The Importance of Data Validation

1
Ensuring High Quality Data

• The Importance of Data Validation
• 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

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)
2
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
3
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
  agencys 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).

4
Data Validation Procedures and Tools

• Data validation tools for PM are in development

5
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
6
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
7
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
8
Level II Consistency Check Guidelines
Chow, 1998
IC ion chromatography XRF energy dispersive
X-ray fluorescence AAS atomic absorption
spectrophotometry
9
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).

11
Reg.
Reg. linear regression fit
Chow, 1998
10
Example Check Sum of Chemical Species vs. PM2.5
Mass
11
Reg.
Chow, 1998

• Compare the sum of species to the PM2.5 mass
  measurements.
• The comparison shown here indicates an excellent
  correlation (r0.98).
• The sum of species concentrations is lower than
  the reported mass because the sum of species does
  not include oxygen.

11
Example Check Chemical and Physical Consistency
(1 of 2)
11
31
Reg.
Reg.
Chow, 1998

• 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.

12
Example Check Chemical and Physical Consistency
(2 of 2)

• 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.

babs vs. Elemental Carbon
Reg.
Chow, 1998
13
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
14
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
15
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
16
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 or 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
17
Example Material Balance
Denver, CO Core Sites

• Geological ( 1.89 ? Al 2.14 ? Si
  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
18
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
19
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
20
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.
21
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
22
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
23
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
24
Are Measurements Comparable?

• Example comparison of 24-hr average TEOM (from
  hourly measurements), IMPROVE (gravimetric mass
  from the A filter), and FRM PM2.5 mass
  measurements made in New Haven, CT during the
  third and fourth quarters of 1998.
• During the colder months at this site, the TEOM
  seems to report a lower concentration than the
  FRM.

PM2.5 average values (mg/m3) New Haven, CT 1998
(No. of samples in the calculated average). For
example, in the third quarter, TEOM and IMPROVE
samples ran concurrently on 24 days. The ten
values where all three samplers ran are a subset
of the 24.
Graham, 1999
25
National Contract Lab Responsibilities

• Discussion of national contract laboratory
  responsibilities to be added.

26
Data Access (1 of 2)

• Official data sources
• AIRS Data via public web at http//www.epa.gov/air
  sdata
• 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 at
  http//capita.wustl.edu/databases/userdomain/pmfin
  e/
• 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

27
Data Access (2 of 2)

• Secondary data sources
• Meteorological parameters from National Weather
  Service (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)

28
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.
29
Summary

• Data validation is vital because serious errors
  in data analysis and modeling results can be
  caused by erroneous individual data values.
• This workbook section provides a discussion of
  data validation levels, example validation
  checks, and other information important to the
  data validation process.

30
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.
• Graham, J. (1999) personal communication.
• 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, lthttp//www.epa.gov/oar/oaqps/pams/a
  nalysisgt 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/amt
  ic/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

31
Critical Criteria Table
U.S. EPA, 1999c
32
Operational Evaluations Table (1 of 2)
U.S. EPA, 1999c
33
Operational Evaluations Table (2 of 2)
U.S. EPA, 1999c
34
Systematic Issues
U.S. EPA, 1999c