Content uploaded by Klaus Schwarzer
Author content
All content in this area was uploaded by Klaus Schwarzer on Sep 07, 2018
Content may be subject to copyright.
ISSN 8755-6839
SCIENCE OF TSUNAMI HAZARDS
Journal of Tsunami Society International
Volume 32 Number 4 2013
A CRITICAL REVIEW AND EVALUATION OF APPLYING SEMI-VOLATILE ORGANIC
COMPOUNDS (SVOCS) AS A GEOCHEMICAL TRACER TO INDICATE TSUNAMI
BACKWASH: The Bilateral, Deutsche Forschungsgemeinschaft (DFG) and National Research
Council of Thailand (NRCT) Funded Project “Tsunami Deposits in Near-Shore- and Coastal
Waters of Thailand (TUNWAT)”
Siwatt Pongpiachan1 & Klaus Schwarzer2
1NIDA Center for Research & Development of Disaster Prevention & Management, School of Social and
Environmental Development, National Institute of Development Administration (NIDA), 118 Moo-3, Sereethai
Road, Klong-Chan, Bangkapi, Bangkok 10240 Thailand*
Corresponding author phone: (66) 2727-3090; mobile phone: (66) 819751456; fax: (66) 27273747; e-mail: pongpiajun@gmail.com
2Institute of Geosciences Sedimentology, Coastal and Continental Shelf Research, Christian Albrechts
University Kiel, Otto Hahn Platz 1, D - 24118 Kiel, Germany
ABSTRACT
Tsunamis symbolize one of the most harmful natural disasters for low-lying coastal zones and their
residents, due to both its destructive power and irregularity. The 2004 Boxing Day tsunami, which
attack the Andaman Sea coast of Thailand, resulted 5,395 of deaths and inestimable casualties,
interrupted economies and social well-being in numerous coastal villages and caused in extreme
alterations of both onshore and offshore coastal morphology. The Great Indian Ocean tsunami also
highlighted that there are many missing jigsaw puzzle pieces in scientific knowledge, starting from
the generating of tsunamis offshore to the countless influences to the marine ecosystems on the
continental shelf, coastal areas and on land and to the economic and social systems consequences. As
with all deposits that do not have a direct physical link to their causative sources, marine tsunami
deposits must be distinguished from other deposits through regional correlation, dating and criteria for
recognition within the deposits themselves. This study aims to provide comprehensive reviews on
using Polycyclic Aromatic Hydrocarbons (PAHs) as a chemical proxy to discriminate tsunami related
Vol. 32, No. 4, page 236 (2013)
deposits from typical marine sediments. The advantages and disadvantages of this chemical tracer will
be critically reviewed and further discussed.
Keywords: Tsunami Deposits, Marine Sediments, Polycyclic Aromatic Hydrocarbons (PAHs),
Andaman Sea
1. INTRODUCTION
Tsunamis has been a cause of concern all over the world for their tremendous destructive power. This
destruction was particularly great in Asia where tsunamis have occurred often throughout history. The
2004 Indian Ocean earthquake is generally recognized as one of the world’s largest and deadliest
natural disasters in modern times, responsible for estimated US$ 10 billions of damages including the
number of deaths approximately 230,000 people. Since tsunamis dominate natural catastrophe
statistics in term of causing casualties and damaging constructions, a large number of studies have
been performed over the last three decades to assess the alteration of geological and sedimentological
features related to tsunami impact around the world (Atwater, 1987; Benson et al., 1997; Bondevik et
al., 1997; Choowong et al., 2007, 2008a, 2008b, 2009; Clague and Bobrowsky, 1994; Dawson et al.,
1988, 1991; Gelfenbaum and Jaffe, 2003; Pinegina and Bourgeois, 2001; Szczuciński et al., 2005,
2006, 2007). Tsunamis are recorded since historical times and numerous investigations have been
done about their origin, wave distribution and energy release along coastlines, potential for coastal
changes as well as on sediment deposition further inland (Bryant, 2001). Tsunamis have been
compared to storms even if they are two genetically unrelated phenomena. Both are remarkably
similar in their physical power and in their ability to transport sediment across the shelf and to deposit
it in deeper water environments, but there is some difference between large storms with long wave
periods and the effects of tsunamis in terms of hydrodynamics on the shelf (Bryant, 2001). The
differences exist in (i) the capacity of eroding sediments from the sea-floor and the beach-face and to
carry and to deposit this material onshore if there are suitable topographic conditions existing and (ii)
in the tremendous backflow of tsunami water masses from the inundated areas carrying land-borne
material offshore. While it is accepted that storm surges result in the deposition of discrete
sedimentary units, tsunami waves generally result in deposition of sediment sheets over relatively
wide areas and considerable distances inland (Dawson, 1999). According to a recent review by
Gusiakov (2005), 688 tsunamis occurred in the tsunamigenic regions of the Indo-Pacific including
Indonesia during 1901 – 2000 and at least 100 megatsunamis have occurred during the past 2000
years worldwide (Scheffers and Kelletat 2003). In historical times 5 Tsunamis hit the coast of
Sumatra (1797, 1833, 1843, 1861 and 1883) and might have affected the coast of Thailand as well
(Department of Mineral Resources, 2005).
Most of the knowledge about offshore, shallow marine tsunami deposits results from onshore
outcrops of ancient lithified strata (Cantalamessa and Di Celma, 2005). Indicators of backrush flow in
Miocene sediments are described by le Roux and Vargas (2005). They consider sandstone injections
and dikes associated with intraclasts, penetrating underlying strata, as well as debris flow including
“wave-smoothed” beach material, as the most reliable evidence. Poor knowledge exists about the
Vol. 32, No. 4, page 237 (2013)
deposition on the upper and lower shore face induced by tsunami across-shelf backwash, which might
produce sediment aprons, sand bars or large sediment waves. Only very few investigations have been
made on submerged unconsolidated Holocene marine deposits (Le Roux and Vargas, 2005, Vargas et
al. 2005). The successive run-up and backwash of tsunami waves can produce strong seaward currents
causing additional erosion and sediment re-deposition, depending on the nature of the coastal
topography and bathymetry (Bondevik et al., 1997; Shi et al., 1998; Dawson and Shaozhong, 2000).
Also, large objects (boulders, coral blocks and human artifacts) may be dragged or deposited on the
seafloor producing a debris field and other evidence on the seafloor. Einsele (1988), who proposed
diagnostic criteria for tsunami deposits, noted that the backflow of tsunamis is commonly focused by
the coastal geomorphology into channelized flows. Due to this channeling, the velocity of the
backwash flow is more erosive and powerful than the run-up flow. Le Roux and Vargas (2005) show
that the backrush of the 1960 tsunami, which hit the coastline of Chile with up to 14 m high waves,
developed as kind of rip currents. In short cores obtained from water depth between 75 – 100 m
Vargas et al (2005) observed features and sediment material in two layers originating from areas
shallower than 50 m. They relate these deposits to the tsunami backwash. By dating with
radioisotopes and lateral correlation they inferred ages as between A.D.1409 – 1449 and A.D.1754 –
1789.
As tsunami backflows advance offshore from the coastline, they will undergo several changes known
as flow transformation (Fisher 1983). Flow channelization seems to play an important role in lateral
flow transformations leading to unusual deposits. Hyper concentrated flow conditions may therefore
develop within the morphologically deepest channel zones, accompanied by intraclasts ripped up from
the substrate. Very dense sediment-water mixtures, which the backrush might be, may initially behave
as Bingham Fluids dominated by frictional grain interactions and shearing, so that shear carpets may
develop at their base. According to Nittrouer and Wright (1994) the midshelf region is in many cases
the dead end for many of the particles transported across continental shelves due to storm events.
Besides the investigations of Le Roux and Vargas (2005) to our knowledge nothing is known about
the fate and the area where most of the tsunami related material is deposited offshore. However,
Shanmugam (2005) assumes that tsunami-related deposits should be volumetrically important in
coastal, shallow-water and deep-water environments.
For a long time it was neglected that on wave dominated coastal and shelf areas the impact of tsunami
waves on shore-face deposits can be preserved in modern sediments. However recent investigations
based on modern high resolution remote sensing mapping techniques for marine areas revealed that
even in highly dynamic areas and even if the perturbed shore face is in a disequilibrium state, a
preservation potential of sediment distribution patterns and shore face geomorphology exist for
decades (Schwarzer et al., 2003; Murray & Thieler, 2004; Ferrini & Flood, 2005; Diesing et al. 2006).
Conceivably, on the deeper parts of the lower shore face and inner shelf the elevated water level may
induce deposition (similar to the classic "Bruun Rule" response) if there is sufficient accommodation
space. Thus, the initial morphological response of the shore face to the tsunami is hypothesized to
have a pattern of sediment deposition on the lower shore face and inner shelf, erosion on the upper
shore face and beach face, and deposition landward of the beach.
Vol. 32, No. 4, page 238 (2013)
1.1 General background of Tsunami Deposits in Near-Shore- and Coastal Waters of Thailand
(TUNWAT) Project
In the months following the tsunami disaster, consultations between the National Research Council of
Thailand (NRCT) and the German Research Foundation (DFG) resulted in a general agreement for a
bilateral cooperation, which was focused on tsunami-related research. Backed by discussions during a
joint NRCT-DFG workshop in Bangkok in July 2006, German and Thai scientists agreed to propose
research projects, which specifically address scientific issues dealing with tsunami triggering, tsunami
wave development and interaction with the sea-bottom and the impacts, alterations and risks to gain a
better understanding of tsunamis, their origin, propagation, physical-, social- and economic impacts,
resulting destructions and long-term effects and, thus, to improve risk management in the Andaman
Sea Region (see Fig.1-3).
Figure 1. Inundation and tsunami flow of the Banda Ache Plain shown in the SPOT2 image. The
image was taken about 4 h after the earthquake. (Includes material from CNES2005, Distribution Spot
Image S.A., France, all rights reserved). Taken from Umitsu, M. et al., 2006. Own comment: Rip
current eddies can be observed along the whole coastline.
Within the framework of this theme, several topics have been brought forth. As there is a common
focus on tsunamis and impacts, the research issues of the individual projects are interlinked by the fact
Vol. 32, No. 4, page 239 (2013)
that they are aiming at providing complementary answers to key questions about tsunamis in the study
region and their potential impacts. These major questions can be described as follows; (i) Where and
how can tsunamis be triggered in the Andaman Sea? (ii) How often have tsunamis struck this coast in
the recent past? (iii) How does a tsunami wave propagate from offshore (deep water) via the shelf and
near-shore zone onto the adjacent low-lying land areas? (iv) What are the impacts of tsunami waves to
the seafloor topography, to the sediment cover and to the marine & littoral ecosystems while
progressing onshore? (v) How does the land-sea loaded backflow influence the marine abiotic and
biotic system? (vi) How is tsunami wave energy attenuated in the near shore zone, beachfront and
hinterland and how is this attenuation influenced by the presence of natural barriers such as coastal
forests? (vii) What factors determine the tsunami-related vulnerability of low-lying coastal areas, their
population, communities and economies? (viii) Which kind of socio-economic, institutional and/or
other factors make coastal societies or communities resilient against tsunami impacts? (ix) How can
risk management, including early warning, be improved to prevent or mitigate future tsunami
disasters along this coast?
Figure 2. The coastal area of Thailand when the Tsunami waves hit the shore. Far offshore the
influence of the sea bottom to the tsunami waves becomes obvious when due to bottom friction by
wave transformation and wave-breaking occurs (Source:
http://www.crisp.nus.edu.sg/tsunami/tsunami.html).
Vol. 32, No. 4, page 240 (2013)
Figure 3. The coast of Thailand 2 hours after the tsunami hit the coast on December 26th, 2004 at
06:25 UTC. The suspension load plume of the backrush extends approximately 20 km offshore.
(Source: http://www.crisp.nus.edu.sg/tsunami/tsunami.html).
Vol. 32, No. 4, page 241 (2013)
The questions above provide a feasible basis for addressing key issues of regional tsunami research.
They also outline some spatial structure, as the planned projects may be seen as lining up along an
offshore – onshore transect from the deep Andaman ocean across the shelf, near shore and beach zone
into the coastal hinterland as far as a mega-tsunami can reach. While in the offshore domain natural
processes dominate, towards onshore and especially on land anthropogenic influences increase and
trigger the vulnerability. Consequently, the intention of the group is to establish a German – Thai
tsunami research network in which project data ought to be exchanged and results ought to be
generated/synthesized as to contribute to the enhancement of the region’s ability to cope with tsunami
risks.
Based on this concept of interlinked and spatially adherent projects the German-Thai research group
attempts to focus on the following issues:
• Analyze the possibility of tsunami-triggering by slides / slumps at the continental margin of
the Andaman Sea;
• Detecting changes in seafloor topography and sediment pathways by use of remote sensing
methods (satellite image investigation, mapping by multi-beam and side scan sonar
techniques), sediment coring, sampling and dating by radio isotopes;
• Detection and reconstruction of previous tsunami events in order to determine the long-term
history of tsunamis along this coast;
• Numerical modeling of wave propagation and run-up during the Dec. 26, 2004 tsunami.
Validation of different models by comparing measured tsunami run-up characteristics with
results from numerical model simulations;
• Determining the influence of coastal ecosystems, in particular coastal forests and mangroves,
on the physical impacts of tsunami waves along the whole western coast of Thailand;
• Generating a scientific knowledge base and developing/validating prediction models for the
tsunami attenuation performance of coastal forests;
• Assessment and evaluation of tsunami risks in flood-prone coastal areas and communities.
Assisted by remote sensing techniques and field surveys, quantitative indicators are to be
derived for ecologic, economic and social vulnerability as well as for local resilience;
• Comparing local and global interactions in disaster prevention and recovery in two regions
strongly hit by the 2004 tsunami: Khao Lak, Thailand and Aceh, Indonesia;
•
The main objective of the proposed sub-projects is to complement individual research issues in such a
way that a clear picture can be drawn about the destructive forces and processes of the recent tsunami
and past tsunamis and to elaborate and to suggest measures how to avoid or mitigate future tsunami
impacts and destructions in Thailand. Most of the deposits that are known to occur or may occur in
marine environments require improvements in the criteria for recognition. Developments of such
criteria are best done when there are deposits of unquestioned origin. The recognition of deposits of
this tsunami gives the opportunity to extend the relatively short or non-existent historical record of
tsunamis in this area. Presently most of the literature regarding marine tsunami deposits comes from
ancient geologic record and cannot be tested. The December 2004 Sumatra-India earthquake and
following tsunami represents an opportunity to catalogue geomarine effects from a very well recorded
series of events, many of which are unknown or poorly known at present. A modern deposit of
Vol. 32, No. 4, page 242 (2013)
unquestioned origin is invaluable for defining the palaeo record. This event represents the best-
recorded mega-tsunami in human history and offers a unique opportunity to collect data, which have
been never recorded yet. Even if offshore records offer the potential of good preservation, good
spatial coverage and long-term span, data should be collected as soon as possible after this event as
much of the information may be altered or destroyed over time.
Numerous field surveys have been carried out to measure the inundation, run up and deposition of
marine derived sediment onshore caused by tsunamis, but no systematically detailed study exist up to
now to measure geomorphological, sedimentological and geological alterations offshore beyond the
upper shore face. This project aims to assess the impact of tsunami waves on changes and/or offsets of
large-scale bed forms, on changes and/or offsets of shelf sediment distribution patterns and on
estimating the spatial extension of erosion as well as on deposition by the backwash. The following
offshore impacts are expected:
• Erosion by the onshore moving tsunami wave;
• Erosion by the channelized backwash currents;
• Transport of huge sediment plumes offshore and deposition from the shore face to the mid- or
outer shelf.
Therefore the scientific questions in detail are:
• How do incoming tsunami waves influence sediments and sea bottom topography on the shelf
and in near shore waters?
• How does the offshore flowing backrush influence sediments and sea bottom topography on
the shelf and in near shore waters?
• Are there distinguished conduits for the sediment backflow?
• At which water-depth and at which offshore distance can a tsunami triggered alteration of the
seafloor be observed?
• How will offshore tsunami deposits look like?
• How does an archive record of tsunami affected shelf deposits look like?
• How far offshore sediments are transported by the tsunami backwash?
• Was more sediment transported and deposited onshore or offshore?
To achieve the objectives of this project a multidisciplinary approach is required combining remote
sensing methods, geophysical and sedimentological methods and geochemical methods.
1.2. Concept of using Semi-Volatile Organic Compounds (SVOCs) as geochemical tracers to
characterize tsunami backwash deposits
Geochemical tracers have been extensively used as environmental tools to investigate the behavior
and fate of target compounds in the environment (Pongpiachan et al., 2012a,b). While onshore
tsunami deposits are supposed to contain organic and inorganic material of marine origin, offshore
transported material should contain tracer-material originating from land, depending on landforms and
Vol. 32, No. 4, page 243 (2013)
deposits, which are exposed in the areas, which were hit by the tsunami waves. For decades, both
PAHs and aliphatics have been used as a “proxy” to distinguish the anthropogenic source and
biogenic source. In this study we make the assumption that (i) terrestrial soils contain many
anthropogenic PAHs coupling with aliphatics from terrestrial plants and (ii) marine sediments contain
many of phytoplankton derived biomarkers. By analyzing these organic tracers from different points
coupling with careful statistical treatments, it should be possible to investigate the impact of the
tsunami to the distribution of terrestrial sediments in the recent deposits of the Andaman Sea.
Understanding the mechanisms and spatial variations of tsunami backwash sediment distribution
patterns and burial within the offshore area of the Andaman Sea requires an ability to discriminate
between terrigenous and marine components of organic compounds. Furthermore, the role of the shelf
as a source area must be distinguished from those of basins, as sink areas and the coupling between
the two should also be understood as well. A number of approaches have been used to distinguish
between marine and terrigenous components in marine sediments including SVOCs such as PAHs,
hopanes and cholestanes (Westerhom et al., 1992; Yunker et al., 2002, 2003; Kalaitzoglou et al.,
2004). SVOCs have the advantage that they are all released into the atmosphere by imperfect
combustion of organic matters and thus can be used as indicators to identify anthropogenic
combustion sources (i.e. traffic emissions, industrial activities, incinerators etc) from natural
combustion sources (i.e. forest fires, volcano activities, re-evaporization, etc.) by using specific binary
ratios (Pongpiachan, 2013a,b,c; Pongpiachan et al., 2013c,d; Tipmanee et al., 2012; Yunker et al.,
2002). Those areas which were most affected by the backrush of tsunami waters should contain more
terrigenous sediments and thus as well relatively higher proportions of anthropogenic source derived
organic compounds. Specific organic biomarkers such as 17 α(H) -22,29,30-Tris (Tm), 17 β(H), 21 β
(H) -hopane, 17 β (H), 21 α (H) -30-northpane (Isoadiatane), 17 β (H), 21 α (H) -hopane (Moretane),
17 α (H), 21 β (H) –hopane, 18 α (H), 22,29,30-Trisnorneohopane (Ts), on the other hand, often yield
clear evidence of sources (terrigenous and marine) even though they represent only a small proportion
of the total organic material found in the sediments (Belicka et al., 2004). Furthermore, the value of
CPI1 (Carbon Preference Index 1), CPI2 (Carbon Preference Index 2), CPI3 (Carbon Preference
Index 3), ACL (Average Carbon Length), Cmax (Carbon Maximum Number), Pristane and Phytane
can be used as biomarkers to distinguish between terrigenous and marine derived sources of
sediments (Yunker et al., 2002, 2003). By calculating SVOC concentrations against background
values the spatial extension of the sediment load of the tsunami backrush can be estimated. In
addition, there are other alternative analytical tools such as Attenuated Total Reflectance Fourier
Transform Infrared Spectroscopy (ATR-FTIR) and micro-beam synchrotron X-Ray Fluorescence (µ-
SXRF) that can be used to characterize the tsunami backwash deposits (Pongpiachan et al., 2013a,b).
1.3. Physicochemical properties, fates and environmental risks of PAHs
PAHs are a class of very stable organic molecules made up of only carbon and hydrogen and contain
two to eight fused aromatic rings. PAHs are formed during incomplete combustion of organic
materials such as fossil fuels, coke and wood. By definition as organic compounds with vapor
pressure ranging from 10-3 – 10-7 Pa (see Table 1), PAHs, alkanes, cholestanes, hopanes and
anthropogenic sourced chemicals like organochlorine pesticides, polychlorinated biphenyls (PCBs),
Vol. 32, No. 4, page 244 (2013)
dioxins can be termed as SVOCs (Paasivirta et al., 1999). These molecules were oriented horizontal
to the surface, with each carbon having three neighboring atoms much like graphite (see Table 2-5).
The structures of a variety of representative PAHs can be seen in Table 2. In order to reduce the
atmospheric concentration of SVOCs, the UK’s Expert Panel on Air Quality Standard (EPAQS) has
selected benzo[a]pyrene (B[a]P) as a marker for levels of ambient PAHs in recognition of its
carcinogenicity and mutagenicity. An annual mean of 0.25 ng m-3 has been set. However, this value
exceeded the annual mean concentration of B[a]P measured in a number of UK cities since 1997. The
long term monitoring of SVOCs in urban air, therefore, is a significant approach to investigate the
annual trends and seasonal variations of targeted compounds. However, the environmental persistence
and a wide range of vapor pressure of SVOCs have made this group of organic compounds globally
ubiquitous and thus subject to unique atmospheric fate/behavior. The complexity of its existence in
the atmospheric environment together with varieties of emission sources attributed the uncertainties in
source apportionment of these specific compounds. Since SVOCs can be measured in the most remote
areas like Antarctica, the worldwide concerns are also focused on the long-range atmospheric
transportation (LRAT).
Epidemiological evidence suggests that human exposures to PAHs, especially B[a]P are high risk
factors for carcinogenic and mutagenic effects. There are hundreds of PAH compounds in the
environment, but only 16 of them are included in the priority pollutants list of US EPA (EPA, 2003).
Many PAHs have also been identified as cancer-inducing chemicals for animals and/or humans
(IARC, 1983). In 1775, the British surgeon, Percival Pott, was the first to consider PAHs as toxic
chemicals with the high incidence of scrotal cancer in chimney sweep apprentices (IARC, 1985).
Occupational exposure of workers by inhalation of PAH-both volatile and bound to respirable
particulate matter- and by dermal contact with PAH-containing materials, occurs at high levels during
coke production, coal gasification, and iron and steel founding. Coke oven workers have a 3- to 7-
fold risk increase for developing lung cancer (IARC, 1984 and IARC, 1987). For this reason, the
monitoring of PAHs in environmental media is a reasonable approach to assess the risk for adverse
health effects. Since the fate of PAHs in the natural environment is mainly governed by its
physiochemical properties, the study of general properties of the compounds is of great concern. In
spite of a large number of publications related to the environmental fates of PAHs around the world,
there are only few studies conducted in Thailand (Boonyatumanond et al, 2007; Chetwittayachan et
al, 2002; Pongpiachan et al., 2009; Pongpiachan, 2013b,c; Pongpiachan et al., 2013c,d, e; Ruchirawat
et al, 2002, 2005, 2007; Tipmanee et al., 2012).
In this study, the authors hypothesize that the employment of PAHs coupled with various analogies of
statistical analysis assist in a better understanding of offshore terrestrial deposit distribution pattern,
which can be subject to erosion by tsunami/monsoon waves and surface runoff by heavy rain in the
tsunami-affected coastal areas of Andaman Sea, Thailand. Note that it is the purpose of this paper to
critically review both the advantages and disadvantages of using SVOCs as an innovative proxy to
separate the terrestrial deposit from the background sediment in tsunami-affected coastal areas.
Neither the source apportionment, nor the analysis of spatial variation of SVOCs in sediments is the
main focus of this study. In addition, the risks and possibilities of applying other geochemical tracers
as an alternative extreme event proxy will be reviewed and discussed.
Vol. 32, No. 4, page 245 (2013)
2. Source characterization of SVOCs
2.1 Source characterization of PAHs
The binary ratio method for PAH source identification, involves comparing ratios between pairs of
frequently found PAH compound characteristics of different sources. Stationary source combustion
emissions from the use of coal, oil and wood are low in coronene (Cor) relative to B[a]P, while
mobile source combustion emissions from diesel and petroleum use are high in B[g,h,i]P and Cor
relative to B[a]P (Stenberg et al., 1979). The ratio of these PAHs can be used to distinguish between
traffic dominated PAH profiles and other sources (Brasser, 1980; Mainwaring and Stirling, 1981).
Lim et al., (1999) used two methods in evaluation of PAH traffic contribution. Both of methods were
adapted from Nielsen (Nielsen, 1996). Equation 1 based on the assumption that all Cor is traffic-
generated, the concentration of B[e]P which is traffic generated is given by
Equation1
Crucial to this method is the assumption that all Cor is traffic generated. Although this is likely to be
an oversimplification, many studies have shown a high correlation between Cor and vehicular
emissions. In addition, the ratios of B[g,h,i]P/B[e]P and Cor/B[e]P can also be used as traffic
indicators, since both B[g,h,i]P and Cor have been reported to correlate closely with traffic emissions .
Other PAHs such as retene (Ret) is also considered as a common biomarker of coniferous wood
combustion, and resin acids such as abietic acid commonly found in smoke samples.
Since PAHs contents in atmosphere are sensitive to both anthropogenic and natural emissions, a more
effective marker of traffic emissions is substantially required. Another potential index to distinguish
the traffic emission is the use of inorganic tracers. It is widely known that particles from road dust
appear to contain Pb, Fe, Mn, Ca and sometimes Zn, (Harrison et al., 1996). This can be explained by
the attrition of road surfaces, resuspension of street dirt and soil dust (i.e. source of Ca and Mn;
Pierson and Brachaczek, 1993), particles from tyre wear (i.e. source of Zn; Ondov et al., 1992), brake-
drum abrasion, and debris from wear and rusting. In order to investigate the contributions of traffic
related pollutants, several attempts were made for source apportionment of PAHs in Birmingham air
by using inorganic source markers such as Cu, Fe, Pb, Zn and Mn. Lim et al (1999) reported high
loading on higher MW PAHs (B[a]A to Cor) and moderate loading on BNT and Ph and the gasoline
powered vehicle marker, Pb.
Based on size distribution of various elements at a road site location in Birmingham (BROS), Fe, Cu,
Mg, Sr, Mo, Ce and Ba were found to be the major components in the traffic related coarse particle
(i.e. 2 – 10 µm aerodynamic diameter, Harrison et al., 2003). Whilst strong correlations of Cu (r2 >
0.74), Mo (r2 > 0.54), Ba (r2 > 0.73), Pb (r2 > 0.68), Mn (r2 > 0.54) and Zn (r2 > 0.61) with NOx
indicated that these elements probably originate in road site particles. Until 1997, Pb and Br had been
Vol. 32, No. 4, page 246 (2013)
widely employed as indicators of road traffic emissions (Harrison et al., 1997). Lead (Pb) is added to
petrol in the form of an organic tetramethyl lead, an anti-knock agent, and is emitted by the exhaust
stream in inorganic particulate forms, predominantly as PbClBr and 2PbClBr*NH4Cl. Thus, the
combination of Pb with Br can be used as classical marker elements for traffic emissions. However,
the worldwide decline of Pb concentrations in the atmosphere has been observed due to the
introduction of unleaded gasoline into the market in 1989 (Salma, et al., 2000). As a consequence, the
Br/Pb ratio for determining the automotive contribution to the atmospheric lead burden may not be
possible. In this study, n-alkanes and petroleum biomarkers such as hopanes and cholestanes had been
introduced into the receptor models instead of inorganic marker elements like Pb. Alternatively,
hopanes and cholestanes can be used as petroleum biomarkers, because: (i) these organic compounds
are found mainly in exhausted gas from petroleum combustion and thus overlap PAHs combustion
profiles, (ii) these organic compounds are not formed by atmospheric chemical reactions, (iii) these
organic compounds are continuously released to the atmosphere by automotive emissions, and (iv)
these organic compounds are prone to degradation in the environment. Because of these unique
characteristics, hopanes and cholestanes have been widely used to define both the fossil origin and
geological source of petroleum residues, (Rogge et al., 1993; Simoneit, 1984).
2.1.1 PAHs in aerosols from different countries
2.1.1.1 UK
PAH monitoring campaign was performed at two sites in London by Kendall et al (2001). The first
sampling site was located at Bounds Green (BG) in North London. The second sampling site was
positioned at St Paul’s Cathedral (SP) in Central London. The annual means of the majority of the
individual PAH compounds and ΣPAHs was higher at SP (7.24 ng m-3) than at BG (4.27 ng m-3).
Both sites showed the same seasonal pattern of the lowest concentration during the summer and the
highest average concentration during the autumn. B[g,h,i]P was the most abundant PAH at both
London sites, followed by B[a]A and Chry, and these three PAHs dominated during all four seasons.
The highest concentrations of these compounds were measured during the sampling week containing
Bonfire Night at BG and during January at SP. Concentrations of these three compounds contributed
over 50% of the ΣPAHs In London, the winter ΣPAHs concentrations were only approximately twice
the summer concentrations, possibly reflecting the small number of PAHs sources in London, with
vehicles dominating PAHs production.
2.1.1.2 Indonesia
The burning of biomass as a method to clear and fertilise land is practiced in Indonesia every year by
farmers and forestry companies. In 1997, Indonesia had been extremely dry due to El Nino, and what
is now known as the Indonesia forest fire started to get out of control in the middle part of June.
Emissions from biomass burning can vary widely because the material and the burning conditions can
differ greatly. In a forest fire of this duration and magnitude, the burning material and conditions can
vary from time to time as well as location to location. The ΣPAHs concentration ranged from 7 ng m-3
to 46 ng m-3 (Fang et al., 1999). The highest value of B[a]P was measured in Pudu on 27-09-97 at 3
ng m-3 while the lowest was at the same site on 04-09-97 at 0.1 ng m-3. These values are comparable
to those measured in Hong Kong, and the highest value matches some of the street-level monitoring
Vol. 32, No. 4, page 247 (2013)
results (Fang et al., 1996). The concentration increase in ΣPAHs from the early to late September
samples was the lowest. Retene, a common biomarker of coniferous wood combustion, and resin
acids such as abietic acid commonly found in smoke samples in controlled studies, were not found in
any of the samples in this study. This may be due to the transformation occurring in the long distance
transportation of the aerosols, although retene was found to be relatively stable in sediments and did
not vary in the same way as other PAHs (Ramdahl, 1983).
2.1.1.3 Greece
The field campaign took place in a conifer forest of central, continental Greece. Measurements were
taken as part of the European Commission project, “Aerosols formation from biogenic organic
carbon” (AEROBIC), during the period of 20 July-12 August, 1997. PAHs and oxy-PAHs detected in
the Greek aerosols present an increase of concentrations with the number of benzene rings, with a
dominance of PAHs with more than 5 rings. ΣPAHs concentrations (0.1 ng m-3 -18.5 ng m-3) were, in
general, higher compared to that reported for a Portuguese forest, 0.1 ng m-3 - 0.9 ng m-3 (Kavouras et
al., 1999), and other rural area of Greece, 0.2 ng m-3 - 2.0 ng m-3 (Gogou et al., 1996). However, these
levels are lower than those presented for urban aerosols of a Greek island, 21.4 ng m-3 - 59.0 ng m-3
(Gogou et al., 1996).
2.1.1.4 Hong Kong
Hong Kong, with an area of 1092 km2 and over 6.5 million in population, is surrounded by the South
China Sea to the south and east, Pearl River Estuary on the west, and the landmass with complicated
terrain on the north. It is also under the influence of the Asian Monsoon System. The summer
monsoon brings in clean oceanic aerosols while a polluted air mass from the mainland comes with the
northeasterly wind in the winter season. Thus the characteristics of Hong Kong’s aerosols are
represented by outside sources superimposed on very strong local emissions. ΣPAHs concentration
was in the range of 0.7 ng m-3 - 12.2 ng m-3 (Zheng et al., 2000). In general, higher PAHs
concentrations were found in the winter PM 2.5 samples. Other studies show that the changes in
emission patterns (stationary and vehicular) and meteorological conditions (including less daylight
hours, reduced ambient temperatures, and lower volatilization and photochemical activity) contribute
to the higher PAHs levels during winter (Ramdahl et al., 1982, 1983; Freeman and Cattell, 1990, Baek
et al., 1991a,b). The association of B[g,h,i]P with vehicle exhaust has long been established (Baek et
al., 1991a,b), and a good correlation with total PAHs was obtained in this study. This suggests that
vehicular emission was the dominant source of PAHs in the PM 2.5 cut of the aerosols in Hong Kong.
The low molecular weight (LMW) PAHs such as Phe, Fluo and Pyr were enriched in the fall and
winter samples. Comparing the spring and summer samples, the winter and fall samples contained
more LMW PAHs. The LMW PAHs tend to be more concentrated in the vapour phase while the
higher molecular weight ones are often associated with particulates. In addition, more details of PAHs
in aerosols around the world are displayed in Table 6.
2.2 Source characterization of aliphatics
The enormous range of organic compounds detected in urban particles may be divided into two major
source groups; primary condensates and oxidized hydrocarbons. Primary condensates (alkanes (C17-
Vol. 32, No. 4, page 248 (2013)
C36), alkenes) originate directly from the incomplete combustion of fossil fuels and are sorbed onto
the surface of particulate matter. Oxidized hydrocarbons (carboxylic acids, aldehydes, ketones,
quinines, esters and phenols) may either be attached to the particulate as a primary condensate or may
be produced during atmospheric oxidation reactions (Cautreels, 1978). Bray and Evans (1961)
developed the carbon preference index (CPI) as an indicator of the extent of odd or even carbon
number homologues within a sample. The CPI is expressed as a summation of the odd number
homologues within a specified range of carbon numbers divided by a summation of the even number
homologues within the same range. This inter-sample comparison is useful in identifying sources and
establishing dominant sources of aerosol organic matter, as certain biologically produced n-alkanes
show a pronounced predominance of odd carbon numbers (Bray and Evans, 1961). For example,
organic matter of recent biogenic origin shows a preference for odd carbon numbered n-alkanes with
CPIodd values of 6-9 and more. Hydrocarbons of abiological origin (e.g. fossil fuels) typically show no
carbon number preference or tend towards low CPI values (i.e. CPI < 1). To reconcile sources of
organic species, CPI was calculated as follows (Tarek et al., 1996):
Equation 2
CPI1 represents whole range for n-alkanes
Equation 3
CPI2 represents petrogenic n-alkanes
Equation 4
CPI3 represents biogenic n-alkanes
The biogenic “wax” concentration of n-alkanes was calculated as follows:
Equation 5
Note that wax* has traditionally referred to a substance that is secreted by bees (i.e. beeswax) and
used them in constructing their honeycombs. Additionally waxes may be natural or artificial. In this
context, wax is natural oily substance. Chemically, a wax may be a combination of other fatty
alcohols with fatty acids.
Vol. 32, No. 4, page 249 (2013)
Scalan and Smith (1970) proposed the odd-to-even predominance n-C29 OEP ratios by using the
following equation;
Equation 6
OEP values were plotted against carbon chain length to construct the OEP curves. The pattern of the
OEP curve is determined by the source of n-alkanes, which indicates the potential fingerprint for
source apportionment. U:R is a good indicator of petroleum residue contribution. Under the resolved
peaks in a typical n-alkane gas chromatogram, there is an unresolved complex mixture (UCM) in the
form of an envelope, which contains branched and cyclic hydrocarbons. The UCM is also termed as
the “hump” and is believed to be the contribution from fossil fuel residue. U:R is the ratio of the areas
of the unresolved to resolved components (peaks) and is an indicator of the contamination by
anthropogenic sources (Mei, 2000).
Hopanes are widespread in both recent and ancient sediments. The origin of most hopane is the
bacterial C35 tetroltetrahydroxybacteriohopane. Hopane hydrocarbons present in sediments range from
C35 and down to C27, usually with the C30 isomers as the predominant. Hopanoid are produced by
most living organism with ββ-configuration (17β(H),21β(H)). Increasing maturity leads to the more
thermodynamically stable αβ-hopanes and βα-hopanes, with the former being the predominant isomer.
Molecular calculations have shown that the αα-isomers are less stable than the αβ-hopanes and βα-
hopanes, but more stable than ββ-isomer (Philip et al., 1984). Most hopanoids are bio-synthesized
with the 17β(H),21β(H) stereochemistry. C29 and higher homologues of hopane are isomerized to
17α(H),21β(H) at any early stage of diagenesis. The degree of maturation can be calculated as
follows:
Equation 7
The ratio of equation 1.7 usually reach one simultaneously with the disappearance of 17β(H),21β(H).
Furthermore, maturation enhances the content of 17α(H),21β(H) relative to 17β(H),21α(H). C27-
Hopanes have no side-chain and the conversion of 17β(H) to17α(H) (Tm) reaches completion at
maturity between the disappearance of 17β(H),21β(H) hopanes and 17α(H),21β(H) hopanes, that is
prior to the onset of intense hydrocarbon generation. As illustrated in Figure 1.2, 18α(H)-
Trisnorhopane (Ts) is not affected by maturity changes, and the ratio: Tm/Ts can thus be used as a
maturity indicator (Yunker et al., 2003). Recent studies have highlighted on the heterogeneous nature
of hopane and sterane biomarkers in several river-dominated margins (Yunker et al., 2003 and
Mudge, 2002). In the last two decades these petroleum biomarker parameters have been developed by
organic geochemists to differentiate between oils, and determine their source in the aquatic
environment and to trace their point sources (Zakaria et al., 2001).
Vol. 32, No. 4, page 250 (2013)
For decades, pristine and phytane have been considered as biodegradable and thus these ratios might
be valuable in the early stages of biodegradation (Prince et al., 1994). In particular, Burns and Teal
(1979) stated that the branched alkanes extracted from marsh sediments at the West Falmouth site
were completely degraded within the first seven years after the oil spill. These findings were based
first on measurements of the pristine/phytane ratios. It was found that this ratio was 1.5 in the original
Florida oil and that it decreased over a period of six years to 0.2. This is in contrast to another recent
finding that have shown that n-alkane band in the fuel oil range (n-C10-n-C24) was no longer present,
whereas all other compound classes such as nor-pristine, pristine and phytane are evident within the
residual oil at the same sampling site (i.e. the West Falmouth site, Reddy et al., 2002). These latest
results suggest that these compounds will be very useful petroleum biomarkers in understanding the
distribution pattern of traffic-emitted aerosol components. In addition, steroid skeletons have been
widely used as an indicator of diagenesis and the biological origin (biomarker) of organic matter
(Jasper, 1993; Huang and Meinschein, 1976; Simoneit, 1984; Venkatesan et al., 2003). Steroids are
also source tracers for biogenic material in complex mixtures of dissolved and particulate matter in
the geosphere, especially in the marine environment (Brault and Simoneit, 1988 and Wang et al.,
2004).
2.3 Cluster analysis & receptor models
2.3.1 Cluster analysis (CA)
Cluster analysis (CA), also called segmentation analysis or taxonomy analysis, seeks to identify
homogeneous subgroups of cases in a population. That is, cluster analysis seeks to identify a set of
groups which both minimise within-group variation and maximise between-group variation. In this
study, CA was conducted using SPSS 13.0 for Windows. CA techniques may be hierarchical (i.e. the
resultant classification has an increasing number of nested classes) or non-hierarchical (i.e. k-means
clustering). Hierarchical clustering allows users to select a definition of distance, then select a linking
method of forming clusters, then determine how many clusters best suit the data. Hierarchical
clustering methods do not require pre-set knowledge of the number of groups. Two general methods
of hierarchical clustering methods are available: divisive and agglomerative. The divisive technique
start by assuming a single group, partitioning that group into subgroups, partitioning these subgroups
further into subgroups and so on until each object forms its own subgroup. The agglomerative
techniques start with each object describing a subgroup, and then combine like subgroups into more
inclusive subgroups until only one group remains. In either case, the results of the application of the
clustering technique are best described using a dendogram or binary tree. The objects are represented
as nodes in the dendogram and the branches illustrate when the cluster method joins subgroups
containing that object. The length of the branch indicates the distance between the subgroups when
they are joined.
After selecting the hierarchical clustering method, it is important to select the clustering algorithm
(i.e. the rules which govern between which point distances are measured to determine cluster
membership). There are many methods available, the criteria used differ and hence different
classifications may be obtained for the same data. Five algorithms, available within SPSS, are (i)
average linkage clustering, (ii) complete linkage clustering, (iii) single linkage clustering, (iv) within
Vol. 32, No. 4, page 251 (2013)
groups clustering and (v) Ward’s method. Average linkage clustering is defined as the dissimilarity
between clusters and calculated using cluster average values. The most common method to calculate
an average is UPGMA (Un-weighted Pair-Groups Method Average). Complete linkage clustering
(Maximum or Furthest-Neighbour Method) can be described as the dissimilarity between two groups
is equal to the greatest dissimilarity between a member of cluster i and a member of cluster j. This
method tends to produce very tight clusters of similar cases. On the other hand, single linkage
clustering can be simply explained, as the dissimilarity between two clusters is the minimum
dissimilarity between members of two clusters. This method has been widely employed in numerical
taxonomy. Besides, it is crucial to note that within groups clustering is similar to UPGMA except
clusters are fused so that within clusters variances is minimized. This tends to produce tighter clusters
than the UPGMA method. Finally, Ward’s Method is assessed by calculating the total sum of squared
deviations from the mean of a cluster. The criterion for fusion is that it should produce the smallest
possible increase in the error sum of squares.
2.3.2 Receptor models
Receptor models can be categorized into two types, namely, Chemical Mass Balance (CMB) and
multivariate models. The most widely used multivariate models are principal component analysis
(PCA), positive matrix factorization (PMF), and UNMIX model. CMB predicts the contribution of
different sources to measured target compound concentrations in atmosphere by means of an inverse
variance weighted least-square linear regression, (Watson et al., 2001). The concept of CMB model,
based on the principle of mass conservation, assumes that the total concentration of chemical species,
Ci, at the receptor site, is the sum of the contributions from all sources j which emit species i.
Equation 8
where Sj is the fractional mass contribution of source j to the pollutant of target compound in the
atmosphere, fij is the fraction of chemical species i in the emission from source j, for p sources. αij is
the coefficient of fractionation used to correct uncertainties in fij between the source and the
atmosphere, (Watson et al., 1990 and Engelbrecht et al., 2002).
Unlike other receptor models, which extract source compositions from the data, CMB model
assumptions are based on: (i) compositions of source emissions are constant over the period of
ambient and source sampling, (ii) chemical species do not react with each other, (iii) all sources with a
potential for contributing to the receptor have been identified and have had their emissions
characterized, (iv) the number of sources or source categories is less than or equal to the number of
species, (v) the source profiles are linearly independent of each other, and (vi) the uncertainties are
random, uncorrelated, and normally distributed, (CMB8 Applications and Validation Protocol for
PM2.5 and VOCs announced by Desert Research Institute US). CMB have been widely used to assess
sources of air pollutants because it: (i) theoretically yields the most likely solutions based on chemical
source profiles, (ii) uses all available chemical measurements, (iii) estimates the uncertainty of the
source contributions based on resolution of both the ambient concentrations and source profiles, (iv)
provides greater influence to chemical species with higher resolution in both the source and receptor
Vol. 32, No. 4, page 252 (2013)
measurements than to species with lower resolution. However, the major weaknesses of the CMB are
its difficulty to account for non-stable compounds and incapability to identify those sources that have
similar contributions. Moreover, CMB requires chemical source profiles as fingerprints for source
apportionment of environmental pollutant.
The fundamental principles of various chemical methods for receptor modeling, including chemical
mass balance (CMB) and multivariate method, have been review in detail (Gordon, 1988; Watson,
1994). Factor analysis offers the advantages of not requiring prior knowledge of the chemical
composition and size distribution of emissions from specific sources (source profiles) but has the
drawback of being mathematically indeterminate, allowing a wide range of possible solutions even
when it is applied to relatively simple simulated data sets. In urban atmosphere, which is composed by
many potential and diverse sources, PCA has been chosen by many workers for source
apportionment. This technique has been widely applied to source apportionment of particulate
pollutants, especially trace metals, and more recently, PAHs. In order to identify sources, multivariate
receptor modeling can be applied to the observed target compound data. Multivariate approaches are
based on the idea that the time dependence of a chemical species at the receptor site will be the same
for species from the same source. Chemical species are measured in a large number of samples
gathered at a single receptor site over time. Species of similar variability are grouped together in a
minimum number of factors that explain the variability of data set. It is assumed that each factor is
associated with a source or source type. However, the method has some limitations in that it can
recognize at most only about eight individual source categories in any study, and poor discrimination
of closely related source categories is commonly found. A further disadvantage of multivariate factor
analysis is that large numbers of ambient air samples must be collected and analyzed (usually at least
50) and the statistically independent source tracers are required for each major source type.
In contrast to CMB model, multivariate techniques such as PCA are preferable since they require no
qualitative insight of the sources of certain chemical species, and thus overcome the limitations of
CMB, (Rachdawong et al., 1998 and Park et al., 2005). The aim of PCA is to identify the major
sources of air pollutant emissions and to select statistically independent source tracers, (Bruno et al.,
2001; Miller et al., 2002 and Gou et al., 2003). All variables are expressed in standardized form with a
mean of 0 and a standard deviation of 1. The total variance therefore equals the total number of
variables, and the variance of each factor expressed as a fraction of the total variance is referred to as
the eigenvalue. If a factor has a low eigenvalue, then it is contributing little to the explanation of
variances in the variables and may be ignored. PCA is generally used when the research purpose is
data reduction (i.e. to reduce the information in many measured variables into a smaller set of
components). PCA seeks a linear combination of variables such that the maximum variance is
extracted from the variables. It then removes this variance and seeks a second linear combination that
explains the maximum proportion of the remaining variance, and so on. This is called the principal
axis method and results in orthogonal (uncorrelated) factors. Thus, the largest combination,
accounting for most of the variance, becomes principal component 1 (PC1), the second largest
accounts for the next largest amount of variances and becomes principal component 2 (PC2), and so
on.
Vol. 32, No. 4, page 253 (2013)
Over a period of about 10 years the combination of interest driven by regulations with advances in
instrumental analytical analysis and the wide availability of computers resulted in the innovative field
of source apportionment/receptor modeling. The first applications of a receptor model using factor
analysis were done by Hopke et al. (1977) and Garrenstrom et al. (1977). The first Ph.D. thesis on
multivariate air quality receptor modeling was that of Henry (1977). This work made explicit the
connection between the statistical and physical models underlying multivariate receptor models for
the first time. Finally, the initial development of UNMIX was fulfilled by Henry and Hidy (1979),
who discovered physically significant air quality patterns in the first multivariate analysis of an air
quality data set that combined pollutant gas concentrations, particulate composition, and
meteorological variables.
UNMIX is a multivariate receptor modeling package that inputs observations of particulate
composition and seeks to find the number, composition, and contributions of the contributing sources
or source types. This model also produces estimates of the uncertainties in the source compositions.
UNMIX uses a generalization of the self-modeling curve resolution method developed by Henry et al
(1997). Using only ambient data, UNMIX outputs the following information: (i) number of sources,
(ii) composition of each source, (iii) source contributions to each sample, (iv) uncertainties in the
source compositions, and (v) apportionment of the average total mass, if total mass is included in the
model. The major advantages of the UNMIX are described as follows: (i) no assumptions about the
number or compositions of sources are needed, (ii) no assumptions or knowledge of errors in the data
are needed, and (iii) UNMIX automatically correct source compositions for effects of chemical
reactions. A major difference between UNMIX and PMF is that UNMIX does not make explicit use
of errors or uncertainties in the ambient concentrations. This is not to imply that the UNMIX approach
regards data uncertainty as unimportant, but rather that the UNMIX model results implicitly
incorporate error in the ambient data. Assume that N air quality samples are analyzed for n species,
which come from m sources. If these species are conservative, then mass balance on each species
requires that
Equation 9
Where Cij is the concentration of the jth (j=1,…..,n) species in the ith sample (i=1,…..,N), ajk is the
mass fraction of species j in source k (k=1,…..,m), and Sik is the total mass of material from source k
in the ith sample. In the parlance of receptor modeling, the ajk are the source compositions, and the Sik
are the source contributions. The equation above includes errors, which may be the results of
analytical uncertainty and variations in the sources’ composition. The existence of errors profoundly
complicates solution of the mixture problem, which is an ill-posed problem in that there are an
insufficient number of constraints to define a unique solution. Several attempts to define a unique
solution had been made by adding the obvious constraints of non-negativity of the source contribution
and narrowing down the range of possible solutions based on physical knowledge of the sources
(Henry et al., 1994, 2003 and 2005). However, the question remains under what conditions can a
unique solution be found using only the data without a priori knowledge. To answer this question, the
Vol. 32, No. 4, page 254 (2013)
graphical approach had been introduced for solving the mixture problem. The main idea is to reduce
the dimension of the problem by projection generalizes to any number of species and sources. Fig. 4
illustrates the single source case in which all the data points lie, except for errors, on a ray coming
from origin. The direction of the ray is determined by the composition of the source. If [p1, p2, p3] is
any point on the line, and [a1, a2, a3] is the source composition then [p1, p2, p3] = k[a1, a2, a3]. Since
the ratio of each element relative to all others in the source composition, the mixture problem can be
solved for the single source case, except that the source composition is only determined up to a
multiplicative constant. In the case of two sources, as demonstrated in Fig. 5, the data are distributed
in a plane through the origin. The problem is to find the vectors (or points) that represent the source
composition. There are two possible choices for the source vectors namely (i) the source composition
vectors of the two sources must lie in the same plane as the data and (ii) the source vectors must also
be non-negative (Henry et al., 2002). This further limits the data points to lie on the plane between the
two rays defined by the two source compositions. If one source is missing from some of the data
points then these data points will lie along a ray defined by the composition of the single, remaining
source. These data points will be distributed so that an edge is apparent (see Fig. 5) and a line drawn
along that edge will give the relative source composition, just as in the single source case.
Figure 4. Plot for the one source, three species case showing the data points lying along the ray
defined by the source composition (Henry et al., 1997).
However, if there are three or more sources, then the points with one source missing have two or more
sources present, and it is not so simple to determine the source compositions. As illustrated in Fig. 6,
all the data points are composed of three sources of three species. In this case the points with one
source missing are located on the planes defined by the rays through the remaining two sources.
While this can be difficult to visualize those remaining two sources in a three-dimensional plot, a
projection from the origin into a plane as demonstrated in Fig. 5 enables the location of the source
vectors much easier to see. In most cases of environmental data, however, data points are composed
of more than three sources and many more than three species. In this case the generalization to more
than three dimensions requires the concepts of a hyperplane and simplex. A hyper-plane is the
Vol. 32, No. 4, page 255 (2013)
generalization of a plane. For instance, a plane in 3-space is two dimensional, so a hyper-plane in n-
space has dimension n-1. Thus, the process of reducing dimensions of the data points in UNMIX is to
find the edges in the data (i.e. finding hyper-planes that define a simplex). Finally, UNMIX uses these
edges to find the source points in data set.
Figure 5. Plot for the two sources, three species case showing the data points lying in the plane
defined by the two source composition rays. The solid circles are edge points, which have one source
missing or low. The edges defined by these points give the relative source composition for the two
sources (Henry et al., 1997).
Figure 6. Plot for the three sources, three species case showing the data points (open circles) and the
projection of the data points from the origin to the plane Species I = 1 (solid circles). Among the
projected points, the edge points are easy to identify (Henry et al., 1997).
Vol. 32, No. 4, page 256 (2013)
3. DISCUSSION & CONCLUSION
Despite of considerably large number of studies focusing on tsunami impact onshore, little is known
about their geomorphologic imprints offshore. In order to gain more insights on return periods of both
earthquake and tsunami hazard, it is therefore crucial to study past tsunami events by carefully
investigate offshore tsunami deposits. There remain substantial difficulties, however, in
discriminating between deposits triggered by tsunamis and those caused by extreme events such as
hurricane/Indian Ocean monsoon (IOM) and/or other sedimentary processes. Hence, it is important to
emphasize that the term of “tsunami deposit” is that it defines deposits that have created by numerous
distinctive geological processes that are not necessarily specific to marine deposits formed during the
tsunami 'backwash' phase (Shanmugam, 2006; Shiki and Yamazaki, 2008). Tipmanee et al (2012)
attempted to use PAHs as a chemical proxy to trace the transport of land-derived materials caused by
the tsunami backwash to better understand how it may have affected the distribution of sedimentary
deposition throughout the seabed of Khao Lak coastal areas. The article provides interesting
application of diagnostic binary ratios of PAHs from coastal sediments and marine deposits in
tsunami 2004 affected coastal area of Thailand. The application of PAHs as a chemical proxy to
identify tsunami backwash patterns is interesting and very challenging, however, there are several
concerns and questions that need clarification as well as some of the following points for a thorough
reconsideration of the proposed concept.
• As the tsunami wave approaches the shoreline its wavelength become shorter and horizontal
water velocity increases. It carries large amount of marine sediments to inundated land. After
reaching its maximum run-up, the backwash phenomenon is occurred and responsible for
channelized and erosions in specific places. The mixture of marine sediments, beach
sediments, minor part of eroded terrestrial soils and eroded older sediments from backwash
channels are transported back to the sea as high density flows, part is transferred as suspension
load as well as simultaneously mobilization of marine sediments. Hence, it is most likely that
offshore tsunami deposits contain both terrestrial signatures and re-deposited marine
sediments. In normal conditions terrestrial component may be delivered to the sea bottom as
well. However, this phenomenon will take longer time than the “tsunami backwash”, which
the debris flow is very fast and sediments are buried rapidly then the "terrestrial set of PAH
signatures" might be hypothetically preserved.
• It is also important to emphasize that not only “tsunami wave” but also “monsoon wave” can
transfer terrigenous components from land to deposit in sea bottom. Therefore, it is crucial to
consider the difference in nature of "tsunami wave" and "monsoon wave". While monsoon
prolongs for several hours with relatively lower magnitude of power, the tsunami wave
occurred in a greater magnitude of power but shorter in backwash time (i.e. several minutes).
The tsunami deposit tends to contain "suspended particle", whereas the monsoon-derived
sediments typically are produced through "bed-load transport". In these particular cases, two
types of hypothesis can be considered;
Vol. 32, No. 4, page 257 (2013)
Hypothesis I (Difference in transportation pattern)
It is well known that different types of PAHs exist in different sizes of particles (Kukkonen
and Landrum, 1996; Wang et al., 2001). Since tsunami waves are capable of transport all
particle sizes of terrestrial sediments via suspended load transport, it is most likely that
tsunami deposit will have a different "distribution pattern of PAHs" from those of monsoon
derived sediments.
Hypothesis II (Difference in erosion time)
Tsunami backwash occurred in relatively short time scale of minutes whereas the inundation
of monsoon prolonged for several hours. Differences in "time dimension" cause the
fractionization of PAHs due to its differences in term of "water solubility". For instance, Phe
tends to have a higher water solubility than B[a]P (Lu et al., 2008). The longer the erosion, the
higher amount of Phe will dissolve into backwash water in comparison with B[a]P. As a result,
the monsoon derived deposit will contain Phe more than B[a]P. However, this fractionization
effect will not happen in the case of tsunami deposit. Firstly, the tsunami involved only three
large waves. Once the waves had passed and receded the event was over. Therefore, both Phe
and B[a]P will be washed back by tsunami wave simultaneously without providing any time
for the fractionization effect. For these reasons, the fingerprint of terrestrial PAHs may
hypothetically be well preserved and possibly be a better proxy than conventional terrigenous
biomarker "lignin" (Tareq et al., 2004), which is highly sensitive to microdegradation
procedure due to its weak chemical structure in comparison with PAHs.
• Fingerprinting reflects the chemical characteristics of various source emissions of the sediment
samples collected at any receptor. Because a simple ratio of two or three PAHs is sensitive to
both photolysis and chemical/biological degradation and may be insufficient to identify PAH
emission sources at fixed monitoring sites, the best strategy to investigate the impact of
backwash tsunami is the use of the entire PAH profiles. Hence, the establishment of PAHs
profile of individual “end member” is therefore essentially crucial.
• It is well known that Ret can be used as indicators of “wood combustion” (Gonçalves et al.,
2011). During the bonfire night episode, the highest contributions of individual PAH came
from Fl, Ac, Ret, B[b+j+k]F, Ind, B[g,h,i]P, while alkane concentrations was following the
decrease order of C29 > C24 > C27 > C22. The chain-saw-distribution of alkanes was evident at
the range of C23 to C31 during the bonfire night episode, suggesting a strong signal of biomass
burnings (Pongpiachan, 2013a). The contribution of diesel engine to atmospheric PAHs
concentrations, especially those of Fl and Phe, has been widely investigated for many years
(Williams et al., 1986). It is well known that diesel fuel is relatively rich in PAHs and most of
them are alkylated PAHs compared to that of the parent PAHs. Several factors like diesel-
engine type and engine operating conditions reflect the ratio of MePhe/Phe in urban air. For
example, the relatively high MePhe/Phe ratio was found when the combustion and exhaust
temperatures were low (Barbella et al., 1989). Takada et al (1991) and Westerholm et al
(1992) reported the values of MePhe/Phe ratios exhausted from diesel engine vehicle, bus and
petrol engine vehicle as 5.5, 4.8 and 0.7 respectively. It is worth mentioning that the
Vol. 32, No. 4, page 258 (2013)
• MePhe/Phe ratio of 5.5 was the average of five types of diesel engine ranging from 1500 cc to
14000 cc running without engine load and at a constant speed of 3000 rpm. In order to
enhance the reliability of using PAHs as proxy of tsunami backwash deposits, one should
consider the analysis of Ret, MePhe and Phe as well as the employment of chain-saw-
distribution of alkanes and CPI index. In addition, it is also important to use cholestanes and
hopanes as alternative biomarkers to distinguish “typical marine sediments” from “tsunami
backwash deposits”.
• The representative of the “tsunami backwash” group as discussed by Tipmanee et al (2012) is
not as far as approximately 25 km from the shoreline. Special signal found in this group could
just be a particular dominant source, not necessary to be tsunami backwash signal. The results
generated from binary ratios of PAHs, HCA coupled with PCA results obtained from
Tipmanee et al (2012) were only surface sediments and there is no direct and strong evidence
that the source of PAHs resulted from the tsunami in 2004. It could increase the strength if
there are dated core sediment samples and showed the temporal distribution in this area.
Furthermore, there are uncertainties in the estimation of source contribution by using PCA and
UNMIX. Further source apportionment techniques such as a positive matrix factorisation
(PMF) and a chemical mass balance (CMB) model should be conducted to increase the
reliability of source contributions.
•
ACKNOWLEDGEMENTS
This work was performed with the approval of Deutsche Forschungsgemeinschaft (DFG) and
National Research Council of Thailand (NRCT). The authors acknowledge the Research Center of
National Institute of Development Administration (NIDA), Thailand for financial support.
Vol. 32, No. 4, page 259 (2013)
REFERENCES
Allard, B., Rager, M.N., and Templier, J., 2002: Occurrence of high molecular weight lipids (C80+)
in the trilaminar outer cell walls of some freshwater microalgae. A reappraisal of algaenan structure.
Org. Geochem., 33, 789-801, 2002.
Atwater, B.F., 1987: Evidence for great Holocene earthquakes along the outer coast of Washington
State. Science., 236, 942-4, 1987.
Baek, S.O., Goldstone, M.E., and Kirk, P.W., 1991: Phase distribution and particle size dependency
of polycyclic aromatic hydrocarbons in the urban atmosphere. Chemosphere., 22, 503-20, 1991a.
Baek, S.O., Field, R.A., Goldstone, M.E., Kirk, P.W., Lester, J.N., and Perry, R., 1991b: A review of
atmospheric polycyclic aromatic hydrocarbons: sources, fate and behavior. Water, Air, and Soil
Pollution., 60, 279-300, 1991b.
Barbella, R., Ciajolo, A., Anna, D.A., and Bertoli, C., 1989: Effect of fuel aromaticity on diesel
emissions. Combust. Flame., 77(3-4), 267-77, 1989.
Belicka, L.L., Macdonald, W.R., Yunker, B.M., and Harvey, R.H., 2004: The role of depositional
regime on carbon transport and preservation in Arctic Ocean sediments. Mar. Chem., 86, 65-88, 2004.
Benson, B. E., Grimm, K. A., and Clague, J. J.: Tsunami deposits beneath tidal marshes on
northwestern Vancouver Island British Columbia. Quaternary. Res., 48, 192-204, 1997.
Blokker P., van Bergen P. F., Pancost R. D., Collinson M. E., Sinninghe Damst J. S., and de Leeuw J.
W.: The chemical structure of Gloeocapsamorpha prisca microfossils: implication for their origin.
Geochim. Cosmochim. Acta., 65, 885-900, 2001.
Bondevik, S., Sevendsen, J. I., and Mangerud, J.: Tsunami sedimentary facies deposited by the
Storegga tsunami in shallow marine basins and coastal lakes, western Norway. Sedimentology., 44,
1115-31, 1997.
Boonyatumanond, R., Murakami, M., Wattayakorn, G., Togo, A., and Takada, H.: Sources of
polycyclic aromatic hydrocarbons (PAHs) in street dust in a tropical Asian mega-city, Bangkok,
Thailand. Sci. Tot. Environ., 384 (1–3), 420-32, 2007.
Brasser, L. J.: Polycyclic aromatic hydrocarbon concentrations in the Netherelands. VDI-Berichete
Nr., 348, 171-80, 1980.
Brault, M., and Simoneit, T.R.B.: Steroid and triterpenoid distributions in bransfield strait sediments:
Hydrothermally-enhanced diagenetic transformations. Org. Geochem., 13(4-6), 697-705, 1988.
Vol. 32, No. 4, page 260 (2013)
Bray, E.E., and Evans, E.D.: Distribution of n-paraffins as a clue to recognition of source beds.
Geochim. Cosmochim. Acta., 22, 2-15, 1961.
Bruno, P., Caselli, M., Curri, L.M., Genga, A., Striccoli, R., and Traini, A.: Chemical characterisation
of ancient pottery from south of Italy by Inductively Coupled Plasma Atomic Emission Spectroscopy
(ICP-AES): Statistical multivariate analysis of data. Anal. Chim. Acta., 410(1-2), 193-202, 2000.
Bryant, E.: Tsunami. The Underrated Hazard: Cambridge, 320, 2001.
Burns, K.A., and Teal, J.M.: The West Falmouth oil spill: hydrocarbons in the salt marsh ecosystem.
Est. Coast. Mar. Sci., 8, 349-60, 1979.
Cantalamessa, G., and Di Celma, C.: Sedimentary features of tsunami backwash deposits in a shallow
marine Miocene setting, Mejillones Peninsula, northern Chile. Sediment. Geol., 178, 259 – 73, 2005.
Cautreels, W., and Van Cauwenburghe, K.: Experiments on the distribution of organic pollutants
between airborne particulate matter and the corresponding gas phase. Atmos. Environ., 12, 1133-41,
1978.
Chetwittayachan, T., Shimazaki, D., and Yamamoto, K.: A comparison of temporal variation of
particle-bound polycyclic aromatic hydrocarbons (pPAHs) concentration in different urban
environments: Tokyo, Japan, and Bangkok, Thailand. Atmos. Environ., 36 (12), 2027-37, 2002.
Choowong, M., Murakoshi, N., Hisada, K., Charusiri, P., Daorerk, V., Charoentitirat, T.,
Chutakositkanon, V., Jankaew, K., and Kanjanapayont, P.: Erosion and deposition by the 2004 Indian
Ocean tsunami in Phuket and Phang-nga Provinces, Thailand. J. Coastal. Res., 23(5), 1270–76, 2007.
Choowong, M., Murakoshi, N., Hisada, K., Charusiri, P., Charoentitirat, T., Chutakositkanon, V.,
Jankaew, K., Kanjanapayont, P., and Phantuwongraj, S.: 2004 Indian Ocean tsunami inflow and
outflow at Phuket, Thailand. Mar. Geol., 248(3–4), 179–92, 2008a.
Choowong, M., Murakoshi, N., Hisada, K., Charoentitirat, T., Charusiri, P., Phantuwongraj, S.,
Wongkok, P., Choowong, A., Subsayjun, R., Chutakositkanon, V., Jankaew, K., and Kanjanapayont,
P.: Flow conditions of the 2004 Indian Ocean tsunami in Thailand inferred from capping bedforms
and sedimentary structures. Terra. Nova., 20, 141–9, 2008b.
Choowong, M., Phantuwongraj, S., Charoentitirat, T., Chutakositkanon, V., Yumuang, S., and
Charusiri, P.: Beach recovery after 2004 Indian Ocean tsunami from Phang-nga, Thailand.
Geomorphology., 104, 134–42, 2009.
Chagué-Goff, C., Schneider, J.L., Goff, J.R., Dominey-Howes, D., and Strotz, L.: Expanding the
proxy toolkit to help identify past events—Lessons from the 2004 Indian Ocean Tsunami and the
2009 South Pacific Tsunami. Earth. Sci. Rev., 107, 107–22, 2011.
Vol. 32, No. 4, page 261 (2013)
Dawson, A.G., Long, D., and Smith, D.E.: The Storegga slides: Evidence from eastern Scotland for a
possible tsunami. Mar. Geol., 82, 271-6, 1988.
Dawson, A.G., Foster, I. D. L., Shi, S., Smith, D.E., and Long, D.: The identification of tsunami
deposits in coastal sediment sequences. Sci. Tsunami. Hazards., 9, 73-82, 1991.
Dawson, A.G.: Linking tsunami deposits, submarine slides and offshore earthquakes. Quarternary
Intern., 60, 119 – 26, 1999.
Dawson, A.G., and Shaozong, S.: Tsunami Deposits. Pure. Appl. Geophys., 157, 875 – 97, 2000.
Department of Mineral Resources, 2005. Goehazard Mitigation: How to survive a tsunami, 34 p.
Diesing, M., Kubicki, A., Winter, C., and Schwarzer, K.: Decadel scale stability of sorted bedforms,
German Bight, Southeastern North Sea. Cont. Shelf. Res., 26, 902 – 16, 2006.
Duan, J., Bi, X., Tan, J., Sheng, G., and Fu, J.: The differences of the size distribution of polycyclic
aromatic hydrocarbons (PAHs) between urban and rural sites of Guangzhou, China. Atmos. Environ.,
78, 190-203, 2005.
Einsele, G.: Event stratigraphy: recognition and interpretation of sedimentary event horizons. In:
Doyle, P. and Bennett, M.R. (eds.). Unlocking the stratigraphic record: advances in modern
stratigraphy, 145 – 193, 1998.
Engelbrecht, P.J., Swanepoel, L., Chow, C.J., Watson, G.J., and Egami, T.R.: The comparison of
source contributions from residential coal and low-smoke fuels, using CMB modelling, in South
Africa. Environ. Sci. Pol., 5(2), 157-67, 2002.
EPA Appendix A to 40 CFR, Part 423–126 Priority Pollutants Available from: http://
www.epa.gov/region01/npdes/permits/generic/prioritypollutants.pdf, (2003).
Fang, M., Wan, T., and Kwan, J.: Characterization of the organic compounds in the aerosols of Hong
Kong-identification, abundance, origin and toxicity. Research Center Publication., 21-96, 1996.
Fang, M., Zheng, M., Wang, F., To, K.L., Jaafar, A.B., and Tong, S.L.: The solvent extractable
organic compounds in Indonesia biomass burning aerosols – Characterisation studies. Atmos.
Environ., 33, 783-95, 1999.
Fang, G.C., Chang. C.N., Wu, Y.S., Fu, P.P.C., Yang, I.L., and Chen, M.H.: Characterization,
identification of ambient air and Road dust polycyclic aromatic hydrocarbons in central Taiwan,
Taichung. Sci. Tot. Environ., 366(2-3), 729-38, 2006.
Vol. 32, No. 4, page 262 (2013)
Ferrini, V.L., and Flood, R.D.: A comparison of Rippled Scour Depressions identified with multibeam
sonar: Evidence of sediment transport in inner shelf environments. Cont. Shelf. Res., 25, 1979–95,
2005.
Fisher, R.V.: Flow transformation in sediment gravity flows. Geology., 11, 273 – 4, 1983.
Freeman, D.J., and Cattell, F.C.R.: Wood-burning as a source of atmospheric polycyclic aromatic
hydrocarbons. Environ. Sci. Technol., 24, 1581-5, 1990.
Garrenstrom, D.P., Perone, P.S., and Moyers, L.J.: Application of pattern recognition and factor
analysis for characterization of atmospheric particulate composition in southwest desert atmosphere.
Environ. Sci. Technol., 10, 795-800, 1977.
Gelfenbaum, G., and Jaffe, B.: Erosion and sedimentation from the July 17, 1998 Papua New Guinea
tsunami. Pure. Appl. Geophys., 160, 1969-99, 2003.
Gogou, A., Stratigakis, N., Kanakidou, M., and Stephanou, E.: Organic aerosol in Eastern
Maditerranean: component source reconciliation by using molecular markers and atmospheric back
trajectories. Org. Geochem., 25, 79-96, 1996.
Gogou, I. A., Bouloubassi, I., and Stephanou, G.E.: Marine organic geochemistry of the Eastern
Mediterranean: 1. Apliphatic and polyaromatic hydrocarbons in Cretan Sea surficial sediments. Mar.
Chem., 68(4), 265-82, 2000.
Gordon, G.E.: Receptor models. Environ. Sci. Technol., 22, 1132–42, 1988.
Gonçalves, C., Alves, C., Fernandes, A. P., Monteiro, C., Tarelho, L., Evtyugina, M., and Pio, C.:
Organic compounds in PM2.5 emitted from fireplace and woodstove combustion of typical Portuguese
wood species. Atmos. Environ., 45 (27), 4533-45, 2011.
Gou, H., Lee, C.S., Ho, F.K, Wang, X.M., and Zou, C.S.: Particle-associated polycyclic aromatic
hydrocarbons in urban air of Hong Kong. Atmos. Environ., 37, 5307–17, 2003.
Grantham, P. J., and Douglas, A. G.: The nature and origin of sesquiterpenoids in some Tertiary fossil
resins. Geochim. Cosmochim. Acta., 44, 1801-10, 1980.
Gusiakov, V.K.: Tsunami generation potential of different tsunamigenic regions in the Pacific. Mar.
Geol., 215, 3 – 9, 2005.
Vol. 32, No. 4, page 263 (2013)
Harrison, R. M., Smith, D. J. T., and Luhana, L.: Source Apportionment of Atmospheric Polycyclic
Aromatic Hydrocarbons Collected from an Urban Location in Birmingham, U.K. Environ. Sci.
Technol, 30(3), 825-32, 1996.
Harrison, R.M., Smith, D.J.T., Pio, C.A., and Castro, L.M.: Comparative receptor modeling study of
airborne particulate pollutants in Birmingham (United Kingdom), Coimbra (Portugal) and Lahore
(Pakistan). Atmos. Environ., 31, 3309-21 1997.
Harrison, M.R., Tilling, R., Callèn Romero, S.M., Harrad, S., and Jarvis, K.: A study of trace metals
and polycyclic aromatic hydrocarbons in the roadside environment. Atmos. Environ., 37, 2391-402,
2003.
Hedberg, H. D.: Significance of high-wax oils with respect to genesis of petroleum. Am. Assoc.
Petrol. Geol. Bull., 52(5), 736-50, 1968.
Henry, C.R., and Hidy, M.G.: Multivariate analysis of particulate sulfate and other air quality
variables by principal components-Part I: Annual data from Los Angeles and New York. Atmos.
Environ., 3, 1581-96, 1979.
Henry, C.R., Lewis, C., and Collins, J.: Vehicle-related hydrocarbon source compositions from
ambient data: The GRACE/SAFER method. Environ. Sci. Technol., 28, 823-32, 1994.
Henry, C.R.: Multivariate receptor modeling by N-dimensional edge detection. Chemom. Intell. Lab.
Syst., 65, 179-89, 2002.
Henry, C.R.: Multivariate receptor modelling by N-dimensional edge detection, Chemom. Intell. Lab.
Syst., 65(2), 179-89, 2003.
Henry, C.R.: Duality in multivariate receptor models, Chemom. Intell. Lab. Syst., 77(1-2), 59-63,
2005.
Holba, A. G., Dzou, L. I. P., Masterson, W. D., Hughes, W. B., Huizinga, B. J., Singletary, M. S.,
Moldowan, J. M., Mello, M. R., and Tegelaar, E.: Application of 24-norcholestanes for constraining
source age of petroleum. Org. Geochem., 29(5-7), pp.1269-83, 1998a.
Holba, A. G., Tegelaar, E. W., Huizinga, B. J., Moldowan, J. M., Singletary, M. S., McCaffrey, M.
A., and Dzou, L. I. P.: 24-norcholestanes as age-sensitive molecular fossils. Geology., 26(9), 783-6,
1998b.
Hopke, K.P., Gladney, S.E., Gordon, E.G., Zoller, H.W., and Jones, G.A.: The use of multivariate
analysis to identify sources of selected elements in the Boston urban aerosol. Atmos. Environ., 10,
1015-25, 1977.
Vol. 32, No. 4, page 264 (2013)
Huang, Y.W., and Meinschein, G.W.: Sterols as source indicators of organic materials in sediments.
Geochimica et Cosmochimica Acta., 40(3), 323-30, 1976.
International Agency for Research on Cancer (1983) IARC Monographs on the evaluation of the
carcinogenic risk of chemicals to man, Vol.32: Polycyclic aromatic hydrocarbons, Part 1: Chemical,
environmental and experimental data. IARC, Lyon, France.
International Agency for Research on Cancer (1984) IARC Monographs on the evaluation of the
carcinogenic risk of chemicals to man, Vol.34: Polycyclic aromatic hydrocarbons, Part 3: Industrial
exposures in aluminium production, coal gasification, coke production, and iron and steel founding.
IARC, Lyon, France.
International Agency for Research on Cancer (1985) IARC Monographs on the evaluation of the
carcinogenic risk of chemicals to man, Bitumens, coal-tars and derived products, shale-oils and soots.
Monnograph No 35, IARC, Lyon, France.
International Agency for Research on Cancer (1987) IARC Monographs on the evaluation of the
carcinogenic risk of chemicals to man, Supplement, IARC, Lyon, France.
Jasper, P.J., and Gagosian, B.R.: The relationship between sedimentary organic carbon isotopic
composition and organic biomarker compound concentration. Geochim. Cosmochim. Acta., 57 (1),
167-86, 1993.
Kalaitzoglou, M., Terzi, E., and Samara, C.: Patterns and sources of particle-phase aliphatic and
polycyclic aromatic hydrocarbons in urban and rural sites of western Greece. Atmos. Environ., 38,
2545-60, 2004.
Kavouras, G.I., Lawrence, J., Koutrakis, P., Stephanou, G.E., and Oyola, P.: Measurement of
particulate apliphatic and polynuclear aromatic hydrocarbons in Santiago de Chile: Source
reconciliation and evaluation of sampling artifacts. Atmos. Environ., 33(30), 4977-86, 1999.
Kendall, M., Hamilton, S. R., Watt, J., and Williams, D. I.: Characterisation of selected speciated
organic compounds associated with particulate matter in London. Atmos. Environ., 35, 2483-95,
2001.
Killops, S. D., Carlson, R. M. K., and Peters, K. E.: High-temperature GC evidence for the early
formation of C40+ n-alkanes in coals. Org. Geochem, 31, 589-97, 2000.
Kukkonen, J., and Landrum, P. F.: Distribution of organic carbon and organic xenobiotics among
different particle-size fractions in sediments. Chemosphere., 32 (6), 1063-76, 1996.
Vol. 32, No. 4, page 265 (2013)
Le Roux, J.P., and Vargas, G.: Hydraulic behaviour of tsunami backflows: Insights from their modern
and ancient deposits. Environ. Geol., 49, 65 – 75, 2005.
Li, C., Fu, J., Sheng, G., Bi, X., Hao, Y., Wang, X., and Mai, B.: Vertical distribution of PAHs in the
indoor and outdoor PM2.5 in Guangzhou, China. Build. Environ., 40, 327–39, 2005.
Lim, H. L., Harrison, M. R., and Harrad, S.: The contribution of traffic to atmospheric concentrations
of polycyclic aromatic hydrocarbons. Environ. Sci. Technol, 33, 3538-42, 1999.
Lu, G. N., Dang, Z., Tao, X. Q., Yang, C., and Yi, X. Y.: Estimation of water solubility of polycyclic
aromatic hydrocarbons using quantum chemical descriptors and partial least squares. Mol. Inform.,
27(5), 618-26, 2008.
Mainwaring, S. J., and Stirling, D. M.: A study of the size distribution and concentrations of
polynuclear hydrocarbons in Melbourne air. In “Proceedings of the 7th International Clean Air”, 1981.
Mei, Z., Ming, F., and Fuwang, K.L.: Characterization of the solvent extractable organic compounds
in PM2.5 aerosols in Hong Kong. Atmos. Environ., 34(17), 2691-702, 2000.
Miller, L.S., Anderson, J.M., Daly, P.E., and Milford, B.J.: Source apportionment of exposures to
volatile organic compounds. I. Evaluation of receptor models using simulated exposure data. Atmos.
Environ., 36(22), 3629-41, 2002.
Moldowan, J. M., Fago, F. J., Lee, C. Y., Jacobson, S. R., Watt, D. S., Slougui, N.E., Jeganathan, A.,
and Young, D. C.: Sedimentary 24-n-propylcholestanes, molecular fossils diagnostic of marine algae.
Science., 247, 309-12, 1990.
Motelay, A., Harner, T.M., Shoeib, M., Diamond, M., Stern, G., and Rosenberg, B.: Using passive air
samplers to assess urban-rural trends for persistent organic pollutants and polycyclic aromatic
hydrocarbons. 2. Seasonal trends for PAHs, PCBs and Organochlorine Pesticides. Environ. Sci.
Technol., 39, 5763-73, 2005.
Mudge, S.M.: Reassessment of the hydrocarbons in Prince William Sound and the Gulf of Alaska:
Identifying the source using Partial Least-Squares. Environ. Sci. Technol., 36, 2354-60, 2002.
Murray, A.B., and Thieler, E.R.: A new hypothesis on exploratory model for the formation of large-
scale inner shelf sediment sorting and “rippeled scour depressions”. Cont. Shelf. Res., 24, 295 – 315,
2004.
Nielsen, T.: Traffic contribution of polycyclic aromatic hydrocarbons in the center of a large city.
Atmos. Environ., 30(20), 3481-90, 1996.
Vol. 32, No. 4, page 266 (2013)
Nittrouer, C.A., and Wright, L.D.: Transport of particles across continental shelves. Rev. Geophys.,
32, 85–113, 1994.
Ohura, T., Amagai, T., Sugiyama, T., Fusaya, M., and Matsushita, H.: Characteristics of particle
matter and associated polycyclic aromatic hydrocarbons in indoor and outdoor air in two cities in
Shizuoka, Japan. Atmos. Environ., 38, 2045–54, 2004.
Ondov, M.J., Kelly, R.W., Holland, Z.J., Lin, C.Z., and Wight, A.S.: Tracing fly ash emitted from a
coal-fired power plant with enriched rare-earth isotopes: An urban scale test. Atmos. Environ. Part B.,
26(4), 453-62, 1992.
Ourisson, G., and Albrecht, P.: Hopanoids 1. Geohopanoids: the most abundant natural products on
Earth? Acc. Chem. Res., 25, 398-402, 1992.
Paasivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., and Wania, F.: Estimation of vapor pressures,
solubilities and Henry’s law constants of selected persistent organic pollutants as functions of
temperature. Chemosphere, 39(5), 811-32, 1999.
Park, S.S., and Kim, J.Y.: Source contributions to fine particulate matter in an urban atmosphere.
Chemosphere., 59(2), 217-26, 2005.
Pierson, R.W., and Brachaczek, W.W.: Particulate matter associated with vehicles on the road II.
Aerosol. Sci. Technol., 2, 1-40, 1993.
Pinegina, T.K., and Bourgeois, J.: Historical and paleo-tsunami deposits on Kamchatka, Russia: long-
term chronologies and long-distance correlations. Nat. Hazard. Earth. Sys. Sci., 1, 177-85, 2001.
Pongpiachan, S., Thamanu, K., Ho, K.F., Lee, S.C., and Sompongchaiyakul, P.: Predictions of gas-
particle partitioning coefficients (Kp) of polycyclic aromatic hydrocarbons at various occupational
environments of Songkhla province, Thailand. Southeast Asian J. Trop. Med., 40 (6), 1377-94, 2009.
Pongpiachan, S., Thumanu, K., Na Pattalung, W., Hirunyatrakul, P., Kittikoon, I., Ho, F. K., and Cao,
J. J.: Diurnal variation and spatial distribution effects of Sulfur Speciation in Aerosol Samples as
Assessed by X-ray Absorption Near-edge Structure (XANES). J. Anal. Methods. Chem., Article ID
696080, 10 pages, doi:10.1155/2012/696080, 2012a.
Pongpiachan, S., Thumanu, K., Kositanont, C., Schwarzer, K., Prietzel, J., Hirunyatrakul, P., and
Kittikoon, I.: Parameters Influencing Sulfur Speciation in Environmental Samples Using Sulfur K-
Edge X-Ray Absorption Near-Edge Structure (XANES). J. Anal. Methods. Chem., Article ID 659858,
12 pages, doi: 10.1155/2012/659858, 2012b.
Vol. 32, No. 4, page 267 (2013)
Pongpiachan, S.: Fingerprint of Carcinogenic Semi-Volatile Organic Compounds (SVOCs) during
Bonfire Night. Asian. Pac. J. Cancer. Prev., 14(5), 3243-54, 2013a.
Pongpiachan, S.: Vertical distribution and potential risk of particulate polycyclic aromatic
hydrocarbons in high buildings of Bangkok, Thailand. Asian. Pac. J. Cancer. Prev., 14(3), 1865-77,
2013b.
Pongpiachan, S.: Diurnal Variation, Vertical Distribution and Source Apportionment of Carcinogenic
Polycyclic Aromatic Hydrocarbons (PAHs) in Chiang-Mai, Thailand. Asian. Pac. J. Cancer. Prev.,
14(3), 1851-63, 2013c.
Pongpiachan, S., Thumanu, K., Na Phatthalung, W., Tipmanee, D., Kanchai, P., Feldens, P., and
Schwarzer, K.: Using Fourier Transform Infrared (FTIR) to Characterize Tsunami Deposits in Near-
Shore and Coastal Waters of Thailand. Sci. Tsu. Haz., 32 (1), 39-57, 2013a.
Pongpiachan, S., Thumanu, K., Tanthanuch, W., Tipmanee, D., Kanchai, P., Schwarzer, K., and
Tancharakorn, S.: Sedimentary features of tsunami backwash deposits as assessed by micro-beam
synchrotron X-Ray Fluorescence (µ-SXRF) at the Siam Photon Laboratory. Sci. Tsu. Haz., 32 (2), 96-
115, 2013b.
Pongpiachan, S., Choochuay, C., Hattayanone, M., and Kositanont, C.: Temporal and spatial
distribution of particulate carcinogens and mutagens in Bangkok, Thailand. Asian. Pac. J. Cancer.
Prev., 14(3), 1879-87, 2013c.
Pongpiachan, S., Ho, K. F., and Cao, J.: Estimation of Gas-Particle Partitioning Coefficients (Kp) of
Carcinogenic Polycyclic Aromatic Hydrocarbons by Carbonaceous Aerosols Collected at Chiang-
Mai, Bangkok and Hat-Yai, Thailand. Asian. Pac. J. Cancer. Prev., 14(4), 3369-84, 2013d.
Pongpiachan, S., Tipmanee, D., Deelaman, W., Muprasit, J., Feldens, P., and Schwarzer, K.: Risk
assessment of the presence of polycyclic aromatic hydrocarbons (PAHs) in coastal areas of Thailand
affected by the 2004 tsunami. Mar. Pollut. Bulletin., In Press, 2013e.
Prince, R.C., Elmendoft, D.L., Lute, R.J., and Hsu, C.E.: 17α(H),21β(H)-hopanes as a conserved
internal marker for estimating the biodegradation of crude oil. Environ. Sci. Technol., 28, 142-5,
1994.
Rachdawong, P., Christensen, R.E., and Chi, S.: Source identification of PCBs in sediments from the
Milwaukee Harbor Estuary, USA. Water. Sci. Technol., 37(6-7), 199-206, 1988.
Ramdahl, T.: Retene- a molecular marker of wood combustion in ambient air. Nature., 306, 580-2,
1983.
Vol. 32, No. 4, page 268 (2013)
Ramdahl, T., and Becher, G.: Characterization of polynuclear aromatic hydrocarbon derivatives in
emissions from wood and cereal straw combustion. Analytica. Chimica. Acta., 144(1), 83-91, 1982.
Ramdahl, T., and Mǿller, M.: Chemical and biological characterization of emissions from a cereal
straw burning furnace. Chemosphere., 12(1), 23-4, 1983.
Reddy, M.C., Eglinton, I.T., Hounshell, A., Xu, K.W., Gaines, B.R., and Frysinger, S.G.: The West
Falmouth oil spill after thirty years: The persistence of petroleum hydrocarbons in marsh sediments.
Environ. Sci. Technol., 36 (22), 4754-60, 2002.
Rogge, W. F., Mazurek, M. A., Hildemann, L. M., and Cass, G. R., and Simoneit, B.R.T.:
Quantification of urban organic aerosols at a molecular level: identification, abundance and seasonal
variation. Environ. Sci. Technol., 27A, 1309-30, 1993.
Rohmer, M., Bouvier-Nav, P., and Ourisson, G.: Distribution of hopanoid triterpenes in prokaryotes.
J. Gen. Microbiol., 130, 1137-50, 1984.
Ruchirawat, M., Mahidol, C., Tangjarukij, C., Pui-ock, S., Jensen, O., Kampeerawipakorn, O.,
Tuntaviroon, J., Aramphongphan, A., and Autrup, H.: Exposure to genotoxins present in ambient air
in Bangkok, Thailand — particle associated polycyclic aromatic hydrocarbons and biomarkers. Sci.
Tot. Environ., 287 (1–2), 121-32, 2002.
Ruchirawat, M., Navasumrit, P., Settachan, D., Tuntaviroon, J., Buthbumrung, N., and Sharma, S.:
Measurement of genotoxic air pollutant exposures in street vendors and school children in and near
Bangkok. Toxicol. Appl. Pharm., 206 (2), 207-14, 2005.
Ruchirawat, M., Settachan, D., Navasumrit, P., Tuntawiroon, J., and Autrup, H.: 2007. Assessment of
potential cancer risk in children exposed to urban air pollution in Bangkok, Thailand. Toxicol. Lett.,
168 (3), 200-9, 2007.
Salma, I., Maenhaut, W., Dubtsov, S., Zemplén-Papp, E., and Záray, G.: Impact of phase out of
leaded gasoline on the air quality in Budapest. Microchemical Journal., 67(1-3), 127-33, 2000.
Sanderson, G.E., Raqbi, A., Vyskocil, A., and Farant, P.J.: Comparison of particulate polycyclic
aromatic hydrocarbon profiles in different regions of Canada. Atmos. Environ., 38, 3417-29, 2004.
Scheffers, A., and Kelletat, D.: Sedimentologic and geomorphologic tsunami imprints worldwide – a
review. Earth. Sci. Rev., 63, 83 – 92, 2003.
Schwarzer, K., Diesing, M., Larson, M., Niedermeyer, R.O., Schumacher, W., and Furmanczyk, K.:
Coastline evolution at different time scales. - Examples from the southern Baltic Sea (Pomeranian
Bight) – Mar. Geol., 194, 79 – 101, 2003.
Vol. 32, No. 4, page 269 (2013)
Shanmugam, G.: The Tsunamite problem. J. Sediment. Res., 76, 718–30, 2006.
Shi, S., Dawson, A.G., and Smith, D.E.: Coastal tsunami sedimentation processes. In: CEA/DASE
(eds.). Proceedings of the International Conference on Tsunamis, Paris, France, May 26 – 28, 43 – 55,
1998.
Shiki, T., and Yamazaki, T.: The term 'Tsunamiite'. In Shiki T. Tsunamiites: features and
implications. Developments in Sedimentology. Elsevier. p. 5. ISBN 978-0-444-51552-0. 2008.
Simoneit, B.R.T.: Organic matter of the troposphere—III. Characterization and sources of petroleum
and pyrogenic residues in aerosols over the western United States. Atmos. Environ., 18, 51–67, 1984.
Stenberg, U., and Alsberg, T.: Capillary GC-MS analysis of PAH emissions from combustion of peat
and wood in hot water boiler. Chemosphere., 8, 487-96, 1979.
Summons, R. E., Jahnke, L. L., and Roksandic, Z.: Carbon isotope fractionation in lipids from
methanothrophic bacteria: relevance for interpretation of the geological record of biomarkers.
Geochim. Cosmochim. Acta., 58, 2853-63, 1994a.
Szczuciński, W., Niedzielski, P., Rachlewicz, G., Sobczyński, T., Zioła, A., Kowalski, A., Lorenc, S.,
and Siepak, J.: Contamination of tsunami sediments in a coastal zone inundated by the 26 December
2004 tsunami in Thailand. Environ. Geol., 49 (2), 321-31, 2005.
Szczuciński, W., Chaimanee, N., Niedzielski, P., Rachlewicz, G., Saisuttichai, D., Tepsuwan, T.,
Lorenc, S., and Siepak, J.: Environmental and geological impacts of the 26 December 2004 tsunami in
coastal zone of Thailand—overview of short and long-term effects. Pol. J. Environ. Stud., 15 (5),
793–810, 2006.
Szczuciński, W., Niedzielski, P., Kozak, L., Frankowski, M., Zioła, A., and Lorenc, S.: Effects of
rainy season on mobilization of contaminants from tsunami deposits left in a coastal zone of Thailand
by the 26 December 2004 tsunami. Environ. Geol., 53 (2), 253-64, 2007.
Takada, H., Onda, T., Harada, M., and Ogura, N.: Distribution and sources of polycyclic aromatic
hydrocarbons (PAHs) in street dust from the Tokyo Metropolitan area. Sci. Total. Environ., 107, 45-
69, 1991.
Tarek, A.T.A., and Bernd, R.T.S.: Lipid geochemistry of surficial sediments from the coastal
environment of Egypt I. Aliphatic hydrocarbons — characterization and sources. Mar. Chem., 54(1-
2), 135-58, 1996.
Tareq, S. M., Tanaka, N., and Ohta, K.: Biomarker signature in tropical wetland: lignin phenol
vegetation index (LPVI) and its implications for reconstructing the paleoenvironment. Sci.Tot.
Environ., 324 (1–3), 91-103, 2004.
Vol. 32, No. 4, page 270 (2013)
Tipmanee, D., Deelaman, W., Pongpiachan, S., Schwarzer, K and., Sompongchaiyakul, P. Using
Polycyclic Aromatic Hydrocarbons (PAHs) as a chemical proxy to indicate Tsunami 2004 backwash
in Khao Lak coastal area, Thailand. Nat. Hazards Earth Syst. Sci., 12, 1441–51, 2012.
Tsapakis, M., and Stephanou, G.E.: Polycyclic aromatic hydrocarbons in the atmosphere of the
Eastern Mediterranean. Environ. Sci. Technol., 39, 6854-90, 2005.
Vargas, G., Ortlieb, L., Chapron, E., Valdes, J., and Marquardt, C.: Paleoseismic inferences from a
high-resolution marine sedimentary record in northern Chile (23° S). Tectonophysics, 399, 381 – 98,
2005.
Venkatesan, I.M., Ruth, E., Rao, S.P., Nath, N.B., and Rao, R.B.: Hydrothermal petroleum in the
sediments of the Andaman Backarc Basin, Indian Ocean. Applied Geochemistry., 18(6), 845-61,
2003.
Volkman, J.K.: Sterols in microorganisms. Appl. Microbiol. Biotechnol., 60, 496-506, 2003.
Wang, L.R., Brassell, C.S., Scarpitta, C.S., Zheng, P.M., Zhang, C.S., Hayde, R.P., and Muench,
M.L.: Steroids in sediments from Zabuye Salt Lake, western Tibet: diagenetic, ecological or climatic
signals? Org. Geochem., 35(2), 157-68, 2004.
Wang, X. C., Zhang, Y. X., and Chen, R. F.: Distribution and Partitioning of Polycyclic Aromatic
Hydrocarbons (PAHs) in Different Size Fractions in Sediments from Boston Harbor, United States.
Mar. Pollut. Bullet., 42 (11), 1139-49, 2001.
Watson, G.J., Robinson, F.N., Chow, C.J., Henry, C.R., Kim, M.B., Pace, G.T., Meyer, L.E., and
Nguyen, Q.: The USEPA/DRI chemical mass balance receptor model, CMB 7.0. Environ. Soft., 5(1),
38-49, 1990.
Watson, J.G., Chow, J.C., Lu, Z., Fujita, E.M., Lowenthal, D.H., and Lawson, D.R.: Chemical mass
balance source apportionment of PM10 during the Southern California air quality study. Aerosol. Sci.
Technol., 21, 1-36, 1994.
Watson, G.J., Chow, C.J., and Fujita, M.E.: Review of volatile organic compound source
apportionment by chemical mass balance. Atmos. Environ., 35(9), 1567-84, 2001.
Westerholm, R., Almen, J., Li, H., Rannug, U., and Rosen, A.: Exhaust emissions from gasoline-
fuelled light duty vehicles operated in different driving conditions: a chemical and biological
characterization. Atmos. Environ., 26B, 79-90, 1992.
Williams, T.P., Bartle, D.K., and Andrews, E.G.: The relationship between polycyclic aromatic
compounds in diesel fuels and exhaust particulates. Fuel., 65(8), 1150-8, 1986.
Vol. 32, No. 4, page 271 (2013)
Yunker, B.M., Macdonald, W.R., Vingarzan, R., Mitchell, H.R, Goyette, D., and Sylvestre, S.: PAHs
in the Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and
composition. Org. Geochem., 33, 489-515, 2002.
Yunker, B.M., and Macdonald, W.R.: Petroleum biomarker sources in suspended particulate matter
and sediments from the Fraser River Basin and Strait of Georgia, Canada. Org. Geochem., 34, 1525-
41, 2003.
Zakaria, P.M., Okuda, T., and Takada, H.: Polycyclic aromatic hydrocarbon (PAHs) and Hopanes in
Stranded Tar-balls on the coasts of Peninsular Malaysia: Applications of biomarkers for identifying
sources of oil pollution. Mar. Pollut. Bulletin., 42(12), pp.1357-1366, 2001.
Zheng, M., Fang, M., Wang, F., and To, K.L.: Characterization of the solvent extractable organic
compounds in PM2.5 aerosols in Hong Kong. Atmos. Environ., 34, 2691-702, 2000.
Zundel, M., and Rohmer, M.: Hopanoids of the methylotrophic bacteria Methylococcus capsulatus
and Methylomonas sp. As possible precursors of C29 and C30 hopanoid chemical fossils. FEMS.
Microbiol. Lett., 28(1), 61-4, 1985a.
Zundel, M., and Rohmer, M.: Prokaryotic triterpenoids 1.3-methylhopanoids from Acetobacter sp.
and Methylococcus capsulatus. Eur. J. Biochem., 150, 23-7, 1985b.
Zundel, M., and Rohmer, M.: Prokaryotic triterpenoids 3. The biosynthesis of 2b-methylhopanoids
and 3b-methylhopanoids of Methylobacterium organophilum and Acetobacter pasteurianus sppl
pasteurianus. Eur. J. Biochem., 150, 35-9, 1985c.
Vol. 32, No. 4, page 272 (2013)
APPENDIX. TABLES
Table&1.&Physiochemical&properties&of&PAHs&
Source: http://www.es.lancs.ac.uk/ecerg/kcjgroup/5.html
MP (oC) Melting Point Kow Octanol-water partition coeffic
BP (oC) Boiling Point H Henry 's Law Constant
PS Vapour pressure of solid substance Koa Octanol-air partition coefficient
PL Vapour pressure of subcooled liquid
Vol. 32, No. 4, page 273 (2013)
Cong
eners
MW
(g/mol)
MP (oC)
BP (oC)
PS
PL
Log Kow
H
Log KOA
Ace
154.2
96
277.5
0.3
1.52
3.92
12.17
6.23
Ac
150.2
92
265-275
0.9
4.14
4.00
8.40
6.47
Fl
166.2
116
295
0.09
0.72; 0.79a
4.18
7.87
6.68
Phe
178.2
101
339
0.02
0.11; 0.06
4.57
3.24
7.45
1-
MePh
192.3
123
359
5.14
An
178.2
216
340
1.00E-03
7.78E-02
4.54
3.96
7.34
Pyr
202.3
156
360
6.00E-04
1.19E-02; 8E-
03
5.18
0.92
8.61
Flu
202.3
111
375
1.23E-03
8.72E-03
5.22
1.04
8.60
B[a]F
216.3
187
407
5.40
B[b]F
216.3
209
402
5.75
Chry
228.3
255
448
5.70E-07
1.07E-04
5.86
6.50E-
02
10.44
Tri
228.3
199
438
2.30E-06
1.21E-04
5.49
1.20E-
02
10.80
p-terp
230.1
213
4.86E-06
6.03
B[a]
A
228.3
160
435
2.80E-05
6.06E-04
5.91
0.58
9.54
B[a]P
252.3
175
495
7.00E-07
2.13E-05
6.04
4.60E-
02
10.77
B[e]P
252.3
178
7.40E-07
2.41E-05
0.02
Per
252.3
277
495
1.40E-08
6.25
3.00E-
03
12.17
B[b]F
252.3
168
481
5.80
B[j]F
252.3
166
480
B[k]F
252.3
217
481
5.20E-08
4.12E-06
6.00
1.60E-
02
11.19
B[g,h
,i]P
268.4
277
2.25E-05
6.50
7.50E-
02
11.02
D[a,h
]A
278.4
267
524
3.70E-10
9.16E-08
6.75
Cor
300.4
>350
525
2.00E-10
6.75
Table&2.&Chemical&structures&of&PAHs&
Congener
Abbreviation
M.W. [g]
Chemical Structure
Acenaphthylene
Ac
152
Acenaphthene
Ace
154
Fluorene
Fl
166
Phenanthrene
Phe
178
Anthracene
An
178
3-Methyl Phenanthrene
3-MePhe
192
9-Methyl Phenanthrene
9-MePhe
192
1-Methyl Phenanthrene
1-MePhe
192
2-Methyl Phenanthrene
2-MePhe
192
1-methyl-7-isopropyl
phenanthrene (Retene)
Ret
234
Fluoranthene
Fluo
202
Pyrene
Py
202
Benz[a]anthracene
B[a]A
228
Chrysene
Chry
228
Triphenylene
Tri
228
&
Vol. 32, No. 4, page 274 (2013)
&
&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
&
Table&3.&Chemical&structures&of&alkanes.&
Congener
Abbreviation
M.W. [g]
Chemical Structure
Tetradecane
C14
198
Pentadecane
C15
212
Hexadecane
C16
226
Heptadecane
C17
268
Octadecane
C18
254
Nonadecane
C19
268
Eicosane
C20
282
Heniacosane
C21
296
Docosane
C22
310
Tricosane
C23
324
Tetracosane
C24
338
Pentacosane
C25
352
Hexacosane
C26
366
Heptacosane
C27
380
Octacosane
C28
394
Nonacosane
C29
408
Triacontane
C30
422
Hentriacontane
C31
436
Dotriacontane
C32
450
Pristane
PC19
268
Phytane
PC20
282
Vol. 32, No. 4, page 275 (2013)
&
&
Table&4.&Chemical&structures&of&hopanes.&
Congener
Abbreviation
M.W. [g]
Chemical Structure
17α(H)-22,29,30-
Trisnorhopane
22,29,30-
trisnorhopane
(th)
370
17α(H),21β(H)-30-
Norhopane
17,21,ab-30-
norhopane
(nh)
398
17α(H),21β(H)-
Hopane
17,21,ab-
hopane
(hop)
412
17α(H),21β(H)-22R-
Homohopane
22R-17,21ab-
30-
homohopane
(homo)
426
Vol. 32, No. 4, page 276 (2013)
&
Table&5.&Chemical&structures&of&cholestanes.&
Congener
Abbreviation
M.W. [g]
Chemical Structure
αββ 20R-Cholestane
20R-abb-
cholestane
(abbC)
372
ααα 20R-Cholestane
20R-aaa-
cholestane
(aaaC)
372
αββ 20R 24S-
Methylcholestane
20R-abb-
methylcholest
ane
(MC)
386
αββ 20R 24R-
Ethylcholestane
20R-abb-
ethylcholestan
e
(EC)
400
Vol. 32, No. 4, page 277 (2013)
Table 6. ΣPAH concentrations in different regions
Location
Sample
Type
ΣPAHs [ng m-3]
Reference
UK (London,Bounds Green)
TSP
4.27 (0.23-27.87)
Kendall et al., 2001
UK (London, St Paul)
TSP
7.24 (1.04-32.04)
Kendall et al., 2001
Indonesia (Pudu)
PM10
26.5 (7-46)
Fang et al., 1999
Greece (Rural Area)
TSP
1.1 (0.2-2.0)
Gogou et al., 1996
Greece (Heraklion)
TSP
17.4 (3.2-44.9)
Gogou et al., 2000
Portugal (Forest)
TSP
0.5 (0.1-0.9)
Kavouras et al., 1999
China (Hong Kong)
PM2.5
9.6 (0.7-12.2)
Zheng et al., 2000
China (Guangzhou)
TSP
73.7 (32.5-153.7)
Duan et al., 2005
Canada (Urban)
TSP
2.35 (1.86-2.83)
Sanderson et al., 2004
Eastern Mediterranean
TSP
0.7 (0.3-1.6)
Tsapakis and Stephanou,
2005
Canada (Toronto)
P+V
36.5 (11.5-61.4)
Motelay et al., 2005
Traffic, Hong Kong
PM2.5
33.96
Gou et al., 2003
Industrial, Hong Kong
PM2.5
16.72
Gou et al., 2003
Industrial (indoor), Shizuoka,
Japan
PM2.5
1.6–23.7
Ohura et al., 2004
Industrial (outdoor),
Shizuoka, Japan
PM2.5
1.1–29.5
Ohura et al., 2004
Pastureland (open) Taichung,
Taiwan
PM2.5
74.47
Fang et al., 2006
Temple (semi-open)
Taichung, Taiwan
PM2.5
284.91
Fang et al., 2006
Traffic Guangzhou, China
PM2.5
57.89
Li et al., 2005
Residential Guangzhou,
China
PM2.5
27.06
Li et al., 2005
Vol. 32, No. 4, page 278 (2013)
Table 7. Biological and/or environmental interpretation of n-alkanes
Biomarker
Biological and/or environmental
interpretation
References
Outstanding
concentrations of n15, n17
and n19 in early Paleozoic
rocks
Marine phytoplankton of uncertain
affinity, probably an alga, identified in
Cambrian Devonian sediments but
most prominent in Ordovician.
Estonian kukersite is a typical source
Blokker et al., 2001
n-C27 with OEP1
Waxes derived from higher plants,
terrestrial input, post-Silurian age
Hedberg, 1968
n-C40
Predominantly degradation products of
aliphatic macromolecules such as
algaenan (marine, lacustrine), cutan
and suberan (terrestrial, plant derived)
Allard et al., 2002
Killops et al., 2000
Table 8. Biological and /or environmental interpretation of hopanes
Biomarker
Biological and/or environmental
interpretation
References
C30-hopanes
Diverse bacterial lineages, few
eukaryotic species (e.g. some
cryptogams, ferns, mosses, lichens,
filamentous fungi, protests)
Rohmer et al., 1984
Extended C31 to C35
hopanes (homohopanes)
Diagnostic for bacterial, biosynthesis
appears to be restricted to lineages that
are not strictly anaerobic
Ourisson and
Albrecht, 1992,
Rohmer et al., 1984
Extended C32 to C36 3b-
methylhopanes
Diagnostic for some microaerophillic
proteobacteria (certain methylotrophs,
methanotrophs, acetic acid bacteria)
Zundel and Rohmer.,
1985a, 1985b,
1985c.
28,30-dinorhopane,
25,28,30-trinorhopane
Often prominent in sediments from
euxinic environment
Grantham et al.,
1980
Vol. 32, No. 4, page 279 (2013)
Table 9. Biological and /or environmental interpretation of cholestanes
Biomarker
Biological and/or
environmental interpretation
References
24-norcholestane (C26)
Possible diatom origin, high
concentrations relative to 27-
norcholestane indicate
Cretaceous or younger crude oil
Holba et al., 1998a and
1998b
Cholestane
In aquatic sources probably
almost exclusively derived from
diverse eukaryotes
Volkman, 2003
24-n-propylcholestane
Pelagophyte algae, a biomarker
for marine conditions with few
exceptions
Moldowan et al., 1990
4-Methylcholestane and
4,4-dimethylcholestane
Diverse eukaryotic sources, high
concentrations likely indicate a
dinoflagellates origin
Summons et al., 1994
Vol. 32, No. 4, page 280 (2013)
