Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how

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.     

Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington

This study investigates how PM2.5 varies spatially and how these spatial characteristics can be used to identify potential monitoring sites that are most representative of the overall ambient exposures to PM2.5 among susceptible populations in the Seattle, WA, area. Data collected at outdoor sites at the homes of participants of a large exposure assessment study were used in this study. Harvard impactors (HIs) were used at 40 outdoor sites throughout the Seattle metropolitan area. Up to six sites at a time were monitored for 10 consecutive 24-hr average periods. A fixed-effect analysis of variance (ANOVA) model that included date and location effects was used to analyze the spatial variability of outdoor PM2.5 concentrations. Both date and location effects were shown to be highly significant, explaining 92% of the variability in outdoor PM2.5 measurements. The day-to-day variability was 10 times higher than the spatial variability between sites. The site mean square was more than twice the error mean square, showing that differences between sites, while modest, are potentially an important contribution to measurement error. Variances of the model residuals and site effects were examined against spatial characteristics of the monitoring sites. The spatial characteristics included elevation, distance from arterials, and distance from major PM2.5 point sources. Results showed that the most representative PM2.5 sites were located at elevations of 80-120 m above sea level, and at distances of 100-300 m from the nearest arterial road. Location relative to industrial PM2.5 sources is not a significant predictor of residential outdoor PM2.5 measurements. Additionally, for sites to be representative of the average population exposures to PM2.5 among those highly susceptible to the health effects of PM2.5, areas of high elderly population density were considered. These representative spatial characteristics were used as multiple, overlapping criteria in a Geographic Information System (GIS) analysis to determine where the most representative sites are located.     

A comprehensive particulate matter monitoring system and dosimetry-based ambient particulate matter standards

A numerical particulate matter (PM) measurement model is developed to characterize and evaluate PM sampling methods. Simulations are conducted using the model to evaluate currently widely used PM samplers, including Federal Reference Method (FRM) samplers. The simulations show that current PM samplers are very vulnerable to both changes in measurement target (i.e., natural variability of particle size distribution) and the sampler's design, manufacturing, and operating conditions, potentially resulting in significant errors in the monitoring data. The numerical model is used in conjunction with two types of commercially available PM monitoring devices to form a Comprehensive Particulate Matter Monitoring System (CPMMS). The first type of device can be any mass-based PM monitor with a well-defined sampling efficiency curve. The second type of device is one capable of measuring particle size distribution with a reasonably good relative accuracy between size categories but not necessarily accurate in measuring absolute mass concentrations. This study shows that CPMMS can produce much higher quality PM monitoring data than the current PM samplers under the same conditions. In addition, unlike past and current PM monitoring data such as total suspended particulates, coarse PM (PM10), fine PM (PM2.5), etc., the CPMMS monitoring data will survive changes in PM regulatory definition. A new concept, dosimetry-based PM metrics and standards, is proposed to define ambient PM level based on the deposition fraction of particles in the human respiratory tract. The dosimetry-based PM metrics is more meaningful because it correlates the ambient PM level with the portion that can be deposited in the respiratory tract without an arbitrary cutoff particle diameter. CPMMS makes dosimetry-based PM metrics and standards feasible.     

Monitoring personal,indoor, and outdoor exposures to metals in airborne particulate matter: Risk of contamination during sampling,handling and analysis

Rigorous sampling and quality assurance protocols are required for the reliable measurement of personal, indoor and outdoor exposures to metals in fine particulate matter (PM_2.5). Testing of five co-located replicate air samplers assisted in identifying and quantifying sources of contamination of filters in the laboratory and in the field. A field pilot study was conducted in Windsor, Ont., Canada to ascertain the actual range of metal content that may be obtained on filter samples using low-flow (4 L min⁻¹) 24-h monitoring of personal, indoor and outdoor air. Laboratory filter blanks and NIST certified reference materials were used to assess contamination, instrument performance, accuracy and precision of the metals determination. The results show that there is a high risk of introducing metal contamination during all stages of sampling, handling and analysis, and that sources and magnitude of contamination vary widely from element to element. Due to the very small particle masses collected on low-flow 24-h filter samples (median 0.107 mg for a sample volume of approximately 6 m³) the contribution of metals from contamination commonly exceeds the content of the airborne particles being sampled. Thus, the use of field blanks to ascertain the magnitude and variability of contamination is critical to determine whether or not a given element should be reported. The results of this study were incorporated into standard operating procedures for a large multiyear personal, indoor and outdoor air monitoring campaign in Windsor.     

Exposure of chronic obstructive pulmonary disease patients to particulate matter: relationships between personal and ambient air concentrations

Mot time-series studies of particulate air pollution and acute health outcomes assess exposure of the study population using fixed-site outdoor measurements. To address the issue of exposure misclassification, we evaluate the relationship between ambient particle concentrations and personal exposures of a population expected to be at risk of particle health effects. Sampling was conducted within the Vancouver metropolitan area during April-September 1998. Sixteen subjects (non-smoking, ages 54-86) with physician-diagnosed chronic obstructive pulmonary disease (COPD) wore personal PM2.5 monitors for seven 24-hr periods, randomly spaced approximately 1.5 weeks apart. Time-activity logs and dwelling characteristics data were also obtained for each subject. Daily 24-hr ambient PM10 and PM2.5 concentrations were measured at five fixed sites spaced throughout the study region. SO4(2-), which is found almost exclusively in the fine particle fraction and which does not have major indoor sources, was measured in all PM2.5 samples as an indicator of accumulation mode particulate matter of ambient origin. The mean personal and ambient PM2.5 concentrations were 18 micrograms/m3 and 11 micrograms/m3, respectively. In analyses relating personal and ambient measurements, ambient concentrations were expressed either as an average of the values obtained from five ambient monitoring sites for each day of personal sampling, or as the concentration obtained at the ambient site closest to each subject's home. The mean personal to ambient concentration ratio of all samples was 1.75 (range = 0.24 to 10.60) for PM2.5, and 0.75 (range = 0.09 to 1.42) for SO4(2-). Regression analyses were conducted for each subject separately and on pooled data. The median correlation (Pearson's r) between personal and average ambient PM2.5 concentrations was 0.48 (range = -0.68 to 0.83). Using SO4(2-) as the exposure metric, the median r between personal and average ambient concentrations was 0.96 (range = 0.66 to 1.0). Use of the closest ambient site did not improve the median correlation of the group for either PM2.5 or SO4(2-). All pooled analyses resulted in lower correlation coefficients than the median correlation coefficient of individual regressions. Personal SO4(2-) was more highly correlated with all ambient measures than PM2.5. Inclusion of time-activity and dwelling characteristics data did not result in a useful predictive regression model for PM2.5 personal exposure, but improved the model fit from simply regressing against ambient concentration (R2 = 0.27). The model for SO4(2-) was predictive (R2 = 0.82), as personal exposures were largely explained by ambient levels. These results indicate a relatively low correlation between personal exposure and ambient PM2.5 that is not improved by assigning exposure to the closest ambient monitor. The correlation between personal exposure and ambient concentration is high, however, when using SO4(2-), an indicator of accumulation mode particulate matter of ambient origin.     

Bayesian hierarchical modeling of personal exposure to particulate matter

In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM_2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM_2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM_2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM_2.5 measurements in characterization of personal exposure to PM_2.5.     

Mesoscale modeling of wintertime particulate matter episodes in eastern Washington, USA

The Spokane, Washington area is classified as a non-attainment area for the 24-h PM₁₀ standard due to a history of high particulate matter concentrations. A Eulerian regional air quality model (CALMET/CALGRID) has been used to characterize the emission, transport and dispersion of PM₁₀ and PM_2.5 in Spokane. Observations from a residential site (Rockwood, RW) and an industrial site (Crown Zellerbach, CZ), spanning July 1994–August 1996 were used to evaluate the current emission inventory. Two major tasks were devised to conduct the objectives of this investigation. First, a simple and efficient urban dispersion model (WYNDValley) was used to simulate important episodes characterized by the highest PM₁₀ and PM_2.5 concentrations. The selected episodes included four days with wet conditions for which no roads would have been emitting and seven days with dry conditions for which roads would emit. In the second step, a single road-emitting event was selected from the previous predicted results for further analysis using the Eulerian regional air quality model to examine the emission inventory. The urban and regional models predicted the observed concentration distributions reasonably well for the source emissions inventoried in Spokane. The mass concentrations of PM₁₀ were well predicted for the roads emitting case examined by both models indicating that the emission inventory based primarily upon area sources including roads is reasonably well characterized, at least at the RW site. The area sources around CZ are less well characterized, so that the PM₁₀ concentrations are underpredicted at CZ. The models appear unable to reach an equilibrium mass balance status at the beginning of the simulation, and the urban model seems unable to properly resolve the nocturnal boundary layer.     

Measurement of personal and integrated exposure to particulate matter and co-pollutant gases

Personal exposure measurement can serve as an effective tool to understand the effect of exposure to air pollutants. Alternatively, exposure assessment using pollutant concentrations in different microenvironments and accurate time–activity information for the subjects can provide good information regarding human integrated exposure. A panel of 18 healthy students of Indian Institute of Technology (IIT) Kanpur in the age group of 18 to 30 years participated in the personal exposure measurements for particulate matter, CO, NO₂ and VOC during post-monsoon and pre-monsoon seasons. Overall, 432 h person exposure data was collected in this study. The major sources of particulate and gaseous co-pollutants were identified. These directly obtained personal exposure values were then compared to the indirectly estimated integrated exposure values. Personal and integrated exposures gave statistically similar results. Through this study, we have shown that integrated exposure values could closely estimate the personal exposure values for particulate matter that can significantly reduce time and cost involved in personal exposure studies. The lung parameters for all the subjects measured during the pre-monsoon and post-monsoon seasons showed statistically significant reduction during pre-monsoon. This was attributed to the high levels of coarse particles during pre-monsoon.     

Seasonal ambient particulate matter and population health outcomes among communities impacted by road dust in British Columbia,Canada

In recent years, many air quality monitoring programs have favored measurement of particles less than 2.5 µm (PM_2.5) over particles less than 10 µm (PM₁₀) in light of evidence that health impacts are mostly from the fine fraction. However, the coarse fraction (PM_10-2.5) may have independent health impacts that support continued measurement of PM₁₀ in some areas, such as those affected by road dust. The objective of this study was to evaluate the associations between different measures of daily PM exposure and two daily indicators of population health in seven communities in British Columbia, Canada, where road dust is an ongoing concern. The measures of exposure were PM₁₀, PM_2.5, PM_10-2.5, PM_2.5 adjusted for PM_10-2.5, and PM_10-2.5 adjusted for PM_2.5. The indicators of population health were dispensations of the respiratory reliever medication salbutamol sulfate and nonaccidental mortality. This study followed a time-series design using Poisson regression over a 2003–2015 study period, with analyses stratified by three seasons: residential woodsmoke in winter; road dust in spring; and wildfire smoke in summer. A random-effects meta-analysis was conducted to establish a pooled estimate. Overall, an interquartile range increase in daily PM_10-2.5 was associated with a 3.6% [1.6, 5.6] increase in nonaccidental mortality during the road dust season, which was reduced to 3.1% [0.8, 5.4] after adjustment for PM_2.5. The adjusted coarse fraction had no effect on salbutamol dispensations in any season. However, an interquartile range increase in PM_2.5 was associated with a 2.7% [2.0, 3.4] increase in dispensations during the wildfire season. These analyses suggest different impacts of different PM fractions by season, with a robust association between the coarse fraction and nonaccidental mortality in communities and periods affected by road dust. We recommend that PM₁₀ monitoring networks be maintained in these communities to provide feedback for future dust mitigation programs.

Implications: There was a significant association between daily concentrations of the coarse fraction and nonaccidental mortality during the road dust season, even after adjustment for the fine fraction. The acute and chronic health effects associated with exposure to the coarse fraction remain unclear, which supports the maintenance of PM₁₀ monitoring networks to allow for further research in communities affected by sources such as road dust.     

Nitro-PAH in ambient particulate matter in the atmosphere of Athens

Nitrated polynuclear aromatic hydrocarbons (NPAH) with a molecular mass of 247 Daltons were found in soot collected in downtown Athens during a campaign performed in 1996. In particular, 2-nitrofluoranthene (2-NFa) and 2-nitropyrene (2-NPy), which are mainly related to photo-induced chemical processes occurring in the atmosphere, were more abundant than 1-nitropyrene (1-NPy) usually associated to motor vehicle exhaust.     

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.     

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.     

Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air

This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time-series. The dynamics of the time-series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time-series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time-series using the two models also supported the same findings.     

A procedure for use in estimating human exposure to particulate matter of ambient origin

Modern epidemiology has shown that fluctuations of mortality data are statistically significantly correlated with fluctuations of ambient particulate matter (PM) concentration data. This relation cannot be confounded by exposure to PM of indoor origin because the concentrations of ambient PM are not correlated with concentrations of PM of indoor origin. It has been suggested, given the above understanding, that modern PM exposure measurements and analysis should create separate estimates of exposure to all PM of ambient origin and exposure to all PM of nonambient origin (primarily of indoor origin), and not exposure to total PM. This paper reviews the developments of the form of the general microenvironmental mass balance equation that can be utilized for estimating human exposure to PM of ambient origin and for estimating the portion of total PM exposure that is attributable to nonambient origin PM. The equation is perfectly general and can be applied to conditions of time-varying factors that influence exposure, such as rapidly changing air-exchange rates in a home as doors and windows are opened and closed, and turning on and off air cleaners in a home. It is suggested that this procedure be applied in exposure assessment studies and validated using independent techniques of estimating exposure to PM of ambient origin available in the literature.     

Personal exposure to fine particulate matter in elderly subjects: relation between personal, indoor, and outdoor concentrations

The time-series correlation between ambient levels, indoor levels, and personal exposure to PM2.5 was assessed in panels of elderly subjects with cardiovascular disease in Amsterdam, the Netherlands, and Helsinki, Finland. Subjects were followed for 6 months with biweekly clinical visits. Each subject's indoor and personal exposure to PM2.5 was measured biweekly, during the 24-hr period preceding the clinical visits. Outdoor PM2.5 concentrations were measured at fixed sites. The absorption coefficients of all PM2.5 filters were measured as a marker for elemental carbon (EC). Regression analyses were conducted for each subject separately, and the distribution of the individual regression and correlation coefficients was investigated. Personal, indoor, and ambient concentrations were highly correlated within subjects over time. Median Pearson's R between personal and outdoor PM2.5 was 0.79 in Amsterdam and 0.76 in Helsinki. For absorption, these values were 0.93 and 0.81 for Amsterdam and Helsinki, respectively. The findings of this study provide further support for using fixed-site measurements as a measure of exposure to PM2.5 in epidemiological time-series studies.     

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.     

Estimating distributions of long-term particulate matter and manganese exposures for residents of Toronto,Canada

Methylcyclopentadienyl manganese tricarbonyl (MMT), a manganese-based gasoline additive, has been used in Canadian gasoline for about 20 yr. Because MMT potentially increases manganese levels in particulate matter resulting from automotive exhausts, a population-based study conducted in Toronto, Canada assessed the levels of personal manganese exposures. Integrated 3-day particulate matter (PM_2.5) exposure measurements, obtained for 922 participant periods over the course of a year (September 1995–August 1996), were analyzed for several constituent elements, including Mn. The 922 measurements included 542 participants who provided a single 3-day observation plus 190 participants who provided two observations (in two different months). In addition to characterizing the distributions of 3-day average exposures, which can be estimated directly from the data, including the second observation for some participants enabled us to use a model-based approach to estimate the long-term (i.e. annual) exposure distributions for PM_2.5 mass and Mn. The model assumes that individuals’ 3-day average exposure measurements within a given month are lognormally distributed and that the correlation between 3-day log-scale measurements k months apart (after seasonal adjustment) depends only on the lag time, k, and not on the time of year. The approach produces a set of simulated annual exposures from which an annual distribution can be inferred using estimated correlations and monthly means and variances (log scale) as model inputs. The model appeared to perform reasonably well for the overall population distribution of PM_2.5 exposures (mean=28 μg m^-3). For example, the model predicted the 95th percentile of the annual distribution to be 62.9 μg m^-3 while the corresponding percentile estimated for the 3-day data was 86.6 μg m^-3. The assumptions of the model did not appear to hold for the overall population of Mn exposures (mean=13.1 ng m^-3). Since the population included persons who were potentially occupationally exposed to Mn (in non-vehicle-related jobs), we used responses to questionnaire items to form a subgroup consisting of non-occupationally exposed participants (671 participant periods), for which the model assumptions did appear to hold. For that subpopulation (mean=9.2 ng m^-3), the model-predicted 95th percentile of the annual Mn distribution was 16.3-ng m^-3, compared with 21.1 ng m^-3 estimated for the 3-day data.     

杭州市空气颗粒物污染特征及变化规律研究

根据2006—2010年杭州市空气颗粒物的监测数据及2002、2006、2008年空气颗粒物来源解析结果,对杭州市空气颗粒物浓度、化学组分与污染来源等特征的变化规律进行分析,以期为空气颗粒物污染控制提供决策依据。结果表明,近年来杭州市PM10浓度有所下降,但一类功能区PM10仍超出《环境空气质量标准》(GB 3095—1996)的要求(≤0.04mg/m3),杭州市空气颗粒物污染以细颗粒物为主,空气颗粒物的二次转化、机动车尾气尘等产生的二次粒子污染相对严重;煤烟尘对杭州市PM10的贡献率下降明显,城市扬尘、二次粒子和机动车尾气尘对PM10的贡献率有所增加,是杭州市PM10的主要来源。     

Assessment of personal integrated exposure to fine particulate matter of urban residents in Hong Kong

Metropolitan residents are concerned about their exposure to airborne pollutants. But establishing these exposures is challenging. A compact personal exposure kit (PEK) was developed to evaluate personal integrated exposure (PIE) from time-resolved data to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) in five microenvironments, including office, home, commuting, other indoor activities (other than home and office), and outdoor activities experienced both on weekdays and weekends. The study was conducted in Hong Kong. The PEK measured PM_2.5, reported location and several other factors, stored collected data, as well as reported the data back to the investigators using global system for mobile communication (GSM) telemetry. Generally, PM_2.5 concentrations in office microenvironment were found to be the smallest (13.0 μg/m³), whereas the largest PM_2.5 concentration microenvironments were experienced during outdoor activities (54.4 μg/m³). Participants spent more than 85% of their time indoors, including in offices, homes, and other public indoor venues. On average, 42% and 81% of the time were spent in homes, which contributed 52% and 79% of PIE (during weekdays and weekends, respectively), suggesting that improvement of air quality in homes may reduce overall exposures and indicating the need for actions to mitigate possible public health burdens in Hong Kong. This study also found that various indoor/outdoor microenvironments experienced by urban office workers cannot be accurately represented by general urban air quality data reported from the regulatory monitoring. Such personalized air quality information, especially while in transit or in offices and homes, may provide improved information on population exposures to air pollution.

Implications: A newly developed personal exposure kit (PEK) was used to monitor PM_2.5 exposure of metropolitan citizens in their daily life. Different microenvironments and time durations caused various personal integrated exposure (PIE). The stationary monitoring method for PIE was also compared and evaluated with PEK. Positive protection actions can be taken after understanding the major contribution to PM_2.5 exposure.