Estimating source regions of European emissions of trace gases

Transcription

Estimating source regions of European emissions of trace gases
Atmospheric Environment 35 (2001) 2507}2523
Estimating source regions of European emissions of trace gases
from observations at Mace Head
D.B. Ryall *, R.G. Derwent , A.J. Manning , P.G. Simmonds, S. O'Doherty
Public Met. Service Research, UK Meteorological Ozce, Bracknell, Berks RG12 2SZ, UK
School of Chemistry, University of Bristol, UK
Received 25 January 2000; received in revised form 4 August 2000; accepted 21 August 2000
Abstract
A technique is described for identifying probable source locations for a range of greenhouse and ozone-depleting trace
gases from the long-term measurements made at Mace Head, Ireland. The Met. O$ce's dispersion model NAME is used
to predict concentrations at Mace Head from all possible sources in Europe, then source regions identi"ed as those which
consistently lead to elevated concentrations at Mace Head. Estimates of European emissions and their distribution are
presented for a number of trace gases for the period 1995}1998. Estimated emission patterns are realistic, given the nature
and varied applications of the species considered. The results indicate that whilst there are limitations, useful information
about source distribution can be extracted from continuous measurements at a remote site. It is probable that much
improved estimates could be derived if observations were available from a number of sites. The ability to assess emissions
has obvious implications in monitoring compliance with internationally agreed quota and protocols. Crown Copyright 2001 Published by Elsevier Science Ltd. All rights reserved.
Keywords: Lagrangian dispersion model; Inversion techniques; European emissions; Greenhouse gases; Ozone depleting gases
1. Introduction
In order to assess the impact of trace gases on global
warming or ozone depletion it is necessary to know both
the magnitude and distribution of their emissions. Similarly, the impact of legislation and other controls on
emissions can only be properly assessed if detailed emission inventories and trends can be established. For many
species, in particular, recently introduced HFCs and
HCFCs, little information is available about source
strengths and their distribution, with estimates mainly
coming from production and sales "gures supplied by
manufacturers (McCulloch and Midgely, 1998).
In previous work we have demonstrated how estimates
of European emissions can be determined for a range of
ozone depleting and radiatively active trace gases from
long-term observations at Mace Head, on the west coast
* Corresponding author.
E-mail address: [email protected] (D.B. Ryall).
of Ireland (Ryall et al., 1998; Derwent et al., 1998a, b). The
dispersion model NAME was used to simulate the transport of European emissions to Mace Head, with emissions based on the CFC-11 inventory of McCulloch et al.
(1994). European source strengths were then determined
by scaling model predictions to "t observations.
The main limitation of this approach is the use of the
CFC-11 inventory to describe the distribution of emissions for all species. Whilst many of the species considered have broadly similar emission patterns, there will
be signi"cant di!erences for other species. For example,
emissions of both halocarbons and radiatively active
trace gases are changing rapidly as legislation banning or
restricting their use takes e!ect, with changes di!ering
widely between di!erent countries. Also some species
have natural as well as anthropogenic sources, or are
emitted from rural rather than populated areas. Another
problem is that observations at Mace Head are most
in#uenced by Irish and UK sources, resulting in estimates of European emissions being biased towards UK
and Irish Source strengths.
1352-2310/01/$ - see front matter Crown Copyright 2001 Published by Elsevier Science Ltd. All rights reserved.
PII: S 1 3 5 2 - 2 3 1 0 ( 0 0 ) 0 0 4 3 3 - 7
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In this paper we describe a technique that goes some
way to estimating possible source strengths. The aim is
to simulate accurately the transport of pollutants to
Mace Head from all possible land sources, then identify
those locations that consistently lead to elevated concentrations at Mace Head, indicating probable source
regions.
2. Mace Head observations
Since 1994, high-frequency (40 min interval) real-time
gas chromatographic measurements of the principal
halocarbons and radiatively active trace gases have been
made as part of the Global Gases Experiment
(GAGE/AGAGE) at Mace Head, Co. Galway, Ireland (Simmonds et al., 1996a; Cunnold et al., 1997). The
species measured include CFC-11, 12 and 113,
methyl chloroform, carbon tetrachloride, carbon
monoxide, methane, nitrous oxide and chloroform. In
addition, a fully automated gas chromatograph-mass
spectrometer (GC-MS) has been used to monitor a range
of additional species, including many HCFCs and HFCs
(Simmonds et al., 1996b) which are now used as CFC
replacements.
The station is situated on the west coast of Ireland
where the prevailing winds are westerly, bringing clean
unpolluted air from the Atlantic. However, for approximately 20}30% of the time the winds have an easterly or
southerly component, bringing polluted air from the UK
and Europe and resulting in observed concentrations
well above baseline levels. Information about European
source strengths and distributions can be determined
from these elevated concentrations, which are due to
recent emissions.
3. The NAME model
The Met. O$ce's dispersion model NAME is used to
simulate the transport of pollutants to Mace Head. The
model is used operationally within The Met. O$ce for
emergency response purposes, and has recently been applied to a range of air quality problems including NO
V
forecasting (Manning, 1999) and secondary PM mod
elling (Malcolm et a1., 1999). Pollutants are represented
by large numbers of imaginary particles which are released into the &model atmosphere' and then advected by
the local mean wind, with various random walk techniques used to represent turbulent di!usion processes.
Parametrisations are also available for entrainment between the boundary layer and the free troposphere, and
for mixing by deep convection. Boundary layer depths
are determined from wind and temperature pro"les using
a Richardson number or parcel technique. Each particle
represents a mass of pollutant which is depleted over time
to represent loss processes such as wet and dry deposition. The model uses wind "elds and other met
"elds from The Met. O$ce's Uni"ed Model (Cullen,
1993).
A key advantage of utilising a Lagrangian approach is
the ability to identify source}receptor relationships. An
attribution facility exists whereby the origin (both time
and location) of all particles reaching a receptor at any
given time can be identi"ed. It is therefore possible to
determine the relative contribution of di!erent sources to
pollutant levels at any given receptor. Further details of
the model can be found in Ryall et al. (1998).
4. Determination of baselines
Determining baseline concentrations is fundamental to
extracting useful information from observations of trace
gases taken at Mace Head. In this work we need to
accurately quantify elevated pollutant levels above baseline values, whilst annual trends and mean atmospheric
concentrations can only be determined by removing polluted observations in#uenced by recent or nearby emissions. We have found that existing techniques do not
always remove polluted observations, especially for species with local sources such as chloroform and methane.
A new technique utilising the NAME model to identify
source regions has been developed to produce improved
baseline curves.
4.1. Existing techniques
Two main approaches have been used to determine
baselines for Mace Head data. In one approach, periods
of baseline are identi"ed by analysing back trajectories
(Derwent et al., 1998a, b). Typically, 96 h back trajectories
are determined and assigned to one of eight 453 sectors.
Observations are considered baseline if the back trajectories originate wholly from within west and northwest
sectors, i.e. the north Atlantic, where there are no signi"cant sources. The main disadvantage of this approach is
that a back trajectory only represents one of the many
possible trajectories that an air parcel could take enroute to Mace Head. In reality, vertical and horizontal
mixing will result in air from a wide area contributing to
Mace Head. Another problem is that the back trajectories are determined from wind "elds taken from globalscale numerical prediction models. These models do not
always resolve small-scale #ow features such as land/sea
breezes, which can result in false indications of clean air.
Fig. 1 shows observations identi"ed as unpolluted for
methane, which has local as well as European and UK
sources. Note that a number of observations that are
clearly polluted are identi"ed as baseline, this would
result in calculated baseline concentrations being too
high.
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
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Fig. 1. (a) Observed methane at Mace Head, (b) baseline observations from wind sector analyses and (c) baseline observations from
NAME analyses.
An alternative approach is to use some form of statistical analyses on the data to remove &polluted' peaks,
making no assumptions about the origin of the data. For
example, Simmonds et al. (1998) describe an iterative
technique in which data falling outside a given number of
standard deviations from the mean are removed, then the
resulting baseline points "tted to an empirical function to
derive seasonal and annual trends. This approach provides baselines that look similar to those produced using
back trajectory analyses, removing most polluted air.
However, it is not clear that the technique is removing all
polluted observations. One problem is that the technique
does not necessarily distinguish low levels of polluted air
from measurement noise.
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D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Fig. 2. All possible sources contributing to Mace Head total between 0900Z and 1500Z on 11 March 1997. Also shown is a four day
850 mb back trajectory for 1200Z on 11 March 1997.
4.2. A new technique for determining baselines
In an attempt to overcome these problems a simple
technique utilising the NAME dispersion model has been
used to identify unpolluted baseline observations.
The NAME model is run with pollutants released
into the boundary layer throughout the main source
regions likely to a!ect Mace Head (typically
1003W}203E, 303N}803N). Using the back attribution
facility all locations that contribute to Mace Head
boundary layer air concentrations are identi"ed for
each observation time at Mace Head. This is done by
identifying the source location and time of all particles
reaching a box of boundary layer depth and area 13;13
centred on Mace Head. As an example, Fig. 2 shows all
possible source locations for air reaching Mace Head
during the 6 h period between 0900UTC and 1500UTC
on 11 March 1997. An 850 mb back trajectory for 1200Z
is also shown for comparison. Note that the source
region identi"ed by NAME covers a much larger area
than the back trajectory indicates, especially at longer
travel times. This is a result of the NAME model representing the detailed transport from all possible source
regions, including the e!ects of horizontal and vertical
mixing.
An observation is considered polluted if more than
a de"ned fraction (typically 1%) of the material detected
at Mace Head originates from east of 93W or south of
453N (to reject southerly air with concentrations below
baseline). In addition, observations are also excluded if
the boundary layer depth as diagnosed by NAME at Mace
Head is below 300 m. This criterion is designed to exclude
low wind and stable boundary layer situations. Under these
conditions local topographic or heating e!ects can result in
complex local wind features (such as land or sea breezes and
anabatic or katabatic winds) which may result in local
land sources being detected at Mace Head, even though
the dominant wind direction may be onshore. Finally,
a 4 week running average is applied to all points identi"ed as baseline to generate the baseline curve.
The lower curve in Fig. 1 shows baseline observations
identi"ed for methane between 1995 and 1997 and can be
compared with baseline observations derived from back
trajectories. The technique shows a clear improvement,
with very few elevated levels being identi"ed as clean air.
Baselines using this approach, therefore, result in slightly
lower baseline values for species with local sources at
Mace Head. Using this technique baselines have been
calculated for all species monitored at Mace Head under
the AGAGE program or by the GC-MS instrument.
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
5. Estimating the spatial distribution and strength of
European emissions
5.1. Model simulations
The NAME model was used to simulate the transport
of pollutants to Mace Head from all possible land sources within the domain 143W}163E, 403N}603N for the
four year period 1995}1998. A land/sea mask was generated from a topography map taken from the global
version of the Met. O$ce's Uni"ed Model at
0.8333;0.5553 resolution. A constant release rate of
1 g m\ s\ was emitted from all areas with non-zero
topography, with all releases being made into the lowest
40 m of the atmosphere. Approximately 12 particles h\
were continuously emitted from each of 611 source regions, resulting in typical &particle' numbers of 0.5}2
million, depending on the prevailing meteorology. Meteorological data were taken from the regional-scale version of the UK Met. O$ce's Uni"ed Model, giving
analysed "elds at three hourly intervals with a horizontal
resolution of about 50 km. In the vertical 12 levels were
used between the surface and 200 mb, with levels concentrated in the lower atmosphere and boundary layer.
Concentrations were calculated every 15 min over a grid
volume of boundary layer depth and area 13;13 centred
on Mace Head. Concentrations are determined by summing the mass of a pollutant carried by all the &particles'
in a given grid volume, then dividing by the grid volume.
Baseline concentrations were removed from observed
data using the technique described in Section 4, to enable
comparisons to be made with short-term #uctuations
above background levels due to recent ((14 days) emissions. In order to directly compare model predictions
with observations, time series were generated for both on
a three hourly timescale. This also helped to reduce noise
levels in both model and observed series.
5.2. Attribution
As Fig. 2 illustrates, a large number of regions can be
identi"ed as possible emission sources. Any one of, or any
combination of, these sources of appropriate magnitude
could generate the same observed peak at Mace Head.
For example, a weak source near Mace Head could result
in the same concentrations at Mace Head as a stronger
but more distant source. In reality only a small subset of
the possible sources is likely to be true emitters.
Depending on the meteorological situation, each occasion a given source contributes to Mace Head, its emissions will probably take a di!erent route to Mace Head.
The more often a given region is associated with elevated
concentrations, the greater is the chance that the source
is a true emitter. In contrast, the more often a given
region is associated with low or zero observed concentrations, the less likely it is to be an actual source. With
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su$cient transport events, it should therefore be possible
to extract information about individual source strengths.
Fig. 3 shows the relative contribution of each source
region to Mace Head between 1995 and 1998, and the
average travel time in days to Mace Head. This shows
that during the four year period, emissions from all of the
source regions are predicted to reach Mace Head. However, the contributions fall o! rapidly to the east and
south, with contributions falling by over three orders of
magnitude towards the eastern Mediterranean. Average
travel times similarly increase to a week or more from the
south and east of the domain. The fall o! in contribution
is a result of both fewer transport events to Mace Head
and greater dilution during transport to Mace Head.
5.3. Determining emissions
As described the NAME model is used to predict
concentrations C for a series of times t"1, ¹ at Mace
GR
Head resulting from each source i of source strength
s ("1), so that the total predicted concentration P at
G
R
Mace Head from all n sources is
L
P" C .
R
GR
G
Given a series of observations o (with baseline removed)
R
at Mace Head, and assuming that there are no model or
measurement errors we can write
L
C e "o for t"1, ¹,
GR G
R
G
where e are the actual emissions from a source i. This
G
describes a set of T simultaneous equations which can be
solved to determine e .
G
This is a highly over-determined set of equations with
¹<n, in addition the equations are sparse, as each
source i only contributes to Mace Head on a subset of
times t. We have tried to determine emissions e using
G
standard least-squares techniques, for example by singular value decomposition (SVD), but this and other
approaches have proved problematic. The solutions
calculated have generally been unrealistic, with large
variations in source strengths and strongly negative
emission values. In addition to being over-determined
and sparse, there are likely to be signi"cant model errors
and biases which cannot be quanti"ed. In future, it may
be possible to constrain the problem better, for example
by using observations at several sites, allowing leastsquares techniques to be more successfully applied.
We can simplify these equations by assuming that the
emission strengths for all sources contributing to Mace
Head at a given time t are equal (e ), we can then write
R
L
e C "o ,
R
GR
R
G
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D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Fig. 3. (a) Average contribution to Mace Head concentrations between 1995 and 1998. (b) Average travel time in days.
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
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Fig. 4. Model-predicted concentrations at Mace Head (upper curve) against observations (plotted as negative values). Model
predictions are based on uniform emissions throughout Europe.
giving
o
e " R.
R P
R
We can then estimate a value for e by taking a weighted
G
mean of e over time, i.e.
R
2 = o /P
e " R R R R .
G
R =
R R
We have used a weighting factor for each sector i of
= "C , this places greater emphasis on e when the
R
GR
R
signal from source i is strongest. If there are no times
where a given source contributes to Mace Head then
e equals zero. This only occurs when deriving emissions
G
using observations over one year or less, or if there are
limited observations available.
This simpli"ed approach has proved the most robust
and successful to date for identifying source locations.
One drawback of not using a least-squares approach is
that an error analyses is not possible. Instead we assess
the technique by analysing and comparing estimated
derived distributions for a range of species.
6. Results
6.1. CFC-11
Model-predicted CFC-11 concentrations before scaling (i.e. assuming uniform emissions throughout Europe)
are plotted against Mace Head observations for 1996 in
Fig. 4. Here, the model series has been scaled by a single
factor equal to the ratio of the mean of all observations to
the mean of all model predictions. Interestingly, there is
already a high correlation of 0.76 between the model and
observed data, indicating that CFC-11 sources are widely
spread across Europe.
Table 1
Statistics for the comparison of NAME model predictions with
observations for CFC-11
r
NMSE
FAC2
1995 before scaling
1995 after scaling
0.70
0.74
2.62
1.89
26.8
28.9
1996 before scaling
1996 after scaling
0.76
0.82
1.82
1.47
28.7
30.9
1997 before scaling
1997 after scaling
0.75
0.80
1.69
1.38
28.6
30.4
1998 before scaling
1998 after scaling
0.79
0.83
2.31
2.16
24.4
24.9
1995}1998 before scaling
1995}1998 after scaling
0.74
0.78
2.1
1.79
27.2
28.1
Notes: (a) r: Pearson's correlation coe$cient; (b) NMSE: normalised mean square error; (c) FAC2: percentage of results
within a factor of 2.
After scaling the emissions from each region in Europe
using the technique described in Section 5, the correlation between model-predicted concentrations and observed values increases signi"cantly, with the correlation
coe$cient r increasing from 0.76 to 0.82. Table 1 shows
similar improvements in other years. Improvements are
also seen in normalised mean square error (NMSE) and
FAC2 (percentage within a factor of 2) statistics. Following application of the spatial scaling technique, the "t
between model-predicted and observed concentrations
improves, indicating that the calculated emission distributions better describe the observed concentrations at
Mace Head. The plots of model versus predicted concentrations in Fig. 5 demonstrate that model appears to
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D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
reproduce the main characteristics of the observed data
throughout the four year period.
Estimated emissions for CFC-11 for each year between
1995 and 1998 are shown in Fig. 5. The estimated distributions for each year show a number of important similarities. These include regions of higher emissions in
southern England and northwest Europe, following the
main areas of high population density, and regions of
lower emissions from Ireland, Scotland, Scandinavia and
Spain, which are typically areas of lower population
density. The largest di!erences between the distributions
are in eastern and southern sectors. As Fig. 3 shows, the
contribution of sources to Mace Head falls o! rapidly
with distance, both due to increased travel times and due
to less frequent transport, so greater uncertainty is to be
expected with increasing distance from Mace Head. The
small source identi"ed in Spain during 1998 is also likely
to be an artifact.
Whilst the technique appears to go someway to identifying source regions, by identifying those regions that
consistently led to elevated levels at Mace Head, when
interpreting estimated distributions a number of limitations should be considered.
(i)
(ii)
(iii)
(iv)
(v)
Di!erent combinations of regions can lead to the
same observed concentrations at Mace Head. For
example, a weak nearby source can lead to the same
concentrations as a more distant, but stronger
source. Given that polluted air often arrives via
similar pathways, such as a high pressure over the
north sea bringing air from Germany, France and
the UK, the result is that it may not be possible to
resolve some regions properly.
The potential number of regions contributing to
Mace Head and their areal coverage generally increases with travel time and distance, reducing the
potential resolution in more distant regions.
Some of the species are detected at very low concentrations, with noise levels being signi"cant compared to #uctuations above baseline. Not only does
this make determining baselines di$cult, it means
that signals from smaller or more distant sources are
di$cult to distinguish from noise.
Modelling errors are likely to be higher for distant
sources with longer average travel times, and this
may introduce bias. Modelling errors may result in
observed peaks not being modelled or peaks being
predicted when none were observed. Such errors will
result in the contributions from some regions either
being suppressed or exaggerated.
Systematic model biases will similarly lead to problems, for example if the model systematically predicts low concentrations due to too much vertical
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mixing or convection then estimated emissions
would be overpredicted.
(vi) Emissions from more distant regions may have been
lost from the computational domain, that would
have otherwise reached Mace Head, leading to
underprediction, requiring higher source strengths
to compensate.
(vii) Emissions are assumed to be continuous and constant throughout the year. Whilst this is a reasonable assumption for some species, others will have
complex time dependencies. For example, those
emitted from tra$c such as carbon monoxide will
have hourly, weekly and seasonal cycles. For some
recent HCFCs or HFCs with limited applications
releases may be erratic and unpredictable, for
example the halon 12B1 which is used in "re extinguishers.
(viii) No deposition or chemistry is assumed. Whilst
many of the species considered can be considered
inert with long lifetimes, some do undergo dry and
wet deposition. For example carbon monoxide is
lost by dry deposition.
As such the estimated source distributions should not
be seen as a de"nitive source distributions, rather as
indicative of the most probable areas of emissions, with
accuracy and resolution increasing closer to Mace Head.
As there is limited information about source strengths
for many of the species monitored at Mace Head it is
di$cult to assess the accuracy of derived emission maps.
An alternative test is to compare derived source distributions for a range of species that might be expected to have
di!erent source distributions due to di!ering uses.
6.2. Comparison of species
Estimated source distributions for the domain
143W}163E and 403N}603N, which covers most of the
populated and industrialised regions of Europe, are presented for a range of species in Fig. 6. All observations
between 1995 and 1998 inclusive were used, resulting in
approximately 41,000 observations being used for the
AGAGE species, and 4000}4500 observations for the
GC-MS species. For HCFC 22, HFC 125 and methyl
chloride measurements were only available for 1998, resulting in approximately 1000 observations being used.
Given the reduced number of observations for the GCMS species due to less frequent monitoring and longer
periods of missing data, the resulting distributions are
likely to be less reliable than those for the AGAGE
species.
Table 2 lists total European source strengths for each
year, and Table 3 lists source strengths for the UK,
䉳&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
Fig. 5. Model-predicted concentrations versus observations and estimated source strengths for CFC-11, 1995}1998. Time series of
observations are plotted as negative values.
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D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Fig. 6. Estimated source distributions for a range of trace gases.
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Fig. 6. (continued).
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D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Fig. 6. (continued).
Table 2
Estimated European emissions in thousand tonnes yr\
Species
1995
1996
1997
1998
1995}1998
CFC-11
CFC-12
CFC-113
Methyl chloroform
Carbon tetrachloride
Carbon monoxide
Methane
Nitrous oxide
Chloroform
Methyl bromide
Methylene dichloride
Methyl chloride
134a
141b
142b
152a
12B1
22
125
8.6
15.1
5.1
29.6
3.0
63,130.0
25,158.0
1376.0
20.6
7.2
69.9
8.4
13.3
3.9
19.0
4.2
74,910.0
27,655.0
1456.0
14.9
6.1
90.4
8.6
12.2
2.3
7.3
2.9
42,730.0
26,452.0
1633.0
18.8
7.9
124.9
2.8
7.1
5.4
0.3
0.9
21.1
6.5
8.0
6.2
0.5
1.0
22.5
10.1
12.3
8.4
0.6
1.3
32.4
8.2
11.1
1.5
1.9
2.9
25,757.0
23,739.0
1516.0
18.0
5.5
89.7
107.8
9.6
11.0
6.4
0.4
0.8
24.1
1.5
8.5
13.0
3.3
15.0
3.3
53,313.0
26,086.0
1511.0
18.0
7.05
102.9
107.8
8.45
10.23
7.11
0.46
1.05
27.4
1.51
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
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Table 3
Estimated UK emissions in thousand tonnes yr\
Species
1995
1996
1997
1998
1995}1998
CFC-11
CFC-12
CFC-113
Methyl chloroform
Carbon tetrachloride
Carbon monoxide
Methane
Nitrous oxide
Chloroform
Methyl bromide
Methylene dichloride
Methyl chloride
134a
141b
142b
152a
12B1
22
125
1.1
1.8
0.6
3.9
0.3
6096.0
3144.0
143.1
3.0
0.8
11.1
0.9
1.4
0.4
1.8
0.3
5822.0
2917.0
139.7
2.1
0.8
10.3
0.9
1.1
0.2
0.8
0.2
3707.0
2871.0
162.1
2.2
0.8
13.0
0.7
0.9
0.2
0.3
0.2
2854.0
2724.0
145.8
1.8
0.5
7.8
9.3
0.8
0.8
0.5
0.03
0.1
1.9
0.2
0.9
1.3
0.4
1.7
0.3
4645.0
2933.0
148.2
2.3
0.8
10.8
9.3
0.8
0.9
0.6
0.03
0.1
2.7
0.2
0.5
0.9
0.5
0.02
0.2
2.9
0.7
0.9
0.5
0.04
0.1
2.5
de"ned as the region between 63W}23E and 503N}603N.
Given the problems discussed, it is clearly ambitious to
subdivide emissions into UK and European components,
and the UK "gures should be treated with caution.
Nevertheless, it is interesting to compare the UK "gures
with national inventories, and identify species with di!ering European to UK components.
6.2.1. CFC-11 and CFC-12
The emission distributions determined for CFC-11
and CFC-12 are shown in Figs. 6a and b. The pattern of
emissions is broadly similar and shows regions of high
emissions over central and southern England with similarly high emissions over northern France, Belgium, The
Netherlands, and spreading eastwards into Germany.
Emissions are found to be relatively low over Ireland,
Scotland, Scandinavia and Spain. Total European source
strengths appear to be dropping for both species, but at
a slow rate.
CFC-11 and CFC-12 manufacture and sales have been
heavily regulated and controlled within the framework of
the international convention to protect the ozone layer
(WMO, 1988). It is therefore likely that the CFC-11 and
CFC-12 emissions required to explain the Mace Head
observations are coming from the &bank' of foams, refrigerators, freezers and other equipment which is either still
in use or has been scrapped. This &bank' of CFC-11 and
CFC-12 is likely to be widely distributed across the
populated areas of northwest Europe which may explain
the widespread nature of the emissions required by the
NAME model.
0.9
1.2
0.7
0.03
0.2
3.3
The relative emissions of CFC-11 and CFC-12 from
the United Kingdom expressed as a fraction of total
European emissions are both close to 0.1. This fraction is
somewhat larger than that expected on population
grounds alone and may re#ect the proximity of the
United Kingdom to the Mace Head monitoring site and
the high frequency with which air parcels travel over the
United Kingdom en route to Mace Head, see Fig. 4a.
Nevertheless, the magnitude of this fraction is still
such that the NAME model requires CFC-11 and CFC12 emissions from an extensive area across Europe if
it is to reproduce the Mace Head observations. The
widespread nature of the best-"t CFC-11 and CFC-12
emission distribution is clearly apparent in Figs. 6a
and b.
6.2.2. CFC-113 and methyl chloroform
The emission distributions for CFC-113 and methyl
chloroform are shown in Figs. 6c and d. There are similarities with CFC-11 and CFC-12, with high emissions
indicated in an extensive region which includes southern
England and the populated regions of northwest Europe.
In contrast to CFC-11 and CFC-12 an additional region
of higher emissions is clearly indicated in France. Calculated European source strengths are reducing rapidly,
with methyl chloroform emissions dropping by over
a factor of 10 in just four years.
Both CFC-113 and methyl chloroform were extensively used as industrial solvents before their manufacture
and sales were phased out under international treaty
obligations. The rapid drop in emissions suggests that
2520
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
their use is falling as expected, with minimal &banked'
stocks.
carbon monoxide emissions which is be re#ected in
Fig. 6f.
6.2.3. Carbon tetrachloride
The emission distribution calculated for carbon tetrachloride is shown in Fig. 6e. There is a clear contrast
between the distribution for carbon tetrachloride and
those for CFC-11 and CFC-12. The distribution indicates emissions which increase progressively eastwards
across Europe, implying that most of the observed carbon tetrachloride at Mace Head originates from Eastern
Europe.
The main use of carbon tetrachloride in Europe was as
an industrial intermediate in the manufacture of CFCs.
This use was phased out in northwest Europe as part of
international treaty obligations. Carbon tetrachloride is
still widely used in Eastern Europe as a solvent, "re
extinguisher, and in the manufacture of CFCs and this
continuing use is clearly apparent in Fig. 6e.
The relative emissions of carbon tetrachloride required
from the UK as a fraction of total European emissions is
0.09, signi"cantly lower than for many of the trace gases
studied.
6.2.5. Methane and nitrous oxide
The estimated emission distributions of methane and
nitrous oxide are presented in Figs. 6g and h. Both
emission distributions show that a widely distributed
source is required across much of northwest Europe
in a source region stretching from Ireland, across the
United Kingdom, through the low countries and into
Germany in the East. In contrast to the distributions
required for CFC-11 and CFC-12, methane and nitrous
oxide emissions are required from Scotland and Scandinavia. These widespread emission regions are consistent
with their emission source types, which include both
industrial and agricultural sources, and for methane
widespread natural sources.
6.2.4. Carbon monoxide
The emission distribution for carbon monoxide is plotted in Fig. 6f. The emission distribution is dominated by
large emissions from Eastern Europe superimposed upon
a broad pattern of release across much of northwest
Europe. Emissions appear to be highest in Poland, Czech
Republic and Hungary and least in Spain, Ireland and
Scandinavia.
This pattern of emission is in stark contrast to that
required by CFC-11 and CFC-12, but is strikingly similar to that required by carbon tetrachloride. It is possible
that this behaviour is due to carbon monoxide being
overpredicted at Mace Head due to loss processes such
as dry deposition and oxidation not being represented.
However, these loss processes are minimal in the short
timescales considered here, so it is possible that the
di!erences in emission patterns between carbon monoxide and CFC-11 (or CFC-12) are signi"cant and that they
re#ect a real di!erence in the long-range transport of
carbon monoxide versus CFC-11 to Mace Head.
Carbon monoxide in European terms is uniquely man
made in origins. Its main source is from the exhaust
emissions of uncontrolled petrol-engine vehicles. This
motor-vehicle source is widely distributed across Europe
and readily accounts for the widespread nature of the
emission distribution required by the NAME model. For
many years now, exhaust emissions in northwest European countries have been controlled through the implementation of exhaust catalyst control systems. Their
widespread introduction imposed by the European Community directives will have resulted in a shifting in
balance between Western and Eastern Europe in their
6.2.6. HFC134a and HFC152a
The emission distributions of HFC134a and HFC152a
required to "t the Mace Head observations are illustrated in Figs. 6j and m. Whereas the distribution required for HFC134a is concentrated in an arc from
southern England through Belgium, the Netherlands,
Germany, Switzerland into Italy, that for HFC152a is
concentrated in Germany. These two patterns are di!erent and probably re#ect real di!erences in the long-range
transport of HFCs to Mace Head. As these species are
relatively new, widespread emissions should not be
expected, so it is possible that these distributions are
indicating manufacturing regions.
The UK contribution as a fraction of the European
total for HFC152a is low at 0.07, indicating the relative
importance of European rather than UK sources.
6.2.7. HCFC-141b and HCFC-142b
The emission distributions for HCFC-141b and
HCFC-142b are shown in Figs. 6k and l. Again, the
distributions are strikingly di!erent for such apparently
similar trace gases. The distribution required for HCFC142b shows an arc shape, quite similar to that found
for HFC 134a. In contrast, the HCFC141b shows a distribution which more closely resembles that of methyl
chloroform, showing up areas of high emissions in
France.
6.2.8. Methyl bromide, methyl chloride, methylene dichloride and chloroform
The emission distributions calculated for the four
halomethane species are presented in Figs. 6l and q}s.
Chloroform emissions are identi"ed in Ireland, Scotland
and Scandinavia, with little evidence of emissions from
the populated regions of Europe. This is in contrast with
the distribution for methylene dichloride which shares
many of the features shown by species which appear to
originate from populated regions, such as HFC-125,
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
Table 4
Emissions of carbon monoxide in thousand tonnes yr\ for the
United Kingdom and Europe from the NAME model and
EMEP
Year UK NAME
UK EMEP
1994
1995
1996
1997
1998
5677.0
5269.0
5000.0
4610.0
6096.0
5822.0
3707.0
2854.0
Europe NAME
63,130.0
74,909.0
42,730.0
25,757.0
Europe EMEP
77,269.0
74,619.0
72,460.0
72,332.0
HFC 142b and CFC-11. Both methyl chloride and
methyl bromide show some similarities with chloroform,
with methyl bromide also showing population-based
sources. Recent measurements in the vicinity of the Mace
Head site show strong natural sources of chloroform and
methyl chloride, it is therefore possible that the derived
distribution is realistic, re#ecting emissions from peaty
soils in northern regions.
Methyl chloride and methyl bromide may also have
ocean sources, which are not considered here.
6.3. Comparison with other inventories
The distribution and strength of CFC-11 emissions
have been published for 1990 by McCulloch et al. (1994).
Their total European source strength for CFC-11 over
the area considered here is over 25 thousand tonnes yr\,
signi"cantly higher than our estimates. However, the
phase-out of European manufacture and sales of CFC-11
under the Montreal Protocol has resulted in such a signi"cant drop in emissions that a detailed comparison
of source strengths or distributions with that from
McCulloch et al. (1994) is inappropriate.
Emission estimates on a country-by-country basis for
carbon monoxide are available through the EMEP program (Tarrason and Schaug, 1999; Mylona, 1999) and
provide an invaluable check of the emission distributions
generated here. In Table 4 the UK and European carbon
monoxide emissions are compared for each year with
those determined from the Mace Head observations.
2521
The NAME model estimates for the United Kingdom
emission of carbon monoxide agree with the literature
estimates to within 20% and to within 40% for Europe as
a whole. This represents a good level of overall agreement. Furthermore, the EMEP inventory presented by
Tarrason and Schaug (1999) shows clear evidence of
emission decreases due to the implementation of catalytic
exhaust gas emission control systems occurring preferentially in western as opposed to Eastern Europe. However,
as Table 4 shows, this preferential impact appears to be
acting more strongly in the NAME model emissions
compared with the published inventories. This may re#ect a bias in our model leading to a biased focus on the
United Kingdom. It could, however, be that carbon monoxide reductions are in reality occurring much faster
compared with the inventories for other reasons concerned with the scrapping or resale of the older uncontrolled vehicles.
CORINAIR (McInnes, 1994) quote European emission estimates of 45.619 and 1.9 million tonnes yr\ for
methane and nitrous oxide respectively for 1990. Whilst
these predate our estimates, they compare favourably
with our mean 1995}1998 estimates of 26.086 and
1.51 million tonnes yr\.
NETCEN publish estimated United Kingdom emissions for a range of species (Salway et al., 1999). Emissions for carbon monoxide, nitrous oxide and methane
are listed in Table 5. With the exception of 1996 carbon
monoxide estimates the agreement between NAME and
NAEI estimates is very good. It is not clear why the 1996
NAME estimate for carbon monoxide is so high compared to other years, though it may be connected with
the unusual number and intensity of pollution episodes
observed during that year.
NETCEN also publish emission estimates for various
hydrocarbons, some of which are monitored at Mace
Head. The published UK emission estimate for methylene dichloride of 14.84 thousand tonnes yr\ for 1996 is
in reasonable agreement with that required by the
NAME model, 10.8 thousand tonnes yr\. In contrast,
the published emission estimate for methyl chlororform
is 15.9 thousand tonnes yr\, much higher than the
NAME-derived estimate of 1.7 thousand tonnes yr\.
However, methyl chloroform emissions are dropping
Table 5
United Kingdom source strengths from NAME and from the National Atmospheric Emissions Inventory in thousands tonnes yr\
Year
Methane
NAME
Methane
NAEI
Carbon monoxide
NAME
Carbon monoxide
NAEI
Nitrous oxide
NAME
Nitrous oxide
NAEI
1995
1996
1997
1998
3144
2917
2871
2724
3751
3712
6096
5822
3707
2854
4939
4645
143
140
162
146
183
189
2522
D.B. Ryall et al. / Atmospheric Environment 35 (2001) 2507}2523
rapidly following the implementation of the Montreal
Protocol, and it is felt that the NAEI fails to represent
this rapid fall o!. Inspection of the methyl chloroform
data at Mace Head shows that concentrations rarely
rise above background, implying there are few active
sources.
7. Discussion and conclusions
Whether any of the above statements and conclusions
concerning European trace gas emissions re#ect realworld behaviour, will depend on the adequacy of the
NAME dispersion model, our ability to generate accurate baseline concentrations and the accuracy of the
absolute calibrations for the trace gases involved. The
emission source distributions have been estimated using
the NAME model over a model domain from 143W to
163E and 403N to 603N. The emission quantities are
annually integrated values and no attempt has been
made to allow for any seasonal variations in emissions.
The model assumes that each species behaves as an inert
tracer so that if deposition or chemical removal occurs in
the real atmosphere, then the estimated emissions generated above would be lower limits to reality. Similarly, we
have made no allowance for any bias in our long-range
transport model. However, we have no reason to believe
that any model bias would act di!erentially across the
range of species modelled.
Despite these limitations our approach shows considerable promise for determining emissions of trace
gases. Useful information about source strengths and
their spatial distribution appears to have been extracted
from continuous high-frequency observations at a single
remote site, speci"cally sited to minimise transport of
pollutants from Europe. Whilst the source distributions
generated are generally consistent with expectations,
there have been some interesting "ndings. These
include the apparent shift of carbon tetrachloride
and carbon monoxide emissions towards eastern Europe,
and the identi"cation of a single source of HFC-152a
in Germany. For some HFCs and HCFCs this approach provides some of the "rst estimates of source
strengths.
Improved resolution and accuracy will require additional observational sites. This will help distinguish
source regions; for example, observations on the east
coast of England would help distinguish European from
UK sources. A few carefully based sites, situated with
source regions and climatology in mind would allow
much improved estimates. It may then be possible to
apply more sophisticated inversion techniques to generate emission distributions; this will also allow us to go
someway towards making error estimates. Future work
will also look at species with more rapid surface exchange
processes, such as carbon dioxide.
Acknowledgements
This work has been supported by the Department
of the Environment, Transport and Regions as part of
the Global Atmosphere Division research programme
through contracts EPG 1/1/57 and EPG 1/1/103 and as
part of the Public Meteorological Services Research and
Development programme of the Meteorological O$ce.
We wish to record our deep appreciation to our colleague, Roy Maryon, for his continued help and encouragement in this work.
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