Introduction
Production of ground-level ozone (O3) is dependent on concentrations of
NOx (NO and NO2), methane, carbon monoxide, and volatile organic
compounds (VOCs) (Jenkin and Clemitshaw, 2000). The formation of
O3 causes substantial deleterious human health and environmental
impacts worldwide (RoTAP; 2012; REVIHAAP, 2013). Development of policies
for the mitigation of these impacts requires understanding of the influences
on O3 concentrations from local, regional and hemispheric-scale
processes. The range in VOC atmospheric lifetimes from a few hours to
several days (Atkinson, 2000) means that the major fraction of the VOC
impact on O3 production occurs on the regional scale of air-mass
movements. At the regional scale, Gauss et al. (2014)
modelled the reductions in O3 impact across Europe on human health
(using the SOMO35 metric) and vegetation (using the deciduous forest
PODY metric) resulting from 15 % reductions in anthropogenic NOx
and VOC emissions across the EU and showed that VOC emission reductions
were more effective than NOx emission reductions in reducing the
O3 impact metrics across much of north-west Europe. Hence, knowledge of
the contribution of individual VOCs to O3 production on the European
(regional) scale will enable targeting of the most effective VOC reductions
for reducing regionally derived O3 exposure relevant to O3
impacts.
Within Europe, the European Monitoring and Evaluation Programme (EMEP) makes
in situ atmospheric composition measurements at sites considered to have
minimal influence from local emission sources (Tørseth et al., 2012). The
UK operates two EMEP Level II monitoring sites (or “supersites”),
Auchencorth and Harwell, at which hourly concentrations of O3, NOx
and 27 VOCs are measured. In this work, chemical climates (defined in Malley
et al., 2014a) are derived to quantify the impact of the measured VOCs on the
regional increment of O3 concentrations (the difference between regional
background and hemispheric background O3 concentrations) measured at
Harwell and Auchencorth. Full definitions of each of these O3 quantities
are given in Sect. 2.1. Monthly-diurnal O3 variation at the EMEP
supersites has previously been shown to be representative of wider
geographical areas, namely rural background air of south-east England and
northern UK for the Harwell and Auchencorth UK supersites, respectively
(Malley et al., 2014b).
The interpretation of VOC measurements at rural sites has previously been
undertaken using positive matrix factorisation (PMF)
(Lanz et al., 2009), trajectory analysis (Sauvage et al., 2009), VOC variability as a measure of source
proximity (Jobson et al., 1999), winter/summer VOC
ratios to indicate changing emission sources
(Jobson et al., 1999), and the ratio of VOCs with
similar reactivity to highlight changes in emission sources (Yates et al., 2010). These studies
identified VOC emission sources based on measured VOC concentrations.
However, the “state” of atmospheric composition variation producing a
regional O3 increment above hemispheric background concentrations is
more rigorously evaluated by considering the chemical loss of the measured
VOCs, since it is the VOC chemical loss in the air mass that drives the
production of a regional O3 increment, not the VOC concentration
remaining in the air mass. In urban environments, the chemical loss of VOCs
has been calculated through the estimation of initial emission ratios of two
VOCs and calculation of photochemical age through parameters such as “OH
exposure” or “VOC consumption” (Shao et al., 2009; Yuan et al., 2012).
This method is not appropriate for rural studies since it assumes that local
sources dominate emissions.
In this work, monthly-averaged diurnal variations of individual VOC
concentrations relative to ethane were used to assess the photochemical loss
of each VOC and its contribution to the regional O3 increment at Harwell
and Auchencorth. Monthly-diurnal averaging was chosen as the annual and daily
cycles are key features of O3 variability associated with the driving
processes on its concentrations and on its impact. For example, the monthly
and diurnal variation in O3 is central to determining the extent and
spatiotemporal trends in health- and vegetation-relevant O3 metrics
(Malley et al., 2015). Ozone variability at hundreds of monitoring sites
globally has also been characterised based on monthly-diurnal variation
(Tarasova et al., 2007). Monthly-diurnal averaging was therefore also
appropriate for setting this work in the wider context, especially given the
relative scarcity of hourly VOC measurements. The magnitude of VOC chemical
loss at each site was linked to anthropogenic emissions by estimating the
integrated VOC emissions along 96 h air-mass back trajectories. These
emissions, from the 11 Selected Nomenclature for Air Pollution (SNAP; EEA,
2013) source sectors, were speciated to compare observed VOC variation with
an estimate of individual VOC integrated back-trajectory emissions.
Integration of emissions, VOC chemistry and O3 production has been
reported previously for one location in the UK using a photochemical
trajectory model with a near-explicit chemical mechanism for a large suite of
VOCs (Derwent et al., 2007a, b). The advantage of the methodology presented
here, based on measurement data, is that uncertainties associated with the
speciation of VOC emission source categories can be identified. A
country-specific disaggregation of emissions into 91 more narrowly defined
Nomenclature for Reporting (NFR; EEA, 2013) source sectors was used to
determine more precisely the activities contributing to VOC back-trajectory
emissions estimates. This current work presents a clear methodology for
achieving a coherent VOC, regional-O3-impact chemical climate and
explores the effect of limited emission and measurement species on the
understanding of the regional contribution to O3 concentrations.
Methodology
This work was undertaken by applying the chemical climatology framework
outlined in Malley et al. (2014a). A chemical climate is
derived through the linkage of a specific “impact” of atmospheric
composition (here, regional O3 increment) through the “state” of
relevant atmospheric composition variation (VOC diurnal photochemical
depletion) to its causal “drivers” (meteorology and emissions). The aim of
this framework is to provide a consistent method for both consideration of
impact severity and the conditions producing it, hence highlighting pathways
for mitigation. The Methods and Results sections are subdivided into impact
(Sects. 2.1 and 3.1 for Methods and Results, respectively), state (Sects. 2.2, 3.2)
and drivers (Sects. 2.3, 3.3) to emphasise the analyses
used to derive the components of the chemical climate. Analyses were
undertaken for the periods 1999–2001 and 2010–2012 at Harwell and 2010–2012
at Auchencorth. Measured data were obtained from UK-AIR (http://uk-air.defra.gov.uk/)
and EMEP (http://ebas.nilu.no/).
For each year, the monthly-averaged diurnal cycles of each atmospheric
component were calculated, i.e. 24 × 12 = 288 values per year.
Regional O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> increment impact
The regional O3 increment is defined as the regional background O3
concentrations minus the hemispheric background O3 concentration. Here,
regional background O3 concentration is defined as that which is
imported into a local spatial domain following modification of hemispheric
background O3 concentrations by European emissions. Examples of local
spatial domains are south-east England and northern UK for which, based on
monthly-diurnal O3 variation, Harwell and Auchencorth, respectively, were
shown previously to be representative (Malley et al., 2014b). The
hemispheric background O3 concentration is in turn defined as that
which is imported into the European domain, with minimal influence from
European emissions.
Hemispheric background O3 concentrations were derived by applying
Ward's method of hierarchical cluster analysis to pre-calculated 96 h air-mass
back trajectories arriving at 3 h intervals at Mace Head, Ireland (R Core
Development Team, 2008; Carslaw and Ropkins, 2012; Draxler and Rolph, 2013),
to identify periods with no European influence. The discrimination achieved
by cluster analysis may be influenced by user choices but the method used
here was shown to be the most accurate of commonly used clustering
techniques (Mangiameli et al., 1996). In Ward's method, each
object (back trajectory) initially constitutes its own cluster. The
algorithm then calculates which two clusters, when merged, give the
smallest increase in total within-cluster variance. The process is repeated
until all trajectories are located in one cluster (Kaufman and
Rousseeuw, 1990). The dendrogram summarising the cluster merging process is
then “cut” at an appropriate level to produce the cluster set. The aim is to
maximise explained inter-trajectory variability using a small number of
clusters to highlight major distinctions between trajectory paths. The
distance between a trajectory and its cluster mean was quantified using the
two-dimensional “angle” of each trajectory (or cluster mean trajectory) from
the origin (i.e. the supersite) at common time points along the trajectory:
d1,2=1n∑i=1ncos-10.5Ai+Bi+CiAiBi,
where
Ai=(X1i-X0)2+(Y1i-Y0)2,Bi=(X2i-X0)2+(Y2i-Y0)2,Ci=(X2i-X1(i))2+(Y2i-Y1(i))2.
d1,2 is the distance between trajectory 1 and trajectory 2,
X0, Y0 are the latitude and longitude coordinates of the origin of
the trajectory, and X1(i), Y1(i), and X2(i), Y2(i) are the
coordinates at time i of trajectories 1 and 2, respectively. The 2920 back
trajectories arriving at Mace Head each year were separated into four
clusters. The monthly-diurnal cycles of O3 concentrations for the
westerly trajectory cluster were used as the estimate of hemispheric
background O3. These values showed excellent agreement with the monthly-average hemispheric background estimates derived by Derwent et al. (2007c)
using Mace Head O3 data and a combination of
pollutant tracers and atmospheric modelling to select “clean” air masses
(r= 0.93, p < 0.001, Fig. 1).
Correlation between monthly hemispheric background O3
concentrations derived by Derwent at al. (2007c) using
pollutant tracers and atmospheric modelling to select “clean” air masses,
and derived by the method described in Sect. 2.1 using cluster analysis.
Black regression line is calculated by the ordinary least squares (OLS)
method, with confidence intervals (95th percentile) shown in grey.
![]()
Regional background O3 concentrations were estimated using the method of
Clapp and Jenkin (2001). In the region of south-east England characterised by
the Harwell supersite nine locations, ranging from rural background to
kerbside, had hourly measurements of O3, NO and NO2. The
y intercept of the linear fit to a total oxidant (O3+ NO2) vs.
NOx (NO + NO2) plot yields the NOx-independent oxidant
contribution, interpreted as the regional background O3 concentration,
i.e. the contribution to O3 within south-east England from processes
occurring outside south-east England. Extraction of the y intercept from an
oxidant vs. NOx plot for each of the 288 “month–hour” averages yielded
the monthly-diurnal cycle of regional background O3 variation in
south-east England. The difference between the hemispheric background and
regional background O3 concentrations provided the magnitude and
direction of the regional modification to hemispheric background O3
concentration. A positive regional O3 increment indicates additional
O3 formation regionally in excess of hemispheric background
concentrations and vice versa.
The spatial domain for which Auchencorth is representative does not have
sufficient co-located NOx and O3 monitoring sites to derive
regional background O3 concentrations by the above method. The regional
O3 increment at Auchencorth was therefore estimated by subtracting the
Mace Head hemispheric background estimates directly from the Auchencorth
monthly-averaged diurnal concentrations.
State
VOC concentrations were determined by automated gas chromatography
(Dernie and Dumitrean, 2013). For 2010–2012, data were
available for 27 species at both Harwell and Auchencorth. Concentrations of
six VOCs at Auchencorth during this period were not above the reported limit
of detection (LOD), so their contribution to the regional O3 increment was
not evaluated. For 1999–2001, data were available for 21 VOCs at Harwell
only.
The VOC data sets had extensive periods during which concentrations were
below LOD, particularly at Auchencorth (e.g. between 6 and 81 % below
LOD at Harwell in 2011, and between 11 and 82 % at Auchencorth).
Therefore, maximum likelihood estimation (MLE) was used to fit three
positively skewed distributions (lognormal, gamma and Weibull) to the
data set for each VOC (Helsel, 2006; Gardner, 2012). The Akaike
information criterion (AIC) was then used to select the distribution which
best fitted the data; this provides a relative estimate of the information
lost when a given distribution is used to represent a data set
(Akaike, 1974). This process was performed on data for each month of
the year, and separately for the 288 monthly-diurnal time periods. The
fitted distributions estimated the probability that a “non-detect” (below
the LOD) was a concentration in the range 0 µg m-3 to the
LOD.
When non-detects occurred for all VOCs in a particular hour, these were
excluded from the MLE analysis on the assumption that this was due to
instrument failure. To avoid the unnecessary omission of valid concentration
measurements, all other data were used, and consequently all remaining
non-detects were assumed to be values below LOD. A number of non-detects due
to the selective failure of the instrument to measure a particular VOC may
be falsely considered to be below the LOD. However, the following evidence
indicates that any bias introduced is likely to be small. Annual medians
were calculated twice using MLE for Harwell in 2011, first, with the
non-detects unique to each VOC, secondly with their omission (i.e. assuming
all these non-detects were due to reasons other than LOD). The increase when
omitted was below 10 % for 11 of the 27 VOCs, including the VOCs with
concentrations consistently well above the LOD. For example, the 5th
percentile concentrations (of all valid concentrations) of propane, ethane
and toluene were 1200, 800 and 175 % above the LOD and
consequently the number of unique non-detects was relatively low (4,
2 and 1 % of values, respectively). The increase when the unique
non-detects were omitted was 10, 8 and 3 % for propane, ethane and
toluene, respectively. Other VOCs had a 5th percentile concentration
much closer to the LOD, increasing the likelihood of periods during which
concentrations were below LOD. For 9 of the 10 VOCs with the largest
annual median increase, the 5th percentile concentration was the LOD.
In summary, for those VOCs with few unique non-detects, the potential
inclusion of non-LOD-related non-detects resulted in a small change in
calculated concentration, while VOCs with a larger proportion of non-detects
had concentrations more frequently close to the LOD, increasing the
likelihood that the unique non-detects resulted from concentrations below the
LOD. This indicates that the
decision to assign all unique non-detects as values below the LOD was
justified, as the potential bias introduced was small, and therefore the
maximum of valid VOC concentration data was preserved and used in the MLE
distribution calculations. Intra-annual and monthly-diurnal variation in VOC
concentrations were summarised using the monthly median concentrations and
the 24 hourly median concentrations for each month from the best-fit
distributions, respectively.
For each VOC, each of the 288 median monthly-diurnal concentrations was
multiplied by the corresponding model-derived photochemical ozone creation
potential (POCP) (Derwent et al., 2007a), to weight the
observed diurnal variation of VOCs according to their different propensities
for O3 formation. In Derwent et al. (2007a), a VOC POCP
was defined as the ratio (multiplied by 100) of the increase in O3 due
to increased emissions of the VOC simulated in a Lagrangian model along a
trajectory traversing from central Europe to the UK, relative to the
modelled increase in O3 from the same mass increase in emissions of
ethene (the reference POCP VOC assigned a value of 100). Multiple studies
have calculated reactivity scales of O3 production potential (OPP) for
a range of VOCs using incremental reactivity methods (Luecken and Mebust,
2008; Derwent et al., 2007a; Hakami et al., 2004; Martien et al., 2003),
multi-parent assignment (Bowman, 2005) and “tagging” of VOC
degradation sequences (Butler et al., 2011). These varying
methods were shown to be generally well correlated (Butler et al.,
2011; Luecken and Mebust, 2008; Derwent et al., 2010). The Derwent et al. (2007a)
POCPs are appropriate to use in this study as they were
calculated under simulated north-western European conditions. Previous
comparison with other VOC reactivity scales indicated uncertainty in POCP
values of up to ±5 POCP units which equates to an average of ±15 %
for the measured VOCs in this study (Derwent et
al., 2007b).
The diurnal variation of individual VOCs due to photochemical depletion was
summarised by calculating the ratio of each POCP-weighted VOC concentration
to the POCP-weighted ethane concentration. Ethane has the second smallest
POCP of the measured VOCs, 87 % smaller than the average, and 20 %
smaller than the next smallest POCP (benzene), so using this ratio removed
the effect on diurnal VOC concentration of changes in boundary layer mixing
depth. The VOC with the smallest POCP, ethyne, had low data capture at Harwell
between 1999 and 2001 (maximum 57 % in 2001). Additionally, ethane has a
smaller rate coefficient for reaction with OH compared with ethyne (Table 1),
and the POCPs were similar (7 for ethyne vs. 8 for ethane). Ratios of
VOC / ethane have been used previously to estimate the photochemical loss
of VOCs (Yates et al., 2010; Helmig et al., 2008; Honrath et al., 2008). It
is also assumed that the diurnal variation of VOCs at the site is not driven
by differences in the magnitude of VOC emissions along the trajectories
contributing VOCs to that site during the day and at night. This can be
verified by the similar monthly median VOC emissions emitted along the path
of 96 h trajectories (outlined in Sect. 2.3) arriving at night (03:00) and
afternoon (15:00). For example, at Harwell in 2011, night trajectory VOC
emissions were no more than ±12 % different from afternoon. Hence, a
daytime decrease in POCP-weighted VOC / ethane ratio indicates greater
photochemical depletion of the VOC relative to ethane. The magnitude of
diurnal photochemical variability for each VOC was derived from the
difference between the average POCP-weighted VOC / ethane ratio at night
(01:00–05:00) and in the afternoon (13:00–17:00). A positive value indicates
daytime photochemical depletion of the VOC relative to ethane. The sum of
positive daytime photochemical depletion of individual VOCs produces the
total VOC diurnal photochemical depletion for each month. The monthly pattern
of total VOC diurnal photochemical depletion was compared with the monthly
pattern of the regional O3 increment. During those months with a
positive regional O3 increment, the relative contribution of each VOC to
total VOC photochemical depletion was used as an estimate of the relative
contribution of each VOC to the VOC chemical loss which contributed to the
production of the positive regional O3 increment.
Summary data for the measured VOCs at Auchencorth and Harwell (note
that m-xylene and p-xylene are reported as a single measurement). The rate
coefficients at 298 K for reactions of each VOC with OH are taken from
Atkinson and Arey (2003), and the POCPs are from Derwent et al. (2007a).
The “main source” column gives the SNAP sector with
the largest contribution of that VOC to UK annual anthropogenic emissions in
2011, with the exception of isoprene which is mainly of biogenic origin
(defined in Sect. 2.3). The listed SNAP sectors are SNAP 2, non-industrial
combustion plants; SNAP 4, production processes; SNAP 5, extraction and
distribution of fossil fuels; SNAP 6, solvent use; SNAP 7, road transport;
and SNAP 8, non-road transport.
VOC
Class
Chemical formula
Main source
OH reaction rate constant
POCP
(1012× k (298 K)
(cm3 molecule-1 s-1))
ethane
alkane
C2H6
SNAP 5 (65 %)
0.248
8
propane
alkane
C3H8
SNAP 5 (36 %)
1.09
14
n-butane
alkane
C4H10
SNAP 6 (44 %)
2.36
31
isobutane
alkane
C4H10
SNAP 5 (61 %)
2.12
28
n-pentane
alkane
C5H12
SNAP 5 (42 %)
3.80
40
isopentane
alkane
C5H12
SNAP 5 (41 %)
3.60
34
n-hexane
alkane
C6H14
SNAP 6 (42 %)
5.20
40
2-methylpentane
alkane
C6H14
SNAP 6 (43 %)
5.20
41
n-heptane
alkane
C7H16
SNAP 5 (43 %)
6.76
35
n-octane
alkane
C8H18
SNAP 5 (64 %)
8.11
34
isooctane
alkane
C8H18
SNAP 4 (100 %)
3.34
25
ethene
alkene
C2H4
SNAP 8 (27 %)
8.52
100
propene
alkene
C3H6
SNAP 4 (36 %)
26.3
117
1-butene
alkene
C4H8
SNAP 7 (26 %)
31.4
104
cis-2-butene
alkene
C4H8
SNAP 5 (87 %)
56.4
113
trans-2-butene
alkene
C4H8
SNAP 5 (90 %)
64.0
116
1,3-butadiene
alkene
C4H6
SNAP 8 (57 %)
66.6
89
isoprene
alkene
C5H8
biogenic
100
114
ethyne
alkyne
C2H2
SNAP 7 (46 %)
0.78
7
benzene
aromatic
C6H6
SNAP 2 (35 %)
1.22
10
toluene
aromatic
C7H8
SNAP 6 (63 %)
5.63
44
ethylbenzene
aromatic
C8H10
SNAP 6 (54 %)
7.0
46
o-xylene
aromatic
C8H10
SNAP 6 (50 %)
13.6
78
m-xylene
aromatic
C8H10
SNAP 6 (71 %)
23.1
86
p-xylene
aromatic
C8H10
SNAP 6 (50 %)
14.3
72
1,2,3-trimethylbenzene
aromatic
C9H12
SNAP 6 (79 %)
32.7
105
1,2,4-trimethylbenzene
aromatic
C9H12
SNAP 6 (74 %)
32.5
110
1,3,5-trimethylbenzene
aromatic
C9H12
SNAP 6 (71 %)
56.7
107
At Auchencorth, the analysis of VOC diurnal photochemical depletion was not
possible in 2010 and 2011 due to low data capture, which compromises the
ability of MLE to accurately estimate median VOC concentrations. This is
particularly important for ethane, as a large error in the fitted
distribution for ethane propagates to all VOC / ethane ratios. In 2011,
the average proportion of non-detects for the measured VOCs was 56 % when
the six VOCs with no measurements above LOD were excluded (34 % for
ethane). In 2012 this decreased to 34 % (10 % for ethane), and VOC
diurnal photochemical depletion was calculated. For comparison, at Harwell,
there were on average 26 % non-detects for each VOC species in 2011
(7 % for ethane).
Drivers
The two main drivers producing the “state” of this chemical climate, i.e.
VOC diurnal photochemical depletion, which are considered here are
meteorology and anthropogenic VOC emissions. Other drivers such as biogenic
VOC emissions and NOx concentrations are drivers of the regional
O3 increment. Meteorology and anthropogenic VOC emissions are the focus
due to the benefits previously outlined in improvement in health- and
vegetation-relevant O3 impacts that result from anthropogenic VOC
emission reductions. The meteorology was characterised by monthly mean,
maximum and minimum temperature, and number of hours of sunshine for Harwell
and Auchencorth obtained from the UK Met Office climate summaries for “South
East and Central South England” and “East Scotland”, respectively
(http://www.metoffice.gov.uk/climate/uk/datasets/#) (Perry and Hollis,
2005).
To investigate geographical emission sources, the locations of each of the
96 hourly time points of the 2920 HYSPLIT 96 h back trajectories arriving at
3 h intervals per year were mapped to the 0.5∘ × 0.5∘
gridded VOC emissions reported by EMEP and used in the EMEP
model (Mareckova et al., 2013; Simpson et al., 2012). This grid
encompasses the region 30.25–75.25∘ N,
29.75∘ W to 60.25∘ E, and the emissions in each grid
square are disaggregated into 11 SNAP source sectors (http://ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase/emissions_emepmodels/).
When the location of the trajectory during a particular hour
fell within the gridded domain, the annual emissions and country of the grid
square over which the trajectory was located were assigned to that time
point. Emissions were assigned to the country which had the greatest
emissions when the grid square straddled an international border. Annual
emissions were modified by prescriptive month, day-of-week and hour-of-day
time factors (Simpson et al., 2012) to
obtain an estimate of the hourly emissions from each SNAP sector during the
hour in which the trajectory passed over the grid cell. The monthly-average
hourly SNAP emission estimates at each of the 96 1 h time points were summed
to give the average European VOC emissions estimate of all the trajectories
arriving in that month (henceforth the VOC trajectory emissions estimate
(TEE)) and the proportions derived from individual countries.
The total VOC TEE from the 11 SNAP sectors were speciated using the 114 VOC
speciated profiles from Passant (2002) to quantify the
proportion of emissions emitted as one of the 27 measured VOCs. The profiles
were first applied to UK annual emissions to obtain speciated profiles for
each SNAP sector which could be applied to the VOC TEEs. Each year, at the
most disaggregated level, the UK National Atmospheric Emissions Inventory
(NAEI) reports total VOC emissions for 337 source activities (http://naei.defra.gov.uk/data/)
(Passant et al., 2013). In Passant (2002), each of these activities
is assigned one of the 114 speciation profiles which in total consider the
contribution from 630 VOCs, including aggregated groups of VOCs, for
example “C7 alkanes”. The total annual UK emissions for each activity were
apportioned between the VOCs in the assigned profile. This resulted in a
matrix of 337 columns of source activities, and 630 rows of VOCs. Activities
were then grouped into the 55 NFR codes used by NAEI, and then into SNAP
sectors 1–9 based on the NFR–SNAP conversion recommended by the EMEP Centre
for Emission Inventories and Projections (CEIP; http://www.ceip.at/fileadmin/inhalte/emep/pdf/nfr09_to_snap.pdf).
There were no reported VOC emissions from activities falling under SNAP 10 (agriculture) and SNAP 11 (other). The
relative contribution of each VOC to total annual UK SNAP emissions was
calculated to provide speciated emission profiles which were used to
speciate the monthly SNAP sector VOC TEEs. This produced an estimate of the
contribution to total monthly VOC TEE from 630 VOCs (Fig. 2). This
contribution was then multiplied by the VOC's POCP to weight it according to
O3 formation potential.
Flowchart demonstrating the process used to calculate the
contribution of 630 individual VOCs to the monthly total VOC trajectory
emissions estimate (TEE; defined in Sect. 2.3). The green rectangles
represent products or data sets, and the blue rounded rectangles represent
processes applied to transform a data set. Further explanation is provided in
Sect. 2.3.
![]()
The EU emissions inventory disaggregates annual emissions from SNAP sectors
1–9 into 91 NFR codes for each EU member state (EEA,
2014). The monthly change in the SNAP sector VOC TEE was attributed to
changes in the contribution from the more narrowly defined NFR codes, based
on the country-specific contributions of each NFR sector to annual SNAP
sector emissions. The VOC TEE from each of the 91 NFR codes for each country
were summed across all countries to obtain the contribution of each NFR code
to the total VOC TEE for each month (Fig. 3).
Flowchart representing the process used to derive the contribution
from NFR codes to monthly TEE. The green rectangles represent products or data sets, and the
blue rounded rectangles represent processes applied to transform a data set.
Note that the separation of the TEE into contributions from two countries is
illustrative, and in most cases a greater number of countries contributed to
the TEE in a given month. Further explanation is provided in Sect. 2.3.
![]()
The emission inventories used in this study have several sources of
uncertainty (EEA, 2013; Koohkan et al., 2013). The
0.5∘ × 0.5∘ grid squares mean that numerous distinct
sources, each with uncertainties in emission factors and activity rates, are
aggregated together to produce the estimate of emissions from a particular
SNAP or NFR source sector. The size of the grid square also does not
necessarily reflect the size of the area from which emissions influence the
atmospheric composition of the trajectory air mass as it passes over. The VOC
TEE is therefore used as a relative comparison spatially and temporally,
rather than a definitive quantification of the VOC emissions emitted into an
air mass. In addition, there are uncertainties in the speciation of total VOC
emissions to individual components (Borbon et al., 2013). However, the
emission inventories used here are the best estimate of the spatial
distribution of anthropogenic VOC emissions across Europe. While studies have
shown discrepancies between the EMEP emission inventory and other estimates
of European emissions (Koohkan et al., 2013), EMEP gridded emissions have
also been shown previously to capture variation in VOC measurement data
(Sauvage et al., 2009; Derwent et al., 2014).
Results and discussion
Impact: regional O<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> production/destruction
assessment
The difference between hemispheric background O3 concentrations and
regional background O3 concentrations relevant for Harwell for 2001
(representative of 1999–2001) and 2011 (representative of 2010–2012), and
for Auchencorth in 2012, is shown in Fig. 4. Although there was inter-annual
variability within each time period, the data for these years illustrate the
main differences between three different phases of the regional O3
increment chemical climate both temporally at Harwell (1999–2001 vs.
2010–2012) and spatially (Harwell vs. Auchencorth). At Harwell, in 2001, a
positive regional O3 increment occurred in each month between May and
September (Fig. 4a). The annual maximum regional O3 increment (i.e. the
difference between hemispheric background and regional background O3
concentrations) occurred in the afternoon in July 2001
(42 µg m-3), while monthly regional O3 increments peaked
in excess of 20 µg m-3 in June and August and in excess of
10 µg m-3 in May and September. A similar pattern occurred in
2000, but with a lower annual maximum (26 µg m-3 in July). In
1999, positive regional O3 increments were greater, extending from April
to September with the annual maximum in July (53 µg m-3), and
increments in excess of 30 µg m-3 in June and August. In
2011, at Harwell, positive regional O3 increments occurred between April
and September (Fig. 4b) but their magnitudes were reduced compared with the
1999–2001 phase. Only 2 months, April and July, had maximum regional O3
increments > 10 µg m-3 (11 and
32 µg m-3,
respectively). In 2012, the monthly regional O3 increment
exceeded 10 µg m-3 in May (12 µg m-3), July (28 µg m-3)
and August (11 µg m-3) and occurred
more modestly in April, June and September. In 2010, the regional O3
increment in June was 24 µg m-3, which then decreased in July
(19 µg m-3). Reductions in regional O3 have been reported
in the UK previously, using high-percentile O3 concentrations as an
indicator of regionally derived episodes, rather than calculation of the
average monthly-diurnal regional O3 increment. For example, Munir et
al. (2013) attributed negative trends in highest O3 concentrations
calculated at 22 UK monitoring sites (13 sites with significant trends) to
regional reduction in O3 precursor emissions between 1993 and 2011.
Monthly–hourly average differences between hemispheric background
O3 and regional background O3 concentrations (µg m-3)
for (a) 2001 and (b) 2011 in south-east England, the area for which Harwell
is representative, and (c) the difference between hemispheric and measured
O3 concentrations for 2012 at Auchencorth.
![]()
Stacked bar chart of median VOC concentrations at
(a) Harwell 2001, (b) Harwell 2011, and
(c) Auchencorth (2012). The error bars show the sum of the 95th
percentile confidence interval in the median VOC concentrations. This
represents the error introduced by representing the data set with the chosen
fitted distribution (see text).
![]()
Monthly variation in VOC diurnal photochemical reactivity as defined
by the difference between night (average of 01:00–05:00) and afternoon
(13:00–17:00) POCP-weighted VOC / ethane ratios for
(a) Harwell 2001, (b) Harwell 2011, and
(c) Auchencorth 2011. Note the very different vertical scales.
![]()
The regional O3 increments at Auchencorth were substantially lower than
at Harwell. Between 2010 and 2012, the maximum regional O3 increment
observed was 14 µg m-3 in July 2011. In 2012 (Fig. 4c), the
maximum regional O3 increment was 4 µg m-3. The spatial
differences in the extent of regional contribution to O3 variation at
Harwell and Auchencorth are consistent with a previous study of rural UK
O3 spatial variability (Jenkin, 2008).
State: VOC concentration and chemical depletion
The monthly median concentrations of the 27 VOCs measured at Harwell and
Auchencorth have a pronounced seasonal cycle with highest total summed VOC
concentrations in winter at each site, albeit with concentrations at
Auchencorth substantially lower than at Harwell (Fig. 5 shows an example year
for each of the three periods). Monthly variation was lower at Auchencorth:
the difference between minimum and maximum monthly total median VOC
concentrations at Auchencorth in 2012 was 6.2 µg m-3,
compared with 9.5 and 13.1 µg m-3 at
Harwell in 2011 and 2001, respectively. Monthly median total VOC
concentrations at Harwell in 1999–2001 and 2010–2012 were similar in winter
months (January, February, December) and generally ranged between 6 and
18 µg m-3. In summer (June, July, August), between 1999 and
2001, total VOC concentrations were between 5 and 13 µg m-3
but between 2010 and 2012 concentrations were lower, between 3 and
6 µg m-3, and only June 2010 had higher total VOC
concentrations than the summer month in 1999–2001 with the lowest total VOC
concentration. In 2001 six VOCs were not measured, and these constituted
between 2.1 and 7.4 % of monthly total measured VOC concentrations in
2011. The non-measurement of these VOCs does not alter the conclusions
relating to the differences in total VOC concentrations observed between
1999–2001 and 2010–2012.
The relative composition of total measured VOCs showed differences between
2001 and 2011 at Harwell. Ethane, propane and n-butane had the largest
measured concentrations. Ethane contributed on average 22 ± 4 % of
total monthly measured VOC concentrations in 2001, compared with
33 ± 6 % in 2011 (annual average monthly-measured ethane
concentration had a small increase from
2.0 ± 0.8 µg m-3 in 2001 to
2.3 ± 1 µg m-3 in 2011), while the relative
contribution from propane did not vary (15 % in each year, average
monthly concentrations in 2001 and 2011 were 1.5 ± 0.9 and
1.2 ± 0.8 µg m-3, respectively) and that from n-butane
decreased from 11 ± 2 to 8 ± 1 %
(1.1 ± 0.6 µg m-3 in 2001 and
0.6 ± 0.4 µg m-3 in 2011). Although these differences
are not large, they may result from differences in the reduction of VOC
emission sources between 1999–2001 and 2010–2012. The aim of this work,
however, was not the determination of long-term trends in absolute VOC
concentrations, and the reader is referred to Dollard et al. (2007), von
Schneidemesser et al. (2010) and Derwent et al. (2014) which have undertaken
analyses of trends in VOC concentrations at multiple UK sites, including
Harwell and Auchencorth.
The extent of diurnal photochemical loss of VOCs over the year is shown in
Fig. 6. At Harwell, periods of increased VOC diurnal photochemical
depletion mirror the monthly magnitude of regional O3 increments
(Fig. 4 cf. Fig. 6). In 2001, at Harwell, both the regional O3 increment
and VOC diurnal photochemical depletion increased from June to July, before
declining in August. In 2011 there was a local maximum in the regional
O3 increment in April, followed by the annual maximum in July, mirrored
by VOC diurnal photochemical depletion. During 2012 the regional O3
increment was minimal at Auchencorth, and the magnitude of VOC diurnal
photochemical depletion was low, with a small peak in August.
The association between the monthly variation in the regional O3
increment and total VOC diurnal photochemical depletion at Harwell indicates
that the variation in VOC chemical loss contributing to the regional O3
increment is represented by the VOC diurnal photochemical depletion. The
relative contribution of each measured VOC to total VOC diurnal photochemical
depletion during months of enhanced regional O3 increment therefore
indicates where emission reductions should be targeted to most effectively
reduce VOC chemical loss and hence to reduce the magnitude of the regional
O3 increment. The contributions of each measured VOC to total VOC
diurnal photochemical depletion during the month of maximum regional O3
increment in 2010, 2011 and 2012 at Harwell are shown in Fig. 7. A positive
value indicates lower POCP-weighted VOC / ethane ratio during the afternoon
compared to night (i.e. photochemical depletion). A higher POCP-weighted
VOC / ethane ratio during the afternoon results in the negative value.
Ethene had the largest contribution during these months (34, 29 and 45 %
of total measured VOC diurnal reactivity in 2010, 2011 and 2012,
respectively). The sum of m+p-xylene also made a major positive
contribution during 2010 (15 %) and 2011 (13 %). The majority of the
remaining measured VOCs made smaller, positive contributions. In July 2011,
71 % of the remaining VOCs (i.e. all VOCs excluding ethene and
m+p-xylene) contributed on average 3.4 ± 2.5 % to total positive
VOC diurnal variation. In July 2012, the maximum regional O3 increment
was 12 % lower than July 2011, and only 58 % of the remaining VOCs made
positive contributions. In June 2010, the maximum regional O3 increment
was 25 % lower, and 54 % of the remaining VOCs contributed. VOCs with
larger VOC / ethane ratios in the afternoon included isoprene, which is
predominantly of biogenic origin (von Schneidemesser et al., 2011). Laurent
and Hauschild (2014) modelled the impact on O3 formation of speciated
VOC emissions from 31 countries and also reported m-xylene and ethene to
have the largest impact of 270 VOCs on regional O3 formation.
Figure 8 is the analogous plot to Fig. 7 for 1999–2001 at Harwell. In
1999–2001, m+p-xylene had the largest diurnal photochemical depletion,
followed by ethene. However, there were much larger negative VOC / ethane
diurnal variations for some anthropogenic VOCs compared to 2010–2012
(Fig. 5), i.e. afternoon POCP-weighted VOC / ethane ratios were
substantially higher than at night. This indicates that processes other than
photochemical depletion, e.g. local emission patterns, contributed to diurnal
variation in POCP-weighted VOC / ethane ratios for these VOCs in
1999–2001. Isopentane had the largest negative difference but had a
consistent positive contribution in 2010–2012. Toluene also had a negative
value in 1999 and 2000. Therefore, from 1999–2001 to 2010–2012 there was a
change in the balance between emissions of isopentane and toluene and their
photochemical removal to the point where photochemical depletion dominated
during the day, and VOC / ethane ratios were lower in the afternoon than
at night. Derwent et al. (2014) calculated exponential decreases in the
concentrations of these VOCs at urban locations in the south-east of England,
where Harwell is located, attributed to the effective control of evaporative
and exhaust emissions from petrol-engine vehicles. Toluene has an
atmospheric lifetime of ∼ 1.9 days with respect to reaction with OH
(Atkinson, 2000), so local daytime toluene emissions would not deplete
substantially during transport to the monitoring site. The observed
decreasing trends at sites close to emission sources in the south-east of
England suggest a decrease in the influence of local isopentane and toluene
emissions in determining the diurnal profile of these VOCs at Harwell and
hence afternoon depletion of regionally emitted toluene and isopentane was
observed in 2010–2012.
Individual VOC diurnal photochemical reactivity as defined by the
difference between night (average of 01:00–05:00) and afternoon (13:00–17:00)
POCP-weighted VOC / ethane ratios for (a) June 2010,
(b) July 2011 and (c) July 2012 at Harwell. A lower ratio
in the afternoon results in a positive value (i.e. photochemical depletion),
while a higher afternoon ratio results in a negative value. These months
correspond to the periods of annual maximum regional O3 increment at
Harwell (see Fig. 2).
![]()
Individual VOC diurnal photochemical reactivity as defined by the
difference between night (average of 01:00–05:00) and afternoon (13:00–17:00)
POCP-weighted VOC / ethane ratios in (a) July 1999,
(b) July 2000 and (c) July 2001 at Harwell. A lower ratio
in the afternoon results in a positive value (i.e. photochemical depletion),
while a higher afternoon ratio results in a negative value. These months
correspond to the periods of annual maximum regional O3 increment. To
emphasise the positive contributions to VOC photochemical cycling, the
negative values have been truncated.
![]()
Drivers of chemical climate state: meteorology and emissions
Meteorology
The monthly-averaged meteorological data for the UK regions relevant for
Harwell in 2001 and 2011 and Auchencorth in 2012 are shown in Fig. 9.
Variation in temperature and sunshine is often associated with
spatiotemporal differences in VOC diurnal photochemical depletion and
regional O3 increment. For example, temperatures were generally lower
in East Scotland than South East and Central South England but the number of
hours of sunshine were comparable, although solar intensity is less in
Scotland, hence a reduced VOC photochemical depletion and regional O3
increment at Auchencorth. At Harwell, in 2001, annual maximum VOC diurnal
photochemical depletion occurred in July, coinciding with annual maximum
monthly temperature, while in July 2011 a combination of relatively high
temperature and hours of sunshine (although neither were annual maxima)
coincided with annual maximum VOC diurnal photochemical depletion. These
summers were typical of the 1999–2012 period; monthly mean temperatures were
between -7 and +4 % compared to the 1999–2012 average, and hours of
sunshine were between -14 to +11 % compared to the average.
Average monthly mean temperatures (blue, maximum and minimum
temperatures shown as whiskers) and hours of sunshine (red) from the UK
Meteorological Office
(http://www.metoffice.gov.uk/climate/uk/datasets/#) for
(a) South East and Central South England 2001, (b) South
East and Central South England 2011 and (c) East Scotland 2012.
![]()
However, not all variation in VOC diurnal photochemical depletion and
regional O3 increment were associated with changes in meteorology. For
example, at Harwell, in April 2011 there was a larger regional O3
increment compared with April 2001. This coincided with a 4 ∘C
higher mean temperature and 95 more hours of sunshine in South East and
Central South England. In May 2011 the temperature and sunshine were similar
to April 2011, but VOC diurnal photochemical depletion and the regional
O3 increment decreased. Hence, other factors, such as the strength of
VOC emission sources over which an air mass passes, also influence VOC
diurnal photochemical depletion and are discussed in Sect. 3.3.2.
Emissions
Variation in the monthly-averaged European anthropogenic VOC TEE is shown in Fig. 10. The VOC TEE is the sum of
hourly emissions from the grid squares the trajectories passed over in the
96 h prior to arrival at the supersites (unit: Mg 96 h-1), rather
than a definitive quantification of the emissions directly impacting upon
the measured atmospheric composition at the supersites. Compared with
Harwell in 2001, the annual average VOC TEE, by mass, was 64 % smaller in
2011 at Harwell and 76 % smaller in 2012 at Auchencorth. For the purposes
of clarity the following assessment focuses on Harwell, where significant
regional O3 increment has been demonstrated (Sect. 3.1). The biggest
change in contribution from the 11 SNAP sectors to average VOC TEE between
2001 and 2011 at Harwell was for SNAP 7 (road transport), which averaged 31 %
of the total 10 VOC TEE in 2001, compared with 9 % in 2011 (Fig. 10). The
biggest change was for SNAP 7 (road transport), which averaged
31 % of the total VOC TEE in 2001, compared to 9 % in 2011. Emissions
from SNAP 6 (solvents) were the largest contribution to the VOC TEE during
both periods, contributing 50 % of total emissions on average in 2011,
compared to 34 % in 2001. Emissions from SNAP 4 (production processes)
were the second largest contributor on average in 2011 (11 % of the total
VOC TEE), followed by SNAP 7 (road transport), and SNAP 5 (extraction and
distribution of fossil fuels), both contributing 9 %.
Monthly-average VOC 96 h back-trajectory emission estimates prior
to arrival at the receptor site, disaggregated into 11 SNAP source
sectors for (a) Harwell 2001, (b) 2011 Harwell, and
(c) Auchencorth 2012.
![]()
Monthly variation in VOC TEE mirrors that of VOC diurnal photochemical
depletion and hence the magnitude of the regional O3 increment. The
period of April–July 2011 provides a useful case study to demonstrate the
nature of the emissions driver. This period shows how variation in both the
magnitude of the VOC TEE, as well as the proportion of emissions emitted
closer to the receptor site (temporally) can influence the extent of VOC
diurnal photochemical depletion and the magnitude of the regional O3
increment. April and May 2011 have similar meteorological conditions (Fig. 9),
but VOC diurnal photochemical depletion was lower in May due to a 62 %
decrease in the VOC TEE compared to April. The VOC TEE decreased in June,
then increased in July. This latter increase, coupled with increased
temperatures and solar intensity in summer, provided conditions conducive to
producing the observed annual maximum in VOC diurnal photochemical depletion
for 2011.
The proportion of the total VOC TEE derived from the final 4 h prior to a
trajectory's arrival, plus the hour of arrival, was labelled as the “final
4 h” VOC TEE to investigate the effect of variation in the proportion of
emissions emitted closer to the monitoring site. In 2011 the final 4 h was
on average 28 % of the total VOC TEE (Fig. 11a). In May and June 2011 it
was above average (36 and 44 %, respectively) and in April and July it
was lower (17 and 20 %, respectively). While the 4 h cut-off for this
calculation was somewhat arbitrary, it was based on consideration of the
average atmospheric lifetimes of the individual VOCs (Atkinson, 2000) which
indicate that most VOCs emitted in the final 4 h have insufficient time to
form O3. Between June and July 2011 there was a 32 % increase in
median VOC concentrations due to an increased VOC TEE (Fig. 11b). However,
there was a 275 % increase in VOC diurnal photochemical depletion as a
larger proportion of emissions were emitted earlier along the air-mass
trajectory (Fig. 11c). Hence, in May and June, lower total VOC TEE compared to
April and July, respectively, coupled with a larger proportion of VOCs
emitted in the final 4 h, resulted in the reduced regional O3 increment
impact (Fig. 11d).
Summary of variables relevant to the assessment of the effect of
variation in the proportion of emissions accumulated close (temporally) to
the monitoring site: (a) the final 4 h TEE metric, i.e. the
proportion of the TEE emitted into the air mass during the 4 h prior to
arrival at the site (defined in Sect. 3.3.2), (b) monthly-average
sum of measured VOCs, (c) monthly-average sum of VOC diurnal
photochemical depletion, (d) monthly maximum difference between
hemispheric background concentrations and regional background concentrations
(a positive value indicates additional regional O3 production).
![]()
The speciated VOC monthly trajectory emission estimates, based on a
UK-specific speciation of the total VOC TEE for nine SNAP sectors, are shown in
Fig. 12 for July 2001 and 2011. Individual VOC trajectory emission estimates
were expressed as the percentage of the total POCP-weighted emissions and the
comparison between 2001 and 2011 illustrates the contrast and similarities in
contribution from individual VOCs to the VOC TEE during the months of maximum
regional O3 increment. The biggest decreases between 2001 and 2011 were
for isopentane (4.1 % total POCP emissions in 2001, 1.7 % in 2011),
and toluene (6.5 % in 2001, 4.5 % in 2011). These decreases mirror
the absence of much greater POCP-weighted VOC / ethane ratios in the
afternoon compared to night for toluene and isopentane in 2010–2012, which
were observed in 1999–2001 and attributed to variation in local emissions
(discussed in Sect. 3.2 and visualised as “negative” VOC diurnal
photochemical depletion in Figs. 7 and 8)
Speciation of average VOC back-trajectory emission estimates in
(a) July 2001, and (b) July 2011 at Harwell. The speciation
was based on source profiles catalogued in Passant (2002) and the relative
contribution of individual activities to annual total VOC emissions.
![]()
Monthly variation in the contribution of measured VOCs to the VOC TEE was
not consistent with variation in the contribution of individual VOCs to
total measured VOC diurnal photochemical depletion. This is in contrast to
the observed changes between 2001 and 2011 in VOC contribution to TEE and
VOC diurnal photochemical depletion and is effectively illustrated using
the April–July 2011 time period as an example. For example, in 2011, the VOC
diurnal photochemical depletion peak in July (Fig. 6) was much greater
than in April due to more intense sunshine and higher temperatures. This
increase was not equally reflected across the measured VOCs, indicating
differences in the speciation of the VOC TEEs prior to arrival at the site.
For example, toluene was 4.2 % of total VOC diurnal photochemical
depletion in April, increasing to 9.6 %, in July and the
1,3,5-trimethylbenzene contribution increased from 0.1 % in April to 8 %
in July. The monthly-averaged speciated VOC TEEs do not reflect these
changes and show little monthly variation within a given year. The
speciated VOC monthly TEE calculation assumes that the SNAP sector component
activities (i.e. the activities for which speciated profiles are defined;
Passant, 2002) contribute similarly to the emissions
exposure of the parent SNAP sector in each month of the year. For example,
it is assumed that an x % increase in SNAP emissions results from an
x % increase in emissions from all component activities. It is unlikely
that the SNAP sector emissions in every region over which an air mass
travels are similarly apportioned between component emission activities.
The inability of this method to account for these spatial differences will
result in the underestimation of the TEE of some VOCs, and the
overestimation of others. Currently, data are only available on changes in
the contribution of more narrowly defined NFR codes to SNAP sector emissions
at a country level and for annual VOC emissions. In 2011 the average
contribution to monthly VOC TEE at Harwell from the UK was 62 %, with
France the second largest contributor at 14 %. Comparing April and July
2011, the contributions from the UK to the VOC TEE were 50 and 95 %,
respectively, with the other 50 % in April resulting from contributions
from Germany, France, Belgium and the Netherlands (Fig. 13). These
countries all have different relative contributions to total SNAP sector
emissions from component NFR source sectors (EEA, 2014).
Contributions to the average VOC 96 h back-trajectory emission
estimates in April 2011 (green bars) and July 2011 (blue bars) from countries
which contributed at least 0.5 % during one of the months. The
contribution of the UK in July 2011 was 95.8 % and has been truncated
in the plot.
![]()
Highly aggregated SNAP source sectors, and a constant contribution of
component activities to SNAP emissions were identified as a potential
contributing factor to inconsistencies between VOC contributions to TEE and
VOC diurnal photochemical depletion. Disaggregation of the VOC TEEs into 91
NFR codes, based on country-specific contributions of these NFR codes to
annual VOC emissions in the 11 parent SNAP sectors, accounted for
country-specific changes in NFR sector contributions to monthly VOC TEE at
Harwell. The aim was to show that within each SNAP sector an increase in VOC
SNAP emissions can result from an increase in a specific source activity
(e.g. specific NFR code), rather than a general overall increase. Variability
in the contribution of constituent activities to SNAP emissions could result
in variation in the contribution of individual VOCs to those emissions. This
would therefore demonstrate that the reporting of gridded VOC emissions in
more disaggregated source sectors was required, so that more flexible VOC
speciation profiles could be derived than those calculated for the nine SNAP
sectors in this study and those calculated previously, e.g. Derwent et
al. (2007a). For example, in 1999–2001, the large contribution from SNAP 7
(road transport; Fig. 10) is more precisely attributed to NFR sectors 1A3bi
(passenger cars) and 1A3bv (gasoline evaporation) which contributed 19 and
11 % to the total VOC TEE in July 2001 (month of maximum regional O3
increment), respectively, and 87 % of the SNAP 7 emissions estimate. The
next largest contribution was from 3D2 (domestic solvent use, 10 %), a
component of SNAP 6 (solvents). Between 2010 and 2012, SNAP 6 was the major
contributor to the VOC TEE. During July 2011, SNAP 6 component NFR sectors
3D2 (domestic solvent use) and 3D3 (other product use) contributed 18 and
12 % of the total VOC TEE (65 % of the SNAP 6 emissions estimate).
The SNAP 4 (production processes) component 2D2 (food and drink) was the
third largest contributor (10 % in July 2011). The two road transport
categories contributed 4 % (1A3bi) and 1 % (1A3bv) to the total VOC
TEE in July 2011.
The difference between the contribution of 91 NFR codes to the average VOC
TEE between April and July 2011 is shown in Fig. 14 to demonstrate the
variability in contribution of component activities to parent SNAP sector
emissions. Between these months, the cumulative change in the contribution
of the nine SNAP sectors to the total VOC TEE was 13.4 %, compared to a
change of 15.9 % for the 91 NFR codes. However, the changes in NFR code
contributions were not equally spread between the constituent activities of
a SNAP sector; they were concentrated in relatively few NFR sectors. For
example, between April and July 85 % of the NFR change resulted from a
decrease in 10 out of the 91 NFR sectors. The sectors “residential:
stationary plant combustion” and “industrial coating application” show the
greatest decrease, while sectors “food and drink” and “venting and
flaring” show the largest increase (identified by stars in Fig. 14). The
disaggregation of SNAP sector VOC TEEs also illustrates changes of opposite
sign in the contribution of component NFR sectors under the net changes in
SNAP sector. For example, SNAP sector 4 (production processes) increased in
contribution between April and July by 2.7 % (12.0–14.7 %). Following
disaggregation, this change was seen to result from a 3.4 % increase in
NFR sector 2D2 (food and drink) and a 0.76 % decrease in 2B5 (other
chemical industry). NFR-sector-level speciated profiles can therefore give
much more specific information on the emission source drivers of VOC
diurnal photochemical depletion, though it is noted that the accuracy of
many emission source speciation profiles is subject to discussion
(Borbon et al., 2013). However, the changes
in contribution of NFR sectors to the VOC TEE calculated here only account
for country-level variation, not for variation in the contribution of NFR
sectors to SNAP emissions on finer spatial scales, such as differences in
NFR sector contribution to SNAP emissions in different 0.5∘ × 0.5∘
grid squares for which the SNAP-sector-gridded
emissions are reported. Hence, the future reporting of gridded emissions to
NFR code level would more accurately represent the true nature of VOC
emissions across Europe.
Difference between NFR source sector contributions to average VOC
back-trajectory emission estimates (VOC TEE) in April and July 2011 at
Harwell. Also shown are the change in contribution of the SNAP source
sectors. These were calculated from the VOC TEE prior to disaggregation and
do not represent the sum of the contribution changes of the constituent NFR
source sectors. The source sectors identified by stars have the largest
changes between April and July (Sect. 3.3.2).
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Uncertainties and implications for future mitigation and
monitoring
Two VOCs, ethene and m+p-xylene, consistently had larger contributions to
total VOC diurnal photochemical depletion compared to the remaining VOC
suite. Therefore, a targeted reduction of these two VOCs (compared to other
measured VOCs) would be most effective in reducing the regional O3
increment. Further reduction of total measured VOC diurnal photochemical
depletion would require a reduction across a larger number of the remaining
measured VOCs. This could be achieved by lowering emissions from large
VOC-emitting sources rather than focusing on individual VOC species. As
previously identified (Sect. 3.3), between 2010 and 2012, the largest
VOC-emitting sources (NFR codes) were 3D2 (domestic solvent use including
fungicides), 3D3 (other product use) and 2D2 (food and drink).
The 27 measured VOCs studied here are a subset of the total VOC species
emitted by a multitude of anthropogenic activities and biogenic processes.
In 2011, 37.5 % of the reported annual UK anthropogenic VOC emissions were
emitted as one of the 27 measured VOCs when speciated using the Passant (2002)
speciation profiles. The UK biogenic VOC emissions
estimate reported to EMEP for 2011 was 91.2 Gg (cf. anthropogenic emissions of
752 Gg) but this value is uncertain and studies have estimated considerably
higher UK annual biogenic VOC emissions, in excess of 200 Gg (Karl et
al., 2009; Oderbolz et al., 2013). Biogenic VOC contributions to regional
O3 increments were not studied using this methodology. The estimate of
752 Gg of UK anthropogenic emissions is also subject to uncertainty
associated with defining accurate activity rates and emission factors for a
large number of source activities (EEA, 2013). The UK
National Atmospheric Emissions Inventory (NAEI) calculated the uncertainty
in UK anthropogenic VOC emissions to be ±10 %
(Misra et al., 2015). Of the 62.5 % of UK
anthropogenic VOC emissions not emitted as one of the VOCs measured at the
supersites, only the additional measurement of ethanol (13 % of 2011
anthropogenic UK emissions), methanol (4 %) and acetone (3 %) would
substantially increase the proportion of the UK VOC suite for which VOC
diurnal photochemical depletion would be quantified. The measurement of
these three VOCs would increase the proportion of UK anthropogenic emissions
emitted as a measured VOC from 37.5 to 57.5 %. Currently, ethanol,
methanol and acetone constitute 35 % of the unmeasured fraction of UK
anthropogenic emissions. Contributions from the 40 unmeasured VOCs with the
next highest emissions are required to make up the same percentage and the
remaining unmeasured emissions fraction comprises 464 VOCs. The large number
of VOCs contributing to the “unmeasured” VOC emissions fraction supports the
argument that the targeting of high VOC-emitting sources would be more
beneficial than reductions in individual VOCs from whatever their source(s).
The large proportion of UK VOC emissions emitted as ethanol, methanol and
acetone (mainly from SNAP 6 (solvents), from which 39, 97 and 91 %
of UK anthropogenic emissions of ethanol, methanol and acetone derived in
2011, and SNAP 4 (production processes), which contributed 57 % of ethanol
emissions) suggests that, like ethene and m+p-xylene, they may have a
disproportionately high contribution to VOC diurnal photochemical depletion
and hence to the magnitude of the regional O3 increment. Measurement of
these oxygenated VOCs at the supersites would allow their contribution to be
quantified.
Other limitations, in addition to using measurements of a subset of the
emitted VOC suite, include use of monthly-diurnal averages. Monthly-diurnal
averages were required to use MLE to derive summary statistics and to
calculate hemispheric and regional background O3 concentrations.
Additionally, it is more appropriate to consider an ensemble of air-mass back
trajectories to reduce the random uncertainty associated with their
calculation. The integration of air-mass back trajectories and gridded
emission inventories therefore also benefitted from use of monthly averages. Hence,
the contribution of VOCs to the average increase in regional O3
increment in a given month was evaluated, rather than any short-term episodic
regional O3 increment increases.
An additional uncertainty is associated with the gridded emissions inventory
itself. The derivation of the inventory requires accurate determination of
emission factors and activity rates for a large number of source activities
(EEA, 2013). Previous studies show the uncertainty associated
with this process. For example, Koohkan et al. (2013)
calculated VOC emissions across Europe using inverse modelling by data
assimilation of measurements for 15 VOCs, and comparison with the EMEP
inventory showed an underestimation of emissions of some VOCs and an
overestimation of others. Hence, there is a requirement for improvement of
emissions inventory derivation. However, this analysis shows that the future
reporting of gridded VOC emissions in source sectors more highly
disaggregated than currently (e.g. NFR codes) would also facilitate a more
precise identification of those VOC sources most important to mitigation
strategies and increase the accuracy in calculating emissions of individual
VOCs. For example, Derwent et al. (2007b) applied the POCP
concept to calculate the contribution of 248 VOC source categories to
regional O3 production using a photochemical trajectory model with a
near-explicit chemical mechanism which followed a “worst case” 5-day
trajectory bringing aged air masses from Europe to a location on the
England–Wales border. A UK-derived VOC emissions speciation was derived and
applied to total gridded VOC emission estimates across north-west Europe.
While the POCP concept provides an effective means of comparison between
different source categories, source category POCPs were calculated without
accounting for the spatial variation in the contribution of the different
source categories to total VOC emissions.
The work presented here highlights the constraints of representing spatial
variation of VOC emissions across Europe with 11 highly aggregated SNAP
sectors in terms of accurately determining the suite of VOCs impacting
atmospheric composition at a site. This results from a fixed contribution of
component activities to the aggregated SNAP sector emissions spatially and
temporally (see Sect. 3.3.2), although emissions from different SNAP
sectors can vary independently of one another. These constraints would be
amplified with no disaggregation of gridded VOC emissions and a constant
contribution from component activities spatially and temporally to total VOC
emissions, i.e. emissions from each aggregated SNAP sector do not vary
independently from one another. The effectiveness of the POCP concept in the
determination of the strongest O3-influencing VOC emission sources, and
hence the most cost effective mitigation strategies, would be substantially
improved by the reporting of gridded emissions at NFR sector level. Finally,
the future measurement at supersites of VOCs which are distinct markers for
source sectors (e.g. NFR codes) could be used to quantify the contribution
from different VOC source sectors.
Conclusions
A methodology has been demonstrated which links the impact of regional
O3 increment to VOC photochemical depletion and spatially gridded
anthropogenic VOC emissions. The utility of this methodology, which
integrates atmospheric composition measurements (O3 and VOCs),
meteorological data and gridded emissions inventory, was shown through the
derivation of policy-relevant conclusions using measurement data at the two
UK EMEP supersites (Harwell and Auchencorth). The regional O3 increment
at Harwell in 2010–2012 was substantially larger than at Auchencorth, but
substantially smaller than in 1999–2001. Of the 27 measured anthropogenic
VOCs, ethene and m+p-xylene consistently contributed the most VOC
photochemical depletion during regional O3 production at Harwell and
therefore reductions in emissions of these VOCs would be most effective in
reducing regional O3 production. To reduce VOC diurnal photochemical
depletion further, reductions across a larger number of the VOCs would be
required. Of these, ethanol, methanol and acetone appear to be the most
important, and measurement of these VOCs at the supersites would provide data
for targeting future emission reductions. Additionally, more detailed
speciated measurement of biogenic VOCs at the supersite, highlighted
previously by von Schneidemesser et al. (2011), would also advance
our understanding of the relative contribution of anthropogenic vs. biogenic VOCs
in determining the regional O3 increment.
Estimates of the integrated anthropogenic VOC emissions along back
trajectories arriving at Harwell have decreased substantially between
1999–2001 and 2010–2012, due to decreases in emissions from SNAP source
sector 7 (road transport). Currently, SNAP sector 6 (solvent and product
use) provides most of the total VOC trajectory emissions estimate. The
disaggregation of highly aggregated SNAP trajectory emission estimates to
NFR codes, accounting for country variation in the NFR sector contribution
to parent SNAP sector, allowed the source sectors which determine the VOC
contribution to the regional O3 impact to be more precisely defined,
i.e. NFR sectors 3D2 (domestic solvent use), 3D3 (other product use) and 2D2
(food and drink), which were the top three contributors to total VOC
emissions exposure at Harwell (2010–2012) during the month of maximum
regional O3 increment. It is concluded that considerable additional
benefits to the interpretation of measurement data, to modelling of future
O3 concentrations and hence to determining policy for abatement of
detrimental O3 impacts would be gained from the availability of gridded
VOC emissions data reported in more narrowly defined source sectors such as
the NFR codes.