removal model for SO2 and sulfate—III. Comparison with the July 1974 sure database

Atmospheric Printed

Enwonmenr

in Great

wo4-6981/88

Vol. 22, No. 9, pp. 2003-201 I, 1988. 0

Bntain.

1988 Pergamon

53.lw+o.Ml Press

plc

AN EULERIAN TRANSPORT/TRANSFORMATION/REMOVAL MODEL FOR SO, AND SULFATE-III. COMPARISON WITH THE JULY 1974 SURE DATABASE MOHAN ~RONAMRAJ~,* LEONARD K. PETERS,* GREGORY R. CARMICHAE~,~ PRASAD KASIBHATLA* and SEOG-Y.EON CHOW * Department of Chemical Engineering, University of Kentucky, Lexington, KY 40506, U.S.A. 7 Department of Chemical and Materials Engineering, University of Iowa, Iowa City, IA 52242, U.S.A. (Received 20 July 1987 and infinuf form 29 January 1988) Abstract--This is the third paper in a series describing an Eulerian trans~rt/transformat~on/removal model (STEM-I). Simulation results for the period 4-9 July 1974 are compared with field data from the SURE experiment for the same period. Statistical analysis of the predicted and observed SO2 and sulfate concentration data sets are used for the comparisons. The STEM-I model overpredicted SO, concentrations and underpredicted the sulfate concentrations. On average, the predicted amount of SO, deposited exceeded the predicted sulfate deposited by eight times, and the predicted amount of SO, deposited exceeded that being converted to sulfate by nearly an order of magnitude. Computed correlation coefficients were reasonably good. Key word index: Eulerian models, transport, chemistry, deposition, SO,, SO:-, model/data comparison, SURE.

Regional-scale air quality simul&ion models provide a means to study complex atmospheric processes. These models attempt to predict the distribution of pollutant concentrations based on the physical and chemical processes occurring in the atmosphere. Because of the accepted significance of the model predictions, it is important to have a measure of the model accuracy in predicting pollutant concentration at a given time and place. Statistical tests are generally used to determine how accurately or poorly an air quality simulation model can reproduce the real situation. Some of the previous evaluation efforts include those of Shir and Shieh (1974), McNaughton (1980) and Stewart et al. (1983a, 1983b). This paper is the third in a series describing an Eulerian combined transport~transfo~ation/removal model referred to as STEM-I (Carmichael and Peters, 1984a, 1984b). In the first paper, the model development was discussed. It is a 3-D grid model that incorporates chemical transformation, dry deposition, spatial variations of topography, spatial and temporal variations of mixing layer heights, wind field, eddy diffusivities, and deposition velocities. In the second paper, the model was applied to SO, transport in the eastern United States. Results for a 72-h simulation using 4 July 1974 meteorological data were presented and discussed. In this paper, we present simulation results for the period 4-9 July 1974. This includes detailed contour plots of the concentrations and dry deposition of SO,

and sulfate and regional mass balances of these speties. Statistical analysis of the data for the entire period are then presented. This statistical analysis involves comparison of the model-predicted data with the observed data from the 1974 Sulfate Regional Experiment (SURE) (Hidy et al., 1976). A model evaluation process is designed to determine the performance of the model. The AMS workshop on dispersion model performance (Fox, 1981) recommended the Performance statistics to be computed for an air quality model evaluation. In this paper, results from the computation of some of these statistics are presented.

DESCRlPTION OF SIMULATIONS

The modeling region is the eastern United States, as shown in Fig. 1. The region contained within the grids (l,i5) to (26,lS) to (26,32) to (1,32) is the same as that used in the SURE experiment. The horizontal grid spacing is 80 km. The vertical grid extends from the Earth’s surface to 3 km and is divided into 11 nonuniform vertical levels. Details concerning the various inputs required for a simulation (emission data, meteorological data, etc.) and the procedures by which inputs are derived from meteorological data have been discussed elsewhere (Carmichael and Peters, 1984a, 198413).Briefly, the meteorological inputs consist of wind field data, eddy diffusivity data, dry deposition velocities of SO, and sulfate, temperatures, and water vapor con~ntrations at each grid point for every time interval.

2003

et al.

MOHANDRONAMRAJU

2004

I3

Fig. 1. Modeling National Weather

6

9

region--eastern

12

United

15

States;

18

circles

21

24

represent

location

Lx

of

Service stations and squares are locations of SO, and sulfate sampling sites.

There were 32 National Weather Service stations providing meteorological data aloft during the period 4-9 July 1974 that could be used to drive the simulation. At a given level, the data included measured values of wind speed, temperature, and dew point. Wind speed was resolved into horizontal wind velocity components u and u. By interpolating between these, the horizontal wind field at that particular level was estimated. The weighting function l/d [rk is the distance between the upper air station and grid point (i,j)] was used in the interpolation scheme. Using the same interpolation method and the predicted dry deposition and eddy diffusivity vaiues at the 32 National Weather Service stations, the eddy diffusivities and the dry deposition velocity values for the entire modeling region were calculated. Similarly, the temperature and dew point depression data for’the entire field were calculated from the measured data at the National Weather Service locations. For the period 49 July 1974, the synoptic situation over the eastern half of the United States was characterized by a high-pressure region over the Atlantic Ocean. Surface winds were light and were generally from the southwesterly direction. High daily average

30-60pg/ma

90-EOpg/m'

Fig. 2. Ground-level

24-h averaged 7 July.

SO, concentrations

for

Eulerian

transport/transformation/removal

<30pq/m' 30-60&m: 60-SOpq/m so-120pqh

model--III

-\

<

IOpq/d

30-6O/Nnr' 60-SOpqh' 90-lWpq/m'

>

120pq/m'

>120pq/m3

Fig. 3. Ground-level 24-h averaged SO, concentrations for 8 July.

Fig. 5. SO2 Concentration

plot for 8 July at 12:OO h.

H

<3Opq/m3

30-60pqh

1 1W-6Opq/d

I

60-90pq/m3

60-90pq/m!

SO-120pq/m3

SO-IPOpq/m

>120pq/m3

> 120pq/m3

r----Y_

Fig. 4. Ground-level

24-h averaged 9 July.

SO, concentrations

ambient temperatures and dew point were observed during this period.

for

temperatures

RESULTS OF SIMULATION Predicted 24-h averaged ground-level SO, concentrations are presented in Figs 2&l for 7, 8 and 9 July.

Fig. 6. SO, Concentration

plot for 8 July at 24:OO h.

These contour plots show high ground-level concentrations near the high source regions such as Chicago, St Louis, western Pennsylvania, and the Ohio River Valley. The instantaneous ground-level SO2 concentration plots for 8 and 9 July at 12:00 h and 24~00 h (see Figs 5-8) show that the predicted night-time concentrations are lower than the predicted daytime concentrations. This is probably due to the stable

MOHANDRONAMRAJU et al.

Fig. 7. SO, Concentration plot for 9 July at 12~00h.

Fig. 9. Ground-level 24-h averaged sulfate concentrations for 7 Juiy.

Fig. 8. SO* Concentration plot for 9 July at 24:OOh.

Fig. 10. Ground-level 24-h averaged sulfate concentrations for 8 July.

stratification prevalent at night, with the result that emissions from elevated sources are mixed to ground very slowly. Predicted 24-h averaged sulfate concentrations at ground level are shown in Figs 9-11 for 7,8 and 9 July. The effect of horizontal transport can be seen by observing the change in direction of the sulfate contours near western Pennsylvania. High sulfate concentrations predicted for 7 July are partly due to high

ambient temperatures (> 75°F) and high average dew point temperatures during that period. For example, the predicted sulfate concentration at Wheeling, WV, for 6 July (T -c 75” F and dew point < 60” F) was I 1.9pg m- 3, whereas it was 17.2 pg m- 3 for 7 July. On 7 July, high sulfate concentrations occurred over western Pennsylvania. These contours moved east on 8 July, crossing the East Coast on 9 July. Figures 12-15 show vertical concentration pro-

Eulerian

transport/transformation/removal

model-III

2007

r

1<

r

30pgf m3

30-60pg/m3

60-90pp/m' so-120*g/m > 120 pg/m’

Fig. 13. Vertical concentration profile of SO, on 9 July at 2400 h. [The vertical extent is from p =O.Ol to p=O.95 for a westeast

Fig. 11. Ground-level

24-h averaged for 9 July.

slice from coordinate (1,22) to coordinate (27.22); see Fig. I.]

sulfate concentrations

(3OPQ/rn’ 30-6Opg/m’ 60-

< 30pg/m3

30-60pgfm’

BOpg/ms

sO-l20p~/r= > 100pp/m’

Fig. 14. Vertical concentration profile of 24-h averaged SO, for 9 July. [The vertical extent is from p=O.Ol to p=O.95 for a west-east slice from coordinate (1,22) to coordinate (27,22); see Fig. 1.1

60-SOPa/m’ 90-

120pg/m3

> 120pg/m3

Fig. 12. Vertical concentration profile of SO, on 9 July at 12:00 h. [The vertical extent is from p=O.Ol to p=O.95 for a west&east slice from coordinate (1,22) to coordinate (27,22); see Fig. 1.1

files of SO, and sulfate. These are the concentration profiles along a vertical section passing through the coordinates (1,22) and (27,22). The vertical extent is from p = 0.01 to p = 0.95, where p = [z - h(x,y)]/ [3OOOm-h(x,y)]. Figures 12 and 13 are the vertical concentration profiles of SO, on 9 July at 12:00 h and 2403 h. Figures 14 and 15 are the vertical concentration profiles of 24-h averaged SO1 and sulfate for 9 July, respectively. These figures illustrate the fact that the SOz concentrations are very source dependent with large concentration gradients, while sulfate concentrations show more gradually changing concentration gradients. The daily accumulated dry deposition contours for

<4

tg/m’

4-8

pg/m’

8-12

fig/m=

12-IS&&g/m’ >I6

#q/m8

Fig. 15. Vertical concentration profile of 24-h averaged sulfate for 9 July. [The vertical extent is from p = 0.01 to p =0.95 for a west-east slice from coordinate (1,22) to coordinate (27,22); see Fig. 1.1

SO2 and sulfate are shown in Figs 16 and 17 for 7 July 1974. These plots show that the SO2 deposition patterns closely follow the ground-level SO, concentration patterns, as was reported by Carmichael and

Peters (1984b). Thus, the SO, dry deposition contours are source dominated, as are the concentrations. Regional daily mass balances predicted from the simulation are presented in Table 1. On average, the amount of SO, deposited exceeded the sulfate deposited by eight times, and the amount of SOz deposited exceeded the amount reacted by nearly an order of magnitude. For sulfate the amount deposited and the amount formed by reaction were of similar magnitude. The regional mass balances can be used to estimate the following regional parameters. (1) Regional-auer~ed enclave SO, reaction rate (rso,). (Mass of SO, reacted per day)/(average inventory in the region during the day)

II

IS-30

(rsoJ

pg/m

= 0.48% h.

(2) Regional-averaged effective conversion rate (f,,,). (Mass reacted per day)/(emissions per day)

10-45 pghi

ts40pghi


pgh

Fig. 16. Daily accumulated dry deposition contours for SO, on 7 July.

(3) 24-h regional-averaged concentration (c). (Average inventory in the region during that day)/(volume of the region). Volume of the region is approx. 1.7 x lOI m3, which corresponds to an atmosphere extending over the entire region shown in Fig. 1 to a height of 3 km. (c,,)=26.1 (c,,)=

fig mm3 9.4 pg me3.

(4) Equivalent box model deposition velocity (v&. (Amount deposited per day)/[(surface area at ground level) (Regional daily averaged concentration)] (v&,>)=4.8cnls-* (udso,)=1.74cms-‘. SURE DATABASE

Fig. 17. Daily accumulated dry deposition contours for sulfate on 7 July.

The major objective of the 1974 SURE experiment was to provide insight into the relationships between sulfur oxide emissions and ambient air quality (Hidy et al., 1976). This program has been followed by more extensive field studies (e.g. 1978 SURE program). One method of data assimilation during this project was to gather data on SO, and sulfate concentrations for a period of I yr between 1974 and 197.5 at 12 existing stations in the northeastern quadrant of the United States. These stations were located in Indiana, in Illinois along the Ohio River Valley, and in Pennsylvania and New York. They were selected on the following basis (Hidy et af., 1976):

Table 1. Regional daily budget based on model simulation (10” pgday - ‘)

SO, reacted SO, deposited Sulfate formed by reaction Sulfate deposited

4 July

5 July

6 July

-0.54 1.47 0.81 0.83

-0.52 5.77 0.78 0.70

-0.50 5.32 0.75 0.69

7 July -0.50 5.47 0.74 0.75 .

8 July

9 July

-0.49 5.64 0.74 0.82

-0.51 5.89 0.77 0.89

Eulerian transport/transformation/removal

(1) location upwind of major local source; (2) number of total suspended particulate filters available for laboratory analyses of sulfates; (3) operations and maintenance records; and (4) geographical distribution over the northeast quadrant of the United States. Figure 1 includes the locations of these stations. Data were not available from all of these stations for every time period. Frequently, data were only available at about half of the stations. STATISTICAL

COMPARISON

OF SIMULATION

WITH

OBSERVED DATA

The SURE database for July 1974 was used to make direct comparisons of the observed data with the

2009

model--III

predicted data. Specifically, the ground-level 24-h averaged SO, and sulfate concentrations were utilized for model evaluation purposes. Comparisons between the observed and the predicted data at different SURE stations for the period 7-9 July 1974 are shown in Tables 2 and 3. These tables indicate that the present model overpredicts SO1 concentrations and underpredicts the sulfate concentrations. Several reasons can be advanced for these differences. These include: (a) the primary sulfate emissions are higher than that used for the model input; (b) the conversion rate of SO2 to sulfate is too low, particularly inasmuch as cloud conversion processes are not included in STEM-I; (c) the modeled rate of dry deposition for sulfate is too large; and

Table 2. Comparison of the observed and predicted SO, concentrations (pg m-‘) for the period 7-9 July 1974 7 July Station Collins, IL Madison, IN Lawrenceburg, IN Huntington, WV Wheeling, WV Scranton, PA

8 July

9 July

Observed

Predicted

Observed

Predicted

Observed

Predicted

7.9 49.8 2.6 144.1 2.6

109.2 118.5 25.2 162.6 25.9

26.2 18.3 89.1 2.6 99.6 5.2

79.9 76.9 110.9 26.5 132.4 32.6

26.2 49.8 34.1 2.6 233.2 10.5

72.0 58.9 51.6 25.8 96.6 34.4

Table 3. Comparison of the observed and predicted SO, concentrations (pg m-j) for the period 7-9 July 1974 7 July Station

Observed

8 July

Predicted

-

Collins, IL Rockport, IN Madison, IN Lawrenceburg, IN Huntington, WV Wheeling, WV Scranton, PA Albany, NY

10.8 12.3 23.4 4.9 53.7 5.0 7.3

10.1 12.5 13.1 7.6 17.2 7.3 6.2

9 July

Observed

Predicted

Observed

Predicted

26.1

8.5

13.1

13.4 22.2 45.7 83.6 7.3 13.2

9.1 14.7 8.7 12.7 7.6 6.9

19.1 36.0 48.7 75.7 27.0 25.3

8.0 8.2 5.9 7.9 11.4 8.1 7.1

Table 4. Statistical summary of 24-h averaged SO, concentrations Mean Period 9 July 8-9 July 7-9 July 6-9 July 5-9 July 4-9 July

Bias* olg m-? 2.9 -16.8 -25.6 - 27.4 -27.1 -28.0

‘kgm-7

Correlation coefficient

Variance of residual? olg mm3)’

56.5 66.5 72.9 73.5 71.2 67.2

0.84 0.66 0.65 0.69 0.71 0.71

4412.1 2552.0 2291.2 1940.3 1488.1 1325.4

RMSE of residual (pg m-“)

Average abs. gross error of residual 01g m-?

60.9 51.2 53.0 51.1 46.6 45.5

42.6 39.5 41.7 39.9 36.6 36.0

*Bias is defined as the difference between the mean observed concentration and the mean predicted value for all sampling stations for the indicated time period. t Residual is defined as the difference between the observed concentration and the corresponding predicted value at a sampling location for the indicated time period.

2010

MOHANDRONAMRAJU et al. Table 5. Statistical

Mean predicted (fig me3)

Bias* (fig m-Y

Period 9 July 8-9 July 7-9 July 6-9 July 5-9 July 4-9 July

summary

26.9 23.7 17.9 15.5 13.6 13.2

of 24-h averaged

Correlation coefficient

8.0 8.9 9.4 9.2 8.5 7.9

0.64 0.44 0.42 0.49 0.55 0.54

sulfate concentrations

Variance of residual t (pg m - ?*

RMSE of residual (pg m-?

Average abs. gross error of residual (fig m-‘)

412.7 501.1 456.4 374.2 309.9 255.3

32.8 32.0 27.5 24.5 22.0 20.6

26.9 23.7 18.4 16.0 14.1 13.6

* Bias is defined as the difference between the mean observed concentration and the mean predicted value for all sampling stations for the indicated time period. t Residual is defined as the difference between the observed concentration and the corresponding predicted value at a sampling location for the indicated time period.

Table 6(a). Peak value statistics

of 24-h averaged

SO,

concentrations

Bias (pg mm-‘)

Variance of residual (pg m-Y

RMSE of residual

Average abs. gross error of residual

(pg m-‘)

(pg m-‘)

5.7* - 7.2t - 29.81

4360.2 5 130.6 3884.9

60.6 65.8 64.2

42.2 52.8 55.7

Table 6(b). Peak value statistics of 24-h averaged concentrations

Bias (pg m --‘) 38.1* 37.9t

34.31

Variance of residual (pg m-Y 570. I 579.4 553.4

sulfate

RMSE of residual (pg m-‘)

Average abs. gross error of residual (pg m-‘)

44.0 ‘43.8 40.5

38.1 37.9 34.3

*Residual is defined as the difference between the observed maximum concentration at a given time over the entire spatial field and the corresponding predicted value at the same location and time. tResidua1 is defined as the difference between the observed maximum concentration at a given time over the entire spatial field and the maximum predicted value in the entire spatial domain at the same time. $ Residual is defined as the difference between the observed maximum concentration at a given time over the entire spatial field and the predicted maximum concentration at that site any time during a time period.

(d) the sampling stations are affected by local sources and do not adequately represent regional air quality. Tables 4 and 5 provide a summary of the statistical analysis. The computed statistics are difference, bias, standardized gross error, and average absolute error. Obviously, lower bias and variance indicate better model performance. The correlation coefficient was also calculated and is a measure of how well the predicted concentration pattern follows the observed concentration pattern. For statistical analysis, six data

subsets were created with the first subset containing data for 9 July, the second subset containing data for 8 and 9 July, and so on. The sixth data subset contained data for 4-9 July. The reason for organizing the data subsets in this manner was to examine how the model performance changed from the start of the simulation to a period where the influence of initial conditions should be reduced. From Tables 4 and 5, it can be seen that the correlation coefficients between observed and predicted data were reasonably good. These coefficients ranged from 0.65 to 0.84 for SO, concentrations and from 0.42 to 0.64 for sulfate concentrations, with higher values calculated for 9 July where the effect of initial conditions on the simulation results was expected to be minimal. This indicated that correlation coefficients computed for this STEM-I model simulation provided a rather optimistic picture of model performance. The predicted and observed values were paired in space and/or time as recommended by Fox (198 1).The statistics for the peak values are summarized in Table 6. These statistics were computed pairing the concentrations as follows: (1) observed maximum concentration at a given time over the entire spatial field and the corresponding predicted value at the same location and time; (2) observed maximum concentration at a given time over the entire spatial field and the maximum predicted value in the entire spatial domain at the same time; and (3) observed maximum concentration at a given time over the entire spatial field and the predicted maximum concentration at that site any time during the simulation. These tables show that the observed peak concentrations are not reproduced well by the model-predicted concentrations. Though the STEM-I model overpredicted most of the observed SO, concentrations, the peak values were underpredicted at Wheeling, WV, on 9 July. The relatively low values of the bias for peak SO, concentrations (Table 6a), as compared with those for peak sulfate concentrations (Table 6b),

Eulerian t~nsport/transformation/removal

are to a large extent due to the effect of underprediction of peak SO2 concentration for one observation (Wheeling, WV, on 9 July). SUMMARY

SO, transport in the eastern United States was simulated using STEM-I, an Eulerian transport/ chemistry/deposition model, Simulation results for the period 49 July 1974 are presented and discussed. The simulation results for 24-h averaged SO, and sulfate ground-level concentrations and SO, and sulfate deposition contours were presented. From the regional daily sulfur budgets, various regional parameters were calculated. On average, the predictions indicated the amount of SO, deposited exceeded the sulfate deposited by eight times, and the amount of SOz deposited exceeded that reacted by about an order of magnitude. The high values of 24-h averaged sulfate concentrations for 7 July 1974 were partly due to high ambient and average dew point temperatures. Statistical analysis of the model was done by comparing the simulated data with the July 1974 SURE database. The correlation coefficients between observed and predicted SO2 and sulfate concentrations were reasonably good. However, peak ground-level concentrations were not predicted well. Acknowledgements-The authors gratefully acknowledge support for this research from NASA through research grant

model--III

2011

NAG l-36, and from EPA and DOE through the Battetle Pacific Northwest Laboratories.

REFERENCES

Carmichael G. R. and Peters L. K. (1984a) An Eulerian transport/transformation/removal model for SO, and sulfate-1. Model development. Atmospheric Environment 18, 937-951. Carmichael G. R. and Peters L. K. (1984b) An Eulerian transport/transformation/~moval model for SO, and suifate--II. Model calculation of SO, transport in the eastern United States. Atmospheric E~~ir~~rn~~~ IS, 953-967. Fox D. G. (1981) Judging air quahty model performance. Buft. Am. met. Sot. 62, 599-609. Hidy G. M., Tong E. Y., Mueller P. K., Rao S., Thomson F., Berlandi F., Muldoon D., McNaughton D. and Majahad A. (1976) Design of the Sulfate Regional Experiment. EPRI, PB 251-701. McNaughton D. J. (1980) Initial comparison of SURE/ MAP3S sulfur oxide observations with long-term regional model predictions. Atmospheric Enoironment 14, 5.5-63. Shir C. C. and Shieh L. J. (1974) A generalized urban air pollution model and its application to the study of SO, distribution in the St Louis metropolitan area. J. appt. Met. 13, 185. Stewart D. A., Morris R. E., Hudischewsky A. B. and Liu M. K. (1983a) Evaluation of episodic regional transport models of interest to the National Park Service. Final report, Systems Applications, Inc., San Rafael, CA. Stewart D. A., Morris R. E. and Liu M. K. (1983b) Evaluation of long-term regional transport models of interest to the National Park Service. Final report, Systems Applications, Inc., San Rafael, CA.