characteristics of organic matter in pm in...
TRANSCRIPT
1
Characteristics of Organic Matter in PM2.5 in Shanghai
Jialiang Feng1, Chak K. Chan1, Ming Fang*, 2
1Department of Chemical Engineering
2Institute for Environment and Sustainable Development
Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Min Hu, Lingyan He, Xiaoyan Tang
State Key Joint Laboratory of Environmental Simulation and Pollution Control
College of Environmental Sciences
Peking University, Beijing, China
*Corresponding author. Email: [email protected], Tel: (852)2358 6916, fax (852)2358 1334
Abstract
Solvent extractable organic compounds (SEOC), organic carbon, elemental carbon and
water soluble organic carbon (WSOC) in PM2.5 samples collected in Shanghai, China in 2002
and 2003 were measured to determine the composition and sources of the organic matter in
atmospheric aerosols. Distinct seasonal variations were detected with higher concentrations
of organic matter in winter. The concentration of total carbon at ~20 μg m-3 in winter was
about three times the summer value. About 30% of the total carbon was water soluble.
Unresolved complex mixture (UCM) and fatty acids were the most abundant components
quantified in SEOC, similar to other Chinese cities previously studied. High ratio of UCM to
n-alkanes (U:R) and the composition of triterpanes indicated that engine exhaust was a major
source of the airborne organic matter. Emissions from coal burning had more impact in the
rural areas, based on the U:R value and PAHs composition. Chemical Mass Balance (CMB)
modeling showed that about one half of the organic carbon was from engine exhaust and
slightly over 20% was from coal burning. No clear spatial variation in the concentration of
This is the Pre-Published Version
2
the organic matter was found between urban and rural areas. Our results showed that due to
the rapid urbanization and relocation of industrial plants from urban areas to rural areas in the
past 20 years, air pollution in rural areas is becoming a serious problem in Shanghai and the
Yangtze River delta.
Keywords: Aerosol; Organic carbon; Water soluble organic carbon; Solvent extractable
organic compound; GC-MS; China
1. Introduction
Shanghai is the largest commercial and industrial city in China with a population of ~15
million. It has the largest steel mill and petrochemical complex in China. Rapid economic
growth in the last two decades has caused soaring energy demand and the annual coal
consumption in Shanghai increased from ~20 million tons in 1989 to ~45 million tons in
2004. Although the annual average concentration of total suspended particulates (TSP) has
dropped since the mid-1990s, fine particle loading has remained high (Shanghai
Environmental Protection Bureau). The annual PM10 loading in recent years (2001-2004) is
high at ~100 μg m-3.
Airborne lead pollution in Shanghai has been found to be mainly from the cement and
metallurgy industries and coal combustion (Zheng et al., 2004; Chen et al., 2005). Only a
small part of the airborne lead (~20%) was from automotive emissions. Chemical Mass
Balance (CMB) model analysis of elemental metals showed that coal combustion,
construction, vehicle emissions, and steel furnaces were the main contributors of TSP (Shu et
al., 2001). Researches on PM2.5 in Shanghai showed that ~42% of the mass was secondary
aerosols of ammonium sulfate and nitrate (Ye et al., 2003) and stationary emissions (coal
burning) are still believed to be the dominant sources (Yao et al., 2002).
A large portion of the PM2.5 mass in Shanghai (~40%) is found to be carbonaceous
3
materials (Ye et al., 2003), but data on the detailed speciation of organic species in aerosols is
not available. Solvent extractable organic compounds (SEOC) in aerosols contain useful
molecular markers that have been successfully used for source apportionment (Simoneit,
1986; Zheng et al., 2000, 2005) and SEOC has been found to be toxic and can cause DNA
mutation even at non-lethal dosages (Hsiao et al., 2000). Knowing the composition of the
organic matter is also important to understanding the impact of continental aerosols on ocean
ecology, because Shanghai is an important starting point of the outflow of continental
pollutants to the East China Sea and the Pacific Ocean (Parungo et al., 1994).
In this paper, we focus on a detailed study of the abundance and characteristics of SEOC
in PM2.5 samples collected in Shanghai on a seasonal basis. The impact of urbanization on the
composition of organic aerosols at rural sites will also be discussed.
2. Experimental
2.1. Sampling
Samples were collected simultaneously at two sites. One was located on the rooftop of a
five-story building, with a height of ~15 meters, on the campus of Fudan University (FDU).
The campus is in the northeast part of the urban area. There is a viaduct with heavy traffic
near the campus. The other site was at the Shanghai Observatory (SHO) located on a small
hill of ~100 meters high called Sheshan in the southwestern countryside of Shanghai. It was
once used as a background site for air pollution monitoring. There was no direct emission
source near the Observatory. The distance between the two sampling site is ~40 km.
PM2.5 samples were collected during 21-28 November 2002 (high loading season) and 14-
21 August 2003 (low loading season) using high-volume samplers with the same procedure
described in our previous publication (Feng et al., 2005).
2.2. SEOC Analysis
The details of the analysis of SEOC were presented in our previous publications (Zheng
4
et al., 1997; Feng et al., 2005). Briefly, prior to extraction, an internal standard mixture
containing eicosane-d42, octacosane-d58, phenanthrene-d10, chrysene-d12, perylene-d12 and
heptadecanoic acid-d33, was spiked onto the filters. Then, the samples were ultrasonically
extracted with three 100-ml aliquots of dichloromethane at room temperature. The combined
extract was concentrated to a volume of 2-3 ml, filtered and reduced further to a volume of
200-300 μl with a stream of high purity N2. The extract was reacted with 14% BF3 in
methanol to esterify the free organic acids and then fractionated with a flash column of silica
gel into four sub-fractions: aliphatics, polycyclic aromatic hydrocarbons (PAHs), fatty acids
(methyl ester) and alkanols.
The total extract and the four fractions were subjected to GC-MS (Finnigan TSQ700
interfaced to a Hewlett Packard Model 5890A gas chromatograph) analyses. The MS was
operated in the electron impact mode at 70 eV and the scan range is 50-550 amu. The GC was
equipped with a HP-5MS capillary column (30m × 0.25 mm i.d., film thickness 0.25 µm),
with helium as carrier gas. The n-alkanol fraction was converted to trimethylsilyl derivatives
by reaction with N,O-bis-(trimethylsilyl) trifluoroacetamide (BSTFA) before analysis. Prior
to the GC-MS analysis, hexamethyl benzene was added to all fractions to be used as an
internal standard for n-alkanols and to check the recovery of the other three fractions.
Alkanes, PAHs and fatty acids were quantified using the corresponding deuterated internal
standards having similar chemical characteristics and retention times.
2.3 OC, EC and WSOC measurements
Organic and elemental carbon (OC and EC) concentrations of the samples were analyzed
by thermal/optical method (Sunset OC/EC analyzer) with the NIOSH temperature program
(Birch, 1998).
Small samples punched from the high-volume filters (5.8 cm2 each) were extracted with
nano-pure water and the concentration of the water soluble organic carbon (WSOC) was
5
measured with a total organic carbon analyzer (Shimadzu TOC-5000A). Hydrochloric acid
was added to each sample before analysis to remove inorganic carbon.
3. Results and discussion
3.1. Loading of OC, EC and WSOC
The average OC in November 2002 (~16 μg m-3, Table 1) was comparable to the reported
winter value (Ye et al., 2003), but the averages of August 2003 (3.9 μg m-3, urban and 4.9 μg
m-3, rural) were lower than the reported average summer value (~10 μg m-3). The occurrence
of several high concentration episodes could be an important cause of the higher reported
summer value (Ye et al., 2003). TC concentrations of ~20 μg m-3 in winter indicated that the
pollution level was high in Shanghai even though TC concentrations were lower than other
Chinese cities such as Beijing and Guangzhou (Cao et al., 2003; Duan et al., 2005).
The average winter OC/EC ratios (3.9 and 4.5 in urban and rural, respectively) were
higher than in summer (2.2 and 2.4 respectively), which could be an effect of lower ambient
temperature. The percentage of organic carbon evaporated at <250 oC (OC1 in the OC/EC
thermogram) in the total carbon was obviously higher in the winter samples than in the
summer samples.
About 30% of the total carbon was water soluble. Higher WSOC at the rural site
suggested a larger contribution from secondary organic carbon due to the longer distance
between the sampling site and the emission sources.
3.2. Loading of SEOC
Quantified SEOC includes n-alkanes, PAHs, n-fatty acids, n-alkanols, and molecular
markers such as pentacyclic triterpanes. Also quantified was the unresolved complex mixture
(UCM) in the aliphatic fraction, which was actually more abundant than the total resolved
components. The total yield of resolved SEOC was 163 ng m-3 at the urban site and 178 ng
m-3 at the rural site in summer, and 650 and 740 ng m-3 respectively in winter. Among the
6
resolved components, fatty acids were the most abundant. In summer, ~80% of the resolved
SEOC yield was fatty acids and ~70% in winter. About 5% of the resolved SEOC in summer
and 8% in winter was PAHs.
3.3. Seasonal variation
Distinct seasonal variations were found in the organic matter with higher concentrations
in winter. The winter to summer concentration ratios for n-alkanes were 6.7 and 4.8 for urban
and rural, respectively, 7.3 and 6.6 for PAHs, 3.1 and 3.4 for n-fatty acids, and 7.0 and 11.1
for n-alkanols. Many of the SEOCs were semi-volatile, contributing to the more pronounced
seasonal variation when compared to elemental carbon (2.2 and 1.8 at the urban and rural
sites, respectively).
High mixing heights are known to lower pollutant concentrations and high mixing heights
are usually caused by higher ambient temperatures in summer. Furthermore, gas/particle
partitioning is also temperature dependent (Bidleman et al., 1986; Pankow and Bidleman,
1992). The difference in ambient temperature (26-30 oC in summer and 8-12 oC in winter
during our sampling periods), therefore, would be the major cause of this seasonal variation.
As a coastal city, Shanghai is affected by summer monsoons, which bring in clean oceanic
winds that dilute local air pollutants. More precipitation in summer also can remove the
pollutants. Low level temperature inversion was also a possible cause for the higher winter
concentrations (Ye et al., 2003).
3.4. Spatial variation
The expected differences in pollutant concentrations between the urban and rural
sampling sites were not found. Actually, the average loading of resolved SEOC was a little
higher at the rural site (Table 1). This phenomenon is the consequence of the change in land
use when a developing nation modernizes or urbanizes. Due to the rapid increase in the value
of land and to the Shanghai government’s efforts to shut down polluting plants, more than
7
1,500 factories in the city were closed down or relocated to rural areas since the mid-1990s
(http://www.sh.xinhua.org/tebiebaodao/tebiebaodao/gongye.htm). Boilers in the urban area
now use natural gas or they are retrofitted to burn more efficiently. In addition, new factories
are being built in the countryside. About 70% of the added value of industrial output of
Shanghai was produced by factories outside the city limits in 2002 (Shanghai Economy
Yearbook, 2003). The change in land use has redistributed pollution sources. What was
countryside once is now studded with manufacturing plants. As a result, it is difficult to
distinguish between rural and urban areas in Shanghai through analysis of air quality data
(http://www.sepb.gov.cn).
The geography of the two sampling stations caused the urban site to be cleaner than the
rural site when the winds were from the sea (east wind in summer and northeast wind in
winter), and vice versa when the winds were from the land (south or southeast winds in
summer and northwest wind in winter) (Table 1).
3.5. Composition of SEOC
3.5.1 Aliphatic hydrocarbons
The concentrations and distributions of n-alkanes (C17-C36) are given in Table 1 and
Figure 1. Carbon preference index (CPI) has been found to be useful in distinguishing the two
main categories of n-alkanes, biogenic and fossil fuel residue (Simoneit, 1986; Rogge et al.,
1993; Fang et al., 1999; Tareq et al., 2005). Alkanes from biogenic sources have high CPI
values (6-9 or above), while alkanes from fossil fuel residue comprise mainly of low carbon
number compounds and show no carbon predominance (CPI value of unity). The low CPI
values (1.4 at the urban site and 1.6 at the rural site in summer, 1.2 at both sites in winter,
Table 1) in Shanghai indicated that fossil fuel residue was the main source of n-alkanes. The
concentration of alkanes from biogenic sources (plant wax) estimated using the method of
Simoneit et al. (1991) showed that only ~20% of the alkanes in summer and <10% in winter
8
were from biogenic sources.
The winter samples had more low carbon number alkanes. The ≤C26 homologues
accounted for only ~20% of the total alkane in summer but ~60% in winter. The carbon
number maximum (Cmax) in the summer samples was C29 at both sites, while it was C23 at
the urban site and C24 at the rural site in the winter samples. Similar phenomenon was also
observed in other cities such as Beijing (Feng et al., 2005) and was attributed to the seasonal
difference in ambient temperature. The ≤C26 homologues of the alkanes are semi-volatile and
will be partitioned between the gas and particle phases (Pankow and Bidleman, 1992).
UCM, composed of unresolvable highly branched and cyclic aliphatic hydrocarbons, was
thought to be from the incomplete combustion of fossil fuels, and has been used to evaluate
the impact of anthropogenic sources (Simoneit, 1986). It is found that vehicular emissions
have higher ratio of unresolved to resolved alkanes (U:R) than coal and wood combustion
emissions (Kavouras et al., 2001). PM2.5 samples in Shanghai had high U:R ratios (19.7 and
11.3 at the urban and rural sites, respectively, in summer, while 7.8 and 6.2 in winter, Table
1), suggesting petroleum residue (engine exhaust or traffic) is the main source of aliphatic
organic matter. Aerosols taken at the rural site had lower U:R ratios, indicating a lower
contribution from traffic.
Triterpanes (hopanes) from C27-C35 without C28 were detected and quantified by the key
ion of m/z 191 (Figure 2). Hopanes are widely used as markers for fossil fuel residue,
especially engine exhaust (Simoneit, 1986; Schauer et al., 1999b). The distribution patterns of
hopanes in Shanghai were quite similar between summer and winter, with the most abundant
compound being 17α(H),21β(H)-hopane, followed by 17α(H),21β(H)-norhopane. The
S/(S+R) ratio at ~0.6 for the isomers of 22S and 22R 17α(H),21β(H)-homohopane suggests
that these triterpanes were from high thermally maturated fossil fuels (or products). The
distribution of triterpanes was similar to reported values from engine exhaust, but different
9
from coal burning (Oros and Simoneit, 2000; Feng et al., 2005), suggesting that engine
exhaust was the main source of these triterpanes in Shanghai.
The yield of triterpanes was higher at the urban site (8.0 ng m-3 in summer and 24.2 ng m-
3 in winter) than at the rural site (5.4 ng m-3 in summer and 18.7 ng m-3 in winter). The ratio
of triterpane yield to non-volatile fossil fuel residue alkanes (sum of the >C26 alkanes that
was not biogenic) was ~0.7 at the urban site and ~0.4 at the rural site, indicating that traffic
emissions was more influential in the urban area.
3.5.2 PAHs
Benzo[b+k]fluoranthene was dominant in all samples, followed by benzo[ghi]perylene
(BgP), indeno[1,2,3-cd]pyrene (IP), benzo[e]pyrene (BeP) and chrysene/triphenylene (Figure
3). One obvious difference in the PAH distributions between summer and winter was the
higher low molecular weight (LMW, molecular weight ≤228) PAH concentrations in winter.
The sum of LMW concentrations was only about 20% of total PAHs in summer while >40%
in winter. Similar to what was found on n-alkanes, this was mainly due to the gas-particle
partitioning of the semi-volatile LMW at different ambient temperatures and is in agreement
with other studies (Bidleman et al., 1986; Zheng et al., 2000; Feng et al., 2005).
The BeP/(BeP+BaP) ratio (BaP is benzo[a]pyrene) is used as an indicator for the decay of
labile BaP in the atmosphere and thus the aging of aerosols (Nielsen, 1988). Most freshly
emitted aerosols have a ratio of ~0.5 (Grimmer et al., 1983). The BeP/(BeP+BaP) ratio in
Shanghai was >0.7 for both summer and winter (Table 1). Higher ratio in summer indicates
the preferential loss of BaP because of the higher ambient temperature and solar radiation;
this is also reported by studies in other Chinese cities (Guo et al., 2003; Feng et al., 2005).
The high ratio in the winter indicates the impact of the non-local and/or aged aerosols and is
in agreement with the higher WSOC/TC ratio in winter.
The IP/(IP+BgP) ratio has been found useful for source apportionment. Aerosols emitted
10
from gasoline vehicles, diesel vehicles, coal combustion were reported to have values of 0.2,
0.37 and 0.56 respectively (Grimmer et al., 1983). In Shanghai, the IP/(IP+BgP) ratio was
0.41 at the urban site and 0.45 at the rural site in summer, and 0.44 and 0.46 respectively in
winter. These numbers indicate a mixed source. The urban site had a lower value than the
rural site in summer indicated a stronger presence of engine exhaust.
3.5.3 Alkanoic acids
C16 and C18 saturated acids were the two largest peaks and they accounted for 50-70% of
the total fatty acids (Figure 4). The strong even carbon number predominance (CPI >10,
Table 1) suggests that the fatty acids were mainly biogenic.
The <C20 homologues of fatty acids are ubiquitous and cooking is one of the important
sources of these compounds (Schauer et al., 1999a; He et al., 2004); while the >C22
homologues are from vascular plant wax (Simoneit, 1986). In Shanghai, the contribution
from plant wax at the rural site (23% in summer and 24% in winter, Table 1) was slightly
higher than at the urban site (16% in summer and 18% in winter). Similar distribution
patterns between summer and winter indicated that the sources of fatty acids had small
seasonal variations.
3.5.4. n-alkanols
Normal alkanols of C12-C32 were detected in all samples (Table 1). The distributions of
the n-alkanols were similar between summer and winter although the concentrations were
quite different (Figure 5). Bi-modal distribution was found with a main peak at C30 (or C28)
and a minor peak at C18 (or C16). Alkanols from vascular plant wax, homologues of >C20
(Simoneit, 1986), accounted for about 65% of the total alkanols. The different Cmax in
summer (C28) and winter (C30) suggested that the alkanols were from different vegetations in
the two seasons.
11
3.6. Source apportionment of carbonaceous components
The sources of the carbonaceous material in the PM2.5 of Shanghai were apportioned
using the Chemical Mass Balance model (CMB8, Watson et al., 1998) with SEOC as tracers.
Source profiles for diesel engine, catalytic and non-catalytic gasoline engine exhausts, meat
cooking and cooking with seed oils are from Schauer et al. (1999a, b; 2002a, b), for
vegetative detritus it is from Rogge et al. (1993), for Chinese kitchen emission it is from He
et al. (2004), for biomass combustion it is from Sheesley et al. (2003), and for Chinese coal
combustion it is from Zheng et al. (2005). Since Si and Al were not measured in this study,
road dust resuspension was not included. The species included in the CMB modeling were
EC, n-alkanes of C25-C34, steranes of C27-C29, hopanes of C27-C30, PAHs (MW 252 and 276,
without BaP), alkanoic acids of C14-C30, cholesterol and β-sitosterol. Considering that non-
local source profiles were used, the CMB results were statistically significant with R2 and
chi-square at 0.74-0.78 and 4.1-4.5, respectively. Modeling results in Table 2 show that
traffic emissions were the largest contributors to particulate OC. Coal burning emission
contributed ~15% of the OC. After taking sulfur (SO2 and sulfate) into account, it was found
that pollution caused by coal usage is still the most important in Shanghai. Kitchen emissions
accounted for about 8% of the OC, warranting more attention. The unexplained OC (denoted
as “Others” in Table 2) was probably due to secondary organic aerosols.
4. Conclusion
No clear spatial variation in SEOC concentrations was observed between the urban and
rural sampling sites and this was attributed to the change in land use in Shanghai due to the
rapid urbanization of the rural areas in the past 20 years. In an attempt to relieve the city of
industrial pollution sources, large-scale relocation of manufacturing plants to rural areas
redistributed the pollution sources. Although the air quality in the city has improved, the rural
areas are now polluted.
12
The concentrations of carbonaceous matter from the PM2.5 samples collected at a rural
site and an urban site in Shanghai in 2002 winter and 2003 summer showed that air pollution
is much more severe in winter. Total carbon concentration at ~20 μg m-3 showed that air
pollution was very severe in winter.
The n-alkanes distribution showed that fossil fuel residue was the main source with not
more than 20% contribution from plant wax during both seasons. The similarity in triterpane
distribution with reported tunnel data and the high U:R ratio of the aliphatic fraction indicated
that engine exhaust was a major source of SEOC. IP/(IP+BgP) ratios suggested a mixed
source of engine exhaust and coal burning for PAHs.
Acknowledgement
The authors are grateful for financial support from NSFC/RGC (N_HKUST613/01) and
NSFC No. 20131160731, 20177002. The authors are also grateful to Messrs Xiaofeng Huang
and Yunliang Zhao for their help in collecting the samples. The authors also wish to thank Dr.
Jianzhen Yu for her help in OC/EC analysis.
References
Bidleman, T.F., Billings, W.N., Foreman, W.T., 1986. Vapor-particle partitioning of
semivolatile organic compounds - Estimates from field collections. Environ. Sci. Technol.
20, 1038-1043.
Birch, M.E., 1998. Analysis of carbonaceous aerosols: interlaboratory comparison. Analyst
123, 851-857.
Cao, J.J., Lee, S.C., Hoa, K.F., Zhang, X.Y., Zou, S.C., Fung, K., Chow, J.C., Watson, J.G.,
2003. Characteristics of carbonaceous aerosol in Pearl River Delta Region, China during
2001 winter period. Atmos. Environ. 37, 1451–1460.
13
Chen, J.M., Tan, M.G., Li,Y.L., Zhang,Y.M., Lu,W.W., Tong,Y.P., Zhang,G.L., Yan Li, Y.,
2005. A lead isotope record of shanghai atmospheric lead emissions in total suspended
particles during the period of phasing out of leaded gasoline. Atmos. Environ. 39, 1245-
1253.
Duan, F., He, K., Ma, Y., Jia, Y., Yang, F., Lei, Y., Tanaka, S., Okuta, T., 2005.
Characteristics of carbonaceous aerosols in Beijing, China. Chemosphere 60, 355-364.
Fang, M., Zheng, M., Wang, F., Chim, K.S., Kot, S.C., 1999. The long-range transport of
aerosols from Northern China to Hong Kong - A multi-technique study. Atmos. Environ.
33, 1803-1817.
Feng, J.L., Chan, C.K., Fang, M., Hu, M., He, L.Y., Tang, X.Y., 2005. Impact of
meteorology and energy structure on solvent extractable organic compounds of PM2.5 in
Beijing, China. Chemosphere 61, 623-632.
Grimmer, G., Jacob, J., Naujack, K.W., 1983. Profile of the polycyclic aromatic compounds
from crude oils. 3. Inventory by GC, GC/MS-PAH in environmental materials. Fresen. Z.
Anal. Chem. 316, 29-36.
Guo, Z.G., Sheng, L.F., Feng, J.L., Fang, M., 2003. Seasonal variation of solvent extractable
organic compounds in the aerosols in Qingdao, China. Atmos. Environ. 37, 1825-1834.
He, L.Y., Hu, M., Huang, X.F., Yu, B.D. Zhang, Y.H., Liu, D.Q., 2004. Measurement of
emissions of fine particulate organic matter from Chinese cooking. Atmos. Environ. 38,
6557–6564.
Hsiao, W.L., Mo Z.Y., Fang, M., Shi, X. M., Wang, F., 2000. Cytotoxicity of PM2.5 and
PM2.5-10 ambient air pollutants assessed by the MTT and the Comet assays. Mutat. Res.
Gen. Tox. En. 471 45-55.
Kavouras, I.G., Koutrakis, P., Tsapakis, M., Lagoudaki, E., Stephanou, E.G., Baer, D.V.,
Oyola, P., 2001. Source apportionment of urban particulate aliphatic and poly-nuclear
14
aromatic hydrocarbons (PAH) using multivariate methods. Environ. Sci. Technol. 35,
2288-2294.
Nielsen, T., 1988. The decay of benzo(a)pyrene and cyclopenteno(cd)pyrene in the
atmosphere. Atmos. Environ. 22, 2249-2254.
Oros, D.R., Simoneit, B.R.T., 2000. Identification and emission rates of molecular tracers in
coal smoke particulate matter. Fuel 79, 515-536.
Pankow, J.F., and Bidleman, T.F., 1992. Interdependence of the slopes and intercepts from
log-log correlations of measured gas-particle partitioning of vapor pressure. I. Theory and
analysis of available data. Atmos. Environ. 26A, 1071-1080.
Parungo, F., Nagamoto, C., Zhou, M.Y., Hansen, A.D.A., Harris, J., 1994. Aeolian transport
of aerosol black carbon from China to the ocean. Atmos. Environ. 28, 3251-3260.
Rogge, W.F., Hildemann, L.M., Mazurek, M A., Cass, G.R., Simoneit, B.R.T., 1993. Sources
of fine organic aerosol. 4. Particulate abrasion products from leaf surfaces of urban plants.
Environ. Sci. Technol. 27, 2700–2711.
Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T.; 1999a. Measurement of
emissions from air pollution sources. 1. C1 through C29 organic compounds from meat
charbroiling. Environ. Sci. Technol. 33, 1566-1577.
Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 1999b. Measurement of
emissions from air pollution sources. 2. C1 through C30 organic compounds from
medium duty diesel trucks. Environ. Sci. Technol. 33, 1578-1587.
Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 2002a. Measurement of emission
from air pollution sources 1. C–C32 organic compounds from gasoline-powered motor
vehicles. Environ. Sci. Technol. 36, 1169–1180.
Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T.; 2002b. Measurement of
emissions from air pollution sources. 4. C1- C27 organic compounds from cooking with
15
seed oils. Environ. Sci. Technol. 36, 567-575.
Sheesley, R. J., Schauer, J.J., Chowdhury, Z., Cass, G.R., Simoneit, B.R.T., 2003.
Characterization of organic aerosols emitted from the combustion of biomass indigenous
to South Asia. J. Geophys. Res. 108(D9), 4285, doi:10.1029/2002JD002981.
Shu, J., Dearing, J.A., Morse, P.A., Yu, L.Z., Yuan, N., 2001. Determining the sources of
atmospheric particles in Shanghai, China, from magnetic and geochemical properties.
Atmos. Environ. 35, 2615-2625.
Simoneit, B.R.T., 1986. Characterization of organic constituents in aerosols in relation to
their origin and transport: A review. Int. J. Environ. An. Ch. 23, 207-237.
Simoneit, B.R.T., Cardoso, J.N., Robinson, N., 1991. An assessment of terrestrial higher
molecular weight lipid compounds in aerosol particulate matter over the south Atlantic
from about 30-70oS. Chemosphere 23, 447-465.
Tareq, S.M., Tanoue, E., Tsuji, H., Tanaka, N., Ohta, K., 2005. Hydrocarbon and elemental
carbon signatures in a tropical wetland: Biogeochemical evidence of forest fire and
vegetation changes. Chemosphere 59, 1655-1665.
Watson, J.G., Robinson, N.F., F ujita, E.M., Chow, J.C., Pace, T.G., Lewis, C., Coulter, T.,
1998. CMB8 Applications and Validation Protocol for PM2.5 and VOCs. Desert
Research Institute Document No. 1808.2D1, Desert Research Institute, Reno NV.
Yao, X.H., Chan, C.K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K.B., Ye, B.M., 2002.
The water-soluble ionic composition of PM2.5 in Shanghai and Beijing, China. Atmos.
Environ. 36, 4223-4234.
Ye, B.M., Ji, X.L., Yang, H.Z., Yao, X.H., Chan, C.K., Cadle, S.H., Chan, T., Mulawa, P.A.,
2003. Concentration and chemical composition of PM2.5 in Shanghai for a 1-year period.
Atmos. Environ. 37, 499-510.
Zheng, J., Tan, M.G., Shibata, Y., Tanaka, A., Li, Y., Zhang, G.L., Zhang, Y.M., Shan, Z.,
16
2004. Characteristics of lead isotope ratios and elemental concentrations in PM10 fraction
of airborne particulate matter in Shanghai after the phase-out of leaded gasoline. Atmos.
Environ. 38, 1191-1200.
Zheng, M., Wan, T.S.M., Fang, M., Wang, F., 1997. Characterization of the non-volatile
organic compounds in the aerosols of Hong Kong -Identification, abundance and origin.
Atmos. Environ. 31, 227-237.
Zheng, M., Fang, M., Wang, F., To, K.L., 2000. Characterization of the solvent extractable
organic compounds in PM2.5 aerosols in Hong Kong. Atmos. Environ. 34, 2691-2702.
Zheng, M., Salmon, L.G., Schauer, J.J., Zeng, L.M., Kiang, C.S., Zhang, Y.H., Cass, G.R.,
2005. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmos. Environ.
39, 3967-3976.
17
Figure 1. Distribution diagrams of n-alkanes average concentrations.
Figure 2 Distribution diagrams of triterpanes average concentrations.
(Ts: 18α(H)-22,29,30-trisnorneohopane; Tm: 17α(H)-22,29,30-trisnorhopane; C29αβ: 17α(H),21β(H)-norhopane; C29βα: 17β(H),21α(H)-norhopane; C30αβ: 17α(H),21β(H)-hopane; C30βα: 17β(H),21α(H)-hopane; C31S: 22S-17α(H),21β(H)-homohopane; C31R: 22R-17α(H),21β(H)-homohopane; C32S: 22S-17α(H),21β(H)-bishomohopane; C32R: 22R-17α(H),21β(H)-bishomohopane)
Figure 3. Distribution diagrams of PAHs average concentrations.
(Low molecular weight PAH: 1 Phenanthrene; 2 Anthracene; 3 Fluoranthene; 4 Pyrene; 5 Benzo[ghi]fluoranthene; 6 Benzo[a]anthracene; 7 Chrysene/Triphenylene. High molecular weight PAH: 8 Benzo[b+k]fluoranthene; 9 Benzo[a]fluoranthene; 10 Benzo[e]pyrene; 11 Benzo[a]pyrene; 12 Perylene; 13 Indeno[1,2,3-cd]fluoranthene; 14 Indeno[1,2,3-cd]pyrene; 15 Benzo[ghi]perylene; 16 Coronene.)
Figure 4. Distribution diagrams of n-fatty acids average concentrations.
(C18:2 is 9,12-octadecadienoic acid; C18:1 is 9-octadecenoic acid)
Figure 5. Distribution diagrams of n-alkanols average concentrations.
18
0
5
10
15
20
25
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36Carbon number
ng m
-3
Winter-ruralWinter-urbanSummer-ruralSummer-urban
Figure 1.
0
1
2
3
4
5
6
7
Winter-rural Winter-urban Summer-rural Summer-urban
ng m
-3
TsTmC29αβC29βαC30αβC30βαC31SC31RC32SC32R
Figure 2.
0.0
2.0
4.0
6.0
8.0
10.0
12.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Compound number
ng m
-3
Winter-ruralWinter-urbanSummer-ruralSummer-urban
Figure 3.
19
0
20
40
60
80
100
120
140
160
180
C12
C13
C14
C15
C16
C17
C18
:2
C18
:1
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
ng m
-3
Winter-ruralWinter-urbanSummer-ruralSummer-urban
Figure 4.
0
5
10
15
20
25
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32Carbon number
ng m
-3
Winter-ruralWinter-urbanSummer-ruralSummer-urban
Figure 5.
20
Table 1. Summary of yields and composition of organic matter in PM2.5 from Shanghai, China n-alkanes PAHs n-fatty acids n-alkanol
Site Date Wind Direction
OC (μg m-3)
EC (μg m-3)
WSOC (μg m-3)
Yield (ng m-3) CPI* Cmax
† U:R‡ Wax% Yield (ng m-3)
BeP/ (BaP+BeP)
Yield (ng m-3) CPI* C18:1/C18 Wax% Yield
(ng m-3) CPI* Cmax† Wax%
FDU 22/11/02 NE 4.8 1.2 2.2 32.9 1.3 24 8.2 11.8 7.8 0.83 123.7 12.9 0.10 13.5 16.8 11.2 28 63.0 (Urban) 23/11/02 NE 9.3 3.0 3.2 83.3 1.2 24 9.5 4.6 28.9 0.78 304.0 13.7 0.10 12.4 36.8 12.7 28 74.5
24/11/02 NW 9.7 3.0 3.4 70.3 1.3 24 6.2 12.1 30.3 0.71 211.2 11.1 0.10 23.0 30.6 7.6 30 65.7 25/11/02 NW 17.0 4.0 6.5 175.5 1.0 23 8.6 3.1 62.7 0.71 397.2 13.3 0.07 14.8 40.2 4.7 30 51.0 26/11/02 NW 27.8 6.8 11.8 226.2 1.3 23 7.0 11.2 82.0 0.70 668.0 9.8 0.18 25.3 170.9 6.6 30 71.4 27/11/02 NW-SE 26.1 5.9 7.4 341.9 1.1 23 6.0 3.7 115.1 0.63 622.9 12.6 0.15 17.3 62.4 8.3 30 77.9 28/11/02 SE 16.2 3.9 5.9 116.6 1.3 24 9.1 9.9 39.7 0.75 401.8 11.5 0.08 20.6 65.2 8.3 30 77.2 Average 15.8 4.0 5.8 149.5 1.2 23 7.8 8.0 52.4 0.73 389.8 12.1 0.11 18.1 60.4 8.5 30 68.7 15/8/03 E 3.4 1.3 1.6 15.6 1.5 31 19.7 20.8 4.3 0.76 108.5 12.5 0.08 14.5 8.2 9.4 28 60.6 16/8/03 E 2.8 1.1 1.3 12.2 1.4 29 24.3 19.9 3.1 0.80 101.3 11.1 0.09 18.6 8.7 9.1 28 71.0 17/8/03 E 6.1 2.7 1.9 39.2 1.2 29 10.7 11.7 13.5 0.78 156.6 10.1 0.08 21.6 11.8 5.9 28 64.6 18/8/03 E 5.0 2.3 1.9 29.6 1.3 29 16.3 15.4 9.2 0.75 167.5 10.8 0.13 17.7 12.2 8.9 28 69.2 19/8/03 S 4.7 2.2 1.6 25.6 1.6 29 22.0 22.9 10.4 0.65 141.0 14.4 0.17 14.1 9.7 8.0 28 59.6 20/8/03 SE 2.5 1.6 0.8 20.5 1.4 29 20.8 17.3 6.1 0.79 93.6 17.1 0.14 8.1 4.4 9.9 28 45.6 21/8/03 SE 2.6 1.3 0.7 14.5 1.6 31 24.0 23.7 3.3 0.76 106.5 14.8 0.19 14.3 5.5 15.1 28 69.2 Average 3.9 1.8 1.4 22.5 1.4 29 19.7 18.8 7.1 0.75 125.0 13.0 0.13 15.6 8.6 9.5 28 62.8
SHO 22/11/02 NE 8.7 2.3 3.5 101.2 1.1 24 5.3 5.1 29.5 0.74 366.2 15.6 0.13 12.5 39.9 12.0 28 72.6 (Rural) 23/11/02 NE 13.5 3.4 4.1 159.8 1.0 24 6.6 1.5 52.1 0.75 455.2 17.1 0.07 10.8 51.1 10.2 30 56.8
24/11/02 NW 11.4 2.1 4.8 92.9 1.3 25 5.2 12.2 31.7 0.70 306.6 9.4 0.07 27.5 94.4 9.8 30 60.3 25/11/02 NW 15.8 3.7 7.0 101.2 1.3 25 7.3 13.0 37.6 0.73 322.8 8.4 0.08 26.0 102.9 6.2 30 67.7 26/11/02 NW 29.8 5.8 12.0 182.3 1.4 24 5.5 14.8 125.6 0.64 613.4 7.4 0.09 36.7 198.7 6.9 30 80.0 27/11/02 NW-SE 16.3 4.6 6.1 153.6 1.2 23 6.8 8.3 76.3 0.64 463.2 10.1 0.07 25.9 142.5 12.5 30 57.1 28/11/02 SE 20.1 3.7 8.7 143.3 1.2 24 6.4 10.2 47.7 0.77 547.7 10.0 0.05 28.0 170.4 14.5 30 64.5 Average 16.5 3.6 6.6 133.5 1.2 24 6.2 9.3 57.2 0.71 439.3 11.1 0.08 23.9 114.3 10.3 30 65.6 15/8/03 E 6.2 2.8 2.7 24.5 1.7 29 9.7 25.7 9.4 0.72 147.4 13.0 0.16 20.8 11.9 9.2 28 65.9 16/8/03 E 7.4 2.9 2.9 58.2 1.0 29 7.9 5.4 11.6 0.80 188.2 11.5 0.08 20.4 13.4 6.8 28 65.3 17/8/03 E 6.0 2.5 2.7 27.8 1.3 29 9.8 15.0 9.1 0.76 137.1 9.8 0.31 23.4 13.2 5.7 28 62.5 18/8/03 E 5.9 2.5 2.5 35.2 1.2 29 7.9 12.1 12.5 0.85 156.9 10.2 0.11 25.5 8.5 7.8 28 70.3 19/8/03 S 4.2 1.5 2.1 19.8 1.7 29 11.1 27.1 4.5 0.72 97.9 12.8 0.18 21.1 11.3 12.9 28 58.1 20/8/03 SE 2.8 1.5 1.1 23.7 1.5 29 15.8 19.6 12.1 0.75 127.9 12.2 0.19 20.8 8.0 13.6 28 66.7 21/8/03 SE 1.7 0.6 0.7 10.2 2.8 29 17.0 45.8 1.3 0.77 60.3 12.7 0.23 26.1 5.9 17.8 28 68.6 Average 4.9 2.0 2.1 28.5 1.6 29 11.3 21.5 8.6 0.77 130.8 11.7 0.18 22.6 10.3 10.6 28 65.4
* Carbon Preference Index: for n-alkanes it is expressed as the summation of odd carbon number homologues divided by the summation of even carbon number homologues; for n-fatty acids and n-alkanols it is the reverse. † Carbon number of the compound with the highest concentration in a homologous series. ‡ Ratio of unresolved complex mixture to resolved n-alkanes.
21
Table 2. Source contributions to organic carbon in PM2.5 of Shanghai in percentage
Diesel and gasoline exhaust
Coal burning
Kitchen emission
Vegetative detritus
Biomass burning
Others
FDU (Urban)
Aug. 54% 12% 9% 6% 1% 18% Nov. 50% 15% 8% 4% 4% 19%
SHO (Rural)
Aug. 45% 13% 7% 8% 2% 25% Nov. 43% 15% 8% 6% 5% 23%