Emission projections from energy production and waste ... · Emission projections from energy production and waste management in the CAM (Spain) J. Lumbrerasl*, M.J. Shnchez2 &M

Emission projections from energy productionand waste management in the CAM (Spain)

J. Lumbrerasl*, M.J. Shnchez2 &M. E. Rodriguezl.1Departamento de Ingenieria Quimica Industrial y del Medio Ambiente,Universidad Politkcnica de Madrid (UPM), C/Josk Guti&rez Abascal, 2.28006- Madrid. Spain2Departamento de Ingenieria de Organizacich, Administraci&z deEmpresas y Estadistica, UPM

Abstract

The Universidad Politicnica de Madrid (UPM) is currently studying industrialactivities that can produce air pollutants. The CORINAIR methodology is beingused and the associated nomenclature called SNAP (Selected Nomenclature forAir Pollution) has been selected to complete an inventory [5]. This inventoryconsiders all the pollutant sources declared in CORINAIR’94, The inventory isbeing time and spatially disintegrated, The reference methodology developed inthis project is very close to those used in the European Union and the GenevaAgreement and could be of guidance for other Spanish regions. The time periodconsidered begins in 1995 and lasts until 2020,

The aim of the study is to obtain detailed information about air pollutantactivities and their current and future emissions in order to identify the incidenceof each activity in air quality, to give useful information for regulatory decisionsand to support decisions in the cases of great disturbances of air quality.

The study presented covers two sectors called SNAP 1 (Public power,cogeneration and district heating plants) and SNAP 9 (waste treatment anddisposal) in the CAM (Autonomous region in the centre of Spain that includesthe city of Madrid). Official data from 1995 through 2000 are used for validatingand evaluating the goodness of the methodology. For the rest of the period westudy the incidence of changing technology and equipment to reduce airpollutant emission. Scenarios are based in statistical predictions, socioeconomic

© 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.Web: www.witpress.com Email [email protected] from: Air Pollution X , CA Brebbia & JF Martin-Duque (Editors).ISBN 1-85312-916-X

302 Air Pollution X

data and estimated consumed energy. Different emission factors are usedapplying the Best Available Technologies (BAT) and fhture legislation.

1 Introduction

The aim of this work is to obtain detailed information about energy productionand waste management and their current and future emissions in order to identifythe incidence of each activity in air quality, to give useful information forregulatory decisions and to support decisions in the cases of great air qualitydisturbances.

The area of the study covers the autonomous region of Madrid (CAM).However, in order to model the emissions it is necessary to work with a largerdomain than the one just including the CAM, which is shown in Figure 1.

%

1

.s ,s W.. w $ww, ,:** ,W” ,.,! !%* :* m

(hogrdphiu)] mtxlcl Iing iinTTHIIn

Figure 1: Map of Spain with detailed geographical modelling domain includingthe CAM,

The goal of the technological study is first to analyse the techniques that arenow being used in the autonomous region and second, possible futurealternatives. The technologies are classified according to the European directiveon integrated pollution prevention and control (IPPC, council directive96/6 l/CE) in Best Available Techniques (BAT) and with a new methodology inEmission Reduction Techniques (ERT). Hence, the technological study and theemission projections are focused on the CAM region shown in Figure 2. As canbe seen in figure 2, there are seven areas and 37 measurement stations including25 in the city centre that will provide usefil information to validate themodelling and calculate its accuracy.


Air Pollution X 303

Figure 2: Map of the CAM (Autononincludes the city of Madrid)stations.

Ious region in the centre of Spain thatand the City centre with measurement

One of the past modelling studies (Palacios, 2001) is represented in figure 3.The influence of traffic in photochemical pollutants was modelled andinteresting results were obtained for methane, NMVOCS, NO,. Ambientconcentration maps for CO, C02, NH3, N20 and S02 are also obtained.

2 Groups considered and their current emissions

Two main industrial sectors have been studied for this work:c Public power, cogeneration and district heating plants (SNAP-1). Waste treatment and disposal (SNAP-9).


304 Air Pollution X

Current significant emissions fi-om SNAP-9 in the CAM are shown in table 1.Also comparative emissions between CAM emissions and total countryemissions are presented in figure 4. For SNAP-1, there are no emissions for themoment in the CAM because there is no plant inside this area, Even so, it isinteresting to study the technology because of its global influence in air qualityand the likely construction of a plant of these characteristics in this area.

[ncin Incin hosp Managed Unmanaged Open Cremation W. water Ww treat in Sludge

domlmun

Compost

wastes w, disposal W, d, burning treat ind.

w,

reslcom spreading production

agric w

Figure 4: Comparison between SNAP-9 activities for Madrid during 1996

Table 1: Emissions from significant SNAP-9 in Madrid during 1996

El❑%CH4.W❑ % CH4<.,

❑ % COVNw.<

❑ $$COVN W-O

■ % No%..!

❑ ?4Nox.sec

SNAP 9.4 SNAP 9.10Total

0401 0402 1001 1002 1003 1005

Emission 130663 126716 3947 10692,76 2012,3 7361,8 47,04 1271,6 141503

CHq ~0totall 72,1 70,0 2,2 5,9 1,1 4,1 0,0 0,7 78,13

‘Yo secto~ 92,3 89,5 2,8 7,6 1,4 5,2 0,0 0,9 100,01percentage of emissions comparing with total@ emissions in Madrid

Zpercentage of emissions comparing with total SNAP-9 emissions in Madrid

As table 1 shows, CHi emissions from SNAP 9.4 (Solid Waste Disposal onLand) and 9.10.1 & 9.10.2 (Waste water treatment) are significant in comparisonwith total emissions. “Total” emissions are the amount of all the emissions fromSNAP-9 sub sectors including 9,4 and 9.10 but no only.

3 Scenarios for SNAP 1 (Public power, cogeneration anddistrict heating plants)

In the case of Madrid, as mentioned above, it is likely that a new plant will bebuilt and three scenarios are proposed for projections on future emissions frompublic power:


Air Pollution X 305

1, BASE (emissions based on the Environmental Impact Assessment for aCombined Cycle Gas Turbine -CCGT-)

2. COR (emissions based on CORINAIR methodology [5])3. LEG scenario; only includes emissions for NOX and SOZ ffom the

current proposal (June 2000) of the revised LCP-Directive, UE.

In each scenario there are three sub-scenarios depending on the powerconsidered in the CCGT. For the 2010 projections we considered threepossibilities: 1200, 1400 and 1500 MW,

It could be possible to study more future scenarios with longer time horizon,such as 2020. However, this has not been done because the SNAP-9 caseinvolves waste estimations with Box-Jenkins models (ARIMA) and theirassociated uncertainty could be at least of 60% when a 20 year period isconsidered. ARIMA models are very useful for predictions up to a 5 year period,

4 Results of SNAP-1 emission projection

The results using the CORINAIR methodology are shown in figure 5,Bmo,ca

I 1

nQCH4(()■co (m

❑c02 (W

❑COVNM (()

■we 0)

❑NOX 0)

■s02 (1]

Figure 5: Emission projections for SNAP-1 activities for Madrid during2010

5 Scenarios for SNAP 9 (waste treatment and disposal).

Several scenarios are studied for waste treatment and disposal. The possibilitiesof waste treatment are: incineration, compost production from waste and biogasproduction, Disposal is another option for waste management, Table 2 shows thescenarios for waste treatment and disposal. In this paper we have only presentedresults for incineration and compost production studies,


306 Air Pollution X

Table 2: Scenarios for waste management

Name Scenario characteristics2003-b Base scenario2003-2 Plan from the authorities with a 5% increase on waste generation from 20002003-3a Plan from the authorities with waste prediction horn ARIMA model2010-b Base scenario

2010-laScenario including the increase of incineration with a 15% increase on wastegeneration from 2000

2010-lbScenario including the increase of incineration with waste prediction fromARIMA model

2010-2aScenario including the increase of compost production with a 15V0increase onwaste generation from 2000

2010-2bScenario including the increase of compost production with waste predictionfrom ARIMA model

2010-3a Plan from the authorities with a 15% increase on waste generation from 20002010-3b Plan from the authorities with waste prediction from ARIMA modelBase scenario: scenario with the same waste generation as in 2000 and taking into accountthe authorities’ plan for waste management

6 Results of the SNAP-9 emission projection

6.1 Results from the ARIMA model

The purpose of this section is to build a model which explains the evolution ofthe variable “waste” along time and through which one can make short termforecasts. We have thus applied univariate time series analysis using ARIMAmodels (Box and Jenkins 1976). The main idea behind these models is to profitfrom the inertia of the data to make forecasts.

Time Series Plot for Waste(x 100000

11 r

12

$ 9

3

00 10 20 30 40 50

Figure 6: Time series plot for waste

Figure 6 shows the waste generation horn 1957 through 2000, in which aclear positive trend can be observed, which is the cause of the non stationarity inmean of the series, i.e, the mean is not constant along time. The series aretransformed into a stationary one and we observe that the structure that appearsin the transformed data corresponds to an autoregressive model of first order(AR(l)). This means that each value just depends on the preceding one, Hence,the estimated model is an ARIMA (1,1,0). Table 3 shows the parameterestimates as well as the t-statistic values for the proposed model. The t-statistics


Air Pollutionx 307

indicate to what extent the hypothesis tests for the parameters have turned outsignificant.

Table 3: Parameter estimates for the ARIMA ( 1,1,0) model

Type of Estimatedt-value Sr2

Box-Pierceparameter value test: p -value

Mean 24842,4 3.48434

4 0.4234 3.00996

7.6379E8 0.926485

Residual Plot for adjusted Waste(x mooo~ ARIMA(l,l ,0) with constant

l-i I I I r I I 4

4

2

0

.2

-4

-60 10 20 30 40 50

Figure 7: Residual plot for adjusted Waste

Residual Autocomelationsfor adjusted WasteANMA(l,l,O) wilh mns!ant

1

06

—— - .— —02

-— -- ----02 —— - ——— . .—— _ .— _

.06

.10 3 6 9 12 15

lag

Figure 8: Residual autocomelations for adjusted Waste

ResidualPartialAutocomelationsfor adjustedWasteASIMA(l,l,O) wtb constant

I

06j

—— —— - .— —— ——— - —— .0,2

—— -—-. .—42 —— — ——— - —— —- ——— - —— —

46

.1 i0 3 6 9 12 15

lag

Figure 9: Residual partial autocomelations for adjusted Waste

Next, the residuals of the model are analysed, they should be distributed

according a N(O, U) and should contain no information. Observing the graph ofthe series of residuals (figure 7) and the simple autoconelation finction andpartial autocomelation function (figure 8 and 9 respectively), it can be seen that


308 Air Pollution X

there appear neither trends nor outliers on the residual plot. These fictionsillustrate how far back the memory of the process (waste generation) goes [2], Inthis case there appears no significant lag which indicates that the estimatedmodel is adequate, A Box-Pierce test to check if the residuals can be taken as asequence of independent random variables has also been performed, The p-valueis 0.92 which confw the adequacy of the model,

6.2 2001-2010 emission projections

We now present the predicted values for the waste generation for the years 2001through 2010, Figure 10 shows a graph with the predictions up to 2010 and intable 4 the values of the predictions as well as 95% confidence intervals. It isimportant to note that it is not adequate to make such long term predictions, 2005is already “dangerous”.

Time Sequence Plot for Waste(x I,E6

1

16

12

0,8

0,4

0

ARIMA(I, 1,0) with comlmt

~’”’’’”””’’”’’’”””””

H f---

❑

0 10 20 30 40 50 60

Figure 10: Residual partial autocorrelations for adjusted Waste

Table 4:2001-2010 projections of waste generation

Year Forecast (XS106)Lower Limit- Upper Limit-

gsyo (X-106) gs~o (x”l@)2001 1.30232 1.24650 1.35813

2002 1,32661 1.22952 1.42370

2003 1.35122 1.21921 1.48323

2004 1,37596 1.21409 1.53784

2005 1.40076 1,21283 1.58870

2006 1,42559 1.21445 1.63673

2007 1.45042 1.21826 1.68259

2008 1.47526 1.22377 1.72676

2009 1.5001 1.23064 1,76957

2010 1.52495 1,23863 1,81126

0

——

actual

forecast95,0% lirh

In order to verify that the model used to produce the forecast is adequate, we laidoff the three last observations, estimated the parameters of the model andcalculated predictions for these three last time points. The forecast errors whererespectively of 1,32’Yo,1.45% and 1.28°/0 which are very satisfactory.


Air Pollutionx 309

6.3 Emission projections from incineration and compost production

According to the preceding scenarios, CHi, CO, COZ, COVNM, NZO, NOX andS02 projection emissions are calculated for incineration plants and CHJ and NH3emissions for compost production plants, The data are plotted in figures 11 and12. The emission factors are taken from [5] except for C02 which is assumed avalue of 324 kg/ton according to [15]. The S02 emissions are produced with acidgas abatement. The VOC are also divided into CHA and NMVOC in the samepercentage as in [15],

m,.

O,M mEn m.,0 mm.,

moms

w, . mm, cow. f.m WA

P.IW*

*,

Figure 11: Emissions from incineration plants for 2003 scenarios

‘“”’oo~

1❑20i0.b42010.1.

❑20?0.lb

❑2010-2,

■2040.2b

Q201O.3,

■ 2010.3b

CH4 co C02(kQ COVNM N20 NOX S02

Pollumlm

Figure 12: Emissions from incineration plants for 2010 scenarios


310 Air Pollution X

The emissions ffom compost production plants are shown in figure 13.Emission factors for CHd are taken from [15] and from[16] for NHs.

2C03.b Z@W.> Z03-3a 2D7C-B 20161. 20i&$b 22>0-2, 20tC-2b 201536 20<0-~

.%marlos

Figure 13: CH~ and NHq emissions from compost plants for fiture scenarios

7 Discussion

For SNAP-1, as was expected in a fwst approach, the more power, the moreemissions are generated. A more thorough analysis provides some interestingconclusions: if legislation standards were achieved (LEG scenario), NO,emissions would be increased in 67% and in 334’%ofor S02. This significantincrease shown in figure 5 is due to the fact that the technology considered in theimplementation of the new plant involved in the scenario is one of the mostemission-effective existing nowadays. It should be taken into account that otherpollutants are not included in legislation standards, so it is impossible to comparetheir emission projections. On the other hand, the emissions associated toCORINAIR are very close to those associated to the BASE case (advancedCCGT technology). Total VOC emissions are 32’%ohigher for the COR case.However, we must take into account that for the BASE case the so-called“methane emissions” include not only methane itself but also NMVOC while forCOR, the emissions are properly dissagregated. Thus, the COR methaneemissions are 97V0 lower, and NMVOC emissions are 1082’%0higher,

Evidently, if no new plant is built, there will be no emissions.

This study has been restricted to large combustion plants using fossil fiels.Alternative technologies (nuclear power plants, aeolian, solar, hydraulic, etc)


Air Pollution X 311

would produce no emissions but would have other costs such as radioactiveresidues, bird mortality, ecological problems, by-product treatment, etc.

For SNAP-9, CHi and NH3 emissions are greater in compost productionplants than in incineration ones. On the contrary, NOX, SOL, CO and CO~emissions are higher for incineration plants than for compost production ones.Projections using ARIMA models are lower for 2003 but higher for 2010compared with other estimations. This is because the expected waste increase isprobably lower than the real fiture one.

For fhture research it would be interesting to compare compost andincineration emissions with other treatment alternatives such as disposal oranaerobic digestion.

8 Conclusions

For SNAP- 1 the CCGT plant seems an adequate alternative when we compare itsemissions with CORINAIR and the future legislation standards.

For SNAP-9, if waste incineration is favoured, CH~ and NH3 emissions willappear while if compost treatment is favoured NO., S02, CO and COZ emissionswill increase. For detailed information on this aspect, fiture research is required.

9 Acknowledgments

The authors want to express their thanks to Dr. JOS6 Mira for his help in theelaboration of the paper.

10 References

[1] Becchis, F. (2001) Economics of integrated pollution prevention policies:introductory remarks, Air Pollution IX

[2] Box, G,E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecastingand Control. Holden Day.

[3] Commission of the European Communities. (1995) European Energy to2020, A scenario approach.

[4] Donaire, I; Rodriguez, M. E,, Palacios, M and Martin, F. (1999). Proyecci6nde las emisiones de contaminants a la atmosfera para el 2020 en Espaiia.Short paper book of the International Conference on EnvironmentalEngineering (Cartagena),

[5] EMEP/CORINAIR Emission Inventory Guidebook - 3rd edition,[6] Grupo de Trabajo de Prospective. La energia en Espaiia 1995-2020.

Simulation provisional del Escenario BASE,[7] IDAE-MCYT. (2000), Prospective Energ&ica y COZ. Escenarios 2010.[8] IDAE-MINER-MEH. (1997), La Energia en Espaiia: 1995-2020.

Simulation Provisional del Escenario BASE.


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[9]

[10

[11

Johansson, M., Guardans, R. y Lindstrow M, (1999). Coupling ofCORINAIR Data to Cost-effective Emission Reduction Strategies Based onCritical Thresholds, CIEMA Z Estudios Tdcnicos,Karvosenoja, N y Johansson, M. (1999). National cost curve analysis forSOZ and NO, emission control. Finnish Environment Institute.Lumbreras, J, Sanchez, M,J, and Rodriguez, M.E. (2001) “Air pollutionestimation for future and current emission scenarios in the CAM (Spain)”.International Conference Measuring Air Pollutants by Diffusive Sampling,Environment Institute (EC Joint Research Centre-EU). Montpelier.

[12] Palacios, M, (2001), Influencia del trafico rodado en la generacitm de lacontaminacitm atmosferica. Aplicacion de un modelo de dispersion al areade influencia de la Comunidad de Madrid. Doctoral thesis.

[13] Palacios, M; Martini, A; Kirchner, F; Martin, F and Rodriguez, ME. 1999.Estimation de contaminacich atmosferica en la Comunidad de Madrid bajoescenarios hipoteticos de emisiones de contaminants. Short paper book ofthe International Conference on Environmental Engineering (Cartagena).

[14] Seinfield, John; Pandis, Spyros, (1998), Atmospheric chemistry and physics.Wisley-Interscience.

[15] Spanish Emission Inventory for years 1990-1996, Edited in September 2000[16] Tchobanoglous, G., Theisen, H, and Vigil, S, (1993), Integrated solid waste

management, McGraw-Hill, Inc.[17] United Nations/Economic Commission for Europe (uN/ECE). (1997),

Convention on Long-Range Transboundary Air Pollution, Draft BATBackground Document. “Task Force on the Assessment of AbatementOptions/Techniques for Nitrogen Oxides flom Stationary Sources”.


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Emission projections from energy production and waste ... · Emission projections from energy production and waste management in the CAM (Spain) J. Lumbrerasl*, M.J. Shnchez2 &M