Partially Supported by Inter-RPO WG – NESCAUM F ast A erosol S ensing T ools fo r N atura l E vent T racking FASTNET CAPITA September 2004 Natural Aerosol Event Detection and Characterization

Partially Supported by Inter-RPO WG – NESCAUM

F ast A erosol S ensing T ools fo r N atura l E vent T racking FASTNET

CAPITA

September 2004

Contents Background, Objectives and and Approach Direct Evidence of Natural Aerosol Events Event Definition: Time Series Approach Origin of US Dust Events Fine Dust Concentration Pattern FAST-CATT: EUS Dust Transport Events Regional PM25 Time Pattern AIRNOW (2003-2004)

Background, Objectives and and Approach

Direct Evidence of Natural Aerosol Events

Event Definition: Time Series Approach

Origin of US Dust Events

Fine Dust Concentration Pattern

FAST-CATT: EUS Dust Transport Events

Regional PM25 Time Pattern AIRNOW (2003-2004)

Background, Objectives and and Approach to the Analysis

Background, Objectives and and Approach to the Analysis Background There is considerable evidence that smoke from biomass fires and windblown dust constitutes a significant component of the PM25 and PM10 over the US. There is a rich pool of research on smoke and dust but the contributions are fragmented, and uneven in spatial, temporal an compositional coverage. Objective Quantify the the spatio-temporal and chemical pattern of dust and smoke over the US Identify and possibly quantify the local and origin of NAM dust and smoke Approach This natural aerosol analysis applies integrative analysis along several dimensions: Access, organize and share historical and recent aerosol-relevant data for the community Combine surface, satellite data, relevant to emissions, transport and effects Participate in open, collaborative projects to conduct integrative analysis Status (Sep. 2004) The analysis of natural aerosols over the US begun at CAPITA in 2000. Since then several funded projects have supported aspects of the analysis. Current (2004) activities support the development of collaboration technologies (NSF), haze regulations (RPO) and the use of satellite data (NASA). The natural aerosol pattern/source analysis progressing well but the collaboration with the community has been marginal.

Background

There is considerable evidence that smoke from biomass fires and windblown dust constitutes a significant component of the PM25 and PM10 over the US.

There is a rich pool of research on smoke and dust but the contributions are fragmented, and uneven in spatial, temporal an compositional coverage.

Objective

Quantify the the spatio-temporal and chemical pattern of dust and smoke over the US

Identify and possibly quantify the local and origin of NAM dust and smoke

Approach

This natural aerosol analysis applies integrative analysis along several dimensions:

Access, organize and share historical and recent aerosol-relevant data for the community

Combine surface, satellite data, relevant to emissions, transport and effects

Participate in open, collaborative projects to conduct integrative analysis

Status (Sep. 2004)

The analysis of natural aerosols over the US begun at CAPITA in 2000. Since then several funded projects have supported aspects of the analysis. Current (2004) activities support the development of collaboration technologies (NSF), haze regulations (RPO) and the use of satellite data (NASA). The natural aerosol pattern/source analysis progressing well but the collaboration with the community has been marginal.

Aerosol Types and their Events: Dust, Smoke and Haze Aerosol are composed of multiple types including urban-industrial sulfates, nitrates and organics (industrial haze), biomass smoke and windblown dust and others. Each type may be considered a different pollutant since it has its own class of sources, aerosol properties and it is associated with different effects. Current regulation lump the different aerosol species into two size classes PM2.5 and PM10, regardless of their composition in each size range. In this sense dust, smoke and haze aerosols (PM25 and PM10) are equivalents to an array of gaseous pollutants, such as SO2, NOx and CO The assessment of PM25 and PM10 aerosols can only be accomplished through a full understanding of the component species and their interaction This analysis assesses the dynamic, event-characteristics of sulfate, organic, dust and nitrate aerosols and their statistical interaction in producing PM25 events.

Aerosol are composed of multiple types including urban-industrial sulfates, nitrates and organics (industrial haze), biomass smoke and windblown dust and others.

Each type may be considered a different pollutant since it has its own class of sources, aerosol properties and it is associated with different effects.

Current regulation lump the different aerosol species into two size classes PM2.5 and PM10, regardless of their composition in each size range.

In this sense dust, smoke and haze aerosols (PM25 and PM10) are equivalents to an array of gaseous pollutants, such as SO2, NOx and CO

The assessment of PM25 and PM10 aerosols can only be accomplished through a full understanding of the component species and their interaction

This analysis assesses the dynamic, event-characteristics of sulfate, organic, dust and nitrate aerosols and their statistical interaction in producing PM25 events.

Regional Haze Rule: Nomenclature and Time Scale Schematics Temporal Components Historical condition, pre 2000 status Baseline condition, established in 2000-2004 Current condition, on 2000-2064 trajectory Natural condition, absence of human aerosol Haze Components 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 Regulatory Components Goal is to attain natural conditions by 2064; Establish baseline during 2000-2004 First SIP & Natural Cond. estimate in 2008; SIP & Natural Cond. revisions every 10 yrs

Temporal Components

Historical condition, pre 2000 status

Baseline condition, established in 2000-2004

Current condition, on 2000-2064 trajectory

Natural condition, absence of human aerosol

Natural Aerosol Analysis Goals Natural Haze (this Project, COHA) Spatially resolve natural (dust, smoke, other), focus on Class I areas Temporally resolve natural haze components for each day Current Conditions (VIEWS, RPO Projects) Spatially resolved haze components, focus on Class I areas Temporally resolve aerosol components for each day Identify worst/best visibility days for total aerosol Natural-Total Haze Apportionment (this & other RPO projects) Estimate the natural-manmade contribution on best/worst visibility days Thus, Current Conditions and the Natural Conditions need to be quantified simultaneously. Thus, coordination with other work is essential.

Natural Haze (this Project, COHA)

Spatially resolve natural (dust, smoke, other), focus on Class I areas

Temporally resolve natural haze components for each day

Current Conditions (VIEWS, RPO Projects)

Spatially resolved haze components, focus on Class I areas

Temporally resolve aerosol components for each day

Identify worst/best visibility days for total aerosol

Natural-Total Haze Apportionment (this & other RPO projects)

Estimate the natural-manmade contribution on best/worst visibility days

Thus, Current Conditions and the Natural Conditions need to be quantified simultaneously. Thus, coordination with other work is essential.

Observational Tools Establishing Aerosol Origin Modern Methods – similar to century-old approaches but with more data Direct Evidence. Photographic, satellite or compelling visual evidence of origin Aerosol Composition. Chemical fingerprinting of different source types (speciation, traces) Temporal pattern. Chemical rPhysical property analysis (satellite, ASOS, PM2.5) Spatial Pattern. Chemical Transport Pattern. Forward, backward trajectory, residence time analysis Chemistry with Transport . Combining chemical fingerprinting and transport (CATT) Dynamic modeling . Simulation model (forward, inversion) quantifying origin/transport Direct Evidence Spatial Pattern Wind Pattern Composition Temporal Pattern Trajectory Historical Methods Source attribution methods have been used for the past 2 centuries A list of methods was given my Egen, 1835. See paper and PPT

Modern Methods – similar to century-old approaches but with more data

Direct Evidence. Photographic, satellite or compelling visual evidence of origin

Aerosol Composition. Chemical fingerprinting of different source types (speciation, traces)

Temporal pattern. Chemical rPhysical property analysis (satellite, ASOS, PM2.5)

Spatial Pattern. Chemical

Transport Pattern. Forward, backward trajectory, residence time analysis

Chemistry with Transport . Combining chemical fingerprinting and transport (CATT)

Dynamic modeling . Simulation model (forward, inversion) quantifying origin/transport

Historical Methods

Source attribution methods have been used for the past 2 centuries

A list of methods was given my Egen, 1835. See paper and PPT

Natural Aerosol Conditions – Default Values The Regional Haze Rule provides initial default values for the Natural Haze Conditions The default haze for the West is 8 deciviews while for the East is 11 deciviews Obtained by estimating the natural concentration of SO4, EC, OC, NO3, Fine, Coarse Soil Weighing each aerosol component by corresponding extinction efficiencies. (Trijonis, 1990) Mass West East Regulatory objective: Fortify the natural condition estimates by the 2008 SIP call.

The Regional Haze Rule provides initial default values for the Natural Haze Conditions

The default haze for the West is 8 deciviews while for the East is 11 deciviews

Obtained by estimating the natural concentration of SO4, EC, OC, NO3, Fine, Coarse Soil

Weighing each aerosol component by corresponding extinction efficiencies. (Trijonis, 1990)

Regional Haze Guide on Fire and Dust Fire Forest and other fires can be either natural or man-induced Many major forest fires can be inherently classified as natural Other fires are intentional (prescribed) to reduce organic fuel accumulation Prescribed burning will likely be increasing to reduce catastrophic wildfires EPA considers some portion of the prescribed fire emissions as ‘natural’. The Western Regional Air Partnership (WRAP) is conducting interesting policy discussions on defining natural and manmade smoke Dust Regional Haze guide document is vague on natural dust A WRAP RPO study has concluded that windblown dust, not manmade fugitive (mechanically stirred up) dust dominated regional dust. Pitchford suggests that a working definition of natural dust could be any dust from non-disturbed soil surfaces; dust from disturbed surfaces would be manmade. Clearly, there is a need to begin both the scientific/technical as well as the policy discussion about natural/manmade dust.

Fire

Forest and other fires can be either natural or man-induced

Many major forest fires can be inherently classified as natural

Other fires are intentional (prescribed) to reduce organic fuel accumulation

Prescribed burning will likely be increasing to reduce catastrophic wildfires

EPA considers some portion of the prescribed fire emissions as ‘natural’.

The Western Regional Air Partnership (WRAP) is conducting interesting policy discussions on defining natural and manmade smoke

Dust

Regional Haze guide document is vague on natural dust

A WRAP RPO study has concluded that windblown dust, not manmade fugitive (mechanically stirred up) dust dominated regional dust.

Pitchford suggests that a working definition of natural dust could be any dust from non-disturbed soil surfaces; dust from disturbed surfaces would be manmade.

Clearly, there is a need to begin both the scientific/technical as well as the policy discussion about natural/manmade dust.

Direct Evidence of Natural Aerosol Events Miscellaneous Observations

Miscellaneous Observations

Aerosol Events Catalog Aerosol events catalog is community-based Each cataloged event has location, time and type codes Software tools allow event submission and browsing Events are described in flexible user-supplied URLs Dust Events Haze Events Smoke Events FASTNET Aerosol Event Catalog

Aerosol events catalog is community-based

Each cataloged event has location, time and type codes

Software tools allow event submission and browsing

Events are described in flexible user-supplied URLs

Example Event Catalog Entry: Texas Dust Event

Aerosol Event Catalog: Astronaut Photos Astronauts routinely photograph dust and smoke events The images and the catalog is on the NASA JSC website Aerosol images are registered in the DataFed Catalog and web-accessible through standard VIEWER interface

Astronauts routinely photograph dust and smoke events

The images and the catalog is on the NASA JSC website

Aerosol images are registered in the DataFed Catalog and web-accessible through standard VIEWER interface

Aerosol Event Catalog: Web pages Catalog of generic ‘web objects’ – pages, images, animations that relate to aerosol events Each ‘web object’ is cataloged by location, time and aerosol type.

Catalog of generic ‘web objects’ – pages, images, animations that relate to aerosol events

Each ‘web object’ is cataloged by location, time and aerosol type.

Temporal Pattern Regional Speciated Analysis - VIEWS Aerosol species time series: ammSO4f OCf ECf SOILf ammNO3f RCFM Regions of Aggregation

Aerosol species time series:

ammSO4f

OCf

ECf

SOILf

ammNO3f

RCFM

Dust Seasonal + spikes East – west events are independent East events occur several times a year, mostly in summer West events are lest frequent, mostly in spring US West East

Seasonal + spikes

East – west events are independent

East events occur several times a year, mostly in summer

West events are lest frequent, mostly in spring

Dust asgasgasfg Northeast Southwest Southeast

asgasgasfg

Dust dfjdjdfjetyj Northwest S. California Great Plaines

dfjdjdfjetyj

Amm. Sulfate wdthehreherh US West East

wdthehreherh

Amm. Sulfate stheherheyju Northeast Southwest Southeast

stheherheyju

Amm. Sulfate shheherh Northwest S. California Great Plaines

shheherh

Organic Carbon sdhdfhefheryj US West East

sdhdfhefheryj

Organic Carbon sdheherh Northeast Southwest Southeast

sdheherh

Organic Carbon erheryeyj Northwest S. California Great Plaines

erheryeyj

Reconstructed Fine Mass estrhertheryu US West East

estrhertheryu

Reconstructed Fine Mass werty3rueru Northeast Southwest Southeast

werty3rueru

Reconstructed Fine Mass wthwrthwerhtr Northwest S. California Great Plaines

wthwrthwerhtr

Event Definition: Time Series Approach Eastern US aggregate time series

Eastern US aggregate time series

Sulfate EUS Daily Average 50%-ile, 30 day 50%-ile smoothing Deviation from %-ile Event – Deviation > percentile value Median Seasonal Conc. Mean Seasonal Conc.

Reconstructed Fine Mass RCFM

Reconstructed Fine Mass RCFM

Organic Carbon

Eelemental Carbon

SOIL

Nitrate

Eastern US PM25 Event Composition The largest EUS PM25 events (as RCFM) are simultaneously ‘events’ in sulfate, organics and soil! Some EUS PM25 events are single species events Some PM25 events are not events in any species; their reinforcing combination causes the PM25 event

The largest EUS PM25 events (as RCFM) are simultaneously ‘events’ in sulfate, organics and soil!

Some EUS PM25 events are single species events

Some PM25 events are not events in any species; their reinforcing combination causes the PM25 event

Origin of US Dust Events

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 Fine Dust Events, 1992-2003 ug/m3

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

Dust Event Detection Determine concentration baseline Use ‘spike filter’ to remove outlier Declare remaining data as ‘baseline’ Plot of the daily avg. fine SOIL Baseline with strong seasonality Events superimposed on baseline Separate event days from baseline Identify spike (event) days Subtract baseline to get event ‘excess’

Determine concentration baseline

Use ‘spike filter’ to remove outlier

Declare remaining data as ‘baseline’

Plot of the daily avg. fine SOIL

Baseline with strong seasonality

Events superimposed on baseline

Separate event days from baseline

Identify spike (event) days

Subtract baseline to get event ‘excess’

Seasonal Pattern of Dust Baseline and Events The dust baseline concentration is has a 5x seasonal amplitude from 0.2 to 1 ug/m3 The dust events (determined by the spike filter) occur in April/May and in July The two April/May and the July peak in avg. dust is due to the events

The dust baseline concentration is has a 5x seasonal amplitude from 0.2 to 1 ug/m3

The dust events (determined by the spike filter) occur in April/May and in July

The two April/May and the July peak in avg. dust is due to the events

Fine Dust Concentration Pattern

Annual Average Fine Dust Pattern (Derived from VIEWS 1992-2002)

Seasonal Average Fine Soil (VIEWS database, 1992-2002) Fine soil concentration is highest in the summer over Mississippi Valley, lowest in the winter In the spring, high concentrations also exists in the arid Southwest (Arizona and Texas) Evidently, the summer Mississippi Valley peak is Sahara dust while the Spring peak is from local sources

Fine soil concentration is highest in the summer over Mississippi Valley, lowest in the winter

In the spring, high concentrations also exists in the arid Southwest (Arizona and Texas)

Evidently, the summer Mississippi Valley peak is Sahara dust while the Spring peak is from local sources

Annual Average Fine Dust Concentration (VIEWS database, 1992-2002) The highest annual fine dust concentration is in southern Arizona and Texas Elevated dust is also evident in the Mississippi Valley

The highest annual fine dust concentration is in southern Arizona and Texas

Elevated dust is also evident in the Mississippi Valley

TOMS and VIEWS, July TOMS – Dust plume from Sahara VIEWS SOILf in July – Sahara dust plume penetrating the continent

TOMS – Dust plume from Sahara

VIEWS SOILf in July – Sahara dust plume penetrating the continent

Local Dust VIEWS SOILf, Spring - Local dust TOMS local dust

VIEWS SOILf, Spring - Local dust

TOMS local dust

Local Dust Dust Storm Damage, 19030-1940 Dust Bowl States (brown), most dust storm damage (red), less damage (yellow). Dust frequency based on visibility obs. – Sehmel (1975?)

Dust Storm Damage, 19030-1940 Dust Bowl States (brown), most dust storm damage (red), less damage (yellow).

Dust frequency based on visibility obs. – Sehmel (1975?)

FAST-CATT: EUS Dust Transport Events Sahara Dust Events over the EUS

Sahara Dust Events over the EUS

Example high fine dust days ( See console for complete list ) Asian Dust April 16, 2001 Sahara Dust July 5, 2001 Southwestern Dust Oct. 16, 2001

AIRNOW PM25 Northeast, Southeast Northeast Southeast 2004 2004 2003 2003

AIRNOW PM25 Great Lakes, Great Plaines Great Lakes Great Plaines 2004 2004 2003 2003

AIRNOW PM25 Northwest, Southwest Northwest Southwest 2004 2004 2003 2003

AIRNOW PM25 Northeast, Southeast Northeast Southeast 2004 2004

AIRNOW PM25 Great Lakes, Great Plaines Great Lakes Great Plaines 2004 2004

AIRNOW PM25 Northwest, Southwest Northwest Southwest 2004 2004