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