Group Atmospheric Pollution & Risks Vladimir Penenko Penenko, - - PowerPoint PPT Presentation

Group ‘Atmospheric Pollution & Risks’

Vladimir Vladimir Penenko Penenko, Institute of Computational Mathematics , Institute of Computational Mathematics and Mathematical Geophysics SB RAS and Mathematical Geophysics SB RAS Alexander Baklanov, Alexander Baklanov, Danish Meteorological Institute Danish Meteorological Institute

Enviro Enviro-

• RISKS CA 2

RISKS CA 2nd

nd-

• year meeting

year meeting Tomsk, 25 July 2007 Tomsk, 25 July 2007

Thematic Focuses and Groups: Thematic Focuses and Groups:

• Atmospheric Pollution and Risks: AR-NARP, EmergPrep,

FUMAPEX, GEMS (DMI), Cities of Siberia, Forecast Methods, Risk (ICMMG), Dust, Hydrocarbons (KazGeoCosmos), Tomsk (SCERT) – Penenko, Baklanov

• Climate/Global Change: TCOS-Siberia (MPI-BGC), AMIP/CMIP

(INM), SGBR (SCERT, IMCES), EACR (ICMMG), CARBO-North (DMI), - Lykosov, MPI-BGC (Marcus, Martin)

• Terrestrial Ecosystems and Hydrology: Siberia-2 (IIASA),

Siberian Taiga (IF), Yugra: Space Monitoring, Water Quality, Land Remediation (URIIT), Great Vasyugan Bog (IMCES), GIS/RS

• Agro, Water Oil Poll (KazGeoCosmos) – Kabanov, Shvidenko
• Info-Systems, Integration and Synthesis: ENVIROMIS, SIREMM

(SCERT), GIS (KazGeoCosmos), all – Gordov, Zakarin, MPI-BGC

Group ‘Atmospheric Pollution & Risks’

• 1. NARP Arctic Risk and NKS Nord-Risk Projects (DMI),
• 2. FUMAPEX: Integrated Systems for Forecasting Urban Meteorology, Air Pollution

and Population Exposure (DMI),

• 3. GEMS: Global and Regional Earth-System Monitoring Using Satellite and In-Situ

Data (DMI),

• 4. ARGOS and DERMA modelling system further development (DMI, DEMA),
• 5. CLEAR cluster of European Air Quality Research, including 9 EU Projects (DMI).
• 6. Methods and Models for Studiing and Forecasting Changes in Environment

(ICMMG),

• 7. Ecological Problems of Siberian Cities (ICMMG),
• 8. Development of Models and Methods for Revealing and Studying Regions of

Increased Ecological Risk taking Siberian Region as Example (ICMMG),

• 9. Development of GIS Technology for Monitoring and Modeling of Dust Storm

(KazGeoKosmos),

• 10. GIS Technology for Monitoring and Modeling of Air Pollution due to Burning of

Hydrocarbons (KazGeoKosmos),

• 11. Tomsk Air Pollution case study (SCERT, TSU)
• 12. Semipalatins…

NIS postgraduates involved into exchange NIS postgraduates involved into exchange program program

SCERT/TSU

• Nuterman R. (DMI) 2 visits

Urban atmospheric pollution modelling KazGeoCosmos

• Pak K. (DMI)
• Tusseeva N. (DMI)
• Balakai L. (DMI)

Atmospheric pollution risk assessment modelling ICMMG

• Penenko A.V. (DMI)

Variational approach and adjoint modelling for sourse-term estimation

Modelling System

1. PSC aerosols 2. Tropospheric aerosols Approaches: Normal distribution, Bin approach Physics: 1. Condensation 2. Coagulation 3. Evaporation 4. Emission 5. Nucleation 6. Deposition

Aerosol Module

1. Gas Phase 2. Aqueous phase 3. Chemical equil. 4. Climate Modeling Approaches: RACM, CBI V, I SORROPI A

Chemical Solvers

Lagrangian transport, 3-D regional scale

UTLS Trans. Models

Eulerian trans- port 0..15 lat-lon grid, 3-D regional scale ECMWF DMI -HI RLAM Eulerian trans- port 0.2-0.05 lat-lon, 25-40

• vert. layer,

3-D regional scale Stochastic Lagrangian transport, 3-D regional scale On-Line Chemical Aerosol Trans. ENVI RO-HI RLAM Off-Line Chemical Aerosol Trans. CAC Emergency Pre- parednes & Risk Assess-

• ment. DERMA

Nuclear, veterinary and chemical. Regional (European) to city scale air pollution: smog and

• zone.

Regional (European) scale air pollution: smog and ozone, pollen.

• Met. Models

City-Scale Obstacle Re- solved and I ndoor Model- ling M2UE-CORM

• Tropo. Trans. Models

ENVIRO-RISKS list of sites for airborne impact simulations

In 2006:

• 1. AZL - Kandalaksha, Russia
• 2. BLP - Semipalatinsk Test Site /Balapan/, Kazakhstan
• 3. CHL - Chelyabinsk, Russia
• 4. CSA - Caspian Sea Site #1, Kazakhstan
• 5. CSB - Caspian Sea Site #2, Kazakhstan
• 6. CSC - Caspian Sea Site #3, Kazakhstan
• 7. DGL - Semipalatinsk Test Site /Degelen/,

Kazakhstan

• 8. EKT - Ekaterinburg, Russia
• 9. KEM - Kemerovo, Russia
• 10. KRA - Krasnoyarsk, Russia
• 11. KVD - Kovdor, Russia
• 12. NTG - Nizhniy Tagil, Russia
• 13. NNN - Norilsk, Russia
• 14. NVK - Novokuznetsk, Russia
• 15. OLE - Olenegorsk, Russia
• 16. ORS - Orsk, Russia
• 17. OZR - Ozersk, Russia
• 18. PRM - Perm, Russia
• 19. SEV - Seversk, Russia
• 20. SMS - Severomorsk, Russia
• 21. SRV - Serov, Russia
• 22. TSF - Semipalatinsk Test Site /Test Field/, Kazakhstan
• 23. ZAR - Zarechny, Russia
• 24. ZHE - Zheleznogorsk, Russia
• 25. ZEL - Zelenogorsk, Russia

in 2007:

• 1. ALM - Almalyik, Uzbekistan
• 2. TAD - Tadaz, Uzbekistan
• 3. ANG - Angren, Uzbekistan
• 4. NAV - Navoi, Uzbekistan
• 5. FER - Fergana, Uzbekistan

Additional sites from previous projects:

• 1. Novaya Zemlya Test Site;
• 2. Sinpo NPP (Korea),
• 3. Vladivostok and
• 4. Kamchatka risk sites (as sub.bases)
• 5. Chernobyl NPP, Ukraine
• 6. Many other European risk sites
• 7. Few risk sites in America

CFD results of computations for urban area

Near surface velocity field and concentration; z = 3.5 m (left) and z = 6.5 m (right)

Atmospheric pollution and risk

EnviroRisk thematic group results/findings

Environmental modeling as a tool for integration, analysis and synthesis

• f knowledge and data
• Synergy of a number of projects of different levels and directions is in

integration of divers fields of knowledge on the base of mathematical models and computational technologies.

• The main issue of synergy is possibility to replace expensive nature

experiments causing irreversible changes of environment by those made

• n computers.
• Analysis of information is made which taken from reports at regular

leading international conferences like CITES, ENVIROMIS, “Atmospheric and Oceanic Optics. Atmospheric Physics”,etc. All- Russian conferences “Control and Rehabilitation of Environment”, “Contemporary Methods of Mathematical Modeling of Natural and Man-made Disasters”, etc. and from scientific papers and personal communications.

Tendencies in environmental modeling in Siberia (Siberian Federal District)

• The leading groups are in Novosibirsk, Tomsk , Irkutsk , Krasnoyarsk,

Tumen, Barnaul, Kemerovo, Yakutsk, Ulan-Ude, Chita, Omsk, Khanti- Mansiisk.

• There are three tendencies in the usage of modeling tools in these

groups:

• using simplified regulatory models ( Gauss type, one/two

dimensions, a few parameters, etc.),(50-70 %)

• adoption of well-known internet-available models, like MM5,

WRF, HYSPLIT, etc. (10-20%, increasing)

• development of original comprehensive models of different

complexity from local to global scales (10-15%, decreasing).

Globalization: Threat or Opportunity? Optimum is mixed strategies

Some problems

• The main obstacle in environmental modeling in Siberia is

lack of data to initialise and run modeling scenarios.

• There are several tools that are quit consistent to solve

environmental tasks of general character. But for emergency situations, when adequate quantitative assessments and

• ptimal solutions are necessary, demand of new generation
• f environmental models is high.

In our opinion, the wide spread forward (direct) models do not answer the requirements of environmental modeling

• n modern level.
• It should be mentioned that the question on predictability
• f models is still open in any case since first attempt
• f using mathematical modeling.

Some problems

• The same situation exists with assessment of uncertainties which arise

from errors in specification of the parameters and initial model state as well as from imperfections in model formulation.

• There is a lack of analytical tools for operational consideration of

current changes in safety restriction and criteria, as well as for assimilation of data, received from the current observations and measurements, into the models of huge dimensions. These are so called “real time” problems.

• A principal difficulty in the integrated analyses of risks, socio-

economical and population health problems is the limitation or even absence of general metrics for differential consideration of various agents and factors.

Recommendations

• To solve environmental problems on modern level, new generation of

models should be intensively developed.

• Models of observations should be designed on level with models of
• processes. In addition, the set of goal criteria expressed by the

functionals in the spaces of the state functions and model parameters as well as the set of functionals describing the integral and distributed restrictions on the state functions and parameters should also be constructed.

• Interrelation and adjustment between all elements of computational

technology may be provided by variational principles defined for non- linear dynamical systems. With the help of variational principles one can generate almost all necessary computational algorithms, sensitivity theory methods for models and functionals, control theory methods and methods of direct and inverse modeling.

Priorities

• Uncertainty assessment
• Atmospheric chemistry ( gases + aerosols)

– algorithms for stiff systems – problem of non-consistency: monotonizators, self-limiting diffusion for divers components – chemical data assimilation ( adjoint problems and sensitivity algorithms) – adaptive algorithms

• Targeting for data assimilation, risk assessment, source

identification

• Optimal problems for design of sustainable development

strategy

Conclusion

• Future evolution in environmental modeling should be directed to the

solution of the above mentioned problems. In this sense the proposed variational methodology might be used for creation of new methods for management of environmental quality, risk assessment from the point

• f view of sustainable development. It is a new class of problems of the

multi-criteria optimization of high dimensions with multiple limitations

• f different character (economical, ecological, health, etc). Because it is a

multi-disciplinary task, the involvement of scientific community from different fields of research activities will be essential.

Thank you for the attention !