Agricultural Burning: Air Monitoring and Exposure ...server.cocef.org/Final_Reports_B2012/20071/20071_Final_Report_EN.pdfAgricultural Burning: Air Monitoring and Exposure Reduction

Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, California

Final Report to U.S./Mexico Border Environmental Cooperation Commission Funded under Technical Assistance Agreement Number TAA08-068

Starting Date: 01-09-09

Total Project Duration: Two Years

Date of Final Report: 05-22-11

Authors:

Environmental Health Investigations Branch (EHIB)

California Department of Public Health

Martha Harnly: Principal Investigator

Kinnery Patel: Project Manager

Environmental Health Laboratory Branch (EHLB)

California Department of Public Health

Stephen Wall: Co-Principal Investigator

Jeff Wagner: Co-Investigator

Diamon Pon: Co-Investigator

School of Public Health

San Diego State University (SDSU)

Christopher Michael Carey: Field Instrumentation Operator

Penelope J. Quintana: Co-Investigator

Summary Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - i -

SUMMARY

Burning of agricultural fields to remove crop stubble occurs throughout the U.S./Mexico border region. Particulate matter (PM), notably PM with an aerodynamic diameter smaller than 2.5 micrometers (µm) (PM2.5), and thousands of chemicals, including polycyclic aromatic hydrocarbons (PAH), are emitted during agricultural burning. Investigators from the California Department of Public Health (CDPH) and San Diego State University (SDSU) obtained funding from the U.S. Environmental Protection Agency‘s Border 2012 Program through the Border Environmental Cooperation Commission (BECC) to: (1) conduct air monitoring in Imperial County, California, from January through March of 2009, a period when burn acreage in the county averaged 218 acres/day (range=0–1400); and (2) develop and distribute exposure reduction recommendations.

AIR MONITORING

The first component of monitoring was conducted by the California Air Resources Board (CARB). To provide accuracy measurements for other samplers, CARB deployed four Environmental Beta Attenuation Monitors (E-BAMs)™ which continuously measured PM2.5 and weather variables, including wind speed, for 69 days. Daily (24-hour average) PM2.5 air concentrations at places of public access (schools and a church) in northern, central, and western Imperial County ranged between < 6 and 21 µg/m3. These levels were:

below 35 µg/m3, a level at which air quality is designated as ―unhealthy‖ by the U.S. Environmental Protection Agency (EPA) Air Quality Index (AQI) guidelines; and

less than average daily concentrations detected in other agricultural and urban counties in California.

However, some (< 5%) of the daily (24-hour average) PM2.5 levels were above 16 µg/m3, a level at which the AQI designates air quality as ―moderate.‖ PM2.5 eight-hour average air concentrations were also 170% higher during evening-to-night (9.3 µg/m3) and night-to-morning (9.9 µg/m3) hours than during the day (5.7 µg/m3). On days with agricultural burning (n=35) this difference was more pronounced (evening-to-night average, 11.1 µg/m3), albeit this difference was within the accuracy of E-BAM measurements (2.5 µg/m3). In addition, daily burn acreage was statistically significantly (p=0.02) correlated with evening-to-night eight-hour average concentrations. Further, on days when an agricultural burn took place within two miles of a monitoring site (n=9), eight-hour average air concentrations were higher (evening-to-night and night-to-early morning, 19.5 and 20.7 µg/m3, respectively) than on days when burns were further from the monitoring site (n=60, p=0.02).

The second component of air monitoring targeted five specific burns of Bermuda grass stubble. Real-time portable monitors including active and passive nephelometers, which measured PM2.5 and PM10 (PM with an aerodynamic diameter less than 10 µm); and aethalometers, which measured black carbon, were successfully deployed to 8–11 locations. In addition, passive samplers, which did not require a field operator, to measure PM and naphthalene were placed at 27 locations. The majority of sampling locations were places of public access near (within 1.5 miles of) the burns, but included co-location at the nearest E-BAM, which was generally further (4–5 miles) away.

At only one of the targeted burns were sampling locations directly downwind. These locations were adjacent to the 120-acre field and alongside a public road. In samples from the downwind locations, but not in samples from an upwind (the E-BAM) site:

PM10 levels measured by a portable monitor personal DataRAMTM (pDR) nephelometer (pDR-1200) were highly elevated for two hours, achieving a maximum hourly level of 6500

Summary Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - ii -

µg/m3 and a 24-hour average level of 276 µg/m3. These levels are above those at which U.S. EPA AQI criteria could designate air quality as ―hazardous‖ (> 526 for hourly and 250

µg/m3 for 24-hour average, respectively). At these levels visibility is expected to be less than one mile. Observations by the sample collectors and photographs taken during the burn document that such reduced visibility occurred.

PM2.5 measured with the passive samplers was primarily carbonaceous, with the distribution of carbonaceous particles showing a peak of very fine particles (aerodynamic diameter < 1 µm).

naphthalene levels were elevated (1.4 µg/m3), but did not exceed a Cal/EPA health reference level of 9.0 µg/m3.

For the other four targeted burns, the winds shifted from the predicted direction and the monitors and samplers were not directly downwind at burn initiation. However, the real-time instruments consistently measured higher PM2.5, PM10, and black carbon during evening, night, and early morning hours than during the day. At one of the burns, monitors were very near the burn and at a school, and PM2.5 levels initially were very low (3 to 6 µg/m3, eight-hour average) during the evening of the burn, but then gradually climbed over the three-day sampling period, reaching peaks that were three- to six-fold over initial levels (19–20 µg/m3) in the night to early morning (12:00 to 8:00 AM) of the second day following the burn. Peaks in PM10 and black carbon concentrations were also apparent over the same periods.

Our study was limited in the number of samples collected. Our results do not represent legal violations: the instrumentation used did not meet requirements for enforcement and hourly or daily elevations above AQI levels are allowed under 24-hour health-based PM standards. Nonetheless, our results suggest that:

directly downwind of burns highly elevated air pollutant concentrations suggestive of ―hazardous‖ air quality may occur. For hazardous PM levels, there is serious risk of respiratory and cardiovascular effects.

during evening, night, and morning hours increases in PM2.5 levels occur. These increases may be associated with agricultural burning in Imperial County and may approach levels corresponding to ―moderate‖ air quality. At moderate air quality levels of PM, respiratory symptoms are considered possible in unusually sensitive people and older adults.

EXPOSURE REDUCTION

To assess health educational needs and attitudes, we conducted qualitative Key Informant Interviews (KIIs) with Imperial County community leaders, residents in areas of agricultural burning, school representatives, and farmers. Community leaders who represented government agencies were interested in training staff on the health effects of smoke, while community leaders who represented organizations working to improve air quality expressed interest in broader outreach and notification of burn events. School representatives had concerns about resources required, but agreed that schools would be an excellent place for outreach. Farmers were interested in the contribution of burn smoke to regional air pollution and in informing neighbors about upcoming burns.

To promote behavioral recommendations to reduce exposures, fact sheets for three separate audiences—the general public, school representatives, and farmers—were developed. Universally, they advised staying away from ground-level smoke plumes. For those who must stay outside and near a burning field, respirators were recommended. Recommendations for farmers included the above plus alerting anyone within a mile and a half of a field to be burned.

Summary Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - iii -

The fact sheets were reviewed by members of the U.S./Mexico Border 2012 Environmental Health Taskforce (BEHT), and the fact sheet for the general public was pre-tested, in both English and Spanish, among 20 residents for clarity and utility. These fact sheets are posted on CDPH‘s website and are being distributed locally by BEHT members.

RECOMMENDATIONS FOR FURTHER PUBLIC HEALTH ACTIONS

The BEHT strongly encouraged project staff to make additional recommendations for further public health actions to reduce exposure. In 2010, the Imperial County Air Pollution Control District (IC APCD) revised its smoke management plan. Recommendations for additional activities that could support or complement that plan, as well as for additional research, were developed and are summarized below:

HEALTH EDUCATION AND OUTREACH

distribution of fact sheets about agricultural burning during the burn permit process, at places of public access, at community meetings, and on agency websites;

development of additional targeted materials for workers;

daily web postings or email alerts for residents of upcoming burns.

EXPOSURE REDUCTION

assessment of whether changes to the meteorological criteria under which burns are allowed would reduce ground-level drift or evening-to-morning air particle levels;

consideration of expanding the IC APCD smoke management plan to include:

o farmers describing alternative techniques which they considered prior to burning;

o daily acreage limitations based on estimated emissions or air quality.

ADDITIONAL RESEARCH

analysis of the benefits and limitations of alternative farming techniques;

outdoor and indoor air monitoring near agricultural burns;

evaluation of the extent to which communities are informed about and follow behavioral recommendations to reduce exposure.

Table of Contents and Abbreviations Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - iv -

TABLE OF CONTENTS Abbreviations ........................................................................................................................... vi I. Introduction / Background / Identified Problem ......................................................... 1 II. Objectives ..................................................................................................................... 3

III. Project Strategies (Approach / Coordination / Obstacles / Milestones) ................... 3

IV. Exposure Assessment ................................................................................................. 5

A. E-BAM Air Monitoring for PM2.5 at Four Locations During A Burn Season ........ 7 1. Methods ......................................................................................................... 7 2. Results ........................................................................................................... 9 3. Discussion ................................................................................................... 13

B. Targeted Burn Event Air Monitoring ................................................................... 15 1. Sampling Locations and Description of Targeted Burns ......................... 15 2. Real-time Monitoring for Particulate Matter and Black Carbon ............... 21

a. Methods ........................................................................................... 21 b. Results ............................................................................................. 23 c. Discussion ....................................................................................... 30

3. Passive Samplers: PM Concentrations and Particle Typing .................... 32 a. Methods ........................................................................................... 32 b. Results ............................................................................................. 34 c. Discussion ....................................................................................... 40

4. Passive Samplers: Vapor-phase Naphthalene Concentrations .............. 42 a. Methods ........................................................................................... 42 b. Results ............................................................................................. 46 c. Discussion ....................................................................................... 48

C. Conclusions ........................................................................................................... 49

V. Exposure Reduction ................................................................................................... 52 A. Needs Assessment ............................................................................................... 52

1. Methods ...................................................................................................... 54 2. Results ........................................................................................................ 54 3. Discussion .................................................................................................. 61

B. Behavioral Recommendations to Reduce Exposures ........................................ 63 1. Downwind Ground-level Plumes ............................................................... 63 2. Evening, Night, and Early Morning Exposures ........................................ 64

C. Fact Sheet Development and Distribution .......................................................... 65 1. Format Development ................................................................................. 65 2. Pre-testing .................................................................................................. 65 3. Additional Input .......................................................................................... 66 4. Distribution ................................................................................................. 66

VI. Recommendations for Further Public Health Action and Research ....................... 67

A. Local Outreach ..................................................................................................... 67 B. Other Public Health Recommendations .............................................................. 68 C. Additional Research ............................................................................................. 70

References Acknowledgement

Table of Contents and Abbreviations Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - v -

Supporting Information: Attachments 1. Regional Air Monitoring (E-BAM): Supporting Tables and Figures for Section IV.A 2. Deployment of Instruments and Samplers at Targeted Burn Events: Supporting Text,

Tables and Figures for Section IV.B.1 3. Real-Time Instrumentation at Targeted Burn Events, Section IV.C: Supporting Text,

Tables and Figures for Section IV.B.2 4. Passive Samplers Particulate Concentrations: Supporting Text, Tables and Figures

for Section IV.B.3 5. Passive Samplers Naphthalene: Supporting Figures for Section IVB.4 6. Fact Sheets on Agricultural Burning for the General Public, Schools, and Farmers Appendices 1. QA/QC Sampling Plan 2. Completed EPA Quality Assessment Checklist

Table of Contents and Abbreviations Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - vi -

ABBREVIATIONS APCD: ............................ Air Pollution Control District

AQI: .............................. Air Quality Index

BEHT: ............................ Binational Environmental Health Taskforce

BECC: ........................... Border Environmental Cooperation Commission

CARB: ............................ California Air Resources Board

CDPH: ........................... California Department of Public Health

CCSEM / EDS: ............... Computer-Controlled Scanning Electron Microscopy / Energy-Dispersive Spectroscopy

CCV: ............................. Comite Civico del Valle, Inc.

CDC: ............................. Centers for Disease Control and Prevention

E-BAM: .......................... Environmentally sealed portable Beta Attenuation Monitors

EHIB: ............................ Environmental Health Investigations Branch

EHLB: ........................... Environmental Health Laboratory Branch

IC: ................................. Imperial County

KII: ................................. Key Informant Interview

L/min: ............................. Liters per minute

LQ: ................................. Limit of Quantification

MPH: ............................. Miles Per Hour

m/s: ................................ meters per second

µm .................................. micrometer, commonly referred to as a micron

N ..................................... Number

ND .................................. Not Detected

PAH: .............................. Polycyclic Aromatic Hydrocarbon

pDR: .............................. personal Data Ram

PHD: .............................. Public Health Department

PI: .................................. Principal Investigator

PHPS: ............................ Public Health Prevention Specialist

PM2.5: ............................. Particulate Matter less than 2.5 µm, aerodynamic diameter

PM10: .............................. Particulate Matter less than 10 µm, aerodynamic diameter

PM2.5–10: ......................... Particulate Matter greater than 2.5 µm and less than 10 µm, aerodynamic diameter

PNV: ............................... Passive Naphthalene Vapor

QA/QC: .......................... Quality Assurance / Quality Control

R2: ........................................................ R correlation coefficient squared; quantity is also the percent of explained variance

RH: ................................ Relative Humidity

SD: .................................. Standard Deviation

SDSU: ........................... San Diego State University

SI: .................................. Supporting Information

µg/m3: ............................ micrograms per cubic meter

UNC ................................ University of North Carolina

I. Introduction Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 1 -

I. INTRODUCTION / BACKGROUND / IDENTIFIED PROBLEM

Throughout agricultural areas of the U.S./Mexico border region, fields are burned to remove weeds, pests, and crop stubble (Choi and Fernando, 2007; Chow and Watson, 2001; Dennis et al., 2002). Combustion products emitted during agricultural burning include carbon gases, metals, nitrates, sulfates, and carcinogenic chemicals, notably polycyclic aromatic hydrocarbons (PAHs) (Naeher et al., 2007). Particulate matter (PM) from agricultural burning is formed by plant matter breaking apart and by gases condensing on particles or forming particles. PM with an aerodynamic diameter smaller than 10 micrometers (µm) is inhalable and is called PM10; PM with a diameter smaller than 2.5 µm is called PM2.5. Unlike PM from road and soil dust, which is primarily larger particles, PM emitted from combustion sources (including from vehicular traffic) is primarily PM2.5 (Watson et al., 2000). PM2.5 tends to be suspended in the air longer, travels further, and penetrates deeper into the lung than PM10. A substantial portion of PM emitted from combustion sources is light-absorbing and called ―black carbon‖ (Naeher et al., 2007). The health effects of compounds in smoke are extensive (Naeher et al., 2007). In California, increases in air levels of PM2.5 and potassium, an indicator of biomass smoke, have been associated with mortality among people of all ages (Ostro et al., 2007). Increased black carbon levels have also been suggested to be associated with increased diagnosis of childhood asthma (Clark et al., 2010).

In Imperial County, California, the annual age-adjusted rates of asthma hospitalizations are the highest of all counties in the state (Milet et al., 2007). The Imperial Valley is also below sea level, where wind cannot easily transport air pollution out of the area. In the winter, nighttime inversions may hold air pollution in the Valley. Fields of Bermuda grass stubble are burned in the winter in Imperial County while wheat stubble is burned during the summer. However, burning does occur throughout the year (Figure I.1). As Imperial County has a warm climate, wood burning for heat is unusual; less than 3% of homes use wood as a house heating fuel (U.S. Census Bureau, 2009).

Figure I.1. Agricultural Burn Acreage by Month, Imperial County, 2007

Farmers are required to obtain a permit prior to burning a field from the Imperial County (IC) Air Pollution Control District (APCD). The APCD notifies the farmer whether the field can be burned, usually the day before the targeted burn date. In 2002, the IC APCD adopted a smoke management plan (IC APCD, 2002). As part of that plan, when a burn is within 1.5 miles of a sensitive location (including schools, the international border, residential areas, or a major highway), it is designated as a ―special burn‖ requiring APCD staff to observe the burn on-site. Special burns near schools are scheduled for the weekends or when school is not in session. In data provided by the APCD, the annual acreage designated as special burns in 2006 was approximately 10,000; in 2007, 9,000 acres were so designated; in total, 36,038 acres were

0

2,000

4,000

6,000

8,000

10,000

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Acr

eage

bur

ned


burned in 2006 and 38,707 in 2007. The APCD has sponsored an incentive program to encourage farmers who have burned their fields in the past to refrain from burning in the coming season. The APCD confirms that a field has not been burned and issues farmers certificates that can be sold to polluters who may then use the certificates to lessen fines.

Since 1985 Imperial County has been designated as a non-attainment area for state and national PM10 standards (Chow and Watson, 2001). PM-based air alerts were issued in December 2010 for U.S. EPA Air Quality Index (AQI) levels above 100, indicating ―unhealthy‖ air. Nonetheless, these alerts have primarily been issued for Calexico, next to the U.S./Mexico border, and associated with urban transport from neighboring Mexicali, Mexico. PM10 air levels are lower further north in El Centro. In 2009, the three-year annual average PM10 air concentration was 48 micrograms per cubic meter (µg/m3) in El Centro, while it was 66 µg/m3 in Calexico (Ethel Street Station), with the Calexico level above the national standard of 50 µg/m3, and both above the state standard of 20 µg/m3 (CARB, 2011). As part of a mandated State Implementation Plan to reduce PM10 emissions, the IC APCD developed an inventory of PM10 sources and estimated that, although some agricultural practices (e.g., tilling) make a significant contribution to PM10, field burning contributes less than 2% to PM10 emissions (IC APCD, 2005). The annual tonnage of PM2.5 emissions from field and weed burning in the county has been estimated to be about the same as the tonnage of PM10 emissions, both approximately two tons per day (CARB, 2009). However, whether PM2.5 emissions from burning activities make a significant contribution to PM2.5 air pollution has not been estimated.

Laboratory chamber studies of crop stubble burns have documented emissions of PM10, PM2.5, and polycyclic aromatic hydrocarbons (PAHs) (U.S. EPA, 1996; Jimenez et al., 2007); one of the most abundant PAHs is naphthalene, a gas- and particulate-phase pollutant and a suspected respiratory carcinogen (Kakareka and Kukharchyk, 2003). However, very few outdoor air monitoring studies to estimate human exposures during agricultural burning have been conducted. Briefly, measurements at one location near an agricultural burn, notably in Imperial County, demonstrated brief (less than an hour) but highly elevated levels of PM10 —above 5,000 µg/m3—and black carbon (Kelly et al., 2003). During a period of rice and wheat burning in India, high monthly average levels of PM10 (100–200 µg/m3) and PM2.5 (50–100 µg/m3) have been reported (Awasthi et al., 2010). In North America, in a populated area of eastern Washington state during a period of wheat and grain field burning, short-term (30 to 90 minutes) elevations of PM2.5 (above 40 µg/m3) have been documented (Jimenez et al., 2006).

Staff of the California Department of Public Health (CDPH), Environmental Health Investigations Branch (EHIB), have been actively involved in assessing the public health impacts of smoke exposure since 1997. Specifically, EHIB staff members demonstrated a relationship between asthma hospitalizations and burning rice stubble in Northern California (Jacobs et al., 1997), and were also part of a team that developed educational materials for public health agencies responding to wildfire events (Lipsett et al., 2008).

The California/Baja California Binational Environmental Health Taskforce (BEHT) is a coalition of state, local, tribal, health, and environmental agencies that identify and address binational health risks related to the environment. CDPH/EHIB and CDPH/Office of Binational Border Health staff are members of the BEHT. In 2008, the members of the BEHT identified ―exposures to smoke from agricultural burning‖ as one of several priority areas for potential funding administered by the U.S. Environmental Protection Agency (EPA) and the U.S. Border Environment Cooperation Commission (BECC). In Imperial County, air pollution agencies continually monitor PM10 and PM2.5 at one to four locations. However, these monitors are mostly located near the border and were not considered sufficiently close enough to burns to evaluate potential human exposures to agricultural burning. Mexican BEHT members were also


interested in the potential health impact from exposures to air pollutants from burn sites in Imperial County, as well as from emissions from burning activities that may occur in Mexico.

II. OBJECTIVES

EHIB, in collaboration with CDPH‘s Environmental Health Laboratory Branch (EHLB), San Diego State University (SDSU), Comite Civico del Valle, Inc. (CCV), and the Imperial County Public Health Department (IC PHD), submitted a plan for potential funding from the U.S./Mexico Border Environmental Cooperation Commission (BECC), which receives funds from the Border 2012 program of the U.S. EPA. In 2008, the project plan was approved for funding. The overall goal of the project was to protect public health by reducing exposure to air pollutants released during agricultural burning in Imperial County. The project had four objectives, the first of which centered on exposure assessment, and the latter three on exposure reduction. Specifically, these objectives were to:

1) assess human exposures during and following agricultural burn events;

2) evaluate health educational knowledge and needs of residents of Imperial County;

3) develop scientifically valid and culturally appropriate behavioral recommendations on how to reduce exposures; and

4) identify methods for distribution of exposure reduction behavioral recommendations.

III. PROJECT STRATEGIES (Approach / Coordination / Obstacles / Milestones)

To address the first objective of exposure assessment, the approach was to conduct air sampling with methods that were easy to deploy. Since agricultural burns in California are not confirmed until the day before a burn, easily moved or portable instruments (see Section IV.B. of this report for specific instruments) and locally based field staff were required. Further, as the flames from any burn will last 30–60 minutes, methods to detect hourly fluctuations in air levels were targeted. Considerable coordination was required among the dispersed study investigators at CDPH (EHIB and EHLB), IC DPH, SDSU, and CCV, who were hiring the field staff. A major obstacle and subsequent milestone was the requirement and then submission of a U.S. EPA–approved Quality Assurance/Quality Control (QA/QC) plan, a 60-page document. This requirement necessitated study investigators putting in the allocated in-kind contribution prior to receipt of funding. Once funding had been awarded (January 5, 2009), immediate training and deployment of field staff was required, as the active burn season had begun. As the sampling progressed, other obstacles included identification of sampling locations near and downwind of burn sites. In order to coordinate the selection of burn events and sampling sites, the principal investigator (PI) traveled to the APCD to review the local wall map of upcoming burns. A constant obstacle was achieving adequate field and laboratory QA/QC for these field deployment methods. After sample collection, competing CDPH responsibilities, including response to the H1N1 flu outbreak and furloughs of the California state workforce, slowed the pace of laboratory analysis and QA/QC. Major milestones for the first objective included: E-BAM deployment, manufacturer calibration of field equipment, completion of the pilot, sampling at four additional burn events, an additional sampling period in which all five instruments were co-located, laboratory-based microscopic analysis of PM and laboratory naphthalene analysis, and review and evaluation of real-time air sampling results by SDSU.

III. Project Strategies Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 4 -

To address our second objective, assessment of health educational needs, our approach was to conduct qualitative Key Informant Interviews (KIIs). Although there were minor hurdles (e.g., it was initially difficult to identify farmers), there were no major obstacles. The ease with which these KIIs were accomplished was due to the fact that a U.S. Centers for Disease Control and Prevention Specialist, located at CDPH/EHIB, an in-kind contribution, was dedicated to this task. The major milestone for this objective was the completion of 20 KIIs.

To address our third objective, the development of exposure reduction recommendations, our approach was to use available air monitoring data and information from the KIIs. First, we developed draft fact sheets promoting behavioral recommendations to reduce potential exposures. A major obstacle was developing language that was concise, technically sound, and at the appropriate literacy level. EHIB staff coordinated review of these fact sheets with members of the BEHT. A major milestone was the completion of three fact sheets targeted for the general public, schools, and farmers.

To address our fourth objective, identification of best methods for distribution of exposure reduction recommendations, our approach was to use responses obtained during the KIIs and pre-testing of fact sheets. In the initial work plan, the IC PHD was to take the lead on a pilot outreach project using established mechanisms. A major obstacle was that responsibilities around the H1N1 flu outbreak prevented IC PHD from having the in-kind resources necessary to coordinate the effort intended. A milestone, however, was that the IC PHD pre-tested the draft fact sheet for the general public among 20 community members who made suggestions for distribution mechanisms. Another task in the initial work plan was to have the BEHT consider early warning mechanism(s) both for Imperial County and for Mexico. Both the magnitude of the unexpected in-kind workload on the air monitoring component (e.g., writing a major QA/QC plan) and low attendance at BEHT meetings led to that task not being accomplished. Nonetheless, results of this project will be presented at future meetings of the Binational Air Quality and Environmental Health Taskforce.

Finally, the BEHT also encouraged project staff to consider recommendations to reduce agricultural burning. Our approach was to develop recommendations based on our air sampling findings. One obstacle was that because air sampling required methods that were portable and easily deployed, the methods used did not meet QA/QC requirements for regulation and enforcement. A major milestone was the incorporation of findings from other researchers and other agencies to complement and support the air monitoring findings. This also led to the development of additional exposure reduction recommendations for regulatory agencies to consider.

IV. Exposure Assessment, Introduction Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 5 -

IV. EXPOSURE ASSESSMENT

Initially, we planned to use two types of easily deployable air monitoring techniques at locations near specific, targeted burn events: i) portable ―real-time‖ instruments (pDRs and aethalometers), which continuously measure PM2.5, PM10, and black carbon; and ii) ―passive‖ samplers, which provide average measurements of PM and the PAH, naphthalene, for the period deployed. The instruments and samplers, including reported accuracy and precision, are described in the following Sections of this report. These instruments and passive samplers, however, provide less accurate measurements than air monitoring methods typically used by regulatory agencies. In the final stages of planning for the study, the California Air Resources Board (CARB) was contacted concerning additional equipment. To assist in the evaluation of the accuracy of the PM2.5 real-time instrument and passive sampler measurements, CARB contributed a third type of instrument: Environmentally-sealed portable Beta Attenuation Monitors (E-BAMs)™ that continuously measure PM2.5 and weather variables.

All three methods involved measuring PM2.5 air concentrations. To evaluate concentrations detected, U.S. EPA Air Quality Index (AQI) levels were used (Table IV.1).

Table IV.1. PM2.5 Air Concentration Values Corresponding to Air Quality Index (AQI) Categories for Short-Term Averages, Expected Visibility at Those Levels, and Recommended Public Health Actionsa

AQI Category

(AQI values) Color Code

Potential Health Impact

b

PM2.5 level (µg/m3)

corresponding to AQI category

Average over:

24 hrs 8 hrs 1 to 3 hrs

Visibilityc

(Miles) Recommended Action

a

Good (0 to 50) Green

None 0–15 0–22 0–38 > 10 —If smoke event forecast, implement communication plan

Moderate (51 to 100) Yellow

—Respiratory symptoms possible in unusually sensitive and older people

16–35 23–50 39–88 6–10 —Advise the public about health effects and ways to reduce exposures

Unhealthy (101 to 300) Orange, Red, and Purple

—Increased respiratory effects in general population

d

36–250 51–300 89–526 1–5 —Consider closing schools and workplaces not essential to public health

d

Hazardous (> 300) Maroon

—Serious risk of respiratory and cardiovascular effects in general population

> 250 > 300 > 526 < 1 —Close schools and workplaces not essential to public health

a Table adapted from Table 3 in Lipsett et al., 2008. Recommendation most appropriate for agricultural burning

smoke selected from multiple recommendations for wildfire smoke. b U.S. EPA, 2006.

c In arid conditions.

d When AQI above 150.

IV. Exposure Assessment, Introduction Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 6 -

The U.S. EPA sets an AQI category of above 100 or ―unhealthy‖ to equate to the primary U.S. National Ambient Air Quality Standard (> 35 µg/m3), which is used to evaluate and enforce legal exceedances in air concentrations. However, even at ―moderate‖ air quality for PM, ―respiratory symptoms are possible in unusually sensitive individuals‖ and ―aggravation of heart or lung disease in people with cardiopulmonary disease and older adults‖ is possible (U.S. EPA 2006, page 8). Recommended public health actions for each category developed for exposures to wildfire smoke are also presented. Notably, even when air quality is ―good,‖ if a smoke event is forecast, a communications plan is recommended.

For this study, shorter-duration PM concentrations estimated to correspond to AQI values were also used (Lipsett et al., 2008). To account for the shorter exposure time, concentrations estimated to correspond to AQI are higher for shorter duration periods (see Table IV.1). For example, for air quality to be classified as moderate, in the present study, based on the levels presented in Table IV.1., a 24-hour average PM2..5 concentration would need to be between 16 and 35 µg/m3 while an eight-hour average would between 23 and 50 µg/m3 and a one- to three-hour average between 39 and 88 µg/m3. These shorter-term concentrations are ―based on a strong body of epidemiological evidence associating 24-hour PM2.5 exposures with respiratory and cardiovascular morbidity and mortality‖ (Lipsett et al., 2008, page 27).

Regulatory agencies, however, base regulatory decisions not only on 24-hour averages, but also on strict monitoring criteria. None of the instruments used in the present study, including the E-BAMs, met those criteria. AQI categories for all concentrations detected for all periods were used as a guide to describe the potential health impact of findings and do not reflect classifications that could be used for legal decisions or to determine whether air quality standards have been exceeded.

All of the measured compounds (PM2.5, PM10, black carbon, and naphthalene) also have sources other than burn smoke, including traffic exhaust. The city of Calexico (population 38,000) has historically high daily PM10 (i.e., many days above 50 µg/m3 and some days above 100 µg/m3) levels at stationary monitoring stations. A major contributor to these higher readings is suggested to be traffic sources associated with neighboring Mexicali, Mexico, which has a population of about a million people (Chow and Watson, 2001). To target smoke from agricultural burns, deployment for all of three types of air sampling equipment (real-time, passive, and E-BAMs) excluded the Calexico area (south of I-8) and attempted to minimize the distance between agricultural burning and the air monitoring locations.

IV. Exposure Assessment; A. E-BAM (PM2.5) Monitoring Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 7 -

IV. A. E-BAM AIR MONITORING FOR PM2.5 AT FOUR LOCATIONS DURING A BURN SEASON

As described, CARB volunteered E-BAMs (Met One Instruments, Oregon) to assist in the evaluation of the accuracy of PM2.5 real-time and passive sampler measurements to be taken at targeted burn events. However, the E-BAMs required fixed, secure locations. Due to this requirement and continuous operation, in addition to providing comparison measurements at targeted burn events, the deployment of E-BAMs provided a description of the temporal variability of PM2.5 and weather variables at four locations during a burn season.

1. METHODS a. E-BAM Measurements

E-BAM deployment required a constant power source and a rooftop where the E-BAM would not be disturbed. Using information provided by the IC APCD, EHIB staff identified and sought the participation of 13 property owners of places of public access (schools, churches) in population centers near previous burns (2006 and 2007). Informational letters with a description of the study were provided to managers of each location. Four places of public access, three schools and one church, geographically dispersed in the Valley (north of Interstate 8), were targeted for E-BAM monitoring. Signed consents were collected from each of the four selected sites. The E-BAMs were installed by staff of CARB on January 14, 2009, at the four locations or ―stations,‖ mapped in Figure IV.A.1. EHIB staff named the stations to correspond to the closest town; however, only the Calipatria station was within city limits.

E-BAMs measured hourly average concentrations of PM2.5, as well as wind direction and speed, and ambient temperature. To measure PM2.5, the installed E-BAMs pulled in air at an average flow rate of 16.7 liters per minute; a cyclone inlet then selected particles less than 2.5 µm and collected them on a glass fiber filter tape. Beta-radiation was transmitted through the filter tape and the mass of PM2.5 derived from the reduction in transmission by the collected particles. The reported accuracy (2.5 µg for a 24-hour sampling period) and precision of E-BAM measurements are sufficient to be considered by the manufacturer a Class III ―Federal Equivalent Method‖ (Met One Instruments, 2009). However, E-BAM measurements are not gravimetric (the mass is not directly weighed) and are not an approved Federal Reference Method, which would be required to determine if air concentrations legally exceeded federal standards.

The E-BAMs remained in these locations until March 23, 2009, a total of 69 days. CARB staff visited the stations every two weeks to ensure proper operation. At the end of the sampling period, E-BAM data were reviewed by CARB, and hourly PM2.5 levels were provided to EHIB staff (CARB, 2009). EHIB staff, in consultation with CARB, considered hourly measurements in which the flow rate was more than one standard deviation below the mean (+/- 3%) as missing. Of the 6296 hourly readings from the four sites, 4.6% (n=304) were missing either because of a low flow rate or instrument malfunction. In addition, 46% of hourly readings were below the quantification limit (< 6 µg/m3) suggested to CARB by Met One (Harnly M., personal communication, July 29, 2009). EHIB, in consultation with CARB, set hourly values less than the quantification limit to half the quantification limit (3 µg/m3). Daily 24-hour (midnight to midnight) PM2.5 averages were computed from the hourly averages. If hourly PM2.5 values were missing, daily averages were computed from available hourly data, except when more than six hours in any given day at any station were missing. In those cases the daily average was not computed.


Figure IV.A.1. Agricultural Burns and E-BAM PM2.5 Monitoring Locations (green dots) in Imperial County from January 14–March 23, 2009.a

a Circles are not the same scale as the fields and are larger than the actual size of the field.

b. Burn Records

A record of agricultural burn events during the E-BAM sampling period of January 14 through March 23, 2009, was provided by the APCD. Several conventional statistical analyses (described below) were conducted to examine air levels by time of day and the potential impact of agricultural burning on air concentrations.


2. RESULTS a. Agricultural Burning During the Air Monitoring Period

Similar to burns in the years 2006 and 2007 (see previous Section), burns during the E-BAM monitoring period occurred primarily in January and February (Figure IV.A.2.). Burns occurred throughout the county, with some apparent clustering of burn events near the U.S./Mexico border (west of the city of Calexico) and in the northern part of the county (Figure IV.A.1.). During the E-BAM monitoring period, 15,686 acres were burned, of which 14,618 acres were Bermuda grass, on 35 allowable burn days.

Figure IV.A.2. Acres Burned by Date: E-BAM Monitoring Period, Imperial County, 2009

b. PM2.5 Measurements

Average 24-hour PM2.5 concentrations were highest at the Calipatria station and lowest in Seeley (Table IV.A.1). These concentrations are graphed by day and station in Attachment 1 of the Supporting Information (SI) (Figure SI.A1.3) and that graph illustrates that this geographical trend is most apparent in the first week of sampling. Table IV.A.1 below also illustrates that the 95th percentile of concentrations was above the 24-hour concentration corresponding to the level for ―moderate‖ air quality of 16 µg/m3 (see Table IV.1) at Calipatria but not at the other stations. To reiterate, the AQI categories used here are a guide to describe the potential health impact (see Table IV.1). At ―moderate‖ air quality for PM, ―respiratory symptoms are possible in unusually sensitive individuals‖ and ―aggravation of heart or lung disease in people with cardiopulmonary disease and older adults‖ is possible (U.S. EPA, 2006). The ―moderate‖ AQI category, however, does not correspond to air levels that exceed standards.

Acre

s B

urn

ed

Month / Day in 2009


Table IV.A.1. Daily (24-hour) Average PM2.5 (µg/m3) Concentrations by Station: E-BAM Monitoring, Imperial County, January 14–March 23, 2009

STATION Number of

Monitoring Days Average

(SDa)

Geometric Mean

b

95th Percentile Maximum

Calipatria 69 11.7 (3.9) 7.1 18.0 21.2

Brawley 69 8.4 (2.9) 5.8 13.0 15.3

El Centro 63 7.4 (3.2) 5.5 13.1 18.0

Seeley 68 6.0 (2.9) 4.5 11.8 20.0 aSD=Standard Deviation

bThe geometric mean is the antilog of the average of the logs of the air concentrations

Bolded values are above the concentration corresponding to the AQI category for moderate air quality (16 µg/m3).

To describe diurnal patterns in PM2.5, concentrations were averaged over eight-hour periods by station (Table IV.A.2). To the north, at the Calipatria and Brawley stations, average concentrations during ―evening‖ (4:00 to 11:59 PM) and ―early morning‖ hours (12:00 AM to 7:59 AM) were at least twice as high as during the ―day‖ (8:01 AM to 3:59 PM). Further to the south, at the El Centro station, higher evening and early morning levels were also observed, but levels were not as high as those observed at the more northern stations. At the westernmost station, Seeley, concentrations for all eight-hour periods were very similar and almost equal.

Table IV.A.2. Eight-hour Average PM2.5 (µg/m3) Concentrations by Location: E-BAM Monitoring, Imperial County, January 14–March 23, 2009

a

Early morning = 12:01 to 8:00 AM, Day=8:01 AM to 4:00 PM, Evening = 4:01 PM to 12:00 PM. b

N=Number of monitoring days, SD=Standard Deviation

Bolded values are above an eight-hour guidance level for moderate air quality (23 µg / m3), Table IV.1.

To further specify the temporal trend, concentrations were averaged for each hour of the day, across all monitoring days. The decline in daytime concentrations at all stations, except Seeley, starts around 5:00 AM and reaches a low at 8:00 AM. Lower concentrations continue until 3:00 PM, and then begin to rise at 4:00 PM (Figure IV.A.3).

STATION Period a

Nb

Average (SD) Geometric Mean 95th Percentile Maximum

Calipateria Early Morning 69 13.7 (7.3) 9.2 25.0 40.9

Calipateria Day 69 4.9 (3.8) 3.7 8.3 31.3

Calipateria Evening 69 14.5 (6.9) 9.9 27.5 34.4

Brawley Early Morning 67 11.9 (5.9) 8.3 25.3 27.2

Brawley Day 65 4.3 (1.8) 3.7 7.1 14.4

Brawley Evening 67 9.3 (4.6) 6.8 18.5 24.8

El Centro Early Morning 62 8.7 (6.3) 6.1 19.5 30.3

El Centro Day 63 5.4 (3.1) 4.3 8.9 23.9

El Centro Evening 63 8.0 (4.0) 6.2 17.8 20.5

Seeley Early Morning 68 5.4 (2.9) 4.4 11.5 17.8

Seeley Day 68 6.3 (4.8) 4.5 16.0 31.0

Seeley Evening 68 6.3 (3.9) 4.7 12.5 24.1

Overall Early Morning 266 10.0 (6.6) 6.7 24.4 40.9

Overall Day 265 5.3 (3.6) 4.1 9.8 31.3

Overall Evening 267 9.3 (5.7) 6.7 20.5 34.8


Figure IV.A.3. Imperial County 2009: Average PM2.5 Concentration at Each Hour of the Day and by Monitoring Location, January 14–March 23, 2009.

c. Weather Measurements

Average temperature and wind speed at each location are listed and graphed in the SI (Attachment 1, Table SI.A2.1 and Figure SI.A2.1). As expected, temperature varied little by sampling location and the average temperature at night (6 PM to 6 AM) was lower (12.8°C / 55°F) compared to the day (19°C / 66.2°F). Of the four stations, wind speed was highest in Calipatria with an average wind speed of 4 MPH and a maximum hourly speed of 26 MPH or 11.6 meters/second (m/s). The predominant wind direction was from the west at three stations, and this was most pronounced at the Seeley station, which was on the western edge of the Valley (Figure IV.A.1, above). At the Brawley station the predominant wind direction was from the south, but winds from the west were also apparent.

d. PM2.5 Measurements and Agricultural Burning

First, we compared the eight-hour average PM2.5 concentrations averaged for the four E-BAM stations on days classified as either ―Field Burn Days‖ (n=35) or ―No Field Burn Days1‖ (n=33). These results are given in Table IV.A.3. During the eight-hour evening period, the average PM2.5 concentrations on ―Field Burn Days‖ (10.1 µg/m3) was 23% higher than on ―No Field Burn Days‖ (8.2 µg/m3). During the early morning hours of the next day, a slightly larger difference in concentrations between the two types of days was observed, albeit this difference is not statistically significant. In contrast, during the day, levels were slightly lower on burn days compared to days with no burns, but this difference was not statistically significant.

1 ―No Field Burn Days‖ included

i) ―Marginal Burn Days‖ [days (n=9) on which other types of burning (e.g., yard waste) were allowed but no agricultural field burns were allowed];

ii) ―No Burns Allowed Days‖ [days (n=12) on which no burning was allowed]; and

iii) ―No Burns, Allowable Burn Day,‖ [days (n=9) on which field burns were allowed but no field burns occurred].

Because PM2.5 concentrations in these three categories were similar (ranging from 5.0 to 6.7 µg/m3, 7.7 to 8.9

µg/m3, and 7.9 to 9.3 µg/m

3 for the eight-hour same day, evening, and early morning of the next day periods,

respectively) these three categories were combined into one classification.

0

2

4

6

8

10

12

14

16

18

20

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 of the day (0=Midnight; 23=11:00 PM)

PM2.

5 (ug

/m3 )

Seeley Brawley El Centro Calipatria


Table IV.A.3. PM2.5 Concentrations (µg/m3) Average at Four Locations in Imperial County January 14–March 23, 2009 during Eight-hour Periods by Type of Burn Day

Eight-hour Period a Type of Burn Day Number

of Days Average Geometric

Mean 95th

Percentile

Day Field Burn Days 35 4.6 3.8 7.1

Day No Field Burn Days 33 5.9 4.3* 16.7

Evening Field Burn Days 35 10.1* 7.0 19.5

Evening No Field Burn Days 33 8.2 6.1 14.0

Early Morning: Next Day Field Burn Days 35 11.0* 7.0 18.7

Early Morning: Next Day No Field Burn Days 33 8.7 6.3 16.4 a Day=8:01 AM to 4:00 PM; Evening=4:01 PM to 12:00 PM; Early Morning=12:01 to 8:00 AM, day following burn day.

* p-value=0.02 to 0.03. Analysis of variance (t-test) between averages on Field Burn Days compared to No Field Burn Days.

Second, linear regression analysis was conducted between acreage burned per day in the county and the average eight-hour concentrations across the four monitors (Table IV.A.4). The beta coefficients for the evening and early morning of the next-day concentrations were 3.0 and 1.0, respectively. These coefficients suggest that for every 1,000 acres burned, the average PM2.5 concentration for the region (i.e., averaged across the four air monitors) increases from 1 to 3 µg/m3, albeit the R2 (equal to the percentage of explained variance) values are low and only the higher coefficient, that for the evening period, is statistically significant.

Table IV.A.4. Correlation of Average PM2.5 Concentrations (n=4 monitors) with Daily Burn Acreage from January 14–March 23, 2009, by Eight-hour Period

Eight-hour Period a N

b days R

2 Intercept Coefficient

c SD

b for

Coefficient

Day 69 0.04 5.6 -1.7 0.1

Evening 68 0.08 8.7 3.0* 1.3

Early Morning: Next Day 69 0.01 9.7 1.1 1.5 a Day=8:01 AM to 4:00 PM, Evening = 4:01 PM to 12:00 midnight; Early Morning = 12:01 to 8:00 AM, next day

b N=Number, SD=Standard Deviation

c Increase in µg PM2.5 / m

3 per 1000 acres burned

* t-test of statistical significance on coefficient, p=0.02 (average values), p=0.05 (log-transformed values)

Distance from specific burns was also examined. During the monitoring period, there were 14 burns within two miles of any of the four monitoring stations, with 10 of these being within two miles of the Calipatria station. Further statistical analysis was restricted to the Calipatria station, due to the small number of days with nearby burns at the other stations. At the Calipatria station, for both the evening period and the next day early morning period, the eight-hour average concentrations were 6–9 µg/m3 higher on days when there was an agricultural burn within two miles (Table IV.A.5).


Table IV.A.5. Average PM2.5 Eight-hour Means at Calipatriaa on Days with Field Burns within Two Miles

Eight-hour Period

Number of Days

Average Geometric Mean

95th Percentile

Day Days with Field Burn within Two miles 9 4.9 4.0 7.1

Day Days with No Field Burns within Two miles

60 4.9 3.8 8.8

Evening Days with Field Burn within Two miles 9 19.5** 14.8** 33.6

Evening Days with No Field Burns within Two miles

60 12.6 9.3 27.0

Early Morning: Next Day

Days with Field Burn within Two miles 9 20.7** 13.5* 40.9

Early Morning: Next Day

Days with No Field Burns within Two miles

60 12.6 8.7 24.1

a At other stations there were few days (0–3) with burns within 2 miles.

b Day=8:01 AM to 4:00 PM; Evening=4:01 PM to 12:00 midnight; Early Morning=12:01 to 8:00 AM, day following the

burn day. *p-value < 0.05 and > 0.01; ** p-value<0.01 and > 0.001: Analysis of variance on average or geometric mean on days with burns within 2 miles compared to days with no field burns.

Finally, because of the high frequency of non-detects, the eight-hour concentrations were potentially not normally distributed, which may violate statistical testing assumptions. To normalize the distribution, these three statistical analyses were repeated using log-transformed values of the eight-hour concentrations. The results of these analyses are also reported in these last three Tables alongside the geometric means: the significance was marginally decreased.

3. DISCUSSION

The E-BAM measurements provided a description of PM2.5 levels at four locations in the western, central, and northern areas of the agricultural area of Imperial County during an agricultural burn season. Average daily (24-hour) PM2.5 concentrations measured by the E-BAMs ranged between < 6 and 21 µg/m3 and were below concentrations corresponding to health-based U.S. EPA AQI categories for ―unhealthy‖ air (<35 µg/m3, daily average). Daily measurements were also generally less than measurements taken throughout the year in other agricultural and urban areas of the state such as Fresno, Kern, Riverside, Sacramento, San Diego, and Santa Clara counties, where daily average concentrations range from 14.8 to 28.8 µg/m3 (Ostro et al., 2007). Nonetheless, some (< 5%) of the average daily PM2.5 E-BAM levels reported here were above 16 µg/m3, a level corresponding to an AQI classification of moderate air quality.

Daily PM2.5 air levels were higher in Calipatria and lowest in Seeley (Table IV.A.1). In Calipatria there were also higher winds. However, the threshold wind velocity for PM10 for non-urban desert soils is estimated at 11 m/s (25 MPH) (Watson et al., 2000) and only the maximum hourly wind speed in Calipatria approached this threshold. Lower daily levels in Seeley, in the western


part of the county, may be due to the predominant wind direction being from the west, whereas potential pollution sources are to the east.

During evening, night, and early morning hours (4 PM to 8 AM), levels were 47%, 84% and 170% higher than levels during the day at the El Centro, Brawley, and Calipatria stations, respectively (Table IV.A.2). Air pollutants may disperse during the day when heating of the Earth‘s surfaces causes greater wind speed and the rising of pollutants to an upper mixing air layer at a higher elevation. The descent of an inversion layer in the cooler evening hours may bring pollutants, such as PM, down to ground level. Higher evening and early morning levels may be more likely in Valley regions where upper mountain passes are at higher elevation than the upper mixing air layer. Similar diurnal patterns in PM2.5 have been observed in Fresno, California (Watson and Chow, 2002) and in the El Paso/Cd. Juarez air quality basin (Li et al., 2001), and in PM10 levels in the Imperial/Mexicali area (Chow and Watson, 2001).

On burn days, eight-hour average evening levels (among the four air monitors) were 23% higher (evening-to-night average, 11.1 µg/m3) than on days when burns did not occur (9.3 µg/m3). A regional impact on air quality is consistent with the estimate that, once aloft, PM2.5 may travel long distances (over 50 km) even at low wind speeds (<1 m/s or 2.2 MPH) (Watson and Chow, 2002). Night and next-day accumulation of smoke has also been described as a possibility in a pamphlet for farmers about agricultural burning developed by CARB (CARB, 1992), and is also consistent with a study in eastern Washington conducted during an agricultural burn season during which day and nighttime averages showed similar significant differences (10 µg/m3, day vs. 13 µg/m3, night) (Jimenez et al., 2006). Here, daily burn acreage was also significantly correlated with the average of the daily PM2.5 concentration at the four monitors. Although that significance was marginal, the regression coefficients (an estimated 1-3 µg/m3 increase in nighttime and early morning levels for every 1000 acres burned) were remarkably similar to an estimated 3 µg/m3 increase in regional and seasonal PM2.5 levels for every 1000 acres burned in forest fires (Jafe et al., 2008).

Higher evening and early morning levels occurred at the Calipatria station on days when there was an agricultural burn within two miles of the station, compared to days when no burns were within two miles of the station. There have been no other similar studies in the U.S. or Mexico with locations within two miles of specific burns where eight-hour or 24-hour PM2.5 concentrations were measured. The next Section of this report describes monitoring where air monitors and samplers were placed closer to agricultural burns.

The E-BAM findings should be interpreted cautiously. As described, the E-BAM measurements are not a federal reference method. Notably, however, air concentrations reported here at the El Centro station (average 7.4 µg/m3) are similar to the annual daily average reported at the stationary air monitor in El Centro (8.0 µg/m3 for the year 2009) (CARB, 2011). Although the accuracy for 24-hour E-BAM averages is good (Met One Instruments, 2010; CARB, 2005), we could find only one other study where the accuracy of shorter-term E-BAM levels had been evaluated. In that study, hourly data were found to have unacceptable error (±10 µg/m3), but co-located E-BAMs ―showed good agreement after averaging over periods of six hours or more‖ (Placer County APCD, 2007, page 4-1). Because of the findings of that study, here we focus on periods longer than one hour, i.e., eight-hour periods. All of these findings are further discussed in conjunction with other air monitoring findings in the final sub-section on air monitoring of this report (Section IV.C).

IV. Exposure Assessment; B. Targeted Burn Event Monitoring; 1. Deployment Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 15 -

IV. B. TARGETED BURN EVENT AIR MONITORING

1. SAMPLING LOCATIONS AND DESCRIPTION OF TARGETED BURNS

a. Targeted Burns: The number of targeted burns and sampling plan were based on available resources and a goal of measuring potential human exposures. In brief, the final sampling plan (Appendix 1) specified that a pilot of monitoring during and following a burn event would first occur, and then four burns would be monitored during January and February of 2009, the same period for which the E-BAMS were deployed. At each targeted burn there would be:

Real-time Instrumentation: At each of three locations, an active personalDataRAMTM (pDR), Thermo Scientific Model 1200), measuring PM2.5; a passive pDR (Thermo Scientific Model 1000), measuring PM10; and an aethalometer, measuring black carbon, would be placed. These instruments required power and secure sites. One of the three targeted sites would be co-located with an E-BAM. All of these instruments would provide continuous readings and be deployed for 24-72 hours, matching the deployment period for the passive samplers.

Passive Samplers: At up to seven locations, two UNC PM samplers and two SKC™ passive naphthalene samplers (one for the first 24 hours and one for 72 hours) would be deployed surrounding the targeted burn. The longer period was chosen to potentially lower quantification limits. These samplers do not require power or an operator. For quality assurance, one location would be at the E-BAM closest to the targeted burn.

The SI, Attachment 2, describes how potential sites were initially identified and how consent was obtained. The Project Manager coordinated with the APCD to identify upcoming burns. Burn events were selected for monitoring based on whether there were pre-identified monitoring locations within two miles of the burn. To select a burn, at least one pre-identified location had to be in the projected downwind direction of the burn.

An SDSU Graduate School of Public Health student was trained to operate the real-time equipment, and that operation is described in the next Section, IV.B2, of this report. Local field staff and the SDSU student were trained to deploy the passive samplers and that deployment is described in subsequent Sections (IV.B3 and IV.B4) of this report.

The first monitored burn event was considered a ―pilot.‖ The SI, Attachment 2, describes how very few sampling locations at places of public access near the burn (within 1.5 miles) were identified for the pilot, and how the Project Manager and the Principal Investigator coordinated to select locations that were further away. Minor adaptations to sampler set-up after the pilot are also described in the SI, Attachment 2. Figure IV.B1.1a shows the final sampler set-up.

Figure IV.B1.1a. E-BAM co-location site on Jan. 27, 2009. Site is 3.3 miles from targeted Dunham burn. Passive samplers (on stand, center of photo) and real-time instruments are co-located with E-BAM (tallest monitor).

Figure IV.B1.1b. Non-targeted Burn Four Miles from Monitoring Location. Upper portion of plume from this burn is observed at monitoring location (Figure a).


All monitored burns were 65-150 acre fields of Bermuda grass stubble (Table IVB1.1). The burns were named for the closest town, although in all instances the closest town was more than three miles away. During the first two monitored burns, there was considerable other burn acreage in the county on the day of the burn and during the following 72 hours. Some of these other burns were observed to potentially impact monitors (Figure IV.B1.1b). For the three other targeted burns, there were few other burns in the county during monitoring, except for the second day following the Brawley burn.

Table IV.B1.1. Monitored Bermuda Grass Burn Events: Jan- Feb, 2009

Targeted Burn

Month / Day

Acres Burned

Wind Speed; Direction, first hour Number of other Burns / Acerage Burned

Day 1: (Targeted Burn Day)

Day 2: (Next Day)

Day 3 (Day after

Day 2)

Day 4 (Day after

Day 3)

Holtville: Pilot

1/14 70 3 mph (1.4 m/s) SE

13 / 1159 11 / 992 4 / 395 3 / 219

Dunham: Near Field

1/27 68 6 mph (2.7 m/s) NW

9 / 824 3 / 335 5 / 319 1 / 140

Brawley 1/31 150 2 mph (0.9 m/s) S/SE

0 / 0 None (No Burn Day)

14 / 1046 None: (Marginal Burn Day)

Imperial 2/15 65 2 mph (0.9 m/s) SE

2 / 60 None (No Burn Day)

0 / 0 4 / 189

Rutherford 2/21 125 2 mph (0.9 m/s) NW

0 /0 None (Marginal Burn Day)

None: (Marginal

Burn Day)

1 / 140

Figure IV.B1.2 maps the monitored field burns, the final 14 real-time instrument and 28 passive sampler locations, and displays the ground level wind direction at the time of the burn and for the subsequent 24 hours. The types of sampling locations are summarized in Table IV.B1.2. Distance of sample locations from the center of the burned field were measured after sample collection in Google Earth.

Table IV.B1.2. Number of Passive and Real-time (in parentheses) Monitoring Locations

Targeted Burn Number of School Locations

Number of Other Public Access Places

Number of Homes

Number of other locations a

Pilot 3 (3) 1 1 0

Dunham 1 (1) 0 0 3 (1)

Brawley 2 (1) 1 (1) 3 (1) 1

Imperial 2 (1) 1 (1) 2 (1) 0

Rutherford 1 (1) 0 2 (2) 3

Total 9 b (7) 3 b (2) 8 (4) 7 (1)

a At Dunham and Brawley, on roadside telephone poles. At Rutherford, on school playground adjacent to field

b Several locations were used at more than one burn event. Totals represent five schools and two other places


FigureIV.B1.2


For the second monitored burn event, the Dunham burn, locations along the roadside of the field were targeted to capture potential resident and worker exposures to drift. Notably, with the exception of the Dunham burn, the wind direction shifted from the predicted direction. For all other burns and sampling locations, the wind direction at the time of burn initiation was away from the instruments and samplers. Figure IV.B1.2 also shows that with the exceptions of the second (the Dunham burn) and the last monitored burn event (the Rutherford burn), monitoring locations were relatively close to state Highways.

E-BAM co-locations were generally further away from the burned field for both the real-time instrumentation and passive monitors (3.8 miles and 5.2 miles, respectively) than the other monitoring locations (1.1 and 1.4 miles, respectively) (see SI, Attachment 2, Table SI.A2.1). Other QA/QC field and co-located duplicates locations are further detailed in the subsequent Sections of this report.

Most importantly, plume observations during the monitored burns provided information about the dispersion of smoke from agricultural burning in Imperial County during the winter. At the pilot burn event, ground-level winds were low and from the south (to the north), the smoke plume from the burn rose up to the apparent height of the inversion layer (the inversion layer must be estimated to be at 3,000 feet or higher or burning of fields is not allowed). Red flames from the field were observed for about 30 minutes. At the height of the upper plume, smoke from the burn was observed to spread out in the opposite direction of the ground wind direction, moving to the south (Figure IV.B1.3). After burning (appearance of red flames) was observed to cease, the ground-level plume was dispersed within about 30 minutes, but smoke from the upper plume remained visible, apparently limited by the inversion layer.

Figure IV.B1.3. Pilot (Holtville) Monitored Burn

At the Dunham burn event, where the wind speed was higher than at the other monitored burns, the ground-level smoke plume was observed to engulf the house on the property of the burned field, the air monitoring equipment mounted on three telephone poles, and to drift onto the adjacent field (Figures IV.B.4a,b,c).


Figure IV.B1.4a. Dunham Burn during Lighting of Field. During lighting there is drift across the adjacent road and onto the adjacent field. Monitors and instruments are next to telephone poles. The first pole used for monitoring is mid-field and is not visible as it is obscured by drift of the plume from the burn. The second pole used for monitoring is in the center of the photo, is 400 feet from the first, obscured pole, and is at the southeast corner of the field, at a 45 degree angle from the center of the field. The third pole used for monitoring is not in the picture.

Figure IV.B1.4b. Dunham Burn: Approximately Ten Minutes after Lighting of Field. First telephone pole used for monitoring is obscured and not visible. The second pole, 400 feet from the first pole, is barely visible. The third pole, 400 feet from the second pole, is the main pole in the photo.

Figure IV.B1.4c. Dunham Burn: Dispersed Ground-Level Plume at Adjacent Field Southeast of Burned Field.


Photographs of the other monitored burns are available in the SI, Attachment 2. After all of the monitored burns, upper smoke plumes were observed to spread and linger till sunset (Figure IV.B.5).

Figure IV.B1.5: Brawley Burn: Upper Layer Plume Extends to the Southwest.

b. Co-location Sub-Study: After burn event monitoring was completed, a separate co-location sub-study was conducted to obtain additional comparison measurements. This sub-study consisted of co-locating the passive particulate samplers and the SDSU real-time instruments next to three of the CARB E-BAMs (Seeley, El Centro, and Calipatria) for three different consecutive 72-hour periods (initiated on 3/8/09, 3/11/09, and 3/14/09), resulting in nine additional co-located measurements (SI, Attachment 2, Table 1). During this period (3/8/09 to 3/20/09), burning occurred but was less than during the targeted burn event monitoring. Specifically, an average of 70 acres per day were burned in the County (see Figure IV.A.3, previous section) during the nine days of the co-location sub-study compared to an average of 240 acres per day during the 60 days prior.

IV. Exposure Assessment; B. Targeted Burn Event Monitoring; 2. Real-time Monitoring Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 21 -

2. REAL-TIME MONITORING FOR PARTICULATE MATTER AND BLACK CARBON

To briefly review, the purpose of the portable real-time instrumentation was to track hourly air concentrations of PM2.5, PM10, and black carbon during and following specific burn events for up to 72 hours. The five targeted burn events and the 14 final real-time sampling locations, including schools, homes, and other roadside locations, are mapped in the preceding Section of this report. As described, a visible ground-level plume was observed to engulf the instruments at one event, the Dunham burn. The Rutherford burn event is also particularly pertinent for human exposure assessment: the burned field was adjacent to a school and a residence, where most of the monitoring instruments were deployed. Air monitoring results from that burn are probably indicative of smoke from one burn, and not other sources, as there were few other nearby buildings, the school was several miles from any busy road, and there were no other agricultural burns in the county during the monitoring period.

a. Methods

Portable real-time instruments measuring PM2.5, PM10, and black carbon were selected based on their ability to record averaged concentrations over short time periods (i.e., five minutes), reported accuracy and precision, and availability.

PM2.5 was measured with active-flow personal DataRAMTM (pDR) nephelometers (model pDR-1200, Thermo Electron Corp., Franklin, MA). These were equipped with a cyclone that selected for PM2.5 by the operator adjusting the flow rate at 4.0 liters per minute (L/min) (SKC AirChek XR 5000, SKC Inc., Eighty Four, PA). Personal DataRAMs do not directly weigh the mass but derive the mass of PM by measuring the intensity of light scattering; the manufacturer reports a quantification range of 1 to 400,000 µg/m3, an accuracy of ± 5%, and a precision of ± 0.2% against gravimetric aerosolized road dust measurements for one-minute averaging times (Thermo Electron Corp., 2005). Researchers deploying these instruments report less, but still good, accuracy and precision for 24-hour averaging times (Chakrabarti, et al., 2004; Liu et al., 2002). At each sample location, downstream of the pDR-1200, we attached a 37-mm filter holder for collection of PM2.5 for gravimetric calibration of 24-hour sample periods.

Similarly, PM10 was measured with a passive pDR nephelometer (model pDR-1000AN, Thermo Electron Corp., Franklin, MA). In contrast to the pDR-1200 for PM2.5, the pDR-1000AN contains no pump for size selection; rather, air enters the instrument through convection and diffusion. The angle of the light scatter is calibrated with standard gravimetric measurements of PM10 in road dust. The manufacturer reports the same precision and accuracy as for the pDR-1200 (Thermo Electron Corp, 2005).

Black carbon was measured with the portable aethalometer (Model AE42; Magee Scientific Company, Berkeley, CA), which estimates the mass in the air by measuring the optical attenuation of aerosols on filter tape with the use of a photodiode at two wavelengths, 370 and 880nm, the latter of which is taken as a measurement of black carbon (Jeong et al., 2004). The quartz fiber filter tape automatically advances during operation, depending on flow rate. A 4.0 L/min flow rate was selected to limit data loss due to tape advancement. The manufacturer reports a quantification limit of 0.1 µg /m3, an accuracy of ±5%, as calibrated with elemental carbon measurements, and a precision of ±0.05 µg/m3 (Hansen, 2005). In field studies, lower, but accurate (r2=0.6-0.8) (Jeong et al., 2004) and excellent precision between co-located readings have been reported (Placer County APCD, 2007).

The pDRs and the aethalometers were calibrated to manufacturer specifications (see above) by the manufacturers in December 2009, before field sampling was initiated. At installation, all instrumentation co-located with an E-BAM was placed on rooftops with inlets at least 1.8 meters away from walls or any obtrusive object and within 1.2 meters of an E-BAM. The pDRs were


attached at 1.2 meters from the ground or rooftop on microphone stands secured with sandbags. A HOBO RH/Temp two-channel Data Logger (H08-003-02, Onset Computer Corp., Bourne, MA) was attached to the side of the pDRs to record relative humidity (RH) and temperature. The inlet of the aethalometer was covered with a mesh screen to prevent objects such as insects from being collected. External power sources were used to power all instrumentation, with the exception of the downwind locations at the Dunham burn, where the pDR was run on batteries. Figure IV.B.2.1. shows the instrumentation setup at a rooftop E-BAM location.

.

Figure IV.B.2.1. PM Instrumentation [E-BAM, Personal DataRAMs (pDR-1200 and pDR-1000AN), Aethalometer, UNC Passive PM and Naphthalene (SKC) Samplers], Imperial County, January 31, 2009.

All instruments were synchronized to current time with a field laptop computer. Averaging time for pollutant concentrations was set to five minutes for all instruments. To minimize baseline drift (Liu, et al., 2002), the pDR-1200 instruments were zeroed by connecting the green zeroing filter to the pDR-1200 cyclone inlet and running the SKC pump, and the pDR-1000ANs were zeroed with a sealed plastic Z-pouch, supplied by the manufacturer. Pre-conditioned and weighed Zefluor filters (37 mm PTFE, 2 µm pore size with support pads, Gellman Sciences, Ann Arbor, MI, cat #P5PJ037) were placed in the filter holder directly downstream of the pDR-1200‘s photometric sensing chamber. The SKC AirChek XR 5000 pump was attached to each active-flow pDR-1200 and calibrated before and after the sampling period with sampling train in-line (primary calibrator Bios Defender 510M; Bios International Corp., Butler, NJ) at a pump flow rate of 4.0 L/min to 0.1 mL/min precision.

Sampling was initiated just prior to burn initiation, which ranged between noon and 2:00 PM, and continued for up to 72 hours following burn events. The aethalometer would automatically advance the filter tape, resulting in 15 minutes of data loss every six hours. After the initial 24 hours of sampling and again at the end of the 68–72-hour sampling periods, the Zefluor filters on the pDR-1200s were changed, and electronic data extracted. The filters were shipped to the CDPH/EHLB in Richmond, California where each was equilibrated for 24 hours and weighed in a controlled-environment chamber (Temperature = 22–24o C, RH = 30–48%.) Filters were weighed to one µg precision using a Cahn 26 Electrobalance (Cerritos, California) and 120 mg substitution weights. Before each weighing session, the balance was zeroed and calibrated


with a 20 mg Class S weight and a control filter was weighed. Measurements were repeated on three successive days to ensure equilibrium, and yielded typical between-session drifts of < 2 µg and < 5 µg for the control filter and sample filters, respectively.

After downloading readings from the pDRs and aethalometer, anomalies in measurements and flow rate due to baseline drift or unusually high values were identified and were either removed or interpolated using methods from previous studies (Wu et al., 2005, Liu et al., 2002, Placer County APCD, 2007); these methods are more fully described in the SI, Attachment 3. The pDR instrumentation had a reported quantification limit of 1.6 µg/m3 (Wallace et al., 2003); values less than 1.6 µg/m3 were replaced with half of this value for this analysis. Humidity, particularly when RH is above 95%, can cause pDR nephelometers to overestimate PM mass (Chakrabarti et al., 2004, Laulainen, 1993). When the co-located HOBO RH/Temp data logger reported humidity > 60% (~33% of five-minute data), the five-minute pDR PM levels were corrected with the equation:

pDR-1200uncorrected/CF = pDR-1200corrected,

where CF = 1+0.25RH2/ (1-RH) (Chakrabarti et al., 2004).

Retained RH-adjusted pDR concentrations as recorded by the instruments were initially planned to be corrected to gravimetric mass equivalents using in-line filter gravimetric mass concentrations obtained from the pDR-1200. However, co-located E-BAM measurements, considered more accurate than the pDR-1200, were also obtained, and linear regressions to predict E-BAM values from pDR-1200 measurements were derived for hourly measurements and 24-hour averages, which met QA/QC criteria, i.e., the E-BAM flow rate was acceptable.

Hourly, four-hour, eight-hour, and 24-hour averages and geometric means of five-minute periods were calculated starting with 12:00 PM of the sampling day. If more than 67% of the minutes of any of the periods of less than 24 hours were missing, and if more than 200 minutes of the 24-hour and 72-hour periods were missing, the concentration for the corresponding period was not calculated. To test for an immediate impact on air levels due to a ground-level plume, the Mann-Whitney test of pairwise comparisons was used to test the difference in five-minute concentrations during the initial four hours (12:00–3:59 PM) compared to the subsequent four hours for each sample location. Correlations between measurements were assessed with the Spearman‘s correlation coefficient.

b. Results

Instrument Operation: Access to power was sometimes limited and reduced the number of available locations. Specifically, of the 14 final sampling locations, pPR 1000s (PM10) were deployed to 13 and pDR-1200s (PM2.5) to 11 for at least 24 hours. One of the aethalometers (black carbon) required repair and was not available for use in this study. Thus, only ten locations were targeted, with successful deployment at eight. In addition, late starts and early stops occurred for some sites at every burn event, and losses ranged from five to 200 minutes. Most notably, due to supply delivery difficulties in a rural area, the pDR PM2.5 sampling was not initiated at the Dunham burn until 4:00 PM, after the burning had ceased. Initially, it had been planned to run all instruments for 72 hours. Due to logistical constraints 72-hour periods were only accomplished at five pDR-1200, eight pDR-1000, and two aethalometer locations. Specifically, monitors at the Dunham burn were run on batteries and were terminated after 24 hours; monitoring at the Imperial burn was terminated early due to rain.

Data Evaluation: Other data losses are summarized in Table IV.B.2.1. The final pump flow rate at the end of the sampling period for the pDR-1200 (PM2.5) never differed more than 10% from the initial rate (average difference -0.2% ± 3.1%), resulting in no data losses due to pump error.


No outliers were found in the pDR-1200 (PM2.5) data and only four five-minute measurements were outliers for the pDR-1000 (PM10) data. However, measurements from the 24-hour period from one pDR-1200 at the Dunham burn events exhibited little variability (five-minute averages ranged from 21–28 µg/m3). Smoke may have contaminated the optics, so the entire sampling period was excluded. Further information is in the SI, Attachment 3. Table IV.B.2.1. Data Losses for Real-time Instrumentation during Burn Event Air Monitoring, January 14–March 17, 2009 (shows percent of five-minute data lost for targeted 24-hour and 72-hour periodsa).

a During the targeted burn events there were 11, 13, and 8 locations for the pDR-1200, pDr-1000, and aethalometers,

respectively, where the targeted sampling period was 24 hours. At 5, 8, and 3 of these locations, the respective instruments were additionally operated for another 48 hours. The total period represented in the Power Failure and Other columns is either the 24-hour or 72-hour sampling period. During the co-location study there were 3, 3, and 2 locations for each of the respective instruments, which were operated for three consecutive 72-hour periods. b

Other type of data loss—i.e., hardware or software failure from pDR-instrumentation, ineffective zeroing procedure

on pDRs, or tape-advancement and negative values from aethalometer. c Number of five-minute samples.

During monitoring, humidity never exceeded 95%, but did exceed 60% every night from approximately 8:00 PM to 4:00 AM, suggesting that the RH correction was appropriate.

Extremely low PM2.5 mass was found on all filters (all < 1 mg). This mass was lower than the amount recommended by the manufacturer for calibration (Thermo Scientific, 2005), and filter mass was not used to calibrate the pDR readings as planned. An E-BAM-correction factor derived from the co-located E-BAM measurements, collected both at the targeted burn events and during the co-location study, was explored. For one-hour averaged concentrations, the slope of the relationship between E-BAM PM2.5 concentrations and RH-adjusted light-scattering pDR-1200 PM2.5 concentrations was 0.73, indicating the pDR was somewhat over-reporting PM2.5 (SI, Attachment 3). However, a low R2 value of 0.16 was observed; inconsistency between the two measurements was particularly apparent when the E-BAM reported values were below the E-BAM quantification limit (6 µg/m3), with the pDR-1200 reading as high as 40 µg/m3 when the E-BAM measurement was less than the quantification limit. For 24-hour averaged E-BAM and pDR concentrations, the slope is similar, but the R2 is much larger, indicating a stronger relationship, with agreement in readings of both instruments at the low and high ends (Figure IV.B.2.1). To adjust for potential systematic over-reporting of PM by the pDR, we then further corrected the RH-corrected pDR-1200 PM2.5 concentrations by multiplying the concentrations by the slope (0.81). However, because it is uncertain if such a correction would be appropriate for PM10, pDR-1000 PM10 concentrations were not further corrected.

Study pDR-1200 (PM2.5) pDR-1000 AN (PM10) Aethalometer (Black Carbon)

Power Failure

Otherb N

c Power

Failure Other

b N

c Power

Failure Other

b N

c

Pilot Burn 66.7% 0.0% 2592 11.5% 0.0% 2592 66.7% 0.7% 1728

Other Burns

8.3% 16.7% 6912 0.0% 8.5% 6912 0.0% 1.9% 3456

Co-location

14.8% 0.0% 7776 14.8% 0.0% 7776 0.0% 2.1% 5184


Figure IV.B.2.2. Regression of 24-hour Averaged E-BAM PM2.5 on RH-adjusted pDR-1200 PM2.5

.

Concentrations during and Immediately Following Burns: Hourly measurements at each location are displayed for two burn events in Figures IV.B.2.3 and IV.B.2.4, and for the other three burn events in the SI, Attachment 3. During the Dunham burn only the PM10 monitor was immediately adjacent and downwind of the burned field. At that monitor highly elevated values were observed: a maximum hourly concentration of 6,500 µg/m3 at 1:00-2:00 PM10, which then declined to a very low level of 4.3 µg/m3 by 4:00 PM (Figure IV.B.2.3.). At the Brawley burn, much smaller peaks in the hour after burn initiation at all sampling locations were apparent in PM10, up to 57 µg/m3 PM10, which then declined to 11 µg/m3 in the next hour (SI, Attachment 3). At the Rutherford burn (Figure IV.B.2.4), immediately after burn initiation there were apparent peaks in air concentrations but these peaks were low (i.e., < 8 µg/m3).


Figure IV.B.2.3. Hourly Averaged Values for Particulate Matter (PM), Black Carbon, Wind Direction (in degrees), Wind Speed, Temperature, and Relative Humidity for the Sampling Period during the Dunham Burn. The burn started at 2:00 PM on January 27, 2009. Distances from the burned field were measured from the center of the field. The monitor at the SE-0.21 location was 50 feet from the edge of the field. Hourly concentrations are plotted at the beginning of the hour. A guideline level for hazardous air quality is 526 µg/m

3, and for moderate air quality, 39 µg/m

3 PM2.5 for one-hour

averages (Lipsett et al., 2008).


Figure IV.B.2.4. Hourly Averaged Values for Particulate Matter, Black Carbon, Wind Direction (in degrees), Wind Speed, Temperature, and Relative Humidity for the Sampling Period during the Rutherford Burn. The burn started at 2:00 PM on February 21, 2009. Distances from the burned field were measured from the center of the field. The monitors at the SE-0.28 and SE-0.31 locations were 500 and 600 feet from the edge of the field. Hourly concentrations are plotted at the beginning of the hour. A guideline for moderate air quality for a

PM2.5 one-hour average is 39 µg/m

3 (Lipsett et al., 2008).


When four-hour concentrations are examined, elevations during and immediately following burns are more apparent. At four of twelve locations, PM10 levels were statistically significantly elevated in the four hours during and following burn hours compared to the subsequent four hours (Table IV.B.2.2). These four included the location immediately downwind of the Dunham burn, two locations at the Rutherford burn, and one location 3.5 miles away from the Dunham burn but where an upper smoke plume from another burn was observed (Figure IV.B.1.1b, previous Section). For PM2.5 and black carbon there were also statistically significant elevations in the initial four hours compared to the subsequent four at some (two to three) locations, most of which were at the Rutherford burn (SI, Attachment 3).

Table IV.B.2.2. Four-hour Averages of Five-minute PM10 (RH-adjusted pDR-1000AN) Concentrations for the First 24 Hours of Sampling by Monitoring Location

Distance from Burn Event (miles)

Burn Event

Afternoon and Evening Day 1: Average [µg/m

3]

(n=number of 5-min samples)

Night and Morning Day 2: Average [µg/m

3]

12 PM– 3:59 PM

4 PM– 7:59 PM

8 PM– 11:59 PM

12 AM– 3:59 AM

4 AM– 7:59 AM

8 AM– 11:59 AM

0.21 Dunham 2182* (36) 3.9(48) 2.5 3.4 4.6 8.3

0.28 Rutherford 10.2** (29) 8.7(48) 5.7 8.3 15.0 17.3

0.30 Rutherford 6.4 (36) 9.3 (48) 8.7 11.2 17.3 17.8

0.77 Imperial 29.3 (48) 39.6***(48) 40.3 34.5 36.9 41.9

0.81 Brawley 37.2(38) 23.3 (48) 31.5 7.4 6.2 12.8

0.87 Brawley 16.1 (45) 20.5** (48) 29.8 7.4 6.0 13.5

1.4 Rutherford 10.4** (23) 7.1 (48) 3.1 7.7 13.9 15.7

1.8 Holtville 15.7 (23) 48.9**(48) 28.6 20.0 14.8 13.6

2.1 Brawley 11.0 (23) 25.8** (48) 47.5 Ended

2.9 Holtville 11.2**(34) 13.0** (48) 37.3 13.1 9.3 16.6

3.5 Dunham 12.7 (36) 6.0 (48) 11.4 6.7 10.7 16.3

3.6 Imperial 22.7 (30) 28.9**(48) 29.4 34.5 34.8 37.7

7.8 Holtville N/A (0) 16.2 (44) 22.3 17.8 14.8 16.4

*p<0.05, ** p<0.01, significance between initial and subsequent four hours off sampling

Bolded value is the peak concentration observed during the sampling period at each location.

Diurnal Fluctuations: At most locations, air concentrations reached maximum values during the evening, night, and morning hours beginning with statistically significant elevations during 4:00 to 7:59 PM compared to the initial four hours of sampling (for PM10, Table IV.B.2.2; for PM2.5 and black carbon, SI, Attachment 3). Average values for sites within two miles of a targeted burn are summarized in Table IV.B.2.3. At sites further than two miles from the targeted burn, night and early morning four-hour levels were sometimes higher than those closer to the targeted burns (for PM10, Table IV.B.2.2; for PM2.5 and black carbon, SI, Attachment 3). However, this occurred at only a few (< 5) of the locations, and, for those locations, other burns occurred in the county on the day of the targeted burns.


Table IV.B.2.3. Four-hour Averages of PM2.5, PM10, and Black Carbon for the First 24 Hours of Sampling at Sites within Two Miles, But Not Downwind, of Targeted Burn.

Measurement Number of Sites

Afternoon and Evening: Day 1 Average (geo mean) [µg/m

3]

Night and Morning: Day 2 Average (geo mean) [µg/m

3]

12 PM– 3:59 PM

4:00– 7:59 PM

8:00– 11:59 PM

12:00–3:59 AM

4:00–7:59 AM

8:00–11:59 AM

PM2.5 (RH-adjusted and E-BAM corrected pDR-1200)

7 4.8

(3.9) 4.9

(3.1) 5.8

(5.4) 7.6

(7.3) 12.5

(12.1)

12.1 (9.8)

PM10 (RH-adjusted pDR-1000AN)

7 17.9 (13.9)

22.5 (19.7)

21.1 (19.5)

13.8 (13.4)

15.7 (15.3)

19.0 (18.2)

Black Carbon (aethalometer)

4 0.29

(0.24) 0.40 (0.38)

0.47 (0.44)

0.62 (0.57)

0.97 (0.93)

0.67 (0.61)

Bolded value is the peak mean during the first 24 hours of sampling.

Over the entire 72 hours of sampling, eight-hour averages revealed similar diurnal trends (SI, Attachment 3). In particular, eight-hour average PM2.5 levels at the school adjacent to the Rutherford burn, where there were no other burns during the sampling period, initially were very low (3 to 6 µg/m3), but then gradually climbed over the sampling period, reaching peaks that were three- to six-fold over initial levels (19–20 µg/m3) in the night to early morning (12:00 to 8:00 AM) of the second day following the burn. PM10 and black carbon show similar peaks during the night-to-early-morning hours of the second day following the burn (SI, Attachment 3) reaching peaks of 26.2 µg/m3 PM10 and 0.58 µg/m3, eight-hour averages. Hourly concentrations at the Rutherford burn show these peaks on the second and third days to occur from 8:00 to 11:00 AM (Figure IV.B.2.4).

At the Brawley burn, on the first two days of sampling there were no other burns in the county; however, on the third day over 1000 acres were burned. On that third day PM2.5, PM10, and black carbon levels climbed several-fold in the evening hours (SI, Attachment 3).

Other Results: Average 24- and 72-hour concentrations during the targeted burn events and the co-location period are detailed in the SI, Attachment 3. Briefly, for PM10, at the site downwind of the Dunham burn the 24-hour average concentration was 276 µg/m3 while the non-downwind sites ranged between 10 and 36 µg/m3. For PM2.5 and black carbon, no sites were directly downwind and the 24-hour concentrations ranged between 3.6 and 25 µg/m3 and between 0.36 and 0.83 µg/m3, respectively.

Table IV.B.2.4 provides correlation coefficients between instrument measurements and these coefficients indicate that the different measurements were highly correlated. Hourly measurements taken at the burn events (e.g., Figure IV.B.2.4) also show close agreement between instruments.

Table IV.B.2.4. Spearman Correlation Coefficient between Co-located 24-hour Average Concentrationsa

pDR-1000AN (PM10) Aethalometer (black carbon)

pDR-1200 (PM2.5) 0.62**(na=25) 0.43 (n=25)

pDR-1000AN (PM10) 1 0.68**(n=25)

E-BAM (PM2.5) — 0.53*(n=15)

*p<0.05; **p<0.01; an=Number of measurements.


c. Discussion

Downwind Exposures: When a pDR monitor was adjacent (50 feet from the edge of the field) and downwind of a burn, PM10 concentrations were highly elevated for two hours (Figure IV.B.2.3). The measured hourly PM10 level (6,500 µg/m3) was above an estimated guideline level for hazardous (>526 µg/m3) air quality (Table IV.1). In addition, photographic evidence (Figure IV.A.3) was consistent with visibility of less than one mile, which is expected at such levels (Table IV.1). For this burn, wind speeds averaged 5.6 MPH (2.7 m/s), which was higher than that at the other monitored burns. Smoke researchers have found that in very light winds (< 2.0 m/s) a ―nearly vertical columnar plume usually developed,‖ but at higher wind speeds there is greater drift (Carroll et al., 1977, page 1044). In a separate study of wheat field burning in Imperial County, comparable or higher PM10 levels, as well as elevated black carbon, were also observed near a burning field for about an hour (Kelly et al., 2003). Although that study and our study together represent only two monitored burn events, they document that hazardous air levels occur.

Non-downwind Exposures: At non-downwind locations during and immediately following a burn, increased concentrations of PM2.5 and PM10 were occasionally measured. However, these increases were very small (hourly PM2.5 was not above 25 µg/m3 and PM10 not above 60 µg/m3) relative to the hourly levels at the location directly downwind of the Dunham burn (6,500 µg/m3). For the other four burns, meteorological conditions, including low wind speeds, probably contributed to smoke rising in a straight upwards direction. Notably, those conditions are what the APCD attempts to achieve when burns are scheduled, and the results presented here suggest that those conditions reduce drift to non-downwind locations.

During evening, night, and morning hours, PM2.5, PM10, and black carbon reached maximum levels across the sampling period at most of the non-downwind locations. During these hours, several hourly PM2.5 concentrations exceeded 40 µg/m3, which is an hourly guideline suggested for moderate air quality (Table IV.1). During a two-month wheat-field burning season in Spokane, Washington, evening increases in PM2.5, were found, including four ―episodes‖ of consecutive 30-minute averages of PM2.5 concentrations exceeding 40 µg/m3 (Jimenez et al., 2006). However, evening PM2.5 levels within two miles of a targeted burn event, as measured by the pDRs, were generally less than those measured at the Calipatria E-BAM, reported in the previous Section of this report (Table IV.A.5). Specifically, the E-BAM PM2.5 evening eight-hour average concentration on days when the Calipatria E-BAM was within two miles of a burn was19.5 µg/m3, while the average concentration measured by the pDRs (SI, Attachment 3) at locations within two miles of a burn was 5.8 µg/m3. One potential explanation is that the monitoring locations of the pDRs were generally further from other sources, such as major roads, than the Calipatria E-BAM.

Over the 72-hour sampling period, continuing peaks at a school following the Rutherford burn suggest that potential exposures may continue for several days and are most apparent, particularly for PM2.5 and black carbon, between 8:00 and 9:00 AM, when children may be arriving at school. However, these peaks are, at most, potentially equivalent to a shift from very good to moderate air quality. The meteorological conditions may have been unusual—e.g., the inversion layer may have remained particularly low—but the results presented here suggest that lingering exposures may occur.

PM10, PM2.5, and black carbon levels were correlated, suggesting exposures to PM2.5, PM10, and black carbon at the same time. These correlations are somewhat expected given that all are different types of measurements of PM. During a period of forest fire burning, lower correlations between PM2.5 and black carbon than those observed here were found, but when the lower UV


wavelength on the aethalometer was used (not reported here) the correlation improved. Those authors interpreted their findings to suggest that the higher UV wavelength correlation is due to the PAH content of biomass burning (Jeong et al., 2006).

Study Limitations: The number of sampling locations at each burn event and, overall, was small. Although the monitors used here do not have sufficient accuracy to be used for regulatory purposes, they have been successfully used in many other studies (Wu et al., 2006; Chakrabarti et al., 2004; Jeong et al., 2006). Most notably, pDRs have been deployed without humidity or gravimetric correction inside and outside homes, as well as to assess personal exposures during an agricultural burn season in Washington State (Wu et al., 2006). Here, PM measurements were humidity-corrected and PM2.5 was E-BAM-corrected. The correlation observed here between 24-hour co-located pDR and E-BAM levels suggests a stronger relationship (R2=0.64), and perhaps greater accuracy, than that achieved in other studies with gravimetric correction (R2=0.56) (Chakrabarti et al., 2004). A weaker relationship was seen here among hourly concentrations, so we generally focused on averages of longer duration. Nonetheless, the pDRs may have error associated with them that may have not been adjusted for by the humidity and E-BAM corrections.

Summary: Results suggest that: i) directly downwind and very near fields being burned, PM10

levels may be hazardous when smoke plumes remain at the surface; and ii) nighttime to morning increases of PM2.5, PM10, and black carbon associated with burns may continue for several days following a burn. Although the monitors used have associated uncertainties and are not equivalent to methods used to determine whether air standards have been exceeded, the findings are consistent with the E-BAM findings, as well as with those in the cited literature. All of these findings are further discussed in conjunction with other air monitoring findings in the final sub-section on air monitoring of this report (Section IV.C.).

IV. Exposure Assessment; B. Targeted Burn Event Monitoring; 3. Passive Samplers: Particulate Matter Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 32 -

3. PASSIVE SAMPLERS: PM CONCENTRATIONS AND PARTICLE TYPING

The passive aerosol samplers deployed were developed at the University of North Carolina (UNC). The UNC samplers are small (about the size of a dime, 1.5 cm wide), inexpensive, and do not require power or operator maintenance during the sampling period (Figure IV.B.3.1) (Wagner et al., 2001 (a)). These samplers were deployed to 28 locations, including schools and homes, as well as along roadsides (see map in previous Section, Figure IV.B.1.2). This Section describes the microscopic analysis of the UNC samplers to determine particle sizes, concentrations, and particle types. Additional information may also be found in the SI, Attachment 4.

Figure IV.B.3.1. UNC Passive PM Sampler

a. Methods

Locations: The locations where UNC samples were collected are described in Section IV.B.1 (Table IV.B.1.2 and Figure IV.B.1.1), and in more detail, with QA/QC samples, in the SI, Attachment 4. Briefly, during each of the four targeted burn events, there were three to six sampling locations near the burn, and one co-located with an E-BAM. A final E-BAM co-location sub-study consisted of UNC samplers co-located with E-BAMs at three locations for three separate 72-hr periods (i.e., nine targeted sampling periods).

Collection: Each UNC sampler was transported to the sampling site inside a sealed individual plastic shipping vial. Field staff were trained in sampler placement technique. To begin sampling, each sampler was removed from its vial using protective gloves and placed inside a shelter, which protected it from wind and rain. The shelter was mounted on a stand or pole and then leveled (see Figure IV.B.2.1.). The stand was then secured with sandbags. When the specified sampling duration—either a 24-, 68–72-, or 120-hr period—was complete, each UNC sampler was removed from the shelter, replaced in its sealed vial, and sent back to EHLB for log-in and analysis. At least one field blank per burn or co-location sampling event was deployed to assess total potential contamination from the manufacture, deployment, and analysis of the sampler. Each field blank was removed from its vial, placed in a shelter, and then immediately replaced back in its vial. (See Section IV.B.1 for further deployment details and maps of the sampling sites). Due to sampler orientation issues that occurred during the pilot, results from this event were analyzed to assess initial concentration ranges and precision, but were not used for the E-BAM comparisons, average concentration levels, or proximity-to-

removable mesh cap

S I D E V I E W T O P V I E W

SEM stub

1 m m collection surface

1.5 cm

stainless steel mesh


burn analyses discussed below. Samples were identified with non-descriptive numerical codes and the analyst was blind to all sampling information until microscopic analysis was completed. In all, 52 field samples, seven field blanks, and two pairs of field replicates (two shelters placed side-by-side) were analyzed.

Laboratory Analysis: The particulate mass collected on the UNC sampler was not directly weighed. Instead, computer-controlled scanning electron microscopy and energy-dispersive spectroscopy (CCSEM/EDS) was used to obtain the collected particles‘ individual sizes and chemistry. Then, PM2.5, PM10–2.5 (particles < 10 µm but >2.5 µm), PM10, and particle size distributions were automatically calculated for each sample using assumed particle density and shape factors in conjunction with a particle deposition velocity model (Wagner et al., 2001 (b)).

Concentration Calculation: Sample results were blank-corrected by subtracting blank concentrations (= the blank‘s weighted loading divided by the appropriate sampling duration; see Equation 1 in the SI, Attachment 4) from the same event. Blank values were also calculated in terms of absolute mass (ng) per sampler. PM2.5 and PM10 24-hour average concentrations were calculated using the samples deployed for the first 24 hours. Concentrations for the two subsequent 48-hour periods were calculated by subtracting weighted averages of the preceding samples (when 72-hr and 120-hr samples were available at a given site; see Equations 6 and 7 in the SI, Attachment 4). All 24-hr and calculated 48-hr results from the targeted burn events were then grouped into: i) sites more than two miles from the nearest burn; and ii) sites less than two miles from a burn. Differences between the averages for these two groups were assessed using single-factor analysis of variance (in the case of two groups, this is the same as a t-test). Then, the analysis was also conducted on the log-transformed data (base-10 logs) to assess the difference between the geometric means (the means of the logged values).

Comparison to E-BAM Results: To assess accuracy, air concentrations measured at the four PM2.5 E-BAMs were compared to co-located UNC sampler PM2.5 measurements. As described in the SI, Attachment 4, four of these comparisons were for sampling durations of 24 hrs, eight for 68–72 hrs, and one for 120 hrs (total=13). For each, the hourly E-BAM results were averaged over the appropriate duration. In addition, a second ―LQ-corrected‖ set of comparisons was made, setting all hourly E-BAM readings below 6 µg/m3 to half the Limit of Quantification (LQ) of the E-BAM: 3 µg/m3. Using an analogous procedure, all UNC results less than the estimated UNC PM2.5 LQ = 10, 3.3, and 2 µg/m3 for 24-hr, 72-hr, and 120-hr samples, respectively, were also set to their respective LQ value divided by 2. The SI, Attachment 4, provides more details on the UNC LQ estimation.

Particle Typing: Each UNC sample was briefly inspected with manual SEM/EDS for uniformity of particle loading prior to CCSEM/EDS analysis. Any particles observed during these inspections with distinctive non-soil morphologies (e.g., spherical, melted, filamentous, spiky) were noted by the analyst, and images and elemental spectra were recorded. Special attention was paid to particles with apparent plant or combustion morphology.

Further analyses were conducted on three samples from the Dunham burn event (see Section IV.B.1 for event description). The measured chemical compositions of each detected particle in these samples were used to generate two different downwind/upwind comparisons. First, particle size distributions were compared for seven select elements plus carbon-oxygen compounds. Second, the proportions of particles in six mutually exclusive ‗particle classes‘ were compared for both the PM2.5 and PM10–2.5 size fractions. Known elemental source signatures were used to define the following six particle classes: 1) carbonaceous (―C-O only‖), indicative of soot, organic carbon, and plant matter; 2) non-aluminum-silicate potassium, any phosphorus, or non-sea-salt chlorine (―K, P, Cl‖), indicative of plant matter and ash; 3) aluminum and silicon, unless they contain phosphorous or non-sea-salt chlorine (―AlSi‖), indicative of


crustal material; 4) silicon and oxygen only (―SiO only‖), indicative of sand and quartz; 5) calcium compounds not already included in classes (2) or (3) (―Ca‖); indicative of limestone and evaporative deposits, and 6) all other particles (―misc.‖).

To further elucidate the morphological and elemental characteristics of airborne particles from burning of Bermuda grass in Imperial Valley, a bulk sample of Bermuda grass was retrieved from a field in Imperial County. Two sub-samples of this grass were exposed to high temperatures (450° C) in a muffle furnace for 10 minutes and 40 minutes, respectively. Sub-samples of the unburned and burned grass were analyzed by SEM/EDS to observe the inorganic elements present.

b. Results

PM Concentrations: Measured blank PM2.5 values averaged 0.6 ng per sampler. Blank PM10 averaged 7.3 ng per sampler. The average precision between field replicates for PM2.5 and PM10 was 2.3 and 2.7 µg/m3, respectively, which corresponds to average coefficients of variation of 23% and 6%, respectively. For all 54 samples, the mean and median blank-corrected UNC sampler PM2.5 were 4.1 and 3.4 µg/m3, respectively, and ranged from 0.7–27 µg/m3. The mean and median blank-corrected UNC sampler PM10 were 25.0 and 20.3 µg/m3, respectively, and ranged from 3.2–74 µg/m3.

Comparison to E-BAM Data: Table IVB.3.1 shows the results of the E-BAM to UNC PM2.5 comparisons. Levels detected were within a fairly small range, E-BAM = 4–13 µg/m3 and UNC = 1–10 µg/m3. The median difference (= PM2.5/E-BAM–PM2.5/UNC) between E-BAM and UNC samplers was 3.6 µg/m3, for an average percent agreement of 66%. Correlation measured across all durations was quite poor (R2 = 0.09), though it was necessarily limited by the small range of values measured. The average difference for 72-hr samples (3.7 µg/m3) and 120-hr samples (2.6 µg/m3) was substantially better than it was for 24-hr samples (6.3 µg/m3).

Table IV.B.3.1. Average PM2.5 Agreement between Co-located E-BAM Monitors and UNC Samplers

Sample Duration

E-BAM PM2.5 (µg/m3) UNC PM2.5 (µg/m

3) Median

Differencea

(µg/m3)

Avg. % Agreement

a

(%) R2 N Median Min. Max. Median Min. Max.

24-hr 4 10 4.0 13 2.7 1.5 4.3 6.3 103 0.04

72-hr 8 8.6 6.3 13 5.6 2.7 9.5 3.7 47 0.18

120-hr 1 4.9 (n/a)b (n/a)

b 2.3 (n/a)

b (n/a)

b 2.6 73 (n/a)

b

All 13 8.0 4.0 13 4.3 1.5 10 3.6 66 0.09 N=number of samples; Min=minimum; Max=maximum; hr=hour. a Positive number denotes a higher E-BAM result.

bNot enough comparisons available.

LQ-corrected statistics were similar to the uncorrected values. That is, on average, LQ-corrected PM2.5 and median differences both increased by 1.0 µg/m3, average agreement improved by 0.8% and R2 decreased 0.02%.

Distance from Nearest Burn: Average PM2.5 and PM10 levels at locations greater than two miles from the nearest burn were significantly lower than levels at locations less than two miles from a burn, as computed both with the untransformed (average levels) and log-transformed (geometric means) data (p < 0.05) (Table IV.B3.2).


Table IV.B.3.2. Mean PM Measured by UNC Samplers at Locations Less Than and More Than Two Miles from the Nearest Field Burn

Distance from Burn

PM2.5 (µg/m3) PM10 (µg/m

3)

N Average p-

value Geometric

Mean p-

value Average p-

value Geometric

Mean p-

value

> 2mi 15 1.8 1.3 17 12.7

< 2mi 22 5.0 0.05 3.8 0.01 27 0.04 22.4 0.02

Particle Typing: For the Dunham burn, the PM2.5 fraction of the two downwind samples was dominated (94%) by carbonaceous particles, most of them submicron (Figure IV.B.3.2, Figure IV.B.3.4, Figure IV.B.3.5). Coarse (PM10–2.5) particles in the downwind samples often had morphologies suggesting plant origins and exposure to high temperatures (molten morphology), and were enriched with calcium, potassium, phosphorus, sulfur, and chlorine (Figure IV.B.3.3, Figure IV.B.3.4). Higher concentrations of coarse (PM10–2.5) carbonaceous particles were also found in the downwind compared to the upwind sample (Figure IV.B.3.4). The coarse silicon and aluminum particles in both the downwind and upwind samples are indicative of crustal material and soil.

Figure IV.B.3.2. Typical SEM Images of the Submicrometer and Fine Carbonaceous Particles Observed in the Downwind UNC Samples from the Dunham Burn. The faint submicron particles are highlighted in the dashed boxes.

1.5 um

300 nm

200 nm


Figure IV.B.3.3. SEM/EDS of Sampled Particles Likely Derived from Plant Matter and Biomass Burning, Observed in Downwind Samples from the Dunham Burn

a) Mottled fiber showing strong potassium, calcium, and phosphorus peaks, with smaller sulfur and chlorine peaks

b) Particle with molten morphology showing strong potassium, sulfur, chlorine, and phosphorus peaks (the center exhibited enriched silicon as well)

c) Fiber with plant fragment morphology and strong potassium, calcium, chlorine, and phosphorus peaks


Figure IV.B.3.4. Elemental Concentrations for Select Elements as a Function of Particle Size for the Dunham Burn. The vertical axis is the normalized, airborne particle mass concentration (dC/dlogda [µg/m3]) and the horizontal axis is the particle aerodynamic diameter (da [µm]). The concentrations are blank-corrected.

(a) 0.2 mile downwind sample (b) upwind sample

Figure IV.B.3.5. shows the percent composition of the particles in each of the Dunham Burn samples, with PM2.5 in the left column and PM10–2.5 in the right column. The two most relevant particle classes for agricultural burning PM are the carbonaceous (soot, organic carbon, plant matter) class and the ―K, P, Cl‖ (plant matter and ash) class. Carbonaceous particles can be seen to dominate the 0.2-mile downwind sample (especially PM2.5), with progressively less influence further from the plume. ―K, P, Cl‖ particles, associated with plant matter and biomass burning, appeared mostly in the coarse PM10–2.5 fraction, where they showed the largest proportion in the 0.2-mile downwind sample. In general, Figure IV.B.3.5 shows a clear trend of decreasing burn-related PM2.5 with distance from the burn, with a total contribution from the two burn-related particle classes of 95%, 59%, and 12% for the 0.2-mile-downwind, 0.3-mile-downwind, and upwind locations, respectively. The analogous burn-related composition trends for PM10–2.5 were less clear-cut but still highest nearest the burn, with a total of 72%, 12%, and 43%, respectively. Total PM2.5 and PM10–2.5 concentrations directly downwind were 17 and 2.7 times higher, respectively, than those at the upwind location.

0

10

20

30

40

50

60

0.1 1 10 100

da (um)

dC

/dlo

gd

a (

ug

/m3

)

Al

Si

P

K

Ca

C-O only

0

10

20

30

40

50

60

0.1 1 10 100da (um)

dC

/dlo

gd

a (

ug

/m3

)

Al

Si

P

K

Ca

C-O only

2.5 2.5


Figure IV.B.3.5. Percent Composition of PM2.5 (left column) and PM10–2.5 (right column) for the 24-hour Dunham Burn UNC Samples, Classed into Carbonaceous PM (“C-O only”), Aluminum Silicates (“Al-Si”), Non-aluminum-silicate Potassium, Any Phosphorus, and Non-sea-salt Chlorine Compounds (“K, P, Cl”), Silicon Oxides (“Si-O only”), Calcium Compounds (“Ca”), and All Other Particles (“misc.”). The size of each pie chart is proportional to the mass concentration of the associated PM fraction.

Ca

7%

misc. 5%

C-O only 0%

K, P, Cl

12%

Si-O only

10%

Al-Si

66%

Si-O only 0%

K, P, Cl

14% C-O only 29%

Al-Si 47%

misc.

0%

Ca

10%

Directly downwind, 0.2 miles from center

of burn

Al-Si

12%

Si-O only

5%

C-O only 57%

K, P, Cl 2%

Ca 1%

misc. 23%

Al-Si

69%

Ca

10%

misc.

4%

C-O only 0%

Si-O only

5%

K, P, Cl

12%

PM2.5

Si-O only

0%

Al-Si

4%

Ca 1%

K, P, Cl

1%

misc.

0%

C-O only 94%

PM10-2.5

misc.

0%

Ca 5%

C-O only

40%

Al-Si

23%

K, P, Cl

32%

Si-O only

0%

45o from downwind, 0.3 miles from center

of burn

Upwind, 3.5 miles from center of

burn

27 ug/m3

22 ug/m3

12 ug/m3

12 ug/m3

1.6 ug/m3

8.2 ug/m3


Bermuda Grass: SEM/EDS of the unburned Bermuda grass sample revealed a carbonaceous matrix with measurable levels of silicon and other compounds (Figure IV.B.3.6(a),(b)). In addition, regions with enriched calcium and silicon were observed (Figure IV.B.3.6(c),(d)), the latter in the form of regular, repeating structures on the outer surfaces of the grass. The ash produced from exposing the Bermuda grass to 450° C (see SI, Attachment 4) was heterogeneous, but consistent with the inorganic elements found in the unburned plant.

Figure IV.B.3.6. Unburned Bermuda Grass Sample from Imperial County

(a) SEM Image of Grass with Studded Silicon Regions along Its Length

(b) EDS of Silicon-rich Studded Features

(c) EDS of Carbonaceous Matrix Showing Sodium, Magnesium, Silicon, Phosphorus, Sulfur, Chlorine, Potassium and Calcium Peaks

(d) EDS of Calcium-rich Region Near Center of (a)


c. Discussion

PM Concentration Measurements near Burns: To date, there has been very limited air monitoring near agricultural burn events (Kelly et al., 2003). Here, we deployed over 50 samplers to 28 locations, 13 of these co-located with E-BAMs. This deployment allowed us to assess UNC PM2.5 measurement accuracy in a rural, outdoor setting. Measured agreement was good with respect to co-located E-BAMs, given the relatively low PM2.5 concentrations measured by both. The average difference between co-located PM2.5 E-BAM and UNC samples was low in absolute terms (3.6 µg/m3, easily meeting the QA/QC plan accuracy criterion of +/-10 µg/m3 agreement), but high in terms of percent difference (66%). The improved agreement for 72-hr and 120-hr samples as compared to the 24-hr samples (Table IV.B.3.1) is likely due to improved particle counting statistics, and is consistent with previously reported limitations of the UNC sampler in low-concentration environments (Wagner et al., 2003). That previous report also noted that a systematic, negative bias in UNC PM2.5 should be expected with EHLB‘s current microscope, due to limited capability of measuring semi-volatile and the smallest submicron particles.

The average UNC sampler PM2.5 field precision of 23% did not meet the QA/QC plan precision criteria (15%), though again, this is partially due to the low PM2.5 concentrations, as the PM2.5 precision in absolute terms was very good (2.3 µg/m3). The average UNC PM10 field precision of 6% easily met precision criteria, but no separate, co-located PM10 measurements were planned as part of this study. Overall, based on these QA/QC criteria, the PM2.5 and PM10 UNC concentrations cannot be used to assess absolute human exposure levels. However, the significance of the PM differences observed with greater distance from a burn is not diminished (Table IV.B.3.2), since measurement imprecision in a regression would only produce an underestimate of the association strength. The higher levels of PM2.5 and PM10 measured by UNC samplers close to burns is also consistent with the assessment of E-BAM PM2.5 concentrations near burns (see previous Section IV.A, Table IV.A.5).

Particle Typing of Plume-impacted Samples: The Dunham Burn represented a direct, ground-level impact of an agricultural burning plume on two UNC samplers at close proximity to the burn, potentially a ―worst-case‖ exposure scenario. The upwind location cannot be regarded as strictly ―clean‖ or background, as other burns were observed to potentially impact the samplers on the sampling day (see previous Section IV.B.1., Figure IV.B.1.1(a), (b)), and carbonaceous particles represented 29% of the coarse fraction there (Figure IV.B.3.5). Nonetheless, the upwind location differs from the other two sites in that it was not directly impacted by a visible ground-level plume. Based on visual observations of the plume and elevated continuous-reading PM monitor values (see previous Section IV.B, Figure IV.B.2.3), the duration of direct plume impact was approximately two hours. Although this represents a small fraction of the 24 hours the UNC samplers were exposed, the impact of those two hours was large enough to create distinct differences between the downwind and upwind samples.

The Dunham plume measurably increased 24-hr average exposures downwind to both fine and coarse particles, though it had a much larger impact in the fine fraction that penetrates more deeply into the lungs. The coarse-mode (PM2.5–10) carbonaceous (―C-O only‖) particles present in the downwind sample, and to a lesser extent the upwind sample, represent relatively few but massive particles. In contrast, the broad, fine mode in the carbonaceous PM size distribution in the downwind sample represents hundreds of times more detected particles. This fine carbonaceous mode is responsible for the corresponding large PM2.5 peak in Figure IV.B.3.4., consistent with direct particle observations in the SEM (Figure IV.B.3.2), and with the dominance of the downwind PM2.5 fraction by carbonaceous PM (Figure IV.B.3.5).


Only a few other studies of agricultural burn smoke have accomplished particle size fractionation. A laboratory chamber study has also demonstrated a similar dominance of very fine (< 1 µm) carbon particles in rice straw and almond pruning smoke (Keshtkar et al., 2007). In air samples collected during an agricultural burning season in Taiwan, the particle size distribution was similar to that seen here, with peaks between 0.1–1.0 µm and in the PM2.5–10 fraction (Chen et al., 2008).

Analysis of Bermuda Grass: Finally, the inorganic elements (sodium, magnesium, silicon, phosphorus, sulfur, chlorine, potassium, and calcium) found in the bulk Bermuda grass analyses (Figure IV.B.3.5) were also seen in similar proportions in particles from downwind air samples directly impacted by the plume (Figure IV.B.3.4). These elements have been observed in microscopy images in another study of smoke from a field burn in Imperial County (Kelly et al., 2003), and potassium has been used as a marker of biomass smoke (Ostro et al., 2007). The characterization of Bermuda grass from the present study may ultimately assist researchers attempting more refined source attribution.

Future Use of UNC Samplers: The UNC sampler‘s ability to measure PM mass concentrations, elemental size distributions, and particle classes made it a useful tool for characterizing PM exposures at rural locations directly and indirectly affected by agricultural burns. The fact that these samplers did not require batteries, AC power, or pump calibration facilitated rapid deployment in often dynamic sampling scenarios. These results suggest that UNC samplers would be useful for PM characterizations and spatial comparisons in other outdoor PM exposure studies, such as in communities impacted by wildfires or urban emissions.

IV. Exposure Assessment; B. Targeted Burn Event Monitoring; 3. Passive Samplers: Naphthalene Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 42 -

4. PASSIVE SAMPLERS: VAPOR-PHASE NAPHTHALENE CONCENTRATIONS

Laboratory chamber studies of crop stubble burns have documented emissions of polycyclic aromatic hydrocarbons (PAHs) (U.S. EPA, 1996; Jimenez et al., 2007); one of the most abundant PAHs is naphthalene, a gas and particulate phase pollutant and a suspected respiratory carcinogen (Kakareka and Kukharchyk, 2003). However, there has been only one study of ambient air concentrations of naphthalene during agricultural burning; that study was done in Taiwan in a rice-burning area (Chen et al., 2008). Based on vapor pressure, naphthalene is partitioned primarily to the vapor-phase; in the Taiwan study, 98% of the naphthalene found was in the vapor-phase.

a. Methods

Samplers: For monitoring at the five targeted burn events, a passive sampler, developed to collect volatile organic compounds, was chosen. The selected passive naphthalene vapor (PNV) sampler was compact (about the size of a quarter), inexpensive, and did not require electric power or an operator during deployment. As specified by the manufacturer (SKC), this combination of diffusive sampler design and sorbant were recommended for the sampling of naphthalene, as well as other reactive and polar organic compounds. A similar passive sampler has been deployed in El Paso, Texas, where the samplers were co-located with routine ambient air monitors (Mukerjee et al., 2004). The selected PNV samplers (SKC Product Code: 575-003) utilize the known diffusion rate of this compound in ambient air to determine the air sampling rate. As shown in Figure IV.B4.1, naphthalene is collected on the Anasorb 727 sorbant after passing through the diffusion barrier at the face of the device. This diffusion-controlled sampler is not designed to collect aerosol particles. The hydrophilic activated carbon diffusion sampler substrate used in the El Paso, Texas, study was reported to under-sample volatile organic compounds at higher humidities (Mukerjee et al., 2004). The Anasorb 727 sorbant on the PNV selected here is derived from cross-linked styrene, providing a large, very non-reactive, and extremely hydrophobic collection surface area. To avoid potential contamination from the adhesive on labels, the samplers were ordered without labels.

Figure IV.B.4.1. SKC Passive Sampler for Organic Vapors

SKC Passive Sampler for Organic Vapors, Anasorb 727, 300 mg, SKC Product Code: 575-003


A sampling structure was designed to deploy the PNV and the passive UNC particulate samplers , described in the previous Section, at the targeted burn events (Figure IV.B4.2). To prevent potential sampling rate artifacts due to air turbulence at the diffusion barrier inlet, as may have occurred in the El Paso, Texas, study (Mukerjee et al., 2004), the diffusive sampler was mounted inside a shelter constructed from a 4‖ diameter PVC tubing end cap (see Figure IV.B.4.2).

Figure IV.B.4.2. VOC Passive Sampler Mounted inside 4” Diameter PVC Pipe Cap Wind Turbulence Shelter on Sampling Bracket Containing the UNC Passive Sampler for Aerosol Collection.

Locations at Targeted Burn Events: The PNV samplers were deployed in tandem with the UNC passive PM aerosol samplers. During each of the five targeted burn events, there were three to six sampling locations near the agricultural field burn, and one co-located at a site with an E-BAM. The locations where the PNV and UNC samples were collected are described in Section IV.B1 in Tables IV.B1.2 and IV.B1.3 and in the Supporting Information (SI), Attachment 2, Tables SI.A2.1 and SI.A2.2. The locations of the targeted burns and the passive samplers are also mapped in Figure IV.B1.1. The average distance of the 28 sampler locations from the center of the field of the targeted burn was 1.4 miles, and the distance of the seven additional sampler locations co-located with an E-BAM was 5.2 miles (SI, Attachment 2, Table SI.A2.2). The ground-level wind direction the hour of the burn, and for the 24 hours following the burn, are also mapped in Figure IV.B1.1. Notably at the Dunham burn, samplers were directly adjacent to and downwind of the burn (50–500 feet from edge of field). At one other burn event, the Rutherford burn, passive samplers were similarly located close to the edge of the field, but the samplers were not directly downwind.

Locations during Co-location Sub-Study: In addition, as previously described, a co-location sub-study was conducted at the end of the targeted burn events. The PNV samplers were co-located next to each of the three CARB E-BAMs (Seeley, El Centro, and Calipatria) for three consecutive 72-hr periods (nine samplers).

Collection: PNV samplers were transported or shipped to the sampling site inside individually sealed aluminized airtight zip-lock bags purchased from the sampler manufacturer, specially designed to provide an impervious diffusion barrier for naphthalene vapor. Field staff received training on sampler deployment and recovery protocols for the PNV sampler, which was deployed along with the UNC sampler. At the start of sampling, the PNV sampler was removed

UNC passive

aerosol sampler

inside shelter

naphthalene

passive

sampler

inside shelter

L-bracket


from the airtight plastic bag using protective gloves and the integral collar clip was used to attach the sampler hanging at the center of a cylindrical 4‖ diameter PVC pipe cap shelter. This provided protection against wind turbulence, windblown dust, and overnight condensation. Details of the PNV sampler shelter and tandem mounting geometry with the UNC sampler shelter are provided in Figure IV.B.4.2. Both shelters attached to the bracket were mounted on a portable stand or fixed pole, and leveled as shown in the previous Section, Figure IV.B.2.1. When portable stands were used, sandbags were employed as extra weight on the base to prevent sampler tip-over in strong winds.

A 24-hour deployment period was targeted at all locations. In addition, at sampling sites with real-time equipment, additional shelters with samplers for 68 and 120 hours were also deployed, albeit for some locations this was not logistically possible (SI, Attachment 2, Table SI.A2.2). At the conclusion of each 24-hr, 68–72-hr, or 120-hr sampling period, the PNV sampler was unclipped and removed from the shelter, returned to the original airtight zip-lock bag, and the batch of samplers from the burn event was sent back to the Environmental Health Laboratory Branch (EHLB) at CDPH in an iced cooler for log-in and analysis.

The location of blanks and replicates are detailed in the SI, Attachment 2, Table SI.A2.1. To summarize, each shipment contained one trip blank which was then carried with the field staff and shipped back to the laboratory, unopened, with field samplers. In addition, at least one field blank per burn or co-location sampling event was deployed to assess total potential contamination from the manufacture, deployment, and analysis of the sampler. Each field blank was removed from the protective zip-lock bag, mounted in a shelter, and then immediately replaced back in the bag. At two locations during the targeted burn events and one location during the co-location sub-study, a second shelter with samplers was placed side-by-side with the initial sampler as a ―replicate.‖ The replicate samplers were attached to the same stand at no further than 18 inches apart. Samples were identified with unique non-descriptive numerical codes and the analyst was blind to all sampling information until chemical analysis was completed.

During the targeted burns, 43 field samples (28, 11, and 4 samples taken for 24 hours, 68–72 hours, and 120 hours, respectively), two additional side-by-side replicates, four trip and two field blanks were collected. During the co-location sub-study nine field samplers were collected, three additional side-by-side replicates, and one field blank were collected.

Laboratory Analysis: PNV samplers in the individual airtight bags returned from the field were stored at freezer temperature (-20° C) until removed for solvent extraction and analysis for naphthalene by gas chromatography/mass spectroscopy (GC/MS). Samples were batched in five GC/MS runs, in which each batch included a duplicate sample, a trip blank, and a method blank.

The PNV sampler was designed with ―filling ports,‖ as shown in Figure IV.B.4.1, for introducing a fixed volume of carbon disulfide to extract the naphthalene without removing the Anasorb collection media from the sealed sampler housing. Details of the extraction protocol, based on the manufacturer‘s instructions, and the GC/MS unit-selective ion storage (u-SIS) chemical analysis using isotope dilution with deuterated naphthalene, are given in the method standard operating procedure (SOP) in the QA/QC Section, Appendix 1.

Extraction of the PNV samplers and analysis by GC/MS followed the SOP detailed in the QA/QC Section, Appendix 1. The Anasorb 727 PNV sampler sorbant was determined to contain naphthalene: the 11 method blanks contained an average of 0.032 µg/sample that was very consistent (SD=0.003 µg/sample). Although unexpected based on the manufacturer‘s specifications, fortuitously, this consistent PNV sampler blank level acted as a standard addition


of the analyte. This method blank level (0.032 µg/sample) was subtracted from all sampler naphthalene results.

An analytical method detection limit (MDL), below which naphthalene cannot be assumed to be present, was established, after subtraction of the method blank, to be 0.0025 µg/sample. The method reporting limit (MRL) below which the amount of naphthalene present cannot be quantified was determined to be 0.005 µg/sample, or twice the MDL.

Concentration Calculation: Results from field samplers were initially reported in µg/sampler. The average air naphthalene concentration for each collected sample was calculated as:

(µg/m3) = Extracted Concentration (µg/sample) X Sampling Period (mins) X Sampling rate.

The sampling flow rate was calculated based on the diffusion rate specific to naphthalene and the diffusion length geometry of the sampler design, based on Frick‘s Law:

Sampling (Uptake) Rate = (AD)/(L) where

D = Analyte Diffusion Coefficient

A = Open Area of Sampler Inlet

L = Path Length of Inlet to Sorbant Media.

As given in the U.S. occupational reference method (U.S. Department of Labor,1982) for naphthalene using the same PNV sampler (SKC #575-003), the naphthalene sampling rate was 0.0000122 m3/minute.

All concentrations in samplers were converted to air concentrations (µg/m3), including concentrations corresponding to Detection Limits (DLs) and Reporting Limits (RLs). As sampling periods increased, the RLs and DLs decreased. Specifically, the DLs were: 0.21 (24 hrs); 0.07 (68, 72 hrs); and 0.01 (120 hrs) µg/m3; and the RLs: 0.42 (24 hrs); 0.14 (68, 72 hrs); 0.02 (120 hrs.) µg/m3. If an air concentration was less than the RL it was reported as ―<RL Value.‖ If an air concentration was less than the DL it was reported as Not Detected (ND.)

Sampling Rate Verification in Laboratory Testing: Since the sampling rate calculation is fundamental to the accuracy of the naphthalene ambient concentration determination, the performance of the PNV was evaluated in the laboratory using test atmospheres. The test atmospheres were generated from a compressed gas cylinder of 2.11 parts per million (ppm) naphthalene in nitrogen prepared by a specialty vendor, Scott Specialty Gases. Although the cylinder concentration was certified, the dilution required to reach the lower expected field sampling levels and potential losses to the 3" diameter cylindrical glass exposure chamber required a direct measurement of the challenge concentration. Accordingly, a direct comparison was made between the PNV sampler and a commercially available sorbant tube (SKC #575-050) containing the same 300 mg of Anasorb 727, which required a pump to provide a recommended constant air sampling rate between 0.00005 and 0.00009 m3/minute.

Test atmospheres in the very low µg/m3 range were chosen as the most challenging for the range of ambient levels expected. Using the 0.0000122 m3/minute PNV sampling rate and the audit sorbant tube–measured sampling rate to normalize the naphthalene, results by similar extraction and GC/MS analysis were compared. Agreement between the PNV and audit sorbant tube samplers ranged from 94% to 104%, with a relative standard deviation between runs of 1.8% and 2% respectively. Challenge naphthalene levels ranged from 0.5 µg/m3 for the four-hour sampling period to 0.2 µg/m3 for the 24-hour period, which was the minimum sampling time used in the field study.


b. Results

QA/QC: Amounts in the four trip and two field blanks collected during the targeted burn event sampling were, on average, equivalent to that in the method blank level (average= 0.032 µg/sample) but showed slightly greater variability (SD=0.006 and 0.008 µg/sample, respectively) than that in the method blanks (SD=0.003 µg/sample). These results suggest little, if any, naphthalene contamination due to handling for transport or field deployment during the targeted burn event sampling. One field blank collected during the co-location sub-study contained a trace amount of naphthalene (0.004 µg/sample, after subtraction of method blank level) that was just above the MDL (0.0025 µg/sample) and below the MRL (0.005 µg/sample).

All reported results met the established QC limits given in the SOP. Laboratory replicates (n=6) of the PNV sample analysis routinely agreed within 1.2%. As might be expected, field replicate (two shelters placed side-by-side) measurements had greater variability. During the targeted burn events, the two samplers of one field replicate pair of 24-hour samplers measured 0.46 and 0.23 µg /m3, both of which were near or below the RL (0.42 µg /m3). During the collocation sub-study, where a longer sampling period, 72 hours, was used levels below or close to the RL (µg /m3) were also obtained: one replicate pair contained 0.19 and 0.04 ug/m3, while another pair contained 0.30 and 0.15 µg/m3.

Naphthalene Concentrations: Naphthalene air concentrations by burn event are summarized in Tables IV.B.4.1a and b. For the 24-hour samplers, concentrations varied by burn event. For the Holtville pilot burn, naphthalene sample levels near the burn and at more distant locations were similar. Notably, on that burn day there were 11 burns in the county with 1159 total acres burned (Table IV.B.1.1). At the Dunham burn, three samplers were very close to the edge of the field (within 50–500 feet) and directly downwind of the burn event. Levels at these close locations were the highest 24-hour average naphthalene levels found: up to 1.37 µg/m3 was reported, with the levels at all three locations higher than at the distant location.

Table IV.B4.1a. Naphthalene Levelsa for Targeted Burns: 24-hour Sampling Periods

Near Burn Locations Distant Locations (at E-BAMs)

Targeted Burn

Number of locations

(mileage from burn)

b

Levels Detected (µg/m3)

Number of locations

(mileage from burn)

Levels Detected (µg/m

3)

Holtville: Pilot 3 (3.0 miles)

c ND, 0.68, 0.91 3 (11 miles) <0.42, <0.42, 0.80

Dunham: Near Field 3 (0.3 miles)

d 0.68, 0.80, 1.37 1 (3.5 miles) <0.42

Brawley 5 (1.3 miles) c ND, ND, <0.42, 0.68, 1.02 1 (9.5 miles) <0.42

Imperial 5 (1.1 miles) c ND , ND, ND, ND, <0.42 1 (3.6 miles) <0.42

Rutherford 6 (0.3 miles) c ND, ND, ND, ND, <0.42, <0.42 Not Applicable

e

aDL= Detection Limit (µg/m3) = 0.21(µg/m

3;); less than DL reported as not detected (ND).

RL =Reporting Limit (µg/m3) = 0.42 (µg/m3); less than RL reported as ―<0.42‖

bDistance from center of field. At Dunham and Rutherford burns, near locations were 50 to 500 feet from field edge.

cAll locations were not directly downwind.

dAll locations were directly downwind.

eE-BAM location was near burn and results are included in ―near burn‖ sites


At the three final burns (Brawley, Imperial, and Rutherford), there were few other burns in the county, but the samplers were not directly downwind of the targeted burn. At these three burns, naphthalene was occasionally detected in the 24 hour samplers (Table IV.B4.1a), with concentrations at two Brawley burn locations (0.68, 1.02 µg/m3) over two times the highest level found at locations more distant from target burns (0.34 µg/m3).

Samplers deployed for longer than 24 hours were lower in naphthalene concentrations (targeted burns, Table IV.B4.1b; co-location sub-study, Table IV.B.4.2). Concentrations measured during the sub-study were also lower than levels during the targeted burn events, and were lowest in Seeley and highest in Calipatria (Table IV.B4.2).

Table IV.B4.1b. Ambient Naphthalene Levelsa for Targeted Burns: Three and Five-day Sampling Periods

Near Burn Locations Distant Locations (at E-BAMs)

Targeted Burn

Sampling Period

Number of locations


3)

Number of locations


3)

Holtville: Pilot 72 hrs 2

b ND, 0.30 1 0.42

Brawley 68 hrs 2 b ND, ND 0

Imperial 68 hrs 3 b ND, ND, ND 1 ND

Rutherford 68 hrs 3 b ND, <0.14, <0.14 Not applicable

c

Brawley 120 hrs 3 b ND, 0.02, 0.07 0

Imperial 120 hrs 1 b ND 0

aDL= Detection Limit (µg/m3) = 0.07 (68, 72hrs); 0.01 (120 hrs); less than DL reported as not detected (ND).

RL =Reporting Limit (µg/m3) = 0.14 (68, 72 hrs); 0.02 (120 hrs); less than RL reported as ―<RL Value‖ b

All locations were not directly downwind. c E-BAM location was near burn and results are included in ―near burn‖ sites

Table IV.B4.2. Naphthalene Levels during Co-location Sub-Study: March 8-23, 2009 a

E-BAM Locationb Levels Detected (µg/m

3)

North: Calipatria 0.15, 0.23, 0.34

Central: El-Centro <0.14, 0.15, 0.19

West: Seeley ND, <0.14, <0.14

DL= Detection Limit (µg/m3) = 0.07 (72hrs); less than DL reported as not detected (ND). RL =Reporting Limit (µg/m3) = 0.14 ( 72 hrs); less than RL reported as ―<0.14 Value‖ aThree consecutive 72-hour sampling periods. During those nine days, agricultural burn acreage in the

County averaged 70 acres/day while during the targeted burn event period it average 265 acres/day.

bLocations are mapped in Figure IV.A.1.


c. Discussion

Utilization of the PNV sampler mounted within the wind shelter provided a measurement of naphthalene. Although the PNV sampler offers the advantages of low cost and no requirements for electric power or a field operator, the low sampling rate requires high naphthalene vapor concentrations (less than 24 hours of sampling) or long deployments for low concentrations (multiday sampling). As utilized, the low, consistent blank level in the Anasorb 727 sorbant must be accounted for in the data analysis. Sampling of low-level naphthalene test atmospheres reported here suggests that the calculation of PNV sampling volume–based naphthalene diffusion rate is appropriate.

The highest levels of naphthalene were detected in the three 24-hour downwind samples at the Dunham burn, and ranged from 0.68 to1.37 µg/m3. These levels were somewhat higher than naphthalene levels 0.38 to 0.44 µg/m3 during an agricultural burn season in Taiwan (Chen et al., 2008). The levels detected here were lower than the reference level for respiratory effects of 9.0 µg/m3 (OEHHA, 2004). However, the co-located PM10 monitor measurement (Section IV.B.2) indicated that the plume impacted the PNV sampler for only the first 1–2 hours of the burn. During this time the prevailing wind was from the northwest (Figure IV.B.1 and wind rose in the SI, Attachment 5, Figure SI.A5.1). Assuming all of the naphthalene emissions occurred within the first one to two hours, the naphthalene levels would be 16 to 32 µg/m3. To compare, vapor-phase naphthalene levels measured in a laboratory, directly above low-temperature burning of agricultural debris have been reported to be near 60 µg/m3 (Kakareka et al., 2002).

An orders-of-magnitude higher diffusion rate for naphthalene vapor compared to PM, suggests that naphthalene levels would drop more quickly with downwind distance from the burn event. This differential dispersion rate distinction is in agreement with an ANOVA analysis conducted on the naphthalene data for sampling at locations greater than and less than two miles from a burn, similar to the Table IV.B.2 analysis for PM2.5 and PM10 UNC sampler measurements. Unlike PM2.5 and PM10, the naphthalene levels were not significantly different (data not shown) (p=0.23) for sites in these two groups based on distance from the nearest burn, as would be expected for this more rapidly dispersed vapor-phase plume component.

However, at the other four burns, naphthalene was detected in about half of the samplers (maximum=1.02 µg/m3) and these detections were more apparent during the pilot burn (Table IV.B4.1a and b) when there was more burning in the County. During the co-location sub-study, when there was agricultural burning in the County, there were also detections (Table IV.B4.2). Most of these detections (up to 0.34 µg/m3) were above or comparable to that measured (0.02–0.06 µg/m3) in similar samplers collected at stationary stations in El Paso, Texas (Mukerjee et al., 2004). Nonetheless, naphthalene has many sources, including mothballs, gasoline, and diesel (OEHHA, 2004). Agricultural burning, or any of those other sources, may have contributed to the levels found during the four other burns and during the sub-study.

Naphthalene concentrations measured during the co-location sub-study were also apparently lower in the western part of the county (Table IV.B.2). E-BAM PM2.5 concentrations revealed a similar geographic trend (Table IV.A.1) that was suggested to be a consequence of wind patterns and pollution sources to the east.

There are thousands of chemicals in smoke (Naeher et al., 2007). Here, we used a novel, easy-to-deploy sampler for a toxic PAH. Not only were higher levels in a smoke plume quantified, but also lower, ambient levels were quantified. These results demonstrate the utility of the PNV sampler in air pollution studies, particularly in high exposure situations. With additional research, naphthalene could potentially be used to indicate the less prevalent, but more highly toxic, vapor-phase PAH compounds.

IV. Exposure Assessment; C. Conclusions Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 49 -

IV. C. CONCLUSIONS

Our air monitoring included two components: monitoring at fixed locations, and at targeted burns. The E-BAM daily (24-hour average) PM2.5 air concentrations at fixed locations in northern, central, and western Imperial County ranged between < 6 and 21 µg/m3

. . In addition,

during the monitoring of targeted burn events—at non-downwind locations—24-hour PM2.5 air concentrations during and following burns ranged between 4 and 25 µg/m3. These levels were mostly below (> 95%) 16 µg/m3, a level at which the AQI would categorize air quality as moderate. These results suggest that regulatory activities to control air pollution from agricultural burns are achieving their objectives. However, air concentrations apparently increased near burns in two situations:

1. DOWNWIND GROUND-LEVEL PLUMES

Directly downwind of one field being burned we found the following:

photo-documentation of drift demonstrating impaired visibility (less than one mile), and indicating PM air levels equivalent to an AQI level of hazardous. This drift was observed to cover a house on the property, the adjacent road, and the field across the road;

highly elevated PM10 levels, as measured by a passive pDR; these levels peaked at an hourly level of 6500 µg/m3 and a 24-hour average level of 256 µg/m3. These levels are above concentrations at which the AQI could designate air as ―hazardous‖ (> 526 for hourly and 250 µg/m3 for 24-hour average, respectively). As noted, similar levels of PM10 were also observed in one other study in Imperial County (Kelly et al., 2010);

elevated PM2.5 levels, as measured by passive samplers, and a PM2.5 fraction that was primarily less than 1.0 micron and primarily carbonaceous;

molten plant fragment particles in microscopic analysis; and

increased levels of naphthalene gas, a respiratory carcinogen.

At hazardous levels of PM there may be risk of: ―serious aggravation of heart or lung disease; premature mortality in persons with cardiopulmonary disease and the elderly; and serious risk of respiratory effects in the general population‖ (Lipsett, et al., 2008). AQI health impacts are based on studies of 24-hour averages (see Table IV.1, page 5) and the downwind exposures here are undoubtedly shorter (1–2 hours). In studies of short-term effects, researchers have suggested that: an increase in PM2.5 concentrations as small as 25 µg/m3 for as little as two hours is associated with heart attacks (Peters et al., 2001); increases in 12-hour PM2.5 concentrations during work shifts of young, healthy taxi drivers in China are associated with altered cardiac autonomic function (Wu et al., 2010); and that a 10 µg/m3 increase in hourly PM2.5 is associated with same-day and next-day risk of mortality (Yang et al., 2009). Those studies were mostly in areas where the primary source of PM2.5 is urban traffic. There has been one study of short-term exposures to wood smoke: two four-hour sessions at 340–380 µg/m3 PM2.5 is associated with inflammation and other mechanisms of cardiovascular disease (Barregard et al., 2006). However, as described above, the downwind 24-hour average concentration was also above the 24-hour AQI concentration level that signifies hazardous air quality, suggesting that the ―strong body of epidemiological evidence associating 24-hour PM2.5

exposures with respiratory and cardiovascular morbidity and mortality‖ (Lipsett et al., 2008, page 27) is applicable. Finally, there may be other exposures, as monitoring was only conducted for one of the thousands of gases potentially emitted during burning (Naeher et al., 2007).


During observation and monitoring of the ground-level plume, wind speeds were higher (2.7 m/s) than they were at the other four monitored burns (< 2 m/s). Smoke researchers have found that in very light winds (< 2 m/s) a ―nearly vertical columnar plume usually developed‖ but at higher wind speeds there was greater drift (Carroll et al., 1977). The particular burn monitered here may have been unusual and the number of sampling locations was small. However, the observations at this event and the levels detected suggest that potentially hazardous exposures may occur. Behavioral and other recommendations to reduce exposures to ground-level plumes are discussed in the next Section of this report.

2. EVENING, NIGHT, AND EARLY MORNING AIR CONCENTRATIONS

To briefly summarize air monitoring results:

Smoke plumes from agricultural burns were observed to rise up to the apparent inversion layer and then to spread out, sometimes in the direction opposite to the ground wind direction. After burning was observed to cease, the ground-level plume visibly dispersed, but smoke from upper plumes remained visible until sunset.

PM2.5 eight-hour average air concentrations were 170% higher during evening-to-night (9.3 µg/m3) and night-to-morning (9.9 µg/m3) hours than during the day (5.7 µg/m3). This difference was more pronounced on days (n=35) with agricultural burning (evening-to-night average, 11.1 µg/m3). Daily burn acreage was also statistically significantly (p=0.02) correlated with evening-to-night eight-hour average concentrations. On days when an agricultural burn took place within two miles of a monitoring site (n=9), eight-hour average air concentrations were higher (evening-to-night and night-to-early-morning, 19.5 and 20.7 µg/m3, respectively) than on days (n=60) when burns were further from the monitoring site (p=0.02).

Night-to-morning increases of PM2.5, PM10, and black carbon related to burns may continue for several days following a burn: eight-hour average PM2.5 levels at a school adjacent to the targeted burn initially were very low (3 to 6 µg/m3). However, levels were higher at night and in the morning on several subsequent days, reaching peaks (19–20 µg/m3) in the early morning (12:00 AM to 8:00 AM) of the third day that were three- to sixfold over initial levels. Because there were no other burns during the sampling period in the county and the school was several miles from any major roads, the targeted burn is the likely source.

These findings are suggestive of an increase in evening, night, and early morning PM2.5 concentrations to levels that may approach levels equivalent to an AQI designation of―moderate air quality levels in areas near (within two miles of) burns.

Although all daily PM2.5 levels measured at non-downwind locations were below AQI levels for unhealthy air (< 36 µg/m3), there is a variety of recent evidence that suggests a potential for public health impact from lower PM air levels. The dose-response relationship between PM2.5 levels and daily mortality has been well defined in multiple studies. Notably, among six counties in California a 1.5% increase in mortality is associated with a 15 µg/m3 increase in daily PM2.5 (Ostro et al., 2007). Some researchers suggest that 24- hour levels as low as 2 µg/m3 of PM2.5 are associated with daily deaths (Schwartz et al., 2002). This and other research has led researchers to suggest that deaths could be prevented by reducing small amounts (1 µg/m3) of PM2.5 (Anenberg et al., 2010). Recently, adverse birth outcomes (preterm birth and small size for gestational age) have been associated with 10 µg/m3 increases in PM2.5 and black carbon (Brauer et al., 2008).

Nonetheless, these studies were mostly conducted in urban areas where PM, black carbon, and naphthalene have many sources, notably traffic. Other studies, however, suggest that burning


of vegetation may have a role. In Australia, hospital admissions were associated with a 10µg/m3 increase in ―bushfire‖ PM10 (Morgan et al., 2010). In Finland during a wildfire episode, a 0.8%–2.0% increase in mortality per 10 µg/m3PM2.5 was observed (Hanninen et al., 2009). In India, during a period of rice- and wheat-field burning, reduced pulmonary function in children, as measured by peak expiratory flow, was associated with 10 µg/m3 increments in PM2.5 (Awashit el al., 2010). As described, hospital admissions in Butte County, California, were associated with acres of rice burned (Jacobs et al., 1999). A study in Washington during a period of prescribed burning suggests that daily PM2.5 levels as low as 11 µg/m3 are associated with cough symptoms in children (Mar et al., 2004).

Behavioral recommendations to reduce exposures to night and early morning hours are further discussed in the next Section of this report. However, our air monitoring results and the results from all of the above-cited studies suggest that reducing agricultural burning in Imperial County could potentially reduce air pollution and thereby improve public health. Additional agency activities and research that may ultimately reduce burning are considered in the final Section of this report.

V. Exposure Reduction; A. Needs Assessment Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA: - 52 -

V. EXPOSURE REDUCTION V. A. NEEDS ASSESSMENT

In 2007, prior to receipt of funding for the current project, a U.S. Centers for Disease Control and Prevention (CDC) Public Health Prevention Specialist (PHPS) placed at CDPH/EHIB began as part of assigned responsibilities an assessment of community knowledge and health educational needs about potential exposures from agricultural burning. The PHPS holds a master‘s degree in epidemiology, and received a year of additional training at CDC prior to the placement at CDPH/EHIB. Although initiated prior to the current funding, the interviews with community residents, described below, were completed with funds from this project. To ensure a complete report, all of the interviews conducted are described below.

1. METHODS

To collect information from a range of residents, a qualitative method—Key Informant Interviews (KIIs)—was selected. Qualitative methods allow for candid and in-depth responses and the characterization of the complexities of community knowledge, and allow public health scientists to understand how people have discovered information and how they have acted on that information (Brown, 2003; Carroll et al., 2004).

Four different groups were targeted for KIIs: community leaders (i.e., representatives of health and environmental agencies and organizations in Imperial County), school representatives, community residents, and farmers. Questions were developed by the EHIB/CDPH team based on similar previously conducted interview projects (Tan et al., 2011; EHIB/CDPH, 2008). Questions were designed to elicit information about:

the perception of smoke from agricultural fields as a health concern for the agency or the respondent, compared to other health concerns (all groups);

exposures to smoke from agricultural burning (school representatives, community residents, and farmers);

the respondent‘s thoughts about their neighbors‘ perception of smoke from field burning compared to other community health concerns (community residents, leaders, and farmers);

the frequency of inquires received by the respondent‘s agency, or inquiries submitted by interviewed residents (all groups);

actions taken in instances of exposure to smoke from burning agricultural fields (all groups);

awareness of current outreach activities, educational efforts, or interagency collaborations to reduce exposures (all groups);

the respondent‘s thoughts about effective methods of health education and community outreach (community leaders and school representatives); and

how frequently farmers burn their fields, whether or not they had heard of field burning alternatives, and, based on the response, if the respondent used field burning alternatives such as tilling (farmers).

Questions were assessed for appropriate reading level, and were orally translated to Spanish as needed during the interview process.


Participants were selected as identified below.

Community Leaders Community leaders were first chosen by identifying representatives of county (government) environmental health organizations in Imperial County. Non-profit agencies were then selected to evenly represent agencies that promote the agricultural industry and those that represent agencies working to promote clean air. In several cases, the interviewee suggested during the actual interview that another organizational leader be interviewed, and that leader was contacted for an interview.

School Representatives Schools that were within five miles of fields that were burned in 2006 or 2007 were targeted for interviews. Geographic Information System (GIS) mapping techniques were used to map historically burned fields and identify schools near these fields. Teachers at these schools were identified through suggestions from community members and governmental organizations. If no existing contacts or connections to the school faculty were available, we searched for the school‘s information through the Internet and phone books, and asked the superintendent or principal of the school to give a KII.

Community Residents Community resident interviews were conducted in two phases, with the first set taking place in summer and fall of 2008, and the second set in March, 2009, after the air monitoring component of the study had been completed. Participants for KIIs conducted before the air monitoring study were identified by suggestions from the BEHT and community leaders who were familiar with residents who would be interested in participating and who lived close to historically burned fields. In 2009, more KIIs were conducted with community residents who lived in areas close to the field where a burn had been monitored for our study. In many cases we had also used their property to set up air monitoring equipment.

Farmers Contacts with farmers were acquired with the help of the local Farm Bureau and the Imperial County Public Health Department. We had some difficulty in gaining participation from farmers. In September, 2008, we had only one successful farmer KII after contacting approximately 10 farmers. With some more research and effort, and contacting approximately 20 additional farmers, two more farmers agreed to participate in March, 2009.

Potential participants were initially contacted for participation by letter, email, telephone, and in person by the PHPS with a pre-written paragraph introducing the PHPS and describing the intent of the interview. All participants were told that the interview would take approximately 30 minutes, that the purpose of the interview was to ―assess the priorities and focus of community outreach‖ and that EHIB/CDHP was ―not a regulatory agency.‖ Community members were given more initial information about air pollution. When a potential participant refused an interview, the reasons for the refusals, if offered, were not recorded because consent had not been obtained.

All respondents agreeing to participate were provided with an informational fact sheet prior to the interview that explained the purpose of the interview process. All participants were provided a consent letter that gave clear details on the intent and purpose of the KIIs, and ensured participants that their identities would remain anonymous. We preferentially obtained oral


consent before an interview as a written consent would document the name of the participant. Respondents were informed that all responses would be anonymous and no identifiers would be used in the analysis.

In all, 25 participants were interviewed from August 2008–March 2009. Interviews conducted with community leaders, teachers, superintendents, and farmers were carried out by the PHPS . We mailed interviews to schools that were unable to give in-person interviews; one interview was obtained in this manner. The community residents were primarily identified during the air monitoring phase and field staff for the air monitoring were trained by the PHPS to interview the community residents. However, the PHPS was present for all interviews. The interviewer took thorough notes of all responses. In addition, if the interviewee gave consent, KIIs were audio-recorded. All interviewers were careful to interview respondents without introducing bias, without leading the respondents, and always allowing the respondent to answer questions without interruption. During the interview, Interviewers did not correct information provided, even if the information was considered by the interviewer to be factually incorrect. Respondents were given ample time to ask any questions or voice any concerns about the interview process.

All of the audio responses were transcribed and qualitative approaches were used to analyze the data. Specifically, KIIs were interpreted using thematic analysis. Bias was minimized by using direct quotes, or by making efforts to adhere to the respondent‘s original response as closely as possible when summarizing. Error was reduced by having only the PHPS analyze all responses, and as she was present for all the interviews, there was also a reduction in misinterpretation of responses.

For the responses of each of the four groups, common themes were identified. Many respondents had similar responses described in different wording and these responses were categorized into one these themes. A theme was considered ―new‖ if the response was apparently a different and independent response compared to other responses in the group. There was usually a spectrum of responses that fell into three to four themes. Although this method is dependent on the views of the person analyzing the results, this study used consistent methods for the entire process, thus minimizing potential categorization errors.

2. RESULTS a. Community Leaders

Ten community leaders were interviewed out of approximately 15 leaders contacted. Four interviews were conducted with representatives from government entities: the APCD, the County Health Department, the Agricultural Commissioner, and the County Environmental Health Department. Three organizations representing the agricultural industry were the UC Agricultural Extension Office, the Vegetable Growers Association, and the Farm Bureau. Three organizations or leaders working to promote clean air included a San Diego State University professor and two non-governmental organizations, Comité Civico del Valle (CCV) and the Institute for Socio-Economic Justice.

Common themes identified during the KIIs are presented in Table V.A.1.


Table V.A.1. Common Themes Identified During Key Informant Interviews Community Leaders (N=10)

Theme N

Agricultural burning in Imperial County is a high or medium priority within the department or agency. 7

Agricultural burning in Imperial County is a low priority within the department or agency. 3

The department, agency, or organization had received resident inquiries about agricultural burning. 5

The organization did not have advisories in place and felt that exposure reduction efforts were needed, including outreach.

4

The department, agency, or organization had the capacity to conduct educational outreach, but not the resources or funding.

7

More than half of the community leaders (n=7) ranked agricultural burning in Imperial County as high or medium priority within their department or agency. For organizations working to promote clean air, agricultural burning was a high priority because it was within their mission as it could ―potentially contribute to the asthma problem.‖ Another leader also cited cancer as a problem attributed to air pollution caused by field burning. However, for those respondents that represented the agricultural industry, it was a high priority because the ―public‘s view of burning is fairly negative.‖

Three respondents ranked burning as a low priority, as:

―burning is a small contributor to ozone and PM overall, (and we) have bigger issues to worry about,‖ and

―(our) regulatory authority lies elsewhere.‖

Many (n=7) thought Imperial County residents would rank agricultural burning as a high or medium priority compared to other health concerns because:

―it is visible, there is a lot of misinformation about ag burns, and (the) public doesn‘t understand the reasoning and all the regulations that the farmers are under,‖ and

―would rank asthma and cancer as high priorities and both may be related to ag burning.‖

Those who thought it was a low concern thought so because:

―many (people) consider it an inevitable problem because they take it as a ‗part of life.‘ Effects are not visible, especially because they are long-term, and asthma attacks occur at night, not during burns.‖

Half of the respondents said that their department, agency, or organization had received resident inquiries about agricultural burning. Most inquiries were about health concerns, exposure reduction methods, and resources and information about field burning. Health concerns included: trouble breathing, allergies, itchy and red eyes, constant coughing such as bronchitis, asthma, and headaches. Most agencies advised the callers on ways to protect their health and then suggested calling the county Air Pollution Control Office for more information or complaints. One agency also suggested that the resident:

―contact farmers and be neighborly,‖ and explained ―the reduction in burning through the years because of credit reduction programs, and why farmers have to burn.‖

One agency (the APCD) and one health advocacy organization had advisories or exposure reduction efforts in place for air pollution exposure. The public is informed of exposure reduction advisories or recommendations through various outreach mechanisms including door-


to-door outreach at homes and public places (for example, at employment development agencies) and through presentations at schools and/or churches. Although there was not a specific target audience for the advisories, most felt that the advisories were effective. In the past, CCV, in collaboration with the Clean Air Initiative, had a ―Heber Project‖ in place in which the residents of Heber were informed of agricultural burn days. Residents were informed of a radio station that announced daily AQI levels and were provided with recommendations to protect themselves if they were exposed to smoke. Presentations on health protection were also given at schools for parents and students.

Half of the eight organizations did not have advisories in place and felt that exposure reduction efforts were needed. Thoughts about outreach included the following:

―I personally believe that any outreach can help: simple recommendations, options of actions to take during a burn, how can they be empowered and how can their voice be heard?‖ and

―educational materials about the decrease in burning, necessity for it, explaining (the) entire inventory of pollution in the Valley, explain that ag burning is less than 2%.‖

Most of the community leaders were aware of effective efforts in place to decrease the occurrence of agricultural burning, such as burn alternative suggestions. Efforts that they were aware of included:

―emission reduction credit program by APCD,‖

―APCD has rules about time of day burning is allowed,‖ and

―ways to plow fields differently, ways to reuse land.‖

Most (seven) of the respondents said that despite having the capacity to conduct educational outreach, the resources or funding were not available. Specifically, they needed:

―personnel with skills and expertise; (there is) no funding, but (this is) also not a priority so (it) would not be pushed for,‖

―funding, exploring burning alternatives, building networks with other agencies,‖

―more specific training on the link between burning and asthma,‖ and

―support for recommendations. Also help interpreting burn laws, and burn alternatives.‖

Other barriers or challenges for agencies in addressing emissions from agricultural burning included:

―institutional barriers because of the dependence on agriculture…entire political structure is all in favor of agricultural interests,‖

[a need for] ―educating the public. And also as our county grows, for those of us that had been here our whole lives, we understand the reasons (for burning). The people that have moved here from the big city or other areas don‘t understand the reasons,‖ and

―it would take something like legislation to get something further than what we‘re already doing. For one they‘ll (farmers) argue that they‘ve gone above and beyond the call, the reductions are really intangible and insignificant, and they‘re actually a de minimis source in Imperial County.‖

Finally, several leaders noted the impact of other air pollution sources, including these remarks:


―ag burning is such a low percentage contributor to our poor air quality compared to the Mexican border,‖ and

―burning is better than the chemical solution alternative to pest and weed infestation. CDPH should understand that there is a place for ag burning, and banning it is not the solution.‖

b. School Representatives

Five teachers or superintendents out of approximately 30 contacted agreed to participate in the interview process; participating schools were located throughout Imperial County in El Centro, Brawley, Calexico, Seeley, and Holtville. Common themes identified are presented in Table IV.A.2.

The frequency of exposure to smoke from agricultural burning was variable. Three schools had been exposed to smoke from agricultural burning while school was in session in the past academic year. One respondent said it was more of a problem in winter, while another said burning occurs throughout the year.

Table V.A.2. Common Themes Identified During Key Informant Interviews School Representatives (N=5)

Theme N

The school had been exposed to smoke from agricultural burning while school was in session in the past academic year.

3

Perception exists that agricultural field burning smoke is a health concern. 3

Interviewee is aware of current outreach activities or educational efforts in place to educate the community about reducing exposure to smoke.

0

Bad air-quality days were addressed by requesting that students stay indoors. 3

Interviewee thought that schools were an appropriate place for initiating outreach and education about air quality to the community.

3

A general curriculum would help the school build capacity to enable educational outreach about reduction of exposure to agricultural burning smoke.

3

When exploring the perception of agricultural field burning smoke as a health concern for the respondent, a majority considered it a medium or low concern, and one teacher said it was a high concern. The following reasons were given that it was not a high concern:

―Farmers never burn while school is in session.‖

―Over the years that they (the APCD and agricultural industry) have put more of a priority on the burning, specific burn days, burn times, to give authorization (permits to burn fields)…‖ and

―The awareness is at the school, but I question the priority. We have a poor population…they do not have advocacy…no clout.‖

None of the schools that were interviewed had ever received inquiries from concerned students or parents. They were also not aware of any current outreach activities or educational efforts in place to educate the community about reducing exposure to smoke.


For general air pollution, bad air quality days had been addressed by three out of five schools on at least one occasion. Students were requested to stay indoors or students with asthma or other respiratory problems were given special advice, and there was no difficulty in implementing these recommendations.

The sentiment that the schools are an appropriate place for initiating outreach and education about air quality to the community was shared by three out of five respondents because:

―as a school we have a responsibility to inform parents and students about the effects that this could be having on their health…‖

Two of the five respondents were not enthusiastic about outreach at schools, responding:

―I don‘t see it as a big concern —there‘s so much that we do have to do.‖

Schools were asked about their opinions on effective methods for community outreach about agricultural burning other than outreach at schools, and some respondents said advertisements in the media, informative posters, and newsletters would be valuable:

―public service announcements, holding people responsible who are burning when they‘re not supposed to be burning … have people call a number if they notice illegal burning or something suspect,‖

―stiff penalties for those who don‘t,‖ and

―move onto the community college level because you‘d have continuity‖ (continuity from what was taught at the grade school level).

When asked what would help the school build capacity to enable educational outreach about reducing exposure to agricultural burning smoke, several respondents said a general curriculum would help. Other suggestions were also given:

―if there were some curriculum…you‘ve already got the programs in place…you have biology classes. … It would be long-term results. You‘d have to have the buy-in of the instructor and the administration…‖ and

―just any type of literature, presentations…‖

At the conclusion of the interviews, respondents were asked to share any remaining thoughts. Responses provided insight into root issues in the communities:

―Agricultural burning will continue to be a thorn in our side as long as our population is uneducated and low-income. The big money interests (agriculture) are ultimately what‘s mandating us. You can do all the studies you want and come up with all the data you want, but until big brother (government) comes down…‖

―There is Mexicali … where people have to burn anything they can to heat themselves in the winter including toxic waste; that pollution is still coming across from one direction or going,‖

―It‘s like a passive acceptance. It becomes part of the background,‖ and

―I just know that over the years that they have put more of a priority on the burning, specific burn days, burn times…it‘s different from 27 years ago when I could just go burn my trash.‖


c. Community Residents:

Seven KIIs were conducted with community residents living throughout Imperial County. About fifteen were approached. Of the seven interviewed, six respondents had been exposed to smoke from agricultural burning in the past year. Common themes identified are presented in Table V.A.3.

Table V.A.3. Common Themes Identified During Key Informant Interviews Community Residents (N=7)

Theme N

Respondent had been exposed to smoke from agricultural burning in the past year. 6

Agricultural burning in Imperial County is a high or medium health concern for respondent. 5

Respondent thought their neighbors would rank agricultural burning as a high health concern. 3

Respondent used an air-conditioning unit in their home on a daily basis in the summer months.

7

Respondent thought that there was more that health departments could do with respect to resident exposure to agricultural burning.

5

A majority (n=5) considered burning a high or medium health concern for themselves and their family compared to other community health concerns:

―You‘re closing doors and windows, just trying to keep the smoke out, and that is a concern because you can‘t enjoy the environment,‖ and

―A lot of individuals in Imperial Valley…suffer from allergies and asthma…You just can‘t go outside. My eyes burn, itch, (and I start) sneezing.‖

One respondent who felt that agricultural burning was a low concern thought that pesticides were a larger concern.

Respondents were asked how they thought their neighbors would rank agricultural burning as a health concern, and three respondents thought their neighbors would rank it as a high health concern, two thought it would be a low concern, and the rest did not know. The following reasons were given for their responses:

―if they knew the health risk, it would be high…‖

―I don‘t think people really think about it because it‘s normal; something we‘ve grown up with,‖ and

―high, because the problem of asthma has increased significantly recently.‖

Despite considering agricultural burning a high health concern, one respondent thought burning affected the overall community‘s health minimally, since the risk depended on how far the burning field was from the population and if the wind was blowing the smoke and pollution towards the public. Four respondents thought that burning somewhat affected the community‘s health:

―It depends on how close they are burning. If the wind is blowing or if it‘s close then I‘m sure it affects our health; if it‘s far away and not that close to the house it doesn‘t affect us that much.‖


Respondents had never called in or inquired with government agencies, other health organizations, or a health care provider for information about agricultural burning. One respondent explained:

―The practical reality of the fact is that we all have to live with our neighbors…it would be difficult to file a complaint or inquiry.‖

All respondents (n=7) used an air-conditioning unit in their home on a daily basis in the summer months, which was generally described as May through October. None of the air-conditioning units in the interviewee homes had a recirculate setting.

Most county residents (n=5) thought that there was more that health departments could do with respect to exposures to agricultural burning. Information about health consequences from exposure to smoke from agricultural burning and exposure reduction actions were the most common requests from respondents. None of the respondents were aware of advisories, outreach, or educational materials for reduction of exposure to agricultural smoke, with one respondent stating:

―I have not seen a lot of documentation on it period…So how can you be aware of how much they provide when you don‘t know?‖

Those that didn‘t think it was a health department responsibility thought it was:

―Something farmers have to do, not the health department‖ and ―farmers are the ones that control everything, not the health department.‖

d. Farmers

Interviews were conducted with three farmers, or growers, in Imperial County out of 30 approached. All three respondents burned their fields approximately once a year, burning Bermuda grass or wheat with field sizes varying from 200 to 500 acres, some years burning thousands of acres. All three farmers also said, ―There have been fewer and fewer burns over the years due to the credit system.‖ Common themes identified during the interviews are presented in Table V.A.4.

Table V.A.4. Common Themes Identified During Key Informant Interviews Farmers (N=3)

Theme N

Agricultural burning in Imperial County is a low or medium health concern. 3

Respondent thought neighbors would rank agricultural burning as a high health concern. 3

Respondent was aware of recommendations for appropriate burning methods. 2

Respondent actively notifies neighbors about planned field burns. 3

Respondent had considered agricultural burning alternatives such as minimum tilling methods and disking the fields, and was aware of pollution credits offered by the APCD.

3

Respondent had been approached by representatives of a community organization concerned about the potential health effects of smoke from agricultural burning.

2


All farmers considered agricultural burning a medium or low health concern, but stated that neighbors would rank it as a high concern ―because of the lack of knowledge and education‖ and because residents forget they live in a very large farming area. Respondents also said:

―…the products being burned, wheat and Bermuda stubble, are not any more harmful, or less harmful, than burning a piece of paper…‖

―it‘s 5% of the total PM10 inventory, so it‘s 5% of my concern…on air quality,‖ and

―…on the California side, we do everything we can…we have a lot of burning in Mexico.‖

Two of the three farmers were aware of recommendations for appropriate burning methods and all three farmers interviewed actively notify their neighbors about their planned field burns. All three had considered agricultural burning alternatives such as minimum tilling methods and disking the fields, and experimentation with these methods was encouraged by the pollution credits offered by the APCD. Farmers reported learning these alternative methods through trial and error.

Two out of three farmers had been approached by representatives of a community organization concerned about the potential health effects of smoke from agricultural burning. Concerns were addressed by open communication:

―I remind people I live here, I have family here… We‘re farmers first and foremost; we are environmentalists,‖ and

―We don‘t do it for fun. I have four to five guys with flags on the field, it‘s a cultural practice which is necessary. I‘m always open to take people on tours and trips to discuss their issues.‖

When asked for final thoughts or comments, farmers said that they felt the U.S. side was taking too much of the blame compared to Mexico, who they felt was the bigger contributor to the pollution.

―Over the last 10–15 years, California has worked very diligently on taking one tool after another from us (farmers)…I know Imperial Valley is not in attainment but there‘s no proof that says that Mexico isn‘t one of the largest contributors to our problem.‖

The farmers also talked about the crop benefits when burning was utilized.

―When all that wheat stubble is breaking down, it‘s putting heat off (when) it‘s breaking down (the nitrogen)… the quality of the crop, non-burned versus burned—was night and day. Economic loss, so many cartons per acre (lost)… Burned fields are more profitable, I will not get the same crop of vegetables that I had if I didn‘t burn.‖

3. DISCUSSION

A broad range of Imperial County residents provided in-depth and candid responses. Common themes mentioned in several of the four groups interviewed included: ranking agricultural burning as a medium or high health concern in the county, interest in greater outreach and education about the health effects of smoke, including actions to take to reduce exposures, and the awareness of other sources of air pollution.


Some respondents were not sure about the health effects themselves, while others felt the community was unaware or had misconceptions about the effects of field burning smoke. The four different groups also had several unique findings specific to them:

Community leaders:

o Representatives of health and environmental agencies were interested in training staff on the health effects of smoke. Some agencies also said they would appreciate scripts or fact sheets for public inquiries, or radio public service announcements.

o Representatives of organizations promoting clean air were interested in broader outreach.

School representatives agreed that schools may be an excellent place for community outreach, but there are concerns about school resources required.

County residents living close to fields were aware of actions that could be taken but thought that there was more that the health department could do.

Farmers and organizations representing the agricultural industry were very interested in the contribution that agricultural burn smoke makes to regional air pollution. Many were also interested in promoting a ―good neighbor‖ policy of informing neighbors when burning was to occur.

The participation rate among community leaders was excellent: representatives from 10 out of fifteen contacted agencies were interviewed. Their interest in training and outreach is representative of a high level of interest by agencies and organizations. However, the number of people interviewed in the other groups, in particular school representatives and farmers, was small. Significant efforts were needed (20–30 contacts) to interview three farmers and five school representatives, and these respondents may represent those that were most interested in the issue. Similarly, almost all of the residents interviewed had been exposed to smoke, and these respondents also may be among those most interested in the issue. In addition, the farmers interviewed farmed large amounts of land, and their responses might not be representative of smaller farmers. Nonetheless, the KII responses by these smaller groups did provide a spectrum of attitudes and knowledge. Their responses, combined with the interest from community leaders, strongly suggest a need for greater education and outreach around agricultural burning.

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V. B. BEHAVIORAL RECOMMENDATIONS TO REDUCE EXPOSURES

Air monitoring levels found and the potential health impact from those levels are summarized in Section IV.C. of this report. In brief, two types of exposures were found: i) directly downwind of a burn visibility and PM10 air concentrations were equivalent to U.S. EPA AQI hazardous levels; and ii) increased air levels of PM2.5 during evening, night, and early morning hours approached or were above AQI guidelines for moderate air quality. To develop behavioral recommendations to reduce the potential health impact from exposures to these air levels, guidance of other agencies for exposure to wildfire (U.S. EPA, 2003; Lipsett et al., 2008) and agricultural burn smoke (CARB, 1992) were reviewed and modified by staff at CDPH/EHIB.

1. DOWNWIND GROUND-LEVEL PLUMES

To avoid smoke from wildfires, other agencies have recommended going inside and running the air-conditioning or ventilation system. Ground-level plumes from agricultural burning are fairly visible and this recommendation was considered applicable to smoke from agricultural burning. However, for agricultural burning, what is apparently downwind may quickly change and a recommendation on how far to remain away from a burning field was needed. Elevated levels and visible drift at the Dunham burn was observed at two tenths of a mile from the center of a burning field and 500 feet from the edge of the field. However, a distance which might be familiar to most people was needed and a somewhat smaller distance was chosen: the distance of a soccer or football field (300 feet). Staff at CDPH/EHIB developed the recommendation that anyone who could see or smell smoke or was within 300 feet of a burning field should go inside.

For those who must stay outside, staying away from the smoke and avoiding ground-level plumes was also recommended. If people had to be outside near a burning field, face-piece particulate respirators (N95, N100, or P100 respirators) were recommended. These masks do have limitations: the designation ‗N95‘ indicates that those respirators only filter 95% of particles, and they do not provide an adequate seal for men with beards or most children. Nonetheless, these respirators are available in hardware stores, and their use may provide protection (Lipsett et al., 2008). Workers who must be outdoors and near a burn must be in a full respiratory protection program, including medical evaluations and testing of the seal of the respirator on an individual face or ―fit-testing‖ (email communication from Cal-OSHA to B.Materna, Chief Occupational Health Branch, CDPH, September 27, 2010).

For farmers, a good-neighbor policy of alerting anyone within a mile and a half of the burning field was also recommended. This distance is the same as required for a designation of a special burn by the APCD, and allows neighbors to take precautions; e.g., using alternative travel routes, or leaving the area during the burn, should they choose.

For schools, as any burn within a mile and a half of a school is designated by the APCD a special burn and scheduled for a weekend, having a burning field near a school on a school day should not occur. Nonetheless, discussions with school representatives suggested a need for knowledge of what to do should smoke be significant at their school. CDHS/EHIB developed the recommendation that if a school representative smelled smoke or if the representative thought that a burn was occurring within a mile and a half of a school, they should first call the APCD. If a burn was confirmed within a mile and a half, the school should then hold recesses and planned outdoor activities inside.

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2. EVENING, NIGHT, AND EARLY MORNING EXPOSURES

As described, evening, night, and early morning PM2.5, PM10, and black carbon air concentrations may be increased during periods of agricultural burning in Imperial County. PM2.5 air concentrations may approach AQI levels corresponding to moderate air quality. When air quality is considered moderate, advising the public on ways to reduce exposures is recommended (U.S. EPA, 2001). In particular, during heavy exercise, breathing rates increase and people tend to breathe through their mouths without the filtering of nasal passages. Not only is it reported here that air PM2.5 concentrations are increased during the evening, night, and early morning hours but also at the Calexico air monitors there is a history of elevations during these hours (Chow et al., 2001). Thus, avoidance of heavy exercise during these hours in the winter was recommended.

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V. C. FACT SHEET DEVELOPMENT AND DISTRIBUTION 1. FORMAT DEVELOPMENT

The KIIs documented that community leaders, school representatives, residents, and farmers were interested in information about the health effects of smoke. Fact sheets could serve as a foundation for outreach. However, recommendations for the general public, farmers, and school representatives were specific to those groups and three separate fact sheets that were targeted to each audience were needed.

To develop specific language, the Project Manager, Principal Investigator, and Community Relations Specialist of EHIB developed succinct language and format, including information on the health effects of smoke. This language was reviewed by three Section Chiefs of EHIB and the Chief of the Occupational Health Branch. Draft fact sheets were reviewed by members of the BEHT, including APCD and CARB staff. All comments were reviewed by the PI, the Project Manager, and the Community Relations Specialist. Most comments were incorporated; the exceptions were when technical words were suggested that were considered to be of a higher literacy level than that of the general public.

2. PRE-TESTING OF THE FACT SHEET FOR THE GENERAL PUBLIC

A draft copy was pre-tested with 20 community members who were randomly approached at a health clinic and at a shopping center. Each respondent was asked for their opinion on the appeal, comprehensibility, and clarity of the content of either the English or Spanish fact sheet.

Of the 20 respondents, 13 would have read the fact sheet if they saw it posted somewhere in the community. Although seven would not have read it, one respondent claimed that s/he would have been more likely to read it if the heading was more ―eye-catching.‖ Most people felt that the fact sheet would be most noticeable and have the most impact if it were placed in a public location that is easily accessed by community members. Suggestions listed by popularity included: doctors offices and clinics, schools, newspapers and similar leaflets, business offices, and community centers and offices such as senior homes, welfare departments, and unemployment centers.

The fact sheet has a picture of a fairly dramatic agricultural burn and respondents were asked what they felt the picture was about. Most (n=15) thought that it had to do with ―burns‖ and of those, nine thought it was specific to agricultural field burning. Six people thought it was about contamination or pollution, including pollution from smoke.

Respondents were asked to recall two actions to take after smelling smoke or seeing smoke within 300 feet. A majority of the respondents remembered the first two recommendations on the fact sheet: close all windows and doors (n=13), and run the air-conditioner on ‗recirculate‘ (n=11). Many also recalled the recommendation to stay away from the smoke (n=6). Three respondents mentioned using a respirator or mask, but did not specify situations in which masks are needed.

Concerns included that one person found the agricultural burning hours to be unclear. Two people suggested the fact sheet be more elaborate about the type of smoke that is dangerous and specific negative impacts that smoke can have on health. Additionally, one respondent was not clear about smoke within 300 feet since he felt that the smoke would still enter his home and impact him. Another concern was the lack of information about what the state, or EHIB, has done to reduce agricultural burning. A couple of people had questions about why or how field burning was legal. In addition, some respondents wanted to know what actions could be taken by a community member if there is a field burning close to him or her. The respondents also had complaints about the length of the fact sheet, and the ratio of words to graphics. In

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addition, the text font was considered not large enough to catch one‘s attention, and instead might deter one from reading it.

Finally, the fact sheets were rated on a scale from one to five, with five being the highest rating. All of the ratings were a four (n=4) or a five (n=16). Seventeen found the recommendations to help protect one‘s health most helpful; two found the information about legal burning hours to be most helpful. Most respondents did find all of the information useful and said that they would follow advice on the fact sheet to protect their own health. No one found sentences in the fact sheet difficult to understand. All of the respondents felt they were able to find the information that they were looking for in the fact sheet. Respondents did not initiate further questions about the content of the fact sheet. Respondents took the advice personally and felt that the recommendations were applicable not only to themselves but also to the rest of the community.

3. ADDITIONAL INPUT

The results from the pre-test were reviewed by EHIB staff. The fact sheets were further edited to address the identified concerns with the overall goal of keeping the fact sheet short (two pages), succinct, and clear. Because all of the information, including that on health effects, recommendations to reduce exposure, and the legal hours of burning, was considered useful, it was not possible to improve the font size or the ratio of text to graphics.

The development of the English and Spanish versions of the fact sheet for the general public was presented at an April, 2010, meeting of the BEHT. Comments were received mostly on format and on the Spanish language translation. These changes were incorporated. In some cases, due to the desire to keep the fact sheets succinct, it was not possible to make the BEHT-suggested changes. For example, it was not possible to add a suggested second graphic.

To further refine the farmer-targeted fact sheet, an Imperial County farmer was interviewed. That farmer reiterated many things stated during the KIIs, including that burning increases crop yield, and suggested that more on the benefits of burning be added. That farmer also suggested that the recommendations on the fact sheet be on the front page, not the second, since most farmers were aware of the health effects of smoke. Both of these recommendations were incorporated.

4. DISTRIBUTION

The final fact sheets were approved by CDPH in November of 2010 and are presented in the SI, Attachment 6. The fact sheets were posted on the CDPH website on December 8, 2010. In addition, CDPH/EHIB staff are working with local organizations to place the fact sheet targeted to the general public in many of the public places identified during the KIIs and the pre-testing of the fact sheet.

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VI. RECOMMENDATIONS FOR FURTHER PUBLIC HEALTH ACTION AND RESEARCH

This BECC-funded project explored exposure assessment methodologies and developed health education materials to reduce exposures. EHIB staff developed a list of activities to further enhance understanding of the contribution of smoke from agricultural burns to air pollution and to reduce exposures to smoke from agricultural burning along the U.S./Mexico Border. The BEHT reviewed a draft list in August, 2010, and added a few additional recommendations that are included below. Within each sub-section, the recommendations are listed in order of the estimated amount of resources necessary to accomplish each task. Those listed first should have the highest priority, due to the relative ease of those activities.

VI. A. LOCAL OUTREACH

Notably, the IC APCD recently revised their Smoke Management Program plan (IC APCD, 2010) and that revision was posted on the IC APCD website in late 2010. Implementation of this plan will significantly enhance educational outreach around agricultural burning in Imperial County. This plan establishes a smoke complaint log and requires that farmers notify neighbors within a half mile of a scheduled burn using a standardized form and, if adjacent to a road, provide traffic re-routing. (IC APCD, 2010). Additional suggested outreach activities include:

HEALTH EDUCATION

The APCD, local government, and other organizations could:

post the CDPH/EHIB fact sheets on their websites;

periodically post or distribute the CDPH/EHIB fact sheets at schools and other community locations suggested in the pre-test of the fact sheet, e.g., libraries;

present the CDPH/EHIB fact sheets at local conferences or community forums; and/or

establish a work group to coordinate and further develop health educational activities.

To further disseminate information on how to reduce exposures, the IC APCD could:

distribute the CDPH/EHIB fact sheets to farmers when they apply for an IC APCD burn permit and when they are notified of their scheduled burn time.

CDPH could:

develop a fact sheet or other health educational materials for workers who may be required to be outdoors near agricultural burns; and/or

develop a Powerpoint, webinar, or other presentation that local agencies could use at local conferences or other community forums.

Most importantly, all educational activities should be coordinated with the IC APCD.


ENHANCED NOTIFICATION

Comments received from BECC on the initial proposal encouraged consideration of an ―early warning system on both sides of the border.‖ Currently, on every burn day the IC APCD notifies local fire departments of scheduled burns and, if the burns are near the border, they also notify fire departments in Mexico. The IC APCD has expressed a willingness to notify staff at local agencies, who could then serve as a central contact for others. However, such contacts at local agencies would first need to be identified. The fact sheets developed here and recent IC APCD policies also promote a good-neighbor policy of farmers alerting their neighbors of when they will burn (IC APCD, 2010). Additionally, interviewed residents requested information on when burning would occur, but also expressed reluctance to call the IC APCD to report neighbors who might be out of compliance. Supplemental IC APCD activities that would promote informational inquiries or compliant reporting could include:

placing instructions on how to make a complaint—including the CARB toll-free phone number for smoke complaints (CARB, 2010)—on the IC APCD website under the menu item ‘complaints,‘

providing a daily website listing of the areas in the county where burns are scheduled, potentially with a map of those areas. Notably, the recently revised IC APCD smoke management plan mentions that such a system is under development;

providing an automated phone and/or electronic alerts for interested residents for when an agricultural burn may be scheduled near where they live or work; and/or

extending the ―good-neighbor‖ policy to include procedures for neighbors to be notified when there is visible drift to neighboring properties.

VI. B. OTHER PUBLIC HEALTH RECOMMENDATIONS

Behavioral recommendations to reduce exposures are considered secondary public health prevention. Not only do such measures require individuals to change activities, but behavioral remedies may only be partially effective. For example, the recommended respirators are not 100% effective. Primary public health prevention includes reducing the source of exposure. Further, the BEHT strongly encouraged project staff to consider recommendations to reduce agricultural burning. CDPH/EHIB staff considered the conclusions of the air monitoring conducted here and elsewhere (see Section IV.C), reviewed the current IC APCD smoke management plan (IC APCD, 2010) as well as the state regulations (CARB, 2010), and developed additional recommendations which may ultimately lead to source reduction in Imperial County. These recommendations may also have some utility for regulatory agencies in other states in the U.S. and Mexico. However, CDPH does not regulate air quality and does not have specific expertise in meteorology. As noted below, all of these recommendations would be subject to review and, if appropriate, implemented by the local and state air quality agencies.

To briefly reiterate, our air monitoring results did suggest that regulatory activities to control air pollution from agricultural burns are achieving their objectives. However, air concentrations were apparently increased in two situations. Recommendations for those two situations follow.


REDUCING DOWNWIND GROUND-LEVEL AIR CONCENTRATIONS

As described in Section IV.C., highly elevated PM10 air levels were found directly downwind of one burn in this study, and another study had similar findings (Kelly et al., 2003). During the hour the burn was conducted, the wind speed was higher (2.7 m/s or 6 MPH) than it had been at the other four monitored burns (< 2 m/s or 4.5 MPH). Currently, the state smoke management plan specifies when a ―burn‖ day is to be declared based on defined meteorological conditions which ensure regional dispersion, e.g., the upper mixing layer (California Code of Regulations, 2001). The IC APCD smoke management plan states that the APCD may place additional restrictions based on meteorological and air quality conditions—including strong surface or gusty winds. However, the meteorological or air quality conditions under which burning is not allowed are not further defined in the plan. The plan also specifies that the IC APCD will monitor the wind speed throughout the day (IC APCD, 2010). To potentially mitigate downwind exposures to drift, state and local regulatory agencies could:

evaluate the wind speed conditions under which burns are allowed to determine whether changes to these conditions should be made, or whether increased monitoring of these conditions during burn days is needed.

REDUCING EVENING, NIGHT, AND EARLY MORNING AIR CONCENTRATIONS

Air monitoring both regionally and near burns suggests an additional impact from agricultural burning in the evening, night, and early morning hours (see Section IV.C). Additional activities that could be considered by the IC APCD and CARB that might reduce the PM impact during those hours include:

reviewing the meteorological or other conditions under which burning is allowed versus prohibited. For example, meteorologists for the ICAPCD and/or CARB could review whether a predicted intense inversion the night of the burn or in the upcoming days could warrant a no burn day declaration. Such a review could also include consideration of whether shifting or shortening the allowable burn hours to earlier in the day would increase upper plume dispersion and lessen the amount of PM descending at night.

reviewing the Smoke Ordinance Plan for the San Joaquin Valley (San Joaquin Valley Unified APCD, 2002) and considering actions that could be more fully incorporated into the Imperial County Smoke Management Program (IC APCD, 2010) including:

o decreasing allowable burn acreage on days when PM air concentrations in the county are forecast to be above 24-hour standards for PM2.5 or PM10. Currently, the IC APCD smoke management plan states that air quality may be used to permit burns on any given day, but does not specify air quality criteria for burns to occur.

o for large burns (>100 acres), requiring farmers or burners to develop Smoke Management Plans prior to burning. Such plans would require the farmer to describe alternatives considered prior to burning.

o limiting the amount of acreage that can be burned in specified burn allocation geographic zones based on estimated emissions. In San Joaquin Valley, this strategy has led to a 52% reduction in PM2.5 and a 47% reduction in PM10 emissions from agricultural burning since 2004 (San Joaquin Valley Unified APCD, 2010). Currently, the IC APCD geographically disperses burning on a given day into four quadrants and limits burning to no more than 2000 acres a day in the county (IC APCD, 2010).


VI. C. ADDITIONAL RESEARCH

Areas of additional research in the Border Region include the following.

AIR MONITORING

A new stationary air monitor installed near Calipatria as part of the Salton Sea network began recording PM2.5, PM10, and meteorological conditions on June 1, 2010 (Imperial County, 2010). Data collected will allow for similar statistical analysis of PM levels on burn days and no burn days, or with burn records from the APCD, as those reported here. As Calipatria is a population center, with several schools, this monitor would reflect community exposures.

Additional air monitoring at locations near and directly downwind of burns, including burns at low wind speeds (< 2 m/s), would fill missing data elements. Placement of such monitors would need to carefully consider where people live and potential exposures. Although there are many areas of the county where burning occurs and there are no housing structures, there are also many relatively isolated farms with single-family homes where exposure may occur to workers and residents, including children and older adults. Additional studies should target those locations. In addition, studies suggest that outdoor PM2.5 may infiltrate buildings, and indoor levels may be equal to outdoor levels (Cortez-Lugo et al., 2008; Hering et al., 2007; Leaderer et al, 1999). Although running air-conditioning may lower levels, the difference in air levels in air-conditioned homes and non-air-conditioned homes is not statistically significant (Leaderer et al, 1999). Because much of the advice developed here involves recommendations to go inside and run any air-conditioning or ventilation system, additional studies on indoor levels are recommended. Our research demonstrates that such a study would require considerable resources to recruit places for monitors near upcoming burns, and also for placement of instruments or samplers inside homes or places of public access. Real-time instrumentation, in particular the portable pDRs and the passive samplers used in this investigation, would be useful in future studies. Although pDRs provide more accurate, continuous readings, passive samplers do not require an operator.

Additional stationary PM2.5 monitors in the county—in areas where burning occurs and people live—would also potentially further the understanding of the impact of agricultural burning on air pollution.

EXPOSURE REDUCTION

Research that may ultimately lead to reduction in agricultural burning includes an evaluation of the benefits and limitations of burning and alternatives to burning. Farmers did report here that burning improves yield. In Northern California, research to study alternatives to burning assisted in a reduction in burning (California Rice Commission, 2011).

Research that may ultimately lead to behavioral changes to reduce exposures include:

evaluation of whether residents are aware of the recommended behaviors to reduce exposures in the developed fact sheets and whether they are taking the recommended actions;

development of educational materials targeted to outdoor/field workers; and

evaluation of the appropriateness of the Spanish-language versions of the fact sheets for distribution in Mexico. The methods used in our study, (i.e., Key Informant Interviews, and pre-testing of fact sheets), may be appropriate.

References Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, CA - 71 -

REFERENCES

Anenberg SC, Horowitz LW, Tong DQ, West J. (2010). An estimate of the global burden of anthropogenic ozone and fine particulate matter on premature human mortality using atmospheric modeling. Environmental Health Perspectives. 118: 1189–1195. Awasthi A, Singh N, Mittal S, Gupta PK, Agarwal R. (2010). Effects of agricultural crop residue burning on children and young on PFTs in North West India. Science of the Total Environment 408: 440–4445. Brauer M, Lencar C, Tamburic L, Koehoorn M, Demers P, and Karr C. (2008). A cohort-study of traffic-related air pollution impacts on birth outcomes. Environmental Health Perspectives. 116: 680–686. Brown, P. (2003). Qualitative methods in environmental health research. Environmental Health Perspectives 111: 1789–1798. CARB (1992). Agricultural Handbook. Available at: www.arb.ca.gov/cap/handbooks/handbooks.htm . CARB (2005) Memo. Operation Support Section E-BAM Evaluation. September 28, 2005. From: Kenneth R. Stroud, Chief/ Air Quality Surveillance Branch; To: Reggie Smith, Manager, Operations Support Section http://www.arb.ca.gov/carpa/docs/E-BAM-eval.pdf . Accessed Dec, 2010.

CARB (2009). Almanac Emission Projection Data by EIC Annual Average Emissions (Tons/Day): Imperial County, Miscellaneous Processes, 670 Managed Burning and Disposal. http://www.arb.ca.gov/app/emsinv/emseic_query.php?F_YR=2008&F_DIV=-4&F_SEASON=A&SP=2009&SPN=2009_Almanac&F_AREA=CO&F_COAB=&F_CO=13&F_EICSUM=670 . Accessed March, 2011. CARB (June 2, 2009). Letter to Stephan Wall and Martha Harnly, CDPH, from Mac McDougall, CARB. CARB (2010). Smoke Management Program. http://www.arb.ca.gov/smp/smp.htm Accessed Oct, 2010. CARB (2011). PM Trends Summary for Imperial County. http://www.arb.ca.gov/adam/trends/trends1.php Accessed Jan, 2011. California Code of Regulations (2001). Title 17, Subchapter 2. Smoke Management Guidelines for Agricultural and Prescribed Burning. California Rice Commission. Environmental & Conservation Balance Sheet for The California Rice Industry. Chapter 4: Air Quality in Relation to Rice Farming. Available at: http://www.calrice.org/Environment/Balance+Sheet/Chapter+4+-+Air+Quality.htm Accessed Jan, 2011. Carroll, AM, Perez M, Toy P. (2004). Performing a Community Assessment Curriculum, Section 4: Key Informant Interviews. Los Angeles: UCLA Center for Health Policy Research. Health DATA Program. Available at: http://www.healthpolicy.ucla.edu/healthdata/ttt_prog24.pdf Accessed Jan, 2011

http://www.arb.ca.gov/cap/handbooks/handbooks.htm

http://www.arb.ca.gov/carpa/docs/ebam-eval.pdf

http://www.arb.ca.gov/smp/smp.htm

http://www.calrice.org/Environment/Balance+Sheet/Chapter+4+-+Air+Quality.htm

http://www.healthpolicy.ucla.edu/healthdata/ttt_prog24.pdf


Carroll, JC, Miller GE, Thompson JF, Darley E. (1977). The dependence of open field burning emissions and plume concentrations on meteorology, field conditions, and ignition technique. Atmospheric Environment 11: 1037–1050. Chakrabarti B, Five PM, Delfino R, Sioutas C. (2004). Performance evaluation of the active-flow

personal Data RAM PM2.5 mass monitor (Thermo Anderson pDR-1200) designed for continuous

personal exposure measurements. Atmospheric Environment 38: 3329–3340. Chen KS, Wang HK, Peng YP, Wang WC, Chen CH. (2008). Effects of open burning of rice straw on concentrations of atmospheric polycyclic aromatic hydrocarbons in Central Taiwan. J Air & Waste Management Assoc 58: 1318–1328. Choi YJ, Fenando HJS. (2007). Simulation of smoke plumes from agricultural burns: application to the San Luis/Rio Colorado airshed along the U.S. Mexico border. Science of the Total Environment 388: 270–289. Chow JC, Watson JG. (2001) . Zones of representation for PM10 measurements along the U.S./Mexico border. Science of the Total Environment 276: 49–68. Clark NA, Demers PA, Karr CJ, Koehoorn M, Lencar C, Tamburic L, Brauer M. (2010). Effect of Early Life Exposure to Air Pollution on Development of Childhood Asthma. Environmental Health Perspectives 118: 284–290. Cortez-Lugo M, Moreno-Macias H, Holguin-Molina F, Chow JC, Watson JG, Guttierrez-Avedoy V, Mandujano F, Hernandez-Avila M, Romieu I. (2008). Relationship between indoor, outdoor, and personal fine particle concentrations for indviduals with COPD and predictors of indoor-outdoor ratio in Mexico City. Journal of Exposure Science and Environmental Epidemiology 18, 109–115. Dennis A, Fraser M, Anderson, S, Allen D. (2002). Air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. Atmospheric Environment 36: 3779–3792. EHIB/CDPH. (December 2008). Naturally Occurring Asbestos: A Needs Assessment of Local Health Agencies. Available at: http://www.ehib.org/papers/NOANAOnline.pdf Hanninen OO, Salonen RO, Kostinen K, Lanki T, Barregard L, Jantunen. (2009). Population exposure to fine particles and estimated excess mortality in Finland from an Eastern European wildfire episode. Journal of Exposure Science and Environmental Epidemiology 19: 414–422. Hansen ADA (2005). The Aethalometer

TM, Magee Scientific Company. Berkeley, California.

http://mageesci.com/support/downloads/Aethalometer_book_2005.07.03.pdf Accessed Dec, 2010. Harnly, M. Personal (phone) communication (July 29, 2009) from CARB to Martha Harnly, Environmental Health Investigations Branch, CDPH. Hering SV, Lunden MM, Thatcher TL, Kirchsteer TW, Brown NJ. (2007) . Using regional data and building leakage to assess indoor concentrations of particles of outdoor origin. Aerosol Science and Technology 41:639–654. Imperial County APCD. (2005). Staff Report. BACM Amendments to Regulation VIII, Fugitive Dust Rules. Nov 8, 2005. Imperial County APCD. (2002). Smoke Management Program. Procedures for Operating the Air District‘s Agricultural Burn Program. April 27, 2010. http://www.co.imperial.ca.us/AirPollution/Web%20Pages/SALTON%20SEA.htm. Accessed November 30, 2010.

http://www.ehib.org/papers/NOANAOnline.pdf

http://mageesci.com/support/downloads/Aethalometer_book_2005.07.03.pdf

http://www.co.imperial.ca.us/AirPollution/Web%20Pages/SALTON%20SEA.htm


Imperial County APCD (2010). Smoke Management Program. Procedures for Operating the Air District‘s Agricultural Burn Program. April 27, 2010. http://www.co.imperial.ca.us/AirPollution/Web%20Pages/SALTON%20SEA.htm. Accessed November 30, 2010. Jacobs J, Kreutzer R, Smith D. (1997) Rice burning and asthma hospitalizations, Butte, County, 1983–1992. Environmental Health Perspectives 105: 980–985. Jafe D, Hafner W, Chand D, Westerling A, and Spracklen D. (2008). Interannual variations in

PM2.5 due to wildfires in the Western United States. Environmental Science and Technology 42:

2812–2818. Jeong CH, Hopke PK, Kim E, Lee DW. (2004). The comparison between thermal-optical transmittance elemental carbon and Aethalometer black carbon measured at multiple monitoring sites. Atmospheric Environment 38: 5193–5204. Jimenez J, Wu CF, Claiborn C, Gould T, Simpson CD, Larson T, Liu LJS. (2006). Agricultural burning smoke in eastern Washington—Part I: Atmospheric Characterization. Atmospheric Environment 40: 639–650. Jimenez J, Claiborn CS, Dhammmapla RS, Simpson CD. (2007). Methoxyphenols and

levoglusan ratios in PM2.5 from wheat and Kentucky bluegrass stubble burning in Eastern Washington and Northern Idaho. Environmental Science and Technology 41: 7824–7829. Kakareka SV, Kukharchyk, TI. (2003). PAH emissions from open burning of agricultural debris. Science of the Total Environment 308: 257–261. Kelly K, Collins K, Quintero Nunez M, Jaramillo C, Arnold N, Wendell K, Gardner J. (2003). Particulate Matter Emissions from Agricultural Burns in the Mexicali / Imperial Valley Region. Report Number A-03-02. Available at: http://scerpfiles.org/cont_mgt/doc_files/Kelly_A0302_dbb.pdf . Accessed Mar, 2011. Keshtkar H, Ashbaugh LL. (2007). Size distribution of polycyclic aromatic hydrocarbon particulate emission factors from agricultural burning. Atmospheric Environment. 41:2729–2739. Laulainen, NS. (1993). Summary of conclusions and recommendations from a visibility science workshop; technical basis and issues for a national assessment for visibility impairment. Prepared for U.S. DOE. Pacific Northwest Laboratory PNL-8606. Leaderer BP, Naeher L, Jankum T, Balenger K, Holford TR, Toth C, Sullivan J, Wolfson JM, Koutrakis P. (1999). Indoor, Outdoor, and Regional Summer and Winter concentrations of PM10, PM2.5, So42-, H+, NH4+, NO3-, NH3, and Nitrous Acid in Homes With and Without Kerosene Space Heaters. Environmental Health Perspectives 107: 223–231. Li W, Orquiz R, Garcia, JH, Espino T, Pingitore N, et al. (2001) Analysis of Temporal and Spatial Dichotomous PM Air Samples in the El Paso-Cd. Juarez Air Quality Basin. Journal of the Air & Waste Management Association. 51:1551–1560. Lipsett M, Materna B, Stone S, Therriault S, Blaisdell R, Cook F. (2008). Wildfire Smoke: A Guide for Public Health Officials. Revised July, 2008. Available at: http://www.arb.ca.gov/smp/progdev/pubeduc/wfgv8.pdf Accessed December, 2010. Liu LJS, Slaughter JC, Larson TV. (2002). Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments. Environmental Science and Technology 2002, 36, 2977–2986.

http://www.co.imperial.ca.us/AirPollution/Web%20Pages/SALTON%20SEA.htm

http://www.arb.ca.gov/smp/progdev/pubeduc/wfgv8.pdf


Mar TF, Larson TV, Stier RA, Claiborn C, Koenig JQ (2004). An analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington. Inhalation Toxicology 16:809–815. Met One Instruments (2009). The Met One E-BAM is a portable real-time beta gauge traceable to U.S. EPA requirements for automated PM2.5 and PM10 measurement. Oregon, U.S. Available at: http://www.metone.com/documents/E-BAM_Datasheet_Rev_Aug09.pdf . Accessed April 13, 2010. Milet M, Tran S, Eatherton M, Flattery J, Kreutzer R. ―The Burden of Asthma in California: A Surveillance Report.‖ California Department of Health Services, Environmental Health Investigations Branch, June 2007. Available at: http://www.cdph.ca.gov/programs/CABreathing/Documents/AsthmaBurdenReport.pdf . Accessed Mar, 2011. Morgan G, Sheppeard V, Khalaj B, Ayyar A, Lincold D, Jalaudin B, Beard J, Corbett S, Lumlel L. (2010). Effects of bushfire smoke on daily mortality and hospital admissions in Sydney, Australia. Epidemiology 21: 47–55. Mukerjee S, Smith LA, Norris GA, Morandi MT, Gonzales M, Noble CA, Neas LM, Qzkaynak. (2004). Field method comparison between passive air samplers and continuous monitors for VOCs and NO2 in El Paso, Texas. Journal of the Air & Waste Management Association 54: 307–319. Naeher LP, Brauer M, Lipsett M, Zeilkoff JT, Simpson CD, Koenig JQ, Smith K. (2007). Woodsmoke Health Effects: A Review. Industrial Toxicology 19: 67–106. Office of Environmental Health Hazard Assessment (OEHHA). (2004). Chronic Toxicity Summary. Naphthalene. Available at http://oehha.ca.gov/air/chronic_rels/pdf/91203.pdf. Accessed Sept 20, 2010. Ostro B, Feng WY, Broadwin R, Green S, Lipsett M. (2007). The effects of fine particulate air pollution on mortality in California. Results from CALFINE. Environmental Health Perspectives 115: 13–19. Materna, B. Personal (email) communication (September 27, 2010) from Cal-OSHA to Barbara Materna, Chief Occupational Health Branch, CDPH Peters A, von Klot S, Heier M, Trentinaglia I, Cyrys J, Hörmann A, Hauptmann M, Wichmann HE, Löwel H. (2005). Particulate air pollution and nonfatal cardiac events. Part I. Air pollution, personal activities, and onset of myocardial infarction in a case-crossover study. Res Rep Health Eff Inst. 124:1–66. Placer County APCD. (2007). Progress Report: Roseville Railyard Air Toxics Project http://pubweb.epa.gov/ttn/amtic/files/ambient/airtox/CSM%200506%20PCAPCD%20FR.pdf Accessed 04/13/10. San Joaquin Valley Unified APCD. (2002). Proposed Smoke Management Program. June 20, 2002. San Joaquin Valley Unified APCD (2010). Proposed Staff Report: Recommendations on Agricultural Burning May 20, 2010.

Schwartz J, Laden F, Zanobetti A. (2002). The concentration-response relation between PM2.5 and daily deaths. Environmental Health Perspectives 110: 1025–1029.

http://www.metone.com/documents/E-BAM_Datasheet_Rev_Aug09.pdf

http://www.cdph.ca.gov/programs/CABreathing/Documents/AsthmaBurdenReport.pdf

http://oehha.ca.gov/air/chronic_rels/pdf/91203.pdf

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Peters%20A%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22von%20Klot%20S%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Heier%20M%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Trentinaglia%20I%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Cyrys%20J%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22H%C3%B6rmann%20A%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Hauptmann%20M%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Wichmann%20HE%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22L%C3%B6wel%20H%22%5BAuthor%5D

http://pubweb.epa.gov/ttn/amtic/files/ambient/airtox/CSM%200506%20PCAPCD%20FR.pdf


Staniswalis Jg, Yang H, Li WW, Kelly K. (2009). Using a continuous time lag to determine the associations between ambient PM2.5 to determine the associations between ambient PM2.5 hourly levels and daily mortality. Journal Air Waste Management Association 59:1173–1185. Tan ML, Ujihara A, Kent L, Hendrickson I, (2011) Communicating fish consumption advisories in California: What Works, What Doesn‘t. Risk Analysis [Epub ahead of print]. Jan 13, 2011. Thermo Electron Corp, 2005. Model pDR-1000AN/1200, personalDATARAM Particulate Monitor Instruction Manual. P/N (100181-00). Thermo Electron Corporation, Environmental Instruments. Franklin, MA. Available at: http://www.raeco.com/products/particulate/MIE-pDR1000AN/mie_pdr1000-1200_man.pdf Accessed February, 2011. Tan ML, Ujihara A, Kent L, Hendrickson I, (2011) Communicating fish consumption advisories in California: What Works, What Doesn‘t. Risk Analysis [Epub ahead of print] Jan 13, 2011. U.S. Census Bureau, 2009. American Community Survey, 2005–2009 Summary Tables; B25117. Tenure by House Heating Fuel. Available at: http://factfinder.census.gov/servlet/DTTable?_bm=y&-context=dt&-ds_name=ACS_2009_5YR_G00_&-mt_name=ACS_2009_5YR_G2000_B25117&-tree_id=5309&-redoLog=true&-_caller=geoselect&-geo_id=01000US&-geo_id=05000US06025&-search_results=01000US&-format=&-_lang=en Accessed for Imperial County, January, 2011. U.S. Department of Labor. (1998). Occupational Safety and Health Administration. Polycyclic Aromatic Hydrocarbons by HPLC Method 5506. http://www.cdc.gov/niosh/docs/2003-154/pdfs/5506.pdf . Accessed February, 2011 U.S. EPA. (1996). Evaluation of Emissions from the Open Burning of Land-clearing Debris. EPA-600/R-96-128 Available at: http://www.epa.gov/ttn/atw/burn/brushburn2.pdf . Accessed December, 2010. U.S. EPA. (2003). How Smoke from Fires can Affect Your Health. Available at: http://www.epa.gov/airnow/smoke/Smoke2003final.pdf . Accessed December, 2010 U.S. EPA. (2006) . Guidelines for the Reporting of Daily Air Quality—the Air Quality Index (AQI). EPA-454-06-001. Available at: http://www.epa.gov/ttncaaa1/t1/memoranda/rg701.pdf . Accessed Juanuary, 2011 Wagner, J. and Leith, D. (2001a). Passive aerosol sampler. Part I: Principle of operation. Aerosol Sci. Technol. (2001) 34:186–192. Wagner, J. and Leith, D. (2001b). Passive Aerosol Sampler. Part II: Wind tunnel experiments. Aerosol Sci. Technol. (2001) 34:193–201. Wagner J. and Macher J. (2003). Comparison of a passive aerosol sampler to size-selective pump samplers in indoor environments. AIHAJ 64:630–639. Wallace, L. A., Mitchell, H., O'Connor, G. T., Neas, L., Lippmann, M., Kattan, M., et al. (2003). Particle concentrations in inner-city homes of children with asthma: the effect of smoking, cooking, and outdoor pollution. Environmental Health Perspectives 111: 1265–1272. Watson JF, Chow JC, and Pace TG. (2000). Air Pollution Engineering Manual, Second Edition, 2000. Air and Waste Management Association, 117–135.

Watson, JG, Chow JC. (2002). A winter-time PM2.5 episode at the Fresno, CA, Supersite. Atmospheric Environment 36, 465–475.

http://factfinder.census.gov/servlet/DTTable?_bm=y&-context=dt&-ds_name=ACS_2009_5YR_G00_&-mt_name=ACS_2009_5YR_G2000_B25117&-tree_id=5309&-redoLog=true&-_caller=geoselect&-geo_id=01000US&-geo_id=05000US06025&-search_results=01000US&-format=&-_lang=en




http://www.epa.gov/ttn/atw/burn/brushburn2.pdf

http://www.epa.gov/airnow/smoke/Smoke2003final.pdf

http://www.epa.gov/ttncaaa1/t1/memoranda/rg701.pdf


Wu CF, Jimenez J, Claiborn C, Gould T, Simpson CD, Larson T, Liu SLJ. (2006). Agricultural burning smoke in Eastern Washington: Part II. Exposure Assessment. Atmospheric Environment 40 5379–5392. Wu S, Deng F, Niu J, Huang Q, Liu Y, Guo X (2010). Association of heart rate variability in taxi drivers with marked changes in particulate air pollution in Beijing in 2008. Environmental Health Perspectives 118: 87–91.

ACKNOWLEDGEMENT

EHIB Section and Branch Chiefs: Marilyn Underwood, Rupali Das, Tivo Rojas-Cheetam, and Michael Lipsett for their careful review and editing of this document.