Data Review, Validation and Verification. All data should be considered VALID unless otherwise proven invalid
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1 Data Review, Validation and Verification All data should be considered VALID unless otherwise proven invalid Reggie Smith, California Air Resources Board PQAO Training 2017
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3 Training Objectives Define data review, verification, and validation Discuss QA handbook critical, operational and systematic criteria Describe general task performed for a multi-level data review Review basic AQS upload process and common upload issues
4 Define Terms Data review Day to day actions used to accept, reject or qualify data in an objective and consistent manner Data validation - evidence that the particular requirements for a specific intended use are fulfilled Data verification - evidence that specified requirements have been fulfilled
5 Data Validation Criteria Code of Federal Regulations QA HB Vol II (Appendix D) SOP & QAPP s Instrument Operating Manuals Quality Assurance Bulletins
6 Critical Data Validation Criteria (red) Operational Criteria (yellow) Systematic Criteria (green)
7 QA Handbook Validation Criteria Critical Observations not meeting each and every criterion should be invalidated unless there is a compelling reason Operational A violation of these may be a cause of invalidation Systematic Important for interpretation of data but usually do not impact data validity
8 Critical Criteria for Continuous PM2.5
9 Best practices or rule of thumb Standard procedures/qapp Review data frequently (recommended daily) Clear concise notes and logs Timely reviews/reporting (data review schedule) Document retention Flag missing or invalid data (reason???)
10 AQS Data Codes and Flags Referred to as Qualifier Codes in AQS 4 Types Quality Assurance (code) Null (code) Informational (flag) Request for Exceptional Event (flag) Typically can be applied at any review level Be consistent Most data systems can auto apply codes/flags
11 Automated QC Checks Done by most Data Management Systems Typical screening criteria Min/Max values Rate of change (hourly ) Sticking or repeating values Excessive negative values May not work at every site Not take place of manual reviews
12 Multi-Level Data Review Process General multi-level review process Station Operator (1 st Level) AM Specialist (2 nd Level) AM Lead /AM Supervisor (3 rd Level) Same person may accomplish two levels at small agencies
13 Qualifier codes can be applied or suggested at any level Level 1 Level 2 Goal Distinguish measurements from measurement errors or interferences Goal Verify Level 1 review and ensure data quality control requirements are met Level 3 Goal Approve agency data suitability for release to AQS
14 1 st Level Reviewer Tasks Most important step in the review process Documentation QC forms Station logs Control charts Missing data or downtime Instrument operation Flag suspect data for further review
15 2 nd Level General Tasks Data Verification Review site operator edits/notes Quality Control review 1-point QC checks Flow rate verification Preventative maintenance Mins, Maxs, NAAQS, SAAQS Site Buddy Checks
16 3 nd Level General Tasks Review data capture rates and completeness Request for exceptional events Sign data validation letter/memo Approve data for upload to AQS
17 AQS Upload Basic AQS upload process Stage, Load, Post Direct submitters v ARB reporting agencies Common AQS Upload issues No monitor available in AQS Uploading over existing data Inconsistent POC
18 AQS Upload Process
19 Post AQS Data Confirmation QA Bulletin 005 Ensures AQS uploads are accurate and complete Quarterly Reviews AMP256 Data Quality Indicator AMP350 Raw Data AMP430 Data Completeness Annual Reviews AMP600- Certification Evaluation
20 Summary Define Terms Data Validation Criteria General multi-level review process AQS submittal process
21 REMEMBER!! All data should be considered VALID unless otherwise proven invalid
22 Break Out Sessions Data Validation/Certification Room - Mountain Vista 1 1:45 3:15 pm (session 1) 3:30 5:00 pm (session 2) AQS Room - Auditorium 1:45 2:25 pm (session 1) 3:30 4: 10 pm (session 2) Janice Lam Snyder (Sac Metro) Nathan Trevino (San Joaquin) Jennifer Williams (US EPA)
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