Transcript Recent Advances in Chemical Weather Forecasting in Support of Atmospheric

Recent Advances in Chemical Weather
Forecasting in Support of Atmospheric
Chemistry Field Experiments
Gregory R. Carmichael
Department of Chemical & Biochemical Engineering
Center for Global & Regional Environmental Research and the
University of Iowa
TRACE-P and ACE-Asia EXPERIMENTS
Satellite data
in near-real time:
MOPITT
TOMS
SEAWIFS
AVHRR
Stratospheric
intrusions
FLIGHT
PLANNING
Long-range transport from
Europe, N. America, Africa
C-130
Boundary layer ASIAN
chemical/aerosol OUTFLOW
processing
DC-8
P-3
ASIA
Emissions
-Fossil fuel
-Biomass burning
-Biosphere, dust
PACIFIC
3D chemical model
forecasts:
- ECHAM
- GEOS-CHEM
- Iowa/Kyushu
- Meso-NH
PACIFIC
Models are an Integral Part of
Field Experiments
• Flight planning
• Provide 4-Dimensional context of the
observations
• Facilitate the integration of the
different measurement platforms
• Evaluate processes (e.g., role of biomass
burning, heterogeneous chemistry….)
• Evaluate emission estimates (bottom-up
as well as top-down)
The Use of Models in Planning
Experimental
measurements
Theoretical
modeling
http://www.cgrer.uiowa.edu/ACESS/acess_index.htm
Model Overview
Regional Transport
Model: STEM
 Structure: Modular (on-line and off-line mode)
 Meteorology: RAMS - MM5 - ECMWF - NCEP
 Emissions: Anthropogenic, biogenic and natural
 Chemical mechanism: SAPRC’99 (Carter,2000)
 93 Species, 225 reactions, explicit VOC treatment
 Photolysis: NCAR-TUV 4.1 (30 reactions)
 Resolution: Flexible 80km x 80km for regional and 16km x 16km
for urban
Photochemistry:
STEM-TUV
Y. Tang (CGRER), 2002
CFORS/STEM Model Data Flow Chart
Meteorological Outputs
from RAMS or MM5
Biomass Emissions
Meteorological Preprocessor
Dust and Sea Salt
emissions
Normal meteorological variables:
wind velocities, temperature, pressure,
water vapor content, cloud water
content, rain water content and PV et al
Biogenic Emissions
Large
Point
Sources
Emission
Preprocessor
CFORS Forecast Model
with on-line TUV
Volcanic SO2 Emissions
Anthropogenic Area Emissions
Satellite Observed total
O3 (Dobson Unit)
Post
Analysis
CFORS/STEM Model Data Flow Chart
Meteorological Outputs
from RAMS or MM5
Biomass Emissions
Biogenic Emissions
Large
Point
Sources
Emission
Preprocessor
Volcanic SO2 Emissions
Anthropogenic Area Emissions
Meteorological Preprocessor
Dust and Sea Salt
emissions
Normal meteorological variables:
wind velocities, temperature, pressure,
water vapor content, cloud water
content, rain water content and PV et al
Tracers/Markers:
SO2/Sulfate
DMS
BC
OC Model
CFORS Forecast
Volcanic
Megacities
with on-line
TUV
CO fossil
CO-Biomass
Ethane
Ethene
Sea Salt
Radon
Lightning NOx Dust 12 size
bins Satellite Observed total
O3 (Dobson Unit)
Post
Analysis
Regional Emission Estimates:
Anthropogenic Sources
Industrial and Power Sector
Coal, Fuel Oil, NG
SO2, NOx, VOC, and Toxics
Domestic Sector
Coal, Biofuels, NG/LPG
SO2, CO, and VOC
Transportation Sector
Gasoline, Diesel, CNG/LPG
NOx, and VOC
Regional Emission Estimates:
Natural Sources
Biomass Burning
In-field and Out-field combustion
CO, NOx, VOC, and SPM
Volcanoes
SO2, and SPM
Dust Outbreaks
SPM
Regional Emission Estimates:
Sectoral Contributions
BB
1%
IND
37%
PP
46%
PP =
Power
Sector
BB =
Biomass
Burning
IND =
Industries
TRAN =
transport
DOM =
Domestic
TRAN
4%
SO2
BB
11%
IND
18%
PP
19%
NOx
DOM
12%
Annual Asian Emissions
for Year 2000
SO2 = 34.8 Tg
CO = 244.8 Tg
NOx = 25.6 Tg
VOC = 52.7 Tg
BB
29%
IND
7%
DOM
8%
TRAN
44%
BB
24%
DOM
38%
IND
16%
DOM
34%
PP
0%
PP
22%
TRAN
4%
CO
TRAN
26%
VOC
Regional Emission Estimates:
% by Economic Sector : SO2 Emissions
Domestic
Industrial
Transport
Power
Regional Emission Estimates:
% by Economic Sector : NOx Emissions
Domestic
Industrial
Transport
Power
Regional Emission Estimates:
Biomass Burning Emissions
For Southeast Asia and Indian Sub-Continent
Original Fire Count(FC)
data(AVHRR)
5-day Fire Count
Satellite Coverage
“Fill-up” Zero Fire
Counts using Moving
Average(MA)
Moving Averaged Fire
Count data (Level 2)
Cloudiness
Precipitation(NCEP)
“Fill-up” Zero Fire Count
using TOMS AI
Mask Grid
(Landcover)
Mask Grid (Never
Fire)
Regress.
Coeff.(AI/FC)
“Extinguish” Fire Count
using Mask Grids
AI Adjusted Fire
Count data (Level 3)
Open Burning Emissions of CO – Based on
AVHRR Fire-count Data
The Importance of Fossil, Biofuels and
Open Burning Varies by Region
Uncertainty analysis has revealed wide differences
in our knowledge of the emissions of particular
species in particular parts of Asia …
900%
800%
SO2
700%
NOx
CO2
600%
CO
500%
CH4
400%
VOC
300%
BC
200%
OC
NH3
?
)
100%
0%
China
Japan
Other East Southeast
Asia
Asia
India
Other
South Asia
Ships
All Asia
March 9 --forecast
3/9
Frontal outflow of biomass burning plumes E of Hong Kong
Biomass burning
CO forecast
(G.R. Carmichael,
U. Iowa)
7
Altitude (km)
6
5
CO Scale
(ppbv)
300+
250 to 300
200 to 250
150 to 200
100 to 150
50 to 100
4
3
2
Observed CO
(G.W. Sachse, NASA/LaRC)
1
0
110
115
120
125
130
135
140
7
Altitude (km)
6
5
Observed aerosol potassium
(R. Weber, Georgia Tech)
K
(ug/m3)
1+
0.8 to 1
0.6 to 0.8
0.4 to 0.6
0.2 to 0.4
0 to 0.2
4
3
2
1
0
110
115
120
125
130
135
140
Using Measurements
and Model – We
Estimate
Contributions of
Fossil, Biofuel and
Open Burning
Sources
Contribution of Asian Fuel Burning to
Tropospheric Ozone
Yienger, et al, JGR 2000
NASA GTE TRACE-P Mar’01Apr’01
 Two aircrafts – DC8 and P3
China
 Chemical evolution during
continental outflow, biomass
burning, dust outbreaks, and
urban plumes
 22 flights out of Hong Kong,
Okinawa and Tokyo
 O3, CO, SOx, NOx, HOx, RH
and J
 100m to 12000m
% Urban Contribution to Regional Photochemistry
Monthly Average March’01 Between 0-500m
Characterization of Urban Pollution
Flight DC8-13 : 03/21/2001
Flight Path
Back Traj.
% Urban HCHO
 1000 ppbv of CO, 10 ppbv of HCHO, 100 ppbv of O3
 Fresh plumes out of Shanghai, < 0.5 day in age
Characterization of Urban Pollution
Flight DC8-16 : 03/29/2001
Flight Path
Back Traj.
% Urban HNO3
 Sunrise experiment 300 ppbv of CO, 60 ppbv of O3
 Pollution entrainment in the high pressure system
 Fresh plumes out of Shanghai, aged plumes from Beijing
Characterization of Urban Pollution
Flight P3-18 : 03/30/2001
Flight Path
Back Traj.
Back Traj.
 200-350 ppbv of CO, 60 ppbv of O3, 5-6 ppbv of NOy, 700-1500 pptv
of NO and 3 ppbv of C2H6
 Fresh plumes out of Seoul and Pusan in one leg, aged plumes from
Beijing and Coastal China in the other
Characterization of Urban Pollution
Back Trajectory Analysis
No. of Points
250
5-5.5
4.5-5
4-4.5
3.5-4
3-3.5
2.5-3
2-2.5
1.5-2
1-1.5
0.5-1
0-0.5
200
150
100
50
Bangkok
Manila
Saigon
Tokyo
Kinki
Pusan
Seoul-Inchon
Shanghai
Qingdao
Santou
Chongqing
Guiyang
Hongkong
Guangzhou
Taiyuan
Wuhan
Tianjian
Beijing
0
Color code
indicates
plume age
in days
from that
city
984
out of
2238
Urban Photochemistry
OH Radical Cycle
VOC + OH --->
Orgainic PM
Fine PM Visibility
(Nitrate, Sulfate,
Organic PM)
Water
Quality
NOx + SOx + OH
(Lake Acidification,
Eutrophication)
PM2.5
.
OH
SOx [or NOx] + NH3 + OH
---> (NH4)2SO4 [or NH4NO3]
Ozone
NOx + VOC + OH
+ hv ---> O3
Acid Rain
SO2 + OH ---> H2SO4
NO2 + OH ---> HNO3
Air Toxics OH <---> Air Toxics
(POPs, Hg(II), etc.)
Urban Photochemistry
 Tropospheric chemistry is characterized by
reaction cycles
 OH plays a key role in tropospheric chemistry
 Reactions lead to removal as well as generation of
pollutants
 NOx to VOC ratio governs Ozone production
Urban Photochemistry
NOx-VOC-Ozone Cycle
RH  OH  R   H 2O
R  O2  RO2 
RO2   NO  RO   NO2
NO2  hv  NO  O3P(  400nm)
O3P  O2  O3
 Organic radical production and photolysis of NO2
 VOC’s and N-species compete for OH radical
Urban Photochemistry
NOx-VOC-Ozone Cycle
CO  OH  H  CO2
H  O2  HO 2 
HO 2   NO  HO   NO2
NO2  hv  NO  O3P(  400nm)
O3P  O2  O3
 In polluted environment, CO contributes to O3
production
Urban Photochemistry
NOx-VOC-Ozone Cycle
CH 4  NO  OH  2O2  HCHO  HO 2   H 2 O  NO2
C 2 H 4  OH  HCHO  RO 2 
HCHO  hv  2O2  CO  HO 2  (45%)
HCHO  hv  CO  H 2 (55%)
HCHO  OH  O2  CO  HO 2   H 2 O
 HCHO – primary intermediate in VOC-HOx chemistry
 Short lived and indicator of primary VOC emissions
Urban Photochemistry
NOx-VOC Emission Ratio
City
Emission Ratio
Dhaka
0.2
New Delhi
0.4
Calcutta
0.3
Mumbai
0.4
Karachi
0.6
Tokyo
0.7
Beijing
0.5
Shanghai
0.6
Chongqing
0.4
Hong Kong
0.8
Seoul
1.4
Manila
0.2
Singapore
1.4
Units:
g NO2 to g C
In 2000
Urban Photochemistry
NOx-VOC-Ozone Cycle
O3 Cycle
Units:
ppbv/hr
STEM Box Model Calculations
For City of Seoul,
O3 Cycle
STEM Box Model Calculations
For City of Shanghai
Urban Photochemistry
Species to Species Comparison
CO Vs VOC: Megacity points from back trajectories
 CO produced due to photolysis of HCHO, a short lived intermediate
from reactions between VOC and HOx
 High O3 and CO concentrations are linked with high VOC
concentrations, especially with urban plume age < 1.0 day
Urban Photochemistry
HCHO to CO Ratios
City
All Points
Shanghai
Plume Age
(days)
Ratio
(Obs.)
Ratio
(Mod.)
< 1 day
0.0102
0.0079
1 to 2 days
0.0069
0.0068
2 to 3 days
0.0061
0.0066
3 to 4 days
0.0061
0.0069
4 to 6 days
0.0070
0.0070
< 1 day
0.0114
0.0079
1 to 2 days
0.0074
0.0066
2 to 4 days
0.0039
0.0047
4 to 6 days
Beijing
Seoul
Pusan
Hong Kong
0.0043
0.0065
0.0071
< 1 day
0.0120
1 to 6 days
0.0078
< 1 day
0.0116
1 to 6 days
0.0077
0.0063
0.0062
Age in days
calculated
from back
trajectories
along the
flight path
Units:
ppbv-HCHO/
ppbv-CO
Urban Photochemistry
Species to Species Comparison
O3 Vs Species: Megacity points from back trajectories
Urban Photochemistry
Loss(N)/(Loss(N)+Loss(R))
NOx-VOC Sensitivity to O3
Production
VOC sensitive
Less than 2
day old
plumes
Model
results
along the
flight path
NOx sensitive
Model NOx (ppbv)
Megacity
points from
back
trajectories
Klienman et al., 2000
Urban Photochemistry
NOx-VOC Sensitivity Implications
 Ozone production in the urban plumes is VOC limited
 Decrease in NOx may actually increase local O3
production
 Though at present, NOx is contributing less to local O3
mixing ratios, it is contributing to local NO2 mixing
ratios (health criteria pollutant) and to O3 production
at downwind sites.
Environmental Integrated Assessment
Ambient
Concentration
Exposure
Emissions
Policy
Issues
Air Quality
Management
System
Technical
Options
Trends in Urban Asia Sulfur Pollution
Model Overview
RAINS-Asia
Developed by IIASA, Austria
SO2, PM, NOx
Energy, Emissions, Controls,
Costs and Optimization modules
ATMOS Dispersion Model
SO2, PM, NOx
Lagrangian Puff Transport
Linear Chemistry
NCEP Winds (1975-2000)
Environmental Integrated Assessment
Case Study of Shanghai, China
32o
Emissions for 1995
PM10 : 166 ktons PM/year
Shanghai
East China
Sea
PM2.5 : 68 ktons PM/year
Sulfur: 458 ktons SO2/year
Shanghai Province
Population: 19 Million
30o36’
120o36’
122o
Source: Li and Guttikunda et al., 2002
Shanghai Urban Air Quality Management
Emission Estimates
Units:
Gg/year
1995
Economic Sector
PM10
(M)
PM2.5 (
C)
PM2.5
(M)
NOx
214.1
80.4
199.9
71.1
40.6
Industry
49.2
Domestic
10.4
6.8
31.9
5.9
Transport
10.1
6.0
11.6
125.8
Total
Economic Sector
18.1
SO2
Power
Other
2020 BAU
PM10 (C
)
31.5
18.3
9.0
7.0
18.0
5.9
4.6
1.0
2.5
117.2
PM10 (C
)
49.5
PM10
(M)
55.1
PM2.5 (
C)
13.7
PM2.5
(M)
458.4
SO2
285.8
NOx
394.3
112.7
214.2
73.2
Power
11.2
Industry
52.1
Domestic
5.2
3.6
16.8
5.4
Transport
31.1
16.7
32.0
276.6
0.0
0.0
Other
0.0
5.1
18.6
36.4
19.6
0.0
5.3
9.3
Shanghai Urban Air Quality Management
Annual Average PM10
Concentrations
120
110
100
90
80
70
60
50
40
30
20
10
5
Units:
mg/m3
PM10
32
32
31.8
31.8
31.6
31.6
31.4
31.4
31.2
31.2
31
31
30.8
30.8
120.8
121
121.2
121.4
in 1995
121.6
121.8
122
120.8
121
121.2
121.4
121.6
2020 BAU
121.8
122
Shanghai Urban Air Quality Management
Health Benefit Analysis
Eij  Pj *  ij * Ai * POP
Dose-response function coefficients
Health Endpoint
Coefficient Source
Mortality
0.84
Lvovsky et al., 2000
Hospital Visit
0.18
Xu et al., 1995
Emergency Room Visit
0.10
Xu et al., 1995
Hospital Admission
0.80
Dockery and Pope, 1994
Chronic Bronchitis
0.10
Xu and Wang, 1993
Coefficient: % change in endpoint per 10 mg/m3 change in annual PM10 levels
Incidence rate: rate of occurrence of an endpoint among the population
Shanghai Urban Air Quality Management
Health Benefit Analysis
No. of cases avoided
Health Endpoint
Power Scenario
(no. of cases)
Industrial Scenario
(no. of cases)
2,808
1,790
Hospital Visit
96,293
61,379
Emergency Room
Visit
48,506
30,918
Hospital Admission
43,482
27,716
Chronic Bronchitis
1,753
1,117
Mortality
Shanghai Urban Air Quality Management
Health Benefit Analysis
Units:
US$ millions in
1998 dollars
Economic Evaluation
Health Benefits
Mortality
Morbidity
Power Scenario
Industrial Scenario
Low
139
88
Medium
347
221
High
1,030
656
Low
38
24
Medium
57
36
119
76
13
8
190 – 1,162
121 – 741
(417)
(266)
High
Work Day Lossess
Total Benefits
(Median Case)
Integrated Assessment Modeling
System (IAMS)
Days & Weeks
Emissions
&
Costs
Energy
Technology
Fuel
Sectors
Scales
Dispersion
Modeling
Source Receptor
Matrix
Seconds
Depositions
&
Concentrations
Exposure
&
Impacts
IAMS Model Schematics
Atmospheric Dispersion Calculations
Emission Sources (PM and SO2)
Central
Heating
Plants
Transportation
Sources
Domestic
Sources
Industrial
Boilers
Large Point
Sources
Transfer Matrix
for
LPS Sources
Transfer Matrix
for
Area Sources
PM and Sulfur
Concentrations
IAMS Software
Tracks
Emission
Changes.
Tracks
Concentration
Changes.
IAMS Software
Calculates Health Damages for
Mortality, Chronic Bronchitis,
Hospital Visits, Work Day Losses.
Tracks Health
Benefits to
Costs Ratio.
U. Iowa/Kyushu/Argonne/GFDL
With support from NSF, NASA (ACMAP,GTE), NOAA, DOE