

3900

3901

348 349 350 351 352 353 354

CALPUFF - Toxics (all sources) - 14 Jul 2001 - 4:00 pm (4:30pm/14th/) AT (#09) 512 Windov Ln.

3897

3898

3899

3900

3901

348 349 350 351 352 353 354

CALPUFF - Toxics (all sources) - 14 Jul 2001 - 7:00 pm (4:30pm/14th/) AT-plus3hr (#09) 512 Windov Ln.

3897

3898

3899

3900

3901

-3hrs +3hrs

AT

Fig. 15. Predicted surface concentrations for all semiconductor manufacturing emissions for report #9 at 4:30pm on 14 July 2001 (bottom center). Also shown are concentration patterns three hours before (upper left) and 3 hours after (upper right) the report time.

Three hours before the report the southeasterly winds were transporting the simulated chemicals toward northwest. At the time of the report, most of the semiconductor facility plume affected receptors from the facility’s fence line to the southeast. Three hours later, the receptors were still within the plume, but maximum concentrations shifted to the south and southwest.

6.3 Annual Simulations for Specific Chemical Emissions The second type of simulation was applied to chemicals in Table 1. These

chemicals were apportioned to each of the 43 sources (Appendix C), then aggregated into the point and line sources of Fig. 13. Emission rates supplied by NMED were given only as a total emission either from scrubbers or thermal oxidizers and consequently an exact amount could not be assigned to a particular source, so emission rates for each chemical were divided equally among scrubber or thermal oxidizer sources.

Model outputs are:

• 1-hr maximum concentrations for each chemical with indicated receptor and date/time of occurrence.


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• Annual maximum concentrations for each chemical with indicated receptor and date/time of occurrence.

Tables of all these maximum concentrations are shown in Appendix E. Generally, the 1-hr maximum concentrations are about an order of magnitude greater than the 24-hr maximum concentrations. Regarding the chemical compounds which have greater emission rates, the maximum concentrations are greater (e.g., 2-propanol, Hydrogen Bromide, Silicon Tetrahydride, etc.). Regarding the receptors, most of the maximum concentrations were estimated to occur at: 1) receptor 16, 2) receptor 14, and 3) receptor 5.

7. LAGRANGIAN RANDOM PARTICLE DISPERSION MODEL The Lagrangian random particle (LAP) model (Koracin et al. 1998; 1999a; 2000)

accurately simulates transport and dispersion of atmospheric pollutants and tracers from various emission sources. A large number of hypothetical particles are continuously released from specified sources and then transported and dispersed based on the simulated meteorological fields. In order to accurately represent the plume, a sufficiently large number of particles (typically 100,000 to 3,000,000 per source) need to be released in small time steps. The model continuously traces the particles in time and space and computes concentrations at specified receptors. The LAP model has three optional turbulence parameterizations which provide scaling parameters for random velocity components that determine the intensity of dispersion. So far, we have been using atmospheric models such as MM5, RAMS, and CALMET to drive the LAP model.

Main inputs to the LAP model are: 3D atmospheric fields of wind components and temperature; emission rate; geometry of sources; and temporal variation of sources. Emission rates were set up same as in the annual simulations using a common chemical (TXS) from each of the sources (see Sections 6.1-6.2 and Figs. 13-14).

Model setup parameters include a choice of a turbulence scheme, time-scale algorithms, receptor positions, and particle release rate. Main outputs of the LAP model are: 3D position of each particle in the domain; concentrations of pollutants at each receptor; and visualization and animation files of the simulated particles. Applications of the coupled MM5 and Lagrangian random particle model have been performed for the regional and mesoscale transport and dispersion of atmospheric pollutants from urban areas in California and Nevada (Koracin et. al. 2001; Gertler et al. 2002; Koracin and Isakov 2002). The applications include the transport and dispersion of various chemical species such as benzene (Koracin et al. 1999), tracers (Koracin et al. 1998), and diesel Particulate Matter (diesel PM) (Koracin and Isakov 2002).

7.1 Cases Simulated with the Lagrangian Random Particle Model Since there were 19 odor and health reports in September (out of 60 for the entire

year), cases were selected from that month:

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• Report #20 (3 September 2001 at 10:30pm; Receptor #5) and # 21 (4 September 2001 at 6:30am; Receptor #5)

• Report #22 (6 September 2001 at 6:30am; Receptor #5) and # 23 (6 September 2001 from 1:00 to 7:00pm; Receptor #7)

• Report #25 (7 September 2001 at 6:15pm; Receptor #5) and # 26 (7 September 2001 at 4:15pm; Receptor #5)

• Report #28 (10 September 2001 at 6:00am; Receptor #11)

• Report #32 (15 September 2001 at 4:00am; Receptor #5) and # 33 (15 September 2001 at 3:39am; Receptor #5)

• Report #34 (18 September 2001 at 6:00am; Receptor #11); # 35 (18 September 2001 at 4:30am; Receptor #5); #36 (18 September 2001 at 4:30-7:30am; Receptor #10); # 37 (18 September 2001 at 4:30am; Receptor #5)

These six 1-2 day episodes included 13 reports. Time series of the concentrations as simulated by the Lagrangian random particle model at each of the 28 receptors (see Table 2) are shown in Appendix F. Type and color of each line indicate concentrations at the each receptor. Vertical red lines indicate time of the complaint. A visual inspection provides a qualitative estimate of the agreement between plume movement and odor/health reports:

• Reports #20 & #21: Excellent coincidence and agreement.

• Reports #22 & #23: Good agreement of the model and two of the receptors for report #22. Report #23 does not show great agreement.

• Reports #25 & #26: Excellent coincidence and agreement, especially about the onset of high concentrations at most of the receptors.

• Report #28 does not show good agreement. It looks like the model indicated some higher concentrations around midnight and then the next elevated concentrations are for the next evening and night.

• Reports #32 & #33: Excellent coincidence and agreement. Both complaints are falling in the relatively short time interval of elevated concentrations at almost all receptors.

• Reports #34-#37: Not good agreement. The complaints were in the early morning while the increased concentrations were simulated later in the day.

All time series, similar to the example in Fig. 16, are included in Appendix F.

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Receptor #11

Receptor #5

Fig. 16. Time series of concentrations for reports #28 (left panel) and #32/#33 (right panel) predicted by the Lagrangian Random Particle Model at all receptors. Time of the report is indicated by the vertical line.

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Figure 16 shows cases when the model results did not indicate elevated concentrations at the time of complaint (Fig. 16, upper panel) and when the model results did show significant elevated concentrations during the time of the complaint (Fig. 16, lower panel). To understand the spatial evolution of the transport and dispersion of atmospheric pollutants emitted from the Intel emission sources, we also created animation for all these 6 episodes. The animation files are given as an electronic attachment (Appendix G).

Although there is a correspondence between the model results and odor/health reports, the subjective and stochastic nature of the reports is not as reliable as ambient concentration measurements would be. The only way of verifying the model results is to compare with fully spatially and temporarily representative monitoring. Also, there are uncertainties in the atmospheric and dispersion models as well as scarcity of necessary data to drive and evaluate the models. This might be addressed in the future by enhancing meteorological and chemical monitoring data and using tracer data to evaluate the atmospheric and dispersion models.

8. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS An air quality dispersion modeling study was undertaken in response to odor and

health reports filed with the New Mexico Environment Department (NMED) by residents of the Village of Corrales in Sandoval County, NM. Most of the reports were filed by residents along the western benches of the Rio Grande river valley located southeast of an Intel semiconductor manufacturing facility. Thirty-six organic and inorganic gases have been identified as potential emissions from this facility at generally low average emission rates.

The purpose of the modeling study was to determine wind flows in the area and to estimate ambient concentrations of semiconductor manufacturing emissions near residences where reports have been filed. This was accomplished by applying the CALMET diagnostic meteorological model to wind measurements from six surface and upper-air monitoring stations in the area for the period of 1 July, 2001 through 30 June, 2002. These CALMET wind fields were coupled with emission rate estimates and locations for the semiconductor facility using the CALPUFF dispersion model to estimate 1-hr. 3-hr., 8-hr, 24-hr, and annual maximum surface concentrations of chemical compounds.

Sixty odor and health reports were filed for the study period, 45 of which identified the time of day. Most of the reports were during nighttime (17) and evening hours (17); morning (7) and afternoon (4) reports were less frequent. Most reports occurred in the summer and fall (28 and 23, respectively). Winter and spring reports may have been fewer due to less time spent outdoors and lower outdoor to indoor infiltration. Although several olfactory characteristics were described in the reports, none of these could be associated at this time with identified chemicals from the semiconductor facility.

During the night, local circulations develop with westerly, northwesterly, and northerly drainages from the western plateau toward the river valley. Wind speeds are low. During the day, mainly southwesterly/southerly flows develop from the cooler valley floor air toward the warmer air over the slope. Windspeeds and the mixing depth are higher during day compared to the nighttime. Flows from the western plateau toward

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the lower benches on the western side of the river valley correspond to the evening and nighttime odor and health reports. Some reports were made when the flow was not favorable to transport from the western plateau to the residences, and not all favorable transport situations corresponded with an odor/health report. Odor and health reports are subjective, and are limited to living (being inside or outside of residence), working (being absent or present), sleeping habits, and sensitivity thresholds. Model results are derived using sparse measurements and in some of the cases cannot fully represent atmospheric flows and thermal stability in this complex terrain. Emissions causing the odor/health symptoms may be sporadic, rather than constant as assumed for the modeling.

Although many of the odor and health reports correspond meteorologically to transport from the semiconductor facility, ambient chemical characterization is insufficient to determine which chemicals are causing the nuisance. Emissions source profiles (fingerprints) could be quantified at different emission points and separated from other contributors at key receptors using receptor models (Watson et al., 2001). More detailed meteorological monitoring and prognostic meteorological modeling would provide better transport estimates. Threshold symptomatic studies, using nearby residents as panelists, might better identify the olfactory and health characteristics of the incidents.

9. REFERENCES Gertler, A., D. Koracin, J. Lewis, M. Luria, J. Sagebiel, and W. Stockwell, 2002: Development of a predictive model to assess the impact of coastal emissions on urban scale air quality. Proceedings, CRC Air Toxics Modeling Workshop, Houston, Texas, 26-27 February 2002. Koracin, D. and V. Isakov, 2002: Local versus regional scale transport and dispersion processes of diesel PM emissions. Proceedings, CRC Air Toxics Modeling Workshop, Houston, Texas, 26-27 February 2002. Koracin, D., V. Isakov and J. Frye, 1998: A Lagrangian particle dispersion model (LAP) applied to transport and dispersion of chemical tracers in complex terrain. Presented at the Tenth Joint Conference on the Applications of Air Pollution Meteorology, Phoenix, AZ, 11-16 January 1998. Paper 5B.5, 227-230. Koracin, D., J. Frye, and V. Isakov, 2000: A method of evaluating atmospheric models using tracer measurements. J. Appl. Meteor., 39, 201-221. Koracin, D., V. Isakov, D. Podnar and J. Frye, 1999a: Application of a Lagrangian random particle dispersion model to the short-term impact of mobile emissions. Proceedings of the Transport and Air Pollution conference, Graz, Austria, 31 May - 2 June 1999. Koracin, D., W.R. Stockwell, D. Freeman, and D. Podnar, 1999b: Impact of offshore Nox emissions on Air Quality in the San Diego area. Technical report. Submitted to the U.S. Generating Company, San Francisco, CA, 42 pp.

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Koracin, D., D. Podnar, T. McCord, V. Isakov, and J. Powers, 2001: Development of an operational version of the atmospheric model (MM5) and dispersion model (Lagrangian random particle model) for Nevada and California, centered in the Truckee Meadows. Phase II: Completion of the operational forecast system and visualization of the possible transport and dispersion of atmospheric pollutants from the Sacramento and Reno sources. Technical report. Submitted to Washoe County District Health Department, Reno, NV, 30 pp. Moschandreas, D.J.; Watson, J.G.; D'Abreton, P.; Scire, J.S.; Zhu, T.; Klein, W.; and Saksena, S. (2002). Chapter three: Methodology of exposure modeling. Chemosphere, 49(9):923-946. Scire, J.S., F.R. Robe, M.E. Fernau, R.J. Yamartino, 2000a: A User’s Guide for the CALMET Meteorological Model. Available from Earth Tech Inc., 196 Baker Ave., Concord, MA 01742. Scire, J.S., D.G. Strimaitis, R.J. Yamartino, 2000b: A User’s Guide for the CALPUFF Dispersion Model. Available from Earth Tech Inc., 196 Baker Ave., Concord, MA 01742. Strimaitis, D.G., J.S. Scire, and J.C. Chang, 1998: Evaluation of the CALPUFF dispersion model with two power plant data sets. Proceedings on 10th Joint Conference on the Applications of the Air Pollution Meteorology, 11-16 January 1998, Phoenix, Arizona. U.S. Government, 2000: Requirements for Preparation, Adoption, and Submittal of State Implementation Plan (Guideline on Air Quality Models), Part II. EPA, Federal Register, April 21, 2000. Watson, J.G.; Chow, J.C.; and Fujita, E.M. (2001). Review of volatile organic compound source apportionment by chemical mass balance. Atmos. Environ., 35(9):1567-1584. Watson, J.G.; Zhu, T.; Chow, J.C.; Engelbrecht, J.P.; Fujita, E.M.; and Wilson, W.E. (2002). Receptor modeling application framework for particle source apportionment. Chemosphere, 49(9):1093-1136.

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10. APPENDIX A – List of registered odor and health reports in Corrales from 1 July

2001 – 30 June 2002.

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11. APPENDIX B – Plots of the surface wind vectors as simulated by the CALMET

atmospheric model for each of the registered odor/health reports. There are three plots for each of the odor and health reports, i.e., 6-hr averages prior, at, and after of the each report. Diamond symbols indicate meteorological stations used in the analysis and modeling (see Table 5).

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12. APPENDIX C – Locations and technological specifics for scrubber and thermal

oxidizer stacks for the semiconductor facility.

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13. APPENDIX D – Plots of CALPUFF concentration patterns produced for the time of

each odor/health report with a time and location, along with patterns before and after the report time from 1 July 2001 – 30 June 2002.

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14. APPENDIX E – Maximum 1-hr, 3-hr, 8-hr, 24-hr, and annual surface concentrations

for all chemicals (suggested by the NMED) with indicated receptors.

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15. APPENDIX F – Time series of the surface concentrations of a common chemical (see

Sections 6.1-6.2) as simulated by the Lagrangian Random Particle Model at each of the 28 receptors (see Table 2) for 13 odor and health reports including six 1-2 day episodes. Type and color of each line indicate concentrations at the each receptor. Vertical red lines indicate time of the odor/health report.

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16. APPENDIX G – Animation files showing the transport and dispersion of the chemical

compounds emitted from the semiconductor facility and simulated by the Lagrangian Random Particle Dispersion Model (see Section 7). The animated episodes are:

• Report #20 (3 September 2001 at 10:30pm; Receptor #5) and # 21 (4 September 2001 at 6:30am; Receptor #5)

• Report #22 (6 September 2001 at 6:30am; Receptor #5) and # 23 (6 September 2001 from 1:00 to 7:00pm; Receptor #7)

• Report #25 (7 September 2001 at 6:15pm; Receptor #5) and # 26 (7 September 2001 at 4:15pm; Receptor #5)

• Report #28 (10 September 2001 at 6:00am; Receptor #11)

• Report #32 (15 September 2001 at 4:00am; Receptor #5) and # 33 (15 September 2001 at 3:39am; Receptor #5)

• Report #34 (18 September 2001 at 6:00am; Receptor #11); # 35 (18 September 2001 at 4:30am; Receptor #5); #36 (18 September 2001 at 4:30-7:30am; Receptor #10); # 37 (18 September 2001 at 4:30am; Receptor #5)

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