Search

Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R2 = 0.74 for PM2.5 and R2 = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.

Although ambient concentrations of particulate matter ≤10 μm (PM10) are often used as proxies for total personal exposure, correlation (r) between ambient and personal PM10 concentrations varies. Factors underlying this variation and its effect on health outcome-PM exposure relationships remain poorly understood.We conducted a random-effects meta-analysis to estimate effects of study, participant, and environmental factors on r; used the estimates to impute personal exposure from ambient PM10 concentrations among 4,012 nonsmoking, participants with diabetes in the Women's Health Initiative clinical trial; and then estimated the associations of ambient and imputed personal PM10 concentrations with electrocardiographic measures, such as heart rate variability.We identified 15 studies (in years 1990-2009) of 342 participants in five countries. The median r was 0.46 (range = 0.13 to 0.72). There was little evidence of funnel plot asymmetry but substantial heterogeneity of r, which increased 0.05 (95% confidence interval = 0.01 to 0.09) per 10 µg/m increase in mean ambient PM10 concentration. Substituting imputed personal exposure for ambient PM10 concentrations shifted mean percent changes in electrocardiographic measures per 10 µg/m increase in exposure away from the null and decreased their precision, for example, -2.0% (-4.6% to 0.7%) versus -7.9% (-15.9% to 0.9%), for the standard deviation of normal-to-normal RR interval duration.Analogous distributions and heterogeneity of r in extant meta-analyses of ambient and personal PM2.5 concentrations suggest that observed shifts in mean percent change and decreases in precision may be generalizable across particle size.

The US Environmental Protection Agency's (US EPA) Detroit Exposure and Aerosol Research Study (DEARS) has provided extensive data on human exposures to a wide variety of air pollutants and their impact on human health. Previous analyses in the DEARS revealed select cardiovascular (CV) health outcomes such as increase in heart rate (HR) associated with hourly based continuous personal fine particulate matter (PM2.5) exposures in this adult, non-smoking cohort. Examination of time activity diary (TAD), follow-up questionnaire (FQ) and the continuous PM2.5 personal monitoring data provided the means to more fully examine the impact of discreet human activity patterns on personal PM2.5 exposures and changes in CV outcomes. A total of 329 343 min-based PM2.5 personal measurements involving 50 participants indicated that ∼75% of these total events resulted in exposures

Appropriate prediction of residential air exchange rate (AER) is important for estimating human exposures in the residential microenvironment, as AER drives the infiltration of outdoor-generated air pollutants indoors. AER differences among homes may result from a number of factors, including housing characteristics and meteorological conditions. Residential AER data collected in the Detroit Exposure and Aerosol Research Study (DEARS) and the Relationships of Indoor, Outdoor and Personal Air (RIOPA) study were analyzed to determine whether the influence of a number of housing and meteorological conditions on AER were consistent across four cities in different regions of the United States (Detroit MI, Elizabeth NJ, Houston TX, Los Angeles, CA). Influential factors were identified and used as binning variables for deriving final AER distributions for the use in exposure modeling. In addition, both between-home and within-home variance in AER in DEARS were quantified with the goal of identifying reasonable AER resampling frequencies for use in longitudinal exposure modeling efforts. The results of this analysis indicate that residential AER is depended on ambient temperature, the presence (or not) of central air conditioning, and the age of the home. Furthermore, between-home variability in AER accounted for the majority (67%) of the total variance in AER for Detroit homes, indicating lower within-home variability. These findings are compared with other previously published AER distributions, and the implications for exposure modeling are discussed.

An investigation of personal fine and coarse particulate matter (PM 2.5 , PM 10–2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) exposures was conducted with an adult asthmatic cohort as part of the U.S. Environmental Protection Agency's Moderate and Severe Asthmatics and their Environment Study (MASAES). The overall goal of the MASAES was to determine the association of particulate matter on the degree of resulting lung inflammation, with those having severe asthma hypothesized to be more highly susceptible to such outcomes. The primary exposure objective was to determine the spatial (personal versus ambient) and temporal relationships associated with the aforementioned air pollutants and establish the precision of a new dual PM 2.5 , PM 10–2.5 monitor (CPEM) for personal exposure monitoring. A total of 16 non-smoking adults of various asthma severities were monitored over the course of a 14 month period during 2008–2009. Participants were monitored for 24 continuous hours each monitoring day with a maximum of five events per participant. Median personal PM 2.5 and PM 10–2.5 exposures were 16.5 and 10.1 μg m −3 , respectively. Daily ambient mass concentrations accounted for less than 1% of the observed variability in personal PM 2.5 or PM 10–2.5 exposures. Duplicate personal measures yielded R 2 values of 0.92 PM 2.5 and 0.77 PM 10–2.5 , respectively. Maximum daily personal exposures of 17.0 ppb NO 2 and 21.7 ppb O 3 occurred with respective mean exposures of 5.8 and 3.4 ppb. Ambient NO 2 and O 3 measures were observed to be poorly associated with personal exposures ( R 2

Exposure models for air pollutants often adjust for effects of the physical environment (e.g., season, urban vs. rural populations) in order to improve exposure and risk predictions. Yet attempts are seldom made to attribute variability in observed outdoor air measurements to specific environmental variables. This research presents a statistical strategy to identify and explain the spatial and temporal components of air pollutant measurement variance using regional predictors and large-scale (with impacts over multiple kilometers of distance) emission source effects. The emission sources considered in this investigation include major highways and industries, and were chosen based on their proximity to monitoring areas designated in the Detroit Exposure and Aerosol Research Study (DEARS). Linear mixed effects models were used to investigate 24-h averaged outdoor residential air measurements of several pollutants, including PM 2.5 mass, PM components (elemental carbon, organic carbon, metals, elements), nitrogen dioxide, and volatile organic compounds (VOCs). Three hierarchal statistical models were utilized to calculate and examine variance component estimates for each analyte before and after adjustment for fixed effects, which included sampling season, day of the week, air concentrations at an ambient (centralized) monitoring site, and the frequency of time a receptor was downwind of specific large-emissions sources. Results indicate that temporal variability accounted for the majority of total measurement variance (90% on average). Adjustments for ambient concentration and sampling season significantly reduced temporal variance estimates for most VOCs and for about half of the PM components (generally with reductions of 24–97%). Major exceptions to this trend were found with metals (Fe, Mn, and Zn), ethyltoluene, and p-dichlorobenzene, where only 4–30% of the temporal variance was explained after the same adjustments. Additional reductions in temporal variance (up to 37%) were observed after adjusting for the large-emission sources and day of the week effects, with the strongest effects observed for PM components, including select metals. Thus, for the Detroit airshed, VOCs appear to have been largely affected by regional factors, whereas PM components were explained by both regional factors and localized large-emissions sources. Examination of the radial directions associated with suspected emission sources generally supported a priori expectations of source–analyte associations (e.g., NO 2 increases from areas of high vehicle traffic). Overall, this investigation presents a statistical multi-pollutant analysis strategy that is useful for simultaneously (1) estimating spatial and temporal variance components of outdoor air pollutant measurements, (2) estimating the effects of regional variables on pollutant levels, and (3) identifying likely emissions sources that may affect outdoor air levels of individual or co-occurring pollutants.

Intra-urban variability in air pollution source impacts was investigated using receptor modeling of daily speciated PM2.5 measurements collected at residential outdoor locations across Detroit, MI (Wayne County) as part of the Detroit Exposure and Aerosol Research Study (DEARS) during summer and winter from 2004 to 2006. Six areas were selected for the residential monitoring in the DEARS to capture impacts from different sources including local industry, motor vehicles, and upwind regional sources. PM2.5 measurements were also collected at the Allen Park, MI Chemical Speciation Network (CSN) site for comparison with the residential outdoor sites. Sources impacting PM2.5 were quantified using the EPA Chemical Mass Balance Model (CMB 8.2). Published source profiles were used as input to CMB along with a mixed industrial profile and a steel manufacturing profile obtained by applying the EPA Positive Matrix Factorization Model (PMF 4.0) to CSN data from a Midwestern U.S. site with industrial sources similar to Detroit. Major PM2.5 sources impacting the Allen Park and residential monitoring areas during DEARS included motor vehicles (24–36% by mass), secondary sulfate/coal combustion (17–35%), secondary nitrate (16–37%) and organic matter (17–21%). Road dust, steel manufacturing, and mixed industrial sources contributed less than 11% by mass. CMB source contribution estimates for Allen Park during the DEARS generally compared well to CMB estimates from the collocated year-long CSN measurements using the same source profiles. CMB source contributions during DEARS showed similar contributions across the residential monitoring areas for secondary sulfate/coal combustion and secondary nitrate consistent with regional impacts for these sources. Contributions from motor vehicles, steel manufacturing, and mixed industrial sources varied across the DEARS monitoring areas, indicating impacts from local sources within the Detroit airshed that may not be well characterized by the Allen Park monitoring location.

Environmental temperatures are inversely related to BP; however, the effects of short-term temperature changes within a 24-hour period and measured with high accuracy at the personal level have not been described. Fifty-one nonsmoking patients living in the Detroit area had up to 5 consecutive days of 24-hour personal-level environmental temperature (PET) monitoring along with daily cardiovascular measurements, including BP, performed mostly between 5 pm and 7 pm during summer and/or winter periods. The associations between hour-long mean PET levels during the previous 24 hours with the outcomes were assessed by linear mixed models. Accounting for demographics, environmental factors, and monitoring compliance, systolic and diastolic BP were positively associated with several hour-long PET measurements ending from 10 to 15 hours beforehand. During this time, corresponding mostly to a period starting from between 1 am and 3 am to ending between 7 am and 9 am, an increase of 1°C was associated with a 0.81 mm Hg to 1.44 mm Hg and 0.59 mm Hg to 0.83 mm Hg elevation in systolic and diastolic BP, respectively. Modestly warmer, commonly encountered PET levels posed a clinically meaningful effect (eg, a 6.95 mm Hg systolic pressure increase per interquartile range (4.8°C) elevation at lag hour 10). Community-level outdoor ambient temperatures were not related to BP. The authors provide the first evidence that personal exposure to warmer nighttime and early-morning environmental temperatures might lead to an increase in BP during the ensuing day.

The US Environmental Protection Agency (EPA) published a National Ambient Air Quality Standard (NAAQS) and the accompanying Federal Reference Method (FRM) for PM 10 in 1987. The EPA revised the particle standards and FRM in 1997 to include PM 2.5. In 2005, EPA proposed revisions to this NAAQS to include PM 10–2.5 but only finalized revisions with a PM 2.5 FRM and the development of a national monitoring network in 2006. Presently, no EPA designated reference or equivalent method sampler has the ability to directly measure the mass concentrations of PM 10 , PM 10–2.5 , and PM 2.5 simultaneously. An additive approach has been used for samplers like the dichotomous monitors to calculate PM 10 mass concentrations from independent measures of PM 10–2.5 and PM 2.5 (i.e. PM 10 = PM 10–2.5 + PM 2.5 ). A differential approach has been used to calculate PM 10–2.5 from identical collocated PM 10 and PM 2.5 samplers (i.e. PM 10–2.5 = PM 10 –PM 2.5 ). Since these two approaches have been used widely for PM measurements, it is informative to evaluate their precision and comparability. EPA performed collocated tests of five different particle samplers in the Research Triangle Park area of North Carolina to evaluate the comparability and to characterize the additive and differential approaches used to determine particle mass concentrations. The intra-sampler precision of MiniVol, Omni, and dichotomous samplers was less than 8.4%. The precision of PM 10 measurements using the additive approach with dichotomous samplers was less than 3.5%. The poorest precision of the various PM 10–2.5 differential approaches was less than 15.1%. No zero or negative PM 10–2.5 concentrations were calculated using the differential approach. A coefficient of determination of 0.81 or higher was obtained for all paired comparison of PM 10–2.5. The reported test results show that concentrations calculated from both the additive and differential approaches generally agree among the portable samplers, the more established dichotomous sampler and the Federal Reference Methods

The U.S. EPA periodically evaluates ambient concentrations, human exposures, and health risks for 180 hazardous air pollutants plus diesel particulate matter using modeled estimates from the National-Scale Air Toxics Assessment (NATA). NATA publishes estimates at the spatial resolution of U.S. Census tracts, which are subdivisions of a county. These local scale, model-predicted estimates from NATA are used extensively in community-based assessments; however, evaluation of NATA’s ambient concentrations and human exposure estimates against measurement data has been limited to date. This paper compares modeled annual average benzene results from the 2002 NATA with measured results from the 2004 to 2007 Detroit Exposure and Aerosol Research Study (DEARS) as a case study of the quality of NATA results. NATA model estimates support community-scale characterization and assessment. Benzene is particularly important as it was estimated by the 2002 NATA as the largest single air toxic pollutant in terms of cancer risk in the U.S. We found that the average ambient concentrations of benzene predicted by NATA were within 5 percent, on average, of the 24-h integrated average ambient concentrations measured in DEARS. The NATA human exposure estimates, which include only outdoor sources for benzene, were, on average, approximately half the measured breathing zone concentrations from DEARS. Our analyses support that the factors driving higher DEARS personal benzene concentrations relative to the NATA predicted exposure values are likely due, at least in part, to indoor sources. This work points to further community-scale modeling research to improve characterizations and assessments of human exposures.

Local industries are often a driving force for the local economy. Due to the inter-dependency between the local economy and the industry, environmental issues perceived by the residents are sometimes overlooked. The community-based participant environmental justice study reported here presents how different groups of stakeholders worked together to solve environmental concerns from the residents of Moncure, North Carolina. Town hall meetings were held to discuss environmental issues of the community with representatives from both the community and local industries. A study design was prepared by scientists from North Carolina Central University and the U.S. Environmental Protection Agency in order to provide independent scientific data on the air pollutants of interest to the residents. Two one-week-long samplings were conducted in January and August in 2007 to conduct a survey concerning the levels of air pollutants including volatile organic compounds (VOC) and aldehydes. During each sampling s...

The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic children's respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PM-2.5 pm [PM2.5] and < or =10 microm [PM10] in aerodynamic diameter), elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used.

The Mechanistic Indicators of Childhood Asthma (MICA) study in Detroit, Michigan introduced a participant-based approach to reduce the resource burden associated with collection of indoor and outdoor residential air sampling data. A subset of participants designated as MICA-Air conducted indoor and outdoor residential sampling of nitrogen dioxide (NO2), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs). This participant-based methodology was subsequently adapted for use in the Vanguard phase of the U.S. National Children’s Study. The current paper examines residential indoor and outdoor concentrations of these pollutant species among health study participants in Detroit, Michigan. Pollutants measured under MICA-Air agreed well with other studies and continuous monitoring data collected in Detroit. For example, NO2 and BTEX concentrations reported for other Detroit area monitoring were generally within 10–15% of indoor and outdoor concentrations measured in MICA-Air households. Outdoor NO2 concentrations were typically higher than indoor NO2 concentration among MICA-Air homes, with a median indoor/outdoor (I/O) ratio of 0.6 in homes that were not impacted by environmental tobacco smoke (ETS) during air sampling. Indoor concentrations generally exceeded outdoor concentrations for VOC and PAH species measured among non-ETS homes in the study. I/O ratios for BTEX species (benzene, toluene, ethylbenzene, and m/p- and o-xylene) ranged from 1.2 for benzene to 3.1 for toluene. Outdoor NO2 concentrations were approximately 4.5 ppb higher on weekdays versus weekends. As expected, I/O ratios pollutants were generally higher for homes impacted by ETS. These findings suggest that participant-based air sampling can provide a cost-effective alternative to technician-based approaches for assessing indoor and outdoor residential air pollution in community health studies. We also introduced a technique for estimating daily concentrations at each home by weighting 2- and 7-day integrated concentrations using continuous measurements from regulatory monitoring sites. This approach may be applied to estimate short-term daily or hourly pollutant concentrations in future health studies.

Citizen science provides quantitative results to support environmental health assessments (EHAs), but standardized approaches do not currently exist to translate findings into actionable solutions. The emergence of low-cost portable sensor technologies and proliferation of publicly available datasets provides unparalleled access to supporting evidence; yet data collection, analysis, interpretation, visualization, and communication are subjective approaches that must be tailored to a decision-making audience capable of improving environmental health. A decade of collaborative efforts and two citizen science projects contributed to three lessons learned and a set of frequently asked questions (FAQs) that address the complexities of environmental health and interpersonal relations often encountered in citizen science EHAs. Each project followed a structured step-by-step process in order to compare and contrast methods and approaches. These lessons and FAQs provide advice to translate citizen science research into actionable solutions in the context of a diverse range of environmental health issues and local stakeholders.

Background Studies examining the health effects of particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) commonly use ambient PM2.5 concentrations measured at distal monitoring sites as proxies for personal exposure and assume spatial homogeneity of ambient PM2.5. An alternative proxy—the residential outdoor PM2.5 concentration measured adjacent to participant homes—has few advantages under this assumption. Objectives We systematically reviewed the correlation between residential outdoor PM2.5 and personal PM2.5 (r̄j) as a means of comparing the magnitude and sources of measurement error associated with their use as exposure surrogates. Methods We searched seven electronic reference databases for studies of the within-participant residential outdoor-personal PM2.5 correlation. Results The search identified 567 candidate studies, nine of which were abstracted in duplicate, that were published between 1996 and 2008. They represented 329 nonsmoking participants 6–93 years of age in eight U.S. cities, among whom r̄j was estimated (median, 0.53; range, 0.25–0.79) based on a median of seven residential outdoor-personal PM2.5 pairs per participant. We found modest evidence of publication bias (symmetric funnel plot; pBegg = 0.4; pEgger = 0.2); however, we identified evidence of heterogeneity (Cochran’s Q-test p = 0.05). Of the 20 characteristics examined, earlier study midpoints, eastern longitudes, older mean age, higher outdoor temperatures, and lower personal-residential outdoor PM2.5 differences were associated with increased within-participant residential outdoor-personal PM2.5 correlations. Conclusions These findings were similar to those from a contemporaneous meta-analysis that examined ambient-personal PM2.5 correlations (r̄j = median, 0.54; range, 0.09–0.83). Collectively, the meta-analyses suggest that residential outdoor-personal and ambient-personal PM2.5 correlations merit greater consideration when evaluating the potential for bias in studies of PM2.5-mediated health effects.

This analysis provides the initial summary of PM 2.5 mass concentrations relationships for all seasons and participants for a general population in the Detroit Exposure and Aerosol Research Study (DEARS). The summary presented highlights the utility of the new methodologies applied, in addition to summarizing the particulate matter (PM) data. Results include the requirement to adjust the exposure data for monitor wearing compliance and measured environmental tobacco smoke (ETS) levels, even though the study design specified a non-smoking household. A 40% wearing compliance acceptance level was suggested as necessary to balance minimizing exposure misclassification (from poor compliance) and having sufficient data to conduct robust statistical analyses. An ETS threshold level equivalent to adding more than 1.5 μg m −3 to the collected sample was found to be necessary to detect changes in the personal exposure factor ( F pex ). It is not completely clear why such a large threshold level was necessary. Statistically significant spatial PM 2.5 gradients were identified in three of the six DEARS neighborhoods in Wayne County. These were expected, given the number of strong, localized PM sources in the Detroit (Michigan) metro area. Some residential outdoor bias levels compared with the central site at Allen Park exceeded 15%. After adjusting for ETS biases, the outdoor contributions to the personal exposure were typically larger by factors from 1.75 to 2.2 compared with those of the non-outdoor sources. The outdoor contribution was larger in the summer than in the winter, which is consistent with the fractions of time spent outdoors in the summer vs. the winter (6.7% vs. 1.1% of the time). Mean personal PM 2.5 cloud levels for the general population DEARS cohort ranged from 1.5 to 3.8 (after ETS adjustment) and were comparable to those reported previously. The personal exposure collections indoors were typically at least 13 times greater than those contributed outdoors.

Factors and sources affecting measurement uncertainty associated with monitoring metals in airborne particulate matter (PM) were investigated as part of the Windsor, Ontario Exposure Assessment Study (WOEAS). The assessment was made using co-located duplicate samples and a comparison of two analytical approaches: ED-XRF and ICP-MS. Sampling variability was estimated using relative percent difference (RPD) of co-located duplicate samples. The comparison of ICP-MS and ED-XRF results yields very good correlations (R2 ≥ 0.7) for elements present at concentrations that pass both ICP-MS and ED-XRF detection limits (e.g. Fe, Mn, Zn, Pb and Cu). PM concentration ranges (median, sample number) of 24-h indoor PM10 and personal PM10 filters, and outdoor PM2.5 filters were determined to be 2.2–40.7 (11.0, n = 48) μg m−3, 8.0–48.3 (11.9, n = 48) μg m−3, and 17.1–42.3 (21.6, n = 18) μg m−3, respectively. The gravimetric analytical results reveal that the variations in PM mass measurements for same-day sampling are insignificant compared to temporal or spatial variations: 92%, 100% and 96% of indoor, outdoor and personal duplicate samples, respectively, pass the quality criteria (RPD ≤ 20%). Uncertainties associated with ED-XRF elemental measurements of S, Ca, Mn, Fe and Zn for 24-h filter samples are low: 78%–100% of the duplicate samples passed the quality criteria. In the case of 24-h filter samples using ICP-MS, more elements passed the quality criteria due to the lower detection limits. These were: Li, Na, K, Ca, Si, Al, V, Fe, Mn, Co, Cu, Mo, Ag, Zn, Pb, As, Mg, Sb, Sn, Sr, Th, Ti, Tl, and U. Low air concentrations of metals (near or below instrumental detection limits) and/or inadvertent introduction of metal contamination are the main causes for excluding elements based on the pass/fail criteria. Uncertainty associated with elemental measurements must be assessed on an element-by-element basis.

The primary objective of the Detroit Exposure and Aerosol Research Study (DEARS) was to compare air pollutant concentrations measured at various neighborhoods, or exposure monitoring areas (EMAs), throughout a major metropolitan area to levels measured at a central site or community monitor. One of the EMAs was located near a busy freeway (annual average daily traffic (AADT) of ∼130,000) so that impacts of mobile sources could be examined. Air pollution concentrations from the roadway-proximate sites were compared to the central site monitor. The volatile organic compounds (VOCs) selected (benzene, toluene, ethylbenzene, m,p- and o-xylene, 1,3 butadiene, 1,3,5-trimethylbenzene and 4-ethyltoluene) are typically associated with mobile sources. Gradients were also evident that demonstrated the amplification of pollutant levels near the roadway compared to the community monitor. A novel distance-to-roadway proximity metric was developed to plot the measurements and model these gradients. Effective distance represents the actual distance an air parcel travels from the middle of a roadway to a site and varies as a function of wind direction, whereas perpendicular distance is a fixed distance oriented normal to the roadway. Perpendicular distance is often used as a proxy for exposures to traffic emissions in epidemiological studies. Elevated concentrations of all the compounds were found for both a summer and winter season. Effective distance was found to be a statistically significant (p < 0.05) univariate predictor for concentrations of toluene, ethylbenzene, m,p-xylene and o-xylene for summer 2005. For each of these pollutants, effective distance yielded lower p-values than the corresponding perpendicular distance models, and model fit improved. Results demonstrate that this near-road EMA had elevated levels of traffic-related VOCs compared to the community monitor, and that effective distance was a more accurate predictor of the degree to which they were elevated as a function of distance. Effective distance produced a range of distance-to-roadway values for a single site based on wind direction, thus increasing the number and range of values that could be used to plot and predict relative differences in pollutant concentrations between two sites.

Ambassador Bridge, connecting Detroit, Michigan and Windsor, Ontario, is the busiest international commercial vehicle crossing in North America, with a large percentage of heavy duty diesel trucks. This study seeks to examine the contribution of diesel truck traffic across Ambassador Bridge to indoor exposure patterns of elemental carbon (EC), a common surrogate for diesel exhaust particles, in homes in close proximity to the bridge. We also aim to understand the relative importance of home ventilation characteristics and wind speed. Measurements were collected as part of the Detroit Exposure and Aerosol Research Study (DEARS). Residential indoor and outdoor EC measurements were collected over five consecutive 24 h periods in both the summer and winter at 16 homes in close proximity to Ambassador Bridge. Ambient concentrations and meteorological data were collected at a central-site monitor, and home air exchange rates were estimated using a perfluorocarbon tracer. The contributions of ambient concentrations and Ambassador Bridge, and potential effect modification by wind speed and home ventilation status were quantified with regression analyses. Both ambient concentrations and the percentage of time a home was downwind from the bridge were associated with an increase in indoor concentrations. Ambient concentrations significantly contributed to indoor concentrations regardless of wind speed category but were a greater influence in home experiencing calm winds. The effect of the percent of time downwind variable on indoor levels was only significant in homes where the ventilation status was high. The distance a home was from the bridge tollbooth complex was not significantly associated with indoor concentrations. We conclude that diesel traffic emissions related to Ambassador Bridge may have an impact on indoor EC exposures. Given that people spend the majority of their time indoors, it is important to evaluate the impact of traffic-related pollution in the home environment.

This study provides descriptive statistical data on daily time spent in three locations of exposure assessment interest for two panel studies of health-compromised elderly individuals >65-year-old having multiple days of human activity data. The panel studies include individuals living in Los Angeles (CA) and Baltimore (MD) in various housing types. Three general locations are evaluated: outdoors, in vehicles, and total indoors. Of particular interest is providing information regarding the within- and between-individual variability in the time use data for the three locations. The data are analyzed using non-parametric statistics and alternative statistical models. Within and between variability are evaluated using intraclass correlation coefficients (ICCs); daily “lag-one” autocorrelation coefficients are also provided for the two samples. There were significant gender differences for selected seasonal and/or day-of-the-week metrics for: (1) outdoor time in Los Angeles, but not in Baltimore, and (2) in-vehicle time in both areas. Elderly women spent more time in these locations than similarly aged men. The ICC statistic indicates that most of the variability in the time spent in the three locations is due to intraindividual variability rather than to inter-individual variability. The results indicate that US Environmental Protection Agency should consider gender, day-of-the-week, and time-of-day data in its exposure modeling of daily activities undertaken by the health-compromised elderly population.

Concentrations of PM2.5, elemental carbon (EC), organic carbon (OC), 30 organic source markers (13 alkanes, 14 polycyclic aromatic hydrocarbons and ketones, and 3 hopanes) and 19 inorganic source markers are reported from residential indoor, residential outdoor, and ambient microenvironments from a nine home pilot study conducted in Tampa, Florida. Mean daily PM2.5 concentrations were 9.3 μg m−3 for residential indoor, 11.3 μg m−3 for residential outdoor, and 12.7 μg m−3 for ambient microenvironments. The EPA Chemical Mass Balance Model (CMB8.2) was used for source apportionment of PM2.5 residential outdoor samples. Four main sources of PM2.5 were identified: sulfate (55±6%) (average outdoor PM2.5±S.D.), gasoline-powered motor vehicles (32±4%), diesel-powered vehicles (8±2%), and road dust (5±1%).

Coarse particulate matter (PM 10 - 2.5 ) concentration data from residential outdoor sites were collected using portable samplers as part of an exposure assessment for the North Carolina Asthma and Children's Environment Studies (NC-ACES). PM 10 - 2.5 values were estimated using the differential between independent PM 10 and PM 2.5 collocated MiniVol measurements. Repeated daily 24-h integrated PM 10 and PM 2.5 residential outdoor monitoring was performed at a total of 26 homes during September 2003–June 2004 in the Research Triangle Park, NC area. This effort resulted in the collection of 73 total daily measurements. This assessment was conducted to provide data needed to investigate the association of exposures to coarse particle PM mass concentrations with observed human health effects. Potential instrument bias between the differential MiniVol methodology and a dichotomous sampler were investigated. Results indicated that minimal bias of PM 10 - 2.5 mass concentration estimates (slope = 0.8, intercept = 0.36 μ g m - 3 ) existed between the dichotomous and differential MiniVol procedures. Residential outdoor PM 10 - 2.5 mass concentrations were observed to be highly variable across measurement days and ranged from 1.1 to 12.6 μ g m - 3 (mean of 5.4 μ g m - 3 ) . An average correlation coefficient of r = 0.75 existed between residential outdoor PM 10 - 2.5 mass concentrations and those obtained from the central ambient monitoring site. Temporal and spatial variability of PM 10 - 2.5 mass concentrations during the study were observed and are described in this report.

Continuous, long-term, and time-resolved measurement of outdoor air pollution has been limited by logistical hurdles and resource constraints. Measuring air pollution in more places is desired to address community concerns regarding local air quality impacts related to proximate sources, to provide data in areas lacking regional air monitoring altogether, or to support environmental awareness and education. This study integrated commercially available technologies to create the Village Green Project (VGP), a durable, solar-powered air monitoring park bench that measures real-time ozone, PM2.5, and meteorological parameters. The data are wirelessly transmitted via cellular modem to a server, where automated quality checks take place before data are provided to the public nearly instantaneously. Over 5500 h of data were successfully collected during the first ten months of pilot testing in Durham, North Carolina, with about 13 days (5.5%) of downtime because of low battery power. Additional data loss (4-14% depending on the measurement) was caused by infrequent wireless communication interruptions and instrument maintenance. The 94.5% operational time via solar power was within 1.5% of engineering calculations using historical solar data for the location. The performance of the VGP was evaluated by comparing the data to nearby air monitoring stations operating federal equivalent methods (FEM), which exhibited good agreement with the nearest benchmark FEMs for hourly ozone (r(2) = 0.79) and PM2.5 (r(2) = 0.76).

The objective of this study is to characterize the airborne particle sources that are common to four different environments (personal, residential indoor, residential outdoor and ambient) and to study the relationships between PM2.5 exposure and emission sources. Twenty-four hour integrated filter samples were collected on 7 days in each of 4 seasons from June 2000 to May 2001 for 38 individuals in four different type of environments in Raleigh and Chapel Hill, NC. An expanded receptor model (since different environments were analyzed simultaneously, it was also called combined model) was established for this study. The organic carbon (OC) and the first component of OC (OC1) measurements were not blank corrected in this study, so an adaptive blank estimation process was integrated into the combined receptor model and the best OC and OC1 blank values were estimated while the source profiles were being extracted. Four external sources (motor vehicle emission, soil, secondary sulfate, and secondary nitrate) and four internal sources (environmental tobacco smoking and its mixture, personal care/activity, Cu-factor mixed with indoor soil, and cooking) were resolved. The contributions of each source to the different environments were estimated. The contribution of external sources to the composition of personal and indoor samples, and various infiltration factors were also discussed. This study also provided a novel method for blank value estimation.

A study of personal exposures and indoor and outdoor concentrations of particles was carried out in 2000–2001 for 37 health-impaired residents of North Carolina. Earlier papers have dealt with the 24-h integrated gravimetric samples; this report adds the continuous data (1-min resolution) using optical scattering devices (personal data RAMs, or pDRs) for personal and indoor measurements. Subjects and their households were sampled for 7 consecutive days in each of four seasons, although not all subjects completed all four seasons. Each subject completed an activity diary with 15-min resolution on each day giving his or her presence in one of six locations and describing activities with the potential for increased exposure to particles. More than 800 person-days and 1.1×10 6 person-min of valid personal exposures and indoor air concentrations were collected. The pDRs compared well with the PM 2.5 gravimetric devices, with an R 2 of 87% compared to the indoor Harvard impactor (HI), and 70% compared to the personal exposure monitor (PEM). As found in previous studies, (and as expected due to the calibration of these devices with an aerosol of higher density than normal ambient and indoor aerosols), the pDRs overestimated the gravimetric concentrations by 20–50%. Because the correction factor varied by season and by type of sample (indoor vs. personal), no overall correction factor could be applied. The estimated mean increases in exposure during three common activities (cooking, cleaning, and personal care) were 56, 28, and 20 μg m −3 , respectively. Several less common activities, such as burning food and using a fireplace were found to result occasionally in very high (>1500 μg m −3 ) short-term peaks of exposure. Although households with smoking were ineligible for participation, two households did have significant smoking, and had the highest average personal exposures and indoor concentrations of all the study homes. The diurnal variation of personal exposures and indoor concentrations was bimodal, with peaks occurring between 10 and 11 AM and 5 and 6 PM. The “damping effect” of the home due to air exchange caused the infiltrated outdoor particles to show only a unimodal variation, with a single broad and mild peak occurring between 6 and 10 AM. A method for identifying peak concentrations due to indoor sources and personal activities was developed. Peaks due to personal activities and indoor sources occurred about 23% and 14% of the time, respectively. On average, outdoor particles contributed about half of the total personal exposures and indoor concentrations, but there was wide variation across subjects. These findings using the MIE data were then compared to previously published estimates using measurements of sulfur from the gravimetric data; good agreement ( R 2 values from 50% to 65%) was found, providing a measure of validation to both methods. Personal exposures exceeded co-located indoor concentrations by 2–3 μg m −3 , confirming findings of a small but statistically significant “personal cloud” for PM 2.5 made in previous studies using gravimetric 24-h integrating monitors.

A simple methodology was developed to collect measurements of duty cycle, the fraction of time the heating and air conditioning (HAC) system was operating, inside residences. The primary purpose of the measurements was to assess whether the HAC duty cycle was related to reductions in indoor particulate matter (PM) concentrations. A miniature temperature logger placed on an HAC outlet vent monitored changes in temperature as the system cooled or heated the residence. Temperature step changes signaling duty cycle periods were identified using spreadsheet macros. Parallel measurements of 24-h integrated air exchange rates (AERs) and indoor PM 2.5 and PM 10 concentrations were collected to determine relationships with the duty cycle. The mild temperatures (mean=18.7°C) present during the Spring season of the RTP PM Panel Study and personal comfort preferences caused low and variable daily duty cycles (mean=0.061, std. dev.=0.054) in both heating and cooling mode. Warmer ambient temperatures during the Fall season of the Tampa Asthmatic Children's Study (TACS) resulted in cooling-only HAC operation, with a higher mean duty cycle of 0.21 (std. dev.=0.11). Statistically significant linear relationships were observed between daily average duty cycle and the ambient temperature for both studies. Duty cycle exhibited a strong diurnal pattern commensurate with ambient temperature fluctuations. Duty cycles were positively associated with the residence AERs for heating-mode operations, but negatively associated when operating in cooling mode. Personal preferences contributed to the variability in the relationship between duty cycle and AER. The relationship between duty cycle and PM 2.5 or PM 10 indoor–outdoor ratios were not statistically significant. The association of duty cycle with indoor–outdoor ratio was confounded by the short time span (mean of 10.3 min for the TACS) of HAC system operation and the presence of strong indoor sources altering the indoor concentration levels.

The Research Triangle Park (RTP) Particulate Matter (PM) Panel Study represented a 1-year investigation of personal, residential and ambient PM mass concentrations across distances as large as 70 km in central North Carolina. One of the primary goals of this effort was to estimate ambient PM 2.5 contributions to personal and indoor residential PM mass concentrations. Analyses indicated that data from the two distinct non-smoking subject populations totaling 38 individuals and 37 residences could be pooled. This resulted in nearly 800 data points for each variable. A total of 55 measurements believed to have been potentially influenced by personal or residential exposure to passive environmental tobacco smoke were not included in the analysis database. Variables to be examined included C ig (concentration of indoor generated PM), E ig (personal exposure to indoor generated PM), F inf (ambient PM infiltration factor), and F pex (personal exposure to PM of ambient origin factor). Daily air exchange rates (AER) were measured and statistical modeling to derive estimates of particle penetration ( P ) and particle deposition ( k ) factors was performed. Seasonality, cohort grouping, participant or combinations of these variables were determined not to be significant influences in estimating group infiltration factors. The mean (±std) mixed model slope estimates were AER=0.72±0.63, P =0.72±0.21, k =0.42±0.19, and F inf =0.45±0.21. These variables were then used in a number of mixed effects models having varying features of single, random or fixed intercepts and/or slopes to determine the most appropriate means of estimating ambient source contributions to personal and residential settings. A mixed model slope for F pex (±SE) was 0.47±0.07 using the model with the highest degree of fit.

The US Environmental Protection Agency has recently performed the Research Triangle Park Particulate Matter Panel Study. This was a 1-year investigation of PM and related co-pollutants involving participants living within the RTP area of North Carolina. Primary goals were to characterize the relationships between ambient and residential PM measures to those obtained from personal exposure monitoring and estimate ambient source contributions to personal and indoor mass concentrations. A total of 38 participants living in 37 homes were involved in personal, residential indoor, residential outdoor and ambient PM2.5 exposure monitoring. Participants were 30 non-smoking hypertensive African-Americans living in a low-moderate SES neighborhood (SE Raleigh, NC) and a cohort of eight individuals having implanted cardiac defibrillators (Chapel Hill, NC). Residential and ambient monitoring of PM10 and PM10–2.5 (coarse by differential) was also performed. The volunteers were monitored for seven consecutive days during each of four seasons (summer 2000, fall 2000, winter 2001, spring 2001). Individual PM2.5 personal exposure concentrations ranged from 4 to 218 μg m−3 during the study. The highest personal exposures were determined to be the result of passive environmental tobacco exposures. Subsequently, ∼7% of the total number of personal exposure trials were excluded to minimize this pollutant's effect upon the overall analysis. Results indicated that a pooled data set (seasons, cohorts, residences, participants) was appropriate for investigation of the basic mass concentration relationships. Daily personal PM2.5 mass concentrations were typically higher than their associated residential or ambient measurements (mean personal=23.0, indoor=19.1, outdoor=19.3, ambient=19.2 μg m−3). Mean personal PM2.5 exposures were observed to be only moderately correlated to ambient PM2.5 concentrations (r=0.39).

A combined epidemiological exposure panel study was conducted during the summer of 1998 in Baltimore, Maryland. The objectives of the exposure analysis component of the 28-day study were to investigate the statistical relationships between particulate matter (PM) and related co-pollutants from numerous spatial boundaries associated with an elderly population, provide daily mass concentrations needed for the epidemiological assessment, and perform an extensive personal exposure assessment. Repeated 24-h integrated PM2.5 (n=394) and PM10 (n=170) data collections corresponding to stationary residential central indoor, individual apartment, residential outdoor and ambient monitoring were obtained using the same sampling methodology. An additional 325 PM2.5 personal air samples were collected from a pool of 21 elderly (65+ years of age) subjects. These subjects were residents of the 18-story retirement facility where residential monitoring was conducted. Mean daily central indoor and residential apartment concentrations were approximately 10 microg/m3. Outdoor and ambient PM2.5 concentrations averaged 22 microg/m3 with a daily range of 6.7-59.3 microg/m3. The slope of the central indoor/outdoor PM2.5 mass relationship was 0.38. The average daily ratio of PM2.5/PM10 mass concentrations across the measurement sites ranged from 0.73 to 0.92. Both the central indoor and mean apartment PM2.5 mass concentrations were highly correlated with the outdoor variables (r0.94). The lack of traditionally recognized indoor sources of PM present within the facility might have accounted for the high degree of correlation observed between the variables. Results associated with the personal monitoring effort are discussed in depth in Part 2 of this article.

A 17-day pilot study investigating potential PM exposures of an elderly population was conducted near Baltimore, Maryland. Collection of residential indoor, residential outdoor, and ambient monitoring data associated with the subjects living at a common retirement facility was integrated with results from a paired epidemiological pilot study. This integration was used to investigate the potential pathophysiological health effects resulting from daily changes in estimated PM exposures with results reported elsewhere. Objectives of the exposure study were to determine the feasibility of performing PM exposure assessment upon an elderly population and establishing relationships between the various exposure measures including personal monitoring. PM 2.5 was determined to be the dominant outdoor size fraction (0.83 PM 2.5 /PM 10 mass ratio by dichot monitoring). Individual 24-h PM 1.5 personal exposures ranged from 12 to 58 μg m −3 . Comparison of data from matched sampling dates resulted in mean daily PM 1.5 personal, PM 2.5 outdoor, and PM 1.5 indoor concentrations of 34, 17, and 17 μg m −3 , respectively. Activity patterns of the study population indicated a generally sedentary population spending a mean of 96% of each day indoors. Future studies would benefit from the use of a consistent sampling methodology across a larger number of PM measurement sites relevant to the elderly subjects, as well as a larger personal PM exposure study population to more successfully collect data needed in matched epidemiological-exposure studies.

examined the cardiac autonomic response to daily variations in PM in 26 elderly (mean age 81) individuals for 3 consecutive weeks. Several standardized methods were used to measure 24-hr average PM concentrations prior to the clinical test inside (indoor PM2.5) and immediately outside (outdoor PM2.5 and PM2.5-10) of participants' residences. Resting, supine, 6-min R wave to R wave (R-R) interval data were collected to estimate high frequency (0.15-0.40 Hz) and low frequency (0.04-0.15 Hz) powers and standard deviation of normal R-R intervals (SDNN) as cardiac autonomic control indices. Participant-specific lower heart rate variability days were defined as days for which the high-frequency indices fell below the first tertile of the individual's high-frequency distribution over the study period. Indoor PM2.5 > 15 microg/m3 was used to define high pollution days. Results show that the odds ratio (95% confidence interval) of low heart rate variability high frequency for high (vs. not high) pollution days was 3.08 (1.43, 6.59). The ss-coefficients (standard error) from mixed models to assess the quantitative relationship between variations in indoor PM2.5 and the log-transformed high frequency, low frequency, and SDNN were: -0.029 (0.010), -0.027 (0.009), and -0.004 (0.003), respectively. This first study of cardiac autonomic control response to daily variations of PM2.5 indicates that increased levels of PM2.5 are associated with lower cardiac autonomic control, suggesting a possible mechanistic link between PM and cardiovascular disease mortality. Images Figure 1

The applicability of genetic regression (GR) to multi-instrument calibration was demonstrated by using several UV-visible spectrophotometers. GR is a calibration technique that optimizes linear regression using a genetic algorithm (GA). Sample spectra of ternary and quaternary mixtures of the pharmaceuticals furaltadone (Fd), doxycycline (Dx), sulfadiazine (Sd), and trimethoprim (Tm) were collected on four different UV-visible spectrophotometers, including one single-beam diode array and three double-beam dispersive instruments. Hybrid calibration models (HCMs) were generated by combining the data collected on multiple instruments into one calibration model as if they had all been collected on a single instrument. For comparison, single-instrument calibration models were also generated for each instrument. Both HCMs and single-instrument models were tested by using a validation set measured on all four instruments. Results obtained from single-instrument models were comparable with a previous study in which partial least-squares (PLS) regression was used for multivariate calibration of these compounds. HCMs for double-instrument cases performed equally well as single-instrument models and slightly worse for the four-instruments models.

Genetic regression (GR) is an application of genetic algorithms to the problem of producing optimal calibration models by wavelength selection. GR has been shown to provide excellent calibration models under many conditions that typically result in poor calibration models with the use of other multivariate techniques. In this study, GR is applied to the calibration of the components of a ternary mixture with the use of near-infrared spectroscopic data. To determine how close GR comes to the true global optimum, a random search of the possible solutions was performed and the distribution of the solutions' predictive abilities determined. Through this study it has been determined that GR is capable of searching through extremely large search spaces and eliminating over 99.9999% of the unsuitable solutions in a matter of minutes. GR is also capable of finding multiple solutions of similar quality, something not available in many other calibration techniques. Comparison with results from partial least-squares (PLS) is also included.

A new procedure for calibrating multiple instruments is presented in which spectra from each are used simultaneously during the construction of multivariate calibration models. The application of partial least-squares (PLS) and genetic regression (GR) to the problem of generating these hybrid calibrations is presented. Spectra of ternary mixtures of methylene chloride, ethyl acetate, and methanol were collected on a dispersive and a Fourier transform spectrometer. Calibration models were generated by using differing numbers of spectra from each instrument simultaneously in the calibration and prediction sets, and then validated by using a set of spectra from each instrument separately. Calibration models were found that perform well on both instruments, even when only a single spectrum from the second instrument was used during the calibration process. As a benchmark, comparison with PLS showed that GR is more effective than PLS in building these hybrid calibration models.

A new nondispersive colorimetric analyzer has been developed with the use of light-emitting diodes (LEDs) as optical sensors coupled to an artificial neural network (ANN). In this application, various colored LEDs are used as optical detectors with tuned bandpasses. The device has been evaluated against conventional spectrometric methods in the determination of pH and heavy metals by the use of colorimetric procedures. The spectrometer benefits from a simple design, ease of application, inexpensive cost of construction, and long life.

The application of a new algorithm, known as genetic regression (GR), to calibration problems with spectra containing complex fluctuating baselines is illustrated with the use of synthetic data. The ability of the algorithm to automatically compensate for the presence of linear and polynomial (quadratic and cubic) baselines in the presence of complex spectral overlap is investigated along with the effect of noise. GR is unique in that it provides an effective wavelength optimization technique by sorting through the spectr al data and selecting and appropriately combining wavelengths that compensate for structured baseline and spectral overlap. The results obtained with GR are compared with those obtained with background-corrected linear regression. GR is shown to give much better results and, in constrast to traditional background correction, is much faster and can compensate for the presence of both structured baseline and complex spectral overlap simultaneously. The results of a noise study show that the method works at low signal-to-noise ratio (SNR) and that the error in the final result is a function of the noise.

Two fungal strains, Cunninghamella elegans and Penicillium zonatum, that grow with crude oil as a sole carbon source were exposed to three crude oils that exhibit a range of mutagenic activity. At regular time intervals following fungal incubation with the various crude oils, extracts were tested for the presence of mutagenic activity using the spiral Salmonella assay. When the most mutagenic of the oils, Pennsylvania crude oil, was degraded by C. elegans or by P. zonatum, its mutagenicity was significantly reduced; corresponding uninoculated (weathered) controls of Pennsylvania crude remained mutagenic. West Texas Sour crude oil, a moderately mutagenic oil, exhibited little change in mutagenicity when incubated with either C. elegans or P. zonatum. Swanson River Field crude oil from Cook Inlet, Alaska is a slightly mutagenic oil that became more mutagenic when incubated with C. elegans; weathered controls of this oil showed little change in mutagenicity. Mycelial mat weights measured during growth on crude oils increased corresponding to the biodegradation of about 25% of the crude oil.

The application of genetic regression (GR) to reflectance spectra of a solid-phase colorimetric extraction for the determination of Hg(II) is demonstrated. GR is a technique that combines wavelengths that optimize linear regression using a genetic algorithm. Solid polystyrene (PS) beads with a molecular weight distribution of 125,000–250,000 were impregnated with Hg(II)-dithizonate, filtered, allowed to dry, and packed in quartz cuvettes, and their reflectance spectra were collected. Quantitation with the use of normal calibration methods could not be achieved because of baseline fluctuations which originate in sample packing, presentation to the spectrometer, and particle size. Results indicate that GR can easily compensate for these baseline fluctuations and provide excellent calibrations.

Surfactants have been used in the development of new methods for colorimetric analysis. Amphiphilic molecules (surfactants, detergents) on dissolution in water from organized molecular assamblies called micelles when an appropriate concentration, the critical micelle concentraiton (cmc), is exceeded. The micelles enhance the solubility of organic compounds in water by rpoviding local nonpolar environments. A simple method for the determination of several metal ions has been developed using diphenyltiocarbazone (dithizone) and a nonionic surfactant, polyoxyethylene tert-octylphenol (Tritonx X-100)

Personal exposure monitoring for select polycyclic aromatic hydrocarbons (PAHs) was performed as part of the National Human Exposure Assessment Survey (NHEXAS) Pilot Study in Baltimore, MD and in four surrounding counties (NHEXAS-Maryland). An objective of this effort was to establish environmental exposure estimates for non-scripted subpopulations involved in their normal activities. Participants, children, and adults (ages 13-84) were randomly selected from urban, suburban, and rural areas near Baltimore. Twenty-four hour PM(10) sample collections (~5.8 m(3)) were performed using personal environmental monitors. Monitoring was performed for 47 households and 6 sampling Cycles during 1995-1996. A total of 233 personal air samples were available from the participants with eight PAHs speciated (e.g., chrysene, benzo(a)pyrene) as well as an aggregate grouping (total carcinogenic PAHs). Results indicate that ~50% of the selected samples had detectable concentrations for 3 to 5 of the individual PAHs depending upon spatial setting. Noted differences were observed between exposure concentrations from individuals living in rural areas as compared to urban/suburban environments. Mean benzo(a)pyrene concentrations were observed to be 0.10 ng/m(3) across the entire sampling population. This represented a value well below the World Health Organization's 1.0 ng/m(3) ambient air guideline for this PAH.

This work explored the association between nitrogen dioxide (NO(2)) and PM(2.5) components with changes in cardiovascular function in an adult non-smoking cohort. The cohort consisted of 65 volunteers participating in the US EPA's Detroit Exposure and Aerosol Research Study (DEARS) and a University of Michigan cardiovascular sub-study. Systolic and diastolic blood pressure (SBP, DBP), heart rate (HR), brachial artery diameter (BAD), brachial artery flow-mediated dilatation (FMD) and nitroglycerin-mediated arterial dilatation (NMD) were collected by in-home examinations. A maximum of 336 daily environmental and health effect observations were obtained. Daily potassium air concentrations were associated with significant decreases in DBP (-0.0447 mmHg/ng/m(3) ± 0.0132, p = 0.0016, lag day 0) among participants compliant with the personal monitoring protocol. Personal NO(2) exposures resulted in significant changes in BAD (e.g., 0.0041 mm/ppb ± 0.0019, p = 0.0353, lag day 1) and FMD (0.0612 ± 0.0235, p = 0.0103, lag day 0) among other findings.

The US Environmental Protection Agency's (US EPA) Detroit Exposure and Aerosol Research Study (DEARS) deployed a total of over 2000 nitrogen dioxide, NO(2,) passive monitors during 3 years of field data collections. These 24-h based personal, residential outdoor and community-based measurements allowed for the investigation of NO(2) spatial, temporal, human and environmental factors. The relationships between personal exposures to NO(2) and the factors that influence the relationship with community-based measurements were of interest. Survey data from 136 participants were integrated with exposure findings to allow for mixed model effect analyses. Ultimately, 50 individual factors were selected for examination. NO(2) analyses revealed that season, exposure to environmental tobacco smoke and residential gas appliances were strong influencing factors. Only modest associations between community-based measures of nitrogen dioxide and personal exposures impacted by various exposure factors for heating (r=0.44) or non-heating seasons (r=0.34) were observed, indicating that use of ambient-based monitoring as a surrogate of personal exposure might result in sizeable exposure misclassification.

The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic children's respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PMor = 2.5 microm [PM2.5] andor = 10 microm [PM10] in aerodynamic diameter), elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used.

The Environmental Protection Agency’s Detroit Exposure and Aerosol Research Study (DEARS) was a complex 3-year personal exposure study. The six geographically defined areas in the Detroit (Wayne County), Michigan, area used as study locations are ethnically diverse; the majority of the residents are African American or Hispanic. Each summer and winter season, the study solicited 40 adult nonsmoking study participants from these predefined areas. Participants were asked to allow home visits each morning for a week, to wear a personal exposure monitoring vest, and to complete an activity diary and follow-up questionnaire each day. Community action groups, recruitment staff, and environmental technicians coordinated the recruitment and environmental sampling activities. Although the study had an overall response rate of 19 percent, recruitment goals were met nearly every season in each geographic area. Over-recruitment was necessary to replace dropouts. Recruitment staff used face-to-face household recruitment to enroll 136 study participants. Among participants, 73 percent participated in two seasons. Details about the recruitment techniques used in exposure studies, as well as the lessons learned, rarely appear in the literature. This report delineates the lessons from the DEARS that may be beneficial to other researchers using similar study designs in low-income, ethnically diverse urban areas.

Background The mechanisms for the relationship between particulate air pollution and cardiac disease are not fully understood. Air pollution-induced myocardial ischemia is one of the potentially important mechanisms. Methods We investigate the acute effects and the time course of fine particulate pollution (PM2.5) on myocardium ischemic injury as assessed by ST-segment height in a community-based sample of 106 healthy non-smokers. Twenty-four hour beat-to-beat electrocardiogram (ECG) data were obtained using a high resolution 12-lead Holter ECG system. After visually identifying and removing all the artifacts and arrhythmic beats, we calculated beat-to-beat ST-height from ten leads (inferior leads II, III, and aVF; anterior leads V3 and V4; septal leads V1 and V2; lateral leads I, V5, and V6,). Individual-level 24-hour real-time PM2.5 concentration was obtained by a continuous personal PM2.5 monitor. We then calculated, on a 30-minute basis, the corresponding time-of-the-day specific average exposure to PM2.5 for each participant. Distributed lag models under a linear mixed-effects models framework were used to assess the regression coefficients between 30-minute PM2.5 and ST-height measures from each lead; i.e., one lag indicates a 30-minute separation between the exposure and outcome. Results The mean (SD) age was 56 (7.6) years, with 41% male and 74% white. The mean (SD) PM2.5 exposure was 14 (22) μg/m3. All inferior leads (II, III, and aVF) and two out of three lateral leads (I and V6), showed a significant association between higher PM2.5 levels and higher ST-height. Most of the adverse effects occurred within two hours after PM2.5 exposure. The multivariable adjusted regression coefficients β (95% CI) of the cumulative effect due to a 10 μg/m3 increase in Lag 0-4 PM2.5 on ST-I, II, III, aVF and ST-V6 were 0.29 (0.01-0.56) μV, 0.79 (0.20-1.39) μV, 0.52 (0.01-1.05) μV, 0.65 (0.11-1.19) μV, and 0.58 (0.07-1.09) μV, respectively, with all p < 0.05. Conclusions Increased PM2.5 concentration is associated with immediate increase in ST-segment height in inferior and lateral leads, generally within two hours. Such an acute effect of PM2.5 may contribute to increased potential for regional myocardial ischemic injury among healthy individuals.