Understanding the composition, temporal variability and
source apportionment of volatile organic compounds (VOCs) is necessary for
determining effective control measures to minimize VOCs and their related
photochemical pollution. To provide a comprehensive analysis of VOC sources
and their contributions to ozone (O3) formation in the Yangtze River
Delta (YRD) – a region experiencing the highest rates of industrial and economic
development in China – we conducted a 1-year sampling exercise using a
thermal desorption GC (gas chromatography) system for the first time at an
urban site in Nanjing (JAES site). Alkanes were the dominant group at the
JAES site, contributing ∼53 % to the observed total VOCs,
followed by aromatics (∼17 %), acetylene (∼17 %) and alkenes (∼13 %). We identified seasonal
variability in total VOCs (TVOCs) with maximum and minimum concentrations in winter and
summer, respectively. Morning and evening peaks and a daytime trough were
identified in the diurnal VOC patterns. We identified VOC sources using
positive matrix factorization (PMF) and assessed their contributions to
photochemical O3 formation by calculating the O3 formation
potential (OFP) based on the mass concentrations and maximum incremental
reactivities of VOCs. The PMF model identified five dominant VOC sources,
with highest contributions from diesel vehicular exhaust (34±5 %), followed by gasoline vehicular exhaust (27±3 %),
industrial emissions (19±2 %), fuel evaporation (15±2 %)
and biogenic emissions (4±1 %). The results of the OFP calculation
inferred that VOCs from industrial and vehicular emissions were found to be
the dominant precursors for OFP, particularly the VOC species of xylenes,
toluene and propene, and top priority should be given to these for the alleviation
of photochemical smog. Our results therefore highlight that priority should
be given to limited VOC sources and species for effective control of O3
formation in Nanjing.
Introduction
Volatile organic compounds (VOCs) are key precursors of O3 and
secondary organic aerosols (SOAs) – a major component of fine particulate
matter (PM2.5). VOCs significantly contribute to the formation of
photochemical smog, atmospheric oxidative capacity, visibility degradation,
and global climate (Jenkin and Clemitshaw, 2000; Seinfeld and Pandis, 2006),
and some VOCs are also known to be toxic to human health. Therefore, in
recent years, much research has focused on the impacts of VOCs due to their
influence on atmospheric chemistry and impacts on human health (Shao et al.,
2009a, b, and references therein).
The Yangtze River Delta (YRD) region (Shanghai–Jiangsu–Zhenjiang region) is
one of the fastest-growing regions in China, having recently undergone rapid
urbanization and industrialization. Rapid economic growth has led to
increased photochemical smog and elevated concentrations of ground-level
O3 and fine particulate matter (PM2.5). These conditions have been
listed as the most important sources of pollution affecting the population
in the YRD region, and they are likely caused by increasing concentrations of
VOCs. Therefore, it has been suggested that controlling VOC emissions is
necessary for the effective alleviation of photochemical smog (Wang et al.,
2009; Zhang et al., 2009; Cai et al., 2010; Kurokawa et al., 2013; Ding et
al., 2016).
To further understand VOC characteristics and to develop effective policies
towards lowering VOC emissions, a number of sampling campaigns have been
conducted to investigate the components, mixing ratios, photochemical
reactivity and emissions of VOCs over the YRD region (Cai et al., 2010; An
et al., 2014; Mo et al., 2015; Pan et al., 2015; Shao et al., 2016; Xu et
al., 2017). For example, based on continuous observation data collected from
March 2011 to February 2012, An et al. (2014) identified clear seasonal
VOC variability in an industrial area of Nanjing, with maximum and minimum
levels observed in summer and winter, respectively. VOC variability was also
found to be strongly influenced by industrial emissions. In contrast, Mo et
al. (2017) found no difference in VOC chemical compositions between
residential, industrial and suburban areas of the coastal industrial city Ningbo. By comparing the emission-based profiles and those extracted from
the positive matrix factorization (PMF) model, the petrochemical industry
was identified as the highest contributor of ambient VOCs due to the unique
industrial structure of Ningbo, which is a coastal city located on the
southern wing of the Yangtze River Delta with petrochemical industry as its
leading industry (Mo et al., 2015, 2016). Pan et al. (2015) conducted emissions
measurements of open biomass burning in the rural area of the YRD region and
examined the major contributors to O3 pollution using a box model
together with the Regional Atmospheric Chemical Mechanism. Overall, these
studies were conducted in industrialized and/or rural areas of the YRD
region and demonstrate the contribution of industrial emissions and biomass
burning towards ambient VOC levels and their contributions to O3
formation. However, VOC studies in urban areas of the YRD region are limited
and could help to improve our understanding of the spatial variability of
VOCs and their environmental impact, particularly as stricter policies on
VOCs and/or photochemical smog have been implemented since 2013 (Fu et al.,
2016). Furthermore, the sampling resolution and sampling duration of these
studies were relatively low as the samples were collected using canisters.
High-resolution VOC datasets can provide more detailed information on the
temporal and spatial variability, source apportionments, and impact factors
of VOCs.
In this study, we collected continuous 1-year observational VOC data at an
urban site in Nanjing in the YRD region. The seasonal and diurnal
characteristics of VOCs were investigated, and their sources were identified
and quantified using the PMF model. Furthermore, we used a box model
together with the Master Chemical Mechanism (MCM) (version 3.2) to identify
the O3–precursor relationships and the contributions of VOC sources to
photochemical O3 formation. Our results were compared with VOCs data
from other Chinese megacities. Based on these findings, we summarize and
propose control strategies to minimize VOC pollution and assess their
implications for Nanjing and the wider YRD region. The results provide
useful information towards lowering photochemical pollution in the YRD
region as well as other regions in China.
MethodologySampling campaign
We continuously measured VOC concentrations from January to December 2016,
at an observation station on the rooftop of an office building
(∼80 m above the ground level) of the Jiangsu Academy of
Environmental Science (JAES). There is a waterproof layer on the rooftop of
the building, but there was no guarantee that it was made of asphalt.
Furthermore, despite this waterproof layer on the rooftop of the building,
the interferences of emissions from this layer were believed to be
insignificant because (1) the waterproof layer was covered a the layer of
concrete, which was further covered with a layer of ceramic tile; (2) the
building was built 3 years before the sampling campaign was
started; and (3) it had been documented that the VOCs emitted from asphalt mainly
included benzene, toluene, ethylbenzene and xylene (Gardiner and Lange,
2005). However, the levels of benzene, toluene, ethylbenzene, m/p-xylene and
o-xylene were lower than those observed in other urban, industrial and rural
environments in different regions (Sect. 3.1, Zhang et al., 2012; An et
al., 2014, 2015; Mo et al., 2015, 2017; He et al., 2019). (4) The sampling
inlet was about 2–3 m above the rooftop of the building.
The station is located
in an urban area of Nanjing, and it is surrounded by heavy road traffic,
residential buildings, a plant and flower market, and several auto repair
shops (Fig. 1). Nanjing, located in the western part of the YRD region, is
one of the most urbanized and industrialized areas in the world, and
consequently it experiences severe air pollution. The site is located downwind
of both Nanjing city center and the wider YRD region (Zhao et al., 2017;
Zhou et al., 2017), and it is therefore ideally placed to determine the
combined impacts of VOCs from both local and regional atmospheric pollution.
Fifty-six VOC species including alkanes, alkenes, aromatics and acetylene
were measured at 1 h intervals using a PerkinElmer online ozone precursor
analyzer based on a thermal desorption GC (gas chromatography) system.
First, the dried air samples were collected by a thermal desorption
instrument and subsequently preconcentrated onto a cold trap. The sampling
flow was 15 mL min-1. After 600 mL of air was sampled, the cold trap was
heated to desorb the compounds adsorbed on to it. By applying the Dean's
Switch technology, whereby the effluent is transferred from
one column to another column with a different stationary phase, the low- and
high-volatility components were injected into the
Al2O3/Na2SO4 PLOT column (50m×0.22mm×1µm) and the dimethyl siloxane column (50m×0.32mm×1µm), respectively, and analyzed using a flame
ionization detector (FID). The temperature increased from 46 ∘C
for 15 min to 170 ∘C at a rate of 5 ∘C min-1 and then
to 200 ∘C at a rate of 15 ∘C min-1. The samples were
finally held at 200 ∘C for 6 min.
A calibration was performed daily for quality control. The calibration
curves showed good linearity with a correlation coefficient of 0.99. Seven
analyses were performed repeatedly to test the precision of the 56 species.
Calibrant concentrations in the gas standard mixture (56 C2–C12
NMHCs (non-methane hydrocarbons), Linde Spectra Environment Gases, Inc, USA) ranged from 20 to 49 ppb of C.
The relative standard deviations of most of the 56 species were < 5 %, representing an error of < 0.5 ppb.
On the other hand, trace gases including CO, NO-NO2-NOx, SO2
and O3 were measured at 1 min resolution using the commercial
instruments of TEI 48i, 42i, 43i and 49i (Thermo Electron Corporation). All
these instruments were zero checked daily, span calibrated weekly and
multi-point calibrated monthly. Furthermore, meteorological conditions,
including temperature, relative humidity, pressure, and wind speed and
direction were monitored at 1 min resolution by a weather station (Vantage
Pro™ and Vantage Pro 2 plus™ weather stations, Davis Instruments).
The PMF model for VOC source identification
In this study, the US EPA PMF (version 4.1) model, which has been widely
used to conduct source apportionment of VOCs (Zhang et al., 2013; Mo et al.,
2017; He et al., 2019, and references therein) was applied to the observed
VOC data to identify potential VOC sources. A detailed description of the
PMF model is provided by Yuan et al. (2009) and Ling et al. (2011). In
brief, the PMF model is a receptor model that can identify the sources and
contributions of given species without prior input of their source profiles.
In this study, a total of 25 species were selected as the input for the PMF
model including species with high abundances as well as typical tracers of
emission sources. Species with high percentages of missing values
(> 25 %) were excluded (i.e., 1,3-butadiene,
cis/trans-2-pentene, dimethylpentane and trimethylpentane). The total
concentration of the 25 selected species accounted for ∼92 % of the total measured VOC composition. Furthermore, we calculated the
total reactivity of the selected 25 species to be ∼90 % of
the total measured VOCs through the analysis of maximum incremental
reactivity (MIR) (Shao et al., 2009a). The high abundance and total
reactivity contributions suggest that the 25 selected species were
appropriate for the PMF model simulation.
The PMF model was tested using a variety of factor numbers, and the optimum
source profiles and contributions were determined based on the correlation
between modeled and observed data, the comparison of modeled profiles with
the results from emission-based measurements, and previous studies involving
PMF or other receptor model simulations (i.e., HKEPD, 2015; Wang et al., 2014;
An et al., 2014; Liu et al., 2008a). For example, different solutions with
different factor numbers were explored, and the source apportionment results
from a five-factor solution that could sufficiently explain the observed
levels of VOCs were selected (details in Sect. 3.3). Compared with
the five-factor solution, the four-factor solution derived two profiles that were
attributable to gasoline and diesel vehicular exhaust, while most of the
aromatic species in these sources and certain amounts of C3–C4
species from fuel evaporation were categorized under industrial emission. On
the other hand, the six-factor solution was split into a factor with a high
abundance of ethyne and certain amounts of ethane (30 % in species total),
C3 species and benzene (∼20 % in species total), while
some alkenes (18 %–80 % in species total) were incorporated into fuel
evaporation. Furthermore, the performance of the five-factor solution was
evaluated using various checks and sensitivity tests. Suitable correlations
between the observed concentrations and those of each species predicted by
the model were observed, with the correlation coefficients (R2) ranging
from 0.60 to 0.91, indicating that the solution adequately reproduced the
observed variations of each species. All the scale residuals were within
±3σ with normal distributions for all species (Baudic et al.,
2016). Moreover, different numbers of start seeds were tested during the
simulation and no multiple solutions were found. The obtained ratio of
Q(robust)/Q(true) was ∼0.93, close to 1 as suggested
by previous studies and the user guide manual (Paatero, 2000; Lau et al.,
2010; Ling et al., 2016). In addition, the results from bootstrapping
analysis for the five-factor solution with bootstrap random seed found that
all the factors were mapped to a basic factor in all 20 bootstrap runs,
while the uncertainties of each species from bootstrapping analysis were
within the range of 1 %–20 %. In this study, different
Fpeak values ranging from -5 to 5 were tested in the five-factor solution
for a more realistic profile (Lau et al., 2010; Baudic et al., 2016). The
profiles with the nonzero Fpeak values were consistent with those with
zero Fpeak value, reflecting that there was little rotation for the
selected solution, confirming that the profiles were reasonably explained by
the five-factor solution (Baudic et al., 2016). The results of the Fpeak
value equals 0.5 run (the base run) was selected for analysis in this study.
Overall, the above features demonstrated that the five-factor solution from
PMF could provide reasonable and stable apportionment results for the
observed VOCs at the JAES site.
Results and discussionVOC observation statistics
Table 1 shows the average concentration and standard deviation of 56
VOC species concentrations measured at the JAES site, while Fig. S1 in the
Supplement presents the time series of all pollution data collected at
the JAES site. The annual-average total VOC (TVOC, sum of the measured VOCs)
concentration in 2016 was 25.7±19.1 ppbv, with highest
contributions from alkanes (13.6±10.5 ppbv, ∼53 %),
followed by aromatics (4.4±4.0 ppbv, ∼17 %),
acetylene (4.5±5.5 ppbv, ∼17 %) and alkenes (3.2±3.3 ppbv, ∼13 %). Annually, the 10 most abundant species were acetylene, propane, ethane, ethylene, butane, toluene,
i-pentane, i-butane, propylene and benzene, with a combined contribution of
∼77 % of the TVOC. This observed VOC composition suggests
that VOCs at the JAES site are predominantly sourced from combustion
emissions (i.e., vehicular emissions). Alkenes are mainly associated with
vehicular emissions and are more photochemically reactive relative to
alkanes and aromatics. The alkenes were found to have higher mixing ratios
during weekdays relative to the weekends (3.5±0.2 vs. 2.9±0.1 ppbv for weekdays and weekend, respectively, p < 0.05), further
confirming the dominant contribution of vehicular emissions to VOC levels at
the JAES site.
The average mixing ratios and standard deviation of VOC species
concentrations measured at the JAES site from January to December 2016. Values in bold font are the sum of mixing ratios of measured VOCs in each group.
SpeciesAverage ± standard deviation (ppbv)SpeciesAverage ± standard deviation (ppbv)Alkanes13.64±10.53Alkenes3.24±3.28Ethane3.63±2.68Ethene1.72±2.00Propane3.70±3.01Propylene0.92±1.16i-Butane1.03±0.871-Butene0.12±0.16n-Butane1.55±1.26cis-2-Butene0.06±0.09Cyclopentane0.08±0.10trans-2-Butene0.16±0.11i-Pentane1.15±1.241-Pentene0.03±0.03n-Pentane0.61±0.60cis-1-Pentene0.02±0.032,2-Dimethylbutane0.02±0.02trans-2-Pentene0.02±0.032,3-Dimethylbutane0.05±0.07Isoprene0.14±0.202-Methylpentane0.26±0.29n-Hexene0.05±0.033-Methylpentane0.16±0.21Aromatics4.40±4.01n-Hexane0.40±0.45Benzene0.80±0.70Methylcyclopentane0.26±0.27Toluene1.40±1.35Cyclohexane0.10±0.16Ethylbenzene0.50±0.622,4-Dimethylpentane0.03±0.01m/p-Xylene0.70±0.712,3-Dimethylpentane0.03±0.02o-Xylene0.25±0.242-Methylhexane0.06±0.09Styrene0.12±0.173-Methylhexane0.07±0.10n-Propylbenzene0.03±0.03Heptane0.09±0.11i-Propylbenzene0.03±0.04Methylcyclohexane0.07±0.09m-Ethyltoluene0.11±0.142,2,4-Trimethylpentane0.02±0.03p-Ethyltoluene0.05±0.072,3,4-Trimethylpentane0.02±0.01o-Ethyltoluene0.04±0.052-Methylheptane0.02±0.021,3,5-Trimethylbenzene0.04±0.063-Methylheptane0.02±0.021,2,4-Trimethylbenzene0.15±0.21Octane0.04±0.061,2,3-Trimethylpentane0.10±0.14Nonane0.02±0.02m-Diethylbenzene0.03±0.06Decane0.04±0.04p-Diethylbenzene0.04±0.08Undecane0.04±0.07Acetylene4.47±5.49Dodecane0.09±0.20––
The TVOC level in this study was lower than previous measurements from an
industrial site in Nanjing, in which 43.5 ppbv of TVOC was reported (An et al.,
2014). However, the high TVOC levels are likely due to the proximity of the
observation site (∼3 km northeast) to the Nanjing chemical
industry area, as well as several iron, steel and cogeneration power plants
(within 2 km) (An et al., 2014). The variability in land use between these
two studies have also resulted in distinct VOC component profiles. In the
industrial area, the relative contributions of alkenes and aromatics were as
high as 25 % and 22 %, while the contribution of alkynes was only 7 %
(An et al., 2014). The alkane, alkene and aromatic concentrations from the
industrial site were 1.4, 3.4 and 2.2 times higher than the concentrations
of this study, respectively, while alkyne concentrations were
∼30 % lower. Given the large variability observed between
the two sites, it is crucial to assess the spatial variability of ambient
VOCs across the city through a collaboration of multiple research groups
using available real-time and online VOC-monitoring systems.
Table S1 in the Supplement compares reported ambient VOCs from continuous measurements of ≥1 year in several megacities in a number of countries, including China.
Continuous online measurements of ambient VOCs have only been available in
China since 2010, unlike many developed countries whereby online VOC
measurements have been available for multiple decades. In China, such
measurements are only concentrated in a few megacities, including Beijing,
Guangzhou and Shanghai. The TVOC level reported in Nanjing was close to
levels measured in Shanghai (another megacity in the YRD, East China, 27.8 ppbv) (Wang et al., 2013), Tianjin (a megacity in North China, 28.7 ppbv)
(Liu et al., 2016), and Wuhan (a megacity located in central China, 24.3 ppbv) (Lyu et al., 2016), but it was considerably lower than Beijing (North
China, 35.2 ppbv) (Zhang et al., 2017) and Guangzhou (southern China, 42.7 ppbv) (Zou et al., 2015). Alkanes were the dominant hydrocarbon group in all
the cities; however, some differences in relative contributions of the four
classes were observed. The contribution from aromatics was highest in
Shanghai (31 %) relative to the other cities, which is likely explained by
the large petrochemical and steel industry in Shanghai (Huang et al., 2011;
Wang et al., 2013). In comparison, the contribution of aromatics in
Guangzhou (Zou et al., 2015) and the industrial area in Nanjing (An et al.,
2014) were 24 % and 22 %, respectively, while in other cities the
contribution ranged from 17 % to 19 %. The current ambient VOC
concentrations in Chinese megacities are generally comparable to the urban
VOC levels in developed countries during the year 2000. However, in
developed countries, the mixing ratios of VOCs were observed to decrease in
the recent decades following the implementation and formulation of VOC
strategies (Warneke et al., 2012). For example, the mixing ratios of VOCs in
Los Angeles have decreased significantly from 1960 to 2002 at an average annual
rate of ∼7.5 %, while the mixing ratios of VOCs in London
presented a higher and faster decrease since 1998 when there were higher
VOC mixing ratios than those in Los Angeles, confirming that the earlier
implementation of VOC reduction strategies in California had clearly led to
the earlier improvement of air quality compared to London (Warneke et al.,
2012; von Schneidemesser et al., 2010). Chinese megacities are therefore
experiencing significantly higher ambient VOC contamination, given the
remarkable decrease in VOC emissions in developed countries over the last
2 decades (Pan et al., 2015; European Environment Agency, 2016; U.S. EPA,
2017). High VOC levels in Chinese megacities are known to impact ambient
ozone and secondary particle pollution and cause adverse impacts on
human health. However, as China has a solid foundation for VOCs monitoring
and control, numerous strict, appropriate and targeted reduction strategies
for VOCs have been and are being formulated and implemented in Chinese
megacities (Guo et al., 2017). It is expected these measures could help
China to reduce VOC emissions and mixing ratios and improve air quality in the
future.
Comparison of annual-average concentrations of ambient VOC in
different cities based on real-time online continuous measurements of at
least 1 year.
Temporal variability
In this study, ambient VOCs showed significant seasonal variability, with
relatively high monthly-average concentrations in winter (40.2±24.0 ppbv) and spring (23.8±15.0 ppbv) and low concentrations in summer
(18.5±14.6 ppbv) and autumn (20.1±12.2 ppbv). As shown in
Fig. S2, the highest monthly-average concentration was observed in
December, followed by January. High pollution levels during the winter
period are usually expected and explained by atmospheric temperature
inversions caused by cooler weather, which inhibits particle dispersion.
Lower concentrations during the summer period are due to both favorable
diffusion conditions and photochemical degradation of VOCs.
High levels of wintertime VOC pollution were also reported in Shanghai (Wang et al.,
2013), Guangzhou (Zou et al., 2015), and Tianjin (Liu et al., 2016), though
some differences in the monthly VOC variability were also observed. Except
for the winter months, similar (and relatively stable) ambient VOC levels in
the remaining months were observed for Guangdong (Fig. 3). In Shanghai,
relatively high levels of VOCs were observed from October to January of the
following year and from June to July based on the 2-year measurements
conducted from 2009 to 2010 (Wang et al., 2013). The inversion layer, the
effect of cold front or uniform pressure in winter resulted in high levels
of VOCs from October to January of the following year, while the frontal
inverted trough or frequently observed stagnant high-pressure system with
southwest flow that could lead to poor diffusion were unfavorable
meteorological conditions for high VOC levels from June to July. In
addition, air masses transported from upwind chemical and petrochemical
industrial factories located in the southwest and south of the monitoring
site were another factor for the high VOC levels in summer (i.e., June and July)
and winter. VOC concentrations in Tianjin showed significant monthly
variability. Highest concentrations were reported in autumn and lowest
concentrations were reported in summer. The observed monthly variability is
affected by several factors including the type and level of emissions and
local meteorological conditions.
Monthly variability of ambient VOCs at the JAES site and three
other Chinese cities: Shanghai (Wang et al., 2013), Guangzhou (Zou et al.,
2015) and Tianjin (Liu et al., 2016).
Figure S3 shows the diurnal trends in ambient VOCs for each month. The
diurnal patterns were generally similar for all the months. The observed
peak at approximately 08:00–09:00 LT (local time) corresponds with the city's
morning traffic rush hours. The concentration begins to decrease after 09:00 LT, with
lowest concentrations observed at approximately 15:00 LT. The observed decline
was likely due to reduced vehicle emissions, growth of the inversion top
and enhanced photochemical VOC degradation. After 15:00 LT, the concentrations
begin to increase gradually as a result of increased vehicle emissions
during the evening rush hour, as well as a reduction in the atmospheric
mixing height under evening meteorological conditions. The second evening
VOC peak was less prominent than the morning peak. Evening concentrations
were generally higher than the daytime concentrations, and the amplitudes of
diurnal variability were larger in autumn and summer compared to winter and
spring.
Source apportionment of VOCs
In this study, we applied the PMF model to apportion the sources of VOCs at
the sampling site. Figure 4 illustrates the source profiles of the VOCs
produced by the PMF model. Five VOC sources were resolved by PMF, including
biogenic emissions (source 1), fuel evaporation (source 2), gasoline
vehicular exhaust (source 3), diesel vehicular exhaust (source 4) and
industrial emissions (source 5).
Source 1 was identified as biogenic emissions due to the high loading of
isoprene – a typical tracer of biogenic emissions (Lau et al., 2010; Yuan
et al., 2012). Source 2 was represented by high proportions of
2-methylpentane, 3-methylpentane, i-pentane and cyclopentane. Pentanes are
mainly associated with profiles from gasoline-related emissions (Barletta et
al., 2005; Tsai et al., 2006). However, the low contributions of incomplete
combustion tracers in this profile suggest that the VOCs are sourced from
fuel evaporation. The high abundance of pentanes in this profile is
consistent with the source profile of gasoline volatilization extracted from
principal component analysis and absolute principal component scores (PCA/APCs)
based on the observed VOC data collected in an industrial area of Nanjing
(An et al., 2014) and the source profile of gasoline evaporation from PMF at
the suburban site and urban sites in Beijing and Hong Kong (Yuan et al.,
2009; Lau et al., 2010). In particular, based on the emission-based
measurement, Liu et al. (2008b) conducted source apportionments of VOCs in
the Pearl River Delta region by the chemical mass balance (CMB) receptor
model, which attributed the source with high loadings of n/i-pentanes,
cyclopentane and 2/3-methylpentane as gasoline evaporation. Therefore,
source 2 here was identified as fuel evaporation.
Sources 3 and 4 were identified as vehicular exhaust due to their
high loadings of incomplete combustion tracers, i.e., C2–C4
alkanes and alkenes (Guo et al., 2011a, b; Zhang et al., 2018). Zhang et al. (2018) compared the VOC composition of vehicular emissions from Zhujiang
Tunnel in 2014 and 2004 in the Pearl River Delta region with those from
other tunnel measurements. C2–C4 alkanes and alkenes were found to
made the greatest contributions to the loading of VOCs emitted from vehicles
in 2014. The higher proportions of n/i-pentane, n-hexane and methylcyclopentane
in source 3 relative to source 4 indicated that VOCs were sourced from gasoline
vehicular exhaust (Liu et al., 2008b; Guo et al., 2011b; Zhang et al.,
2018). Source 4 was identified as diesel vehicular exhaust due to the high
percentages of ethyne, ethane and propene, as well as C2–C4
alkenes (Ho et al., 2009; Cai et al., 2010; Ou et al., 2015; Liu et al.,
2008c). Source 5 was characterized by high concentrations of aromatics. In
addition to gasoline vehicle emissions, industrial emission could be another
important contributor to ambient aromatic hydrocarbons in the Yangtze River
Delta, Pearl River Delta and North China Plain (Yuan et al., 2009; Zhang et
al., 2013; An et al., 2014; Mo et al., 2015, 2017; He et al., 2019).
The tunnel studies and emission-based measurement results found that
aromatic hydrocarbons from gasoline vehicular exhaust were coherently emitted
with pentanes, butenes, n-hexane and cyclopentane, which were more
consistent with the profile in source 3 mentioned above (Liu et al., 2008a, b, c;
Ho et al., 2009; Yuan et al., 2009; Zhang et al., 2018). Therefore, the
absence of the above species in source 5 indicated that this source could be
related to industrial emission (Zhang et al., 2014). In particular, the high
abundances of toluene, ethylbenzene, xylenes, ethyltoluene and
trimethylbenzene were consistent with the emission-based measurement results
conducted in the paint and printing industries (Yuan et al., 2010) and
manufacturing facilities (Zheng et al., 2013). On the other hand, the
profile with a high abundance of aromatic hydrocarbons (C7–C9
aromatics) and a certain amount of ethene was also in agreement with the
profiles measured in the areas dominated by industrial emissions in the
Yangtze River Delta region (An et al., 2014; Shao et al., 2016; Mo et al.,
2017). For example, An et al. (2014) reported that toluene, ethylbenzene,
xylenes and trimethylbenzenes could be emitted from different industrial
processes, and they identified that the factors with high loadings of these
species as industrial production, solvent usage and industrial production of
volatilization sources by PCA/APCs at the industrial area in Nanjing. On the
other hand, Mo et al. (2017) identified the factors with high concentrations
of C7–C9 aromatics and ethene, as used in residential solvent usage and the
chemical, paint, and petrochemical industries, with the PMF model
applied to the data collected in an industrialized coastal city of the Yangtze
River Delta. To further identify sources 3 and 5, the ratio of
toluene to benzene (T / B, ppbv ppbv-1) in each profile was compared with those
obtained from emission-based measurements and tunnel study results (Zhang et
al., 2018, and references therein). The ratios of T / B were ∼8.2 and ∼1.2 for sources 5 and 3, respectively, and were
consistent with those of “industrial processes and solvent application”
and “roadside and tunnel study”, respectively (Zhang et al., 2018, and
references therein). This further confirmed that source 3 was related to
gasoline vehicular exhaust, while source 5 was associated with industrial
emission.
Source profiles of VOCs identified using the PMF model and the
relative contributions of the individual VOC species.
Vehicular exhaust was found to be the most significant contributor to the
TVOCs at the JAES site, with average contributions of ∼34%
and ∼27% for diesel and gasoline exhaust, respectively,
followed by industrial emissions (19 %), fuel evaporation (∼15%), and biogenic emissions (∼4%). Our results are
inconsistent with previous results observed at industrial sites in Nanjing
(An et al., 2014; Xia et al., 2014a). An et al. (2014) found that industrial
activities were the most significant source of VOCs, contributing
45 %–63 % (mainly aromatic VOCs), followed by vehicle emission at
34 %–50 %. Similarly, Xia et al. (2014a) reported solvent usage and
other industrial sources to account for most (31 %) of the VOCs in a
suburban site in southwestern Nanjing, close in proximity to Nanjing's
industrial zone. Fossil fuel, biomass and biofuel combustion were the second
highest contributors at 28 %, while the average contribution of vehicular
emissions was 17 %, mainly from the northern center of Nanjing (Xia et
al., 2014a). Combined, these results infer vehicular emissions to be a major
component of urban emissions in Nanjing. The observed spatial variability in
the contributions of VOC sources infers the complex emissions
characteristics of VOCs in Nanjing, likely due to the city's unique
industrial structure. For example, the sampling site (i.e., the JAES site)
was located in a more residential and urban area compared to other sites
listed in An et al. (2014) and Xia et al. (2014a). There are more than 0.22 million people living in the areas surrounding the sampled site (within 3 km
of the observation site), which is composed of residential communities, schools,
government agencies and business centers. These results also demonstrate
that local emissions are dominant contributors to ambient VOC levels in
Nanjing.
The dominant contribution of vehicular emissions to ambient VOCs in Nanjing
is consistent with the urban or central areas of other large cities, including
Hong Kong, Guangzhou, Shanghai and Beijing, as identified and quantified by
the PMF model (Yuan et al., 2009; Cai et al., 2010; Guo et al., 2011a; Zhang
et al., 2013; Wang et al., 2015). In addition, our results are in agreement
with the anthropogenic VOC source emission inventory of Jiangsu Province in
2010 (Xia et al., 2014b), indicating vehicular emissions and industrial
emissions (i.e., solvent usage and industrial process source) to be the two
dominant sources of VOCs in the region. However, the contributions of
vehicle-related emissions (i.e., ∼25 %) and industrial
emissions were lower and higher than those quantified by the PMF model in
this study, respectively. The observed discrepancy between the two studies
may be due to differences in source categories, measured VOC species, and/or
sampling locations and methods used in the different models. For example,
the VOC sources in Jiangsu Province were categorized into vehicular related
emission (∼26 %), industrial solvent usage (∼25%), fossil fuel combustion (∼24 %), industrial
processes (∼22 %) and biomass burning (∼3%). Further, vehicle-related emissions only included emissions from
motor vehicles and ships and the volatilization of fuel, while solvent
usage included organic solvents volatilized from a variety of industries
(the industrial production of electronic equipment,
furniture, printing, packaging, inks, adhesives, etc. as well as
other dry cleaning, catering and architectural decoration processes).
The higher vehicular emission contribution in this study may also be due to the
increasing number of vehicles from 2010 to 2014 as a result of increased
urbanization and industrialization (Statistical yearbook of Nanjing, 2014).
Figure 5 illustrates the mean diurnal variability of all identified sources
at the JAES site. These trends were influenced by the variability in
emission strength, mixing height, and the concentrations and photochemical
reactivity of individual species in each source profile. For example, we
observed a typical diurnal pattern with a broad peak between 09:00 and 18:00 LT (local time) for
biogenic emissions, as the emission rate of isoprene from vegetation is
largely depended on ambient temperature and sunlight intensity. Higher
levels of diesel and gasoline vehicular emissions were observed in the
evening and early morning due to a reduced mixing height and increased
emissions from the morning and evening rush hours. Lower concentrations
observed during daytime hours were likely due to decreased emissions, an
increased mixing height and enhanced photochemical loss (Gillman et al.,
2009; Yuan et al., 2009; Wang et al., 2013). A diurnal pattern of fuel
evaporation was similar to that of vehicular emissions. Though the
evaporation of fuel is dependent on temperature, the average temperature in
the morning and evening (i.e., 08:00–10:00 and 17:00–19:00 LT, respectively) when
peaks of fuel evaporation were found was only about ∼1.2∘C lower than that observed from noon to afternoon (11:00–16:00 LT),
which may not result in much higher fuel evaporation at noon (the difference
between maximum and minimum values for fuel evaporation was found to be
∼6µg m-3). On the other hand, in addition to
evaporation from the gas station, fuel could evaporate from hot engines,
fuel tanks and the exhaust system when the car is running. Furthermore, the
engine remains hot for a period of time after the car is turned off, and
gasoline evaporation continues when the car is parked (Technology Center,
University of Illinois;
https://mste.illinois.edu/tcd/ecology/fuelevap.html, last access: 25 December 2019). The similarity of diurnal variations of fuel evaporation to
vehicular emissions suggest that the prominent peak in the morning and
evening hours are related to the increased vehicles in the traffic rush
hours and emissions accumulated in the relatively low boundary layer.
Moreover, we identified higher concentrations of industrial emissions at
night and in the early morning, with values remaining fairly stable during
daytime hours. This finding is consistent with other observations in urban
and rural areas (Yuan et al., 2009; Leuchner and Rappenglück, 2010).
Diurnal patterns in source concentrations of the five identified
sources.
Contributions of VOC sources to O3 formation
As important O3 precursors, information on the contributions of VOC
sources and related species to O3 formation is necessary for the
formulation and implementation of VOC control measures. To achieve this
goal, the maximum incremental reactivity (MIR) method, which evaluates the
O3 formation potential (OFP) on the basis of mass concentrations and
maximum incremental reactivities of VOCs with the OH radical, was adopted
in the present study (Shao et al., 2009b, 2011; Mo et al., 2017). Figure 6
presents the relative contributions of individual VOC sources and related
VOC species from PMF to OFP at the JAES site. Industrial emissions were
found to have the largest OFP at JAES due to the high loadings of aromatic
VOC species that have relatively high OH reactivities in this source profile
(Atkinson and Arey, 2003), with the OFP value of ∼43µg m-3 and the contribution percentage of ∼32 % to the
total OFP of all VOC sources, followed by diesel vehicular exhaust
(∼36µg m-3, ∼27 %), gasoline
vehicular exhaust (∼32µg m-3, ∼24 %) and fuel evaporation (∼13µg m-3,
∼10 %). Furthermore, though the MIR value of isoprene was
much higher than other VOC species, biogenic emissions only contributed
∼7 % (∼9µg m-3) to the total OFP
of all VOC sources as the relatively low mixing ratio of isoprene at the
JAES site. Similarly, using the same method to evaluate OFP of different VOC
sources, Mo et al. (2017) found that industrial emissions (including the
emissions of the petrochemical industry, chemical and paint industries, solvent
usage) and vehicular emissions were the dominant VOC sources of the total
OFP in an industrialized coastal city (i.e., Ningbo) in the YRD region.
Therefore, our results further demonstrate the need to minimize VOC
emissions from industrial emissions and vehicle exhaust in order to lower
O3 formation and photochemical pollution in the YRD.
(a) The contribution of individual sources to the total OFP of all
sources extracted from PMF and (b) OFP values of the top 10 VOC species in
the different source categories.
Based on the mass concentrations of individual species in each source, we
found that m,p-xylene and toluene in industrial emissions and gasoline
vehicular emissions; propene, ethene, toluene, and m,p-xylene in diesel
vehicular emissions; and o-xylene,1,2,4-trimethylbenzene, and ethene in
industrial emissions to be the dominant species from VOC emissions
contributing to photochemical O3 formation. Thus, only a small number
of VOC species can be monitored for the effective control of O3
formation.
Policy summary and implications
To effectively control photochemical pollution, the Prevention and Control
of Atmospheric Pollution Act was passed in 1987 and amended in 2015. As a
result, a series of measures to prevent and control VOC levels have been
and are being implemented by central and local governments, including the
implementation of new laws and regulations and the advancement of
technology. The results of this study suggest that photochemical O3
formation within the urban areas of Nanjing city are VOC limited, which is
consistent with observations in the urban locations of other regions,
including the North China Plain, the Yangtze River Delta and the Pearl River
Delta. Minimizing VOC emissions and their concentrations should therefore be
prioritized in order to alleviate O3 pollution in urban environments.
The prevention and control of VOC pollution has been listed as one of the
key tasks of the “Blue Sky” project initiated in 2012 by the Department of
Environmental Protection of Jiangsu Province. Furthermore, the
administrative measures on the Prevention and Control of Volatile Organic
Compounds Pollution in Jiangsu (Order No. 119 of the Provincial Government)
was enacted on 6 March 2018 and implemented on 1 May 2018, with the aim of
controlling VOC emissions in Jiangsu Province.
In order to achieve these goals, various measures have been implemented
(Table S2), including (1) investigating the current pollution status and
identifying the progress of VOC prevention and control in Jiangsu Province
(Provincial Office of the Joint Conference on the prevention and control of
air pollution [2012] No. 2); (2) conducting a strict industry access system,
under the Advice on Promoting Air Pollution Joint Prevention and Control
Work to Improve Regional Air Quality (Office of the State Council [2010] No.
33); (3) strengthening the remediation on existing sources of VOCs and
reducing VOC emissions from these sources, under the guidelines for the
implementation of Leak Detection and Repair (LDAR) in Jiangsu Province
(Trial) (Provincial Office of Environmental Protection [2013] No. 318); (4) strengthening the VOC monitoring capacity, under the Guidelines for Control
of Volatile Organic Compounds Pollution in Key Industries in Jiangsu
Province (Provincial Office of Environmental Protection [2013] No. 128); (5) improving standards regarding VOC emissions for key industries, including
standards for surface coating of the automobile manufacturing industry
(DB32/2862-2016), the chemical industry (DB32/3151-2016) and furniture
manufacturing operations (DB32/3152-2016), which are still effective since
their enforcement; (6) implementing the Pilot Measures for Volatile Organic
Compounds Discharge Charges (Ministry of Finance [2015] No. 71) on 1 October 2015 to raise awareness pertaining to emissions reduction in factories
and to control VOC emissions from industrial sources; (7) encouraging the
public to live a low-carbon life and supervise and offer recommendations in
accordance with the laws, under the Measures for Public Participation in
Environmental Protection in Jiangsu Province (Trial) (Provincial Regulation
of Environmental Protection Office [2016] No. 1).
Based on the VOC source apportionment results in this study, we identified
vehicular emissions and industrial emissions as the two major VOC sources
contributing to photochemical O3 formation. Other measures and/or
regulations have been conducted in Jiangsu Province to effectively
control VOC emissions from vehicles and industry. For vehicular emissions,
the Regulations on Prevention and Control of Vehicle Exhaust Pollution in
Nanjing was amended in July 2017 and subsequently in March 2018
(http://www.jiangsu.gov.cn/art/2019/4/30/art_59202_8323581.html, last access: 28 March 2020). The new regulation not
only focuses on vehicle emissions but also incorporates a number of
additional topics, including optimizing the function and distribution of
urban areas, limiting the number of vehicles in the region, promoting new
green energy vehicles, and improving the quality of fuel. The promotion of
intelligent traffic management, implementation of a priority strategy for
public transportation and construction of more efficient traffic systems to
promote pedestrian and bicycle use is recommended. Further studies should be
conducted to estimate and manage the increasing quantity of vehicles on the
road. As of 1 January 2017, regulation stipulate that all new and used
vehicles should meet the fifth phase of vehicle emission standards,
including vehicle manufacture, sales, registration and importation. For
vehicles already in use, an environmental protection examination should be
conducted annually, based on the standards of GB 14622-2016, GB 18176-2016,
GB 19755-2016 and HJ 689-2014. Penalties are issued if qualified vehicles
excessively emit pollutants due to poor maintenance.
For industrial emissions, various policies have been implemented to reduce
VOC emissions, particularly in chemical industries, including (1) investigations on the VOC emissions of the chemical industry and the
establishment of an archive system for VOC pollution control, particularly
the inspection of industry information, products and materials, and unorganized
emission of storage and exhaust gas treatment facilities, under the Plan for
Investigation of Volatile Organic Pollutant Emissions in Jiangsu Province,
mentioned in the Provincial Office of Environmental Protection [2012] No.
183; (2) exhaust gas remediation in the chemical industry park, under the
Technical Specifications for Prevention and Control of Air Pollution in
Chemical Industries in Jiangsu Province (Provincial Office of Environmental
Protection [2014] No. 3), which requires the establishment of the long-term
supervision of exhaust gas remediation in the chemical industry park of
Jiangsu Province; (3) a pilot project on the leak detection and repair (LDAR)
technology in the chemical industry park, was conducted according to the documents from Provincial Office of Environmental Protection (No. 157 in 2015). The TVOC removal efficiency of
organic exhaust vents should be > 95 % and higher for areas of
excessive environmental pollution at > 97 % (GB 31571-2015).
Furthermore, though measures have been adopted to improve standards and
control vehicle VOC emissions, most of these policies only focus on total
VOC emissions (or the mass of total emissions) and do not consider the
impacts of individual VOC species. To accelerate the implementation of
existing policies and to strengthen collaborative regional prevention and
control, priority should be placed on specific high-impact VOC species
(i.e., m,p-xylene and toluene in the industrial emission and gasoline vehicular
emission) by considering both their reactivity and abundance.
Last but not least, O3 pollution is a regional cross-boundary
environmental issue rather than a local pollution problem. Apart from VOCs,
NOx is another important precursor for O3 formation with its dual
roles in O3 production (enhancing O3 formation in non-NOx-saturated environment and titrating O3 in NOx-saturated
environment). In other areas (i.e., the rural environment and/or the
downwind areas of the urban center in the same region) where the concentrations
of NOx are low and/or there is a non-NOx-saturated environment,
the situation may be different and controlling VOCs should be conducted
cautiously (Zheng et al., 2010; Yuan et al., 2013; Ou et al., 2016).
Therefore, from a regional perspective, the benefits of VOC control
measures could be further evaluated with those of NOx (i.e., the
appropriate ratios of VOC/NOx for the reduction of O3 pollution)
as well as the associated O3–VOCs–NOx sensitivity. Therefore, one
important concern for the policy formulation and implementation system is
whether controlling VOCs and NOx individually or controlling both VOCs
and NOx is more effective and appropriate for alleviating O3
pollution. It is necessary to consider the reduction ratios of VOC/NOx
when VOCs and NOx are simultaneously controlled. Finally, long-term
monitoring studies are necessary to determine the costs, benefits and
performance of each policy.
Conclusions
In this study, a 1-year field sampling campaign was conducted to
investigate the VOC characteristics at an urban site in Nanjing (the JAES
site), Jiangsu Province. In total, 56 VOCs including 29 alkanes, 10 alkenes,
16 aromatics and acetylene were identified and quantified. The composition
analysis found that alkanes were the dominant group of VOCs observed at the
JAES site (∼53 %), followed by aromatics, acetylene and
alkenes. This finding is consistent with the VOC measurements in studies
conducted in the North China Plain, Pearl River Delta and Yangtze River
Delta. We observed distinct seasonal patterns of TVOCs, with maximum values
in winter and minimum values in summer. Similarly, prominent morning and
evening peaks were observed in the diurnal variability of TVOCs, influenced
by local emissions and meteorology.
Based on the observed VOC data, we identified five dominant VOC sources at
the JAES site using a PMF model. By considering both the abundance and
maximum incremental reactivity of individual VOC species in each source, the
OFP values identified industrial and vehicular emissions, particularly
m,p-xylene, toluene and propene, as the main contributors of O3 pollution.
Local governments have strengthened several measures to minimize VOC
pollution from vehicle and industrial emissions in Jiangsu Province in
recent years, though most of these policies focus particularly on lowering
the total emissions of VOCs. Furthermore, from a regional perspective, it is
suggested that appropriate ratios of VOC/NOx, their associated
sensitivity to O3 formation, and relative benefits and disadvantages of
reducing VOCs/NOx should be investigated and evaluated when control
measures of VOCs and NOx are conducted.
Data availability
The research data belongs to Jiangsu Academy of Environmental Sciences, which can be accessed by application. Detailed application procedures can be obtained by request to QY Zhao (qiuyue.zhao@163.com) of Jiangsu Academy of Environmental Sciences.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-3905-2020-supplement.
Author contributions
JB, ZL and QZ designed the
research and carried it out. ZL performed the data simulation.
QZ and GS performed the observation data analysis. QZ prepared the article with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Hai Guo of Hong Kong Polytechnic University for the suggestion of model simulation, and they also thank the handling editor and two anonymous referees for their constructive comments for the article.
Financial support
This work was supported by the National Key R&D Program of China (nos. 2016YFC0207607, 2017YFC0210106), the National Science Foundation of Jiangsu Province of China (General Program, no. BK20161601), the Open Research Fund of Jiangsu Province Key Laboratory of Environmental Engineering (no. ZX2016002) and the National Natural Science Foundation of China (no. 41775114). This work was also partly supported by the Pearl River Science and Technology Nova Program of Guangzhou (grant no. 201806010146).
Review statement
This paper was edited by Thomas Karl and reviewed by two anonymous referees.
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