Introduction
Aromatic compounds is an important class of hydrocarbons taking a
significant part in the formation of tropospheric ozone and the secondary
pollutants, including organic aerosols, that can lead to photochemical smog
(Wayne, 2000; Baltaretu et al., 2009). Being toxic and carcinogenic, these
gases, even without chemical transformation, have multiple impacts on the
environment and human health (Derwent et al., 2000; Calvert et al., 2002;
Lippmann, 2009). Thus, it is very important to study the role of aromatic
compounds in air pollution for improving air quality.
A dominant source of aromatic compounds, particularly in urban environments,
is automobile emissions with significant emissions being also from their use
as solvents in various manufacturing processes (Wayne, 2000). Aromatic
compounds can make up to 30–40 % of the total non-methane hydrocarbon
(NMHC) content in the atmosphere, and are responsible for about 40 % of the
ozone producing potential of all NMHCs (Derwent et al., 2000; Calvert et
al., 2002; Mugica et al., 2003; North et al., 2015).
In anthropogenically loaded regions, the most abundant aromatic compounds
usually include benzene and toluene, which we focused our effort on in this
study. They are often associated with air emissions from petroleum
production industries (motor vehicle exhaust, incomplete combustion of
fossil fuels, oil and gas service stations, and other industrial and human
activities). Filella and Peñuelas (2006), White et al. (2009) and Parra et al. (2006)
also point to biogenic sources of benzene and toluene in summer
rural conditions.
Many studies on benzene and toluene in urban and rural atmosphere have been
carried out around the world (e.g., Keymeulen et al., 2001; Barletta et al.,
2005; Parra et al., 2006; Velasko et al., 2007; Tivary et al., 2010; Miller
et al., 2011; Civan et al., 2011; Chaudhary et al., 2012; Shaw et al.,
2015).
However, there is still a substantial lack of information on benzene
and toluene abundance and spatial localization in remote areas of the North
Eurasia, although some studies were performed by Elansky at al. (2001) and Timkovsky et al. (2010). This gap is partially filled from the TROICA-12 campaign on a
mobile carriage laboratory in summer 2008, during which complex
measurements of surface air chemical composition were conducted along the
Trans-Siberian Railway from Moscow to Vladivostok (∼ 9300 km).
This study aims to investigate variation in benzene and toluene in the
surface air over Russia from PTR-MS measurements to determine their
characteristic levels in urban, rural, and remote atmosphere in different
geographic regions with respect to their major regional sources including
large towns and industrial areas along the railway. The relative importance
of benzene and toluene emissions in photochemical near-surface ozone
production is then assessed with respect to a major biogenic ozone
precursor, isoprene (Chameides et al., 1992; Geng et al., 2011).
Schematic representation of the TROICA-12 route from Moscow (MSW)
to Vladivostok (VLK). Thin solid lines across the route represent
approximate boundaries of various geographic regions: European Russia (ER),
Ural Mountains region (UR), southern parts of west (SWS), central (SCS), and
east (SES) Siberia, and the Far East region (FE). Backward 2-day trajectories with
endpoints at Trans-Siberian Railway at local noon of each successive day of
carriage movement are shown for east (black solid) and west (gray solid)
routes of the campaign. Open circles mark air particle positions at 0, 24,
and 48 h along the each trajectory.
Methods
TROICA experiments
TROICA experiments over Russia on a mobile laboratory have been carried out
regularly since 1995 (Elansky et al., 2009). About 10 inorganic compounds
(ozone, nitric oxides, carbon oxides, methane, sulfur dioxide etc.) as well
as aerosols and meteorological parameters (air temperature, pressure and
humidity, solar radiation, vertical temperature profiles, wind speed and
direction; at stops) were measured continuously and simultaneously by a
specially constructed automated system. The system was built on a railway
carriage with air inlets at the height of about 4 m above the ground. VOC
concentrations have been measured routinely since 2008 (TROICA-12, -13 and
-14 campaigns). The TROICA carriage laboratory is equipped in accordance
with the measurement requirements of the Global Atmospheric Watch (WMO), and
is located just after the electric locomotive to minimize various effects of
near-surface air perturbations due to moving train. The possible impact of
oncoming trains, e.g., those transporting oil products as well as human
activities in the train (all conveniences were placed at the end part of the
train) on the measurements, has been removed through the respective time
filtering of the original data. We also note that this impact is expected to
be generally non-significant as demonstrated previously in Crutzen et al. (1998),
Elansky et al. (2000) and Panin et al. (2001).
In present study, the data from the summer experiment TROICA-12 (21 July–4 August 2008) along the Trans-Siberian Railway (Fig. 1) are analyzed. The
train covers the total length of the route from Moscow to Vladivostok
(∼ 9288 km) for approximately 6 days, so the total duration of
a single campaign is about 2 weeks. (Henceforth, we denote the forward path
from Moscow to Vladivostok and the return path from Vladivostok to Moscow as
east and west segments of the whole route, respectively.)
Undoubtedly, the results of the observations at each particular location
performed from the moving carriage are strongly influenced by specific
weather conditions (synoptic patterns), as well as by complex interplay of
local pollution sources and atmospheric transport by turbulent eddies on a
variety of scales, the latter being considered as a source of random noise
in the measurement data. Generally, straightforward quantification of the
effects of atmospheric dilution and absolute strength of the associated
nearby emission sources is inhibited in data analyses. Yet, the passage of
each location twice (in the forward and return paths of the TROICA campaign)
allows for some qualitative assessment of the impact of weather conditions,
since the time between the two subsequent measurements is of the order of
1 week, which is comparable to the characteristic time of boundary
layer ventilation in mid-latitudes.
Various types of meteorological conditions along the railway can be
generalized into three distinctive weather patterns when traversing mountain
area adjacent to Lake Baikal (∼ 110∘ E) in east and
west segments. There was clear and warm (> 20 ∘C at
noon) weather on the route from Moscow to Lake Baikal, hot weather (> 24 ∘C at noon) between Lake Baikal area and Vladivostok (east and
west segments), and cool (daytime temperatures of 15–20 ∘C) and
rainy weather between Lake Baikal and Moscow on the return west segment of
the route. Nighttime surface temperature inversions and stagnant air
conditions were common for the east segment, although light winds were
typical for both the east and west segments of TROICA. This feature is
clearly seen in Fig. 1, where 2-day back trajectories along the TROICA route
calculated with the use of NOAA HYSPLIT model (Stein et al., 2015) based on 3-D wind fields are shown as averages of corresponding
ensembles of trajectories originated within a height range from 0 to 400 m a.g.l. According to Fig. 1, the characteristic distances of transport within
a planetary boundary layer do not exceed 500–600 km in the 2 days
preceding measurement time, so the measured chemical composition of the
respective air masses can be considered as representative of the cumulative
impact of pollutant sources at local to regional scales. Relatively low wind
speeds and short chemical lifetimes of the analyzed species (∼ 12 days for benzene and ∼ 2–4 days for toluene) support a
regional approach to data analyzes implemented in present study (see Sect. 3) to quantify impact of various anthropogenic sources (large towns and
industrial facilities) localized primarily along the railway. The exception
is the Far East region, where regional advection by southerly winds may
contribute to measured pollutant concentrations from highly urbanized areas
of the northeast China.
VOC measurements
VOC concentrations were measured by a compact proton transfer reaction mass
spectrometer (compact PTR-MS) from Ionicon Analytik GmbH, Austria. One of the
advantages of the compact PTR-MS is portability, which allows its use
for continuous measurements on a mobile moving platform. Another large
advantage of the compact PTR-MS is that the volatile compound samples do not
need to be specially prepared before the measurement, e.g., involving
preconcentration procedures; thus, headspace samples can be introduced
directly into the reaction chamber consisting of the drift tube. The fact
that, due to their low proton affinities, H3O+ ions do not react
with any of the major components present in clean air is an additional
advantage as it allows the analyzed air to be used directly as the buffer
gas. To set a correct transmission in the software, a gas standard (Ionicon
Analytik GmbH, Innsbruck) containing 17 VOCs including benzene, toluene and
isoprene was used. Error in calibration by the gas standard did not exceed
15 %. The instrument allows the simultaneous online monitoring of 20
VOCs, including benzene, toluene, xylene, propylbenzene, isoprene,
monoterpenes and others.
The compact PTR-MS consists of three main components: an ion source, a drift
tube, and a mass analyzer (quadrupole mass spectrometer) in conjunction with
an ion detector/amplifier. H3O+ ions are produced in high
concentrations from pure water vapor within a hollow cathode discharge ion
source. These primary ions pass into the drift tube, where they undergo
(mostly) non-dissociative proton transfer to the VOCs that are to be
detected. The air sample to be analyzed is introduced into the drift tube
(which is maintained at about 2 mbar and 40 ∘C with a drift
voltage of 600 V) close to its entrance at a typical flow rate of about 11 mL min-1.
The compact PTR-MS measurement range depends on the substances measured,
integration time and system setup. Its detection limits for the
investigated VOCs are of the order of pptv. The ions associated with benzene and
toluene are free from significant interferences of other VOC species
(Warneke et al., 2003). In the case of isoprene, other aldehydes and ketones are known to be detectable at this mass (Warneke et al.,
2003; Fortner et al., 2009). However, isoprene has been found to be the
dominant species at mass 69 within various kinds of air masses (de Gouw and
Warneke, 2007). Nevertheless, the isoprene signal should be interpreted
carefully, particularly from coniferous forests, where emissions of
2-methyl-3-buten-2-ol, which is also determined at mass 69, take place.
Other components and meteorology
Concentrations of non-methane hydrocarbons (NMHCs) were measured with an
APHA-360 instrument (Horiba, Japan). This gas analyzer separates
CH4 and NMHC by using selective catalytic absorbers and measures the
gas concentrations with a flame ionization detector. The total error in the
CH4 and NMHC concentration measurements does not exceed ±5 ppb.
To supply the flame ionization detector with hydrogen, which is necessary
for its operation, the instrumentation set has hydrogen generators of
different types. To make proper measurement accuracy, the zero calibration
for every 20 min of the measurements and daily span calibrations were
carried out in the campaign. The calibrations were performed using the gas
standards provided by the D.I. Mendeleev All-Russian Institute for
Metrology.
CO concentration was measured with a TE48S instrument (Thermo Electron
Corp.). This instrument uses an advanced method based on the measurements
with the correlation of gas filters. It allows for measuring background CO
concentrations at a level of less than 100 ppb with the total error of
±10 ppb.
SO2 concentration was measured with an APSA-360 instrument (Horiba, Japan). This instrument measures SO2chemiluminescence induced
by UV radiation. For scheduled calibrations zero-air generator is used.
NO and NO2 concentrations were measured at different times with a
TE42C-TL instrument (Thermo Electron Corp., USA) and with a M200AU
instrument produced by Teledyne Corp. (USA). These instruments apply the
chemiluminescence method. The minimum NO and NO2 concentrations
detectable with these instruments are equal to 0.05 ppb, which makes it
possible to measure the so-called background concentrations not influenced
by the pollution sources. NOx concentration is the sum of NO and
NO2 concentrations.
Ozone concentration was measured with Dasibi 1008RS and 1008AH gas
analyzers. These instruments are based on the photometric method. They allow
measuring the ozone concentration in the range from 1 to 1000 ppb with a
total error of ±1 ppbv. These instruments undergo scheduled
calibrations against the secondary standard, the O3-41M no. 1294 instrument,
which undergoes in turn annual calibrations against the SRP no. 38
standard owned by the Mendeleev Research Metrology Institute (Russia).
For monitoring of meteorological parameters, the instrumentation set was
constructed including the following instruments: an ACAT-3M acoustic anemometer (Russia), HMP233 transmitter and PTA427 barometric pressure transducer (Vaisala, Finland), and CM6B pyranometer (Driesen & Kem, Germany).
The temperature profile up to a height of 600 m was measured with MTP-5
meteorological temperature profiler (ATTEX, Russia).
Statistics for the filtered original 10 s data of benzene and
toluene (in ppb) from the TROICA-12 campaign (E – east segment; W – west
segment). N – total number of the original 10 s filtered samples; σ – standard deviation; P – percentiles.
TROICA segment
N
Mean
σ
Min
Max
P10
P50
P90
Benzene
E
39 873
0.23
0.60
0.01
36.51
0.05
0.15
0.40
W
36 322
0.18
0.27
0.01
14.32
0.05
0.13
0.35
Toluene
E
39 873
0.34
0.79
0.01
45.58
0.07
0.21
0.62
W
36 322
0.27
0.48
0.01
25.89
0.06
0.17
0.50
Relative frequency of NO / NO2 values in the TROICA-12 campaign.
Data processing
The major problem in the TROICA data analyses is the correct elimination of
screening effects (relative to regional-scale pollution sources) produced by
local pollution sources along the railway. Except for small areas of
biomass burning and smoldering in the vicinity of the railway, such sources are
mainly of anthropogenic origin and characterized by highly limited spatial
extents (and, hence, an impact upon a chemical composition), so they can be
effectively filtered out by applying some objective criteria to the original
10 s dataset. Commonly, the chemical impact is characterized by strong
short-time variations in CO, O3, and NOx concentrations and by an
increase in the NO / NO2 ratio well above its regional characteristic value
of ∼ 0.1. Upon a set of trials, we found that eliminating data
points satisfying the criteria NO / NO2 > 0.2 was sufficient to
obtain robust statistics on measurement data concerning the regional
effects of anthropogenic pollution. According to Fig. 2, such an approach
results in eliminating ∼ 25 % of data from the subsequent
analyses, with the remaining data (which we hereafter refer to as the “filtered dataset”) being safely attributed to the local- to regional-scale effects
produced by sustained anthropogenic sources.
Statistical and graphical data analysis was performed with the free software
environment for statistical computing and graphics, R
(http://www.r-project.org/), and with the data analysis and graphing
software OriginPro (OriginLab Corporation).
(a) 10 min concentrations of benzene, toluene, and T / B ratio as well
as NMHC, CO and SO2 along the Trans-Siberian Railway in the east
segment of the TROICA-12 campaign. Some parameters are plotted in a
logarithmic scale for better data presentation. White circles on the top
of the figure indicate the times of local noon. The cities with a population from 250 000 to 1 million or more are shown in capital letters. A dashed
line shows the T / B boundary between transport and industrial emissions (Tiwary
et al., 2010; Carballo-Pat et al., 2014).
(b) Same as (a) but for the west segment of the TROICA-12 campaign.
A statistical summary of the filtered dataset is shown in Table 1. In the
east segment, toluene and benzene concentrations and their variations are
somewhat higher compared to those for the west segment. As discussed above,
this feature is most probably attributed to the observed differences in
meteorological conditions, as the regional anthropogenic emission sources
are not expected to vary at any appreciable rate on a sub-seasonal scale.
Although atmospheric dilution rates and advection paths (see Fig. 1) vary
significantly between the east and west segments, spatial localization of
high levels of toluene and benzene near their emission sources (mostly large
towns and their suburbs) strongly restricts the impact of meteorology on
their near-surface abundance, at least at the regional scale, due to their
relatively short chemical lifetime. Consequently, the observed systematic
differences in toluene and benzene statistics between east and west segments
were relatively small for both mean and percentile values.
Results and discussion
Spatial distribution of benzene and toluene along the Trans-Siberian
Railway
The areas adjacent to the Trans-Siberian Railway are markedly different in
the amount of urbanization and anthropogenic load. Eighty-seven towns are
located immediately on the railway, 68 towns are in the Ural
Mountains region and west Siberia, with the remaining ones located in east
Siberia and the Far East region. Yet, it is in the area of the first tens to hundreds
of kilometers from the Trans-Siberian Railway, where the most significant
regional anthropogenic sources are commonly located in all the regions
considered. Figure 3 shows 10 min averages of the surface concentrations of
benzene and toluene derived from the filtered 10 s dataset for the east
and west segments of the TROICA-12 route. Simultaneous measurements of
surface NMHCs, CO, and SO2 concentrations are also shown in the figure.
The observed simultaneous peaks in the concentrations of VOCs and inorganic
compounds shown in the figure are spatially connected to the most
significant regional anthropogenic sources along the railway (mostly in
urban environments), which are mainly expected to be motor vehicle transport,
industry, central heating, and power plants.
The regional statistics of benzene, toluene (in ppb) and T / B along
the Trans-Siberian Railway: ER – European Russia; UR – Ural Mountains
region;
SWS – southern parts of west Siberia; SCS – southern parts of central
Siberia; SES – southern parts of east Siberia; FE – Far East region.
Region
N
Mean
σ
Min
Max
P10
P50
P90
Benzene
ER
7456
0.28
1.10
0.01
36.51
0.08
0.16
0.43
UR
5464
0.28
0.42
0.01
12.22
0.08
0.20
0.49
SWS
10 714
0.22
0.44
0.01
22.28
0.07
0.14
0.37
SCS
13 120
0.25
0.64
0.01
35.35
0.07
0.16
0.45
SES
26 228
0.20
0.26
0.01
14.99
0.07
0.15
0.36
FE
13 212
0.22
0.28
0.01
15.81
0.08
0.17
0.38
Toluene
ER
7456
0.35
0.65
0.01
32.10
0.09
0.23
0.67
UR
5464
0.39
0.86
0.01
32.82
0.10
0.27
0.70
SWS
10 714
0.28
0.56
0.01
17.26
0.07
0.17
0.48
SCS
13 120
0.37
0.89
0.01
38.58
0.08
0.21
0.71
SES
26 228
0.31
0.59
0.01
45.58
0.09
0.21
0.55
FE
13 212
0.28
0.63
0.01
40.67
0.08
0.18
0.45
Although an exact quantitative input of various types of toluene and benzene
sources into the observed concentrations remains unknown, some qualitative
assessment of their relative importance can be done from a ratio of toluene
to benzene (T / B). The T / B ratio is frequently used as an indicator of motor
transport emissions, since toluene content in gasoline and automobile
exhausts is in 3–4 times higher than the benzene content (D. Brocco et al.,
1997). Therefore, T / B ≈ 1–3 is widely accepted to indicate motor
vehicle transport, whereas T / B > 3 points to industrial
exhausts (Tiwari et al., 2010; Shaw et al., 2014; Carballo-Pat et al., 2014).
Furthermore, the chemical activity of toluene in the atmosphere is
approximately 5 times higher than that of benzene. Hence, the T / B ratio can
serve as an indicator of chemical aging and, consequently, proximity of the
sampled air to the associated pollution source (Mugica et al., 2003; Tiwari
et al., 2010; Carballo-Pat et al., 2014; Shaw et al., 2014).
Following our previous study on greenhouse gas emissions (Berezina et al.,
2014), in the present analysis, we divide the whole path from Moscow to
Vladivostok into six lengthy segments according to climatological conditions
and anthropogenic load intensity: European Russia (ER), the Ural Mountains region
(UR), southern parts of west (SWS), central (SCS), and east (SES) Siberia,
and the Far East region (FE; see Fig. 1). Statistics for benzene, toluene, and T / B from
10 s filtered data for different regions are shown in Table 2. The
highest concentrations of benzene and toluene were observed in ER, UR, and
SCS, which are the regions of most significant anthropogenic emission
sources and proximity of the TROICA route to the strong pollution sources.
Since the low/high T / B ratio measured at a given location can be equally
attributed (in the absence of prior information) either to a
photochemically aged/young air mass or specific chemical composition of the
primary pollutants affected the measured air mass, we must distinguish
between both factors to use the T / B ratio as characteristic of the
associated emission source. The problem is further complicated by the fact that the
measured air represents commonly a mixture of air parcels with different
photochemical age and/or anthropogenic loading. One partial solution
consists in separating air masses according to their chemical aging
(transport times from the regional pollution sources) based on some
additional data on their chemical composition and/or transport times.
T / B ratio for moderately to strongly polluted air (NOx > 2 ppb).
Region
N
Mean
σ
Min
Max
P10
P50
P90
ER
4247
2.20
2.63
0.04
41.71
0.49
1.53
4.26
UR
3801
2.26
2.90
0.04
49.00
0.52
1.51
4.40
SWS
6685
2.27
3.28
0.01
57.65
0.40
1.37
4.63
SCS
10 732
2.28
3.08
0.02
72.13
0.48
1.50
4.44
SES
21 824
2.58
3.73
0.03
187.77
0.49
1.61
5.30
FE
11 645
1.84
3.01
0.01
87.86
0.33
1.10
3.62
In the present work we utilized the former approach by invoking contemporary
measurements of NO and NO2 to distinguish between clean remote air
(NOx < 2 ppb), moderately polluted air (2 ≤ NOx < 20 ppb) and highly polluted air (NOx≥ 20 ppb), the latter being
representative of urban and suburban environments. Here, the exact threshold
values of NOx were chosen based on our experience in processing
multiple datasets from TROICA campaigns as well as continuous measurements
of NOx at ZOTTO Tall Tower, a remote site in Central Siberia, which is
occasionally affected by transport of polluted air from major regional
anthropogenic sources in south Siberia (Vasileva et al., 2011). The
statistical analysis of the filtered dataset showed that about 80 % of the
data account for moderately polluted air, 18 % for clean remote air and
only about 2 % for highly polluted air. Thus, the measurements in the
TROICA-12 campaign were performed mostly in a moderately polluted urban
atmosphere (2 ≤ NOx < 20 ppb), with maximum concentrations of
toluene and benzene reaching values of 45.6 and 36.5 ppb,
respectively, which is significantly less than the short-term exposure
limits (94 and 159 ppb for benzene and toluene, respectively).
Since high (≥ 2 ppb) NOx are found to be a robust characteristic of
photochemically young air (Vasileva et al., 2011), we use associated T / B
(NOx > 2 ppb) values to infer some qualitative information on
the pollution sources characteristic for the given region. According to
Table 3, average and median values of T / B (NOx > 2 ppb) were in
the ranges of 1.8–2.6 and 1.1–1.6, respectively, which is well below the
commonly accepted threshold value for the motor vehicle exhausts
(T / B < 3). Yet, significantly higher T / B ratios (the column P90
of the table) were found in all the regions, suggesting other important
regional sources of air contamination. These high values were measured
commonly as short-lasting events where the mobile laboratory crossed the
plumes of strongly contaminated air originating from large upwind emission
sources, as evidenced from air trajectory analyses and prior data on
industrial facilities. In such cases, high toluene and benzene
concentrations are accompanied with enhanced levels of NMHC, CO, and
SO2 as well, indicating the petrochemical and refining exhausts. These
include heat and power plants in the vicinities of large towns: Perm,
Tyumen, Omsk, Ulan-Ude, Chita, Khabarovsk (Fig. 3a), Yekaterinburg,
Tyumen, Krasnoyarsk, Kansk, Irkutsk, Mogocha, Birobidzhan, and Khabarovsk
(Fig. 3b).
The bulk contribution of motor vehicle exhausts, χ(%), to the
near-surface abundance of T and B along the Trans-Siberian Railway (see Eq. 1).
TROICA segment
T‾low
tlow
T‾high
thigh
χ(%)
Benzene
East
0.274
0.77
0.123
0.22
89
West
0.274
0.80
0.119
0.20
90
Toluene
East
0.316
0.77
0.603
0.22
65
West
0.333
0.80
0.704
0.20
65
Assuming that high (> 2 ppb) NOx is a signature of freshly
contaminated air, we estimated the bulk contribution of motor vehicle
exhausts, χ(T), to the near-surface abundance of toluene and benzene
along the Trans-Siberian Railway from a simple relation:
χ(T)Tlow⋅tlowTlow⋅tlow+Thighthigh⋅100%,
where tlow and thigh are the fractions of measurement time within air masses having low
(< 3 ppb) and high (≥3 ppb) T / B ratios and NOx > 2 ppb (tlow+thigh= 1), and
Tlow and Thigh are average toluene concentrations in the low- and
high-T / B air masses, respectively (see Table 4).
One can see from Table 4 that motor vehicle exhausts were responsible for
∼ 90 % of benzene levels and ∼ 65 % of
toluene levels during the campaign, that is, toluene emission from motor
vehicle exhausts is 25 % lower than the benzene one. It is comparable with
the relative source contributions for benzene, and toluene presented in
Karl et al. (2009). Thus, the motor vehicle exhausts were the most
significant anthropogenic source of air pollution by toluene, and benzene in
all the areas adjacent to the TROICA-12 route (densely populated areas along
the Trans-Siberian Railway).
Benzene levels from the TROICA-12 campaign are broadly comparable in
magnitude with other published data on their abundance in the summer urban
and rural continental surface air (Elansky et al., 2000; Barletta et al.,
2005; Na et al., 2005; Parra et al., 2006; Hoque et al., 2008; Tiwary et al.,
2010; Seco et al., 2013; Wagner and Kuttler, 2014). However, toluene levels
tended to be lower than those reported in earlier publications. This is
possibly due to the more significant contribution of evaporative and
industrial emissions in toluene levels (Karl et al., 2009) than that of
mobile transport exhausts (which are about 65 % for toluene from TROICA-12
measurements). It should be noted that no significant
biomass burning along the Tran-Siberian railway was observed in the campaign, so this
emission source is not expected to impact significantly on benzene and
toluene levels.
Diurnal variations in benzene and toluene
To determine the contribution of diurnal variations in benzene and toluene
to their surface levels and spatial variability, we analyzed their hourly
mean concentrations measured in the campaign.
Observations in several locations (Filella et al., 2006; Zalel et al., 2008;
Tiwary et al., 2010; Wagner and Kuttler, 2014) reported the highest levels of
benzene and toluene being in the morning and evening hours due to the increase
in the motor vehicle transport exhausts at this time. Contrary to these
studies, no clear diurnal variation in benzene and toluene was observed in
the TROICA-12 campaign (Fig. 4). We suppose that it is due to spatial
smoothing of their diurnal variations in conditions of a lack of strong
local pollution sources, which is clearly seen from median values. Somewhat
higher levels in the morning (at 04:00–05:00 LT, local time) and in the
evening (at 20:00 and 23:00 LT) are most probably due to accumulation of
benzene and toluene in the stable atmospheric conditions in the vicinities
of their regional sources transected on the TROICA route. Episodic crossing
of anthropogenic pollution plumes during the campaign caused the midday peak
(which falls occasionally at ∼ 12:00 LT), seen in Fig. 4
from P90 profile.
Surface levels of the studied impurities in urban and rural areas
along the Trans-Siberian Railway. All impurities are in ppb except for NMHC and
CO (ppm).
Compound
N
Mean
σ
Min
Max
P10
P50
P90
Urban
N6H6
10 492
0.37
0.33
0.01
2.94
0.12
0.26
0.67
C7H8
10 492
0.70
1.34
0.04
22.56
0.17
0.39
1.30
NMHC
7571
0.26
0.24
0.09
2.65
0.14
0.20
0.39
NOx
11 052
11.95
16.14
0.75
205.64
2.78
7.57
23.82
CO
7239
0.27
0.08
0.14
0.68
0.20
0.25
0.36
SO2
7518
1.38
1.16
0.04
10.74
0.35
1.16
2.48
Rural
N6H6
65 703
0.21
0.43
0.01
36.51
0.07
0.15
0.37
C7H8
65 703
0.28
0.47
0.01
45.58
0.08
0.20
0.51
NMHC
51 497
0.16
0.09
0.03
3.42
0.11
0.14
0.21
NOx
79 941
4.38
5.07
0.56
237.98
1.46
3.38
7.70
CO
50 256
0.23
0.07
0.05
2.77
0.17
0.22
0.28
SO2
53 502
1.22
0.82
0.10
9.80
0.30
1.10
2.20
Thus, diurnal variations in the planetary boundary layer mixing regime did not
contribute significantly to the spatial variability in benzene and toluene
along the Trans-Siberian Railway in the TROICA-12 campaign. This allows us to
neglect them further in summarizing the data at different spatial scales (for
example, by different Russian regions and cities).
Benzene and toluene levels in urban and rural surface air
We found that the surface concentrations of VOCs, NOx, CO, and SO2
were, in general, notably higher in urban areas (as would be expected),
as most of regional pollution sources are located in cities and their
suburbs (Table 5). It was found, however, that the highest (> P95) concentrations of all the pollutants including benzene and toluene were
measured outside the cities, so they can not be attributed to the direct impact
of urban pollution sources. A closer examination showed that these events
were most probably connected to specific transport conditions favorable for
maintaining anthropogenic plumes from large upwind sources, i.e., towns and
industrial manufacturers away from the railway, as highly coherent
structures at timescales of the order of a few hours to 10 h. The most
prominent events of crossing industrial plumes took place in ER (up to 37 ppb for benzene), SCS (up to 39 ppb for benzene), SES (up to 46 ppb for
toluene) and FE (up to 41 ppb for benzene).
Pearson correlation matrix for urban and rural measurements along
the Trans-Siberian Railway. R≥ 0.5 are shown in bold. Asterisks show
statistically significant correlations for P = 0.05. All impurities are in ppb
except for NMHC and CO (ppm).
N6H6
N5H8
NOx
CO
SO2
NMHC
Urban
N6H6
1
0.53*
0.23*
0.57*
0.35*
0.47*
C7H8
0.53*
1
0.15*
0.49*
0.18*
0.21*
NOx
0.23*
0.15*
1
0.47*
0.25*
0.21*
CO
0.57*
0.49*
0.47*
1
0.25*
0.21*
SO2
0.35*
0.18*
0.25*
0.25*
1
0.01
NMHC
0.47*
0.21*
0.21*
0.21*
0.01
1
Rural
N6H6
1
0.52*
0.10*
0.11*
0.08*
0.21*
C7H8
0.52*
1
0.17*
0.14*
0.07*
0.17*
NOx
0.10*
0.17*
1
0.22*
0.16*
0.16*
CO
0.11*
0.14*
0.22*
1
0.13*
0.18*
SO2
0.08*
0.07*
0.16*
0.13*
1
0.13*
NMHC
0.21*
0.17*
0.16*
0.18*
0.13*
1
One can see from Table 6 that there was a statistically significant moderate
correlation (R≈ 0.5) between benzene and toluene both in urban and
rural areas. In urban environments, there was a stronger correlation between
benzene and toluene and CO (R≈ 0.6), as well as between NOx and
CO, which can point to motor vehicle transport as their main common
emission source. In urban areas, benzene also had a significant moderate
correlation with NMHC, which can indicate partial input from the industrial
exhausts: hydrocarbon processing, refining industry, fuel transportation and
storage, tank and pipeline leaks, etc.
In rural areas (identified as those outside the towns with additional
constraint NOx < 0.2 ppb; see Sect. 3.1), correlation between all
the species studied was very poor, except for that between benzene and
toluene. Evidently, the diversity of transport pathways, photochemical
aging, and irreversible mixing of air masses subjected to different
rates of anthropogenic contamination precludes direct quantification of
primary pollutant sources for the substantial part of the TROICA route away
from areas of their immediate impact.
Diurnal profiles of the surface levels of benzene and toluene from
TROICA-12 (hourly mean values): average (solid) and percentiles (dashed).
To study atmospheric pollution in Russian cities along the Trans-Siberian
Railway, 29 cities were selected, for which the total amount of measurement
time was at least 25 % of the whole residence time in the city. The
highest concentrations of benzene and toluene (up to 5 ppb) were observed in industrial towns: Perm and Kirov (European Russia); Kungur and
Yekaterinburg (south Ural Mountains region); Tyumen (west Siberia); Angarsk,
Irkutsk, and Ulan-Ude (east Siberia); and Birobidzhan and Khabarovsk (Far East
region; Fig. 5). In these cities the highest levels of NMHC, NOx, and CO
were also measured (Fig. 6). Evidently, high CO abundances found in some of
these towns point to the significant contribution to the overall
pollution rates from refineries and central heating and power plans, which
was also confirmed by the high T / B ratios (> 3–4 based on
P90
regional values). The specific T / B ratios also indicate that Khabarovsk,
Birobidzhan, Skovorodino, Tulun, Tajshet, and Tyumen are mainly polluted by
industrial emissions, whereas Vladimir, Kungur, Yurga, and Krasnoyarsk are mainly polluted by
transport exhausts. In other cities, motor vehicle transport was found to be
a main pollution source, even though the contribution from other sources
(including industrial emissions, coal burning and gasoline evaporation) was
also important, as seen from the significantly higher P90 values comparing
to the average one calculated for rural regions (see Fig. 5). Benzene and
toluene surface levels in the medium-sized towns of Siberia (e.g., Achinsk,
Taishet, Nizhneudinsk) were close to an average rural concentration
calculated for the campaign (less than 0.3 ppb).
Unfortunately, exact quantification of inputs from various types of sources
into anthropogenic contamination of urban air was inhibited when using the
TROICA data due to the very limited amount of observations collected within
a particular town. Considering that transport emissions occur when the T / B
is in the range of 1–3 (Mugica et al., 2003; Tiwari et al., 2010;
Carballo-Pat et al., 2014; Shaw et al., 2015), and supposing well-mixed conditions
such that each measured air parcel represents a uniform mixture of pollutants
from various sources within a town, we found that motor vehicle transport
accounts for approximately 75 % of anthropogenic emissions in the Russian
cities along the Trans-Siberian Railway, with the remaining 25 % being
attributed to other sources (Fig. 7). These estimates correspond well to
those derived in Sect. 3.1 based on the whole TROICA dataset. As seen from
Fig. 8, the T / B ratio for vehicle urban exhausts in the Russian cities along
the Trans-Siberian Railway is usually in the range of 2.3–2.8.
Benzene, toluene and T / B in Russian cities along the
Trans-Siberian Railway. The cities with a population from 250 000
to 1 million or more are shown in capital letters, and the cities with a
population from 50 000 to 250 000 in lowercase. Asterisks show cities with a population less than 50 000. The cities are
shown in accordance with their location along the railway. The shaded area
represents the mean, 10th and 90th percentiles calculated from the data measured in
rural regions. The dotted line is a border between vehicle and industrial
exhausts (Mugica et al., 2003; Tiwari et al., 2010; Carballo-Pat, 2014; Shaw
et al., 2015).
Inorganic impurities in Russian cities along the Trans-Siberian
Railway. The cities with a population from 250 000 to 1 million
or more are shown in capital letters, and the cities with a population
from 50 000 to 250 000 in lowercase. Asterisks show the cities with a
population less than 50 000. The cities are shown in accordance with
their location along the railway. The shaded area represents mean, 10th and 90th
percentiles calculated from the data measured in rural regions.
Averaged concentrations and photochemical properties of benzene,
toluene and isoprene (± standard deviation) from the TROICA-12
campaign.
VOC
1012 × KOHa
MIRa
Concentration (ppb)
OFPa (µg m-3)
PEa (µg m-3)
Benzene
1.23
0.42
0.20 ± 0.33
0.29 ± 0.48
0.06 ± 0.09
Toluene
5.96
2.70
0.28 ± 0.51
3.10 ± 5.69
0.44 ± 0.81
Isoprene
101.00
9.10
0.60 ± 0.55
16.65 ± 15.19
11.52 ± 10.51
a Rate constants of VOCs with OH at 298 K (sm3 molecule-1 s-1; Atkinson, 1989; Atkinson and Arey, 2003).
b Maximum incremental reactivity (g O3/g VOC; Carter, 1994).
Frequency distribution of T / B ratio for the Russian cities along
the Trans-Siberian Railway.
Ozone-forming potential (OFP) along the Trans-Siberian Railway
from daytime measurements (12:00–17:00 LT).
Contribution of VOCs to ozone formation potential over Russia
Along the whole route of the TROICA campaign, the lower troposphere chemical
regime was found to be essentially NOx sensitive, both in rural and urban
environments, with typical morning NMHC / NOx ratios being well above 20.
Hence, ozone production rates are expected to be controlled by regional NOx
emissions (Silman, 1999) and their complex interplay with both natural and
anthropogenic sources of VOCs. As mentioned above, the meteorological
conditions during most of the TROICA campaign were favorable for
studying both chemical composition of fresh air masses contaminated by regional
sources and ozone production from the emitted precursors due to
high daytime surface air temperatures and solar radiation. To estimate the
impact of the measured VOCs on regional ozone production, we employed
widely used quantities: propylene-equivalent concentration (PE) and
ozone-forming potential (OFP; Carter, 1994; So and Wang, 2004), which
utilize the measured concentrations of VOCs along with their reactivity with
the
hydroxyl radical. These coefficients are defined as
PEVOC[ppbC]=CVOC×kOH,VOC/kOH,propylene,OFPVOC[µgm-3]=CVOC×MIRVOC,
where is a VOC concentration in units of ppbC and µg m-3 in Eqs. (1) and (2) is the rate constant for the
reaction of VOC with the OH radical, the rate constant for the reaction
between OH and propylene, and a maximum incremental reactivity. The
last of these is a dimensionless quantity defined as grams of O3 produced per gram
of the VOC, which is equal to the maximum ozone concentration formed from
chemical destruction of the given VOC.
We calculated PE and OFP values for benzene and toluene based on the daytime
observations from 12:00 to 17:00 LT, the time for which the highest
correlations between ozone and its precursor species were observed in TROICA
as well. The calculated PE and OFP values were compared against those for
isoprene, the latter of which is known as one the most important biogenic ozone
precursors in rural as well as urban settings (Chameides et al., 1988;
Fuentes et al., 2000; Wagner and Kuttler, 2014).
As seen from Table 7, the average value of OFP of isoprene along the TROICA
route is much higher compared to those for benzene and toluene, owing to
relatively high near-surface abundances of isoprene (approx. 3 times as much
as that for the sum of benzene and toluene) and its exceptionally high
reactivity with hydroxyl radical. According to Fig. 8, the process of
oxidizing of isoprene proves to be the most important chemical source of
ozone in all the regions along the TROICA route, as could be expected if
one takes into account that the greater part of the railway crosses the areas
with very weak to moderate anthropogenic load. The highest OFPs of isoprene
seen in the figure in the Far East are due to its high biogenic emissions
from broad-leaved forests as well as high surface air temperatures measured
in this region in both the east and west segments of the route.
As seen from Fig. 8, the OFPs of benzene and toluene do not have
significant large-scale spatial variations along the railway, contrary to
that for isoprene. The highest OFPs shown as peaks in Fig. 8 are spatially
connected to large towns and their vicinities along the railway where the
relative input of benzene and toluene into ozone production reaches as high
as 60–70 % compared to that of isoprene. This supports our general notion
of the reduced impact of regional anthropogenic sources on the regional
ozone budget compared to long-range advection and regional biogenic VOC
emissions (Shtabkin et al., 2016). Taking into account the important role of
biogenic emissions of isoprene in the regional ozone photochemistry, we recommend that a detailed analyses of isoprene observations from the TROICA campaigns should
be done in a separate publication.
Summary
Surface concentrations of two important anthropogenic VOCs, benzene and toluene,
as well as inorganic compounds were measured simultaneously along the
Trans-Siberian Railway on a mobile railway laboratory in the TROICA-12
campaign in summer 2008. It is demonstrated that the TROICA-12 measurements
were carried out mostly in a moderately polluted (2 ≤ NOx < 20 ppb)
environment (∼ 78 % of measurements), with the remaining part
of measurement time divided between weakly polluted (NOx ≤ 2 ppb) and
highly polluted (NOx > 20 ppb) urban environments (20 and 2 % of
measurements, respectively). Maximum values of benzene and toluene during
the campaign reached 36.5 and 45.6 ppb, respectively, which is
significantly less than their short-term exposure limits (94 and 159 ppb for
benzene and toluene, respectively). However, the weather conditions
during the major part of the TROICA campaign were favorable for accumulating
anthropogenic pollutants in the lower atmosphere, the absence of clear
diurnal variations in benzene and toluene along with their low abundances
apart from the immediate vicinity of large towns and industrial manufactures
points out to mostly unpolluted air conditions along the Trans-Siberian
Railway during the campaign.
We estimated that motor vehicle exhaust accounts for ∼ 90 %
of benzene levels and ∼ 65 % of toluene levels during the
campaign, with the remaining 10 and 25 %, respectively, provided by
other important regional anthropogenic sources: industrial enterprises, coal
burning, evaporative emissions of VOCs etc.
The highest near-surface abundances of benzene and toluene, both in urban
environment and on the regional scale, were observed in areas with the
highest anthropogenic burden. They are industrial regions of the Southern
Ural Mountain, European Russia, and the southern part of central Siberia, where
spatially averaged benzene and toluene levels, representative of rural
conditions, equal ∼ 0.3 and ∼ 0.4 ppb,
respectively. Vehicle emissions constitute the major part of total
anthropogenic pollution in these regions, with a typical ratio of T / B of 2.2–2.3. Similarly, the highest concentrations of benzene (up to 5 ppb) and
toluene (up to 7 ppb) along with high levels of NMHC, CO and NOx were
observed in the following industrial towns: Perm and Kirov (European Russia);
Kungur and Yekaterinburg (south Ural Mountains region); Tyumen (west
Siberia); Angarsk, Irkutsk, and Ulan-Ude (east Siberia); and Birobidzhan and
Khabarovsk (Far East region).
Considering that transport emissions occur when the T / B is in the range
of
1–3 and supposing well-mixed conditions such that each measured air
parcel represents a uniform mixture of pollutants from various sources within
a town, we found that motor vehicle transport accounts for approximately
75 % of anthropogenic emissions in the Russian cities along the
Trans-Siberian Railway, with the remaining 25 % attributed to
industrial sources. T / B ratio for vehicle urban exhausts in the Russian
cities along the Trans-Siberian Railway is usually in the range of 2.3–2.8.
The contribution of benzene and toluene to the local photochemical ozone
production along the Trans-Siberian Railway is generally not significant
compared to biogenic VOCs in rural environment and reaches as much as 16 %
of that of isoprene. However, in large towns the contribution of benzene and
toluene to ozone formation reaches 60–75 % compared to isoprene,
supporting the important role of anthropogenic sources in local pollution.