UTLRT: A Statistical Long Range Transport Model S.T. T h o m a s

Sirrine Environmental Consultants, Inc., Greenville, SC 29606, USA

A. K u m a r

Department of Civil Engineering, The University of Toledo, Toledo, Ohio 43606, USA

ABSTRACT

This paper describes the application of a long range transport model which has been adapted for use on a microcomputer. The model used to simulate dispersion and transport is a statistical-type model developed at the University of Toledo and is based on the analytical solution of convective diffusion equations. Certain concepts which have been found to be important to the long range transport of pollutants are incorporated in the model including mixing height prediction, plume penetration effects, and receptor-specific precipitation data. The model has been applied in studies of midwestern NOX and SO2 source regions impacting northeastern United States receptors.

KEYWORDS: Acid Rain Model, Personal Computer, Long Range Transport, Dispersion Modeling

INTRODUCTION

Many mathematical models have been developed to simulate the long range transport (LRT) and deposition of those pollutants thought to be responsible for acid precipitation. This paper discusses the software for the computer model UTLRT [1,2] which is a statistical-type LRT model. It uses long term averages of meteorological data to determine the impact of mean plumes. It makes use of statistics on the premise that long term concentrations are insensitive to short term fluctuations in flow variables. Therefore, the concentration and deposition estimates are representative of yearly values and/or seasonal values.

The UTLRT model calculates the concentrations of primary and secondary pollutants, dry deposition and wet deposition from pollution sources in the eastern and central United States which impact receptors in the northeastern United States. The model differs from other statistical models in that it includes the effects of source height and variation of mixing height with the time of day on acid deposition. UTLRT incorporates theoretical concepts such as prediction of diurnal variation of mixing height and plume penetration which allow much greater flexibility than most statistical type LRT models seen to-date.

The main modules of UTLRT are as follows:

Paper received 15 October 1988 and in final form 25 June 1989 Referees: Drs. A.J. Janssen and Christopher Fung

.

2. 3.

Mixing height module Dispersion module Plume penetration module

The mixing height module uses an analytical equation to predict the boundary layer height as a function of time according to the theory of Kumar and Djurfors [31. The solution given in reference [3] is an extension of the work done by Tennekes [4] by incorporating a finite mixing height at sunrise. The dispersion module consists of the mathematical equations for the primary and secondary pollutants. The basis of the transport mechanism in UTLRT is the analytical solution to the steady-state, two-dimensional diffusion equation as proposed by Fay and Rosenzweig [5]. The plume penetration module adapts the theoretical concepts of Kumar and Djurfors [3] to long range transport. The penetration equation is a generalization of Briggs' work for any value of entrainment coefficient. It calculates the extent of plume penetration above the mixing layer and thus the portions of the plume susceptible to short range and iong range transport as shown in Table I. Plume rise is calculated according to Briggs [6] and Well and Brower [7].

The model uses meteorological data and source emission data as input for studying the potential for long-range transport and acid deposition from a given

Environmental Software, 1989, Yol. 4, No. 4 179

Table I - PLUME PENETRATION CONDITIONS

CONDITION PENETRATION PLUME SUBJECT TO

Mixing Height > 2 He 0 Long range transport (LRT)

Mixing Height < He 1.0 (Complete) LRT

Mixing Height 1 < . . . . . . . . . . . . . . < 2

He Upper portion: LRT Lower portion: Limited Mixing

where: He = effective emission height at the source P = penetration fraction

source. Note that the results Obtained from the model are applicable at long distances from the sources (greater than 100 kilometers). The meteorological variables are averaged over long periods of time before calculations are made for concentrations and depositions. A given source's potential for long range transport is influenced by many factors, not the least of which is the height of the mixing layer during the transport process and the extent of plume penetration into the layer.

This paper addresses the BASIC version of the U T L R T computer code (UTLRT.BAS) adapted for use on a personal computer. It is a condensed version of a mainframe computer model originally written in F O R T R A N which has been adapted for use on a personal computer. For practical applications UTLRT.BAS is sufficient. Model results should be interpreted in light of the model's underlying assumptions and restrictions.

P R O G R A M R E Q U I R E M E N T S

The requirements to run the program are shown in Table II.

Table 11 - P R O G R A M NAME: UTLRT.BAS

I N P U T FILES: 1. SORC.DAT (Source and receptor file) 2. M E T . D A T (Meteorological data file)

OUTPUT OPTIONS AND FILES: Three Options

I. Screen 2. Printer 3. Output file C O U T . P R N

and DEPOS.PRN •

• Produced only if requested. This file contains information on unit deposition estimates.

INPUT DATA

In general, UTLRT requires the specification of four types of input data: meteorological, source, rate parameters and receptor data. For UTLRT.BAS, program source data and receptor data are combined in the file SORC.DAT while meteorological data and rate parameters are combined in another file MET.DAT (see Table II).

For the source and receptor deck, U TLRT requires site specific parameters defined as input including: source location (longitude/latitude), emission rate, and stack data such as stack height, stack diameter, stack velocity and stack temperature. These can be obtained from existing emission inventories. Some time should be spent reviewing the emission inventory in order to

construct a reasonable size database as input to the model. Computer CPU time/cost should be optimized with the value of the resulting data.

The optimum database would theoretically be large enough to include greater than 90% of the total emissions for a source region and yet small enough to run the LRT models without accumulating exorbitant computer costs/time. All sources can be roughly marked on a regional map in order to determine which regions are the largest contributors. On the basis of this map, preliminary grouping of sources may be initiated. In order to group point sources, two criteria may be followed:

A. Smaller sources in the same vicinity could be grouped if their stack velocities and stack heights are in similar ranges.

B. Most large sources should be left intact.

For example, sources could be grouped by county according to the stack height ranges. Each group's

180 Environmental Software, 1989. Vol. 4, No. 4

initial rates could then be summed while other parameters, such as stack height, stack diameter, stack temperature and stack velocity could be averaged. An example of this initial grouping for NOx sources in Ohio is shown graphically in Kumar, et al. [8].

The SURE network of receptors are desirable receptor locations because they provide easily accessible data for model verification and comparison studies. In the absence of other user-specified receptors, the five primary SURE receptors can be included as the minimum number of receptors for output. Other specific receptors can be input to UTLRT using UTM (Universal Transverse Mercator) grid coordinates. A separate computer program for converting latitude/longitude to UTM coordinates is available from the attthors.

There are two categories of meteorological data required as input to UTLRT. The first category of data is essential to the computation of primary and secondary pollutant concentrations and form an integral part of the underlying diffusion theory. These data include wind data i.e. wind speed and frequency of occurrence for seven sets of wind direction.

The second set of data is not required by the theory directly but is required to compute certain optional parameters built into UTLRT. These options are the calculation of plume rise and penetration effects, the calculation of the boundary layer height, and the calculation of wet deposition rate based on actual receptor precipitation data.

Seven wind direction sectors were chosen as having impact areas in the eastern U.S. and Canada from midwestern sources. Wind direction, frequency of occurrence, and average wind speed are required as input. The values for averaged wind velocities and wind directions can be compiled from monthly summary sheets obtained from the National Climatic Center in Asheville, North Carolina. The computer program is designed to be flexible in specifying the number of wind sectors and can therefore be modified to consider other source regions and impact areas in the United States.

The following meteorological data are optional because they are only required when certain options are triggered in the model. These can be summarized as follows:

a. Plume Rise/Penetration

This option to calculate plume decoupling effects, along with plume rise and effective plume rise, requires the following meteorological data: ambient temperature, wind speed, atmospheric stability, and boundary layer height (calculated or given as input). The model uses sigma theta for identifying atmospheric stability class. To reflect an average stability for a region, the results

for three stabilities (unstable, neutral and stable) are computed and the results are averaged based on the frequency of occurrence of each stability in that region.

b. Boundary Layer Height

UTLRT has been designed to address, in a simplified manner, the question of variable versus constant mixing layer height at the source. A constant layer height may be used in the input deck, or one of two theoretical approaches may be used to calculate layer height on an hourly basis: the analytical solution of Kumar and Djurfors [3], or the interpolation scheme using maximum mid-day layer height values. The BASIC version of UTLRT does not include the interpolation scheme. The meteorological data required in calculating layer height at the source using the first scheme include: day, month, year, time of day, a tmospher ic pressure, ambient t empera ture , sunrise/sunset data, solar radiation data for a typical day in the month, and cloud cover.

c. Receptor Specific Precipitation Data

UTLRT has been designed to optionally calculate wet deposition rates on the basis of receptor-specific precipitation data. Precipitation data for the receptor points may be obtained for the closest major airports reporting such data in the NWS network. These data should be averaged for each hour per month for all the days, and then, an overall average can be computed. The frequencies of precipitation events can be obtained from hours of wet periods versus dry. It is realized that average wet deposition is not equal to the wet deposition at the average precipitation rate. However, it is felt that the use of receptor specific precipitation (rather than average precipitation rate for a whole region) will produce estimates which are closer to reality.

Parameters defining wet and dry deposition and conversion rates are required in the deposition and depletion calculations. The dry deposition of pollutants is described using dry deposition velocities on a diurnal basis. Those used in NOx related studies are according to Endlich, et al.[9] and Kleinman [10] as given in Table III. The values for other pollutants can be compiled from literature.

Tablc Ill - D R Y D E P O S I T I O N V E L O C I T I E S

DAY N I G H T cnl/sec cm/~¢c

P R I M A R Y - N O 2 0.2 to 0.4 0.00 to 0.07

S E C O N D A R Y - N 0 3 l .g to 3.0 0.07 to 0.6

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Table IV - WET DEPOSITION RATES FOR ATMOSPHERIC NITROGEN COMPOUNDS

COMPOUND

NO 2, NO

HNO 3

RELATIVE R A T E / h r

0.25 x Alpha

0.5 x Alpha

Alpha = a (PR b) = /hr. where PR = rainfall mm/hr.

Winter

a = 0.009 b = 0.70

a = 0.021 b = 0.70

Spring/Fall

a = 0.036 b = 0.53

a = 0.091 = 0.27

Summer

a = 0.14 b = 0.12

a = 0.39 b = 0.06

The wet deposition of pollutants is described using scavenging rates along with precipitation and layer height data. For NOx related studies, the Endlich [9] values used in the ENAMAP-2N model are used. These are seasonally adjusted values which are related to SOx values and are shown in Table IV.

The chemical transformation of primary to secondary pollutants was assumed to be a linear conversion in the model. This, therefore, requires the specification of a single conversion rate, tau c. For example, to represent the daytime and nighttime conversion of N02 to N03, the following was used: tau c = 90,000 sec (4%/hr)

For other applications, separate daytime and nighttime values may be appropriate.

The value of horizontal diffusivity is a user input and depends on atmospheric stability. This indirectly takes into account an association between diffusivity parameters and the wind field. The program can be easily modified to specify a relationship between the diffusivity parameter and the wind field.

M O D E L APPLICATION

The input data structure for both the source and meteorological data files is shown in Tables V and VI. Sample input data decks are given in Table VII. The sample data represents an application requesting N02 and N03 concentrations at five receptors for three NOx sources. An example of the output corresponding to these input data examples is given in Table VIII.

The F O R T R A N version of this model has been successfully applied in studies of the potential of midwestern NOx sources to impact the northeastern United States [11] . Other studies have also been completed which use the model to compare the impact of NOx sources in Ohio, Chicago, and New Jersey at receptors in the northeastern United States [12]. An

earlier version of this model was used to study the impact of midwestern sulfur dioxide sources on sulfate deposition in the eastern United States as well as plume penetration effects of tall stacks on sulfate deposition [13].

CONCLUSION

A long range transport model has been developed to study the potential of S02 or N02 source regions to impact northeastern regions of the country. This paper addresses the microcomputer version of this model referred to as UTLRT.BAS.

A C K N O W L E D G M E N T S

This research work was supported by research grants from the Ohio Air Quality Development Authority and the Toledo Edison Company.

REFERENCES

[1}

121

[3]

[41

Kumar, A., S.T. Thomas, "Acid Deposition due to NOx Emissions from Ohio", Volumes 1 and 2, Prepared for the Ohio Air Quality Development Authority, Ohio EPA, August 1985.

Thomas, S.T.,A. Kumar, V.N. Ravidran, "Analysis of aStatistical Type Long Range Transport Model", Paper #85-5.8, 78th Annual Meeting of APCA, Detroit, June, 1985.

Kumar, A., S.G. Djurfors, " A Model to Predict Violation of Clean Air Regulations", Proc. of 4th Joint Conf. on Sensing of Environmental Pollutants, American Chemical Society, 1978.

Tennekes, H.,"A Model for the Dynamics of the Inversion Layer Above a Convective Boundary Layer",J. Atmospheric Sciences, Volume 30, p.558-567, 1973.

182 Environmental Software, 1989, Vol. 4, No. 4

TABLE V - DESCRIPTION OF SOURCE INPUT DATA (UTLRTC.BAS)

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1" 2: 3: 4:

5: 6: 7: 8: 9: 10:

TNS NS(JK)JK = 1 to WF SLON (K,JK), SLAT (K,JK) : JK = 1 TO WF AND K = 1 TO NS(K) QQS (K,JK), VVS (K,JK), DDS (K,JK), TTS(K,JK), SSTH(K,JK) : JK = 1 to WF and K = 1 to NS (JK) NR RX(M),RY(M) : M = 1 to NR VDD1, VDD2, VDN1, VDN2, TAUC DH(J) J = 1 to NST A2(J), B2(J), A3(J), B3(J), J = 1 to 3 (seasons) WFACP, WFACS

where,

TNS NS (JK)

SLON (K,J K) SLAT (K,J K) QQS (K,J K) VVS (K,JK) DDS (K,J K) "FFS (K,J K) SSTH (K,J K) NR RX(M) RY(M) VDDI VDD2 VDNI VDN2 TAUC DH(J)

A2(J), B2(J) A3(J), B3(J) WFACP WFACS

= total number of sources in all wind fields = number of sources in each wind field (number of wind ficld (WF) is

listed in met data) = longitude of each source (decimal deg.) = latitude of each surce (decimal deg.) = emission strength, tons/yr. = stack velocity, m/sec. = stack diameter, ft. = stack temperature, *K = stack height, ft. = number of receptors = X-distance from (90", 25*) origin = Y-distance from (90", 25*) origin = dry deposition velocity for dry duration (primary duration) = dry deposition velocity for wet duration (primary pollutant) = dry deposition velocity for dry duration (secondary pollutant) = dry deposition velocity for wet duration (secondary pollutant) = Conversion rate from primary pollutant to secondary pollutant (scc) = horizontal diffusivity for atmospheric stability conditions specified by

NST (from stable to unstable) = a and b for a *((PR)**b): primary pollutant = a and b for a *((PR)**b): secondary pollutant = 1 (for SO 2 or SO4) or .25 (for NO 2 or NO3) = 1 (for SO 2 or SO4) or .50 (for NO 2 or NO3)

[5]

[6]

[71

Fay, J.A., J.J. Rosenzweig, "An Analytical Diffusion Model for Long Distance Transport of Air Pollutants", Atm.Env. 14,pp.355-365, 1980.

Briggs, G.A.,"Plume Rise Predictions," Lectures on Air Pollution and Environmental Imnact Analysis, AMS, pp. 59-111, 1975.

Weil,J.C., R.P.Brower, "An Updated Gaussian Plume Model for Tall Stacks", JAPCA, Volume 34, pp.818-827, 1984.

[8]

[91

[10]

Kumar, A., S.T. Thomas, H.G. Rao, " Acid Deposition Due to NOX Emissions From Ohio", University of Toledo, Toledo, Ohio, August 1985.

Endlich, R.M., K.C. Nitz, R. Brodzinsky, G.M. Bhumralkar, "The ENAMAP-2 Air Pollution Model for Long Range Transport of Sulfur and Nitrogen Compounds", Project Summary, EPA- 600/5683-059, January, 1984.

Kleinman,L.I.,"A Regional Scale Lagrangian

Environmental Software, 1989, Vol. 4, No. 4 183

TABLE VI - DESCRIPTION OF METEOROLOGICAL INPUT DATA FILE MET.DAT (UTLRTC.BAS)

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1: 2: 3: 4: 5: 6: 7: 8: 9: 10:

WF NSEC, NST PaX(M), ERR(M) ; M = 1 to NR SIG(J) J = 1 to NST PC(J), J = 1, NST WIND(J,JK),FREQ(J,JK), DIR(J,JK) : JK = 1 to WF and J = 1 to NSEC ID, IMON, IYR HITE TSR, TSS, CC, HO, GMO, TA QM(IT) : IT = 1 to 24

where,

WE =

NSEC = NST = PaX(M) , EaR(M) =

SIG(1), SIG(2), SIO(3) =

PC(l), PC(2), PC(3) =

WlND(J,JK) =

EREQ(J,JK) =

D I R ( J , J K ) =

ID, IMON, IYR = HITE =

TSR, TSS = CC = HO = GMO = TA = QM(IT) =

number of wind field for sources number of wind sectors considered number of atmospheric stabilities considered receptor specific precipitation rate and frequency of precipitation at each receptor (total number of receptors = NR) (rate mm/hr) Sigma Theta for three stabilities (start with stable atmosphere fraction of time for each stability identified above wind speed for each sector (J) for each wind field (JK), m/sec freq. of wind speed for each sector (J) for each wind field (JK) wind direction from North (wind blowing from), deg. day, month and year mixing layer height chosen for constant cases, meters sunrise, sunset times cloud cover, tenths initial layer height at sunrise, meters initial lapse rate, *K/m ambient temperature, *K solar radiation data for each hour of typical daY in month kJ/m 2

[111

[121

Non-Linear Chemistry Model for Acid Deposition", 4th Joint Conf. on Applic. of Air Poll. Met., Portland, Oregon, October, 1984.

Thomas, S.T., A. Kumar, "Determination of Long Range Transport Potential of NOx and SOx Sources on the Northeastern United States", 79th Annual Meeting of APCA, Minneapolis, June, 1986.

Thomas,S.T., A. Kumar,"Relative Contribution

[13]

of Three Major NOx Source Regions on Acid Deposition in the Northeastern United States", APCA Specialty Conference on Environmental Challenges in Energy Utilization During the '90s, Cleveland, Ohio, October, 1988.

Thomas, S.T., A. Kumar, V.N. Ravidran, "The Impact of Plume Penetration Effects on Long Range Transport of Emissions", Paper #84-25.8, 77th Annual Meeting of APCA, San Francisco, June, 1984.

184 Environmental Software, 1989, Vol. 4, No. 4

TABLE VII - INPUT DATA STRUCTURE (UTLRTC.BAS)

5 receptors

SORC.DAT 3 2,1 83.1,39.96,82.2,39.40,83.5,39.50 1386.,8.93,6.,445.,92. 3831.,7.33,15.,540.,500. 2000.,8.50,10.,450.,200. 5 710008,1650051. 849081,1418822. 972052.,1213491. 1257519.,1822086. 1534202,1946025. 0.2,1.0,07,07,90000 200000,2000000,20000000 .009,.7,.021,.7 .036,.53,.09,.27 .14,.12,.39,.06 .25,.5

Card 1 Card 2 Card 3 Card 4

Card 5 Card 6

Card 7 Card 8 Card 9

Card 10

MET.DAT

2 7,3 0.0443,0.168,0.0458,0.149,0.0543,0.141,0.0646,0.138,0.0480,0.145 4.,9.,20. 0.33,0.33,0.34 10.1,.065,180.,9.33,0.097,215.,9.50,0.032,236.7,14.3,0.097,256.7 17.6,0.065,275.,12.6,.129,300.,9.90,0.032,325. 10.0,065,180.,9.00.0.097,215.,8.5,0.032,236.7,10.3,0.097,256.7 12.6,0.065,275., 10.6,. 129,300.,9.0,0.032,325 15,3,80 1000. 6.46,18.42,0.0,20,.02,293. 5.,5.,5.,5.,5.,5.,120.,476.,844.,1165.,1411.,1548.,1557.,1446. 1207.,855.,480., 121.,5.,5.,5.,5.,5.,5.

Card 1 Card 2 Card 3 Card 4 Card 5 Card 6

Card 7 Card 8 Card 9 Card 10

Environmental Software, 1989, Vol. 4, No. 4 185

Table VIII - O U T P U T RESULTS (BASIC)

(c) UTLRTC.BAS

UNIVERSITY OF TOLEDO, T O L E D O , OHIO Contract: Dr. A. Kumar 419-537-2312/2640

1986

LONG RANGE TRANSP O RT MO D EL U T L R T - Basic Version 1.02

The Primary Pollutant is NO2 The secondary pollutant is NO3 The Source and Receptor data file is SORCU.DAT The Met Data file is METU.DAT

Total Number of Source = 3 Number of Sources in all Wind Fields NS(1) = 2 NS(2) = 1 Location

Source Data

Long. Lat. QQS VVS DSS TTS SSTH 83.1 39.96 1386 8.93 6 445 92 82.2 39.4 3831 7.33 15 540 500 83.5 39.5 2000 8.5 10 450 200

LOPT = IBOPT =

MODEL OPTIONS 2 (does calculate both plume rise and penetration effects) 1 (Calculates bot, ndary layer height using solar radiation data)

PRIMARY CONCENTRATION ESTIMATES, micrograms/cu.m. S OUR C E # R E C E P T O R #

1 2 3 4 5 1 0.60E-02 0.18E-02 0.66E-03 0.64E-03 0.28E-03 2 0.25E-01 0.74E-02 0.23E-02 0.20E-02 0.81E-03 3 0.70E-02 0.28E-02 0.10E-02 0.65E-03 0.26E--03 TOTALS 0.38E-01 0.12E-01 0.39E-02 0.33E-02 0.13E-02

SECONDARY CONCENTRATION ESTIMATES, micrograms/cu.m. SOURCE # R E C E P T O R #

1 2 3 4 5 1 0.29E-03 0.27 E-03 0.17E-03 0.16E-03 0.10E-(B 2 0.86E-03 0.84E-03 0.53E-03 0.52E-03 0.33E-03 3 0.53E-03 0.48E-03 0.30E-03 0.24E-03 0.14E-03 TOTALS 0.17E-02 0.16E-02 0.10E-02 0.92E-03 0.57E-03

DEPOSITION RESULTS (KG/HECTARE/YEAR)

Receptor D ~ Dcp. Wet Dep. (Primary) (Secondary) (Primary)

1 0.12E-01 0.15E-02 0.11E-02 2 0.39E-02 0.14E-02 0.32E-03 3 014E-02 0.92E-03 0.11E-03 4 0.11E-02 0.84E-03 0.94E-04 5 0.48E-03 0.54E-03 0.34E-04

(Secondary) 0.55E-03 0.47E-03 0.29E-03 0.27E-03 0.17E-03

Total Deposition

0.15E-01 0.61E-02 0.27E-02 0.23E-02 0.12E-02

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