Impact of Wood Combustion for Secondary Heating and Recreational

Mar 3, 2015 - Little information is available on the concentrations of ambient fine particles (PM2.5) in residential areas where wood combustion is co...
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Impact of Wood Combustion for Secondary Heating and Recreational Purposes on Particulate Air Pollution in a Suburb in Finland Tarja Yli-Tuomi,*,†,∥ Taina Siponen,†,∥ R. Pauliina Taimisto,†,∥ Minna Aurela,‡ Kimmo Teinila,̈ ‡ Risto Hillamo,‡ Juha Pekkanen,†,§,∥ Raimo O. Salonen,†,∥ and Timo Lanki†,∥ †

Department of Environmental Health, National Institute for Health and Welfare (THL), FI-70701, Kuopio, Finland Finnish Meteorological Institute, Air Quality Research, FI-00101, Helsinki, Finland § Institute of Public Health and Clinical Nutrition, University of Eastern Finland, FI-70211, Kuopio, Finland ‡

S Supporting Information *

ABSTRACT: Little information is available on the concentrations of ambient fine particles (PM2.5) in residential areas where wood combustion is common for recreational purposes and secondary heating. Further, the validity of central site measurements of PM2.5 as a measure of exposure is unclear. Therefore, outdoor PM2.5 samples were repeatedly collected at a central site and home outdoor locations from a panel of 29 residents in a suburb in Kuopio, Finland. Source apportionment results from the central site were used to estimate the contributions from local sources, including wood combustion, to PM2.5 and absorption coefficient (ABS) at home outdoor locations. Correlations between the central and home outdoor concentrations of PM2.5, ABS, and their local components were analyzed for each home. At the central site, the average PM2.5 was 6.0 μg m−3 during the heating season, and the contribution from wood combustion (16%) was higher than the contribution from exhaust emissions (12%). Central site measurements predicted poorly daily variation in PM2.5 from local sources. In conclusion, wood combustion significantly affects air quality also in areas where it is not the primary heating source. In epidemiological panel studies, central site measurements may not sufficiently capture daily variation in exposure to PM2.5 from local wood combustion.

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INTRODUCTION

about 25% of the total primary PM2.5 emissions in Finland in 2000.3 Exposure to ambient air pollution is one of the main causes of disability and death worldwide.4 Wood smoke contains a large number of chemicals, including significant quantities of known health-damaging pollutants. Respiratory health effects of wood smoke have been acknowledged for a long time,5 and the 2005 global update of the WHO air quality guidelines concluded that there was little evidence that the toxicity of particles from biomass combustion would differ from the toxicity of more widely studied urban PM.6 More recently WHO concluded, based mainly on new source apportionment studies, that exposure to particles from residential wood combustion may be associated also with cardiovascular health.7−10 As residential wood combustion occurs in areas where people spend a majority of their time, it is important to study and

Residential wood combustion for heat production is becoming more common in suburban areas as the prices of fossil fuels and electricity increase. Wood combustion is generally perceived as natural and environmentally friendly, and fireplaces are also becoming more popular because they create a cozy feeling. In Finland, wood-heated sauna stoves are often preferred to electrical stoves in single family houses. However, wood combustion also has its downsides due to the relatively high emission of particles compared to other sources, especially because small combustion appliances are used without a flue gas filtering system. The magnitude of emissions from wood combustion depends considerably on the combustion device, fuel quality, and operating conditions.1 In Finland, the use of masonry heaters and sauna stoves is widespread. Typically, these appliances are operated for a short time and at a high combustion rate. The insufficient supply of air, due to pyrolysis occurring too quickly, and increased ash release caused by the high combustion temperature lead to a high particle and gas emissions from these appliances.2 Residential wood combustion produced © 2015 American Chemical Society

Received: Revised: Accepted: Published: 4089

November 3, 2014 January 29, 2015 March 3, 2015 March 3, 2015 DOI: 10.1021/es5053683 Environ. Sci. Technol. 2015, 49, 4089−4096

Article

Environmental Science & Technology

could not be observed from the time series of pollutant concentrations. At the same time, a panel of 29 residents was followed for home outdoor PM2.5 concentration; each resident had four to six repeated measurements at about one month intervals (12− 91 days). Locations of the central site and study homes are shown in Figure S1. The maximum distance between homes and the central site was 1.6 km. The field campaign was carried out during the heating season between November 18, 2009 and May 19, 2010. No filter samples were collected from December 20, 2009 to January 2, 2010 because of holidays. The total number of sampling days was 170. Measurements. Two EPA-WINS impactors (EPA Well Impactor Ninety-Six, BGI Inc., Waltham, MA) with a GrasebyAndersen PM10 inlet preceding the WINS PM2.5 impactor were used to collect daily PM2.5 samples on Teflon (Zefluor 2 μm, 47 mm, Pall Life Sciences) and quartz fiber filters (2500QAT-UP 47 mm, Pall Life Sciences). Teflon filters were used to analyze PM2.5 gravimetric mass and concentrations of inorganic ions, while levoglucosan, EC, and OC were analyzed from the quartz fiber filters. Filters were changed daily at 9 a.m., and the impactor was cleaned and greased with Dow Corning high vacuum grease (Dow Corning Corporation, Midland, MI) weakly. A flow rate of 16.7 l min−1 was monitored with TSI 4043 flow meters. Eberline FH62-IR (Eberline Instruments, Santa Fe, NM) equipped with a Graseby-Andersen PM10 inlet preceding the WINS PM 2.5 impactor was used for continuous PM 2.5 measurements at a flow rate of 16.7 l min−1. Data were collected with an ENVS-101 data acquisition system, and daily averages (from 9 a.m. to 9 a.m.) were calculated from 1 min values. A total of 75% of min data was demanded for a valid value. The portable aethalometer AE42-2 (Magee Scientific Company, Berkeley, CA) was used with a SCC-1.828 PM2.5 inlet (5 l min−1 flow rate) for continuous measurements (in 5 min periods) to determine the light absorption of urban PM2.5 particles at two wavelengths: 880 and 370 nm. Optical attenuation was converted to a mass of BC using σ880 nm = 16.6 and σ370 nm = 39.5.18 Data were corrected for the loading effect according to the procedure presented by Virkkula et al.,19 and daily averages (from 9 a.m. to 9 a.m.) were calculated from 5 min values. A total of 75% of data was demanded for a valid value. For comparison with home outdoor measurements, samples were also collected with a personal environment monitor (PEM) containing in series a PM2.5 cyclone (GK 2.05 KTL, BGI, Waltham, MA), a data logging photometer (pDR-1200 X, MIE, Bedford, MA), a filter holder (M000037A0, Millipore, Bedford, MA), and an air pump (AFC400S, BGI). Samples were collected on 37 mm, 2 μm pore sized Pall Life Sciences Zefluor membrane filters with Nuclepore Drain Disc (Whatman International Ltd.) as support. The volume flow rate was 4.0 L min−1, and the sampling period concurred with the EPAWINS impactors. The system has been described in more detail by Lanki et al.20 At home outdoor locations, the photometer was added to the PEM system at the end of January. A Davis Vantage Pro2 Plus weather station was installed at the central measurement site. The wind monitor was located on the roof of a nearby apartment building (height 10 m) in order to monitor regional wind direction. In addition, NO2, PM2.5, and PM10 results from a municipal urban background measurement site in Kasarminpuisto (4.5 km

assess the population exposure to wood smoke. Especially during periods of low vertical mixing due to stagnant weather conditions, the effects on local air quality can be substantial.11−14 Wood smoke pollution levels within a residential area can vary considerably because even a small number of badly operated heaters can have a large influence on local air quality. 15,16 Therefore, the wood smoke concentration measured at the central site may not reliably predict the exposure in home outdoor environments. This knowledge is important for epidemiological studies. Longitudinal panel studies are commonly used to evaluate acute physiological effects of air pollution. Typically, exposure assessment is based on outdoor measurements of air pollution at a central site. In this study setting, valid exposure assessment requires, at minimum, that central outdoor concentrations reflect well daily variation in air pollution outside the home. Molnar et al. reported weak cross-sectional associations between home indoor and central outdoor concentrations of the wood combustion-related elements Cl, Mn, Rb and Pb in Sweden.17 However, no previous studies on exposure to wood smoke with repeated measurements at a central site and panel home outdoor locations have been published. Also, to our knowledge, there is no information available on the effect of secondary heating and recreational wood combustion on exposure concentrations although the use of wood for these purposes have been increasing. Secondary heating systems are those that provide space heating which is in addition to that provided by the main heating, such as masonry heaters used in conjunction with the district heating or electric heating to reduce the costs. In the study area, the main types of recreational burning are sauna stoves and indoor fireplaces. The first aim of the present study was to determine the sources and their contribution to PM2.5 mass and black smoke concentrations at a central site of a residential area where wood combustion is used for recreational purposes and secondary heating during the heating season. The second aim was to evaluate the validity of the central site as a proxy for wood smoke concentration at home outdoor locations.

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MATERIALS AND METHODS Sampling Sites and Measurement Period. Outdoor PM2.5 samples were collected in Kuopio, Finland, in a suburb called Jynkkä (62° 51′ N, 27° 39′ E). About 3400 inhabitants live in this low density suburb located 6−8 km south of the city center. The housing stock mainly consists of terraced houses and detached houses, and only one-fifth of the residents live in apartment buildings. Although nearly 100% of the houses are connected to a district heating network, residential wood combustion is common as a secondary heating source, and saunas are often heated with wood-fired stoves. In addition, many houses have a fireplace for recreational activities. The monitoring site was located between a court of a day care center and an area of apartment buildings in order to measure representative air quality and to avoid high impact from a single chimney. The sample intakes were about 5 m above ground level. The nearest residential house with a stove was located about 70 m south of the measurement site. The distance to the nearest road with a traffic intensity of 5000 or more vehicles per 24 h was over 1 km. There was a small oilfired (low-sulfur heavy fuel oil) district heating center 300 m south−southwest from the measurement site. It was operated 32, 0, 118, 79, 0, 50, and 24 h monthly from November 2008 to May 2009, respectively, but the effect of the heating center 4090

DOI: 10.1021/es5053683 Environ. Sci. Technol. 2015, 49, 4089−4096

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Environmental Science & Technology from the central site) were utilized because no marker species for road dust and traffic exhaust emissions were measured at the central site. The decision to include variables measured at another site was based on the assumption that although the absolute concentrations of traffic-related PM and road dust vary spatially, daily variation is highly correlated. Indeed, in Kuopio, two municipal stations (urban background and traffic stations) had correlation coefficients (r) of 0.85 and 0.88 for NO2 and PM10, respectively, during the measurement campaign. The City of Kuopio measures NO2 with a Monitor Laboratories 9841 B and particulate matter with a TEOM 1400A. Daily averages were calculated from 1 h values for the days with data from the central site. Chemical Analysis. Concentrations of chlorine (Cl−), nitrate (NO3−), sulfate (SO42−), oxalate (C2O42−), sodium (Na+), ammonium (NH4+), and potassium (K+) ions were analyzed simultaneously using two Dionex ICS-2000 ion chromatography systems, one for anions and one for cations. Details of the analysis are described in the Supporting Information. The detection limits of the analyzed ions are around 1 ng mL−1. NO3− and NH4+ were excluded from the analysis because the filters had high and variable background concentration of these ions. For SO42−, Na+, and K+ median blank levels were 4, 8, and 7% of the median sample concentration, respectively. Organic carbon (OC) and elemental carbon (EC) were analyzed from a 1 cm2 punch-out of the quartz fiber filters using a thermal optical carbon analyzer (TOA; Sunset Laboratory Inc., Tigard, OR). The instrument uses a two-phase thermal method to separate OC and EC.21 Details of the analysis are described in the Supporting Information. For OC, a backup value was subtracted from the value of the front filter in order to minimize the error caused by the absorption of organic gases to the quartz fiber filters. The concentration of levoglucosan was analyzed using a Dionex ICS-3000 system coupled to a quadropole mass spectrometer (Dionex MSQ).22 Before the analysis, the filters were stored at +5 °C for 0−7 days, then sent to Helsinki in a cooler with ice bricks and stored at −20 °C for up to 5 months. Details of the analysis are described in the Supporting Information. The detection limit (DL) of the levoglucosan is around 2 ng mL−1. Particle reflectance was measured from the PEM filters using a smoke stain reflectometer (M43D, Diffusion Systems Ltd., London, UK) and transformed into an ABS according to ISO 9835. This method is based on the old black smoke protocol with the exception of the particle cut size and filter material. Although the black smoke method involves a transformation from reflectance units into mass concentrations, these calculated mass concentrations are considered unreliable. ABS is therefore expressed in m−110−5. Wood Smoke Tracer Species. In source apportionment studies, levoglucosan is a commonly used organic particle phase biomass burning emission tracer. It is a sugar anhydride produced during the combustion of cellulose. According to Jordan et al.,23 levoglucosan is stable during atmospheric transport and present at expected levels. However, recent detailed kinetic studies on the reactivity of levoglucosan with OH, NO3, and SO4− radicals in aqueous solutions indicate that it may not be as stable in the atmosphere as previously thought.24 Levoglucosan emissions from woodstoves have been considered to be relatively constant.25

In addition to the chemical markers, an optical method using the principle of aerosol light absorption has been used to provide an indicator for wood combustion particles in previous studies. Certain organic aerosol components present in wood smoke have enhanced optical absorption at 370 nm relative to 880 nm (Delta-C = UVPM370 nm − BC880 nm). In traffic-related aerosol or diesel soot, a significantly lower increase in light absorption is observed at these wavelengths.26,27 However, in mobile monitoring in Northern New York state, the ratio of Delta-C to PM2.5wood varied by a factor of 6 or more on a 3 min time scale, indicating that the ratio is a function of many factors such as type of wood combusted, combustion conditions, and the age of the smoke.28 In the present study, the aethalometer Delta-C signal was used as a wood smoke marker together with levoglucosan. Source Apportionment. Sources of fine particles were determined using the U.S. Environmental Protection Agency’s model EPA PMF 3.0.2.2.29 Details of the model are described in the Supporting Information. All sources were constrained to have non-negative species concentration, and no sample was allowed to have negative source contribution. The model was operated in a robust mode so that, for any data point for which the residual exceeded 4 times the error estimate, the value was processed as an extreme value, and its weight was decreased. In the input data, missing values were approximated as the geometric mean of the corresponding species concentration, and values below the species DL were replaced by DL/2. For each data point, uncertainty (σ) was determined as 5/6 DL if the concentration ≤ DL and ((percentage uncertainty × concentration)2 + DL2)1/2, if the concentration > DL. The percentage uncertainty consists of the measurement and lab errors. Extra modeling uncertainty of 5% was applied to all species to encompass errors associated with the modeling assumptions, like variation of source profiles and chemical transformations in the atmosphere.29 Back Trajectories. The three-dimensional HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model30 was used to reconstruct the air parcel movements before arrival above the measurement site. Five-day backward trajectories were calculated at 12, 18, 00, and 06 UTC for sampling periods representing the five highest concentrations for each source factor. The starting height of the trajectories was 250 m. The computations were performed on the NOAA Web site using an archived meteorological data set (GDAS). Conditional Probability Function. The conditional probability function (CPF)31 was used to analyze wood smoke impacts from various wind directions. The CPF estimates the probability that a given source contribution from a given wind direction will exceed a predetermined threshold criterion.32 The CPF was defined as mΔθ/nΔθ, where nΔθ is the total number of 5 min wind direction observations from direction sector Δθ and mΔθ is the number of wind direction observations from the same sector during the days when the source contribution exceeded the threshold. In this study, Δθ was set at 22.5° and the threshold was set at the 90th percentile of the daily averages measured at the central site. Central Site Wood Smoke As a Proxy of Home Outdoor Concentration. In addition to gravimetric PM2.5 weight, only ABS was analyzed from the home outdoor and central site PEM samples. At the central site, correlation coefficients of 0.63 and 0.70 were observed between PM 2.5Eberline and PM2.5PEM and between BC and ABS, respectively. For the comparison, PM2.5PEM at the home 4091

DOI: 10.1021/es5053683 Environ. Sci. Technol. 2015, 49, 4089−4096

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Table 1. Description of the Chemical Components of Central Site PM2.5 and Marker Species Used in the Source Apportionment (PM2.5, OC, PMCkuo, and NO2kuo in μg m−3; others in ng m−3)a PM2.5 OC Levogluc SO42− Na+ K+ PMCkuo NO2kuo Delta-C BC

MEAN

GMEAN

GSTD

MIN

P75

P95

MAX

DL

BDL %

missing %

uncert. %

6.0 1.2 44 1939 85 108 6.3 15 110 704

5.1 1.0 33 1401 64 91 1.5 13 81 589

1.8 1.9 2.1 2.4 2.2 1.8 16 1.6 2.3 1.8

0.42 0.08 7.6 14 6.9 17 0.00 2.7 4.5 148

7.7 1.5 57 2628 115 146 8.3 18 135 977

13 2.8 126 5104 222 227 25 26 294 1508

16 5 164 9135 292 272 45 43 617 1931

1 0.1 10 2 2 2 1 2 5 200

0 0 14 0 0 0 27 0 8 0

0 2 2 4 4 4 2 1 9 4

10 30 15 10 10 10 20 10 30 20

a

GMEAN = geometric mean; GSTD = geometric standard deviation; P75 and P95 = 75th and 95th percentiles; DL = detection limit; BDL % = the percentage of values below the DL.

The correlation coefficient (r) was 0.71 between EC and BC, 0.60 between EC and ABS, and 0.70 between BC and ABS. In order to avoid double counting, EC and ABS were excluded from the data during the basic analysis. OC, levoglucosan, and Delta-C are all related to emissions of organic compounds but do not measure exactly the same fraction of the emissions from different sources. Thus, they were all included in the data. Afterward, the selected model was rerun with EC and without OC, but the effects on the interpretation of the solution were negligible, and thus these results are not presented. The addition of ABS into the model increased the number of solutions (local minima) found and the rotational ambiguity based on the G-space plots. In particular, distribution of ABS and Delta-C between the factors varied between the solutions. Thus, ABS was excluded from the data. Source Apportionment. Models with four to six factors were examined. The most feasible solution was found with five factors. The compositions and contributions of the factors are presented in Figures 1 and 2, respectively. In addition, contributions are shown as a stacked column chart in Figure S3 in the Supporting Information. Determination of the interquartile ranges around the base run profile was based on 1000 bootstrap runs. A minimum correlation R value of 0.6, seed set of 20, and block size of 9 were used in the bootstrapping. The first factor was characterized by high SO42− concentration and was thus interpreted to represent long/regionalrange transported sulfate-rich aerosol (LRT). This factor explained 26% and 19% of the variation of BC and OC concentrations, respectively. Relatively high contents of OC in secondary sulfate factors have been attributed to the condensation of semivolatile compounds on the high specific surface area of ammonium sulfate.37 This factor also explained 22% of the variation of levoglucosan, indicating long-/regionalrange transport of wood smoke. The five highest concentrations occurred when the air masses arrived from known industrial areas where fossil fuels are used (Figure S4 in the Supporting Information). The second factor was related to traffic emissions, because it was characterized with NO2kuo, and BC and OC were also attributed to this factor. As expected, PM2.5 concentrations associated with traffic emissions were higher during weekdays than weekends. However, about 4% of levoglucosan was explained by this factor, indicating less than ideal source separation.

outdoor was transformed to PM2.5Eberline using an equation obtained from the central site data (Eberline = 0.5747 × PEM + 2.992). Daily PM2.5 and BC concentrations from nonlocal sources at the central site were calculated based on the source apportionment. The amount of nonlocal ABS was estimated assuming that the ratio of nonlocal to total is the same for ABS and BC. The contribution from nonlocal sources was approximated to be evenly distributed over the study area, and thus the contribution from local sources (including wood combustion) was calculated for the home outdoor locations as the difference between total and nonlocal PM2.5 and ABS. Spearman’s rank correlation coefficients (rho) between the central site and home outdoor concentrations of PM2.5, ABS, and local components of these pollutants were analyzed for each home separately.

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RESULTS AND DISCUSSION

Meteorological Conditions. A summary of the meteorological conditions during the measurement period is presented in Table S1. Generally, winter 2008−2009 and spring 2009 were milder than average in Finland.33,34 Mild winters probably decreased the use of wood for home heating. Composition Data. Table 1 lists the characteristics of the chemical components of PM2.5 and marker species used in the source apportionment. The concentration of coarse particles (PMCkuo) was calculated as the difference between PM10 and PM2.5 measured at the Kuopio municipal site and was used as a marker for road dust. PM2.5 was marked as the total variable, and thus, its uncertainty was tripled in the analysis. Gaseous acids, which are present in polluted air, can cause displacement of chlorine as the volatile HCl. Thus, the temporal variation of Cl may deviate from the variation of other components from the same source. Indeed, if Cl− was included in the model as a strong variable, it formed a onecomponent factor explaining only the variation of Cl−. Because of this, Cl− had a low relevance as an indicator of any source. Furthermore, 27% of the Cl− concentrations were below the detection limit, and thus Cl− was excluded from the data. Oxalate has been suggested as a wood smoke marker. Oxalate can originate from primary emissions of biomass burning35 and/or be formed as a secondary product by the oxidation of gaseous organic compounds.36 In Kuopio, the ratio of oxalate to levoglucosan increased in spring with increasing solar radiation (Figure S2), and thus, oxalate could not be used as a tracer of biomass burning and was excluded from the analysis. 4092

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Figure 1. Factor profiles in Kuopio, Jynkkä. Columns present the concentrations of species (PM2.5, OC, PMCkuo and NO2kuo in μg m−3; others in ng m−3), while the median and interquartile ranges around the base run profile based on 1000 bootstrap runs are shown with markers. LRT = long/regional-range transported aerosol. Figure 2. Source-specific contributions to daily PM2.5 in Kuopio. LRT = long/regional-range transported aerosol.

The third factor described the suspended soil and road dust with a high concentration of PMC. The temporal variation showed typical spring episodes of road dust in Finland (Figure 2). The fourth factor was related to long-range transported seaspray aerosol particles, because it had a high abundance of Na+ and K+ and had its highest contributions when the backward trajectories had passed over sea areas (Figure S5 in the Supporting Information). The K+/Na+ ratio (0.05) was close to the composition of seawater (0.04).38 The factor associated with wood combustion was characterized by high concentrations of levoglucosan, Delta-C, K+, BC, and OC. About 81% of Delta-C, 68% of the levoglucosan, 41% of OC, 35% of BC, and 21% of K+ were attributed to this factor. The relative levoglucosan to PM2.5 concentration was 3%. Correlation between outdoor temperature and PM2.5wood was low (r = −0.15), because wood smoke was also emitted from sauna stoves in addition to secondary heating. Relatively high PM2.5wood concentrations were observed often on Wednesdays and Saturdays, which are the most popular days for heating saunas in Finland (Figure S6). BCwood contributed,

on average, 45% and 32% of the modeled BC concentration on Saturdays and other days, respectively. High PM 2.5wood concentrations were frequently observed when the wind was between 146° and 191° (Figure S7 in the Supporting Information). The nearest residential houses with wood combustion were located in that direction. There were no indications that high concentrations would be caused by temperature inversion because concentrations of PM2.5traffic were not elevated at the same time as PM2.5wood (Figure S3). Although the contribution from local sources was remarkable, this factor also included wood smoke transported from more distant sources. The average ambient concentration of PM2.5 was 6 μg m−3 and BC 0.7 μg m−3. LRT, traffic, road dust, sea spray, and wood-combustion-related particles comprised 58, 12, 9, 2 and 16% of the measured PM2.5 and 26, 29, 6, 0 and 35% of the measured BC, respectively. Concentrations of PM2.5wood varied from 0 to 4.3 μg m−3 (Table 2) and BCwood from 0 to 1.1 μg m−3 during the heating season. 4093

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Table 2. Description of the Source Specific PM2.5 Concentrations in μg m−3 and Percentage of the Total Measured PM2.5 LRTa μg m MIN P10c P25c MEDIAN P75c P90c maxb mean

−3

0.0 0.8 1.4 2.5 4.8 7.9 13.3 3.5

traffic

road dust

sea spray

wood

%

μg m−3

%

μg m−3

%

μg m−3

%

μg m−3

%

0 25 37 54 72 84 121 58

0.0 0.2 0.4 0.7 1.0 1.3 2.8 0.7

0 4 7 12 19 30 62 12

0.0 0.0 0.1 0.2 0.8 1.7 3.8 0.6

0 0 1 5 18 35 85 9

0.0 0.0 0.1 0.1 0.2 0.3 0.5 0.1

0 0 1 2 5 9 34 2

0.0 0.1 0.4 0.7 1.3 2.2 4.3 1.0

0 2 8 15 24 34 67 16

a Long/regional-range transported aerosol. bDecember 1, 2008 was included in the analysis, but excluded from the percentage calculation, because measured PM2.5 was below 1 μg m−3 and less than the sum of the chemical components. cP10, P25, P75, and P95 = 10th, 25th, 75th and 95th percentiles

Correlations between pairs of marker species used in the source apportionment and the resolved source contributions are presented in Table S2 in the Supporting Information. DeltaC correlated strongly with the wood smoke source (r = 0.74). Although Delta-C also had moderate correlation with NO2kuo, correlation with the traffic exhaust source was weak. Weak correlations between Delta-C and motor vehicle particles have been published previously from North American studies.39−41 Levoglucosan, the other wood smoke marker used in this study, had an even higher correlation with wood smoke source (r = 0.83), but also showed moderate correlation with LRT, while correlation between Delta-C and LRT was weak. This may indicate a difference in atmospheric stability between these two wood smoke markers. Central Site Wood Smoke As a Proxy of Home Outdoor Concentration. The average PM2.5 and ABS concentrations at 29 home outdoor locations varied from 4.5 to 8.2 μg m−3 and from 0.15 to 0.70 m−1 × 10−5, respectively (Figure 3a). The ratio of the average home outdoor concentration to central site concentration was calculated for PM2.5 and ABS for each home. The home outdoor/central site ratios were higher for ABS than PM2.5 concentrations. In about 75% of the homes, ABS was higher at the home location than at the central site (Figure 3b). Spearman’s rank correlation coefficients (ρ) between the central site and home outdoor daily average concentrations of PM2.5, ABS, and local components of these pollutants were analyzed for each home separately in order to estimate how well the central site data predicts the longitudinal variation at home locations. Since the traffic intensities on the residential streets were low, most of the variation between the central site and home outdoor locations was likely caused by local wood combustion. The distributions of ρ are presented in Figure 4a for PM2.5 and b for ABS. As expected, the best correlations were observed between central site and home outdoor total PM2.5 and ABS including the long-/regional-range transported aerosol. Central site PM2.5local gave a remarkably better prediction than total PM2.5 and PM2.5wood for the variation of home outdoor PM2.5local. The best prediction for home outdoor ABSlocal was given by PM2.5wood, but central site ABSlocal gave only slightly lower ρ values. However, even with the best predictor variables, 66% and 52% of homes had a ρ lower than 0.5 between the central site and home outdoor PM2.5local and ABSlocal, respectively. In epidemiologic studies, exposure misclassification causes the relative risk of disease associated with the exposure to be biased toward the null value.42 Therefore, in health studies, exposure to wood smoke needs to

Figure 3. (a) Distribution of the mean PM2.5 and ABS at home outdoor locations and (b) distribution of the HomeOut/Central site ratios for PM2.5 and ABS. The box shows the 25th, 50th, and 75th percentiles; whiskers present the minimum and maximum values, while the square is the marker for the mean value.

be estimated preferably by analyzing marker species concentrations in home outdoor or indoor air. At least, home outdoor PM2.5 concentration and source apportionment results from a central site are needed to estimate exposure to the local component of the air pollution. High differences of ρ values were observed between homes located near each other (Figures S9 and S10). Thus, the correlation did not depend on the distance between the central site and home, or on the home location relative to the central site. One explanation for this is that badly operated heaters and stoves in or nearby the study home can lead to high local concentrations if the smoke plume reaches the measurement instruments before it has been diluted. Since only two homes were measured simultaneously, differences in wind direction, 4094

DOI: 10.1021/es5053683 Environ. Sci. Technol. 2015, 49, 4089−4096

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the measurements. The authors gratefully acknowledge the NOAA Air Resources Laboratory for the provision of the HYSPLIT transport and dispersion model and use of the READY website (http://www.ready.noaa.gov) in this publication.

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Figure 4. Distributions of individual Spearman’s correlation coefficients (ρ) between the central site and home outdoor components of (a) PM2.5 and (b) ABS analyzed for each home (n = 29) separately. The box shows the 25th, 50th, and 75th percentiles; whiskers present the minimum and maximum values, while the square is the marker for mean value.

temperature, and other meteorological variables, as well as day of the week, can also partly explain the variation in ρ.

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ASSOCIATED CONTENT

S Supporting Information *

Details of the chemical analysis, details of the PMF modeling, information on meteorological conditions, map of the measurement site, further information used for factor identification, comparison between predicted and measured concentrations, and a map of the Spearman’s correlation coefficients between the central site and home outdoor location for the local components of PM2.5 and ABS. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*Tel: +358 29 524 6302. Fax: +358 29 524 6498. E-mail: tarja. yli-tuomi@thl.fi. Present Address ∥

Current affiliation information: Department of Health Protection, National Institute for Health and Welfare (THL), FI-70701, Kuopio, Finland Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Academy of Finland (contract no. 10155) and intramural funding from THL. Timo Mäkelä from the Finnish Meteorological Institute and Erkki Pärjälä from the City of Kuopio are acknowledged for their help with 4095

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Article

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