Transcript Air Quality Modeling - Georgia Institute of Technology

Air Quality Modeling
Rosa Sohn and Sun-Kyoung Park
1. Introduction
Atmospheric
Chemistry
Emissions
Inputs
Emissions
Modeling
Inputs:
Population
Roads
Land Use
Industry
Meteorology
Numerical
Routines
Meteorological
Fields
Meteorological
Modeling
Inputs:
Topography
Observed
Meteorology
Solar insolation
Pollutant
Distributions
Effects
Visualization
Economics
Controls
2. Eulerian and Lagrangian models
2.1. Eulerian Model
– The behavior of species is described relative to a
fixed coordinate system
(1) Single box model:
• Focus: atmospheric chemistry
• Lack physical realism - horizontal and vertical transport, etc.
(2) Multi-dimensional grid-based air quality model
• Potentially the most powerful
• Involving the least-restrictive assumption
2.2. Lagrangian Model
– The concentration changes are described relative to the
moving fluid
3. Air Quality Model Formulation(1)
 ci 
ci
   (U ci )   Di    Ri (c1 , c2 , ... , cn , T , t )  Si ( x , t )
t

i=1,2,3, . . . , n
ci : concentration of species i.
U : wind velocity vector
Di : molecular diffusivity of species i
Ri : rate of concentration change of species i by chemical reaction
Si : source/sink of i
 : air density
n : number of predicted species
3. Air Quality Model Formulation(2)
Reynolds decomposition + K theory
Assumption:   U   0
c 
c 
  u '  ci '  

 Di  i    K  i      i

  
  


  ci 
   ( U  ci )
t
  ci  
  Ri ( c1 ,  c2 , ... ,  cn , T , t )   Si ( x , t ) 
  K 
  
Atmospheric Diffusion Equation (ADE)
4. Model components and process descriptions(1)
• Turbulent transport and diffusion
– K-theory
• K: Function of the atmospheric stability class and the mixing height
• Deposition
– Dry deposition: vd=(ra + rb + rc ) –1
ra: aerodynamic resistance, controlled by the atmospheric turbulence
rb: resistance in the fluid sublayer very near the plant surface
rc: surface(or canopy) resistance, the function of pollutant, land-use,
surface condition(dew, rain or dry..) and season
– Wet deposition
• Because of the meteorological model’s uncertainty in the formation of
the clouds and the precipitation, wet deposition has still much
uncertainty.
4. Model components and process descriptions(2)
• Chemical kinetics
– Homogeneous gas-phase chemistry
– Heterogeneous chemistry
• Acid deposition, aerosol formation
– Radiative transfer (Approach)
• To adjust the sea-level photolysis rates for solar zenith angle,
wavelength, changes in altitude, haze and clouds, preferably
using measurements.
• To use a look-up table derived from a detailed radiative transfer
model and then modify the results for clouds (e.g., CMAQ)
• To use radiative fluxes calculated by meteorological model being
used to provide other field
4. Model components and process descriptions(3)
• Particulate matter
– Impact health, visibility and gas phase species levels
(e.g., In the presence of aerosol, scattering increase
 Increase in ozone formation)
– Particulate matter modeling
• Formation and growth: Sectioning size distribution
• Size and chemical composition:
Condensation, Coagulation, Sedimentation and Nucleation
• Individual sources are simulated to emit a set of aerosol
packets with specific sizes and compositions (Cass, et al).
5. Mathematical and computational implementation(1)
• Horizontal transport algorithms
– Based on Finite Difference, Finite Element and Finite Volume
– Spectral Method, Lagrangian approach
– Problem with solving the set of equations
• Spatial discretization artificial numerical dispersion, which is
manifested by the formation of spurious waves and by pollutant
peaks being spread out
• Currently small error/uncertainty in application
• Chemical dynamics: 80 % of the computer time
– QSSA(quasi-steady-state approximation)
– Hybrid method
– Gear-type method
• Good for the large integration time step (e.g., 1hr)
5. Mathematical and computational implementation(2)
• Monoscale, nested multiscale and adaptive grids
– Large grid size is inappropriate for the non-linear reactions (e.g.,
ozone) with significant chemical gradient in cities
– Considering computational resources, using finer grids in urban
area and coarser grids over rural area
• Plume modeling
– Concentrated sources of some pollutants in coarse resolution
(e.g., power plant)
• Mixing is at a finite rate  local  volume average concentration.
• Assumption of immediate mixing often leads to overestimating the
oxidation rate of NO (e.g., in VOC rich environment).
– Adaptive mesh technique
• Mesh is generated automatically to capture the fine scale features
5. Mathematical and computational implementation(3)
• Mass conservation in air quality models
– Without using the continuity equation explicitly, models diagnose
air density from pressure and temperature (e.g., MM5)
– Even the continuity equation is satisfied,
• Meteorological model output is stored at a certain interval
(e.g., 1hr)
• Air Quality Model’s time step (e.g., 10 min)  Interpolation
• The interpolation of density and momentum does not
guarantee the mass conservation

  U   0
Vertical or horizontal velocity is recomputed.
t
5. Mathematical and computational implementation(4)
• Advanced Analysis routines
– Integration of specific physical and chemical processes terms
• Applied to Lagrangian Box Modeling studies
– Direct sensitivity analysis
• Brute Force
• Decoupled Direct Method(DDM)
• Adjoint Approach
– Limitation: cannot capture nonlinear response
6. Model Input (1) - Meteorology
• Meteorological Input
– Horizontal and Vertical Wind fields, Temperature, Humidity, Mixing
depth, Solar insolation fields, Vertical diffusivities, cloud
characteristics(liquid water content, droplet size, cloud size, etc.),
rain fall
• How to prepare input fields?
– Interpolating relatively sparse observations over the modeling
domain using the objective analysis
– Meteorological Model (e.g., MM5) output because of the
sparseness of data
6. Model Input (2) – Emission
• Emission
– CO, NO, NO2, SO2, VOCs, SO3, NH3, PM2.5 and PM10
– Emissions are one of the most uncertain, but the most
important inputs into air quality models
• Temporal Processing
– The inventory is the yearly averaged data, but the AQM
needs short interval emission input(e.g., hourly).
• Spatial Processing
– The inventory is county based data, but the AQM needs
gridded emission inventory if you are doing the multidimensional grid-based air quality model
UNPROJECTED LATITUDE-LONGITUDE
Map Projection
• “Grid” defined in the AQM depends on the map projection.
• Map Projection
– Attempt to portray the surface of the earth or a portion of the earth on
a flat surface
•
•
•
•
•
Cylindrical
Psuedo-cylindrical
Conical
Azimuthal
Other
(1) Cylindrical Projection
• Mercator
• Lambert’s Cylindrical Equal-area
• Gall’s Sterographic Cylindrical
• Miller Cylindrical
• Behrmann Cylindrical Equal-area
• Peters
• Transverse Mercator
(2) Psuedo-Cylindrical Projections
• Mollweide Equal-area
• Eckert IV Equal-area
• Eckert VI Equal-area
• Sinusoidal Equal-area
• Robinson
(3) Conical Projections
• Albers Equal Area Conical Projection
• Lambert Conformal Conical Projection
• Equidistant Conical Projection
CONICAL
TANGENT
(4) Azimuthal Projections
• Equidistant Azimuthal Projection
• Lambert Equal Area Azimuthal Projection
7. Air Quality Model Evaluation (1)
• Assessment of the adequacy and correctness of the science
represented in the model through comparison against empirical
data
•


1 N CP ( xi , t )  CO ( xi , t )
Normalized Bias, D D  
100(%) , t  1, ... ,24
N i 1
CO ( xi , t )
• Normalized Gross Error, Ed
1
Ed 
N
N
C P ( xi , t )  CO ( xi , t )
i 1
CO ( xi , t )

100 (%) , t  1, ... ,24
• Unpaired Peak Prediction Accuracy
Au 
CP ( x , t ) max  CO ( x' , t ' ) max
CO ( x' , t ' ) max
100(%)
7. Air Quality Model Evaluation (2)
• Statistical Benchmark for the model performance (US EPA,
1991 Tesche et al.)
• Normalized Bias :
 5 ~  15 %
• Normalized Gross Error :
 30 ~  35 %
• Unpaired peak prediction accuracy:  15 ~  20 %
7. Model Evaluation (3) – Input Meteorology
• Mean Bias Error (MBE)
1
MBE 
N
 V
N
i
i 1
s
 Vi o



• Mean Normalized Bias (MNB):
1 N Vi s  Vi o
MNB  
100%
o
N i 1 Vi
• Root Mean Square Error (RMSE)
1
RMSE 
N
• Mean Absolute Gross Error (MAGE)
1 N s
MAGE   Vi  Vi o
N i 1
•
1
Mean Normalized Gross Error (MNGE) MNGE 
N
 V
2
N
i 1
N

i 1
s
i
 Vi o
Vi s  Vi o
Vi
o

100 %
7. Model Evaluation (4) – Input Meteorology
Statistical Benchmarks
Wind Speed
RMSE  2 m/s
Bias  0.5 m/s
Wind Direction
Gross Error  30 deg
Bias  10 deg
Temperature
Gross Error  2 K
Bias  0.5 K
Specific
Humidity
Gross Error  2g/kg
Bias  1g/kg
Source : Environmental Report: MM5 Performance Evaluation Project
Matthew T. Johnson, Kirk Baker (2001)
8. Application (1)
• Sensitivity to Process Parameterizations
• Sensitivity to Model Numerics/Structure
– Small uncertainty in numerical technique
– Grid size, number of the vertical layers.
(e.g., Difference in ozone prediction
Horizontal grid size: 5 km, 10 km and 20km
Number of the vertical layers : 6 ~ 15 layers)
8. Application (2)
• Sensitivity to Model Input
– Emissions, meteorological conditions, boundary conditions,
initial conditions, HONO formation rate and deposition
– Emission control  Ozone and PM control
(e.g., SO2 control  sulfate decrease, but nitrate increases)
– VOCs with different reactivity  Ozone, PM, …
(e.g., Methanol based fuels would be beneficial for ozone
control because of its atmospheric low reactivity)
9. Current Status of AQM
• 1st generation: simple chemistry at local scales
• 2nd generation: local, urban, regional addressing each scale
with a separate model and often focusing on a single pollutant.
• 3rd generation: multiple pollutants simultaneously up to
continental scales and incorporate feedbacks between chemical
and meteorological components.
– Models3 (SMOKE, MM5 and CMAQ(Community Multiscale
Air Quality) Modeling system: urban to regional scale air
quality simulation of tropospheric ozone, acid deposition,
visibility and fine particulate).
• 4th generation (Future): extend linkages and process feedback
to include air, water, land, and biota to simulate the transport
and fate of chemical and nutrients throughout an ecosystem.
Step 1. Getting Started with AQM
• Download the existing model with free of charge
• Models3
– MM5 (http://www.mmm.ucar.edu/mm5/mm5-home.html)
• PSU/NCAR mesoscale model version 5
• Meteorological Field
– SMOKE (http://edge.emc.mcnc.org/uihelp/docs/smoke.html)
• Sparse Matrix Operator Kernel Emissions modeling system
• Converting emissions inventory data into the formatted
emission files required by an AQM
– CMAQ (http://www.epa.gov/asmdnerl/models3/)
• Community Multiscale Air Quality Modeling System
• Atmospheric chemistry combined with the numerical routine
Step 2. Meteorological Input – MM5 (1)
• MM5 (PSU/NCAR mesoscale model version 5) download
– http://www.mmm.ucar.edu/mm5/mm5-home.html
– MM5 Input Data: http://dss.ucar.edu/catalogs/
• Topography and Landuse data
• Gridded atmospheric data with sea-level pressure, wind, temperature,
relative humidity and geopotential height
• Observation data that contains soundings and surface reports
Step 2. Meteorological Input – MM5 (2)
• MM5 Output Manipulation for the evaluation, etc.
– MM5toGrADS
http://www.mmm.ucar.edu/mm5/mm5v3/tutorial/mm5tograds/mm5tograds.html
– GrADS(Grid Analysis and DisplaySystem)
http://grads.iges.org/grads/
• Converting the MM5 output to the format required in
SMOKE and CMAQ (NetCDF format)
– MCIP2(Meteorology-Chemistry Interface Processor Version 2)
: Inside of the CMAQ model system
Step 3. Emission Input – SMOKE
• National Emission Inventory (1996 yr, 1999 yr)
(http://www.epa.gov/ttn/chief/net/index.html)
• Projection to the model year
– EGAS 4.0 (http://www.epa.gov/ttn/chief/emch/projection/egas40/)
• Spatial Processing
– Converting the county based inventory data to the gridded emission
inventory which fits to the multi-dimensional grid-based air quality model
– ESRI ArcGIS – ArcView, ArcInfo, ArcMap, ArcToolbox and ArcCatalog
(Architecture Computer Lab (Rm 359))
• SMOKE (http://edge.emc.mcnc.org/uihelp/docs/smoke.html)
– Converting emissions inventory into the formatted emission files required
by an AQM (NetCDF format)
Step 4. Air Quality Modeling - CMAQ
• CMAQ Modeling system
– (Community Multiscale Air Quality)
– http://www.epa.gov/asmdnerl/models3/
• CMAQ Document
– http://www.epa.gov/asmdnerl/models3/doc/science/science.html
• CMAQ Tutorial
– http://www.epa.gov/scram001/cmaq.htm
Step 5. Tools
• PAVE
– The Package for Analysis and Visualization of Environmental Data
– Visualization and the analysis of NetCDF data
– http://www.epa.gov/asmdnerl/models3/vistutor/pave.html
• NetCDF IO/API
– The Models-3 Input/Output Applications Programming Interface
– The standard data access library for EPA's Models-3 available from both C
and Fortran. (ASCII or Binary  NetCDF)
– http://www.emc.mcnc.org/products/ioapi/AA.html
– AQM testing with arbitrary input (e.g., zero emission)
– Evaluation
Questions
Comments
Reference: NARSTO critical review of photochemical models and modeling, Armistead
Russell and Robin Dennis, 2000 Atmospheric Environment 34. 2283 - 2324