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LEADS/EMS DATA VALIDATION PowerPoint Presentation
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LEADS/EMS DATA VALIDATION

LEADS/EMS DATA VALIDATION

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LEADS/EMS DATA VALIDATION

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  1. LEADS/EMS DATA VALIDATION

  2. WHAT IS VALIDATION? From The Dictionary: 1a. To Make Legally Valid 1b. To Grant Official Sanction By Marking • Validation Using LEADS Is A Process Of Data Review Followed By An Actual Marking Of The Data As Validated • The Act Of Validating Data Is Restricted To Only Certain User Classes • There Is An Automatic Audit Trail Entry Generated Every Time Data Is Validated

  3. WHY BOTHER WITH VALIDATION? Data Validation Is Required By Federal Regulations: Air Quality Data Submitted For Each Reporting Period Must Be Edited, Validated, And Entered Into The AIRS-AQS For Updating Pursuant To Appropriate AIRS-AQS Procedures. ~ 40 CFR 58.35

  4. VALIDATING DATA • The Actual Act Of Validating Data With The Manual Validation Interface Is Almost Trivial • What Is Difficult Is Gathering All Of The Background And Supporting Information That Allows The Data Validator To Validate Data With Confidence • This Has To Be A Collaborative Effort Between Everyone Involved – Field Operators, QA Staff, Maintenance Personnel, And Data Validators

  5. VALIDATOR RESPONSIBILITIES • Validate NAMS/SLAMS Data Using Available Data Screens And Reports • Provide Technical Support On Data Management Issues That May Arise • Document All Data Management Activities • Receive, Process, And Send All Network Data To The EPA AIRS Database • Prepare NAMS/SLAMS Data Summaries • Prepare Annual SLAMS Certification For EPA

  6. OPERATORS AND VALIDATORS • If Any Unusual Or Nonstandard Conditions Are Noticed By The Field Operator, They Should Enter Any Pertinent Information In The Electronic Operator Log • Then… • Validators Review The Ambient Data, Electronic Logs, Automatic Checks, And Quality Control Records To Determine If There Is A Reason To Invalidate The Data

  7. HIGHEST DATA RETURN • How Can You Get The Highest Data Return Possible? • Field Operators Record Everything They Do That Might Affect The Monitors In The Operator Log • Everyone Know And Follow The QAPP, SOPs, And Other Training Materials • Maintain A Station Inventory And Keep The System Configuration Up-To-Date

  8. EFFECTIVE OPERATOR LOGS • Operators Should Make A Log Entry Every Time They Do Something At The Station – Including Just A Short “Well Station” Visit • Record Any PMI’s That Are Performed • Record Any Pertinent Instrument Readings (Status, Mode, Etc.) • Record The Cause Of Monitor Malfunctions And Automatic Check Failures And What Was Done To Correct The Problem • Record Whether Monthly Or Quarterly Audits And Verifications Pass Or Fail – Include The Percentages

  9. SITE OPERATION STRATEGIES • Keep The Station Temperature Within The Established Limits – Both Upper And Lower • Monitor The Sites Closely – Do Not Let Instruments Drift Until There Is A Failure • Keep Communications Between The Field And The Data Validator(s) Open • Be Pro-Active And Aggressive In Isolating And Resolving Problems And Issues – If A Problem Is Causing A Loss Of Data, It Must Be Addressed With The Highest Priority

  10. STRATEGIES FOR SUCCESS • Establish A Quality Assurance Project Plan • Develop And Implement Standard Operating Procedures • Train Everyone On The QAPP And SOP’s • Use The Quarterly (Or However Frequently The QAPP Calls For Them) Audit Visits To Each Site To Verify That The QAPP And SOP’s Are Being Used At Each Site • Everyone Use The LEADS Tools – Including The Web Pages And Manual Validation

  11. DID YOU KNOW? • Data Validation Is Dependent Upon The Correct QA And QC Methods Having Been Complied With And The Validator Notified • If An Instrument Fails An Audit, The Data Collected Since The Previous Audit May Be Invalid

  12. DID YOU KNOW? • If The Calibrator Is The Cause Of An Automatic Check Failure, That The Data Is Likely To Be Recoverable? • Make An Entry In The Operator Log To Let The Data Validator Know What Happened!

  13. DID YOU KNOW? • An Equipment Problem Is Often First Evident In The 5 Minute Data • Data Is Compared Between Nearby Sites, And With Same-site Parameters Which Correlate Inversely Or Proportionally

  14. FINI This Concludes Data Validation