Gregory R. Carmichael Department of Chemical & Biochemical Engineering
description
Transcript of Gregory R. Carmichael Department of Chemical & Biochemical Engineering
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 EXPERIMENTSTRACE-P and ACE-Asia EXPERIMENTS
Emissions-Fossil fuel-Biomass burning-Biosphere, dust
Long-range transport fromEurope, N. America, Africa
ASIA PACIFIC
P-3
Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRR
DC-8
3D chemical model forecasts: - ECHAM - GEOS-CHEM - Iowa/Kyushu - Meso-NH
FLIGHTPLANNING
Boundary layerchemical/aerosolprocessing
ASIANOUTFLOW
Stratosphericintrusions
PACIFIC
C-130
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
Experimentalmeasurements
Theoreticalmodeling
http://www.cgrer.uiowa.edu/ACESS/acess_index.htmhttp://www.cgrer.uiowa.edu/ACESS/acess_index.htm
Model OverviewRegional Transport Model: STEM
Structure: Modular Modular (on-line and off-line mode)
Meteorology: RAMSRAMS - MM5MM5 - ECMWFECMWF - NCEP NCEP
EmissionsEmissions: Anthropogenic, biogenic and natural
Chemical mechanism: SAPRC’99 SAPRC’99 (Carter,2000)
93 Species, 225 reactions, explicit VOC treatment
Photolysis: NCAR-TUV 4.1 NCAR-TUV 4.1 (30 reactions)
Resolution: Flexible 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
Meteorological Preprocessor
CFORS Forecast Modelwith on-line TUV
Normal meteorological variables:wind velocities, temperature, pressure,water vapor content, cloud water content, rain water content and PV et al
Dust and Sea Saltemissions
Emission Preprocessor
Biomass Emissions
Volcanic SO2 Emissions
Anthropogenic Area Emissions
Biogenic Emissions
Large PointSources
Satellite Observed total O3 (Dobson Unit)
PostAnalysis
CFORS/STEM Model Data Flow Chart
Meteorological Outputs from RAMS or MM5
Meteorological Preprocessor
CFORS Forecast Modelwith on-line TUV
Normal meteorological variables:wind velocities, temperature, pressure,water vapor content, cloud water content, rain water content and PV et al
Dust and Sea Saltemissions
Emission Preprocessor
Biomass Emissions
Volcanic SO2 Emissions
Anthropogenic Area Emissions
Biogenic Emissions
Large PointSources
Satellite Observed total O3 (Dobson Unit)
PostAnalysis
Tracers/Markers:Tracers/Markers:
SO2/Sulfate DMS
BC OC
Volcanic Megacities
CO fossil CO-Biomass
Ethane Ethene
Sea Salt Radon
Lightning NOx Dust 12 size bins
Regional Emission Estimates:
Anthropogenic Sources
Industrial and Power Sector Coal, Fuel Oil, NG
SO2, NOx, VOC, and Toxics
Domestic SectorCoal, Biofuels, NG/LPG
SO2, CO, and VOC
Transportation SectorGasoline, Diesel, CNG/LPG
NOx, and VOC
Regional Emission Estimates:
Natural Sources
Biomass Burning In-field and Out-field combustion
CO, NOx, VOC, and SPM
VolcanoesSO2, and SPM
Dust OutbreaksSPM
PP0%
BB24%
IND16%
TRAN26%
DOM34%
IND7%
PP22%
BB29%
TRAN4%
DOM38%
IND18%
DOM8%
TRAN44%
PP19%
BB11%
IND37%
DOM12%
TRAN4%
PP46%
BB1%
Regional Emission Estimates:
Sectoral Contributions
COCONONOxx
SOSO22
VOCVOC
SO2 = 34.8 TgNOx = 25.6 Tg
CO = 244.8 TgVOC = 52.7 Tg
Annual Asian Emissions for Year 2000
PP = Power SectorBB = Biomass BurningIND = IndustriesTRAN = transportDOM = Domestic
Regional Emission Estimates:
% by Economic Sector : SO2 Emissions
IndustrialIndustrialDomesticDomestic
TransportTransport Power Power
Regional Emission Estimates:% by Economic Sector : NOx Emissions
IndustrialIndustrialDomesticDomestic
TransportTransport Power Power
For Southeast Asia and Indian Sub-Continent
Original Fire Count(FC) data(AVHRR)
“Fill-up” Zero Fire Counts using Moving
Average(MA)
“Fill-up” Zero Fire Count using TOMS AI
Satellite Coverage
Cloudiness
Mask Grid (Landcover)
Precipitation(NCEP)
“Extinguish” Fire Count using Mask Grids
Mask Grid (Never Fire)
Moving Averaged Fire Count data (Level 2)
AI Adjusted Fire Count data (Level 3)
5-day Fire Count
Regress. Coeff.(AI/FC)
Regional Emission Estimates:
Biomass Burning Emissions
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 differencesin our knowledge of the emissions of particular
species in particular parts of Asia …
0%
100%
200%
300%
400%
500%
600%
700%
800%
900%
China Japan Other EastAsia
SoutheastAsia
India OtherSouth Asia
Ships All Asia
(95%
Con
fiden
ce In
terva
l,
? )SO2
NOx
CO2
CO
CH4
VOC
BC
OC
NH3
3/9
March 9 --forecast
Frontal outflow of biomass burning plumes E of Hong Kong
Observed CO(G.W. Sachse, NASA/LaRC)
Observed aerosol potassium(R. Weber, Georgia Tech)
110 115 120 125 130 135 1400
1
2
3
4
5
6
7
CO Scale(ppbv)300+250 to 300200 to 250150 to 200100 to 15050 to 100
110 115 120 125 130 135 1400
1
2
3
4
5
6
7
K(ug/m3)1+0.8 to 10.6 to 0.80.4 to 0.60.2 to 0.40 to 0.2
Biomass burning CO forecast(G.R. Carmichael,U. Iowa)
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
Two aircrafts – DC8DC8 and P3P3
Chemical evolution during continental outflow, biomass burning, dust outbreaks, and urban urban plumesplumes
2222 flights out of Hong Kong, Okinawa and Tokyo
O3, CO, SOx, NOx, HOx, RH and J
100m to 12000m
110 E 120 E 130 E 140 E 150 E 160 E
Longitude
0 N
10 N
20 N
30 N
40 N
50 N
Lat
itu
de
DC-8 FlightsP-3B Flights
China
NASA GTE TRACE-P Mar’01-Apr’01
% Urban Contribution to Regional Photochemistry Monthly Average March’01 Between 0-500mMonthly Average March’01 Between 0-500m
1000 ppbv of CO, 10 ppbv of HCHO, 100 ppbv of O3
Fresh plumes out of ShanghaiShanghai, < 0.5 day in age
% Urban HCHO% Urban HCHOFlight PathFlight Path Back Traj.Back Traj.
Characterization of Urban Pollution
Flight DC8-13 : 03/21/2001
Sunrise experiment 300 ppbv of CO, 60 ppbv of O3
Pollution entrainment in the high pressure system
Fresh plumes out of ShanghaiShanghai, aged plumes from BeijingBeijing
% Urban HNO% Urban HNO33Flight PathFlight Path Back Traj.Back Traj.
Characterization of Urban Pollution
Flight DC8-16 : 03/29/2001
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 Seoul and Pusan Pusan in one leg, aged plumes from Beijing Beijing and Coastal China Coastal China in the other
Flight PathFlight Path Back Traj.Back Traj. Back Traj.Back Traj.
Characterization of Urban Pollution
Flight P3-18 : 03/30/2001
0
50
100
150
200
250
Be
ijing
Tia
njia
n
Wu
ha
n
Taiyu
an
Gu
an
gzh
ou
Ho
ng
kon
g
Gu
iyan
g
Ch
on
gq
ing
Sa
nto
u
Qin
gd
ao
Sh
an
gh
ai
Se
ou
l-Inch
on
Pu
san
Kin
ki
Tokyo
Sa
igo
n
Ma
nila
Ba
ng
kok
5-5.54.5-54-4.53.5-43-3.52.5-32-2.51.5-21-1.50.5-10-0.5
Color code Color code indicates indicates plume age plume age in days in days from that from that citycity
984 984 out of out of 22382238
No.
of
Poin
tsCharacterization of Urban Pollution
Back Trajectory Analysis
Urban Photochemistry
OH Radical Cycle
Air ToxicsAir Toxics
OzoneOzone
Acid RainAcid Rain
VisibilityVisibility
PM2.5PM2.5
Water Water QualityQuality
..OHOHNOx + VOC + OH + hv ---> O3
SOx [or NOx] + NH3 + OH ---> (NH4)2SO4 [or NH4NO3]
SO2 + OH ---> H2SO4NO2 + OH ---> HNO3
VOC + OH --->Orgainic PM
OH <---> Air Toxics (POPs, Hg(II), etc.)
Fine PM(Nitrate, Sulfate, Organic PM)
NOx + SOx + OH (Lake Acidification,
Eutrophication)
Tropospheric chemistry is characterized by reaction cycles
OHOH plays a key role in tropospheric chemistry
Reactions lead to removalremoval as well as generationgeneration of pollutants
NONOxx to VOC ratio to VOC ratio governs Ozone production
Urban Photochemistry
Urban Photochemistry
NOx-VOC-Ozone Cycle
32
2
22
22
2
3
)400(3
OOPO
nmPONOhvNO
NORONORO
ROOR
OHROHRH
Organic radical production and photolysis of NO2
VOC’s and N-species compete for OH radical
Urban Photochemistry
NOx-VOC-Ozone Cycle
32
2
22
22
2
3
)400(3
OOPO
nmPONOhvNO
NOHONOHO
HOOH
COHOHCO
In polluted environment, CO contributes to O3 production
Urban Photochemistry
NOx-VOC-Ozone Cycle
OHHOCOOOHHCHO
HCOhvHCHO
HOCOOhvHCHO
ROHCHOOHHC
NOOHHOHCHOOOHNOCH
222
2
22
242
22224
%)55(
%)45(2
2
HCHO – primary intermediate in VOC-HOx chemistry
Short lived and indicator of primary VOC emissions
Urban Photochemistry
NOx-VOC Emission Ratio
Units:g NO2 to g C
In 2000
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
Bangkok 0.2
Urban Photochemistry
NOx-VOC-Ozone Cycle
O3 CycleSTEM Box Model Calculations
For City of Seoul,
O3 CycleSTEM Box Model Calculations
For City of Shanghai
Units: ppbv/hr
CO Vs VOC: Megacity points from back trajectoriesCO 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
Species to Species Comparison
Age in days Age in days calculated calculated from back from back trajectories trajectories along the along the flight pathflight path
Units:Units:
ppbv-HCHO/ ppbv-HCHO/ ppbv-COppbv-CO
Urban Photochemistry
HCHO to CO Ratios
CityCity Plume Age Plume Age (days)(days)
Ratio Ratio (Obs.)(Obs.)
Ratio Ratio (Mod.)(Mod.)
All Points < 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
Shanghai < 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 0.0043
Beijing 0.0065 0.0071
Seoul < 1 day 0.0120
1 to 6 days 0.0078
Pusan < 1 day 0.0116
1 to 6 days 0.0077
Hong Kong 0.0063 0.0062
Tokyo 0.0102
Manila 0.0192
OO33 Vs Species: Megacity points from back trajectories Vs Species: Megacity points from back trajectories
Urban Photochemistry
Species to Species Comparison
Urban Photochemistry
NOx-VOC Sensitivity to O3 Production
VOC sensitive
NOx sensitive
Loss(N
)/(L
oss(N
)+Loss(R
))
Model NOx (ppbv)
Model results along the flight path
Megacity points from back trajectories
Klienman et al., 2000Klienman et al., 2000
Less than 2 day old plumes
Urban Photochemistry
NOx-VOC Sensitivity Implications
Ozone production in the urban plumes is VOC VOC limitedlimited
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 pollutantcriteria pollutant) and to O3 production at downwind sites.
EmissionsEmissions
Ambient Ambient ConcentrationConcentration
ExposureExposure
Air Quality Air Quality ManagemeManagement Systemnt System
Policy Policy IssuesIssues
Technical Technical OptionsOptions
Environmental Integrated Assessment
Trends in Urban Asia Sulfur Pollution
Model Overview
RAINS-AsiaDeveloped by IIASA, Austria
SO2, PM, NOx
Energy, Emissions, Controls, Costs and Optimization modules
ATMOS Dispersion ModelSO2, PM, NOx
Lagrangian Puff TransportLinear Chemistry
NCEP Winds (1975-2000)
Shanghai Province
Shanghai
3030oo36’36’120120oo36’36’
3232oo
122122oo
East China Sea
Emissions for 1995Emissions for 1995
PMPM1010 : 166 ktons PM/year : 166 ktons PM/year
PMPM2.52.5 : 68 ktons PM/year : 68 ktons PM/year
Sulfur: 458 ktons SOSulfur: 458 ktons SO22/year/year
Population: 19 MillionPopulation: 19 Million
Source: Li and Guttikunda et al., 2002
Environmental Integrated Assessment
Case Study of Shanghai, China
202
0
202
0
BA
UB
AU
Units:Gg/year
Economic SectorEconomic Sector PMPM1010
(C )(C )PMPM1010
(M)(M)PMPM2.52.5
( C)( C)PMPM2.52.5
(M)(M)SOSO22 NONOxx
Power 11.2 5.1 394.3 112.7
Industry 52.1 18.6 19.6 5.3 214.2 73.2
Domestic 5.2 3.6 16.8 5.4
Transport 31.1 16.7 32.0 276.6
Other 0.0 36.4 0.0 9.3 0.0 0.0
Total 99.699.6 55.055.0 45.045.0 14.614.6 657.2657.2 468.0468.0
Economic SectorEconomic Sector PMPM1010
(C )(C )PMPM1010
(M)(M)PMPM2.52.5
( C)( C)PMPM2.52.5
(M)(M)SOSO22 NONOxx
Power 40.6 18.1 214.1 80.4
Industry 49.2 31.5 18.3 9.0 199.9 71.1
Domestic 10.4 6.8 31.9 5.9
Transport 10.1 6.0 11.6 125.8
Other 7.0 18.0 5.9 4.6 1.0 2.5
Total 117.2117.2 49.549.5 55.155.1 13.713.7 458.4458.4 285.8285.8
199
51
995
Shanghai Urban Air Quality Management
Emission Estimates
in 1995in 1995 2020 BAU2020 BAU
120.8 121 121.2 121.4 121.6 121.8 122
30.8
31
31.2
31.4
31.6
31.8
32
5102030405060708090100110120
Units: g/m3 PM10
120.8 121 121.2 121.4 121.6 121.8 122
30.8
31
31.2
31.4
31.6
31.8
32
Shanghai Urban Air Quality Management
Annual Average PM10 Concentrations
Shanghai Urban Air Quality Management
Health Benefit Analysis
POPAPE iijjij ***
Dose-response function coefficientsDose-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, 1993Coefficient: % change in endpoint per 10 g/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 avoidedNo. of cases avoided
Health EndpointHealth Endpoint Power Scenario Power Scenario
(no. of cases)(no. of cases)
Industrial ScenarioIndustrial Scenario
(no. of cases)(no. of cases)
Mortality 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
Shanghai Urban Air Quality Management
Health Benefit Analysis
Units: US$ millions in
1998 dollars Economic EvaluationEconomic Evaluation
Health BenefitsHealth Benefits Power ScenarioPower Scenario Industrial ScenarioIndustrial Scenario
Mortality
Low 139 88
Medium 347 221
High 1,030 656
Morbidity
Low 38 24
Medium 57 36
High 119 76
Work Day Lossess 13 8
Total Benefits 190 – 1,162 121 – 741
(Median Case) (417) (266)
Emissions Emissions & &
CostsCosts
Emissions Emissions & &
CostsCostsDispersion Dispersion ModelingModeling
Dispersion Dispersion ModelingModeling
Depositions Depositions & &
ConcentrationsConcentrations
Depositions Depositions & &
ConcentrationsConcentrations
EnergyEnergyTechnologyTechnology
FuelFuelSectorsSectorsScalesScales
EnergyEnergyTechnologyTechnology
FuelFuelSectorsSectorsScalesScales
ExposureExposure&&
ImpactsImpacts
ExposureExposure&&
ImpactsImpacts
Days & WeeksDays & Weeks
Source ReceptorSource ReceptorMatrixMatrix
SecondsSeconds
Integrated Assessment Modeling System (IAMSIAMS)
Central Heating Plants
Central Heating Plants
Transfer Matrix for
Area Sources
Transfer Matrix for
Area Sources
Domestic Sources
Domestic Sources
IndustrialBoilers
IndustrialBoilers
Transportation Sources Large Point
Sources
Large Point Sources
Emission Sources (PM and SO2)
Transfer Matrix for
LPS Sources
Transfer Matrix for
LPS Sources
PM and Sulfur Concentrations
PM and Sulfur Concentrations
IAMS Model Schematics
Atmospheric Dispersion Calculations
IAMS Software
Tracks Concentration
Changes.
Tracks Emission
Changes.
IAMS Software
Tracks Health Benefits to
Costs Ratio.
Calculates Health Damages for Mortality, Chronic Bronchitis,
Hospital Visits, Work Day Losses.
U. Iowa/Kyushu/Argonne/GFDL
With support from NSF, NASA (ACMAP,GTE), NOAA, DOE