The degradation of micropollutants in water via ultraviolet (UV)-based advanced oxidation processes (AOPs) is strongly dependent on the water matrix. Various reactive radicals (RRs) formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants. Hence, process selection and optimization are crucial. This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation (k′_p,MP) in various UV-AOPs by combining model simulation with portable measurement. Portable methods for measuring the scavenging capacities of the principal RRs (RRSCs) involved in UV-AOPs (i.e., $ \mathrm{HO}^{·}$, $ \mathrm{SO}_{4}^{·-}$, and $ \mathrm{Cl}^{·}$) using a mini-fluidic photoreaction system were proposed. The simulation models consisted of photochemical, quantitative structure–activity relationship, and radical concentration steady-state approximation models. The RRSCs were determined in eight test waters, and a higher RRSC was found to be associated with a more complex water matrix. Then, by taking sulfamethazine, caffeine, and carbamazepine as model micropollutants, the k′_p,MP values in various UV-AOPs were predicted and further verified experimentally. A lower k′_p,MP was found to be associated with a higher RRSC for a stronger RR competition; for example, k′_p,MP values of 130.9 and 332.5 m²·einstein^–1, respectively, were obtained for carbamazepine degradation by UV/H₂O₂ in the raw water (RRSC = 9.47 × 10⁴ s⁻¹) and sand-filtered effluent (RRSC = 2.87 × 10⁴ s⁻¹) of a drinking water treatment plant. The developed method facilitates process selection and optimization for UV-AOPs, which is essential for increasing the efficiency and cost-effectiveness of water treatment.

• Water matrix scavenging capacities for $ \mathrm{HO}^{·}$, $ \mathrm{SO}_{4}^{·-}$ and $ \mathrm{Cl}^{·}$ were measured portably.

• Model simulation consisted of photochemical, QSAR, and SSA models.

• k′_p,MP values in UV-AOPs were predicted in real waters and verified experimentally.

• The developed method facilitates the selection and optimization of UV-AOPs.