Automated quality control of atmospheric aerosol time series in near real-time

Ensuring reliable and comparable aerosol optical data across monitoring networks demands automated, standardized quality control (QC) procedures. We present an integrated, real‑time QC system for AE33 Aethalometer equivalent black carbon (eBC) and Aurora 4000 nephelometer scattering coefficient (σ_sca) measurements. Built on the open-source SaQC framework and distributed through the actris_qc package, the system operationalizes community QA/QC guidelines through a structured, machine-readable rule hierarchy that performs device-control, channel-level, and derived-variable diagnostics. The workflow is configured using an anomaly catalogue from the urban TROPOS site and validated at the contrasting rural Melpitz station, confirming robust adaptability across diverse environments. It consistently detects outliers, noise, plateaus and instrument failures while preserving plausible atmospheric variability, reaching real-time execution on standard computing platforms. Its modular and declarative architecture facilitates seamless extension to further instruments and network infrastructures, enabling harmonized QC flagging, traceable provenance, and FAIR‑compliant, AI‑ready data streams for next‑generation atmospheric monitoring systems.