Satellite Data Us in PM Management: A Retrospective Assessment Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN MexicanSmoke

Early Satellite Detection of Manmade Haze, 1976 Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B. Mon. Weather Rev. 1976 SMS GOES June 30 1975

Scientific Challenge: Description of PM Gaseous concentration: g ( X, Y, Z, T ) Aerosol concentration: a ( X, Y, Z, T , D, C, F, M ) The ‘aerosol dimensions’ size D, composition C, shape F, and mixing M determine the impact on health, and welfare. Particulate matter is complex because of its multi-dimensionality It takes at leas 8 independent dimensions to describe the PM concentration pattern Dimension Abbr. Data Sources Spatial dimensions X, Y Satellites, dense networks Height Z Lidar, soundings Time T Continuous monitoring Particle size D Size-segregated sampling Particle Composition C Speciated analysis Particle Shape/Form F Microscopy Ext/Internal Mixture M Microscopy

Gaseous concentration: g ( X, Y, Z, T )

Aerosol concentration: a ( X, Y, Z, T , D, C, F, M )

The ‘aerosol dimensions’ size D, composition C, shape F, and mixing M determine the impact on health, and welfare.

Technical Challenge: Characterization PM characterization requires many different instruments and analysis tools. Each sensor/network covers only a limited fraction of the 8-D PM data space . Most of the 8D PM pattern is extrapolated from sparse measured data. Some devices (e.g. single particle electron microscopy) measure only a small subset of the PM; the challenge is extrapolation to larger space-time domains. Others, like satellites, integrate over height, size, composition, shape, and mixture dimensions; these data need de-convolution of the integral measures.

PM characterization requires many different instruments and analysis tools.

Each sensor/network covers only a limited fraction of the 8-D PM data space .

Most of the 8D PM pattern is extrapolated from sparse measured data.

Some devices (e.g. single particle electron microscopy) measure only a small subset of the PM; the challenge is extrapolation to larger space-time domains.

Others, like satellites, integrate over height, size, composition, shape, and mixture dimensions; these data need de-convolution of the integral measures.

Vertical Pattern of Global Aerosol Windblown Dust (crustal elements) Biomass Smoke (organics, H 2 0 ) Sea H 2 0 salt (NaCl. H 2 0) Stratospheric (Volcanic) (H2SO4) Biogenic (Non-sea salt sulfate, org) Urban-Industrial Haze (SO4, org. H 2 0) Dust, smoke, volcanic aerosol and industrial haze originate from land The global aerosol concentration is highest over land and near the continents over the oceans (coastal regions) Sea salt is significant over some of the windy oceanic regions and biogenic sulfate and organic aerosols also occur …

Windblown Dust (crustal elements)

Biomass Smoke (organics, H 2 0 )

Sea H 2 0 salt (NaCl. H 2 0)

Stratospheric (Volcanic) (H2SO4)

Biogenic (Non-sea salt sulfate, org)

Urban-Industrial Haze (SO4, org. H 2 0)

Dust, smoke, volcanic aerosol and industrial haze originate from land

The global aerosol concentration is highest over land and near the continents over the oceans (coastal regions)

Sea salt is significant over some of the windy oceanic regions and biogenic sulfate and organic aerosols also occur …

Just like the human eye, satellite sensors detect the total amount of solar radiation that is reflected from the earth’s surface ( R o ) and backscattered by the atmosphere from aerosol, pure air, and clouds. A simplified expression for the relative radiatioin detected by a satellite sensor (I/I o ) is: I / I o = R o e -  + (1- e -  ) P Satellite Detection of Aerosols Today, geo-synchronous and polar orbiting satellites can detect different aspects of aerosols over the globe daily. where  is the aerosol optical thickness and P the angular light scattering probability.

Information ‘Refinery’ Value Chain (Taylor, 1975) Organizing Grouping Classifying Formatting Displaying Analyzing Separating Evaluating Interpreting Synthesizing Judging Options Quality Advantages Disadvantages Deciding Matching goals, Compromising Bargaining Deciding e.g. CIRA VIEWS e.g. Langley IDEA RAW System e.g. WG Summary Rpt e.g. RPO Manager Informing Knowledge Action Productive Knowledge Information Data

Asian Dust Cloud over N. America On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65  g/m 3 In Washington State, PM10 concentrations exceeded 100  g/m 3 Asian Dust 100  g/m 3 Hourly PM10

The Asian Dust Event of April 1998 On April 19, 1998 a major dust storm occurred over the Gobi Desert The dust cloud was seen by SeaWiFS, TOMS, GMS, AVHRR satellites The transport of the dust cloud was followed on-line by an an ad-hoc international group China Mongolia Korea

Supporting Evidence: Transport Analysis Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and entering the continent. The air masses arrive to Big Bend, TX form the east (July) and from the west (April)

Sahara PM10 Events over Eastern US The highest July, Eastern US, 90 th percentile PM10 occurs over the Gulf Coast ( > 80 ug/m3) Sahara dust is the dominant contributor to peak July PM10 levels. Much previous work by Prospero, Cahill, Malm, Scanning the AIRS PM10 and IMPROVE chemical databases several regional-scale PM10 episodes over the Gulf Coast (> 80 ug/m3) that can be attributed to Sahara. June 30, 1993 July 5, 1992 June 21 1997

The highest July, Eastern US, 90 th percentile PM10 occurs over the Gulf Coast ( > 80 ug/m3)

Sahara dust is the dominant contributor to peak July PM10 levels.

May 15, 1998 Fire locations detected by the Defense Meteorological Satellite Program (DMSP) sensor. Smoke is detected by SeaWiFS and TOMS (green) satellites and surface visibility data, Bext Smoke from Central American Fires The smoke plume extends from Guatemala to Hudson May in Canada The Bext values indicate that the smoke is present at the surface

Fire locations detected by the Defense Meteorological Satellite Program (DMSP) sensor.

Smoke is detected by SeaWiFS and TOMS (green) satellites and surface visibility data, Bext

The smoke plume extends from Guatemala to Hudson May in Canada

The Bext values indicate that the smoke is present at the surface

July 2020 Quebec Smoke Event Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002 – PM2.5 time series for New England sites. Note the high values at White Face Mtn. Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002

PM2.5 time series for New England sites. Note the high values at White Face Mtn.

Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

May-June 2003 Siberian Fires

What kind of neighborhood is this anyway? May 9, 1998 A Really Bad Aerosol Day for N. America Asian Smoke C. American Smoke Canada Smoke

What kind of neighborhood is this anyway?

Global Oceanic Aerosol Pattern (1997) Tropical zones dominate; Dust and Smoke dominate Husar, Prospero, Stowe, 1997

Tropical zones dominate; Dust and Smoke dominate

Pattern of Fires over N. America The number of ATSR satellite-observed fires peaks in warm season Fire onset and smoke amount is unpredictable Fire Pixel Count: Western US North America

The number of ATSR satellite-observed fires peaks in warm season

Fire onset and smoke amount is unpredictable

GOES 8 Images for every ½ hour processed daily Processing includes georeferencing and gamma correction

Images for every ½ hour processed daily

Processing includes georeferencing and gamma correction

Retrieved Optical Depth 2000.08.15

Retrieved Optical Depth 2000.08.18

CAPITA NASA REASON Project: Application of Satellite Data to PM Management Schematic representation of data sharing in a federated information system. Based on the premise that providers expose part of their data (green) to others Data Federation Concept and the FASNET Network

Please Visit DataFed.Net

Web-based data delivery: Analysts Console

Near Real Time Public Satellite Data Delivery

Interactive Virtual Workgroup Websites July 2002 Quebec Smoke

R eal-Time A erosol W atch (RAW) RAW is an open communal facility to study non-industrial (e.g. dust and smoke) aerosol events , including detection, tracking and impact on PM and haze. RAW output will be directly applicable, to public health protection, Regional Haze rule, SIP and model development as well as toward stimulating the scientific community. The main asset of RAW is the community of data analysts, modelers, managers and others participating in the production of actionable knowledge from observations, models and human reasoning The RAW community will be supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools evolving under the umbrella of Fast Aerosol Sensing Tools for Natural Event Tracking (FASTNET) . Initially, FASTNET is composed of the Community Website for open community interaction, the Analysts Console for diverse data access and the Managers Console for AQ management decision support.

Summary Satellite data have aided the science of Particulate Matter since the 1970s Satellite data have supported PM air quality management since the 1990s. Past satellite data helped the qualitative description of PM spatial pattern Quantitative satellite data use and fusion with surface data is still in infancy Satellite data applications will require collaboration across disciplines

Satellite data have aided the science of Particulate Matter since the 1970s

Satellite data have supported PM air quality management since the 1990s.

Past satellite data helped the qualitative description of PM spatial pattern

Quantitative satellite data use and fusion with surface data is still in infancy

Satellite data applications will require collaboration across disciplines