A Classification of Bias Correction Methodologies used in Air Quality Scenarios Modelling Applications

Air quality modelling systems (AQMS) cannot always reproduce observed pollutant concentrations due to limited input data and incomplete knowledge of atmospheric processes. Bias correction methodologies (BCM) are continuously developed to address these limitations when predicting air concentrations for emission scenarios. We propose a consistent framework for classifying BCMs along four key dimensions: the sequence of operations (spatialization, calibration, and application of adjustment), the type of adjustment (additive, multiplicative, linear, etc.), the calibration strategy (per station, per grid cell, or global), and the spatialization approach (e.g. thin-plate spline, ordinary kriging, kriging with external drift). Examples presented in this report show how existing BCMs can be mapped into this framework. The proposed classification aims to improve comparability and reproducibility within the scientific community and to support clear communication of results to stakeholders.