Interpretive and User-Facing Approaches to Urban Air Quality Assessment: A Critical Review

Urban air pollution remains the single largest environmental health risk factor for premature mortality in the European Union (EU) and a central concern of the European Green Deal and the Zero Pollution Action Plan. Numerical dispersion and chemistry-transport models have become increasingly sophisticated, yet the communities who live with polluted air rarely interact with raw concentration fields. Instead, they interact with interpretive, user-facing representations such as colour-coded indices, source–receptor diagrams, heuristic risk judgements, and conceptual policy frameworks including the Drivers–Pressures–State–Impact–Response (DPSIR) model. This paper critically examines five families of interpretive approaches, including Explainable AI (XAI) as an emerging entrant. Drawing on recent reviews, the Directive (EU) 2024/2881 on Ambient Air Quality and Cleaner Air for Europe (hereafter the recast Directive), and the 2026 WHO roadmap on air quality indexes, the paper argues that these approaches are vital tools for translating and governing what numbers alone cannot communicate. Strengths, limitations, and trade-offs are synthesised into three comparative tables, and the implications are illustrated through a conceptual discussion of Malta. The nation represents a small, densely populated island state, where limited monitoring networks, street-canyon geometries, and maritime emissions make interpretable frameworks particularly valuable. The review concludes that the five families are complementary rather than interchangeable, and that in Malta, and plausibly similar jurisdictions, this translation layer is essential for converting stringent requirements into practical public-health and policy action.