1 AirWare : AirWare : C OMPREHENSIVE A IR QUALITY M ODEL WITH E X TENSIONS VERSION 4.30, PBM DDr. Kurt Fedra Environmental Software & Services GmbH A-2352.

Slides:



Advertisements
Similar presentations
A numerical simulation of urban and regional meteorology and assessment of its impact on pollution transport A. Starchenko Tomsk State University.
Advertisements

A numerical simulation of wind field and air quality above an industrial center Alexander Starchenko Tomsk State University, Tomsk, Russia
AIR POLLUTION. ATMOSPHERIC CHEMICAL TRANSPORT MODELS Why models? incomplete information (knowledge) spatial inference = prediction temporal inference.
PREV ’AIR : An operational system for large scale air quality monitoring and forecasting over Europe
Title EMEP Unified model Importance of observations for model evaluation Svetlana Tsyro MSC-W / EMEP TFMM workshop, Lillestrøm, 19 October 2010.
PLANNING BY NEURAL NETWORKS OF INTERVENTIONS FOR THE REDUCTION OF OZONE CONCENTRATION IN LOMBARDY Giorgio Corani Dipartimento di Elettronica ed Informazione.
University of Aveiro Final Meeting and Project Review 23/24 June 2003 Gdansk University of Aveiro Emissions and Air Quality Modelling Department of Environment.
METO 637 Lesson 15. Polar meteorology In the winter months the poles are in perpetual darkness. This causes extremely cold temperatures in the stratosphere.
THE PUMA PROJECT AND SUBSEQUENT AIR QUALITY MODELLING AT THE UNIVERSITY OF BIRMINGHAM ROY M. HARRISON AND XIAOMING CAI SCHOOL OF GEOGRAPHY, EARTH AND ENVIRONMENTAL.
1 AirWare : R elease R5.3 beta AERMOD/AERMET DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA
Atmospheric modelling activities inside the Danish AMAP program Jesper H. Christensen NERI-ATMI, Frederiksborgvej Roskilde.
1 00/XXXX © Crown copyright URBAN ATMOSPHERIC CHEMISTRY MODELLING AT THE METEOROLOGICAL OFFICE Dick Derwent Climate Research Urban Air Quality Modelling.
X ONE-BOX MODEL Atmospheric “box”;
Evaluation of the AIRPACT2 modeling system for the Pacific Northwest Abdullah Mahmud MS Student, CEE Washington State University.
Jenny Stocker, Christina Hood, David Carruthers, Martin Seaton, Kate Johnson, Jimmy Fung The Development and Evaluation of an Automated System for Nesting.
Next Gen AQ model Need AQ modeling at Global to Continental to Regional to Urban scales – Current systems using cascading nests is cumbersome – Duplicative.
Improving Cloud Simulation in Weather Research and Forecasting (WRF) Through Assimilation of GOES Satellite Observations Andrew White Advisor: Dr. Arastoo.
The Control Method of Photochemical Smog Lee Jin-Young Civil & Environmental System Engineering 2006 년 6 월 12 일.
Session 9, Unit 17 UAM and CAMx. UAM and CAMx UAM - Urban Airshed Model Currently available versions:  UAM-V 1.24  UAM-V 1.30  Available from Systems.
Air Pollution & Control. Thickness of Atmosphere The atmosphere is a very thin (relatively) layer of gas over the surface of the Earth Earth’s radius.
CMAQ (Community Multiscale Air Quality) pollutant Concentration change horizontal advection vertical advection horizontal dispersion vertical diffusion.
Template CAMx Ancillary Input Development Chris Emery ENVIRON International Corporation, Novato CA November 14, 2012.
©2005,2006 Carolina Environmental Program Sparse Matrix Operator Kernel Emissions SMOKE Modeling System Zac Adelman and Andy Holland Carolina Environmental.
1 CCOS Seasonal Modeling: The Computing Environment S.Tonse, N.J.Brown & R. Harley Lawrence Berkeley National Laboratory University Of California at Berkeley.
Chapter 17.1 Atmospheric Characteristics
EFFICIENT CHARACTERIZATION OF UNCERTAINTY IN CONTROL STRATEGY IMPACT PREDICTIONS EFFICIENT CHARACTERIZATION OF UNCERTAINTY IN CONTROL STRATEGY IMPACT PREDICTIONS.
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar Abhilash Vijayan Kanwar Siddharth Bhardwaj University of Toledo.
Transport & Deposition of Air Pollutants David Gay Coordinator National Atmospheric Deposition Program University of Illinois, Champaign, IL ,
The Atmosphere as a Chemical Reactor OutputsInputs Chemistry Radiation (energy) Biogeochemical Cycling.
4. Atmospheric chemical transport models 4.1 Introduction 4.2 Box model 4.3 Three dimensional atmospheric chemical transport model.
Georgia Institute of Technology Initial Application of the Adaptive Grid Air Quality Model Dr. M. Talat Odman, Maudood N. Khan Georgia Institute of Technology.
M.K. Neophytou 1&2, D. Goussis 2, E. Mastorakos 1, R.E. Britter 1 1 University of Cambridge, Department of Engineering, Trumpington Street, Cambridge CB2.
Wildland Fire Impacts on Surface Ozone Concentrations Literature Review of the Science State-of-Art Ned Nikolov, Ph.D. Rocky Mountain Center USDA FS Rocky.
Introduction to Modeling – Part II Marti Blad Northern Arizona University College of Engineering & Technology Dept. of Civil & Environmental Engineering.
Application of Models-3/CMAQ to Phoenix Airshed Sang-Mi Lee and Harindra J. S. Fernando Environmental Fluid Dynamics Program Arizona State University.
Sensitivity of Air Quality Model Predictions to Various Parameterizations of Vertical Eddy Diffusivity Zhiwei Han and Meigen Zhang Institute of Atmospheric.
學生:張立農 NUMERICAL STUDY ON ADJUSTING AND CONTROLLING EFFECT OF FOREST COVER ON PM 10 AND O 3.
For more information about this poster please contact Gerard Devine, School of Earth and Environment, Environment, University of Leeds, Leeds, LS2 9JT.
Introduction to Modeling – Part II
Deguillaume L., Beekmann M., Menut L., Derognat C.
Model Evaluation and Assessment ALBERT EINSTEINALBERT EINSTEIN: Things should be made as simple as possible, but not any simpler. Theodore A. Haigh Confederated.
TEMIS user workshop, Frascati, 8-9 October 2007 TEMIS – VITO activities Felix Deutsch Koen De Ridder Jean Vankerkom VITO – Flemish Institute for Technological.
TEMPLATE DESIGN © A high-order accurate and monotonic advection scheme is used as a local interpolator to redistribute.
Impact of high resolution modeling on ozone predictions in the Cascadia region Ying Xie and Brian Lamb Laboratory for Atmospheric Research Department of.
Session 5, CMAS 2004 INTRODUCTION: Fine scale modeling for Exposure and risk assessments.
An Exploration of Model Concentration Differences Between CMAQ and CAMx Brian Timin, Karen Wesson, Pat Dolwick, Norm Possiel, Sharon Phillips EPA/OAQPS.
Analysis of Ozone Modeling for May – July 2006 in PNW using AIRPACT3 (CMAQ) and CAMx. Robert Kotchenruther, Ph.D. EPA Region 10 Nov CMAQ O 3 Prediction.
Lagrangian particle models are three-dimensional models for the simulation of airborne pollutant dispersion, able to account for flow and turbulence space-time.
Seasonal Modeling of the Export of Pollutants from North America using the Multiscale Air Quality Simulation Platform (MAQSIP) Adel Hanna, 1 Rohit Mathur,
Types of Models Marti Blad Northern Arizona University College of Engineering & Technology.
Peak 8-hr Ozone Model Performance when using Biogenic VOC estimated by MEGAN and BIOME (BEIS) Kirk Baker Lake Michigan Air Directors Consortium October.
___________________________________________________________________________CMAQ Basics ___________________________________________________Community Modeling.
Intro to Modeling – Terms & concepts Marti Blad, Ph.D., P.E. ITEP
Breakout Session 1 Air Quality Jack Fishman, Randy Kawa August 18.
Sensitivity of PM 2.5 Species to Emissions in the Southeast Sun-Kyoung Park and Armistead G. Russell Georgia Institute of Technology Sensitivity of PM.
Transport Simulation of the April 1998 Chinese Dust Event Prepared by: Bret A. Schichtel And Rudolf B. Husar Center for Air Pollution Impact and Trend.
Synthesis of work on Budget of Water Vapor and Trace gases in Amazonia Transport and Impacts of Moisture, Aerosols and Trace Gases into and out of the.
Atmospheric Lifetime and the Range of PM2.5 Transport Background and Rationale Atmospheric Residence Time and Spatial Scales Residence Time Dependence.
CO 2 Mixing/Advection by Fronts Aaron Wang Colorado State University.
1 DUST modeling AirWare AQMS: urban/industrial and regional air quality modeling and management: DUST modeling DDr. Kurt Fedra Environmental Software and.
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
A.Liudchik, V.Pakatashkin, S.Umreika, S.Barodka
CRC NARSTO-Northeast Modeling Study
Models of atmospheric chemistry
MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching
Introduction to Modeling – Part II
M. Samaali, M. Sassi, V. Bouchet
Alison Redington* and Derrick Ryall* Dick Derwent**
UNCERTAINTIES IN ATMOSPHERIC MODELLING
Development and Evaluation of a Hybrid Eulerian-Lagrangian Modeling Approach Beata Czader, Peter Percell, Daewon Byun, Yunsoo Choi University of Houston.
Presentation transcript:

1 AirWare : AirWare : C OMPREHENSIVE A IR QUALITY M ODEL WITH E X TENSIONS VERSION 4.30, PBM DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA

2 CAMxCAMx CAMx simulates the emission, dispersion, chemical reaction, and removal of pollutants in the troposphere by solving the pollutant continuity equation for each chemical species (l) on a system of nested three- dimensional grids.

3 CAMxCAMx The Eulerian continuity equation describes the time dependency of the average species concentration (cl) within each grid cell volume as a sum of all of the physical and chemical processes operating on that volume.

4 Where: C l = concentration of chemical species l V H = horizontal wind vector  = net vertical entrainment rate  = atmospheric density K = turbulent diffusion CAMx basic structure:

5 CAMx (simple version) Regular terrain-following 3D grid, supports nesting; Describes conservative substances, particulates and aerosols, various alternative chemistry mechanisms including complete photochemistry (ozone). Regular terrain-following 3D grid, supports nesting; Describes conservative substances, particulates and aerosols, various alternative chemistry mechanisms including complete photochemistry (ozone).

6 CAMx model grid: 2 level nesting: 1 km master domain (240*180) 1 km master domain (240*180) up to 9 sub- domains (city level, km at 250m) up to 9 sub- domains (city level, km at 250m) 8 vertical layers. 8 vertical layers.

7 CAMx Performance Performance independent of the number of (gridded) sources; Depends on: horizontal and vertical extent and resolution of the model domain, run duration 24 hours Cyprus (240*180*8, 9 sub- domains of 20*20 km 250m) conservative  3 hours 24 hours Cyprus, ozone (CBM IV, no sub- domain nesting  10 hours. Performance independent of the number of (gridded) sources; Depends on: horizontal and vertical extent and resolution of the model domain, run duration 24 hours Cyprus (240*180*8, 9 sub- domains of 20*20 km 250m) conservative  3 hours 24 hours Cyprus, ozone (CBM IV, no sub- domain nesting  10 hours.

8 CAMxCAMx The Eulerian continuity equation describes the time dependency of the average species concentration (cl) within each grid cell volume as a sum of all of the physical and chemical processes operating on that volume.

9 CAMx implementation Used for scheduled runs: Daily 24 hour forecasts (SO2, NOx, PM10) Hourly now-casts (waiting for real-time data assimilation, nudging the initial conditions with monitoring data. Used for scheduled runs: Daily 24 hour forecasts (SO2, NOx, PM10) Hourly now-casts (waiting for real-time data assimilation, nudging the initial conditions with monitoring data.

10

11

12

13

14

15

16

17

18

19

20

21

22 AirWare R5.3 PBM photochemical box model DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA

23 PBM photochemical box model Simple, fast and efficient numerical model for urban scale ( km) photochemical smog. Initial and boundary conditions can be taken from the daily regional ozone forecasts by CAMx. Input data include: –Meteorology (temperature, wind speed, mixing height, insolation/cloud cover) can be taken from the MM5 weather forecasts; –Dynamic (hourly) emission data of NOx and VOC are taken from the city domain level emission scenarios Simple, fast and efficient numerical model for urban scale ( km) photochemical smog. Initial and boundary conditions can be taken from the daily regional ozone forecasts by CAMx. Input data include: –Meteorology (temperature, wind speed, mixing height, insolation/cloud cover) can be taken from the MM5 weather forecasts; –Dynamic (hourly) emission data of NOx and VOC are taken from the city domain level emission scenarios

24 PBM photochemical box model Simple representation: Variable volume (vertical movement of the mixing layer), well mixed reactive cell (box); Transport and dispersion of pollutants through the cell; Dynamic emission of primary precursor species; Chemical transformations into intermediate and secondary products. Simple representation: Variable volume (vertical movement of the mixing layer), well mixed reactive cell (box); Transport and dispersion of pollutants through the cell; Dynamic emission of primary precursor species; Chemical transformations into intermediate and secondary products.

25

26 PBM photochemical box model Simulation runs: Daily forecast runs for the period from –05:00 LST (at/around/before sunrise) –23:00 LST (after sunset Hourly model output. Speed/efficiency of the model makes it ideal for stochastic/ensemble simulations: probability of exceedances of daily/next day ozone warning and alert levels. Simulation runs: Daily forecast runs for the period from –05:00 LST (at/around/before sunrise) –23:00 LST (after sunset Hourly model output. Speed/efficiency of the model makes it ideal for stochastic/ensemble simulations: probability of exceedances of daily/next day ozone warning and alert levels.

27

28 PBM data requirements: Meteorology (hourly): Date, location Wind speed Mixing height (can be simulated) Ambient air temperature Solar radiation or UV radiation (can be simulated if cloud cover is known) Cloud cover, amount and (optional) height Meteorology (hourly): Date, location Wind speed Mixing height (can be simulated) Ambient air temperature Solar radiation or UV radiation (can be simulated if cloud cover is known) Cloud cover, amount and (optional) height

29 PBM data requirements: Air Quality data: Initial conditions (concentrations) Boundary conditions/concentrations: upwind monitoring station ? Observed concentrations (optional) Hydrocarbon speciation factors for –Initial concentrations –Boundary conditions –Observed concentrations Air Quality data: Initial conditions (concentrations) Boundary conditions/concentrations: upwind monitoring station ? Observed concentrations (optional) Hydrocarbon speciation factors for –Initial concentrations –Boundary conditions –Observed concentrations

30 PBM reactivity classes: Non-reactives (NONR) Ethylene (ETH) Olefins/alkenes, minus ethylene (OLE) Paraffins/alkanes, minus methane (PAR) Formaldehyde (FOR) Other aldehyde species (ALD) Toluene (TOL) Other aromatic species (ARO) Non-reactives (NONR) Ethylene (ETH) Olefins/alkenes, minus ethylene (OLE) Paraffins/alkanes, minus methane (PAR) Formaldehyde (FOR) Other aldehyde species (ALD) Toluene (TOL) Other aromatic species (ARO)

31 PBM data requirements: Emissions (hourly): CO (area, point sources) NOx (area, line, point sources) THC (total hydrocarbon) emissions NMHC (non-Methane compounds) NO2/NOx ratio in emissions (0.1) CH4/THC ratio in emissions Emissions (hourly): CO (area, point sources) NOx (area, line, point sources) THC (total hydrocarbon) emissions NMHC (non-Methane compounds) NO2/NOx ratio in emissions (0.1) CH4/THC ratio in emissions

32 PBM model dynamics: Mixing height growth: option to interpolate between minimum and maximum; Photolytica rate constants: diurnal variation of photolytica rate constants based on theoretical clear-sky insolation and attenuation based on cloud cover or oberserved insolation; Chemical kinetics: 63 reactions and 41 chemical species in 8 hydrocarbon classes (Demerjian generalized chemical kinetics) Mixing height growth: option to interpolate between minimum and maximum; Photolytica rate constants: diurnal variation of photolytica rate constants based on theoretical clear-sky insolation and attenuation based on cloud cover or oberserved insolation; Chemical kinetics: 63 reactions and 41 chemical species in 8 hydrocarbon classes (Demerjian generalized chemical kinetics)

33 PBM model governing equation:

34 PBM major assumptions: 1.Well mixed box, no significant spatial variation inside the box/surface; 2.Low wind to near stagnant conditions (< 2m/s) 3.Emission sources are homogeneously distributed within the domain; 4.Entrainment/exchange 1.laterally by advective transport 2.Vertically by rising mixing layer 5.Molecular and turbulent diffusion neglected (well mixed…) 6.Horizontal and vertical wind shear neglected. 1.Well mixed box, no significant spatial variation inside the box/surface; 2.Low wind to near stagnant conditions (< 2m/s) 3.Emission sources are homogeneously distributed within the domain; 4.Entrainment/exchange 1.laterally by advective transport 2.Vertically by rising mixing layer 5.Molecular and turbulent diffusion neglected (well mixed…) 6.Horizontal and vertical wind shear neglected.