1. TowardanOpenResourcesUsingServices
Air Pollution Modeling Application
Didin Agustian Permadi
Prof. N. T. Kim Oanh

2. Content Introduction Air Quality Model Framework1 2
Input data (i.e. emission inventory, meteorological fields, etc) 3
Several air quality models used by the AIT 4
Link to TORUS project 5
Air Quality Model Framework
Content

3. Introduction Air Pollution Problems: Health impacts Ecosystem impacts
1976
1989
2004
Rapid development of the city (Parasati, 2011)
Biomass open burning practices
Air Pollution Problems:
Health impacts
Ecosystem impacts
Urgent calls for mitigation
High rate of motorization
High population growth

4. Why do we need modeling tool?
Environment effects are product of complex dynamic system driven by multiple processes (e.g. main processes determining air pollutant dispersion)
Atmospheric transport by mean wind field
Atmospheric turbulent diffusion
Atmospheric chemical and photochemical reactions
Interactions between surface (sea, land) and atmosphere
Wet and dry removal process
Modeling tool used to integrate these processes in a systematic approach to assess impacts of alternative scenarios on environment (causal links)
Hindcast, nowcast, and forecast are possible
Why do we need modeling tool?

5. Category of Air Pollution Models
Dispersion Model
Receptor Model
Statistical Model
based on a set of analytical or numerical algorithms (mathematical equations) describing physical, chemical aspects of air pollution to estimate ambient concentrations
These models are observational techniques which use the chemical and physical characteristics of gases and particles measured at source and receptor to both identify the presence of and to quantify source contributions to receptor concentrations
Statistical models for predictions: time series, synoptic climatological models, etc
Concentration at receptors
Source emissions

6. Evolution of Dispersion Models (US EPA)
1st-generation AQM (1970s s)
Gaussian dispersion models for primary pollutants : ISC, Calpuff, AERMOD, etc.
Mixing cell: single box model
Photochemical box models (OZIP/EKMA): e.g. Lagrangian box model for O3
2nd-generation AQM (1980s s)
Photochemical grid models (UAM)
3rd-generation AQM (1990s s)
Community-Based “One-Atmosphere” Modeling System (e.g., U.S. EPA’s Models-3/CMAQ): taking all pollutants and their interactions into account chemistry transport model (CTM), Chimere, STEM, etc
“On-line” couple chemistry –meteorology CTM: WRF/Chem, GATOR
Evolution of Dispersion Models (US EPA)

7. Advance Chemistry Transport Model Framework
Atmospheric Concentration
Surface Deposition
3D- Meteorology Data (output of meteorology model)
Emission data of SO2, NOx and NH3 as output of emission inventory
Chemistry Transport /Dispersion Model
Chemistry Mechanisms
Land cover data
Boundary condition from global CTM
Advance Chemistry Transport Model Framework

8. Air Quality Model Input: Emission Inventory
Emission Inventory - a comprehensive listing by sources of air pollutant emissions in a geographic area during a specific time period
Biomass Open Burning
Top-down approach
Bottom-up approach
Air Quality Model Input: Emission Inventory

9. Examples of gridded EI dataset
NOx
NMVOC
Source: Kingkaew, 2012
2007 BC emission
2007 NOx emission
Source: Permadi, 2013
Examples of gridded EI dataset

10. Examples of Available Global/Regional EI
EI Initiatives
Web-address
Remarks
The Emission Database for Global Atmospheric Research (EDGAR)
GHGs, ozone precursors, and particulates (1 degree gridded data)
The Atmospheric Composition Change by the European Network of Excellence (ACCENT)
a compilation of several inventories of anthropogenic emissions
The Regional Emission inventory in ASia (REAS),
NOx, CO2, SO2, CO, N2O, NH3, BC, OC, CH4, and NMVOC of 1995 and 2000 (0.5 degree)
CGRER/UIOWA
NOx, CO, BC, OC, PM, VOCs, SO2 (0.5 degree)
Global Fire Emission Database (GFED)
CO2, CO, CH4, N2O, NOx, NMHC, OC, BC, PM2.5, TPM, and SO2 (0.5 degree)
Examples of Available Global/Regional EI

11. Meteorological models
System Applications International Mesoscale Model (SAIMM) prognostic meteorological model developed by SAI (1995) to provide met fields of photochemical smog model UAM-V
European Center for Medium Range Weather Forecasts (ECMWF) meteorological model Produces routine global analyses
Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) pre-processors for CAMx (MM5-CAMx) and CMAQ (MCIP) Input data (NCEP Final Analysis, Global Reanalysis) :
Weather research forecast (WRF) model  pre-processors for CAMx (WRF- CAMx), and CMAQ (MCIP) similar input data for MM5
Meteorological models

12. Summary of Air Quality Models used by AIT
Model/platform
Met model
Emission data 
Domain
Simulation period
Resolution
CAMx (linux)
WRF
Independent EI for Vietnam
Vietnam (Hanoi, South VN)
Jan – Dec 2010
Outer: 12km
Inner: 4km
CAMx/CMAQ (linux)
MM5/WRF
THEM + AIT BMR EI
BMR
February, August, November 2010
Outer: 6 km
Inner: 2 km
CHIMERE (linux cluster)
ABC EI CGRER emission (2006), on-line biogenic emission
SEA
January – December 2007
30km
CMAQ (linux)
MM5
CGRER emission (2000 and 2006) GEIA
CSEA
January 26 – 29, 2004
March 24 – 26, 2004
56km
CMAQ (linux cluster)
PCD database
CENTHAI
January 1-30, 2006
11.2km
2000 CGRER, GEIA , independent emission inventory
JMA
Oct 17-18, 2003
Outer: 10.8km
Inner: 3.6km
Updated PCD database
March 24-26, 2004
4km
2000 CGRER , GEIA , independent emission inventory
HCMC
March 1-13, 2005
PCD database, 2006 CGRER, GEIA, Crop residue OB
January, 2007
August, 2007
2006 CGRER , independent emission inventory, Updated UNEP male declaration project 2000 database
Dhaka
June, 2006
December, 2006
Outer: km
Inner: 5.55 km
UAM-V (linux)
SAIMM
Independent emission inventory
Hanoi
March 3-4, 2003
UAM-V (Sun station)
PCD data for 1997
January 13-14, 1997
CHIMERE (Sun station)
ECMWF
Summary of Air Quality Models used by AIT

13. Modeling domains Kimoanh et al. (2012); Permadi (2012)
CMAQ/MM5, CAMx/MM5, UAM/SAIMM, CHIMERE/ECMWF, CHIMERE/WRF D7
Kimoanh et al. (2012); Permadi (2012)
Modeling domains

14. Example of Model Structure (CAMx)
Source: ENVIRON (2014)
Example of Model Structure (CAMx)

15. Model Performance Evaluation
Source: US. EPA (1991) for ozone
Statistical measure
Equation
Criteria
Mean Normalized Bias (MNB)
±15%
Mean Normalized Error (MNE)
35%
Unpaired Peak Prediction Accuracy (UPA)
±20%
Source: Boylan and Russell (2006) for PM
Parameters
Formula
Suggested Criteria
Mean fractional bias (MFB)
≤ ±60%
Mean fractional error (MFE)
≤ 75% (criteria)
Model Performance Evaluation

16. NOx-VOC sensitivity study for BMR, Thailand
Constructed by photochemical smog model application (UAMV-SAIMM)
BMR is more VOC sensitive →common condition found in other large cities
VOC reduction should be more effective to reduce ozone concentration
Ozone isopleth plot (ppb) over Bangkok
Source: Kim Oanh and Zhang, 2004
NOx-VOC sensitivity study for BMR, Thailand

17. Regional scale ozone simulation: CMAQ/MM5 in CSEA
Permadi et al. (2012)
January 2020 (IPCC-A2)
January 2006
January 2020 (IPCC-B2)
Regional scale ozone simulation: CMAQ/MM5 in CSEA

18. SEA aerosol simulation using Chimere/WRF (Permadi, 2013)
Hotspot active fire
PM2.5 Jan
2007
January
PM2.5 Aug
2007
August
SEA aerosol simulation using Chimere/WRF (Permadi, 2013)

19. Simulated AOD vs Satellite AOD (Permadi, 2013)
Modeled AOD Jan 2007
Modeled AOD April 2007
Modeled AOD Dec 2007
Simulated AOD vs Satellite AOD (Permadi, 2013)

20. Summary of model performances
Model applications
Statistical parameters for O3
Statistical parameters for PM
MNGE
MNBE
UPA
MFB
MFE
Criteria
<35%
±15%
±20%
≤ ±60%
≤ 75%
CMAQ/MM5 for CSEA regional ozone simulation
9 – 42
(-22) – 42
(-19) – 32 -
UAM-V/SAIMM for Ozone in BMR
24 – 31
(-24) – 10
(-22) – (-52)
CHIMERE/SAIMM for Ozone in BMR
23 – 47
(-13) – (-43)
(-20) – 19
CAMx/MM5 for ozone in BMR 26
-17
-6.5
CAMx/MM5 for ozone in JMA 47 19 20
CAMx/MM5 for ozone in HMR 21
-12.5
-18.3
CAMx/MM5 for ozone in HCMC
19 – 31
(-13) – (-27)
(-0.5) – (-14)
CAMx/WRF ozone and PM in Vietnam/Hanoi
44-125
-53 – (-112)
CAMx/WRF for ozone in BMR
27-71
-39-71
CHIMERE/WRF for SEA domain
-52-77
-9 - (-56)
Summary of model performances

21. Ways Forward: benefiting from TORUS
Air quality models require extensive data transfer and storage (input – output of meteorology and chemistry)
High performance computation is important (models are supported for paralell computation)
Integrated application for data visualization/dissemination through web-based interface can be developed
Therefore, through TORUS project network we seek for cooperation and collaboration (i.e. technology transfer through partners)
Ways Forward: benefiting from TORUS

22. Thank you