- Email: [email protected]
A comparison study of three urban air pollution models
Atmospheric Enoironment Vol. 23, No. 4, pp. 793-801,
olm&6981/89 $3.oo+o.wJ Fwgamon Press plc
1989.
Printed in Great Britain.
A COMPARISON
STUDY OF THREE URBAN AIR POLLUTION MODELS K. SHANKARRAO
Atmospheric Turbulence and Diffusion Division, NOAA/ARL, P.O. Box 2456, Oak Ridge, TN 37831,
U.S.A.
and JIA-YEONG Ku rind S. TRIVIKRAMARAO New York State Department of Environmental Conservation, Division of Air Resources, Albany, NY 12233, U.S.A. (First received 15 February 1988 and injinalform
21 October 1988)
Abstract-The predictions of three urban air pollution models with varying degrees of mathematical and computational complexities are compared against the hourly SO, ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction of urban area source concentrations. Statistics for the paired comparison of predictions of each model with the observations are presented. The RAM and the ATDL model with stable diffusion coefficients overestimated the observed night-time concentrations. The results show that the performance of the ATDL model with near-neutral diffusion coefficients is comparable to the more sophisticated 3-D grid numerical model. Key word index: Urban air pollution models, ATDL model, RAM model, numerical grid model, RAPS data, urban area sources, model performance statistics.
1. INTRODUCTION
Urban air pollution models, which permit quantitative determination of ambient air concentrations in relation to emission sources and meteorological conditions, are widely used in regulation and urban planning for impact analyses of existing or new sources, forecasting of pollution episodes, and evaluation of control strategies. Mathematical models used for urban air pollution studies range from simple empirical models to very complex numerical models. In this paper, we compare the predictions of three urban air pollution models with varying degrees of mathematical and computational complexities. The performance of the models is assessed by comparing the hourly SO2 concentrations estimated from each model with the corresponding observed concentrations on ten winter days of the St. Louis Regional Air Pollution Study (RAPS) data base. These comparisons are limited to night-time data with low mixing depths, when the ground-level concentrations are primarily dictated by the area sources.
occur only in the details of how area source summation is carried out and in how the relevant meteorological parameters are included. The ubran area-source emission inventory is developed by dividing the city into equal-sized grid cells, each typically a square of 3-5 km size, and representing the total of all distributed low-level emissions in each grid cell by an equivalent area-source emission. Elevated large point sources, which are treated separately, are excluded from this area-source inventory. The first model considered in this study is the ATDL urban air pollution model described by Gifford and Hanna (1971, 1973). This model is selected because of its simplicity and its application in many of the regulatory models. Consider two equal grid squares, one of them containing the area-source emissions Q, assumed to be located at ground-level at the center of the square, and the other containing a ground-level receptor R at its center. The mean wind U blows along the line from Q to R as shown in Fig. l(a). The concentration C,, at R due to the area source Q is given by
(1)
2. DESCRIPTION OF MODELS In urban air pollution models based on the Gaussian-diffusion framework, the line and area source problems are generally treated by integrating the point-source diffusion algorithms along a crosswind line or over an area. Differences between these models
Assuming u, is given by a power law of the form a,(x) = axb
(2)
where x is in m, and a and b are constants depending 793
794
K. SHANKAR RAO et al. GRIO SOUARE WITH EMISSIONS\
GRID SOUARE WITH RECEPTOR\
I =
t
143
"23
I %4
"24
(bj Fig. 1. Schematic diagram for area-source concentration algorithms showing (a) a grid square with emissions Q and a grid square with receptor R, and the distances; (b) an emission grid square with receptor R, and four upwind grid squares with emissions. The distances xii and xzi are measured upwind from the receptor R,.
only on the atmospheric stability, we obtain
c,=
JT
Q (x:-b-xf-b). n Ua(1 -b)
-
(3)
Here Q has units rate of the were located at the itself, then xi = 0, comes
of M L-* t- ‘, representing the area source, and 0 < x1 c x2. If R center of the emission grid square x2 = Ax/2, and Equation (3) be-
emission
c,
= 4
Uo;_b)
[$q-”
(4)
If N area sources are located upwind of a single receptor R,, then the total surface concentration at R, can be obtained by summing up the individual contributions of ail N sources: .-
c,=
JI.oat:.$ n
Qi(X!ib-Xiib).
_b)
I
0
(5)
This is schematically illustrated in Fig. l(b). This algorithm for urban area-source concentrations was given by GitTord and Hanna (1971) in a slightly different form by using xI~ =
(2i- 1)6x/2, xzi = (2i+ l)Ax/2,0 < xii < X2(. (6)
This equation can be easily adapted to account for multiple receptors, or pollutant depletion mechanisms such as deposition and chemical transformation (Rao, 19&t), and weighted to account for the frequency with which wind blows from the 16 major directions. Many of these techniques are straightforward and do not necessarily require a digital computer; often, results can be obtained by using a scientific calculator. The concentration estimates are sensitive to the numerical values choeen for o and b. Hanna (1972) recommended the values of a=040 and b~0.91 for a sunny day (unstable conditions), D=0.15 and b =0.75 for a
Comparison study of air pollution models
cloudy day (near-neutral conditions), and a = 0.06 and b =0.71 at night (stable conditions). The second model considered in this study is the RAM model (Turner and Novak, 1978) of the U.S. Environmental Protection Agency (EPA). This urban air quality model, based on steady state Gaussian plume assumptions, is designed for regulatory purposes to predict short term (l-24 h) average groundlevel concentrations at multiple receptors. The concentrations from area sources are calculated using the method of Gifford and Hanna (1971), except that the area sources are considered to have an effective height H. This requires the integration to be accomplished numerically. Thus, the equivalent of Equation (1) in RAM is given by
Briggs’ (1973) urban diffusion formulations, which are based on the diffusion data from near-surface releases of a neutrally-buoyant tracer in St. Louis by McElroy and Pooler (1968), are used for Q, in Equation (7). The RAM model includes a meteorology preprocessor, which accepts hourly surface observations and daily maximum and minimum mixing height data as input, to determine the hourly stability class and mixing height for use in the model. Several innovative computational techniques are used in RAM to minimize the computation time. However, calculations would require at least a personal computer with sufficient storage capacity. It should be noted that both the ATDL model and RAM ignore horizontal diffusion, on the basis of the narrow-plume hypothesis (Gifford, 1959). The latter postulates that surface concentration at a receptor due to distributed area sources depends only on sources located in a rather narrow upwind sector whose angular width is less than the usual 22.5” resolution of observed wind directions. The contribution of the emission grid cell containing the receptor is therefore the dominant one, and the contributions of more remote upwind area sources to this receptor concentration are comparatively small. Thus, it is generally adequate to consider only the four emission grid cells immediately upwind of each receptor grid square. The third urban air pollution model is a timedependent three-dimensional grid model described by Ku et al. (1987a,b). This model numerically integrates the mass conservation equation which includes advection, turbulent diffusion, chemical transformation, emissions and removal of pollutants:
z+&(~iC)=&Kig I
.( .>
+Ri+Q.
(8)
A non-divergent wind field interpolation scheme based on objective analysis is used to generate the mean transport winds ui from available measurements. Recent advances in PBL dynamics are utilized
195
to specify the eddy diffusivities Ki. This model uses sophisticated numerical techniques that require a large digital computer, and is suitable for research and evaluation of urban air pollution problems. 3. THE RAPS SO, DATA
The St. Louis Regional Air Pollution Study experiment and data base have been described by Schiermeier (1978). A set of hourly SO, concentrations observed at 20 surface stations on 10 winter days of 1975-1976 are used in the present study; these 10 days, selected from the large RAPS data base, contained detailed emissions, meteorology and concentration data which were considered to be appropriate for model development and evaluation by the EPA (see Pendergrass and Rao, 1984). The locations of the monitoring stations in the RAPS experiment are shown in Fig. 2. The diurnal variations of total point and total area source emissions of SO2 for a winter day and a summer day in the RAPS data are shown in Fig. 3. The variability in emissions is more significant in summer than in winter. For the latter case, the hourly total area-source emission is about 5% of the corresponding total point-source emission. Nevertheless, area sources play a dominant role in determining the surface concentrations in urban areas at night-time in winter. This is demonstrated in Figs 4(a) and (b) which show the variations of the observed hourly SO, concentrations for a winter day at several monitoring stations, and the corresponding numerical simulation results of Ku et al. (1987b). The contributions to the total concentration from the area sources and from the point sources, calculated separately by the numerical model, are also shown in these figures. It is clear that the contribution from area sources dominates during the night, while the point-source contribution dominates during the day. The diurnal variation of the average mixing height for the 10 winter days considered in this study is shown in Fig. 5. During the winter night, the low mixing height (about 150 m) prevents the emissions from elevated point sources with effective stack heights > 150 m from diffusing to the surface, while the small eddy diffusivity prevents the area source emissions near the surface from mixing in the vertical. In contrast, during the daytime, increased mixing height and eddy diffusivity reduce the contribution from area sources. The temporal, spatial and seasonal variations of surface concentration distributions as functions of the area and point source SO2 emissions in the St. Louis urban area were discussed by Ku et al. (1987b). 4. RESULTS AND DISCUSSION
In view of these results, and the emphasis on areasource emissions in the present comparison study, we
796
K. SHANKAK RAO et al.
Fig. 2. Location of air monitoring stations in St. Louis during the RAPS field experiment. Thirty-one per cent of total area source emissions of SO2 occur in the 20 km x 20 km area (shown in dots) in downtown St. Louis.
0 0
4
12
El LOCAL
nw mm
20
Fig. 3. Diurnal variations of total point source (dashedline) and total area source (solid line) emissions of SO, in RAPS.
Comparison study of air pollution models
. .
. .
E
-
798
K. SHANKARRAO ct ~1
Fig. 5. Variation of the houriy mixing height, averaged over t0 winter days in the RAPS data base, in St. Louis.
calculated only the night-time (1~ h local time) hourly SO, concentrations at various monitoring stations for 10 winter nights of RAPS data with each of the three models, and compared them with the corresponding observations in order to assess each model’s performance. On these nights, the winds were blowing primarily from the SE to SSW (39%), or NNW to NE (32%); the hourly mean wind speeds ms-’ were 1.5-3 m s- ’ (44%), 3-5 ms-‘(32%),0-1.5
(13%), and 5-7.5 ms-’ (1 IX), where the numbers in the parentheses give the percentage frequency of winds in each direction or speed class. For each hour, the stability class and the urban mixing height are determined by the RAM preprocessor, following a procedure described by Turner and Novak (1978). The stability classes on the 10 winter nights are either E or F. Figure 6 shows the comparison of the hourly SO, ATDL M 88W5
400 r
I l
-
L - -----
I
I
I
I
Observations Numerical
Model
RAM Model
_I
ATDL Model
22 Local Time
2
0
4
(hr)
Fig. 6. Comparison of the hourly SO, concentrations (fig m- ‘f averaged over all stations, predicted by each of the three models and the corresponding observations Near-neutral values of a and b are used in the ATDL model calculations. The times shown on the abscissa correspond to the beginning of each hourly sampling period.
799
Comparison study of air pollution models
surface concentrations (averaged over all of the monitoring stations within the area source domain) pmdicted by the three models, and the corresponding observations. The average observed hourly SO, wncentrations remain fairiy uniform ( < 100 j.4gm- 3, through most of the night, with a slight increase evident after midnight. Both the numerical model and the ATDL model (using the near-neutral coefficients of a = 0.15 and b = 0.75) simulate the average observed concentrations very well, while the RAM model significantly overpredicts at all hours, especially after 11:OOp.m. Previous studies evaluating the performance of RAM with the RAPS data base noted the bias towards overestimation of urban concentrations during nighttime stable conditions by the model. Turner and Irwin (1985) and Rao et al. (1985) attributed this stabilityrelated bias of RAM to poor characte~~tion of emissions of point and area sources, and lack of treatment in the model of the physical processes such as building downwash and heat island effects, locally produced turbulence, and plume penetration of elevated stable layers. Weil (1984) discussed the shortcomings of various components of urban models, and suggested that the use of Turner stability classification may not be appropriate for urban areas. Figure 7 shows the results of the ATDL model using the stable values for coefficients, u = 0.06 and 6 = 0.7 1; the other curves are same as those shown in Fig. 6. These results show that the ATDL model significantly overpredicts (much worse than the RAM model) when a stable value of u, is used in the model. Gifford and Hanna (1973) argue that the atmospheric diffusion conditions in the lower atmosphere over cities tend to be of the near-neutral type, without the strong diurnal
600
I
I
I
I
I
Observations
0
“E a
variations found at airport (rural) locations. There is evidence that the frequency and intensity of nocturnal inversions over cities are decreased due to the enhanced thermal and mechanical turbulence resulting from urban heat island and increased roughness effects (e.g. see Oke, 1974). Thus, at night, the appropriate values of a and b to be used in the ATDL model are those recommended for near-neutral conditions (Pasquill type-D). Table 1 shows the statistics for the performance of each of the models when compared against the observations. The statistics are those discussed by Fox (1981), Willmott (1982) and Rao et al. (1985). The compared data consist of 1528 hourly SO* concentrations from various stations. The mean (6) of observed concentrations is 77pgrnm3, while the mean (P) of predicted concentrations is 87 pgrnm3 for the ATDL model (with near-neutral a and b), 103 pgrne3 for the numerical model, and 220 pgrn- 3 for the RAM. The corresponding means of (Pi/O,) are 4.3,3.8 and 7.7 for the three models. It should be noted that whereas the ratios of the mean predicted to the mean observed concentration are 1.1,1.3 and 2.9 for the ATDL model (with near-neutral a and b), numerical model and RAM, respectively, the computed values for the mean of (Pi/‘Oi) for the three models are significantly high due to the strong influence of a few large values (outhers) in the sample. The standard deviations of (P,/O,) indicate that there is a large scatter in the values for the ratio. The index of agreement performance measure indicates that the numerical model is 19% better than the simple ATDL model with nearneutral coefficients. Though the numerical model shows a slightly larger correlation with observations, the performance of the simple ATDL model (with
-
Numerical
--
RAM Model
Model
-----
ATDL Model
#fix I ’\ r’
‘t,
400
b
5
3
E E 8 g 0
200
0 18
I
I
I
I
I
20
22
0
2
4
Local Tlme
(hr)
Fig. 7. Same as in Fig. 6, except stable values of a and b are used in the ATDL model calculations.
800
K. SHANKARRAO et al. Table 1. Comparison of model performance statistics Sample size
1528 Observed (0) Numerical model
Range Mdan SD. Correlation coefficient Slope Intercept Mean of (Pi/O,) S.D. of (PilO,) Bias (6-P) Avg. abs. gross error RMSE Mean of fraction error Index of agreement MSE(u)/MSE MSE(s)/MSE
1-1672 77.0 130.8
Predicted (P) RAM ATDL*
ATDLt
(r989 103.4 105.2
____~~__._~_~ _-...~ ~___ 3-8062 3-1145 11-354s 219.9 87.2 280.7 409.7 99.0 318.2
0.13 0.11 95.3 3.8 6.6 - 26.4 89.0 158.8 -3.1 0.34 43% 51%
0.07 0.21 203.9 7.7 20.3 - 142.9 186.5 445.1 -8.7 0.13 84% 16%
- 0.07 -0.05 91.3 4.3 9.0 - 10.2 87.9 169.7 -1.6 0.15 34% 66%
- 0.07 --0.16 292.9 13.6 28.8 - 203.7 239.9 406.4 - 13.2 0.10 61% 39%
Note: The units of range, mean, standard deviation (SD.), intercept, bias, average absolute gross error, and root mean square error (RMSE) are pg m - 3. MSE(s) and MSE(u) are the systematic and unsystematic parts of mean square error (MSE). * ATDL model with near-neutral values of a and h. t ATDL model with stable values of a and b.
near-neutral a and b), as revealed by the root mean square error and the average absolute gross error, is comparable to that of the more sophisticated numerical model for the night-time diffusion conditions. The statistics of the ATDL model with stable values of a are also given in Table 1 for comparison.
5. CONCLUSIONS The predictions of three urban air pollution models of varying degrees of complexity are compared against the hourly SO, ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction 6f concentrations due to the urban area source emissions. The RAM model significantly overpredicted the observed concentrations. The ATDL model with stable values of a and b performed poorly, overestimating concentrations worse than RAM. The performance of the simple ATDL model with nearneutral values for the coefficients a and b was found to be comparable to the more sophisticated 3-dimensional numerical grid model. For the usual case of surface receptors within a city, as Gifford and Hanna (1973) pointed out, the area source component of urban air pollution is strongly dominated by the source-strength pattern, and by transport by the mean wind. However, the gross influences of atmospheric stability variations should also be included, since the choice of appropriate values for a and b is important for good performance of the ATDL model. Given the uncertainties in the area source emission inventory and our limited knowledge of the dynamics
of the nocturnal boundary layer in urban areas, it is difficult to improve upon the numerical model’s performance (see Ku et al., 1987b). Detailed models wit1 always be needed for research applications, where there is a need to understand the complexities associated with urban air pollution. However, for many practical applications, it is desirable to retain the essential simplicity in urban diffusion models so that meteorological effects can be treated in a relatively uncomplicated manner. Such a simple modeling approach can serve as a useful screening tool for urban air quality impact assessment.
Acknowledgements-This work was performed under agreements among the National Oceanic and Atmospheric Administration, the U.S. Department of Energy and the New York State Department of Environmental Conservation. REFERENCES Briggs G. A. (1973) Diffusion estimation for small emissions. ATDL Annual Report, NOAA, Oak Ridge, TN, 83-146 Fox D. G. (1981) Judging air quality model performance. Bull. Amer. Met. Sot. 62, 59e609. Gifford F. A. (1959) Computation of pollution from several sources. Inc. J. Air Pollut. 2, X0-109. Gifford F. A. and Hanna S. R. (1971) Urban air pollution modelhng. Proc. of the Second Id. Clean Air Congress (editedby H. M. Enghmd and W. T. Beery), pp. 1029-1032. Academic Press, New York. GiIford F. A. and Hanna S. R. (1973) Modelling urban air pollution. Atmospheric Environment 7, 131-136. Hanna S. R. (1972) Description of ATDL computer model for dispersion from multiple sources. Proc. of the Second Annual Industrial Air Poll. Control Conf., Knoxville, TN. ATDL Report 56, NOAA, Oak Ridge. Ku J. Y., Rao S. T. and Rao K. S. (1987a) Numerical
Comparison study of air potlution models simulation of air pollution in urban areas: model development. Atmospheric Environment 21, 201-212. Ku J. Y.. Rao S. T. and Rao K. S. (1987bl Numerical simulation of air pollution in urban‘areaa:’ model ner* formance. Atmospkric Environment 21,213-232. McElroy J. L. and Pooler F. (1968) St. Louis dispersion study. U.S. Public Health Service. National Air Pollution Control Administration, Publication AP-53, Vol II. Oke T. R. (19741 Review of Urban Climatoloav 19681973. Technical Note No. 134, World Meteorolo~~i Organization No. 383, Geneva. Pendergrass W. R. and Rao K. S. (1984) Evaluation of the Pollution Episodic Model using the RAPS Data. U.S. Environmenial Protection Agency Report EPA-@O/3-84087. Research Trianale Park. NC. Available as PB 84-232 537.from NTIS, Sp&gfIeld,‘VA. Rao K. S. (1984) Plume concentration algorithms with deposition, sedimentation and chemical transformation. U.S. Environmental Protection Agency Report EPA600/3-84-942, Research Triangle Park, NC. Available as PB 84-138 742 from NTIS, Springfield, VA.
801
Rao S. T., Sistala G., Pagnotti V., Petersen W. B., Irwin J. R. and Turner D. B. (1985) Evaluation of the performance of RAM with the Regional Air Pollution Study data base. Atmospheric Environment 19,229245. Schiermeier F. A. (1978) Air monitoring milestones: RAPS field measurements are in. Envir. Sci. Technol. 12,644-651. Turner D. B. and Irwin J. S. (I985) The relation of urban model performance to stability. Air Pollution Modelling and its Application IV (edited by C. D. Wispelaere), pp. 721-732. Pienum Press, New York. Turner D. B. and Novak J. H. (1978) User’s Guide for RAM. Vols. I and II. U.S. Environmental Protection Agency Renorts EPA-@O/8-78-016 a and b. Research Trianale Paik, NC. Weil J. C. (1984) Review of urban dispersion models. In Review of the Attributes and Performance of Six Urban Difision Models. U.S. Environmental Protection Agency Report EPA-600/3-84-089, Research Triangle Park, NC, 5681. Willmott C. J. (1982) Some comments on the evaluation of model performance. Bull. Amer. Met. Sot. 82, 1309-1313.
olm&6981/89 $3.oo+o.wJ Fwgamon Press plc
1989.
Printed in Great Britain.
A COMPARISON
STUDY OF THREE URBAN AIR POLLUTION MODELS K. SHANKARRAO
Atmospheric Turbulence and Diffusion Division, NOAA/ARL, P.O. Box 2456, Oak Ridge, TN 37831,
U.S.A.
and JIA-YEONG Ku rind S. TRIVIKRAMARAO New York State Department of Environmental Conservation, Division of Air Resources, Albany, NY 12233, U.S.A. (First received 15 February 1988 and injinalform
21 October 1988)
Abstract-The predictions of three urban air pollution models with varying degrees of mathematical and computational complexities are compared against the hourly SO, ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction of urban area source concentrations. Statistics for the paired comparison of predictions of each model with the observations are presented. The RAM and the ATDL model with stable diffusion coefficients overestimated the observed night-time concentrations. The results show that the performance of the ATDL model with near-neutral diffusion coefficients is comparable to the more sophisticated 3-D grid numerical model. Key word index: Urban air pollution models, ATDL model, RAM model, numerical grid model, RAPS data, urban area sources, model performance statistics.
1. INTRODUCTION
Urban air pollution models, which permit quantitative determination of ambient air concentrations in relation to emission sources and meteorological conditions, are widely used in regulation and urban planning for impact analyses of existing or new sources, forecasting of pollution episodes, and evaluation of control strategies. Mathematical models used for urban air pollution studies range from simple empirical models to very complex numerical models. In this paper, we compare the predictions of three urban air pollution models with varying degrees of mathematical and computational complexities. The performance of the models is assessed by comparing the hourly SO2 concentrations estimated from each model with the corresponding observed concentrations on ten winter days of the St. Louis Regional Air Pollution Study (RAPS) data base. These comparisons are limited to night-time data with low mixing depths, when the ground-level concentrations are primarily dictated by the area sources.
occur only in the details of how area source summation is carried out and in how the relevant meteorological parameters are included. The ubran area-source emission inventory is developed by dividing the city into equal-sized grid cells, each typically a square of 3-5 km size, and representing the total of all distributed low-level emissions in each grid cell by an equivalent area-source emission. Elevated large point sources, which are treated separately, are excluded from this area-source inventory. The first model considered in this study is the ATDL urban air pollution model described by Gifford and Hanna (1971, 1973). This model is selected because of its simplicity and its application in many of the regulatory models. Consider two equal grid squares, one of them containing the area-source emissions Q, assumed to be located at ground-level at the center of the square, and the other containing a ground-level receptor R at its center. The mean wind U blows along the line from Q to R as shown in Fig. l(a). The concentration C,, at R due to the area source Q is given by
(1)
2. DESCRIPTION OF MODELS In urban air pollution models based on the Gaussian-diffusion framework, the line and area source problems are generally treated by integrating the point-source diffusion algorithms along a crosswind line or over an area. Differences between these models
Assuming u, is given by a power law of the form a,(x) = axb
(2)
where x is in m, and a and b are constants depending 793
794
K. SHANKAR RAO et al. GRIO SOUARE WITH EMISSIONS\
GRID SOUARE WITH RECEPTOR\
I =
t
143
"23
I %4
"24
(bj Fig. 1. Schematic diagram for area-source concentration algorithms showing (a) a grid square with emissions Q and a grid square with receptor R, and the distances; (b) an emission grid square with receptor R, and four upwind grid squares with emissions. The distances xii and xzi are measured upwind from the receptor R,.
only on the atmospheric stability, we obtain
c,=
JT
Q (x:-b-xf-b). n Ua(1 -b)
-
(3)
Here Q has units rate of the were located at the itself, then xi = 0, comes
of M L-* t- ‘, representing the area source, and 0 < x1 c x2. If R center of the emission grid square x2 = Ax/2, and Equation (3) be-
emission
c,
= 4
Uo;_b)
[$q-”
(4)
If N area sources are located upwind of a single receptor R,, then the total surface concentration at R, can be obtained by summing up the individual contributions of ail N sources: .-
c,=
JI.oat:.$ n
Qi(X!ib-Xiib).
_b)
I
0
(5)
This is schematically illustrated in Fig. l(b). This algorithm for urban area-source concentrations was given by GitTord and Hanna (1971) in a slightly different form by using xI~ =
(2i- 1)6x/2, xzi = (2i+ l)Ax/2,0 < xii < X2(. (6)
This equation can be easily adapted to account for multiple receptors, or pollutant depletion mechanisms such as deposition and chemical transformation (Rao, 19&t), and weighted to account for the frequency with which wind blows from the 16 major directions. Many of these techniques are straightforward and do not necessarily require a digital computer; often, results can be obtained by using a scientific calculator. The concentration estimates are sensitive to the numerical values choeen for o and b. Hanna (1972) recommended the values of a=040 and b~0.91 for a sunny day (unstable conditions), D=0.15 and b =0.75 for a
Comparison study of air pollution models
cloudy day (near-neutral conditions), and a = 0.06 and b =0.71 at night (stable conditions). The second model considered in this study is the RAM model (Turner and Novak, 1978) of the U.S. Environmental Protection Agency (EPA). This urban air quality model, based on steady state Gaussian plume assumptions, is designed for regulatory purposes to predict short term (l-24 h) average groundlevel concentrations at multiple receptors. The concentrations from area sources are calculated using the method of Gifford and Hanna (1971), except that the area sources are considered to have an effective height H. This requires the integration to be accomplished numerically. Thus, the equivalent of Equation (1) in RAM is given by
Briggs’ (1973) urban diffusion formulations, which are based on the diffusion data from near-surface releases of a neutrally-buoyant tracer in St. Louis by McElroy and Pooler (1968), are used for Q, in Equation (7). The RAM model includes a meteorology preprocessor, which accepts hourly surface observations and daily maximum and minimum mixing height data as input, to determine the hourly stability class and mixing height for use in the model. Several innovative computational techniques are used in RAM to minimize the computation time. However, calculations would require at least a personal computer with sufficient storage capacity. It should be noted that both the ATDL model and RAM ignore horizontal diffusion, on the basis of the narrow-plume hypothesis (Gifford, 1959). The latter postulates that surface concentration at a receptor due to distributed area sources depends only on sources located in a rather narrow upwind sector whose angular width is less than the usual 22.5” resolution of observed wind directions. The contribution of the emission grid cell containing the receptor is therefore the dominant one, and the contributions of more remote upwind area sources to this receptor concentration are comparatively small. Thus, it is generally adequate to consider only the four emission grid cells immediately upwind of each receptor grid square. The third urban air pollution model is a timedependent three-dimensional grid model described by Ku et al. (1987a,b). This model numerically integrates the mass conservation equation which includes advection, turbulent diffusion, chemical transformation, emissions and removal of pollutants:
z+&(~iC)=&Kig I
.( .>
+Ri+Q.
(8)
A non-divergent wind field interpolation scheme based on objective analysis is used to generate the mean transport winds ui from available measurements. Recent advances in PBL dynamics are utilized
195
to specify the eddy diffusivities Ki. This model uses sophisticated numerical techniques that require a large digital computer, and is suitable for research and evaluation of urban air pollution problems. 3. THE RAPS SO, DATA
The St. Louis Regional Air Pollution Study experiment and data base have been described by Schiermeier (1978). A set of hourly SO, concentrations observed at 20 surface stations on 10 winter days of 1975-1976 are used in the present study; these 10 days, selected from the large RAPS data base, contained detailed emissions, meteorology and concentration data which were considered to be appropriate for model development and evaluation by the EPA (see Pendergrass and Rao, 1984). The locations of the monitoring stations in the RAPS experiment are shown in Fig. 2. The diurnal variations of total point and total area source emissions of SO2 for a winter day and a summer day in the RAPS data are shown in Fig. 3. The variability in emissions is more significant in summer than in winter. For the latter case, the hourly total area-source emission is about 5% of the corresponding total point-source emission. Nevertheless, area sources play a dominant role in determining the surface concentrations in urban areas at night-time in winter. This is demonstrated in Figs 4(a) and (b) which show the variations of the observed hourly SO, concentrations for a winter day at several monitoring stations, and the corresponding numerical simulation results of Ku et al. (1987b). The contributions to the total concentration from the area sources and from the point sources, calculated separately by the numerical model, are also shown in these figures. It is clear that the contribution from area sources dominates during the night, while the point-source contribution dominates during the day. The diurnal variation of the average mixing height for the 10 winter days considered in this study is shown in Fig. 5. During the winter night, the low mixing height (about 150 m) prevents the emissions from elevated point sources with effective stack heights > 150 m from diffusing to the surface, while the small eddy diffusivity prevents the area source emissions near the surface from mixing in the vertical. In contrast, during the daytime, increased mixing height and eddy diffusivity reduce the contribution from area sources. The temporal, spatial and seasonal variations of surface concentration distributions as functions of the area and point source SO2 emissions in the St. Louis urban area were discussed by Ku et al. (1987b). 4. RESULTS AND DISCUSSION
In view of these results, and the emphasis on areasource emissions in the present comparison study, we
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Fig. 2. Location of air monitoring stations in St. Louis during the RAPS field experiment. Thirty-one per cent of total area source emissions of SO2 occur in the 20 km x 20 km area (shown in dots) in downtown St. Louis.
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Fig. 5. Variation of the houriy mixing height, averaged over t0 winter days in the RAPS data base, in St. Louis.
calculated only the night-time (1~ h local time) hourly SO, concentrations at various monitoring stations for 10 winter nights of RAPS data with each of the three models, and compared them with the corresponding observations in order to assess each model’s performance. On these nights, the winds were blowing primarily from the SE to SSW (39%), or NNW to NE (32%); the hourly mean wind speeds ms-’ were 1.5-3 m s- ’ (44%), 3-5 ms-‘(32%),0-1.5
(13%), and 5-7.5 ms-’ (1 IX), where the numbers in the parentheses give the percentage frequency of winds in each direction or speed class. For each hour, the stability class and the urban mixing height are determined by the RAM preprocessor, following a procedure described by Turner and Novak (1978). The stability classes on the 10 winter nights are either E or F. Figure 6 shows the comparison of the hourly SO, ATDL M 88W5
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Fig. 6. Comparison of the hourly SO, concentrations (fig m- ‘f averaged over all stations, predicted by each of the three models and the corresponding observations Near-neutral values of a and b are used in the ATDL model calculations. The times shown on the abscissa correspond to the beginning of each hourly sampling period.
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surface concentrations (averaged over all of the monitoring stations within the area source domain) pmdicted by the three models, and the corresponding observations. The average observed hourly SO, wncentrations remain fairiy uniform ( < 100 j.4gm- 3, through most of the night, with a slight increase evident after midnight. Both the numerical model and the ATDL model (using the near-neutral coefficients of a = 0.15 and b = 0.75) simulate the average observed concentrations very well, while the RAM model significantly overpredicts at all hours, especially after 11:OOp.m. Previous studies evaluating the performance of RAM with the RAPS data base noted the bias towards overestimation of urban concentrations during nighttime stable conditions by the model. Turner and Irwin (1985) and Rao et al. (1985) attributed this stabilityrelated bias of RAM to poor characte~~tion of emissions of point and area sources, and lack of treatment in the model of the physical processes such as building downwash and heat island effects, locally produced turbulence, and plume penetration of elevated stable layers. Weil (1984) discussed the shortcomings of various components of urban models, and suggested that the use of Turner stability classification may not be appropriate for urban areas. Figure 7 shows the results of the ATDL model using the stable values for coefficients, u = 0.06 and 6 = 0.7 1; the other curves are same as those shown in Fig. 6. These results show that the ATDL model significantly overpredicts (much worse than the RAM model) when a stable value of u, is used in the model. Gifford and Hanna (1973) argue that the atmospheric diffusion conditions in the lower atmosphere over cities tend to be of the near-neutral type, without the strong diurnal
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variations found at airport (rural) locations. There is evidence that the frequency and intensity of nocturnal inversions over cities are decreased due to the enhanced thermal and mechanical turbulence resulting from urban heat island and increased roughness effects (e.g. see Oke, 1974). Thus, at night, the appropriate values of a and b to be used in the ATDL model are those recommended for near-neutral conditions (Pasquill type-D). Table 1 shows the statistics for the performance of each of the models when compared against the observations. The statistics are those discussed by Fox (1981), Willmott (1982) and Rao et al. (1985). The compared data consist of 1528 hourly SO* concentrations from various stations. The mean (6) of observed concentrations is 77pgrnm3, while the mean (P) of predicted concentrations is 87 pgrnm3 for the ATDL model (with near-neutral a and b), 103 pgrne3 for the numerical model, and 220 pgrn- 3 for the RAM. The corresponding means of (Pi/O,) are 4.3,3.8 and 7.7 for the three models. It should be noted that whereas the ratios of the mean predicted to the mean observed concentration are 1.1,1.3 and 2.9 for the ATDL model (with near-neutral a and b), numerical model and RAM, respectively, the computed values for the mean of (Pi/‘Oi) for the three models are significantly high due to the strong influence of a few large values (outhers) in the sample. The standard deviations of (P,/O,) indicate that there is a large scatter in the values for the ratio. The index of agreement performance measure indicates that the numerical model is 19% better than the simple ATDL model with nearneutral coefficients. Though the numerical model shows a slightly larger correlation with observations, the performance of the simple ATDL model (with
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1528 Observed (0) Numerical model
Range Mdan SD. Correlation coefficient Slope Intercept Mean of (Pi/O,) S.D. of (PilO,) Bias (6-P) Avg. abs. gross error RMSE Mean of fraction error Index of agreement MSE(u)/MSE MSE(s)/MSE
1-1672 77.0 130.8
Predicted (P) RAM ATDL*
ATDLt
(r989 103.4 105.2
____~~__._~_~ _-...~ ~___ 3-8062 3-1145 11-354s 219.9 87.2 280.7 409.7 99.0 318.2
0.13 0.11 95.3 3.8 6.6 - 26.4 89.0 158.8 -3.1 0.34 43% 51%
0.07 0.21 203.9 7.7 20.3 - 142.9 186.5 445.1 -8.7 0.13 84% 16%
- 0.07 -0.05 91.3 4.3 9.0 - 10.2 87.9 169.7 -1.6 0.15 34% 66%
- 0.07 --0.16 292.9 13.6 28.8 - 203.7 239.9 406.4 - 13.2 0.10 61% 39%
Note: The units of range, mean, standard deviation (SD.), intercept, bias, average absolute gross error, and root mean square error (RMSE) are pg m - 3. MSE(s) and MSE(u) are the systematic and unsystematic parts of mean square error (MSE). * ATDL model with near-neutral values of a and h. t ATDL model with stable values of a and b.
near-neutral a and b), as revealed by the root mean square error and the average absolute gross error, is comparable to that of the more sophisticated numerical model for the night-time diffusion conditions. The statistics of the ATDL model with stable values of a are also given in Table 1 for comparison.
5. CONCLUSIONS The predictions of three urban air pollution models of varying degrees of complexity are compared against the hourly SO, ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction 6f concentrations due to the urban area source emissions. The RAM model significantly overpredicted the observed concentrations. The ATDL model with stable values of a and b performed poorly, overestimating concentrations worse than RAM. The performance of the simple ATDL model with nearneutral values for the coefficients a and b was found to be comparable to the more sophisticated 3-dimensional numerical grid model. For the usual case of surface receptors within a city, as Gifford and Hanna (1973) pointed out, the area source component of urban air pollution is strongly dominated by the source-strength pattern, and by transport by the mean wind. However, the gross influences of atmospheric stability variations should also be included, since the choice of appropriate values for a and b is important for good performance of the ATDL model. Given the uncertainties in the area source emission inventory and our limited knowledge of the dynamics
of the nocturnal boundary layer in urban areas, it is difficult to improve upon the numerical model’s performance (see Ku et al., 1987b). Detailed models wit1 always be needed for research applications, where there is a need to understand the complexities associated with urban air pollution. However, for many practical applications, it is desirable to retain the essential simplicity in urban diffusion models so that meteorological effects can be treated in a relatively uncomplicated manner. Such a simple modeling approach can serve as a useful screening tool for urban air quality impact assessment.
Acknowledgements-This work was performed under agreements among the National Oceanic and Atmospheric Administration, the U.S. Department of Energy and the New York State Department of Environmental Conservation. REFERENCES Briggs G. A. (1973) Diffusion estimation for small emissions. ATDL Annual Report, NOAA, Oak Ridge, TN, 83-146 Fox D. G. (1981) Judging air quality model performance. Bull. Amer. Met. Sot. 62, 59e609. Gifford F. A. (1959) Computation of pollution from several sources. Inc. J. Air Pollut. 2, X0-109. Gifford F. A. and Hanna S. R. (1971) Urban air pollution modelhng. Proc. of the Second Id. Clean Air Congress (editedby H. M. Enghmd and W. T. Beery), pp. 1029-1032. Academic Press, New York. GiIford F. A. and Hanna S. R. (1973) Modelling urban air pollution. Atmospheric Environment 7, 131-136. Hanna S. R. (1972) Description of ATDL computer model for dispersion from multiple sources. Proc. of the Second Annual Industrial Air Poll. Control Conf., Knoxville, TN. ATDL Report 56, NOAA, Oak Ridge. Ku J. Y., Rao S. T. and Rao K. S. (1987a) Numerical
Comparison study of air potlution models simulation of air pollution in urban areas: model development. Atmospheric Environment 21, 201-212. Ku J. Y.. Rao S. T. and Rao K. S. (1987bl Numerical simulation of air pollution in urban‘areaa:’ model ner* formance. Atmospkric Environment 21,213-232. McElroy J. L. and Pooler F. (1968) St. Louis dispersion study. U.S. Public Health Service. National Air Pollution Control Administration, Publication AP-53, Vol II. Oke T. R. (19741 Review of Urban Climatoloav 19681973. Technical Note No. 134, World Meteorolo~~i Organization No. 383, Geneva. Pendergrass W. R. and Rao K. S. (1984) Evaluation of the Pollution Episodic Model using the RAPS Data. U.S. Environmenial Protection Agency Report EPA-@O/3-84087. Research Trianale Park. NC. Available as PB 84-232 537.from NTIS, Sp&gfIeld,‘VA. Rao K. S. (1984) Plume concentration algorithms with deposition, sedimentation and chemical transformation. U.S. Environmental Protection Agency Report EPA600/3-84-942, Research Triangle Park, NC. Available as PB 84-138 742 from NTIS, Springfield, VA.
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Rao S. T., Sistala G., Pagnotti V., Petersen W. B., Irwin J. R. and Turner D. B. (1985) Evaluation of the performance of RAM with the Regional Air Pollution Study data base. Atmospheric Environment 19,229245. Schiermeier F. A. (1978) Air monitoring milestones: RAPS field measurements are in. Envir. Sci. Technol. 12,644-651. Turner D. B. and Irwin J. S. (I985) The relation of urban model performance to stability. Air Pollution Modelling and its Application IV (edited by C. D. Wispelaere), pp. 721-732. Pienum Press, New York. Turner D. B. and Novak J. H. (1978) User’s Guide for RAM. Vols. I and II. U.S. Environmental Protection Agency Renorts EPA-@O/8-78-016 a and b. Research Trianale Paik, NC. Weil J. C. (1984) Review of urban dispersion models. In Review of the Attributes and Performance of Six Urban Difision Models. U.S. Environmental Protection Agency Report EPA-600/3-84-089, Research Triangle Park, NC, 5681. Willmott C. J. (1982) Some comments on the evaluation of model performance. Bull. Amer. Met. Sot. 82, 1309-1313.