A Reproducible QA/QC, Imputation and Robust-Series Workflow for Air-Quality Monitoring Time Series

This study develops a reproducible and auditable workflow to prepare regulatory air-quality monitoring time series for subsequent temporal analysis, including observational PRE/POST applications around coal-fired power plant closures in northwestern Spain. The dataset comprises daily concentrations from 28 monitoring stations (2006–2023) for PM10, PM2.5, NO, NO2, NOx, O3, SO2, and CO, affected by missingness, structural inconsistencies, and extreme values. The contribution of this study lies in integrating standardized data ingestion and QA/QC chained-equation imputation with Bayesian Ridge regression, hold-out validation, physicochemical consistency checks, and robust extreme-value handling within a traceable processing workflow. Missing values are reconstructed per pollutant using plant-level multi-station pooling to improve stability. Performance is evaluated using a 5% masked hold-out and summarized with MAE, RMSE, R2, and bias, complemented by an operational fit-quality label. Post-imputation controls enforce NO–NO2–NOx consistency and the physical constraint PM2.5 ≤ PM10, while extreme values are screened through a hierarchical robustness framework combining a Hampel filter, winsorization, and a Tukey IQR criterion. The workflow outputs documented diagnostics and robust daily series while preserving the traceability of observed values, flags, edits, and final decisions.