Air-Quality Forecasting Across Monitoring, Predictor, and Validation Regimes: A Systematic Mapping Review and Decision Framework

Air-quality forecasting models are often compared by architecture, although reported skill also depends on the pollutant, monitoring density, forecast horizon, predictor latency, validation design, and deployment objective. We conducted a systematic mapping review of 533 unique records published between 2000 and 15 June 2026; 409 met the forecasting eligibility criteria. The evidence was analysed in two layers: a metadata-derived map of the full corpus and a targeted full-text synthesis of representative studies. The non-exclusive metadata categories show that general machine learning or benchmark studies were most common ( n = 207 ), followed by recurrent deep learning ( n = 109 ), hybrid or decomposition methods ( n = 60 ), Transformer or attention models ( n = 51 ), tree ensembles ( n = 48 ), CNN/ConvLSTM models ( n = 43 ), classical statistical methods ( n = 26 ), graph neural networks ( n = 13 ), and physics-informed or CTM-coupled methods ( n = 7 ). These counts describe topical prevalence, not comparative effectiveness. The main contribution is a decision framework that links the forecasting setting to a defensible starting model, the evidence available for that model family, and the minimum validation needed to support temporal, spatial, or external generalisation. The synthesis favours transparent statistical and tabular baselines for short or sparse single-station records; spatial deep models only when network geometry and leave-site-out testing support them; and CTM-coupled postprocessing when operational physical fields are available at issue time. Diffusion and foundation models remain promising but unevenly validated for pollutant forecasting. A leakage-safe daily PM 2.5 case study in Naples illustrates the practical consequence: model rankings change with the metric, and every fitted model underestimates the highest 5% of concentrations.