1. Institute of Problems of Chemical Physics Remote Recognition of Aerosol Chemicals B. Bravy, V.Agroskin, G.Vasiliev Laser Chemistry Laboratories

2. LIDAR (Light Identification, Detection And Ranging) Institute of Problems of Chemical Physics

3. r<< Rayleigh scattering scattering indicatrix   ~ -4

4. ~R (0.1 <R<10  100 ) Mie scattering and spectral resonanse scattering indicatrix back forward

5. Institute of Problems of Chemical Physics R>> scattering indicatrix back forward

6. Dependences of absorption and aerosol backscattering spectra of dibutylamin (DBA) on size of cuvette or aerosol particles Traditional spectroscopyAerosol backscattering spectroscopy T=exp(-  ( )·  ) Nonanalytic dependence

7. Spectral dependences of backscattering coefficient for DBA, petroleum, and turbine oil Identical particle size distributions, but different substances

8. In contrast to traditional spectroscopy, aerosol backscattering spectra are measured with restricted number of spectral channels

9. Different particle size distributions and different substances without noise 5% - noise

10. 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 recognition 2. Dependence of backscattering coefficient on size distribution parameters is complicated 3. Spectrum of aerosol backscattering is measured on finite number of spectral channels 4. Noise of measurement is much greater than in traditional spectroscopy

11. Execution cycle for numerical modeling Haze + TBA (T-oil, Petroleum or Water) SPECTRUM NOISE INPUT SPECTRUM RECOGNITION PROCEDURE OUTPUT: SUBSTANCE & CONCENTRATION background aerosol components of the atmosphere impurity aerosol

12. RECOGNITION PROCEDURE 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

13. algorithms of random search: (Monte-Carlo) strictly determined algorithms: (gradient methods) algorithms of intermediate type: (evolutionary algorithms) the large time of calculation for casual enumeration of possible combinations of 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 discovers a region of values of variables, in which there is a minimum of discrepancy, may be not a global minimum, but one of most “deep” minimums. Further correct determination of this minimum proceeds slowly Genetic algorithm, then gradient descent

14. 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 dust with a visibility range of 10 km Input spectrum was calculated for the haze with DBA aerosol. Concentration of DBA was varied from 1 mg/m 3 to 0.1 mg/m 3

15. Demonstration of numerical modeling

20. Probability (%) of correct aerosol substance identification in single measuring. Conditions: aerosol impurity on background of haze under visibility range of 10 km. SUBSTANCEC, mg/m 3 NOISE LEVEL, % 12358 TBA 3>99 98 1>99 9894 0.3>9997<90 T-oil 3>99 9896<90 1>999792<90 0.396<90 Demonstration of numerical modeling

21. Concentration and size distribution parameters recovered in single measuring. Input data* NOISE LEVEL, % 125810 r mod,  m 1011 121415  23.2 3.53.63.8 C, 1/l5.06.156.8 7.76.6 C, mg/m 3 0.150.140.130.140.190.18 Demonstration of numerical modeling

22. 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