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Source characterization of organic aerosols using Monte Carlo source apportionment of PAHs at two South Asian receptor sites
Atmospheric Environment 45 (2011) 3874e3881
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Source characterization of organic aerosols using Monte Carlo source apportionment of PAHs at two South Asian receptor sites Rebecca J. Sheesley*, August Andersson, Örjan Gustafsson Dept of Applied Environmental Science (ITM) and the Bert Bolin Climate Research Centre, Stockholm University, Stockholm, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 June 2010 Received in revised form 12 January 2011 Accepted 14 January 2011
The quantification of source contributions is of key importance for proposing environmental mitigation strategies for particulate organic matter. Organic molecular tracer analysis of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes was conducted on a set of winter samples from two regional receptor sites in South Asia: the Island of Hanimaadhoo (the Republic of Maldives) and a mountain top near Sinhagad (W. India). Monte Carlo source apportionment (MCSA) techniques were applied to the observed PAH ratios using profiles of a representative range of regional combustion sources from the literature to estimate the relative source contributions from petroleum combustion, coal combustion and biomass burning. One advantage of this methodology is the combined use of the mean and standard deviation of the diagnostic ratios to calculate probability distribution functions for the fractional contributions from petroleum, coal and biomass combustion. The results of this strategy indicate a higher input from coal combustion at the Hanimaadhoo site (32e43 21%) than the Sinhagad site (24e25 18%). The estimated biomass contribution for Sinhagad (53 22%) parallels previous radiocarbon-based source apportionment of elemental carbon at this location (54 3%). In Hanimaadhoo, the MCSA results indicate 34 20% biomass burning contribution compared to 41 5% by radiocarbon apportionment of EC. While the MCSA based on PAH ratio diagnostic distributions are less precise than the radiocarbon-based apportionment, it provides additional information of the relative contribution of two subgroups, coal and petroleum combustion, within the overall contribution from fossil fuel combustion. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: PAH Alkane Monte Carlo India Particulate matter
1. Introduction The far reaching impact of South Asian carbonaceous aerosols on regional climate and health is an emerging concern (Lelieveld et al., 2001; Ramanathan et al., 2007; Ramanathan and Carmichael, 2008). Apportionment of combustion and biogenic sources are crucial to improve characterization and regulation of this particulate air pollution. Natural abundance radiocarbon (14C) source apportionment of Indian aerosols recently provided a high precision constraint on the wintertime dry-, non-monsoon season sources of the particulate total organic carbon (TOC) with twothirds being of biomass origin (Gustafsson et al., 2009). Radiocarbon source apportionment of two proxies of combustion-derived black carbon (BC) similarly each yielded tight constraints on the contribution from biomass burning with elemental carbon (EC) at 46 8% and soot carbon (SC) at 68 6% (Gustafsson et al., 2009). Emission
* Corresponding author. Present address: Baylor University, Waco, TX 76798, USA. Tel.: þ1 254 710 3158; fax: þ1 254 710 3409. E-mail address: [email protected] (R.J. Sheesley). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.01.031
inventory models of BC sources in India are roughly consistent with the SC estimate (Bond et al., 2004; Venkataraman et al., 2005) whereas recipient studies, using various elemental data to diagnose sources have generally projected a smaller contribution from biomass burning to the South Asian black carbon (Novakov et al., 2000; Mayol-Bracero et al., 2002). While the radiocarbon approach clearly delineates between contemporary and fossil sources of any specific carbon isolate, it cannot alone provide information on specific types of emission sources within these two classes. In order to implement effective mitigation strategies it is of great interest to know the contribution of different types of fossil and biomass combustion sources including coal, motor vehicle exhaust, residential biofuel and open crop burning as well as the urban vs rural contributions. Organic tracers, including n-alkanes and polycyclic aromatic hydrocarbons (PAHs,) can provide additional source information. A limited number of pioneering studies of organic aerosol tracers in South Asia, measuring PAHs in the Maldives, the northern Indian Ocean and urban areas in India have been reported in the literature (Crimmins et al., 2004; Chowdhury et al., 2007; Stone et al., 2007). Each of these studies has attempted some type of source
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apportionment including diagnostic ratios, molecular markerchemical mass balance modeling and principal component analysis. Chowdhury et al. (2007) showed that fossil fuel contributions to organic carbon (OC), including diesel, gasoline and coal combustion, dominated over biomass burning in urban areas in India. However, the study also highlighted differences in major cities located in different regions within India, i.e., Delhi vs Mumbai, in contribution of coal combustion (Chowdhury et al., 2007). Crimmins et al. (2004) focused on the relationship between PAHs and EC, concluding that EC-associated PAHs were dominated by fossil fuel combustion rather than biomass/biofuel combustion but that this split was dependent on the source region of the air masses sampled in the Northern Indian Ocean. This source region-dependence for atmospheric components and emission sources will be explored in the current contribution by qualitatively combining organic tracer analysis and previously published back trajectory analysis (Gustafsson et al., 2009). Like any tracer, the molecular (or elemental) markers can be inconclusive. The characteristic composition and tracer ratios of different sources (hereafter called end-members) may overlap, the emission factors may vary considerably and the ratios of tracers may undergo change during transport due to differential degradation and/or scavenging. Although there have been molecular marker studies in this region, tighter constraints on the relative contribution of different fossil fuels are needed on a regional level. This is particularly important for regional S. Asia studies where the emission sources are poorly constrained and represent a wide range of source types (Bond et al., 2004). In order to incorporate the variability of emission source PAH diagnostic ratios in the source apportionment, a recently developed Monte Carlo source apportionment (MCSA) approach was applied in this study (Vonk et al., 2010). Previous studies have combined MC methods with source apportionment applications in environmental science (Kuik et al., 1993; Miller et al., 2002; Veldkamp and Weitz, 1994). The advantage of the present MCSA strategy is that the contributions from petroleum combustion, coal combustion and biomass combustion are visualized via distributions which reflect a range of source contributions based on emission source variability. For this study, samples were collected during a 2006 campaign at two sites of the Atmospheric Brown Cloud (ABC) Project located on the northern part of the island of Hanimaadhoo, Republic of the Maldives (6.78 N, 73.18 E) and a mountain top outside Sinhagad, Maharashtra, western India (18.35 N, 73.75 E, 1400 m above sea level). These two sites were established as part of the Atmospheric Brown Cloud network based on their lack of significant local upwind sources and remote locations (Corrigan et al., 2006; Ramana and Ramanathan, 2006; Stone et al., 2007; Gustafsson et al., 2009) The meteorological setting and trends in ambient concentrations and radiocarbon source apportionment of bulk total organic carbon and black carbon have been reported for this sampling campaign (Gustafsson et al., 2009). The major goal of this sampling campaign was to characterize the average dry season outflow from South Asia. Therefore it is not a major concern that long sample times (wweekly) resulted in samples with multiple source sub-regions within the greater S. Asia. This organic tracer study builds on that radiocarbon-based source characterization by adding source apportionment of PAHs using a recently developed MCSA method as well as analysis of long-chained n-alkanes to shed further light on the aerosol sources. The n-alkanes have both anthropogenic combustion and biogenic sources (Rogge et al., 1993a,b) while particle-phase PAHs in the atmosphere are almost uniformly of combustion origin and may be used to differentiate among different fossil fuel and biomass combustion sources.
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2. Methods 2.1. Atmospheric sampling Total suspended particulate matter was collected on pre-combusted 142 mm quartz fiber filters during January to March 2006 in Hanimaadhoo, Maldives (Han) and March to April 2006 in Sinhagad, India (Sin) as reported previously (Gustafsson et al., 2009). To assure sufficient sample sizes, a series of long-term collections on the order of one-half to two weeks each were conducted. In total, six long-term high-volume filters were collected in Hanimaadhoo (4e14 days each) and three filters were collected in Sinhagad (5e11 days each) resulting in continuous wintertime records of 50 and 20 days, respectively (Table 1). Filters were placed in pre-combusted aluminum foil envelopes using clean techniques and stored in a freezer/refrigerator during the sampling study and then shipped by courier to Stockholm University in triple plastic bags to prevent contamination. The concentrations and carbonisotopic composition of total organic carbon, elemental carbon and soot carbon fractions have been previously reported (Gustafsson et al., 2009). Filter blanks were collected at each site and stored and transported in the same manner as the sampled filters. The limitation of particle phase analysis is that semi-volatile organic species will be split between the particle and gas phase. Depending on factors including temperature, particle composition, and molecular weight PAHs and alkanes will be thusly split. 2.2. Organic compound analysis A small fraction of each sampled filter (3e7.5 cm2) and one field blank each from Han and Sin was spiked with deuterated internal standards and extracted for 24 h by Soxhlet using cyclohexane with a protocol based on Mandalakis et al. (2004a,b). The extracts were then concentrated and partitioned by a dimethylformamide e n-pentane method (Mandalakis et al., 2004a,b). Each alkane and PAH fraction was finally reduced to 0.2 ml and chrysene-d12 was added as an injection standard prior to analysis. Alkanes were analyzed using a Fisons 8060 GC interfaced to a Fisons MD 800 mass spectrometer with the MS operating in scan mode. The PAHs were analyzed on a Thermoquest Finnigan GC8000 interfaced with a Voyager mass spectrometer operating in selective ion mode. Each analyte was blank subtracted using the average of the Han and Sin field blanks. Median recoveries of the internal standards were 59% for alkanes and 53% for PAHs. The limit of quantification (LOQ) for analysis was calculated as the average filter blank plus the standard deviation of filter blanks. The analyte list for alkanes includes n-eicosane thru hexatriacontane (nC20-36) with an LOQ of 50 ng. The quantified PAHs include molecular weights 192e300 amu, with an LOQ of 0.02 ng for 192 amu (i.e. methyl anthracene), an LOQ of 0.3 ng for 202e228 amu (i.e. fluoranthene), an LOQ of 1.9 ng for 252 amu (i.e. benzo[b]fluoranthene), an LOQ of 1.1 ng for 276 amu
Table 1 Sampling details for 2006 South Asia campaign at Hanimaadhoo, Maldives (Han) and Sinhagad, India (Sin). Sample
Start date
Stop date
No. days
Han 1 Han 2 Han 3 Han 4 Han 5 Han 6 Sin 1 Sin 2 Sin 3
28-Jan 31-Jan 9-Feb 16-Feb 23-Feb 2-Mar 27-Mar 1-Apr 6-Apr
9-Feb 4-Feb 16-Feb 23-Feb 2-Mar 16-Mar 1-Apr 6-Apr 18-Apr
12 4 7 7 7 14 5 5 12
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(i.e. benzo[ghi]perylene) and an LOQ of 0.1 ng for 278e300 amu (i.e. coronene). A full table of analytes and ambient concentrations has been included in the Supplementary materials (Table S1). 2.3. Source apportionment using PAH diagnostic ratios The PAH-ratio pairs fluoranthene/(fluoranthene þ pyrene) [Flu/(Flu þ Pyr)] and indeno[cd]pyrene/(indeno[cd]pyrene þ benzo [ghi]perylene) [Icd/(IcdP þ BgP)] form a solvable system of equations since the end-member values for petroleum, coal and biomass combustion have been reported in the literature. The MCSA strategy is governed by the following set of equations:
Fo ¼ xFp þ yFc þ zFb
(1a)
Io ¼ xIp þ yIc þ zIb
(1b)
Where Fp, Fc and Fb are the Flu/(Flu þ Pyr) end-member values for petroleum, coal and biomass combustion, Ip, Ic and Ib respectively, the analogous quantities for Icd/(I þ BgP) and x, y and z are the respective fractions (noting that x þ y þ z ¼ 1). Flu/(Flu þ Pyr) and IcdP/(IcdP þ BghiP) were chosen because the paired PAHs have similar volatility and atmospheric lifetime (Finlayson-Pitts and Pitts, 2000; Perraudin et al., 2007). Additionally, these PAH ratios are well represented in the emission profiling literature, have been used as diagnostic ratios (Yunker et al., 2002; Dickhut et al., 2000; Mandalakis et al., 2005) and their signals are relatively invariant across the 9-week study, mimicking the radiocarbon results. Although Flu and Pyr are partitioned between the gas and particle phase, it is assumed that their ratio is relatively well-preserved across phases and that estimating the total Flu and Pyr based on the particle-phase would introduce an unnecessary source of uncertainty without significantly improving the accuracy. The implicit assumptions of the MCSA method are: (1) the diagnostic ratios will not be altered during transport (2) the three combustion source categories (petroleum, coal and biomass) are sufficient to explain possible combustion source contributions (3) the dominant limitations and uncertainty in the MCSA method are due to the inherent uncertainty of the diagnostic ratios as source tracers. These assumptions are common for receptor-based source apportionment methodology, eg chemical mass balance modelling, where one is using literature-based emission profiles. However, the advantage of the MCSA method for this particular study, lies in its ability to incorporate the variability of the source-diagnostic ratios into the apportionment calculations. For each of the three source categories, 8e10 published PAH ratios were used to calculate the three end-member diagnostic ratios for the MCSA sources. We note that few of these address India-specific combustion situations. For the source categories: petroleum combustion includes profiles for gasoline and diesel motor vehicle exhaust at different loads, no. 2 fuel oil and crude oil combustion; coal combustion includes brown to hard coals for industrial and residential burning; biomass combustion includes biofuel, wood and open burning of forested area. The No. 2 fuel and crude oil combustion represent industrial boilers and will be considered surrogates for ship emissions for the purpose of the current study. While India does not have the same extent of residential coal burning as China, residential coal burning emission ratios were included to represent lower temperature, unregulated coal combustion practices such as in brick kilns (Bond et al., 2004). Similarly, a range of biomass combustion processes occur across India (Sinha et al., 1998; Venkataraman et al., 2005), including, but not limited to, household fuel such as wood and dung cakes, agricultural crop residue burning and vegetation fires. A complete list of
the emission sources and PAH end-member values with associated references is included in the Supplementary materials (Table S2). This is a significant improvement over the single or average emission profile used in previous diagnostic ratio studies (Yunker et al., 2002) and CMB (Sheesley et al., 2005). 2.4. Monte Carlo simulations The present MCSA strategy is a source apportionment model, which includes both the numerical variability and the average end-member ratios for emission source categories using Eqs. (1)aeb. Eqs. (1)aeb can be solved analytically. However, such an approach would introduce bias, since it is implicitly assumed that the calculated fractions are independent of the variability of the emission source values. To account for these effects we adopt a random sampling (Monte Carlo simulation) strategy. Each diagnostic ratio was represented by a Normal distribution, with mean and standard deviation defined by the source data. The Normal distribution was chosen to characterize the data as this distribution is the most unbiased representation of data where the real distribution is not known. Other distributions, e.g., the log-normal distribution may also be implemented within the current approach. However, a detailed comparison of how well different distributions fit the source data was not conducted, since such should be performed on a larger source dataset than was available in the current study. Pseudo-random numbers were then generated from the Normal distributions of the diagnostic ratios (by random sampling) generating Fp, Fc, Fb, Ip, Ic and Ib values for which Eqs. (1)aeb were solved. A large number of solutions (400,000) of the fractional source contributions (x, y and z) were generated using this methodology. The results were sorted into histograms, generating probability density functions (PDFs) describing the fractional influence of the different sources. These PDFs were then used to calculate the mean (m), standard deviation (s) and median (x) of source contributions according to Eqs. (S6e8) (in Supplementary materials), as Summarized in Table 2. These resulting source contribution distributions were all normalized to the range between 0 and 1. All values outside this range were discarded and all calculated values for x, y and z simultaneously satisfied this boundary condition. MCSA simulations were run using PAH diagnostic ratios for three individual Hanimaadhoo samples (Han 1, 2 and 4), the average of the three Hanimaadhoo samples, three individual Sinhagad samples (Sin 1, 2 and 3) and the average of the three Sinhagad samples. Tests were conducted to validate the method. To assess the veracity of assumption 3 from Section 2.3 (uncertainty in the MCSA is due to the diagnostic ratios), calculations were repeated ten times giving a variation in the mean value that was typically lower than 0.003. An assessment of the predictability is outlined in the Supplementary materials. The source with the narrowest endmember value distribution and least overlap with the other sources has the best predictability, in the present case, petroleum combustion. Table 2 Observed PAH ratios from Hanimaadhoo, Maldives (Han) and Sinhagad, India (Sin) with results of Monte Carlo simulations for the fraction contributions from petroleum combustion, coal combustion and biomass combustion (given in the format mean standard deviation of the distribution function). Site
Flu/ (Flu þ Pyr)
IcdP/ (IcdP þ BghiP)
Petroleum
Coal
Han 1 Han 2 Han 4 Sin 1 Sin 2 Sin 3
0.53 0.53 0.53 0.48 0.49 0.48
0.30 0.60 0.54 0.50 0.52 0.53
0.41 0.11 0.15 0.24 0.21 0.20
0.43 0.33 0.32 0.25 0.25 0.24
0.20 0.10 0.12 0.14 0.13 0.13
Biomass
0.22 0.21 0.20 0.18 0.18 0.18
0.16 0.56 0.52 0.50 0.54 0.55
0.11 0.25 0.23 0.21 0.22 0.20
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The MCSA simulations were processed with scripts written by the authors for Matlab version 7.9.0 (The MathWorks, Natick, MA, USA). The simulations were run using 400,000 iterations and the resulting distributions for x, y and z were calculated using a histogram bin size of 256. Additional technical information on the repeatability and predictability of the model has been included in the Supplementary materials. 3. Results and discussion 3.1. Meteorological setting Sampling occurred in the second half of the winter dry season, well before the start of the monsoons (Table 1). Carbon concentrations are typically quite high during the South Asian dry season and are drastically reduced during monsoon season (Novakov et al., 2000; Corrigan et al., 2006). Ten day back trajectory analysis starting at 50 m was reported previously for both locations for the entire sampling campaign (Gustafsson et al., 2009) with details in the caption of Fig. 1. Although the particulate phase of the aerosol collected at Hanimaadhoo is highly impacted by emission sources in India, a direct sourceereceptor relationship should obviously not be assumed between Hanimaadhoo and Sinhagad. Nevertheless, back trajectory analysis suggests that Han 6 has similar source
Carbon mg m -3
A
7
Sinhagad
Hanimaadhoo
EC OC
6 5 4 3 2 1 0
B 0.05
Pyrene Chrysene Benzo(a)Pyrene
PAH ng (mg OC)-1
0.04
Benzo(ghi)Perylene
0.03
0.02
0.01
0.00
Alkane ng (mg OC)-1
C
regions as the sampled Sinhagad aerosols (i.e. it is the only one of the Han samples which is solely impacted by the 2 trajectory classification e western Indian margin, etc.). 3.2. Abundance and temporal trends of organic tracers The general trends in atmospheric concentrations of the molecular tracers broadly follows the previously published trends of the bulk aerosol carbon (Gustafsson et al., 2009) for example, with the highest Han values in the JaneFeb samples (Table S1). The four PAHs and two alkanes selected to demonstrate the overall temporal variability were normalized by OC concentrations (Fig. 1bec) to illustrate changes in source contributions relative to bulk carbonaceous aerosol (Fig. 1a). A spectrum of common PAHs spanning from molecular weight 202 (pyrene) to 276 (benzo[ghi]perylene) is displayed (Fig. 1b). For Sinhagad, the normalized PAH concentrations stay rather invariant during the three week observational period (Fig. 1b). For Hanimaadhoo, the PAH concentrations decrease relative to TOC concentrations (Fig. 1a) throughout the study period (Fig. 1b). As mentioned previously, back trajectory analysis indicates a switch from Eastern to Western Indian origin over the course of the Maldives sampling. The higher molecular weight PAHs are relatively more abundant for Han 1 than for Han 2 despite overlapping sample times. Han 1 is quite enriched in benzo[ghi]perylene and somewhat enriched in coronene compared to the remaining Hanimaadhoo aerosol samples (Supp Matl Table S1). Motor vehicle emission characterization studies have suggested that gasoline exhaust is enriched in benzo[ghi]perylene and coronene relative to diesel exhaust (Phuleria et al., 2007) and recent compound-specific radiocarbon analysis of PAHs found indeno[cd]pyrene plus benzo[ghi]perylene to be have slightly higher fossil contribution than the lower molecular weight PAHs (Sheesley et al., 2009). The sum of PAHs (not including coronene) reported in Stone et al (2007) (0.75 ng m3), for the same site on the Maldives, are similar to the concentrations reported here (0.86 ng m3). For these Maldives samples, the C29 and C31 homologs were the two most abundant n-alkanes, which signals impact by biogenic emissions (Rogge et al., 1993a,b). There is a larger temporal variability in the alkane abundance at Hanimaadhoo as compared to Sinhagad, where there is less than a factor of 2 difference in normalized concentration for the six selected tracers. At Hanimaadhoo, there is a factor of five difference between February Han 3 and early March Han 6 for the C29 alkane (Fig. 1c). This reflects the trend for PAHs discussed previously.
5 c29 c31
4
3
2
1
0 28-Jan-06
D
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11-Feb-06
1 1
1
25-Feb-06
2,1
11-Mar-06
2,1
2
Han 1,2 Han 3 Han 4 Han 5
Han 6
25-Mar-06
08-Apr-06
3
3
Sin 1 Sin 2
22-Apr-06
3 Sin 3
Fig. 1. Ambient concentrations for Hanimaadho (the Maldives) and Sinhagad (western India) with air mass source regions. (A) Organic carbon (OC) and elemental carbon (EC) concentrations (B) OC-normalized concentrations for four ubiquitous PAHs (pyrene, chrysene, benz[a]pyrene and benzo[ghi]perylene) (C) OC-normalized concentrations for n-nonacosane (C29) and heneitriacontane (C31) (D) Source region by back trajectory analysis: 1) Ganges Valley in North India and the eastern Indian continental margin (2) Northern Arabian Sea, Northwest India, Pakistan and the western Indian continental margin (3) Mumbai region, the Northern Arabian Sea, Northwest India and Pakistan.
3.3. Source diagnostics of organic tracers and carbon isotopes Source-diagnostic ratios of individual n-alkanes and PAHs have been applied in both atmospheric and geochemistry source studies. It is necessary to stress that individual species may have different source contributions and any given ratio is not highly source specific. It is therefore valuable to combine several organic tracer classes in concert with source-diagnostic carbon-isotope signals. Hypotheses of source contributions can then be tested against a broader set of constraints. 3.3.1. Alkanes There is a well-known odd-to-even predominance in high molecular weight n-alkane chain lengths for biogenic emissions but an odd-to-even ratio near one for higher temperature combustion processes. This feature is commonly evaluated by calculating the carbon preference index (CPI). For this campaign the carbon number range 25e35 (CPI25e35) was chosen to most closely correspond to previously published atmospheric CPI, using the equation from Yue and Fraser (2004).
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Fig. 2. Apportionment and characterization of total organic carbon by Cwax, CPI25e35 and d13C-TOC for Hanimaadhoo, Maldives and Sinhagad, India.
The CPI of natural products is preserved in biogenic emissions from leaf detritus (Rogge et al., 1993a,b), which has a reported CPI around 12 for fine particulate green and dead leaf detritus and a Cmax of 31 (Rogge et al., 1993a,b). Alkanes emitted during fossil fuel combustion have a CPI of 1.0 (Rogge et al., 1993a,b). Reported CPI for wood combustion and biomass combustion in a wood stove lies in the range of 1e2.7, with most below 2 (Sheesley et al., 2003; Lee et al., 2005). Grass and cereal combustion have been reported with CPI over 5.0 and up to 20 (Simoneit, 2002; Zhang et al., 2007). Therefore, CPI values averaging below 2 are indicative of fossil fuel and high temperature biomass combustion, while CPI over 10 are indicative of biogenic emissions and potential impact of low temperature combustion of grasses and cereals. Another source-diagnostic alkane ratio is the Cwax, which sums biogenic alkane concentration (Yue and Fraser, 2004). Cwax was originally designed to separate biogenic from combustion alkanes (Yue and Fraser, 2004), but it does not take into account that CPI values over 1.0 are common for biomass combustion. Because of this omission, the Cwax will just be used as an additional visualization of the odd-over-even predominance. Alkane-based fingerprint source information was compared with d13C-TOC (Fig. 2), which is the stable carbon ratio relative to the reference material Vienna PeeDee Belemnite (d13Csample ¼ {(13C/12C sample)/(13C/12C standard)e1} 1000). The CPI25e35 and Cwax for the aerosols at Hanimaadhoo is lower than for Sinhagad. This suggests a lower contribution at Hanimaadhoo from biogenic emissions and/ or low temperature biomass burning. However, the radiocarbon data constrains that the fraction of total contemporary biomass is nearly identical for Hanimaadhoo and Sinhagad (68 3 and 65 2% biomass for TOC, respectively) as is the EC/TC (0.16e0.22 for both sites) (Gustafsson et al., 2009). The d13C of TOC for the Hanimaadhoo aerosol is enriched, indicating greater impact from C4 plants which includes grasses, corn and sugarcane (Gustafsson et al., 2009). Taken together, the different d13C and CPI25e35 at the two sites, with similar and near invariant D14C, may reflect a transition in sources from Eastern to Western India rather than the addition of an emission source. A comparison with a previous urban study of eastern and western Indian cities (Chowdhury et al., 2007) confirms that the winter urban CPI (1.23e1.48) is significantly lower than measured at the regional background sites in this study as Han 1-Han 5 is 2.6e3.4 and Sin samples are 4.2e5.2. This would indicate that the
Fig. 3. Diagnostic ratios of PAHs to indicate source contributions for Hanimaadhoo, Maldives and Sinhagad, India with comparison to grouped combustion sources for (A) fluoranthene/fluoranthene þ pyrene (Flu/(Flu þ Pyr)) (B) indeno[cd]pyrene/(indeno[cd] pyrene þ benzo[ghi]perylene) (IcdP/(IcdP þ BghiP)). Ratios of grouped combustion sources (Supp matl Table S2) are visualized using box plots where the ends of the boxes represent the 25th and 75th percentiles, the whiskers represent the 10th and 90th percentiles, outliers are plotted beyond the whiskers and the median is represented by the internal box line. Median lines for each combustion type have been extended.
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rural and residential impact is an important contribution to the contemporary biomass signature evident in the radiocarbon and alkane CPI25e35 values of S. Asian background aerosols. The current dataset does not allow constraining the total biogenic contribution to atmospheric TOC. The primary particulate biogenic contribution can be represented by alkanes with CPI > 10, but the total biogenic component of TOC would have to include secondary biogenic carbon ie. from the oxidation of isoprene (Edney et al., 2005). A rough estimation of the fraction of organic carbon coming from primary biogenic emissions, following Rogge et al, (1993a,b) and using total alkanes (n25e35)-to-organic carbon ratios indicate that 2e6% of OC is primary biogenic in Hanimaadhoo and 5e7% is primary biogenic at Sinhagad. 3.3.2. PAH ratios Various PAH ratios have been suggested to elucidate PAH and carbon sources in atmospheric aerosol (McVeety and Hites, 1988; Benner et al., 1995; Lima et al., 2005; Mandalakis et al., 2005) and in sediments (Readman et al., 1987; Yunker et al., 2002; Lima et al., 2005; Elmquist et al., 2007). Fig. 3 presents ambient results and end-member ratios for two diagnostic PAH ratios, Flu/(Flu þ Pyr) and IcdP/(IcdP þ BghiP)(see the full list of sources and references in Supp Mat, Table S2). Unfortunately, IcdP was not quantified in three of the six of the Hanimaadhoo samples, however the three available samples represent nearly three weeks of the dry season in Hanimaadhoo and two different trajectory classifications (see Section 3.1). The Sinhagad set is complete for both ratios. 3.4. Monte Carlo simulations The current MCSA strategy has been outlined in detail in the Methods section, with complementary information available in
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the Supplementary materials. Accordingly, three probability density functions were obtained for each sampling point (time and site), illustrating the fractional influence of the three sources. Fig. 4 depicts examples of PDFs for Hanimaadhoo and Sinhagad illustrating the source contribution output from the MCSA. The remaining plotted results are included in the Supplementary materials Figs. S1e2. Median values are drawn as vertical lines in the figures while mean and standard deviations are included in Table 2. Focusing on the Sinhagad site, the two PAH ratios observed for the three sampling periods are fairly similar (Table 2). Little temporal variation is observed in the Sinhagad values, with mean fractions of biomass (z) ranging from 0.50 to 0.55. The coal and petroleum has roughly equal contribution ranges, 0.24e0.25 and 0.20e0.24, respectively. Han 2 and Han 4 have similar contributions from biomass combustion as Sinhagad, while Han 1 has significantly lower biomass and higher petroleum combustion contributions. Differences between median and mean values are indicators of skewness in a PDF. The probability density distributions depicted in Fig. 4, S1 and S2 are asymmetric. The difference for the mean and median ranges is between 0.00e0.03 for all sampling periods. Sin 1 illustrates the impact of skewed distribution on the results (Fig. 4def). Even though the main probability mass is centered around 0.6, values 0e0.6 are more probable than values 0.9e1. In other words, occurrences of low biomass influence are more probable than occurrences of high biomass influence in the Sin 1 sample. The calculated standard deviations (0.10e0.24) reflect the width of the PDFs, which in turn reflect the variability of the source data. These values should therefore not be interpreted as the precision of the MCSA calculations in itself, as discussed in Section 2.4. These inherent variations are important to keep in mind when comparing with more precise data, such as source apportionment by 14C measurements.
Fig. 4. Calculated distribution functions for the fractional contributions from different combustion sources for Han 4 (A, biomass, B, coal, C petroleum) and Sin 1 (D, biomass, E, coal, F, petroleum). The dashed vertical lines correspond to the median fraction of respective source and the solid grey vertical lines depict the width of the distribution (1.s.d.).
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Table 3 Comparison of biomass contribution for Hanimaadhoo, Maldives and Sinhagad, India for Monte Carlo source apportionment (MCSA) (this study) and radiocarbon apportionment of total organic carbon (TOC), soot carbon (SC), and elemental carbon (EC) (Gustafsson et al., 2009). MCSA results given in the format mean (median) standard deviation of the distribution function, 1 s.d.
Hanimaadhooa Sinhagadb
MCSA
14
31(29) 18% 54(57) 22%
68 3% 65 2%
C TOC
14
C SC
69 6% 64 3%
14
C EC
41 5% 54 3%
a Average observed PAH ratio used in MCSA was 0.53 for Flu/(Flu þ Pyr) and 0.42 for IcdP/(IcdP þ BghiP). b Average observed PAH ratio used in MCSA was 0.48 for Flu/(Flu þ Pyr) and 0.48 for IcdP/(IcdP þ BghiP).
Since z represents the fraction biomass in the PAH calculations, and x þ y represent the fraction fossil fuel combustion, the calculated values may therefore be compared with our earlier results from a 14C-based source apportionment (Gustafsson et al., 2009) (Table 3). D14C is the ratio of radiogenic 14C to 12C relative to the reference standard NBS oxalic acid as discussed in Stuiver and Polach (1977). In order to get an accurate error estimate for the average source contributions for Hanimaadhoo and Sinhagad, the MCSA was also run with the time-weighted average values of Han 1,2 and 4 and then Sin 1,2 and 3. Interestingly, the timeweighted values of biomass obtained from Monte Carlo simulation of PAH values (54%) matches the 14C-derived fraction biomass for EC (54%) in Sinhagad. However, the precision of the MCSA-derived values are considerably lower (22% as compared with 2e6%). These values both have lower biomass contribution as compared to TOC and SC for Sinhagad. Gustafsson et al. (2009) suggested, based on false positive results of the EC method (Hammes et al., 2007), that the discrepancy between the EC, SC and OC data could be due to a higher contribution of fine coal dust or coal combustion particles in EC than the more selective SC. Previous studies have shown a large wintertime increase in Se, a tracer for coal burning, in Hanimaadhoo (Stone et al., 2007). The mega-cities Delhi and Kolkata, which according to the back trajectories are influencing samples Han1eHan4, each were estimated to have a 14% coal contribution to fine particulate matter during the winter season, based on picene as a molecular marker in chemical mass balance modeling, while the western India city of Mumbai, impacting samples Han5-Han6 and Sin, had an estimated 8% coal contribution (Chowdhury et al., 2007) The present study supports the hypothesis that there is a significant coal contribution in the particle phase of the aerosol collected at Han. The current PAH-based source apportionment suggests that the fraction biomass in Hanimaadhoo is more variable through time than in Sinhagad, as calculated from PAH data (Table 2). This is in contrast to the radiocarbon data, which show a very stable biomass contribution during the same period (Gustafsson et al., 2009). However, this discrepancy is mainly due to the low IcdP/(IcdP þ BghiP) ratio for Han 1. As expected, the Hanimaadhoo mean biomass contribution (31 18%) is lower than obtained by radiocarbon source apportionment, but still within a standard deviation of the EC biomass contribution (41%, Table 3). The present MCSA for particulate PAHs shows good agreement with the previously obtained 14C-based results and contributes novel information by refining the fraction fossil fuels into separate petroleum and coal combustion source classes. 4. Conclusion By applying an MC-based source apportionment strategy to PAHratios greater total source resolution is obtained when combined with 14C-based source information (Gustafsson et al., 2009).
Radiocarbon source apportionment constrained that the background S. Asian aerosol is a mixture of biomass and fossil sources, with biomass sources dominating the TOC and composing 40e50% of the EC isolate (Gustafsson et al., 2009). The present model shows good agreement with previous estimates of the fraction of biomass, but refines the fraction fossil fuels into petroleum and coal combustion. The organic tracers here add some resolution within each of those two broad source classes, within the constraints of the dual carbon-isotope results. Taking the isotopes and organic tracers together, it appears that both Hanimaadhoo and Sinhagad provide regional background composites as discussed previously (Stone et al., 2007; Gustafsson et al., 2009), not dominated by local emissions but rather providing long-term integration of both urban and rural contributions. The CPI25e35 reported here is significantly higher than seen in Indian mega cities (Chowdhury et al., 2007), which suggests that the rural component of the regional aerosol is primarily due to biomass combustion and/or biogenic emissions. Another significant outcome of the MC-based source apportionment is that coal is contributing to the fossil signal of the regional carbonaceous aerosol. Acknowledgments This research is a contribution of the Stockholm University Bert Bolin Centre for Climate Research. We gratefully acknowledge access to and support of the Han field site for the 2006 campaign by the Maldives Meteorology Office and H. Nguyen of the international ABC program and of the Sin field site in 2006 by the Indian Institute of Tropical Meteorology in Pune, India and the Department of Meteorology, Stockholm University. We particularly value the assistance during S. Asian field work by P.S. Praveen, P.S.P. Rao and P.D. Safai and the advice and support of Henning Rodhe, Lennart Granat and Caroline Leck in preparations and logistics. We appreciate the technical assistance of Martin Kruså in field sampling and chemical analysis. We thank Jens Danielsson for valuable discussions on the Monte Carlo simulations. This study was financed by the Swedish Strategic Environmental Research Foundation (MISTRA contract nr2002-057); the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS contract nr 214-2006-527); and the Swedish International Development Agency, Department for Research CoOperation (SIDA-SAREC contract nr SWE-2006-148). ÖG also acknowledges support as an Academy Researcher from the Swedish Royal Academy of Science. AA gratefully acknowledges the Knut and Alice Wallenberg Foundation and the Magnus Bergwall Foundation for financial support. Appendix. Supplementary material Supplementary material associated with this paper can be found, in the online version, at doi:10.1016/j.atmosenv.2011.01.031. References Benner, B.A., Wise, S.A., Currie, L.A., Klouda, G.A., Klinedinst, D.B., Zweidinger, R.B., Stevens, R.K., Lewis, CW., 1995. Distinguishing the contributions of residential wood combustion and mobile source emissions using relative concentrations of dimethylphenanthrene isomers. Environmental Science & Technology 29, 2382e2389. Bond, T.C., Streets, D.G., Yarber, K.F., Nelson, S.M., Woo, J.H., Klimont, Z., 2004. A technology-based global inventory of black and organic carbon emissions from combustion. Journal of Geophysical Research-Atmospheres 109, D14203. doi:10.1029/2003JD003697. Chowdhury, Z., Zheng, M., Schauer, J.J., Sheesley, R.J., Salmon, L.G., Cass, G.R., Russell, AG., 2007. Speciation of ambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities. Journal of Geophysical Research-Atmospheres 112. doi:10.1029/2007JD008386.
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Source characterization of organic aerosols using Monte Carlo source apportionment of PAHs at two South Asian receptor sites Rebecca J. Sheesley*, August Andersson, Örjan Gustafsson Dept of Applied Environmental Science (ITM) and the Bert Bolin Climate Research Centre, Stockholm University, Stockholm, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 June 2010 Received in revised form 12 January 2011 Accepted 14 January 2011
The quantification of source contributions is of key importance for proposing environmental mitigation strategies for particulate organic matter. Organic molecular tracer analysis of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes was conducted on a set of winter samples from two regional receptor sites in South Asia: the Island of Hanimaadhoo (the Republic of Maldives) and a mountain top near Sinhagad (W. India). Monte Carlo source apportionment (MCSA) techniques were applied to the observed PAH ratios using profiles of a representative range of regional combustion sources from the literature to estimate the relative source contributions from petroleum combustion, coal combustion and biomass burning. One advantage of this methodology is the combined use of the mean and standard deviation of the diagnostic ratios to calculate probability distribution functions for the fractional contributions from petroleum, coal and biomass combustion. The results of this strategy indicate a higher input from coal combustion at the Hanimaadhoo site (32e43 21%) than the Sinhagad site (24e25 18%). The estimated biomass contribution for Sinhagad (53 22%) parallels previous radiocarbon-based source apportionment of elemental carbon at this location (54 3%). In Hanimaadhoo, the MCSA results indicate 34 20% biomass burning contribution compared to 41 5% by radiocarbon apportionment of EC. While the MCSA based on PAH ratio diagnostic distributions are less precise than the radiocarbon-based apportionment, it provides additional information of the relative contribution of two subgroups, coal and petroleum combustion, within the overall contribution from fossil fuel combustion. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: PAH Alkane Monte Carlo India Particulate matter
1. Introduction The far reaching impact of South Asian carbonaceous aerosols on regional climate and health is an emerging concern (Lelieveld et al., 2001; Ramanathan et al., 2007; Ramanathan and Carmichael, 2008). Apportionment of combustion and biogenic sources are crucial to improve characterization and regulation of this particulate air pollution. Natural abundance radiocarbon (14C) source apportionment of Indian aerosols recently provided a high precision constraint on the wintertime dry-, non-monsoon season sources of the particulate total organic carbon (TOC) with twothirds being of biomass origin (Gustafsson et al., 2009). Radiocarbon source apportionment of two proxies of combustion-derived black carbon (BC) similarly each yielded tight constraints on the contribution from biomass burning with elemental carbon (EC) at 46 8% and soot carbon (SC) at 68 6% (Gustafsson et al., 2009). Emission
* Corresponding author. Present address: Baylor University, Waco, TX 76798, USA. Tel.: þ1 254 710 3158; fax: þ1 254 710 3409. E-mail address: [email protected] (R.J. Sheesley). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.01.031
inventory models of BC sources in India are roughly consistent with the SC estimate (Bond et al., 2004; Venkataraman et al., 2005) whereas recipient studies, using various elemental data to diagnose sources have generally projected a smaller contribution from biomass burning to the South Asian black carbon (Novakov et al., 2000; Mayol-Bracero et al., 2002). While the radiocarbon approach clearly delineates between contemporary and fossil sources of any specific carbon isolate, it cannot alone provide information on specific types of emission sources within these two classes. In order to implement effective mitigation strategies it is of great interest to know the contribution of different types of fossil and biomass combustion sources including coal, motor vehicle exhaust, residential biofuel and open crop burning as well as the urban vs rural contributions. Organic tracers, including n-alkanes and polycyclic aromatic hydrocarbons (PAHs,) can provide additional source information. A limited number of pioneering studies of organic aerosol tracers in South Asia, measuring PAHs in the Maldives, the northern Indian Ocean and urban areas in India have been reported in the literature (Crimmins et al., 2004; Chowdhury et al., 2007; Stone et al., 2007). Each of these studies has attempted some type of source
R.J. Sheesley et al. / Atmospheric Environment 45 (2011) 3874e3881
apportionment including diagnostic ratios, molecular markerchemical mass balance modeling and principal component analysis. Chowdhury et al. (2007) showed that fossil fuel contributions to organic carbon (OC), including diesel, gasoline and coal combustion, dominated over biomass burning in urban areas in India. However, the study also highlighted differences in major cities located in different regions within India, i.e., Delhi vs Mumbai, in contribution of coal combustion (Chowdhury et al., 2007). Crimmins et al. (2004) focused on the relationship between PAHs and EC, concluding that EC-associated PAHs were dominated by fossil fuel combustion rather than biomass/biofuel combustion but that this split was dependent on the source region of the air masses sampled in the Northern Indian Ocean. This source region-dependence for atmospheric components and emission sources will be explored in the current contribution by qualitatively combining organic tracer analysis and previously published back trajectory analysis (Gustafsson et al., 2009). Like any tracer, the molecular (or elemental) markers can be inconclusive. The characteristic composition and tracer ratios of different sources (hereafter called end-members) may overlap, the emission factors may vary considerably and the ratios of tracers may undergo change during transport due to differential degradation and/or scavenging. Although there have been molecular marker studies in this region, tighter constraints on the relative contribution of different fossil fuels are needed on a regional level. This is particularly important for regional S. Asia studies where the emission sources are poorly constrained and represent a wide range of source types (Bond et al., 2004). In order to incorporate the variability of emission source PAH diagnostic ratios in the source apportionment, a recently developed Monte Carlo source apportionment (MCSA) approach was applied in this study (Vonk et al., 2010). Previous studies have combined MC methods with source apportionment applications in environmental science (Kuik et al., 1993; Miller et al., 2002; Veldkamp and Weitz, 1994). The advantage of the present MCSA strategy is that the contributions from petroleum combustion, coal combustion and biomass combustion are visualized via distributions which reflect a range of source contributions based on emission source variability. For this study, samples were collected during a 2006 campaign at two sites of the Atmospheric Brown Cloud (ABC) Project located on the northern part of the island of Hanimaadhoo, Republic of the Maldives (6.78 N, 73.18 E) and a mountain top outside Sinhagad, Maharashtra, western India (18.35 N, 73.75 E, 1400 m above sea level). These two sites were established as part of the Atmospheric Brown Cloud network based on their lack of significant local upwind sources and remote locations (Corrigan et al., 2006; Ramana and Ramanathan, 2006; Stone et al., 2007; Gustafsson et al., 2009) The meteorological setting and trends in ambient concentrations and radiocarbon source apportionment of bulk total organic carbon and black carbon have been reported for this sampling campaign (Gustafsson et al., 2009). The major goal of this sampling campaign was to characterize the average dry season outflow from South Asia. Therefore it is not a major concern that long sample times (wweekly) resulted in samples with multiple source sub-regions within the greater S. Asia. This organic tracer study builds on that radiocarbon-based source characterization by adding source apportionment of PAHs using a recently developed MCSA method as well as analysis of long-chained n-alkanes to shed further light on the aerosol sources. The n-alkanes have both anthropogenic combustion and biogenic sources (Rogge et al., 1993a,b) while particle-phase PAHs in the atmosphere are almost uniformly of combustion origin and may be used to differentiate among different fossil fuel and biomass combustion sources.
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2. Methods 2.1. Atmospheric sampling Total suspended particulate matter was collected on pre-combusted 142 mm quartz fiber filters during January to March 2006 in Hanimaadhoo, Maldives (Han) and March to April 2006 in Sinhagad, India (Sin) as reported previously (Gustafsson et al., 2009). To assure sufficient sample sizes, a series of long-term collections on the order of one-half to two weeks each were conducted. In total, six long-term high-volume filters were collected in Hanimaadhoo (4e14 days each) and three filters were collected in Sinhagad (5e11 days each) resulting in continuous wintertime records of 50 and 20 days, respectively (Table 1). Filters were placed in pre-combusted aluminum foil envelopes using clean techniques and stored in a freezer/refrigerator during the sampling study and then shipped by courier to Stockholm University in triple plastic bags to prevent contamination. The concentrations and carbonisotopic composition of total organic carbon, elemental carbon and soot carbon fractions have been previously reported (Gustafsson et al., 2009). Filter blanks were collected at each site and stored and transported in the same manner as the sampled filters. The limitation of particle phase analysis is that semi-volatile organic species will be split between the particle and gas phase. Depending on factors including temperature, particle composition, and molecular weight PAHs and alkanes will be thusly split. 2.2. Organic compound analysis A small fraction of each sampled filter (3e7.5 cm2) and one field blank each from Han and Sin was spiked with deuterated internal standards and extracted for 24 h by Soxhlet using cyclohexane with a protocol based on Mandalakis et al. (2004a,b). The extracts were then concentrated and partitioned by a dimethylformamide e n-pentane method (Mandalakis et al., 2004a,b). Each alkane and PAH fraction was finally reduced to 0.2 ml and chrysene-d12 was added as an injection standard prior to analysis. Alkanes were analyzed using a Fisons 8060 GC interfaced to a Fisons MD 800 mass spectrometer with the MS operating in scan mode. The PAHs were analyzed on a Thermoquest Finnigan GC8000 interfaced with a Voyager mass spectrometer operating in selective ion mode. Each analyte was blank subtracted using the average of the Han and Sin field blanks. Median recoveries of the internal standards were 59% for alkanes and 53% for PAHs. The limit of quantification (LOQ) for analysis was calculated as the average filter blank plus the standard deviation of filter blanks. The analyte list for alkanes includes n-eicosane thru hexatriacontane (nC20-36) with an LOQ of 50 ng. The quantified PAHs include molecular weights 192e300 amu, with an LOQ of 0.02 ng for 192 amu (i.e. methyl anthracene), an LOQ of 0.3 ng for 202e228 amu (i.e. fluoranthene), an LOQ of 1.9 ng for 252 amu (i.e. benzo[b]fluoranthene), an LOQ of 1.1 ng for 276 amu
Table 1 Sampling details for 2006 South Asia campaign at Hanimaadhoo, Maldives (Han) and Sinhagad, India (Sin). Sample
Start date
Stop date
No. days
Han 1 Han 2 Han 3 Han 4 Han 5 Han 6 Sin 1 Sin 2 Sin 3
28-Jan 31-Jan 9-Feb 16-Feb 23-Feb 2-Mar 27-Mar 1-Apr 6-Apr
9-Feb 4-Feb 16-Feb 23-Feb 2-Mar 16-Mar 1-Apr 6-Apr 18-Apr
12 4 7 7 7 14 5 5 12
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(i.e. benzo[ghi]perylene) and an LOQ of 0.1 ng for 278e300 amu (i.e. coronene). A full table of analytes and ambient concentrations has been included in the Supplementary materials (Table S1). 2.3. Source apportionment using PAH diagnostic ratios The PAH-ratio pairs fluoranthene/(fluoranthene þ pyrene) [Flu/(Flu þ Pyr)] and indeno[cd]pyrene/(indeno[cd]pyrene þ benzo [ghi]perylene) [Icd/(IcdP þ BgP)] form a solvable system of equations since the end-member values for petroleum, coal and biomass combustion have been reported in the literature. The MCSA strategy is governed by the following set of equations:
Fo ¼ xFp þ yFc þ zFb
(1a)
Io ¼ xIp þ yIc þ zIb
(1b)
Where Fp, Fc and Fb are the Flu/(Flu þ Pyr) end-member values for petroleum, coal and biomass combustion, Ip, Ic and Ib respectively, the analogous quantities for Icd/(I þ BgP) and x, y and z are the respective fractions (noting that x þ y þ z ¼ 1). Flu/(Flu þ Pyr) and IcdP/(IcdP þ BghiP) were chosen because the paired PAHs have similar volatility and atmospheric lifetime (Finlayson-Pitts and Pitts, 2000; Perraudin et al., 2007). Additionally, these PAH ratios are well represented in the emission profiling literature, have been used as diagnostic ratios (Yunker et al., 2002; Dickhut et al., 2000; Mandalakis et al., 2005) and their signals are relatively invariant across the 9-week study, mimicking the radiocarbon results. Although Flu and Pyr are partitioned between the gas and particle phase, it is assumed that their ratio is relatively well-preserved across phases and that estimating the total Flu and Pyr based on the particle-phase would introduce an unnecessary source of uncertainty without significantly improving the accuracy. The implicit assumptions of the MCSA method are: (1) the diagnostic ratios will not be altered during transport (2) the three combustion source categories (petroleum, coal and biomass) are sufficient to explain possible combustion source contributions (3) the dominant limitations and uncertainty in the MCSA method are due to the inherent uncertainty of the diagnostic ratios as source tracers. These assumptions are common for receptor-based source apportionment methodology, eg chemical mass balance modelling, where one is using literature-based emission profiles. However, the advantage of the MCSA method for this particular study, lies in its ability to incorporate the variability of the source-diagnostic ratios into the apportionment calculations. For each of the three source categories, 8e10 published PAH ratios were used to calculate the three end-member diagnostic ratios for the MCSA sources. We note that few of these address India-specific combustion situations. For the source categories: petroleum combustion includes profiles for gasoline and diesel motor vehicle exhaust at different loads, no. 2 fuel oil and crude oil combustion; coal combustion includes brown to hard coals for industrial and residential burning; biomass combustion includes biofuel, wood and open burning of forested area. The No. 2 fuel and crude oil combustion represent industrial boilers and will be considered surrogates for ship emissions for the purpose of the current study. While India does not have the same extent of residential coal burning as China, residential coal burning emission ratios were included to represent lower temperature, unregulated coal combustion practices such as in brick kilns (Bond et al., 2004). Similarly, a range of biomass combustion processes occur across India (Sinha et al., 1998; Venkataraman et al., 2005), including, but not limited to, household fuel such as wood and dung cakes, agricultural crop residue burning and vegetation fires. A complete list of
the emission sources and PAH end-member values with associated references is included in the Supplementary materials (Table S2). This is a significant improvement over the single or average emission profile used in previous diagnostic ratio studies (Yunker et al., 2002) and CMB (Sheesley et al., 2005). 2.4. Monte Carlo simulations The present MCSA strategy is a source apportionment model, which includes both the numerical variability and the average end-member ratios for emission source categories using Eqs. (1)aeb. Eqs. (1)aeb can be solved analytically. However, such an approach would introduce bias, since it is implicitly assumed that the calculated fractions are independent of the variability of the emission source values. To account for these effects we adopt a random sampling (Monte Carlo simulation) strategy. Each diagnostic ratio was represented by a Normal distribution, with mean and standard deviation defined by the source data. The Normal distribution was chosen to characterize the data as this distribution is the most unbiased representation of data where the real distribution is not known. Other distributions, e.g., the log-normal distribution may also be implemented within the current approach. However, a detailed comparison of how well different distributions fit the source data was not conducted, since such should be performed on a larger source dataset than was available in the current study. Pseudo-random numbers were then generated from the Normal distributions of the diagnostic ratios (by random sampling) generating Fp, Fc, Fb, Ip, Ic and Ib values for which Eqs. (1)aeb were solved. A large number of solutions (400,000) of the fractional source contributions (x, y and z) were generated using this methodology. The results were sorted into histograms, generating probability density functions (PDFs) describing the fractional influence of the different sources. These PDFs were then used to calculate the mean (m), standard deviation (s) and median (x) of source contributions according to Eqs. (S6e8) (in Supplementary materials), as Summarized in Table 2. These resulting source contribution distributions were all normalized to the range between 0 and 1. All values outside this range were discarded and all calculated values for x, y and z simultaneously satisfied this boundary condition. MCSA simulations were run using PAH diagnostic ratios for three individual Hanimaadhoo samples (Han 1, 2 and 4), the average of the three Hanimaadhoo samples, three individual Sinhagad samples (Sin 1, 2 and 3) and the average of the three Sinhagad samples. Tests were conducted to validate the method. To assess the veracity of assumption 3 from Section 2.3 (uncertainty in the MCSA is due to the diagnostic ratios), calculations were repeated ten times giving a variation in the mean value that was typically lower than 0.003. An assessment of the predictability is outlined in the Supplementary materials. The source with the narrowest endmember value distribution and least overlap with the other sources has the best predictability, in the present case, petroleum combustion. Table 2 Observed PAH ratios from Hanimaadhoo, Maldives (Han) and Sinhagad, India (Sin) with results of Monte Carlo simulations for the fraction contributions from petroleum combustion, coal combustion and biomass combustion (given in the format mean standard deviation of the distribution function). Site
Flu/ (Flu þ Pyr)
IcdP/ (IcdP þ BghiP)
Petroleum
Coal
Han 1 Han 2 Han 4 Sin 1 Sin 2 Sin 3
0.53 0.53 0.53 0.48 0.49 0.48
0.30 0.60 0.54 0.50 0.52 0.53
0.41 0.11 0.15 0.24 0.21 0.20
0.43 0.33 0.32 0.25 0.25 0.24
0.20 0.10 0.12 0.14 0.13 0.13
Biomass
0.22 0.21 0.20 0.18 0.18 0.18
0.16 0.56 0.52 0.50 0.54 0.55
0.11 0.25 0.23 0.21 0.22 0.20
R.J. Sheesley et al. / Atmospheric Environment 45 (2011) 3874e3881
The MCSA simulations were processed with scripts written by the authors for Matlab version 7.9.0 (The MathWorks, Natick, MA, USA). The simulations were run using 400,000 iterations and the resulting distributions for x, y and z were calculated using a histogram bin size of 256. Additional technical information on the repeatability and predictability of the model has been included in the Supplementary materials. 3. Results and discussion 3.1. Meteorological setting Sampling occurred in the second half of the winter dry season, well before the start of the monsoons (Table 1). Carbon concentrations are typically quite high during the South Asian dry season and are drastically reduced during monsoon season (Novakov et al., 2000; Corrigan et al., 2006). Ten day back trajectory analysis starting at 50 m was reported previously for both locations for the entire sampling campaign (Gustafsson et al., 2009) with details in the caption of Fig. 1. Although the particulate phase of the aerosol collected at Hanimaadhoo is highly impacted by emission sources in India, a direct sourceereceptor relationship should obviously not be assumed between Hanimaadhoo and Sinhagad. Nevertheless, back trajectory analysis suggests that Han 6 has similar source
Carbon mg m -3
A
7
Sinhagad
Hanimaadhoo
EC OC
6 5 4 3 2 1 0
B 0.05
Pyrene Chrysene Benzo(a)Pyrene
PAH ng (mg OC)-1
0.04
Benzo(ghi)Perylene
0.03
0.02
0.01
0.00
Alkane ng (mg OC)-1
C
regions as the sampled Sinhagad aerosols (i.e. it is the only one of the Han samples which is solely impacted by the 2 trajectory classification e western Indian margin, etc.). 3.2. Abundance and temporal trends of organic tracers The general trends in atmospheric concentrations of the molecular tracers broadly follows the previously published trends of the bulk aerosol carbon (Gustafsson et al., 2009) for example, with the highest Han values in the JaneFeb samples (Table S1). The four PAHs and two alkanes selected to demonstrate the overall temporal variability were normalized by OC concentrations (Fig. 1bec) to illustrate changes in source contributions relative to bulk carbonaceous aerosol (Fig. 1a). A spectrum of common PAHs spanning from molecular weight 202 (pyrene) to 276 (benzo[ghi]perylene) is displayed (Fig. 1b). For Sinhagad, the normalized PAH concentrations stay rather invariant during the three week observational period (Fig. 1b). For Hanimaadhoo, the PAH concentrations decrease relative to TOC concentrations (Fig. 1a) throughout the study period (Fig. 1b). As mentioned previously, back trajectory analysis indicates a switch from Eastern to Western Indian origin over the course of the Maldives sampling. The higher molecular weight PAHs are relatively more abundant for Han 1 than for Han 2 despite overlapping sample times. Han 1 is quite enriched in benzo[ghi]perylene and somewhat enriched in coronene compared to the remaining Hanimaadhoo aerosol samples (Supp Matl Table S1). Motor vehicle emission characterization studies have suggested that gasoline exhaust is enriched in benzo[ghi]perylene and coronene relative to diesel exhaust (Phuleria et al., 2007) and recent compound-specific radiocarbon analysis of PAHs found indeno[cd]pyrene plus benzo[ghi]perylene to be have slightly higher fossil contribution than the lower molecular weight PAHs (Sheesley et al., 2009). The sum of PAHs (not including coronene) reported in Stone et al (2007) (0.75 ng m3), for the same site on the Maldives, are similar to the concentrations reported here (0.86 ng m3). For these Maldives samples, the C29 and C31 homologs were the two most abundant n-alkanes, which signals impact by biogenic emissions (Rogge et al., 1993a,b). There is a larger temporal variability in the alkane abundance at Hanimaadhoo as compared to Sinhagad, where there is less than a factor of 2 difference in normalized concentration for the six selected tracers. At Hanimaadhoo, there is a factor of five difference between February Han 3 and early March Han 6 for the C29 alkane (Fig. 1c). This reflects the trend for PAHs discussed previously.
5 c29 c31
4
3
2
1
0 28-Jan-06
D
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11-Feb-06
1 1
1
25-Feb-06
2,1
11-Mar-06
2,1
2
Han 1,2 Han 3 Han 4 Han 5
Han 6
25-Mar-06
08-Apr-06
3
3
Sin 1 Sin 2
22-Apr-06
3 Sin 3
Fig. 1. Ambient concentrations for Hanimaadho (the Maldives) and Sinhagad (western India) with air mass source regions. (A) Organic carbon (OC) and elemental carbon (EC) concentrations (B) OC-normalized concentrations for four ubiquitous PAHs (pyrene, chrysene, benz[a]pyrene and benzo[ghi]perylene) (C) OC-normalized concentrations for n-nonacosane (C29) and heneitriacontane (C31) (D) Source region by back trajectory analysis: 1) Ganges Valley in North India and the eastern Indian continental margin (2) Northern Arabian Sea, Northwest India, Pakistan and the western Indian continental margin (3) Mumbai region, the Northern Arabian Sea, Northwest India and Pakistan.
3.3. Source diagnostics of organic tracers and carbon isotopes Source-diagnostic ratios of individual n-alkanes and PAHs have been applied in both atmospheric and geochemistry source studies. It is necessary to stress that individual species may have different source contributions and any given ratio is not highly source specific. It is therefore valuable to combine several organic tracer classes in concert with source-diagnostic carbon-isotope signals. Hypotheses of source contributions can then be tested against a broader set of constraints. 3.3.1. Alkanes There is a well-known odd-to-even predominance in high molecular weight n-alkane chain lengths for biogenic emissions but an odd-to-even ratio near one for higher temperature combustion processes. This feature is commonly evaluated by calculating the carbon preference index (CPI). For this campaign the carbon number range 25e35 (CPI25e35) was chosen to most closely correspond to previously published atmospheric CPI, using the equation from Yue and Fraser (2004).
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Fig. 2. Apportionment and characterization of total organic carbon by Cwax, CPI25e35 and d13C-TOC for Hanimaadhoo, Maldives and Sinhagad, India.
The CPI of natural products is preserved in biogenic emissions from leaf detritus (Rogge et al., 1993a,b), which has a reported CPI around 12 for fine particulate green and dead leaf detritus and a Cmax of 31 (Rogge et al., 1993a,b). Alkanes emitted during fossil fuel combustion have a CPI of 1.0 (Rogge et al., 1993a,b). Reported CPI for wood combustion and biomass combustion in a wood stove lies in the range of 1e2.7, with most below 2 (Sheesley et al., 2003; Lee et al., 2005). Grass and cereal combustion have been reported with CPI over 5.0 and up to 20 (Simoneit, 2002; Zhang et al., 2007). Therefore, CPI values averaging below 2 are indicative of fossil fuel and high temperature biomass combustion, while CPI over 10 are indicative of biogenic emissions and potential impact of low temperature combustion of grasses and cereals. Another source-diagnostic alkane ratio is the Cwax, which sums biogenic alkane concentration (Yue and Fraser, 2004). Cwax was originally designed to separate biogenic from combustion alkanes (Yue and Fraser, 2004), but it does not take into account that CPI values over 1.0 are common for biomass combustion. Because of this omission, the Cwax will just be used as an additional visualization of the odd-over-even predominance. Alkane-based fingerprint source information was compared with d13C-TOC (Fig. 2), which is the stable carbon ratio relative to the reference material Vienna PeeDee Belemnite (d13Csample ¼ {(13C/12C sample)/(13C/12C standard)e1} 1000). The CPI25e35 and Cwax for the aerosols at Hanimaadhoo is lower than for Sinhagad. This suggests a lower contribution at Hanimaadhoo from biogenic emissions and/ or low temperature biomass burning. However, the radiocarbon data constrains that the fraction of total contemporary biomass is nearly identical for Hanimaadhoo and Sinhagad (68 3 and 65 2% biomass for TOC, respectively) as is the EC/TC (0.16e0.22 for both sites) (Gustafsson et al., 2009). The d13C of TOC for the Hanimaadhoo aerosol is enriched, indicating greater impact from C4 plants which includes grasses, corn and sugarcane (Gustafsson et al., 2009). Taken together, the different d13C and CPI25e35 at the two sites, with similar and near invariant D14C, may reflect a transition in sources from Eastern to Western India rather than the addition of an emission source. A comparison with a previous urban study of eastern and western Indian cities (Chowdhury et al., 2007) confirms that the winter urban CPI (1.23e1.48) is significantly lower than measured at the regional background sites in this study as Han 1-Han 5 is 2.6e3.4 and Sin samples are 4.2e5.2. This would indicate that the
Fig. 3. Diagnostic ratios of PAHs to indicate source contributions for Hanimaadhoo, Maldives and Sinhagad, India with comparison to grouped combustion sources for (A) fluoranthene/fluoranthene þ pyrene (Flu/(Flu þ Pyr)) (B) indeno[cd]pyrene/(indeno[cd] pyrene þ benzo[ghi]perylene) (IcdP/(IcdP þ BghiP)). Ratios of grouped combustion sources (Supp matl Table S2) are visualized using box plots where the ends of the boxes represent the 25th and 75th percentiles, the whiskers represent the 10th and 90th percentiles, outliers are plotted beyond the whiskers and the median is represented by the internal box line. Median lines for each combustion type have been extended.
R.J. Sheesley et al. / Atmospheric Environment 45 (2011) 3874e3881
rural and residential impact is an important contribution to the contemporary biomass signature evident in the radiocarbon and alkane CPI25e35 values of S. Asian background aerosols. The current dataset does not allow constraining the total biogenic contribution to atmospheric TOC. The primary particulate biogenic contribution can be represented by alkanes with CPI > 10, but the total biogenic component of TOC would have to include secondary biogenic carbon ie. from the oxidation of isoprene (Edney et al., 2005). A rough estimation of the fraction of organic carbon coming from primary biogenic emissions, following Rogge et al, (1993a,b) and using total alkanes (n25e35)-to-organic carbon ratios indicate that 2e6% of OC is primary biogenic in Hanimaadhoo and 5e7% is primary biogenic at Sinhagad. 3.3.2. PAH ratios Various PAH ratios have been suggested to elucidate PAH and carbon sources in atmospheric aerosol (McVeety and Hites, 1988; Benner et al., 1995; Lima et al., 2005; Mandalakis et al., 2005) and in sediments (Readman et al., 1987; Yunker et al., 2002; Lima et al., 2005; Elmquist et al., 2007). Fig. 3 presents ambient results and end-member ratios for two diagnostic PAH ratios, Flu/(Flu þ Pyr) and IcdP/(IcdP þ BghiP)(see the full list of sources and references in Supp Mat, Table S2). Unfortunately, IcdP was not quantified in three of the six of the Hanimaadhoo samples, however the three available samples represent nearly three weeks of the dry season in Hanimaadhoo and two different trajectory classifications (see Section 3.1). The Sinhagad set is complete for both ratios. 3.4. Monte Carlo simulations The current MCSA strategy has been outlined in detail in the Methods section, with complementary information available in
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the Supplementary materials. Accordingly, three probability density functions were obtained for each sampling point (time and site), illustrating the fractional influence of the three sources. Fig. 4 depicts examples of PDFs for Hanimaadhoo and Sinhagad illustrating the source contribution output from the MCSA. The remaining plotted results are included in the Supplementary materials Figs. S1e2. Median values are drawn as vertical lines in the figures while mean and standard deviations are included in Table 2. Focusing on the Sinhagad site, the two PAH ratios observed for the three sampling periods are fairly similar (Table 2). Little temporal variation is observed in the Sinhagad values, with mean fractions of biomass (z) ranging from 0.50 to 0.55. The coal and petroleum has roughly equal contribution ranges, 0.24e0.25 and 0.20e0.24, respectively. Han 2 and Han 4 have similar contributions from biomass combustion as Sinhagad, while Han 1 has significantly lower biomass and higher petroleum combustion contributions. Differences between median and mean values are indicators of skewness in a PDF. The probability density distributions depicted in Fig. 4, S1 and S2 are asymmetric. The difference for the mean and median ranges is between 0.00e0.03 for all sampling periods. Sin 1 illustrates the impact of skewed distribution on the results (Fig. 4def). Even though the main probability mass is centered around 0.6, values 0e0.6 are more probable than values 0.9e1. In other words, occurrences of low biomass influence are more probable than occurrences of high biomass influence in the Sin 1 sample. The calculated standard deviations (0.10e0.24) reflect the width of the PDFs, which in turn reflect the variability of the source data. These values should therefore not be interpreted as the precision of the MCSA calculations in itself, as discussed in Section 2.4. These inherent variations are important to keep in mind when comparing with more precise data, such as source apportionment by 14C measurements.
Fig. 4. Calculated distribution functions for the fractional contributions from different combustion sources for Han 4 (A, biomass, B, coal, C petroleum) and Sin 1 (D, biomass, E, coal, F, petroleum). The dashed vertical lines correspond to the median fraction of respective source and the solid grey vertical lines depict the width of the distribution (1.s.d.).
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Table 3 Comparison of biomass contribution for Hanimaadhoo, Maldives and Sinhagad, India for Monte Carlo source apportionment (MCSA) (this study) and radiocarbon apportionment of total organic carbon (TOC), soot carbon (SC), and elemental carbon (EC) (Gustafsson et al., 2009). MCSA results given in the format mean (median) standard deviation of the distribution function, 1 s.d.
Hanimaadhooa Sinhagadb
MCSA
14
31(29) 18% 54(57) 22%
68 3% 65 2%
C TOC
14
C SC
69 6% 64 3%
14
C EC
41 5% 54 3%
a Average observed PAH ratio used in MCSA was 0.53 for Flu/(Flu þ Pyr) and 0.42 for IcdP/(IcdP þ BghiP). b Average observed PAH ratio used in MCSA was 0.48 for Flu/(Flu þ Pyr) and 0.48 for IcdP/(IcdP þ BghiP).
Since z represents the fraction biomass in the PAH calculations, and x þ y represent the fraction fossil fuel combustion, the calculated values may therefore be compared with our earlier results from a 14C-based source apportionment (Gustafsson et al., 2009) (Table 3). D14C is the ratio of radiogenic 14C to 12C relative to the reference standard NBS oxalic acid as discussed in Stuiver and Polach (1977). In order to get an accurate error estimate for the average source contributions for Hanimaadhoo and Sinhagad, the MCSA was also run with the time-weighted average values of Han 1,2 and 4 and then Sin 1,2 and 3. Interestingly, the timeweighted values of biomass obtained from Monte Carlo simulation of PAH values (54%) matches the 14C-derived fraction biomass for EC (54%) in Sinhagad. However, the precision of the MCSA-derived values are considerably lower (22% as compared with 2e6%). These values both have lower biomass contribution as compared to TOC and SC for Sinhagad. Gustafsson et al. (2009) suggested, based on false positive results of the EC method (Hammes et al., 2007), that the discrepancy between the EC, SC and OC data could be due to a higher contribution of fine coal dust or coal combustion particles in EC than the more selective SC. Previous studies have shown a large wintertime increase in Se, a tracer for coal burning, in Hanimaadhoo (Stone et al., 2007). The mega-cities Delhi and Kolkata, which according to the back trajectories are influencing samples Han1eHan4, each were estimated to have a 14% coal contribution to fine particulate matter during the winter season, based on picene as a molecular marker in chemical mass balance modeling, while the western India city of Mumbai, impacting samples Han5-Han6 and Sin, had an estimated 8% coal contribution (Chowdhury et al., 2007) The present study supports the hypothesis that there is a significant coal contribution in the particle phase of the aerosol collected at Han. The current PAH-based source apportionment suggests that the fraction biomass in Hanimaadhoo is more variable through time than in Sinhagad, as calculated from PAH data (Table 2). This is in contrast to the radiocarbon data, which show a very stable biomass contribution during the same period (Gustafsson et al., 2009). However, this discrepancy is mainly due to the low IcdP/(IcdP þ BghiP) ratio for Han 1. As expected, the Hanimaadhoo mean biomass contribution (31 18%) is lower than obtained by radiocarbon source apportionment, but still within a standard deviation of the EC biomass contribution (41%, Table 3). The present MCSA for particulate PAHs shows good agreement with the previously obtained 14C-based results and contributes novel information by refining the fraction fossil fuels into separate petroleum and coal combustion source classes. 4. Conclusion By applying an MC-based source apportionment strategy to PAHratios greater total source resolution is obtained when combined with 14C-based source information (Gustafsson et al., 2009).
Radiocarbon source apportionment constrained that the background S. Asian aerosol is a mixture of biomass and fossil sources, with biomass sources dominating the TOC and composing 40e50% of the EC isolate (Gustafsson et al., 2009). The present model shows good agreement with previous estimates of the fraction of biomass, but refines the fraction fossil fuels into petroleum and coal combustion. The organic tracers here add some resolution within each of those two broad source classes, within the constraints of the dual carbon-isotope results. Taking the isotopes and organic tracers together, it appears that both Hanimaadhoo and Sinhagad provide regional background composites as discussed previously (Stone et al., 2007; Gustafsson et al., 2009), not dominated by local emissions but rather providing long-term integration of both urban and rural contributions. The CPI25e35 reported here is significantly higher than seen in Indian mega cities (Chowdhury et al., 2007), which suggests that the rural component of the regional aerosol is primarily due to biomass combustion and/or biogenic emissions. Another significant outcome of the MC-based source apportionment is that coal is contributing to the fossil signal of the regional carbonaceous aerosol. Acknowledgments This research is a contribution of the Stockholm University Bert Bolin Centre for Climate Research. We gratefully acknowledge access to and support of the Han field site for the 2006 campaign by the Maldives Meteorology Office and H. Nguyen of the international ABC program and of the Sin field site in 2006 by the Indian Institute of Tropical Meteorology in Pune, India and the Department of Meteorology, Stockholm University. We particularly value the assistance during S. Asian field work by P.S. Praveen, P.S.P. Rao and P.D. Safai and the advice and support of Henning Rodhe, Lennart Granat and Caroline Leck in preparations and logistics. We appreciate the technical assistance of Martin Kruså in field sampling and chemical analysis. We thank Jens Danielsson for valuable discussions on the Monte Carlo simulations. This study was financed by the Swedish Strategic Environmental Research Foundation (MISTRA contract nr2002-057); the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS contract nr 214-2006-527); and the Swedish International Development Agency, Department for Research CoOperation (SIDA-SAREC contract nr SWE-2006-148). ÖG also acknowledges support as an Academy Researcher from the Swedish Royal Academy of Science. AA gratefully acknowledges the Knut and Alice Wallenberg Foundation and the Magnus Bergwall Foundation for financial support. Appendix. Supplementary material Supplementary material associated with this paper can be found, in the online version, at doi:10.1016/j.atmosenv.2011.01.031. References Benner, B.A., Wise, S.A., Currie, L.A., Klouda, G.A., Klinedinst, D.B., Zweidinger, R.B., Stevens, R.K., Lewis, CW., 1995. Distinguishing the contributions of residential wood combustion and mobile source emissions using relative concentrations of dimethylphenanthrene isomers. Environmental Science & Technology 29, 2382e2389. Bond, T.C., Streets, D.G., Yarber, K.F., Nelson, S.M., Woo, J.H., Klimont, Z., 2004. A technology-based global inventory of black and organic carbon emissions from combustion. Journal of Geophysical Research-Atmospheres 109, D14203. doi:10.1029/2003JD003697. Chowdhury, Z., Zheng, M., Schauer, J.J., Sheesley, R.J., Salmon, L.G., Cass, G.R., Russell, AG., 2007. Speciation of ambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities. Journal of Geophysical Research-Atmospheres 112. doi:10.1029/2007JD008386.
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