Introduction
Solar radiation plays a key role in the atmospheric chemistry by
photo-dissociation of gas molecules. Photolysis reactions, which are mainly
driven by the ultraviolet part of the spectrum (100–400 nm), have a
significant impact on the formation of tropospheric air pollutants like
ozone (Madronich and Flocke, 1999; Seinfeld and Pandis, 2016). The
photolysis of ozone leads to its self-destruction (R1) and in the presence
of water vapor it becomes the main source of hydroxyl radicals (OH) in the
troposphere (R2), while the photolysis of NO2 will lead to ozone
production via reactions R3 and R4 (Madronich and Flocke, 1999;
Monks, 2005):
O3+hvλ<320nm⟶JO3→O1DO(1D)+O2O(1D)+H2O→OH+OHNO2+hvλ<420nm⟶JNO2NO+O(3P)O(3P)+O2+M→O3+M.
The photolysis rate coefficient (J) of a gas is wavelength (λ)
dependent and is described by the following equation (Madronich and
Flocke, 1999):
J=∫F(λ)⋅φλ,T⋅σλ,Tdλ,
where F is the solar actinic flux (photons cm-2 s-1 nm-1)
which represents the solar radiation that is incident to a volume element,
and φ and σ are the quantum yield and absorption cross
section (cm2), respectively, of the gas. φ and σ depend
on the gaseous species and the air temperature T (K), as well as on air
pressure for some species, while F depends on the position of the Sun and
the transmissivity of the atmosphere which is mainly influenced by the
presence of clouds, aerosols and radiatively active gases (e.g., O2,
O3, water vapor) (Wild et al., 2000; Bian and Prather, 2002). Since
the atmosphere can be considered as an optical medium, the total light
extinction is governed by the optical depth of the clouds (COD), which mainly
scatter light, and of the aerosols (AOD), which either scatter or absorb light
(aerosol–radiation interactions (ARIs), which are also referred to as direct
aerosol effects) depending on their optical properties (Yu et al., 2006;
Seinfeld and Pandis, 2016), as well as by the absorption of gases. In
addition, aerosols have an indirect influence on the atmospheric
transmissivity (aerosol–cloud interactions (ACIs), which are also referred
to as indirect aerosol effects) as they play a role in the formation of
clouds by serving as cloud condensation nuclei (CCN) and they can also alter
the optical properties and lifetime of clouds (Lohmann and Feichter, 2005;
Seinfeld and Pandis, 2016). Aerosols are either directly emitted (primary
aerosols) by anthropogenic (e.g., industries, heating processes, vehicles,
ships, biomass burning) and natural sources (e.g., volcanos, oceans, deserts),
or they are formed through chemical reactions (secondary aerosols) of
precursor gases, i.e., SO2, NH3, nitrogen oxides
(NOx = NO2 + NO), volatile organic
compounds (VOCs) (Fuzzi et al., 2015; Seinfeld and Pandis, 2016). Hence, the
human activities can affect the incoming solar radiation by influencing the
aerosol loading and radiatively active gas concentrations in the atmosphere.
The multi-decadal changes in aerosol concentrations in the 20th century
are considered to be responsible for the changes in surface solar radiation
(SSR) in several areas in western Europe and North America. There was a
decrease in the SSR between the 1950s and mid-1980s (referred to as solar
“dimming”) due to increased industrial and urban production of aerosols,
followed by an increase in the SSR since the mid-1980s (referred to as solar
“brightening”) when air quality regulations were imposed (Stanhill and
Cohen, 2001; Wild et al., 2005; Streets et al., 2006; Ohmura, 2009; Wild,
2009, 2012; Allen et al., 2013; Imamovic et al., 2016). Moreover,
extraterrestrial changes or changes in radiatively active gases were ruled
out as potential drivers of the solar dimming and brightening (Kvalevåg and Myhre, 2007; Wild, 2009). On the other hand, there are
studies arguing that these changes in SSR, especially in pristine or remote
areas, were mainly driven by natural changes in cloud cover and/or cloud
properties (Dutton et al., 2006; Long et al., 2009; Augustine and Dutton,
2013; Stanhill et al., 2014). However, for Europe, several studies have
reported either no statistically significant trends in cloud cover since
1990 or no strong evidence that changes in cloud cover were mainly
responsible for the observed SSR trends (Norris and Wild, 2007;
Sanchez-Lorenzo et al., 2009, 2012, 2017a; Vetter and Wechsung, 2015).
Furthermore, other studies that focused on Europe pointed to aerosols, and
especially the ARI, as the main driver for the brightening since the
mid-1980s (Ruckstuhl et al., 2008, 2010; Ruckstuhl and Norris, 2009;
Folini and Wild, 2011; Sanchez-Lorenzo and Wild, 2012; Wang et al., 2012a;
Cherian et al., 2014; Nabat et al., 2014; Turnock et al., 2015; Manara et
al., 2016). The relative contribution of clouds and aerosols to the SSR
trends might also have a seasonal and spatial dependence, which could be
related to changes in large-scale atmospheric circulation patterns like the
North Atlantic Oscillation (Stjern et al., 2009; Chiacchio and Wild,
2010; Chiacchio et al., 2011; Parding et al., 2016) or can also depend on
the method of study, e.g., surface measurements, satellite observations, SSR
proxies like sunshine duration (Sanchez-Lorenzo et al., 2008, 2017b). In
addition, it is not clear yet if and to what extent aerosol–cloud
interactions influenced the SSR trends in Europe since the mid-1980s (Wild, 2009; Ruckstuhl et al., 2010; Boers et al., 2017).
Tropospheric ozone in Europe has either not decreased as much as expected or
even increased in spite of large reductions of precursor emissions since the
1990s (Wilson et al., 2012; Aksoyoglu et al., 2014; Colette et al.,
2016). In addition to precursor emissions, European ozone concentrations
might also be affected by the hemispheric baseline ozone and changes in
photochemical activity (Ordóñez et al., 2005; Andreani-Aksoyoglu
et al., 2008). The radiative impact of aerosols on photochemistry and
tropospheric ozone over Europe has been examined in several studies (Real
and Sartelet, 2011; Forkel et al., 2012, 2015; Kushta et al., 2014; Makar et
al., 2015a; San José et al., 2015; Xing et al., 2015a; Mailler et al.,
2016). Real and Sartelet (2011) used an offline model
(where meteorology and chemistry are decoupled) and performed simulations
with and without ARI. They reported that the photolysis rates at the ground
level were reduced in the summer, due to the ARI, by 10–14 %, which led to
an average surface ozone reduction of 3 and up to 8 % in more polluted
areas. A different approach was followed by Xing et al. (2015a, b) to
investigate and quantify the impact of multi-decadal ARI changes on surface
ozone between 1990 and 2010 over the Northern Hemisphere, by using an
online-coupled model. For Europe, they reported a total average increase of
0.3 and up to 3 % for more polluted days over the 21 years when they
included the ARI (compared to the no-feedback case). In other words, they
suggested that higher AOD (and thus larger ARI) led to higher ozone
concentrations due to an increase of atmospheric vertical stability (lower
planetary boundary layer (PBL) height) as a result of the ARI surface
cooling and above-PBL warming, which resulted in an increase of ozone
formation by the accumulation of pollutants close to the surface. This
feedback overcompensated for the decreased photolysis rates (due to solar
radiation reduction by ARI), although increased photolysis rates do not
always lead to higher ozone production (as discussed above), but they can
also lead to higher ozone destruction in NOx-limited environments (Bian et al., 2003).
On the other hand, other modeling studies for different summer periods and
regions (US and Europe) showed that the influence of ARI on ozone varies
spatially, leading to either ozone enhancement or reduction depending on the
local meteorological and chemical conditions (Forkel et al., 2012, 2015;
Hogrefe et al., 2015; Kong et al., 2015; Makar et al., 2015a; Wang et al.,
2015). Moreover, they showed that the impact on ozone (both enhancement and
reduction) was even stronger when the ACI were also taken into account. In
addition, Forkel et al. (2012) suggested that the spatial
patterns of changes in meteorological features due to the aerosol effects
should not be taken as a general feature, because they will depend on the
prevailing meteorological conditions. Makar et al. (2015a, b) further
pointed out that the modeling results of ACI on weather (and consequently on
chemistry) will vary based on the model parameterization when comparing the
no-feedback case (some models use a “no aerosol” atmosphere while
others use different simple parameterizations for aerosol radiative
properties and CCN formation) with the one including the ACI. Overall, the
research about the aerosol radiative effects (especially the ACI) and their
implementation in the online-coupled models to consistently simulate their
feedbacks on meteorology and chemistry is still going on, along with the
efforts to overcome the problems of high computational demand (Zhang,
2008; Baklanov et al., 2014).
The focus of this study was to investigate the impact of changes in solar
radiation in Europe between 1990 and 2010 on summer surface ozone with the
following main differences from pre-existing studies. First, we used an
offline model, thus excluding ACI, following suggestions from several
studies that the ARI was the main driver for the brightening in Europe
during the period 1990–2010. In this way, we also excluded the
meteorological feedbacks on chemistry due to ARI, emphasizing the more
direct and less uncertain impact of ARI on chemistry via the photolysis
rates, compared to the more uncertain meteorology–chemistry interactions in
the online-coupled models as discussed above. Second, we designed specific
sensitivity tests to simulate, as consistently as possible, the observed
changes in AOD and SSR in Europe between 1990 and 2010, which is different
from the general “switch on/off” ARI approach. Third, we modeled and
compared only the initial (1990) and final year (2010) of the studied period
using same model input (i.e., the one of 2010; thus, the actual year 1990 was
not simulated to avoid the effects from emissions and meteorology, but
rather the AOD and SSR conditions representative of the year 1990 were used;
see Sect. 2.3) to isolate the influence of ARI on ozone from other factors.
Furthermore, this approach is unaffected by any potential masking of the
effects of ARI on ozone from interannual variability of key ozone
influencing factors (such as meteorology, emissions and boundary
conditions), compared to multi-year (with “switch on/off” ARI) simulation
studies. Fourth, we included and investigated for the first time (to the
best of our knowledge) the impact on biogenic emissions and their effects on
ozone. The methods and design of the aforementioned sensitivity tests are
described in Sect. 2, accompanied by a particulate matter (PM) trend
analysis and discussion (that the model runs were based on) in Sect. 3. The
model results are presented and discussed in Sect. 4. Finally, the
conclusions are summarized in Sect. 5.
Methodology
Model Setup
We used the offline (i.e., the meteorology is prescribed) regional air
quality model, CAMx (Comprehensive Air Quality Model with
Extensions; http://www.camx.com, last access: 2 July 2018) version 6.30. We modeled the summer season (June, July,
August – JJA) in 2010 plus the last 2 weeks of May which were used as
spin-up time. The model domain had a horizontal resolution of
0.250∘ × 0.125∘ and covered all of Europe
from 15∘ W to 35∘ E and 35∘ N to 70∘ N. The vertical
extension was up to 460 hPa using 14 sigma layers. The thickness of the
first layer was ∼ 20 m but its modeled values corresponded to
∼ 10 m, as the concentrations are calculated at the midpoint
of each layer. We used the CB6r2 (Carbon Bond mechanism, version 6,
revision 2; Hildebrandt Ruiz and Yarwood, 2013) gas-phase mechanism, and we
simulated the PM concentrations using a static two-mode (fine/coarse) scheme
for the aerosol size distribution. For the inorganic thermodynamics and
gas–aerosol partitioning calculations, the ISORROPIA scheme (Nenes et al., 1998, 1999) was used, while for the
calculations of the organic aerosol concentrations we used the SOAP model (Strader et al., 1999). The dry deposition was calculated
according to the scheme of Zhang et al. (2003). The MOZART (Model
of Ozone and Related Chemical Tracers) global model data for 2010 (Horowitz et al., 2003) served as initial and boundary conditions
for the chemical species. The MOZART data had a time resolution of 6 h and were interpolated to the size and resolution of our grid using the CAMx
preprocessor MOZART2CAMx (Ramboll Environ, 2016). The full-science
tropospheric ultraviolet and visible (TUV) radiation model (NCAR,
2011) is used as a preprocessor to provide CAMx with clear-sky photolysis
rates, where a climatological aerosol profile determined by Elterman (1968) is used. Then, these rates are internally adjusted in CAMx every hour
for clouds and aerosols as well as for pressure and temperature using a fast
in-line version of TUV (Emery et al., 2010; Ramboll
Environ, 2016). The internal adjustment for clouds and aerosols inside CAMx
is performed in two steps. First, the clear-sky radiative transfer
calculations with in-line TUV are repeated inside CAMx. In the second step,
the radiative transfer calculations are repeated including the impact of
clouds and aerosols (simulated by CAMx). A ratio of cloudy- (and aerosols)
to clear-sky solar radiation is derived by the aforementioned two-step
radiative transfer calculations in CAMx. This ratio is then applied to the
clear-sky photolysis rates and SSR which were calculated by the full-science
TUV preprocessor at the beginning. This internal adjustment (i.e., in-line
TUV) is carried out only for a single representative wavelength (350 nm), as
tests against the full-science TUV indicated a difference smaller than 1 %
in the ratio of cloudy- to clear-sky solar actinic flux for a variety of
cloudy conditions (Emery et al., 2010). Inside CAMx, the COD
is calculated for each model grid cell based on the approach of Genio et al. (1996) and Voulgarakis
et al. (2009), while the dry extinction efficiency of the aerosol species,
which is needed for the calculation of the AOD, as well as the
single-scattering albedo (SSA) were provided by Takemura et al. (2002) for the wavelength of 350 nm (Table S1 in the Supplement). These values of aerosol
optical properties were provided for sulfate, organics, soot, total dust and
sea salt, and the sulfate values were extended to nitrate and ammonium (Ramboll Environ, 2016). The asymmetry factor for aerosols was set
to have a default value of 0.61 regardless of their composition. For clouds,
the default values of the asymmetry factor and SSA were 0.85 and 0.99,
respectively. In addition, the eight-stream discrete ordinates scheme was used
for the radiative transfer calculations compared to the more common (and
computationally faster) two-stream delta-Eddington approximation scheme, as
the calculations' accuracy increases with the number of streams (Stamnes
et al., 1988; Toon et al., 1989). The choice of eight streams has been suggested
to offer high accuracy (1 % or better compared to 32 streams) without
having a significantly higher computation cost (Petropavlovskikh, 1995). TOMS (Total Ozone Mapping
Spectrometer) data, which were provided by NASA (National Aeronautics and
Space Administration; ftp://toms.gsfc.nasa.gov/pub/omi/data/, last access: 2 July 2018), were used as
input for total ozone column in both TUV and CAMx. In addition, the
radiative transfer algorithms of both full-science TUV and CAMx (i.e.,
in-line TUV) were modified to extract the modeled AOD and SSR data. In other
words, both the SSR (used in the photolysis rate calculation) and the
photolysis rates were calculated according to the same parameterization that
was described above.
The required meteorological input for CAMx was generated by the WRF-ARW
(Advanced Research Weather Research and Forecasting model, version 3.7.1;
Skamarock et al., 2008). Reanalysis global data, with time resolution of
6 h and horizontal resolution of 0.72∘ × 0.72∘, were provided by ECMWF (European Centre for
Medium-Range Weather Forecasts) and served as initial and boundary
conditions for WRF. Both CAMx and WRF had the same model domain and
horizontal resolution. However, for the WRF runs, 31 vertical layers, up to
100 hPa were used instead of 14, which was the case for the CAMx runs for
computational efficiency. More details about the WRF parameterization are
provided in Oikonomakis et al. (2018).
For the anthropogenic emissions, we used the TNO-MACC-III emission
inventory for 2010. This inventory was provided by the Netherlands
Organization for Applied Scientific Research (TNO) and is an extension of the
TNO-MACC-II emission inventory (Kuenen et al., 2014). More details about the
TNO-MACC-III emission inventory are given in Kuik et al. (2016). The TNO
European emission domain is the same as our domain but with a finer
horizontal resolution (0.125∘ × 0.0625∘). The
mineral dust, sea salt and wildfire emissions are not included in the
inventory. However, in the model's initial and boundary conditions, the
concentrations of mineral dust and sea salt are included. For the calculation
of the biogenic emissions (isoprene, monoterpenes, sesquiterpenes and soil
NO), we followed the methods described by Andreani-Aksoyoglu and
Keller (1995) using temperature and SSR data from the WRF output (the SSR
data from WRF were not used in any calculation in CAMx) as well as land use
data from the GlobCover 2005–2006 land use inventory
(http://due.esrin.esa.int/page_globcover.php, last access: 2 July 2018) and the United States Geological
Survey (USGS). All emissions were injected in the first model layer and were
treated as area emissions. A detailed discussion and values of the emissions
used in this study are given in Oikonomakis et
al. (2018).
Observations
The European Air Quality Database v7 (AirBase; Mol and de Leeuw, 2005)
provided observational surface data for the air pollutant concentrations
(http://acm.eionet.europa.eu/databases/, last access: 2 July 2018) with an hourly time resolution, which were used
for chemical model evaluation. For a better comparison between the model and
the observations, we used only rural background stations due to our grid
resolution. Furthermore, we evaluated the daily mean of the chemical species
in order to be able to compare our results with other studies (e.g., Bessagnet
et al., 2016). More details about the observational data treatment and the
statistical methods are described in the model evaluation part of Oikonomakis
et al. (2018). Furthermore, PM10 (particles with an aerodynamic
diameter, d<10 µm) and PM2.5 (d<2.5 µm)
data from the AirBase database as well as from the Swiss National Air
Pollution Monitoring Network (NABEL; Empa, 2010) were used for trend
analysis. Switzerland and the Netherlands were chosen for the PM trend
analysis as they have PM10 data going back to 1990 and 1992,
respectively. For Switzerland, the PM10 data until 1997 are actually
corrected total suspended particle (TSP) data (Empa, 2010), but they
are suitable for PM10 trend analysis (Barmpadimos
et al., 2011). Hourly high-quality SSR data from the Baseline Surface
Radiation Network (BSRN; König-Langlo et al., 2013) for seven stations
were used for model evaluation. An overview of the seven BSRN stations is given
in Table S2. Finally, AOD data were retrieved by the Aerosol Robotic Network
(AERONET), which is a network of ground-based Sun photometer measurements of
aerosol optical properties (Holben et al., 1998; O'Neill et al., 2003). We
used level 2.0 (quality assured) data for the 340 nm wavelength band to
compare with the respective modeled AOD values. The calibration error of the
AOD measurements is of the order of 0.015 (Holben et al., 1998; Eck et
al., 1999). Since the temporal resolution of the AOD measurements is not
constant (e.g., at specific hours), the calculated daily mean does not
correspond to a 24 h time interval but to intra-day time intervals with
available measurements. The daily average of the modeled AOD was calculated
using only the times of available AOD measurements for each site, for a more
consistent comparison between model and observations.
Model runs
The description of 12 model runs is shown in Table 1. We used two base case
scenarios: one with the default parameterization (BASE) and a second one
with increased NOx emissions (BASE_NOx) which
produced higher ozone concentrations, in order to incorporate any potential
underestimation of the ARI effects on ozone due to underestimated modeled
ozone production as suggested by Oikonomakis et al. (2018). All sensitivity tests were performed using both base case scenarios
(see Table 1).
Summary of model runs. All runs used the emissions and meteorology
of 2010.
Scenario
Description
BASE
Base case using the default parameterization as described in Sect. 2.1.
BASE_NOx
Same parameterization as BASE scenario but with doubled NOx emissions for each SNAP (Selected Nomenclature for Air Pollution) category to be used as a second base case with higher ozone production according to Oikonomakis et al. (2018).
PHOT1
Increased concentrations of SO42-, NH4+, NO3-, POA, ASOA, EC and FPRM by a factor of 2 over land only in the calculation of AOD.
PHOT1_NOx
Same method as PHOT1 but applied on the BASE_NOx scenario.
PHOT2
Increased concentrations of SO42-, NH4+, NO3-, POA, ASOA, EC, FPRM by a factor of 3 over land only in the calculation of AOD.
PHOT2_NOx
Same method as PHOT2 but applied on the BASE_NOx scenario.
PHOT3
Increased concentrations of only SO42- by a factor of 3.4 and only in the calculation of AOD.
PHOT3_NOx
Same method as PHOT3 but applied on the BASE_NOx scenario.
BIO
Rerun of the BASE scenario with new biogenic emissions generated after decreasing SSR by 3 % in the biogenic emission model.
BIO_NOx
Same method as BIO but applied on the BASE_NOx scenario.
COMBO
A combination of the PHOT3 and BIO scenarios.
COMBO_NOx
A combination of the PHOT3_NOx and BIO_NOx scenarios.
The impact of solar radiation changes due to the ARI on ozone chemistry was
investigated via two pathways: (i) via impact on photolysis rates and (ii)
via impact on biogenic volatile organic compound (BVOC) emissions. In
order to quantify these impacts, we first simulated the summer of 2010, then
applied sensitivity tests that would represent the radiation conditions in
the summer of 1990 (i.e., different solar radiation due to ARI) and finally
compared the two cases. In other words, we used the same meteorology and
emissions for both cases (except for the BVOC emission sensitivity tests
where we used different BVOC emissions) and we designed special sensitivity
tests to isolate and quantify the effect of changes in the ARI between those
years on ozone concentrations. Finally, it is noted that the chemistry
simulated by CAMx (for any scenario) does not affect the meteorology, as it
is prescribed (see Sect. 2.1), and hence the impact of ARI on atmospheric
dynamics and other meteorological related effects (e.g., vertical mixing, dry
deposition, Xing et al., 2017) are excluded in
this study.
Impact via photolysis rates
In order to quantify only the changes in ARI, we had to isolate them from
other effects such as the gas–aerosol chemical interactions. For this
reason, we modified the radiative transfer algorithm in CAMx (i.e., the
in-line version of TUV) by applying an adjustment factor (pf) in the
AOD calculation to represent the aerosol concentrations in 1990 but without
changing the concentrations themselves and thus avoiding any change due to
chemistry. So, the adjusted AOD for N vertical layers and M aerosol
species was calculated as shown below:
AOD=∑j=1NΔzj⋅∑i=1Mμexti⋅f(RHj)⋅Cij⋅pfi,
where μext is the aerosol dry extinction efficiency (see Table S1),
f(RH) is the relative humidity (RH) adjustment factor (FLAG,
2000), C is the aerosol species concentration, and Δz is the
layer's thickness. Hence, the product pf⋅C represents the PM
concentrations in 1990 but purely in AOD calculations in order to generate
only AOD, solar radiation and photolysis rates as in 1990. The value of
pf for sulfate (SO42-), ammonium
(NH4+), nitrate
(NO3-), primary organic aerosol (POA),
anthropogenic secondary organic aerosol (ASOA), elemental carbon (EC) and
fine other primary aerosol (FPRM) varies with the sensitivity test, while
there was no adjustment (i.e., pf=1) for biogenic secondary aerosol
(BSOA), sodium chloride (NaCl), fine (FCRS) and coarse (CCRS) crustal
aerosols, and coarse other primary aerosol (CPRM). We have excluded the
natural aerosols (biogenic SOA, sea salt and dust (FCRS + CCRS)) from the
AOD adjustment since the anthropogenic aerosol concentration reductions
were suggested as a likely explanation for the brightening (see Sect. 1);
moreover, no significant change in their contribution to the AOD trends was
reported (Streets et al., 2009). Although large natural aerosol
contributors like volcanic eruptions (e.g., El Chichón in 1986 and
Pinatubo in 1991) can introduce large spikes in the SSR time series, they do
not alter the longer-term trends (Wild, 2009). In addition, we also
excluded the coarse mode (PM10–PM2.5) of the anthropogenic
aerosols from the AOD adjustment, assuming that the fine mode (PM2.5)
dominated the decreasing trend of the total aerosol mass (discussed in
detail in Sect. 3; Barmpadimos et al., 2012; Tørseth et al., 2012). The
pf values 2 and 3 (corresponding to ∼ 50 and 65 %
reductions in PM2.5 concentrations, respectively, in 2010 compared to
1990) for the first two sensitivity tests (PHOT1 and PHOT2, respectively, in
Table 1), were inferred by a PM trend analysis based on observations
(discussed in detail in Sect. 3) and they represent an estimated range of
reductions in PM2.5 concentrations between 1990 and 2010 in Europe,
i.e., PM2.5_1990 = PM2.5_2010 ⋅ pf. The assumption for PHOT1 and PHOT2 scenarios is that
the estimated observed changes in PM2.5 are the same for all species,
which does not necessarily correspond to reality as some species decreased
more (SO42-) than others
(NH4+), while for some others (EC)
trends are not known as there were no measurements during the 1990s in
Europe (Tørseth et al., 2012). However,
sulfate was and still is one of the single most important components that
contribute to the total aerosol mass concentration in Europe (Putaud et
al., 2010; Tørseth et al., 2012). Moreover, the sulfate measurements
started in 1972, so its trends and changes (between -60 and -80 %) are
well known for our period of study (Tørseth et al., 2012; Banzhaf et
al., 2015; Xing et al., 2015c; Colette et al., 2016) and are within the
same range as the changes considered in PHOT1 and PHOT2 scenarios.
Therefore, we consider the PHOT1 and PHOT2 scenarios to be good proxies for
the purpose of this study, at a regional scale. Furthermore, in order to
investigate the impact of sulfate in more detail, we included another
sensitivity test (PHOT3 scenario) where we adjusted only the sulfate
concentrations in 2010 by a factor of 3.4, which represents approximately a
-70 % total change in sulfate concentrations between 1990 and 2010 based
on the aforementioned studies. Another aspect to be considered was the
anthropogenic aerosols originating directly or indirectly from ship
emissions. Since marine emissions were not regulated during 1990–2010
(Eyring et al., 2005; Aksoyoglu et al., 2016), we did not adjust the AOD
(i.e., pf=1) over the sea and ocean (for PHOT1, PHOT2 and PHOT3
scenarios), where the contribution of ship emissions to the PM2.5
concentrations is more significant (up to 50 %) compared to continental
Europe (up to 10 %) as shown by Aksoyoglu et al. (2016) for
the summer of 2006. This way, we expect that the photolysis rate sensitivity
tests will represent in general more consistently the AOD conditions of
1990, even though this approach might be conservative as the European
maritime AOD trends suggest a decline (significant at the 95 or 99 %
level) since the early/mid-1990s (Mishchenko et al., 2007; Cermak et al.,
2010; Li et al., 2014a).
Impact via BVOC emissions
We investigated the effect of changes in the solar radiation on biogenic VOC
emissions and the subsequent impact on ozone, in two steps. The first step
was to generate new biogenic emissions after decreasing the solar radiation
input values in the biogenic emission model by 3 % (corresponding to the
SSR conditions of 1990), as the observed relative change of SSR in Europe in
the summer season between 1990 and 2009 (i.e., the SSR was 3 % lower in
1990 compared to 2009) according to Turnock et al. (2015). These new emissions
would correspond to 1990 conditions with respect to the SSR factor; changes
in other parameters due to SSR changes, like temperature and photosynthesis
as well as diffuse to direct radiation ratio, were not taken into account.
The second step was to rerun CAMx with these new biogenic emissions (BIO
scenario) and compare with the base case (BASE scenario, Table 1). Finally,
we included a scenario (COMBO) with the combined effects of biogenic
emissions (BIO scenario) and photolysis rates (PHOT3 scenario; it was chosen
as it was considered to be the least uncertain scenario compared to PHOT1
and PHOT2) to assess the overall impact of the ARI changes on surface ozone.
PM trends
As discussed in Sect. 2.3, the adjustment factor (pf) used in the
sensitivity tests represents the total relative change in aerosol
concentrations between 1990 and 2010 for the summer season. Although for the
SSR such a value was available in the literature (Turnock et al., 2015) for a
similar time period (1990–2009) as in this study for the summer season, this
was not the case for the total aerosol concentrations. Therefore, we
performed a trend analysis to estimate the total relative change of aerosol
concentrations for the time period 1990–2010. Several studies report a
decreasing trend in both PM10 and PM2.5 concentrations in Europe
since the 1990s, following the reductions in the anthropogenic emissions of
PM10, PM2.5 and gas precursors responsible for secondary aerosol
formation (EEA, 2014, 2017). Barmpadimos et al. (2012) and Tørseth et
al. (2012) suggested that the decreasing trend in PM10 concentrations
was dominated by the reductions in the PM2.5 concentrations for the
periods 1998–2010 and 2000–2009, respectively, as the aerosol coarse mode
(PM10–PM2.5) had either a very small decrease or in some cases
even a small increase. Although Wang et al. (2012b)
claimed a smaller decrease in PM2.5 than in PM10 during 1992–2009,
this could be attributed to the difference in number and type of the sites as
discussed by Fuzzi et al. (2015).
Hence, for our trend analysis, we assumed that the aerosol coarse mode
remained constant throughout the period 1990–2010. Therefore, we subtracted
the 2010 aerosol coarse mode from the PM10 concentrations of all years
to infer the PM2.5 concentrations' trend, as there are no PM2.5
measurements available for the whole examined period (i.e., from 1990–1992
to 2010), and calculate their total change over the period of study. The
adjustment factors (pf) were then based on the total relative changes of
the estimated PM2.5 concentrations for the summer season (see Table 2).
Trends (and their standard errors) and total changes in PM10
concentrations measured at three stations in Switzerland (1990–2010) and at
three stations in the Netherlands (1992–2010). The total relative changes in the
estimated PM2.5 concentrations are also reported in parentheses. All
trends are statistically significant (at the 99 % confidence level).
Trend (µg m-3 yr-1)
Absolute change (µg m-3)
Relative change (%)
Annual
Summer
Annual
Summer
Annual
Summer
Switzerland
-0.64 ± 0.08
-0.56 ± 0.08
-12.7
-11.2
-41 (-48)
-45 (-53)
The Netherlands
-0.92 ± 0.11
-1.04 ± 0.14
-16.6
-18.6
-43 (-55)
-50 (-65)
The linear trends were calculated with the Theil–Sen method (Sen, 1968) and their significance was evaluated with the
Mann–Kendall test (Mann, 1945; Kendall, 1948). The stations
selected for the trend analysis (three for Switzerland and three for the
Netherlands) fulfilled the following criteria: (i) they covered the whole
period (1990–2010) (Switzerland) or 1992–2010 (the Netherlands) for
PM10 data; (ii) they had at least 70 % of daily PM10 and
PM2.5 data in each month; and (iii) they had both PM10 and
PM2.5 data for 2010 in order to calculate the 2010 aerosol coarse mode
(PM10–PM2.5). An overview of the stations is given in Table S3. Regarding the data treatment, the monthly average was calculated
initially for each station separately and the 2010 aerosol coarse mode was
subtracted to estimate PM2.5 concentrations as discussed above. Then,
an average over the stations was taken before the annual (or summer) average
was calculated requiring all 12 (or 3) months to be available for a year to
be considered in the analysis. The slope of the Theil–Sen trend gave the
absolute concentration change per year (µg m-3 yr-1), which
was then multiplied by the number of year intervals (number of years - 1) to
yield the total absolute change for the respective period. The total
relative change was estimated by dividing the total absolute change by the
regression value of the respective period's initial year.
The changes in the measured PM10 and estimated PM2.5 concentrations
at selected stations over the studied period and the results of the trend
analysis are shown in Fig. 1 and Table 2, respectively, for summer as well as
for the whole year. A steeper decreasing trend in PM10 concentrations is
evident for the Netherlands (-0.92 ± 0.11 µg m-3 yr-1) compared to Switzerland
(-0.64 ± 0.08 µg m-3 yr-1), especially in the
summer (-1.04 ± 0.14 and -0.56 ± 0.08 µg m-3 yr-1, respectively). The annual total relative
change in PM10 concentrations is -43 % for the Netherlands and
-41 % for Switzerland. This is in line with the -44 % PM10
change in Europe for the time period 1992–2009 that was reported by Wang et al. (2012b). Our PM10 trend results for
Switzerland are also in line with the results (-0.53 and -0.58 µg m-3 yr-1, for annual and summer trends, respectively)
reported by Barmpadimos et al. (2011) for the time
period 1991–2008; small differences in the trends between the studies are
attributed to the inclusion of more sites (with available data later than
1990) in Barmpadimos et al. (2011).
Annual (a, b) and summer (c, d) concentrations of
PM10 (blue) and PM2.5 (red) measured at three stations in Switzerland
(a, c) and at three stations in the Netherlands (b, d) for the
period 1990–2010 and 1992–2010, respectively. Dashed lines show the linear
regression fit. PM2.5 concentrations were estimated as described in Sect. 3.
Results and discussion
Model evaluation
The model performance evaluation for both WRF and CAMx models was carried
out and discussed in detail in Oikonomakis et al. (2018). A summary of the statistical metrics and model performance
evaluation is given in Tables 3 and 4, respectively, for the daily mean
O3, PM2.5 and PM10 (see also Fig. S1). The model performance
for O3 and PM2.5 was satisfactory, as discussed in detail by Oikonomakis et al. (2018). On the other hand, there
was a consistent underestimation of PM10 with a mean bias (MB) of -6 µg m-3 and normalized mean bias (NMB) of -33 %. However, the
correlation coefficient for the PM10 is 0.5, suggesting that the model
can capture the observed PM10 temporal evolution (Fig. S1). Also, since
the model performance for PM2.5 is better, this implies that the
discrepancy in the PM10 is more likely due to missing emissions in the
coarse mode such as sea salt, mineral dust and wildfires (see Sect. 2.1).
Even with the inclusion of such emissions, models still have difficulties
simulating the PM10 concentrations accurately, as the uncertainties
related to these emissions are large and meteorological uncertainties (e.g.,
in wind speed, vertical mixing) also play an important role (Karamchandani et al., 2017; Solazzo et al., 2017).
Definition of statistical metrics for model performance evaluation.
Mi and Oi stand for modeled and observed values, respectively, and
N is the total number of paired values.
Metric
Definition
Mean bias (MB)
MB=1N∑i=1NMi-Oi
Mean gross error (MGE)
MGE=1N∑i=1NMi-Oi
Root mean square error (RMSE)
RMSE=1N∑i=1NMi-Oi2
Normalized mean bias (NMB)
NMB=∑i=1NMi-Oi∑i=1NOi
Normalized mean error (NME)
NME=∑i=1NMi-Oi∑i=1NOi
Pearson correlation coefficient (r)
r=∑i=1N(Mi-M¯)⋅(Oi-O¯)∑i=1N(Mi-M¯)2⋅∑i=1N(Oi-O¯)2
Statistical summary of model performance evaluation for summer 2010.
The units for MB, MGE and RMSE are in ppb for O3,
in µg m-3 for PM and in W m-2 for SSR, while the
units for NMB and NME are in percent.
No. of
MB
MGE
RMSE
NMB
NME
r
stations
O3
382
4
7
8
13
22
0.7
PM2.5
35
0.3
5
7
10
49
0.5
PM10
128
-7
8
11
-34
45
0.5
SSR
7
14
35
50
6
15
0.8
AOD
47
-0.15
0.16
0.20
-47
51
0.6
The systematic model underestimation of the PM10 concentrations is also
evident in the AOD (Table 4), where the model consistently underestimates
the AERONET observations (MB of -0.15, MGE of 0.16). Despite this
systematic negative bias, the model is able to represent quite accurately
the spatial and temporal variability of the observed AOD, indicated by the
relatively high correlation (r=0.6) between the model and the
observations which is shown in more detail in Fig. S1. Other possible error
sources for the modeled AOD could be (i) the simplified treatment of the
aerosol size distribution, the optical properties and the mixing state (Curci et al., 2015); (ii) the use of
the constant climatological aerosol Elterman (1968) profile for the upper
troposphere and stratosphere; (iii) uncertainties in RH and f(RH) for
inorganic aerosols; and/or (iv) uncertainties due to grid resolution
(horizontal or vertical). Overall, our model AOD discrepancies are within
range with other modeling studies (Cesnulyte et al., 2014; Im et al.,
2015), where they underline the importance of dust and sea salt treatment in
the models.
In the case of SSR, the model performance is better, with a slight
overestimation (NMB of 6 %; Table 4). The diurnal and inter-daily
variability was captured as well (Figs. 2 and S1; r=0.8). In general,
the overestimation of the downward shortwave radiation is a long-standing
issue in the models (Wild, 2008; Wild et al., 2013), which indicates that
it might be related not only to aerosols but also to other important sources
of uncertainty such as parameters related to clouds and water vapor. Since
the modeling framework of this study is based on the PM2.5, we believe
that the systematic PM10 model bias would not affect the results and
conclusions significantly.
Mean diurnal profiles of observed and modeled (BASE scenario) SSR
at seven European sites from the BSRN network in summer 2010.
PM species
The modeled daytime (10:00–18:00 LMT) concentrations of the fine PM species
to be adjusted for AOD and SSR calculations are shown in Fig. 3. Sulfate
concentrations were predicted to be the highest in summer among all seven
species (Fig. 3a) especially over the Mediterranean Sea and southeastern
Europe. Although ship emissions are considered to be the main source of
elevated sulfate concentrations over the sea, their contribution to the land
areas in Southeastern Europe (e.g., Greece and Turkey) is much smaller
compared to other emission sources, such as power generation, industries
and road transport (Tagaris et al., 2015; Aksoyoglu et al.,
2016). Particulate nitrate concentrations, on the other hand, are higher in
regions with high NOx and NH3 emissions (around the English
Channel, Benelux region, northern Italy). The concentrations of
anthropogenic SOA (Fig. 3d) are very low, and the spatial distribution of
primary species POA, EC and FPRM is similar to their emission
patterns (Fig. 3e–g). The high POA concentrations on the eastern boundary of
the model domain are consistent with the summer 2010 Russian wildfires,
which influenced mainly the areas around Moscow and to a lesser extent the
eastern part of Europe (Mei et al., 2011; Portin et al., 2012;
Péré et al., 2015). It is noted that, although wildfire emissions
are not included in the model (see Sect. 2.1), they enter the model domain
from the model boundaries.
Seasonal daytime (10:00-18:00 LMT) mean concentrations
(µg m-3) of (a) sulfate (SO42-),
(b) nitrate (NO3-), (c) ammonium (NH4+),
(d) anthropogenic secondary organic aerosol (ASOA), (e) primary organic
aerosol (POA), (f) elemental carbon (EC), (g) fine other primary aerosols
(FPRM) and (h) sum of panels (a–g), for the BASE scenario in summer 2010.
Results of PM adjustment scenarios
Changes in AOD
In this section, the AOD in the base case (BASE) is compared to the AOD
after the adjustment of fine PM species to represent the conditions in 1990
(see Table 1 for the adjustment scenarios). The simulated AOD in the base
case (Fig. 4a) has a similar spatial distribution over the European domain
to the anthropogenic aerosols (see Fig. 3h), although the highest AOD values
in the whole grid are in the dust-enriched northwest Africa (in the model,
dust is included only in the boundary conditions). The European (i.e.,
excluding northwest Africa) land (land and marine) grid mean of the AOD is
0.14 (0.13), while in more polluted regions (e.g., Po Valley, Benelux region,
western Turkey), the AOD values are as high as 0.20–0.25. The spatial
distribution of modeled AOD is in line with modeling results and satellite
observations (at around 550 nm) from other studies for different summer
periods (Real and Sartelet, 2011; Xing et al., 2015b; Mailler et al.,
2016), as well as with the eight-model ensemble results of the PM10
spatial distribution for 2010 by Colette et al. (2017).
Seasonal daytime (10:00-18:00 LMT) mean AOD at 350 nm for the
BASE scenario (a) and AOD differences between the BASE scenario and the PHOT1,
PHOT2 and PHOT3 scenarios (b–d), respectively, in summer 2010. Note the
reversed color order in the color scales of panels (b–d).
The changes in the calculated AOD after the adjustment of the PM species
according to the descriptions given in Table 1 are shown in Fig. 4b–d. The
largest difference in AOD was obtained with the PHOT2 scenario (Fig. 4c) of
up to -0.41, followed by the PHOT3 (Fig. 4d) and PHOT1 scenarios (Fig. 4b) with
up to -0.33 and -0.21, respectively. The continental European grid averages
for the AOD differences between the base case (BASE) and PHOT1, PHOT2 and
PHOT3 scenarios are -0.10, -0.21 and -0.15, respectively. The changes in AOD
in all three tests consistently follow the spatial distribution of
anthropogenic aerosols (see Fig. 3h), with southwestern and northern Europe
having the smallest values due to higher contribution of dust and BSOA,
respectively, to aerosol concentrations in these regions (Fig. S2). The
spatial distribution of the simulated AOD differences (Fig. 4b–d) is
similar to that from the modeled difference in PM10 concentrations
between 1990 and 2010 (Colette et al., 2017),
supporting the assumptions used in our sensitivity tests. Xing et al. (2015b) reported that the simulated
trends of AOD (at 533 nm) in summer in Europe were -0.007 and -0.003 yr-1, for the periods 1990–2000 and 2000–2010, respectively, resulting
in -0.1 for the whole period (1990–2010). They also calculated an AOD summer
trend of -0.002 to -0.007 yr-1 from the analysis of satellite
observations for the period of 2000–2010. Turnock et al. (2015) reported modeled and
observed (from AERONET sites) summer AOD (at 440 nm) trends of -0.005 and
-0.014 yr-1, respectively, for the period 2000–2009, which are higher
than the ones reported by Xing et al. (2015b)
probably due to the lower wavelength used by Turnock et al. (2015). This could be an
indication that the fine-mode particles were mainly responsible for the
decreasing AOD trends, as their scattering efficiency is higher at smaller
wavelengths (Seinfeld and Pandis, 2016). Li et al. (2014b) also suggested that the AOD reduction in Europe might have been
driven by decreases in the fine-mode particles. The authors reported
decreasing trends in the AOD (at 440 nm), as well as in the Ångström
exponent (at 440/870 nm), for the vast majority of the European AERONET
sites between 2000 and 2013; the largest AOD decrease was observed in western
Europe with -0.1 decade-1 (i.e., -0.010 yr-1). Another study by Bin et al. (2017) further supported the conclusions about the AOD
decreasing due to the smaller particles. They showed that the AOD (at 555 nm) trend from satellite observations for western Europe in summer was
∼-0.003 yr-1 between 2001 and 2015. Assuming that the
AOD trend between 1990–2000 and 2000–2010 was the same, we estimated the
AOD trend in Europe for 1990–2010 to be ∼-0.005, -0.010 and
-0.008 yr-1 for the PHOT1, PHOT2 and PHOT3 scenarios, respectively. Our
results about the change in AOD are in the same range as the other studies,
by taking into account that (i) for smaller wavelengths (350 nm in our case
and > 440 nm in the aforementioned studies) larger changes are
expected due to the higher decreasing trend in the fine-mode particle
concentrations as discussed above; (ii) the AOD reduction might have been
larger for 1990–2000 than 2000–2010 (Xing et al.,
2015b).
Changes in SSR
In this section, the SSR in the base case (BASE) is compared to the SSR
after the adjustment of fine PM species to represent the conditions in 1990
(see Table 1 for the adjustment scenarios). The modeled SSR for the base
case (BASE) is shown in Fig. 5a. The model captured both the magnitude and
the spatial distribution with the south–north latitudinal gradient and the
lowest values over the northwest Atlantic Ocean, as also shown by other
studies (Forkel et al., 2012, 2015). The average (maximum) differences in
SSR over land between the base case (BASE) and PHOT1, PHOT2 and PHOT3 tests
are 9 (20), 17 (35) and 11 (26) W m-2, respectively (Fig. 5b–d).
Following the same method as for the AOD, we estimated the SSR trend as
0.45, 0.85 and 0.55 W m-2 yr-1 for PHOT1, PHOT2 and PHOT3,
respectively. Other studies reported modeled and observed SSR trends within
a range of 0.35–0.55 W m-2 yr-1 for different periods between
1986 and 2012 (Norris and Wild, 2007; Allen et al., 2013; Cherian et al.,
2014; Nabat et al., 2014; Sanchez-Lorenzo et al., 2015; Turnock et al.,
2015). Based on these studies, PHOT1 and PHOT3 are more realistic scenarios
than PHOT2 which seems to present a slight overestimation of the ARI
changes. Xing et al. (2015a) reported for Europe SSR changes
between 1990 and 2010 in the range of 6–18 W m-2 in line with PHOT1 and
PHOT3 scenarios (Fig. 5b and d). In general, our simulated AOD and SSR changes
between 1990 and 2010 for PHOT1 and PHOT3 scenarios seem to be consistent
with respective observed and modeled changes from other studies, while the PHOT2
scenario can be considered rather an upper limit of the ARI changes.
Seasonal daily mean SSR for the BASE scenario (a) and SSR
differences between the BASE scenario and the PHOT1, PHOT2 and PHOT3 scenarios (b–d), respectively, in summer 2010. Note the different color scale between
panel (a) and panels (b–d).
Effects on ozone via photolysis rates
The simulated (in the base case) ground-level photolysis rate of NO2,
J(NO2), consistently follows the south-to-north latitudinal gradient of
SSR and temperature, as shown in Fig. 6a. The modeled continental mean
absolute (relative) differences in ground-level J(NO2) between the base
case (BASE) and PHOT1, PHOT2 and PHOT3 tests are 0.7 (3 %), 1.3 (6 %)
and 0.9 (4 %) h-1, respectively (Fig. 6b–d). The spatial
distribution and relative changes are the same for the ground-level
photolysis rate of O3, J(O3 → O1D), with changes in
absolute terms being 0.0015, 0.0029 and 0.0020 h-1, respectively, for
PHOT1, PHOT2 and PHOT3 tests (Fig. S3). As discussed in Sect. 1, changes in
the photolysis rates will affect the chemical production and destruction of
ozone as well as other chemical processes in the troposphere such as the
secondary aerosol (SA) formation, which in turn can affect the
photolysis rates. This implication, however, is rather small with the change
in SA concentration (continental grid mean) between base case (BASE) and
PHOT1, PHOT2 scenarios being 0.01 and 0.02 µg m-3, respectively
(Figs. S4–S5) or in relative terms 0.3 and 0.6 %, respectively (the
respective results for the PHOT3 test are very similar to the ones of the
PHOT1 test and are therefore not shown). We conclude that these changes in
SA have negligible impact on the photolysis rates. However, the changes in
SA might not be negligible if the impact of ARI on meteorology and
subsequent effects on chemistry are also taken into account (which is not
the case for this study).
Seasonal daytime (10:00-18:00 LMT) mean J(NO2) at ground
level for the BASE scenario (a) and J(NO2) differences between the BASE
scenario and the PHOT1, PHOT2 and PHOT3 scenarios (b–d), respectively, in summer
2010. Note the different color scale between panel (a) and panels (b–d).
The enhancement of the photolysis rates leads to higher ozone formation
especially in regions where there are significant ozone precursor emissions
(i.e., central Europe, northern Italy; Fig. 7). The magnitude of the effect
of enhanced photolysis rates is, however, rather small on average. The
difference in the surface ozone between BASE and PHOT2 scenarios during the
daytime (10:00–18:00 LMT) varies between 0.4 and 0.7 ppb (1–1.5 %)
over central Europe and up to 1.4 ppb (2.5 %) in the Po Valley, while it
is smaller for the other two scenarios (up to 0.7–0.8 ppb, 0.7–1.4 %).
However, on an hourly resolution, the largest difference in surface ozone
between BASE and PHOT2 scenarios can go up to 8 ppb (16 %) and up to
4 ppb (10 %) for the PHOT1 and PHOT3 scenarios in
high-NOx areas on land (Fig. S6).
We repeated similar tests based on a second base case (BASE_NOx) with increased NOx emissions which improved the model
performance for ozone production as discussed in Oikonomakis et al. (2018).
In the BASE_NOx case, ozone production was higher over a
larger area compared to the BASE (see Figs. 7a and 8a). Consequently, the
difference in ozone between BASE_NOx and the
PHOT1_NOx, PHOT2_NOx and
PHOT3_NOx scenarios was more pronounced over a larger area; the
magnitude of the impact, however, only slightly increased (Figs. 8 and S6).
Seasonal daytime (10:00-18:00 LMT) mean O3 mixing ratios for
the BASE scenario (a) and O3 differences between the BASE scenario
and the PHOT1, PHOT2 and PHOT3 scenarios (b–d), respectively, in summer 2010.
Note the different color scale between panel (a) and panels (b–d).
Seasonal daytime (10:00–18:00 LMT) mean O3 mixing ratios for
the BASE_NOx scenario (a) and O3 differences
between the BASE_NOx scenario and the PHOT1_NOx, PHOT2_NOx and PHOT3_NOx
scenarios (b–d), respectively, in summer 2010. Note the different color
scale between panel (a) and panels (b–d).
We also investigated the impact of ARI changes on daily maximum ozone, but
it was higher only by up to ∼ 0.1 ppb (not shown) compared to
the daytime (10:00-18:00 LMT) average. Therefore, the ARI did not have a
significantly higher impact on daily maximum ozone. The reason is that the
daily maximum ozone occurs at a different time (mid-afternoon) than the times
the maximum ARI occurs (morning and evening), as also shown in other studies (Xing et al., 2015a, 2017).
Effects on ozone via BVOC emissions
The response of isoprene emissions (2.5–3 % changes) to SSR changes
(3 %) is nearly linear (Fig. 9), in line with the literature (Guenther et al., 2006). On the contrary, terpene
(monoterpene and sesquiterpene) emissions are less sensitive to SSR with
changes up to 0.7 % (Fig. 9). Nevertheless, the BVOC emissions'
sensitivity to solar radiation can vary depending on the model
parameterization of physical processes such as the emission dependence on
light and the canopy calculations of diffuse and direct radiation as well as
the relative contribution between shaded and sunlit leaves over multiple
leaf area index (LAI) layers (Messina et al.,
2016). In general, BVOC emission estimates have high uncertainties (a
factor of 2–3) due to uncertainties in the land use, LAI and
parameterization of physical processes, the large number of compounds and
biological sources, and the lack of observations (Guenther et al., 2006; Karl et al., 2009; Guenther, 2013; Oderbolz et al., 2013). Despite these
uncertainties, Stavrakou et al. (2014) also
reported a linear response of isoprene emissions with the respective SSR
changes in Asia between 1979 and 2012, using a different biogenic emission
model. On the other hand, other studies suggested that the
photosynthetically active radiation (PAR), which depends more on the diffuse
component of solar radiation, did not have a significant impact on the
increasing BVOC trends in Europe during the solar brightening (after
1980) probably due to the diffuse to direct radiation ratio decrease,
compensating for the total increase in SSR (Mercado et al., 2009; Yue et
al., 2015). In fact, during the solar dimming (i.e., when the total SSR
decreased) between 1960 and 1980, both the diffuse fraction of PAR and the
photosynthesis were enhanced (Mercado et al., 2009). It is
further suggested that the BVOC emissions are less sensitive to the SSR
compared to the temperature, which is identified as a more important driver
for the BVOC emission trends (Guenther et al., 2006; Lathière et
al., 2006; Yue et al., 2015; Gustafson et al., 2017).
Total (i.e., JJA sum) of isoprene (left panels) and terpene
(monoterpene and sesquiterpene; right panels) emissions per km2 for the
BASE scenario (top panels) and relative difference between BASE and BIO
scenarios (bottom panels) in summer 2010.
The impact of a 2.5–3 and 0.7 % increase in isoprene and terpene
emissions (BIO scenario), respectively, on daytime (10:00–18:00 LMT)
average surface ozone is rather small (up to 0.08 ppb, ∼ 0.2 %; Fig. 10a) and an order of magnitude smaller than the respective
ozone impact via photolysis rates (see Fig. 8). Both the daytime average and
largest hourly (∼ 1 ppb) impacts are higher in central Europe
where both BVOC and NOx emissions are ample (Figs. 10a and S7). The
effects of increased BVOC emissions are higher in magnitude (up to 0.11 ppb, ∼ 0.3 %) and spatial coverage when applied to the base
case with higher NOx emissions (i.e., BASE_NOx–BIO_NOx), as shown in Figs. 10b and S7. The combined
effects via BVOC emissions and photolysis rates (COMBO and
COMBO_NOx scenarios) on surface ozone appear to be
roughly additive, with the photolysis rates effects dominating the overall
impact (daytime average difference was up to 0.8 ppb, 1.5 %; Figs. 10c–d
and S7). Overall, the direct effects of SSR changes on the BVOC emissions
(with the assumptions and parameterizations of this study) were small, and
as a result this was also the case for the consequent impact on surface
ozone. However, SSR trend implications related to temperature and CO2
changes (Wild et al., 2007; Storelvmo et al., 2016) might have a more
significant impact on BVOC emissions and thus on surface ozone, but this
was beyond the scope of this study.
Seasonal daytime (10:00–18:00 LMT) mean O3 differences
between the (a) BASE and BIO, (b) BASE_NOx and
BIO_NOx, (c) BASE and COMBO, and (d) BASE_NOx and COMBO_NOx scenarios in summer 2010.
ARI and ozone trends
Although the effects of ARI changes via photolysis rates and BVOC emissions
on surface ozone seem to be small compared to the total ozone
concentrations, it might be more meaningful to compare with the magnitude of
the observed ozone concentration trends. Wilson
et al. (2012) reported an annual (summer) increasing trend of 0.16 ± 0.02 (0.12 ± 0.06) ppb yr-1 in the European ground-level ozone
(stations' average) for the period 1996–2005. The total ozone difference
(0.2–0.8 ppb) via both the effects on photolysis rates and BVOC emissions
(COMBO scenario) would translate (considering the full 20-year time period)
to a summer trend of 0.01–0.04 ppb yr-1. These values should not be
considered for a direct comparison with the absolute values of the
aforementioned observed ozone trends, not only due to differences in the
data analysis like time averaging and spatial coverage but most importantly
due to the exclusion of other physical and chemical processes influencing
the ozone trends. Nevertheless, the comparison of the order of magnitude
between the aforementioned values and the reported ozone trends suggests a
higher importance of the impact of ARI (only via photolysis rates and BVOC emissions) on surface ozone than when just comparing to the total ozone
concentrations. Therefore, this comparison indicates that the ARI (as
investigated in this study) might have had an accountable impact on the
European surface ozone trends since the 1990s and could have partially
dampened the effects of ozone precursor emissions' reduction along with other
more influential physical processes like intercontinental transport and
stratosphere–troposphere exchange (Ordóñez et al., 2007; Derwent
et al., 2008, 2015).
Conclusions
We investigated the impact of the ARI changes on European summer surface
ozone between 1990 and 2010 using the CAMx air quality model. We modeled the
summer of 2010 as base case and designed various sensitivity tests based on
literature review as well as an observational PM trend analysis performed in
this study to represent the AOD and SSR conditions of the year 1990. One of
the main assumptions in this study was that the change in ARI was the main
driver for the solar brightening in Europe and thus excluded the ACI
and cloud cover natural variability. Moreover, this study focused on the
less uncertain effects of ARI via the impact on photolysis rates and BVOC emissions, compared to the more uncertain ARI-induced meteorological
effects. Lastly, in the model scenarios, we assumed that the AOD changes
between 1990 and 2010 in Europe were predominantly driven by changes in the
anthropogenic PM2.5 concentrations, and hence we excluded any AOD
changes due to variations in PM10 or natural PM2.5 concentrations.
Regarding the impact on ozone via photolysis rates, the PHOT1 and PHOT3
model scenarios (doubling anthropogenic PM2.5 concentrations and
increasing only sulfate concentrations by 3.4 times, respectively) were
considered to be closer to the observed and modeled AOD and SSR changes
reported by other studies (see Sect. 4.3.1 and 4.3.2) compared to PHOT2
scenario (tripling anthropogenic PM2.5 concentrations) that should be
regarded as an upper limit. Furthermore, the PHOT3 scenario was based on
less uncertain assumptions (well-documented sulfate concentration trends;
see Sects. 2.3.1 and 3), and therefore we considered it to be more
realistic (except for southeastern Europe where the effects might be
overestimated). The differences in AOD, SSR and the main ground-level
photolysis rates (J(NO2) and J(O3 → O1D)) between the
BASE and PHOT3 scenarios (representing the changes between summer of 1990
and 2010) were -0.33, 11 W m-2 and 4 %, respectively, and the
consequent impact on daytime (10:00–18:00 LMT) surface ozone was on average
0.2–0.4 ppb (0.5–1 %) over central and western Europe. Moreover, the
largest hourly difference in surface ozone could be as high as 4 ppb (10 %), while the same test performed on a base case with higher NOx
emissions and ozone production (BASE_NOx–PHOT3_NOx) resulted in an extension of the spatial
coverage of the ARI effects on ozone (apart from the VOC-limited Benelux
region).
On the other hand, the impact of -3 % SSR change resulted in a near-linear
response in isoprene emissions (2.5–3 %) but less in the terpene
(monoterpene and sesquiterpene) emissions (0.7 %), with the subsequent
effects on daytime ozone being small (up to 0.08 ppb, ∼ 0.2 %). Compared to the impact on ozone via the photolysis rates, the
effects of BVOC emission changes were about an order of magnitude smaller,
and thus the former dominated the latter impact when they were combined, as
their effects were nearly additive. Therefore, the overall impact of SSR
changes on ozone remained relatively small. Nevertheless, the role of the
ARI changes (as quantified in this study) in the European summer surface
ozone trends was suggested to be more important when comparing to the order
of magnitude of the ozone trends instead of the total ozone concentrations.
Finally, the inclusion of the impact of ARI on meteorology and ACI might
have additional increasing or, conversely, decreasing effects on surface
ozone as discussed in Sect. 1. However, climate modeling studies show that
the decline of aerosols can also affect the global atmospheric circulation
as well as the atmospheric stability (Rotstayn et al., 2014; Wang et al.,
2016; Navarro et al., 2017) and this entanglement might have compelling
implications for air quality at a regional scale. It is therefore suggested
that future air quality studies take into account the possible repercussions
of declining aerosols on climate and atmospheric circulation at a global
scale for a better understanding of the anthropogenic influence on air
quality and climate as well as their complex interlinkage.