What is the purpose of data substitution policies in ambient air data QA/QC?

• A. To replace all missing data with zeros.
• B. To replace data flagged as invalid or missing with audit-approved estimates to preserve dataset continuity while acknowledging uncertainty.
• C. To delete any data with calibration issues.
• D. To anonymize data before reporting.

Answer: (B)

Data substitution policies in ambient air QA/QC are about keeping the data record intact when a measurement is invalid or missing. The best approach is to replace those gaps with audit-approved estimates—methods that are documented, reviewed, and defensible—so the time series remains continuous while clearly acknowledging the uncertainty in those filled values. This allows trend analysis, regulatory reporting, and modeling to proceed without introducing artificial gaps, and it preserves transparency because every substituted value has an auditable trail and a stated level of uncertainty. Substitutions are used only when a reliable estimate exists, and they should be clearly documented, so users understand which data are raw versus filled. This differs from filling with zeros, deleting data, or anonymizing before reporting, which would misrepresent measurements or obscure data provenance.