Grantee Research Project Results
2000 Progress Report: Personal PM Exposure Assessment
EPA Grant Number: R827355C003Subproject: this is subproject number 003 , established and managed by the Center Director under grant R827355
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Water Environment and Reuse Foundation's National Center for Resource Recovery and Nutrient Management
Center Director: Olabode, Lola
Title: Personal PM Exposure Assessment
Investigators: Liu, Sally , Claiborn, Candis , Larson, Timothy V. , Gundel, Lara
Current Investigators: Liu, Sally , Claiborn, Candis , Koenig, Jane Q. , Larson, Timothy V. , Simpson, Chris , Kalman, Dave , Allen, Ryan
Institution: University of Washington , Washington State University
Current Institution: University of Washington
EPA Project Officer: Chung, Serena
Project Period: June 1, 1999 through May 31, 2004 (Extended to May 31, 2006)
Project Period Covered by this Report: June 1, 2000 through May 31, 2001
Project Amount: Refer to main center abstract for funding details.
RFA: Airborne Particulate Matter (PM) Centers (1999) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air
Objective:
The goal of Project 2b is to assess particulate matter exposures among the susceptible subpopulations, including chronic obstructive pulmonary disease (COPD) and cardiovascular (CV) patients, asthmatic children, and healthy adults in Seattle. The objectives of this study are to:- Characterize personal, indoor, and outdoor exposures to the organic components of PM10 and PM2.5 among susceptible subpopulations.
- Determine the strength of the relationship of the particle exposures of the high-risk subpopulations to the concentrations measured by a central monitoring station.
- Characterize the key factors influencing this relationship and develop models for predicting personal PM exposures.
- Provide exposure models for the concurrent epidemiologic study to reach unbiased estimation of health effects.
- Update the study design and analysis goals based on the new findings and new hypotheses.
Progress Summary:
To achieve these goals, we collected daily personal, biological (urine and breath), indoor, and outdoor samples for PM10, PM2.5, CO, SO2, and NO2 samples from subjects in the Seattle metropolitan area over high and low wood smoke seasons. Airborne particulate samples are analyzed for mass, trace elements, organic carbon (OC), PAHs, elemental carbon (EC), and wood smoke and fossil fuel markers. Urine samples were collected for analysis of wood smoke and fossil fuel biomarkers. Measurements of ambient PM1, PM2.5, PM10, CO, and NO2 also were collected at several "central" sites, including Beacon Hill (residential hill), Duwamish (industrial valley), and Lake Forest Park (residential valley). Concentrations of trace elements, EC/OC, PAHs, and wood smoke compounds from various microenvironments (personal, indoor, outdoor, and central sites) and collection media (filters, PUFs, and urine samples) will be used for source apportionment modeling. Using the wood smoke compounds as a tracer for ambient PM exposures is a unique approach in this study.We also collected information on personal time-location-activity, symptoms and medication use, PM events occurring in residences that may generate or reduce PM, building characteristics, continuous CO2 concentrations, temperature, and relative humidity. The exposure data are supplemented with concurrent health data, including peak expiratory flow rate, pulse rate, oxygen saturation, blood pressure, and electrocardiogram measurements.
The monitoring periods included two "monitoring years," each containing high and low wood smoke seasons. Year 1 was between October 1999, and August 2000, but Year 2 started in October 2000, and completed in May 2001. On average, eight subjects were monitored concurrently in Year 1, but six subjects were monitored concurrently in Year 2 during each 10-day session. We have completed all data entry and quality control for the Year 1 data. Organic speciation from the Year 1 urine samples is underway. Teflon filters and PUF extracts have been collected and archived for planned XRF and GC-MS analysis.
We have completed monitoring four populations (N= 107). These 107 monitored subjects included 32 elderly COPD patients, 31 elderly healthy subjects, 25 CV patients, and 19 pediatric asthmatics. Approximately 50 percent of these subjects, including 12 COPD, 11 healthy, 11 CV, and 13 asthmatic subjects were monitored over two seasons, but 5 COPD subjects and 1 CV subject were monitored over three seasons. The number of personal samples collected totaled 1,670 subject*days [(89 subjects in Year 1 + 78 subjects in Year 2)*10 samples/subject].
Quality Control for Year 1 Samples
The total number of field blanks ranged between 11 percent and 21 percent of field samples (Table 1a). The total number of field collocated samples was 8 percent to 17 percent of field samples (Table 1b). In addition, our PM and NO2 samples were collocated with the federal reference method (FRM) sampler at the Beacon Hill central site (Table 1c). The limit of detection (LOD), precision, and accuracy for all types of samples are within the proposed values in our Quality Assurance Project Plan (Table 1). Note that the LOD for the 24-hour integrated Harvard Personal Environmental Monitors (HPEM) for PM2.5 (4 l/min), was 6.2 mg/m3 for the first four sessions of the study and 4.6 mg/m3 afterwards. The 4.6 mg/m3 LOD was similar to the LOD reported by Sarnat, et al. (2000). The LOD for HPEM2.5 was improved by (1) replacing the oiled porous impaction plate with vacuum grease to reduce contamination from the oil, and (2) adding a drain disc downstream to the Teflon filter. The LOD for the air exchange rate (ACH) measurements represents the maximum air exchange rate that could be accurately measured (i.e., 180/hour). The mean difference between the duplicates for all type of samples was not significantly different from zero (Table 1b). The low correlation (0.61) between the Ogawa NO2 measurements and the central site chemiluminescence (CL) method was because the CL method measured total NOy instead of NO2 (Table 1c). Nephelometers, which were used to measure continuous indoor and outdoor PM1 concentrations at each private and group home, had an imprecision of 3 percent (data not shown here).
Table 1. Method Limit of Detection, Precision, and Accuracy
| (a) Limit of detection (LOD) | HI2.5 & HI10 | HPEM (oiled) |
HPEM (greased) |
SO2 | NO2 | ACH |
| # of blanks | 132 | 38 | 97 | 41 | 45 | 45 |
| % of total field samples | 11% | 15% | 19% | 21% | 12% | |
| Blank mean | 1.0 µg | 13.7 µg | 11.3 µg | 0.12 µg | 0.51 µg | 0.005 pl |
| Blank STD | 4.8 µg | 12.0 µg | 8.9 µg | 0.15 µg | 0.15 µg | 0.005 pl |
| LOD* | 1 µg/m3 | 6.2 µg/m3 | 4.6 µµg/m3 | 0.4 ppb | 0.87 ppb | 180 h-1 |
| *LOD was calculated for 24-hour measurements except for the 10-day integrated SO2 and NO2 measurements. | ||||||
| (b) Precision |
HI2.5 (µg/m3) |
HI10 (µg/m3) |
HPEM (µg/m3) |
CO (ppm) |
SO2 (ppb) |
NO2 (ppb) |
ACH (h-1) |
| # of collocated pairs |
52 |
55 |
46 |
93 |
36 |
36 |
39 |
| % of total field samples |
8% |
9% |
10% |
17% |
17% |
10% | |
| Duplicate mean |
8.9 |
15.7 |
10.4 |
1.36 |
0.63 |
15.8 |
9.49 |
| Duplicate difference |
-0.0 |
-0.3 |
0.2 |
0.02 |
0.08 |
0.36 |
-0.12 |
| Std Dev of dup difference |
0.9 |
1.2 |
3.3 |
0.3 |
1.21 |
2.37 |
0.87 |
| Precision (SD/ 2) |
0.6 |
0.9 |
2.3 |
0.2 |
0.9 |
1.7 |
0.6 |
| Correlation (r) |
0.98 |
0.99 |
0.95 |
0.95 |
0.94 |
0.95 |
0.99 |
| (c) Accuracy (all in the units of g/m3 except for NO2 | ||||||
| Reference Method | Federal Reference Method2.5 | CL1 | HI vs. HPEM | |||
| Our samplers | HI2.5 | HPEM-oiled | HPEM-greased | NO2 (ppb) | Indoor | Outdoor |
| # of dup pairs | 55 | 27 | 25 | 11 | 17 | 25 |
| Reference mean | 10.7 | 18.1 | 8.5 | 20.7 | 6.9 | 8.5 |
| Mean difference | -0.4 | -7.72 | -0.7 | 0.2 | 0.1 | -0.5 |
| SD of the difference | 1.1 | 4.6 | 1.5 | 1.9 | 1.2 | 1.4 |
| Correlation (r) | 0.99 | 0.96 | 0.93 | 0.61 | 0.96 | 0.94 |
| 1. Continuous chemiluminescence NOy monitor. | ||||||
| 2. This bias was corrected in the data analysis. | ||||||
Summary of Exposures to PM2.5 and Co-Pollutants
Table 2 summarizes personal exposures to PM2.5 and other co-pollutants, including CO, SO2, and NO2 in our Year 1 measurements. The concentrations of PM and co-pollutants were relatively low during Year 1, with no significant differences in PM and co-pollutant exposures between COPD and healthy subjects. In a cross-sectional analysis, personal PM2.5 exposures were marginally correlated to alveolar CO measured in exhaled breath (r=0.15, p=0.07), while 10-d average alveolar CO was negatively correlated with 10-d personal exposures to SO2 (r=-0.36) and NO2 (r=-0.24). Longitudinal correlation for personal exposures to PM2.5 and alveolar CO concentrations ranged between 0 and 0.78 (median=0.11).
Table 2. Summary of PM and Co-pollutants Measurements in Year 1
| Pollutant | Subjects | N | Mean | SD | Min | Max | |
| Location | PM2.5 | COPD | 458 | 13.9 | 11.7 | -1.2 | 81.2 |
| (µg/m3) | Healthy | 419 | 12.8 | 12.2 | 0.8 | 103.3 | |
| Alveolar CO | COPD | 458 | 3.91 | 2.80 | 0.00 | 15.00 | |
| Personal | (ppm) | Healthy | 419 | 3.76 | 2.88 | 0.00 | 13.00 |
| NO2 | COPD | 46 | 9.7 | 4.6 | 3.5 | 21.7 | |
| (ppb) | Healthy | 42 | 10.2 | 3.7 | 4.4 | 17.5 | |
| SO2 | COPD | 46 | 0.2 | 0.3 | -0.2 | 1.2 | |
| (ppb) | Healthy | 42 | 0.0 | 0.2 | -0.3 | 0.4 | |
| PM2.5 | COPD | 458 | 8.2 | 5.2 | 1.0 | 49.9 | |
| (µg/m3) | Healthy | 419 | 7.6 | 4.4 | 0.4 | 38.0 | |
| PM10 | COPD | 458 | 13.4 | 6.5 | 2.5 | 38.6 | |
| (µg/m3) | Healthy | 419 | 12.5 | 6.6 | 1.6 | 62.2 | |
| Indoor | CO | COPD | 458 | 1.57 | 0.90 | 0.15 | 5.25 |
| (ppm) | Healthy | 419 | 1.56 | 0.89 | 0.05 | 4.55 | |
| NO2 | COPD | 46 | 9.8 | 5.2 | 2.1 | 23.0 | |
| (ppb) | Healthy | 42 | 11.1 | 5.4 | 2.1 | 22.3 | |
| SO2 | COPD | 46 | 0.1 | 0.2 | -0.2 | 0.9 | |
| (ppb) | Healthy | 42 | 0.1 | 0.2 | -0.2 | 0.5 | |
| PM2.5 | COPD | 458 | 8.7 | 4.7 | 1.6 | 25.7 | |
| (µg/m3) | Healthy | 419 | 9.3 | 4.9 | 1.4 | 24.6 | |
| PM10 | COPD | 458 | 13.8 | 6.7 | 2.9 | 54.9 | |
| Outdoor | (µg/m3) | Healthy | 419 | 14.5 | 6.8 | 2.9 | 54.9 |
| NO2 | Healthy | 46 | 16.1 | 6.1 | 6.9 | 36.5 | |
| (ppb) | COPD | 42 | 16.8 | 6.0 | -0.3 | 28.1 | |
| SO2 | Healthy | 46 | 0.8 | 0.4 | 0.2 | 2.1 | |
| (ppb) | COPD | 42 | 0.9 | 0.6 | 0.3 | 3.5 | |
| Central Site | PM2.5 (µg/m3) |
All | 88 | 8.5 | 4.5 | 1.4 | 22.4 |
| PM10 (µg/m3) |
All | 88 | 14.5 | 8.2 | 2.7 | 46.3 | |
| CO (ppm) | All | 88 | 1.5 | 0.4 | 0.6 | 2.7 | |
| NO2 (ppb) | All | 88 | 20.7 | 2.7 | 14.4 | 23.6 | |
| SO2 (ppb) | All | 88 | 1.2 | 0.4 | 0.5 | 2.1 | |
| Note: NO2 and SO2 samples are 10-d average samples. | |||||||
Relationship Between PM Exposures and Central Site Measurements
Cross-sectionally, central site PM2.5 and PM10 were weakly but significantly related to personal PM2.5 exposures (r=0.17-0.29) in both elderly COPD and healthy subpopulations. The longitudinal correlation coefficients for personal PM2.5 exposures and central site measurements, calculated over the 10-day period for each subject, ranged between 0 and 0.79. Using the random component superposition model (Ott, et al., 2000), we estimated that 40 percent of the personal PM2.5 exposures could be contributed to ambient sources, while 60 percent was from non-ambient sources.
References:
Ott W, Wallace L, Mage D. Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model. Journal of the Air and Waste Management Association 2000;50:1390-1406.
Sarnat JA, Koutrakis P, Suh HH. Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD. Journal of the Air and Waste Management Association 2000;50:1184-1198.
Future Activities:
We have started modeling and characterizing key factors influencing the relationship of central site, outdoor, indoor, and personal measurements of PM and co-pollutants (some results were presented at the ISEA 2000 conference). We plan to develop models for predicting personal PM exposures and apply the exposure models to the concurrent epidemiologic study to reach unbiased estimation of health effects.Results from the 2 years of monitoring work and four subpopulations should provide extremely valuable information on the relationships of personal PM and co-pollutant exposures to ambient PM and co-pollutant levels. We expect that the Year 2 measurements in particular will provide a more comprehensive understanding of PM exposures due to the dry, cold winter with several PM episodes. These results will enhance our knowledge on key factors influencing exposures of sensitive subpopulations to PM and the spatial, temporal, and magnitude components of these exposures.
We are updating our study design and analysis goals based on new findings and new hypotheses emerging from current PM studies in our center and other PM centers. The new focus of the Year 3 study is to capture and characterize short-term peak PM exposures and health effects in sensitive populations. Modified directions in our study design include the following:
- Characterize short-term peak PM exposures to forest fire smoke in the northwest region during the wild fire season (July-September 2001).
- Conduct a 1-month pilot project in Pullman to study exposure and health effects of Agricultural burning in the fall of 2001 and spring of 2002.
- Conduct episodic monitoring to capture high wood smoke periods in Seattle during the heating season (October 2001-February 2002). The study subjects will be those who were monitored in the past 2 years.
- Continue sample analysis, data analysis, and exposure modeling with the Years 1 and 2 results.
- Explore further the relationship between central site CO and personal CO and PM exposures.
- Work with other PM centers to compare our results with those from other similar studies.
Journal Articles:
No journal articles submitted with this report: View all 65 publications for this subprojectSupplemental Keywords:
ambient particles, fine particles, combustion, health, exposure, biostatistics, susceptibility., RFA, Health, Scientific Discipline, Air, Geographic Area, particulate matter, air toxics, Environmental Chemistry, Health Risk Assessment, Epidemiology, State, Northwest, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, Biochemistry, genetic susceptability, indoor air, Atmospheric Sciences, ambient aerosol, ambient air quality, asthma, biostatistics, health effects, particulates, PM10, sensitive populations, air pollutants, cardiopulmonary responses, fine particles, health risks, human health effects, morbidity, PM 2.5, toxicology, stratospheric ozone, exposure and effects, ambient air, exposure, hazardous air pollutants, animal model, combustion emissions, air pollution, children, Human Health Risk Assessment, particle exposure, cardiopulmonary response, human exposure, inhalation, PAHs, atmospheric aerosols, ambient particle health effects, mortality studies, hydrocarbons, human susceptibility, Seattle, Washington, incineration, indoor air quality, mortality, California (CA), allergens, aerosols, air quality, atmospheric chemistry, cardiovascular disease, exposure assessment, human health riskRelevant Websites:
http://depts.washington.edu/pmcenter/
Progress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R827355 Water Environment and Reuse Foundation's National Center for Resource Recovery and Nutrient Management Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R827355C001 Epidemiologic Study of Particulate Matter and Cardiopulmonary
Mortality
R827355C002 Health Effects
R827355C003 Personal PM Exposure Assessment
R827355C004 Characterization of Fine Particulate Matter
R827355C005 Mechanisms of Toxicity of Particulate Matter Using Transgenic Mouse Strains
R827355C006 Toxicology Project -- Controlled Exposure Facility
R827355C007 Health Effects Research Core
R827355C008 Exposure Core
R827355C009 Statistics and Data Core
R827355C010 Biomarker Core
R827355C011 Oxidation Stress Makers
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.