Group ‘Atmospheric Pollution & Risks’ Vladimir Penenko Penenko, Institute of Computational Mathematics , Institute of Computational Mathematics Vladimir and Mathematical Geophysics SB RAS and Mathematical Geophysics SB RAS Alexander Baklanov, Danish Meteorological Institute Danish Meteorological Institute Alexander Baklanov, RISKS CA 2 nd nd - Enviro- -RISKS CA 2 -year meeting year meeting Enviro 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 Aerosol Module Chemical Solvers UTLS Trans. Models 1. PSC aerosols 1. Gas Phase Lagrangian 2. Tropospheric Eulerian trans- 2. Aqueous phase transport, 3-D aerosols port 0..15 3. Chemical equil. regional scale lat-lon grid, 4. Climate Modeling Approaches: 3-D regional Normal distribution, Approaches: scale Bin approach RACM, CBI V, ECMWF I SORROPI A Met. Models Physics: 1. Condensation DMI -HI RLAM 2. Coagulation 3. Evaporation 4. Emission Eulerian trans- 5. Nucleation port 0.2-0.05 Stochastic 6. Deposition lat-lon, 25-40 Lagrangian vert. layer, transport, 3-D regional 3-D regional scale scale Tropo. Trans. Models City-Scale Obstacle Re- solved and I ndoor Model- ling M2UE-CORM Emergency Pre- On-Line Chemical Off-Line Chemical Aerosol Trans. parednes & Risk Assess- Aerosol Trans. ENVI RO-HI RLAM ment. DERMA CAC Regional (European) scale air Nuclear, veterinary and Regional (European) to city pollution: smog and ozone, chemical. scale air pollution: smog and pollen. ozone.
ENVIRO-RISKS list of sites for airborne impact simulations In 2006: 21. SRV - Serov, Russia 1. AZL - Kandalaksha, Russia 22. TSF - Semipalatinsk Test Site /Test Field/, Kazakhstan 2. BLP - Semipalatinsk Test Site /Balapan/, Kazakhstan 23. ZAR - Zarechny, Russia 3. CHL - Chelyabinsk, Russia 24. ZHE - Zheleznogorsk, Russia 4. CSA - Caspian Sea Site #1, Kazakhstan 25. ZEL - Zelenogorsk, Russia 5. CSB - Caspian Sea Site #2, Kazakhstan 6. CSC - Caspian Sea Site #3, Kazakhstan in 2007: 7. DGL - Semipalatinsk Test Site /Degelen/, 1. ALM - Almalyik, Uzbekistan Kazakhstan 2. TAD - Tadaz, Uzbekistan 8. EKT - Ekaterinburg, Russia 3. ANG - Angren, Uzbekistan 9. KEM - Kemerovo, Russia 4. NAV - Navoi, Uzbekistan 10. KRA - Krasnoyarsk, Russia 5. FER - Fergana, Uzbekistan 11. KVD - Kovdor, Russia 12. NTG - Nizhniy Tagil, Russia Additional sites from previous projects: 13. NNN - Norilsk, Russia 1. Novaya Zemlya Test Site; 14. NVK - Novokuznetsk, Russia 2. Sinpo NPP (Korea), 15. OLE - Olenegorsk, Russia 3. Vladivostok and 16. ORS - Orsk, Russia 4. Kamchatka risk sites (as sub.bases) 17. OZR - Ozersk, Russia 5. Chernobyl NPP, Ukraine 18. PRM - Perm, Russia 6. Many other European risk sites 19. SEV - Seversk, Russia 7. Few risk sites in America 20. SMS - Severomorsk, Russia
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 of 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 on 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 optimal solutions are necessary, demand of new generation of environmental models is high. In our opinion, the wide spread forward (direct) models do not answer the requirements of environmental modeling on modern level. • It should be mentioned that the question on predictability of models is still open in any case since first attempt of 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
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