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INVESTIGATION OF LOW-WIND/HIGH PARTICULATE MATTER

EPISODES IN THE IMPERIAL VALLEY/MEXICALI REGION

Project Number: A-05-07

KERRY KELLY, CRISTINA JARAMILLO, DAVID WAGNER, AND JOANN LIGHTY, UNIVERSITY OF UTAH KIMBERLY COLLINS AND MARCO BEJARANO, SAN DIEGO STATE UNIVERSITY, IMPERIAL VALLEY MARGARITO QUINTERO NÚÑEZ, JOEL VILLAR, AND CARLOS GONZALEZ, UNIVERSIDAD AUTÓNOMA DE BAJA CALIFORNIA

INTRODUCTION Currently, the EPA classifies Imperial County, CA as a nonattainment area for PM10

(particulate matter smaller than 10 m in diameter); the Calexico Ethel St. monitoring station violated ambient air quality standards for airborne PM on 219 days in 2004 (CARB, 2006). In general, high concentrations of PM10 have been linked to increased mortality and morbidity rates (Pope et al. 1991, Dockery et al. 1993, Maynard and Maynard 2002, Peters et al. 2001). In the border region, PM levels have been linked to adverse health effects. In Imperial County, the death rate from respiratory diseases is more than double that of California, and the childhood asthma rate is the highest in the state (CDHS 2002). In Mexicali, chronic bronchitis is one of the leading causes of death (Ramírez, 2000). Associated with PM10, Imperial County reports the highest child asthma index in California, and Mexicali reports the highest child asthma index in Baja California (Reyna et al., 2003). Using Poisson regression models, Reyna et al. (2005) found that asthma is more sensitive to PM10 than air respiratory infections (ARI).

Although high annual and daily average PM levels can have negative health and economic effects, a potentially more important problem is that of short-term spikes in PM levels, connected with low-wind conditions in the evenings and in the mornings. These PM episodes are especially relevant in light of the recent studies showing changes in heart rate variability (Gold et al. 2000; Magari et al. 2002) with short-term (1-

4 hour) peaks in PM levels. Peters et al. (2001) identified a link between a 22 g/m3 increase in PM2.5 concentration and cardiac arrhythmia.The maximum PM10 and PM2.5

concentrations in the study were 62.2 g/m3 and 53.2 g/m3, respectively. The PM episodes along the border involve PM concentration spikes at least 10-times greater than those reported by Peters et al. and maximum PM10 concentrations that sometimes

exceed 500 g/m3.

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The PM spikes have been observed in Calexico/Mexicali as well as other U.S.-Mexico border locations including El Paso, Hidalgo/Reynosa, Calexico/Mexicali, and Douglas/Agua Prieta (Staniswalis et al. 2005; Li et al. 2001, 2005; Jeon et al. 2001; Sheya et al. 2002; Anderson 2003).These PM episodes typically last a few hours, occur in the evenings and sometimes mornings, and are more prevalent during the winter season.

Although the high PM episodes associated with low-wind conditions/transition fronts may have a significant health impact, little is understood about them. For example, their frequency, association with meteorological conditions, location, composition, and sources are poorly understood. Because current EPA rules only require the reporting of 24-hour PM2.5 and PM10 averages and annual arithmetic means, the presence of these transient high PM episodes goes unnoticed by regulators because the episodes only last a few hours and are obscured by the daily average.

RESEARCH OBJECTIVES As a first step toward understanding these short-term PM events, researchers at the University of Utah, Universidad Autónoma de Baja California (UABC), and San Diego State University at Imperial Valley (SDSU-IV) investigated the frequency, severity, and composition of low-wind/high particulate matter (PM) episodes in the Imperial Valley/Mexicali Region. Specifically, the objectives of this study were to:

Determine the frequency and severity of PM episodes in the Imperial/Mexicali Valley.

Develop an automated PM recorder and sampler and deploy it in Calexico and Mexicali.

Collect and analyze filter samples of PM from these episodes.

Using x-ray fluorescence (XRF) for inorganic components and thermal-desorption gas-chromatography/mass spectrometry (TD GC/MS) for organic components, identify the composition of the PM during these high PM episodes.

RESEARCH METHODOLOGY The SCERP team collected hourly PM data to complement existing state data and filter samples from high-PM/low-wind episodes. Hourly PM2.5 data are currently available in Calexico at the Ethel Street monitoring station. Meteorological data is available for the region. The filter samples and hourly PM data were analyzed to identify the frequency and severity of the episodes and composition of PM during high-PM/low-wind episodes. To accomplish this, the approach was divided into four tasks.

Task 1: Develop a small, robust, lightweight sampling system to provide hourly PM results and to trigger the collection of PM samples during a high PM episode. The University of Utah developed a sampling system to provide hourly PM data and collect samples during high PM episodes. Figure 1 illustrates the sampling system. It includes a DustTrak (TSI Inc.) nephelometer, two dichot filter samplers with pumps, and a switching mechanism to permit only one sample. The DustTrak recorded a PM10

reading every 5 seconds, and when PM10 concentrations reached 150 g/m3 it triggered

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the pumps on the dichot filter samplers. One Dichot sampler held a 23 mm Teflon filter (used for inorganic analysis) and the other held a 23 mm quartz filter (used for organic analysis). If triggered, the PM sampler collected one three-hour quartz sample and one three-hour Teflon sample. Students from UABC and SDSU checked the sampler daily, reset the switch, and the replaced filters. The DustTrak uses light scattering to provide a qualitative measurement of PM mass concentration, although measurements can be calibrated with a corresponding mass sample. Figure 1 provides a depiction of the PM sampler

Task 2: Deploy the PM sampler at SDSU and train students at UABC and SDSU-IV to operate the PM sampler. Ms. Kelly from the University of Utah visited SDSU-IV to deploy the PM sampler atop their library (Figure 2) and trained the SDSU-IV and UABC students in the assembly and operation of the PM sampler and sample handing. Every day, the sampler required: downloading of the DusTrak PM results, description of the weather and any relevant observations, resetting of each of the switches, and replacement of the treated filter, if a sample was collected. Once the filter was removed, it was placed in its individual stainless steel container and labeled with date and time, and a new filter was placed in the Dichot sampler. The filters were then weighed and analyzed as discussed in Task 3.

Sampling began on January, 16th 2006 in Calexico and continued until February 17th, 2006. At that time UABC Dr. Quintero and his students transported the PM sampler to UABC (Figure 2), deployed it on the roof of the second floor of the Institute of Engineering at UABC Main Campus located at Av. de la Normal Ave. The UABC students began collecting PM samples on February 24th, 2006 and continued until March 23rd. Table 1 indicates the dates and times the PM filters were collected, the average PM10 concentration and windspeed during the sample. Figure 2 provides a map of sampling locations and the Calexico-Mexicali region.

Task 3: Analyze the filter samples for organics and inorganic composition by GC/MS and XRF. All filters were weighed using a Mettler Toledo AX balance, calibrated weekly using internal and external weight adjustment. The filters were placed on the weighing pan and the draft shield was closed. The weighing result was recorded as soon as the stability detector symbol indicated that the weighing reading was stable. The quartz filters were pretreated to eliminate possible adsorbed organic material by baking at 600-725 oC over approximately 12 hours in a porcelain container in a furnace prior to being weighed.

A few unintended filter samples were collected (as described in Table 1) because the DusTrak occasionally provides short-term spurious readings (see Figure 3), which might be caused by a wind gust or a large particle passing through the instrument. Because the alarm function of DusTrak uses a 5-second interval (could not be modified) to trigger the sampling system, it was not possible to eliminate the collection of unintended filter samples.

Once the filters were collected and weighed, selected samples were analyzed using x-ray fluorescence (West Coast Analytical, Santa Fe Springs, CA), as well as the organic,

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elemental, and total carbon samples (Sun Laboratories, Hillsboro, NC). All of the filters were analyzed by TD GC/MS, as described below.

The University of Utah developed a thermal desorption GC/MS technique for analyzing microgram quantities of particles captured on a filter. This technique enables researchers to shorten filter sample collection periods. The results from the thermal desorption technique compare well to the traditional solvent extraction GC/MS techniques (Jeon et al., 2001). Before the filter sample is analyzed, a 1.5 x 18 mm2 strip is cut from the filter, which represents approximately 10% of the exposed filter area. The University of Utah developed the strip cutter, which consists of parallel razor blades and a pair of ultrasonically cleaned forceps. The filter strips are then placed inside a glass reaction tube that is lined with ferromagnetic foil characterized by the Curie-point

temperature of 315 C for desorption, and placed into a Curie-point reactor for desorption of volatile and semivolatile organic compounds. In the Curie-point reactor, a high-frequency induction coil surrounds the glass tube and heats the foil by induction. The foil is heated until its Curie point is reached. Flash desorption of organic compounds from the ambient particles on the quartz-fiber filter chips is achieved using a total heating time of 10 seconds under a continuous flow of helium, which transfers the analytes from the reaction zone into the fused silica capillary column of the GS/MS. The compounds analyzed by GC/MS are presented in Table 4.

Subsequent to thermal desorption, a temperature-programmed separation of analytes coupled with simultaneous MS monitoring is begun. The GC analysis is performed with a Hewlett-Packard Model 5890 gas chromatograph using an HP-5MS column with a 25-

m-long, 0.20-mm-i.d. fused silica capillary coated with a 0.33 m-thick film equipped with a thermal desorption inlet. The oven is temperature programmed from 50 to 320

C at a rate of 3 to 15 C/min. Compound identification and peak area integration are performed with quadrupole-type mass selective detectors (MSD 5972 or MSD 5973 from HP) operated in the electron impact mode with an electron energy of 70 eV.

Task 4: Analyze the results. The SCERP team analyzed the results, reviewed the original sampling notes, and integrated the meteorological conditions, the hourly PM data from the PM monitoring stations, the x-ray fluorescence, and the GC/MS results.

PROBLEMS ENCOUNTERED Although the sampling and analysis campaigns were successful, several problems arose:

The weather became windier as the sampling at UABC progressed; therefore, fewer PM peaks were identified at this location.

The University of Utah developed a Teflon filter holder that was similar to the Quartz filter holder, for a specialized 23 mm filter size. This size works well for the TD/GC-MS method, but few analytical labs were able to perform XRF on this size filters. Consequently, the XRF analyses were much more expensive than

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planned ($200/filter) instead of $75/filter (as budgeted), which limited the number of samples that could be analyzed for inorganic constituents.

Because the filter areas were small, the collecting an accurate weight was a challenge. In addition, a few filters ripped during handling, and occasionally small amounts of the filter material stuck to the gasket, making the weight inaccurate.

RESEARCH FINDINGS Real-time results during study Figures 3 and 4 provide graphical examples of an evening low-wind, high PM event in Calexico and Mexicali. In Figure 3 the decrease in windspeed at approximately 19:30 corresponds with an increase in PM levels, although the windspeeds were low throughout this sampling period. The PM levels continue to increase substantially to a

level of 500 g/m3. Note that EPA’s 24-hour National Ambient Air Quality Standard for

PM10 is 150 g/m3. An additional PM event is evident in the early morning hours. These two events are responsible for 75% of the mass concentration during this sampling period. Figure 4 shows another example of a low-wind, high PM evening and a smaller morning event in Mexicali, which is responsible for 49% of the PM mass during this sampling period.

The DusTrak measurements shown in the figures were corrected with a mass calibration factor from the corresponding filter mass. These factors ranged from 0.68 – 1.22 (filter mass/DusTrak mass) with an average factor of 0.99 and a standard deviation of 0.19. Figures 3 and 4 typify the PM10 concentration trends that were seen during low-wind days during the study. Note that occasionally DusTrak provided a spurious reading that was unlikely to be representative of the ambient PM levels. Figure 4 shows the DusTrak PM10 values for March 15 – 16th, 2006. The filter sample time is noted; wind data were not yet available.

California Air Resources Board Data The DusTrak results presented in Figure 3 and 4 are also similar to average PM2.5 trends during the winter months available from the California Air Resources Board for the Ethel Street monitoring station in Calexico, located a few blocks from the SDSU-IV library (Figures 1, 5 and 6). The winter-season hourly PM concentrations show a diurnal pattern, with an increase in PM levels at about 17:00 hours continuing until 2:00 hours, with another smaller increase at around 4:00 hours. During the summer season, this diurnal pattern is not as apparent. Figure 5 gives the hourly average PM2.5 concentrations during December, January, and February (2004-2006), from the Ethel St. monitoring station, managed by the California Air Resources Board. Figure 6 shows the hourly average PM2.5 concentrations during June, July, and August (2004-2006) at the Ethel St. monitoring station. During the winter months (December - February), 9% of hours exceeded the PM2.5 NAAQPS 24-hour standard, but during the summer months (June – August) only 1% of

hours exceeded the standard of 65 g/m3 (Figures 7 and 8). Figure 7 shows how the

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number of PM exceedances increases dramatically at 19:00 hrs during the winter months. The winds during the winter months in this region tend to come from the northwest (Figure 9), whereas the winds during the summer months tend to come from the southeast (Figure 10). The highest PM2.5 concentrations during the winter months are associated with winds from the west. Filters collected and conditions The sampling team collected a total of 34 Teflon and 34 quartz filters during the study. Table 1 shows the sampling conditions, and Table 2 shows the filter mass and carbon content. Because the filters were small (23mm diameter), occasionally some material would stick to the filter gasket or the filter would tear, causing an inaccurate weight reading. Zeros in Table 2 indicate that a filter had a mass of less than zero or was torn. All of the filters were analyzed by GC/MS, and selected filters were analyzed by XRF, total carbon, elemental carbon, and organic carbon. We selected filters for XRF and carbon analysis to include several low-wind, high PM events, locations in both countries, and a few PM samples collected during windier days. XRF results The XRF results summarized in Table 3 show that on windy days, such as 2/15/06, the mass contribution from silica and other inorganic constituents was greater than on the low-wind days. Another interesting result of the XRF analysis is that the chlorine contribution to PM mass. If one compares the two filters that were collected during low-wind/high PM events (1/17 and 1/20) with the one collected during a time without an event but also without a lot of wind (2/3), one can see a 10-fold increase in Cl contribution for the high PM events. The Mexicali filters were a challenge. Because the winds were gusty during this period, it was difficult to collect a filter during calm wind conditions, so the XRF results for Mexicali were not particularly enlightening. EC/OC results Table 2 also shows the elemental (EC), organic (OC), and total carbon (TC) content of selected filters. Filters 18 and 25 have the lowest TC content and correspond to samples collected during windier conditions. These results can provide an idea of how important the GC/MS or organic contributions are to the total PM collected on the filter. These will be more important during the subsequent source attribution studies. GC/MS results Figures 11 and 12 are indicative of the GC/MS results from Calexico and Mexicali. They tended to be enriched in alkanes, alkanoic acids, PAHs, terpenoids, esters, phtalates, ethoxy ethanol and nitrogen containing compounds are the most representative groups present in the samples. These results suggest that vehicles,

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trash burning, and agricultural sources may be the primary sources of the organic portion of the PM. Because of the widespread use of phthalates in plastics, it is possible that the phthlates are an artifact from the sampling or analysis. The chromatograms from Calexico and Mexicali, regardless of the wind conditions and the presence of a low-wind/high PM episode, tended to look similar. These results were also similar to those of samples collected during the summer of 2004 (Kelly et al. 2005). This would suggest that these low-wind/high PM episodes are caused by meterological phenomena as opposed to a few specific sources. Because of the windy nature of the weather during the Mexicali sampling, the Mexicali filters had fewer peaks. CONCLUSIONS The results indicate that low-wind high PM episodes are relatively common during winter months. Over the course of three years, 9% of hours exceeded the PM2.5 NAAQPS 24-hour standard during winter months, but during the summer months only 1% of hours exceeded the standard. These episodes can be responsible for a large portion of the daily PM mass, and can involve PM10 concentrations that can exceed 500

g/m3 and PM spikes of hundreds of g/m3. In light of recent studies showing the

adverse health effects of short-term spikes on the order of tens of g PM/m3, these events could have significant adverse health effects on the population of Calexico-Mexicali.

The organic analyses of the filters show that all of the samples regardless of whether they were collected during a low-wind high PM episode tended to be enriched in enriched in alkanes, alkanoic acids, PAHs, terpenoids, esters, ethoxy ethanol and nitrogen-containing compounds. These compounds are indicative of cars, trash burning, and agricultural sources. The SCERP team will be performing a source attribution of the samples in a subsequent SCERP project.

Given the frequency, severity, and potential impacts of these episodes on the region, they merit further study to assess their potential causes, health effects, and policy implications.

RECOMMENDATIONS FOR FURTHER RESEARCH

Based on the results of this study, the SCERP team has the following recommendations:

Collect more hourly PM data. If the air quality authorities could collect and make available more hourly PM data, this would help elucidate the distribution of these events and might eventually be used to warn sensitive individuals about poor air quality in an area.

In order to develop a better understanding of the sources of these PM events,

the investigators are beginning a source attribution study (as part of a follow-on SCERP project).

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If the study were repeated with a little more funding, the team could collect and

analyze more Teflon filters, which could provide a more complete picture of both the organic and inorganic sources of low-wind/high PM events, both organic and inorganic.

Integrate PM and meterological data.

RESEARCH BENEFITS

This study demonstrated numerous low-wind, high PM events in Calexico and Mexicali during the study period (winter 2006), and these trends are confirmed by several years of CARB’s hourly PM2.5 data. Some of the ambient PM10 concentrations during the

study period reached 500 g/m3, with corresponding windspeeds below 5 mph. In light of recent studies showing the adverse health effects of short-term elevations in PM level, these events could have significant adverse health effects on the population of Calexico-Mexicali. The SCERP team is currently working to publicize their results in the scientific and local communities. The results were presented at the annual SCERP conference, and the team is working on combining the analytical results from this study with source attribution work in a new SCERP study for publication in a peer-reviewed technical journal. The SCERP investigators have also been invited to present their results to the Imperial Valley air quality partners association. This SCERP project contributed to the training of three students, Cristina Jaramillo (PhD ChE, University of Utah), and Joel Villar an undergraduate student in electronic engineering at UABC, and Carlos Gonzalez is an undergraduate student in International Business at UABC. Ms. Jaramillo performed the GC/MS analysis and interpretation, and she will perform source attribution on the results from this study. This work helped to support Cristina’s thesis on low-wind, high PM events on the U.S.-Mexico border. Mr. Villar assisted with the sampling in Mexicali, and he received course credit for his work. Prof. Quintero from UABC was one of the co-investigators in this project and provided international cooperation in the border region. His students performed a significant portion of the field sampling. ACKNOWLEDGMENTS

This work was sponsored by the Southwest Consortium for Environmental Research and Policy (SCERP) through a cooperative agreement with the U.S. Environmental Protection Agency. SCERP can be contacted for further information through www.scerp.org and scerp@mail.sdsu.edu.

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REFERENCES

Anderson John. 2003. “Transborder Flux of Fugitive Dust at Douglas, Arizona - Agua Prieta, Sonora", Southwest Center for Environmental Research and Policy Spring meeting, San Francisco, 1, April 2003.

CARB (California Air Resources Board) 2006. “PM10 Trends Summary: Calexico-Ethel Street”, (cited August 3, 2006) http://www.arb.ca.gov/adam/cgi-bin/db2www/polltrendsb.d2w/Branch CDHS (California Department of Health Services, Environmental Health Investigations Branch) 2002. “California Environmental Health Indicators”. Brook R.D., J.R. Brook, and S. Rajagopalan. 2003. “Air Pollution: the ‘Heart’ of the Problem”. Current Hypertension Report 5(1):32-39.

Brunekreef B. and B. Forsberg 2005. Epidemiological Evidence of Effects of Coarse Airborne Particles on Health. European Respiratory Journal 26(2):309-318.

Dockery, D.W. A.C. Pope, X. Xiping, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. Speizer. 1993. “An Association between Air Pollution and Mortality in Six U.S. Cities”. New England Journal of Medicine 329 (24) 1753-1759. EPA Region 9. 2002. Program to Improve Air quality in Mexicali (2000-2005) http://www.epa.gov/region09/border/airplans/mexicalieng.pdf Fernando, H.J. S. Personal communication, 2003. Gold D.R., A. Litonjua, J. Schwartz, E. Lovett, A. Larson, B. Nearing, G. Allen, M. Verrier, R. Cherry, and R. Verrier. 2000. “Ambient Pollution and Heart Rate Variability”. Circulation 101(11):1267-1273. Hunt, J.C. R., H.J.S. Fernando, and M. Princevac. 2003. “Unsteady Thermally Driven Flows on Gentle Slopes. “ Journal of Atmospheric Sciences. 60 (7), 2169-2182. Jeon, S.J., H.L.C. Meuzelaar, S.N. Sheya, J.S. Lighty, W.H. Jarman, C. Kastelar, and A.F.S. Sarofim, "Exploratory Studies of PM10 Receptor and Source Profiling by GC/MS and Principal Component Analysis of Temporally and Spatially Resolved Ambient Samples", Journal of the Air and Waste Management Association, 51, 766-784.

Kelly, K.E., I.C. Jaramillo, N. Arnold, K. Wendell, J. Gardner, K. Collins, M. Quintero-Nunez, 2006.“Particulate Matter Emissions from Agricultural Burns in the Mexicali/Imperial Valley Region,” Project Number: A03-02, SCERP project report, San Diego State University Press, www.scerp.org.

Li, W.W., R. Orquiz, N.E. Pingitore Jr., J.E. Garcia, T.T. Espino, J. Gardea-Torresdey, J. Chow, and J.G. Watson 2001. “Analysis of Temporal and Spatial Dichotomous PM Air Samples in the El Paso-Cd. Juarez Air Quality Basin”. Journal of the Air and Waste Management Association, 51:1511-1560.

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Magari S.R., J. Schwartz, P.L. Williams, R. Hauser, T.J. Smith, and D.C. Christiani. 2002. “The Association between Personal Measurements of Environmental Exposure to Particulates and Heart Rate Variability”. Epidemiology 13(3):305-310.

Maynard, A.D. and R.L. Maynard 2002 “A Derived Association between Ambient Aerosol Surface Area and Excess Mortality using Historic Time Series Data”. Atmospheric Environment. 36, 5561-5567. Pope CA. 2000a. “Epidemiology of Fine Particulate Air Pollution and Human Health: Biologic Mechanisms and Who's at Risk?” Environmental Health Perspectives. 108 Suppl 4:713-723.

Pope CA. 2000b. “What do Epidemiologic Findings Tell us About Health Effects of Environmental Aerosols?” Journal of Aerosol Medicine 13(4):335-354.

Parks, N.J., J.G. Staniswalis, J. Bader, and Y. Munoz. 2000 “Association of Particulate Matter and Other Priority Air Pollutant Levels with Daily Mortality after Windstorms, Still-Air Inversions and Closely Timed Combinations.” Report to the Center for Border Health Research El Paso, Texas. Peters, A. E. Liu, R.L. Verrier, J. Schwartz, D.R. Gold, M. Mittleman, M. Baliff, J. Oh, J.A. Allen, G. Monahan, and D.W. Dockery. 2000. “Air Pollution and Incidence of Cardiac Arrhythmia”. Epidemiology 11(1), 11–17. Peters A. D. Dockery, J.E. Muller, and M.A. Mittleman. 2001 “Increased Particulate Air Pollution and the Triggering of Myocardial Infarction”. Circulation, 2810-2815. Peters A., S. von Klot, M. Heier, I. Trentinaglia, A. Hormann H.E. Wichmann, and H. Lowel 2004. “Exposure to Traffic and the Onset of Myocardial Infarction”. New England Journal of Medicine 351(17): 1721-1730. Pope, C.A., Dockery D.W., Spengler J.D., and Raizenne M.E. 1991. “Respiratory Health and PM10 Pollution. A Daily Time Series Analysis”. American Review of Respiratory Disease 1991 144 (3 Pt 1), 668-674. Ramírez S.C. 2000. “Public Health Issues in Mexicali and the Imperial Valley”. California Center for Border and Regional Economic Studies. http://www.ccbres.sdsu.edu/Community_Outreach/Workshops/Workshop4/Workshop4.PDF Reyna Carranza, M.A., Quintero Núñez, M., Collins, K., and Vildósola Reyes, R.L. 2003. “Análisis de la relación del PM10 con las enfermedades respiratorias en la población urbana de Mexicali, Baja california: Un Estudio de Series de Tiempo.” Revista Mexicana de Ingeniería Biomédica, Septiembre XXIV (2), 116-125. Reyna Carranza, M.A., Quintero Núñez, M., and Collins, K., 2005. “Correlation Study of the Association of PM10 with the Main Respiratory Diseases in the Populations of Mexicali, Baja California and Imperial County, California. Revista Mexicana de Ingeniería Biomédica, Marzo XXVI (1), 22-36.

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Sheya, Suzanne, 2002. “Development of Thermal Desorption-Gas Chromatography/Mass Spectrometry (TD-CG/MS) as a Rapid Method for Ambient Particulate Characterization”, PhD Thesis, University of Utah, College of Engineering. Romieue, I., M.R. Aguilar, H.M. Macias, A.B. Villarreal, L.H. Cadena, and L.C. Arroya. “Health Impacts of Air Pollution on Morbidity and Mortality among Children of Ciudad Juárez, Chihuahua, Mexico. Commission for Environmental Cooperation (CEC), Montréal, Canada. Staniswalis J.G., Parks N.J., Bader J.O., and Moldanado Y.M. 2005. “Temporal Analysis of Airborne Particulate Matter Reveals a Dose-Rate Effect on Mortality in El Paso : Indications of Differential Toxicity for Different Particle Mixtures“. Journal of the Air & Waste Management Association 55:893-902.

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Table 1. Filters collected and selected analysis. Filter Sample

Time Sample Date

Avg wind during sample

(mph)

Analysis Notes

Calexico samples

2 21:21

1/17 1.0 TC, XRF, GC Sunny and calm, noticed plumes from agricultural burns

4 14:19 1/20 4.6 - Poor sample*

5 18:17 1/21 3.5 GC

6 14:15 1/24 2.5 GC foggy, missed most of peak*

7 0:54 1/26 2 GC hazy overcast, small peak

8 18:56 1/27 1.7 TC, GC, XRF Calm

9 13:54 1/28 5.8 - Breezy

10 18:27 1/31 1.5 GC light winds, hazy, nice peak

11 13:55 2/1 5.8 GC Breezy, no peak sample

12 14:20 2/2 3.5 - Poor sample*

13 15:50 2/3 2.8 TC, XRF, GC No peak sample

14 4:45 2/9 4.6 - Breezy, no peak sample

15 6:37 2/10 2.3 - Poor sample*

16 6:19 2/12 8.1 - Breezy, no peak sample

17 21:21 2/14 2.3 GC Breezy day, no peak sample

18 13:50 2/15 18.5 TC, XRF, GC Windy

19 23:30 2/16 9 GC Windy

Mexicali samples

20 18:46 2/27 8.75 - windy

22 20:32 2/28 9.2 XRF, GC Breezy

23 19:59 3/1 10.4 - Breezy

24 19:24 3/2 2.1 XRF, GC Light winds, captured small peak

25 12:12 3/6 6.25 TC, GC Breezy

26 13:25 3/7 13.4 - Windy

27 13:20 3/8 4.6 - Light winds, poor sample*

28 15:46 3/9 10.8 - Windy

29 20:31 3/13 3.5 - Light winds, very small peak

31 20:03 3/15 1.5 GC sunny day, captured small peak

33 14:50 3/16 3.75 GC Light winds, captured peak

36 16:50 3/22 4.6 GC Light winds, no peak sample

* Sample triggered due to presumably spurious, 5-second PM spike.

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Table 2. Filter mass.

Filter Quartz TFE TC EC OC

# (mg) (mg) (%) (%) (%)

1 0.0045 0.7333

2 0.9529 0.8903 23.5 1.24 4.2 0.72 19.3 0.52

3 NA 0.8683

4 NA 0.9116

5 0.0528 0.6955

6 0.3848 2.1822

7 0.4086 0.907

8 0.5817 0.1555 18.9 1.75 3.5 0.91 15.4 0.84

9 0.2479 0.1773

10 0.7626 1.1378 13.2 2.16 3.2 1.6 10.0 0.56

11 0.5631 0

12 0.2877 2.3484

13 0.3551 4.1583 18.7 1.45 2.5 0.53 16.2 0.92

14 0.4289 0

15 0.4721 2.798

16 0.8834 2.9266

17 0.1503 0

18 2.4756 0 5.9 1.47 0.2 1.17 5.7 0.3

19 0 0

20 0 0.0156

21 0 0

22 0 1.4221

23 0.1057 0

24 0 0

25 1.5563 0.2595 2.2 1.99 0 1.86 2.2 0.13

26 0.0221 0

27 0 0

28 0.0713 NA

29 0.4315 NA

30 0.1235 0.0195

31 0.6025 0

32 0.6195 0.0676

33 0.1477 0

34 0 0.0214

35 0.0834 0

36 0 0

NA: filter blew away.

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Table 3. XRF results (weight percent).

Sample date 1/17 1/26 2/3 2/15 2/28 3/2

Calexico Calexico Calexico Calexico Mexicali Mexicali

Peak YES YES No No No No

Avg wind (mph) 1 2 2 7 8.75 5.33

Si 0.86 0.5 0.25 2.99 0.22 ND

Al 0.35 0.23 0.11 1.19 0.02 0.005

Mg 0.15 0.099 0.046 0.48 0.007 0.003

Ca 0.086 0.042 0.012 0.566 ND ND

Na 0.074 0.037 0.015 0.417 ND ND

Cl 0.067 0.041 0.005 0.193 ND ND

K 0.037 0.015 0.004 0.203 ND ND

Si 0.031 0.015 0.01 0.136 ND ND

P 0.008 0.0043 0.0023 0.025 ND ND

Ti 0.003 0.0019 ND 0.025 ND

Mn 0.0008 0.0007 0.0006 0.0027 ND ND

Fe ND ND ND ND ND ND

Co ND ND ND ND ND ND

Ni ND ND ND ND ND ND

Zn ND ND ND ND ND ND

Ga ND ND ND ND ND ND

Ge ND ND ND ND ND ND

As ND ND ND ND ND ND

Se ND ND ND ND ND ND

Rb ND ND ND ND ND ND

Total 1.6668 0.9859 0.4549 6.2277

ND = not detected

16

Table 4. Compounds identified by GC/MS. ID GROUP Component

1 C16

2 C17

3 alkanes(C19-22) C18

4 n-nonadecane (19)

5 n-eicosane (20)

6 n-heneicosane (21)

7 n-docosane (22)

8 alkanes(C23-26) n-tricosane (23)

9 n-tetracosane (24)

10 n-pentacosane (25)

11 n-hexacosane (26)

12 alkanes(C27-33) n-heptacosane (27)

13 n-octacosane (28)

14 n-nonacosane (29)

15 n-triacontane (30)

16 n-hentriacontane (31)

17 n-dotriacontane (32)

18 n-tritriacontane (33)

19 C34

20 alkanoic acids(10,12 13,18) n-decanoic acid

21 n-dodecanoic acid

22 n-tridecanoic acid

23 n-octadecanoic acid

24 alkanoic acids(14,16,17) n-tetradecanoic acid

25 n-pentadecanoic acid

26 n-hexadecanoic acid

27 alkanoic acids(9, 28,30) n-nonanoic acid

28 n-octacosanoic acid

29 n-triacontanoic acid

30 alkahols and ketones hexadecanol

31 docosanol

32 nonanal

33 tetradecanal

34 tridecanal

35 dehydrobietic acid dehydroabietic acid

36 methyldehydroabietate

37 benzaldehyde & amyrin 3-methoxy-4-hydroxybenzaldehyde

38 trimethylbenzaldehyde

39 4-hydroxy-3,5-dimethoxy-benzaldehyde (syringaldehyde)

40 -amyrin (3 -olean-12-en-3-ol)

41 small and medium (PAH) phenanthrene

42 anthracene

43 1-methyl-7-isopropylphenanthrene (retene)

44 fluoranthene

45 pyrene

46 benz[a]anthracene

47 chrysene/triphenylene

48 benzo[j]fluoranthene

17

49 benzo[e]pyrene

50 benzo[a]pyrene

51 large PAH perylene

52 indeno[1,2,3-cd]pyrene

53 indeno[1,2,3-cd]fluoranthene

54 benzo[ghi]perylene

55 arones 1H-phenalen-1-one

56 9,10-anthracenedione (anthraquinone)

57 terpenoids (20S and R)-5"(H),14 (H),17 (H)-cholestanes

58 22,29,30-trisnorneohopane

59 17(H), 21(H)-29-norhopane

60 17(H) 21(H)-hopane

61 N-containing& O-Containing quinoline

62 1-methyl-2-pyrrolidinone (m-pyrol) 1-methyl-2-pyrrolidinone (m-pyrol)

63 2-(2-butoxyethoxy)ethanol 2-(2-butoxyethoxy)ethanol

64 2-(2-(2-methoxyethoxy)ethoxy)ethanol

65 4-acetyl-2-methoxyphenol

66 benzoic acid benzoic acid

67 ester N,N-dibutylformamide

68 propanoic acid 2-methyl-2,2-dimethyl-1-(2-hydrxy-1-methylethyl)propyl ester

69 propanoic acid 2-methyl-3-hydroxy-2,4,4-trimethylpentyl ester

70 propanoic acid 2-methyl-1-(1,1-dimethylethyl)-2-methyl-1,3-propanediyl ester

71 tetradecanoic acid methyl ester

72 hexadecanoic acid 1-methylethyl ester

73 phtalates dibutyl phthalate

74 bis-(2-ethyhexyl)phthalate

75 diethyl phthalate

76 others o,o(o,p, or o,m)-quaterphenyl

77 1-(2-hydroxy-3-methoxy-4-methylphenyl)-ethanone

78 6,10,14-trimethyl-2-pentadecanone

79 tri-n-butylphosphate

80 additional caprolactam

81 octanoic acid

82 2,5-Cyclohexadiene-1,4-dione, 2,6-bis(1,1-dimethylethyl)-"2,6-Di-tert-butylbenzoquinone"

83 Pentadecane, 2,6,10

84 Hexadecane, 2,6,10,14-tetramethyl

85 extended list Isopropyl Myristate

86 Diisooctyl Adipate

87 Eicosanol

88 Squalene

18

Figure 1. PM sampler

Dichot Sampler:

PM10 Quartz

Source Attribution of PM

Dichot Sampler:

PM10 Teflon

Environmental Enclosure

Switch Switch

DusTrak

19

Figure 2. Map of sampling locations and Calexico/Mexicali region

20

Figure 3. DusTrak PM10 and windspeed (California Air Resources Board, Calexico Ethel St.) values for January 17-18, 2006. The DusTrak values were corrected using

corresponding filter mass measurements. The filter sample time is noted.

Filter #2 (Jan 17 - 18)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

16

:31

17

:06

17

:40

18

:15

18

:49

19

:24

19

:58

20

:33

21

:07

21

:42

22

:16

22

:51

23

:25

0:0

0

0:3

4

1:0

9

1:4

3

2:1

8

2:5

2

3:2

7

4:0

1

4:3

6

5:1

0

5:4

5

6:1

9

6:5

4

7:2

8

8:0

3

8:3

7

9:1

2

9:4

6

10

:21

10

:55

11

:30

12

:04

12

:39

13

:13

Time

PM

10

(m

g/m

3)

0

0.5

1

1.5

2

2.5

3

3.5

4

Win

ds

pe

ed

(m

ph

)

PM Wind

Filter sample

21

Figure 4. DusTrak PM10 values for March 15 – 16th, 2006. The filter sample time is noted. Note wind data were not yet available

Filter # 31 (March 15 - 16)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

13:1

4

13:5

6

14:3

8

15:1

9

16:0

1

16:4

3

17:2

4

18:0

6

18:4

8

19:2

9

20:1

1

20:5

3

21:3

4

22:1

6

22:5

80:

211:

031:

442:

263:

083:

494:

315:

135:

546:

367:

187:

598:

419:

23

10:0

4

10:4

6

11:2

8

12:0

9

12:5

1

Time

PM

10

(m

g/m

3)

Filter sample

22

Figure 5. Hourly average PM2.5 concentrations during December, January, and February (2004-2006), Ethel St. monitoring station, California Air Resources Board.

BAM2 (Winter)

1

10

100

1000

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

Time (hr)

PM

2.5

co

nc

en

tra

tio

n (

ug

/m3

)

The solid box indicates the mean, and the bars indicate the 25th and 75th percentile.

The hollow box indicates the maximum. 174 days

23

Figure 6. Hourly average PM2.5 concentrations during June, July, and August (2004-

2006), Ethel St. monitoring station, California Air Resources Board.

BAM2 (Summer)

1

10

100

1000

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

Time (hr)

PM

2.5

co

nc

en

tra

tio

n (

ug

/m3

)

153 daysThe solid box indicates the mean, and the bars indicate the 25th and 75th percentile.

The hollow box indicates the maximum.

24

Figure 7. Count of hours (December – February 2004 –2006) for which PM2.5

concentration exceeds 65 g/m3. The legend shows the total number of hours for which data were available

Winter Count of Hourly PM2.5> 65 ug/m3

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

Co

un

t

BAM1 (4225 hr)

BAM2 (4176 hr)

25

Figure 8. Count of hours (June – August 2004 –2006) for which PM2.5 concentration

exceeds 65 g/m3. The legend shows the total number of hours for which data were available

Summer Count of Hourly PM2.5> 65 ug/m3

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

Co

un

t

BAM1 (4322 hr)

BAM2 (3623 hr)

26

Figure 9. Hourly average windspeed and direction for winter (December, January, and

February 2004-2006).

0 1 2 3

0

1

2

3

0123

0

1

2

3

PM2.5 (0-15) (ug/m3)

PM2.5 (15-25) (ug/m3)

PM2.5 (25-32)(ug/m3)

N

S

W

30

60

90

120

150

180

210

240

270

300

330

0

E0 1 2 3

0

1

2

3

0123

0

1

2

3

PM2.5 (0-15) (ug/m3)

PM2.5 (15-25) (ug/m3)

PM2.5 (25-32)(ug/m3)

N

S

W

30

60

90

120

150

180

210

240

270

300

330

0

E

27

Figure 10. Hourly average windspeed and direction for summer (June, July, and August 2004-2006).

0 1 2

0

1

2

012

0

1

2

PM2.5 (15-25) (ug/m3)

N

S

W

30

60

120

150

180

210

240

270

300

330

E0 1 2

0

1

2

012

0

1

2

PM2.5 (15-25) (ug/m3)

N

S

W

30

60

120

150

180

210

240

270

300

330

E

28

Figure 11. GC/MS results summary for March 15 – 16th, 2006 for January 17-18,

2006, showing enrichment in alkanes, alkanoic acids, and esters.

others(76-88)

phtalates(73-75)

ester(68-72)

benzoic acid66

2-(2-butoxyethoxy)ethanol(63-65)

1-methyl-2-pyrrolidinone (m-pyrol)62

N-containing& O-Containing(61 &67)

terpenoids(57-60)

Arones55-56

PAH(41-54)

benzaldehyde & amyrin(37-40)

dehydrobietic acid35-36

alkahols and ketones(30-34)

alkanoic acids(20-29)

alkanes(1-19)

others(76-88)

phtalates(73-75)

ester(68-72)

benzoic acid66

2-(2-butoxyethoxy)ethanol(63-65)

1-methyl-2-pyrrolidinone (m-pyrol)62

N-containing& O-Containing(61 &67)

terpenoids(57-60)

Arones55-56

PAH(41-54)

benzaldehyde & amyrin(37-40)

dehydrobietic acid35-36

alkahols and ketones(30-34)

alkanoic acids(20-29)

alkanes(1-19)

29

Figure 12. GC/MS results summary for the quartz filter from March 15 – 16th, 2006.

30