From Weather to Optical Water Assays: A Review of Modelling Approaches and a Proposed Framework for EcoExposure AI Field Assay Platform

Weather may help explain variation between field water-assay runs, but the relevance of antecedent conditions depends on sample provenance, collection timing and the measurement process. This targeted critical review examines rainfall-lag models, accumulation and wash-off concepts, and statistical or machine-learning prediction using meteorological data. Seven established studies provide the main comparison, with three recent publications screened as contextual updates. Their outcomes include microbial indicators, turbidity, microplastic measurements, transported loads and water temperature. These endpoints are distinct from a reagent-generated optical feature. The review supports testing antecedent weather as a candidate predictor while separating environmental history from assay development, preserving sample independence and evaluating added information against an available baseline. We specify an exploratory framework using one reproducible optical outcome, site and seasonal information, measured assay conditions and a limited rainfall summary. Model development would be restricted to training observations, with paired assessment on later independent samples. A project and archive audit found no assay-level dataset suitable for fitting; predictive improvement, sample size and calibration therefore remain unestablished. The present contribution is an evidence-linked proposed framework and data-readiness assessment for a future EcoExposure evaluation, rather than a validated weather-to-assay model.