Use of a continuous nephelometer to measure personal exposure to particles during the U.S. Environmental Protection Agency Baltimore and Fresno Panel studies

In population exposure studies, personal exposure to PM is typically measured as a 12- to 24-hr integrated mass concentration. To better understand short-term variation in personal PM exposure, continuous (1-min averaging time) nephelometers were worn by 15 participants as part of two U.S. Environmental Protection Agency (EPA) longitudinal PM exposure studies conducted in Baltimore County, MD, and Fresno, CA. Participants also wore inertial impactor samplers (24-hr integrated filter samples) and recorded their daily activities in 15-min intervals. In Baltimore, the nephelometers correlated well (R2 = 0.66) with the PM2.5 impactors. Time-series plots of personal nephelometer data showed each participant's PM exposure to consist of a series of peaks of relatively short duration. Activities corresponding to a significant instrument response included cooking, outdoor activities, transportation, laundry, cleaning, shopping, gardening, moving between microenvironments, and removing/putting on the instrument. On average, 63-66% of the daily PM exposure occurred indoors at home (about 2/3 of which occurred during waking hours), primarily due to the large amount of time spent in that location (an average of 72-77%). Although not a reference method for measuring mass concentration, the nephelometer did help identify PM sources and the relative contribution of those sources to an individual's personal exposure.     

The influence of human activity patterns on personal PM exposure: a comparative analysis of filter-based and continuous particle measurements

Particulate matter (PM) exposure data from the U.S. Environmental Protection Agency (EPA)-sponsored 1998 Baltimore and 1999 Fresno PM exposure studies were analyzed to identify important microenvironments and activities that may lead to increased particle exposure for select elderly (>65 years old) subjects. Integrated 24-hr filter-based PM2.5 or PM10 mass measurements [using Personal Environmental Monitors (PEMs)] included personal measurements, indoor and outdoor residential measurements, and measurements at a central indoor site and a community monitoring site. A subset of the participants in each study wore passive nephelometers that continuously measured (1-min averaging time) particles ranging in size from 0.1 to approximately 10 microm. Significant activities and locations were identified by a statistical mixed model (p < 0.01) for each study population based on the measured PM2.5 or PM10 mass and time activity data. Elevated PM concentrations were associated with traveling (car or bus), commercial locations (store, office, mall, etc.), restaurants, and working. The modeled results were compared to continuous PM concentrations determined by the nephelometers while participants were in these locations. Overall, the nephelometer data agreed within 6% of the modeled PM2.5 results for the Baltimore participants and within approximately 20% for the Fresno participants (variability was due to zero drift associated with the nephelometer). The nephelometer did not agree as well with the PM10 mass measurements, most likely because the nephelometer optimally responds to fine particles (0.3-2 microm). Approximately one-half (54 +/- 31%; mean +/- standard deviation from both studies) of the average daily PM2.5 exposure occurred inside residences, where the participants spent an average of 83 +/- 10% of their time. These data also showed that a significant portion of PM2.5 exposure occurred in locations where participants spent only 4-13% of their time.     

The Influence of Human Activity Patterns on Personal PM Exposure: A Comparative Analysis of Filter-Based and Continuous Particle Measurements

ABSTRACT

Particulate matter (PM) exposure data from the U.S. Environmental Protection Agency (EPA)-sponsored 1998 Baltimore and 1999 Fresno PM exposure studies were analyzed to identify important microenvironments and activities that may lead to increased particle exposure for select elderly (>65 years old) subjects. Integrated 24-hr filter-based PM_2.5 or PM₁₀ mass measurements [using Personal Environmental Monitors(PEMs)] included personal measurements, indoor and outdoor residential measurements, and measurements at a central indoor site and a community monitoring site. A subset of the participants in each study wore passive nephelometers that continuously measured (1-min averaging time) particles ranging in size from 0.1 to ~10 um. Significant activities and locations were identified by a statistical mixed model (p < 0.01) for each study population based on the measured PM_2.5 or PM₁₀ mass and time activity data. Elevated PM concentrations were associated with traveling (car or bus), commercial locations (store, office, mall, etc.), restaurants, and working.

The modeled results were compared to continuous PM concentrations determined by the nephelometers while participants were in these locations. Overall, the nephelometer data agreed within 6% of the modeled PM_2.5 results for the Baltimore participants and within ~20% for the Fresno participants (variability was due to zero drift associated with the nephelometer). The nephelom-eter did not agree as well with the PM₁₀ mass measurements, most likely because the nephelometer optimally responds to fine particles (0.3–2 um). Approximately one-half (54 ± 31%; mean ± standard deviation from both studies) of the average daily PM_2.5 exposure occurred inside residences, where the participants spent an average of 83 ± 10% of their time. These data also showed that a significant portion of PM_2.5 exposure occurred in locations where participants spent only 4–13% of their time.     

Evaluation and comparison of continuous fine particulate matter monitors for measurement of ambient aerosols

To provide a scientific basis for the selection and use of continuous monitors for exposure and/or health effects studies, and for compliance and episode measurements at strategic locations in the State of New Jersey, we evaluated the performance of seven continuous fine particulate matter (PM2.5) monitors in the present study. Gravimetric samplers, as reference methods, were collocated with realtime instruments in both laboratory and field tests. The results of intercomparison of real-time monitors showed that the two nephelometers used in this study correlated extremely well (r2 approximately 0.97), and two tapered element oscillating monitors (TEOM 1400 and TEOM filter dynamics measurement system [FDMS]) correlated well (r2 > 0.85), whereas two beta gauges displayed a weaker correlation (r2 < 0.6). During a summertime controlled (laboratory) evaluation, the measurements made with the gravimetric method correlated well with the 24-hr integrated measurements made with the real-time monitors. The SidePak nephelometer overestimated the particle concentration by a factor of approximately 3.4 compared with the gravimetric method. During a summertime field evaluation, the TEOM FDMS monitor reported approximately 30% higher mass concentration than the Federal Reference Method (FRM); and the difference could be explained by the loss of semi-volatile materials from the FRM sampler. Results also demonstrated that 24-hr average PM2.5 mass concentrations measured by beta gauges and TEOM (50 degrees C) in winter correlated well with the integrated gravimetric method. Seasonal differences were observed in the performance of the TEOM (50 degrees C) monitor in measuring the particle mass attributed to the higher semi-volatile material loss in the winter weather. In applying the realtime particulate matter monitoring data into Air Quality Index (AQI) reporting, the Conroy method and the 8-hr end-hour average method were both found to be suitable.     

Estimates of community exposure and health risk to sulfur dioxide from power plant emissions using short-term mobile and stationary ambient air monitoring

To estimate plausible health effects associated with peak sulfur dioxide (SO₂) levels from three coal-fired power plants in the Baltimore, Maryland, area, air monitoring was conducted between June and September 2013. Historically, the summer months are periods when emissions are highest. Monitoring included a 5-day mobile and a subsequent 61-day stationary monitoring study. In the stationary monitoring study, equipment was set up at four sites where models predicted and mobile monitoring data measured the highest average concentrations of SO₂. Continuous monitors recorded ambient concentrations each minute. The 1-min data were used to calculate 5-min and 1-hr moving averages for comparison with concentrations from clinical studies that elicited lung function decrement and respiratory symptoms among asthmatics. Maximum daily 5-min moving average concentrations from the mobile monitoring study ranged from 70 to 84 ppb (183–220 µg/m³), and maximum daily 1-hr moving average concentrations from the mobile monitoring study ranged from 15 to 24 ppb (39–63 µg/m³). Maximum 5-min moving average concentrations from stationary monitoring ranged from 39 to 229 ppb (102–600 µg/m³), and maximum daily 1-hr average concentrations ranged from 15 to 134 ppb (40–351 µg/m³). Estimated exposure concentrations measured in the vicinity of monitors were below the lowest levels that have demonstrated respiratory symptoms in human clinical studies for healthy exercising asthmatics. Based on 5-min and 1-hr monitoring, the exposure levels of SO₂ in the vicinity of the C.P. Crane, Brandon Shores, and H.A. Wagner power plants were not likely to elicit respiratory symptoms in healthy asthmatics.

Implications: Mobile and stationary air monitoring for SO₂ were conducted to quantify short-term exposure risk, to the surrounding community, from peak emissions of three coal-fired power plants in the Baltimore area. Concentrations were typically low, with only a few 5-min averages higher than levels indicated during clinical trials to induce changes in lung capacity for healthy asthmatics engaged in exercise outdoors.     

Assessment of Human Exposure to Ambient Particulate Matter

ABSTRACT

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM).

This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD

We conducted a multi-pollutant exposure study in Baltimore, MD, in which 15 non-smoking older adult subjects (> 64 years old) wore a multi-pollutant sampler for 12 days during the summer of 1998 and the winter of 1999. The sampler measured simultaneous 24-hr integrated personal exposures to PM2.5, PM10, SO4(2-), O3, NO2, SO2, and exhaust-related VOCs. Results of this study showed that longitudinal associations between ambient PM2.5 concentrations and corresponding personal exposures tended to be high in the summer (median Spearman's r = 0.74) and low in the winter (median Spearman's r = 0.25). Indoor ventilation was an important determinant of personal PM2.5 exposures and resulting personal-ambient associations. Associations between personal PM2.5 exposures and corresponding ambient concentrations were strongest for well-ventilated indoor environments and decreased with ventilation. This decrease was attributed to the increasing influence of indoor PM2.5 sources. Evidence for this was provided by SO4(2-) measurements, which can be thought of as a tracer for ambient PM2.5. For SO4(2-), personal-ambient associations were strong even in poorly ventilated indoor environments, suggesting that personal exposures to PM2.5 of ambient origin are strongly associated with corresponding ambient concentrations. The results also indicated that the contribution of indoor PM2.5 sources to personal PM2.5 exposures was lowest when individuals spent the majority of their time in well-ventilated indoor environments. Results also indicate that the potential for confounding by PM2.5 co-pollutants is limited, despite significant correlations among ambient pollutant concentrations. In contrast to ambient concentrations, PM2.5 exposures were not significantly correlated with personal exposures to PM2.5-10, PM2.5 of non-ambient origin, O3, NO2, and SO2. Since a confounder must be associated with the exposure of interest, these results provide evidence that the effects observed in the PM2.5 epidemiologic studies are unlikely to be due to confounding by the PM2.5 co-pollutants measured in this study.     

APCA Financial Statements for FY 1979-80

The U.S. Environmental Protection Agency (EPA) has proposed a new secondary standard based on visibility in urban areas. The proposed standard will be based on light extinction, calculated from 24-hr averaged measurements. It would be desirable to base the standard on a shorter averaging time to better represent human perception of visibility. This could be accomplished by either an estimation of extinction from semicontinuous particulate matter (PM) data or direct measurement of scattering and absorption. To this end we have compared 1-hr measurements of fine plus coarse particulate scattering using a nephelometer, along with an estimate of absorption from aethalometer measurements. The study took place in Lindon, UT, during February and March 2012. The nephelometer measurements were corrected for coarse particle scattering and compared to the Filter Dynamic Measurement System (FDMS) tapered element oscillating microbalance monitor (TEOM) PM_2.5 measurements. The two measurements agreed with a mass scattering coefficient of 3.3 ± 0.3 m²/g at relative humidity below 80%. However, at higher humidity, the nephelometer gave higher scattering results due to water absorbed by ammonium nitrate and ammonium sulfate in the particles. This particle-associated water is not measured by the FDMS TEOM. The FDMS TEOM data could be corrected for this difference using appropriate IMPROVE protocols if the particle composition is known. However, a better approach may be to use a particle measurement system that allows for semicontinuous measurements but also measures particle bound water. Data are presented from a 2003 study in Rubidoux, CA, showing how this could be accomplished using a Grimm model 1100 aerosol spectrometer or comparable instrument.

Implications: Visibility is currently based on 24-hr averaged PM mass and composition. A metric that captures diurnal changes would better represent human perception. Furthermore, if the PM measurement included aerosol bound water, this would negate the need to know particulate composition and relative humidity (RH), which is currently used to estimate visibility. Methods are outlined that could accomplish both of these objectives based on use of a PM monitor that includes aerosol-bound water. It is recommended that these techniques, coupled with appropriate measurements of light scattering and absorption by aerosols, be evaluated for potential use in the visibility based secondary standard.     

Comparison of particle light scattering and fine particulate matter mass in central California

Particle light scattering (Bsp) from nephelometers and fine particulate matter (PM2.5) mass determined by filter samplers are compared for summer and winter at 35 locations in and around California's San Joaquin Valley from December 2, 1999 to February 3, 2001. The relationship is described using particle mass scattering efficiency (sigmasp) derived from linear regression of Bsp on PM2.5 that can be applied to estimated PM2.5 from nephelometer data within the 24-hr filter sampling periods and between the every-6th-day sampling frequency. An average of sigmaSp = 4.9 m2/g was found for all of the sites and seasons; however, sigmasp averaged by site type and season provided better PM2.5 estimates. On average, the sigmasp was lower in summer than winter, consistent with lower relative humidities, lower fractions of hygroscopic ammonium nitrate, and higher contributions from fugitive dust. Winter average sigmasp were similar at non-source-dominated sites, ranging from 4.8 m2/g to 5.9 m2/g. The sigmasp was 2.3 m2/g at the roadside, 3.7 m2/g at a dairy farm, and 4.1 m2/g in the Kern County oilfields. Comparison of Bsp from nephelometers with and without a PM2.5 inlet at the Fresno Supersite showed that coarse particles contributed minor amounts to light scattering. This was confirmed by poorer correlations between Bsp and coarse particulate matter measured during a fall sampling period.     

Outdoor versus indoor contributions to indoor particulate matter (PM) determined by mass balance methods

This study compares an indoor-outdoor air-exchange mass balance model (IO model) with a chemical mass balance (CMB) model. The models were used to determine the contribution of outdoor sources and indoor resuspension activities to indoor particulate matter (PM) concentrations. Simultaneous indoor and outdoor measurements of PM concentration, chemical composition, and air-exchange rate were made for five consecutive days at a single-family residence using particle counters, nephelometers, and filter samples of integrated PM with an aerodynamic diameter of less than or equal to 2.5 microm (PM2.5) and PM with an aerodynamic diameter of less than or equal to 5 microm (PM5). Chemical compositions were determined by inductively coupled plasma mass-spectrometry. During three high-activity days, prescribed activities, such as cleaning and walking, were conducted over a period of 4-6 hr. For the remaining two days, indoor activities were minimal. Indoor sources accounted for 60-89% of the PM2.5 and more than 90% of the PM5 for the high-activity days. For the minimal-activity days, indoor sources accounted for 27-47% of PM2.5 and 44-60% of the PM5. Good agreement was found between the two mass balance methods. Indoor PM2.5 originating outdoors averaged 53% of outdoor concentrations.     

Sources of personal exposure to fine particles in Toronto, Ontario, Canada

Individuals are exposed to particulate matter from both indoor and outdoor sources. The aim of this study was to compare the relative contributions of three sources of personal exposure to fine particles (PM2.5) by using chemical tracers. The study design incorporated repeated 24-hr personal exposure measurements of air pollution from 28 cardiac-compromised residents of Toronto, Ontario, Canada. Each study participant wore the Rupprecht & Patashnick ChemPass Personal Sampling System 1 day a week for a maximum of 10 weeks. During their individual exposure measurement days the subjects reported to have spent an average of 89% of their time indoors. Particle phase elemental carbon, sulfate, and calcium personal exposure data were used in a mixed-effects model as tracers for outdoor PM2.5 from traffic-related combustion, regional, and local crustal materials, respectively. These three sources were found to contribute 13% +/- 10%, 17% +/- 16%, and 7% +/- 6% of PM2.5 exposures. The remaining fraction of the personal PM2.5 is hypothesized to be predominantly related to indoor sources. For comparison, central site outdoor PM2.5 measurements for the same dates as personal measurements were used to construct a receptor model using the same three tracers. In this case, traffic-related combustion, regional, and local crustal materials were found to contribute 19% +/- 17%, 52% +/- 22%, and 10% +/- 7%, respectively. Our results indicate that the three outdoor PM2.5 sources considered are statistically significant contributors to personal exposure to PM2.5. Our results also suggest that among the Toronto subjects, who spent a considerable amount of time indoors, exposure to outdoor PM2.5 includes a greater relative contribution from combustion sources compared with outdoor PM2.5 measurements where regional sources are the dominant contributor.     

Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless "attenuation factor" between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities. Using a fixed 24-hr attenuation factor as a first approximation derived from regression analysis for the respondents, the distributions of nonambient PM10 personal exposures were obtained for each city. Although the mean ambient PM10 concentrations in the three cities varied from 27.9 micrograms/m3 in Toronto to 60.9 micrograms/m3 in Phillipsburg to 94.1 micrograms/m3 in Riverside, the mean nonambient components of personal exposures were found to be closer: 52.6 micrograms/m3 in Toronto; 52.4 micrograms/m3 in Phillipsburg; and 59.2 micrograms/m3 in Riverside. The three frequency distributions of the nonambient components of exposure also were similar in shape, giving support to the hypothesis that nonambient concentrations are similar across different cities and populations. These results indicate that, if the ambient concentrations were completely controlled and set to zero in all three cities, the median of the remaining personal exposures to PM10 would range from 32.0 micrograms/m3 (Toronto) to 34.4 micrograms/m3 (Phillipsburg) to 48.8 micrograms/m3 (Riverside). The highest-exposed 30% of the population in the three cities would still be exposed to 24-hr average PM10 concentrations of 47-74 micrograms/m3; the highest 20% would be exposed to concentrations of 56-92 micrograms/m3; the highest 10% to concentrations of 88-131 micrograms/m3; and the highest 5% to 133-175 micrograms/m3, due only to indoor sources and activities. The distribution for the difference between personal exposures and indoor concentrations, or the "personal cloud," also was similar in the three cities, with a mean of 30-35 micrograms/m3, suggesting that the personal cloud accounts for more than half of the nonambient component of PM10 personal exposure in the three cities. Using only the ambient measurements in Toronto, the nonambient data from THEES in Phillipsburg was used to predict the entire personal exposure distribution in Toronto. The PM10 exposure distribution predicted by the model showed reasonable agreement with the PM10 personal exposure distribution measured in Toronto. These initial results suggest that the RCS model may be a powerful tool for predicting personal exposure distributions and statistics in other cities where only ambient particle data are available.     

Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization

As part of a large exposure assessment and health-effects panel study, 33 trace elements and light-absorbing carbon were measured on 24-hr fixed-site filter samples for particulate matter with an aerodynamic diameter <2.5 microm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subject's residence, inside each residence, and on each subject (personal sample). Both two-way (PMF2) and three-way (PMF3) positive matrix factorization were used to deduce the sources contributing to PM2.5. Five sources contributing to the indoor and outdoor samples were identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed more PM2.5 mass on average than any other source in all microenvironments, with average values estimated by PMF2 and PMF3, respectively, of 7.6 and 8.7 microg/m3 for the outdoor samples, 4 and 5.3 microg/m3 for the indoor samples, and 3.8 and 3.4 microg/m3 for the personal samples. Personal exposure to the combustion-related particles was correlated with outdoor sources, whereas exposure to the crustal and chlorine-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the child subjects.     

Comparison of short-term variations (15-minute averages) in outdoor and indoor PM2.5 concentrations

Measurements of 15-min average PM2.5 concentrations were made with a real-time light-scattering instrument at both outdoor (central monitoring sites in three communities) and indoor (residential) locations over two seasons in the Minneapolis-St. Paul metropolitan area. These data are used to examine within-day variability of PM2.5 concentrations indoors and outdoors, as well as matched indoor-to-outdoor (I/O) ratios. Concurrent gravimetric measurements of 24-hr average PM2.5 concentrations were also obtained as a way to compare real-time measures with this more traditional metric. Results indicate that (1) within-day variability for both indoor and outdoor 15-min average PM2.5 concentrations was substantial and comparable in magnitude to day-to-day variability for 24-hr average concentrations; (2) some residences exhibited substantial variability in indoor aerosol characteristics from one day to the next; (3) peak values for indoor short-term (15-min) average PM2.5 concentrations routinely exceeded 24-hr average outdoor values by factors of 3-4; and (4) relatively strong correlations existed between indoor and outdoor PM2.5 concentrations for both 24-hr and 15-min averages.     

Predicting Particulate (PM10) Personal Exposure Distributions Using a Random Component Superposition Statistical Model

ABSTRACT

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless “attenuation factor” between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM₁₀ measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities.     

Year-long continuous personal exposure to PM2.5 recorded by a fast responding portable nephelometer

Personal exposure to particulate matter of aerodynamic diameter under 2.5 μm (PM_2.5) was monitored using a DustTrak nephelometer. The battery-operated unit, worn by an adult individual for a period of approximately one year, logged integrated average PM_2.5 concentrations over 5 min intervals. A detailed time-activity diary was used to record the experimental subject’s movement and the microenvironments visited. Altogether 239 days covering all the months (except April) were available for the analysis. In total, 60 463 acceptable 5-min averages were obtained. The dataset was divided into 7 indoor and 4 outdoor microenvironments. Of the total time, 84% was spent indoors, 10.9% outdoors and 5.1% in transport. The indoor 5-min PM_2.5 average was higher (55.7 μg m^?3) than the outdoor value (49.8 μg m^?3). The highest 5-min PM_2.5 average concentration was detected in restaurant microenvironments (1103 μg m^?3), the second highest 5-min average concentration was recorded in indoor spaces heated by stoves burning solid fuels (420 μg m^?3). The lowest 5-min mean aerosol concentrations were detected outdoors in rural/natural environments (25 μg m^?3) and indoors at the monitored person’s home (36 μg m^?3). Outdoor and indoor concentrations of PM_2.5 measured by the nephelometer at home and during movement in the vicinity of the experimental subject’s home were compared with those of the nearest fixed-site monitor of the national air quality monitoring network. The high correlation coefficient (0.78) between the personal and fixed-site monitor aerosol concentrations suggested that fixed-site monitor data can be used as proxies for personal exposure in residential and some other microenvironments. Collocated measurements with a reference method (β-attenuation) showed a non-linear systematic bias of the light-scattering method, limiting the use of direct concentration readings for exact exposure analysis.     

Estimating Separately Personal Exposure to Ambient and Nonambient Particulate Matter for Epidemiology and Risk Assessment: Why and How

ABSTRACT

This paper discusses the legal and scientific reasons for separating personal exposure to PM into ambient and nonambient components. It then demonstrates by several examples how well-established models and data typically obtained in exposure field studies can be used to estimate both individual and community average exposure to ambient-generated PM (ambient PM outdoors plus ambient PM that has infiltrated indoors), indoor-generated PM, and personal activity PM. Ambient concentrations are not highly correlated with personal exposure to nonambient PM or total PM but are highly correlated with personal exposure to ambient-generated PM. Therefore, ambient concentrations may be used in epidemiology as an appropriate surrogate for personal exposure to ambient-generated PM. Suggestions are offered as to how exposure to ambient-generated PM may be obtained and used in epidemiology and risk assessment.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.