Fossil Fuels Utilization - American Chemical Society

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Modeling Future Emissions of Atmospheric Pollutants Kristin Graves, Edward H . Pechan, and Donna L. Turner Ε. H . Pechan & Associates, Inc., 5537 Hempstead Way, Springfield, VA 22151 This paper provides an overview of the techniques used by policy analysts to estimate air pollution emission from anthropogenic sources. The development of an inventory of emissions from a large nimber of emission sources by direct measurement would be prohibitively expensive. Therefore, estimation techniques are used which, although somewhat less reliable at the specific unit or boiler level, provide a reasonable assessment of emissions on average. Although no physical measurement i s used, policy analysts are able to generate historic and current estimates, and future forecasts, of air pollutant emissions. This paper provides an overview of the techniques used to provide such estimates and forecasts. Each year, millions of dollars are expended to fund research on the causes and effects of environmental air pollution. While chemists and biologists quantify and measure the physical relationships between emission sources, emissions, and deposition, policy analysts attempt to estimate historic, current, or future emissions and deposition without making measurements at each emission source or deposition site. The work of each group is quite dependent upon the work of the other. For example, policy analysts use the results obtained by chemists and biologists as an input to their calculations. In turn, the estimates provided by the policy analysts provide information to policy-makers regarding which pollutants are likely to be the cause of current or future problems — these problem areas are those for which basic research, such as that conducted by the biologists and chemists, will be funded. The purpose of this paper i s to provide chemists with an overview of the techniques used by policy analysts to estimate future air pollutant emissions. A comparison of modeling techniques for future periods i s also made with techniques for historical and current periods. In addition, sample estimates of forecasted sulfur dioxide (SO2) emissions provide an illustration of the uncertainty inherent in such estimates. 0097-6156/86/0319-0360S06.00/0 © 1986 American Chemical Society

Markuszewski and Blaustein; Fossil Fuels Utilization ACS Symposium Series; American Chemical Society: Washington, DC, 1986.

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Environmental Estimation Methodology General Methodology. The f o l l o w i n g i s a schematic equation o f the procedure used t o estimate emissions: EMIS = ACTLEV * EMISCOEFF * (1 - CONTEFF) where

(1)

EMIS = emission l e v e l of pollutant of interest, e.g., metric tons SC^ p g year r

ACTLEV = l e v e l of a c t i v i t y relevant t o estimated emission, e.g., coal use by u t i l i t i e s ( f o r u t i l i t y SO^ emissions) or v e h i c l e miles traveled ( f o r transportation nitrogen oxides emissions) EMISCOEFF = uncontrolled emissions per u n i t of a c t i v i t y l e v e l (e.g., tons of S 0 emitted per ton o f coal burned)

2

CONTEFF = proportion o f uncontrolled emissions removed by emission control dev ice This general procedure i s used t o obtain emissions estimates f o r h i s t o r i c , current, and f u t u r e years. Ihe primary difference between emission estimates f o r h i s t o r i c , current, and f u t u r e years i s the use of h i s t o r i c a l l y available, currently reported, or f u t u r e estimated values o f the required input information. The s p e c i f i c information used t o provide estimates o f a c t i v i t y l e v e l s v a r i e s with the emission source sector being examined. For u t i l i t i e s , fuel use i s desired. For the i n d u s t r i a l sector, information on f u e l use alone i s not adequate since many i n d u s t r i a l process emissions do not r e s u l t from f u e l combustion. Usually, some approximation f o r product output, such as estimates o f value added or earnings, i s often used. For motor v e h i c l e emissions, estimates of v e h i c l e miles traveled i s more useful than f u e l use because most emissions are unrelated t o v e h i c l e e f f i c i e n c y , i . e . , a small car emits about the same amount of p o l l u t i o n per m i l e as a l a r g e r car. Emission c o e f f i c i e n t s are estimates o f emissions per u n i t of a c t i v i t y l e v e l . These c o e f f i c i e n t s are generally estimated using actual measurements o f emissions and information about a c t i v i t y l e v e l s from a subset of representative sources. The emission measurements used as an input to these c o e f f i c i e n t s are subject t o considerable uncertainty, being based on measurements a t ten sources or, at most, at a few hundred sources. Usually, no d i r e c t estimates of associated v a r i a b i l i t y or uncertainty, are a v a i l a b l e . Depending upon the l e v e l of d e t a i l i n the model being used t o make the emission estimates, emission c o e f f i c i e n t s may be assumed constant over time or across regions, or new combustion and other technologies may be assumed t o be a v a i l a b l e . M u l t i p l y i n g a c t i v i t y l e v e l s by emission c o e f f i c i e n t s provides information on uncontrolled emissions over time. To obtain information on controlled emissions, the uncontrolled emissions t o t a l


FOSSIL FUELS UTILIZATION: ENVIRONMENTAL CONCERNS

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can be m u l t i p l i e d by the quantity one minus the control e f f i c i e n c y . Estimates of control e f f i c i e n c y can be obtained from the manufacturer or from independent t e s t s . As with emission c o e f f i c i e n t estimates, control e f f i c i e n c y information i s generally somewhat crude with l i t t l e known about i t s v a r i a b i l i t y over time or uncertainty. Differences Between Estimates. The combination of these three f a c t o r s r e s u l t s i n estimates of emissions t h a t may be, as evidenced here, somewhat crude. The general methodology described above may be embellished or used a t f i n e r l e v e l s of d e t a i l t o provide an appropriate l e v e l of resolution i n the generated emission estimates. For example, EPA s National finissions Data System (NEDS), a major emission inventory covering point and area sources, uses such c a l c u l a t i o n s a t the point or b o i l e r l e v e l ; other l e s s - d e t a i l e d data f i l e s would use emissions calculated a t a county or regional l e v e l (1). I t i s important t o stress, however, that the f a t e of b i l l i o n s of d o l l a r s hinges on analyses such as these. Estimates o f emissions obtained s i m i l a r l y to those described here are used i n the a n a l y s i s of proposed l e g i s l a t i o n and regulation. For example, under House of Representatives b i l l nimber H.R. 3400 (also known as the Sikorski-Waxman b i l l ) , the 50 highest emitting p l a n t s i n 1980 were targeted f o r scrubber i n s t a l l a t i o n ; methods akin t o those described here, a l b e i t i n f i n e r d e t a i l , would l i k e l y be used t o i d e n t i f y those plants. In f a c t , E.H. Pechan & Associates, Inc., i d e n t i f i e d the top c o a l - f i r e d u t i l i t y SOp emitters f o r the U.S. Environmental Protection Agency (EPA) i n 1982 using a s i m i l a r methodology (2,3). For that study, information on the coal quantity and q u a l i t y ( s u l f u r content) were a v a i l a b l e , as w e l l as information on e x i s t i n g control technologies. In general, the greatest r e s o l u t i o n can be obtained i n estimates of current or recent h i s t o r i c a l (within the l a s t 10 t o 15 years) emissions. This i s because r e l i a b l e data on f u e l use (both q u a l i t y and quantity) and other a c t i v i t y l e v e l s are available, and good estimates o f emission c o e f f i c i e n t s and control e f f i c i e n c i e s are a v a i l a b l e . As one goes f u r t h e r back i n time, the data needed f o r d e t a i l e d emission estimates are either not a v a i l a b l e or are l e s s r e l i a b l e . Recently, SOp and nitrogen oxide emissions were estimated f o r EPA f o r the period 1900 t o 1980 a t the state l e v e l by fuel and source sector (ÎU5.). I t was p a r t i c u l a r l y d i f f i c u l t t o obtain r e l i a b l e estimates of pre-1940 fuel use and q u a l i t y , control e f f i c i e n c y , and emission c o e f f i c i e n t s . Obviously, the l e s s data that are a v a i l a b l e , the simpler the methodology that must be used. A discussion o f a data set required f o r d e t a i l e d a n a l y s i s of emissions and deposition i s beyond the scope of t h i s paper, but i s a v a i l a b l e elsewhere (6). Obviously for f u t u r e periods, no measured data are a v a i l a b l e and a l l v a r i a b l e s must be estimated. Some models, such as the Advanced U t i l i t y Simulation Model (Z.) being developed f o r EPA, use complex algorithms and modeling s t r u c t u r e s t o i n t e r n a l l y simulate the operations of a sector or sectors of the economy t o obtain estimates of a c t i v i t y l e v e l s , control e f f i c i e n c i e s , and emission c o e f f i c i e n t s . Other models, such as the Environmental Trends A n a l y s i s Model I I (8,9), o r i g i n a l l y developed f o r the U.S. Department of Energy, use exogenous forecasts of a c t i v i t y l e v e l s and assumed values of control e f f i c i e n c i e s and emission c o e f f i c i e n t s . The uses f o r these 1


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various types o f forecasting models are discussed more f u l l y i n the next section o f t h i s paper. Emissions Forecasting Introduction. Environmental projections are needed f o r three primary reasons. Ihe f i r s t reason i s the long l a g time between beginning the process of s e t t i n g of environmental standards and the change i n environmental conditions associated with the new standards. Ihe procedures needed t o pass l e g i s l a t i o n or e s t a b l i s h agency standards take months or years t o complete, and the l a g time between completion o f those procedures and t h e i r implementation may be even longer. During t h a t e n t i r e period of time, i t i s important t o be able t o predict the e f f e c t s o f the new standards so t h a t i t can be determined whether or not a d d i t i o n a l controls may be necessary. Furthermore, the longer the l a g time, the greater the l i k e l i h o o d t h a t other relevant f a c t o r s may also change, thereby having an impact on the expected e f f e c t of the standards. For example, changing f u e l costs may encourage f u e l switching, say from coal to o i l , and thus reduce the need f o r additional controls on c o a l - f i r e d b o i l e r s . Ihe second reason f o r environmental projections i s the s i g n i f i c a n c e of control costs. For example, proposed a c i d r a i n l e g i s l a t i o n could cost u t i l i t i e s and t h e i r customers b i l l i o n s o f d o l l a r s . Before t h i s money i s spent, i t i s important t o be able t o evaluate the e f f e c t s that controls may have on emissions, on employment, i n d i f f e r e n t regions o f the country, and the l i k e . A large share of S 0 emissions are from older, d i r t i e r , c o a l - f i r e d power plants. I f these plants are r e t i r i n g i n a few years, i t may not make economic sense t o spend m i l l i o n s or b i l l i o n s of d o l l a r s t o r e t r o f i t them with controls. On the other hand, i f such plants w i l l be operating f o r many years t o come, the benefits may be considered worth the expense. Ihe t h i r d reason f o r environmental projections i s t h a t they may be able to i d e n t i f y emerging issues o f importance. For example, the increased use of scrubbers over time may r e s u l t i n a problem i n the disposal of scrubber sludge. As another example, the increasing importance of c e r t a i n high-tech i n d u s t r i e s over time may indicate emerging hazardous waste disposal problems i n c e r t a i n regions o f the country. 2

Differences Between Models. As described above, there are several reasons f o r making emission forecasts. A number of models e x i s t that have been developed t o provide such forecasts. Ihe differences between the models cause one model to be more appropriate f o r c e r t a i n types o f analyses than another model. In general, policy analysts use two s t r u c t u r a l types o f models t o make forecasts of f u t u r e a c t i v i t y l e v e l s : econometric and engineering or process models. Econometric models use h i s t o r i c data and r e l a t i o n s h i p s t o estimate future trends i n v a r i a b l e s of i n t e r e s t . Engineering or process models use the physical r e l a t i o n s h i p s o f production processes ( i . e . , the r e l a t i o n s h i p between inputs t o a production process and i t s outputs) t o predict l e v e l s of the dependent v a r i a b l e s . In general, the use of econometric modeling techniques t o forecast a c t i v i t y l e v e l s provides a better long-term (beyond 20 years) trend because i t r e l i e s on long-term, h i s t o r i c a l


364


trend analysis, and thus can capture the general upward or downward trends. For shorter-term forecasts, engineering or process models may provide a more accurate p i c t u r e ; i n addition, use of engineering or process models f a c i l i t a t e s the pin-pointing of the cause of a p a r t i c u l a r trend or change i n a trend. For some types of analyses, i t may be important t o examine i n d i v i d u a l u t i l i t i e s or even i n d i v i d u a l power plants. For other analyses, i t may be s u f f i c i e n t t o examine e f f e c t s a t the state l e v e l . Factors a f f e c t i n g the l e v e l of d e t a i l i n a model include the data contained i n the model and the types o f c a l c u l a t i o n s used. Generally, l e s s - d e t a i l e d models are appropriate f o r use i n scoping analyses f o r time periods f u r t h e r i n t o the future, say 50 years, t o i d e n t i f y major trends t h a t may be of future importance. The more d e t a i l e d models can then be used t o examine those i d e n t i f i e d trends i n greater depth. Associated with the l e v e l of d e t a i l i n a model, the costs of development and use vary considerably among models. Some models, usually those attempting t o provide extremely d e t a i l e d r e s u l t s , may be developed over periods of ten years or more a t a m u l t i - m i l l i o n d o l l a r cost. Other models, usually providing l e s s d e t a i l e d r e s u l t s , have been developed i n a period o f a few months a t a cost of several thousand d o l l a r s . Ihe cost of model use, i n terms o f computer time and expense, also v a r i e s considerably among models. Model runs may take a few hours i n turn-around time and cost tens o f d o l l a r s , or turn-around and a n a l y s i s time may be several weeks and cost hundreds to thousands o f d o l l a r s . As with development costs, the costs of model use generally vary with the l e v e l of d e t a i l . As with the l e v e l of d e t a i l , the l e s s costly models are best used t o i d e n t i f y major trends, which can ( i n terms o f time and cost) then be examined i n a d d i t i o n a l d e t a i l by the more expensive models. In determining which model to use, the analyst needs t o determine what outputs or r e s u l t s are needed t o successfully accomplish the desired o b j e c t i v e s o f the study. Factors that should be considered include the l e v e l of regional d e t a i l , the forecast period, the p o l l u t a n t s t o be considered, and the sectoral d e t a i l desired. A v a i l a b l e funds and resources f o r running a model also need to be taken i n t o account. For example, i f a control strategy a f f e c t i n g a l l emission sectors i s proposed, then a model examining only, say i n d u s t r i a l emissions may not be s u f f i c i e n t . Interaction between emission source sectors and the c a p a b i l i t y of the models needs t o be considered. Ihe treatment of uncertainty v a r i e s considerably from one model to another. Simpler models allow uncertainty i n the input data t o be treated through the use of a l t e r n a t i v e estimates o f uncertain v a r i a b l e s . For example, i n the next section o f t h i s paper, uncertainty about the length of u t i l i t y coal plant l i f e t i m e s i s addressed by comparing r e s u l t s of two assumptions about the l i f e t i m e — 60 years and 40 years. In t h a t way, ranges o f emission estimates are obtained that, i t i s hoped, provide reasonable upper and lower bounds on emissions. More sophisticated models allow users t o specify percentage ranges of uncertainty for input data; the model then incorporates these uncertainty estimates i n t o upper and lower bounds on estimated emissions. Ihe E l e c t r i c Power Research I n s t i t u t e has sponsored a number of such models (10).


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Sample Emission Estimates

For i l l u s t r a t i v e purposes, r e s u l t s from the Environmental Trends Analysis Model I I (ETAM I I ) are presented here. ETAM I I i s an engineering and process model that uses exogenous a c t i v i t y l e v e l information ( i . e . , supplied from outside the model rather than produced w i t h i n i t ) , to which production r e l a t i o n s h i p s are applied as a means o f generating estimates o f pollutant emissions. ETAM I I was o r i g i n a l l y developed t o provide environmental trend analyses i n support of DOE s 1983 National Energy Policy Plan (NEPP). ETAM I I i s one example of a simple model, designed f o r use as an a i d i n long-term emission trends analysis. In f a c t , i t has been used f o r policy and s e n s i t i v i t y a n a l y s i s i n support of such programs as the Interagency V i s i b i l i t y Task Force and the Interagency Prevention o f S i g n i f i c a n t Deterioration Task Force. To provide an i l l u s t r a t i o n o f the type of ouputs provided by these models, Figure 1 presents r e s u l t s based on the examination o f a l t e r n a t i v e coal plant l i f e t i m e s f o r f a c i l i t i e s operated by the e l e c t r i c u t i l i t y industry. Figure 1 shows a comparison o f assumed 40 and 60 year u t i l i t y plant l i f e t i m e s on emissions t o 2030. As can be seen, these assumptions have no e f f e c t on emissions from n o n - u t i l i t y sectors, but have a tremendous e f f e c t on both the pattern and the magnitude of t o t a l emissions. The s i g n i f i c a n t impact of a plant l i f e t i m e assumption i s due t o the stringent controls placed on new plants through new source performance standard requirements; with e a r l i e r retirement of plants, the newer and more s t r i n g e n t l y regulated p l a n t s come on-line much sooner. For S 0 emissions, the assumption regarding coal plant l i f e t i m e a f f e c t s the year i n which i n d u s t r i a l emissions become the primary contributor t o t o t a l emissions. Under the 40-year l i f e t i m e assumption, u t i l i t y S 0 emissions f a l l below i n d u s t r i a l emissions by 2010. I f a 60-year l i f e t i m e i s assumed, t h i s switch w i l l not occur u n t i l about 2030. As indicated e a r l i e r , the difference caused by u t i l i t y plant l i f e t i m e assumptions, i d e n t i f i e d here, could be examined i n greater d e t a i l by a more-detailed model. 1

2

2

Summary Although measured l e v e l s o f emissions are used as a basis f o r c a l c u l a t i n g emissions i n f u t u r e years, such measurements are not used as d i r e c t l y as one might think. Rather, combinations o f estimated or average emission factors, a c t i v i t y l e v e l s , and control e f f i c i e n c i e s are used t o obtain estimates of emissions. The l e v e l of d e t a i l a t which these emission estimates are provided v a r i e s considerably. The choice of a projection model f o r use i n a n a l y s i s i s dependent upon the desired d e t a i l i n and the intended use f o r the r e s u l t s .



366

S02 EMISSIONS (MILLION TONS PER YEAR) OTHER INDUSTRY UTILITY

LO

HI

1980

LO

HI

1990

LO

HI

2000

LO

HI

2010

LO

HI

2020

LO

HI

2030

LO= 40 YEARS; HI = 60 YEARS

F i g u r e 1. E m i s s i o n f o r e c a s t s under a l t e r n a t i v e u t i l i t y p l a n t l i f e t i m e assumptions.


coal

RECEIVED

29.

G R A V E S E TA L .


367

Literature Cited 1.

"National Air Pollutant Emission Estimates, 1940-1984." U.S. Environmental Protection Agency. EPA-450/4-85-014. January 1986.

2.

E.H. Pechan & Associates, Inc. "Estimates of Sulfur Oxide Emissions from the Electric Utility Industry," Volume I Summary and Analysis. Prepared for U.S. Environmental Protection Agency; EPA-600/7-82-061a. July 1982.

3.

E.H. Pechan & Associates, Inc. "Estimates of Sulfur Oxide Emissions from the Electric Utility Industry," Volume II Databook. Prepared for U.S. Environmental Protection Agency; EPA-600/7-82-061b. July 1982.

4. Gschwandtner, G.; Gschwandtner, K.; Eldridge, K.; Mann, C.; Mobley, D. "Historic Emissions of Sulfur and Nitrogen Oxides in the United States from 1900 to 1980." Journal of the Air Pollution Control Association. Vol. 36, No. 2. February 1986; p. 139-149. 5.

"Historic Emissions of Sulfur and Nitrogen Oxides in the United States from 1900 to 1980," Volume I - Results. U.S. Environmental Protection Agency, Air and Energy Engineering Research Laboratory; EPA-600/7-85-009a. April 1985.

6.

Saxena, P.; Seigneur, C.; Pollack, A.K. "A Critical Assessment of Existing Data Bases for Acid Deposition Modeling Studies," Journal of the Air Pollution Control Association. Vol. 36, No. 1. January 1986; p. 48-54.

7.

"AUSM - Analytical Documentation," Volumes I, II, and III. Assembled by U.S. Environmental Protection Agency, Industrial and Environmental Research Laboratory, prepared by Universities Research Group on Energy. March 1983.

8.

E.H. Pechan & Associates, Inc. "The Environmental Trends Analysis Model: Technical Documentation." March 1984.

9.

E.H. Pechan & Associates, Inc. "The Environmental Trends Analysis Model: User Documentation." March 1984.

10.

Electric Power Research Institute. "Air Quality Models Update: Decision Frameworks and Risk Assessment Models." Energy Analysis and Environment Division Technical Newsletter, Issue #1. February 10, 1984. April 2, 1986


Fossil Fuels Utilization - American Chemical Society

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