Krzysztof Olendrzynski EMEP/MSC

EMEP /MSC-W
Date:
Note 4/99
July 1999
DET NORSKE METEOROLOGISKE INSTITUTT
Norwegian Meteorological Institute
Research Note no. 29
Operational EMEP Eulerian Acid Deposition Model
Krzysztof Olendrzynski
EMEP/MSC-W
1999
ISSN 0332-9879
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CONTENTS
Preface and Acknowledgements...................................................................... 3
Introduction ..................................................................................................... 4
1. Recent updates to the model ......................................................................... 4
1.1 Extension of the model domain ......................................................... 5
1.2 PARLAM-PS meteorology ............................................................... 5
1.3 Modifications in the code .................................................................. 6
1.4 New countries in the budget and source-receptor computations ....... 7
2. Measurement database for 1997 .................................................................. 8
3. Annual scatter plots for 1997 ..................................................................... 11
3.1 Concentrations in air ........................................................................ 11
3.2 Concentrations in precipitation ........................................................ 14
3.3 Wet depositions ............................................................................... 18
4. Comparison of computed and measured daily time series in 1997 ............ 21
5. Maps of computed concentration and deposition fields for 1997 ............. 28
Conclusions ................................................................................................... 33
References ..................................................................................................... 35
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Preface
This report was prepared for the twenty third session of the Steering Body of EMEP (Co-operative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe). EMEP is one of the scientific programs of the UN Convention on Longrange Transboundary Air Pollution (LRTAP Convention). The main objective of this report is
to present the status of the development of the EMEP Eulerian Acid Deposition model, which
is run operationally for the first time this year. The report provides a detailed description of the
model performance with regard to 1997 meteorological and measurement data. The application
of the model with regard to source-receptor matrix computations and country budgets is described in Bartnicki (1999, EMEP/MSC-W Note 5/99).
Acknowledgements
The author would like to acknowledge his current and former colleagues from the EMEP modelling group at MSC-W who helped in preparation of this report through numerous discussions
on the model, its implementation and its post-processing tools. In particular I would like to
thank: Jan-Eiof Jonson, Erik Berge, Hugo Jakobsen, and Jerzy Bartnicki. I would also like to
thank Egil Stoeren for his valuable contributions in various parts of this work, and Leonor Tarrason for reviewing the manuscript and inspiring comments.
The work would not be completed without support from: the DNMI’s meteorological section,
CCC/NILU, and staff of the computer department at the Technical University in Trondheim, on
which CRAY T3E computer, the model has been developed and run operationally.
-3-
Introduction
The main objective of Meteorological Synthesizing Centre - West (MSC-W) of the EMEP Programme is computation of atmospheric transboundary transport and deposition of air pollutants (sulfur, nitrogen compounds and tropospheric ozone) in Europe. The computations of
annual concentration and deposition fields, as well as country-to-grid matrices for acidifying
species, have been performed routinely for 20 and 13 years respectively for sulfur and nitrogen
compounds. Two-dimensional (2-D) Lagrangian model (Eliassen and Saltbones, 1983; Barret
and Berge, 1996) had been successfully used for routine computations up till 1998. However,
one important limitation of the 2-D Lagrangian model is its simplified vertical structure, which
makes it is very difficult to parameterize atmospheric transport of pollutants above the mixing
height. In order to overcome this limitation, a 3-D Eulerian transport/deposition model has
been developed at MSC-W since 1993 (Berge, 1993; Jakobsen et al., 1995; Jonson and Berge,
1995; Jakobsen et al., 1996; Jakobsen et al., 1997; Berge, 1997; Berge and Jakobsen, 1998,
Jonson et al., 1998a, 1998b, Olendrzynski et al., 1998a, 1998b, 1999).
This year - for the first time - the model was applied to operational computations. The results of
routine calculations: country budgets and country-to-country pollution exchange, are presented
in Bartnicki (1999). The current status of the model and its performance with respect to 1996
data was extensively described in EMEP 1997 Report (Bartnicki et al., 1998, Olendrzynski et
al., 1998), and is available at: http://www.emep.int. Therefore, only the recent updates to the
model are presented here (Section 1) together with the analysis of the model performance for
1997. The model was run for 1997 meteorological and emission data. EMEP measurement database for 1997 - used for model validation - is described in Section 2. In Section 3 annual scatter plots are presented and discussed. Examples of daily time series are given in Section 4, while
computed maps of annual concentration and deposition fields are presented in Section 5 followed by conclusions and references.
1. Recent updates to the model
This section documents significant changes introduced in the model during 1998 and early
1999. The most important development was a rather detailed analysis of pollutant mass conservation, which led to the modifications in the chemistry scheme and to the debugging of the
model “book keeping”. The overall outcome was an updated code which produces annual or
monthly results with mass conservation error below 1%. Also, the comparison of the model
performance with respect to the EMEP Lagrangian model (Bartnicki and Tarrason, 1998),
showed that the Eulerian model performs comparably or better in most tests. Some of the tests
for 1996 data were repeated with the updated Eulerian model (not discussed here). They
showed that the conclusions reached in 1998 are still valid. These developments made the
EMEP Eulerian acid deposition model ready for operational use. The individual developments
with respect to the model are discussed in detail below.
The following modifications were made in the model compared to the 1998 version:
- extension of the model domain to 170 x 133 grid
- application of the PARLAM-PS meteorological model for producing meteorological data
- modifications in the physical routines imposed by the new meteorological data; mass conservation analysis
- introduction of new countries/Parties to the CRLTAP Convention in the budget and source
receptor computations
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1.1 Extension of the model domain
The model domain has been extended from [1:151] x [1:133] to [1:170] x [1:133] in the EMEP
50km grid. The description of the EMEP 50km grid system is given e.g. in the Appendix of
EMEP/MSC-W Report 1/98 (see also Figure 1.1 below). The extension of the model domain in
the x direction from [1:151] to [1:170] grid cells, enabled the inclusion of Cyprus and Armenia
(Parties to the LRTAP Convention), as well as the entire Mediterranean Sea, entire Turkey,
Armenia, Azerbaijan, the Caspian Sea, Syria, and parts of Iran and Irak. At the same time, the
deposition calculation area increased from ([6:38] x [2:36]) used by the EMEP Lagrangian
model to ([1:44] x [1:37]). This corresponds to grid cells: ([36:167] x [12:122]) in the 50km
notation (Figure 1.1). The extension of the domain required extra input data for the parts not
covered by the model so far. These data included - among others - anthropogenic emissions,
natural marine emissions from eastern part of the Mediterranean Sea, landuse and surface
roughness. The extended emission and meteorological data are discussed in other EMEP/
MSC-W Notes: Mylona (1999), and Tsyro and Støren (1999). The landuse data were processed - aggregated to the classes of the original RIVM formulation - by David Simpson based
on the database developed at Stockholm Environment Institute (Simpson - private communication, 1999).
122
120
110
100
90
80
70
60
50
40
30
20
12
36 40
50
60
70
80
90
100
110
120
130
140
150
160
167
Figure 1.1 Extended EMEP 50km grid deposition area. It corresponds to [1:44] x [1:37] of the
EMEP 150km grid.
1.2 PARLAM-PS meteorology
In 1998 computations, the meteorological data for 1996 were derived from the output of the
LAM50E meteorological model. This year, however, for the first time the PARLAM-PS model
was applied to produce the meteorological data for use in the EMEP Eulerian model. PAR-
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LAM-PS is a dedicated version of the HIRLAM model - operational weather forecast system
of the Norwegian Meteorological Service. In PARLAM-PS the σ-coordinates replaced HIRLAM’s hybrid η−coordinates, and polar stereographic projection replaced rotated spherical
horizontal grid. The detailed description of the PARLAM-PS model and its output is given by
Tsyro and Støren (1999). Here, only the basics are given.
The domain of the PARLAM-PS model covers the entire domain of the EMEP Eulerian model.
The horizontal and vertical grids of both model are identical. The temporal resolution of the
meteorological data increased from 6 hours in the LAM50E model to 3 hours for PARLAMPS. For the purposes of the EMEP Eulerian model eight 3-D and four 2-D fields of data are
needed. The 3-D fields are:
- wind u-component [m/s]
- wind v-component [m/s]
- specific humidity [kg/kg]
- potential temperature. [K]
- cloud water [(kg water)/(kg air)]
- cloud cover [%]
- precipitation at each vertical level accumulated over 3-hours [mm]
- vertical velocity [1/s]
Compared to the LAM50E data, 3-D cloud cover data were added. Previously, the cloud cover
was given for two layers: above and below the normalized sigma level 0.85 (~850hPa). The 2D surface fields include:
- air pressure [hPa]
- temperature at 2m [K]
- surface flux of sensible heat [W/m2]
- surface stress [N/m2]
Because of the increased temporal resolution, extension of the grid and adding 3-D cloud data,
the size of the meteorological data files increased to the total of 21 900 MB required for one
year of meteorological data. This figure is the product of: 365days times eight 3-hour intervals
per day, times 7.5MB - the size of an individual binary file. The size of the meteorological data
together with enormously abundant model output (annual, monthly and daily data from runs
for individual countries) determines the disk space requirements for the Eulerian model. As in
1998, the model is run on parallel computer CRAY T3E at the Technical University in Trondheim (Norway). Depending on the load at particular time, the model is run on 8-to-16 processors out of the total number of ca. 88 processors available to users. A run for entire year takes
(16 processors) about 11 hours of real time. The computation time is practically inversely proportional to the number of used processors.
1.3 Modifications in the code
Several modifications in the code were made to account for the changes in the meteorological
and other input data. The main temporal loop now is 3 hours (previously 6 hours). At the
beginning of the 3-hour loop new set of meteorological data is read from input files. The basic
time step is 10 minutes. It is reduced to 200s for chemistry calculations. By increasing the temporal resolution of the basic meteorological data, it became possible to more accurately simulate meteorological conditions in the model. Especially, the diurnal course of basic
meteorological variables can be simulated more precisely. Every 10 minutes, there is a linear
-6-
interpolation of wind, humidity and temperature data within each 3-hour interval.
Cloud cover is now given at all twenty vertical layers. It is a an arithmetic mean of 15-minute
intervals over the 3-hour period. The 3-D cloud cover data are used directly when computing
in-cloud SO2 oxidation and in-cloud scavenging. The maximum cloud cover above the given
layer (including the surface layer) is used in computing NO2 dissociation and in computing the
wet part of grid cells in the dry deposition scheme.
Precipitation is taken as a 3-hour cumulative value. It was found in the original PARLAM-PS
data that there are common occasions when there is net evaporation for a given vertical layer.
Physically, it means that rain water formed in cloud layers evaporates below the clouds before
reaching the surface. Precipitation reaching the ground is thus the difference between the rain
water and evaporation. The latter process is not taken into account in the present parameterization of cloud scavenging. Instead, wet scavenging is parameterized in terms of the actual precipitation reaching the surface. The code was modified accordingly to neglect the evaporation
process. A detailed parameterization of cloud process in PARLAM-PS provides a good basis
for a review of the cloud scavenging processes in the EMEP Eulerian model. The introduction
of the evaporation into the pollutant scavenging parameterization, is intended to begin next fall.
Horizontal velocities (u and v) are now defined for the staggered grid. The first value at the left
boundary is given at the node x = 1+1/2. Similarly the first the value in the y direction is given
at y=1+1/2. Previously, in the model version with LAME50e meteorology, the horizontal
velocities were given at the centers of grid cells. They were interpolated linearly to grid cell
boundaries when computing horizontal advection.
Vertical velocities - on the other hand - are now given at the grid centers. The values at the
“half-grids” between vertical layers are “recovered” precisely through interpolation and by setting to zero velocities at the surface and at the top of the atmosphere. A reverse calculation is
done in PARLAM-PS, when vertical velocities are stored for output. Previously with LAM50e,
the situation was just the opposite.
Equilibrium chemical reactions leading to the production of ammonium sulfate and ammonium nitrate were taken outside of the iteration within the TWOSTEP method. This modification in the numerical scheme for solving the chemistry, helped to maintain the mass balance
especially for reduced nitrogen. It should be noted that, in general, the TWOSTEP conserves
mass only if a sufficient number of iterations is performed. In the current implementation only
single iteration is used with a time step of 200s. This was found sufficient for practical applications.
Pollutant mass conservation is an important and desired feature of an operational air pollution
model. It is especially important in case of budget computations and transboundary exchange.
For the annual runs with all emissions accounted for, the total mass of pollutants is conserved
in the model domain within 1% range for total sulfur, total oxidized nitrogen and reduced
nitrogen. Similar accuracy is achieved in runs for individual countries.
1.4 New countries in the budget and source-receptor computations.
Due to the extension of the computational domain described in paragraph 1.1, several new
countries - Parties to the LRTAP Convention - could be included in the budget calculations.
-7-
These include: Armenia, Cyprus, Malta and Turkey. For each of these countries as well as for
all countries and seas added to the computational domain, it was necessary to determine the
fraction of the grid cell area belonging to the respective country or sea (Caspian or Mediterranean). This was done based on the geographical coordinates of coastlines and the country borders.
In the following sections, results from model validation against 1997 data are presented. But
first comes the basic information about the measurement database for 1997.
2. Measurement database for 1997
The Eulerian model has been run with meteorological and emission data refer for 1997. Computed concentrations and depositions have been compared with available measurements in the
model domain. Altogether, 87 EMEP stations have been used for the model validation (89 stations in 1996). These stations reported daily values of air concentrations and concentrations in
precipitation. The number of days in 1997 for which measurements were performed varied
form one station to another as well as the number of measured species at each station.
The list of the stations used for the Eulerian model validation is given in Table 2.1. Geographical positions of these stations in the model domain are shown in Figure 2.1. The density of the
EMEP measurement network is quite high in Central and Western Europe (Germany, France,
UK, Switzerland, Poland, Czech Republic, Slovakia) and Scandinavia (Finland, Norway, Sweden and Denmark). The spatial coverage is less satisfactory in Southern Europe (Italy, Greece,
Spain, the Balkans), where in general the quality of measurements is lower than in Central and
Northern Europe.
Figure 2.1 Geographical locations of the EMEP stations in the Eulerian model domain. The stations marked by black circles have been used for the model validation.
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Table 2.1. List of EMEP stations used for the model validation.
Station
No
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
AT2
AT4
AT5
CH1
CH2
CH3
CH4
CH5
CS1
CS3
DE1
DE2
DE3
DE4
DE5
DE7
DE8
DE9
DK3
DK5
DK8
EE9
EE11
FI4
FI9
FI17
FI22
FI37
FR3
FR5
FR8
FR9
FR10
FR11
FR12
GB2
GB4
GB6
GB7
GB13
GB14
GB15
GB16
GR1
HU2
Position
Name
Illmitz
St. Koloman
Vorhegg
Jungfraujoch
Payerne
Tönikon
Chaumont
Rigi
Svratouch
Kosetice
Westerland
Langenbrügge
Schauinsland
Deuselbach
Brotjacklriegel
Neuglobsow
Schmücke
Zingst
Tange
Keldsnor
Anholt
Lahemaa
Vilsandy
Ähtari
Utö
Virolahti II
Oulanka
Ähtari II
La Crouzille
La Hague
Donon
Revin
Morvan
Bonnevaux
Iraty
Eskdalemuir
Stoke Ferry
Lough Navar
Barcombe Mills
Yarner Wood
High Muffles
Strath Vaich D.
Glen Dye
Aliartos
K-puszta
Lat. N
EMEP grid
Lon. E
47°46’
47°39’
46°40’
46°33’
46°48’
47°29’
47°03’
47°04’
49°44’
49°35’
54°55’
52°48’
47°55’
49°46’
48°49’
53°09’
50°39’
54°26’
56°21’
54°44’
56°43’
59°30’
58°23’
62°33’
59°47’
60°31’
66°19’
62°35’
45°50’
49°37’
48°30’
49°54’
47°16’
46°49’
43°02’
55°19’
52°34’
54°26’
50°52’
50°36’
54°20’
57°44’
56°58’
38°22’
46°58’
16°46’
13°12’
12°58’
7°59’
6°57’
8°54’
6°58’
8°27’
16°02’
15°05’
8°18’
10°45’
7°54’
7°03’
13°13’
13°02’
10°46’
12°44’
9°36’
10°44’
11°31’
25°54’
21°49’
24°13’
21°23’
27°41’
29°24’
24°11’
1°16’
-1°50’
7°08’
4°38’
4°05’
6°11’
-1°05’
-3°12’
0°30’
-7°54’
-0°02’
-3°43’
-0°48’
-4°46’
-2°25’
23°05’
19°35’
-9-
i-50
112.05
108.34
109.74
103.87
102.17
103.56
101.82
103.65
107.80
107.09
91.60
97.31
101.66
97.86
106.40
99.03
100.72
96.67
90.73
94.28
91.93
97.90
97.33
91.26
94.52
21.00
17.59
91.18
95.91
86.93
99.84
94.77
21.26
22.39
20.69
78.76
86.28
74.14
87.73
16.44
16.21
13.49
14.60
137.31
116.43
Altitude
j-50
60.48
56.11
54.18
48.42
47.80
51.09
48.28
49.86
62.72
61.41
63.69
62.25
50.84
53.38
58.08
65.03
58.60
66.83
67.24
65.50
69.47
86.56
81.26
88.71
82.72
89.42
97.14
88.73
40.35
45.41
51.14
51.38
45.84
47.05
32.39
55.95
53.07
51.40
49.32
46.04
55.58
59.86
59.70
55.17
62.76
(m)
117
851
1020
3573
510
540
1130
1028
737
633
12
74
1205
480
1016
62
937
1
13
9
40
32
6
162
7
4
310
180
497
133
775
390
620
836
1300
243
15
126
8
119
267
270
85
110
125
Table 2.1. List of EMEP stations used for the model validation.
Station
No
Code
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
IE1
IE2
IE3
IS2
IT1
IT4
LT15
LV10
LV16
NL9
NL10
NO1
NO8
NO15
NO39
NO41
NO42
NO55
PL2
PL3
PL4
PL5
PT1
PT3
PT4
RU1
RU13
RU16
SE2
SE5
SE8
SE11
SE12
SE13
SI8
SK2
SK4
SK5
SK6
TR1
YU5
YU8
Position
Name
Valentia Obs.
Turlough Hill
The Burren
Irafoss
Montelibretti
Ispra
Preila
Rucava
Zoseni
Kollumerwaard
Vreedepeel
Birkenes
Skreådalen
Tustervatn
Kårvatn
Osen
Spitzbergen, Z
Karasjok
Jarczew
Sniezka
Leba
Diabla Gora
Braganca
V. d. Castelo
Monte Velho
Janiskoski
Pinega
Shepeljovo
Rörvik
Bredkälen
Hoburg
Vavihill
Aspvreten
Esrange
Iskrba
Chopok
Stará Lesná
Liesek
Starina
Cubuk11
Kamenicki vis
Zabljak
Lat. N
EMEP grid
Lon. E
51°56’
53°02’
52°59’
64°05’
42°06’
45°48’
55°21’
56°13’
57°08’
52°20’
51°32’
58°23’
58°49’
65°50’
62°47’
61°15’
78°54’
69°28’
51°19’
50°44’
54°45’
54°09’
41°49’
41°42’
38°05’
68°56’
64°42’
59°58’
57°25’
63°51’
56°55’
56°01’
58°48’
67°53’
45°34’
48°56’
49°09’
49°22’
49°03’
40°30’
43°24’
43°09’
- 10 -
-10°15’
13°11’
-9°06’
-21°01’
12°38’
8°38’
21°04’
21°13’
25°55’
6°17’
5°51’
8°15’
6°43’
13°55’
8°53’
11°47’
11°53’
25°13’
21°59’
15°44’
17°32’
22°04’
-6°46’
-8°48’
-8°48’
28°51’
43°24’
29°07’
11°56’
15°20’
18°09’
13°09’
17°23’
21°04’
14°52’
19°35’
20°17’
19°41’
22°16’
33°00’
21°57’
19°08’
i-50
13.13
77.34
73.97
6.47
117.19
105.86
22.76
22.27
22.80
91.81
93.89
86.49
84.49
15.19
15.56
17.05
59.01
79.20
110.50
105.76
100.46
105.27
88.32
84.99
88.59
81.61
20.21
21.61
91.21
83.60
97.21
20.17
19.79
15.38
113.86
112.77
113.03
112.06
115.06
142.33
125.78
123.20
Altitude
j-50
44.83
49.33
47.69
67.30
45.86
47.74
76.44
77.77
83.76
59.16
55.51
69.63
69.24
85.59
77.49
77.01
104.35
97.68
71.93
63.96
71.98
75.87
24.84
23.03
14.62
99.47
107.55
90.20
70.96
83.58
75.76
69.77
77.79
93.08
54.61
65.67
66.84
66.43
69.16
74.68
60.75
56.37
(m)
9
420
100
61
48
209
5
18
183
1
28
190
475
439
210
440
474
474
180
1604
2
157
691
16
43
118
28
4
10
404
58
172
20
475
520
2008
808
892
345
1169
813
1450
3. Annual scatter plots for 1997
3.1 Concentrations in air
We begin the model validation by comparing measured and computed annual concentrations in
air and in precipitation. For the comparison of air concentrations, only those stations from Table
2.1 were selected which reported measurements for at least 274 days in 1997, i.e. 75% of days.
The number of stations that fulfil the requirement varied between 25 for nitric acid and nitrate,
and 60 for sulfate. The same requirement had been applied in previous years in case of EMEP
Lagrangian acid deposition model. A new element this year are the quality classes updated recently by CCC/NILU. The results of the comparison are presented in the form of scatter plots.
Scatter plots illustrate the differences between calculated and measured annual concentrations
and depositions for all selected stations available for a given compound. Five stations (DE3,
FR12, PL3, SK2 and NO42) have been excluded from the comparison due to either their location on mountain tops, or due to influence of local emission sources (Hjellbrekke, 1998).
For annual average concentrations of sulfur dioxide in air, the scatter plot is shown in
Figure 3.1. Calculated, mean concentration over 56 stations (2.88 µg(S)m-3) is 91% higher than
the mean concentration from the observations (1.51 µg(S)m-3). The difference is much larger
for sulfur dioxide than for all other compounds both in air and in precipitation. In general, model
overestimates high concentrations above 1.5 µg(S)m-3, and underestimates those below 0.6
µg(S)m-3. The ‘factor of two’ agreement between computed and measured annual concentrations lies between external dotted lines in Figures: 3.1 - 3.12. When the station is located in the
factor of two zone in the scatter plots, annual average computed value for this station must be
no more than two times higher or no less than two times lower than the measured value. Logarithmic scale is applied in order to better display stations with low concentration or deposition
values. In case of linear plots, those stations typically are grouped together in the lower left corner of the plot, thus making station identification difficult.
SO2 in air [ug S/m3] in 1997
0.08 0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
PL2
6.0
CS1
10.0
DE8
IT1
10.0
CS3
SK6
8.0
SK4
NL10
6.0
DE7
SI8 DE4
FR9
DE2
PL5
4.0
HU2
SK5
GB4
8.0
6.0
8.0 10.0
AT2
4.0
FR8
DE5
IT4
FR10
2.0
GB13
2.0
GB2
SE11
IE3
IE2
DK3
LV10
LV16 CH3RU16
EULERIAN
GB6
CH5
1.0
1.0
CH4
0.8
CH2
FI17
0.8
PL4
0.6
0.6
TR1 DE9 GB7
SE13
DK5
FI22
0.4
SE12
0.4
LT15
GB16
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
1.51
2.88
0.74
56
SE2 DK8
NO1
FI9
DE1
0.2
0.2
SE8
FR5
0.1
0.08
0.1
SE5
0.08
IE1
0.08 0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
OBSERVED
6.0
8.0 10.0
Figure 3.1 Computed versus measured annual average sulfur dioxide concentrations in air in
1997. Units: µg(S)m-3.
- 11 -
Figure 3.1 and all other scatter plots (Figures 3.2 - 3.13) include EMEP stations of quality classes ranging between A (best quality; within +/- 10%) to D (lowest quality; worse than 30%). The
quality classes for all station/component combinations have been determined by EMEP Chemical Coordinating Centre (CCC) at the Norwegian Institute for Air Research (NILU). CCC arrived at the quality ranking through inter-laboratory comparisons and analysis of various
measurement and analytical methods. The tables with quality classes are included in the Appendix of EMEP/MSC-W Report 1/99.
Correlation coefficient shown in Figure 3.1, between measured and computed annual concentrations, is 0.74. If only those stations with quality class A are considered (not shown here), the
correlation coefficient increases to 0.81. This increase occurs also for other components. However, in general, no major improvement of the annual model performance (scatter and bias) is
noted when only class A stations are taken into account. This is partly because there are relatively few stations ranked B-D for components other than SO2.
The performance of the EMEP Eulerian model for SO2 concentrations is not satisfactory. The
model generally overestimates concentrations in source areas (Central and Western Europe) and
underestimates concentrations in remote locations (Scandinavia). There are two possible reasons for this. On one hand, EMEP stations have been designed to monitor air pollution levels in
relatively remote and clean areas. Measurements at such point locations are not usually representative for pollution levels in larger areas, to which model results should be compared - due
to its grid cell area of ca. 2000-2500 km2. Similar situation was in the case of EMEP Lagrangian
acid deposition model, which - in addition - had grid cell area ca. nine times larger than the
EMEP Eulerian model.
SO2 in air [ug S/m3] in 1997
0.04
0.06
0.08 0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
PL2
4.0
CS1
IT1
GB7
SK4
SK6
0.8
GB6
CH5
0.6
SE8
0.4
SE12
FI9
IE3
2.0
NL10
DE7
DE4
SI8
IT4 FR9
DE2
GB16
FR8
GB13
PL4
PL5
SE11
DE9
DK3
GB2
DK8
SE2
DK5 DE5
CH4
CH2
IE2 RU16
CH3
FR5
FR10
LV10
FI17
1.0
EULERIAN
CS3
GB4
DE8
2.0
4.0
SK5
HU2
AT2
1.0
0.8
0.6
DE1 LT15
LV16
0.4
IE1
NO1
SE13
0.2
FI22
0.2
TR1
0.1
0.1
0.08
0.08
0.06
0.06
0.04
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
1.51
1.25
0.78
56
0.04
SE5
0.04
0.06
0.08 0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
OBSERVED
Figure 3.2 Computed versus measured annual average sulfur dioxide concentrations in air in
1997. Test with fixed vd for SO2. Units: µg(S)m-3.
- 12 -
Another reason for relatively poor performance with respect to SO2 air concentrations, is the
unsatisfactory performance of the dry deposition scheme applied in the Eulerian model. Figure
3.2 presents analogous results for a test run in which the SO2 dry deposition velocity at 1m was
fixed at 0.008 m/s for all meteorological conditions and all surface types. The computed mean
of SO2 concentrations decreased to 1.25 µg(S)m-3. Now, both the underestimations and overestimations seen in Figure 3.1 have been significantly reduced. The results of this model version
for other than SO2 components (not shown here), are only slightly affected. This tests shows
that most probably the computed by the model sum of the surface and laminar resistances is on the average - overestimated in the source regions and underestimated in remote areas. That
results in the opposite estimation for the respective dry deposition velocities for SO2. Dry deposition parameterization for SO2 has to be, therefore, re-evaluated. It is intended to start with
this task is in the coming months.
The scatter plot for annual average concentrations of sulfate in air is shown in Figure 3.3. Calculated mean concentration over 60 stations (0.73 µg(S)m-3) is 12% lower than the mean concentration from the observations (0.83 µg(S)m-3). Model underestimates low concentrations
below 0.60 µg(S)m-3, especially very low values measured at Norwegian and Swedish stations.
However, for sulfate in air, 82% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations (0.87) is better in this case than for sulfur
dioxide.
The scatter plot for annual average concentrations of nitrogen dioxide in air is shown in
Figure 3.4. Calculated mean concentration over 47 stations (2.06 µg(N)m-3) is almost identical
(1% difference) to the mean concentration from the observations (2.04 µg(N)m-3). The agreement between model results and measurements is best for NO2 among all components. For nitrogen dioxide, 87% of the stations can be found in the factor of two area. Correlation between
measured and computed concentrations (0.80) is also good in this case.
SO4-- in air [ug s/m3] in 1997
0.06
0.08
0.1
0.2
0.4
0.6
0.8
1.0
2.0
PL2
HU2
2.0
2.0
CS1
CS3 SK6
SK5
GB4
SK4
IT1
NL10
SI8
GB7
DE8
DE7
DE4
DE2
1.0
0.9
DE5
DE9
FR9
NL9
PL5
FR8
0.8
0.7
IE3
0.6
EULERIAN
0.5
0.4
1.0
0.9
PL4
IT4
0.8
FR5
DK5
FR10GB13
DE1
FR11
SE11DK8
CH2
DK3
GB16
LV10
CH5 GB2SE2
IE2SE8
LV16
RU16
FI17
GB6
FI9
SE12
0.3
0.7
0.6
0.5
0.4
IE1
0.3
GB15
NO8 NO1
FI22
0.2
0.2
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
0.83
0.73
0.87
60
NO41
NO55
SE13
0.1
0.1
0.09
0.09
SE5 IS2
0.08
0.07
NO15
0.06
0.06
0.08
NO39
0.07
0.08
0.1
0.2
0.06
0.4
0.6
0.8
1.0
2.0
OBSERVED
Figure 3.3 Computed versus measured annual average sulfate concentrations in air in 1997.
Units: µg(S)m-3.
- 13 -
NO2 in air [ug N/m3] in 1997
0.08
0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
6.0
8.0
NL10
8.0
8.0
6.0
6.0
DE4
4.0
IT4
PL2
HU2
SE2CS3
DE2 IT1
SE11
DE8
DK8
DE5 CH4
SK5
DE7
DE9
CH5
SK6
PL4
2.0
4.0
CH3
CH2
2.0
DE1
SE12 PL5
SE8
SK4
FI17
FI9
LT15
EE11
EE9
EULERIAN
1.0
RU16 LV10
1.0
NO1
NO8
0.8
0.8
LV16
0.6
0.6
IE1
NO41
YU8
YU5
0.4
0.4
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
2.04
2.06
0.80
47
FI22
SE5
TR1
NO39
0.2
0.2
NO15
0.1
0.1
NO55
0.08
0.08
0.1
0.2
0.08
0.4
0.6
0.8
1.0
2.0
4.0
6.0
8.0
OBSERVED
Figure 3.4 Computed versus measured annual average nitrogen dioxide concentrations in air in
1997. Units: µg(N)m-3.
The scatter plot for annual average concentrations of nitric acid plus nitrate in air is shown in
Figure 3.5. Calculated mean concentration over 25 stations (0.62 µg(N)m-3) is in good agreement (only 13% higher) with than the mean concentration from the observations (0.55
µg(N)m-3). In general, model reproduces measured concentrations well, but there is underestimation of the concentrations in the range below 0.2 µg(N)m-3. For nitric acid plus nitrate, 88%
of the stations can be found in the factor of two area. Correlation between measured and computed concentrations is 0.69.
The scatter plot for annual average concentrations of ammonia plus ammonium nitrate in air is
shown in Figure 3.6. Calculated mean concentration over 26 stations considered (1.25
µg(N)m-3) is 8% lower than the mean concentration from the observations (1.36 µg(N)m-3. For
ammonia plus ammonium nitrate, 85% of the stations can be found in the factor of two area.
Correlation between measured and computed concentrations is 0.80.
3.2 Concentrations in precipitation
In case of concentrations in precipitation a different temporal coverage requirement is applied.
For a station to be included in the comparison, there must be at least 25% of common days with
measured concentrations between the model and the station. The same requirement has been
used so far, when analyzing the results of the EMEP Lagrangian model. Figure 3.7 shows the
differences between measured and computed total annual precipitation. It should be noted here
that the computed value refers to the grid cell mean while the measured one is a point measurement.
- 14 -
HNO3 and NO3- in air [ug N/m3] in 1997
0.01
0.02
0.04
0.06
0.08 0.1
0.2
0.4
0.6
0.8 1.0
GB14
2.0
2.0
2.0
DK5
HU2
PL2
DK8
DK3
1.0
SE11
SE2 PL4
GB2
0.8
FI9
SI8
0.6
1.0
0.8
CH2
PL5
0.6
LT15
LV10
0.4
0.4
FI17
SE12
LV16
0.2
0.2
EULERIAN
NO8
NO1
0.1
0.1
0.08
0.08
0.06
0.06
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
0.55
0.62
0.69
25
NO41
0.04
0.04
0.02
0.02
FI22
SE5
0.01
0.01
0.02
0.04
0.06
NO39
0.01
0.08 0.1
0.2
0.4
0.6
0.8
1.0
2.0
OBSERVED
Figure 3.5 Computed versus measured annual average nitric acid plus nitrate concentrations in
air in 1997. Units: µg (N)m-3.
NH3 and NH4+ in air [ug N/m3] in 1997
0.04
0.06
0.08
0.1
0.2
0.4
0.6
0.8
1.0
2.0
4.0
PL2
4.0
4.0
DK3
CH2
GB14
PL5
SI8
2.0
GB2
DK5
SE11
2.0
DK8
PL4
SE2
LV10
1.0
1.0
LV16
LT15
0.8
0.8
SE12
0.6
0.6
EULERIAN
FI17
NO8
0.4
0.4
FI9
NO1
NO41
0.2
0.2
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
1.36
1.25
0.80
26
FI22
SE5
NO55
0.1
0.1
NO39
0.08
0.08
0.06
0.06
NO15
0.04
0.04
0.06
0.08
0.1
0.2
0.4
0.6
0.8
1.0
0.04
2.0
4.0
OBSERVED
Figure 3.6 Computed versus measured annual average ammonia plus ammonium concentration
in air in 1997. Units: µg(N)m-3.
- 15 -
In case of precipitation no individual point is a good measure for an area equivalent to the grid
cell area cell (ca. 2000 to 2500km2). It should be noted that the computed mean (574 mm) is
significantly lower (29%) than the observation mean (808 mm). As it is discussed below, it has
consequences for wet deposition.
The scatter plot for annual mean sulfate concentration in precipitation is shown in Figure 3.8.
Calculated, mean concentration over 50 stations (0.72 mg(S)l-1) is 43% higher than the mean
concentration from the observations (0.51 mg(S)l-1), which means significant overestimation.
In this case, still 87% of the stations can be found in the factor of two area. Correlation (0.64)
between measured and computed concentrations in precipitation of sulfate is not so good as in
the case of air concentrations (0.87).
The scatter plot for annual mean concentration of nitrate in precipitation is shown in Figure 3.9.
There is little difference (3%) between calculated mean over 52 stations (0.36 mg(N)l-1yr-) and
the observation mean (0.35 mg(N)l-1yr-). In general, model slightly underestimates measured
mean concentrations in the range below 0.1 mg(N)l-1yr-1. For concentration in precipitation of
nitrate, 90% of the stations can be found in the factor of two area. Correlation between measured
and computed depositions (0.59) is, however, worse than in the case of sulfate.
The scatter plot for annual mean concentration of ammonium in precipitation is shown in
Figure 3.10. Calculated mean concentration in precipitation (0.48 mg(N)l-1yr-1) over 54 stations, is 14% higher than the mean over observations (0.42 mg(N)l-1yr-1). In general, model reproduces measured concentrations well except for high values above 0.5 mg(N)l-1yr-1 which
tend to be overestimated. For nitrate concentration in precipitation, 74% of the stations can be
found in the factor of two area. Correlation between measured and computed depositions is
0.55. Although, agreement between mean computed and measured values, as well as correlation
are good, the scatter of the stations within a factor of two area is larger than for air concentrations. The same remarks applies to concentrations of sulfate and nitrate in precipitation.
Accumulated precipitation in 1997
200
300
400
500
600
700
800
900 1000
2000
2000
2000
YU8
NO8
NO39
SK4
NO15
IE2
SK5
1000
GB15
900
1000
FR10
IE3
GB13
GB2
GB6
800
LV16
900
IS2
800
NO1
700
700
RU16
600
FI22
TR1
RU13
SE12
EE11
FI17
EE9
500
FI9
400
600
PL5
DK3
SE2
DE1
FI4
NO41
SE11
SK6
GB14
DE4
DK5
LT15
AT2
SE5
NL9
FR11
LV10
CH5
DE5
500
FR9
FR5
DE8
DK8
NO55
AT4
AT5
CS1
CH4
YU5 CH2
PL2
PL4
400
CH3
FR8
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
808.12
574.51
0.53
67
DE2
RU1 DE7
300
IT4
HU2
DE9
300
CS3
200
200
IT1
200
300
400
500
600
700
800
900 1000
OBSERVED
2000
Figure 3.7 Computed model versus measured mean annual precipitation in 1997.
Units: mm yr-1.
- 16 -
Conc. in prec. of SO4-- corr. [mg S/l/year] in 1997
0.08
0.1
0.2
0.4
0.6
0.8
1.0
SK6
YU5
IT4
PL2
CS3
SK5
CH3
AT5
AT4
1.0
0.9
FR8
0.8
0.7
0.6
EULERIAN
0.9
DE9
DK5
YU8
FI17
NL9
DK3
LV16
FI9
SE2
DE1
NO1
EE9
RU16
FR11
0.5
1.0
DE2
0.8
CH4 CH2
CH5 FR9
LV10
SK4
DE8
DE7
DE4
PL4DE5
PL5
0.7
0.6
0.5
FR10
NO41FI4
0.4
FR5
0.4
GB2
NO8
0.3
0.3
RU13
FI22
IE2
0.2
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
0.51
0.72
0.64
50
0.2
IE3
0.1
0.1
NO39
0.09
IS2
0.09
0.08
0.08
NO15
0.08
0.1
0.2
0.4
0.6
0.8
1.0
OBSERVED
Figure 3.8 Computed model versus measured mean annual sulfate (corrected for sea spray) concentrations in precipitation in 1997. Units: mg(S)l-1 yr-1.
Conc. in prec. of NO3- [mg N/l/year] in 1997
0.04
0.06
0.08
0.1
0.2
0.4
0.6
0.8
1.0
1.0
1.0
0.9
0.9
0.8
0.8
CH3
0.7
AT5
0.6
FR8
FR9 SK6
0.5
LV10
0.4
FR11
0.3
CH4
CH2
FR5
NO8
GB2
0.5
SE2
0.4
YU5
0.3
FI9
SK4
YU8
EULERIAN
0.6
DE9
FI17
GB13
IE2
GB6
0.7
DE5
CS3
DE7
NL9
DE8
DE1
DK5
DE4
DK3
PL2
PL4
NO1
GB14PL5
CH5
SK5
FR10
0.2
DE2
AT4
LV16
0.2
EE9
RU16
IE3
FI4
NO41
GB15
0.1
0.1
0.09
0.09
0.08
0.08
0.07
0.07
0.06
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
0.35
0.36
0.59
52
0.06
FI22
0.05
0.05
RU13
0.04
0.04
NO15
NO39
0.03
0.04
0.06
0.08
0.03
0.1
0.2
0.4
0.6
0.8
1.0
OBSERVED
Figure 3.9 Computed versus measured mean annual nitrate concentrations in precipitation in
1997. Units: mg (N)l-1 yr-1.
- 17 -
Conc. in prec. of NH4+ [mg N/l/year] in 1997
0.02
0.04
0.06
0.08
0.1
0.2
0.4
CH3
1.0
CH4
0.8
FR11
0.6
LV10
0.6
AT4
0.8
1.0
DE5
CH5
IT4
DE2
SK6 CH2
PL2
AT5 DE8CS3
FR8
DE7
SK5
FR9
DE4
YU5
DK5
DE1
NL9
DE9
SK4
DK3 PL5
PL4
0.4
0.8
0.6
0.4
SE2
FR10
1.0
NO1
GB2
LV16
FR5
GB14
FI17
YU8
FI9 GB13
IE2
0.2
0.2
EE9
EULERIAN
GB6
NO8
RU16
NO41
FI4
IE3
0.1
0.1
GB15
0.08
0.08
0.06
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
0.42
0.48
0.55
54
0.06
FI22
RU13
0.04
0.04
NO39
NO55
0.02
0.02
NO15
0.02
0.04
0.06
0.08
0.1
0.2
0.4
0.6
0.8
1.0
OBSERVED
Figure 3.10 Computed versus measured mean annual ammonium concentrations in precipitation in 1997. Units: mg(N)l-1 yr-1.
3.2 Wet depositions
The scatter plot for annual wet deposition of sulfate is shown in Figure 3.11. Calculated, mean
concentration over 50 stations (385.4 mg(S)m-2yr-1) is merely 3% higher than the mean concentration from the observations (395.8 µg(S)m-2yr-1), which means very good agreement. In
this case, 90% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations in precipitation of sulfate is good (0.63) but not so good as
in the case of respective air concentrations(0.87; see Figure 3.3). The same conclusion applies
to nitrate, and the sum of ammonia and ammonium (see below and Figures 3.5 and 3.6).
The scatter plot for annual wet deposition of nitrate is shown in Figure 3.12. Calculated mean
over 52 stations (189.12 mg(N)m-2yr-1) is 30% higher than the mean concentration from the observations (268.80 mg(N)m-2yr-1). In general, model underestimates measured values, especially for the range above 100.0 mg(N)m-2yr-1. However, despite the significant underestimation,
still 83% of the stations can be found in the factor of two area. Correlation between measured
and computed depositions (0.64) is similar to the one in case of sulfate.
It can be noticed that although, the model on the average computes reasonable values for concentration in precipitation (Figures 3.8-3.10), the respective computed wet depositions are on
the average underestimated. A reason for that can be the underestimation of modelled total precipitation at model grid cells where the measurement stations are located. That means that although the concentrations in precipitation are on the average correct, insufficient amount of
precipitation makes the total mass deposited to be underestimated.
- 18 -
Wet deposition of SO4-- corr. [mg S/m2/year] in 1997
80
100
200
400
600
800
1000
SK5
SK4
YU8
1000
1000
900
900
SK6
800
800
700
700
YU5
600
600
AT5
500
AT4
PL2
LV16
PL5
500
FR10
DE8NO8
DE5
DE4
400
CH3
DK3 CH5 FR11 FR9
LV10
DK5
FI9
IT4
400
CS3
FR8
DE2RU16
SE2
GB2
DE1
FI17
CH2 CH4
DE7
EE9
300
NO1
PL4
IE2
300
DE9
NL9
NO41
200
200
FI4
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
395.80
385.40
0.63
50
FR5
IE3
FI22
RU13
NO39
100
100
NO15
90
90
80
80
IS2
80
100
200
400
600
800
1000
OBSERVED
Figure 3.11 Computed versus measured annual wet deposition of sulfate in 1997.
Units: mg(S)m-2 yr-1.
Wet deposition of NO3- [mg N/m2/year] in 1997
30
40
50
60
70
80
90 100
200
300
400
500
600
600
600
500
500
SK5
400
400
NO8
SK4
AT5
300
DE5
SK6
LV10
200
EULERIAN
YU8
AT4
FI9
GB15
100
300
NO1
CH3
DE8
DE1
FR9
DK3
DE2
FR10 GB13
DE4GB2
IE2
FR11CH5 FR8
SE2
PL5
NL9
DK5
DE7
GB14
LV16
CS3
CH2
CH4 PL2
PL4
DE9
YU5
IE3
FR5
FI17
200
GB6
EE9
100
RU16
90
90
80
80
NO41
70
70
FI4
60
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
268.80
189.12
0.64
52
60
50
50
40
30
40
NO39
NO15
30
FI22
RU13
30
40
50
60
70
80
90 100
200
300
400
500
600
OBSERVED
Figure 3.12 Computed versus measured annual wet deposition of nitrate in 1997.
Units: mg(N)m-2 yr-1.
- 19 -
The scatter plot for annual wet deposition of mean of ammonia and ammonium is shown in
Figure 3.13. There is a large difference (28%) between calculated mean (243.98 mg(N)m-1yr1) over 54 stations and the mean from the observations (338.27 mg(N)m-1yr-1). As in the case
of nitrate, the model underestimates measured depositions, especially for the range above 100.0
mg(N)m-2yr-1. For wet deposition of ammonia and ammonium nitrate, 72% of the stations can
be found in the factor of two area. Correlation between measured and computed depositions is
0.58.
Wet deposition of NH4+ [mg N/m2/year] in 1997
10
20
40
60
80
100
200
400
800
600
800
800
SK5
AT4
SK4
600
600
CH5 DE5
CH3
SK6
AT5
YU8
DE8
FR10
CH4
CH2
FR11
PL2 FR9
DE2
DE4
FR8
GB2 PL5
NO1
DK3
DE1
NO8
IE2
YU5
DE7
LV10
LV16
CS3
GB13
400
DK5
200
SE2
400
IT4
200
NL9
PL4
GB6 DE9
FR5
FI17
EULERIAN
100
EE9
GB14
IE3
100
RU16
FI9
NO41
80
GB15
80
60
FI4
60
NO39
40
40
OBSERVED mean =
EULERIAN mean =
correlation
=
no. of stations =
338.27
243.98
0.58
54
FI22
NO15
RU13
20
20
10
10
NO55
10
20
40
60
80
100
200
400
600
800
OBSERVED
Figure 3.13 Computed versus measured annual wet deposition of ammonium in 1997.
Units: mg(N)m-2 yr-1.
- 20 -
4. Comparison of measured and computed daily results
In this section, computed daily concentrations are compared with daily measurements at EMEP
stations in Europe. To present all available graphs - several hundreds - would be a formidable
task. Therefore, a reasonable selection must be made. Below, we give examples which we think
are typical for describing model behavior for a given component/location combination. For each
of the combinations, there are stations for which model performs better than in the displayed
graphs, and there are stations where the model performance is poorer.
Five components are included in the analysis: sulfur dioxide, nitrogen dioxide, sulfate, sum of
nitric acid and nitrate, and sum of ammonia and ammonium. The first two components are gases
while the last two are measured as gases plus aerosols. Sulfate is measured as aerosol. In all
graphs the units are: µgS(or N) m-3.
Before we turn to the computed daily values, one important limitation has to be revoked. EMEP
Eulerian acid deposition model has not been designed to simulate day-to-day variations. Its
main objective is to compute long-term (monthly, annual) mean concentrations and accumulated depositions. Long-term transboundary fluxes and country budgets constitute another important part of model output.
To model daily concentrations more precisely, one would need much better input data (e.g. daily variations of emissions on a grid cell basis) and finer horizontal resolution. The former is very
difficult or almost impossible to obtain. Introduction of the latter would result in a very complicated model. Then, the required computation time would make long-term calculations practically impossible, at least in the operational mode.
Point measurements at stations are compared with the grid cell values in the model (roughly 50
km × 50 km, in the horizontal direction). For gases the computed values are scaled from the lowest model layer (approximately 45 m) to the height of 1m. Measurements can vary significantly
even for locations close to each other. Local topographical effects and local emission sources
may influence measurements and result in poor representativeness of a given station.
For each component we selected three different stations: one located in northern part of Europe,
one in middle latitudes, and one in southern Europe. One possibility would be to take the same
locations for presentation of various pollutants. However, while there are stations fulfilling this
requirement in the northern and Central Europe, no such station exists in the South. Therefore,
to better illustrate the model performance different stations have been selected for different
components.
For both Virolahti II and Cubuk 11 (Figure 4.1), there is a fairly good agreement between calculated and measured SO2 concentrations. The seasonal variations in the observations are well
captured by the model, especially at Virolahti II. As one could expect not all peaks are well simulated, but the model dynamics and its response to rapid concentration changes in winter and
spring looks satisfactory. For Neuglobsow in Germany the agreement is not good. Model overestimates concentrations throughout the year. As discussed in Section 3, this is typical model
behavior in Central and Western Europe, where calculated SO2 concentrations are usually overestimated.
- 21 -
___
EMEP measurements
......
EMEP Eulerian model
Virolahti II- FI17: SO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
5
5
4
4
3
3
2
SO2 in air [ug S/m3]
DE 7 :
Neuglobsow ( 62m a.s.l.)
2
1
0
1
___
20
......
EMEP
measurements
40
60
80
100
120
0
140
160
180
200
220
240
260
280
300
320
340
360
260
280
300
320
340
360
1997
EMEP Eulerian model
Neuglobsow- DE 7: SO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
25
25
20
20
15
15
10
SO2 in air [ug S/m3]
TR 1 :
Cubuk11 (1169m a.s.l.)
10
5
0
5
___
20
......
EMEP
measurements
40
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
260
280
300
320
340
360
1997
Cubuk11- TR1: SO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
20
40
60
80
100
120
140
160
180
200
1997
220
240
260
280
300
320
340
360
Figure 4.1 Comparison of measured and computed SO2 concentrations in air at Virolahti II,
Neuglobsow and Cubuk11. Solid line - observations, dotted line - model results. Units: µg(S)
m-3.
- 22 -
___
EMEP measurements
......
EMEP Eulerian model
Vavihill - SE11: SO4 in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.0
SO4-- in air [ug S/m3]
CH 2 :
Payerne ( 510m a.s.l.)
1.5
1.0
0.5
0.0
0.5
___
20
......
EMEP measurements
40
60
80
100
120
0.0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
260
280
300
320
340
360
1997
Payerne - CH2: SO4 in air
20
40
60
80
100
120
140
160
180
200
220
240
6
6
5
5
4
4
3
2
3
SO4-- in air [ug S/m3]
GR 1 :
Aliartos ( 110m a.s.l.)
2
1
0
1
___
20
......
EMEP measurements
40
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
260
280
300
320
340
360
1997
Aliartos - GR1: SO4 in air
20
40
60
80
100
120
140
160
180
200
220
240
10
10
8
8
6
6
4
4
2
2
0
0
20
40
60
80
100
120
140
160
180
200
1997
220
240
260
280
300
320
340
360
Figure 4.2 Comparison of measured and computed SO4 concentrations in air at Vavihill, Payerne and Aliartos. Solid line - observations, dotted line - model results. Units: µg (S) m-3.
- 23 -
___
EMEP measurements
......
EMEP Eulerian model
Birkenes - NO1: NO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
10
10
8
8
6
4
6
NO2 in air [ug N/m3]
IT 1 : Montelibretti ( 48m a.s.l.)
4
2
2
___
0
EMEP measurements
20
40
......
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
260
280
300
320
340
360
1997
Montelibretti - IT1: NO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
10
10
9
9
8
8
7
7
6
6
5
5
NO2 in air [ug N/m3]
DE 1 :
Westerland ( 12m a.s.l.)
4
3
4
3
2
2
1
1
___
0
20
......
EMEP measurements
40
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
1997
Westerland - DE 1: NO2 in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
20
20
15
15
10
10
5
5
0
0
20
40
60
80
100
120
140
160
180
200
1997
220
240
260
280
300
320
340
360
Figure 4.3 Comparison of measured and computed NO2 concentrations in air at Birkenes, Montelibretti and Westerland. Solid line - observations, dotted line - model results. Units: µg(N)m-3.
- 24 -
___
EMEP measurements
......
EMEP Eulerian model
Virolahti II - FI17: HNO3 and NO3- in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
2.0
2.0
1.5
1.5
1.0
HNO3 and NO3- in air [ug N/m3]
LT 15 :
Preila ( 5m a.s.l.)
1.0
0.5
0.0
0.5
___
20
......
EMEP measurements
40
60
80
100
120
0.0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
1997
Preila - LT15: HNO3 and NO3- in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
6
6
5
5
4
4
HNO3 and NO3- in air [ug N/m3]
HU 2 :
K-puszta ( 125m a.s.l.)
3
2
3
2
1
1
___
0
20
......
EMEP measurements
40
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
1997
K-puszta - HU2: HNO3 and NO3- in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
20
40
60
80
100
120
140
160
180
200
1997
220
240
260
280
300
320
340
360
Figure 4.4 Comparison of measured and computed nitric acid and nitrate concentrations in air
at Virolahti II, Preila and K-puszta. Solid line - observations, dotted line - model results. Units:
µg(N)m-3.
- 25 -
___
EMEP measurements
......
EMEP Eulerian model
Oulanka - FI22: Ammonia and ammonium in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
1.0
1.0
0.8
0.8
0.6
0.6
0.4
NH3 and NH4+ in air [ug N/m3]
PL 2 :
Jarczew ( 180m a.s.l.)
0.4
0.2
0.0
0.2
___
20
......
EMEP measurements
40
60
80
100
120
0.0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
300
320
340
360
1997
Jarczew - PL2: Ammonia and ammonium in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
12
12
10
10
8
8
6
4
NH3 and NH4+ in air [ug N/m3]
SI 8 :
Iskrba ( 520m a.s.l.)
6
4
2
0
2
___
20
......
EMEP measurements
40
60
80
100
120
0
140
160
EMEP Eulerian model
180
200
220
240
260
280
300
320
340
360
1997
Iskrba - SI8: Ammonia and ammonium in air
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
6
6
5
5
4
4
3
3
2
2
1
1
0
0
20
40
60
80
100
120
140
160
180
200
1997
220
240
260
280
300
320
340
360
Figure 4.5 Comparison of measured and computed ammonia and ammonium concentrations in
air at Oulanka, Jarczew Birkenes and Iskrba. Solid line - observations, dotted line - model results. Units: µg(N)m-3.
- 26 -
In case of SO4 measurements (Figure 4.2), reasonable agreement is seen in all three graphs. At
Swedish station Vavihill, the episodes of high concentrations associated with advection from
central Europe are well represented. Also at Payerne (Switzerland) the model fairly well captures the day-to-day variations in all seasons except winter, when the computed values are too
low. The agreement between measured and computed values is not so good at Aliartos (Greece).
The agreement for other stations in southern Europe - not shown here - is generally worse than
in central and northern Europe. This can be explained by the gaps and poorer quality of emission
data in southern Europe. Also the density of the measurement network is lower as well as the
lower - on the average - quality of measurements estimated by NILU during inter-laboratory
comparisons (Hjellbrekke, 1998).
NO2 concentrations (Figure 4.3).are well reproduced in all three stations: Birkenes (Norway),
Montelibretti (Italy) and Westerland (Germany). As in the case of SO4, good agreement is typical for most of Scandinavian stations and those located in Central and Western Europe. There
is a clear seasonal course in the results at Westerland. Higher concentrations are computed for
the colder period of the year. This is the result of the seasonal variations of emissions. According to NILU (Hjellbrekke, 1998), the main reason for the relatively large variations of NO2 concentrations at Montelibretti, is the influence of local NOx emission sources.
In case of HNO3 and NO3- observations (Figure 4.4), a better agreement is for the Finish station
Virolahti II and Lithuanian Preila, than for the Hungarian K-Puszta. The seasonal trend at KPuszta is captured by the model, but its amplitude in winter is underestimated. At Virolahti, the
episodes of high concentrations are - on the average - well simulated by the model. It is not the
case at Preila.
The graphs for the sum of ammonia and ammonium (Figure 4.5), show considerable day to day
variations. Model simulates the variations quite well at Oulanka (Finland) and in the second half
of the year at Jarczew (Poland). At Iskrba (Slovenia) model simulates significant variations on
a daily basis, but the variations do not exactly follow the observed ones. The computed values
in the colder part of the year are underestimated.
The graphs presented in this section give an insight into the model behaviour on a daily basis.
The dynamics of concentration changes is frequently well captured by the model. This is particularly evident at stations in Scandinavia on occasions of long-range transport from the continental Europe. The agreement between model results and observations becomes worse when
moving towards southern Europe. This conclusions holds for all five components analysed here.
More plots are of this type are presented in the Appendix of the EMEP/MSC-W Report 1/99.
Plots for all stations and all components can be found at: http://www.emep.int
- 27 -
5. Maps of computed concentration and deposition fields for 1997
The results of the EMEP Eulerian model run for 1997 are presented in this section as concentration and deposition maps. Annual average air concentrations of sulfur dioxide, sulfate, nitrogen dioxide, nitric acid + nitrate, and ammonia + ammonium are shown in Figures 5.1, 5.2,
5.3, 5.4 and 5.5, respectively. Annual depositions of total sulfur, oxidized nitrogen and reduced
nitrogen are shown in Figures 5.6, 5.7 and 5.8, respectively.
In the map of computed annual mean sulfur dioxide air concentration at the surface
(Figure 5.1), one can clearly see the influence of the distribution of emission sources in Europe
(Mylona, 1999). Southern Italy around Sicily is the location with the absolute maximum of the
computed concentrations, the same where the largest single source of both natural and anthropogenic emissions - Mt. Etna - is. A large area with high concentrations exceeding 10 µg(S)/
m3 is the so called ‘Black Triangle’ region (southern Poland, eastern Germany and northern
part of the Czech Republic), where also the largest sources of anthropogenic emissions of sulfur dioxide are. Outside Black triangle, there are several other large emission sources (e.g
southern U.K., northern Yugoslavia, Bulgaria, Belgium, Kola Peninsula) which are reflected in
the concentration map. Overall concentration pattern computed by the Eulerian model is similar to the concentration pattern produced by the Lagrangian model for 1996 (see Appendix C
of the Numerical Addendum to EMEP/MSC-W Report 1/98). However, because of the better
spatial resolution of the Eulerian model (50 km instead of 150 km), more details are visible in
the map computed by this model. In addition, due to fully three-dimensional structure of the
Eulerian model, certain climatological and terrain features appear clearly only in the maps produced by the Eulerian model. For example, the presence of Alps appears as an area with significantly lower concentrations in the Eulerian map. This and other similar features can be seen
when comparing all concentration and deposition fields produced by the two models.
Computed surface concentration field for sulfates (sum of SO4 and ammonium sulfate) (Figure
5.2) is smoother than the one for sulfur dioxide. This is because unlike SO2 which is emitted,
sulfates are mainly produced in the atmosphere - as a result of SO2 oxidation. The influence of
emissions is not so pronounced in this case, however, regions with higher sulfate concentrations follow, to some extent, the regions with high emissions of sulfur dioxide. Concentrations
exceeding 5 µg(S)/m3 are found in southern Poland, northern Czech Republic, south-western
Germany (‘Black Triangle’), northern Yugoslavia, parts of Britain and Bulgaria.
Map of computed annual average surface concentrations of nitrogen dioxide is shown in Figure
5.3. Again, as in the case of sulfur dioxide, concentration pattern for nitrogen dioxide is driven
by the distribution of NOx emissions in Europe. However, the location of maxima is a bit different compared to maps for SO2 and sulfates. A large area with high concentrations above 5
µg(N)/m3 covers northern Germany, the Netherlands, part of Belgium and southern United
Kingdom. Similar concentrations are also found in southern Poland and south-western Germany.
Computed annual average surface concentrations of nitric acid + nitrate are shown in Figure
5.4. Here, we note high concentrations exceeding 2 µg(N)/m3 in and around the English Channel. Another large area of high concentrations is in the northern Italy and off the south-western
coast of Spain.
- 28 -
Computed annual average surface concentration of ammonia + ammonium (Figure 5.5) has a
very similar pattern to ammonia emissions (Mylona, 1999). Three regional maxima can be distinguished with concentrations exceeding 5 µg(N)/m3: the Netherlands and northern Belgium,
southern Germany (Bavaria) and northern Italy. Concentrations above 2 µg(N)/m3 are noted in
most of western and Central Europe extending from western France, through Germany, Czech
Republic, Poland up to the eastern borders of Lithuania and Ukraine.
Total deposition of oxidized sulfur in 1997 is shown in Figure 5.6. There is a large area with
depositions above 2000 mg(S)m-2yr-1 which covers large parts of Europe. In this area, scattered local deposition maxima are in the following locations: ‘Black Triangle’, southern and
central Poland, Sicily, northern Yugoslavia, Bulgaria, English Channel and central U.K., northern Yugoslavia and eastern Bulgaria. These locations coincide with the locations of the main
emissions sources for SO2.
Figure 5.7 shows the total deposition of oxidized nitrogen in 1997. Maxima of the deposition
exceeding 1000 mg(N)m-2yr-1 can are found in the Benelux countries, western Germany,
northern Italy and France, southern and central U.K, northern Czech Republic and southern
Poland (Silesia).
Annual total (dry + wet) deposition of reduced nitrogen is shown in Figure 5.8. The maxima of
total deposition are located in western Europe: the Netherlands, northern Italy, Ireland, northwestern France and southern Germany. These are also the regions with high ammonia emissions in Europe.
120
’97 SO2 conc. [ug/m3]
110
100
90
80
70
60
50
40
> 10.0
5.0 - 10.0
2.0 - 5.0
1.0 - 2.0
0.5 - 1.0
0.2 - 0.5
0.1 - 0.2
< 0.1
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.1. Map of computed annual average sulfur dioxide concentrations in 1997.
Units: µg(S)m-3.
- 29 -
120
’97 SO4 conc. [ug/m3]
110
100
90
80
70
60
50
40
> 5.0
2.0 - 5.0
1.0 - 2.0
0.5 - 1.0
0.2 - 0.5
0.1 - 0.2
< 0.1
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.2. Map of computed annual average sulfate air concentrations in 1997.
Units: µg(S)m-3.
120
’97 NO2 conc. [ug/m3]
110
100
90
80
70
60
50
40
> 10.0
5.0 - 10.0
2.0 - 5.0
1.0 - 2.0
0.5 - 1.0
0.2 - 0.5
0.1 - 0.2
< 0.1
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.3. Map of computed annual average nitrogen dioxide air concentrations in 1997.
Units: µg(N)m-3.
- 30 -
120
’97 HNO3 [ug/m3]
110
100
90
80
70
60
50
40
> 5.0
2.0 - 5.0
1.0 - 2.0
0.5 - 1.0
0.2 - 0.5
0.1 - 0.2
< 0.1
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.4. Map of computed annual average nitric acid + nitrate air concentrations in 1997.
Units: µg(N)m-3.
120
’97 NH3+NH4 [ug/m3]
110
100
90
80
70
60
50
40
> 5.00
2.00 - 5.00
1.00 - 2.00
0.50 - 1.00
0.20 - 0.50
0.10 - 0.20
< 0.10
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.5. Map of computed annual average ammonia + ammonium air concentrations in 1997.
Units: µg(N)m-3.
- 31 -
120
’97 S dep. [mg/m2/yr]
110
100
90
80
70
60
50
40
> 2000
1000 - 2000
500 - 1000
200 - 500
100 - 200
50 - 100
<
50
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.6. Map of computed annual total (dry + wet) deposition of oxidized sulfur in 1997.
Units: mg(S)m-2 yr-1.
120
’97 ox.N d. [mg/m2/yr]
110
100
90
80
70
60
50
40
30
> 1000
500 - 1000
200 - 500
100 - 200
50 - 100
<
50
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.7. Map of computed annual total (dry + wet) deposition of oxdized nitrogen in 1997.
Units: mg(N)m-2 yr-1.
- 32 -
120
’97 re.N d. [mg/m2/yr]
110
100
90
80
70
60
50
40
> 2000
1000 - 2000
500 - 1000
200 - 500
100 - 200
50 - 100
<
50
30
20
40
50
60
70
80
90
100
110
120
130
140
150
160
Figure 5.7. Map of computed annual total (dry + wet) deposition of reduced nitrogen in 1997.
Units: mg(N)m-2 yr-1.
Conclusions
Several important modifications have been made in the EMEP Eulerian acid deposition model
since 1998. The model has been adapted to new meteorological data produced by PARLAMPS - a dedicated meteorological model. Mass conservation properties of the model have been
reviewed and updated, leading to pollutant mass conservation within 1% error, for long-term
computations as required by EMEP. The model domain has been extended to account for the
new countries - Parties to the CLRTAP Convention - located outside the traditional EMEP modelling domain.
Moreover, as in 1998 (Olendrzynski et al., 1998), also this year an extensive evaluation of the
model performance was carried out. This time, it was based on 1997 meteorological and measurement data. In addition to comparison of modelled and measured annual average concentrations in air and precipitation, model results were also compared with daily measured values.
Based upon the recent updates to the model, and upon the analysis of the results of the model
validation, the following conclusions can be drawn:
• After several years of development, EMEP Eulerian acid deposition model has become
ready for operational use. On the average, the model performs comparably or better than the
EMEP Lagrangian model with regard to measured values. High precision of mass conservation makes the Eulerian model suitable for the analysis of transboundary exchange and
country budgets. Higher, than in the Lagrangian model, horizontal resolution makes the
Eulerian model a better tool for applications not only on regional but also on sub-regional
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scales.
• Scatter diagrams demonstrate that the computed annual mean air concentrations are generally within a factor of two agreement with observations in most of the EMEP stations in
1997. Only for SO2 the model tends to overestimate high concentrations close to emission
sources, and to underestimate low concentrations in remote areas of Europe.
• A test with fixed dry deposition velocity at 1m for SO2, showed that the agreement with
measurements improves significantly for SO2, while the results for other components are except SO4 - only slightly affected. The possible cause of the inadequate performance of the
dry deposition scheme is the computation of surface and quasi-laminar boundary layer
resistances. A closer look into this problem is intended in the near future.
• For compounds other than SO2, computed annual mean air concentrations are in relatively
good agreement with observations. The difference between computed and measured air
concentrations, averaged over all stations considered, varies between 1-13% with NO2 and
NH3+NH4 having the best agreement. Correlation coefficient between computed and measured concentrations is rather high, varying between 0.69 and 0.87 depending on the compound. Between 82-88% of all stations are within the zone of ‘the factor of two’ difference.
• Computed annual concentrations in precipitation are also in fairly good agreement with
observations. The difference between computed and measured air concentrations, averaged
over all stations is 43%, 3% and 14% for sulfate, nitrate and ammonium, respectively. Correlation coefficient between computed and measured concentrations is lower than for air
concentrations, varying between 0.55 and 0.64. Between 74-87% of all stations are within
the zone of ‘the factor of two’ difference. A still unresolved problem in the model, is the
missing parameterization of the pollutant evaporation from the rain water below the clouds.
This task is planned for implementation in the coming months.
• Computed wet depositions are also in fairly good agreement with observations. The difference between computed and measured annual values, averaged over all stations is 3%, 30%
and 28% for sulfate, nitrate and ammonium, respectively. Correlation coefficient between
computed and measured wet depositions varies between 0.58 and 0.64. Between 72-90% of
all stations are within the zone of ‘the factor of two’ difference.
• Analysis of the daily measured and computed concentrations indicated that the dynamics of
the concentration changes is frequently well captured by the model. This is particularly evident at stations in Scandinavia on occasions of long-range transport from continental
Europe. Model performance for southern Europe is, in general less satisfactory than for the
rest of the continent.
• Examination of the concentration and deposition maps computed by the Eulerian model,
and their comparison with corresponding maps produced by the Lagrangian model showed
that there are more local scale details in the Eulerian maps, mainly due to better spatial resolution and multilayer structure. Lagrangian model produces smoother concentration and
deposition fields but general patterns are quite similar for both models.
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