Indoor and Outdoor Levels and Sources of Submicron Particles (PM1

Article pubs.acs.org/est

Indoor and Outdoor Levels and Sources of Submicron Particles (PM1) at Homes in Edmonton, Canada Md. Aynul Bari,*,† Warren B. Kindzierski,† Lance A. Wallace,‡ Amanda J. Wheeler,§ Morgan MacNeill,§ and Marie-Ève Héroux§,∥ †

School of Public Health, University of Alberta, 3-57 South Academic Building, 11405-87 Avenue, Edmonton, Alberta T6G 1C9, Canada ‡ Consultant, 428 Woodley Way, Santa Rosa, California 95409, United States § Health Canada, 269 Laurier Avenue West, Ottawa, Ontario K1A 0K9, Canada S Supporting Information *

ABSTRACT: Exposure to submicron particles (PM1) is of interest due to their possible chronic and acute health effects. Seven consecutive 24-h PM1 samples were collected during winter and summer 2010 in a total of 74 nonsmoking homes in Edmonton, Canada. Median winter concentrations of PM1 were 2.2 μg/m3 (interquartile range, IQR = 0.8−6.1 μg/m3) and 3.3 μg/m3 (IQR = 1.5− 6.9 μg/m3) for indoors and outdoors, respectively. In the summer, indoor (median 4.4 μg/m3, IQR = 2.4−8.6 μg/m3) and outdoor (median 4.3 μg/m3, IQR = 2.6−7.4 μg/m3) levels were similar. Positive matrix factorization (PMF) was applied to identify and apportion indoor and outdoor sources of elements in PM1 mass. Nine sources contributing to both indoor and outdoor PM1 concentrations were identified including secondary sulfate, soil, biomass smoke and environmental tobacco smoke (ETS), traffic, settled and mixed dust, coal combustion, road salt/road dust, and urban mixture. Three additional indoor sources were identified i.e., carpet dust, copper-rich, and silver-rich. Secondary sulfate, soil, biomass smoke and ETS contributed more than 70% (indoors: 0.29 μg/m3, outdoors: 0.39 μg/m3) of measured elemental mass in PM1. These findings can aid understanding of relationships between submicron particles and health outcomes for indoor/outdoor sources.

1. INTRODUCTION There is an increasing interest in understanding human exposure to ambient submicron particles, i.e., PM1 (with aerodynamic diameter less than 1.0 μm). Recent epidemiological studies suggest an association between short-term exposure to number and mass concentrations of ambient submicron particles and increased morbidity, mortality, and emergency hospital admissions for cardiovascular, cerebrovascular, and ischemic heart disease.1,2 Others have reported that submicron particles can penetrate into the alveolar region of human lungs, induce inflammatory responses in lung epithelial cells,3 damage acinar lung units, and activate cells of the immune system.4 PM1 characterization indoors at home is complex and an understanding of their sources, sinks, and temporal and spatial variation is needed. At present, there is not enough evidence at the population level to identify differences in the effects of particles with different chemical compositions or emanating from various sources. Nevertheless, there is a body of evidence that chemical composition of particles may be a valuable and appropriate parameter for evaluating PM health risk in addition to particle mass concentrations.5,6 From a toxicological point of view, some trace elements and their compounds (e.g., arsenic, cadmium, cobalt, chromium, mercury, manganese, nickel, lead, © 2015 American Chemical Society

antimony, and selenium) are considered hazardous under the United States Environmental Protection Agency Clean Air Act7 and fall under the list of toxic substances (arsenic, lead, mercury, cadmium, and nickel) as defined by Health Canada under the Canadian Environmental Protection Act.8 Some studies indicate that crustal-derived elements (e.g., silicon, aluminum, calcium, and iron) are predominant in coarse particles (PM10−2.5), and potentially appear to be less toxic than combustion-derived trace elements (e.g., lead, arsenic chromium, nickel, zinc, cadmium, and selenium), which are more abundant in fine particle fractions (i.e., PM2.5, PM1) suggesting that smaller sizes, composition, and sources of particles are important factors in toxicity.7,9 To date, only a few studies10−12 have focused on characterization of indoor and outdoor PM1 concentrations for residential homes. For ambient PM1 source apportionment, different receptor models, e.g., positive matrix factorization (PMF) and Unmix have been used.13−15 Until now no receptor modeling studies have been undertaken to identify and Received: Revised: Accepted: Published: 6419

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2, 2014 4, 2015 13, 2015 22, 2015 DOI: 10.1021/acs.est.5b01173 Environ. Sci. Technol. 2015, 49, 6419−6429

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Environmental Science & Technology

collected. The homes were selected randomly based on an a priori hypothesis that age of home would impact indoor air quality. A single stage stratified sampling design26,27 was used for the study where the stratification variable was Edmonton neighborhoods with different average age of housing stock defined by 2006 Canadian census data. Details of the home selection procedure are described elsewhere.28 Indoor measurements were taken at a breathing height of 1.5 m within the family or living room, while outdoor sampling equipment was located in the backyard and as far away from any combustion sources such as barbeques, automobiles and other localized outdoor sources as possible. To assess instrument precision, duplicate samples were taken at an Environment Canada National Air Pollution Surveillance (NAPS) station in south Edmonton. Three size fractions of particulate matter i.e., PM1, PM1−2.5 and PM2.5−10 were collected using Harvard Coarse Impactors (HCI, Harvard School of Public Health, Boston, MA, U.S.A.), which have been previously reported to function well in comparison to dichotomous samplers.29 The indoor and outdoor HCIs operated at a flow rate of five liters per minute (LPM) using a BGI pump (model #400−10, BGI Inc., Waltham MA, U.S.A.). PM1 mass was collected on Teflon filters (37 mm, 2 μm pore size, Pall Inc. Port Washington, NY) and gravimetric analysis was performed using the U.S. EPA method outlined in the Quality Assurance Guideline Document 2.12.30 Elemental carbon (EC) and organic carbon (OC) were collected on prefired quartz fiber filters indoors and outdoors for 24-h during both seasons using ChemComb samplers (flow rate 10 LPM) with a PM2.5 cutoff (Model 3500, Thermo Scientific, Waltham, MA, U.S.A.). Gaseous pollutants, e.g., nitrogen dioxide (NO2) and sulfur dioxide (SO2) were also measured for seven consecutive 24-h periods using Ogawa passive samplers (Ogawa & Co., Pompano Beach, FL, U.S.A.). VOC sampling was performed using Summa canisters (Scientific Instrumental Specialists, Inc.). Measurements of home air exchange rates, temperature and relative humidity were reported in Bari et al. (2014).28 2.2. Chemical Analysis. PM1 samples were analyzed quantitatively for 34 heavy and trace elements using energy dispersive X-ray fluorescence (ED-XRF, ThermoFisher QuanX) and inductively coupled plasma mass spectrometry (ICP−MS, PerkinElmer Elan DRC-II). Elements with more than 80% of data below the method detection limit were excluded from further statistical analyses. Measurements of elements by the two independent analytical methods were examined to evaluate data consistency and 27 elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Cl, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, Pb, S, Sb, Si, Sn, Tl, V, and Zn) were chosen based on data completeness and/or higher signal-to-noise (S/N) ratios that could better explain resolved variability in the data set. Further details on chemical analysis are provided in the SI. 2.3. Source Apportionment Method. The multivariate receptor model PMF was used to identify and quantify possible emission sources of measured PM1 elements. The EPA PMF 3.0 program based on the second version of the multilinear engine (ME-2) was used. Details of the model, including data treatment procedures, are described in the SI. For source apportionment 27 trace elements were used in the PMF data set. EC/OC and secondary pollutants (e.g., sulfate, nitrate, and ammonium) were not analyzed in the same PM1 mass and therefore could not be incorporated in the PMF modeling for this study. However, measurements of 24-h EC/OC in PM2.5,

apportion PM1 sources in residential environments. A number of receptor modeling attempts have been made to apportion fine particles (i.e., PM2.5) indoors and outdoors. Yli-Tuomi et al.16 applied a multilinear engine model to determine indoor and outdoor PM2.5 sources from repeated daily measurements (1−8 times) in 33 homes in Amsterdam, The Netherlands, and in 45 homes (4−6 times) in Helsinki, Finland. A study in Barcelona17 investigated indoor and outdoor sources influencing PM2.5 exposures for 54 pregnant women using personal and indoor/outdoor measurements over two consecutive days (repeated twice for 11 women). Meng et al.18 used PMF to investigate indoor and outdoor sources of PM2.5 for 279 nonsmoking homes from 48 h measurements (1−4 homes at a time). Other studies19−21 also used receptor modeling techniques for apportioning personal, indoor and outdoor PM2.5 sources using a single daily measurement at multiple homes. Edmonton is the second largest city in Alberta with a population within its municipal boundaries of 782 439 in 2009.22 It covers an area of over 680 km2, and is the northernmost North American city with a metropolitan population over 1.1 million. It has a relatively humid continental climate with wide variations in seasonal temperatures. Daily average temperatures range from −11.7 °C in January to 17.5 °C in July. Prevailing wind directions are westnorthwesterly and early morning ground-based inversions are frequent throughout the whole year.23 The city is surrounded by a number of industries (coal-fired power plants located 60 km to the west, petroleum refineries, steel foundries, a coke processing plant, asphalt roofing and a cement manufacturing plant) and agricultural farming lands (Supporting Information, SI, Figure S1).23,24 According to Environment Canada’s National Pollution Release Inventory (NPRI),25 major contributors of fine particulate matter (PM2.5) emissions during 2010 in Alberta were transportation (12 490 tonnes), industrial sources (12 343 tonnes) including the upstream oil and gas industry (6309 tonnes), nonindustrial sources (7862 tonnes) including coal-fired electric power generation (2009 tonnes) and natural gas (1219 tonnes), open sources, e.g., agriculture (15 074 tonnes), construction operations (129 949 tonnes), dust from paved and unpaved roads (218 476 tonnes) as well as natural events like forest fires (6879 tonnes). Health Canada, in collaboration with the University of Alberta School of Public Health, undertook a study during 2010 to characterize indoor and outdoor concentrations for a number of air pollutants, including PM1, in different homes of Edmonton. This manuscript presents the results of an investigation of indoor and outdoor levels and sources of airborne elements in PM1 based on receptor modeling for these homes. Understanding the sources contributing to PM1 exposures can support development of risk management options and strategies for air pollution control.

2. EXPERIMENTAL SECTION 2.1. Sampling Strategy. PM 1 concentrations were measured indoors and outdoors for seven consecutive 24-h periods in 50 homes during winter (January to April) and summer (June to August) 2010, with 26 homes participating in both seasons. Home locations are identified in SI Figure S1. There were nine consecutive seven-day sampling periods per season, with 5 to 6 homes being measured concurrently per period. Data on relative humidity, temperature, questionnairebased housing characteristics, and occupants’ activities were 6420

DOI: 10.1021/acs.est.5b01173 Environ. Sci. Technol. 2015, 49, 6419−6429

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60% of homes. Visitors smoked outside at least 1 day during monitoring in 32% and 18% of homes during the winter and the summer, respectively. Participants used personal care products and performed cleaning activities on at least 1 day during the monitoring period in almost all homes. 3.3. PM1 Mass and Elemental Composition. Indoor and outdoor PM1 mass concentrations for both seasons are shown in SI Table S2. During the winter, median concentrations of PM1 were 2.2 μg/m3 (IQR = 0.8−6.1 μg/m3) and 3.3 μg/m3 (IQR = 1.5−6.9 μg/m3) for indoors and outdoors, respectively. During the summer, concentrations were found to be similar for both indoors (median 4.4 μg/m3, IQR = 2.4−8.6 μg/m3) and outdoors (median 4.3 μg/m3, IQR = 2.6−7.4 μg/m3). Further details on PM1 comparison with other studies and seasonal patterns (Figures S2, S3) are discussed in the SI. Concentrations of measured elements in PM1 mass are shown in SI Table S2. Average mass concentrations of total measured elements in PM1 were 0.39 μg/m3 and 0.50 μg/m3 for indoors and outdoors, respectively. In this study, major crustal elements (i.e., Al, Ca, Fe, Si, Mg, and Mn) were found both indoors and outdoors contributing 3.4% and 1.4% to PM1 indoor mass concentrations and 3.4% and 1.6% to PM1 outdoor mass concentrations during winter and summer, respectively. Median indoor/outdoor ratios of crustal elements were 0.97 in summer and 0.61 in winter, indicating higher transport and penetration of these elements from outdoors to indoors in summer than in winter, which can occur from open windows (SI Table S1) and dust tracked in on footwear during the summer. Concentrations of K, a typical marker element for biomass smoke, were abundant (range 0.3−736 ng/m3) during summer especially during forest fire episodes in Alberta and nearby provinces, while some peak concentrations were also observed during winter indoors at home. The most dominant element found in both indoor and outdoor PM1 samples was S, which can be found in the atmosphere in the particulate form as secondary sulfate and can remain airborne for hundreds of kilometres.34 Altshuller35 reported that secondary sulfate can occur predominately in the accumulation mode with a diameter between 0.1 and 1.0 μm. Outdoor S concentrations (range 45− 868 ng/m3) were higher than indoor concentrations (range 13−707 ng/m3) in both winter and summer in this study. Marine-like components (Na, Cl) were observed indoors and outdoors in both seasons. However, presence of these elements in PM1 are unlikely to represent a marine aerosol source, the closest of which is more than a thousand kilometres west of Edmonton. Instead, use of deicing road salt containing Na and Cl, commonly used in Edmonton during winter, represents a more-likely source. Concentrations of other trace elements that normally occur at very low levels in the environment were found in less abundance in PM1. Concentrations of Cr, Cu, B, Zn, Pb were higher outdoors and Cr, B, Cu, As, V, Pb, Sn, Mo, Cd, and Bi were higher indoors in summer than in winter. The association between indoor and outdoor elements in PM1 is discussed in the SI (Figure S4, Table S3). Median outdoor concentrations of potential carcinogens, e.g., As (winter: 0.14 ng/m3, summer: 0.17 ng/m3) and Ni (winter: 0.65 ng/m3, summer: 0.36 ng/m3) were far below the Alberta Ambient Air Quality Objectives and Guidelines (10 ng/m3 for As and 50 ng/m3 for Ni).36 3.4. Indoor and Outdoor Sources of Airborne Elemental Mass in PM1. In this study, 12-factor and 9-factor solutions were chosen to represent indoor and outdoor sources respectively using EPA PMF modeling. Model performance

gaseous NO2 and SO2, and some VOCs (e.g., benzene, toluene, and acetaldehyde) were used in linear regression analysis to investigate their relationship with identified sources and assist in the interpretation of source profiles (i.e., chemical composition of the emissions). The sum of measured airborne elemental mass concentrations in PM1 (EM-PM1) was included as an input variable (set as a total variable) in the PMF model to directly obtain the source contributions instead of using the conventional multiple linear regression analysis.31 In this study, outdoor data (n = 275) were used in the model to characterize outdoor sources. Then pooled indoor and outdoor data (n = 529) were analyzed to identify common sources for both environments and to define indoor-generated sources. It was assumed that infiltration of outdoor emissions could affect indoor air quality. On the basis of the results of the pooled indoor outdoor model, indoor-only PMF modeling was conducted to apportion indoor sources. Plausibility and interpretability of solutions with four to 15 factors were checked. The optimal number of factors was chosen after analyzing several model performance criteria, e.g., goodness-offit Q-values for the entire run, scaled residual matrices, scatter plots between elements, agreement between predicted and measured mass, and physical meaningfulness of the source profiles and contributions.32 To understand the probable contributions from local point sources, conditional probability function (CPF) plots33 were generated using source contribution estimates from PMF coupled with available wind direction values measured on Edmonton south and east sites. In addition, backward trajectory analysis including potential source contribution function (PSCF) was also performed. Further details on CPF and backward trajectory analysis are provided in the SI.

3. RESULTS AND DISCUSSION 3.1. Data Quality. Data from 50 homes in the summer and 26 homes in the winter were analyzed, for a total of 74 homes. Results from samples collected at 24 homes during the first four 7-day sampling periods in winter were deemed invalid and were excluded. No blank corrections were applied to the PM1 concentrations, as more than 50% of the blank Teflon filters were below the detection limit of 4 μg/filter. From duplicate sampling, the median precision of PM1 mass during summer and winter was 15.4% (inter quartile range, IQR = 4.4−38%, n = 60) and 14.7% (IQR = 6.8−49%, n = 33), respectively. Median absolute difference of PM1 mass during summer and winter was 0.7 μg/m3 and 0.5 μg/m3, respectively. Strong associations between ambient PM1 concentrations were observed in duplicate measurements at the NAPS site during summer (r2 = 0.99) and winter (r2 = 0.89). 3.2. Home Characteristics. Home characteristics and daily activities that were predicted a priori to influence individual exposures to indoor PM1 are shown in SI Table S1. Most of the Edmonton homes in the study were single-family, detached homes with limited use of air conditioning (AC) during summer (26% of homes had AC). About 40% of the homes were located within 90 m or 1 block of a new construction site and/or any dust sources. Eight of 26 homes in the winter campaign and 17 of 50 homes in the summer campaign had attached garages with connecting doors. Natural gas was used as the main heating system fuel in all homes. Almost all homes had carpets in the room where sampling was conducted. Participants used an electric stove for cooking in 80% of homes. Barbeque use was reported during summer months at about 6421


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Figure 1. PMF-resolved indoor and outdoor source profiles (concentration and % of species apportioned to factor) and average contributions (in the parentheses) of individual sources to measured total PM1 elements (EM-PM1) for indoors and outdoors, respectively.

profiles (concentration and percentage of species apportioned to each factor) of each of the identified source factors from the base run (bootstrap results in SI Figures S6 and S7) and average percent mass contribution of each source factor to total measured elemental mass concentrations in PM1 for both environments. Figure 2 displays the time series plots of PMFderived source contributions (in μg/m3) for indoors and outdoors. PM1 concentrations in Edmonton homes were inferred to originate from both indoor and outdoor sources. From identified PMF-resolved factors, 9 factors were found to

criteria are shown in the SI (Tables S4 and S5). To evaluate the quality of the PMF model results, a comparison of modelpredicted contributions of total elemental mass in PM1 from all identified indoor and outdoor sources with measured elemental mass in PM1 is shown in SI Figure S5. The fit between modeled and measured total elemental concentrations for both environments showed good agreement based on the square of the Pearson correlation coefficient of R, with modeled indoor and outdoor resolved source factors explaining 82% (p = 0.03) and 87% (p = 0.04) of the variance in measured elemental mass concentrations in PM1, respectively. Figure 1 shows source 6422


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Figure 2. Time series plots of PMF-derived source contributions (in μg/m3) for indoors and outdoors. Daily source contribution values are averages of 5−6 homes.

between indoor and outdoor contributions of this factor (SI Table S6, Figure S10). Along with gas- and oil-fired sources, use of natural gas for heating in Edmonton homes under stable weather and inversion conditions could explain peak contributions during winter months. A CPF plot of this factor showed high contributions at homes in Edmonton south and east sites, when winds originated from east and southeast directions (SI Figure S11a). HYSPLIT trajectory analysis also indicated similar wind directions (i.e., southeast) leading to the observed spike on April 12, 2010 (Figure 2, SI Figure S12). Industrial facilities to the south and east of Edmonton were likely to be associated with this factor.39 A PSCF plot of this factor showed high PSCF-valued grid cells to the northeast and

contribute to both indoor and outdoor sources. Factor 1 was characterized by a large mass fraction of S, which explained more than 60% of the variation indoors and outdoors. This factor was assigned to secondary sulfate and it was the largest source in Edmonton, accounting for 44% (0.16 μg/m3) and 43% (0.21 μg/m3) of the total measured elemental mass in PM1 for indoors and outdoors, respectively. This is interpreted to be related to Alberta’s background regional sulfate that is found in high abundance due to oil and gas extraction and production activities (SI Figures S8 and S9), other industrial processes like coal- and gas-fired industrial boilers and power plants and other nonspecific industrial sources.37,38 A substantial correlation was found (r = 0.53, p < 0.0001) 6423


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observed both indoors and outdoors on August 18−21, when winds originated from west and south−southwest directions reflecting the major forest fire intrusion from British Columbia as shown in CPF and PSCF plots (SI Figures S3, S11−13). Some indoor peak contributions of this factor were also found during late winter (mid-April) in two homes where daily questionnaires indicated that visitors smoked cigarettes several times a day inside and outside of the homes. The predominant elements found in factor 4 were Mn (71%), Cr (54%), Fe (41%), and Mo (28%) with some fractions of Ni, Bi, As, Sb, Ba, Co, and Cu (7% to 36% of their total mass). These elements are associated with traffic-related emissions.24,40,48−51 Fe is released from engine oil or catalyst equipped gasoline vehicles,52 while Mn, Ba, Zn, Sb are linked to nonexhaust emissions such as brake and tire wear.46,53−55 Therefore, factor 4 was interpreted as vehicle (exhaust and nonexhaust) emissions and the contribution of this factor was 2.9% (0.011 μg/m3) and 4.3% (0.021 μg/m3) to total measured elemental mass in PM1 for indoors and outdoors, respectively. Outdoor concentrations of traffic markers such as OC, NO2, benzene, and toluene were correlated with this traffic factor. The CPF plot for this factor indicated the south−southwest and south−southeast as dominant directions, suggesting the influence of local highways (SI Figure S11b). This is similar to what others have found in their source apportionment studies for VOCs and PM2.5 in Edmonton.39,40,56 The influence of outdoor vehicular traffic emissions on indoor PM1 concentrations has been also demonstrated elsewhere.57 Indoor concentrations of NO2 and EC were also correlated with this factor, which may initially suggest the influence of electric and gas cooking. However, daily mass contributions of this factor were not consistent with cooking behavior obtained from daily questionnaires. Factor 5 was characterized by a high abundance of Al explaining up to 79% of the variation. Some fractions of Cr (46%), Ni (36%), Na (23%) and small fractions of Si, Mg, Co, and Sn (7% to 8%) were also observed. Contributions of this factor were 3.9% (0.015 μg/m3) and 3.7% (0.019 μg/m3) to total measured elemental mass in PM1 for indoors and outdoors, respectively. Outdoor contributions were 2-fold higher during summer than winter. At both environments, the AlCrNi rich factor was most likely to be associated with resuspension of settled and mixed dust from traffic and industrial activities.58−60 However, no correlations were found between this factor and any of the combustion pollutants. A weak yet significant correlation (r = 0.23, p = 0.05) was observed indoors with the soil factor. CPF plots showed dominant wind directions of west, northwest and east where the higher concentrations were observed at Edmonton homes. From trajectory analysis one observed spike on June 25, 2010 was found to be associated with winds coming from the west and possibly may have been influenced by the presence of a cement factory in the northwest and major power generating units in the west of Edmonton (SI Figure S1). Therefore, factor 5 was determined to be settled and mixed dust. Factor 6 was described by high levels of Pb and As explaining up to 77%, and 39% of their variation, respectively along with some contributions of Cd, Sb, Sn, and Cu (23% to 33% of their explained variation). These are typical marker elements for coal emissions.61−64 Significant correlations of this factor were found between both environments (r = 0.54, p < 0.0001). Outdoor concentrations of NO2 and benzene were also correlated with this factor. From CPF and PSCF analysis,

southeast (SI Figure S13). This factor showed positive correlations with indoor and outdoor relative humidity and negative correlation with outdoor temperature suggesting that dominant heterogeneous oxidation of SO2 from local sources and regional transport of oil and gas activities may be apparent during summer and winter months. These results agree well with previous studies,24,40 where secondary sulfate was the dominant contributor to ambient fine particulate matter (PM2.5) in Edmonton. Factor 2 was distinguished by high levels of typical soil components i.e., Ca, Mg, and Si representing up to 67% and 81% of the explained variation indoors and outdoors. Some mass fractions of Fe, Tl, Co, K, Ba, and Bi (20% to 50% of their total mass) were also present in this factor. This factor was interpreted as soil and it contributed 15.8% (0.06 μg/m3) and 18.7% (0.09 μg/m3) to the total measured elemental mass in PM1 for indoors and outdoors, respectively. In this factor, observed Al/Si and K/Si ratios for indoors (0.35, 0.28) and outdoors (0.13, 0.17) resembled the corresponding ratios of 0.26 and 0.09 identified by Wedepohl.41 The contributions of this factor were higher in winter than in summer (SI Table S7). Soil elements are assumed to be emitted from windblown dust sources (e.g., soil and road dust resuspension) in summer. Normally, a low inferred contribution of windblown dust would be expected in winter due to a greater predominance of light winds, and presence of snow cover and/or wet/frozen ground conditions. However, the observed abundance of outdoor soil contribution during March/April was hypothesized to be attributed to spring dust episodes during the sampling days. Edmonton area snowfall over the period when the most winter snowfall occurs (November to the end of February) was less than 30% of the long-term (1981−2010) average of 78 cm during November 2009 to the end of February 2010.42 Minimal or lack of ground snow cover existed in the Edmonton area along with an above-average monthly temperature in March 2010 (1 °C actual versus −2.5 °C long-term average). This factor showed negative correlations with temperature and relative humidity for both environments (SI Tables S8 and S9). Outdoor concentrations of NO2 were positively correlated with this factor, which was consistent with an observed PMFresolved soil dust factor for other Canadian cities like Toronto and Montreal.40 Factor 3 was identified as biomass smoke and environmental tobacco smoke (ETS) and was represented by the high abundance of tracer elements such as K, and Cd (representing from 37% to 62% of their total mass) with some contributions from Cl and S (up to 24% of their total mass). These elements are typically emitted from smoke-related sources for example, forest fire smoke and ETS.43−46 This factor contributed 17.8% (0.07 μg/m3) and 17.1% (0.09 μg/m3) to total measured elemental mass in PM1 for indoors and outdoors, respectively. In this factor, significant correlation (r = 0.76, p < 0.0001) was found between indoor and outdoor environments. Indoor and outdoor contributions of this factor showed significant associations with OC, EC, benzene, toluene, and acetaldehyde as well as positive correlation with temperature (SI Tables S8 and S9). A high summer trend of this factor was clearly observed suggesting the influence of forest fire smoke and frequent use of barbeque (62% of homes). Several forest fire episodes occurred in Alberta and nearby provinces such as British Columbia, Saskatchewan, and Manitoba during the 2010 summer months (e.g., episodes during June 24, July 13, July 28−29, August 5−6, 18−21).47 Highest concentrations were 6424


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Significant correlation (r = 0.48, p < 0.0001) was found between indoor and outdoor contributions and concentrations of OC, EC, NO2, SO2, benzene, and toluene. Therefore, factor 9 was interpreted as an urban mixture and the contribution of this factor was 2.0% (0.007 μg/m3) and 5.0% (0.025 μg/m3) to total measured elemental mass in PM1 indoors and outdoors, respectively. In this study, three additional factors were identified (factors 10 to 12) that contributed to indoor sources. Factor 10 was characterized by a high abundance of Sb explaining 78% of the variation. Common airborne Sb sources are crustal material, brake dust,54 coal combustion,71,72 carpets and clothing treated with Sb-containing flame retardant materials (SbO3 and Sb2O5).73−75 The ratio of Cu/Sb has been used to discriminate different airborne sources such as upper continental crust (64),41 traffic including brake dust (2.3−10.1),76,77 coal fly ash (∼20),78 and carpet samples (0.18).75 In this study, the ratio of Cu/Sb in indoor PM1 samples was 0.04, suggesting an influence of resuspension of dust from flame retardant-treated carpet. A similar ratio (0.08) was found in the PM1 fraction inside an elementary school in Flagstaff, Arizona, indicating a carpet source.75 In Edmonton, almost all homes had carpets, and the proportion of homes that had the total area in the living room and bedrooms covered in carpets greater than 300 ft2 was 38% and 48% in winter and summer homes, respectively. Therefore, factor 10 was interpreted as flame retardant-treated carpet dust, and the contribution of this factor was 4.3% (0.016 μg/m3) to total measured elemental mass in PM1. Factor 11 was distinguished by Cu accounting for 88% of its total mass and contribution of this factor was 1.7% (0.006 μg/m3) to total measured elemental mass in PM1. Possible indoor sources of Cu are electric devices, including vacuum cleaners, blenders and hair dryers that use copper commutators for motor rotation and personal care products, such as cosmetics.16,79,80 Daily questionnaires indicated that people used personal care products and performed cleaning activities (dusting, vacuuming) at least 1 day during monitoring in almost all Edmonton homes. An indoor Cu-factor was also found in other source apportionment studies for indoor PM2.5 concentrations.16,81 The dominant element found in factor 12 was Ag, explaining 84% of the variation. Small mass fractions of Si, Ni, Ca, and Cl (5% to 10% of their total mass) were also observed. Possible indoor sources of Ag in nanoparticular form are consumer products such as textiles, socks, cosmetics, toys and childrens’ goods, antibacterial electronics/cleaning products, and food storage containers.82−84 A high winter trend of this factor and negative correlation with temperature were found. Therefore, factor 12 was represented by Ag-factor, and it contributed 1.6% (0.006 μg/m3) to total measured elemental mass in PM1. 3.4.1. Relation between Sources. Good correlations were found between indoor and outdoor source-specific PM1 concentrations for secondary sulfate, biomass smoke and EST, traffic, coal combustion, and the oil and gas industry (SI Table S6). Therefore, outdoor concentrations of these sources could be considered as surrogates for indoor concentrations.85 One of the important features of the PMF model is the nonorthogonality of resolved factors to each other.86 In this study, notable correlations were also observed among some identified sources at both environments (SI Tables S10 and S11). For example, the urban mixture source factor outdoors showed substantial correlations with secondary sulfate, biomass smoke and EST, traffic, road dust, and coal combustion. Indoors, a correlation was also found between coal

higher concentrations (e.g., on March 20, 2010) were observed when winds originated from northeast, northwest, and southwest directions. The most dominant direction was northeast suggesting the influence from Alberta’s industrial heartland located north/northeast of Edmonton. There is no known presence of permitted coal-burning activities/operations outside of Edmonton to the north/northeast. However, this area has numerous petrochemical plants, upgraders and refineries, and may be plausible that some nonapproved burning of coal material was occurring in this area during our sampling program (January through August 2010).65 Since some elements (e.g., Pb, Cd) are reported to be transported over long distances by atmospheric flow and deposited far from their emission sources,61,66 the presence of one coal-fired cement factory in the northwest, three major coal-fired power generating units 60 km west of Edmonton (in Wabamun Lake) and one coal-fired power plant in 200 km southeast (in Battle River) are inferred to be associated with this factor (SI Figures S8, S11−13). Therefore, factor 6 was interpreted as coal combustion and it accounted for 2.7% (0.010 μg/m3) and 3.6% (0.018 μg/m3) of total measured elemental mass in PM1 for indoors and outdoors, respectively. The most abundant element found in factor 7 was V (76% of variation explained) with some amounts of Mo, Cr, and Tl (explaining 12% to 33% of the variation). V is typically emitted from oil and petrochemical refining and natural gas extraction and processing.67−69 This factor was assigned to the oil and gas industry and the contributions of this factor were 2.1% (0.008 μg/m3) and 2.4% (0.012 μg/m3) to total measured elemental mass in PM1 for indoors and outdoors, respectively. As mentioned previously, this is more likely due to the influence of oil and gas production activities in and around Edmonton as well as within the province of Alberta.37 There was a significant association (r = 0.92, p < 0.0001) between indoor and outdoor contributions and notable correlations of this factor with SO2 was observed. In addition, the CPF plot indicated the northeast and southeast as the most dominant directions. This was further confirmed from backward trajectory analysis where highest concentrations on June 9, and June 28, 2010 were contributed from the southeast and northeast directions (SI Figures S12, S13). Alberta’s industrial heartland including two major oil refineries (Imperial Oil and Suncor Energy with crude oil processing capacities of 187 200 and 142 000 barrels per day, respectively70), located in the north/northeast of Edmonton (SI Figure S1) are likely associated with this factor. Factor 8 was highly enriched with Na accounting for up to 77% of its total mass. A winter-high trend was observed, suggesting the contribution of deicing road salt in this factor. Source profile indicated some contributions of Ba, Sb, As, Cd, and Ni accounting for 21% to 73% of their total mass. As mentioned previously, these elements are released from trafficrelated exhaust and nonexhaust emissions including brake/tire wear and road abrasion.48,53,54 This factor was well correlated with outdoor concentrations of NO2 and benzene and negatively correlated with ambient temperature. Therefore, factor 8 was assigned to road salt/road dust and it contributed 1.2% (0.004 μg/m3) and 2.6% (0.013 μg/m3) to total measured elemental mass in PM1 indoors and outdoors, respectively. Factor 9 was rich in Zn and Cl accounting for up to 79% and 68% of total mass, respectively. Some amounts of K, Cd, Tl, Pb, As, Bi (10% to 37% of their total mass) were also found in this factor. These elements are typically found in mixed urban sources such as traffic and industrial emissions. 16,48,53 6425


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combustion, and urban mixture.16,20,21 In this study, the identified source profiles and composition found outdoors were in agreement with ambient PM2.5 source apportionment studies conducted in Edmonton and other Alberta cities.24,40 A number of limitations exist in our study. In characterizing indoor and outdoor levels of PM1 and their sources, sampling of homes were clustered in space and time, resulting in an inability to assess the spatial variability of PM1 concentrations in the greater Edmonton area. EC/OC and secondary pollutants (sulfate, nitrate, and ammonium) were not measured in PM1 mass. Furthermore, other ions (e.g., Na+, Cl−, and Ca2+) were also not analyzed. However, this study does provide key insights about indoor and outdoor levels of PM1 elements in residences; and it also adds to a very limited base of information in the scientific literature on sources of PM1 elements in indoor and outdoor environments. We acknowledge that all source factors identified by the PMF model in this study are preliminary, given the limited number of species and samples measured. More work is needed to further resolve source contributions to both indoor and outdoor air in Edmonton in order to inform policy makers about important sources of population exposure to fine particulate matter.

combustion and secondary sulfate. The results comply with the context of real-world emissions, where sources are not orthogonal and can be temporally correlated to each other due to meteorological influences.87 The variability in identified sources impacting outdoor homes across different neighborhoods in Edmonton was also evaluated using a nonparametric one-way Wilcoxon score (SI Table S12). Intraurban variations (p < 0.01) in different sources including traffic, road salt/road dust, and coal combustion were found in most of the neighborhoods. However, no statistically significant differences (p > 0.05) were observed in contributions from secondary sulfate and oil and gas industry between different neighborhoods, suggesting that regional and long-range transport of pollutants from these sources are evenly distributed over the study area. 3.4.2. Influence of Particle Infiltration. The infiltration factor (Finf, the fraction of outdoor particles that penetrates indoors and remains suspended)88 of each PMF-derived source was estimated using paired factor contributions from the pooled indoor outdoor PMF model and compared with average indoor to outdoor (I/O) source contribution ratios (obtained from separate indoor and outdoor PMF model) and estimates from a tracer-based approach (SI Table S13). Finf estimates based on the slopes of the linear regression of indoor factor concentrations on outdoor concentrations are shown in SI Figure S14 derived from the pooled indoor outdoor PMF model. The I/O ratio of S has also been used as an estimate of Finf for fine particles.89−91 In this study, Finf estimates were calculated using tracer elements (S for secondary formation, Zn for primary combustion including traffic, coal combustion, urban mixture, V for oil and gas industry and Si for mechanical suspension including soil, settled and mixed dust, and road salt/ road dust18,92) and based on the regression slopes of measured indoor concentrations on outdoor concentrations. In general, comparable results of Finf estimates were observed across the methods. Higher infiltration was found during summer than winter and larger infiltration was observed for secondary PM1 compared to primary combustion and mechanical resuspension factors. During summer, I/O ratios of biomass smoke and ETS and settled and mixed dust were >1.0, suggesting that additional indoor sources may be associated with these factors. Infiltration factors may depend largely on air exchange rate, particle size distribution, housing characteristics, meteorology and occupant behavior.85,93,94 SI Figure S15 shows sulfur-based Finf estimates by Edmonton homes. The median daily summertime Finf (0.78, n = 50 homes) was higher than in winter (0.45, n = 26 homes). In this study, the Finf values for different sources were comparatively lower than estimates conducted on PM2.5 source types in California and New Jersey homes,18 which were likely in part due to the lower air exchange rates observed during winter (median = 0.22 h−1) and summer (median = 0.31 h−1) months in Edmonton homes compared to other cities.28,95 3.4.3. Comparison with Other Source Apportionment Studies. The indoor and outdoor sources of elements in PM1 obtained in this study were consistent with other available source apportionment studies for PM2.5. For residential indoor environments, soil and resuspension, electrical appliances and cooking, cleaning, personal care products, personal activities, and ETS have been found as indoor sources.16,19−21,81,96 Common outdoor sources of PM2.5 that infiltrated to indoors have been identified in other studies and these include secondary sulfate, soil, traffic, sea-salt, vegetative burning, oil

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ASSOCIATED CONTENT

* Supporting Information S

Additional information as noted in the text. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01173.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1 780 248 1984; fax: +1 780 492 0364; e-mail: [email protected]. Present Address

∥ WHO European Centre for Environment and Health, Platz der Vereinten Nationen 1, 53113 Bonn, Germany

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors are grateful to Health Canada staffs (Keith Van Ryswyk, Ryan Kulka, Hongyu You, Mélissa St-Jean, and Tae Maen Shin), field technicians and study and laboratory personnel at the University of Alberta; and RTI Laboratories and Alberta Innovates−Technology Transfers. This study was funded by Health Canada under contract no. 4500220223.

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REFERENCES

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