Assess learners' ability to identify, quantify, and interpret common sources of uncertainty in environmental air quality monitoring data — including instrument precision, detection limits and drift; sampling design and representativeness (spatial/temporal variability); environmental interferences and sampling artifacts; and data-processing, QA/QC, and analytical errors. Expect analysis of random vs. systematic errors, methods for uncertainty estimation and propagation (error propagation, Monte Carlo, sensor intercomparison), and practical strategies for mitigation and transparent reporting to support data-driven decisions.