MODELS FOR ENVIRONMENTAL POLLUTION CONTROL© 1973 Ann Arbor Science Publishers, Inc.

CHAPTER 11A SURVEY OF AIR POLLUTION CONTROL MODELS

Ellison S. Burton, Edward H. Pechan, III,and William Sanjour*

I. INTRODUCTION

The development of a methodology for analysis ofair pollution abatement should be guided by severalkey requirements:

The method must have application to a wide rangeof urban environments with different pollutionsources, fuel use and cost structures, meteorology,and different abatement objectives.The method must be capable of generalization toaccommodate abatement analysis of many differentpollutants.The method must be useful immediately with presentdata and become increasingly more powerful asresearch provides more and better data concerningair pollution.The method must be useful at the federal, state,and local levels for examining the cost-effectivenessrelationships implied by a given abatement policy,for judging the efficacy of competing incentives forabatement, and for indicating productive avenues ofresearch in pollution control.

Given the large number of individual emissionsources in most urban regions, the scale of anymultiple-source abatement analysis is combinatoriallyso great that computer-assisted simulation is theonly analytical method available which can meet theabove requirements practically.

*Messrs. Burton, Pechan and Sanjour are with the Office ofPlanning and Evaluation, Environmental Protection Agency,Washington, D.C. 20460, U.S.A.

219

220 Models for Environmental Pollution;,Cbritrol

Before discussing the details of models now inuse it will be helpful to discuss how modeling fitsinto an overall framework for analysis of air pollu-tion control policy.

II. GENERAL DECISION FRAMEWORKFOR AIR POLLUTION CONTROL

Figure 11.1 shows a macro level flow chart of asystem to evaluate possible pollution controlstrategies within a geographic region. Such anoverall model could be used either to determineappropriate standards for the region or to assistin the decision process of selecting the beststrategy to meet existing standards.

The overall system works with regional andnonregional data and other inputs based on costs,benefits, possible control alternatives, etc. toproduce an enumeration of all possible pollutioncontrol strategies. Each possible strategy (oronly those strategies meeting standards) is charac-terized by costs, benefits, and nonquantitativefactors. The decision process then consists of theselection of the best or most appropriate strategy.

The blocks of the model shown in Figure 11.1 aredescribed below.

1. Emission Inventory

The emission inventory consists of data describingeach pollution source in the region under study. Thedata must be inclusive enough to permit later phasesto determine feasible control alternatives and theircosts and effects on air quality in the region. Someof the data elements for these inputs would be sourcetype (point or area), and process type (i.e., SIC andprocess codes), geographic location, current controldevices, current emissions, fuels burned, and per-centage of facility currently used.

2. Regional Growth Factors

The regional growth factors are used with theemission inventory by the growth model (block 6) toupdate current data to the specific year under study.These data should indicate growth in emissions frompresent sources as well as fairly specific data onexpected new sources. The accuracy of this

Air Pollution Control 221

REGIONAL INPUTS ,1 Emission 2 Regional 3 Regional 4 Meteoro- 5 Demo-

Inventory Growth Cost Data logical graphicFactors Data Data, ,

6 GrowthModel,

7 PredictedInventory

•8 ControlTechnologies

and Costs,9 Alternative

Controlsfor eachSource

r---i--- EVALUATED FOR EACH COMBINATION OF CONTROLS -- -I 10 Meteoro- I 11 Dose-

logical Response

I Model Model

I • •I 12 Standards 16 Benefit

I Compli-t Model

ance

I • 14 Economic 13 Regional

I Impact ....!Non-Regional15 Resultant 4- Economic

I Cost and FactorsAir

I Quality

I •L_ - --- - --------- 17 - - - --- --- --- --- ---Decision

--,IIIIIIIIII......J

Figure 11.1. General decision framework for air pollutioncontrol.

222 Models for EnvironmentalPollution Control

information can be quite important to the decisionprocess particularly in an area experiencingsignificant growth.

3. Regional Cost Data

These data are used to develop costs specificto the region under study of the various possiblecontrol alternatives. Examples of these data arelabor costs for installation and operation, capitalcosts for devices, interest rates and tax data, andfuel costs.

4. Meteorological Data

Meteorological data are needed by the system toproduce information on air quality given the emissionsand their geographic locations.

~5. Demographic Data

The demographic data are required to determinethe effects pollution has on the population of theregion. The population should be classified in somedetail, including age and location data.

6. Growth Model

The growth model accepts data on current emis-sions and expected growth patterns and produces anupdated emission inventory for the time period understudy.

7. Predicted Inventory

The predicted emissions inventory produced bythe growth model is a key input to the remainder ofthe system. The predicted inventory file wouldcontain data elements similar to those of theinitial emission inventory but adjusted to reflectconditions in the study period.

8. Control Technologies and Costs

This model uses the predicted inventory andregional cost data to produce a file containing allpossible control alternatives for each pollution

Air PoZZution ControZ 223

source. Each alternative is characterized by aspecific abatement action, costs (capital, operationand maintenance, etc.), resource requirements (labor,fuels, etc.), resultant emissions, and productionconstraints (if any).

9. AZternative ControZs for Each Source

This block represents the file produced by thecontrol technologies and costs model. It is struc-tured by source with each source having the uncontrolledstate and all possible alternatives presented. Theset of all possible strategies is determined byselecting one alternative for each source until allpossible combinations have been selected.

10. MeteoroZogicaZ ModeZ

For each strategy considered, the meteorologicalmodel is used to predict resultant air quality overthe entire region.

11. Dose-Response ModeZ

For each strategy, the dose-response modelevaluates, to the extent known, the cost of thehealth and welfare damages caused by air pollutionusing the air quality developed by the meteorologicalmodel and the demographic and community data developedfor the region. The outputs of this model would bethe pecuniary value of lost income because of earlydeath and illness, the medical costs of morbidity,and the value of damage done to materials, property,plants and animals, and other pollution-relateddamage. Since not much is known about these damagefunctions, this model remains more theoretical thanreal.

12. Standards CompZiance

The standards compliance model determines fromthe resultant air quality whether or not eachstrategy under consideration meets the standardswhich have been established. If desired, strategieswhich fail to meet the standards can be eliminatedfrom the remainder of the analysis. An optimizationmodel may be a component of this model.

224 Models for EnvironmentalPollution Control

13. Regional and Nonregional Economic Factors

These data are required as an input to theeconomic impact model. The nonregional factorsconsist of data used to predict such effects asrelocation of production (and jobs) to other geo-graphic regions because of other productionfacilities available elsewhere. Such factors areextremely difficult to quantify. Regional factorsinclude employment data, capital invested, etc. andare used to determine the economic impacts of eachstrategy considered.

14. Economic Impact

This model attempts to predict the economicimpact of a control strategy on the region. Inputsinclude the regional and nonregional economic dataas well as control costs. Output includes effects onregional gross product, prices, unemployment,investment, etc.

15. Resultant Costs and Air Quality

This model evaluates quantitatively each strategyon a cost/benefit basis using as inputs the costs ofthe strategy, benefits, and air quality.

16. Benefit Model

The benefit model evaluates the human damagereduction implied by the air quality improvementsachieved and by the dose-response model. Like thatmodel, little is quantified about benefit values.

17. Decision

The decision process involves the evaluation foreach strategy. The inputs include costs of thestrategy, air quality produced by the strategy,benefits from the strategy, and estimated economicimpacts of the strategy.

III. DISCUSSION OF MODEL TYPES

The models that have been implemented to estimatecosts and air quality for regions of Air Quality

Air Pollution Control 225

Control Region (AQCR) size can be compared in twoways. One comparison is between macro and microlevel models, and the other is between analytic (ortheoretical) models and heuristic models. Generally,analytic models tend to be macro models whereas mostmicro models resort to heuristics.

Two models illustrating the difference areKohn'sl model of air quality in St. Louis, and theImplementation Planning Program (IPP) model developedby TRW Systems2 for the Environmental ProtectionAgency. In comparing the two models, a major empha-sis will be placed on which type of model is of mosthelp in assisting the policy decision maker.

The Kohn model is a macro level model in thesense that it measures air quality at a single pointand treats sources in groups. This means that theresultant air quality computed by the model is baseddirectly on total emissions regardless of their geo-graphic location. An advantage of the model is thatit is in linear programming format and can easily berun using existing linear programming codes.

The IPP model is a micro level heuristic model.It treats each point source in the AQCR individually,simulating the application of each feasible controlalternative (of which there are 50) in turn. Theeffects of emissions on air quality are simulated byan atmospheric diffusion model which estimatespollutant concentrations at a number of receptorpoints. The original IPP did not possess an optimi-zation capability; a heuristic integer programalgorithm was later added to the system to determinenear-optimal solutions. A further disadvantage ofIPP was that it treated only two pollutantssimultaneously.

The characteristics of the models are summarizedin Table 11.1. Clearly the Kohn model offers anumber of advantages. Similar approaches have beendeveloped and documented in the literature (e.g.,Norsworthy and Teller3).

The decision maker must use some additionalcriteria in selecting a modeling technique. Forexample, if a model is used in developing standardsand the standards are challenged, the agency settingthe standard may be required to demonstrate con-vincingly its logic to nonexperts in a courtroomsituation. If this occurs, the micro level modelhas numerous advantages.

While pollution can be viewed easily as a macrolevel phenomenon and strategies defined by tons ofemissions reduced, an implementation of any strategy

226 Models for Environmental Pollution Control

Table 11.1

Characteristics of IPP and St. Louis Models

CharacteristicKohn St. Louis

ModelTml IPP Mode i

(with revisions)

Type analytic (LP) heuristic (simulation)Source groups macro microPollutants 5 2

Data requirements less moreOutput general detailedCost determination optimal heuristic "near optimal"Computer requirements low high

must involve a micro level application of each controlstrategy source-by-source. Thus, the aggregating ofdata on sources in the macro model does not reallyrepresent the physical realities of implementingcontrols on sources with different characteristics.For example, for some industrial processes, switchingfuel types is accomplished relatively easily whilefor others it is close to impossible. The micromodel may better reflect this difference.

In pollution control, there has been much moreexperience in designing and implementing models andsetting standards than in actually implementingstandards. Because of this there is also littleexperience in evaluating the accuracy of the modelsavailable. Thus, until more information is availableconcerning the accuracy of model predictions, caremust be taken to assure that the procedures used inarriving at cost and air quality estimates are asaccurate as possible. This goal can often be helpedby using a more detailed level model.

IV. DESCRIPTION OF A MICRO MODEL

One attempt that has been made to assist in theanalysis of control strategies is the IPP programmentioned earlier. The stated purpose of IPP was toassist the state governments in preparing implementa-tion plans for control of sulfur oxides and particulates.

Air Pollution Control 227

A macro flow chart of IPP is given in Figure11.2. Each of the programs is discussed brieflybelow.

1. Source Data Management Program

This program creates, updates and lists theprimary data file (defined as the Source File). TheSource File will contain all sources of pollutantswhich may be considered by the ImplementationPlanning Program.

2. Air Pollutant Concentration Program

This program utilizes a diffusion model to trans-form source emission data from the source file intoaverage, long-term, ground-level concentrations anda statistical portion to determine correspondingfrequency distributions for ground-level concentra-tions with short-term averaging times. The outputis presented on printed tables and on magnetictape (defined as the Source Contribution File) .

3. Source Contribution File Merge Program

This program is designed to merge the filesproduced by multiple (subregional) Air PollutantConcentration Program runs into a single regionalfile.

4. Con tro L Cos t Program

The purposes of this program are to simulatethe application of alternative control devices avail-able to each point source and to determine estimatesof the total annual cost and efficiency of pollutantremoval for each such application. The output ispresented on printed tables and on magnetic tape,defined as the Control Cost File.

5. Control Cost File Update Program

This program allows the user to correct or updateinformation contained on an existing Control CostFile. In general, use of this program will reducethe number of control cost program runs required toproduce a complete, error-free Control Cost File.Output is a corrected Control Cost File.

AIR POLLUTANT CONCENTRATION SEGMENTr------,l. 2. + 3.r--t-... 4.

SOURCE DATA AIR POLLUTANT USER SOURCEMANAGEMENT - CONCENTRATION - VALIDATION - CONTRIBUTION

'"PROGRAM PROGRAM (MANUAL FILE MERGEOPERATION) PROGRAML----

CONTROL COST SEGMENT CONTROL STRATEGIES SEGMENT

5. 6. 7. 8.9. '~

CONTROL I I CONTROL I EMISSION EMISSION REGIONALCOST FILE STANDARDS STANDARDS STRATEGIES('os'!' - --I -I ,RooM. 1 -I ,~=I rl ;ROO... I l "~~~TE I -I 'ROO,," I-IFigure 11.2. Implementation planning program structure.

~~Co

~~<;--lCil

?0-,:~<::!<".0-,:

~~<;i-~<;--l

~<;--l<;--l

~<;i-<".~&~~()<;--l

Air Pollution Control 229

6. Emission Standards Program

This program applies all candidate emissionstandards to the sources within each applicablepolitical jurisdiction. The emission standard re-quirements are matched with the data on the ControlCost File so that the most cost-effective devicefor each point source, under each standard, isproduced. The output is presented on printed tablesand on magnetic tape, defined as the EmissionStandards File.

7. Emissions Standards File Update Program

This program is designed to update emissionstandard application data contained on an existingEmission Standards File. As in Control Cost FileUpdate Program, this program can eliminate costlyreruns of the Emission Standards Program. Outputis a corrected Emission Standards File.

8. Regional Strategies Program

This program applies specified emission controlregulations and produces summary tables of the re-sulting emission reductions, control cost-effective-ness, and air quality values. The output from thisprogram is the culmination of IPP. The output froma series of runs becomes input for the determinationof the Control Plan for achieving ambient airquality standards.

A comparison of the IPP structure given inFigure 11.2 with the general framework of Figure11.1 shows that the IPP system can be consideredas a subset of the overall model.

Inputs required by IPP are emission inventory,regional cost, and meteorological data. The IPPControl Cost segment serves the purpose of the Con-trol Technologies and Costs model in Figure 11.1.The Air Pollution Concentration segment deals withmeteorological effects. Finally, the ControlStrategies segment generates a strategy (withcorresponding air quality and costs) based on userinputs to the system. The original IPP selectedthe strategy by applying the input criteria to eachsource individually. This did not permit a regionaloptimization capacity. Later, a heuristic optimization

230 Models for Environmental Pollution Control

section was added to permit generation of a regionalleast cost solution given designed air qualitystandards at selected receptors.

The original IPP model was designed to assistthe policy planner in standards evaluation. Therevised model, which indicated least cost solutionsand specific control alternatives selected, aidedthe planner in choosing near-optimal standards.

IPP did not provide for emissions growth or humaneffects. In addition, economic impacts were notincluded. However, despite these weaknesses, themodel was able to show significant results.

V. USING A MICRO LEVEL MODEL

This section describes some of the results fromusing the modified IPP model. The modificationsmade to this model permit the use of a heuristicoptimization procedure described as follows.

There are N sources of emission, each sourceemitting k different kinds of pollution. The nthsource has S(n) different control states (includingthe present state) which cost C(n,s) [n = 1, •.. , NiS = 1, ... , S(n)], where C(n,s) is the cost of the8th state of the nth source. The emission from thesth state of the nth source is E(n,s,k) [n = 1, ... ,Ni s = 1, ..• , S(n)i k = 1, ... , K] for the kthpollutant. Furthermore, M receptor points have beenspecified at different locations in the area ofinterest. With the use of the appropriate meteoro-logical diffusion model, we compute the matrixA (n, s, k ,m) [n = 1, ••• , N i s = 1, ••• , S (n) i k = 1,.•. , Ki m = 1, ... , M], which is the concentrationof the kth pollutant at the mth receptor due toemissions from the nth source in the sth state.

The optimization routine is formulated mathe-matically as follows:

minimize costN s tn)L L C(n,s) X(n,s)

n=l s=l(1)

subject to:N 5(n)L L E(n,s,k) X(n,s) ~ B(k) (k=l,..•,N) (2)

n=l s=l

5(n)L X(n,s)

s=l1 (n 1, ••• ,N) (3)

Air Pollution Control 231

and X(n,s) can take on values of zero or one only.This problem states that costs are to be minimizedin such a way that the total emissions of eachpollutant do not exceed a given amount B(k).

The set of equations (2) can be replaced by

N S(n)L L A(n,s,k,m) X(n,s) < B'(k), (m=l, ••• ,M; k=l, ••• ,K)

n=l s=l

which states that the total concentration of anypollutant at any receptor point cannot exceed agiven amount B' (k). The optimization works byminimizing costs over the set of strategies whilemeeting air quality constraints for the two pollu-tants studied, sulfur dioxide and particulates.

Although the model was run for a number of AQCR's,for the sake of brevity only the results from theNew York AQCR are summarized here.

The New York AQCR consists of 18 counties locatedin the States of New York, New Jersey, and Connecticut.The emission inventory for the region identified 678point sources, the largest single group of which was92 steam-electric power plants. The region was alsodivided into 863 area sources on a grid basis. Ofthese area sources, 151 were analyzed in detailmanually to determine a reduction efficiency appli-cable to all area sources. This was done by analyzingthe fuels being utilized and their alternatives.During computer runs, the air quality values reflectedthis reduction but no cost was applied since the costwas identical for all control strategies.

The 678 point sources averaged four feasiblecontrol alternatives, each includin~ the base state(uncontrolled). This represents 468 possiblecontrol strategies. In addition, air quality wasmodeled at 270 receptor locations.

Eleven control strategies were run for the modelof which three required regional optimization. Twoof the regional optimization strategies and twoothers requiring only individual source optimizationwill be discussed.

Initially, the Control Cost segment was run todetermine all of the possible control alternativesfor each point source in the model. (Area sourceswere reduced by a constant amount for air qualitypurposes but a corresponding cost was not computed.The area source reductions were constant over allstrategies produced.) Then, a number of differentcontrol strategies were run. The most significantstrategies are outlined below.

232 Models for EnvironmentalPollution Control

1. Least Cost Subject to PrimaryAir Quality Standards

In this strategy, pollutants at each receptorwere constrained so as not to exceed concentrationsof 80 ~g/m3 SOx and 75 ~g/m3 particulate. Theheuristically obtained least-cost solution meetingthese constraints was then determined.

2. Least Cost Subject to SecondaryAir Quality Standards

This strategy is similar to strategy 1 butconstrained both SOx and particulates to 60 ~g/m3at each receptor. However, as indicated in thesummary table, these constraints proved infeasiblefor the simulated New York AQCR.

3. Maximum Reduction Efficiency

This strategy maximizes the total reduction ofpollutants by weight. The reduction of each alter-native was determined by summing the SOx and particu-late reductions. This is not an overall optimizingstrategy because the optimum selection is made foreach point source regardless of cost.

4. Least Cost with Emission Charges

This strategy involved the selection of theleast-cost solution for each point source based onthe sum of the costs of the device plus a cost foremissions produced. A uniform cost of 10¢/lb foremissions of each pollutant was used as the penaltycost.

Table 11.2 shows some of the effects of thefour strategies listed above. It also presents asummary of some of the data produced by the model.Additional data not presented here included thevarious types of fuel and the amounts required byeach strategy, the control alternatives selectedfor each point source by strategy, and the resultantair quality at each of 270 receptors by strategy.

The summary data leads to several interestingconclusions.

A comparison of the maximum control strategy (3)with the primary standards strategy (1) shows

Air Pollution Control 233

Table 11.2

Results of Applying Strategies for the New York AQCR

1Least Cost

PrimaryStandards

2Least CostSeoondaryStandards

MaximwnControl

4Least CostEmissionCharges

Device cost 54.98 92.16 406.30 91.29(millions $)Emission penalties 0 0 0 72.21(millions $)SOx reduction 252.21 269.40 453.00 144.60(103 tons)Part reduction 38.88 103.14 111.72 93.15(103 tons)Worst receptor SOx 80.00 79.61* 76.77 123.36(flg/m3

)

Worst receptors 74.79 65.29* 64.78 68.52particula te (flg/m3)

Base state emissions were 658.99 103 tons SOx and 124.75103 tons particulate.*Since standards could not be met, goals were set at 5% aboveminimum feasible for those receptors not meeting standards.

that while costs and reductions were significantlyhigher under maximum control resultant air qualityat the worst receptor was not significantly affected.The secondary standards strategy (2) was onlyslightly more expensive for devices than thepenalties strategy (4) but had significantly better SOxair quality at the worst receptors. This indicatesthat the geographic location of each point sourceis significant with respect to the contributions to"worst case" air quality. Thus, emissions chargesare not as efficient in reducing emissions as arespecific alternatives selected by the optimizationroutine. Note also that the total cost of strategy 4to private industry was much higher than strategy 2because of the penalty charges.A further comparison of strategies 2 and 4 indicatesthat the penalty cost was more effective in reducingparticulate than SOx emissions.

234 Models for Environmental Pollution Control

As indicated, a variety of conclusions can beobtained from analysis of the micro models output.By analyzing the devices industry selects for aspecific strategy, a series of industry-basedstandards can be developed. Alternatively, stan-dards could be based on the geographic locationsince the model indicated that "hot spots" occurredat several receptors.

VI. FURTHER WORK WITH A MICRO MODEL

Some of the more basic problems to be encounteredwith use of a micro type model are the difficultyof obtaining detailed data required, the problem ofprojecting growth of emissions, the difficulty ofdealing with area sources at a micro level, and thedifficulty arising in setting up and running a largemodel on a production basis.

The problem of obtaining data will probably besolved with the passage of time. Data collectionefforts are underway in a number of AQCR's todefine the emissions inventory. Estimating growthis difficult at a micro level. An interim solutionmay be to use macro level techniques until bettermethods are developed.

Area sources are a significant contribution toambient air pollution but the unavailability ofdetailed data does not permit them to be handledas easily as point sources. Most area sources areeither transportation (mobile sources) or fixed, butsmall, heating and incineration sources. Transpor-tation sources will continue to be treatedat an aggregate level. Heating sources can becharacterized by the amounts and types of fuelsburned, and control strategies would consist offuel switching. Incineration sources are difficultto define exactly, and more work is needed in thisarea.

The setting up and operation of micro models isdifficult and requires sophisticated personnel.Generally, manual analysis of certain parts of thedata base are required and computer programs mustoften be modified to devise and test new strategies.For example, tests on a version of the heuristicoptimization algorithm to permit constraints onfossil fuels indicated the great difficulty ofmodeling simultaneous constraints on fuels. Addi-tionally, the dimensionality of the problem wasfurther increased. These difficulties suggest theneed for a large scale mixed integer LP.

Air Pottution Controt 235

While the IPP type of micro model has many weak-nesses, it still provides a powerful tool to thepolicy planner. Work in expanding the usefulnessand capability of such models will be rewarded bymaking strategy development and implementation moreefficient and accurate.

REFERENCES

1. Kohn, R. E. "Abatement Strategy and Air Quality Standards"In Development of Air Quality Standards, Arthur Atkissonand Richard S. Gaines, eds. (Columbus, Ohio: Charles E.Merrill Publishing Co., 1970).

2. TRW Systems. "Air Quality Implementation PlanningProgram," Contract PH 22-68-60 Environmental ProtectionAgency (1970).

3. Norsworthy, J. R. and A. Teller. "The Evaluation of theCost of Alternative Strategies for Air Pollution Control,"APCA Paper 69-172, New York City (June 22-26, 1969).