Assessment of the Speciated PM Network

(Initial Draft, November 2004 )

Washington University, St. LouisCIRA/NPS VIEWS Team

Contents

• Background and Approach– Aerosol Speciation and the NCore Network– Aerosol Species in IMPROVE and SPEC Networks– Technical Approach to the Network Assessment

• Temporal Data Coverage– Long-Term Trend Data: Fine Mass, SO4, K (1988-03)– Recent Trends (1999-03): Fine Mass, Sulfate, Nitrate, OC (IMPROVE),

OC_NIOSH (SPEC), EC (IMPROVE), EC_NIOSH (SPEC)

• Spatial Data Coverage – Total Data Stock Maps: Fine Mass, So4, NO3, Ocf, Ocf_NIOSH, Euf– Fine Mass, Sulfate, Nitrate, Europium

• Information Value of Stations: Error for SO4

– Estimation Error: Single Day– Estimation Error: Long-term Average

• Temporal and Spatial SO4 Characterization• Assessment Summary

Aerosol Speciation and the NCore Network

• NCore is to characterize the pollutant pattern for many applications • Speciation provides the data for aerosol source apportionment• In NCore, ‘core species’ are measured at Level-2 and L1 sites• Currently (2003), the speciation sites exceed 350• The challenge is to assess the evolution and status of the speciation network

Technical Approach to the Network Assessment

• This draft Network Assessment is a collaboration of the CAPITA and CIRA groups• CIRA has created an integrated speciation database as part of the RPO VIEWS project• CAPITA has applied the analysis tools of DataFed to the VIEWS database• The results of the assessment analysis are presented (this PPT) to the NCore team• Guided by the evaluation and feedback from NCore, the assessment is revised

CIRA/ VIEWS

Database

CAPITA/ DataFed Database

Network Assessment

PPT

IMPROVE

EPA SPEC

CIRA Tools and Processes

DataFed Tools and Processes

Analysis Tools and Processes

Speciated Data Flow and Processing

EPA NCore Process

Evaluation, Feedback

Aerosol Species Monitored Snapshot for Aug 19, 2003

• IMPROVE monitors over 30 species• EPA monitors over 40 species• About 25 species are reported by both

Species Abbriv Species NameALf Aluminum ammNO3f Ammonium Nitrate ammSO4f Ammonium Sulfate ASf Arsenic BRf Bromine CAf Calcium CEf Cerium CHLf Chloride CLf Chlorine CM_calculated PM2.5-10: massCRf Chromium CUf Copper EC1f Carbon (elem frac 1)EC2f Carbon (elem fract 2)EC3f Carbon (elem fract 3)ECf Carbon (total) (eleml)FEf Iron Kf Potassium MGf Magnesium MNf Manganese N03f PM10: MassN2f Nitrite NBf Niobium NH4f Ammonium ion NIf Nickel OCf Carbon (total) (organic)OMCf Carbon Mass (organic)PBf Lead Pf Phosphorus RBf Rubidium RCFM Reconstructed Fine MassSEf Selenium Sf Sulfur SIf Silicon SO4f Sulfate SOILf Soil SRf Strontium Vf Vanadium ZNf Zinc ZRf Zirconium

Species Abbriv Species NameAGf Silver ALf Aluminum ASf Arsenic AUf Gold BAf Barium BRf Bromine CAf Calcium CDf Cadmium CEf Cerium CLf Chlorine COf Cobalt CRf Chromium CSf Cesium CUf Copper ECf_NIOSH Carbon (eleml) (NIOSH)EUf Europium FEf Iron GAf Gallium HFf Hafnium HGf Mercury INf Indium IRf Iridium Kf Potassium Kf_ion Potassium ion LAf Lanthanum MF PM2.5: massMGf Magnesium MNf Manganese MOf Molybdenum MT PM10: MassN03f PM10: MassNAf_ion Sodium ion NBf Niobium NH4f Ammonium ion NIf Nickel OCf_NIOSH Carbon (organic) (NIOSH)OCX2f Carbon (org NIOSH frac 2)PBf Lead Pf Phosphorus RBf Rubidium SBf Antimony SCf Scandium SEf Selenium Sf Sulfur SIf Silicon SMf Samarium SNf Tin SO4f Sulfate SRf Strontium TAf Tantaium TBf Terbium Tif Titanium v_nitrate Volatile Nitrate Vf Vanadium WOLF WOLFYf Yttrium ZNf Zinc ZRf Zirconium

IMPROVE Species

SPECIATION

IMP + SPEC Data Count

Species Abbriv Species Name Network Data CountMF PM2.5: mass IMP & SPEC & FRM1328MT PM10: Mass IMP & SPEC & FRM641SO4f Sulfate IMP & SPEC 353ALf Aluminum IMP & SPEC 349ASf Arsenic IMP & SPEC 349BRf Bromine IMP & SPEC 349CAf Calcium IMP & SPEC 349CEf Cerium IMP & SPEC 349CLf Chlorine IMP & SPEC 349CRf Chromium IMP & SPEC 349CUf Copper IMP & SPEC 349FEf Iron IMP & SPEC 349Kf Potassium IMP & SPEC 349MGf Magnesium IMP & SPEC 349MNf Manganese IMP & SPEC 349NBf Niobium IMP & SPEC 349NIf Nickel IMP & SPEC 349PBf Lead IMP & SPEC 349Pf Phosphorus IMP & SPEC 349RBf Rubidium IMP & SPEC 349SEf Selenium IMP & SPEC 349Sf Sulfur IMP & SPEC 349SIf Silicon IMP & SPEC 349SRf Strontium IMP & SPEC 349Vf Vanadium IMP & SPEC 349ZNf Zinc IMP & SPEC 349ZRf Zirconium IMP & SPEC 349N03f PM10: Mass IMP & SPEC 337NH4f Ammonium ion IMP & SPEC 218ECf_NIOSH Carbon (eleml) (NIOSH)SPEC 208OCf_NIOSH Carbon (organic) (NIOSH)SPEC 208SBf Antimony SPEC 208SCf Scandium SPEC 208OCX2f Carbon (org NIOSH frac 2)SPEC 207AGf Silver SPEC 206AUf Gold SPEC 206BAf Barium SPEC 206CDf Cadmium SPEC 206COf Cobalt SPEC 206CSf Cesium SPEC 206EUf Europium SPEC 206GAf Gallium SPEC 206HFf Hafnium SPEC 206HGf Mercury SPEC 206INf Indium SPEC 206IRf Iridium SPEC 206Kf_ion Potassium ion SPEC 206LAf Lanthanum SPEC 206MOf Molybdenum SPEC 206NAf_ion Sodium ion SPEC 206SMf Samarium SPEC 206SNf Tin SPEC 206TAf Tantaium SPEC 206TBf Terbium SPEC 206Tif Titanium SPEC 206WOLF WOLF SPEC 206Yf Yttrium SPEC 206ammNO3f Ammonium Nitrate IMP 147ammSO4f Ammonium Sulfate IMP 147CHLf Chloride IMP 147EC1f Carbon (elem frac 1) IMP 147EC2f Carbon (elem fract 2) IMP 147EC3f Carbon (elem fract 3) IMP 147ECf Carbon (total) (eleml) IMP 147N2f Nitrite IMP 147OCf Carbon (total) (organic) IMP 147OMCf Carbon Mass (organic) IMP 147RCFM Reconstructed Fine MassIMP 142SOILf Soil IMP 142CM_calculated PM2.5-10: mass IMP 141v_nitrate Volatile Nitrate SPEC 8

Long-Term Monitoring: Fine

Mass, SO4, K

• Long-term speciated monitoring begun in 1988 with the IMPROVE network

• Starting in 2000, the IMPROVE and EPA networks have expanded

• By 2003, the IMPROVE + EPA species are sampled at 350 sites

• In 2003, the FRM/IMPROVE PM25 network is reporting data from over 1200 sites

Fine Mass

Sulfate

Potassium

Fine Mass, Sulfate, Nitrate

Monitoring (1999-03)

• Daily valid station counts for sulfate has increased from 50 to 350

• About 250 sites sample every 3rd day, 350 sites every 6th day

Fine Mass

Nitrate

SulfateEvery 6th day

Every 3rd day

Every 6th day

Every 3rd day

Every 6th day

Every 3rd day

OC (IMPROVE), OC_NIOSH (SPEC), 1999-2003

• Organic Carbon at the IMPROVE (OCf) and EPA (Ocf_NIOSH) sites are not fully compatible• Since 2000, the IMPROVE OCf monitoring has increased from 50 to 150 sites• Since 2001, the EPA network has grown from 20 to over 200 sites• (Need to separate STN?? Which are the STN site codes?)

IMPROVE OCf

EPA OCf_NIOSH

EC (IMPROVE), EC_NIOSH (SPEC), 1999-2003

• Same as Organic Carbon … redundant??

IMPROVE ECf

EPA ECf_NIOSH

Data Stock: Fine Mass, So4

• The data stock is the accumulated data resource

• The IMPROVE sites that begun in 1988 have over 1500 samples (red circles)

• The more recent EPA sites have 400 or less samples

IMPROVE + EPA Fine Mass

IMPROVE + EPA Sulfate

Data Stock: Ocf, Ocf_NIOSH, NO3, EUf

• The IMPROVE data stock for OCf and NO3f over 1500, for EPA OCf_NIOSH is < 400• Europium is only measured at the EPA sites, ~400 samples or less

IMPROVE OCf

IMPROVE/EPA NO3f

EPA Ocf_NIOSH

EPA Europium

Evolution of Spatial Data Coverage: Fine Mass 1998-2003

• Before 1998, IMPROVE provided much of the PM2.5 data (non-FRM EPA PM25 not included here)

• In the 1990s, the mid-section of the US was not covered • By 2003, the PM2.5 sites (1200+) covered most of the US

1998 1999 2000

200320022001

Evolution of Spatial Data Coverage: Fine Sulfate, 1998-2003

1998 1999 2000

200320022001

• Before 1998, IMPROVE provided much of the PM2.5 sulfate• In the 1990s, the mid-section of the US was not covered • By 2003, the IMPROVE and EPA sulfate sites (350+) covered most of the US

Evolution of Spatial Data Coverage: Nitrate, 1998-2003

• Ditto as Sulfate

1998 1999 2000

200320022001

Evolution of Spatial Data Coverage: Europium, 1998-2003

• Europium and many other trace elements are only measured in the EPA network• Starting 2001, the EPA network expanded to over 200 sites

Site Information Value • The information value of a site can be measured by how much it reduces the

uncertainty of concentration estimates

• If the concentration at a site can be estimated accurately for auxiliary data, (estimation error is low), then the information content of that site is low

• If the estimate is poor (estimation error is high), then the information content of that site is high, since having that site reduces the concentration uncertainty

• Thus, estimation error is a measure of the information content of a specific site

Estimation Error Calculation

• Cross-validation estimates the information value of individual stations by – removing each monitor, one at a time– estimating the concentration at the removed monitor location by the ‘best available’ spatial extrapolation scheme– calculation the error as difference (Estimated – Measured) or ratio (Estimated/Measured)

• The ‘best available’ estimated concentration is calculated using– de-clustering by the method of S. Falke– interpolation using 1/r-4 weighing– Using the nearest 10 stations within a 600 km search radius

• In the following examples, the error is estimated for two extreme cases:– for a single day with significant spatial gradients (Aug 19, 2003)– for the grand average concentration field with smooth pattern

SO4 Estimation Error: Single Day (Aug 19, 2003)

• The measured concentration contour plot is shown for all data• The estimated contour uses only values estimated from neighboring sites• The difference map indicates +/- 5 ug/m3 estimation errors (need to check )

• The ratio map shows the error to be over 50%

Measured Estimated

RatioDifference

Estimation Error: Long-term Average (2000-2003)

• The long-term average concentration pattern are smoother than for single day• The concentration error difference is below +/- 1 ug/m3 for most stations in the East• The error/measured ratio is below 20% except at few (<5) sites• In the West, the errors are higher due to varying site elevation/topographic barriers

Measured Estimated

RatioDifference

Average SO4: Estimated - Measured

• The Estimated-Measured correlation at Eastern sites is r2 ~ 0.8 • For the Western US sites the correlation is only r2 ~ 0.5

Western US SO4: Measured vs. Estimated

y = 0.9391x

R2 = 0.4865

0

1

2

3

4

0 1 2 3 4

Measured, ug/m3

Est

imat

ed, u

g.m

3

Eastern US SO4: Measured vs. Estimated

y = 0.9968x

R2 = 0.8022

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8

Measured, ug/m3

Estim

ated

, ug.

m3

Temporal and Spatial SO4 Characterization

• For SO4, the temporal coverage is every 3 or 6 days

• Typical ‘transport distance’ between 3-day samples (at 5 m/sec wind speed) is about 1500 km

• On the other hand, the characteristic distance between sites is about 160 km (total area 9x106 km2, 350 sites)

• Thus, the spatial sampling provides at least 10-fold more ‘characterization' than the 3rd day temporal sampling.

3 Day Transport

1 Day Transport

Speciation Network Assessment Summary (Initial Draft, November 2004 )

• Since 2000, speciated aerosol monitoring has grown from 50 to 350 sites

• IMPROVE and EPA sites have accumulated 1500 and 400 data points, respectively

• By 2003, the spatial coverage for speciated sampling was high throughout the US

• For long-term SO4 averages, the estimation error over the East was below 1 ug/m3

• For a specific day with strong SO4 gradient, the error was below up to 5 ug/m3

• The 350 sites provide at least 10-fold more ‘characterization' than the 3rd day sampling

AIRNOW PM25 - ASOS RH- Corrected Bext

2004 July 20 14:00

July 21, 2004 July 22, 2004 July 23, 2004

ARINOW PM25 ARINOW PM25ARINOW PM25

ASOS RHBext

ASOS RHBext

ASOS RHBext

Quebec Smoke July 7, 2002Satellite Optical Depth & Surface ASOS RHBext

A note to the NCore implementation managers:From ad hoc to Continuous Network Assessment

• By design, NCore will be changing in response to the evolving conditions

• Nudging NCore toward desired goals, requires assessment and feedback

• FRM mass and speciation monitoring is now ready to be ‘monitored’

• The indicators can be calculated from the VIEWS integrated database

• Many assessment tools (maps, charts, CrossVal) are developed or feasible

… so, it may be time to consider …

Automated Network Assessment as a routine part of monitoring

Continuous Speciated Network Assessment: A Feasibility Pilot Project

• Currently, network assessments are done intermittently with ad hoc tools• Network status and trends monitoring is now possible with web-based tools• A ‘pilot feasibility project’ could aid the design of operational network assessment • Such automatic feedback would contribute to the agility of the monitoring network

CIRA/ VIEWS

Database

CAPITA/ DataFed Database

Automatic Assessment WebTool

IMPROVE EPA SPEC Monitoring Networks

CIRA Tools and Processes

DataFed Tools and Processes

Analysis Tools and Processes

Network Assessmt

PPT

Analysis Tools and Processes

Network Adjustment Many Other Factors

Data Life Cycle: Acquisition Phase – Usage Phase

• Need a ‘force’ to move data from one-shot to reusable form • External force – contracts• Internal – humanitarian, benefits

The Researcher/Analyst’s Challenge

“The researcher cannot get access to the data;if he can, he cannot read them;if he can read them, he does not know how good they are;and if he finds them good he cannot merge them with other data.”

Information Technology and the Conduct of Research: The Users ViewNational Academy Press, 1989

Data Flow Resistances

These resistances can be overcome through a distributed system that catalogs and standardizes the data allowing easy access for data manipulation and analysis.

• The user does not know what data are available• The available data are poorly described (metadata)• There is a lack of QA/QC information• Incompatible data can not be combined and fused

The data flow process is hampered by a number of resistances.