PLANNING BY NEURAL NETWORKS OF INTERVENTIONS FOR THE REDUCTION OF OZONE CONCENTRATION IN LOMBARDY

1. PLANNING BY NEURAL NETWORKS OF INTERVENTIONS FOR THE REDUCTION OF OZONE CONCENTRATION IN LOMBARDY Giorgio Corani Dipartimento di Elettronica ed Informazione - Politecnico di Milano

2. Outline • Ozone problem in Lombardy • Traditional simulation approaches • A novel approach: use ANN to generate the results produced by traditional deterministic models, greatly shortening the computation times. • A two-objectives problem: ozone reduction (min concentrazioni) and minimization of removal costs (min costi) • Results

3. Introduction • In the stratosphere (some 30000m over the ground), ozone protects the Earth from dangerous UV radations (see the ozone hole problem) • but in the atmosphere, ozone is dangerous for both humans and crops (ground ozone)

4. Ozone recommended standards • World Health Organization: prescribes 120 g/m3 on the 8-hours moving average • Quality standard: 200 g/m3 on the hourly mean, to be exceeded no more than once in a month (objective often not met) • Ozone is a secundary polluttant

5. Polluttats trends in Milan Yearly average in Milan (g/m3) Source: “Rapporto 2001 sulla qualità dell’aria a Milano”

6. Air quality in Milan and Lombardy • SO2, NOx, CO are well under control; they have been largely reduced over the last 15 years • PM10 and O3 ozone (summer only) constitute instead a major health concern

7. Problem overview (1) • Ozone formation depends mainly on “precursors”: • NOx • mainly due to road transports (76%) and heating (21%) • VOC - volatile organic compounds, such as CO, CH4 • mainly due to solvent use (44%) and road transport (49%) • Since chemical reactions develop in some hours (or in a few days), ozone values over a certain site are due to NOx-VOC sources located at many km of distance (transport) • Ozone peaks are usually observed in suburban areas secondary polluttant

8. Problem overview (2) • High ozone ground levels concentrations observed since the 70’s in USA and Europe • The process takes place only at high temperatures (over 30 C) • In Lombardy increasing ozone trends claim for effective reduction policies

9. Sources classificanion • CORINAIR: defines 13 typologies of emission sources (e.g.: road transports, industrial plants, waste disposal,.. ecc.) • The costs of the implementation of reduction policies for different polluttants in the different sectors have been estimated by (IAASA, 2000)

10. Aims of the research ? 100% 60% ozone pollution reduction [% max] 30% 0% 20% 40% 60% 80% 100% reduction costs[% max] • To design effective ozone reduction policies for Lombardy region solving a multi-objective optimisation problem…

11. Methodology • Selection of a meaningful ozone indicator (max 8h average) • Scenarios simulations through CALGRID, an eulerian photochemical model (time consuming) • ANN training to map CALGRID inputs to the simulated ozone indicator • Precursors reduction costs evaluation (IAASA, 2000) • Decision variables selection (precursors reduction rates in each emission sector) • Solution of the multi-objective optimization problem, modelling ozone dynamics through ANN

12. Photochemical simulation (1) Orography VOC emissions Windfield • Requires as inputs on each cell: • Orography • Hourly wind field • Hourly emissions • Such gridded data are obtained through ad hoc pre-processing

13. Photochemical simulation (2) - 35% VOC and NOx + 35% VOC, NOx -35% NOx - 35% VOC • Returns 3-D Ozone concentration fields • Given the computational effort, we analysed few scenario simulations, assuming a uniform VOC/NOx reduction rate on the whole domain • Meteorological conditions: 5-7 June 1996 How to perform an optimization analysis?

14. Artificial neurons • Weighted sum of the inputs (cfr. dendriti) xt w1,1 xt-1 xt-2 neuron  • Logistic activation function ... b xt- w1,r xt --1 1 ... input = f(Wx+b)

15. Artificial Neural Networks (ANN) x0  w1,1 x1 x2 f  ... xr wn,r  Forecast Hidden layer (n neurons) Output neuron input

16. Emission and receptors 4 km 4 km 4 km • Receptor(4km * 4km) : a given cell in the gridded domain (ozone indicator evaluation) • Emissions (12km * 12 km) : cells in the square centered in the receptor (emission patterns, initial concentration conditions)

17. The neural emission-receptor model Hiddenlayer:    Ozone indicator (max 8h average on the receptor)    Inputs(at each emission cell): • Daily Noxemissions • (24 h) • Daily VOC emissions (24 h) • NOx and VOC initial conditions • Elevation above sea level

18. Neural network training • PCA analysis (45 inputs -> 22 inputs) • Generalization ability: early stopping • Levenberg - Marquardt algorithm • 26 hidden nodes

19. ANN Results 12000 km^2 Correlation R = 0.912 • The mountain part of the region is insensitive to both NOx and VOC reductions on the whole domain; thus, we focus on the plain part (VOC -limited) • The network fits well the data, and can be exploited for optimization purposes.

20. Reduction policies • The policy design requires to select a VOC reduction rates for: • solvent use (470 ton/day) • road transport (408 ton/day) • waste treatment (110 kg/day) • fossil fuel distribution (50 ton/day) • production without combustion (23 ton/day) • Reduction costs for each sector are known (IAASA, 2000)

21. Optimization • rs: reduction for sector s: Rs maximum feasible • Eijs : VOC emission on cell (i,j) for sector s • cs: reduction costs function for sector s • Ii,j : ozone indicator on cell (i,j)

22. Pareto Boundary 70% maximum feasible ozone reduction 30% maximum costs

23. Solution analysis VOC

24. Conclusions • The optimisation problem has been solved thanks to ANN ability in non linear dynamic and computational speed • The main result is that noticeable improvements in ozone level are reachable even through moderate investments, provided that these are targeted to some sectors, such as road transport and industrial solvents.