Multi-objective analysis to control ozone exposure

1. Multi-objective analysis to control ozone exposure C. Carnevale, G. Finzi, E. Pisoni, M. Volta Dipartimento di Elettronica per l’Automazione Università degli Studi di Brescia, Italy

2. Research aim To develop a secondary pollution control plan: • Multi-objective optimization: • Objective 1: Air Quality Index (AQI) • Objective 2: Internal Costs (C) • Objective 2: External Costs (ExC) • for a mesoscale domain • Milan CityDelta domain (Northern Italy)

3. Problem formulation: objective 1 the Air Quality Indicator (AQI) daily cell NOx and VOC emissions in the reference case for CORINAIR sector s; decision variable set: CORINAIR sector precursor emission reductions;

4. Problem formulation : objective 2 the emission reduction cost (C) unit costs related respectively to NOx and VOC emission reduction; decision variable set: CORINAIR sector precursor emission reductions;

5. Study domain 300x300km2 Milan domain

6. AQI model identification • Pollutant concentration are computed by 3D deterministic chemical transport multiphase modelling system • Time consuming • Identification of source-receptor models (Neural Networks), describing the nonlinear relation between decision variables (emission reduction) and air quality objective, processing the simulations of TCAM

7. Shell Core 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

8. 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 • the run of such a simulation takes about 12 days of CPU time • simulation period: 1999 april to september • alternative scenario simulations: • CLE: current legislation • MFR: most feasible reduction

9. Source-receptor models (NN) • Elman NN architecture: • Nodes of input layer: 2 • Nodes of output layer: 1 • Nodes of hidden layer: 8 • One neural network for each group of 2x2 (10x10 km2) domain cells • Input data: daily NOx and VOC emissions • Target data: cell AOT40 daily values computed by the GAMES system

10. Source-receptor models (NN) • Identification and validation dataset: • 3 TCAM seasonal simulations • Base Case; • Current LEgislation; • Most Feasible Reduction. • Validation dataset (126 values): • Third week of each month. • Identification dataset (423 values): • Remaining patterns

11. Source-receptor models (NN) NBIAS r=0.97

12. Cost functions • Cost curves used are estimated on the basis of RAINS-IIASA database (http://www.iiasa.ac.at) • An emission reduction cost curve has been assessed for each CORINAIR sector. • Decision variables = emission reduction for sectors: • VOC: 2, 3, 4 ,5, 6, 7, 8, 9 • NOx: 2, 3, 4, 7, 8

13. 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:

14. Optimization problem solution • Weighted Sum Method • Constraints • Maximum Feasible Reductions 2. Technologies reducing both precursors

15. Results • Pareto boundaries Utopia

16. Results NOx reductions VOC reductions

17. Results VOC emissions NOx emissions

18. Conclusions • A procedure to formulate a multi-objective analysis to control ozone exposure has been presented • The procedure implements Elman neural 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 ozone exposure (60% of the maximum air quality improvement) can be attained with a small fraction of the emission reduction technology costs (about 12%)

19. Current activities • Uncertainty analysis: • Source-receptor models • Cost curves • VOC/NOx reduction functions for transport sectors • CityDeltaIII simulations to extend source-receptor model calibration and validation sets; • source-receptor models for SOMO35,AOT60, max8h, mean PM10 and PM2.5 concentrations; • PM10 and PM2.5 precursor (NOx, VOC, primary PM10, NH3, SO2) cost curves; • PM10 and PM2.5 two-objective optimization

20. 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.

21. References • Finzi, G., Guariso, G., 1992. Optimal air pollution control strategies: a case study. Ecological Modelling 64, 221–239. • Barazzetta, S., Corani, G., Guariso, G., 2002. A neural emission-receptor model for ozone reduction planning. In: Proc. iEMSs 2002. • Volta, M. 2003. Neuro-fuzzy models for air quality planing. The case study of ozone in Northern Italy. European Control Conference. • Guariso, G., Pirovano, G., Volta, M., 2004. Multi-objective analysis of ground level ozone concentration control. Journal of Environmental Management 71, 25–33. • Carnevale C., Finzi G., Pisoni E., Volta M., 2006. Identification of source-receptor models for secondary tropospheric pollution control. 14th IFAC Symposium on System Identification. 29-31 march, 2006 (pp. 762-767). IFAC Ed., CDROM published by Causal Productions. • M Carnevale C., Finzi G., Pisoni E., Volta M., 2006. Multi-objective analysis to control ozone exposure, 28th ITM-NATO.

22. Constraints • Maximum feasible reductions allowed by technologies for macrosector s: • Technologies reducing both NOx and VOC emissions

23. Optimization problem solution Constraints (2): technologies reducing both precursors macrosector 8 macrosector 7 VOC reduction VOC reduction NOx reduction] NOx reduction

24. Utopia A scenario A

25. basecase emission scenario NOx emissions VOC emissions

26. AOT40 scenarios source-receptor model simulations basecase Scenario A ppb*h

27. control priorities 2 3 2 1 1 3 2 2 emission reductions (ton/year) 1 1 scenario A: emissions