Identify typical data quality issues that can arise in ambient air data and how they are addressed.

• A. Instrument malfunctions; calibration drift; gaps due to power or sampler downtime; flow errors; environmental interference; addressed through QA/QC procedures, validation flags, and data substitution policies.
• B. Data always perfect; no issues occur.
• C. Only missing data is addressed; other issues are ignored.
• D. Issues are not addressed until after regulatory reporting.

Answer: (A)

Data quality in ambient air monitoring centers on catching and handling issues that can distort measurements. Typical problems include instrument malfunctions that stop or skew readings, calibration drift that gradually shifts the scale, gaps when power or sampler downtime interrupts data collection, flow errors that misrepresent the sampled air volume, and environmental interference from conditions like humidity or extreme temperatures. We address these through QA/QC procedures that include routine maintenance and calibration checks, validation flags that label data as good, suspected, or bad, and data substitution or imputation policies to fill or replace questionable data in a documented, defensible way. This systematic approach keeps datasets reliable for analysis and reporting, which is why this approach is correct.