ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-13973-2015Formation of secondary organic aerosol in the Paris pollution plume and its impact on surrounding regionsZhangQ. J.qzhang@aria.frBeekmannM.FreneyE.https://orcid.org/0000-0002-9363-9115SellegriK.PichonJ. M.SchwarzenboeckA.ColombA.https://orcid.org/0000-0002-2595-3911BourrianneT.MichoudV.BorbonA.Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA/IPSL),
UMR CNRS 7583, Université Paris Est Créteil (UPEC) and Université Paris Diderot (UPD), Paris, FranceLaboratoire de Météorologie Physique, Clermont-Ferrand, FranceCentre National de Recherches Météorologiques, Météo-France, Toulouse, URA1357, Francenow at: Aria Technologies, Boulogne-Billancourt, Francenow at: Mines Douai, SAGE, 59508 Douai, FranceQ. J. Zhang (qzhang@aria.fr)18December20151524139731399219January201517March20156September201516November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/13973/2015/acp-15-13973-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/13973/2015/acp-15-13973-2015.pdf
Secondary pollutants such as ozone, secondary inorganic aerosol,
and secondary organic aerosol formed in the plumes of megacities can affect
regional air quality. In the framework of the FP7/EU MEGAPOLI (Megacities: Emissions, urban, regional and Global
Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) project, an
intensive campaign was launched in the greater Paris region in July 2009.
The major objective was to quantify different sources of organic aerosol
(OA) within a megacity and in its plume. In this study, we use airborne
measurements aboard the French ATR-42 aircraft to evaluate the regional
chemistry-transport model CHIMERE within and downwind of the Paris region. Two
mechanisms of secondary OA (SOA) formation are used, both including SOA
formation from oxidation and chemical aging of primary semivolatile and
intermediate volatility organic compounds (SI-SOA) in the volatility basis set (VBS) framework.
As for SOA formed from traditional VOC (volatile organic compound) precursors (traditional SOA), one
applies chemical aging in the VBS framework adopting different SOA yields
for high- and low-NOx environments, while another applies a single-step
oxidation scheme without chemical aging. Two emission inventories are used
for discussion of emission uncertainties. The slopes of the airborne OA levels
versus Ox (i.e., O3+ NO2) show SOA formation normalized with
respect to photochemical activity and are used for specific evaluation of
the OA scheme in the model. The simulated slopes were overestimated slightly
by factors of 1.1, 1.7 and 1.3 with respect to those observed for the three
airborne measurements, when the most realistic “high-NOx” yields for
traditional SOA formation in the VBS scheme are used in the model. In
addition, these slopes are relatively stable from one day to another, which
suggests that they are characteristic for the given megacity plume
environment. The configuration with increased primary organic
aerosol (POA) emissions and with a
single-step oxidation scheme of traditional SOA also agrees with
the OA / Ox slopes (about ±50 % with respect to the observed
ones); however, it underestimates the background. Both configurations are coherent
with observed OA plume buildup, but they show very different SI-SOA and
traditional anthropogenic SOA (ASOA) contributions. It is hence concluded
that available theoretical knowledge and available data in this study are
not sufficient to discern the relative contributions of different types of
anthropogenic SOA in the Paris pollution plume, while its sum is correctly
simulated. Based on these simulations, for specific plumes, the
anthropogenic OA buildup can reach between 8 and 10 µgm-3. For
the average of the month of July 2009, maximum OA increases due to emissions
from the Paris agglomeration are noticed close to the agglomeration at
various length scales: several tens (for primary OA) to hundreds (for SI-SOA
and ASOA) of kilometers from the Paris agglomeration. In addition, BSOA
(SOA formed from biogenic VOC precursors) is an important contributor to
regional OA levels (inside and outside the Paris plume).
Introduction
The number of large agglomerations (“megacities”) is increasing due to
population clustering in urban regions (UN, 2014). Human activities in the
megacities cause important negative effects on air quality (Gurjar et al.,
2010). Pollutants like ozone and fine particulate matter (PM2.5) have
recently been the focus of several studies as a result of concerns for human
health, impacts on ecosystem (Seinfeld and Pandis, 2006) and climate (IPCC,
2013). Due to their lifetime (several days to weeks), PM2.5 and ozone
have impacts at both the local and regional level. Therefore, adequate
emission control strategies for air quality management need to take into
account impacts on different scales.
Ratios of OOA vs. Ox from studies in Mexico City, Houston, Los
Angeles, Tokyo and Paris. Ratios for Houston, Los Angeles and Tokyo are
derived from ground-based measurements during typically 1 month and
located in the metropolitan area. For Houston, the ratio derived during influences
from a combination of urban and petrochemical emissions, typically
0.03 µgm-3ppb-1 (Wood et al., 2010), is presented. Ratios for
Paris and Mexico City are derived from three and two individual flights,
respectively, performed at about 100–150 km downwind from the
agglomeration.
Photochemical ozone formation is related to precursor molecules: nitrogen
oxides (NOx), and volatile organic compound (VOC) species, emitted
mainly from human activities – such as traffic, industrial production and
solvent use – as well as from biogenic emissions. In large European
agglomerations, a VOC-limited chemical regime is in general realized
(Beekmann and Vautard, 2010), where ozone production is directly related
to that of VOC precursors. Secondary aerosol formation is induced by
formation of condensable or semivolatile species from precursors like
NOx, SO2, NH3 and VOCs (Seinfeld and Pandis, 2006). Due to the
large number of chemical reactions occurring in different phases, secondary
organic aerosol (SOA) formation pathways are still uncertain (e.g., Hallquist
et al., 2009), its formation is still difficult to estimate quantitatively
(e.g., Hodzic et al., 2010; Zhang et al., 2013), and the contribution of
anthropogenic versus biogenic sources are still under debate (e.g., Hallquist
et al., 2009; Beekmann et al., 2015). Also, the relative contribution of SOA
from traditional anthropogenic VOC precursors (ASOA) and from semivolatile
(SVOC) or intermediate volatility (IVOC) organic compounds (SI-SOA) is still
under debate and difficult to constrain from field data (as for example
discussed in Hayes et al., 2015, for the case of Los Angeles).
Field data provide strong constraints on SOA-related processes. In
particular, the relation between SOA and Ox (O3+NO2) has
been used to express SOA formation as a function of photochemical products
formation (Herndon et al., 2008; Wood et al., 2010; Hayes et al., 2013;
Morino et al., 2014). Indeed, in a “VOC-limited regime” in an urban area,
such as Paris, VOC oxidation by OH, ozone or NO3 is the rate limiting
step for both SOA and ozone or Ox production.
VOC+OH→…→…→αOx+βSOA+…
The ratio or slope of SOA vs. Ox, given by the term β/α
represents the ratio of the photochemical production of SOA to the
photochemical production of Ox, both from VOC oxidation, that is, the
SOA yield normalized by current photochemical conditions characterized by
the availability of VOC precursors and oxidant agents. It is expected to
vary for different VOC species, in particular as a function of their SOA
yields, which are for instance large for aromatics and terpenes while low
for alkanes and alkenes (Wood et al., 2010). This is reflected in larger SOA
vs. Ox slopes observed in Mexico City (typically 0.16 µgm-3ppb-1),
Los Angeles (0.15 µgm-3ppb-1;
Hayes et al., 2013) and Tokyo (0.19 µgm-3ppb-1; Morino
et al., 2014), where aromatic emissions are large, than in Houston (typically
0.03 µgm-3ppb-1) where petrochemical alkene emissions are
large (Wood et al., 2010) (Fig. 1).
The main objective of the MEGAPOLI (Megacities: Emissions, urban, regional and
Global Atmospheric POLlution and climate effects, and Integrated tools
for assessment and mitigation) Paris campaign in summer 2009 was to
determine organic aerosol sources in a post-industrial megacity and in its
plume. In this work, we apply the regional chemistry transport model (CTM)
CHIMERE (Menut et al., 2013) in order to evaluate the model performance
against airborne measurements especially for organic aerosol and to assess
the impact of Paris agglomeration emissions on OA (organic aerosol) formation in surrounding
regions.
Different configurations of the SOA formation schemes have been implemented
into CHIMERE, in particular the volatility basis set (VBS) approach
(Robinson et al., 2007; Donahue et al., 2006; Murphy and Pandis, 2009; Lane et
al., 2008a). Based on ground level evaluation with data from the MEGAPOLI
summer campaign, Zhang et al. (2013) show a better agreement with OA
measurements when taking into account the volatility of primary organic
aerosol (POA), the existence of additional IVOC, and
as the chemical aging of the semivolatile VOC from anthropogenic and
biogenic origin. However, SOA was overestimated during long-range-transport
episodes of polluted plumes to the Paris agglomeration. In addition,
uncertainties of POA (or SI-VOCs) emissions were made also evident and estimated
as at least a factor of 3 (Zhang et al., 2013).
Comparison of measured (a1, a2, a3) and modeled BC from VBS-LA (b1, b2, b3) and VBS-LNOX (c1, c2, c3) during the flights on 16, 21
and 29 July, respectively.
Comparison of measured (a1, a2, a3) and modeled NOx
from VBS-LA (b1, b2, b3) and VBS-LNOX (c1, c2, c3) during the flights on 16,
21 and 29 July, respectively.
Comparison of measured (a1, a2, a3) and modeled O3 from VBS-LA (b1, b2, b3)
and VBS-LNOX (c1, c2, c3) during the flights on 16,
21 and 29 July, respectively.
Comparison of measured (a1, a2, a3) and modeled OA from VBS-LA (b1, b2, b3), VBS-LNOX (c1, c2, c3) and VBS-HNOX (d1, d2, d3) during the flights
on 16, 21 and 29 July, respectively.
For megacities, sources of organic aerosol are still under debate and need
to be quantified (e.g., Molina et al., 2010). While in Beekmann et al. (2015), the local versus advected and the fossil versus non-fossil nature of
OA sources within the agglomeration is analyzed, here we focus on additional
OA buildup in the agglomeration plume and on its impact on aerosol
concentrations in the surrounding of Paris. In the framework of the MEGAPOLI
project, airborne measurements were performed with the French ATR-42
aircraft operated by SAFIRE (a CNRS-MétéoFrance–CNES-headed
unit) in order to document the evolution of pollutants within the Paris
agglomeration pollution plume during the MEGAPOLI summer campaign (Freney et
al., 2014). The advantage of the airborne measurements over the ground-based
ones is that they allow following the evolution of the city plume over time and space up to
200 km downwind of the emissions. Data from these flights will be used to
extend the model evaluation performed in Zhang et al. (2013) for urban and
suburban sites in the Paris agglomeration to plume conditions. The focus is to
monitor the buildup of secondary organic aerosol within the plume in
relation with tracers of primary emissions and photochemical activity.
Among the various formulations that have been derived in the framework of
the VBS scheme (for example, Lane et al., 2008a; Murphy and Pandis, 2009;
Dzepina et al., 2011; Shrivastava et al., 2013; Zhao et al., 2015, etc.),
specifically two are chosen for this paper (as already for Zhang et al.,
2013) because they either favor large ASOA or large SI-SOA buildup in the
plume. In this way, we intend to address uncertainty linked to the
co-existence of different VBS schemes in the formation of different SOA
types within the plume. Another important aspect of this paper is to analyze
the OA/Ox ratio, specifically used for model evaluation, as it
normalizes SOA formation with respect to photochemical reactivity and
precursor load.
Airborne chemical instruments deployed, the measurements including
the maximum and 30th percentile (P30) of pollutant concentrations by
these instruments are used to discuss general findings during the campaign
(Freney et al., 2014) and evaluate the model simulations.
a Measurement of nitrogen on aircraft developed by the Laboratoire
Interuniversitaire des Systems Atmosphériques (LISA). NO, NO2 and
NOy are measured (Freney et al., 2014, Supplement).b Thermal-environmental instruments O3 UV analyzer.c Radiance research ® particulate soot
absorption photometer.d Aerodyne compact time-of-flight aerosol mass spectrometer.
The paper is organized as follows. In Sect. 2, the airborne measurements
during the MEGAPOLI summer campaign are described. The model configurations
and simulation setups for the VBS approach to model POA and SOA are
introduced in Sect. 3. The evaluation of model performance for plume
simulations is presented in Sect. 4, and the impact on regional air
quality is described in Sect. 5. From comparison of different setups of
the VBS scheme, uncertainties in the formation of different SOA types in the
Paris plume are discussed.
Airborne measurements during the MEGAPOLI summer campaign
Flight patterns flown during the MEGAPOLI campaign (Fig. 2) consisted of
several transects of the pollution plume at increasing distances from the
urban area (Freney et al., 2014). Perpendicular flight legs to the plume
axis were chosen ranging from 50 to 100 km in order to sample both the plume
and rural background conditions at the lateral plume edges. Taking into
account the aircraft autonomy of about 3.5 h, this allowed flying four
legs across the plume. The maximal distance for a flight was about
200 km
from the Paris agglomeration center. The flight level was chosen to lie well
inside of the well-developed afternoon convective boundary layer, at about
500–700 m above ground. In addition to measurements inside and outside the
Paris plume, each flight included a complete circle around the
agglomeration, performed after takeoff and before landing at the
Cergy–Pontoise airport. In this work, we focus only on measurements downwind
of Paris to study the pollution production from Paris emissions. The
measurements started in the early afternoon in order to sample
photochemically processed air. Because of a limited number of flight hours,
and in line with the principal objective of documenting the photochemical
production of pollutants, flights were performed on days with light wind
(<3ms-1) and cloud-free weather conditions. For this study,
three flights were chosen on the 16, 21 and 29 July,
all of which encountered well-pronounced plumes of primary and secondary
pollutants. Meteorological conditions for these days were characterized by
southerly winds, low wind speed over the agglomeration, elevated temperature
and cloudless skies. These conditions favor the accumulation of primary
pollutants and photochemical processes leading to the formation of secondary
pollutants like O3 and SOA.
An extensive set of gas-phase pollutants, aerosol species and properties
were measured on each flight (Freney et al., 2014). For this work, for each
flight a complete measurement set of primary pollutants, BC and NOx
(NO and NO2), and secondary pollutants, O3 and OA, is available
and analyzed. Measurement frequencies of all instruments, including the
aerosol chemical composition, are rapid enough (<40s) to have a
relatively good spatial resolution. All measurements during the flights are
corrected to temperature (22 ∘C) and pressure (950 hPa) of the
plane (Freney et al., 2014). Thus, compared to other values given in this
paper and taken at standard conditions, our values are about 5 % lower.
Table 1 summarizes the deployed instruments and the measured concentration
levels for these pollutants during these flights. Only measurements at a
stable flight altitude are used for this study.
The 30th percentile concentration of a pollutant on the flight legs
downwind of Paris is close to the median concentration outside the Paris
plume and represents its background level. For NOx and BC, the
30th percentile concentrations are 1.11, 1.03 and 1.14 ppb, and 0.33,
0.49 and 0.38 µgm-3 on 16, 21 and 29 July,
respectively (Table 1). The rather homogeneous background pollutant levels
(Figs. 2, 3) correspond to the absence of major urban pollution sources in
the south of the Paris agglomeration (rural “center” region). The Paris
pollution plumes are always clearly identifiable as sharp concentration
increases, with continuity on all flight legs at different distances from
the agglomeration (Figs. 2, 3). The plume half-widths are about several
tens of kilometers. Maximum plume concentrations of NOx and BC are
13.5, 7.98 and 12.2 ppb, and 2.00, 2.01 and 2.30 µgm-3,
respectively, for the three flights (Table 1).
The maximum plume ozone concentrations are 62.0, 79.0 and 62.4 ppb during
these flights, respectively, compared to the 30th percentile (i.e., background) concentrations of 49.0, 58.0 and 50.0 ppb (Table 1). The largest
O3 values are observed at the flight leg most distant from the
agglomeration, allowing for the longest photochemical processing (Fig. 4).
For the 16 July, the transects across the plume show a double maximum and
a relative central minimum due to ozone titration by NO.
The background concentrations of OA are 3.87, 6.47 and 4.13 µgm-3,
respectively, during these three flights (Table 1, Fig. 5). Maximum
plume OA concentrations are 5.97, 12.33 and 7.36 µgm-3,
respectively. Thus, additional OA buildup within the plume is about 2–6 µgm-3 (see also below in Sect. 4.2). Maximum concentrations
appear in the three outer flight legs. OA plumes are wider and less
homogeneous than primary pollutant ones, which could be due to a secondary
organic aerosol production from additional biogenic sources in addition to
formation from emissions in the Paris agglomeration.
OA vs. Ox(a1, a2, a3), SI-SOA vs. Ox(b1, b2, b3), ASOA
vs. Ox(c1, c2, c3) and BSOA (d1, d2, d3) vs. Ox during the
flights on 16, 21 and 29 July, respectively. For OA vs.
Ox(a1, a2, a3), results from the measurement, VBS-LA, VBS-LNOX and
VBS-HNOX are presented. For others, only results from VBS-LA and VBS-HNOX
are presented.
OA versus Ox (Ox=O3+NO2) plots from measurements on
these flights are used to study the ratio of the photochemical productivity
of OA and Ox buildup in the plume from Paris emissions, following an
approach first proposed by Herndon et al. (2008). In this study, OA is used
instead of SOA, because it is directly measured. Among OA factors derived
from positive matrix factorization (PMF) of AMS (aerosol mass spectrometer)
measurements, LV-OOA (low-volatility-oxygenated OA) and SV-OOA (semivolatile-oxygenated OA) are commonly
attributed to SOA (Hallquist et al., 2009). These LV-OOA and SV-OOA factors
contributed on average about 65 % of resolved OA factors and 37 % of the
total OA during these three MEGAPOLI flights. HOA (hydrocarbon-like OA) makes up for the remaining 35 % of resolved OA factors and 20 % of the total
OA. While the HOA factor is generally attributed to POA, it might partly
also correspond to oxidized POA, considered as SOA (Aumont et al., 2012;
Cappa and Wilson, 2012), and to cooking-related OA (Freutel et al., 2013).
Using total OA avoids these problems arising from the interpretation of PMF-derived factors.
The Pearson R correlation between OA and Ox on the three flights on
16, 21 and 29 July is around 0.70 (Table 4, Fig. 6). It
indicates a similar ratio of photochemical production of ozone and OA from
VOC precursors, though as expected the match between OA and Ox is not
perfect, due to differences in SOA and Ox yields for different VOC
precursors. The OA / Ox slopes for these flights are 0.14–0.15 µgm-3ppb-1. This result is close to the one obtained from a
previous flight study of urban air mass in Mexico City (0.14–0.15 µgm-3ppb-1, Wood et al., 2010). It is also close to ground-based
studies of downwind urban emissions from ground-based measurements in Mexico
City (median OOA vs. Ox slope of 0.16 µgm-3ppb-1;
Wood et al., 2010), in Los Angeles (0.15 µgm-3ppb-1;
Hayes et al., 2013) and in Tokyo (0.19 µgm-3ppb-1;
Morino et al., 2014).
SimulationsModel configuration
In this work, we used the CHIMERE v2008b model (see http://www.lmd.polytechnique.fr/chimere/) (Vautard al., 2001; Bessagnet et
al., 2009; Menut et al., 2013), widely used for operational regional air
quality forecasts (Honoré et al., 2008; Zhang et al., 2012) and simulations
in Europe (e.g., Beekmann and Vautard, 2010; Sciare et al., 2010). With a few
exceptions (noted below), the same model configuration as in Zhang et al. (2013) was set up. Two nested domains are applied: a continental domain
covering Europe with a resolution of 0.5∘ (35–57.5∘ N,
10.5∘ W–22.5∘ E) and a regional domain over northern
France covering all the flight patterns during this campaign with a
resolution of 3 km (called MEG3 domain). Eight hybrid-sigma vertical layers are
used, with the first layer extending from the ground to about 40 m, and a model
top at 500 hPa. Tropospheric photochemistry is represented using the reduced
MELCHIOR chemical mechanism (Lattuati, 1997; Derognat et al., 2003),
including 120 reactions and 44 prognostic gaseous species. For the
simulation of the particulate phase, eight bins of particulate sizes are used in
the model with diameters ranging from 0.04 to 10 µm. The
thermodynamic equilibrium of the inorganic species (sulfate, nitrate, and
ammonium) between the gas and particle phase is interpolated from a
tabulation calculated with the ISORROPIA model (Nenes et al., 1998). The
evaporation and condensation processes related to departures from the
thermodynamic equilibrium are kinetically controlled.
Two SOA formation mechanisms are used and are described in more detail in
Sect. 3.2. For SI-SOA (SOA from oxidation of primary semivolatile and
intermediate volatile VOCs, previously referred to as OPOA in Zhang et al.,
2013) formation, both mechanisms use the VBS formulation as described in
Robinson et al. (2007). For traditional anthropogenic and biogenic SOA (ASOA
and BSOA) formation from VOC precursors, one uses the classical single-step
oxidation scheme (Pun et al., 2006; Bessagnet et al., 2009) and the other
one a volatility basis set (VBS) scheme with differences in high-NOx
and low-NOx parametrizations. The VBS approach is implemented in the
model as in Murphy and Pandis (2009) and Lane et al. (2008a). In our work,
BSOA gas-phase aging is also included with the same rate constant as for
ASOA (1×10-11molcm3s-1). Gas-phase chemical
aging of BSOA is supported by laboratory (see in Zhao et al., 2015) and box
model experiments with the very detailed GECKO-A mechanism (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere; Valorso et al.,
2011). In Zhang et al. (2013), it had been shown that including BSOA aging
was necessary to reproduce several OA peaks occurring during the summer
campaign at Paris urban sites in the model.
For the large domain, anthropogenic gas-phase emissions are calculated from
EMEP (European Monitoring and Evaluation Programme)annual totals (http://www.ceip.at/emission-data-webdab/),
while black carbon (BC) and primary organic aerosol (POA) are prescribed
from an emissions inventory prepared by Laboratoire d'Aérologie (LA)
(Junker and Liousse, 2008). In the different simulation set-up's in Sect. 3.2,
emissions for the inner domain MEG3 over northern France are either
taken from the same EMEP-LA inventory or from an alternative inventory
specifically designed for the MEGAPOLI project, the Airparif-TNO-MEGAPOLI
inventory, in which the refined Paris emissions from Airparif with a
resolution of 1 km are integrated into the European-wide TNO inventory
(Timmermans et al., 2013). In this latter inventory, BC and POA emissions
for the Paris agglomeration are about 2 and 3 times lower than in the
EMEP-LA inventory, respectively, and VOC emissions are about a third lower,
while NOx emissions are similar. These differences are explained by use
of spatial downscaling techniques in the EMEP-LA inventory using proxies
that generally tend to overestimate megacity or urban emissions, while the
Airparif-TNO-MEGAPOLI inventory is constructed following a bottom-up
approach (Timmermans et al., 2013). Both inventories are affected by
additional uncertainties in activities and related emission factors. Cooking
emissions, which have been shown to be significant for the Paris
agglomeration (Freutel et al., 2013; Crippa et al., 2013) are not included
in either of these emission inventories. In this work, we assume that
differences in BC and POA emissions in both inventories span the range of
uncertainty for these emissions in the Paris region. This is compatible
with the Petetin et al. (2015) study which evaluates these emission
inventories with respect to ground-based measurements within the
agglomeration and ground-based ones around it. As explained in more detail
in Zhang et al. (2013), POA / SVOC emission factors for the main source in
summer, traffic, are obtained from laboratory measurements under low level
of dilution (with OA loading of 1000 µgm-3). Under these
conditions, the POA / SVOC emissions are emitted mostly in the particle phase.
A volatility distribution following Robinson et al. (2007) was affected to
these emissions. Additional IVOC emissions (factor 1.5 of POA / SVOC) were
also considered according to Robinson et al. (2007).
Biogenic emissions are calculated using the MEGAN (Model of Emissions of Gases and Aerosols from Nature) model data and
parametrizations (Guenther et al., 2006). Meteorological fields are
simulated with the MM5 mesoscale model (PSU/NCAR mesoscale model; Dudhia, 1993). Boundary conditions
are taken from a monthly climatology of the LMDz-INCA2 and LMDz-AERO general
circulation models (Hauglustaine et al., 2004).
Secondary organic aerosol (SOA) mass yields used in this work. These
yields are for surrogate VOC species with saturation concentrations of 1,
10, 100 and 1000 µgm-3 at 298 K (Murphy and Pandis, 2009).
VOCsVBS-LNOX with low-NOx condition VBS-HNOX with high-NOx condition 11010010001101001000ALK4a00.0750000.037500ALK5b00.30000.1500OLE1c0.00450.0090.060.2250.00080.00450.03750.15OLE2d0.02250.04350.1290.3750.0030.02550.08250.27ARO1e0.0750.2250.3750.5250.0030.1650.30.435ARO2f0.0750.30.3750.5250.00150.1950.30.435TERPg0.10730.09180.35870.60750.0120.12150.2010.507ISOPh0.0090.030.01500.00030.02250.0150
Here, a brief summary on the three distinct simulation configurations used
in this study is given. They are intended to take into account both
uncertainties in SOA formation schemes and in POA / SI-VOC emissions.
The VBS-LNOX (low-NOx) simulation, in which all SI-SOA, ASOA and BSOA are affected by
chemical aging with the VBS approach. High SOA yields under low-NOx
conditions (Murphy and Pandis, 2009; Lane et al., 2008a) are used for both
simulation domains (the same as the so-called VBS-MPOLI simulation in
Zhang et al., 2013; see Table 2), assuming that most of the OA is advected to
the Paris agglomeration from outside (Petetin et al., 2014) and probably
formed under low-NOx conditions. Usually, a limiting VOC/NOx ratio
of 3 and 10 ppbCppb-1 is used to discern a high- and a low-NOx
regime, respectively (Lane et al., 2008b). While the ratio of 10 ppbCppb-1 is close to or above the value for most of northern France,
indicating that it is close to a low-NOx regime, it is close to or
below the ratio of 3 ppbCppb-1 on the north of Paris in the plume
region (Fig. S1 in the Supplement). This low-NOx configuration is thus expected to
overestimate ASOA formation in the Paris pollution plume under high-NOx
conditions around Paris. The emission inventory for the MEG3 domain is the
specific MEGAPOLI inventory.
The VBS-HNOX (high-NOx) simulation, in which lower SOA yields under high-NOx
conditions (Murphy and Pandis, 2009; Lane et al., 2008a) are used for the
inner MEG3 domain (see Table 2). This is more realistic for SOA formation in
its plume close to the Paris agglomeration than in low-NOx conditions. The
low-NOx condition is still applied on the continental domain for
background OA simulation. All other model settings are equal to the VBS-LNOX
configuration. Although using high-NOx conditions with lower yields,
which are more realistic for the plumes from Paris emissions, this
configuration might still overestimate ASOA formation in the plume following
the new ASOA yields fitted to laboratory experiments in SOA formation
schemes described in Zhao et al. (2015).
The VBS-LA simulation (same as VBS-T1 simulation in Zhang et al., 2013), in
which a single-step oxidation scheme (Pun et al., 2006) is used for
traditional ASOA and BSOA formation, as is VBS scheme for SI-SOA formation
and for the other two configurations. The EMEP-LA emission inventory with
3 times larger POA / SI-VOC emissions is used for the inner MEG3 domain.
The fact that POA / SI-VOC emissions are 3 times larger and the absence of
chemical aging for ASOA will favor SI-SOA with respect to ASOA formation.
Model evaluation with airborne measurements
In this section, modeling results of NOx, BC, Ox, and OA are
presented and compared to the airborne measurements at the same location and
time. Outputs from simulations are interpolated to the exact flight location
and time. NOx and BC are compared as primary urban tracers to indicate
the location of the Paris plume in observations and in simulations. Only the
VBS-LNOX simulations are used for BC, NOx and Ox, because a change
of the SOA yields does not affect the simulation of the concentrations of
these species between the VBS-LNOX and VBS-HNOX simulations. The evaluation
with the VBS-LA simulations gives an insight on effects of emission
uncertainties. Individual species comparisons are presented in Sect. 4.1
while model observation comparisons of the OA/Ox ratio are presented in
Sect. 4.2. For each of the four to five transects through the pollution plume of
a flight, the simulated and observed maximum concentrations are depicted and
averaged over all transects of a flight. The same procedure is applied for
the 30th percentiles (P30) over each transect, considered as representative
for background conditions outside of the plume.
Model statistics for maximum and 30th percentile (P30) of
pollutant concentrations from VBS-LNOX (and VBS-HNOX for OA as well) and
VBS-LA.
Individual species model to observation comparisons
The qualitative inspection of simulated and observed BC plumes shows that
the plume direction is correctly simulated on 21 and 29 July,
while a difference of about 20∘ occurs on 16 July (Fig. 2). This
will have little influence on the study of the OA impact from Paris on its
surroundings due to the rather circular structure of the agglomeration
(Shaiganfar et al., 2015). In both the modeled fields and
observations, the largest concentrations appear close to the Paris
agglomeration during these three flights.
The modeled maximum BC concentrations from VBS-LNOX are underestimated by
-0.7 (-35 %), -1.5 (-74 %) and -1.6µgm-3 (-70 %) with
respect to the measurement, respectively, for 16, 21, and
29 July, while they are overestimated by 0.4 (21 %) and underestimated
by -1.2 (-62 %) and -1.2µgm-3 (-53 %) from VBS-LA,
respectively (Tables 3, S1; Fig. 2). Thus, for the last two flights, the BC
underestimation appears for both emission inventories. An average
underestimation of plume BC measurements by -20 % (over 10 flights during
July 2009) was already noticed by Petetin et al. (2015), which could be
attributed to errors in emission inventories, uncertainty in measurements
and in the choice of the mass specific absorption coefficient (Petetin et
al., 2015). Our study focusses on 3 days with particularly low wind
speeds with variable wind direction during morning hours in the Paris
agglomeration, allowing for primary pollution buildup and subsequent
secondary pollution buildup in the plume. As shown in Petetin et al. (2015),
it turns out that the meteorological model (MM5, but similar results
are obtained for WRF) forcing the CTM simulations is not capable of
simulating these wind direction variations for two of the 3 flight days,
on 21 and 29 July, thus underestimating the pollution
accumulation in the Paris region and subsequently in the plume. The modeled
30th percentile BC concentrations taken as representative for
background concentrations are also underestimated by -0.17 (-51 %),
-0.31
(-62 %) and -0.22µgm-3 (-59 %) from VBS-LNOX and by
-0.22
(67 %), -0.34 (-69 %) and -0.27µgm-3 (-71 %) from
VBS-LA, respectively, during these flights (Tables 3, S1; Fig. 2).
Similar to BC, VBS-LNOX underestimates NOx concentrations by -4.3
(-32 %), -5.2 (-65 %) and -7.3ppb (-60 %) for the maximum
concentrations and by -0.64 (-58 %), -0.42 (-41 %) and
-0.59ppb
(-52 %) for the background concentrations with respect to the measurements
(Tables 3, S1; Fig. 3). VBS-LA shows slightly more underestimation by
-7.3
(-54 %), -5.3 (-72 %) and -8.7 (-71 %) ppb for the maximum
concentrations and slightly less underestimated for the background
concentrations by -0.57 (-51 %), -0.36 (-35 %) and -0.51 (56 %) ppb
(Tables 3, S1). Also similar to BC, the modeled NOx maximum
concentrations for both simulations are located close to the Paris
agglomeration (Figs. 2, 3).
The modeled O3 concentrations are slightly overestimated with respect
to the measured O3 concentrations, by 7.5 (12 %), 4.3 (5 %) and 8.3 ppb (13 %)
from VBS-LNOX and 12.9 (21 %), 4.3 (5 %) and 9.8 ppb
(16 %) from VBS-LA for the maximum concentrations, and by 4.3 (9 %), 11.3
(20 %) and 3.0 ppb (6 %) from VBS-LNOX and 4.6 (9 %), 11.7 (20 %)
and 3.3 ppb (7 %) from VBS-LA for the background concentrations during the
three flights, respectively (Tables 3, S1; Fig. 4). Note that, for Ox,
the concentrations can be slightly less overestimated than for O3
by, respectively, 3.0 (6 %), 11.0 (19 %) and 1.8 ppb (4 %) from VBS-LNOX
and 3.4 (7 %), 11.4 (19 %) and 2.1 ppb (4 %) from VBS-LA for the
background concentrations due to the opposite sign in measured O3 and
NOx differences (Tables 1, 3, S1; Fig. S2) and sometimes for the
maximum concentrations by 8.5 (13 %), 3.6 (4 %), 8.0 ppb (12 %) from
VBS-LNOX and 13.1 (21 %), 3.5 (4 %) and 9.2 ppb (14 %) from VBS-LA.
Similar to the observations, the modeled maximum O3 and Ox levels
are located farthest from the agglomeration. Differences
between VBS-LNOX and VBS-LA are apparently small. The rather correct
simulation of O3 and Ox in spite of the NOx underestimation
is less astonishing in light of the following discussion about NOx–OH
relationships.
Direct comparisons for POA are not shown here because of the uncertainty in
HOA factors from the PMF analysis, and because of the incomplete match
between HOA and POA. However, the BC underestimation in simulations lets us
also expect a POA underestimation.
Consequences of these underestimations in primary pollutants on the
buildup of secondary pollutants are briefly discussed here.
First, as shown above for BC, the underestimation is alleviated in the
alternative VBS-LA simulation with larger BC (and POA) emissions. Thus,
unexpressed uncertainty in meteorological data is partly taken into account
by that in emissions.
Second, the use of OA vs. Ox slopes for evaluation of the SOA production
efficiency normalizes the effect of errors in primary pollutants, as
in Eq. (1); it merely depends on the ratio of the product yields. This is
why the use of this ratio is important for this study.
Third, in a VOC-limited regime, as is characteristic for the Paris region
(e.g., Deguillaume et al., 2008), the rate of secondary pollutant buildup is far
from proportional to the primary precursor concentration. On the contrary,
in the extreme case that NOx compounds represent the only OH loss,
NOx and OH concentrations are inversely proportional (e.g., Kleinman et
al., 1997). When assuming a constant ratio in primary pollutants, the flux
in Eq. (1) is then independent of VOC concentrations and only depends
on the production rate of odd hydrogen radicals (OH+HO2+RO2).
The measured OA plume is correlated with the measured BC plume on 16 July,
while it appears translated to the west on 21 and 29 July, as is
also the ozone plume (Figs. 3–5). This is probably due to an
additional contribution from other sources besides the Paris emissions, such
as background levels from regional contributions related to both
anthropogenic and biogenic sources. In the simulations, OA, O3, BC, and
NOx plumes coincide for all dates indicating a contribution from the
Paris emissions. As expected from results in Zhang et al. (2013) for urban
and suburban Paris sites, the VBS-LA simulation with single-step oxidation
scheme for ASOA and BSOA formation generally underestimates OA measurements,
in particular for the background concentrations, by up to 80 % (Fig. 5). A
slight overestimation of 1 µgm-3 is observed in the plume for
the maximum concentration during the flight on 16 July. This is
related to high POA and SI-SOA contributions of up to 70 % to the total OA concentrations
in the plume (Figs. S3, S4). Thus, apparently, the
increased plume SI-SOA buildup due to increased POA emissions in the
agglomeration is able to overcompensate the lack in background SOA at least for
this day.
Simulations with the VBS-HNOX configuration show lower plume concentrations
than those with the VBS-LNOX configuration, because of lower yields in SOA
formation in the inner MEG3 domain. Background simulations are similar for
both simulations corresponding to low-NOx yields chosen for both
simulation configurations in the larger domain. The maximum concentration of
OA simulated with VBS-LNOX is overestimated with respect to the measurement
by 1.7 (28 %), 0.4 (3 %) and 1.5 µgm-3 (21 %) on
16, 21 and 29 July, respectively, while it fits well with
the observations in VBS-HNOX, being slightly underestimated by -0.5
(-8 %), -1 (-8 %) and -0.5µgm-3 (-7 %) (Tables 3,
S1; Fig. 5). The modeled OA background concentrations are underestimated both on
16 and 29 July by VBS-LNOX by, respectively, -1.6 (-41 %) and
-1.0µgm-3 (-25 %) and by VBS-HNOX by,
respectively, -1.9
(-50 %) and -1.4µgm-3 (-33 %). They are overestimated with
both configurations on 21 July by 2.3 (36 %) and 1.9 µgm-3 (29 %),
respectively (Tables 3, S1). All in all, simulated and
observed OA concentrations are rather similar, which is a satisfying result
in light of often very large model to observation differences reported in
literature (e.g., Sciare et al., 2010, for the Paris region). However, as the
primary pollutants are generally underestimated by the model, this might be
the result of compensating errors for different OA compounds. In a later
section (Sect. 4.3), we will thus rely on OA versus Ox slopes in the Paris
plume for further analysis.
Correlation coefficients between OA and Ox, SI-SOA and Ox,
ASOA and Ox and BSOA and Ox for the flights on 16, 21 and 29 July,
both from the measurements (AMS) and simulations with VBS-LNOX, VBS-HNOX and
VBS-LA.
Slopes of OA vs. Ox, SI-SOA vs. Ox, ASOA vs. Ox and BSOA
vs. Ox for the flights on 16, 21 and 29 July, both from the
measurements (AMS) and simulations with VBS-LNOX, VBS-HNOX and VBS-LA.
First, the plume productions of OA (and of Ox) are derived here from
the difference between maximum and background (30th percentile)
concentrations over flight transects as given in the last section. Ox
is preferred here with respect to O3 since it is not affected by
titration with NO. For the 3 flight days (16, 21 and
29 July), the measured values of OA plume buildup are 2.1, 5.9 and 3.2 µgm-3,
respectively, while they are 5.4, 4.0 and 5.8 µgm-3 in VBS-LNOX, 3.5, 3.0 and 4.1 µgm-3 in VBS-HNOX and
5.9, 1.4 and 3.1 µgm-3 in VBS-LA (Tables 1, 3). Thus,
independent of the model configuration used, overestimations of plume OA
occur on 2 days (16 and 29 July), while an underestimation
appears on 21 July. The plume's Ox production for the 3 flight days, calculated again
from the difference between the maximum concentrations and the background
concentrations are, respectively, 12.9, 21.8 and 12.6 ppb from the measurements, 18.3,
14.4 and 18.8 ppb from VBS-LNOX, and 22.5, 13.9 and 19.7 ppb from VBS-LA
(Tables 1, 3). As for OA, we
encounter an overestimation of plume Ox on 16 and
29 July, and an underestimation on 21 July. This suggests that the
representation of photochemical conditions might be partially responsible
for differences observed for OA and, thus, that the given data set could not
be used directly for evaluation of the OA scheme in the model.
To overcome these problems, we analyze here OA versus Ox plots. As
explained in the introduction, the slopes of these plots can represent in-plume OA buildup, normalized with respect to the availability of VOC
precursors and oxidant agents (OH, O3 and NO3). This holds under
the ideal hypothesis of a constant mix of VOC, SVOC and IVOC precursors on
one hand, and oxidant agents on the other hand, for the considered data
points of a flight. As explained in Sect. 2, we did not plot OOA or SOA
vs. Ox because of the uncertainties related to PMF analysis and the definition
of HOA comparing to POA or/and SI-SOA and cooking-related OA. In Sect. 2
we also presented correlations of about 0.7 (R) between OA and Ox
measured on the flight legs for a given day. Modeled OA and Ox on these
flight legs show an even higher correlation of about 0.87 from VBS-LA, except
for 0.67 on 21 July, and of more than 0.95 from VBS-HNOX and VBS-LNOX (Table 4).
These good correlations suggest that we are close enough to the
“constant mix” hypothesis to make the OA vs. Ox slope a useful
metric. The simulated slopes of OA / Ox are 0.23, 0.29 and 0.26 µgm-3ppb-1 with the VBS-LNOX configuration for the three
flights on 16, 21 and 29 July, respectively (Fig. 6, Table 5).
They overestimate the measured slopes of 0.13, 0.14 and 0.15 µgm-3ppb-1 by a factor between 1.7 and 2. We noticed that the
small variability in the relative differences between flights are due to the
normalizing method (i.e., plotting OA vs. Ox to normalize with respect
to photochemical conditions). This overestimation can be related to the SOA
formation scheme: it is likely that the high-SOA yields under low-NOx
conditions are incorrect under plume conditions. The corresponding slopes in
the VBS-HNOX simulation with lower yields under high-NOx conditions are
0.15, 0.24 and 0.19 µgm-3ppb-1, respectively. These
slopes show a much lower overestimation with factors of 1.1, 1.7 and 1.3 for
the 3 days. As for VBS-LA, the simulated OA / Ox slope is
overestimated by 46 % on 16 July with up to 70 % of the contribution of
SI-SOA to the total OA, while it is underestimated by 50 and 27 % on
21 and 29 July, respectively (Table 5). Thus, for all three
flights, simulated OA / Ox slopes with both VBS-HNOX and VBS-LA show a
similar range of errors with respect to the observed slopes (even if the sign of
errors is different). However, as we will show in Sect. 5, these two
simulations show a very different distribution of ASOA, BSOA and SI-SOA
buildup in the plume (Figs. S4–S6). Apparently, observed OA / Ox
slopes cannot constrain these distributions.
The measured slopes of OA vs. Ox during the first two flight legs on
these days are close to the ones during the last two flight legs (Figs. S7, S8).
This analysis focuses on the VBS-HNOX scheme. The modeled slopes of OA vs.
Ox (0.12, 0.23 and 0.17 µgm-3ppb-1 for the 3 flights) are
close to the measured ones (0.12, 0.18 and 0.16 µgm-3ppb-1) during the first two flight legs. Conversely,
these slopes (0.17, 0.25 and 0.21 µgm-3ppb-1) are
overestimated by factors of 1.3, 1.9, and 1.3 with respect to the measured
ones during the last two flight legs. Thus, the overestimation of slopes
occurs especially during the last two flight legs, which is related to
relatively higher anthropogenic SOA formation due to continuous chemical
aging when flights are farther away from fresh Paris emissions. The higher
slopes during the last two flight legs than during the first two
flight legs are not seen for BSOA, probably because the biogenic VOC
emissions are more diffuse (Figs. S7, S8). Even if some differences are
made evident, the good agreement of OA vs. Ox slopes between
simulations and measurements represents a valuable validation of the
VBS-HNOX scheme for the conditions of the Paris plume.
Impact of the Paris plume on surrounding regions
In this section we analyze the contribution of OA buildup from emissions
in or near the Paris agglomeration to regional OA levels. This analysis is
based on simulations with the VBS-HNOX configuration and the VBS-LA
simulations, which show similar errors with respect to observed OA / Ox
slopes indicative for plume OA buildup. The VBS-LNOX simulation clearly
showed larger errors. We will first analyze the individual buildup of OA
species for the 3 flight days (Sect. 5.1), then we will study the time
evolution of a pollution plume on 16 July (Sect. 5.2) and,
finally, we will present the averaged results for July 2009.
Plume buildup of individual OA speciesVBS-HNOX simulation
The slopes of modeled SI-SOA, ASOA (anthropogenic SOA) and BSOA (biogenic
SOA) versus Ox are well correlated, generally with R>0.7
(Table 4, Fig. 6). The slopes are used here to analyze the plume production of
individual OA species. SI-SOA is formed by functionalization and
condensation of evaporated POA and IVOC species (Robinson et al., 2007)
which are thought to be constituted by long alkane chains. SI-SOA vs.
Ox slopes are 0.04, 0.02, and 0.03 µgm-3ppb-1 for
the three flights, respectively (Table 5). They represent 27, 8, and 16 %
of the total OA vs. Ox slopes. Thus, SI-SOA has only a minor
contribution to the Paris plume OA formation in this simulation. The
anthropogenic photochemical production of ASOA from aromatics dominates the
OA production on 16 and 29 July, with slopes of 0.10 and 0.09 µgm-3ppb-1,
respectively, and of 0.08 µgm-3ppb-1 on 21 July
(Table 5). On 29 and especially on 16 July, the SOA production
is strongly influenced by anthropogenic emissions (by more than 90 %). A
major contribution of anthropogenic VOC emissions to OA buildup in the
Paris plume during MEGAPOLI flights has also been found by Freney et al. (2014),
from a conjoint analysis by AMS OA measurements and PTR-MS (proton transfer reaction - mass spectrometry) VOC
compounds. These results imply important SOA formation from the Paris
agglomeration VOCs and to a lesser extent POA / SI-VOC emissions. Borbon
et al. (2013) found emission ratios for C7–C9 aromatics in Paris which were by a
factor of 2–3 higher than in Los Angeles and other French and European
Union urban areas. This clearly could favor large anthropogenic SOA
formation of OA in the Paris plume. Conversely, BSOA formation
dominates the SOA production on 21 July, with a slope of BSOA vs. Ox
of 0.15 µgm-3ppb-1 (Table 5), about 63 % of the slope
of OA vs. Ox. BSOA formation can be due to both fresh BVOC (biogenic VOC) emissions
from mainly isoprene emitting forests north of Paris and from condensation of
biogenic SVOC when temperatures decrease in the later afternoon. Recently,
the comparison of different VBS-based SOA schemes to chamber measurements in
Zhao et al. (2015) suggests lower SOA formation from traditional VOC
precursors (ASOA), by explicitly simulating the first-generation products,
than when using the parametrization from Lane et al. (2008a) as in our
study. In addition, Lane et al. (2008a) do not account for BSOA chemical
aging, while we do based on the results of Zhang et al. (2013). Thus, the
relative contributions of ASOA and BSOA to plume SOA buildup in the
VBS-LNOX and VBS-HNOX configurations used in this paper are considered as an
upper limit, while the primary SI-VOC emissions for SI-SOA formation are
considered as a lower limit. Others studies taking into account
fragmentation reactions (Jimenez et al., 2009; Shrivastava et al., 2011;
Murphy et al., 2012) reduce OA formation.
Urban OA (PM10 fraction) plume (µgm-3) evolution
on 16 July from VBS-HNOX, the triangle represents the location of
Paris, illustrated by six panels (from a to f) corresponding to
07:00–22:00 (UTC + 2) with a time step of 3 h.
VBS-LA simulation
In the alternative VBS-LA simulation, the contribution of SI-SOA to the
total slopes is dominant, except on 21 July (Table 5; Fig. 6). SI-SOA
vs. Ox slopes are 0.15, 0.03, and 0.07 µgm-3ppb-1
for the three flights, respectively, and represent 79, 42, and 64 % of the
total OA vs. Ox slopes. ASOA vs. Ox slopes are negligible (0.00 or
0.01 µgm-3ppb-1). BSOA vs. Ox slopes are 0.02,
0.03, and 0.03 µgm-3ppb-1 for the three flights,
respectively, and represent 10, 42, and 27 % of the total OA vs. Ox
slopes. However, we noticed that for the VBS-LA simulation the uncertainty in
the determination of the slope is larger than for VBS-HNOX. For all three
SI-SOA vs. Ox plots, two regimes with two different slopes are
observed. If the slope was only taken for points with larger SI-SOA and
Ox values, which are closer to the plume, a larger slope would have
been determined. To a lesser extent, this feature also appears for VBS-LNOX
simulations.
The larger SI-SOA vs. Ox slopes in the VBS-LA simulation are easily
explained by the larger POA emissions in this configuration. The range of
SI-SOA vs. Ox slopes between the VBS-HNOX and the VBS-LA configuration
represents the uncertainty due to POA emissions (Table 5). Even larger
SI-SOA vs. Ox slopes during these flights would also be expected if the
more aggressive SI-SOA formation scheme by Grieshop et al. (2009) had been
used. With the Grieshop et al. (2009) formulation, SVOC species have a
reaction rate constant 2 times lower than in this study with Robinson et
al. (2007) but are shifted to 2 orders of magnitude lower volatility
(instead of one), with a mass increase by 40 % for each oxidation step
(instead of 7.5 %). Box simulations by Dzepina et al. (2011) for Mexico
City and by Hayes et al. (2015) for Los Angeles yield about 2 times larger
SI-SOA yields with the Grieshop et al. (2009) scheme than with the Robinson et al. (2007)
scheme. These results suggest an additional possibility to increase
SI-SOA contributions to plume SOA.
As a result of these comparisons, we come to the conclusion that, due to
uncertainty both in POA emissions and in the SOA formation formulations,
the constraint of observed OA vs. Ox slopes on the SOA
distribution in the Paris plume is unfortunately weak. ASOA and SI-SOA
could be the major anthropogenic SOA products for two flights, with varying
contributions of BSOA.
Time evolution of the plume on 16 July
Figure 7 gives a typical picture of the OA species evolution in the Paris
plume (at surface), simulated with both the VBS-HNOX scheme and the VBS-LA
scheme.
With the VBS-HNOX scheme, on 16 July at 07:00 UTC, a morning peak of OA
is formed inside the Paris agglomeration as a result of POA emissions, low
wind speeds, and a low boundary layer height. This peak of OA is then
transported northeast. It disappears in the later morning (10:00 UTC) due to an
increase of the PBL (planetary boundary layer) height and stronger winds. In the early afternoon (13:00 UTC),
an OA plume is formed at about 50 km from the agglomeration center due
to photochemical SOA production. At 16:00 UTC, the plume travels further
northeast. The largest OA values occur between 49.5 and
50∘ N, about 100 km north of Paris, in agreement with
measurements. Major contributors, ASOA and SI-SOA, add more than 5 and 2 µgm-3
of OA to the plume maximum (Fig. 8). The ASOA and SI-SOA
plumes are clearly separated from the Paris agglomeration, because of (i) the
time needed for processing of precursor emissions and (ii) the largest
accumulation of precursor emissions in the early morning hours when wind
speeds over the agglomeration are very low (also seen in the POA peak at
the same location). BSOA contributes to the regional background and is
little affected by the anthropogenic Paris agglomeration emissions (Fig. 8). The
highest OA concentrations of about 10 µgm-3 occur in the
evening at 19:00 UTC in northern France (at ∼150km
from the agglomeration center) due to continuous photochemical SVOC
production and aging, and due to lower temperatures. At 22:00 UTC, the plume is
leaving the MEG3 model domain.
Urban POA (a), SI-SOA (b), ASOA (c) and BSOA (d) (in PM10)
plumes (µgm-3) from VBS-HNOX at 16:00 (UTC + 2) on 16 July; the triangle represents the location of Paris.
This phenomenon of continuing SOA formation which is detached from the
original rush hour emission area due to transport is very similar to that
observed for Los Angeles in the CalNex study (Hayes et al., 2013).
The corresponding results obtained with the VBS-LA scheme are shown in the
Supplement (Fig. S9). Morning OA concentrations are about 3
times larger than in VBS-HNOX due to larger emissions, while the background
concentrations are all lower than 1 µgm-3. At 13:00 UTC, an OA
plume with a concentration about 6 µgm-3 higher than
in VBS-HNOX is formed at about 50 km from the agglomeration center. While
ASOA is the major contributor to OA plume formation in VBS-HNOX, SI-SOA
formation contributes the most to the plume in VBS-LA and produces the
maximum concentration of about 6 µgm-3 in the later afternoon
at 16:00 UTC towards northern France. POA, ASOA and/or BSOA contribute less
than 1 µgm-3 to the OA plume maximum (Fig. S10).
Modeled monthly mean OA concentration in PM10 (µgm-3)
from VBS-HNOX; the triangle represents the location of Paris.
Modeled monthly mean POA (a), SI-SOA (b), ASOA (c) and
BSOA (d)
concentration in PM10 (µgm-3) from VBS-HNOX; the
triangle represents the location of Paris.
Average July 2009 urban OA contribution to the surroundings of
Paris
Here, we analyze the regional-scale OA buildup from the Paris emissions for
the average of July 2009 from the VBS-HNOX (Figs. 9, 10) and VBS-LA
simulations (Figs. S11, S12). For VBS-HNOX, average OA concentrations around the Paris
agglomeration do not show distinctive pollution plumes but instead a strong
W–E gradient near the agglomeration, presumably due to averaging different
plume directions and due to differences in background conditions. OA values
also show strong decreasing gradients at about 100–150 km in the N–NE of
Paris. Contrary to VBS-HNOX, OA values from VBS-LA show a distinct OA plume
from the Paris agglomeration: the absolute plume concentrations are lower. This
behavior can be analyzed by considering specifically the contributions to
OA.
For the VBS-HNOX simulation, the average POA from Paris emissions is only about
0.15 µgm-3 over Paris and the area of enhanced values
extends to the E–NE because of the largest climatological frequency of
southwesterly to westerly winds in July (Fig. 10). The areas of
enhancements of POA occur on a length scale of some tens of kilometers
around the agglomeration. ASOA is enhanced within the agglomeration and
within the SW and NNE plume, up to 100–150 km downwind of the
agglomeration, respectively. The maximum concentrations in these plumes are 0.4 and 0.35 µgm-3,
respectively (always for the July 2009 average). In the
NNE direction, enhanced values originate from pollution events under SW flow,
such as those studied in this work (see Sect. 5.2). The enhanced values in
the SW originate from a pronounced pollution plume occurring in the
beginning of July, for which no measurements were available. SI-SOA is most
enhanced in the NNE direction, where its maximum concentration is about 0.35 µgm-3,
thus somewhat smaller than the ASOA concentration. It is
worth noting that these increases in ASOA and SI-SOA concentrations are much
larger when analyzing individual events than when looking at averages, due
to different plume angles on different days and thus diluting the average
fields. The BSOA component does not show distinct plumes, but a continuous
NW/W–SE/E gradient that is the continental character of air masses
implies large average BSOA concentrations. BSOA is the strongest contributor
to OA over the domain. Its gradient is responsible for the W–E OA gradient
noticed earlier, with smaller contributions from the other components.
For the VBS-LA simulation, the larger primary S/IVOC emissions within the LA
inventory lead to a larger average POA concentration of up to 0.7 µgm-3
within the Paris agglomeration. The monthly maximum plume SI-SOA
concentration is about 0.3 µgm-3 in VBS-LA (Fig. S12). The
lower SI-SOA concentration albeit with higher POA concentrations is due to lower
OA load in the plume. Indeed, the monthly average plume ASOA concentration is
small, below 0.05 µgm-3, and the plume BSOA concentration is
below 0.3 µgm-3. BSOA shows a similar spatial pattern in VBS-LA but with
lower absolute values than in VBS-HNOX.
In conclusion, both the VBS-HNOX and VBS-LA simulations show different
monthly average OA product distributions. As discussed above, the ASOA and
BSOA contributions in VBS-HNOX represent an upper limit for ASOA and BSOA
produced in the plume (and in background air masses). Conversely, the
small plume and background values in VBS-LA simulated without any chemical
aging are probably underestimated, in particular because they underestimate
the SOA observations within the Paris agglomeration (Zhang et al., 2013). Moreover, for SI-SOA, the differences between both simulations are weak.
However, alternative VBS schemes (Grieshop et al., 2009) would simulate
a higher SI-SOA formation, as noted above (about a factor 2 from box model
studies in other urban plumes).
Conclusion
CHIMERE simulations are used to study the secondary pollutant formation in
the Paris plume and its impact on the surrounding regions. This study
focusses on 3 photochemically active days for which airborne
observations are available. Three simulation configurations are set up in
order to cover the range of uncertainty in emissions and in different
formulations of the SOA buildup in the frame of the VBS scheme. Primary
pollutants within the plume, such as NOx, BC and probably also POA,
are clearly underestimated in the model when using the MEGAPOLI inventory,
and to a lesser extent with the EMEP-LA inventory. For two of the three
flights, this underestimation is probably due to too high wind speeds in the
morning over the Paris agglomeration, not allowing for strong enough
pollution accumulation. Conversely, ozone is slightly overestimated in
the plume and in background air masses, as is Ox. This is not
contradictory since the chemical regime in Paris and its surroundings is
generally NOx saturated (Deguillaume et al., 2008). Both in
observations and simulations, predicted (and measured) OA is very well
correlated with predicted (and measured) Ox. The ratio of the
photochemical productivities of SOA and Ox (represented by the slope of
OA vs. Ox) is well simulated (overestimation of less than 30 % on the
average of 3 days) for the Paris plume from VBS-HNOX when low SOA yields
are applied on the SOA formation scheme. The overestimation might be related
to too large yields of ASOA in the VBS scheme set up in this work, which was
based on the parameters given in Lane et al. (2008a) and Murphy and Pandis (2009).
Nevertheless, this good agreement is an important result in
evaluating the VBS scheme with field data. Combined with similar recent
results for the Tokyo megacity (Morino et al., 2014), it shows good
performance of the VBS schemes in large urban areas and in their plumes. When
considering the OA to Ox slopes, the day to day variability in model to
observation results is much lower than for OA alone. Observed OA vs. Ox
slopes of about 0.14–0.15 µgm-3ppb-1 compare well to
those observed in the Mexico City, Los Angeles and Tokyo plumes with
different emissions and photochemical conditions during different seasons
(Fig. 1).
However, an alternative scheme with 3 times larger POA emissions, and
without ASOA and BSOA aging, also shows good agreement with observed OA vs.
Ox slopes, though it strongly underestimates background and urban Paris
OA. This leads us to the conclusion that due to uncertainties both in POA
emissions and in the SOA formation formulations, uncertainly in the SOA
product distribution remains large. The constraint of observed OA vs.
Ox slopes on the SOA product distribution in the Paris plume is
unfortunately weak and does not reduce this uncertainty (while it does for
anthropogenic OA yields). Both ASOA and SI-SOA could be the major
anthropogenic SOA products for two of the flights. In the simulations, anthropogenic
SOA is the major contributor to plume SOA on two flight days, while BSOA is
the major or equal contributor on the third day.
Predicted maximum OA is found on the flight leg most distant from the
agglomeration (at about 150 km), as for observations, indicating secondary
anthropogenic SOA formation from Paris emissions over all the distances and
during several hours. On a monthly average, OA from Paris emissions
contributes to the OA regional buildup at different length scales, from
several tens of kilometers for POA to several hundreds of kilometers for ASOA and SI-SOA.
Clearly, a combination of ASOA and SI-SOA buildup from precursor emissions
in the Paris agglomeration affects atmospheric composition at a regional
scale. Simulating this buildup has been possible only after an original
model evaluation showing good agreement between simulated and observed OA to
Ox slopes. This slope is an interesting parameter to measure the SOA
buildup efficiency of a given environment.
The Supplement related to this article is available online at doi:10.5194/acp-15-13973-2015-supplement.
Acknowledgements
The research leading to these results has received funding from the European
Community's Seventh Framework Programme FP/2007-2011 under grant agreement
no. 212520. Support from the French ANR project MEGAPOLI – PARIS
(ANR-09-BLAN-0356), from the CNRS-INSU/FEFE via l'ADEME (no. 0962c0018) and the Ile de France/SEPPE are acknowledged. We would like to
thank the pilots, the flight crew, and the whole SAFIRE team for operating
the ATR-42 aircraft. A part of the work was supported by a PhD grant from
CIFRE (ANRT) to Q. J. Zhang (at LISA/CNRS and ARIA Technologies).
Edited by: J.-L. Jimenez
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