Ensuring High Quality Data

1. The Importance of Data Validation Data Validation Procedures and Tools Data Validation Levels Level 0: Field and Laboratory Checks Level I: Internal Consistency Checks and Examples Level II/III: Unusual Value Identification and Examples Information to be Provided with PM Sampler Data Are Measurements Comparable? National Contract Lab Responsibilities Data Access Sample Size Issues Summary References 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

2. The Importance of Data Validation (1 of 2) • 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

3. The Importance of Data Validation (2 of 2) • 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

4. Data Validation Procedures and Tools • Data validation procedures for the PM2.5 chemical speciation network are discussed in Research Triangle Institute (2000) available at <http://www.epa.gov/ttn/amtic/amticpm.html>. The report includes a discussion of the validation process, Level 0 and Level 1 validation, data review and change submission form, field sampling data validation criteria, laboratory validation flagging, and mapping of validation criteria onto AIRS codes. • A PM2.5data management tool was developed to assist in the process of collecting data from the monitors, administering and housing the data, and reporting it to the EPA is available at <http://home.pes.com/marama/>. This database tool has the ability to accept electronic files from both Andersen and R&P monitors as inputs; accept filter data from laboratories in the form of an Excel spreadsheet; rapidly input filter identification data using a bar code scanner; review, edit, quality check and report their data; and produce transaction files that can be sent to AIRS. • Data validation tools for PM are in development PM Data Analysis Workbook: Data Validation

5. Data Validation Levels • Level 0. Routine checks during the initial data processing and generation of data including proper data file identification; review of unusual events, field data sheets, and result reports; do instrument performance checks and deterministic relationships. • Level I. Internal consistency tests to identify values in the data that appear atypical when compared to values of the entire data set. • Level II. Current data comparisons with historical data to verify consistency over time. • Level III. 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

6. Level 0: 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

7. Validation of PM2.5 Mass • Consistent validation of PM2.5 mass concentrations across the United States is needed. To facilitate consistency, three criteria tables were developed. • 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 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

8. CRITICAL CRITERIA TABLE aS-Single Filter, G-Group of filters (i.e., batch), G1-Group of filters from one instrument QA Guidance Document 2.12 Reference Samples Impacteda 40 CFR Reference Criteria Frequency Acceptable Range U.S. EPA, 1999c PM Data Analysis Workbook: Data Validation

9. OPERATIONAL EVALUATIONS Table (1 of 2) aS-Single Filter, G-Group of filters (i.e., batch), G1-Group of filters from one instrument QA Guidance Document 2.12 Reference Samples Impacteda 40 CFR Reference Frequency Acceptance Range Criteria U.S. EPA, 1999c PM Data Analysis Workbook: Data Validation

10. OPERATIONAL EVALUATIONS Table (2 of 2) aS-Single Filter, G-Group of filters (i.e., batch), G1-Group of filters from one instrument QA Guidance Document 2.12 Reference Samples Impacteda 40 CFR Reference Frequency Acceptance Range Criteria U.S. EPA, 1999c PM Data Analysis Workbook: Data Validation

11. SYSTEMATIC ISSUES aS-Single Filter, G-Group of filters (i.e., batch), G1-Group of filters from one instrument QA Guidance Document 2.12 Reference Samples Impacteda 40 CFR Reference Criteria Frequency Acceptable Range U.S. EPA, 1999c PM Data Analysis Workbook: Data Validation

12. Level I: Internal Consistency Checks To be demonstrated on the following pages: • 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

13. Level I: Consistency Check Guidelines IC = ion chromatography XRF = energy dispersive X-ray fluorescence AAS = atomic absorption spectrophotometry * Dichotomous data may be an exception to this check Chow, 1998 PM Data Analysis Workbook: Data Validation

14. 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. All plots prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

15. 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. Plot prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

16. 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. Plots prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

17. 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 (r = 0.93). Plot prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

18. 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. Plot prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

19. Ammonium Sulfate Ammonium Bisulfate Linear (Ammonium Sulfate) Linear (Ammonium Bisulfate) Ammonia Balance Plot prepared using MS Excel. Chow, 1998 Equations to calculate an ammonia balance (g/m3) are as follows: 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=) Next, plot the calculated ammonium vs. measured ammonium for both forms of sulfate. PM Data Analysis Workbook: Data Validation

20. Nitrate Volatilization Check San Joaquin Valley, CA 1:1 • Particularly for the western United States, 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 at San Joaquin Valley sites during a 1990 study. In the winter, nitrate volatilization was not as significant. 1:1 Plots prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

21. 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). Plot prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

22. Material Balance = 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 Denver, CO Core Sites Plot prepared using MS Excel. Measured mass = 15.4  10.8g/m3. Bar labels list concentration in g/m3. Data collected in Denver, CO. Chow, 1998 PM Data Analysis Workbook: Data Validation

23. 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 Plot prepared using MS Excel. Chow, 1998 PM Data Analysis Workbook: Data Validation

24. Level II/III: 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

25. 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 MS Excel. PM Data Analysis Workbook: Data Validation

26. 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). Plots prepared using MS Excel. Poirot, 1999 PM Data Analysis Workbook: Data Validation

27. 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

28. 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 PM Data Analysis Workbook: Data Validation

29. National Contract Lab Responsibilities The National Contract Laboratory (Research Triangle Institute—RTI) is responsible for the following activities: • Most laboratory analyses for the PM2.5 program. • Scheduling the distribution and receipt of sampler components to and from the monitoring agencies that operate the sites. • Entering and managing all field and laboratory data. • Performing preliminary Level 0 and Level 1 data validation. • Reporting the preliminary validated data to the monitoring agencies on a monthly basis. • Finalizing the validated data set based on the monitoring agencies’ reviews. • Formatting the data and uploading the validated data to AIRS. PM Data Analysis Workbook: Data Validation

30. Data Access(1 of 2) EPA-sponsored 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) • EPA PM2.5 Data Analysis clearinghouse at <http://www.epa.gov/oar/oaqps/pm25/> • PM2.5 “super sites” at <http://www.epa.gov/ttn/amtic/ssprojec.html>including Atlanta, Los Angeles, St. Louis, Pittsburgh, Fresno, Houston, Baltimore, and New York. Other data sources: • PM2.5 Data Analysis Workbook at <http://capita.wustl.edu/PMFine/> • Northern Front Range Air Quality Study at<http://www.nfraqs.colostate.edu/index2.html> • NEARDAT at<http://capita.wustl.edu/NEARDAT> PM Data Analysis Workbook: Data Validation

31. 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) PM Data Analysis Workbook: Data Validation

32. 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

33. 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. PM Data Analysis Workbook: Data Validation

34. References (1 of 2) Ayers G.P., Keywood M.D., and Gras J.L. (1999) TEOM vs. manual gravimetric methods for determination of PM2.5 aerosol mass concentrations. Atmos. Environ. 33, 3717-3721. Chow J.C. (1995) Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air Waste Manage. Assoc., 45, 320-382. Chow J.C. (1998) Descriptive data analysis methods. Presentation prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by Desert Research Institute, Reno, NV, November. Chow J.C., Watson J.G., Lu Z., Lowenthal D.H., Frazier C.A., Solomon P.A., Thuillier R.H., and Magliano K. (1996) Descriptive analysis of PM2.5 and PM10 at regionally representative locations during SJVAQS/AUSPEX. Atmos. Environ., 30(12), 2079-2112. Chow J.C. and Watson J.G. (1998) Guideline on speciated particulate monitoring. Draft report 3 prepared for the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, by Desert Research Institute, Reno, NV, August. Graham, J. (1999) Personal communication. Homolya J.B., Rice J., and Scheffe R.D. (1998) PM2.5 speciation - objectives, requirements, and approach. Presentation. September. Lai C.-Y. and Chen C.-C. (2000) Performance characteristics of PM10 samplers under calm air conditions. J. Air & Waste Manag. Assoc.50, 578-587. 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. Mignacca D. and Stubbs K. (1999) Effects of equilibration temperature on PM10 concentrations from the TEOM method in the lower Fraser Valley. J. Air & Waste Manage. Assoc. 49, 1250-1254. Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at <http://capita.wustl.edu/NEARDAT/Reports/TechnicalReports/potass/Ktext.htm>. Poirot R. (1999) Personal communication. Research Triangle Institute (2000) Data validation process for the PM2.5 Chemical Speciation Network. Draft report prepared for the U.S. Environmental Protection Agency. RTI/07565/12-01F. July. PM Data Analysis Workbook: Data Validation

35. References (2 of 2) 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/files/ambient/pm25/qa/valdtmpl.pdf>. PM Data Analysis Workbook: Data Validation