Discerning Influences

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August 1999 PM Data Analysis Workbook: Characterizing PM 1 Discerning Influences Key reference: Meteorology: explore the affect of meteorological conditions on PM concentrations and composition. Emissions: explore the affect of emissions on PM concentrations and composition. Inter-pollutant relationships: explore the relationships among PM components. Natural Events: explore the affect of natural events (e.g., dust storms, fires) on PM concentrations.

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Discerning Influences. Meteorology: explore the affect of meteorological conditions on PM concentrations and composition. Emissions: explore the affect of emissions on PM concentrations and composition. Inter-pollutant relationships: explore the relationships among PM components. - PowerPoint PPT Presentation

Transcript of Discerning Influences

August 1999 PM Data Analysis Workbook: Characterizing PM 1

Discerning Influences

Key reference:

• Meteorology: explore the affect of meteorological conditions on PM concentrations and composition.

• Emissions: explore the affect of emissions on PM concentrations and composition.

• Inter-pollutant relationships: explore the relationships among PM components.

• Natural Events: explore the affect of natural events (e.g., dust storms, fires) on PM concentrations.

August 1999 PM Data Analysis Workbook: Characterizing PM 2

Emissions

August 1999 PM Data Analysis Workbook: Characterizing PM 3

Meteorology

August 1999 PM Data Analysis Workbook: Characterizing PM 4

Formation and Removal

August 1999 PM Data Analysis Workbook: Characterizing PM 5

Inter-pollutant Relationships

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Natural PM Events: Dust and Smoke

• Dust storms and forest fires are major PM events that occur several times a year over different parts of the US.

• Many of these events originate outside the US, e.g. dust from Sahara and the Asian desserts and smoke from forest fires in Central America and Canada.

• Exceedances of the NAAQS caused by dust and smoke events are uncontrollable ‘acts of God’. Nevertheless, states are required to provide evidence that such events (outside their jurisdiction) have occurred.

Key reference: Capita

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Natural PM Events

• For this reason, control agencies need to be able to detect and document the impact of such events on their control region. The existing tools for such documentation are poorly developed.

• The natural PM events are illustrated by two extreme examples: Asian dust impacting on the West Coast and the Central American forest fire smoke impacting the Eastern US.

Key reference: Capita

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Smoke from C. American Forest Fires

• During a ten-day period, May 7-17, 1998, smoke from fires in Central America drifted northward into USA and Canada.

• The smoke caused exceedances of the PM standard, health alerts, and impairment of air traffic, as well as major reductions of visual range.

• It has been argued that some ozone exceedances in the Eastern US may have been due to ozone generated by the forest fire smoke.

GOES 8 View of the SmokeSeaWiFS View of the Smoke

Key reference: Capita

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PM10 over the Eastern U.S. during the smoke event

24-hr PM10 concentrations in g/m3 are shown for several cities. The likely smoke impact on these cities is highlighted.

The vertical line at … in each figure represents….

Key reference: Capita

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The Asian Dust Event of April 1998

• On April 15th, 1998 an unusually intense dust storm began in the western Chinese Province of Xinjiang, just in time for the east Asian dust season.

• By April 20, the dust front covered a 1000 mile stretch on the east coast of China and within five days it moved across the Pacific. It reached the West Coast by April 25.

Key reference: Capita

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Asian Dust over the West Coast

• In Vancouver and in Washington State the PM10 and PM2.5 concentrations reached 100 and 40 g/m3, respectively.

• Based on public complaints and monitoring data, the State of Washington issued a ban on open burning on April 29. Thus, a dust cloud from another country impacted activity in the US.

Key reference: Capita

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Methods and Tools

August 1999 PM Data Analysis Workbook: Characterizing PM 13

Decision Matrix for Spatial and Temporal AnalysesStatus and Trends Example Projects Future Analyses

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TECHNICAL TOPIC AREASTemporal Patterns

Diurnal Day-of-Week Episodic Seasonal

Spatial PatternsUrban Urban/rural Elevational Regional National/International

Compositional PatternsMass General Species Detailed Species

Discerning InfluencesMeteorology Emissions Inter-pollutant Relationships Natural Events

Decision matrix to be used to select example projects that will illustrate how others have explored the characterization of PM. To use the matrix, find your technical topic area at the left. Follow this line across to see which example projects illustrate analyses pertaining to the topic area. For each of these projects, go to the next page to see which data and data analysis tools were used.

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Decision Matrix for Spatial and Temporal AnalysesStatus and Trends Example Projects Future Analyses

EP

A T

rend

s R

epor

t

US

Vis

ibili

ty T

rend

s, 1

980-

1995

Impa

ct o

f 199

8 C

entr

al A

mer

ican

Fire

s

Use

of T

ime

Ser

ies

Ana

lysi

s -

Chi

cago

Tren

ds u

sing

IMP

RO

VE

Dat

a A

naly

sis

and

Mod

elin

g -

Pho

enix

IMS

95

Loca

l and

reg

iona

l… M

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Trac

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of O

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ty -

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Nor

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Aug

. 19

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data

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APPLICABLE DATAPM Mass

FRM Mass Non-FRM Mass Continuous Mass

PM SpeciationNAMS Trends State Supersites IMPROVE Special Studies

MeteorologySurface Upper-Air

Other Air Quality DataSecondary (e.g., Ozone) Primary (e.g., SO2, NOx, CO) VOCs

TOOLS DEMONSTRATEDAMDAS Other Statistical Methods Voyager Trajectory Methods Factor, cluster analyses PMFUNMIXCMB8 SPECIATE

For each of the projects that are of interest to you, follow down the column to see which data and data analysis tools were used.

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References