Local, Regional, Global Pollution

Before 1950s:

LocalSmoke, Fly ash

Post- 2000s:

Global, HTAP

Ozone, PM,Global Change

1970s-1990s:

Regional, LRTPAcid Rain, Haze

• The LRTP/HTAP flow of air pollutants is likely to increase as overseas economies grow.• Pollutant influx leads to significant exceedances of O3 PM NAAQS in some regions• Even after domestic controls, some US areas will be no-compliant because of LRTP

Aircraft Detection of Siberian Forrest Smoke near Seattle, WA

Jaffe et. al., 2003

Asian Dust Cloud over N. America

On April 27, 1998 the dust cloud arrived in North America.

Regional average PM10 concentrations increased to 65 g/m3

In Washington State, PM10 concentrations exceeded 100 g/m3

Asian Dust 100 g/m3

Hourly PM10

Origin of Fine Dust Events over the US

Sulfate is local, no major spikes

Gobi dust transport in springSahara dust import in summer

Fine dust spikes over the entire US are mainly from intercontinental transport

http://www.igospartners.org/http://www.fz-juelich.de/icg/icg-ii/iagos/

http://www.fz-juelich.de/icg/icg-ii/mozaic/home

http://earthobservations.org/

http://www.epa.gov/ttn/amtic/monstratdoc.html

National Ambient Air Monitoring StrategyOffice of Air Quality Planning and StandardsResearch Triangle Park, NCDecember 2005

http://www.al.noaa.gov/AQRS/reports/monitoring.html

http://www.empa.ch/gaw/gawsis/

http://www.nesdis.noaa.gov/

http://www.cmdl.noaa.gov/

Barrow

Mauna Loa

Trinidad Head

A. Samoa

S. Pole

L2

NCORE L3

L1

http://www.emep.int/

GAW

CENR/AQRSGEOSS

NO

AA

CM

DL

NOAA NESDIS

EMEP

Terrestrial

Airborne

Near-Space

LEO/MEO Commercial Satellites and Manned Spacecraft

Far-Space

L1/HEO/GEO TDRSS & CommercialSatellites

Dep

loya

ble

P

erm

anen

t Coordinating Earth Observing Systems

Forecasts & Predictions

Aircraft/Balloon Event Tracking and Campaigns

User Community

Vantage Points Capabilities

`

Products

Products

State & Local

Canadian Providences

NOAANWS

HHSCDC-EPHTN

Aerosol Optical Depth(GASP)

TERRA MODISTERRA MODISAQUA MODISAQUA MODIS

ProductsCMAQ Forecast Data

US EPAAQS

ProductsSLAMS/NAMS SURACE PM2.5 Data

Air Quality/Public Health NTO Integrated Observed-Modeled Air Quality FieldsAir Quality/Public Health NTO Integrated Observed-Modeled Air Quality Fields

ProductsSpatial surface Predictions Satellite/Model/Surface Data Fusion

State Public Health

Departments

~10:30 local overpass~1:30 local overpass

Algorithms/QA

NASAGFSC

Science Team

NASAGFSCDACC

*Note: Regional East Atmospheric Lidar Mesonet (REALM) is university led federated network by UMBC and is identified as a NTO in the implementation plan.

Products Aerosol Optical Depth(MOD04_L2)

NOAANESDIS

NOAANESDIS/ORA &

CREST Institutes

?UMBC

CREST Institute

GEOS-12GEOS-12CONUS every 30 minutes

REALMREALMContinuous VerticalResolution Data

ProductsAlgorithms/QA

EPAOAR & ORD

ProductsCMAQ Assessment Data

ProductsStudies and Impacts to human health

US EPA OAQPS/ORD/OEI

RSI Gateway

Challenge 21: Science – Management Link

Sensing and recognition (monitoring)

Reasoning and explaining (sciences)

Decision making, action (management)

Sustainable Development in an ever-changing world:Sensory-Motor Loop:

Collaboration (culture)

empire building?

Observation technologies {e.g., satellites}

Computational power

Science, talent{embodied in AQ models and young geniuses}

Budgets, agency collaboration resource/program accountability

Accountability, ↓ regulatoryassessments {e.g., NAS, CASAC}

Information technologies{e.g., data sharing protocols}

Alignment

Stars aligned?

Example User Client Missions

Apparent divergences?

NPSProtect ecosystems, AQ WQ

NASAExplore fundamental Earth System Properties

USDAProtect/optimize Ag and forest resources

EPA (Protect human health and environment)Improve air, water, ecosystem health

CDCTo promote health and quality of life by preventing and controlling disease, injury, and disability.

NOAATo understand and predict changes in the Earth’s environment and conserve and manage coastal and marine resources to meet our nation’s economic, social and environmental needs”

Or substantial convergence and optimization potential?

Common data

Client Data Overlaps

Summary • Global Sensing – Modeling Revolution – ‘May you live in interesting

times’– We are in the midst of an observational revolution (satellites, monitoring

networks).– The global distribution and transport of some pollutants can be monitored daily– Global models are maturing into effective analytical and predictive tools

• Results to Date: – Compelling evidence for global-scale transport of PM and Ozone

– Qualitative evidence of ‘extra-jurisdictional’ impact on the US air quality– Potential for quantification of natural and non-US impact

• Such massive job will require:– International, interagency, interdisciplinary collaboration.– Open flow of data/knowledge– Scientific ‘value-adding chains’

FUTURE

For instance,

• Health– effects/outcomes associations (PHASE)– Public health warnings/forecasting

• Air program support– defining attainment/nonattainment areas

(and projection, current practice)– developing emission strategies– accountability

• Environmental– Ecosystem deposition assessments/support– AQ trends in National Parks– Regional haze assessments

• Atmospheric science– Diagnosing emissions and models

Benefit from Air quality characterizations

And benefit even more from rich (t,s,c) AQ characterizations

Building an integrated observation-modeling complex: an air program perspective

• Health– effects/outcomes associations (PHASE)– Public health warnings/forecasting

• Air program support– defining attainment/nonattainment areas

(and projection, current practice)– developing emission strategies– accountability

• Environmental– Ecosystem deposition assessments/support– AQ trends in National Parks– Regional haze assessments

• Atmospheric science– Diagnosing emissions and models

Benefit from Air quality characterizations

And benefit even more from rich (t,s,c) AQ characterizations

Note: IGACO; AQ, ox eff., strat-O3, climate

Consequently

• A simple overarching goal or vision,

– Strive for maximum and efficient AQ characterization in time, space and compositional terms

• the intersecting or common link between air programs and satellite data and integrated advanced systems

• Assimilation of data to improve

– air quality models for forecast

– Current and– Retrospective

assessments

• Global-Regional Air Quality Connections

• Climate-AQ connections

TGAS/Aerosol Satellite Measurements and Numerical Predictive Models

Land AQ Monitors

Total column depth(through Satellites)

AQ model results

Vertical Profiles

Integrated Observation- Modeling

Optimized PM2.5, O3

Characterizations

Health

Air management

ecosystems

Process Scales relevant to Air Quality vs. Space Observations

12Z

18Z

RAQMS 330K NOy 18Z July 17, 2004

Lo

ng

Ran

ge

Tra

nsp

ort

Reg

ion

al T

ran

spo

rt

Pollution Dynamics

Intercontinental Transport

Regional Haze/Subsidence

Residual layer/Nocturnal Jet

Convective exchange/rainout

Urban Canyon

Strat/Trop Exchange

Global Composition/Climate

Collaboration (culture)

empire building?

Observation technologies {e.g., satellites}

Computational power

Science, talent{embodied in AQ models and young geniuses}

Budgets, agency collaboration resource/program accountability

Accountability, ↓ regulatoryassessments {e.g., NAS, CASAC}

Information technologies{e.g., data sharing protocols}

Alignment

Stars aligned?