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The Importance of Data Validation Data Validation Procedures and Tools Data Validation Levels Level I: Field and Laboratory Checks Level II: Internal Consistency Checks and Examples Level III/IV: Unusual Value Identification and Examples Validation of PM 2.5 Mass

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ensuring high quality data
The Importance of Data Validation

Data Validation Procedures and Tools

Data Validation Levels

Level I: Field and Laboratory Checks

Level II: Internal Consistency Checks and Examples

Level III/IV: Unusual Value Identification and Examples

Validation of PM2.5 Mass

Information to be Provided with PM Sampler Data

Are Measurements Comparable?

National Contract Lab Responsibilities

Data Access

Sample Size Issues

References

Appendix: Criteria Tables for PM2.5 Mass Validation

Critical Criteria Table

Operational Evaluations Table

Systematic Issues

Ensuring High Quality Data

“The purpose of data validation is to detect and then verify any data values that may not represent actual air quality conditions at the sampling station.” (U.S. EPA, 1984)

PM Data Analysis Workbook: Data Validation

the importance of data validation
The Importance of Data Validation
  • Data validation is critical because serious errors in data analysis and modeling results can be caused by erroneous individual data values.
  • Data validation consists of procedures developed to identify deviations from measurement assumptions and procedures.
  • Timely data validation is required to minimize the generation of additional data that may be invalid or suspect and to maximize the recoverable data.

Main et al., 1998

PM Data Analysis Workbook: Data Validation

the importance of data validation3
The Importance of Data Validation
  • The quality and applicability of data analysis results are directly dependent upon the inherent quality of the data. In other words, data validation is critical because serious errors in data analysis and modeling results can be caused by erroneous individual data values. The EPA's PM2.5 speciation guidance document provides quality requirements for sampling and analysis. The guidance document also discusses data validation including the suggested four-level data validation system. It is the monitoring agency’s responsibility to prevent, identify, correct, and define the consequences of difficulties that might affect the precision and accuracy, and/or the validity, of the measurements.
  • Once the quality assured data are provided to data analysts, additional data validation steps need to be taken. Given the newness and complexity of the PM2.5 speciation monitoring and sample analysis methods, errors are likely to pass through the system despite rigorous application of quality assurance and validation measures by the monitoring agencies. Therefore, data analysts should also check the validity of the data before conducting their analyses.
  • While some quality assurance and data validation can be performed without a broad understanding of the physical and chemical processes of PM (such as ascertaining that the field or laboratory instruments are operating properly), some degree of understanding of these processes is required. Key issues to understand include PM physical, chemical, and optical properties; PM formation and removal processes; and sampling artifacts, interferences, and limitations. These topics were discussed in the introduction and references therein. The analyst should also understand the measurement uncertainty and laboratory analysis uncertainty. These uncertainties may differ significantly among samplers and analysis methods which, in turn, have an affect on the interpretation and uses of the data (e.g., in source apportionment).

PM Data Analysis Workbook: Data Validation

data validation procedures and tools
Data Validation Procedures and Tools

Data validation tools for PM are in development

PM Data Analysis Workbook: Data Validation

data validation levels
Data Validation Levels
  • Level I. Routine checks during the initial data processing and generation of data (e.g., check file identification; review unusual events, field data sheets, and result reports; do instrument performance checks).
  • Level II. Internal consistency tests to identify values in the data that appear atypical when compared to values of the entire data set.
  • Level III. Current data comparisons with historical data to verify consistency over time.
  • Level IV. Parallel consistency tests with data sets from the same population (e.g., region, period of time, air mass) to identify systematic bias.

U.S. EPA, 1999a

PM Data Analysis Workbook: Data Validation

level i field and laboratory checks
Level I: Field and Laboratory Checks
  • Verify computer file entries against data sheets.
  • Flag samples when significant deviations from measurement assumptions have occurred.
  • Eliminate values for measurements that are known to be invalid because of instrument malfunctions.
  • Replace data from a backup data acquisition system in the event of failure of the primary system.
  • Adjust measurement values of quantifiable calibration or interference bias.

Chow et al., 1996

PM Data Analysis Workbook: Data Validation

level ii internal consistency checks
Level II: Internal Consistency Checks
  • Compare collocated samplers (scatter plots, linear regression).
  • Check sum of chemical species vs. PM2.5 mass (multielements Al to U + sulfate + nitrate + ammonium ions + OC + EC - Sulfur).
  • Check physical and chemical consistency (sulfate vs. total sulfur, soluble potassium vs. total potassium, soluble chloride vs. chlorine, babs vs. elemental carbon).
  • Balance cations and anions.
  • Balance ammonium.
  • Investigate nitrate volatilization and adsorption of gaseous organic carbon.
  • Prepare material balances and crude mass balances.

Chow, 1998

PM Data Analysis Workbook: Data Validation

level ii consistency check guidelines
Level II: Consistency Check Guidelines

Chow, 1998

IC = ion chromatography

XRF = energy dispersive X-ray fluorescence

AAS = atomic absorption spectrophotometry

PM Data Analysis Workbook: Data Validation

example compare collocated samplers
Example: Compare Collocated Samplers
  • Data from collocated samplers should be compared - between the same sampler type and different sampler types.
  • During the 1995 Integrated Monitoring Study (IMS95) in California, the collocated PM2.5 samplers (same type) at Bakersfield showed excellent agreement.
  • SSI 1 and TEOM measurements did not correlate very well during the winter/fall season. The two samplers showed much better agreement during March-September (not shown).

1:1

Reg.

Reg. = linear regression fit

Chow, 1998

PM Data Analysis Workbook: Data Validation

example check sum of chemical species vs pm 2 5 mass
Example: Check Sum of Chemical Species vs. PM2.5 Mass

1:1

  • Compare the sum of species to the PM2.5 mass measurements.
  • The comparison shown here indicates an excellent correlation (r=0.98).
  • The sum of species concentrations is lower than the reported mass because the sum of species does not include oxygen.

Reg.

Chow, 1998

PM Data Analysis Workbook: Data Validation

example check chemical and physical consistency 1 of 2

Soluble Potassium vs. Total Potassium

Sulfate vs. Total Sulfur

Example: Check Chemical and Physical Consistency (1 of 2)

1:1

3:1

  • Chemical and physical consistency checks include comparing sulfate with total sulfur (sulfate should be about three times the sulfur concentrations) and comparing soluble potassium with total potassium.
  • In the examples shown, the sulfur data compare well while the potassium data comparison shows a considerable amount of scatter.

Reg.

Reg.

Chow, 1998

PM Data Analysis Workbook: Data Validation

example check chemical and physical consistency 2 of 2
Example: Check Chemical and Physical Consistency (2 of 2)

babs vs. Elemental Carbon

Reg.

  • Another consistency check that can be performed (if data are available) is to compare the elemental carbon concentrations with particle absorption (babs) measurements.
  • In the example shown, the two measurements agree well.

Chow, 1998

PM Data Analysis Workbook: Data Validation

example anion and cation balance
Example: Anion and Cation Balance
  • Equations to calculate anion and cation balance (moles/m3)

Anion equivalence

e = Cl- + NO3- + SO4=

35.453 62.005 48.03

Cation equivalence

e = Na+ + K+ + NH4+

23.0 39.098 18.04

Plot cation equivalents vs. anion equivalents

Reg.

Chow 1998

PM Data Analysis Workbook: Data Validation

example ammonia balance
Example: Ammonia Balance
  • Equations to calculate ammonia balance (g/m3)

Calculated ammonium based on NH4NO3 and NH4HSO4 = 0.29 (NO3-)+ 0.192 (SO4=)

Calculated ammonium based on NH4NO3 and (NH4)2SO4 = 0.29 (NO3-)+ 0.38 (SO4=)

Plot calculated ammonium vs. measured ammonium for both forms of sulfate

Chow 1998

PM Data Analysis Workbook: Data Validation

example nitrate volatilization check
Example: Nitrate Volatilization Check

San Joaquin Valley, CA

  • Particularly for the western U.S., the analyst should understand the extent of possible nitrate volatilization in the data set.
  • This example shows that nitrate volatilization was significant during the summer.

Chow 1998

PM Data Analysis Workbook: Data Validation

example adsorption of gaseous oc check
Example: Adsorption of Gaseous OC Check
  • Some VOCs evaporate from a filter (negative artifact) during sampling while others are adsorbed (positive artifact).
  • The top figure shows the organic carbon (OC) concentrations on the backup filters were frequently 50% of more of the front filter concentrations. The error bars reflect measurement standard deviation.
  • The bottom figure shows the ratio of the backup OC to the front filter OC as a function of PM2.5 mass. Relatively larger organic vapor artifacts at lower PM2.5 concentrations suggests that particles provide additional adsorption sites on the front filters (Chow et al., 1996).

Chow 1998

PM Data Analysis Workbook: Data Validation

example material balance
Example: Material Balance

Denver, CO Core Sites

= Geological ( [ 1.89  Al ] + [ 2.14  Is ] + [ 1.4  Ca ] + [ 1.43  Fe ] )

+ Organic carbon ( 1.4  OC )

+ Elemental carbon

+ Ammonium nitrate ( 1.29  NO3– )

+ Ammonium sulfate ( 1.38  SO4= )

+ Remaining trace elements (excluding Al, Si, Ca, Fe, and S)

+ Unidentified

Chow 1998

PM Data Analysis Workbook: Data Validation

example crude mass balance
Example: Crude Mass Balance
  • Crude mass balances can be constructed to investigate estimated source contributions.
  • Do the crude estimates make sense spatially and temporally?

Las Vegas, NV

Site types

Sites

Chow 1998

PM Data Analysis Workbook: Data Validation

level iii iv unusual value identification
Level III/IV: Unusual Value Identification
  • Extreme values
  • Values that normally track the values of other variables in a time series
  • Values that normally follow a qualitatively predictable spatial or temporal pattern

The first assumption upon finding a measurement that is inconsistent

with physical expectations is that the unusual value is due to a measurement error.

If, upon tracing the path of the measurement, nothing unusual is found,

the value can be assumed to be a valid result of an environmental cause.

Chow et al., 1996

PM Data Analysis Workbook: Data Validation

example unusual value identification
Example: Unusual Value Identification
  • Potassium nitrate (KNO3) is a major component of all fireworks.
  • This figure shows all available PM2.5 K+ data from all North American sites, averaged to produce a continental average for each day during 1988-1997.
  • Fourth of July celebration fireworks are clearly observed in the potassium time series.
  • Fireworks displays on local holidays/events could have a similar affect on data.

Poirot (1998)

Regional averaging and count of sample numbers were conducted in Voyager, using variations of the Voyager script on p. 6 of the Voyager Workbook Kvoy.wkb. Additional averaging and plotting was conducted in Microsoft Excel.

PM Data Analysis Workbook: Data Validation

data validation continues during data analysis
Data Validation Continues During Data Analysis
  • Two source apportionment models were applied to PM2.5 data collected in Vermont, and the results of the models were compared.
  • Excellent agreement for the selenium source was observed for part of the data while the rest of the results did not agree well.
  • Further investigation showed that the period of good agreement coincided with a change in laboratory analysis (with an accompanying change in detection limit and measurement uncertainty - the two models treat these quantities differently.)

Poirot, 1999

PM Data Analysis Workbook: Data Validation

validation of pm 2 5 mass
Validation of PM2.5 Mass
  • Consistent validation of PM2.5 mass concentrations across the U.S. is needed. To aid in this, three tables of criteria were developed and are provided in the appendix to this section of the workbook.
  • Observations that do not meet each and every criterion on the Critical Criteria Table should be invalidated unless there are compelling reasons and justification not to do so.
  • Criteria that are important for maintaining and evaluating the quality of the data collection system are included in the Operational Evaluations Table. Violation of a criterion or a number of criteria may be cause for invalidation.
  • Criteria important for the correct interpretation of the data but that do not usually impact the validity of a sample or group of samples are included on the Systematic Issues Table.

U.S. EPA, 1999c

PM Data Analysis Workbook: Data Validation

information to be provided with pm sampler data
Information to be Provided with PM Sampler Data

These supplemental measurements will be useful to help

explain or caveat unusual data

40 CFR 50 Appendix L, Table L-1

PM Data Analysis Workbook: Data Validation

are measurements comparable
Are Measurements Comparable?

To be added, a discussion of the following:

  • FRM vs. continuous vs. speciation
  • IMPROVE vs. Federal PM samplers

PM Data Analysis Workbook: Data Validation

national contract lab responsibilities
National Contract Lab Responsibilities

To be added, a discussion of the following:

  • Levels 0 and 1 validation
  • AIRS reporting

PM Data Analysis Workbook: Data Validation

data access 1 of 2
Data Access(1 of 2)

Official data sources:

  • AIRS Data via public web at http://www.epa.gov/airsdata
  • AIRS Air Quality System (AQS) via registered users

register with EPA/NCC (703-487-4630)

  • PM2.5 websites via public web

PM2.5 Data Analysis Workbook athttp://capita.wustl.edu/databases/userdomain/pmfine/

EPA PM2.5 Data Analysis clearinghouse at http://www.epa.gov/oar/oaqps/pm25/

Northern Front Range Air Quality Study at http://nfraqs.cira.colostate.edu/index2.html

NEARDAT at http://capita.wustl.edu/NEARDAT

PM Data Analysis Workbook: Data Validation

data access 2 of 2
Data Access (2 of 2)

Secondary data sources:

  • Meteorological parameters from NWS http://www.nws.noaa.gov
  • Meteorological parameters from PAMS/AIRS AQS register with EPA/NCC (703-487-4630)
  • Collocated or nearby SO2, nitrogen oxides, CO, VOC from AIRS AQS
  • Private meteorological agencies (e.g., forestry service, agricultural monitoring, industrial facilities)

PM Data Analysis Workbook: Data Validation

sample size issues
Sample Size Issues

How complete must data be to show that an area meets the NAAQS for PM?

U.S. EPA, 1999b

Sample size requirements for data analyses will vary depending upon the analysis type, the analysis goals, the variability in the data, and other factors.

PM Data Analysis Workbook: Data Validation

references
References

Ayers G.P., Keywood M.D., Gras J.L. (1999) TEOM vs. manual gravimetric methods for determination of PM2.5 aerosol mass concentrations. Atmos. Environ., 33, pp. 3717-3721.

Chow J.C. and J.G. Watson (1998) Guideline on speciated particulate monitoring. Draft report 3 prepared by Desert Research Institute for the U.S. EPA Office of Air Quality Planning and Standards. August.

Chow J.C. (1998) Descriptive data analysis methods. Presentation prepared by Desert Research Institute for the U.S. EPA in Research Triangle Park, November.

Chow J.C., J.G. Watson, Z. Lu, D.H. Lowenthal, C.A. Frazier, P.A. Solomon, R.H. Thuillier, K. Magliano (1996) Descriptive analysis of PM2.5 and PM10 at regionally representative locations during SJVAQS/AUSPEX. Atmos. Environ., Vol. 30, No. 12, 2079-2112.

Chow J.C. (1995) Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air Waste Manage. Assoc., 45, pp.320-382.

Homolya J.B., Rice J., Scheffe R.D. (1998) PM2.5 speciation - objectives, requirements, and approach. Presentation. September.

Main H.H., Chinkin L.R., and Roberts P.T. (1998) PAMS data analysis workshops: illustrating the use of PAMS data to support ozone control programs. Web page prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, <http://www.epa.gov/oar/oaqps/pams/analysis> STI-997280-1824, June.

Poirot R. (1999) personal communication

Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at http://capita.wustl.edu/PMFine/Workgroup/SourceAttribution/Reports/In-progress/Potass/ktext.html

U.S. Environmental Protection Agency (1984) Quality assurance handbook for air pollution measurement systems, volume ii: ambient air specific methods (interim edition), EPA/600/R-94/0386, April.

U.S. Environmental Protection Agency(1999a) Particulate matter (PM2.5) speciation guidance document. Available at http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/specpln3.pdf

U.S. Environmental Protection Agency(1999b) Guideline on data handling conventions for the PM NAAQS. EPA-454/R-99-008, April.

U.S. Environmental Protection Agency(1999c) PM2.5 mass validation criteria. Available at http://www.epa.gov/ttn/amtic/pmqa.html

PM Data Analysis Workbook: Data Validation

critical criteria table
Critical Criteria Table

U.S. EPA, 1999c

PM Data Analysis Workbook: Data Validation

operational evaluations table 1 of 2
Operational Evaluations Table (1 of 2)

U.S. EPA, 1999c

PM Data Analysis Workbook: Data Validation

operational evaluations table 2 of 2
Operational Evaluations Table (2 of 2)

U.S. EPA, 1999c

PM Data Analysis Workbook: Data Validation

systematic issues
Systematic Issues

U.S. EPA, 1999c

PM Data Analysis Workbook: Data Validation