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Optimal air pollution control strategies: a case study
Ecolugical Modelling, 64 ( 1992) 221-239 Elscvier Science Publishers B.V., Amsierdam
Optimal
airpollutio9
221
control
stí-ategies:
a case study
G. Finzi and G. Guariso Center for Enrironmemal
Computer Scìence (CIRITA), Departnzent P~litecrtico di Milam. ~Wano, Italy
af E!ectrouics,
ABSTRACT
Finzi, G. and Guariso, G., 1992. Optimal Modelhg, 64: 221-239.
air pollution
controi
strategies;
a case study.
Ecol.
Air pollution can be controlled at a iegional leve1 in several different ways, s;rch as emission standards, taxes. permits, etc. The European Community decided to set standards on cnvironmental quaiity, namely on the distribution of pollutant concentrations measurcd at ground leve]. This paper deals with the problem of evaluating the trade-offs between such ambient standards and pollution abaicment costs. For this purpose, a two-objective linear program is formulated and solved for a 300 km? region in northern Italy, using a simulation model to evaluate the effects of each poIlution source. The software developed forms the basis of a more complete decision support system for this type of complex probfem. Its structure and components are describcd in detail.
1. INTRODUCTION
Air quality managemenr studies have become more and more interesting for the scientific community dwing the last 20 years due to the stwrgthening of the interactions between energy resources demand, pollution contrci polities and the related social costs. Decision makers involved in air quality con:
Correspondence CO: G. Finzi, Center Department of Electron&, Politecnico
O304-3800/92/$05.00
0 1992 - Elsevier
for Environmental Computer Scicnce (CIRITA), di Milano, Via Ponzio 34/5, 20133 Milano, Italy,
Science
Publishers
B.V. Ah rights
reserved
222
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FIN21
AND
TJ.
GUARISO
large number of factors (meteoroíogy, chemical reactions, solar radiation, complex terrain, etc.); thc technolo=T for abating pollution is not yet wel1 established, at least for some types of emissions. The existente of these difficulties is confirmed by the recent history af air quality ïegislation, which bas gone through a process of evolutionary changes, different from country to country. As a matter ol fact, several control strategies have been applied, such as: - imposition of emisGon standards at the sources; - control of ambient pollution; - taxation of polluteas; - establishment of a pollution permÎt market in an homogeneous air basin (bubble}, This demonstrates that decision makers urgently need objective methuds to support the develtzpment of rational ezergy resources plans, taking into account both econonnic and environmental aspects. The EEC countries have chusen the criterion of controlling pollutant concentrations at gronnd level; so the actual iegislation takes into account the probability distributions uf measured values. Consequently, it is reasonable to analyze thv problem in a multiobjective framework, where air quality is explicitly considered among the objectives, as wil1 be shown in the follo!ving section. In particular, the problem of satisfying the thermal energy demand in an assigned region, minimizing both the costs (including these d5e to c mission reduction) and the air quality damage, wil1 be considered, The proposed methodology allows the analysis of several economie and social scenarios in order to quantify the corresponding trade-offs betwcen econumic and environmental objectivcs. It has been implemented in an interactive software package (ARIA) for personal computers, which wil 1 be illustrated in the. third section. ARIA is a prototype decision support system for air pollution problems; it provides law makers with several capabilities to evaluate, comparand update standards and control strategies. The package has been used in a real case, a region of about 300 km* in northern Italy. Al1 steps of the application and the results obtained wil1 be illustrated in Section 4. It should be t>ointed out that, although the methodology and the package have genera1 applicability, in the foliowing a particular pollutant (S021 wil1 be taken into cunsideration. This is due to the availability of data in the area? where SO, was the _mGn pollution problem. Consequently, the choice amon g different emission abatement technologies was restricted to the following (sec, for instanqe, Amman and Kornai, 1987): use of low sulfur fuels;
OPTIMAL
-
-
AIR
POLLLJTIC~N
CONTROL
Sl-RATEGIES
223
combustion modification (i.e. desulfurization during the combustion process) with a mean reduction of 50% in sulfur emissions; flue gas desulfurization by a wet Zmestone scrubbing process with a remova! efficiency of 90%; regenerative process, an advanced technology which achieves an efficiency of 98%.
2. PROBLEM
FC?RMULATION
The problem outlincd above can bc formally stated as a deterministic mtllti-objective mathematica1 programming problem with the objectives of minimizing the overall energy production costs of a set of plants and maximizing a suitable indicator of environmental quality of the atmosphere in the region. The decision variables of such a problem are the quantities, qi,i, of the various fuels, k, used by each plant, i, in the area, measured, for instance, in tonnes per unit time. Since normally there are only a few treatment alternatives for each type of pohutant, different treatments have also been consi&red as different fuel options, k. For instance, a HSF (high sulfur fuel) with an original sulfur emission concentration of 3% may be treated with a flue gas technology allowing a sulfur reduction of 90%. Ht can then 3s consider,zd as a different fuel with thr same heating power, aq emission .vith 0.3% sulfer content, and a tost equal to the original purchase tost of MSF plus the tost of the desulfurization process. Bbviously, not al1 the piants can use al1 the available treatment technologies, since their application may be limited to particular industrial processes or by suitable dimensions of the plant. For instance, a Iarge power plant with three or four heater-stack groups can have several different treatment alternatives for each of them, while a smal1 production unit may only be able to switch to a less polluting fucl ivithout any specific treatment.
The tost objective min(costs)
= min z tq,L) i=
funct:nn C
is intuitively
formulated
as
ckclik
f k uk’,
where qik is the quantity of fuel k used by plant i in a given time interval; ck is the unit tost of fuel k; 1 is the total number of sources considered in the region; and K, is the set of energy oytions avaitable for source i.
G. FINZi
224
AND
G. GUARKO
The tost ck includes, as already mentioned, the treatment of effluents. Their quantitative evaluation requires estimates of the costs of different air cleaning technologies. The costs of X3, removal processes used in the case study presented later have been developed by Bocola (19871, Baterman et al. (1986) and Amman and Kornai (1987). Estimates of abatement costs for other pollutants are presented, for instance, in the proceedings of the ENCLAIR C on f erence (ENEA, 1986). However, as recent events have clearly demonstrated, al1 economie evaluations of fuel costs may be subject to wide ffuctuations and a planner may be interested in testing the robustness of a proposed energy prodwction/air quality scenario with fuel costs very different from the present ones (considering, for instance, their long-term trends). This must be explicitly considered when designing a software package to support encrgy planning activities.
‘Fhe environmental objective can be represented by the minimization of the ground leve1 concentrations of the pollutant under examlnation, as suggested by the directives of the European Community. More precisely, EEC legislation for various pollutants (Directives NOS. 80/779, 84,/360 and 85/203) takes into consideration the yearly statistical distribution of the daily mean values at some receptor sites as a significant indicator of air quality. The Xtalian law for sulfur dioxide, in particular, prescribes two restrictions: one on the 50th percentiie, representative of the mean prevailing conditions, and the second on the 98th percentile, representative of the most critical episodes with low occurrence probability. The mean daily SB, concentration must Se lower than 80 pg/m” for at least 50% of the days of the year and lower than 250 pg/‘m” for at least 98% of the days. Both these thresholds and their probabilities of occurrence constitute the air pollution indicator and may thus be regarded as independent objectives of a genera1 energy production plan. Wowever, to adhere tc, the current legislation and to simplify the solution of the problem in the following example, oniy the median threshold, T’, has been cc3nsidered as a variable, while the ratio between the 5Uth and the 98th pe-centile values has been fixed according to law (i.e. VSO/250). The air quality objective may thus be formulated as mintair
pollution)
= min T’
at each point of with e set of constraints specifymg that thc concentration the measurement network is less than or equal to T’ 50% of the time and less than or equal to T” (= 250/80T’) 98% of the time.
OPTIMAL
AIR
POLLUTlON
The fornrulation
CONTROL
STRATEGIES
proposed
abuve implies that, for each pollution
225
sensor
gj( - ,..) is a function relatlng the pollutant concentration at the j-ih sensor to the cmissions ei of the considered sources. These emissions may easily be computed, once the quantities, qik, of fuePs used and their specific pollution emissions, sk, are known, as
where
$
-
where J is the total number of measurement points in the area, 6j is the background pollulion, i.e. that present independent of the sources considered in the planning problem, and LY’and LY”are the percentages of time (namely, 50 and 98% in our case) for which the thresholds T’ and ‘F” must not be exceeded. Two slmplifications must be applied to constraints (3) to solve the problem in real cases, where the number of decision variables can easily exceed a hundred. The first concerns the form of the function gj( *), which is assumed to be a linear function of the emission of each plant, as commonly suggested in the literature (Atkinsons and kewis, 1974, 1975; Krupnick et al., $983). This means that constraints (3) can be rewritten in the forrn
where *he so-called “transfer coefficients” aij are random variables depending un the meteorological condition WZ.The probability distribution of such conditions is usually expressed in discrete terms through the joint frequency distribution of atrnospheric stability classes, wind speed and direction classes, and thermal inversion elevation classes. The rationale behind fosmulation (4) is that the pollutant conceratration at each measure-
C. FINZI AND G. GUARISU
226
poiui (i.e.
ment
weighted
the variable sum of the emissions
constrained
by law) is interpreted
as a
The weights ajj depend upon the meteorological situation HZ, which is in turn a random variable. The physical dimensions of the aii are units of concentration/units of emission and their evaïuation wil1 be dealt with in Section 2.4. Formulation (4) requires that the effect of the different sources is simply additive, that is it implies that chemical reactions of the pollutant are negligible and that the pollutant itself is sufficiently stabIe. FOP sulfur dioxide and particulate matter, for instance, this assumption is usuahy wel1 satisfied. For other pollutants, such as NOX, it must be regarded with some caution, because the phenomenon of dispersion is much more complex. Nevertheless, the recent literature presents interesting apphcations of NO, dispersion models (e.g., Simpson et al., 1990) which could fit the preceding assumption. The second simplifkation introduced refers to the transformation of the probabiiistic constraints into deterministic ones. This can be accomplished in an exact way by adopting a mixed integer programming formulation (Fronza and Melli, 1983; Guariso and Lancini, 1989), but this has Enacceptable solution times in practica1 cases. Consequently, one is forced to look for a suboptimum solution by deciding in advance which meteorological conditions have to be taken into account. This can be accomplished on the basis of the following considerations. For a given meteorological situation HI, constraints (4b) are certainly satisfied if (4a) are satisfied (remember that 7’” > T’j; thus the situation PE may be considered only once, for (4a) or (4b). The total probabilities of occurrence must be ti’ and ti” and the probability rm of the occurrence of situation %n is provided by meteorological data. Thus, one must choose a first set of M, metesrological conditions such that
and write the retative
C
aij(m
i=l
~
T” -
bj
air quality constraints j=
l,...,J
mi?= l,...,M,
in the farm
~PTIMAL
AIR
P~LLUTI~N
CONTROL
and a secowd additional
227
SI-RATEGIES
set of (M2 - M,) situations
such that
M2 c
71;,),d’-d
m=M,+Z
with the relative air quality constraints in the form (6) with m = M, + 1 , . . . , M2, ad substituting T” for 7”. Provided that constraints (5) and (7) are satisfied, one must choose the least criticat meteorological conditions from the pollution viewpoint for inclusion i;l the problem. For instance, if emissions are mainly from high stacks, g;ne could first select streng wind or stable atmospheric conditions and then add situations with weaker wind and increased instability up to the required probability sf occurrence M”. The inclusion of constraints referring ts very rare, but dangerous, episodes would mean the adoption of control strategies more restrictive than those required by law, and hence more expensive than optimal ones, The clevesness of the analyst and his/her knowledge of the particular sïte are essential to determine which meteorologieal conditions must be disy-zgarded to find a good suboptimal solution. When all the constraints are expressed in the form (61, rhey have the additïonal advantage of allowing a preliminary check of whieh of them is dominated bye others, i.e. not influencing the optimaj solution. T’he elimination of such constraints can strongly reduce the computational complexity, and thus the solution time, of the simplex method used to solve the resulting linear programming problem (sec Seetion 2.4). 2.4 Evahalion
of the transfer
cueffìcimts
The last problem to examine is how ts compute the transfer coefficient a,,(m) for every particular meteorological condition m. A suitable model must be used to simulate the diffusion of the pollutant in the atmosphere., on the basis of the stabihty class, the wind direction and speed, and the thermal inversion classes as wel1 as al1 the characteristics of the emissions (gas temperature and speed, stack elevation in the case of point sources ar area in the case of distributed sources such as domestic heating, etc.). For instance, any of the available Gaussian models can be used for this purpose, since the specific form of the air quality constraints adopted above is independent of the simulation model used. Once the pollutant concentration, Cij, produced at Iocation j by emission ei has been evaluated, the transfer eoefficient aij is simply computed as a,j = Cii/e,. If the diffusion model is hnear in the emissions, such as for al1 the Gaussian models proposed by EPA (15X36), the transfer coefficient
G. FI.NZI AND
223
C. GWARISO
enclosed in the model; if it is and non-hnear, the ~zij may simply be considered as linear approximations thus they are more precise if the emissions used fox their evaluation are close to those produced by the final solution of the problem. Some iterations of the overall procedure may be required to attain this result. Tt is interesting to note that this kind of technique has been proposed (e.g., Gorelick, 1883) in an apparently quite different domain, underground water management, which presents similar modelling difficulties (for instance, the presehce of scveral pollution sources in a three-dimensional field). already cag%.~~~s ail the reilevant Hnformation
The last set of constraints to Dr included in the problem is that requiring that the production of each sCrurce meets its reskective dzmand Dj (energy demanded per unit time). AsLuming that pk represents the heating power (energy per unit weight) of the kth fuel and that Di takes into account the Tfficiency cif the plant, these constraints can be written as IC g,q,,, 2 Di kEKi
ì = IJ
(81
Only one of these constraints wil1 hold for those sourees that may werk in a cooperztive way. Fur instance, large power stations having more than one heater.stack group have to be assigned an aggregate demand. Wris demand may ba representative of the actual werking condition of the plant and, in this case, it can be evaluated trom past fuel consumption, or it may be possi& to forecast future demands, for instance by means of a statistical trend anafysis.
The final two-objective mathematica1 !hat emission e, = x sp,‘~~~1): kEKi T’
program
to be solved is (remember
OPTWvIAL ALR POLLVTLON
CONTROL
229
STRATEGIES
(12)
gik
2
0
k=
l,...,Ki
ì=
(15)
l,.*.,I
where al1 the decision variablts are no>r-negative since they represent fuel consumptions. Once the transfer coefficilents have been evaluated through M, simulations of the polhrtion dispersion model, the problem reduces to a simple two-objective linear program? for which efficient solutions can be computed using, for instance, the corstraint method and a linear program solver. The results of an accurate implementation in a northern Italian regi+xr are discussed in Section 4. 3. A SOFTWARE DECISIONS
PACKAGiZ FOR SWPPORTING
ENERGY
PRODUCTION
As already noted, severai variables in the above problem may not be precisely known in practice: fuel and treatmeat costs, energy demands, and meteorological conditions are surely parameters for which every decision maker would like to have some sensitivity analysis. Fur this reason, the most signitïcant result of this study is perhaps a software package allowing an interactive analysis of different economie and physical scenarios. The package, called ARIA, the Italian acronym for “air pollution reduction alternatives”, resembles, quite closely the classica1 structure of a decisfon support system (Guariso and Werthner, 1989) with a data base, a model base and some modules decdicated to communication with the user. Ht has been implemenced using programs written in the Fortran, Pascal and C languages, coordinated by a main C program. It has been used on a 386 FC, where the complete study of a new area, from data input to the determination of aar efficient energy production plan, requires from 1 to 6 h of computation, depending upon rhe number of plants and the accuracy of the description of the meteorology. The main modules of the package are: (1) plant data base (2) area definition
230
G
FINZI
AND
G. GUARISO
(3) meteorology definition (4) simulation and optimization model base (5) presentation and analysis of the results. 3.1 T’e plant duta buse The economie and physical data necessary for a complete study are numerous and complex; this is why they have been structured in a hierarchical data base, specifically designed to serve as the unique input data module. Tt offers al1 the traditional tools of a data base (storage, retrieval, deletion, correction, listing, etc., of the data relative to a very large number of plants). Stack heights and positions, gas flow, temperature and velocity, fuels used, working period and total energy produced are examples of the mformation stored for each plant. Startlng from these data, the module also computcs and stores the fuel and treatment costs, using standard values and formulae proposed in the literature or more precise information for the case under study supplied by the user. A specific option allows for a distinction ttr De made between the fuel andfor treatment technologie5 aiready used 2 y the plant and those which the user deeides to analyse in an energy plan. A centralized data base shows efficient access of the information by al1 the other modules and frees the use from thr necessitv of sge.cifjGng the data necessary for a model in a rigid format every time. The plant data base can in fact store data for a Sarge area and can be used as an When independent tool for normal surveying und archiving activities. developing a specific energy plan, the required data referring to the plants in the region unCur study wil1 automatically be retrieved and used by oth:r modules.
This module simply implements graphical access to the data basc. The user in fact decides the region to be studied by giving its extreme coordinates, checking on the screen whsre the plants are located and assigning the monitoring statioir to be considered. The gra&mic preseiatation shows immediate feedback to the user, who can easily reshage the area when he/she is not satisfied with the previous choice. In the same module the legal constraints of the problem, namely the pollution thresholld kalues (7’ and 7”‘) and their limit probability of occurrence Ia and a”), are also entered.
231
3.3 The meteorology defiinitionmodule
This is a separate program which selects the meteorological situations to be included in the optimization problem on the basis of an objective criterion chosen by the user. The choice made in this module is obviously critical, because a very detailed description of the meteorology corresponds to a large number of simulations of pollutant diffusion and to a high complexity of the optimization problem. At present, the options available are very few. For instance, one may choose the situations with the highest probability of occurrencc up to the probability LX”.This criterion is cumpletely site-independent and guarantees the minimum number of conhighly straints and thus the highest execution speed, but may provide suboptimal solutions. Another option available is the choice of the situations with the highest stability and the highest wind speed within each stability class. This criterion provided better results in the application described in Section 4 below. A single meteorological situation may also be selected to understand the impact of a specific scenario on the final energy plan. The user may easily add other options (i.e. other programs) implementing different criteria which are more adequate for a specific study. The input to this module are normally the tables of the joint frequency function of atmosphcric stability, wind direction and speed, and thermal inversion classes in a standard format (such as that provided bv the Italian Air Force Meteorological Service). 3.4 Simuìatiunand optimization modeE base The model base constitutes the core of the system and contains both the air gollution simulatiom and the tost minimizatiou models. A modified version of the Gaussian diffusion model (Cirillo et ar., 1986) and a linear programming algorithm form tne basic elements of the model base. However, the software is conceived in such a way that the user can insert in the package his/her own models in a very simple and straightforward way. A specific model management module allows the insertion of new user-supphed modeis into the base, their retrieval, deletion OF modification of their description. The user may decide how the model should be referred t:o in the menus and insert/edit a few lines of explanation uf the model scope and performance. In this way, the only computer-related information zhat is required for adding a new mode? to the package is the name of the executable program which implements it. PB user-supplied routine for modifying the data format is necessary if the new model does not comply with the standard input/output of those already present in the package.
G. FINZI
232 0
3
!j
7
? #
9
0
11
12
13
14
15
16
17
18
19
20
AN13 G. GUARISO 21
22
23
24
12
1%
II
11
10
10
9
9
8
8
7
7
6
6
5
5
4
-l-_ 23
Fig. 1, Sulphur dioxide concentralions (pg/m”) in the 24 X 12 km region of Piacenza-Castel San Giovanni (northern Italy). T’he vah*es are plotted from the results compllted for eight sensors for a median threshold value of 160.
This option may be vergr useful particularly when adding new simulation modeIs taking into accou~$ the characteristics of the examined sites irough terrain, coastal sites, urban areas, etc.) and the different mathematica1 formulations (Gaussian, puffs, particlcs, etc.). A simiaar option als0 exists for optimization algorithms, but the ger,zrality and reliability of the method already available suggest thut a large expansion of the base in this field is qui te unliI
Tlae user can analyze the result obtained in several ways: the probability distribution of the pollution values at each monitoring station; the spatial interpolation of values corresponding to an assigned percentile of those distributions (Fig. 1.corresponds, for Icstance, to the values of the medians%; or the pollutian cosrcsponding to a given meteorological situation. The possibility of comparing two different ener&y plans or, in gencral, two pollution scenarios, by computing and visualizing their differences is alsc under iinplementation.
OPTIMAL
AIR
POLLUTION
CQNTROL
311m
233
w
CASTEL S. CIOVANNI
STRATEGIES
SARMATO mm A
Aa
Ah
PIACENZA
AREAL
EMISSION SOURCE
LOW EMISSION
SOURCE
HIGH EMISSION SOURCE
Fig. 2. Piacema study area.
This module provides the basic information required to implement a multi-attribute choice method such as those proposed by Meeney and Raiffa (1976), Goicoechea et al. (1982) and Colorni and Laniado (1987) in order to accomplish a final selection among alternative energy plan& 4. THE PIACENZA
CASE STUDY
The methodology and the package described in the precrding paragraphs have been tested in Piacenza province, located in thc Po Valley (northern Italy). In particulx, the study was limited to a rectangular area (24 x 12 km) that inchrdes the town of Piacenza and the municipalities of Castel San Giovanni tind Sarmato (Fig. 2). The hypothese3 and the results of this test casG are reported in this section simply for the purpose of illustration. Alrhough we have done our best to ensure the maximum reliability of the data, WG do not want to give any particular emphasis to these results. The real ou:come of the study is the ARIA package, which can be used by decision makers to compute the best energy plan deriving from any assumption about the economie and physical condition of the system.
234
G. FINZI
AND
G. GUAKISO
E
Wind speed (calm
Frequenries Numbe
Fig 3. Piacenza
wind rose fcx the period
1951-1987.
Calm conditions
prevail.
‘ï’he area under examination is affected by severe pollutant emissions due to two large p~wer plants and several industrial activities and to the urban emissions from domestic hcating and traffic. En particular, a 640 MW power station and two cement factories. are close ter, the tswn Of Piacenza; the area of Castel San Giovanni-Sarmato includes a 1280 MW puwer plant and a large sug-r Jactory. Both the electric power pjants use mainly fuel si1 with a sulfur concentration. aanging from 0.5 to 2.0%; their consumptisn totals around 80% of the energy budget of the entire prsvince and they are responsibk for 95% of sulfur disxide emissions, i.e. around llOM0 tonnes per year. The impact of this pollutiun klad on the area is relevant, in view of the high population density and the concentration of agriculture resources (Cirillo et al., 1988). The polftitant under study, i.e. sulfur dioxide, may reach critical concentrutions in conditiony; of particularly unfavsurable meteorology. The data supplicd by the Air Furce Meteorological Service show that a highly stable atmosphere.and pr-crknged inversions with fog episodes are frequent in the Pe Valley, especiall:! during the eold season. As shown in the wind rose of ‘Fig. 3, the circulatiun iu the area is very wenk and talm canditiûns prevail fmrire than 50% of the time). Such conditiori; are essentially due tio an anticyclonic circulation over n~xthern Italy which Zeads to tampzrature inversisns of abuut 300-400 m height, giving rise to two phenomena: - in urban areas, the “beat island” effect is quite frequent, especially in the morning hours; in this case, the temperature is higher in the city than
~PTIMAI.
AIR
P~LLUTION
CONTROL
STRATEC~IES
235
in the countryside and urban emissions are trapped in a highly stabk, closed system; - in countryside areas, the night temperature inversion is usually broken by solar radiation late in the mornlng, giving rise to instability at ground leve1 with fumigation episodes due to emissions from tal1 stacks (morning fumigation). An accurate inventory of emission data was available for the area (Cirillo et al., 1988j and recorded the following sources: - two Marge power plants (one at ka Caseha with two 200 m high stacks, and the second near Piacenza with two sta& with a height of 200 m and two of 47 m), - 13 main industrial plants, - 21 minor industrial and domestic sources that account for less than 1% of the total emissions. This does not mean that their impact is neghgible from the pollution point of view, since they contribute about 8% of the spatial average of the annual SO, concentration and a 1% increase in their emission would entail a 25% increase of the average concentratlon (Cirillo et al., 1988). This configuration of the sources, consistent with the most recent available data, has nut been changed in the following simulations, even though the overall procedure can be easily repeated with different source scenarios, using the ARIA package. Complex emission abatement technologies were taken into consideration only for large plants, while the change of fuel option was available for al1 the sources. Nevertheless, the decision variables for this probPem were 129. For the classification of the meteorological situation, Q-re following categories were taken into account: - seven atmospheric stability classes, - 16 wind direction sectors, - six wind speed ranges, - 14 thermal inversion height intervals. Since not al1 the com.binations have been reported (some are not even physically possible) the totnl number of meteorological scenarios was 2268. on air quaiity As aiready mentioned, the present EEC regulations standards take into consideration the mean daily SO, concentration, while the meteorological data elaborated by the Italian Air Force Mcteorological office are on a 3 h basis. This meax~ that the simulation. of diffusion of pollutants had to be performed on this time interval and consequently that the two objectives were the total treatment and fuel costs and the 3 h f concentration threshold. Two different ways of selecting the meteorological scenarios were examined:
(a) According to their probabifity of occurrence, i.e. adding the most probable situations up to the probability a” imposed by law, so that a large number of occasional meteorological situations are neglected. In this case the number of scenarios to be considered was reduced to 244 (b) According to the risk of pollution at ground level, starting from the least dangerotts classes, that is from stable situations (typical of the phenomena of “fanning” and “lofting” of c.b0 e plumes at the height of the stacks) and high wind speeds (leading also to the dispersion of pollutants in urban areas), and then adding less stable and less windy c.onditions up to a”. The resulting number of scenarios was 360. In the case at hand, where polfution from high stacks is predominant, this method provided a better solution, paid for by a longer execution time. The simulation of emissions’ diffusion for each source and for each scenario was performed by means of the modified Gaussian model DIMULA (Cirillo and Cagnetti, ig82), assuming the existente of a monitormg network in the area with eight stations corresponding to the present ones. The number of constraints to the problem could be reduced stil1 further by leaving out al1 the nul1 or dominated oncs (see Section 2.3). In the first case (a) the constraints decreased from 1952 to 146, in the second case (b) from 2880 to 139, so that the final number is quite similar for the two alternatives. Finally, 37 energy demand constraints were added to the problem, one for each plant. Figure 4 shows the set of non-dominated energy plans for Piacenza province. The estimates of the energy production costs are plotted as functions of the value of the lower SO, concentration threshold T’, assuming a constant ratio of 80/25Q between T’ and T”. It can be seen how aiternative (b) afways gives lower costs than case (a). This is due to the .Tact that some meteorological situations leading to high pollution have not been included in the problem or have been considered in set M, (which is constrained by a higher threshold) instead of set M,. Curve (b) thus represents an approximation of the optimal compromise between energy production (at the present level) and air quality, in the sense specified above. Most of the energy glans below a threshold value of 150 pg/m3 involve extensive use of low sulfur fuel and some desulfurization for the La Casella power plant. Point IJ in the figure represents the unreackable utopia condition, where both cos& and pollution are minimum. ft is computed by solving problem @)-(15) twic e with a scalar o’bjective function: once for the minimum tost, and once for the minimum pollution. Thus, beìow the valtrE: of the SO, threshold corresponding to ‘IJ, there is no solution of the problem satisfying the energy demands; whiie costs cannot be reduced below their valuc at U even if environmental quality is disrcgarded. Approaching such a condition,
OFFIMAL
AIK
I’OLLUTION
Efficient
CONTROL
STRATEG1ES
237
energy plans
Piacenza
area
2000
1500
lOCK2
500
J
minimum tost
0 0
(no polluti07
200
100
Env. quslity
contdl
300
400
@g/m3 SOJ
Fig. 4. Energy production tost versus air quality (measured in terms of SO, pollution threshold observed 50% of the time) for the region of Piacenza, with different choices of meteorological conditions to be included in the problem. (al Based on the probability of occurrence; (bl based on the effect on pollution. Point IJ represents the utopic situation in which both air pollution and energy costs are at their absolute and independent minima.
the choice of meteorology to be included in the problem obviously becomes less rellevant and the curves (a) and (b) come close to each other. 5. CONCLUDING
RIEMARKS
software environment for determining and analysing the efficient trade-offs between energy production and air quality (measured in probabilistic terms) has been developed on a personal computer. It combines the use of simulation models for computing the spataal distribution of a pollutant and of tlptimization methods to determine the optimal solutions within the physical, economie and legal scenario defined by the user. Furthermore, the suftware structure is completely independent of the particular simulation model usp,d and different models can easily be inteA
grated by the user. These characteristics of the package allow a decision maker to easily test several different energy production plans or to examine their robustness to changes in fuel or treatment costs, in the legal framework, or in energy demands. The effects of each plan can be analyzed from both economie and physieal points of view, by Iooking at the time and space distribution of the pollutant concentration. Although addressed originally only to a specific problem (planning of fuels and treatments of a set of plants in a region), the software supports the determination and analysis of several kinds of related decisions. For a 300 km* area in Piacenza province, a set of efficient compromise solutions for 41 sulfur dioxidc emissions has been determined through repetitive optimizations, each requiring from < 1 to 5 h of computation. It shorr!d be noted, however, that these energy pllans may be in force for years and thus speed of computation is not an essential requirement. Thanks to the use of the data base for input data storage and retrieval, the application of the same procedure for a different area (the municipality of Brescia, 100 krll to the north-east with very different meteorology due to the closeness of the /Ups) has been straightforward. The analysis of quite different economie-legal frameworks such as taxing pollution sources or creating a market for pollution permits, which limits the overall emission in the area, wil1 also be investigated in the near future by slightly modifying the objective function or the constraints of the optimization problem. ACKNOWLEDGMENTS
This study was supported by the ENEA VESE Project and the Italian Ministry of Education. The authors would like to thank W. Boco%a and M. Cirillo, EN&%, Rome, and G. MamoM, ENEE, Piacenza, for their suggestions and fcr supplying tht: case study data,
Amman, M. and Kornai, G.* 1987. Ccpst Fur,ction for Controliing SC& Emission in Europe. WP-87065, IIASA, L.axenburg, Auslria. Atkinstms, S. and Lmvis, D,, 1974. A wsl-cflC~tivcne~s analysis of altcrnativc air quality control stnrtcgies. J. Enviwn. Ecnn. Manage., 1: 237-2561. Atkinsons, 5. zmd Lcwiss D., 1975, Determination and implemcntatinn of sptimai air quality standard. J. Environ. Econ. Manage., 3: 363-380. Batterman, S., Amman, M., Hettetingh, J.-P., Hordijk, L. and Kornai, G., 1986. Optimal SOI Abatement Polities in Europe: Somc examples. WP-86-42, HASA, Laxenburg, Austria. Bocola, W., 1987. TccnoIogic e tosti per la riduzione dclf’inquinar~lcnto atmosferico di ccntrali tcrmnefcttrichc a carbonc. Energla, 2: 26-41. (in Itafian).
OPTIMAL
AIR POLLUTION
CONTROL
STRATEGIES
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Cirillo, M.C. and Cagnetti, P., 1982. DIMULA: un codice multivariato per il calcolo deile concentrazioni in aria, al live110 del suolo, dcgli inquinanti atmosferici. RTl/STUDIVALSAMP (8218, ENEA, Y. le Regina Margherita 125, Roma, Italy (in Italian). Cirillo, M.C., Clerici, G.C. and Manzi, D., 1986. Manuale d’uso del codice DIMULA Versione 1.0. RT/STUDI/86/2, ENEA, V. le Regina Margherita 125, Roma, Italy (in Italian). Cirillo, M.C., Bocola, W. and ‘I’riolo, L., 1988. Environmcntal impact of air pollutants with emphasis on crops. The Piacenza case study. In: A. Marani (Editor), Advances in Environmental rvlodelling. Elsevier, Amsterdam, pp. 243-312. Colorni, A. and Laniado, E., 1987. A decision support system for choosing among alternative projects. Seminar on Multipurpose Agriculture and Forestry. Wissensc.taEt Vauk, Kiel, pp. 39-51. ENEA, 1986. Froceedings ENCLAIR 86, Taormina, Italy, October 1986. ENEA, V. le Regina Margherita 125, Roma, Italy. EPA, 1986. Guideline on Air Quality Models. U.S. Environmental Protection Agency Report 450/2-78-027R, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Fronza, G. and MeHi, P., 1983. Assignment of emission abatement Eevels by stochastic programming. Atmos. Environ., 17: 2005-2029. Goicoechea, A., Mansen, D.R. and Du&tein, L., 1982. Multiobjective Decision Analysis with Engineering and Business Applications. John Wiley, New York. Gorelick, S.M., 1983. A review of distributed parameters groundwater management modeling methods. Water Resour. Res., 19: 305-319. Guariso, 6. and Lan’zini, R., 1989. Yroduzione di clnergia e salvaguardia dell’ambiente: verso un compromesso ottimalc. Rit. Operativa, 50: 27-48 (in Ttalian). Guariso, G. and Werthner, W., 1989. Environmental Decision Support Systems. Ellis l-lorwood, Cbichester. Keeney, R.L. and Raiffa, H., 1975. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley, New York. Krupnick, A.J., Oates, W.E. and Van De Verg, E., 1983. On marketable air pollution permits: the case for a system of pollution offsets. J. Environ. Econ. Manage., 10: 233-247. Simpson, D , PL:iin, D.A., V arey, J.E. and Williams, M.L., 1990. Dispersion modelling of nitrogen Jxides in the United Kingdom. Atmos. Environ., 24A(73: 1713-1733.
Optimal
airpollutio9
221
control
stí-ategies:
a case study
G. Finzi and G. Guariso Center for Enrironmemal
Computer Scìence (CIRITA), Departnzent P~litecrtico di Milam. ~Wano, Italy
af E!ectrouics,
ABSTRACT
Finzi, G. and Guariso, G., 1992. Optimal Modelhg, 64: 221-239.
air pollution
controi
strategies;
a case study.
Ecol.
Air pollution can be controlled at a iegional leve1 in several different ways, s;rch as emission standards, taxes. permits, etc. The European Community decided to set standards on cnvironmental quaiity, namely on the distribution of pollutant concentrations measurcd at ground leve]. This paper deals with the problem of evaluating the trade-offs between such ambient standards and pollution abaicment costs. For this purpose, a two-objective linear program is formulated and solved for a 300 km? region in northern Italy, using a simulation model to evaluate the effects of each poIlution source. The software developed forms the basis of a more complete decision support system for this type of complex probfem. Its structure and components are describcd in detail.
1. INTRODUCTION
Air quality managemenr studies have become more and more interesting for the scientific community dwing the last 20 years due to the stwrgthening of the interactions between energy resources demand, pollution contrci polities and the related social costs. Decision makers involved in air quality con:
Correspondence CO: G. Finzi, Center Department of Electron&, Politecnico
O304-3800/92/$05.00
0 1992 - Elsevier
for Environmental Computer Scicnce (CIRITA), di Milano, Via Ponzio 34/5, 20133 Milano, Italy,
Science
Publishers
B.V. Ah rights
reserved
222
0.
FIN21
AND
TJ.
GUARISO
large number of factors (meteoroíogy, chemical reactions, solar radiation, complex terrain, etc.); thc technolo=T for abating pollution is not yet wel1 established, at least for some types of emissions. The existente of these difficulties is confirmed by the recent history af air quality ïegislation, which bas gone through a process of evolutionary changes, different from country to country. As a matter ol fact, several control strategies have been applied, such as: - imposition of emisGon standards at the sources; - control of ambient pollution; - taxation of polluteas; - establishment of a pollution permÎt market in an homogeneous air basin (bubble}, This demonstrates that decision makers urgently need objective methuds to support the develtzpment of rational ezergy resources plans, taking into account both econonnic and environmental aspects. The EEC countries have chusen the criterion of controlling pollutant concentrations at gronnd level; so the actual iegislation takes into account the probability distributions uf measured values. Consequently, it is reasonable to analyze thv problem in a multiobjective framework, where air quality is explicitly considered among the objectives, as wil1 be shown in the follo!ving section. In particular, the problem of satisfying the thermal energy demand in an assigned region, minimizing both the costs (including these d5e to c mission reduction) and the air quality damage, wil1 be considered, The proposed methodology allows the analysis of several economie and social scenarios in order to quantify the corresponding trade-offs betwcen econumic and environmental objectivcs. It has been implemented in an interactive software package (ARIA) for personal computers, which wil 1 be illustrated in the. third section. ARIA is a prototype decision support system for air pollution problems; it provides law makers with several capabilities to evaluate, comparand update standards and control strategies. The package has been used in a real case, a region of about 300 km* in northern Italy. Al1 steps of the application and the results obtained wil1 be illustrated in Section 4. It should be t>ointed out that, although the methodology and the package have genera1 applicability, in the foliowing a particular pollutant (S021 wil1 be taken into cunsideration. This is due to the availability of data in the area? where SO, was the _mGn pollution problem. Consequently, the choice amon g different emission abatement technologies was restricted to the following (sec, for instanqe, Amman and Kornai, 1987): use of low sulfur fuels;
OPTIMAL
-
-
AIR
POLLLJTIC~N
CONTROL
Sl-RATEGIES
223
combustion modification (i.e. desulfurization during the combustion process) with a mean reduction of 50% in sulfur emissions; flue gas desulfurization by a wet Zmestone scrubbing process with a remova! efficiency of 90%; regenerative process, an advanced technology which achieves an efficiency of 98%.
2. PROBLEM
FC?RMULATION
The problem outlincd above can bc formally stated as a deterministic mtllti-objective mathematica1 programming problem with the objectives of minimizing the overall energy production costs of a set of plants and maximizing a suitable indicator of environmental quality of the atmosphere in the region. The decision variables of such a problem are the quantities, qi,i, of the various fuels, k, used by each plant, i, in the area, measured, for instance, in tonnes per unit time. Since normally there are only a few treatment alternatives for each type of pohutant, different treatments have also been consi&red as different fuel options, k. For instance, a HSF (high sulfur fuel) with an original sulfur emission concentration of 3% may be treated with a flue gas technology allowing a sulfur reduction of 90%. Ht can then 3s consider,zd as a different fuel with thr same heating power, aq emission .vith 0.3% sulfer content, and a tost equal to the original purchase tost of MSF plus the tost of the desulfurization process. Bbviously, not al1 the piants can use al1 the available treatment technologies, since their application may be limited to particular industrial processes or by suitable dimensions of the plant. For instance, a Iarge power plant with three or four heater-stack groups can have several different treatment alternatives for each of them, while a smal1 production unit may only be able to switch to a less polluting fucl ivithout any specific treatment.
The tost objective min(costs)
= min z tq,L) i=
funct:nn C
is intuitively
formulated
as
ckclik
f k uk’,
where qik is the quantity of fuel k used by plant i in a given time interval; ck is the unit tost of fuel k; 1 is the total number of sources considered in the region; and K, is the set of energy oytions avaitable for source i.
G. FINZi
224
AND
G. GUARKO
The tost ck includes, as already mentioned, the treatment of effluents. Their quantitative evaluation requires estimates of the costs of different air cleaning technologies. The costs of X3, removal processes used in the case study presented later have been developed by Bocola (19871, Baterman et al. (1986) and Amman and Kornai (1987). Estimates of abatement costs for other pollutants are presented, for instance, in the proceedings of the ENCLAIR C on f erence (ENEA, 1986). However, as recent events have clearly demonstrated, al1 economie evaluations of fuel costs may be subject to wide ffuctuations and a planner may be interested in testing the robustness of a proposed energy prodwction/air quality scenario with fuel costs very different from the present ones (considering, for instance, their long-term trends). This must be explicitly considered when designing a software package to support encrgy planning activities.
‘Fhe environmental objective can be represented by the minimization of the ground leve1 concentrations of the pollutant under examlnation, as suggested by the directives of the European Community. More precisely, EEC legislation for various pollutants (Directives NOS. 80/779, 84,/360 and 85/203) takes into consideration the yearly statistical distribution of the daily mean values at some receptor sites as a significant indicator of air quality. The Xtalian law for sulfur dioxide, in particular, prescribes two restrictions: one on the 50th percentiie, representative of the mean prevailing conditions, and the second on the 98th percentile, representative of the most critical episodes with low occurrence probability. The mean daily SB, concentration must Se lower than 80 pg/m” for at least 50% of the days of the year and lower than 250 pg/‘m” for at least 98% of the days. Both these thresholds and their probabilities of occurrence constitute the air pollution indicator and may thus be regarded as independent objectives of a genera1 energy production plan. Wowever, to adhere tc, the current legislation and to simplify the solution of the problem in the following example, oniy the median threshold, T’, has been cc3nsidered as a variable, while the ratio between the 5Uth and the 98th pe-centile values has been fixed according to law (i.e. VSO/250). The air quality objective may thus be formulated as mintair
pollution)
= min T’
at each point of with e set of constraints specifymg that thc concentration the measurement network is less than or equal to T’ 50% of the time and less than or equal to T” (= 250/80T’) 98% of the time.
OPTIMAL
AIR
POLLUTlON
The fornrulation
CONTROL
STRATEGIES
proposed
abuve implies that, for each pollution
225
sensor
gj( - ,..) is a function relatlng the pollutant concentration at the j-ih sensor to the cmissions ei of the considered sources. These emissions may easily be computed, once the quantities, qik, of fuePs used and their specific pollution emissions, sk, are known, as
where
$
-
where J is the total number of measurement points in the area, 6j is the background pollulion, i.e. that present independent of the sources considered in the planning problem, and LY’and LY”are the percentages of time (namely, 50 and 98% in our case) for which the thresholds T’ and ‘F” must not be exceeded. Two slmplifications must be applied to constraints (3) to solve the problem in real cases, where the number of decision variables can easily exceed a hundred. The first concerns the form of the function gj( *), which is assumed to be a linear function of the emission of each plant, as commonly suggested in the literature (Atkinsons and kewis, 1974, 1975; Krupnick et al., $983). This means that constraints (3) can be rewritten in the forrn
where *he so-called “transfer coefficients” aij are random variables depending un the meteorological condition WZ.The probability distribution of such conditions is usually expressed in discrete terms through the joint frequency distribution of atrnospheric stability classes, wind speed and direction classes, and thermal inversion elevation classes. The rationale behind fosmulation (4) is that the pollutant conceratration at each measure-
C. FINZI AND G. GUARISU
226
poiui (i.e.
ment
weighted
the variable sum of the emissions
constrained
by law) is interpreted
as a
The weights ajj depend upon the meteorological situation HZ, which is in turn a random variable. The physical dimensions of the aii are units of concentration/units of emission and their evaïuation wil1 be dealt with in Section 2.4. Formulation (4) requires that the effect of the different sources is simply additive, that is it implies that chemical reactions of the pollutant are negligible and that the pollutant itself is sufficiently stabIe. FOP sulfur dioxide and particulate matter, for instance, this assumption is usuahy wel1 satisfied. For other pollutants, such as NOX, it must be regarded with some caution, because the phenomenon of dispersion is much more complex. Nevertheless, the recent literature presents interesting apphcations of NO, dispersion models (e.g., Simpson et al., 1990) which could fit the preceding assumption. The second simplifkation introduced refers to the transformation of the probabiiistic constraints into deterministic ones. This can be accomplished in an exact way by adopting a mixed integer programming formulation (Fronza and Melli, 1983; Guariso and Lancini, 1989), but this has Enacceptable solution times in practica1 cases. Consequently, one is forced to look for a suboptimum solution by deciding in advance which meteorological conditions have to be taken into account. This can be accomplished on the basis of the following considerations. For a given meteorological situation HI, constraints (4b) are certainly satisfied if (4a) are satisfied (remember that 7’” > T’j; thus the situation PE may be considered only once, for (4a) or (4b). The total probabilities of occurrence must be ti’ and ti” and the probability rm of the occurrence of situation %n is provided by meteorological data. Thus, one must choose a first set of M, metesrological conditions such that
and write the retative
C
aij(m
i=l
~
T” -
bj
air quality constraints j=
l,...,J
mi?= l,...,M,
in the farm
~PTIMAL
AIR
P~LLUTI~N
CONTROL
and a secowd additional
227
SI-RATEGIES
set of (M2 - M,) situations
such that
M2 c
71;,),d’-d
m=M,+Z
with the relative air quality constraints in the form (6) with m = M, + 1 , . . . , M2, ad substituting T” for 7”. Provided that constraints (5) and (7) are satisfied, one must choose the least criticat meteorological conditions from the pollution viewpoint for inclusion i;l the problem. For instance, if emissions are mainly from high stacks, g;ne could first select streng wind or stable atmospheric conditions and then add situations with weaker wind and increased instability up to the required probability sf occurrence M”. The inclusion of constraints referring ts very rare, but dangerous, episodes would mean the adoption of control strategies more restrictive than those required by law, and hence more expensive than optimal ones, The clevesness of the analyst and his/her knowledge of the particular sïte are essential to determine which meteorologieal conditions must be disy-zgarded to find a good suboptimal solution. When all the constraints are expressed in the form (61, rhey have the additïonal advantage of allowing a preliminary check of whieh of them is dominated bye others, i.e. not influencing the optimaj solution. T’he elimination of such constraints can strongly reduce the computational complexity, and thus the solution time, of the simplex method used to solve the resulting linear programming problem (sec Seetion 2.4). 2.4 Evahalion
of the transfer
cueffìcimts
The last problem to examine is how ts compute the transfer coefficient a,,(m) for every particular meteorological condition m. A suitable model must be used to simulate the diffusion of the pollutant in the atmosphere., on the basis of the stabihty class, the wind direction and speed, and the thermal inversion classes as wel1 as al1 the characteristics of the emissions (gas temperature and speed, stack elevation in the case of point sources ar area in the case of distributed sources such as domestic heating, etc.). For instance, any of the available Gaussian models can be used for this purpose, since the specific form of the air quality constraints adopted above is independent of the simulation model used. Once the pollutant concentration, Cij, produced at Iocation j by emission ei has been evaluated, the transfer eoefficient aij is simply computed as a,j = Cii/e,. If the diffusion model is hnear in the emissions, such as for al1 the Gaussian models proposed by EPA (15X36), the transfer coefficient
G. FI.NZI AND
223
C. GWARISO
enclosed in the model; if it is and non-hnear, the ~zij may simply be considered as linear approximations thus they are more precise if the emissions used fox their evaluation are close to those produced by the final solution of the problem. Some iterations of the overall procedure may be required to attain this result. Tt is interesting to note that this kind of technique has been proposed (e.g., Gorelick, 1883) in an apparently quite different domain, underground water management, which presents similar modelling difficulties (for instance, the presehce of scveral pollution sources in a three-dimensional field). already cag%.~~~s ail the reilevant Hnformation
The last set of constraints to Dr included in the problem is that requiring that the production of each sCrurce meets its reskective dzmand Dj (energy demanded per unit time). AsLuming that pk represents the heating power (energy per unit weight) of the kth fuel and that Di takes into account the Tfficiency cif the plant, these constraints can be written as IC g,q,,, 2 Di kEKi
ì = IJ
(81
Only one of these constraints wil1 hold for those sourees that may werk in a cooperztive way. Fur instance, large power stations having more than one heater.stack group have to be assigned an aggregate demand. Wris demand may ba representative of the actual werking condition of the plant and, in this case, it can be evaluated trom past fuel consumption, or it may be possi& to forecast future demands, for instance by means of a statistical trend anafysis.
The final two-objective mathematica1 !hat emission e, = x sp,‘~~~1): kEKi T’
program
to be solved is (remember
OPTWvIAL ALR POLLVTLON
CONTROL
229
STRATEGIES
(12)
gik
2
0
k=
l,...,Ki
ì=
(15)
l,.*.,I
where al1 the decision variablts are no>r-negative since they represent fuel consumptions. Once the transfer coefficilents have been evaluated through M, simulations of the polhrtion dispersion model, the problem reduces to a simple two-objective linear program? for which efficient solutions can be computed using, for instance, the corstraint method and a linear program solver. The results of an accurate implementation in a northern Italian regi+xr are discussed in Section 4. 3. A SOFTWARE DECISIONS
PACKAGiZ FOR SWPPORTING
ENERGY
PRODUCTION
As already noted, severai variables in the above problem may not be precisely known in practice: fuel and treatmeat costs, energy demands, and meteorological conditions are surely parameters for which every decision maker would like to have some sensitivity analysis. Fur this reason, the most signitïcant result of this study is perhaps a software package allowing an interactive analysis of different economie and physical scenarios. The package, called ARIA, the Italian acronym for “air pollution reduction alternatives”, resembles, quite closely the classica1 structure of a decisfon support system (Guariso and Werthner, 1989) with a data base, a model base and some modules decdicated to communication with the user. Ht has been implemenced using programs written in the Fortran, Pascal and C languages, coordinated by a main C program. It has been used on a 386 FC, where the complete study of a new area, from data input to the determination of aar efficient energy production plan, requires from 1 to 6 h of computation, depending upon rhe number of plants and the accuracy of the description of the meteorology. The main modules of the package are: (1) plant data base (2) area definition
230
G
FINZI
AND
G. GUARISO
(3) meteorology definition (4) simulation and optimization model base (5) presentation and analysis of the results. 3.1 T’e plant duta buse The economie and physical data necessary for a complete study are numerous and complex; this is why they have been structured in a hierarchical data base, specifically designed to serve as the unique input data module. Tt offers al1 the traditional tools of a data base (storage, retrieval, deletion, correction, listing, etc., of the data relative to a very large number of plants). Stack heights and positions, gas flow, temperature and velocity, fuels used, working period and total energy produced are examples of the mformation stored for each plant. Startlng from these data, the module also computcs and stores the fuel and treatment costs, using standard values and formulae proposed in the literature or more precise information for the case under study supplied by the user. A specific option allows for a distinction ttr De made between the fuel andfor treatment technologie5 aiready used 2 y the plant and those which the user deeides to analyse in an energy plan. A centralized data base shows efficient access of the information by al1 the other modules and frees the use from thr necessitv of sge.cifjGng the data necessary for a model in a rigid format every time. The plant data base can in fact store data for a Sarge area and can be used as an When independent tool for normal surveying und archiving activities. developing a specific energy plan, the required data referring to the plants in the region unCur study wil1 automatically be retrieved and used by oth:r modules.
This module simply implements graphical access to the data basc. The user in fact decides the region to be studied by giving its extreme coordinates, checking on the screen whsre the plants are located and assigning the monitoring statioir to be considered. The gra&mic preseiatation shows immediate feedback to the user, who can easily reshage the area when he/she is not satisfied with the previous choice. In the same module the legal constraints of the problem, namely the pollution thresholld kalues (7’ and 7”‘) and their limit probability of occurrence Ia and a”), are also entered.
231
3.3 The meteorology defiinitionmodule
This is a separate program which selects the meteorological situations to be included in the optimization problem on the basis of an objective criterion chosen by the user. The choice made in this module is obviously critical, because a very detailed description of the meteorology corresponds to a large number of simulations of pollutant diffusion and to a high complexity of the optimization problem. At present, the options available are very few. For instance, one may choose the situations with the highest probability of occurrencc up to the probability LX”.This criterion is cumpletely site-independent and guarantees the minimum number of conhighly straints and thus the highest execution speed, but may provide suboptimal solutions. Another option available is the choice of the situations with the highest stability and the highest wind speed within each stability class. This criterion provided better results in the application described in Section 4 below. A single meteorological situation may also be selected to understand the impact of a specific scenario on the final energy plan. The user may easily add other options (i.e. other programs) implementing different criteria which are more adequate for a specific study. The input to this module are normally the tables of the joint frequency function of atmosphcric stability, wind direction and speed, and thermal inversion classes in a standard format (such as that provided bv the Italian Air Force Meteorological Service). 3.4 Simuìatiunand optimization modeE base The model base constitutes the core of the system and contains both the air gollution simulatiom and the tost minimizatiou models. A modified version of the Gaussian diffusion model (Cirillo et ar., 1986) and a linear programming algorithm form tne basic elements of the model base. However, the software is conceived in such a way that the user can insert in the package his/her own models in a very simple and straightforward way. A specific model management module allows the insertion of new user-supphed modeis into the base, their retrieval, deletion OF modification of their description. The user may decide how the model should be referred t:o in the menus and insert/edit a few lines of explanation uf the model scope and performance. In this way, the only computer-related information zhat is required for adding a new mode? to the package is the name of the executable program which implements it. PB user-supplied routine for modifying the data format is necessary if the new model does not comply with the standard input/output of those already present in the package.
G. FINZI
232 0
3
!j
7
? #
9
0
11
12
13
14
15
16
17
18
19
20
AN13 G. GUARISO 21
22
23
24
12
1%
II
11
10
10
9
9
8
8
7
7
6
6
5
5
4
-l-_ 23
Fig. 1, Sulphur dioxide concentralions (pg/m”) in the 24 X 12 km region of Piacenza-Castel San Giovanni (northern Italy). T’he vah*es are plotted from the results compllted for eight sensors for a median threshold value of 160.
This option may be vergr useful particularly when adding new simulation modeIs taking into accou~$ the characteristics of the examined sites irough terrain, coastal sites, urban areas, etc.) and the different mathematica1 formulations (Gaussian, puffs, particlcs, etc.). A simiaar option als0 exists for optimization algorithms, but the ger,zrality and reliability of the method already available suggest thut a large expansion of the base in this field is qui te unliI
Tlae user can analyze the result obtained in several ways: the probability distribution of the pollution values at each monitoring station; the spatial interpolation of values corresponding to an assigned percentile of those distributions (Fig. 1.corresponds, for Icstance, to the values of the medians%; or the pollutian cosrcsponding to a given meteorological situation. The possibility of comparing two different ener&y plans or, in gencral, two pollution scenarios, by computing and visualizing their differences is alsc under iinplementation.
OPTIMAL
AIR
POLLUTION
CQNTROL
311m
233
w
CASTEL S. CIOVANNI
STRATEGIES
SARMATO mm A
Aa
Ah
PIACENZA
AREAL
EMISSION SOURCE
LOW EMISSION
SOURCE
HIGH EMISSION SOURCE
Fig. 2. Piacema study area.
This module provides the basic information required to implement a multi-attribute choice method such as those proposed by Meeney and Raiffa (1976), Goicoechea et al. (1982) and Colorni and Laniado (1987) in order to accomplish a final selection among alternative energy plan& 4. THE PIACENZA
CASE STUDY
The methodology and the package described in the precrding paragraphs have been tested in Piacenza province, located in thc Po Valley (northern Italy). In particulx, the study was limited to a rectangular area (24 x 12 km) that inchrdes the town of Piacenza and the municipalities of Castel San Giovanni tind Sarmato (Fig. 2). The hypothese3 and the results of this test casG are reported in this section simply for the purpose of illustration. Alrhough we have done our best to ensure the maximum reliability of the data, WG do not want to give any particular emphasis to these results. The real ou:come of the study is the ARIA package, which can be used by decision makers to compute the best energy plan deriving from any assumption about the economie and physical condition of the system.
234
G. FINZI
AND
G. GUAKISO
E
Wind speed (calm
Frequenries Numbe
Fig 3. Piacenza
wind rose fcx the period
1951-1987.
Calm conditions
prevail.
‘ï’he area under examination is affected by severe pollutant emissions due to two large p~wer plants and several industrial activities and to the urban emissions from domestic hcating and traffic. En particular, a 640 MW power station and two cement factories. are close ter, the tswn Of Piacenza; the area of Castel San Giovanni-Sarmato includes a 1280 MW puwer plant and a large sug-r Jactory. Both the electric power pjants use mainly fuel si1 with a sulfur concentration. aanging from 0.5 to 2.0%; their consumptisn totals around 80% of the energy budget of the entire prsvince and they are responsibk for 95% of sulfur disxide emissions, i.e. around llOM0 tonnes per year. The impact of this pollutiun klad on the area is relevant, in view of the high population density and the concentration of agriculture resources (Cirillo et al., 1988). The polftitant under study, i.e. sulfur dioxide, may reach critical concentrutions in conditiony; of particularly unfavsurable meteorology. The data supplicd by the Air Furce Meteorological Service show that a highly stable atmosphere.and pr-crknged inversions with fog episodes are frequent in the Pe Valley, especiall:! during the eold season. As shown in the wind rose of ‘Fig. 3, the circulatiun iu the area is very wenk and talm canditiûns prevail fmrire than 50% of the time). Such conditiori; are essentially due tio an anticyclonic circulation over n~xthern Italy which Zeads to tampzrature inversisns of abuut 300-400 m height, giving rise to two phenomena: - in urban areas, the “beat island” effect is quite frequent, especially in the morning hours; in this case, the temperature is higher in the city than
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in the countryside and urban emissions are trapped in a highly stabk, closed system; - in countryside areas, the night temperature inversion is usually broken by solar radiation late in the mornlng, giving rise to instability at ground leve1 with fumigation episodes due to emissions from tal1 stacks (morning fumigation). An accurate inventory of emission data was available for the area (Cirillo et al., 1988j and recorded the following sources: - two Marge power plants (one at ka Caseha with two 200 m high stacks, and the second near Piacenza with two sta& with a height of 200 m and two of 47 m), - 13 main industrial plants, - 21 minor industrial and domestic sources that account for less than 1% of the total emissions. This does not mean that their impact is neghgible from the pollution point of view, since they contribute about 8% of the spatial average of the annual SO, concentration and a 1% increase in their emission would entail a 25% increase of the average concentratlon (Cirillo et al., 1988). This configuration of the sources, consistent with the most recent available data, has nut been changed in the following simulations, even though the overall procedure can be easily repeated with different source scenarios, using the ARIA package. Complex emission abatement technologies were taken into consideration only for large plants, while the change of fuel option was available for al1 the sources. Nevertheless, the decision variables for this probPem were 129. For the classification of the meteorological situation, Q-re following categories were taken into account: - seven atmospheric stability classes, - 16 wind direction sectors, - six wind speed ranges, - 14 thermal inversion height intervals. Since not al1 the com.binations have been reported (some are not even physically possible) the totnl number of meteorological scenarios was 2268. on air quaiity As aiready mentioned, the present EEC regulations standards take into consideration the mean daily SO, concentration, while the meteorological data elaborated by the Italian Air Force Mcteorological office are on a 3 h basis. This meax~ that the simulation. of diffusion of pollutants had to be performed on this time interval and consequently that the two objectives were the total treatment and fuel costs and the 3 h f concentration threshold. Two different ways of selecting the meteorological scenarios were examined:
(a) According to their probabifity of occurrence, i.e. adding the most probable situations up to the probability a” imposed by law, so that a large number of occasional meteorological situations are neglected. In this case the number of scenarios to be considered was reduced to 244 (b) According to the risk of pollution at ground level, starting from the least dangerotts classes, that is from stable situations (typical of the phenomena of “fanning” and “lofting” of c.b0 e plumes at the height of the stacks) and high wind speeds (leading also to the dispersion of pollutants in urban areas), and then adding less stable and less windy c.onditions up to a”. The resulting number of scenarios was 360. In the case at hand, where polfution from high stacks is predominant, this method provided a better solution, paid for by a longer execution time. The simulation of emissions’ diffusion for each source and for each scenario was performed by means of the modified Gaussian model DIMULA (Cirillo and Cagnetti, ig82), assuming the existente of a monitormg network in the area with eight stations corresponding to the present ones. The number of constraints to the problem could be reduced stil1 further by leaving out al1 the nul1 or dominated oncs (see Section 2.3). In the first case (a) the constraints decreased from 1952 to 146, in the second case (b) from 2880 to 139, so that the final number is quite similar for the two alternatives. Finally, 37 energy demand constraints were added to the problem, one for each plant. Figure 4 shows the set of non-dominated energy plans for Piacenza province. The estimates of the energy production costs are plotted as functions of the value of the lower SO, concentration threshold T’, assuming a constant ratio of 80/25Q between T’ and T”. It can be seen how aiternative (b) afways gives lower costs than case (a). This is due to the .Tact that some meteorological situations leading to high pollution have not been included in the problem or have been considered in set M, (which is constrained by a higher threshold) instead of set M,. Curve (b) thus represents an approximation of the optimal compromise between energy production (at the present level) and air quality, in the sense specified above. Most of the energy glans below a threshold value of 150 pg/m3 involve extensive use of low sulfur fuel and some desulfurization for the La Casella power plant. Point IJ in the figure represents the unreackable utopia condition, where both cos& and pollution are minimum. ft is computed by solving problem @)-(15) twic e with a scalar o’bjective function: once for the minimum tost, and once for the minimum pollution. Thus, beìow the valtrE: of the SO, threshold corresponding to ‘IJ, there is no solution of the problem satisfying the energy demands; whiie costs cannot be reduced below their valuc at U even if environmental quality is disrcgarded. Approaching such a condition,
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energy plans
Piacenza
area
2000
1500
lOCK2
500
J
minimum tost
0 0
(no polluti07
200
100
Env. quslity
contdl
300
400
@g/m3 SOJ
Fig. 4. Energy production tost versus air quality (measured in terms of SO, pollution threshold observed 50% of the time) for the region of Piacenza, with different choices of meteorological conditions to be included in the problem. (al Based on the probability of occurrence; (bl based on the effect on pollution. Point IJ represents the utopic situation in which both air pollution and energy costs are at their absolute and independent minima.
the choice of meteorology to be included in the problem obviously becomes less rellevant and the curves (a) and (b) come close to each other. 5. CONCLUDING
RIEMARKS
software environment for determining and analysing the efficient trade-offs between energy production and air quality (measured in probabilistic terms) has been developed on a personal computer. It combines the use of simulation models for computing the spataal distribution of a pollutant and of tlptimization methods to determine the optimal solutions within the physical, economie and legal scenario defined by the user. Furthermore, the suftware structure is completely independent of the particular simulation model usp,d and different models can easily be inteA
grated by the user. These characteristics of the package allow a decision maker to easily test several different energy production plans or to examine their robustness to changes in fuel or treatment costs, in the legal framework, or in energy demands. The effects of each plan can be analyzed from both economie and physieal points of view, by Iooking at the time and space distribution of the pollutant concentration. Although addressed originally only to a specific problem (planning of fuels and treatments of a set of plants in a region), the software supports the determination and analysis of several kinds of related decisions. For a 300 km* area in Piacenza province, a set of efficient compromise solutions for 41 sulfur dioxidc emissions has been determined through repetitive optimizations, each requiring from < 1 to 5 h of computation. It shorr!d be noted, however, that these energy pllans may be in force for years and thus speed of computation is not an essential requirement. Thanks to the use of the data base for input data storage and retrieval, the application of the same procedure for a different area (the municipality of Brescia, 100 krll to the north-east with very different meteorology due to the closeness of the /Ups) has been straightforward. The analysis of quite different economie-legal frameworks such as taxing pollution sources or creating a market for pollution permits, which limits the overall emission in the area, wil1 also be investigated in the near future by slightly modifying the objective function or the constraints of the optimization problem. ACKNOWLEDGMENTS
This study was supported by the ENEA VESE Project and the Italian Ministry of Education. The authors would like to thank W. Boco%a and M. Cirillo, EN&%, Rome, and G. MamoM, ENEE, Piacenza, for their suggestions and fcr supplying tht: case study data,
Amman, M. and Kornai, G.* 1987. Ccpst Fur,ction for Controliing SC& Emission in Europe. WP-87065, IIASA, L.axenburg, Auslria. Atkinstms, S. and Lmvis, D,, 1974. A wsl-cflC~tivcne~s analysis of altcrnativc air quality control stnrtcgies. J. Enviwn. Ecnn. Manage., 1: 237-2561. Atkinsons, 5. zmd Lcwiss D., 1975, Determination and implemcntatinn of sptimai air quality standard. J. Environ. Econ. Manage., 3: 363-380. Batterman, S., Amman, M., Hettetingh, J.-P., Hordijk, L. and Kornai, G., 1986. Optimal SOI Abatement Polities in Europe: Somc examples. WP-86-42, HASA, Laxenburg, Austria. Bocola, W., 1987. TccnoIogic e tosti per la riduzione dclf’inquinar~lcnto atmosferico di ccntrali tcrmnefcttrichc a carbonc. Energla, 2: 26-41. (in Itafian).
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Cirillo, M.C. and Cagnetti, P., 1982. DIMULA: un codice multivariato per il calcolo deile concentrazioni in aria, al live110 del suolo, dcgli inquinanti atmosferici. RTl/STUDIVALSAMP (8218, ENEA, Y. le Regina Margherita 125, Roma, Italy (in Italian). Cirillo, M.C., Clerici, G.C. and Manzi, D., 1986. Manuale d’uso del codice DIMULA Versione 1.0. RT/STUDI/86/2, ENEA, V. le Regina Margherita 125, Roma, Italy (in Italian). Cirillo, M.C., Bocola, W. and ‘I’riolo, L., 1988. Environmcntal impact of air pollutants with emphasis on crops. The Piacenza case study. In: A. Marani (Editor), Advances in Environmental rvlodelling. Elsevier, Amsterdam, pp. 243-312. Colorni, A. and Laniado, E., 1987. A decision support system for choosing among alternative projects. Seminar on Multipurpose Agriculture and Forestry. Wissensc.taEt Vauk, Kiel, pp. 39-51. ENEA, 1986. Froceedings ENCLAIR 86, Taormina, Italy, October 1986. ENEA, V. le Regina Margherita 125, Roma, Italy. EPA, 1986. Guideline on Air Quality Models. U.S. Environmental Protection Agency Report 450/2-78-027R, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Fronza, G. and MeHi, P., 1983. Assignment of emission abatement Eevels by stochastic programming. Atmos. Environ., 17: 2005-2029. Goicoechea, A., Mansen, D.R. and Du&tein, L., 1982. Multiobjective Decision Analysis with Engineering and Business Applications. John Wiley, New York. Gorelick, S.M., 1983. A review of distributed parameters groundwater management modeling methods. Water Resour. Res., 19: 305-319. Guariso, 6. and Lan’zini, R., 1989. Yroduzione di clnergia e salvaguardia dell’ambiente: verso un compromesso ottimalc. Rit. Operativa, 50: 27-48 (in Ttalian). Guariso, G. and Werthner, W., 1989. Environmental Decision Support Systems. Ellis l-lorwood, Cbichester. Keeney, R.L. and Raiffa, H., 1975. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley, New York. Krupnick, A.J., Oates, W.E. and Van De Verg, E., 1983. On marketable air pollution permits: the case for a system of pollution offsets. J. Environ. Econ. Manage., 10: 233-247. Simpson, D , PL:iin, D.A., V arey, J.E. and Williams, M.L., 1990. Dispersion modelling of nitrogen Jxides in the United Kingdom. Atmos. Environ., 24A(73: 1713-1733.