Air quality decision support under uncertainty (case study analysis) Piotr Holnicki Systems Research Institute PAS Warszawa, Newelska 6

Applications of air pollution transport models – air quality analysis and forecast, – exceedance of critical levels for concentration or critical loads for deposition, – assessment of environmental impact of emission sources, – selection of emission reduction technology, – selection of new investments location, – analysis of new technologies of energy generation, – IAM – Integrated Assessment Models.

Integrated system of air quality management

The basic processes of air pollution dispersion

Initial conditions Notation: – domain considered, c – pollution concentration, Q – total emission field. (2)(2) – wind field vector, – outward normal vector, – horizontal diffusion coefficient, – pollution reduction coefficient, (1a) (1b) Boundary conditions Transport equation Mathematical model of air pollution dispersion (1)

Gaussian trail model (local scale) Assumptions: Pollutant concentration:

Regional and urban scale pollution dispersion models Eulerian model Lagrangian model

Main sources of modeling uncertainty 1)Input data a) emission sources (point – energy sector, heavy industry; area – housing, industry; linear – transportation system) b) meteorological data (wind field, atmospheric stability, mixing height, temperature, humidity, precipitation,…..) 2)Model parameters a) model type (Lgrangian, Eulerian, other, temporal & spatial scale) b) simplifications of mathematical description c) parameterization of some processes (horizontal & vertical diffusion, dry & wet deposition, chemical transformations, …..) d) numerical implementation (approximation of transport equations, time & space discretization step, numerical diffusion effect, ….) 3)Physical description of the domain a) orography b) topography c) terrain coverage

Impact of the input data – emission sources Categories of emission sources a) high point sources (energy sector) – relatively low uncertainty, necessary analysis of initial puff formation b) intermediate point sources (other industry) – higher uncertainty and imprecision of emission data (technological parameters and fuel are not known) c) area sources (industry, housing) – high uncertainty – emission data are assessed basing on some aggregated information d) linear sources (transportation systems) – high uncertainty -- emission depends on the traffic, car parameters, fuel use and characteristics

Impact of the input data – meteorology (wind field, mixing height, temperature, cloudeness, precipitation, …)

Impact of the input data – meteorology (atmospheric stability) – unstable conditions – neutral conditions – stable conditions Pasquill stability categories

Case study example – regional scale Domain – 110 x 56 km Discretization – 2km x 2km Eulerian model RGFOR3

NoSourceCoordinatesHe [m] Emisson (Winter) SO 2 [t/d] Emission (Summer) SO 2 [t/d] 1Jaworzno III(21,24) Rybnik(1,20) Siersza A(30,23) SierszaB(30,23) Skawina(43,11) Łaziska I(8,20) Będzin B(18,31) Łęg(46,12) Katowice(13,25) Będzin A(18,31) Łaziska II(8,20) Łaziska III(8,20) Jaworzno IIA(21,24) Jaworzno IIB(21,24) Halemba(8,25) Bielsko-Biała(14,2) Bielsko-Km.(15,1) Chorzów(12,27) Jaworzno I(20,23) Tychy(13,19) Parameters of emission sources

Concentration map for nominal emissions Season-averaged (Winter) distribution of SO 2 in the domain

ParameterUncertainty range (for 95% of data) Distribution Emission [g/s]± 20%N / L-N Outlet gas velocity [m/s]± 15%N / L-N Outlet gas temperature [ o K]± 15%N / L-N Mixing height [m]± 25%N / L-N Components of geostrophic wind [m/s]± 25%N / L-N Components of anemometric wind [m/s]± 25%N / L-N Temperature [ o C]± 25%N / L-N Precipitation intensity [mm/h]± 25%N / L-N Atmospheric stability class [ - ]± 1- Uncertainty range of the input data

Application of Monte Carlo method REGFOR3 – regional, three-layer Eulerian model ↓

Concentration uncertainty due to input data uncertainty emission intensity source parameters basic meteo data atmospheric stability class

Uncertainty in decision support due to air quality forecast uncertainty

Optimal strategy of emission abatement – notation Quality functional Current concentration Current emission – admissible level of concentration Emission reduction cost Notation – source —> receptor transfer matrix – admissible level of concentration N – number of controlled sources, M – number of desulphurization technologies, – „0-1” control variable matrix – effectiveness of emission reduction– emission vector – matrix of the unit costs – background concentration;

Discrete problem (DP) of the optimal abatement strategy Find the optimal solution of the following problems (DP-A) – minimization of environmental cost (DP-B) – minimization of technological cost The set of admissible solutions subject to the total cost constraint minimize the environmental cost function subject to the constraint of environmental standard minimize the cost of emission abatement

Find the optimal solution of the following problems (MP-A) – minimization of environmental cost subject to the total cost constraint minimize the environmental cost function (MP-B) – minimization of technological cost subject to the constraint of environmental standard The set of admissible solutions Modified problem (MP) of the optimal abatement strategy minimize the cost of emission abatement

1) "doing nothing" technology (e = 0 ), 2) low sulfur fuel (e = 0.30 ), 3) dry desulphurization method (e = 0.35 ), 4) low sulfur fuel + dry desulphurization method (e = ), 5) half-dry desulphurization method (e = 0.75 ), 7) wet desulphurization method (e = 0.85 ), 8) Low sulfur fuel + wet desulphurization method (e = ), 6) low sulfur fuel + half-dry desulphurization method (e = ), The real data case study – desulphurization technologies Computational domain Efficiency of abatement technologies Location of emission sources 110 km x 76 km – rectangle domain 20 – power plants (emission sources)

Characteristics of the controlled emission sources No SourceCoord.Stack [m] Emiss [t/d] Unit abatement cost 1Jaworzno III(21,24) Rybnik(1,20) Siersza A(30,23) SierszaB(30,23) Skawina(43,11) Łaziska I(8,20) Będzin B(18,31) Łęg(46,12) Katowice(13,25) Będzin A(18,31) Łaziska II(8,20) Łaziska III(8,20) Jaworzno IIA(21,24) Jaworzno IIB(21,24) Halemba(8,25) Bielsko-Biała(14,2) Bielsko-Km.(15,1) Chorzów(12,27) Jaworzno I(20,23) Tychy(13,19)

Application of Monte Carlo method Optimization algorithm ↓

Optimal selection of emission reduction technologies initial index - J i = a) uncertain (fuzzy) solution b) reference solution

Optimal selection of emission reduction technologies initial index - J i = a) uncertain (fuzzy) solution b) reference solution

Optimal selection of emission reduction technologies initial index - J i = a) uncertain (fuzzy) solutionb) reference solution

Histogram of the optimal emission; initial index - J i = reference solutionuncertain (fuzzy) solution –

Histogram of the optimal emission; initial index - J i = uncertain (fuzzy) solution reference solution

Histogram of the optimal emission; initial index - J i = uncertain (fuzzy) solution reference solution

General conclusions limited impact of the model uncertainty on accuracy of the optimal problem solution, mainly qualitative character of environment-oriented decisions, final accuracy of numerical test – sufficient for decision support in environmental policy, application of sophisticated and time-consuming methods in such applications is (due to uncertainty) rather unfounded, simpler and computationally efficient heuristic algorithms are more motivated in such decision tasks.

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