Oil and natural gas are important for energy supply around the world. The
exploring, drilling, transportation and processing in oil and gas regions can
release a lot of volatile organic compounds (VOCs). To understand the VOC
levels, compositions and sources in such regions, an oil and gas station in
northwest China was chosen as the research site and 57 VOCs
designated as the photochemical precursors were continuously measured for an
entire year (September 2014–August 2015) using an online monitoring system.
The average concentration of total VOCs was 297
Volatile organic compounds (VOCs) are ubiquitous
in ambient air and originate from both natural processes (i.e., vegetation
emissions, volcanic eruption and forest fire) and anthropogenic activities
such as the fossil fuel combustion, industrial processes and solvent usage
(Cai
et al., 2010; Leuchner and Rappenglück, 2010; Baudic et al., 2016). As
the key precursors of O
Previous studies in China mainly focused on the measurements of VOCs in urban agglomerations such as the Pearl River Delta (PRD) region (Tang et al., 2007; Liu et al., 2008; Cheng et al., 2010; Ling et al., 2011), Yangtze River Delta (YRD) region (An et al., 2014; Li et al., 2016; Shao et al., 2016), and Beijing–Tianjin–Hebei (BTH) region (Li et al., 2015) and key megacities including Beijing (Song et al., 2007; Wang et al., 2010; Yuan et al., 2010), Shanghai (Cai et al., 2010; Wang, 2014), Guangzhou (Zou et al., 2015) and Wuhan (Lyu et al., 2016). These studies found that vehicle emissions and solvent usage contributed most to the ambient VOCs in urban areas. A few studies were also conducted in industrial areas (An et al., 2014; Wei et al., 2015; Shao et al., 2016) and petrochemical industrial regions with a lot of VOC emissions (Lin et al., 2004; Wei et al., 2015; Jia et al., 2016; Mo et al., 2017). These studies conducted in industrial areas found that the VOC sources and compositions are complex due to the different emissions and atmospheric processes (Warneke et al., 2014). However, the research conducted in oil and gas areas in China is still limited while the VOC emission characteristics in these types of regions are common around the world (Buzcu-Guven and Fraser, 2008; Simpson et al., 2010; Rutter et al., 2015; Bari et al., 2016). For instance, Leuchner and Rappenglück (2010) found that natural gas or crude oil sources contributed most to the VOC emissions in Houston. Gilman et al. (2013) found that oil and gas emissions strongly contribute to propane and butanes in northeast Colorado. Therefore, studies concerning VOC emission characteristics in oil and gas areas in China are very important.
In previous studies, the ambient air was sampled for a few days (weeks) or
at a certain season with low time resolution. The diurnal, monthly and
seasonal variations were mostly overlooked, which prevented the
understanding of the VOCs' temporal behaviors influenced by the real-time
emissions, photochemical reaction and meteorological condition. Therefore, a
long-term monitoring of VOCs with a high time resolution is desired
(Baudic
et al., 2016; Liu et al., 2016). It should be emphasized that in the
September of 2013, the VOC control in petrochemical regions has been listed
as one of the main objectives of the Action Plan of Atmospheric Pollution
Control released by the central government of China
(
To identify the VOC sources, receptor models including chemical mass balance
(CMB), positive matrix factorization (PMF) and principal component
analysis/absolute principal component scores (PCA/APCSs) have been widely
used
(Guo
et al., 2004; Rodolfo Sosa et al., 2009; An et al., 2014; Liu et al., 2016).
Meanwhile, dispersion models including conditional probability function
(CPF), backward trajectory, potential source contribution function (PSCF)
and concentration-weighted trajectory (CWT) are also employed to locate the
potential source origins
(Song
et al., 2007; Chan et al., 2011; Liu et al., 2016). Recently, the
combination of these two types of models has been developed to figure out the
locations of various air pollutant sources
(Zhang
et al., 2013a; Bressi et al., 2014; Chen et al., 2016). These practices
mainly focus on the atmospheric fine particles (PM
In this study, an oil and gas field located in northwest China was chosen as the study area to conduct long-term monitoring of VOCs with high time resolution. The main objectives are to (1) compare VOC concentrations, compositions and OFP at this oil and gas station with other areas, (2) discuss the relationships between VOC concentrations and meteorological parameters on different timescales, (3) identify the possible VOC sources by PMF, and (4) identify the local source contributions and regional origins of VOCs based on PMF and dispersion models. This study is the first VOC research with high time resolution at the oil and gas field in China, and provides new information on the temporal variation, OFP, and local and regional contributions of VOCs. This study will be helpful to establish control measures of VOCs at this type of region around the world.
The study area (44.1–46.3
The spatial distribution of oil- and gas-bearing basins in China
From September 2014 to August 2015, 57 ambient VOCs designated as
O
Concentrations (mean
Continued.
The target compounds involved 57 VOC species: alkanes (30), alkenes (9), alkynes (acetylene) and aromatics (17). The standard gases from PAMS were used for the equipment calibration and verification through the five-point method every 2 weeks (Lyu et al., 2016). The correlation coefficients of the calibration curves usually varied from 0.991 to 0.998. The detection limits were in the range of 0.04 to 0.12 ppbv (Table 1). The missing value was due to power failure or instrument maintenance and was not included in the data analysis.
Other datasets such as the 3 h resolution meteorological parameters
(atmospheric pressure,
Meteorological parameters at the observation site from September 2014 to August 2015 for every 3 h.
The hourly CO, NO
The PMF model has been widely employed for VOC source apportionment (Buzcu-Guven and Fraser, 2008; Leuchner and Rappenglück, 2010; Liu et al., 2016; Lyu et al., 2016). In this study, the EPA PMF 5.0 (US EPA, 2014) was employed and additional information is given in Appendix A.
The VOC concentrations are not proportional to the OFP due to their wide ranges of photochemical reactivity with OH radicals (Table 1). Two methods including propylene-equivalent concentrations (propy-equiv) and the maximum incremental reactivity (MIR) were adopted to analyze the OFP of VOCs. More details can be found in the research of Atkinson and Arey (2003) and Zou et al. (2015).
The CPF is widely used to locate the direction of sources based on wind
direction data (Song et al., 2007). In this study, the directions of various
VOC sources were explored based on the
The 48 h backward trajectories with 2 h intervals (starting from 00:00 to
20:00 local time, LT) were run each day by the TrajStat – plug-in of
MeteoInfo software (
The PSCF and CWT models have been previously used to identify the possible source
regions based on the backward trajectory analysis
(Cheng
et al., 2013; Bressi et al., 2014; Liu et al., 2016). The PSCF gives the
proportion of air pollution trajectory in a given grid and the CWT reflects
the concentration levels of trajectories. The geographic domain
(31–71
Local and regional source contributions of the observed VOCs were calculated
using raster analysis. In previous studies, the domain was divided into 12 sectors (each was 30
Several factors affect the calculated results of regional and local source
contributions, including the radius of the circle and CWT value. In this
study, the 12 h backward trajectories were chosen to differentiate the local
area from regional area. In fact, the longer the backward trajectories were,
the lower the regional contributions that were produced. In addition, the PMF model was
employed to VOC source apportionment and the contribution of each
identified source was introduced into CWT calculation. However, the negative
value of source contribution was inevitably generated despite the
application of
Time series of 2-hourly concentrations (ppbv)
for the four VOC categories including alkanes
The statistics of observed VOCs are summarized in Table 1 and every 2-hourly variations in four VOC categories are shown in Fig. 3. Among the four
different VOC groups, the average concentrations of alkanes were highest
(129
Comparison of the VOC concentrations
The VOC concentrations, compositions and the top five species in this study
and other areas around the world were compared and are shown in Fig. 4. The
total VOC concentrations in this study (297
The profiles of different VOC categories with concentrations expressed on
different scales are shown in Fig. 5. The top 10 VOC species for OFP
obtained using the propy-equiv and MIR methods are listed in Table S1 in the Supplement. Among the top
10 compounds calculated using the two methods, six compounds were the same,
but differed in their rank order. Considering the kinetic activity,
1-pentene ranked first with the propy-equiv method. However,
Box and whisker plots of VOC profiles based on different scales during the whole sampling period. Box and whisker plots are constructed according to the 25th–75th and 5th–95th percentiles of the calculation results.
Seasonal and daily variations in ethane
Figure 6 shows the temporal variations in ethane, ethylene, acetylene and
benzene on different timescales. Though differences existed, the selected
compounds broadly represent the respective alkanes, alkenes, alkynes and
aromatics
(Lyu
et al., 2016). Significant differences were found between the meteorological
parameters in different seasons (
Diurnal variation in boundary layer height (BLH), VOC, NO
The diurnal variations in VOCs and trace gases (NO
Correlations (
Ambient ratios for VOC species holding similar reaction rates with OH
radicals can reflect the source features, as these compounds are equally
affected by the photochemical processing and the new emission inputs
(Russo
et al., 2010; Baltrėnas et al., 2011; Miller et al., 2012). For example,
Diurnal variations in benzene, toluene, ethylbenzene, and
Information on the photochemical removal process can be obtained by
comparing the ambient ratios of aromatics due to their differences in
atmospheric lifetimes. For example, the atmospheric lifetimes of benzene
(9.4 days), toluene (1.9 days) and ethylbenzene (1.6 days) are relatively
longer than
Generally speaking, when the reaction with OH radicals was the only factor
controlling the seasonal ratio of longer atmospheric lifetime to shorter
lifetime compounds (i.e., benzene
Source profiles of five factors resolved with PMF modeling
including oil refineries
Five sources including oil refining process, NG, combustion source, asphalt and fuel evaporation were identified by the PMF analysis, and their source profiles and daily contributions are shown in Fig. 10. The monthly, seasonal and annual contributions were calculated and are shown in Fig. 11. The relationships among daily source contributions and meteorological parameters and trace gases were analyzed using scatter plots (Fig. 12). The source apportionment of this high-resolution dataset provided a unique opportunity to discuss the diurnal variation in different sources as shown in Fig. 13.
Variation in monthly averaged
The emissions from the refining process are complex due to the diversities
of VOC species, which depend on the production processes
(Vega
et al., 2011; Mo et al., 2015). The crude oil is composed of
Scatter plots of daily concentrations of trace gas and source
contributions including oil refineries
The annual contribution of the oil refining source was relatively stable
throughout the year (3.8
The diurnal pattern of this source contribution was well correlated to the
methylcyclohexane (
Ethane and propane are the most abundant nonmethane hydrocarbon compounds
in natural gas
(Xiao et
al., 2008; McCarthy et al., 2013). The
Diurnal variation in the contributions (ppbv) of
five identified sources including oil refining processes
The annual contribution of the NG leakage source was 53 ppbv, accounting for
62.6
This source was dominantly weighted by ethylene (95
Asphalt released predominantly C
The annual contribution of asphalt was the lowest among the five sources and
only contributed 1.3
The diurnal variation in asphalt was different from other sources and
followed the diurnal patterns of decane (
The gasoline evaporation profile holds high proportions of
The fuel evaporation is controlled by temperature, leading to higher
contributions in summer. The highest contribution was found in summer
(22.9 %) in this study. The same results were also observed previously
(Baudic
et al., 2016; Liu et al., 2016). A significant correlation between the
contributions of NG and fuel evaporation was observed (
The contributions of five identified VOC sources to OFP were also evaluated
using the
Comparison of VOC source apportionment results with former studies.
The source apportionment results showed that the dominant source in this
study was the natural gas source, contributing 62.6
Annual conditional probability function (CPF) plots of five
identified VOC sources including oil refineries
Annual weight potential source contribution function (WPSCF) maps
for five identified sources derived from PMF analysis including oil refineries
The contributors to VOCs in urban areas were complex, with at least five
different sources including fuel evaporation, LPG/NG, industrial emissions,
vehicle emissions and solvent usage (Table 2). The number of VOC
sources apportioned in industrial areas was fewer compared to the cities. For
example, only three sources including vehicle emissions (58.3 %), solvent
usage (22.2 %) and industrial activities (19.5 %) were apportioned by
principle component analysis – multiple linear regression (PCA-MLR) in
Lanzhou, a petrochemical industrialized city in northwest China
(Jia et al., 2016). The same result was also found in Houston
that only fuel evaporation, industrial emissions and vehicle emissions were
identified
(Leuchner and
Rappenglück, 2010). In these studies, the vehicle emissions was an
important source both in urban and industrial areas and contributed about
11–58.3 % to the total VOCs (Table 2). However, the vehicle emission
source was not identified in this study due to several reasons. First,
despite the similarity between the source profile of combustion or
fuel evaporation in this study and the vehicle emissions (i.e., high loadings
on acetylene, ethylene, BTEX, butanes and pentanes), the temporal variations
in these species did not show a distinct increase during the traffic
rush hour. In fact, the identified combustion source in this study
represented the characteristics of coal burning and torch burning in oil
refineries (to eliminate the hazardous gases). Secondly, differences existed
in sampling location and vehicle amounts. In previous urban studies, the
sampling location was in megacities with huge vehicle flows. For example, in
the research of Wuhan
(Lyu
et al., 2016), the sampling site located in the city center and the car
population was 2.2
LPG and NG sources are usually apportioned in both urban and industrial
areas. These sources contribute 10–32 % to the total VOCs and are
mainly from household or fugitive industrial emissions. However, in this
study, the NG source was mainly from the NG exploitation and NG chemical
industry due to its abundance in this area and accounted for 62.6
Solvent usage also accounts for a large proportion of total VOCs in urban
areas (4.7–36.4 %). In this study, a similar source related to asphalt
was identified with heavy weights on C
Annual weight concentration-weighted trajectory (WCWT) maps for
five identified sources derived from PMF analysis including oil refineries
The possible geographic origins of five identified VOC sources were explored using CPF, PSCF and CWT as shown in Figs. 14, 15 and 16, respectively. These methods aimed at providing insights on the potential geographic origins of VOC sources but did not claim to be precise at the cell level or pixel level.
Contributions (%) of local sources and regional transport of five sources in different seasons.
The highest CPF value of oil refineries was found east of the sampling site (Fig. 14a), which indicated the potential location of this source. However, the oil refineries are mainly located to the southwest of the sampling site (Fig. 1c) and a high CPF value (0.95) was also found in the southwest direction. Therefore, the CPF results were able to reflect the location of the oil refineries. Similarly, high probabilities and concentrations of oil refineries were also found from the southeast to southwest of the sampling site according to the PSCF (Fig. 15a) and CWT plots (Fig. 16a). As shown in Fig. 1a and b, the sampling site is located to the west of the Junggar Basin, which is the second largest oil and gas basin in China. Indeed, high values of CPF, PSCF and CWT were found in the east (Figs. 14b, 15b and 16b), which indicated the potential geographic origins of NG. Given the fact that the NG source was composed of long atmospheric lifetime species (i.e., ethane, propane and butanes), the high probabilities and concentrations of this factor likely resulted from aged air masses from each direction. The combustion source showed high potentials from the ESE to SE according to the CPF, PSCF and CWT plots. There were no high values to the northwest of the sampling site, where the urban area is located. This also indicated that the combustion from vehicle emissions was insignificant in this study. For the asphalt source, the highest CPF value was found in the east while the PSCF and CWT plots showed high values to the northeast. As discussed above, the asphalt source in this study was from the natural source (black oil hill to the northwest of the sampling site) and oil refineries (southwest direction). The CPF, PSCF and CWT results indicated that these methods failed to locate the natural source of asphalt. The potential geographic origins of fuel evaporation were widespread from the ESE to W, which was similar to the oil refinery source.
Diversities of geographic origins were also found in different seasons (Figs. S6–S13). The potential source areas of the five sources spread from northeast to southwest in autumn. In winter, both PSCF and CWT methods indicated that the VOC sources were probably from the southeast and southwest. In spring, VOCs were mainly from long-range transport from the west. However, high probabilities and contributions existed around the sampling site. In summer, high potential and contribution were from the west to the southeast. Overall, the five sources exhibited different local source areas proved by the CPF plots on the annual scale. Similar regional distributions of these sources were found on the seasonal scale. To quantify the contributions of local emissions and long-range transport to the sampling site, raster analysis based on CWT was used and the results are summarized in Table 3. Annually, except for the combustion source, the identified VOC sources were mainly from the local emissions, with contributions of 53.6 % for oil refining, 54.5 % for NG, 50.5 % for asphalt and 50.6 % for fuel evaporation. The seasonal patterns were the same as the annual pattern, exhibiting higher contributions from local areas and the differences only existed in the proportions. The highest local contributions of oil refining (69.4 %) and combustion (69.2 %) were observed in summer, while the local sources contributed most to the NG (74.6 %), asphalt (65.4 %) and fuel evaporation (68.3 %) in autumn.
Based on 1 year of continuously online monitoring VOCs in an oil and gas
field, and on the use of PMF receptor, back trajectory, PSCF and CWT
dispersion models, this study compared the VOC levels and compositions with
other studies, identified the VOC source and explored the potential
geographic origins of five identified VOC sources. The main findings are
summarized as follows.
The total VOC concentrations in this study were not only higher than
those in urban areas but also higher than those measured in petrochemical
areas. Alkanes contributed most to the total VOCs (accounting for 87.5 %
and 128 Five sources with local characteristics were identified. The NG
contributed most to the VOCs (62.6 The geographic origins of five VOC sources were the same during the whole
period. The differences existed in the seasonal variations in them. For
instance, VOCs were mainly from the northeast and southwest in autumn, while
they originated from the southeast and southwest in winter. The raster analysis
indicated that the VOCs in this study were mainly from local emissions with
contributions ranging from 48.4 to 74.6 % in different seasons.
In summary, this study found that the VOC concentrations, compositions, ozone formation potential and sources were different from those in urban and industrial areas and similar to those in oil and gas rich areas. This study will be helpful for the VOC control in these type of regions around the world.
We thank the Qingyue Open Environmental Data
Center (
Two files including species concentration and uncertainty are required to be
introduced into the EPA PMF 5.0 model. The concentration file is an
Choosing the optimal number of factors (
As shown in Fig. A2a,
Samples of highly collinear species.
Scatter plots between the total predicted and observed VOC concentrations based on the five-factor PMF solution.
After choosing the five-factor solution, the bootstrap (BS) method was used to
detect and estimate disproportionate effects of a small set of observations
on the solution and also, to lesser extent, effects of rotational ambiguity.
BS datasets are constructed by randomly sampling blocks of observations from
the original dataset (US EPA, 2014). The base run with the
lowest
Bootstrap displacement (BS-DISP) estimates the errors associated with both random and rotational
ambiguity. A key file containing the number of cases accepted, largest
decrease in
Finally, the
Pearson coefficients between the observed and predicted VOC concentrations for the five-factor solution.
Mapping of bootstrap factors to base factors.
BS-DISP diagnostics.
The potential source contribution function (PSCF) values were calculated to explore the potential geographic origins
of VOC sources using the source contributions apportioned from the PMF model
and the backward-trajectory. The PSCF is defined as
Since the PSCF value just gives the proportion of potential sources in a grid
but struggles to distinguish the pollution levels of different potential
regions, a concentration-weighted trajectory (CWT) model was employed in
this study. The geographical domain was sliced into grid cells with a
resolution of 0.5
Grids within a given radius were excluded in case they could be too close to
the origin. Usually, the radius was set according to the area covered by
first 6 h of trajectories
(Bari et al., 2003; Wang
et al., 2015, 2016). In this study, we referred to it with
modification. 12 h backward trajectories were set as the radius to
distinguish the local and regional areas due to the following reasons.
The duration of The endpoint of each backward trajectory in the first 24 h was tested
to find the optimum range of “local and nearby” area (Fig. C1). As the
backward time increased from 1 to 24 h, the area covered by the long air
mass increased significantly. Before the first 5 h, the air masses were
mainly from the northwest and the east of the sampling site. From 7 to 12 h, air masses came from the northeast, southeast and southwest of the sampling
site and the “shape of local area” formed. After 12 h, the air masses,
especially for the trajectories from the west transported for a long distance
and reaching the sampling site, indicated regional contributions.
The statistics of VOC sources in each season obtained using raster analysis.
The region covered by the first 24 h backward trajectories.
Example of
It can be seen from Fig. C1 that the farthest endpoint of 12 h backward was
about 7
The CWT results obtained with the TrajStat software were stored in shapefile
format and were then introduced into the ArcGIS software (10.1, Esri, US).
The first step was to remove the negative value from the shapefile and then
convert the shapefile into raster format. The local and regional area was
extracted by a circle with a radius of 7
The authors declare that they have no conflict of interest.
This study was financially supported by the Key Program of Ministry of Science and Technology of the People's Republic of China (2016YFA0602002; 2017YFC0212602). The research was also supported by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan. The authors are grateful to the local Environmental Monitoring Center Station for their sampling work. Edited by: Kimitaka Kawamura Reviewed by: four anonymous referees