ATARS v3.1 — A Formal Statistical Framework with an 18-Step Data-Quality Engine, Machine-Learning Enhancements, Atmospheric Contextual Modelling, and a Nine-Module Verification Suite for Trustworthy LLM Reporting of Urban Air Quality

Urban air-quality stations generate hundreds of millions of sensor records per year, yet open, reproducible, equation-numbered analysis pipelines that also verify the AI-generated narratives built on top of them do not exist. This paper presents ATARS v3.1 (Automated Time-Series Analysis and Reporting System), the final open-source release of a single-file Python pipeline that extends the formally specified ATARS v2.0 framework with four major additions: (1) an 18-step automated data-quality engine with a seven-dimension quality composite; (2) a formal statistical-inference layer (Mann–Kendall trend test and time-series decomposition) added to sixteen core operators; (3) a contextual-enrichment phase integrating live Open-Meteo weather, a Pasquill–Gifford atmospheric-stability proxy, an eight-type pollution-episode classifier, and a seven-day pollution-risk forecast; and (4) a nine-module verification suite — numerical grounding (RGV, G_rate), temporal (TCV), causal (CCD), anomaly-explanation (AEE), report-drift (RDD), uncertainty-propagation (UPT), cross-variable-consistency (CVCC), narrative-specificity (NSS), and benchmark-comparison (BNC) — unified by a composite Master Novelty Score (MNS). The principal contribution is the combination: no prior open-source system unifies a scored data-quality engine, an equation-numbered statistical-and-inference core, atmospheric episode typing, and multi-axis verification of an AI narrative into one offline, deterministic, hash-reproducible pipeline. On the public CPCB Gurugram 2024 dataset (8,784 hourly records) ATARS achieves a grounding rate G_rate = 92.9%, surfaces 16 multivariate anomaly days missed by univariate analysis, classifies pollution episodes by physical type, and produces an 18-section Word report, 15 charts, and a 15-slide executive deck — all offline, deterministic, and MIT-licensed.