Data Management Concepts James Payne Environmental Protection Department Morongo Band of Mission Indians

2 Why data management?  Ensure data are scientifically valid  i.e., you have done what you said you’d do, in the way you said you’d do it  How? Equipment checks, flagging data, scheduled maintenance  Ensure data are legally defensible  i.e., you can prove that you did it with evidence  How? Chain of custody forms, logbooks/logsheets, audit reports  Remember: If you don’t write it down, it didn’t happen

3 How your data is managed and assessed depends on…  How the data will be used and by whom  How good the data needs to be  Data quality objectives  How the data will be collected  Newly collected data vs. Previously collected data  To which databases it will need to be uploaded  EPA’s Air Quality System (AQS)  EPA’s National Emission Inventory (NEI)  Other

4 Collecting the Data  Requires Data Management System  Involves  Planning data collection  Implementing data collection  Assessing collected data  Mechanism to continually improve system  EPA provides data management “tools”

5 EPA’s Quality Management Tools  Quality Management Plans (QMPs)  Management Systems Reviews (MSRs)  Data Quality Objectives (DQOs)  QA Project Plans (QAPPs)  Standard Operating Procedures (SOPs)  Technical Systems Audits (TSAs)  Data Quality Assessment (DQA)

6 Quality Assurance Project Plan (QAPP)  Detailed description of what, where, when, who, how and why of project activities  Opportunity to review systems in place for quality assurance (QA)  Documentation of QA (if not written, didn’t happen for QA purposes)

7 Why do I need a QAPP?  Provides overview of project goals, organization  Details information for every aspect of the project  Outlines clear chain of authority for QA  Describes need for the measurements  Defines QA/QC for project  Usually required for EPA-funded air monitoring activities

8 What is in a QAPP?  Program staff & management structure  Samplers/equipment  Project activities & Standard Operating Procedures (SOPs)  Lab & contract staff  Data reviewers & auditors

Turbo QAPP a road to happiness…

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20 What is QA/QC?  Quality Assurance (QA): integrated system of management activities involving  Planning  Implementation  Documentation  Assessment  Reporting  Quality improvement  Good QA ensures that a process is of good quality

21 What is QA/QC? (cont.)  Quality Control (QC): overall system of technical activities  Measures performance of process against defined standards  Verifies that processes meet requirements established by QAPP  QC is part of QA

22 Morongo QA Activities  Standard Operating Procedures  Equipment checks  Automated status check  Daily data review  Weekly physical check  Quarterly solar adjustment  Semi-yearly independent equipment calibration/performance testing  Inter-tribal

23 QA Activities (cont.)  Data checks  Daily data review  Summary tables; daily, monthly  Event log  Second-person check needed but currently not done  Check against local air quality management district data

24 Morongo QC Activities  Technical activities that control data quality, should be part of standard operating procedures (SOPs)  Weekly automated calibrations  Quarterly Calibration machine check  Sampler audits  Check against local air quality management district data  (Field blanks)  (Collocated replicate samples)  (Duplicate analyses)  (Lab QC)  (Data handling checks)

25 Data-Collection Methods  Ambient air monitoring  40 CFR Part 50–National Primary and Secondary Ambient Air Quality Standards  Source sampling  40 CFR Part 60–Standards of Performance for New Stationary Sources  Emissions inventory  Other sources  EPA, states, tribes, other  Data provided by facility  Data from prior studies  Technical literature & reports

26 Examples of Data Uses  Source emissions  Ambient concentration  Baseline data  Attainment/Non-attainment  Source compliance  AQS  Code development  Permit writing

27 Morongo Data Usage  Establish baseline and seasonal trends of Ozone & PM-2.5 levels on tribal lands  Determine meteorological trends  Determine feasibility of realignment of non- attainment boundary  Upload into AQS  Monitor long-term ozone & PM-2.5 changes  Regulate tribal activities  Issue alerts

28 Data Quality Considerations  Is the quality of the data known?  Are errors within acceptable limits?  Are the data usable?  Were enough data collected?

29 Data Quality Objectives (DQOs)  Method detection limits (MDLs)  Data completeness  Accuracy of measurements  Spatial coverage (representativeness)  Data quantity (frequency of measurements)

30 Morongo Monitoring DQOs  Measure continuous ozone & PM-2.5 concentrations on tribal lands using 1-hour averages  Measure meteorological conditions on tribal lands using 1-hour averages  Maintain data recovery and quality at >75%

31 Basic “Toolbox” For Assessing Data Uncertainty  Collect and analyze specific QC samples  Use basic statistical methods to calculate and evaluate  Sampling variability  Measurement error

32 Data Review/Validation  Using all available QC information  Review Data Quality Objectives (DQOs)  Weed out any big problems (flag bad data)  Determine MDLs, accuracy of results  Determine if any corrective action is required  Can be an expensive and time-consuming step  Most important step in data management “No data is better than bad data”

33 Tools for Data Validation  Microsoft Excel has numerous tools for reviewing/validating data  Sorting/filtering  Calculations (built-in and custom)  Charting/graphing functions  Excel is relatively straightforward and easy to use  Microsoft Access  Powerful database application  Extremely useful for managing large, complex sets of data  Not as intuitive or accessible as MS Excel for the “casual” user  Software specific  Datalogger software will send for set criteria  Tribal Data Toolbox

34 Data Reporting  Internal and project-related reporting  Reports to tribal council  Progress reports to EPA or other funding agency  External reporting  Reports to community and other audiences  Reporting data to national databases (AQS, NEI)

Tribal Data Toolbox a great help…

“The Toolbox is a form-driven database designed to walk users through the data entry, validation, and archival processes.” ttdt.asp  Form driven-Intuitive step by step process  Database-Catalog of data that can be queried  Data entry-Uploading of admin, AQ, and QC data  Validation-Data satisfies acceptance criteria  Archival-Storage of records for retrieval

Report & chart screenshots from TDT helpfile

Screenshots from TDT helpfile

Additional Training and Information  Online class to be determined  Tribes interested in the Toolbox should contact: Angelique Luedeker at

47 Data Reporting (cont.)  Air Quality System (AQS) not easy to use  Data requires specific format to be accepted into database  Not known for user-friendliness  Tribal concerns about data sharing  Critical for increasing our collective understanding of effects of air pollution  Necessary for continued political support of tribal monitoring activities

48 Other Data Sharing  National Emissions Inventory (NEI) database  Tribal EI data has large gaps  ITEP attempting to help tribes enter data  Tribal Exchange (TREX) Network Project  Nine tribes from Southwest uploading continuous data into national network – 4 others showing interest  Walker River Paiute Tribe (NV) leads the project

49 Preserving/Protecting Data  Make sure data is accessible  Changing sensor and acquisition hardware  Changing software and operating systems  Bad IT  Backup data  Automated backup  Multiple location repository  Hardcopy

50 Key Take-Home Messages  Good data management requires organizational commitment to  Good planning  Realistic allocation of resources (esp. staff time)  Consistent execution  Good documentation  No data is better than bad data!

51 Take-Home Messages (cont.)  QAPP is vehicle by which data-management process is defined for a specific project  Data Quality Objectives  Quality-control measures  Data-validation steps  Data reporting  Preserve/protect your data