DEA - Università degli Studi di Brescia Multi-objective optimization to select effective PM10 control policies in Northern Italy C. Carnevale, E. Pisoni,

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DEA - Università degli Studi di Brescia Multi-objective optimization to select effective PM10 control policies in Northern Italy C. Carnevale, E. Pisoni, M. Volta Dipartimento di Elettronica per l’Automazione Università degli Studi di Brescia, Italy

DEA - Università degli Studi di Brescia Methodology: research aim To develop a secondary pollution control plan: Multi-objective optimization: –Objective 1: Air Quality Index (AQI) –Objective 2: Internal Costs (C) for a mesoscale domain –Milan CityDelta domain (Northern Italy)

DEA - Università degli Studi di Brescia Methodology: multi-objective problem emission reduction costs PM exposure index (meanPM) set of the feseable solutions decision variables: reduction of the precursor emissions

DEA - Università degli Studi di Brescia daily (d) cell (i,j) precursor (p) emissions for CORINAIR sectors (s) for the basecase scenario decision variable set: precursor (p) reduction for CORINAIR sector (s) domain yearly mean PM exposure ( g/m 3 ): source-receptor models PM precursors CORINAIR sectors (s) Methodology: Obj 1 - the Air Quality Index:  (  )

DEA - Università degli Studi di Brescia Methodology: Obj 2 - emission reduction Costs (C) unit cost curve for precursor (p) and CORINAIR sector (s) Cost curves used are estimated on the basis of RAINS-IIASA database An emission reduction cost curve has been assessed for each CORINAIR sector.

DEA - Università degli Studi di Brescia Case study: domain 300x300km 2 Milan domain

DEA - Università degli Studi di Brescia Case study: Obj 1: AQI Pollutant concentration are computed by 3D deterministic chemical transport multiphase modelling system –Time consuming Identification of source-receptor models (Neuro-fuzzy Networks), describing the nonlinear relation between decision variables (emission reduction) and air quality objective, processing the simulations of TCAM

DEA - Università degli Studi di Brescia Case study: Obj 1 - GAMES Continental model output Land use Topography Diagnostic Model Output Local Measurements RAMS-CALMET Meterological Model TCAM Initial and Boundary condition Pre-processors 3D concentration fields POEM-PM Emission Model Radiosounding Emission inventories Emission Fields 3D meteo fields VOC, PM speciation Profiles Temporal Profiles System Evaluation Tool IC, BC

DEA - Università degli Studi di Brescia Case study: Obj 1 - TCAM model gas phase chemical mechanisms: SAPRC90, SAPRC97, COCOH97, CBIV 21 aerosol chemical species 10 Size classes –Size varying during the simulation –Fixed-Moving approach processes involved: –Condensation/Evaporation –Nucleation –Aqueous Chemistry Shell Core

DEA - Università degli Studi di Brescia Case study: Obj 1 - TCAM simulations base case simulation: –300 x 300 km2, 60 x 60 cells, cell resolution: 5x5 km2 –11 vertical layers –emission and meteorological fields: JRC (CityDelta Project) –initial and boundary conditions: EMEP –simulation takes several days of CPU time –simulation period: year 1999 alternative scenario: –CLE: current legislation –MFR: most feasible reduction emissionscenario precursorBase case [ton/year] CLE [%] MFR [%] NOx VOC SOx NH PM

DEA - Università degli Studi di Brescia Case study: Obj 1 - SR models 4-layer NF architecture –Number of MF for input: 2 –Number of rules: 2 5 =32 –Nodes of hidden layer: 8 Input data: daily NOx,VOC, PM10, NH3, SOx emissions (CDII) Target data: daily PM10 concentration computed by the GAMES system (CDII)

DEA - Università degli Studi di Brescia Case study: Obj 1 - SR models Identification of a neural network for each group of 6x6 TCAM grid cells

DEA - Università degli Studi di Brescia Case study: Obj 1 - SR models validation BIASScatter Plot

DEA - Università degli Studi di Brescia Case study: Obj 2 - Cost functions Fitting the costs of the available technologies: –considering 2nd order polynomial functions –with the constraint of estimating a monotonically increasing and convex function. NOx, sector 3:

DEA - Università degli Studi di Brescia Case study: optimization problem solution Weighted Sum Method Constraints 1.Maximum Feasible Reductions: 2. Technologies reducing both precursors S1S1 S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 S8S8 S9S9 S 10 S

DEA - Università degli Studi di Brescia Case study: Results (pareto boundary) Optimisation performed only on the 50% cells with highest mean PM concentration

DEA - Università degli Studi di Brescia Case study: Results (VOC) road transport (7), resid. combustion plants (2) road transport (7)

DEA - Università degli Studi di Brescia Case study: Results (NOx) industrial combustion (3), public power plants (1), production processes (4) road transport (7), public power plants (1), production processes (4)

DEA - Università degli Studi di Brescia Case study: Results (PM) waste treatment (9), production processes (4), other mobile sources (8) road transport (7), production processes (4), other mobile sources (8)

DEA - Università degli Studi di Brescia Case study: Results (SO2) production processes (4), road transport (7), other mobile sources (8) production processes (4),

DEA - Università degli Studi di Brescia Conclusions –A procedure to formulate a multi-objective analysis to control PM exposure has been presented; –The procedure implements neuro-fuzzy networks tuned by the outputs of a deterministic 3D modelling system; –The methodology has been applied over Milan CityDelta domain (Northern Italy): a strong reduction of 70% of air qualiy index can be attained with only 15% of maximum costs

DEA - Università degli Studi di Brescia Current activities –Uncertainty analysis: cost curves NOx/VOC and NOx/PM reduction functions for transport sectors sensitivity of source-receptor models to NH3 emission reduction –CityDeltaIII simulations to extend source-receptor model calibration and validation sets; –source-receptor models for mean PM10 and PM2.5 concentrations: spatial resolution 10x10km 2 ; –PM2.5 two-objective optimization –Ozone and PM10 two-objective optimization

DEA - Università degli Studi di Brescia Thanks to… This research has been partially supported by MIUR (Italian Ministry of University and Research). The authors are grateful to the CityDelta community. The work has been developed in the frame of NoE ACCENT (T&TP, Atmospheric sustainability).