Semi-volatile and intermediate-volatility organic
compounds (S–IVOCs) are considered critical precursors of secondary
organic aerosol (SOA), which is an important component of fine particulate
matter (PM2.5). However, knowledge of the contributions of S–IVOCs
to SOA is still lacking in the Pearl River Delta (PRD) region,
southern China. Therefore, in this study, an emission inventory of S–IVOCs
in the PRD region was developed for the first time for the year 2010. The
S–IVOC emissions were calculated based on a parameterization method involving
the emission factors of POA (primary organic aerosol), emission ratios of
S–IVOCs to POA, and domestic activity data. The total emissions of S–IVOCs
were estimated to be 323.4 Gg, with major emissions from central cities in the
PRD, i.e., Guangzhou, Foshan, and Shenzhen. On-road mobile sources and
industries were the two major contributors of S–IVOC emissions, with
contributions of ∼42 % and ∼35 %,
respectively. Furthermore, uncertainties of the emission inventory were
evaluated through Monte Carlo simulation. The uncertainties ranged from
-79 % to 229 %, which could be mainly attributed to mass fractions of OC
(organic carbon) to PM2.5 from on-road mobile emissions and emission
ratios of IVOCs / POA. The developed S–IVOC emission inventory was further
incorporated into the Weather Research and Forecasting with Chemistry
(WRF-Chem) model with a volatility basis-set (VBS) approach to improve the
performance of SOA simulation and to evaluate the influence of S–IVOCs on
SOA formation at a receptor site (Wan Qing Sha (WQS) site) in the PRD. The
following results could be obtained. (1) The model could resolve about
34 % on average of observed SOA concentrations at WQS after considering
the emissions of S–IVOCs, and 18 %–77 % with the uncertainties of the
S–IVOC emission inventory considered. (2) The simulated SOA over the PRD
region was increased by 161 % with the input of S–IVOC emissions, and it
could be decreased to 126 % after the reaction coefficient of S–IVOCs with
OH radical was improved. (3) Among all anthropogenic sources of S–IVOCs,
industrial emission was the most significant contributor of S–IVOCs for SOA
formation, followed by on-road mobile, dust, biomass burning, residential,
and off-road mobile emissions. Overall, this study firstly quantified
emissions of S–IVOCs and evaluated their roles in SOA formation over the PRD,
which contributes towards significantly improving SOA simulation and better
understanding of SOA formation mechanisms in the PRD and other regions in China.
Introduction
As the key component, secondary organic aerosol (SOA) accounts for
20 %–80 % of organic aerosol (OA), while OA accounts for 20 %–90 % of
fine particulate matter (PM2.5) (Kanakidou et al., 2005; Carlton et al., 2009; Zhang et al., 2007, 2013). They not only affect
atmospheric chemistry, climate change, radiation balance, visibility, and
air quality
(Kanakidou
et al., 2005; Pope et al., 2002), but also endanger human and vegetation
health
(Gehring
et al., 2013; Zhou et al., 2014). In recent years, although PM2.5
concentrations in major city clusters including the Pearl River Delta (PRD)
region have shown a declining trend, the annual PM2.5 concentrations
are still higher than the World Health Organization (WHO) air quality
standards and Air Quality Guideline
(Li
et al., 2015; Lin et al., 2018). Moreover, the contribution of SOA to
PM2.5 is increasing
(Huang
et al., 2015; Zheng et al., 2014), and it dominates the composition of
PM2.5 during episodes of photochemical smog. Therefore, investigating
the formation mechanism of SOA is a prerequisite for better control over its
precursors and PM2.5, which is becoming increasingly more prominent as
the concentrations of SOA precursors continue to increase over the years
(Guo et
al., 2017).
Three-dimensional chemical transport models (CTMs) have been widely used to
investigate the formation and sources of SOA. Initially, organic compounds
with similar properties or sources were clustered together in OA (organic
aerosol) modules within gridded models due to the high complexity of OA and
large varieties of compounds incorporated in the detailed chemical schemes
(Johnson
et al., 2006), where large uncertainties occurred in simulations of SOA
formation. Then a two-product model (Odum
et al., 1996) based on the absorptive partitioning theory of
Pankow (1994) and fitting
methods developed from chamber study data was widely used in the simulation
of SOA formation. However, this empirical two-product model was reported to
largely underpredict SOA yield because it could not account for the wide
range of volatility of organic compounds. Recently, another scheme,
i.e., the volatility basis-set approach (VBS, typically one-dimensional VBS; 1-D VBS), was used to overcome the limitation of the two-product model
(Donahue et al., 2006). In VBS,
organic compounds are classified by their volatility, and it was developed
on the basis of the absorptive partitioning theory. The VBS approach
improves the modeling of further multigenerational oxidation processes and
incorporates low-volatility precursors of SOA, which consequently reduces
the discrepancy between observation and simulation results. Furthermore, to
capture the fragmentation process and oxidation of OA more accurately, the
two-dimensional VBS (2-D VBS) was proposed; 2-D VBS features a more detailed
classification of organic compounds in different ranges of volatility and
oxidation state. However, despite its potential for accurately simulating
the evolution of SOA, it has rarely been used in CTMs because it involves
much more complexity and computational expense compared to the widely used
1-D VBS
(Donahue
et al., 2011, 2012; Zhao et al., 2016a).
Although the scheme for SOA formation has been advanced significantly in
recent years, large discrepancies have still been found between the observed
and predicted abundance of SOA due to uncertainties in the formation
mechanisms of SOA as well as the related parameterization (i.e., SOA yields of
precursors of SOA) and the omittance of key precursors. For example, through
recent chamber experiments, SOA yields of aromatics have been updated to be
much higher than previous ones
(Ng et al., 2007), while
suggestions have also been made to consider wall losses of SOA in the SOA
yields of each VOC precursor extracted from chamber experiments
(Hildebrandt
et al., 2009; Li et al., 2017a). In addition to the SOA yields, recent
studies indicated that in-cloud aqueous-phase formation
(Lim,
Carlton and Turpin, 2005; Ervens et al., 2011) and oxidation of
VOCs (volatile organic compounds) that were previously not considered in
models (i.e., isoprene, benzene, and acetylene) could be important pathways for
SOA formation
(Claeys
et al., 2004; Martín-Reviejo and Wirtz, 2005; Volkamer et al., 2009).
In addition to the traditional precursors (i.e., VOCs), recent laboratory and
modeling studies have suggested that semi-volatile and intermediate-volatility organic compounds (S–IVOCs), which have effective saturation
concentrations in the ranges of 10-2–103 and
104–106µg m-3 at 298 K and 1 atm, respectively,
are key factors affecting the underestimation of SOA in numerical
simulations
(Donahue
et al., 2006; Robinson et al., 2007; Jiang et al., 2012). To date, S–IVOCs
are found to mainly include straight-chain and branched alkanes with carbon
numbers >12, alkylcyclohexanes, unsubstituted and substituted
polycyclic aromatic hydrocarbons (PAHs), alkylbenzenes, and cyclic and
polycyclic aliphatic material (Zhao et al., 2015; Li et al., 2018; Drozd et
al., 2019). However, a vast majority of S–IVOC mass still has not been
speciated at the molecular level, which is defined as an unresolved complex
mixture (UCM) (Jathar et al., 2012; Zhao et al., 2015; Drozd et al., 2019).
Incomplete combustion, such as the combustion of fossil fuel, especially
vehicle exhaust, has been reported to be a large contributor to S–IVOC
emissions in developed regions (May et al., 2013a, b; Ots et
al., 2016; Khare and Gentner, 2018). Recent studies have also shown that
consumer products and commercial or industrial products, processes, and
materials are significant sources of unspeciated S–IVOCs (Czech et al.,
2016; Khare and Gentner, 2018). Conversely, biogenic S–IVOCs have
recently been demonstrated to have a non-negligible impact on SOA formation,
but very few measurements have been reported on their emissions (Palm et
al., 2016, 2017). Therefore, to improve the performance of models simulating
SOA formation, anthropogenic emissions and the chemical mechanisms of
S–IVOCs have been incorporated into different models, including box,
regional, and global models
(Robinson
et al., 2007; Shrivastava et al., 2008, 2011, 2015; Grieshop et al.,
2009; Pye and Seinfeld, 2010; Tsimpidi et al., 2010; Ahmadov et al., 2012; Woody et al., 2015).
In terms of chemical mechanisms, although 1-D VBS is still widely used in
current models, one of the most important improvements is the adoption of
the 2-D VBS scheme as mentioned above
(Woody
et al., 2015; Zhao et al., 2016a). For emission inventories, which is a
prerequisite condition for improving model simulation of SOA formation and
evaluating the roles of S–IVOCs in SOA production, previous studies
typically estimated S–IVOC emissions from various sources based on the
relationship of S–IVOCs with POA, NMHCs (non-methane hydrocarbons), or
naphthalene as well as emission profiles and source-specific volatility
distribution factors for S–IVOC emissions extracted from various studies
(Robinson
et al., 2007; Pye et al., 2010; Shrivastava et al., 2011; May et al., 2013a, b, c; Woody et al., 2015; Zhao et
al., 2016a). However, very few studies have directly developed an emission
inventory of S–IVOCs appropriate for CTMs. For example, only
Liu et al.
(2017) reported an emission inventory of vehicular IVOCs for China,
estimating total emissions of IVOCs from vehicles in different provinces
based on emission factors obtained from measurements of vehicle exhaust in
the United States. However, this emission inventory was flawed as it could
not be applied to CTMs because the total IVOC emissions had not been
spatially allocated into grid cells, and it was not sufficiently localized
as the emission factors of IVOCs from vehicle exhaust were completely based
on measurements in the United States
(Zhao
et al., 2015, 2016b). As such, most modeling studies used the same
parameterizations and volatility distributions of all emissions independent
of source types to simulate SOA formation. However, S–IVOC emissions and
characteristics of SOA and particles widely vary among different countries
and regions.
The significance of S–IVOCs has been demonstrated through lab and modeling
studies in different environments, but the emissions of S–IVOCs and their
roles in the formation of SOA in China are still poorly understood,
especially in the PRD region, where photochemical smog and high oxidative
capacity are frequently observed
(Hofzumahaus
et al., 2009; Xue et al., 2016). Therefore, in the present study, a gridded
emission inventory of S–IVOCs for the PRD region was first developed and
then incorporated into the WRF-Chem model (Weather Research and Forecasting
model with Chemistry) with the 1-D VBS approach. The objectives of this
study are as follows: (i) to examine the potential of considering S–IVOCs
for improving the simulation of SOA formation and (ii) to evaluate the
contributions of S–IVOC to SOA over the PRD region. This study is the first
report focusing on the emissions of S–IVOCs and their contributions to SOA
formation in the PRD region, which could advance the understanding of SOA
formation mechanisms in the PRD and could be extended to other regions in China.
It should be noted that this study mainly focused on anthropogenic S–IVOCs
and their roles in SOA formation in the PRD region as anthropogenic S–IVOCs
were found to have much greater contributions to SOA formation than biogenic
S–IVOCs in developed regions (Palm et al., 2016, 2017; Khare and Gentner,
2018).
Datasets of all input parameters used in the emission
inventory model. Ellipses indicate that not all data were present in the table as the number of data was large (∼400).
SourceFOCOM/OCO/CH/CN/CESVOCs/EPOAEIVOCs/EPOAIndustrym0.03a1.77d0.19d1.26d0.008d––0.3a1.91e0.56e1.61e0.02e––…………………Residential sourcesn0.1a1.39e0.17e1.8e0.004e––0.3a1.44f0.19f1.78f0.036f––…………………On-road mobile sources0.58a1.4f0.15f1.77f0.045f0.67h8k0.37b1.46g0.19g1.78g0.05g0.49i30j…………………Off-road mobile sourceso0.33a––––––0.32b––––––…………………Dustp0.1a––––––0.05a––––––…………………Biomass burning0.58c1.55d0.26d1.62d0.06d0.65i0.75l0.6a1.62c0.32c1.47c0.06c0.8j1.5j…………………
a Li et al. (2017b). b Zhao et al. (2011).
c He et al. (2011). d Huang et al. (2011). e Hu
et al. (2016). f Xu
et al. (2015). g Ye
et al. (2017). h May et al. (2013a). i Louvaris et al. (2017). j Zhao et al. (2016a). k Zhao et al. (2015). l Shrivastava et al. (2008), etc. m Data of ratios of S–IVOCs to POA for industry are the same as those for on-road mobile sources. n Data of ratios of S–IVOCs to POA for residential
sources are the same as those for on-road mobile sources. o Data of ratios
of S–IVOCs to POA, O/C, H/C, N/C, and OM/OC for off-road mobile sources are
the same as those for on-road mobile sources. p Data of ratios of S–IVOCs
to POA for dust are the same as those for on-road mobile sources; data of
ratios of O/C, H/C, N/C, and OM/OC for dust are the same as those for
industry.
MethodologyEstablishment of the S–IVOC emission inventory
In this study, a gridded emission inventory of S–IVOCs was determined using
Eq. (1).
1ES/IVOCs,j=∑j,kAj,k×EFS/IVOCs,j×(1-μ)×10-3,
where j and k denote the specific sector and city, respectively; ES/IVOCs
denotes the annual emissions of S–IVOCs; and A, EF, and μ
represent the activity level, mean emission factor, and removal efficiency,
respectively. Emission factors of S–IVOCs were calculated on the basis of
existing traditional POA emission factors using source-specific linear
scaling factors because available emission factors of S–IVOCs are limited.
Moreover, the traditional POA emission factors for different source
categories (e.g., industry, on-road and off-road mobile sources, residential
sources, dust, and biomass burning) were obtained from POC (primary organic
carbon) emission factors using source-specific ratios of OM/OC (mass ratios
of organic matter to organic carbon), while the POC emission factors were
obtained from PM2.5 emission factors by applying the source-specific
mass fractions of OC to PM2.5. Therefore, Eq. (1) was extended to Eq. (2). The related parameters of S–IVOCs, including the activity levels,
removal efficiency, and spatiotemporal allocations, were assumed to be the
same as those of POA and POC for all source categories. S–IVOC emissions in
the PRD region for the year 2010 were calculated using Eq. (3), among which
the activity levels, removal efficiency, and emission factors were combined
and expressed as PM2.5 emissions (EPM2.5,j in Eq. 3). Note that
the PM2.5 emissions in this study were obtained from a highly resolved
spatial anthropogenic PRD regional emission inventory for the year 2010 with
a horizontal resolution of 3 km
(Zheng et al., 2010b).
2ES/IVOCs,j=∑j,kAj,k×EFPM2.5,j×FOC,j×OMOCj×ESVOCs,jEPOA,j+EIVOCs,jEPOA,j×(1-μ)×10-33ES/IVOCs,j=EPM2.5,j×FOC,j×OMOCj×ESVOCs,jEPOA,j+EIVOCs,jEPOA,j
The above parameters used in the development of the emission inventories of
S–IVOCs of each source category were extracted from recent studies (Table 1). In order to calculate the oxygen fraction and ratios of the non-oxygen
component to the carbon component of each species in all anthropogenic
sources, which would be required in modeling (Sect. 2.2.2), elemental
ratios of O/C, H/C, and N/C were also estimated.
Furthermore, uncertainties of the emission inventory of S–IVOCs, which can
be attributed to uncertainties in all parameters, were evaluated and
quantified using statistical methods and Monte Carlo simulation, as
suggested by Zheng
et al. (2010a). Sample correlation coefficients between total S–IVOC
emissions and model input parameters or S–IVOC emissions of each specific
source category have been calculated to identify the key sources of
uncertainties in the estimation of S–IVOC emissions (NARSTO, 2005). Based on
the different values of model input parameters from previous studies (Table 1), probabilistic distributions representing uncertainty ranges of different
parameters, including FOC, ESVOCs/EPOA,
EIVOCs/EPOA, OM/OC, O/C, H/C, and N/C, from different source
categories are summarized in Table 2. Additionally, uniform distribution
based on the results of uncertainty assessment in
Zhong et al. (2018) was applied to all source categories of PM2.5 emission in the
present study.
Probabilistic distributions with uncertainty range at the
95 % confidence interval in model input parameters.
InputSourceDistributionPara1Para2MeanUncertainty rangeparameterstypevalue(95 % confidence level)FOCindustryWeibull0.091.070.08(0.005, 0.28)residential sourcesnormal0.460.170.45(0.17, 0.70)on-road mobile sourcesWeibull0.392.020.33(0.07, 0.70)off-road mobile sourcesnormal0.260.110.25(0.06, 0.70)dustWeibull0.085.260.08(0.05, 0.10)biomass burninglognormal-1.010.340.38(0.19, 0.68)OM/OCindustrygamma111.460.021.69(1.43, 1.94)residential sourceslognormal0.280.051.33(1.26, 1.43)on-road mobile sourceslognormal0.340.051.39(1.31, 1.46)off-road mobile sourceslognormal0.340.051.39(1.31, 1.46)dustgamma111.460.021.69(1.43, 1.94)biomass burninglognormal0.430.091.51(1.40, 1.61)ESVOCs/EPOAindustrylognormal-0.320.230.70(0.51, 0.97)residential sourceslognormal-0.320.230.70(0.51, 0.97)on-road mobile sourceslognormal-0.320.230.70(0.51, 0.97)off-road mobile sourceslognormal-0.320.230.70(0.51, 0.97)dustlognormal-0.320.230.70(0.51, 0.97)biomass burningnormal0.760.140.80(0.58, 0.97)EIVOCs/EPOAindustrylognormal1.860.888.00(1.79, 25.45)residential sourceslognormal1.860.888.00(1.79, 25.45)on-road mobile sourceslognormal1.860.888.00(1.79, 25.45)off-road mobile sourceslognormal1.860.888.00(1.79, 25.45)dustlognormal1.860.888.00(1.79, 25.45)biomass burninggamma0.660.820.40(0.002, 1.33)O/CindustryWeibull0.492.700.44(0.19, 0.73)residential sourcesnormal0.130.050.13(0.08, 0.19)on-road mobile sourceslognormal-1.840.260.16(0.11, 0.21)off-road mobile sourceslognormal-1.840.260.16(0.11, 0.21)dustWeibull0.492.700.44(0.19, 0.73)biomass burninglognormal-1.290.350.30(0.19, 0.47)H/Cindustrygamma71.810.021.59(1.30, 1.90)residential sourcesWeibull1.7632.931.72(1.60, 1.80)on-road mobile sourcesWeibull1.7790.921.75(1.71, 1.78)off-road mobile sourcesWeibull1.7790.921.75(1.71, 1.78)dustgamma71.810.021.59(1.30, 1.90)biomass burninglognormal0.450.051.55(1.48, 1.62)N/Cindustrylognormal-4.020.760.02(0.01, 0.07)residential sourceslognormal-4.451.010.02(0.00, 0.05)on-road mobile sourcesnormal0.030.020.03(0.01, 0.05)off-road mobile sourcesnormal0.030.020.03(0.01, 0.05)dustlognormal-4.020.760.02(0.01, 0.07)biomass burninglognormal-3.620.810.03(0.01, 0.06)
para1: the mean for normal, the mean of lnx for lognormal, and the scale
parameter for gamma and Weibull distributions.
para2: the standard deviation for normal, the standard deviation of lnx for
lognormal, and the shape parameter for gamma and Weibull distributions.
Model description and settingsModel settings
To further evaluate the roles of S–IVOCs in SOA formation, the newly
developed S–IVOC emission inventory was incorporated into the WRF-Chem model
to simulate the formation of SOA and investigate its impact factors in the
PRD region. The WRF-Chem model (https://ruc.noaa.gov/wrf/wrf-chem/, last access: 8 May 2019) is a fully coupled online
meteorology–chemistry model that can be used to simulate physical and
chemical processes simultaneously
(Grell et al., 2005). This model has been widely used to simulate the formation of
secondary products (i.e., SOA and O3), including their relationship with
precursors, the influence of meteorological conditions, and contributions
from anthropogenic and biogenic emissions from regional to cloud resolving
scales
(Fast
et al., 2009, 2006; He et al., 2015; Jiang et al., 2012; Li et al., 2011;
Liu, 2014; Sharma et al., 2017; Tie et al., 2013).
The model configuration is presented in Table S1 in the Supplement, and the model domain is presented in Fig. 1. The simulation was
conducted from 12:00 UTC on 17 November 2008 to 00:00 UTC on 26 November 2008 because measured data of SOA were available from 19 to 25 November 2008 at the receptor site of the PRD region, i.e., the Wan Qing Sha (WQS) site.
During the simulation period, the first 24 h was consumed as the spin-up
time for the simulation. The initial meteorological field and boundary
meteorological conditions were provided by the ERA-Interim reanalysis
dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF)
with the resolution of 0.5∘×0.5∘, while the
chemical boundary condition was obtained from the Model for Ozone and
Related chemical Tracers (MOZART) global simulation of trace gases and
aerosols (Emmons et al.,
2010). The above initial field and boundary meteorological conditions have
been confirmed to be appropriate for the reproduction of observed
meteorological parameters in the PRD (Situ et
al., 2013).
The gas-phase chemistry mechanism used in the simulation was SAPRC-99
(Statewide Air Pollution Research Center), including 235 reactions of 80
gases (Carter, 2000). It should be noted
that the gas-phase photochemical oxidation of gas-phase organic species for
the formation of SOA, e.g., the gas-phase chemistry of BVOCs (biogenic VOCs)
such as isoprene, monoterpenes, and sesquiterpenes, has been recently updated
and incorporated in the mechanism (Situ
et al., 2013). In addition, the Model for Simulating Aerosol Interactions and
Chemistry (MOSAIC) aerosol chemistry mechanism coupled with two-species VBS
treatment was used to represent aerosol processes
(Zaveri et al., 2008). The MOSAIC scheme
includes aerosol species, such as sulfates, nitrates, ammonium salts, sodium
salts, chlorine salts, calcium salts and other inorganics (OIN), organic
carbon (OC), elemental carbon (EC), and water, but does not consider the
formation of SOA from organic vapors
(Fast
et al., 2009). Therefore, this mechanism was coupled with a simplified
two-species 1-D VBS (Sect. 2.2.2) developed by
Shrivastava et al. (2011) to
simulate the formation of OA.
Modeling domain and locations of Wan Qing Sha air quality
monitoring sites.
Anthropogenic emissions of PM10, PM2.5, VOCs, NOx, SO2, and
CO in the PRD region were derived from a highly resolved spatial
anthropogenic PRD regional emission inventory for the year 2010 with a
horizontal resolution of 3 km, whereas emissions outside the PRD region were
based on the Guangdong provincial emission inventory
(Zheng et al., 2010b). This
emission inventory was developed using the best available domestic emission
factors and activity data, including the sectors of industry, on-road and
off-road mobile sources, residential sources, dust, and biomass burning
(Zheng
et al., 2009). In our emission inventory, dust mainly includes road fugitive
dust and building construction dust. Particles containing many toxic metals
and organic contaminants such as PAHs and long-chain alkanes from various
sources (e.g., weathered materials of street surfaces, automobile exhaust,
lubricating oils, gasoline, diesel fuel, tire particles, construction
materials, and atmospherically deposited materials) can be deposited on roads
and construction sites, which are known as road fugitive dust and building
construction dust (Takada et al., 1990; Rogge et al., 1993; Chen et al.,
2012). Furthermore, dust is a large source of POA at urban locations, and
S–IVOCs are frequently co-emitted with POA (Zheng et al., 2012; Shrivastava
et al., 2013; van Drooge and Grimalt, 2015). Indeed, S–IVOCs, such as
n-alkanes (C19-C39) and PAHs, have been identified in dust samples,
confirming that dust could be a source of S–IVOCs (Takada et al., 1990;
Rogge et al., 1993; Schefuß et al., 2003; Dong and Lee, 2009). In
addition, BVOC emissions were derived from the Model of Emissions of Gases
and Aerosols from Nature (MEGAN, https://sites.google.com/uci.edu/bai, last access: 8 May 2019) developed by
Guenther
et al. (2012).
VBS approach
With the configuration mentioned above, the WRF-Chem model used in this
study provides a simplified and computationally efficient two-species 1-D VBS
scheme coupled with MOSAIC that includes V-SOA (SOA formed by the oxidation
of VOC-traditional SOA precursors emitted from varied anthropogenic and
biogenic sources) and SI-SOA (SOA formed by the oxidation of
S–IVOC-untraditional SOA precursors emitted from anthropogenic sources).
This scheme was simplified from the detailed nine-species VBS, a scheme with
more surrogate organic compounds categorized by different ranges of
volatility (Shrivastava et al.,
2011). The simplified two-species scheme categorized S–IVOCs into two
volatility species with effective saturation concentration C∗ equal to 0.01
and 105µg m-3 at 298 K and 1 atm. C∗ is the inverse of the
Pankow-type equilibrium partitioning coefficient, which describes the
fraction of gas and particle components in SOA formation. Note that gas-phase SVOCs and all IVOCs in this mechanism are represented by species with
C∗ equal to 105µg m-3, and IVOCs were considered to remain in
the gas phase before photochemical oxidation in the atmosphere. POA and
SI-SOA were assumed to be non-volatile in the model. POA is the remaining
aerosol component after the evaporation of gas-phase SVOCs. In this
mechanism, POA with SI-SOA are represented by species with C∗ equal to 0.01 µg m-3. This simplified two-species VBS has been confirmed to be more
suitable for computationally extensive models (i.e., WRF-Chem) for running
complex coupled cloud–aerosol–meteorology because of its similar predictions
of total OA mass, individual OA components, and evolution of organic
aerosols in addition to its reduction in computational cost by a factor of
2, compared to the detailed nine-species VBS
(Shrivastava et al., 2011).
In terms of the formation of SI-SOA in the two-species VBS, the primary
oxidation of S–IVOCs transformed the gas-phase high-volatility S–IVOCs
(C∗=105µg m-3) into the extremely low-volatility SI-SOAs
(C∗=0.01µg m-3) with the OH reaction rate constant (kOH) of
0.57×10-11 cm3 molecule-1 s-1 and oxygen
yield of 50 %. In order to align the SOA predictions between two-species and
nine-species VBS schemes, kOH in the two-species VBS had been reduced by a
factor of 7 (i.e., 0.57×10-11 cm3 molecule-1 s-1)
from that of the nine-species VBS (4×10-11 cm3 molecule-1 s-1) because the order of magnitude reduction in
volatility through one generation of oxidation in the two-species VBS was 7
times that in the nine-species VBS. Note that the kOH of 4×10-11 cm3 molecule-1 s-1 in the nine-species VBS was
assumed to be ∼50 % higher than that of a typical large
saturated n-alkane as suggested by previous studies
(Atkinson
and Arey, 2003; Robinson et al., 2007). The specific oxidation reaction
equations are as follows (the detailed description of the parameters in the
equations are provided in the Supplement):
4S/IVOCg2,e,c+OH→SI-SOAg1,e,c+0.5SI-SOAg1,e,o5S/IVOCg2,e,o+OH→SI-SOAg1,e,o+OH
where (g) denotes gas phase; the subscript “1” denotes the low-volatility
species (C∗=0.01µg m-3 at 298 K and 1 atm) and “2” denotes the
high-volatility species (C∗=105µg m-3 at 298 K and 1 atm);
e denotes the emission categories, including biomass burning and other
anthropogenic emissions; c denotes the non-oxygen (C, H, N) component of the
species; and o represents the oxygen component.
In addition, V-SOA formed by the oxidation of anthropogenic and biogenic
VOCs in this mechanism was considered using one-species treatment with the
configuration of the saturation concentration C∗ of V-SOA as 1 µg m-3 at 298 K and 1 atm. NOx-dependent fixed one-product yields for all
VOC precursors were proposed by
Shrivastava et al. (2011).
Gas-particle partitioning between the gas and aerosol phases of both SI-SOA
and V-SOA was calculated using the absorptive partitioning theory as
described by Donahue et al. (2006):
Ci,a=Ci,tot1+Ci∗/M,
where Ci,a denotes aerosol-phase SOA mass concentration at a
given volatility bin i (here, its bin boundaries are C∗ values of 0.01 and/or 105µg m-3); Ci,tot denotes the total mass
concentrations of gas- and aerosol-phase SOA for bin i;
Ci∗ denotes the saturation concentration for
bin i; and M denotes total mass concentrations of OA, which includes POA and SOA.
To calculate the influence of temperature on C∗, the Clausius–Clapeyron
equation was used:
Ci∗=Ci,0∗T0TexpΔHiR1T0-1T,
where Ci∗ and Ci,0∗ denote saturation concentration at T and T0 (reference temperature
298 K), respectively, for bin i; R is the universal gas constant; and
ΔHi denotes the enthalpy of vaporization for
bin i.
Model scenarios
In order to evaluate the roles of S–IVOCs in the formation of SOA over the
PRD region, 13 simulations were performed from 19 to 25 November 2008, including one control BASE simulation and 12 sensitivity CASE
simulations. Table 5 provides detailed descriptions on the base and
sensitivity scenarios. For the base scenario, the simulation was conducted
without the input of S–IVOC emissions. For CASE1, this simulation was
conducted with the input of S–IVOCs from all anthropogenic emissions
(Sect. 3) in order to estimate the contributions of S–IVOCs to the
formation of SOA.
A large uncertainty of 0.57×10-11 cm3 molecule-1
s-1 was found for the kOH of S–IVOC species in the two-species VBS
used in the current WRF-Chem model, which was calculated on the basis of the
kOH of the nine-species VBS that was assumed to be about 50 % higher than
that of a typical large saturated n-alkane
(Atkinson
and Arey, 2003; Robinson et al., 2007). In this study, the kOH of
S–IVOC species was updated according to the emission factors and kOH of
57 speciated IVOCs from the vehicular emission measurements
(Zhao
et al., 2015, 2016b) using the molar weighting method by the following
equation (Carter, 2000):
kOH=kOH,i×EFiEFtot,
where i denotes the specific S–IVOC species; tot denotes all S–IVOC species;
EF denotes the emission factor; and kOH denotes the OH reaction rate
constant. The kOH of S–IVOCs was calculated to be 3×10-11 cm3 molecule-1 s-1, which is smaller than the
original kOH of 4×10-11 cm3 molecule-1 s-1 in the model. Then, the reaction rate with OH radicals was reduced
to 0.42×10-11 cm3 molecule-1 s-1 by a factor
of 7 in order to ensure its applicability to the two-species VBS scheme, as
suggested in Sect. 2.2.2. To evaluate the effect of the OH reactivity of
S–IVOCs on the formation of SOA, CASE2 was conducted using the new updated
kOH with the input of the same S–IVOC emission as in CASE1.
For CASE3–6, the simulations were designed with varied amounts of S–IVOC
emissions at the 50 % and 95 % confidence intervals (Sect. 3.2) using
the new updated kOH of S–IVOCs in order to evaluate the sensitivity of
the SOA simulation to the magnitude of S–IVOC emissions and quantify the
uncertainty ranges in SOA prediction attributable to uncertainties of S–IVOC
emissions. Note that CASE3 and CASE6 were conducted with the lower and upper
limits of the uncertainty ranges of S–IVOC emissions estimated at the 95 %
confidence interval (which was 21 % and 329 % of the amounts in the
inventory as suggested in Sect. 3.2) as presented in Table 4, whereas
CASE4 and CASE5 were conducted with the edges of the uncertainty ranges of
S–IVOC emissions estimated at the 50 % confidence interval (45 % and
127 % of the amounts in the inventory as suggested in Sect. 3.2).
Furthermore, CASES7–12 were simulated using the new updated kOH of
S–IVOCs with only the input of individual S–IVOC emissions, i.e., biomass
burning, dust, industry, off-road mobile, on-road mobile, and residential
sources, to quantify the contributions of each S–IVOC emission to the
formation of SOA.
Source-specific emissions in each city for the year 2010.
S–IVOC emission inventory in the PRD region for the year
2010.
SourceS–IVOC emissionContribution(Gg yr)(%)Industry114.635.4Residential sources8.42.6On-road mobile sources134.441.6Off-road mobile sources4.81.5Dust46.814.5Biomass burning14.44.5Total323.4100.0
Uncertainty assessment of the S–IVOC emission inventory
for the year 2010.
Using the parameterization method described in Eq. (3), hourly gridded
S–IVOC emissions in the PRD region for the year 2010 with a resolution of 3 km × 3 km were estimated with parameters given in Table 2 and the
high-resolution PM2.5 emission inventory
(Zheng et al., 2010b). As
shown in Table 3, the total S–IVOC emissions in the PRD region are 323.4 Gg in
2010, of which on-road mobile sources contribute about 41.6 % (134.4 Gg),
industry about 35.4 % (114.6 Gg), dust about 14.5 % (46.8 Gg), biomass
burning about 4.5 % (14.4 Gg), residential sources about 2.6 % (8.4 Gg),
and off-road mobile sources about 1.5 % (4.8 Gg). Regarding city-level
contributions, Guangzhou was the largest contributor to S–IVOC emissions
with a contribution of 23.9 %, followed by Foshan (18.4 %), Shenzhen
(15.1 %), Jiangmen (11.9 %), and Dongguan (11.7 %) as shown in Fig. 2.
Notably, as expected, on-road mobile sources and industry, which involve
large amounts of vehicles and industrial plants, were the top two
contributors in all cities, except for Zhongshan, where the contribution of
dust to total S–IVOC emissions was higher than industry because of the
accelerating urbanization with frequent urban constructions but much fewer
industrial plants than in Guangzhou, Foshan, Dongguan, and Shenzhen
(Pan et al., 2015; Yin et al., 2015;
GSY, 2010). It was also of interest to find that the magnitudes of S–IVOC
emissions from dust and industry in Zhaoqing and Shenzhen were similar, but
the contributions were different. Dust contributed about 21.1 % (3.6 Gg)
and 7.4 % (3.6 Gg) to the S–IVOC emissions in Zhaoqing and Shenzhen, and
industry contributed about 22.8 % (3.9 Gg) and 8.8 % (4.3 Gg),
respectively. The contributions of dust and industry to S–IVOC emissions in
Shenzhen were smaller than those in Zhaoqing, attributable to the dominance
of on-road mobile S–IVOC emissions in Shenzhen (81.3 %, 39.6 Gg) because
of the dense traffic (Pan et al., 2015). As Shenzhen and Zhaoqing have much
fewer industrial point sources than cities located in the southeastern PRD
such as southern Guangzhou and Foshan (Pan et al., 2015), their
corresponding industrial S–IVOC emissions were also less. There were
relatively higher S–IVOC emissions from road fugitive dust and lower
emissions from building construction dust in Zhaoqing than in Shenzhen
because of shorter road lengths and more developed construction industries
in Shenzhen (GSY, 2011; Peng et al., 2013), resulting in similar magnitudes
of S–IVOC emissions from dust in these two cities.
Spatial distribution of S–IVOC emissions from different
source categories for the year 2010: (a) biomass burning, (b) dust, (c) industry, (d) off-road mobile sources, (e) on-road mobile sources, (f) residential sources.
Spatial distribution of total S–IVOC emissions for the
year 2010.
Figure 3a–f show the spatial distributions of S–IVOCs emitted from different
sectors for the year 2010. In general, the spatial characteristics of S–IVOC
emissions in 2010 (Fig. 4) were consistent with the distribution of on-road
mobile and industrial emissions (Fig. 3), the top two S–IVOC contributors in
this region. Furthermore, the spatial distributions of total S–IVOC
emissions agreed well with the road network with the high S–IVOC emissions
located in central cities including Guangzhou, Foshan, Dongguan, and
Shenzhen. Large amounts of emissions from biomass burning were found in
Zhaoqing, Jiangmen, and Huizhou, which are characterized by extensive
combustion of household firewood and straw associated with the large rural
populations (Fig. 3a), contributing nearly 43 % to total rural populations
in the PRD region in 2010 (GSY,
2011; Yuan et al., 2010). In contrast, high S–IVOC emissions from dust were
mainly concentrated in Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan,
associated with heavy traffic flows and frequent urban constructions
because of preparation for the 2010 Asian Games and accelerating
urbanization processes in recent years (Fig. 3b). The high industrial
emissions of S–IVOCs were mainly concentrated in Foshan, Dongguan,
Zhongshan, and Guangzhou, where numerous industrial point sources and power
plants are located (Fig. 3c). The spatial distributions of S–IVOCs emitted from
on-road mobile sources were very consistent with the patterns of road
networks in the PRD region. The emissions were concentrated in central
economically developed cities with large numbers of vehicles (Fig. 3e).
Compared with the abovementioned sectors, S–IVOC emissions from residential
and off-road mobile sources in the PRD region were lower (Fig. 3d and f).
Nevertheless, the total S–IVOC emissions (323.4 Gg) were only a quarter of
total VOC emission (1224.5 Gg) in the PRD region in 2010 (Fig. 5), but they were more than 6 times the total OC emission (52.9 Gg). Moreover, the
contributions of different sectors varied in different emission inventories.
For example, biogenic and solvent use sources contributed to the
overall VOC emissions by 45 % in total but did not contribute to emissions of
S–IVOCs, PM2.5, and OC. The contribution of biomass burning (4 %,
14.4 Gg) to S–IVOCs was much smaller than that to OC (24 %, 12.9 Gg)
because the emission ratio of IVOCs to POA for biomass burning is much
smaller than that of other sectors. Industrial sources contributed less to
S–IVOC emissions than to PM2.5 with contributions of 35 % and 52 %,
respectively, while on-road mobile sources contributed more to S–IVOC emissions
(42 %, 134.4 Gg) as the fraction of OC in PM2.5 (FOC) in
on-road mobile emissions was higher than that in industrial emissions, when
other parameters in the emission model for these two sectors were similar
(Table 2).
Comparisons with emissions of other pollutants in the PRD
region for the year 2010.
Uncertainties in S–IVOC emissions
An uncertainty assessment of the 2010 PRD regional S–IVOC emission inventory
together with a sensitivity analysis based on the Monte Carlo simulation and
sample correlation coefficient method
(Zheng et al.,
2010a) were performed to determine the ranges of uncertainties and identify
the key sources of uncertainties in S–IVOC emission estimates. Table 4 lists
estimated ranges of uncertainties and associated correlation coefficients
with estimated total S–IVOC emissions for the different parameters used in
calculating the total S–IVOC emissions in different source categories. As
shown in Table 4, the uncertainty in the total S–IVOC emissions was very
high with a relative error of -79 %–229 % at the 95 % confidence
interval, which could be mainly attributed to uncertainties in the S–IVOC
emissions of the on-road mobile sources because of the largest correlation
coefficient of the on-road mobile S–IVOC emissions with total S–IVOC
emissions among all the source categories. It is noteworthy that the
uncertainty ranges of the emission inventories of S–IVOCs were wider than
those of VOCs and PM2.5, which were only -6 %–99 % and -6 %–77 %,
respectively
(Zhong et al.,
2018). For input parameters in the emission model, the correlation
coefficients between total S–IVOC emissions and FOC for the on-road
mobile sources or ratios of EIVOCs/EPOA for all source categories,
except biomass burning, were very large, indicating that these parameters
were the key sources of high uncertainties in the S–IVOC emission estimates.
It should be noted that the actual uncertainties in S–IVOC emission
estimates should be larger because the same ratios of EIVOCs/EPOA
and ESVOCs/EPOA were used for all source categories, except
biomass burning, which was only based on measurements of vehicular
emission. These results indicated that more measurement of FOC from
on-road mobile emission and source-specific measurements of
EIVOCs/EPOA and ESVOCs/EPOA is key to reducing
uncertainties in S–IVOC emission estimates.
Comparisons of SOA between simulations and observations at
the WQS monitoring site: (a) time series; (b) ratios of temporal average SOA
concentration to observed SOA concentration during the study period (the box
represents the uncertainty range in SOA prediction, the central line is the
ratio in CASE2, and the edges of the box are the ratios in CASE4 and CASE5, the
edges of the whiskers are the ratios in CASE3 and CASE6).
Comparison with other emission inventories
To indicate the differences in S–IVOC emission inventories developed using
different methods, the S–IVOC emission inventory developed in this study was
further preliminarily compared to recently proposed global emissions of PAHs
by Shen et al. (2013) (detailed data for the emission inventory are provided in Table S2 in
the Supplement) because PAHs are important components of
S–IVOCs. In this study, emissions of five PAHs including NAP (naphthalene),
ACY (acenaphthylene), FLO (fluorene), PHE (phenanthrene), and PYR (pyrene)
with high fractions in total PAH emissions were selected for comparison with
corresponding PAH emissions of
Shen et al. (2013)
for the year 2010 in the PRD region. Note that the emission of individual
PAHs in all source categories in this study was extracted from the total
IVOC emission using the ratio of specific individual PAHs to total IVOCs from
vehicular emission measurements
(Zhao
et al., 2015, 2016b). Large deviations were found for emissions of the
abovementioned PAHs between the present and previous studies, especially for
NAP (Fig. S1 in the Supplement). For example, NAP emissions were larger with more distinct
spatial characteristics in this study than in Shen's inventory over most of
the PRD region. The characteristics of road networks were also observed for
the spatial distribution of NAP emission in the present study, which was not
reflected in Shen's inventory. Furthermore, the total emissions of the five
selected PAHs over the PRD in this study were about 3.4 times those in Shen's
inventory, with multiples ranging from 1.2 to 13.4 in nine individual cities
of the PRD (Table S2). The discrepancies in PAH emissions in different studies
can be mainly attributed to the following factors: (1) differences in the
resolution of emission inventories. For example, the spatial resolution of
the emission inventory in this study was 3 km × 3 km, which is much
higher than that of the previous study (0.1∘×0.1∘). (2) The second factor is differences in the parameterization methods for
developing different inventories. For example, emission factors of PAHs in
the present study were calculated on the basis of those of IVOCs using the
ratio of specific individual PAH to total IVOCs, wherein the emission
factors of IVOCs were obtained from those of existing traditional POAs using
source-specific linear scaling factors. However, emission factors of PAHs in
Shen's study were directly obtained from actual measurements from various
reports, which were further calculated to be time-specific based on the
regression model and technology splitting approach. Nevertheless, by
considering the uncertainties of different inventories (i.e., -55 %–27 % and
-34 %–62 % at the 50 % confidence interval for emission inventories of
the present and previous studies, respectively), it is reasonable to
conclude that the emissions of selected PAHs between the two studies are
comparable. Moreover, further investigation revealed that the spatial
variations in PAH emissions in this study may be more reasonable than those
in Shen's inventory. For example, high centers of PAH emissions in Shen's
inventory were located only in Guangzhou and Shenzhen. Conversely, in
this study, high PAH emissions were found in central cities including
Guangzhou, Foshan, Shenzhen, and Dongguan, which have dense traffic and
population. This result is consistent with the result that traffic was
frequently found to be one of the most important sources of PAH emissions
(Riva et al., 2017).
Relative difference of SOA between CASE runs and BASE: (a) CASE1, (b) CASE2, (c) CASE3, (d) CASE4, (e) CASE5, (f) CASE6.
The simulation results of SOA formationEffects of S–IVOCs on SOA concentration
In this study, daily measured concentrations of SOA at the WQS site in
Guangzhou, a receptor site of the PRD region during autumn and winter, were used to evaluate the model performance for the simulation of
SOA
(Ding
et al., 2012). The monitoring data of this site could represent the regional
air pollution in the PRD because it is surrounded by farmland and not much
traffic with flat terrain
(Ding
et al., 2012). The time series of SOA for BASE, CASE1, CASE2, and
observations during the study period is plotted in Fig. 6a. Both BASE and
CASE simulations reproduced the day-to-day variations in SOA well, although
the model could not capture the observed high concentrations of SOA. Another
remarkable feature in Fig. 6a is that the predicted concentrations of SOA
became much closer to the observed values after the S–IVOC emissions were
considered, with the discrepancy between simulations and observations
decreasing from -9.15 to -6.39µg m-3 for CASE1
(30 % decrease, p<0.01). Moreover, the performance of SOA
simulation was improved by 196 % for CASE1 compared to BASE. The ratios of
predicted SOA to observed SOA in CASE1–2 and BASE runs are presented in Fig. 6b. The model could resolve 39 % of the observed SOA with an increase of
26 % compared to BASE when the S–IVOC emissions were included and the
original kOH of S–IVOCs was used. Figure 7a shows the relative
variations in SOA between CASE1 and BASE in the whole modeling domain. An
obvious increase of 40 %–375 % in SOA is found over the PRD region with an
average regional increase of 161 % when S–IVOC emissions were incorporated
into the model. The most remarkable increase patterns are found in the
cities of Foshan, Shenzhen, Dongguan, and Jiangmen, with the increments
ranging from 240 % to 375 %. This is consistent with the spatial SI-SOA
in Fig. 8 and can probably be attributed to the high anthropogenic S–IVOC
emissions in these cities (Fig. 4). Furthermore, a substantial increase in
SOA was found in the southwest downwind area of the PRD region with
increments of 240 %–325 % attributable to the influences of both local
pollutants and pollutants transported from the upwind area because the
dominant wind direction over the PRD region was northeasterly during the
pollution period (Fig. 8). Notably, high increasing ratios of SOA
concentrations in Guangzhou only appeared in a small southwestern part of
Guangzhou, probably because high S–IVOC emissions in Guangzhou mainly
resulted in considerable SOA growth in downwind areas, especially Foshan,
which lies to the southwest of Guangzhou. Nevertheless, the above results
demonstrated that S–IVOCs are significant precursors for forming SOA, and
the model performance for SOA formation could be improved significantly if
S–IVOC emissions were considered. Therefore, in addition to traditional VOCs,
S–IVOC emissions should be included in CTMs to achieve accurate modeling of
the formation of SOA and regional air quality.
In contrast, the predicted SOA in CASE2 decreased by 14 % compared to
CASE1 after the kOH of S–IVOCs was improved. Moreover, the model could
resolve 34 % of the observed SOA at the WQS site, which is smaller than
the resolved fraction of 39 % in CASE1 (Fig. 6a–b). The average regional
increase ratio of SOA decreased to 126 % in CASE2 with the newly updated
decreased kOH of S–IVOCs and the same S–IVOC emissions as in CASE1 (Fig. 7b). This suggests that the decreased OH reactivity coefficient indeed
decreased the formation rate of SOA, and a more precise OH reactivity is
required for the model to better simulate SOA.
Spatial distribution of temporal average (a) SOA and (b) SI-SOA during the study period over the modeling domain in the CASE2 run.
Overview of simulations.
TestS–IVOC emissionkOH of S–IVOCsNotesnameinventory(cm3 molecule-1 s-1)BASENo S–IVOC emissionsCASE1All anthropogenic S–IVOC emissions0.57×10-11To evaluate the effect of kOH on theCASE2All anthropogenic S–IVOC emissions0.42×10-11formation of SOA.CASE321 % of the S–IVOC emissions in CASE20.42×10-11To evaluate the sensitivity of SOACASE445 % of the S–IVOC emissions in CASE20.42×10-11simulation to S–IVOC emission.CASE51.27 times the S–IVOC emissions in CASE20.42×10-11CASE63.29 times the S–IVOC emissions in CASE20.42×10-11CASE7Only S–IVOC emissions from biomass burning0.42×10-11CASE8Only S–IVOC emissions from dust0.42×10-11To quantify the contributions of S–IVOCsCASE9Only industrial S–IVOC emissions0.42×10-11emitted from different source categoriesCASE10Only off-road mobile S–IVOC emissions0.42×10-11to the formation of SOA.CASE11Only on-road mobile S–IVOC emissions0.42×10-11CASE12Only residential S–IVOC emissions0.42×10-11
CASE3–6 were simulated with the input of varied amounts of S–IVOC emissions
on the basis of the uncertainty ranges of the estimates of S–IVOC emissions
(Table 5 and Sect. 2.2.3). The uncertainty ranges of the ratios of
predicted SOA concentrations to observed ones, attributable to uncertainties
in S–IVOC emissions, are presented as an error bar in Fig. 6b. As expected,
the ratios of temporal average simulated SOA to observed SOA at the WQS site
during the study period varied from 18 % to 77 % after taking the
uncertainties of S–IVOC emissions into account. Figure 7c–d show minor
increases in SOA with the input of lower S–IVOC emissions for CASE3 and
CASE4, compared to CASE1, with average regional increases of 27 % and
57 %, respectively. Figure 7e–f show larger increases in SOA with the
input of higher S–IVOC emissions for CASE5 and CASE6, with average regional
increases of 158 % and 395 %, respectively. The results suggest that SOA
is strongly sensitive to the amounts of S–IVOC emissions. Consequently, it
is of great importance to reduce the uncertainties in the S–IVOC emission
inventory to achieve accurate simulations of SOA.
Relative difference of SOA between CASE runs and BASE: (a) CASE7, (b) CASE8, (c) CASE9, (d) CASE10, (e) CASE11, (f) CASE12.
Key anthropogenic S–IVOCs for SOA formation
Six simulations including CASE7–12 were conducted with only the input of
S–IVOC emissions from individual source categories in order to identify the
key anthropogenic source of S–IVOCs to form SOA, as described in Table 5.
The spatial distributions of the relative differences of predicted SOA
concentrations between CASE simulations and BASE are presented in Fig. 9.
The increasing ratio of SOA in CASE9 was found to vary in the range of
5 %–190 % over the PRD region with an average increase of 52 % when the
industrial S–IVOC emission was incorporated into the model. The center of
increasing SOA was located in Foshan, which is an industrially developed
city (Fig. 9c). After including on-road mobile S–IVOC emissions in the
model, the predicted SOA was increased by 5 %–180 % with an average
regional increase of 43 % (Fig. 9e), and high amounts were detected over
central cities including Shenzhen, Guangzhou, Foshan, and Zhongshan, which
feature a high rate of vehicle ownership, contributing to 71 % of total
vehicle ownership in the PRD region (GSY, 2011). After considering S–IVOCs
emitted from dust, the average regional increase ratio of SOA was 18 %
(Fig. 9b), and the centers were located in Guangzhou, Foshan, Zhongshan,
Dongguan, and Shenzhen. These cities have high traffic flows and frequent
urban constructions, and their vehicle ownerships and floor space of
buildings under construction contributed to ∼86 % and
∼81 % of those in the PRD region
(Pan et al., 2015; GSY,
2011). With the input of S–IVOC emissions from biomass burning, the average
regional increasing ratio of SOA was up to 8 % (Fig. 9a), and high values
were mainly distributed in Zhaoqing. This city has expansive agricultural
areas and large rural populations, accounting for ∼31 % and
∼15 %, respectively, of the total in the PRD
(Yang et al., 2013;
Pan et al., 2015; GSY, 2011). Nevertheless, the average regional SOA
increased by only 2 % and 4 % with the input of S–IVOCs emitted from
off-road mobile and residential sources, respectively (Fig. 9d and f).
Notably, similar high centers of increasing SOA and S–IVOC emissions could
be found in Figs. 9 and 3, respectively, for six specific sectors,
indicating that the increment in SOA concentrations was highly correlated
with the input of S–IVOC emissions. Overall, the industry and on-road mobile
sources were the main anthropogenic sources of S–IVOCs contributing to the
formation of SOA in the PRD region, followed by dust, biomass burning,
residential, and off-road mobile sources. However, it was of interest to
find that though the emission strength of on-road mobile S–IVOCs was
stronger than that of industrial S–IVOCs in the PRD, the contribution of
industry to SOA formation was higher than on-road mobile sources. This is
related to different transport patterns in varied simulations with the input
of S–IVOC emissions from different source categories. For example, high
industrial S–IVOC emissions outside the PRD would induce considerable SOA growth
downwind inside the PRD; however, high on-road mobile S–IVOC emissions in
coastal cities such as Shenzhen would bring the SOA growth to the South
China Sea, resulting in a loss of SOA inside the PRD.
Discussion and conclusions
In this study, a highly resolved gridded emission inventory of S–IVOCs for
the PRD region in 2010 was developed. The estimates showed that total S–IVOC
emission in the PRD region for the year 2010 was 323.4 Gg, 77 % of which
could be attributed to on-road mobile and industrial sources. Large
uncertainties were still observed in S–IVOC emission estimates, with a
relative error ranging from -79 % to 229 %. These uncertainties could be
attributed to the FOC of the on-road mobile source and
EIVOCs/EPOA ratio of all source categories, except biomass
burning. Therefore, these parameters should be prioritized in further
experimental studies in order to improve future S–IVOC emission inventories.
Moreover, 13 simulations using the WRF-Chem model were conducted to
investigate the effects of S–IVOCs on SOA and identify the key anthropogenic
source of S–IVOCs contributing to SOA formation over the PRD region. The
analysis of the simulation results indicated that the performance of SOA
simulation was greatly improved after considering the reaction pathway
producing SI-SOA from S–IVOCs. S–IVOCs could result in considerable SOA
growth, and the kOH of S–IVOCs had a non-negligible effect on the
production of SI-SOA. After considering the uncertainties of S–IVOC
emissions, the model could resolve 18 %–77 % of observed SOA
concentrations at the WQS site. These indicate the need for more experimental
data of kOH for S–IVOCs to reduce the uncertainties of this parameter
within the model, and reduction of uncertainties of S–IVOC emissions to
achieve more accurate simulation of SOA formation. In addition, the
industrial and on-road mobile sources were the top two important
anthropogenic sources of S–IVOCs contributing to SOA formation, followed by
dust, biomass burning, residential, and off-road mobile sources.
Although the performance of the model in simulating SOA could be
significantly improved, many issues still remain to be resolved. The
observed SOA concentrations could not be accurately reproduced in the
present study, especially for high SOA concentrations (Fig. 6a). We inferred
that the incomplete and inaccurate formation mechanism of SOA and large
unresolved uncertainties in the S–IVOC emission inventory were the main
reasons for the underestimation of SOA concentrations in the simulation. For
example, large uncertainties still remained within source-specific and
season-specific S–IVOC emissions, reaction rates of S–IVOCs, and SOA yields
of VOCs and S–IVOCs. Furthermore, specific profiles of S–IVOCs were lacking.
The approach including the distribution of S–IVOC emissions was based on
inadequate data from domestic and foreign studies without sufficient
localization in the PRD region, which further included large uncertainties.
Furthermore, the assumption that SVOC emissions were included in POA
emissions was not sufficiently constrained because of the limited
observation data of hydrocarbon-like organic aerosol (HOA) and biomass burning organic aerosol (BBOA). Therefore, much more local experimental
work is needed to quantify all the abovementioned parameters in the future.
In addition, the introduction of complete and complex physical and chemical
processes of SOA formation, e.g., gas- and aqueous-phase oxidation,
heterogeneous and accretion reactions, acid catalysis reactions of SOA from
glyoxal, and chemical aging of SOA, may be useful in estimating SOA
concentrations more accurately, although it will increase experimentation
costs and introduce larger uncertainties
(Carlton
et al., 2008; Denkenberger et al., 2007; George and Abbatt, 2010; Hallquist
et al., 2009; Kroll and Seinfeld, 2008; Liggio et al., 2005; Pun and
Seigneur, 2007; Washenfelder et al., 2011). For example,
Dzepina et
al. (2011) found that including chemical aging of V-SOA resulted in larger
regional overprediction of SOA, whereas
Ahmadov et al. (2012)
reported a good agreement with observations after considering it.
Shrivastava et al. (2011)
pointed out that aging parameterization based on smog chamber measurements
involves large uncertainties because the timescales of photochemical ages
are longer than that accessible in chambers.
Shrivastava et al. (2015) also pointed out that neglecting fragmentation reactions in aging
parameterizations leads to large model overpredictions of SOA concentrations
at all surface sites. Therefore, we plan to test more chemical processes
that have not yet been considered in the WRF-Chem model and introduce the
parameters required for establishing the S–IVOC emission inventory and model
parameterization with fewer uncertainties based on more local experimental
work in the future. Furthermore, we plan to build the S–IVOC emission
inventory based on ample local directly measured S–IVOC emission factors and
volatility distribution factors of POA in future work instead of scaling
POA emission factors.
Data availability
The underlying research data and the newly developed emission inventory of
S–IVOCs in this study are available to the community and can be accessed by
request to Xuemei Wang (eciwxm@jnu.edu.cn) of Jinan University.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8141-2019-supplement.
Author contributions
In this study, the analysis methods were developed and the whole structure
for the paper was designed by ZL and XW. LW conducted the data processing and wrote the original version of the
paper. SL and MS provided the related data and
made revisions of the paper. Furthermore, the simulations using the
WRF-Chem model were designed and conducted by LW. Finally, the
paper was finalized by ZL and XW.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
The authors thank Junyu Zheng of Jinan University for providing the Guangdong emission inventory, and
Xiang Ding of the Guangzhou Institute of Geochemistry, Chinese Academy of
Sciences for providing measurement data of SOA at the WQS site.
Financial support
This research has been supported by the State Key Program of the National Natural Science Foundation of China (grant no. 91644215), the National Key Research and Development Program of China (grant nos. 2017YFC0210106 and 2016YFC0202206), and the National Nature Science Fund for Distinguished Young Scholars (grant no. 41425020). This research has also been partly supported by the Pearl River Science & Technology Nova Program of Guangzhou (grant no. 201806010146).
Review statement
This paper was edited by Jianmin Chen and reviewed by two anonymous referees.
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