Ecological risk control in the context of sustainable development:

methods

Vladimir Penenko

ICM&MG SD RAS, Novosibirsk

Tools for scenario approach: models & techniques

Models of hydrodynamics

Models of transport and transformation of pollutants (gases and aerosols)

Functionals for management strategies( generalized description of the system , restrictions, cost, etc.)

Sensitivity and observability algorithms

Combination of forward and inverse techniques

Joint use of models and data

• Extraction of multi- dimensional and multi-component factors from data bases

• Classification of typical situations with respectto main factors

• Investigation of variability

• Formation of “leading” spaces

Analysis of the climatic system for constructionof long-term scenarios

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Symmetrized form for operators of turbulent exchange and transformation of substances

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The algorithm for calculation of sensitivity functions

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Sensitivity relation for estimation of risk/vulnerability and observability

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safe ecological conditions

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Risk assessment with the help of sensitivity functions

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Risk domain

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•specifying the set of receptors and the structure of the functionals;• constructing and calculating the sensitivity relations ( solutions of the forward and adjoint problems);•revealing the risk/vulnerability domains for given set of receptors and functionals;•detecting the sources located in the risk domains;•grading the sources in accord with the degree of potential danger and the level of significance of the sensitivity functions;•separating the sources into two groups: open to control and placed beyond one’s reach ;•construction of management strategy according to the goal criteria and restrictions.

System organisation of the risk management algorithms

Numerical algorithm of control and identification

1. Calculation of SFs for goal functionals. Assessment of parameter variations

2. Calculation of SFs for restriction functionals

3. Formation of linearized manifold to take into account every restriction

4. Projection of estimations of item 1 on the restriction manifold of items 2&3

5. Check of convergence criteria

Global and regional models of hydrodynamics

Models of pollutants’ transport

Hybrid vertical coordinate system (p-sigma)

Fast data assimilation

Reanalysis NCEP/NCAR data base

Applications

Risk assessment of volcano eruption

Source of emission: ShiveluchRelease time 19-21.05.2001

Surface level150 mb

1

1

12

2

2

3

3

45

6

11 110 0.59 0.18 0.057 0.016 0.0055 0.0014 0.00053 0.00012 5E-051 1E-05

Risk domain to get pollution within the time interval 19.05-19.06.2001.Surface level, gases.Source of emission: volcano Shiveluch.

Release time 19.05-21.05 2001

1

1

1

1

2

2

3

33

45

7

11 110 0.59 0.18 0.057 0.016 0.0055 0.0014 0.00053 0.00012 5E-051 1E-05

Risk domain to get pollution within the time interval 19.05-19.06.2001.150mb, gases.Source of emission: volcano Shiveluch.

Release time 19.05-21.05 2001

Risk assessment for two versions of military action in Iraq:•winter•spring

From Iraq's Weapons of Mass Destruction Programs, U.S. Director of Central Intelligence, October 2002

Winter scenario animation

Spring scenario animation

Risk assessment for two versions of military action in Iraq:•winter•spring

Transboundary transport and risks in the Russian Far East, China and Korea

Forward problem:cities as aggregated sources of pollution

Shenyang Pyonguyang Laoyang Seoul Anshan Khabarovsk Teling Vladivostok Fu-shun Dalian Dantung In-Cou Jin-Jou Fu-Sin Beijng Harbin Changchun

animation

Inverse problem: cities as receptors

Vladivistok Khabarovsk Beijng Shenyang Dalian Seoul

animation

Conclusion

Combination of the forward and inverse modeling seems to be advanced technology for risk / vulnerabilitystudies

Joint use of sensitivity and observability techniques givesthe possibility to detect the unreachable and uncontrolled sources

Forecasting and management of ecological risks is a key element in the choice of the strategies of sustainable development and social safety

Subjects require amplification: •refinement of goal criteria and constrains;•forecasting and management in the conditions of uncertainties

Acknowledgements

The work is supported by•RFBR

Grant 04-05-64562•Russian Ministry of Science and Education

Contract № 37.011.11.0009• Russian Academy of Sciences

Program 13Program 14Program 1.3.2

•Siberian Division of Russian Academy of SciencesIntegrating projects 130, 131, 137, 138