Institute of Problems of Chemical Physics

Remote Recognition of Aerosol Chemicals

B. Bravy, V.Agroskin, G.Vasiliev

Laser Chemistry Laboratories

LIDAR

(Light Identification, Detection And Ranging)

Institute of Problems of Chemical Physics

r<<

2,5 3,0 3,5 4,0 4,5

1E-7

1E-6

, a.u.

, m

Rayleigh scattering

scattering indicatrix

~ -4

~R (0.1<R<10100)

Mie scattering and spectral resonanse

scattering indicatrix

back forward2,5 3,0 3,5 4,0 4,5

1E-3

0,01

0,1

, a.u.

m

2,5 3,0 3,5 4,0 4,5

0,01

0,1 , a.u.

m

Institute of Problems of Chemical Physics

R>>

scattering indicatrix

back forward

3,0 3,2 3,4 3,6 3,8 4,0 4,2

1E-6

1E-5

1E-4

1E-3

0,01

0,1

1

=150 m=30 m=6 m

T

m

3,0 3,2 3,4 3,6 3,8 4,0 4,21E-3

0,01

0,1

rmod

=375 mr

mod=75 m

rmod

=15 m

rmod

=7.5 m, a.u.

m

Dependences of absorption and aerosol backscatteringspectra of dibutylamin (DBA) on size of cuvette or aerosol particles

Traditional spectroscopy Aerosol backscatteringspectroscopy

T=exp(-()·) Nonanalytic dependence

3,0 3,2 3,4 3,6 3,8 4,0

1E-3

0,01

0,1 C=1cm-3

DBA

T-oil

rmod

= 10m, = 2

Petroleum , k

m-1st

er-1

m

Spectral dependences of backscattering coefficient for DBA,

petroleum, and turbine oil

Identical particle size distributions,but different substances

3,5 3,6 3,7 3,8 3,9 4,0

0,01

0,1

Petroleum

T-oil

DBA

, k

m-1st

er-1

m

In contrast to traditional spectroscopy, aerosol backscattering spectra are measured with restricted number

of spectral channels

3,5 3,6 3,7 3,8 3,9 4,0

0,01

0,1 , a.u.

T-oil

Petroleum

DBA

m

3,5 3,6 3,7 3,8 3,9 4,0

0,01

0,1 , a.u. Petroleum

T-oil

DBA

m

Different particle size distributionsand different substances

without noise 5% - noise

The principal question : can aerosol impurities in the atmosphere be recognized under the circumstances and to what extent?

The rest of the report will be devoted to numerical modeling

of the situation in attempt to give the answer to this question.

Resume

1. Aerosol backscattering spectrum in a spectral range of absorption band of aerosol matter is specific and gives bases for substance and microphysical characteristics recognition2. Dependence of backscattering coefficient on size distribution parameters is complicated 3. Spectrum of aerosol backscattering is measured on finite number of spectral channels4. Noise of measurement is much greater than in traditional spectroscopy

Execution cycle for numerical modeling

Haze + TBA (T-oil, Petroleum or Water)

SPECTRUM

NOISE

INPUT SPECTRUM

RECOGNITION PROCEDURE

OUTPUT: SUBSTANCE & CONCENTRATION

background aerosolcomponents of the atmosphere impurity aerosol

RECOGNITION PROCEDURE

2

iπ

*π (i)β(i)(arg)βS(arg)

arg haze parameters + impurity aerosol substance with distribution parameters

Input spectrum: (i); i – number of spectral channel

min S(arg) output arg

Calculation of output spectrum *(i)(arg)

Choice of arguments

Choice of arguments

algorithms of random search:(Monte-Carlo)

strictly determined algorithms:(gradient methods)

algorithms of intermediate type:(evolutionary algorithms)

the large time of calculation for casualenumeration of possible combinationsof values for variables is necessary

the gradient methods very promptly discover a minimum of discrepancy for a set of values nearest to initial one, but availability of many local minima levels this advantage

the genetic algorithm promptly enough discoversa region of values of variables, in which there isa minimum of discrepancy, may be not a global minimum, but one of most “deep” minimums. Further correct determination of this minimumproceeds slowly

Genetic algorithm, then gradient descent

Demonstration of numerical modeling

Restriction on number of possible substances of aerosol impurity:only one out of DBA, turbine oil, petroleum, water (fog)

Haze (natural atmospheric aerosol): fine dispersed water and dustwith a visibility range of 10 km

Input spectrum was calculated for the haze with DBA aerosol.Concentration of DBA was varied from 1 mg/m3 to 0.1 mg/m3

0 5 10 15

DBA

%

Demonstration of numerical modeling

0 5 10 15

T-oil

DBA

%

Demonstration of numerical modeling

0 5 10 15

Petr.

T-oil

DBA

%

Demonstration of numerical modeling

0 5 10 15

Water

Petr.

T-oil

DBA

%

Demonstration of numerical modeling

0 5 10 15

1 mg/m3

WaterPetr.

T-oilDBA

%

0.3 mg/m3

0.1 mg/m3

Demonstration of numerical modeling

 

Probability (%) of correct aerosol substance identification in single measuring.Conditions: aerosol impurity on background of haze under visibility range of 10 km.

SUBSTANCE C, mg/m3

NOISE LEVEL, %

1 2 3 5 8

TBA

3 >99 >99 >99 >99 98

1 >99 >99 >99 98 94

0.3 >99 97 <90    

T-oil

3 >99 >99 98 96 <90

1 >99 97 92 <90  

0.3 96 <90      

 

Demonstration of numerical modeling

Concentration and size distribution parameters recovered in single measuring.

 Input data*

NOISE LEVEL, %

1 2 5 8 10

rmod,

m

10 11 11 12 14 15

2 3.2 3.2 3.5 3.6 3.8

C, 1/l 5.0 6.15 6.8 6.8 7.7 6.6

C, mg/m3

0.15 0.14 0.13 0.14 0.19 0.18

Demonstration of numerical modeling

1. The possibility of recognition of composition and microphysical characteristics of aerosol impurities in the atmosphere with the use of finite number of spectral channels has been shown.

2. The validity of impurity recognition and errors of determination of aerosol microphysical characteristics have been obtained using some particular substances and conditions.

3. The analysis has shown an possibility of aerosol recognition at the accessible requirements to precision of backscattering spectrum recovery.

CONCLUSIONS