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

Giorgio Corani

Dipartimento di Elettronica ed Informazione - Politecnico di Milano

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

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)

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

Polluttats trends in Milan

1990 2000 %

SO2 30 8 - 73%

CO 4.8 1.8 -62%

NOx 270 140 -48%

O3 25 38 +50%

PM10 40(1998)

40 -

Yearly average in Milan(g/m3)

Source: “Rapporto 2001 sulla qualità dell’aria a Milano”

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

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, CH4mainly 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 polluttantsecondary polluttant

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

0

100

200

300

400

500

1991 1993 1995 1997 1999

mg

/m^3

Maximum obs. hourly O3Hourly O3 law thresholdsummer average (Apr -Sep)

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)

Aims of the research To design effective ozone reduction policies for

Lombardy region solving a multi-objective optimisation problem…

ozo

ne p

ollu

tion

red

ucti

on

[%

max]

0%

30%

60%

100%

20% 40% 60% 80% 100%

reduction costs [% max]

?

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

Photochemical simulation (1)Orography

Wind field

VOC emissions

Requires as inputs on each cell: Orography

Hourly emissions

Hourly wind field

Such gridded data are obtained through ad hoc pre-processing

Photochemical simulation (2)

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

- 35% NOx - 35% VOC

- 35% VOC and NOx

+ 35% VOC, NOx

How to perform an optimization analysis?

Artificial neurons

xt

xt-1

xt-2

...

w1,1

w1,r

b

1

input

neuron

= f(Wx+b)

xt-

xt --1

...

jkkjj bxwz

Weighted sum of the inputs (cfr. dendriti)

Logistic activation function

Artificial Neural Networks (ANN)

x0

x1

x2

xr

...

f

w1,1

wn,r

input Hidden layer(n neurons)

Forecast

Output neuron

1ty

Emission and receptors

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) 4 km4 km 4 km4 km 4 km4 km

The neural emission-receptor modelInputs (at

each emission cell):

•Daily Nox emissions (24 h)

•Daily VOC emissions(24 h)

• NOx and VOC initial conditions

•Elevation above sea level

Ozone indicator (max 8h average on the receptor)

Hidden layer:

Neural network training

PCA analysis (45 inputs -> 22 inputs) Generalization ability: early stopping Levenberg - Marquardt algorithm 26 hidden nodes

ANN Results

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)

Data Set 8 - Test Set - BACINO di PIANURATarget (t) vs. Output rete (a)

Correlazione: R = 0,912

T

A

40

50

60

70

80

90

100

40 50 60 70 80 90 100

Correlation Correlation R = R = 0.9120.912

The network fits well the data, and can be exploited for optimization purposes.

12000 km^2

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)

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)

s

jisjir

sss

ij

ssji

r

R

rIpollution

rcErcosts

s

,,

,

r0

:by costrained

)(min)min(

)(min)min(

Pareto Boundary

0

0.5

1

0 0.2 0.4 0.6 0.8 1

Costs (% with respect to the maximum)

Ozo

ne r

educ

tion

(%

wit

h re

spec

t to

the

max

)

70% maximum feasible ozone reduction30% maximum costs

Solution analysis

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Costs [% maximum]

Ozo

ne

reduct

ion [%

max

imum

]

Proc wth comb Fuel distribSolventRoad transportWaste

VO

C

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.