Passive sampling has been developed for representative and reliable monitoring of organic micropollutants such as pesticides in the aquatic environment. This technique allows the determination of time-weighted average (TWA) concentrations, providing first, prior determination of the sampling rates (Rs) of all target micropollutants via calibration, and second, a possible correction for in situ application, depending on the micropollutant and the experimental conditions. Indeed, flow rate, temperature and the concentration of dissolved organic carbon (DOC) in water may have an impact on the accumulation kinetics of micropollutants in passive samplers, and may imply biased TWA concentrations. Recently, we developed the passive stir bar sorptive extraction (SBSE) for the determination of moderately hydrophobic to hydrophobic pesticides in freshwaters. The effects of flow velocity, temperature, and the concentration of DOC on the accumulation of 13 pesticides in polydimethylsiloxane (PDMS)-coated stir bars and the elimination of three candidates for performance reference compounds (PRC) were determined precisely through a full factorial screening design of experiments. Eight simultaneous accumulation and elimination experiments were run in a flow-through system to determine the effect of experimental parameters on the sampling rates Rs, the water-PDMS phase partition coefficient Ksw, and the elimination constant ke. The processing of these data allowed us to determine the factors and the two-factor interactions with significant effects. These pesticides were then sorted in five groups regarding their behavior, i.e., the nature and number of factors with a significant effect. Moreover, the three PRC could be assigned to three groups, being more representative of the pesticides behaviors and able to provide a more efficient correction. Finally, a discriminant function analysis allowed us to discriminate three of these five groups regarding physical chemical properties of the pesticides, and to predict the behavior of 7 other pesticides from their physical chemical data, making it easier the identification of PRC for future quantitative monitoring.