Environ. Sci. Technol. 2008, 42, 3316–3323
Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter JISCA SANDRADEWI,† A N D R E S . H . P R É V Ô T , * ,† S Ö N K E S Z I D A T , †,‡ N O L W E N N P E R R O N , † M. RAMI ALFARRA,† VALENTIN A. LANZ,§ ERNEST WEINGARTNER,† AND URS BALTENSPERGER† Laboratory of Atmospheric Chemistry, Paul Scherrer Institut, 5232 Villigen, Switzerland, Department of Chemistry and Biochemistry, University of Bern 3012, Bern, Switzerland, and Empa, Swiss Federal Laboratories for Materials Testing and Research, 8600 Duebendorf, Switzerland
Received September 7, 2007. Revised manuscript received January 4, 2008. Accepted January 8, 2008.
A source apportionment study was performed for particulate matter in the small village of Roveredo, Switzerland, where more than 70% of the households use wood burning for heating purposes. A two-lane trans-Alpine highway passes through the village and contributes to the total aerosol burden in the area. The village is located in a steep Alpine valley characterized by strong and persistent temperature inversions during winter, especially from December to February. During two winter and one early spring campaigns, a seven-wavelength aethalometer, high volume (HIVOL) samplers, an Aerodyne quadrupole aerosol mass spectrometer (AMS), an optical particle counter (OPC), and a Sunset Laboratory OCEC analyzer were deployed to study the contribution of wood burning and traffic aerosols to particulate matter. A linear regression model of the carbonaceous particulate mass in the submicrometer size range CM(PM1) as a function of aerosol light absorption properties measured by the aethalometer is introduced to estimate the particulate mass from wood burning and traffic (PMwb, PMtraffic). This model was calibrated with analyses from the 14C method using HIVOL filter measurements. These results indicate that light absorption exponents of 1.1 for traffic and 1.8–1.9 for wood burning calculated from the light absorption at 470 and 950 nanometers should be used to obtain agreement of the two methods regarding the relative wood burning and traffic emission contributions to CM(PM1) and also to black carbon. The resulting PMwb and PMtraffic values explain 86% of the variance of the CM(PM1) and contribute, on average, 88 and 12% to CM(PM1), respectively. The black carbon is estimated to be 51% due to wood burning and 49%
* Corresponding author phone: +41 56 3104202, fax: + 41 56 3104525: e-mail:
[email protected]. † Paul Scherrer Institut. ‡ University of Bern. § Swiss Federal Laboratories for Materials Testing and Research. 3316
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 42, NO. 9, 2008
due to traffic emissions. The average organic carbon/total carbon (OC/TC) values were estimated to be 0.52 for traffic and 0.88 for wood burning particulate emissions.
1. Introduction Atmospheric aerosols have direct and indirect effects on the earth’s radiative balance. The scattering and/or absorption of shortwave and longwave radiation describes the direct effect. The indirect effect of aerosols is determined by their role as cloud condensation nuclei, which changes the microphysical properties and the lifetime of clouds (1). On a regional scale, the effects of atmospheric aerosols on air quality and visibility are of concern (2, 3). Aerosols have been related to adverse health effects in various studies, for example, long-term exposure to combustion-related fine particulates has been reported as an important environmental risk factor for cardiopulmonary and lung cancer mortality (4, 5). Combustionrelated aerosols may come from fossil fuel combustion engines, biomass burning, and industrial incinerators. Wood smoke is known to contain abundant organic materials such as polycyclic aromatic hydrocarbons (PAH), aromatics and humic-like substances (HULIS) (6–9), which strongly enhance the absorption of light in the ultraviolet wavelength range compared to that in the near-infrared range, where black carbon (BC) dominates the absorption. Traffic emission particles from diesel engines contain a higher fraction of BC than organic material (7, 10). Light absorption measurements at ultraviolet, visible, and infrared wavelengths by an aethalometer were used to provide a qualitative measure of the relative presence of wood smoke and traffic emissions in ambient air (11–16). Unlike other optical methods where the aerosol is measured in its airborne state, the aethalometer collects aerosols on a quartz fiber filter. Thus, the absorption measurements must be carefully corrected for artifacts caused by the filter (17, 18). Once this is performed, the BC concentration is determined by division of the aerosol light absorption coefficient (units of m-1) by the aerosol light absorption cross-section σabs (units of m2 g-1). The value of σabs varies with the wavelength and the mixing state of the particles. For example: Kirchstetter and Novakov (19) reported a value of ∼8.5 m2 g-1 (at λ ) 530 nm) for BC generated by a diffusion flame, which is comparable to the values of 7.5 ( 1.2 m2 g-1 (at λ ) 550 nm) for airborne uncoated soot particles reported by Bond and Bergstrom (20). For aged BC measured at the high Alpine research station Jungfraujoch (3580 m asl, Switzerland), Cozic et al. (21) reported σabs values of 7.6 m2 g-1 in winter and 11 m2 g-1 in summer (at λ ) 630 nm). The calculation of the UV-absorbing material concentrations, however, is nontrivial due to the highly variable absorption cross-sections of these UVabsorbing compounds and their dynamic change in composition in ambient air. Thus, the UV-absorption coefficients can be determined but cannot be quantitatively interpreted as an exact amount of a specific compound (22) unless the mixture of UV-absorbing species remains constant enough and an average absorption cross-section can be assumed. An attempt to use the aethalometer data for quantitative wood smoke determination was introduced by Allen et al. (23). A receptor model was applied on field data consisting of a few mass components [volatile and nonvolatile PM2.5 (particulate matter with an aerodynamic diameter lower than 2.5 µm)], carbon indicators (aethalometer BC and ∆C, which is the enhanced optical absorption at 370 relative to 880 nm by the organic aerosol components of wood smoke PM) and collocated gaseous species (SO2, NO, and CO). Our method 10.1021/es702253m CCC: $40.75
2008 American Chemical Society
Published on Web 04/02/2008
1 h time resolution b
results are published in ref 24. 14C
Details on sampling time and a
same as above November 24 to December 15, 2005
AMS results from these two campaigns are published in ref 8.
10 min time resolution. 5 min time resolution. collection efficiency ) 0.7b
2 min time resolution. collection efficiency ) 0.5b morning and evening filters, 16 h sampling time per filter. PM1 size-cut. four filters analyzeda same as above March 1-16, 2005
n/a morning and evening filters, 16 h sampling time per filter. PM10 size-cut. four filters analyzeda 2 min time resolution. wavelengths: 370, 470, 520, 590, 660, 880, 950 nm January 13-24, 2005
HIVOL filters for 14C analysis aethalometer campaign
TABLE 1. Summary of Campaign Dates, Instrumentation, and Data Availability
2.1. Measurement Site. Measurements were performed in the village of Roveredo, Switzerland with around 2200 inhabitants. The village is located in the Mesolcina valley south of the main Alpine crest (46°14′18′′N, 9°07′45′′E, 298 m asl). A two-lane highway connecting the San Bernardino Pass and the Gotthard highway passes through the village. A 3-m high concrete wall separates the highway from the measurement container and the residential area. In the winter period between December and February, the village lies most of the time in the shadow of the surrounding steep hills and mountains, providing favorable condition for persistent and strong temperature inversions. Around 77% of the houses in this village use wood burning for heating in winter, with similar numbers in neighboring villages. These are mostly old wood stoves and open fire places, where approximately 70% beech and 25% other hardwoods, for example, wood from chestnut, ash, locust, and birch trees are burnt (24). More details on the measurements during summer and winter at this site can be found in Sandradewi et al. (16). Data from two winter and one early spring campaigns are reported here. The dates of these campaigns along with the deployed instruments are summarized in Table 1. 2.2. Instrumentation. In addition to the continuous meteorology and air quality measurements performed in this village by the local environmental agency (16), the following instruments were operated (see also Table 1): an aethalometer (Magee Scientific, USA, type AE31) was used to measure the BC mass concentration and the aerosol light absorption coefficients (babs) at 370, 470, 520, 590, 660, 880, and 950 nm. Corrections of the multiple scattering of the light beam within the filter fibers when the filter is relatively unloaded with aerosols and the “shadowing” effect of the particles that occurs as the filter gets more highly loaded were performed using the procedure described by Weingartner et al. (17). In the January campaign, the aethalometer sampled through an inlet with no size cut, whereas in the March and November-December campaigns the aethalometer was installed parallel to the AMS with a PM1and PM2.5-sized cut, respectively. However, a parallel measurement with two identical aethalometers, one with a PM1 and the other with an open inlet, performed in a separate campaign (not shown) suggested no significant differences in the absorption coefficients and the absorption exponents values due to these different sized cuts. High volume samplers (HIVOL, DIGITEL Elektronik AG, Switzerland, type DHA-80) with a flow rate of 0.5 m3/min and either PM10 or PM1 inlets were used (see Table 1) for aerosol collection on quartz fiber filters. Selected filters were treated and analyzed for their fractions of modern carbon (fM), which is the 14C/12C isotopic ratio of the sample compared to the 14C/12C isotopic ratio of the reference year 1950 (24, 25). A quadrupole aerosol mass spectrometer (AMS, Aerodyne Research Inc., Massachusetts, USA) was used to provide quantitative, online measurements of the chemical composition and mass size distribution of the nonrefractory
AMS
2. Experimental
24 h sampling time per filter. PM1 size-cut. four filters analyzed
n/a n/a
n/a n/a
OPC
OCEC analyzer
differs from the method by Allen et al. (23) in that we take into account the contribution of wood burning not only at low wavelengths but also its contribution to BC. During winter in the Alpine valley under investigation, PM1 (particulate matter with an aerodynamic diameter lower than 1 µm) is basically composed of only two sources, wood smoke and traffic emission particles (8, 24). We use this unique situation to develop a model to quantitatively estimate the contribution of both sources using the aethalometer multiwavelength light absorption measurements. The results of this model are presented here and are compared to 14C measurements of the fossil and nonfossil EC (elemental carbon) and OC (organic carbon), where the nonfossil fractions were attributed to wood burning (24).
VOL. 42, NO. 9, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3317
fraction of submicrometer aerosols (26). Details of the AMS measurements in Roveredo can be found in Alfarra et al. (8). An optical particle counter (OPC, GRIMM Aerosol Technik, Germany, type 1.108) was used to measure the particle number size distribution in the diameter range D ) 0.3–20 µm by means of light scattering. The OPC particle sizing is based on a calibration with latex spheres, performed by the manufacturer. To calculate the particulate mass (PM1, PM2.5, and PM10), a diameter-independent particle density was chosen such that the computed OPC PM10 values correlated well (slope ) 0.9, r2 ) 0.8) with the PM10 data of the β-attenuation monitor (Thermo ESM Andersen FH62 I-R). Particles smaller than 0.3 µm are assumed in this calculation to not significantly contribute to the particulate mass. The OPC was placed in a temperature-controlled container at 25 °C. The particles were thus measured at the relative humidity corresponding to the ambient air heated to this temperature. An OCEC analyzer (Sunset Laboratory, Inc., USA) was used to measure the OC and EC concentrations (27, 28). The temperature program used was comparable to the NIOSH 5040 protocol (27, 29). 2.3. The Aethalometer Model. The wavelength dependence of the aerosol absorption coefficients (babs) is proportional to λ–R, where λ is the wavelength and R is the absorption exponent. Day et al. (15) measured the aerosol light absorptions of fresh wood smoke aerosol between 370 < λ < 950 nm and reported R-values (i.e., the absorption exponent calculated with the BC concentration as a function of wavelength) between 0.9 and 2.2 that strongly depended on the type of wood and burning conditions. High R-values were observed in wood combustion aerosols that are due to material with strong absorption in the UV and lower visible wavelength range. From traffic or diesel soot studies, the R-values ranged between 0.8 and 1.1 (12, 30, 31). These reported R-values were computed without correction of the aerosol light absorption coefficients as recommended for filter-based aerosol optical measurements (17, 18). During winter periods, strong diurnal trends in R had been shown, with high R-values observed in the evening hours due to increased wood burning activity (16). The R-values for wood burning particles for this site in winter were shown to be dependent on the used wavelengths, that is, they were lower when they were computed using the absorption coefficients of all seven wavelengths (R370–950nm) compared to using only the lowest 3 wavelengths (R370,470,520nm). Clearly distinct diurnal cycles were found for the light absorption at the lowest and highest wavelengths. Because of problems with the 370 nm lamp of the aethalometer during the third measurement period (November 24 to December 15, 2005), which were detected after a parallel test in the laboratory with other identical aethalometers, we instead used the next lowest wavelength (470 nm) in the following calculation algorithm. In principle, the same concept should be applicable to any of the three lowest wavelengths. Using the Beer–Lambert’s Law, we obtain the equations relating the absorption coefficients (babs), the wavelengths, and the absorption exponents for conditions of pure traffic and pure woodburning (wb). babs(470 nm)traffic 470 ) babs(950 nm)traffic 950
-Rtraffic
( )
babs(470 nm)wb 470 ) babs(950 nm)wb 950
-Rwb
( )
babs(λ) ) babs(λ)traffic + babs(λ)wb
(1) (2) (3)
For given Rtraffic and Rwb values and using the field data of the light absorption measurements at 470 and 950 nm, the values for babs(470nm)traffic, babs(950nm)traffic, babs(470nm)wb, and babs(950nm)wb can be computed with eqs 1-3. In this study, 3318
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 42, NO. 9, 2008
FIGURE 1. A stacked plot showing the contributions of the aerosol light absorption from traffic and wood burning to the total aerosol light absorption. The light absorption coefficients babs(470nm)wb and babs(950nm)traffic used in eq 4 are indicated in the graph. The sum of babs(λ)traffic and babs(λ)wb corresponds to the mean value of babs(λ) of the November-December campaign. the absorption exponent for pure traffic condition Rtraffic is set at 1.1, which is comparable to literature values (12, 30, 31) and those obtained in a Roveredo summer campaign (16). However, the absorption exponent for pure woodburning conditions Rwb has a higher and wider range than that of traffic (depending on the wood type and combustion process). By comparison with the 14C results and linear unmixing by positive matrix factorization of the babs(470nm) and babs(950nm), we determined an average Rwb(470nm, 950nm) value using all data in Roveredo. In the following, Rtraffic and Rwb always refer to the exponents computed with the 470nm and 950-nm wavelengths. In the following, the contribution of wood burning and traffic emission aerosols to the carbonaceous material within PM1 is approximated by a linear regression of the light absorption of the two sources, assuming a constant light absorption cross-section (eq 4). The calculated parameters c1 and c2 relate the light absorption to the particulate mass of both sources. A third constant (c3), which accounts for the background concentration, is not required (c3 ) 0), as will be shown later in Section 3.2. Figure 1 illustrates the light absorption coefficients used in eq 4.
(4) The carbonaceous material (CM) is calculated by eq 5, CM(PM1) ) OM + BC
(5)
where OM is the total organic mass measured by the AMS, and BC is the black carbon concentration measured by the aethalometer. Here we neglect possible additional atoms such as hydrogen and oxygen in the BC fraction, assuming that the necessary correction is within the accuracy of the aerosol light attenuation cross-section used, that is, 16.6 m2 g-1 (at 880 nm). An alternative method for calculating CM(PM1) is provided in eq 6, where we assume that PM10 consists only of coarse mode particles (PM10-PM1), CM(PM1) and inorganic PM1 (ammonium nitrate and ammonium sulfate). CM(PM1) ) PM10β-gauge (coarse mode)OPC - inorganicsAMS ≡ ″estimated carbonaceous PM1″
(6)
Besides assuming other PM sources being negligible, the two alternatives to calculate CM(PM1) involve various uncertain-
FIGURE 2. Time series of the mass contributions of the OPC coarse mode, the AMS inorganics and organics, and the aethalometer black carbon (at 880 nm) for the November-December campaign (stacked plot). The sum of these four variables is in good agreement with the continuous PM10 concentration measured by the β-attenuation monitor (black line). ties of the individual measurements, including the mass absorption cross-section and the shadowing effects of the aethalometer, the collection efficiency of the aerosol mass spectrometer for the organic mass and the inorganics (32), the possible aerosol size dependent density and refractive index used in the OPC calculation, and the losses of volatile material in the slightly heated β-attenuation monitor (operating temperature ) 30 °C). 2.4. The 14C Analyses. The application of the 14C method to atmospheric aerosols enables the determination of the contributions of fossil and nonfossil carbon concentrations (33). The radiocarbon measurement results are expressed as fractions of modern carbon (fM), which is the 14C/12C isotopic ratio of the sample related to that of the reference year 1950. A time-dependent correction factor to compensate for bombderived 14C is applied (34). Fossil material is characterized by fM ) 0 due to its age and the extinction of 14C. Contemporary sources show slightly elevated fM levels compared to the theoretical value of 1 as a consequence of the nuclear bomb excess; fM ) 1.24 ( 0.05 was set for carbonaceous aerosols from residential wood burning using 30–50 year-old plants and fM ) 1.072 ( 0.015 for present biogenic OC (24, 35). Because of the exchange of the 14Cenriched CO2 with the marine and terrestrial environments, these values show a slight annual decrease so that for future studies recalculation of these reference fM values will be necessary (36). The HIVOL filter sample containing the collected aerosol was separated into its OC and EC components, and the 14C contents of these fractions were then determined separately using accelerator mass spectrometry (25). Following Szidat et al. (24), the total carbon mass (TCM) for the Roveredo samples are calculated using the following values, OM TCMnonfossil ) OC
EM × OCnonfossil + × ECnonfossil nonfossil EC (7)
( )
TCMfossil )
( OM OC )
( )
fossil
× OCfossil +
× EC ( EM EC )
fossil
(8)
where OM (organic mass) and EM (elemental carbon mass) are the particle masses containing OC and EC, respectively. The ratio of (OM/OC)nonfossil is higher than (OM/OC)fossil because the organic aerosol from wood smoke (i.e., the main nonfossil contributor in Roveredo) contains a higher mass fraction of heteroatoms than the organic aerosol from traffic (37). The values used here are the same as the ones used by Szidat et al. (24), that is, (OM/OC)nonfossil ) 2.25, (OM/OC)fossil
FIGURE 3. Scatterplot of OM + BC (eq 5) versus estimated carbonaceous PM1 (eq 6) showing a slope of 0.99 with r2 ) 0.79.
TABLE 2. The values of c1 and c2 obtained with regression on the aethalometer model with rtraffic(470nm,950nm) = 1.1 and various rwb(470nm,950 nm) (November-December period) rwb(470nm,950nm) a,b
c1 c2a
1.6
1.7
1.8
1.9
2.0
2.1
258831 486861 546005 601132 652516 700411 745054
a The units c1 and c2 are µg/m2. b The aethalometer model results in a constant c1 value for a fixed Rtraffic.
) 1.4, and (EM/EC) ) 1.1. For further discussion we refer to Section 3.2.
3. Results and Discussions 3.1. Alternative Measurements of the Carbonaceous Matter CM(PM1). For the determination of c1 and c2 in eq 4, we used the November-December period because the most complete data set was available during that period (Table 1). Figure 2 shows the time series of all parameters needed for calculating the carbonaceous mass (CM) in eqs 5 and 6. The sum of the BC, AMS inorganics and organics, and the OPC coarse mode (i.e., PMtotal - PM1) follows the same strong diurnal trend shown by the PM10 data from the β-attenuation monitor. Not included in Figure 2 are possible other particulate components such as KCl and K2SO4 from wood combustion (38), because these cannot be quantitatively measured with the AMS. However, ion-chromatography analysis of five HIVOL filters from the November-December campaign indicated that the contribution of the sum of KCl and K2SO4 to PM10 is less than 2.5%. VOL. 42, NO. 9, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3319
FIGURE 4. The sensitivity analysis of the aethalometer model by varying rwb(470nm,950nm) is described with the scatterplots of PMwb/ PMtraffic derived from the aethalometer model vs TCMnonfossil/TCMfossil from the 14C analysis. The four filters from the November-December period (a) and the eight filters from the January and March periods (b) are shown. The solid line corresponds to the 1:1 correlation.
FIGURE 5. (a) Scatterplots of babs(950nm)traffic/babs(950nm)total derived from the aethalometer model versus ECfossil/ECtotal determined by the 14C method, and (b) PMwb/PMtraffic from the aethalometer model versus TCMnonfossil/TCMfossil from the 14C method for all three campaign periods with rtraffic ) 1.1 and rwb(470nm, 950nm) ) 1.86. The 1:1 line is included for comparison.
FIGURE 6. Time series of the OM + BC as well as PMtraffic + PMwb (computed with eq 4) for the November-December campaign. From the scatterplot of OM + BC versus the estimated carbonaceous PM1 calculated with eqs 5 and 6, we obtained a high correlation with a slope of 0.99 and r2 ) 0.79 (Figure 3). Considering that the estimated carbonaceous PM1 has more uncertainty due to its three variables (β-attenuation monitor, OPC particle density estimation, and AMS collection efficiency), we chose to use OM + BC for the determination of c1 and c2 (eq 4). 3.2. Comparison of the Aethalometer Model with 14C Results. In the aethalometer model (eq 4), the Rtraffic was fixed at 1.1, and Rwb was allowed to vary such that the model could be calibrated with the 14C results. For every chosen Rwb, a linear regression was performed according to eq 4 to deliver the c1 and c2 values, which were further used to determine the PMtraffic and PMwb. Table 2 summarizes these two constants with respect to Rwb for the November-December period. 3320
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 42, NO. 9, 2008
The c1 and c2 values summarized in Table 2 were then applied to the January and March 2005 periods to obtain the concentrations of PMtraffic and PMwb. For comparison with 14C data (TCM fossil, TCMnonfossil), these PMtraffic and PMwb were averaged for the duration of the HIVOL sampling time. The TCM values used in this study (eqs 7 and 8) were computed with (OM/OC)nonfossil ) 2.25, (OM/OC)fossil ) 1.4, and EM/EC ) 1.1 as used in Szidat et al. (24). A sensitivity study using (OM/OC)nonfossil ) 2.0 and (OM/OC)fossil ) 1.2 yielded very similar TCM results. Due to the long sampling time of the HIVOL filters (see Table 1) and possible condensation of the gas phase on the filter material, artifacts might affect the concentrations of TCMnonfossil and TCMfossil determined by the 14C method (see also ref 24). Thus, for the comparison with the aethalometer model, we use the ratio of PMwb/PMtraffic and TCMnonfossil/
FIGURE 7. Boxplots describing the diurnal profiles of (a) CM(PM1) concentrations in terms of the sum of OM and BC measured by the AMS and aethalometer, respectively, (b) PMwb/(PMtraffic + PMwb) derived from the aethalometer model with rtraffic ) 1.1 and rwb ) 1.86, (c) BCwb/BCtotal at 950 nm also from the aethalometer model, and (d) OC/TC from the Sunset Laboratory OCEC analyzer. The crosses represent the group medians. The vertical hinges represent data points between the 25th- and 75th-percentiles. The whiskers represent data points between the 5th- and 95th-percentiles. TCMfossil instead of the absolute concentrations (Figure 4), because the ratios are expected to be less susceptible to these artifacts. We found that Rwb values between 1.8 and 1.9 resulted in the best agreement with the 1:1 line in Figure 4. Choosing an Rwb value lower than 1.8 or higher than 1.9 results in over- or underestimation of the wood burning contribution in the aethalometer model, respectively. The absorption exponent was also estimated by linear unmixing by positive matrix factorization (PMF) (39) of the original babs(470nm) and babs(950nm). In the PMF analysis we adjusted the fpeak value (40) so that one source approximated the known traffic ratio, Rtraffic(470nm,950nm) ) 1.1. The corresponding value for the wood burning source was then calculated to be Rwb(470nm,950nm) ) 1.86. Besides the comparison of our model data to the fossil and nonfossil TCM values, we also compared the calculated traffic emission and wood burning aerosol fractions of the light absorption at 950 nm with the fossil and nonfossil EC fractions. Traffic aerosols contribute more to EC than to TCM. This provides an additional verification of the method (Figure 5). In Figure 5a we note that the model using absorption exponents Rtraffic ) 1.1 and Rwb ) 1.86 yields a relatively good representation of the fossil and nonfossil EC fractions (slope ) 1.1, r2 ) 0.5). Using the same absorption exponents, the corresponding PMwb/PMtraffic versus TCMnonfossil/TCMfossil is plotted for comparison in Figure 5b, yielding a slope of 0.9 and r2 ) 0.8. A comparison of the time series of OM + BC and PMtraffic + PMwb (Figure 6) shows that the two parameters agree well with each other. From the scatterplot of PMtraffic + PMwb versus OM + BC (not shown), we obtain a slope of 0.95 ( 0.01 (( uncertainty) and r2 ) 0.86. 3.3. Diurnal Profiles of the Carbonaceous Materials (November-December Campaign). Figure 7a describes the diurnal profile of the CM(PM1) in terms of OM + BC data (eq 5) for the November-December campaign with lowest and highest concentrations observed at 04:00–08:00 CET and 14:00–24:00 CET, respectively. Its average (( standard deviation) mass concentration was 26.0 ( 19.4 µg/m3. Figure 7b shows the diurnal profile of PMwb/(PMwb + PMtraffic) calculated with the aethalometer model having a similar trend as CM(PM1). On average, the contributions of wood burning and traffic to the CM(PM1) were computed to be 88% (22.9 ( 17.1 µg/m3) and 12% (3.1 ( 2.2 µg/m3), respectively.
In contrast to the dominance of wood burning aerosol on CM(PM1), the BC concentration was on average 51% due to wood burning and 49% due to traffic aerosols (diurnal cycle shown in Figure 7c). Nevertheless, this contribution of wood burning to BC is exceptionally high considering that elemental carbon (assuming that the latter is equivalent to BC) is usually dominated by traffic (24). Figure 7d shows the diurnal profile of OC/TC obtained with the OCEC analyzer that has a trend similar to that of PMwb/(PMwb + PMtraffic), that is, low values in the morning and high values in the evening. Because of the high emissions of organic compounds during the combustion process, aerosols from wood combustion are known to have a higher OC/TC ratio than those from motor vehicles. This is supported by various references related to fire places, biomass burning episodes, test bench and tunnel studies (e.g. refs 10,11, and 41). During the rush hour traffic between 06:00– 10:00 CET, when the highest number of diesel engines such as buses, trucks, delivery vans, and trailers were counted on the highway passing this village (24), the average OC/TC ratio of 0.76 in Roveredo was considerably higher than the range of average diesel particulate emission ratios of 0.22–0.37 (dependent on age and weight) reported by Lough et al. (42). This is in line with the observation that a substantial contribution from wood combustion was found in the morning (Figure 7b) (see also ref 24). Also, in an urban site such as Zürich, Switzerland, a higher contribution of wood burning compared to traffic was observed (43), indicating the need of abatement strategies for wood combustion emissions. The least-squares regression of OC/TC as a function of PMwb/(PMwb + PMtraffic) excluding the outliers (i.e., including only the data range between the 2.5th- and 97.5th-percentiles of PMwb/(PMwb + PMtraffic)) yields a slope of 0.36 ( 0.02 and an intercept of 0.52 ( 0.02 (r2 ) 0.5). The intercept corresponds to the OC/TC value with PMwb/PMtotal ) 0, that is, for zero wood burning contribution. This value is slightly higher than OC/TC ) 0.22–0.37 reported by Lough et al. (42) for various motor vehicles or 0.40 reported by Lonati et al. (44) for a tunnel study in summer. The extrapolation of this linear regression to PMwb/PMtotal ) 1 (i.e., for pure wood burning) yields OC/TC ratios of wood burning particles of 0.88 ( 0.04, which is comparable to the value of 0.87 ( 0.04 derived from the literature values summarized in Table 3 in Szidat et al. (35). VOL. 42, NO. 9, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3321
Acknowledgments We thank the Office of Nature and Environment of Canton Graubünden for their data and field support and the Swiss Federal Office for the Environment (FOEN) for funding the AEROWOOD project.
Literature Cited (1) Intergovernmental Panel on Climate Change. Climate Change 2007: The Physical Science Basis — Summary for Policymakers. http://www.ipcc.ch/SPM2feb07.pdf (accessed 2007). (2) Vingarzan, R.; Li, S.-M. The Pacific 2001 air quality study — Synthesis of findings and policy implications. Atmos. Environ. 2006, 40, 2637–2649. (3) Park, R. J.; Jacob, D. J.; Kumar, N.; Yantosca, R. M. Regional visibility statistics in the United States: Natural and transboundary influences, and implications for the Regional Haze Rule. Atmos. Environ. 2006, 40, 5405–5423. (4) Pope, C. A. 3rd; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Ito, K.; Thurston, G. D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 2002, 287 (9), 1132–1141. (5) Jerret, M.; Burnett, R. T.; Ma, R. J.; Pope, C. A.; Krewski, D.; Newbold, K. B.; Thurston, G.; Shi, Y. L.; Finkelstein, N.; Cale, E. E.; Thun, M. J. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology. 2005, 16, 727–736. (6) Hoffer, A.; Gelencsér, A.; Guyon, P.; Kiss, G.; Schmid, O.; Frank, G.; Artaxo, P.; Andreae, M. O. Optical properties of humic-like substances (HULIS) in biomass-burning aerosols. Atmos. Chem. Phys. 2006, 6, 3563–3570. (7) Kochbach, A.; Li, Y.; Yttri, K. E.; Cassee, F. R.; Schwarze, P. E.; Namork, E. Physicochemical characterisation of combustion particles from vehicle exhaust and residential wood smoke. Particle Fibre Toxicol 2006, 3, 1,; doi: 10.1186/1743-8977-3-1. (8) Alfarra, M. R.; Prevot, A. S. H.; Szidat, S.; Sandradewi, J.; Weimer, S.; Lanz, V. A.; Schreiber, D.; Mohr, M.; Baltensperger, U. Identification of the mass spectral signature of organic aerosols from wood burning emissions. Environ. Sci. Technol. 2007, 41, 5770–5777,; doi: 10.1021/es062289b. (9) Weimer, S.; Alfarra, M. R.; Schreiber, D.; Mohr, M.; Prévôt, A. S. H.; Baltensperger, U. Organic aerosol mass spectral signatures from wood burning emissions: Influence of burning conditions and wood type. J. Geophys. Res. 2008, doi: 10.1029/2007JD009309, in press. (10) Burtscher, H. Physical characterization of particulate emissions from diesel engines: A review. Aerosol Sci. 2005, 26, 896–932. (11) Jeong, C.-H.; Hopke, P. K.; Kim, E.; Lee, D.-W. The comparison between thermal-optical transmittance elemental carbon and aethalometer black carbon measured at multiple monitoring sites. Atmos. Environ. 2004, 38, 5193–5204. (12) Kirchstetter, T. W.; Novakov, T. Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. J. Geophys. Res. 2004, 109, D21208,; doi: 10.1029/ 2004JD004999. (13) Hand, J. L.; Malm, W. C.; Laskin, A.; Day, D.; Lee, T.; Wang, C.; Carrico, C.; Carrillo, J.; Cowin, J. P.; Collett, J.; Iedema, M. J. Optical, physical, and chemical properties of tar balls observed during the Yosemite Aerosol Characterization Study. J. Geophys. Res. 2005, 110, D21210,; doi: 10.1029/2004JD005728. (14) Park, K.; Chow, J. C.; Watson, J. G.; Trimble, D. L.; Doralswamy, P.; Arnott, W. P.; Stroud, K. R.; Bowers, K.; Bode, R.; Petzold, A.; Hansen, A. D. A. Comparison of continuous and filter-based carbon measurements at the Fresno Supersite. J. Air Waste Manage. Assoc. 2006, 56 (4), 474–491. (15) Day, D. E.; Hand, J. L.; Carrico, C. M.; Engling, G.; Malm, W. C. Humidification factors from laboratory studies of fresh smoke from biomass fuels. J. Geophys. Res. 2006, 111, D22202,; doi: 10.1029/2006JD007221. (16) Sandradewi, J.; Prévôt, A. S. H.; Weingartner, E.; Schmidhauser, R.; Gysel, M.; Baltensperger, U. A study of wood burning and traffic aerosols in an Alpine valley using a multi-wavelength aethalometer. Atmos. Environ. 2008, 42, 101–112. (17) Weingartner, E.; Saathoff, H.; Schnaiter, M.; Streit, N.; Bitnar, B.; Baltensperger, U. Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers. J. Aerosol Sci. 2003, 34, 1445–1463. (18) Arnott, W. P.; Hamasha, K.; Moosmüller, H.; Sheridan, P. J.; Ogren, J. A. Towards aerosol light-absorption measurements with a 7-wavelength aethalometer: Evaluation with a photoacoustic instrument and 3-wavelength nephelometer. Aerosol Sci. Technol. 2005, 39, 17–29. 3322
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 42, NO. 9, 2008
(19) Kirchstetter, T. W.; Novakov, T. Controlled generation of black carbon particles from a diffusion flame and applications in evaluating black carbon measurement methods. Atmos. Environ. 2007, 41, 1874–1888. (20) Bond, T. C.; Bergstrom, R. W. Light absorption by carbonaceous particles: An investigative review. Aerosol Sci. Technol. 2006, 40, 27–67. (21) Cozic, J.; Verheggen, B.; Mertes, S.; Connolly, P.; Bower, K.; Petzold, A.; Baltensperger, U.; Weingartner, E. Scavenging of black carbon in mixed phase clouds at the high alpine site Jungfraujoch. Atmos. Chem. Phys. 2007, 7, 1797–1807. (22) Andreae, M. O.; Gelencsér, A. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmos. Chem. Phys. 2006, 6, 3131–3148. (23) Allen, G. A.; Babich, P.; Poirot, R. L. Evaluation of a new approach for real time assessment of wood smoke PM. In Proceedings of the Regional and Global Perspectives on Haze: Causes, Consequences and Controversies—Visibility Specialty Conference, Asheville, North Carolina, 2004; Air and Waste Management Association: 2004. (24) Szidat, S.; Prévôt, A. S. H.; Sandradewi, J.; Alfarra, M. R.; Synal, H.-A.; Wacker, L.; Baltensperger, U. Dominant impact of residential wood burning on particulate matter in Alpine valleys during winter. Geophys. Res. Lett. 2007, 34, L05820,; doi: 10.1029/ 2006GL028325. (25) Szidat, S.; Jenk, T. M.; Gäggeler, H. W.; Synal, H.-A.; Hajdas, I.; Bonani, G.; Saurer, M. THEODORE, a two-step heating system for the EC/OC determination of radiocarbon (14C) in the environment. Nucl. Instrum. Methods Phys. Res., Sect. B 2004, 223–224, 829–836. (26) Canagaratna, M. R.; Jayne, J. T.; Jimenez, J. L.; Allan, J. D.; Alfarra, M. R.; Zhang, Q.; Onasch, T. B.; Drewnick, F.; Coe, H.; Middlebrook, A.; Delia, A.; Williams, L. R.; Trimborn, A. M.; Northway, M. J.; Kolb, C. E.; Davidovits, P.; Worsnop, D. R. Chemical and microphysical characterization of ambient aerosols with the aerosol mass spectrometer. Mass Spectrom. Rev. 2007, 26, 185–222. (27) Birch, M. E.; Cary, R. A. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Sci. Technol. 1996, 25, 221–241. (28) Bae, M.-S.; Schauer, J. J.; DeMinter, J. T.; Turner, J. R.; Smith, D.; Cary, R. A. Validation of a semi-continuous instrument for elemental carbon and organic carbon using a thermal-optical method. Atmos. Environ. 2004, 38, 2885–2893. (29) Chow, J. C.; Watson, J. G.; Crow, D.; Lowenthal, D. H.; Merrifield, T. Comparison of IMPROVE and NIOSH carbon measurements. Aerosol Sci. Technol. 2001, 34, 23–34. (30) Schnaiter, M.; Horvath, H.; Möhlter, O.; Naumann, K.-H.; Saafhoff, H.; Schöck, O. W. UV-VIS-NIR spectral optical properties of soot and soot-containing aerosols. J. Aerosol Sci. 2003, 34, 1421–1444. (31) Schnaiter, M.; Linke, C.; Möhler, O.; Naumann, K.-H.; Saathoff, H.; Wagner, R.; Schurath, U. Absorption amplification of black carbon internally mixed with secondary organic aerosols. J. Geophys. Res. 2005, 110, D19204,; doi: 10.1029/2005JD006046. (32) Alfarra, M. R.; Coe, H.; Allan, J. D.; Bower, K. N.; Boudries, H.; Canagaratna, M. R.; Jimenez, J. L.; Jayne, J. T.; Garfortha, A. A.; Lie, S.-M.; Worsnop, D. R. Characterization of urban and rural organic particulate in the Lower Fraser Valley using two Aerodyne aerosol mass spectrometers. Atmos. Environ. 2004, 38, 5745–5758. (33) Currie, L. A. Evolution and multidisciplinary frontiers of 14C aerosol science. Radiocarbon 2000, 42, 115–126. (34) Levin, I.; Kromer, B. The tropospheric 14CO2 level in mid-latitudes of the northern hemisphere (1959–2003). Radiocarbon 2004, 46, 1261–1272. (35) Szidat, S.; Jenk, T. M.; Synal, H.-A.; Kalberer, M.; Wacker, L.; Hajdas, I.; Kasper-Giebl, A.; Baltensperger, U. Contributions of fossil fuel, biomass-burning, and biogenic emissions to carbonaceous aerosols in Zurich as traced by 14C. J. Geophys. Res. 2006, 111, D07206,; doi: 10.1029/2005JD006590. (36) Mohn, J.; Szidat, S.; Fellner, J.; Rechberger, H.; Quartier, R.; Buchmann, B.; Emmenegger L. Discrimination between biogenic and fossil CO2 emitted by waste incineration based on 14CO and mass balances Bioresour. Technol. 2007, doi: 10.1016/ 2 j.biortech.2007.11.042, in press. (37) Turpin, B. J.; Lim, H.-J. Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 2001, 35, 602–610. (38) Khalil, M. A. K.; Rasmussen, R. A. Tracers of wood smoke. Atmos. Environ. 2003, 37, 1211–1222.
(39) Paatero, P.; Tapper, U. Positive matrix factorization: A nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics, 1994, 5, 111–126. (40) Paatero, P. User’s Guide for Positive Matrix Factorization Programs PMF2 and PMF3, Part 1: Tutorial; University of Helsinski, Finland, 2007. (41) Saarikoski, S.; Sillanpa, M.; Sofiev, M.; Timonen, H.; Saarnio, K.; Teinila, K.; Karppinen, A.; Kukkonen, J.; Hillamo, R. Chemical composition of aerosols during a major biomass burning episode over northern Europe in Spring 2006: Experimental and modeling assessments. Atmos. Environ. 2007, 41, 3577–3589. (42) Lough, G. C.; Christensen, C. G.; Schauer, J. J.; Tortorelli, J.; Mani, E.; Lawson, D. R.; Clark, N. N.; Gabele, P. A. Development of molecular marker source profiles for emissions from on-
road gasoline and diesel vehicle fleets. J. Air Waste Manage. Assoc. 2007, 57, 1190–1199. (43) Lanz, V. A.; Alfarra, M. R.; Baltensperger, U.; Buchmann, B.; Hueglin, C.; Szidat, S.; Wehrli, M. N.; Wacker, L.; Weimer, S.; Caseiro, A.; Puxbaum, H.; Prévôt, A. S. H. Source attribution of submicron organic aerosols during wintertime inversions by advanced factor analysis of aerosol mass spectra. Environ. Sci. Technol. 2008, 42, 214–220. (44) Lonati, G.; Giugliano, M.; Butelli, P.; Romele, L.; Tardivo, R. Major chemical components of PM2.5 in Milan (Italy). Atmos. Environ. 2005, 39, 1925–1934.
ES702253M
VOL. 42, NO. 9, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3323
