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Validation of a multiple source gaussian air quality model
Afmasphrric
Enwronn~enr
Vol. I I, pp.791-795. Pergamon Press 1977. Prmted in Great Britain.
VALIDATION
OF A MULTIPLE SOURCE AIR QUALITY MODEL LARS P.
PFCAHM
and
GAUSSIAN
MAKS CHRISTENSEN
Danish Meteorological Institute, Air Pollution Section, Lyngbyvej 100 ~~-2100, Copenhagen 0, Denmark (First received
24 May
1976)
Abstract-A multiple source stationary Gaussian atmospheric dispersion model is tested in the Copenhagen area for 20 dispersion and decay parameter combinations, using different parameters for high and low sources. Optimal correspondance between measured and computed SO2 values is obtained for parameters, representing rapid dispersion and decay. Use of different dispersion parameters does not change the spatial correlation coefficient between measured and computed concentrations significantly, but changes the slope of the regression line. The spatial correlation for 22 receptor points between the climatological SO2 average concentration through a period of three months gives a correlation coefficient r larger than 0.9, explaining more than 80% of the variation. The slope of the regression line is between 0.9 and 1.0 for optimal dispersion parameter combinations. The model is found to be an applicable tool for urban planning and establishment of air quality strategies.
1.
INTRODUCTION
Most computations of atmospheric dispersion of pollutants from single sources and from multiple sources, e.g. urban areas, are based on the steady state Gaussian dispersion formulation (Stem, 1970). Larger discrepancies between measured and computed concentrations are found for short averaging times, caused by, among other things, non-homogeneous and nonstationary meteorological conditions. For long averaging times, e.g. months, the Gaussian dispersion concept has been accepted as adequate for design of chimney heights (Turner, 1970). Superposition of the contribution from multiple sources, e.g. an urban area, reduces the deviation between measured and computed results, caused by statistical averaging of the errors. The Gaussian model has been validated by Koch et al. (1971) inter alia. The report shows that the computed concentrations are sensitive to the choice of dispersion parameters and decay rates, and that further analysis of these parameters should be performed. The present study concerns a validation of a multiple Gaussian urban air quality model for the Copenhagen area in Denmark. Computed three months average SO, concentrations are compared with measured results at 24 stations, covering an area of about 500 square kilometres of “flat” urban area (Fig. 1). The spatial correlation and regression between measured and computed concentrations is determined for a number of different dispersion parameters and decay rates. The relative contribution of pollutant from low and high sources is discussed. 2. THE GAUSSIAN
MODEL
The Gaussian dispersion concept has been used for a number of multiple urban air quality models. These models determine concentrations of pollutants in the
atmosphere as an average concentration in a number of meteorological discrete and stationary states, weighted with the probability of occurrences. Our model is an extension of the Climatological Dispersion Model (CDM) documented by Busse & Zimmerman (1973). The technical details are described by Calder (1973). Both theory and tracer experiments have shown that low and high sources should be treated separately (Pasquill, 1974). The main improvement of the model in this study is the introduction of a separate set of dispersion parameters for area (low) and point (high) sources. As no other important changes have been made in the CDM model used, the reader is referred to the original description of the model for further information on model formulation. 3. METEOROLOGICAL
The model is tested on the three months period 1 January-l April 1971 with a relatively high measured pollution level. Meteorological input data consist of a joint frequency function based on wind direction, wind speed and atmospheric stability. These data are taken from hourly airport observations in the Copenhagen area (Kastrup). During the considered period a mean wind velocity at 6 (m s-l) was measured. Mixing height is determined from radiosonde observations twice a day. Average mixing heights during night and day were 500m and 700m, respectively. The plume rise is computed after the “2/3 law” due to Briggs (1969). In order to test the sensitivity of the model towards the dispersion parameters, different parameter combinations are used. Three sets of dispersion parameters gL are used for area sources: The original Pasquill parameters (Pasquill a), these parameters shifted one class towards the unstable region Pasquill b and the parameters 791
AE. 11,‘9%A
DATA
792
LARS P. PRAHM
and MAKS CnRlsreNsl-:N
determined by McElroy (1969). Thermal effects and the a~rodyna~c roughness of the urban area introduce an increased turbulent mixing compared with dispersion over the open country. These conditions are taken into account in the last two sets of dispersion parameters. To some degree an initial dispersion nio at the source can account for the mixing introduced by the urban area (McElroy, 1969). In our tests a cio value of 20, 30 or 40m is used. The dispersion from high sources is determined hy the Wiigstriim parameters, and the Singer & Smith parameters, both reviewed by Brummage (1968). or the McElroy (1969) parameters. The last set gives a significantly faster mixing than the two other parameter combinations. reposition and the chemical oxidation rate of SO2 to SO4 is described with a half life of 1, 3 or X, hours. In Table 1 is shown the combination of the chosen input parameters in 20 tests. 4.
EMISSION
Test hi,,.
!:I
Ih
?;r
5.
MiCASliKEMENTS
Air quality was measured at 23 stations in the Copenhagen area by StorktThenhavns luftforureningsudvalg (Fig. 1). Samplers were installed initially at the height of 2-12 metres over the ground and consisted of a semi--automatic instrument. pumping 2 m3
UATA
The sulphur dioxide cm&ion originates from industrial activity and house heating. The emission sources are divided in two groups, point sources and area sources. All stacks, emitting sulphur from more than 400 cubic tons per year of oil (heat equivalents), are classified as point sources, and i~ldi~~duai dispersion computations are performed for each of these. based on information on stack dimensions, gas temperatures, gas flow and yearly fuel consumption. Information on 252 point sources was used in this test. The emission from all other fuel consumption was connected to a rectangular grid array of uniform-sized squares with grid spacing of t km. The area source emission inventory was based on a study of heated building square metres. A total of 484 area sources is used in our computations. Towns (10000-30000 inhabitants) in distances of less than 30 km from the area are accounted for as distant sources. By different cross checks the error on the emission inventory is found to he less than lo’:,. The total yearly emission in 1970 71 was 65000 tons of SO,. In the model comTable 1. Parameter combination
putations yearly average emission data are used. During the winter months. however, the emission is about 2O”/babove average level. The emission inventory for both point and area sources was performed hq: “Storknbenhavns luftforureningsudvalg”.
Fig. I. Map of the Cuprnhagen ares with the coast lint at Oresund. The coordinates are in the UTM system with a unit of 1 km. The measurement stations are marked with their names referring to Table 2. In the town centre (10 x IO km’ dashed squnrc) the emission strength is r~sually larger than 50 (tons SO, year- i km- ‘1 for each single square of the six 1 (km2). Outside this area the emission strength is usually smaller.
in dispersion model and comparison between computed and measured concentrations __-___~ --ll_____ 3
3c
i:,
lh
3c
J;r
ih
F;i
‘h
hl
-
7!1
i.,
sh
‘I:1
91,
793
Validation of a multiple source Gaussian air quality model air a day through a solution of hydrogen peroxide. The sulphur was analysed for each station. The sampling method was originally recommended by Warren Spring Laboratory (1966). 6. RESULTS No calibration
of any parameters in the dispersion model is performed. In Table 1 is shown the parameter combinations and the main results from 20 tests. The squared linear correlation coefficient between computed and measured 3 months average SO, values varies between 0.6 and 0.7. Two data points deviated significantly from the linear regression line in all tests. These two points represent measurement stations situated in questionable surroundings, and they were excluded in the further analyses. Using 22 measurement stations the ?-values vary between 0.82 and 0.85, thus the model explains more than 80% of the spatial variation of the SO,-concentrations in the urban area. The correlation coefhcient is not sensitive to the parameter combinations used, possibly a result of the large number of sources in varying distances from the receptor points. The slope of the linear regression line, however, is sensitive to the parameter combination. The cut-off of the regression line is between 31 and 43 pg/m3. About half of this is originating from the rural “background” concentration, and from sources not accounted for. The measurements are probably 5-20pg/m3 above the computed level caused by the increased emission during the winter months January-March. The slope of the regression line is generally less than one in our tests and in other studies, referred to below. Apart from the use of too weak dispersion, a possible reason is a positive truncation of measured SOz concentrations at low concentration levels. The truncation is caused
r----140 -
y~o.98xt39 r =0.91
120 -
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+
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+:’
T7 E rz 602
+
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+ 20-
w
I
40
60
60
100
120
Calculated SO&g/m31
Fig. 2. Measured and computed 3 months average SO2 concentrations at 22 recentor ooints (test No. 3ct. . . I
+ + .E 0.5 z \ E ';5 0.4 ,P s f D
0.3.
s
t
8
0.2
c
?
F
o 0.1
E
I
20
I>,
4
40
60
I
*
60
I
>.
I
100
120
/.
140
Measured SO&Wn3~ Fig. 3. Relative computed SO* con~ntration at 22 receptor points, originating from point and area sources. Area
sources dominate at the receptor points with high concentrations, i.e. in the central parts of the town (test No. 3b). by a “detection limit” of the sampling and chemical analysis method, close to or below the lowest measured daily average SO, concentrations. The slope of the regression line is to a great extent determined by the computed concentrations in the “high emission” areas, i.e. the central areas of the town. The largest slope is found for a short half life, and for use of dispersion parameters, giving rapid dilution for area sources. Both conditions give a relatively low computed concentration in the central parts of the town. A few tests have been performed on data from a two months period in the beginning of 1972. In these tests equally high correlations between measured and computed concentrations were obtained. In 1973-74 the emission of SOz decreased, partly caused by the oil crisis and partly by a lower sulphur content in the fuel Model computations did not correlate equally well with the relatively low measured pollution level during this period, Figure 2 shows the measured and compute results of test No. 3~. An important problem in urban air quality strategy is whether emission from mainly point or area sources should be minimized. Figure 3 shows the relative contributions from area sources and point sources, computed in test No. 3b. It is seen that point sources are relatively less important in the central parts of the town, where we find the high pollution level, The total emission from point sources exceeds slightly the emission from area sources in the Copenhagen area. Table 2 gives information on the position of measurement stations in UTM (Universal Transverse Mercator coordinate system) coordinates on emission strength in a square kilometre at the station, on
194
LARS P. PRAHM
and
MAKS CHRISTENSEE~
Table 2.
LBHS* BELA SUNP VALR HUSH ELHV FRBR STOM GENT HELL LYNu(i* OkLV VANL GLOS BALL GLAD HVtV ROD\’ SMCIR SOLL AMVA MAGL STAV FLON
* Station not included in regression analysis caused by questionable surroundings. measured and on computed concentrations. The last columns in Table 2 show the relative contribution from point sources to the total computed concentrations for a number of tests. The measurement stations situated at highest area emission strength is in the top of the table and positions with lowest emission at the bottom. The geographical position of the measurement stations in the Copenhagen area is shown on the map (Fig. 1). For all dispersion parameter combinations the same tendency is found as shown at Fig. 3 with strongest influence from point sources in the regions outside the town centre. Comparison between a and b tests (Table 2) shows that the oxidation time have no important influence on the relative contribution from the point sources. The transport velocity for each source, however, is determined by use of logarithmic wind profile, and the relatively large velocity for high sources results in less effect of the change in oxidation time for these sources than for area sources. Table 2, tests 1 and 3, shows that increased initial dispersion at the area sources increases the relative contribution from point sources. IJse of the Pasquill parameters (Table 2, test 8) introduces a relatively slow dispersion from area sources. resulting in decreased influence from the point sources. The HGgstr8m parameters with more rapid dispersion than the Singer & Smith parameters for neutral stability result in relatively large point source contribution in areas with relatively weak area source emission strength. but have no significant influence in the central part of the town (Table 2, tests 3 and 5). 7. DISCUSSION It is shown that a stationary Gaussian multiple source dispersion model for the Copenhagen area
adequately accounts for the spatial distribution ol SO2 for a period of three months, with a correlation coefficient Y between measured and computed results at 22 receptor points better than 0.9. The correlation is not sensitive to the choice of dispersion parameters. The slope of the regression line is largest for dispersion parameters. representing rapid dilution. oxidation and deposition. A slope from 0.9 to 1.0 is obtained for optimal parameter combinations. The correlation between measured and computed results is improved, compared with the results given by Calder (1973) with intercept at 20 50,~g S0,im3. slope of 0.2-0.4 and a correlation coeficient I’ of 0.77-0.79. The validation of a Gaussian dispersion model by Koch et (I/. (1971 I includes similar tests at &IO receptor points and with intercept5 liom -5 to 60 kg SOz/m3. slopes from 0.60 to 0.98 and correlation coefficients r between 0.68 and 0.87. Recently a large number of applications of the Gaussian dispersion model has been reported by Salter & Tikvart (1974). In computations with more than IO receptor points. correlation coefficients r in the range from 0.5 to 0.9 are achieved. by averaging SO2 concentration through about one year. Simple air quality models can bc based on the assumption of direct proportionality between the ambient air quality and the cmihsion of pollutant4 in the nearest surroundings (square kilometres). Gifford & Hanna (1973) suggest ;I general area source air pollution concentration formula \ =. (~Q;I, where s is the estimated u an average wind speed, squares centred as closely position. The constant c
III
concentration of pollutant, and Q is the emission in as possible at the sampling is a weak function of city
Validation of a multiple source Gaussian air quality model size. The formula is considered for computation of long period mean concentrations of different pollutants. The constant c is found to be between 7 and 218 for SO2 mean concentrations in 29 towns in the United States. Table 2 shows computed concentrations from the Copenhagen area based on the calibrated Guassian model and based on a simple area source emission proportionality model: x = (cQ/u) + d
(2)
where x(pg m--j) is the estimated SO, concentration, Q (pg SO, see-’ me2 ) the area source emission strength and u (m/set) the mean wind speed. The constants c and d are determined by linear regression analysis between Qu- ’ and measured concentrations, giving c = 94 and d = 60. The correlation coefficient is 0.8. The constant c is within the interval determined for c in Formula (1) by Gifford & Hanna (1973) with mean concentrations in the 29 towns in United States. The area source emission proportionality model (2) were used at 4 winter periods in Copenhagen and the results are shown in Table 3. Variation of the regression constants gives strict limitations on the applicability of this simple area source emission proportionality model. Compared with other studies, both the Gaussian model and the simple model give relatively high correlation with measured concentrations, probably caused by the spatiality detailed emission survey for the Copenhagen area. The absolute concentration level is of course especially sensitive to the sulphur content in the fuel used, which might be difficult to estimate accurately. Although the stationary Gaussian model is based on a rudimentary description of the atmospheric dispersion process, it is shown that the model is adequate as a tool for urban planning and establishment of air quality strategies on a climatological basis. if a sufficiently detailed emission inventory and some measured concentrations for model calibration are available. In case of non-homogeneous and non-stationary meteorological conditions, short time averages should be treated by other methods, such as puff models (Sheih & Moroz, 1973: Start & Wendell, 1974) for single point sources and e.g. the newly developed pseudospectral model (Christensen & Prahm, 1976; Berkowicz & Prahm, 1977) or the moment model (Pedersen & Prahm, 1974) for area sources. The present study shows the effect on the spatial correlation of ambient SO, air concentrations, measured and computed, using various dispersion Table 3. SO, emission proportxmahty
Period Oct~Dec Jan Mar Ott- Dee Jan Mar
1970 1971 1971 1972
Meat? wind u (m/se4 ___.~~___._ 6 6 8 7
Sl0pe 69 94 112 121
model x = cQ/s + d @g/m’)
IntCfCCpt
55 60 32 49
Correlation 0.5 0.8 0.7 0.6
79.5
parameters in the Gaussian model. Further studies should show the ability of the model to reflect the changing emission strength and the meteorological dispersion conditions from year to year. Acknowledgements-This study is based on the extensive emission survey for the Copenhagen area, and we would like to thank T. C. Windfeld Lund, Storkebenhavns luftforureningsudvalg, who is responsible for the collection of these data and who kindly made the material for these computations available. We would like to thank K. Conradsen, the institute for mathematical statistics and operational analysis, The Technical University of Denmark, for his advice during the study. The SO,-air quality measurements used were performed by: The Danish Boiler Association for Stork@benhavns Luftforureningsudvalg. REFERENCES
Berkowitz R. & L. P. Prahm (1977) Pseudospectral simulation of dry deposition from a point source. Preprint volume from the International Symposium on Sulphur in the Atmosphere. Sept. 7-14. Dubrovnik. Yugoslavia. To appear in Atmospheric Environment 12, No. 1. Briggs G. A. (1969) Plume Rise. A.E.C. Critical Review Series. U.S. Atomic Energy Comission, Division of Technical Information. TID-25075. Brummage K. G. (1968) The calculation of atmospheric dispersion from a stack. Atmospheric Environment 2, 197-224. Busse A. D. & J. R. Zimmerman (1973) User’s guide for the climatological dispersion model EP.4-R4-73-024. Report. Calder K. L. (1973) A climatological model for multiple source urban air pollution. EPA-R4-73-024. Report. Christensen 0. & L. P. Prahm (1976) A pseudospectral model for dispersion of atmospheric pollutants. J. Appl. Meteor. 15, 1284-1294. Gifford F. A. & S. R. Hanna (1973) Modelling urban air pollution. Afmospheric Enoironment 7, 131-~136. Koch C. K. & S. D. Thayer (1971) Validation and sensitivity analysis of the gaussian plume multiple-source urban diffusion model. National Technical Information Service, U.S. Dept. of Commerce. Springfield Va 22151. McElroy J. L. (1969) A comparative study of urban and rural dispersion. J. Appl. Meteor. 8, 19-31. Pasquill F. (1974) Atmospheric Di;qicsion. John Wiley, London. Pedersen L. B. & L. P. Prahm (1974) A Method for numerical solution of the advection equation. Tellus, 26, 594-602. Sheih C. M. & W. J. Moroz (1973) A Lagrangian puff diffusion model for the prediction of pollutant concentrations over urban areas. Proceedings oj the Third International Clean Air Conaress. Diisseldorf. Oct. 8-12 1973. B43-B52. Slater H. H. & J: A. Tikvart (1974) Application of a multisource urban model. Proceedings of the fifth meeting of the expert panel on air pollution modelling. N.35 NATO, CCMS. Start G. E. & L. L. Wendell (1974) Regional effluent dispersion calculations considering spatial and temporal meteorological variations. Symposium ON atmospheric difi fusion andair pollution. Santa -Barbara, Califoinia. Sept. 9-13. American Met. Sot. Boston. U.S.A. 202-208. Stern A. C. (1970) Proceedings of symposium on multiplesource urban dijiision models. Air Pollution Control Office Publication AP-86. Turner D. B. (1970) Workbook of atmospheric dispersion estimates. li.S. Environmental Protection Agency. AP-26. Warren Spring Laboratory (1966) National suroey of smoke and sulphur dioxide. Instruction manual.
Enwronn~enr
Vol. I I, pp.791-795. Pergamon Press 1977. Prmted in Great Britain.
VALIDATION
OF A MULTIPLE SOURCE AIR QUALITY MODEL LARS P.
PFCAHM
and
GAUSSIAN
MAKS CHRISTENSEN
Danish Meteorological Institute, Air Pollution Section, Lyngbyvej 100 ~~-2100, Copenhagen 0, Denmark (First received
24 May
1976)
Abstract-A multiple source stationary Gaussian atmospheric dispersion model is tested in the Copenhagen area for 20 dispersion and decay parameter combinations, using different parameters for high and low sources. Optimal correspondance between measured and computed SO2 values is obtained for parameters, representing rapid dispersion and decay. Use of different dispersion parameters does not change the spatial correlation coefficient between measured and computed concentrations significantly, but changes the slope of the regression line. The spatial correlation for 22 receptor points between the climatological SO2 average concentration through a period of three months gives a correlation coefficient r larger than 0.9, explaining more than 80% of the variation. The slope of the regression line is between 0.9 and 1.0 for optimal dispersion parameter combinations. The model is found to be an applicable tool for urban planning and establishment of air quality strategies.
1.
INTRODUCTION
Most computations of atmospheric dispersion of pollutants from single sources and from multiple sources, e.g. urban areas, are based on the steady state Gaussian dispersion formulation (Stem, 1970). Larger discrepancies between measured and computed concentrations are found for short averaging times, caused by, among other things, non-homogeneous and nonstationary meteorological conditions. For long averaging times, e.g. months, the Gaussian dispersion concept has been accepted as adequate for design of chimney heights (Turner, 1970). Superposition of the contribution from multiple sources, e.g. an urban area, reduces the deviation between measured and computed results, caused by statistical averaging of the errors. The Gaussian model has been validated by Koch et al. (1971) inter alia. The report shows that the computed concentrations are sensitive to the choice of dispersion parameters and decay rates, and that further analysis of these parameters should be performed. The present study concerns a validation of a multiple Gaussian urban air quality model for the Copenhagen area in Denmark. Computed three months average SO, concentrations are compared with measured results at 24 stations, covering an area of about 500 square kilometres of “flat” urban area (Fig. 1). The spatial correlation and regression between measured and computed concentrations is determined for a number of different dispersion parameters and decay rates. The relative contribution of pollutant from low and high sources is discussed. 2. THE GAUSSIAN
MODEL
The Gaussian dispersion concept has been used for a number of multiple urban air quality models. These models determine concentrations of pollutants in the
atmosphere as an average concentration in a number of meteorological discrete and stationary states, weighted with the probability of occurrences. Our model is an extension of the Climatological Dispersion Model (CDM) documented by Busse & Zimmerman (1973). The technical details are described by Calder (1973). Both theory and tracer experiments have shown that low and high sources should be treated separately (Pasquill, 1974). The main improvement of the model in this study is the introduction of a separate set of dispersion parameters for area (low) and point (high) sources. As no other important changes have been made in the CDM model used, the reader is referred to the original description of the model for further information on model formulation. 3. METEOROLOGICAL
The model is tested on the three months period 1 January-l April 1971 with a relatively high measured pollution level. Meteorological input data consist of a joint frequency function based on wind direction, wind speed and atmospheric stability. These data are taken from hourly airport observations in the Copenhagen area (Kastrup). During the considered period a mean wind velocity at 6 (m s-l) was measured. Mixing height is determined from radiosonde observations twice a day. Average mixing heights during night and day were 500m and 700m, respectively. The plume rise is computed after the “2/3 law” due to Briggs (1969). In order to test the sensitivity of the model towards the dispersion parameters, different parameter combinations are used. Three sets of dispersion parameters gL are used for area sources: The original Pasquill parameters (Pasquill a), these parameters shifted one class towards the unstable region Pasquill b and the parameters 791
AE. 11,‘9%A
DATA
792
LARS P. PRAHM
and MAKS CnRlsreNsl-:N
determined by McElroy (1969). Thermal effects and the a~rodyna~c roughness of the urban area introduce an increased turbulent mixing compared with dispersion over the open country. These conditions are taken into account in the last two sets of dispersion parameters. To some degree an initial dispersion nio at the source can account for the mixing introduced by the urban area (McElroy, 1969). In our tests a cio value of 20, 30 or 40m is used. The dispersion from high sources is determined hy the Wiigstriim parameters, and the Singer & Smith parameters, both reviewed by Brummage (1968). or the McElroy (1969) parameters. The last set gives a significantly faster mixing than the two other parameter combinations. reposition and the chemical oxidation rate of SO2 to SO4 is described with a half life of 1, 3 or X, hours. In Table 1 is shown the combination of the chosen input parameters in 20 tests. 4.
EMISSION
Test hi,,.
!:I
Ih
?;r
5.
MiCASliKEMENTS
Air quality was measured at 23 stations in the Copenhagen area by StorktThenhavns luftforureningsudvalg (Fig. 1). Samplers were installed initially at the height of 2-12 metres over the ground and consisted of a semi--automatic instrument. pumping 2 m3
UATA
The sulphur dioxide cm&ion originates from industrial activity and house heating. The emission sources are divided in two groups, point sources and area sources. All stacks, emitting sulphur from more than 400 cubic tons per year of oil (heat equivalents), are classified as point sources, and i~ldi~~duai dispersion computations are performed for each of these. based on information on stack dimensions, gas temperatures, gas flow and yearly fuel consumption. Information on 252 point sources was used in this test. The emission from all other fuel consumption was connected to a rectangular grid array of uniform-sized squares with grid spacing of t km. The area source emission inventory was based on a study of heated building square metres. A total of 484 area sources is used in our computations. Towns (10000-30000 inhabitants) in distances of less than 30 km from the area are accounted for as distant sources. By different cross checks the error on the emission inventory is found to he less than lo’:,. The total yearly emission in 1970 71 was 65000 tons of SO,. In the model comTable 1. Parameter combination
putations yearly average emission data are used. During the winter months. however, the emission is about 2O”/babove average level. The emission inventory for both point and area sources was performed hq: “Storknbenhavns luftforureningsudvalg”.
Fig. I. Map of the Cuprnhagen ares with the coast lint at Oresund. The coordinates are in the UTM system with a unit of 1 km. The measurement stations are marked with their names referring to Table 2. In the town centre (10 x IO km’ dashed squnrc) the emission strength is r~sually larger than 50 (tons SO, year- i km- ‘1 for each single square of the six 1 (km2). Outside this area the emission strength is usually smaller.
in dispersion model and comparison between computed and measured concentrations __-___~ --ll_____ 3
3c
i:,
lh
3c
J;r
ih
F;i
‘h
hl
-
7!1
i.,
sh
‘I:1
91,
793
Validation of a multiple source Gaussian air quality model air a day through a solution of hydrogen peroxide. The sulphur was analysed for each station. The sampling method was originally recommended by Warren Spring Laboratory (1966). 6. RESULTS No calibration
of any parameters in the dispersion model is performed. In Table 1 is shown the parameter combinations and the main results from 20 tests. The squared linear correlation coefficient between computed and measured 3 months average SO, values varies between 0.6 and 0.7. Two data points deviated significantly from the linear regression line in all tests. These two points represent measurement stations situated in questionable surroundings, and they were excluded in the further analyses. Using 22 measurement stations the ?-values vary between 0.82 and 0.85, thus the model explains more than 80% of the spatial variation of the SO,-concentrations in the urban area. The correlation coefhcient is not sensitive to the parameter combinations used, possibly a result of the large number of sources in varying distances from the receptor points. The slope of the linear regression line, however, is sensitive to the parameter combination. The cut-off of the regression line is between 31 and 43 pg/m3. About half of this is originating from the rural “background” concentration, and from sources not accounted for. The measurements are probably 5-20pg/m3 above the computed level caused by the increased emission during the winter months January-March. The slope of the regression line is generally less than one in our tests and in other studies, referred to below. Apart from the use of too weak dispersion, a possible reason is a positive truncation of measured SOz concentrations at low concentration levels. The truncation is caused
r----140 -
y~o.98xt39 r =0.91
120 -
;;++
% n" 5
+ //1./
+
100"
+
+
x.
/./.. $f
so-
+:’
T7 E rz 602
+
_:+ *
E 40
+ 20-
w
I
40
60
60
100
120
Calculated SO&g/m31
Fig. 2. Measured and computed 3 months average SO2 concentrations at 22 recentor ooints (test No. 3ct. . . I
+ + .E 0.5 z \ E ';5 0.4 ,P s f D
0.3.
s
t
8
0.2
c
?
F
o 0.1
E
I
20
I>,
4
40
60
I
*
60
I
>.
I
100
120
/.
140
Measured SO&Wn3~ Fig. 3. Relative computed SO* con~ntration at 22 receptor points, originating from point and area sources. Area
sources dominate at the receptor points with high concentrations, i.e. in the central parts of the town (test No. 3b). by a “detection limit” of the sampling and chemical analysis method, close to or below the lowest measured daily average SO, concentrations. The slope of the regression line is to a great extent determined by the computed concentrations in the “high emission” areas, i.e. the central areas of the town. The largest slope is found for a short half life, and for use of dispersion parameters, giving rapid dilution for area sources. Both conditions give a relatively low computed concentration in the central parts of the town. A few tests have been performed on data from a two months period in the beginning of 1972. In these tests equally high correlations between measured and computed concentrations were obtained. In 1973-74 the emission of SOz decreased, partly caused by the oil crisis and partly by a lower sulphur content in the fuel Model computations did not correlate equally well with the relatively low measured pollution level during this period, Figure 2 shows the measured and compute results of test No. 3~. An important problem in urban air quality strategy is whether emission from mainly point or area sources should be minimized. Figure 3 shows the relative contributions from area sources and point sources, computed in test No. 3b. It is seen that point sources are relatively less important in the central parts of the town, where we find the high pollution level, The total emission from point sources exceeds slightly the emission from area sources in the Copenhagen area. Table 2 gives information on the position of measurement stations in UTM (Universal Transverse Mercator coordinate system) coordinates on emission strength in a square kilometre at the station, on
194
LARS P. PRAHM
and
MAKS CHRISTENSEE~
Table 2.
LBHS* BELA SUNP VALR HUSH ELHV FRBR STOM GENT HELL LYNu(i* OkLV VANL GLOS BALL GLAD HVtV ROD\’ SMCIR SOLL AMVA MAGL STAV FLON
* Station not included in regression analysis caused by questionable surroundings. measured and on computed concentrations. The last columns in Table 2 show the relative contribution from point sources to the total computed concentrations for a number of tests. The measurement stations situated at highest area emission strength is in the top of the table and positions with lowest emission at the bottom. The geographical position of the measurement stations in the Copenhagen area is shown on the map (Fig. 1). For all dispersion parameter combinations the same tendency is found as shown at Fig. 3 with strongest influence from point sources in the regions outside the town centre. Comparison between a and b tests (Table 2) shows that the oxidation time have no important influence on the relative contribution from the point sources. The transport velocity for each source, however, is determined by use of logarithmic wind profile, and the relatively large velocity for high sources results in less effect of the change in oxidation time for these sources than for area sources. Table 2, tests 1 and 3, shows that increased initial dispersion at the area sources increases the relative contribution from point sources. IJse of the Pasquill parameters (Table 2, test 8) introduces a relatively slow dispersion from area sources. resulting in decreased influence from the point sources. The HGgstr8m parameters with more rapid dispersion than the Singer & Smith parameters for neutral stability result in relatively large point source contribution in areas with relatively weak area source emission strength. but have no significant influence in the central part of the town (Table 2, tests 3 and 5). 7. DISCUSSION It is shown that a stationary Gaussian multiple source dispersion model for the Copenhagen area
adequately accounts for the spatial distribution ol SO2 for a period of three months, with a correlation coefficient Y between measured and computed results at 22 receptor points better than 0.9. The correlation is not sensitive to the choice of dispersion parameters. The slope of the regression line is largest for dispersion parameters. representing rapid dilution. oxidation and deposition. A slope from 0.9 to 1.0 is obtained for optimal parameter combinations. The correlation between measured and computed results is improved, compared with the results given by Calder (1973) with intercept at 20 50,~g S0,im3. slope of 0.2-0.4 and a correlation coeficient I’ of 0.77-0.79. The validation of a Gaussian dispersion model by Koch et (I/. (1971 I includes similar tests at &IO receptor points and with intercept5 liom -5 to 60 kg SOz/m3. slopes from 0.60 to 0.98 and correlation coefficients r between 0.68 and 0.87. Recently a large number of applications of the Gaussian dispersion model has been reported by Salter & Tikvart (1974). In computations with more than IO receptor points. correlation coefficients r in the range from 0.5 to 0.9 are achieved. by averaging SO2 concentration through about one year. Simple air quality models can bc based on the assumption of direct proportionality between the ambient air quality and the cmihsion of pollutant4 in the nearest surroundings (square kilometres). Gifford & Hanna (1973) suggest ;I general area source air pollution concentration formula \ =. (~Q;I, where s is the estimated u an average wind speed, squares centred as closely position. The constant c
III
concentration of pollutant, and Q is the emission in as possible at the sampling is a weak function of city
Validation of a multiple source Gaussian air quality model size. The formula is considered for computation of long period mean concentrations of different pollutants. The constant c is found to be between 7 and 218 for SO2 mean concentrations in 29 towns in the United States. Table 2 shows computed concentrations from the Copenhagen area based on the calibrated Guassian model and based on a simple area source emission proportionality model: x = (cQ/u) + d
(2)
where x(pg m--j) is the estimated SO, concentration, Q (pg SO, see-’ me2 ) the area source emission strength and u (m/set) the mean wind speed. The constants c and d are determined by linear regression analysis between Qu- ’ and measured concentrations, giving c = 94 and d = 60. The correlation coefficient is 0.8. The constant c is within the interval determined for c in Formula (1) by Gifford & Hanna (1973) with mean concentrations in the 29 towns in United States. The area source emission proportionality model (2) were used at 4 winter periods in Copenhagen and the results are shown in Table 3. Variation of the regression constants gives strict limitations on the applicability of this simple area source emission proportionality model. Compared with other studies, both the Gaussian model and the simple model give relatively high correlation with measured concentrations, probably caused by the spatiality detailed emission survey for the Copenhagen area. The absolute concentration level is of course especially sensitive to the sulphur content in the fuel used, which might be difficult to estimate accurately. Although the stationary Gaussian model is based on a rudimentary description of the atmospheric dispersion process, it is shown that the model is adequate as a tool for urban planning and establishment of air quality strategies on a climatological basis. if a sufficiently detailed emission inventory and some measured concentrations for model calibration are available. In case of non-homogeneous and non-stationary meteorological conditions, short time averages should be treated by other methods, such as puff models (Sheih & Moroz, 1973: Start & Wendell, 1974) for single point sources and e.g. the newly developed pseudospectral model (Christensen & Prahm, 1976; Berkowicz & Prahm, 1977) or the moment model (Pedersen & Prahm, 1974) for area sources. The present study shows the effect on the spatial correlation of ambient SO, air concentrations, measured and computed, using various dispersion Table 3. SO, emission proportxmahty
Period Oct~Dec Jan Mar Ott- Dee Jan Mar
1970 1971 1971 1972
Meat? wind u (m/se4 ___.~~___._ 6 6 8 7
Sl0pe 69 94 112 121
model x = cQ/s + d @g/m’)
IntCfCCpt
55 60 32 49
Correlation 0.5 0.8 0.7 0.6
79.5
parameters in the Gaussian model. Further studies should show the ability of the model to reflect the changing emission strength and the meteorological dispersion conditions from year to year. Acknowledgements-This study is based on the extensive emission survey for the Copenhagen area, and we would like to thank T. C. Windfeld Lund, Storkebenhavns luftforureningsudvalg, who is responsible for the collection of these data and who kindly made the material for these computations available. We would like to thank K. Conradsen, the institute for mathematical statistics and operational analysis, The Technical University of Denmark, for his advice during the study. The SO,-air quality measurements used were performed by: The Danish Boiler Association for Stork@benhavns Luftforureningsudvalg. REFERENCES
Berkowitz R. & L. P. Prahm (1977) Pseudospectral simulation of dry deposition from a point source. Preprint volume from the International Symposium on Sulphur in the Atmosphere. Sept. 7-14. Dubrovnik. Yugoslavia. To appear in Atmospheric Environment 12, No. 1. Briggs G. A. (1969) Plume Rise. A.E.C. Critical Review Series. U.S. Atomic Energy Comission, Division of Technical Information. TID-25075. Brummage K. G. (1968) The calculation of atmospheric dispersion from a stack. Atmospheric Environment 2, 197-224. Busse A. D. & J. R. Zimmerman (1973) User’s guide for the climatological dispersion model EP.4-R4-73-024. Report. Calder K. L. (1973) A climatological model for multiple source urban air pollution. EPA-R4-73-024. Report. Christensen 0. & L. P. Prahm (1976) A pseudospectral model for dispersion of atmospheric pollutants. J. Appl. Meteor. 15, 1284-1294. Gifford F. A. & S. R. Hanna (1973) Modelling urban air pollution. Afmospheric Enoironment 7, 131-~136. Koch C. K. & S. D. Thayer (1971) Validation and sensitivity analysis of the gaussian plume multiple-source urban diffusion model. National Technical Information Service, U.S. Dept. of Commerce. Springfield Va 22151. McElroy J. L. (1969) A comparative study of urban and rural dispersion. J. Appl. Meteor. 8, 19-31. Pasquill F. (1974) Atmospheric Di;qicsion. John Wiley, London. Pedersen L. B. & L. P. Prahm (1974) A Method for numerical solution of the advection equation. Tellus, 26, 594-602. Sheih C. M. & W. J. Moroz (1973) A Lagrangian puff diffusion model for the prediction of pollutant concentrations over urban areas. Proceedings oj the Third International Clean Air Conaress. Diisseldorf. Oct. 8-12 1973. B43-B52. Slater H. H. & J: A. Tikvart (1974) Application of a multisource urban model. Proceedings of the fifth meeting of the expert panel on air pollution modelling. N.35 NATO, CCMS. Start G. E. & L. L. Wendell (1974) Regional effluent dispersion calculations considering spatial and temporal meteorological variations. Symposium ON atmospheric difi fusion andair pollution. Santa -Barbara, Califoinia. Sept. 9-13. American Met. Sot. Boston. U.S.A. 202-208. Stern A. C. (1970) Proceedings of symposium on multiplesource urban dijiision models. Air Pollution Control Office Publication AP-86. Turner D. B. (1970) Workbook of atmospheric dispersion estimates. li.S. Environmental Protection Agency. AP-26. Warren Spring Laboratory (1966) National suroey of smoke and sulphur dioxide. Instruction manual.