Support by Inter-RPO WG - NESCAUM Performed by CAPITA & Sonoma Technology, Inc F ast A erosol S ensing T ools fo r N atura l E vent T racking FASTNET Project Synopsis Haze levels should be reduced to the ‘natural conditions’ by 2064. The space, time, composition features of natural aerosols are not known This long-term project goal is to better characterize the natural haze conditions Focus is on detailed analysis of major natural events, e.g. forest fires and windblown dust FASTNET is primarily a tools development project for data access, archiving and analysis This, first year pilot project focuses on demonstrating the feasibility and utility of approach

Support by Inter-RPO WG - NESCAUM

Performed by

CAPITA & Sonoma Technology, Inc

Regional Haze Rule: Natural Aerosol The goal is to attain natural conditions by 2064; The baseline is established during 2000-2004 The first SIP & Natural Condition estimate in 2008; SIP & Natural Condition Revisions every 10 yrs Natural haze is due to natural windblown dust, biomass smoke and other natural processes Man-made haze is due industrial activities AND man-perturbed smoke and dust emissions A fraction of the man-perturbed smoke and dust is assigned to natural by policy decisions

The goal is to attain natural conditions by 2064;

The baseline is established during 2000-2004

The first SIP & Natural Condition estimate in 2008;

SIP & Natural Condition Revisions every 10 yrs

Significant Natural Contributions to Haze by RPO Judged qualitatively based on current surface and satellite data Natural forest fires and windblown dust are judged to be the key contributors to regional haze The dominant natural sources include locally produced and long-range transported smoke and dust WRAP Local Smoke Local Dust Asian Dust VISTAS Local Smoke Sahara Dust MRPO Local Smoke Canada Smoke Local Dust CENRAP Local Smoke Mexico/Canada Smoke Local Dust Sahara Dust MANE-VU Canada Smoke

Natural forest fires and windblown dust are judged to be the key contributors to regional haze

The dominant natural sources include locally produced and long-range transported smoke and dust

Natural Aerosol Features and Event Analysis Natural Aerosol Features: Intense – natural event concentrations can be much higher than manmade emissions Large – major natural events frequently have global-scale impacts Episodic – the main impact is on the extreme, not on the average concentrations Seasonal - dust and smoke events are strongly seasonal at any location Uncontrollable –natural events can seldom be suppressed; they may be extra-jurisdictional. Natural Aerosol Event Analysis: Much understanding can be gained from the study of major natural aerosol events Their features are easier to quantify due to the intense aerosol signal Subsequently, smaller events can be evaluated utilizing the gained insights

Natural Aerosol Features:

Intense – natural event concentrations can be much higher than manmade emissions

Large – major natural events frequently have global-scale impacts

Episodic – the main impact is on the extreme, not on the average concentrations

Seasonal - dust and smoke events are strongly seasonal at any location

Uncontrollable –natural events can seldom be suppressed; they may be extra-jurisdictional.

Natural Aerosol Event Analysis:

Much understanding can be gained from the study of major natural aerosol events

Their features are easier to quantify due to the intense aerosol signal

Subsequently, smaller events can be evaluated utilizing the gained insights

National Ambient Air Monitoring Strategy (NAAMS) Focus on PM & Ozone (Slide for Scheffe) Insightful Measurements Enhanced real-time data delivery to public Increase capacity for hazardous air pollutant measurements Increase in continuous PM measurements Support for research grade/technology transfer sites Multiple pollutant monitoring must be advanced AQ is integrated through sources, atmo. processes, health/eco effects Technological advances must be incorporated Information transfer technologies Continuous PM monitors High sensitivity instruments Model-monitor integration FASTNET pursues several of the NAAMS recommendation:

Insightful Measurements

Enhanced real-time data delivery to public

Increase capacity for hazardous air pollutant measurements

Increase in continuous PM measurements

Support for research grade/technology transfer sites

Multiple pollutant monitoring must be advanced

AQ is integrated through sources, atmo. processes, health/eco effects

Technological advances must be incorporated

Information transfer technologies

Continuous PM monitors

High sensitivity instruments

Model-monitor integration

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.

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.

Data Federation Concept and the FASNET Network Schematic representation of data sharing in a federated information system. Based on the premise that providers expose part of their data (green) to others Schematics of the value-adding network proposed for FASTNET Components embedded in the federated value network

Origin of Fine Dust Events over the US Gobi dust in spring Sahara in summer Fine dust events over the US are mainly from intercontinental transport

Daily Average Concentration over the US Dust is seasonal with noise Random short spikes added Sulfate is seasonal with noise Noise is by synoptic weather VIEWS Aerosol Chemistry Database

Dust is seasonal with noise

Random short spikes added

Sahara and Local Dust Apportionment: Annual and July The maximum annual Sahara dust contribution is about 1  g.m 3 In Florida, the local and Sahara dust contributions are about equal but at Big Bend, the Sahara contribution is < 25%. The Sahara and Local dust was apportioned based on their respective source profiles. In July the Sahara dust contributions are 4-8  g.m 3 Throughout the Southeast, the Sahara dust exceeds the local source contributions by w wide margin (factor of 2-4) Annual July

The maximum annual Sahara dust contribution is about 1  g.m 3

In Florida, the local and Sahara dust contributions are about equal but at Big Bend, the Sahara contribution is < 25%.

In July the Sahara dust contributions are 4-8  g.m 3

Throughout the Southeast, the Sahara dust exceeds the local source contributions by w wide margin (factor of 2-4)

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)

Seasonal Fine Aerosol Composition, E. US Upper Buffalo Smoky Mtn Everglades, FL Big Bend, TX

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.

MODIS Rapid Response FASTNET Event Report: 040219TexMexDust Texas-Mexico Dust Event February 19, 2004 Contributed by the FASNET Community Correspondence to R Poirot , R Husar

Satellites detect dust most storms in near real time The MODIS sensor on AQUA and Terra provides 250m resolution image s of the dust storm Visual inspection reveals the dust sources at the beginning of dust streaks. The NOAA AVHRR sensor highlights the dust by its IR sensors In the TOMS satellite image, the dust signal is conspicuously absent – too close to the ground

Surface met data from the 1200 station network documents the strong winds that cause the windblown dust and resulting low-visibility regions

High Wind Speed – Dust Spatially Correspond The spatial/temporal correspondence suggests that most visibility loss is due to locally suspended dust, rather than transported dust Alternatively, suspended dust and ‘high winds’ travel forward at the same speed Wind speed animation ; Bext animation . (material for model validation?)

The spatial/temporal correspondence suggests that most visibility loss is due to locally suspended dust, rather than transported dust

Alternatively, suspended dust and ‘high winds’ travel forward at the same speed

Wind speed animation ; Bext animation . (material for model validation?)

PM10 > 10 x PM25 During the passage of the dust cloud over El Paso, the PM10 concentration was more than 10 times higher than the PM2.5 AIRNOW PM10 and Pm25 data Schematic Link to dust modelers for faster collective learning?

AIRNOW PM10 and Pm25 data

Monte Carlo simulation of dust transport using surface winds (just a toy, 3D winds are essential!) See animation Note, how sensitive the transport direction is to the source location (according to this toy)

See animation Note, how sensitive the transport direction is to the source location (according to this toy)

VIEWS Fine Mass, Sulfate, OC, Dust, 02-07-01 OC OC Mass SO4 Dust

OC

SeaWiFS AOT – ASOS FBext, 02-07-01

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

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.

2002 Quebec Smoke over the Northeast Smoke (Organics) and Sulfate concentration data from VIEWS integrated database DVoy overlay of sulfate and organics during the passage of the smoke plume

Smoke (Organics) and Sulfate concentration data from VIEWS integrated database

DVoy overlay of sulfate and organics during the passage of the smoke plume

Please Visit http://datafed.net

NCore Integration NOAA/NASA Satellite: Global/Continental transport Other Networks: Deposition, Ecosystems Intensive/diagnostic Field Programs Longer Term Goal: Integrated Observation-modeling Complex Similar to Meteorological Models (FDDA) Model Adjustments Through Obs. All in Near Real Time Full Model Dims (x, y, z, t, chemistry, size)

NCore Integration

NOAA/NASA Satellite: Global/Continental transport

Other Networks: Deposition, Ecosystems

Intensive/diagnostic Field Programs

Longer Term Goal:

Integrated Observation-modeling Complex

Similar to Meteorological Models (FDDA)

Model Adjustments Through Obs.

All in Near Real Time

Full Model Dims (x, y, z, t, chemistry, size)