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

Sources of personal PM2.5 exposure during pregnancy in the MADRES cohort

BACKGROUND

Personal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy.

OBJECTIVE

Identify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA.

METHODS

We measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources.

RESULTS

Mean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources.

IMPACT

We conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Adequacy of stationary measurements as proxies for residential personal exposure to gaseous and particle air pollutants

People are exposed to myriad of airborne pollutants in their homes. Owing to diverse potential sources of air pollution and human activity patterns, accurate assessment of residential exposures is complex. In this study, we explored the relationship between personal and stationary air pollutant measurements in residences of 37 participants working from home during the heating season. Stationary environmental monitors (SEMs) were located in the bedroom, living room or home office and personal exposure monitors (PEMs) were worn by the participants. SEMs and PEMs included both real-time sensors and passive samplers. During three consecutive weekdays, continuous data were obtained for particle number concentration (size range 0.3-10 μm), carbon dioxide (CO2), and total volatile organic compounds (TVOC), while passive samplers collected integrated measures of 36 volatile organic compounds (VOCs) and semi volatile organic compounds (SVOCs). The personal cloud effect was detected in >80% of the participants for CO2 and >50% participants for PM10. Multiple linear regression analysis showed that a single CO2 monitor placed in the bedroom efficiently represented personal exposure to CO2 (R2 = 0.90) and moderately so for PM10 (R2 = 0.55). Adding a second or third sensor in a residence did not lead to improved exposure estimates for CO2, with only 6-9% improvement for particles. Selecting data from SEMs when participants were in the same room improved personal exposure estimates by 33% for CO2 and 5% for particles. Out of 36 detected VOCs and SVOCs, 13 had at least 50% higher concentrations in personal versus stationary samples. Findings from this study aid improved understanding of the complex dynamics of gaseous and particle pollutants and their sources in residences, and could support the development of refined procedures for residential air quality monitoring and inhalation exposure assessment.

The impact of GPS-derived activity spaces on personal PM2.5 exposures in the MADRES cohort

BACKGROUND

In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM_2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM_2.5 exposures during pregnancy.

METHODS

We collected 48-h integrated personal PM_2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3^rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM_2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM_2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM_2.5 exposure and identify key contributing factors.

RESULTS

Indoor PM_2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM_2.5 exposure, while greenness was associated with lower personal PM_2.5 exposure (β = -3.09 μg/m³ per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM_2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m³ per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM_2.5 concentrations.

CONCLUSIONS

Our findings highlight the importance of activity spaces and the indoor environment on personal PM_2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM_2.5 exposure.

Longitudinal assessment of personal air pollution clouds in ten home and office environments

Elevated exposure to indoor air pollution is associated with negative human health and well-being outcomes. Inhalation exposure studies commonly rely on stationary monitors in combination with human time-activity patterns; however, this method is susceptible to exposure misclassification. We tracked ten participants during five consecutive workdays with stationary air pollutant monitors at their homes and offices, and wearable personal monitors. Real-time measures of size-resolved particulate matter (within range 0.3-10 μm) and CO₂ , and integrated samples of PM₁₀ , VOCs, and aldehydes were collected. The PM₁₀  cloud magnitude (excess of PM₁₀ beyond stationary room concentration) was detected for all participants in homes and offices. The PM₁₀  cloud magnitude ranged within 5-37 μg/m³ and was the most discernible in the coarse particle size fraction. Particles associated with "Urban mix," "Traffic," and "Human activities" sources contributed the most to PM₁₀ exposures. The personal CO₂  clouds were detected for participants with the SEMs in their living rooms and private or low-occupancy offices. The stationary monitors placed in bedrooms were better predictors of personal PM₁₀ and CO₂ exposures. An overall of 33 VOCs and aldehydes were detected in both microenvironments, with the majority exhibiting high correlation between personal and stationary stations.

The relationship between personal exposure and ambient PM2.5 and black carbon in Beijing

This study is part of the "Air Polluion Impacts on Cardiopulmonary disease in Beijing: an integrated study of Exposure Science, Toxicologenomics & Environmental Epidemiology (APIC-ESTEE)" project under the UK-China joint research programme "Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China)". The aim is to capture the spatio-temporal variability in people's exposure to fine particles (PM_2.5) and black carbon (BC) air pollution in Beijing, China. A total of 120 students were recruited for a panel study from ten universities in Haidian District in northwestern Beijing from December 2017 to June 2018. Real-time personal concentrations of PM_2.5 and BC were measured over a 24-h period with two research-grade portable personal exposure monitors. Personal microenvironments (MEs) were determined by applying an algorithm to the handheld GPS unit data. On average, the participants spent the most time indoors (79% in Residence and 16% in Workplace), and much less time travelling by Walking, Cycling, Bus and Metro. Similar patterns were observed across participant gender and body-mass index classifications. The participants were exposed to 33.8 ± 27.8 μg m^-3 PM_2.5 and to 1.9 ± 1.2 μg m^-3 BC over the 24-h monitoring period, on average 24.3 μg m^-3 (42%) and 0.8 μg m^-3 (28%) lower, respectively, than the concurrent fixed-site ambient measurements. Relative differences between personal and ambient BC concentrations showed greater variability across the MEs, highlighting significant contributions from Dining and travelling by Bus, which involve potential combustion of fuels. This study demonstrates the potential value of personal exposure monitoring in investigating air pollution related health effects, and in evaluating the effectiveness of pollution control and intervention measures.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing

In the study, personal PM_2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM_2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM_2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM_2.5 exposures and produced poor correlations between personal and ambient PM_2.5. These results demonstrate that individual behavior strongly affects personal PM_2.5 exposure. Six primary sources of personal PM_2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM_2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

Determinants of personal exposure to fine particulate matter (PM2.5) in adult subjects in Hong Kong

Personal monitoring for fine particulate matter (PM_2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM_2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM_2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM_2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM_2.5 was the dominant predictor of (R²=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM_2.5 mass and most components. For all subjects, a one-unit (2.72μg/m³) increase in ambient PM_2.5 was associated with a 0.75μg/m³ (95% CI: 0.59-0.94μg/m³) increase in personal PM_2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM_2.5 components in personal exposure (R²_β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM_2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM_2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM_2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.

Chemical characterization and sources of personal exposure to fine particulate matter (PM2.5) in the megacity of Guangzhou, China

Concurrent ambient and personal measurements of fine particulate matter (PM_2.5) were conducted in eight districts of Guangzhou during the winter of 2011. Personal-to-ambient (P-C) relationships of PM_2.5 chemical components were determined and sources of personal PM_2.5 exposures were evaluated using principal component analysis and a mixed-effects model. Water-soluble inorganic ions (e.g., SO₄^2-, NO₃^-, NH₄⁺, C₂O₄^2-) and anhydrosugars (e.g., levoglucosan, mannosan) exhibited median personal-to-ambient (P/C) ratios < 1 accompanied by strong P-C correlations, indicating that these constituents in personal PM_2.5 were significantly affected by ambient sources. Conversely, elemental carbon (EC) and calcium (Ca²⁺) showed median P/C ratios greater than unity, illustrating significant impact of local traffic, indoor sources, and/or personal activities on individual's exposure. SO₄^2- displayed very low coefficient of divergence (COD) values coupled with strong P-C correlations, implying a uniform distribution of SO₄^2- in the urban area of Guangzhou. EC, Ca²⁺, and levoglucosan were otherwise heterogeneously distributed across individuals in different districts. Regional air pollution (50.4 ± 0.9%), traffic-related particles (8.6 ± 0.7%), dust-related particles (5.8 ± 0.7%), and biomass burning emissions (2.0 ± 0.2%) were moderate to high positive sources of personal PM_2.5 exposure in Guangzhou. The observed positive and significant contribution of Ca²⁺ to personal PM_2.5 exposure, highlighting indoor sources and/or personal activities, were driving factors determining personal exposure to dust-related particles. Considerable discrepancies (COD values ranging from 0.42 to 0.50) were shown between ambient concentrations and personal exposures, indicating caution should be taken when using ambient concentrations as proxies for personal exposures in epidemiological studies.

Variability of environmental exposure to fine particles, black smoke, and trace elements among a Swedish population

Mixed-effects models were used to estimate within-person and between-person variance components, and some determinants of environmental exposure to particulate matter (PM(2.5)), black smoke (BS) and trace elements (Cl, K, Ca, Ti, Fe, Ni, Cu, Zn, and Pb) for personal measurements from 30 adult subjects in Gothenburg, Sweden. The within-person variance component dominated the total variability for all investigated compounds except for PM(2.5) and Zn (in which the variance components were about equal). Expressed as fold ranges containing 95% of the underlying distributions, the within-person variance component ranged between 5-fold and 39-fold (median: sixfold), whereas the between-person variance component was always <sixfold (median: threefold). The relatively large within-person variance components can lead to attenuation bias in exposure-response relationships and point to the importance of obtaining repeated samples of PM exposure from study subjects in epidemiological investigations of urban air pollution. On the basis of the variance components estimated for the various particulate species, between 3 and 39 repeated measurements per subject would be required to limit attenuation bias to 20%. Significant determinants for personal exposure levels were urban background air concentrations (PM(2.5), BS, Cl, Zn, and Pb), cigarette smoking (PM(2.5), BS, K, and Ti), season (PM(2.5), Fe, and Pb), and the time spent outdoors or in traffic (Fe).

Predicting personal nitrogen dioxide exposure in an elderly population: integrating residential indoor and outdoor measurements, fixed-site ambient pollution concentrations, modeled pollutant levels, and time-activity patterns

Predicting chronic exposure to air pollution at the intra-urban scale has been recognized as a priority area of research for environmental epidemiology. Exposure assessment models attempt to predict and proxy for individuals' personal exposure to ambient air pollution, and there are no studies to date that explicitly attempt to compare and cross-validate personal exposure concentrations with pollutants modeled at the intra-urban level using methods such as interpolated surfaces and land-use regression (LUR) models. This study aimed to identify how well personal exposure to NO(2) (nitrogen dioxide) can be predicted from ambient exposure measurements and intra-urban exposure estimates using LUR and what other factors contribute to predicting variations in personal exposure beyond measured pollutant levels within home. Personal, indoor and outdoor NO(2) were measured in a population of older adults (>65 yr old) living in Hamilton, Canada. Our results show that personal NO(2) was most strongly associated with contemporaneously collected indoor and outdoor concentrations of NO(2). Predicted NO(2) exposures from intra-urban LUR models were not associated with personal NO(2), whereas interpolated surfaces of particulates and ozone were modestly associated. Combinations of variables that best predicted personal NO(2) variability were derived from time-activity diaries, interpolated surfaces of ambient particulate pollutants, and a city wide temporally matched average of NO(2). The nonsignificant associations between personal NO(2) and the modeled ambient NO(2) concentrations suggest that observed associations between NO(2) generated by LUR models and health effects are probably not produced by NO(2), but by other pollutants that follow a similar spatial pattern.

Quality of indoor residential air and health

About 90% of our time is spent indoors where we are exposed to chemical and biological contaminants and possibly to carcinogens. These agents may influence the risk of developing nonspecific respiratory and neurologic symptoms, allergies, asthma and lung cancer. We review the sources, health effects and control strategies for several of these agents. There are conflicting data about indoor allergens. Early exposure may increase or may decrease the risk of future sensitization. Reports of indoor moulds or dampness or both are consistently associated with increased respiratory symptoms but causality has not been established. After cigarette smoking, exposure to environmental tobacco smoke and radon are the most common causes of lung cancer. Homeowners can improve the air quality in their homes, often with relatively simple measures, which should provide health benefits.

Relationships between personal, indoor, and outdoor exposures to trace elements in PM(2.5)

Twenty-four hour average fine particle concentrations of 23 trace elements (TEs) were measured concurrently in (a) ambient air in three urban neighborhoods (Battle Creek-BCK; East St. Paul-ESP; and Phillips-PHI), (b) air inside residences of participants, and (c) personal air near the breathing zone of healthy, non-smoking adults. The outdoor (O), indoor (I), and personal (P) samples were collected in the Minneapolis/St. Paul metropolitan area over three seasons (Spring, Summer, Fall) using either the federal reference (O) or inertial impactor (I,P) inlets to collect PM(2.5). In addition to descriptive statistics, a hierarchical, mixed-effects statistical model was used to estimate the mutually adjusted effects of monitor location, community, and season on mean differences between monitoring locations while accounting for within-subject and within-monitoring period correlation. The relationships among P, I, and O concentrations varied across TEs. The O concentrations were usually higher than P or I for elements like Ca and Al that originate mainly from entrained crustal material, while P concentrations were often highest for other elements with non-crustal sources. Unadjusted mixed model results demonstrated that O monitors more frequently underestimated than overestimated P TE exposures for elements associated with non-crustal sources. This finding was true even though the O TE measurements were taken in the same neighborhoods as the P and I measurements. Further adjustment for community or season effects in the mixed models reduced the number of significant O-P and O-I differences compared to unadjusted models, but still indicated a tendency for underestimation of personal and indoor TE exposures by central site monitors, particularly in the PHI community. These results indicate that community and season are important covariates for developing long term TE exposure estimates, and that personal exposure to trace elements in PM(2.5) is likely to be underestimated by outdoor central site monitors.