The Formation of Stable Components in the Atmosphere due to Energy Production

W O R K I N G P A P E R

THE F D R M A T I m OF SI'AELE

P

IN TKE ArYOsr'HERE DUE 7'0 IDJERGY PRODUCIlON

July 1988 WP-88-49

l n t e r n a l i o n a l l n s t ~ t u l e for Appl~ed Sys~ems Analysts

THE FORMATION OF STABLE COMPONENTS

IN

THE ATMOSPHERE DUE TO ENERGY PRODUCllON

M. Antonovsky

July 1988 W P-88-49

Working P a p e r s are interim r e p o r t s on work of t h e International Institute f o r Applied Systems Analysis and h a v e r e c e i v e d only limited review. Views or opinions e x p r e s s e d h e r e i n d o not n e c e s s a r i l y r e p r e s e n t t h o s e of t h e I n s t i t u t e or of i t s National Member Organizations.

INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS A-2361 Laxenburg, Austria

Foreword

This paper i s devoted to the possibility of taking into account the pressures of human activity on the atmosphere. It was presented a t the International Workshop Envrisk '88 'Tnergy and Environment: the European Perspective on Risk" organized by the Italian Commission f o r Nuclear and Alternative Energy Sources, May 1988.

THE

FORWATION

OF EWABLE COMPONENTS IN THE ATMOSPHERE DUE TO ENERGY PRODUCTION

M. Antonousky

The problem of t h e inter-relations between

Man

and Nature i s a preoccupation of industrial societies. Moreover, t h e n a t u r e of t h e c o n c e r n i s not s t a t i c o v e r time, but may undergo r a p i d changes. Until recently, Man h a s played a relatively passive r o l e in t h e Biosphere, influenced by, but not strongly influencing, i t s na- tural rhythms.

However, in r e c e n t c e n t u r i e s t h e r e h a s been a new "geological" phenomenon on t h e planet. For t h e f i r s t time, Man h a s become a large-scale geological "force"

influencing Nature through threshold effects, s u r p r i s e s , discontinuities, r i s k s , and catastrophical events (both slow and sudden).

Similar t o ongoing r e s e a r c h on t h e prediction of earthquakes, r e s e a r c h e r s are investigating such problems as climatic changes owing to t h e "greenhouse ef- fect", t h e frequency of such d i s a s t e r s as Chernobyl o r Bhopal, and how t o p r e v e n t them. The hope e x i s t s t h a t societies c a n respond t o perceived ecological t h r e a t s more easily t h a n in t h e c a s e of a n earthquake. Even in such a c a t a s t r o p h i c event as war, t h e c u r r e n t generation i s much wiser than previous generations regarding t h e environmental r i s k s entailed in such a n event.

Now i t i s a l s o known t h a t t h e r e i s a set of ' l i f e zones" o r "life intervals" con- sisting of a narrow r a n g e of p a r a m e t e r s necessary f o r life; f o r example, intervals ik ( k =1,

. .

,n ⁾ of t e m p e r a t u r e , s o l a r radiation, radioactivity, precipitation, pres- s u r e , concentrations of gases such

as

C02, SO2, CO, NO,, toxic materials, and s o on.

For example, a d e c r e a s e of s o l a r radiation flow ( o r flux) by only a few p e r - cent would b e sufficient to c a u s e t h e freezing of a l a r g e f r a c t i o n of t h e E a r t h ' s s u r f a c e . An i n c r e a s e of this flux by s e v e r a l p e r c e n t could cause t h e evaporation of most of t h e existing liquid

water

on t h e planet, and s o on. Analogous conse- quences may a r i s e if C02 concentrations d e c r e a s e o r increase. respectively.

Moreover, many organisms could die owing t o smaller Variations of C02 in com- parison with oscillations which could bring about climatic c a t a s t r o p h e s .

The life i n t e r v a l s have been under t h r e a t many times in c r i t i c a l periods of geological history. If t h e probability pk does not exceed t h e limits of t h e intervals it, with k

=I, . . .

^, N. t h e n t h e probability p of t h e existence of life is:

The value of p i s a v e r y s m a l l number. Nevertheless, life h a s existed in dif- f e r e n t forms f o r many millions of years. The significance of this f a c t i s t h a t on o u r planet t h e r e are some unique conditions t h a t r e t a i n t h e oscillations of e v e r y "life interval" within a n admissible limit. However, t h e activities of industrial societies a r e causing l a r g e r oscillations t h a t may eventually lead t o t h e p a r a m e t e r s extend- ing beyond t h e admissible limits.

For example, t h e c u r r e n t energy-production technologies are associated with impacts for e a c h s t e p of t h e production cycle, including mining, t r a n s p o r t and use.

Thus, t h e u s e of fuel, hydropower and atomic e n e r g y h a s local, regional and global impacts

at

d i f f e r e n t i n t e r v a l s of time. Local effects c a n become global problems if t h e number a n d distribution of s m a l l power stations i s large. As t h e number of sta- tions i n c r e a s e s , t h e i n t e g r a t e d impacts a l s o i n c r e a s e , p e r h a p s non-linearly. To t a k e a n o t h e r extreme,

a

unique event such

as

t h e Chernobyl a c c i d e n t not only causes material damage, but

also

h a s enormous emotional, philisophical, scientific and social ramifications.

This event and t h e connected release of radioactivity into t h e environment have focused t h e i n t e r e s t of t h e international, scientifio community on t h e meteorological aspects of transport of radionuclides a n d t h e mathematical model- ing of p r o c e s s e s by which radioactive materials contaminate t h e

natural

environ- ment. This i n t e r e s t h a s a l r e a d y spawned a n i n c r e a s e d number of publications de- voted

to

modeling t h e atmospheric t r a n s p o r t of radionuclides a n d t h e i r fallout o n t o t h e s u r f a c e a f t e r t h e accidents. These m o d e l s can b e used

to

assess t h e r i s k within t h e close vicinity of t h e n u c l e a r power plant, a n d

to

recommend p r o c e d u r e s by which populations within t h i s area c a n b e b e t t e r p r o t e c t e d from possible accidents in t h e future. (See f o r example, I z r a e l

et

d . , 1987; Dickerson

et

^d., 1986; Itogo- vyi doklad, 1986.)

Furthermore, even seemingly s a f e technologies such

as

hydropower merit g r e a t e r attention in t h e future. E x p e r i e n c e has shown t h a t many undesirable consequences are caused by hydropower stations. These include: changes in t h e hydrological a n d biological s t r u c t u r e s of r i v e r s , changes in local climate, immense damage t o ecologically r i c h lands around r i v e r s , a n d so on. F o r example, t h e ar- tificial s t o r a g e facilities for hydropower production o f t e n create a disequilibrium between t h e volume of

water

and geometry of t h e n a t u r a l containment: t h e collapse of enormous quantities of soil from t h e banks and subsequent settling

at

t h e bottom of t h e

water

body c a n r e s u l t in g r e a t l y r e d u c e d

water

d e p t h s and change t h e geometry (i.e. from a cup-like s h a p e to a plate), t h e consequence being r a p i d eu- trophication of a r e s e r v o i r . The ecological consequences of t h e fossil use are well-known (Bolin 1987).

In o r d e r

to

make more p r e c i s e assessments of t h e e f f e c t of anthropogenic ac- tivities o n t h e environment, i t i s n e c e s s a r y

to

design a n a p p r o p r i a t e system of monitoring. The goal of such a system i s

to

organize t h e s t r e a m of corresponding d a t a in mathematical models in o r d e r

to

make predictions a b o u t t h e condition of t h e environment. Ecological t h e o r y h a s not y e t completely succeeded in identify- ing t h e major quantitative mechanisms of stability of t h e n a t u r a l ecosystem s t a t e , i r r e s p e c t i v e of t h e considerable e f f o r t s undertaken in t h i s s p h e r e . This means t h a t we d o not y e t know exactly what t h e probable s o u r c e s of instability are or might b e ,

as

a r e s u l t of both conventional a n d new anthropogenic stresses on na- t u r a l ecosystems and t h e b i o s p h e r e as a whole. A s a r e s u l t ,

as

noted above, w e may face not only gradual ecological transformations b u t a l s o a sudden loss of stability which may c a u s e non-linear and s u r p r i s i n g ecological effects. So, once more, t h e a u t h o r wishes

to

stress t h a t mathematical modeling i s one of t h e most powerful and effective instruments available for investigating t h e complexity of problems men- tioned above.

However, one c o n s t r a i n t may b e t h e duration of

t i m e

r e q u i r e d

to

develop t h e s e models. History h a s shown t h a t t h i s time scale could b e significant. F o r ex- ample, i t took almost 2,000 y e a r s

to

evolve from the s t a t i c mechanics of A r - chimedes

to

t h e dynamic mechanics of Newton, with t h e invention of differential and integral calculus. P r i o r

to

Newton, t h e arithmetic and geometry of A r - chimedes w a s insufficient

to

d e s c r i b e t h e relation between force and velocity. The

challenge of today i s

to

condense and organize t h e enormous accumulation of d a t a and information into "usable" knowledge concerning t h e fundamental behavior of t h e biosphere. In t h i s r e p o r t t h e a u t h o r briefly a d d r e s s e s

some

important ques- tions. (See also, I z r a e l e t d , 1987; Alcamo e t d . , (1987); Antonovsky e t d . , 1983.)

F i r s t of all i t i s worth mentioning t h a t in t h e last s e v e r a l d e c a d e s t h e r e h a s been a r a p i d development in t h e number and sophistication of air-quality monitor- ing networks. This i s

true

both nationally (including c o u n t r i e s s u c h

as

Canada, G r e a t Britain, USSR, USA, and o t h e r s ) and internationally (including the ECE, Eu- r o p e a n Monitoring and Evaluation P r o g r a m (EMEP), t h e WHO Background Air Pollu- tion Monitoring Network (BAPMON) and o t h e r s (see WMOEP-VI/DOC8 (18.IV.1986)).

These initiatives h a v e lead

to

national and international p r o j e c t s f o r t h e develop- ment of extensive, environmental d a t a bases, such as WDDES (IGVACA)

-

^{1000 MB,}

NOAANET (NDAA / 6 0 MB, GRID/GEMS/UNEP/NASA) and s e v e r a l o t h e r s .

The primary c r i t e r i a in a monitoring system are a ) t h e identification of t h e optimal number and disposition of t h e stations on

a

given landscape a n d b ) t h e level of information obtained from t h e

set

of d a t a g a t h e r e d from t h e stations.

These c r i t e r i a are fundamental b u t work c o u n t e r t o e a c h o t h e r . Hence, a compromise must b e r e a c h e d .

The necessity of establishing a limited, spatially-organized network i s mainly r e l a t e d

to

t h e high cost of constructing and o p e r a t i n g monitoring stations. At t h e same time, t h e established network should,

at

a minimum, b e sufficient to e n s u r e t h a t t h e

m o s t

salient f e a t u r e s of t h e deposition p a t t e r n under investigation

are

detected, i.e., t h e d e g r e e of information obtained should b e a d e q u a t e f o r t h e goals of t h e analysis. The d a t a

f r o m

such stations provides t h e analyst with a time string. However, if one were

to

consider t h i s time s e r i e s as merely a s e r i e s of numbers, t h e n t h e d a t a would h a v e no prognostic value. R a t h e r , e a c h of t h e s e numbers should b e considered a s a r e p r e s e n t a t i v e of some interval. The problem is t o define t h e width of e a c h of t h e s e intervals. The probability of containment of a d a t a point within a n i n t e r v a l i s q u i t e d i f f e r e n t t h a n t h e probability of placement

at

o n e specific value.

In t h e following w e attempt

to

demonstrate such a n a p p r o a c h . Firstly, w e d e s c r i b e t h e p r o c e d u r e for identifying statistically s t a b l e p a t t e r n s in t h e time s e r i e s of observations of background atmospheric pollutants ( s e e Izrael, Anto- novski

et

al., 1987). The d a t a were collected as daily mean measurements of con- c e n t r a t i o n of t r o p o s p h e r i c sulfur dioxide a n d a e r o s o l d u s t at five monitoring sta- tions. These stations

are

as follows: Boroval (1976-1983), Berezina (1980-1983) and Repetek (1980-1983) in t h e USSR; t w o stations in Sweden and Norway, where corresponding d a t a were collected

to

provide information on t h e long-term t r a n - s p o r t of t h e s e pollutants. In our a p p r o a c h w e used well-developed s t a t i s t i c a l tech- niques a n d

a

s p e c i a l

case

of t h e main model l a w

-

two p a r a m e t e r logarithm

-

^normal

distribution

(LN2).

W e also used t h e mixture of such distributions. The usual method f o r constructing multimodel distributions i s

to

choose local m o d e l s a n d ap- ply a l a w of composition. These s t e p s permit u s

to

d e r i v e a global model based o n a set of local m o d e l s . Our local m o d e l i s based o n t h e normal law.

For construction of the multimodel l a w w e applied t h e o p e r a t i o n of composi- tion of density functions f l ( z ) and f 2 ( z ) :

where nl, TT;! are constant; a i s a switch point between d i f f e r e n t laws washed by normal white noise with dispersion u; ~ ( t ) i s

a

distribution function of t h e normal law N(0,l). I t was shown t h a t t h e model gives a good approximation of t h e empiri-

c a l density of t h e seasonal s e r i e s of observations with t h e value obtained f o r a sub-sample of 150 observations (see Izrael, Antonovski e t d . , 1987).

S o t h e problem of obtaining t h e s t a b l e statistical f e a t u r e s of background con- tamination in t h e atmosphere i s subdivided into two p a r t s : t h e f i r s t i s a decomposi- tion of t h e seasonal s e r i e s of observations in o r d e r t o obtain a n informative description of e a c h season s e p a r a t e l y ; t h e second i s

an

investigation of such descriptions in o r d e r

to

d e r i v e s t a b l e s t a t i s t i c a l c h a r a c t e r i s t i c s of t h e e n t i r e

set

of observations of t h e phenomenon.

The a u t h o r would again stress t h a t t h e main hypothesis of t h e investigation i s t h a t dispersion p r o c e s s e s change one a n o t h e r in such a way t h a t in t h e zone of in- fluence of one p r o c e s s ( n e a r i t s mode) t h e "tails" of a n o t h e r

are

not observed.

Each seasonal s e r i e s was subdivided into some

set

of i n t e r v a l s on t h e a x i s of logarithm of concentration. In

terms

of t h e model, i t i s possible

to

identify t h e i-th interval a s a n i n t e r v a l of t h e log concentration a x i s between t h e points ai

+

3 u and at ^+'

-

3 u , where ail i =I,

. . . ,

N are switch points (see above) and a, and a ~ + ' are t h e b o r d e r points of t h e distribution.

Sequences are constructed by methods of classification where areas of group- ings a r e established through a g r a p h analysis of log concentration distribution functions with a remote l i n e a r trend. Thus w e obtained a

set

of intervals of selec- tive grouping, i.e., sample W in s p a c e J of all intervals of t h e log concentration axis. Defining t h e measure p as t h e proximity of two intervals

see t h a t -1

<

^{p(11,12) S} 1 , and if p 2 0, t h e n &11,12)

=

I 1 12' , where , I

I

^h

I11

U

121 t h e length of interval I.

Using p , w e c o n s t r u c t t h e algorithm f o r determining s t a b l e grouping inter- vals. S o if we have t h r e e elements,

n,

W c J, a sample of intervals, and a n interval I,. then w e c a n c o n s t r u c t a n estimating t h e extension of t h e i n t e r v a l I, a s a n inter- val of t h e statistical grouping of concentration.

1. L e t po E ( 0 , l ) ;

2. We c o n s t r u c t section S ( I o , p o )

=

lIe,Ie E W, I =I.

...,

L j k

-

t h e s e t of intervals a r r a n g e d according t o t h e sequence belonging t o t h e c o r t a g e and where

&IIIO)

>

P o ;

3. Using section S(I,,p,)

=

!Ie j w e c o n s t r u c t matrix M with sizes L xL whose ele- ment (k , e ) i s equal

to

&I1, ,Ie );

4. From matrix

M

w e calculate t h e value FwIpo(I,) by t h e formula

The functional F

=

Fr.,& : J

-.

R', corresponding

to

e a c h i n t e r v a l a number gives t h e estimation using sample W, and shows t h e e x t e n t

to

which t h i s interval could b e r e g a r d e d as t h e i n t e r v a l of t h e statistical concentration grouping.

An i n t e r v a l I* f o r which FW,& i s maximum i s called p,,. The value ~ r , i s de- fined

to

b e s t a b l e f o r t h e i n t e r v a l I* if

F J " . ~ , ( I *

=

FWek *,,(I3

.

i.e., when a s m a l l variation of p,, does not change t h e value of t h e functional. Thus, a s t a b l e i n t e r v a l of s t a t i s t i c a l grouping f o r t h e given sample W i s found, if

m,,

8 p,, a n d s t a b l e i n t e r v a l I* are such t h a t FWed (I' )

>

1 -r.

Tables 1 and 2 contain t h e r e s u l t s of t h e s t a t i s t i c a l analysis of t h e seasonal s e r i e s of SO2 a n d lead concentrations f o r t h e period 1982-1983, which i l l u s t r a t e t h e application of t h e suggested technique. For longer time p e r i o d s see Table 3.

Columns contain i n t e r v a l sequences of selective grouping; rows contain sec- tions corresponding

to

i n t e r v a l s of s t a t i s t i c a l grouping which are a r r a n g e d ac- cording

to

t h e i r p l a c e on t h e a x i s of concentrations a n d observation areas. Let u s i l l u s t r a t e t h e technique f o r establishing a s t a b l e i n t e r v a l (-0.74

-

1.5) using d a t a from t h e Borovoye station.

Values of p calculated f o r t h e corresponding seasonal sections 1-4 are as fol- lows: 0.77, 0.75, 0.95, 0.86. Matrix M f o r t h i s section i s shown in Table 4. The s t a b l e i n t e r v a l of s t a t i s t i c a l grouping given in Table 2 h a s been calculated o n t h e basis of a l l available d a t a from t h e Borovoye station and, as o n e c a n s e e ,

d i f f e r s but slightly from t h e i n t e r v a l calculated o n t h e basis of d a t a f o r 1982-1983.

Tables 2 and 3 i l l u s t r a t e t h e description of t h e whole d a t a

set

in t h e form of a n a r r a n g e d

set

of s t a b l e i n t e r v a l s and sections corresponding

to

them. Let u s d e s c r i b e examples of r e g u l a r i t i e s r e v e a l e d in t h e whole d a t a b a s e in

terms

of t h e obtained description.

A section i s a

set

of i n t e r v a l s a r r a n g e d on t h e a x i s of time according

to

sea- sons, which allows u s

to

examine t h e dependence of t h e forming f a c t o r e f f e c t on seasons in observation areas. Thus, at t h e station in t h e Berezino Biological R e s e r v e ( s e e Table 2) SO2 concentrations form f o u r s t a b l e i n t e r v a l s and corresponding sections. A s t h e t a b l e shows, t h e f i r s t and last i n t e r v a l s o c c u r only in t h e summer and winter sections respectively. Intervals in between o c c u r equally during all seasons. The t a b l e s contain d a t a on t h e o c c u r r e n c e of t h e frequency of i n t e r v a l s in t h e w a r m and cold

seasons

(seasons designated by W a n d C). One c a n see t h a t as f a r as lead i s concerned, a c l e a r l y marked winter seasonal variation e x i s t s in t h e grouping i n t e r v a l f o r t h e highest concentration logarithms

at

Boro- voye station, and in t h e Repetek Biological Reserve. A s f o r SO2, c l e a r l y marked summer seasonal and winter seasonal variations

are

displayed in Borovoye and Berezino Biological Reserve, while t h e station in t h e Repetek Biological R e s e r v e r e v e a l s no difference in SO2 behavior in summer

or

in winter in any section. Dust sections show no seasonal peculiarities

at

any station e x c e p t f o r a v e r y slight winter variation in Berezino Biological Reserve.

The discovered r e g u l a r i t i e s could b e used in t h e assessment of regional levels of background atmospheric pollution f o r removing t h e components of seasonal and anthropogenic effects.

A set of s t a b l e i n t e r v a l s f o r o n e station c a n b e r e g a r d e d as an informative description of all d a t a obtained

at

t h i s station. If w e form, o u t of t h e s e sets (se- quences) a b a s e of i n t e r v a l s of statistically s t a b l e grouping,

w e

c a n apply t h e above-described algorithms. The established intervals are statistically s t a b l e with r e g a r d

to

specific regional f a c t o r s . An example

to

i l l u s t r a t e t h i s method i s t h e es- tablishing of a s t a b l e interval f o r SO2. Stable interval ( -1.3: 1.5) defines t h e sec- tion as follows: combination of i n t e r v a l s (-1.3 -0.2), (-0.2, 1.4) f o r Borovoye

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m

I 1

' 4 * N S r : r l c u m m *

nnn

* r q *

N m

* , +es'*

4 @ l m

w w w

h h h h

? a ? - !

G

44,

a ^*

h h h h h h h h

q ~ ? ~ ! r n o ? I

r?tr ?

N N m m * ^{4 4 N} m

h h h

".'9 LC!

C N e7)

h h h h m

".

₄ q t c : _{N N N}

* _*

N N N H L n O O d d m m d

w w w w

* d m N

n n n n n

N H N N N rn(D(OF0 d N N N d

w w w w w

giM- b e - i i d O u ¶ Q ) d 0 d N

w w w w

n n n

N N H d Ln Q)

NLnN

w w w

d F F

n n n n n

N H N H H 0 r n Q ) O F d N L n 0 . d

w w w w w

d ( O F i n N

n n n n

N N N H r n F C D F 0 N N ( O d

w w w w

L n h h d

n n n n

N H N N F F t n S O

d S 3 m

d a m

n n n

N N N

2 % $

w w w

N O C O

n n n

N N H O d m m N i n N

w w w

dd'd'

n n n n n

N H N N N

m o L L L n 0

d L n N d d

w w w w w

n n n n

n n n n n n n

N N H M O F O d O

xssg

m m d

Table 4: Similarity matrix f o r t h e section of interval (-0.2

-

^1.5)

tion; combination of i n t e r v a l s (-1.5, -0.3), (-0.1, 1.4) f o r Berezina station; i n t e r v a l s (-1.2, 0.8) and (0.6, 2.2) f o r Repetek station.

I t follows, f o r quality considerations, t h a t background concentration levels should possess d i f f e r e n t kinds of stability. The regional background pollution level i s c h a r a c t e r i z e d by stability in time; hemispheric level

-

by stability in time and s p a c e ,

etc.

T h e r e f o r e , s t a b l e grouping i n t e r v a l s suggested f o r t h e description of samples are s t a t i s t i c a l d a t a i n t e r p r e t e d as c h a r a c t e r i s t i c s f o r background con- c e n t r a t i o n levels.

The following section of t h e r e p o r t p r e s e n t s a n integral estimation of t h e im- p a c t of t h e e n e r g y system o n some model t e r r i t o r i e s , a n d summarizes t h e r e s u l t s of r e s e a r c h work on ecological-economic modeling c a r r i e d o u t in Naturuf Enuiron- ment and t h e Climate Monitoring L a b o r a t o r y Goskomgidromet and USSR Academy of Sciences in t h e period 1979-1987, embodied as as multigoal, automized system (MARS) ( s e e Antonovsky and Litvin, 1987). This i s a decision s u p p o r t system f o r protecting t h e atmosphere from pollution a t t h e level of mesoscale regions o r ci- ties: t w o corresponding modifications of t h e program exist.

Management decisions on d e c r e a s i n g atmospheric pollutions are made in p r a c - t i c e

at

t h e corresponding administration t e r r i t o r i a l level, i.e., a n economic region, a territorial-production complex, a republic, a s e p a r a t e

state

a n d

so

on. Such a t e r r i t o r i a l scale c o r r e s p o n d s

to

t h e notions of a mesoscale region. One c a n con- s i d e r a city as a n elementary t e r r i t o r i a l administrative level.

Currently, management aims of activities t h a t pollute t h e atmosphere are not simple. On t h e o t h e r hand, i t i s impossible to formulate a n "ideal" c r i t e r i o n t o esti- mate t h e

state

of t h e lower atmosphere. Besides, management t a s k s in c i t i e s and mesoscale regions h a v e t h e i r c o n c r e t e f e a t u r e s (peculiarities) t h a t define t h e u s e of d i f f e r e n t m o d e l s f o r t h e calculation of c r i t e r i o n indexes.

Cities a n d mesoscale regions, as

a

r u l e , contain hundreds or even thousands of s o u r c e s of atmospheric pollutants. To d e c r e a s e t h e pollution of t h e lower l a y e r s of t h e atmosphere, various c o n c r e t e pollution-control measures c a n b e e n a c t e d at e a c h of t h e sources. Each measure may h a v e a d i f f e r e n t r e s u l t in controlling pol- lution. Hence. p r a c t i c a l decisions f o r which measures

to

implement r e q u i r e infor- mation as t o t h e i r r e l a t i v e effectiveness.

The MARS program i s c a p a b l e of providing t h i s information f o r s t a t i o n a r y sources. A mesoscale region and a c i t y are exemplified in MARS by a r e g u l a r grid with a step/pitch from 0.5 km

to

1 0 km (usually 1 cm f o r a city and 1 0 km f o r a re- gion). On such a g r i d f o r e a c h s c r e e n cell t h e r e is information o n t h e economic damage as a r e s u l t of atmospheric pollution (scattering), and t h e pollutant distri- bution p a r a m e t e r in t h e atmosphere.

Principally different a p p r o a c h e s

to

modeling pollutant distribution

are

used f o r cities and regions.

Maximum surface l a y e r atmosphere concentrations of pollutants

are

calculat- e d f o r c i t i e s according t o t h e bearing and velocity of wind on t h e basis of t h e ana- lytic solution of atmospheric diffusions under t h e bound limits corresponding t o 20-30 minute averaging of meteorological parameters. Such

a

situation c o r r e s p o n d s

to

unfavorable weather conditions which are relatively common r e c o r d e d during a y e a r .

The chosen time i n t e r v a l of averaging and low velocity of wind s e r v e t o minim- ize t h e e f f e c t s on

a

model t e r r i t o r y / c i t y .

Thus,

calculations

to

minimize t h e in- dices of pollutant concentration p a t t e r n s may lead

to

a possible realization of t h e c r i t e r i a f o r decreasing emissions and economic damage in cities.

Deposition of sulfur, as

w e l l

as evaluation of t h e potential h a z a r d f o r coni- f e r o u s f o r e s t s , are calculated f o r mesoscale regions.

Each model index i s

an

additive one; t h a t is, f o r in any calculated point i t i s qualified by t h e covering of e m i t t e n t a f f e c t e d zones. These zones are not equally distributed since t h e distances o v e r which pollutants are d i s p e r s e d d i f f e r greatly.

This differentiation becomes a p p a r e n t if calculated values

are

compared with limit- ing values.

Thus, t o make cost-effective decisions in t h e field of atmospheric protection in t h e a b s e n c e of ideal c r i t e r i a , i t is necessary t o analyze t h e r a n g e complex of in- dices and p a r t a k e in t h e

art

of i n t e r p r e t a t i o n of t h e results.

MARS i s a powerful tool f o r analyzing t h e effectiveness of pollution control measures. I t contains a compiled d a t a bank f o r making all possible (permissible) s e t s , and optimizing d i s c r e t e atmospheric protection s t r a t e g i e s . The exhaustive s e a r c h of a l a r g e number of v a r i a n t s becomes possible thanks t o t h e use of dynam- ic programming models and a functional diagram f o r distributing t h e expenditures.

I t i s possible t o work o u t optimal s t r a t e g i e s in MARS f o r each of t h e c r i t e r i a with t h e use of two kinds of economic expenditure: c a p i t a l and discounted ones;

t h a t is, comparing expenditure and r e s u l t s of d i v e r s e economic processes. These two t y p e s of expenditures r e f l e c t t h e r e s o u r c e p a r t of t h e index of effectiveness of atmospheric protection activities.

The application of MARS r e q u i r e s a relatively small d a t a bank, consisting of two p a r t s : a ) information on n a t u r a l climatic f e a t u r e s of t h e t e r r i t o r y , and param-

eters

of emission s o u r c e s and; b) information on technology t o r e d u c e emission sources. The f i r s t p a r t of t h e d a t a bank i s w e l l established. I t s p r e p a r a t i o n does not c a u s e any difficulties o r r e q u i r e much time. The second p a r t of t h e d a t a bank supposes a designed study of possible technological

measures

f o r reducing ef- fluents

at

t h e sources. For t h i s i t i s a l s o necessary

to

generalize analogues f o r o t h e r cities/regions. I t should b e

stressed

t h a t MARS. in t h i s r e s p e c t , gives minimum opportunities

to create

a d a t a b a s e of atmosphere protection measures in cities, republics,

states

a n d s o on.

Proposed models, algorithms. and package programs were multilaterally ex- amined and

are

used in t h e USSR t o b a s e s t r a t e g i e s of atmosphere protection in ci- t i e s and regions. Located in t h e t e r r i t o r y of a region (which could b e chosen from cities, industrial c e n t e r s , territorial-production complexes, administrative

re-

gions, republics) a r e , as a r u l e , s e v e r a l thousand sources. e a c h contributing

to

t h e pollution of t h e lower atmosphere. To control t h a t pollution level i t i s neces- s a r y , within t h e framework of atmosphere monitoring.

to

perform various actions which are c h a r a c t e r i z e d by d i f f e r e n t efficiencies:

W,,, = E , / R , , m E M i , i E Z

(*I

where Rm

-

t h e volume of expenses e.g., investments. overall needed f o r t h e realization of m

-

^action;

M f

-

t h e s e r i e s of actions technically permissible f o r i

-

^source;

I

-

_{t h e set} of a i r pollution s o u r c e s in t h e region.

The e f f e c t from m-action linked directly with a source i s determined as a difference:

E m

= ~21) ^-

^{EAZ) (I.2)}

Component EA1) i s a basic (before t h e realization of m-action) s h a r e of a c e r - tain

source

in t h e lower atmosphere pollution level, and E$) i s a s h a r e of t h e s o u r c e a f t e r t h e realization of m-action which could b e defined as atmospheric protection if ~ $ 1

>

EA2).

The volume of atmospheric protection r e s o u r c e s i s limited:

and f o r e v e r y limit R * t h e corresponding optimum program c a n b e found:

W=CW,,, --,max,R = ~ * , m € M f , i € I

(**I

The r e v e r s e setting of a problem, reflecting t h e achievement of a d e s i r e d

state

of t h e atmosphere W* is a l s o possible:

R

=CR,

- - , m i n , w = w * ,

m

€ M i , i E I

(***I

The succession of t h e optimum programs with t h e monotonously increasing W efficiency values, and t h e all-permissible R r e s o u r c e consumption determines t h e function of t h e efficiency of t h e atmospheric protection actions in t h e region. The synthesis of t h e function i s necessary f o r calculating t h e amount of resources used f o r t h e protection of t h e atmosphere. The optimum function of t h e efficiency of

at-

mospheric protection activities in a region i s t h e final p r o d u c t of calculations done through MARS.

The MARS permits t h e obtaining of efficiency functions of t h e atmospheric protection activities on five types of c r i t e r i a

(~2')

^{and EA2)} calculation methods), including t h e analysis of t h e two kinds of expenses (investments and total*). Each of t h e c r i t e r i a may b e i n t e r p r e t e d depending

on

t h e goals of the analysis. The r e s u l t s of t h e calculations on t h e c r i t e r i a chosen r e p r e s e n t t h e solution of t h e p r a c t i c a l problems

in

atmospheric p r o t e c t i v e from pollution.

The MARS h a s

t w o

modifications designated f o r calculations on a level of a n in- d u s t r i a l c e n t e r o r a city (MARS-1, and on t h e level of a me-scale region (MARS- 2). Those modifications d i f f e r one from a n o t h e r by t h e composition of a n initial d a t a base, by model blocks of pollutant dissipation in the atmosphere, and by

sets

* The total expenses could be calculated using one of the known methods of commensuration of diverse economic expenditures directed at the realization of an atmospheric protection action;

e.g..

P =

C

+

^{EK, where C}

-

expenses on exploitation for one industrial cycle (1-year), K

-

^in-

vestments realized during several industrial cycles, and E

-

norm of the investments efficiency.

of c r i t e r i a f o r t h e efficiency of atmospheric protection actions.

Calculations using MARS r e q u i r e no special knowledge in mathematical model- ing o r computers. However, at t h e same time, MARS provides good possibilities f o r environmental managing based on assessment, monitoring. and c o n t r o l of a n a i r basin pollution level.

An assessment of a pollution level includes t h e following:

a ) Mapping of pollutant discharge.

b) Calculation of a s t m c t u r e of economic damage f o r e a c h of t h e s o u r c e s and pollutants.

c ) Mapping of economic damage.

d ) Calculation of fields of pollutant maximum concentrations under normal un- favorable conditions (MARS-1).

e ) Calculation of mean annual sulfur compounds concentrations (MARS-2).

f ) Calculation of mean annual sulfur compounds dry deposition o v e r a region (MARS-2).

g) Calculation of mean annual

w e t

deposition of sulfur compounds o v e r a region (MARS-2).

h) Calculation of mean

annual

values of sulfur exportation out a region of eight s e c t o r s and of t o t a l exportation (MARS-2).

i) Calculation of indices of potential damage t o coniferous f o r e s t s of a region from total sulfur deposition (MARS-2).

j) Mapping of sulfur concentrations and deposition and of indices of potential damage

to

coniferous f o r e s t s (MARS-2).

The information f o r decision-making regarding t h e monitoring and control of atmosphere pollution is provided by:

a ) Analysis of efficiency of initial atmosphere protection efforts.

b) Analysis of efficiency of a l l permissible s e r i e s of t h e atmosphere protection efforts.

c ) Calculation of a n optimum efficiency function.

d ) Plotting of a n optimum function.

e) Establishing a n optimum s e r i e s of t h e atmospheric protection actions in ac- cordance with (**) o r (***).

f ) Determination of permissible waste norms f o r s o u r c e s in a c c o r d a n c e with t h e optimum s e r i e s .

g ) Calculation of complete expenses f o r t h e optimum s e r i e s of actions.

h) Determination of a n expedient amount of expenses f o r t h e atmosphere protec- tion e f f o r t s in a region.

i) Determination of t h e e f f e c t of a r a n g e of regional s o u r c e s on coniferous f o r e s t s (an index of damage f o r coniferous f o r e s t s from deposition of sulfur compounds) (MARS-2).

The MARS,

run

on a personal computer of IBM-PC-AT class. analyzes t h e e f f e c t of 1,000 l a r g e s o u r c e s emitting seven different pollutants in t h e a i r basin of a city o r a meso-scale region, presented by a r e g u l a r grid 50 x 50 with a s t e p (space) from 0.5km up t o 10km. correspondingly.

The MARS s t ~ c t u r e contains 34 blocks.

Block of organization, following t h e development of d a t a bank.

Bank of environmental a n d climatic c h a r a c t e r i s t i c s .

Bank of p a r a m e t e r s of s t a t i o n a r y atmospheric pollution s o u r c e s .

Block for modeling

structure

a n d spatial distribution of economic damage due

to

atmospheric pollution.

Assessments of economic damage f o r e a c h of the sources and t h e pollutants.

Spatial distribution of economic damage for e a c h of t h e pollutants a n d

total.

Block for modeling of t h e dissipation of pollutants in the atmosphere o v e r a city or a n industrial c e n t e r .

Pollutant concentration fields o v e r a city or a n industrial c e n t e r .

Block f o r modeling of s u l f u r compound concentrations, d r y and

w e t

deposition f o r a

mesoscale

region, a n d s u l f u r exportation.

onc on cent ration

^field.

SO:- concentration field.

SO2 d r y deposition field.

SO: - d r y deposition field.

SO2

w e t

deposition field.

SO:-

w e t

deposition field.

Deposition field of total s u l f u r compounds.

Assessments of s u l f u r exportation out of a region o n eight sectors and of t o t a l exportation.

Block f o r modeling t h e index of potential damage

to

coniferous f o r e s t s in a re- gion d u e t o total deposition of s u l f u r compounds.

Spatial distribution of t h e index of potential damage

to

coniferous forests.

Block for modeling p a r a m e t e r s of initial atmosphere protection actions.

Bank of initial atmosphere protection actions.

Block of expenses mode selection.

Block of selection c r i t e r i o n of t h e efficiency of atmospheric protection ef- f o r t s .

Block of organization of t h e bank of a c t i o n s efficient enough for selected m o d e of e x p e n s e s and c r i t e r i o n .

Bank of actions efficient f o r selected mode of expenses a n d c r i t e r i o n .

Block for modeling of permissible technological chains for decreasing

wastes

from s o u r c e s , and f o r analysis of t h e i r efficiency f o r a selected mode of ex- penses and c r i t e r i o n .

Efficient s e r i e s of a c t i o n s f o r e a c h of t h e sources (initial information for an optimization model).

Block f o r modeling t h e optimum s t r a t e g i e s of t h e atmospheric protection ef- forts.

Information on p a r a m e t e r s of t h e optimum s t r a t e g y . Block of selection t h e specific optimum s t r a t e g y .

Block of organization of t h e s e r i e s of t h e atmospheric protection actions corresponding to t h e optimum s t r a t e g y selected.

32. The optimum s t r a t e g y f o r achieving a predetermined norm of t h e a i r basin s t a t e .

33. The optimum s t r a t e g y f o r distribution of expenses of atmospheric protection actions with a predetermined limit of r e s o u r c e .

34. Block of listing of t h e r e g i s t e r .

Apparently,

at

p r e s e n t t h e r e is no "ideal1' index f o r

Em

e f f e c t determination and t h e r e f o r e t h e r e i s no "ideal" efficiency of m-action f o r t h e atmospheric p r e tection. That i s why, depending on t h e aim of t h e atmospheric protection s t r a t e g y , t h e calculated assessments of e f f e c t used in MARS could b e divided into five types.

The f i r s t t y p e i s based on t h e calculation of t h e emitted

mass

of o n e o r s e v e r a l pollutants. Comparison of pollutants included in t h e e f f e c t index could b e done by t h e coefficients of toxicity ( r e v e r s e value

to

t h e maximum allowable concentra- tion). In t h a t c a s e , t h e efficiency of a n action i s t h e reduction of emitted mass p e r unit of expenses. This index i s simple and convenient and t h e r e now e x i s t s a developed d a t a b a s e f o r i t s usage. However, t h e e f f e c t of pollution s o u r c e s i s determined, not only by t h e amount of pollutants entering t h e lower atmosphere, but also by t h e peculiarities of pollutant dissipation in t h e atmosphere and, t h e r e - f o r e , by t h e s t r u c t u r e of r e c i p i e n t s suffering from a d v e r s e pollutants.

Those moments could b e considered while using t h e index of t h e second type, namely, of t h e economic damage from pollution of t h e lower l a y e r of t h e atmo- s p h e r e (Antonovski, Litvin, 1987):

where

Y - t h e economic damage (roubles/year);

7

-

t h e specific economic damage (roubles/comparison ton), a v e r a g e f o r t h e U S S R i s 7

=

2.4;

a

-

t h e dimensionless value characterizing t h e s t r u c t u r e of r e c i p i e n t s lo- cated in t h e zone of a s o u r c e of a c t i v e pollution (0.05 5 a 5 30);

f - t h e dimensionless c o r r e c t i o n f o r t h e mode of t h e dissipation of a pollu- t a n t in t h e atmosphere,

to

b e dependent on t h e a c t i v e height of a source, mean annual wind velocity, and r a t e of admixture disposition (I S f 5 10).

For t h e organized s o u r c e s ( s t a c k s of height h

<

1 0 m ) t h e zone of

an

active pollution i s presented by a c i r c l e with a c e n t e r in a point of a s o u r c e location and with a radius of 50 h, b u t f o r h

r

10m t h e zone i s a r i n g formed by radiuses Rinn,

=

2 q h , Rat,,

=

2 0 q h , where q i s a dimensionless c o r r e c t i o n f o r a plume raising:

where AT i s t h e d r o p in t e m p e r a t u r e between t h e mouth of a source and t h e am- bient atmosphere (mean annual temperature).

The given m a s s of pollutants emitted from a s o u r c e (comparison ton/year):

where

M, =

t h e m a s s of a n annual emission of j-pollutant (ton/year).

Coefficient of r e l a t i v e potency of a pollutant:

where aj

=

a n index of r e l a t i v e danger of a pollutant inhaled by a human; a,

- -

t h e c o r r e c t i o n f o r probability of a pollutant accumulating in environmental com- partments, in food chains, and pollutant intake into humans by any

means

o t h e r than inhalation;

6, =

t h e c o r r e c t i o n f o r a pollutant e f f e c t on various recipients o t h e r than humans; Aj

=

t h e c o r r e c t i o n f o r probable secondary discharge of a pollutant into t h e atmosphere;

Bj =

t h e c o r r e c t i o n f o r probable formation of secondary pollutants which are more dangerous than

an

initial pollutant. A, values f o r t h e most frequently o c c u r r i n g pollutants are calculated in Antonovsky and Litvin (1987) and lie within t h e limits of 1

to

1 2 x

lo5.

While using t h e index of t h e second t y p e t h e efficiency a p p e a r s t o prevent economic damage p e r unit of ex- pense.

The suggested method f o r calculating economic damage i s addressed t o partic- ular sources, and based on emission accounting. Therefore, i t keeps t h e advan- tages of t h e indices of t h e f i r s t type. However, t h e e f f e c t on recipients i s con- sidered in t h e n e a r e s t vicinity of a s o u r c e (the zone of t h e active pollution).

The r e s e a r c h r e s u l t s show t h a t pollutants may b e t r a n s p o r t e d o v e r long dis- tances, and transformed and deposited on t h e underlying s u r f a c e , thus affecting various recipients f a r beyond t h e limits of t h e active pollution zone. The calcula- tion of t h e atmospheric deposition (indices of t h e t h i r d type) is of special impor- t a n c e when t h e size of a region i s on t h e o r d e r of s e v e r a l hundred kilometers. In t h a t case, t h e d e c r e a s e in total deposition p e r unit of expenses i s t h e c r i t e r i o n of t h e efficiency. The f o u r t h type of indice i s sulfur exportation out of a region's limit (total o r directional). This type of indice is of p a r t i c u l a r i n t e r e s t f o r analyz- ing t h e e f f e c t of a p a r t i c u l a r region's pollution s o u r c e s on bordering regions. I t can b e also used f o r facilitating decision-making regarding t h e implementation of t h e Convention on Transboundary Transport of sulfur compounds. I t can also b e used f o r linking t h e r e s u l t s obtained through MARS-2 with t h e models of trans- boundary t r a n s p o r t . The special meso-scale models of transportation, transforma- tion and deposition of atmospheric pollutants a r e used f o r calculating t h e indices of t h e t h i r d and f o u r t h types.

The fifth t y p e of indice i s based on t h e comparison of t h e maximum lower

at-

mospheric pollutant concentrations t o t h e i r maximum allowance values, calculated f o r so-called normal unfavorable meteorological conditions.* This indice i s most important f o r urban t e r r i t o r i e s where t h e major recipient i s population. If t h e r e are o t h e r r e c i p i e n t s sensitive

to

t h e pollution of t h e t e r r i t o r y of a city, i t i s necessary

to

compare t h e concentrations t o t h e maximum allowable values f o r those recipients (secondary norms), but w e must b e a r in mind t h a t such a base of s t a n d a r d s i s c u r r e n t l y not sufficiently developed.

*

The normal unfavorable meteorological conditions presuppose the non-stable stratification of the atmosphere and occur rather frequently during a year. Assume that a decrease in maximum pollu- tant concentrations t o t h e level of t h e maximum allowable concentrations would provide the mean daily maximum allowance concentrations, whereas the r e v e r s e premise i s not correct. That i s why the criterion of minimization of "the maximum pollutant concentrations" indices i s realized in MARS-1.

The model of a n admixture dissipation in t h e atmosphere w a s modified f o r t h e purpose of calculating t h e maximum pollutant concentrations in t h e s u r f a c e l a y e r o v e r urban t e r r i t o r i e s under normal unfavorable conditions. The peculiarity of t h e modification i s a considerable reduction of time needed f o r calculation, which i s most important when using t h a t block in optimization blocks of MARS-1 complex.

When using t h e indices of t h e fifth type, t h e efficiency of actions i s evaluated by t h e d e c r e a s e of t h e index of pollutant concentrations p e r unit of expense. The corresponding index i s d e c r e a s e d p e r unit of expenses. The corresponding index i s determined f o r t h o s e elements of t h e r e g u l a r grid where t h e exceeding of t h e cal- culated concentrations o v e r t h e maximum allowable ones exists:

qi

= C C

c J ( ~ ~ ~ ) / P f o r V C ~ ( ~ ' ~ ) > P

i k 1 f

where q j

=

t h e index of calculated maximum concentrations of t h e j 2 - pollutant (the sum of e x c e s s e s of maximum occasional allowable concentrations in a city);

cti

e k )

=

t h e maximum calculated concentration of j

-

pollutant in

( t

, k ) r a s t e r element of a r e g u l a r grid presenting a city;

P j

=

t h e maximum occasional allowable concentration of j-pollutant in t h e at- mosphere o v e r a settlement.

The s t a t e of a n a i r basin o v e r a city could b e considered s a t i s f a c t o r y when:

qj s1 f o r Vj

(*'I

The g e n e r a l index of a n a i r basin pollutant f o r s e v e r a l pollutants o v e r a city*:

A t t h e same time, f o r reaching t h e satisfactory s t a t e of a n a i r basin of a c i t y f o r s e v e r a l pollutants simultaneously, a more s t r i c t condition in comparison t o (*') should b e c a r r i e d out:

T h e r e i s a possibility in MARS t o produce additional efficiency c r i t e r i a such a s (*) under different methods of

Em

calculation, if any of t h e five indices listed above a r e used. The simplest "dose-effect" model permitting calculation of t h e in- dex of potential damage from sulfur compounds

to

coniferous f o r e s t s of a region could s e r v e as a n example of such a n additional c r i t e r i o n , illustrating t h e ex- pediency of t h e use of MARS in o r d e r

to

p r o t e c t t h e atmosphere from pollution.

Also worth mentioning i s t h e IIASA Regional Acidification Information and Simulation (RAINS) model, which i s recognized by t h e ECE as a major prediction model

to

be used in f u t u r e discussions with r e s p e c t

to

going beyond t h e 30% sulfur emission protocol. This m o d e l w a s developed by t h e Acid Rain P r o j e c t of IIASA's Environmental Program, and i s a

set

of computer models that

can

b e used

to

evalu- a t e t h e abatement policies of acidification in Europe. The P r o j e c t t r i e s as much as possible

to

use existing models and a d a p t s them as a bridge between scientists and modelers. The major u s e r of t h i s work i s expected t o b e t h e Executive Body of t h e Convention on Long Range Transboundary Air Pollution which w a s established in

* F o r a group o f p o l l u t a n t s having t h e p r o p e r t y o f summarizing t h e i r e f f e c t .

REFERENCES

Alcamo, J.. H. ApSimon and P. Builtjes (eds.) (1987) Interregional Air Pollutant Transport: The Linearity Question, Research Report RR-87-20 (International Institute f o r Applied Systems Analysis, Laxenburg, Austria).

Antonovsky, M.Ya., N.M. Vinogradova (1983) Modeling technique of prediction of antopogenic impact o n environment, Moscow. Gidrometeoizdat. 15p.

Antonovsky, M.Ya.. V.A. Litvin (1987) Evaluation and prediction of contamination of atmosphere in system of regional monitoring. Goskomizdat, 15pp.

Bolin, B. (ed.) (1987) SCOPE 29. The greenhouse effect, climatic change and ecosys- tems.

Budiko, M.I. (1980) Climate in p a s t and future. Leningrad Hydrometeoizdat, 359pp.

Dicker, M.H., T.J. Sullivan, (1986) A r a c response

to

t h e Chernobyl reactor ac- cident L,L.N.L. UCID-20834.

Itogovyi doklad MKGYaB o soveshanii p o passmotreniu prichin i posledstvig a v a r i i v Chernobyle, MAGATE, Generalnaja Konferentsija, Vena, 30. VIII-05.IX 1986, dok

.

Izrael, Yu.A. (1984) The problems of protection of environment and t h e ways of i t s solutions. Leningrad. Hydrometioizdat.

Izrael, Yu.A, V.N. P e t r o v , D.A. Severov (1987) RADIOACTIVE FALLOUT simulation in t h e closed-in area of Chernobyl NPP. Soviet Journal of Meteorology and Hy- drology. N7.

Izrael, Yu.A.. M.Ya. Antonovsky, B.M. Buchshtaber, E.A. Zelenuk (1987) Doklad Academia of Sci., USSR, Tom 292, N2.