Memory effects of unintentional persistent organic pollutants during industrial flue gas purification: A conceptual framework from PCDD/Fs to emerging contaminants

“Memory effects” in air pollution control devices (APCDs) represent a critical, yet often overlooked, challenge to the removal efficiency of unintentionally produced persistent organic pollutants (UP-POPs) during industrial flue gas purification. While well-documented for polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), their extent and mechanism across broader UP-POPs remain fragmentary. This paper proposes a conceptual framework synthesizing evidence on memory effects in representative APCDs, including wet scrubber system (WSS), wet flue gas desulfurization, selective catalytic reduction, activated carbon adsorption, baghouse filter, and wet electrostatic precipitators. It is postulated that key mechanisms like pollutant accumulation and re-release, catalytic transformation, and byproduct formation, operate similarly across diverse UP-POPs, varying by physicochemical properties. Analogous to PCDD/Fs, highly halogenated congeners are preferentially removed from the gas through adsorption, whereas volatile, low-halogenated species may pass through or even increase post-treatment. Case studies indicate that secondary formation of polychlorinated naphthalenes, halogenated polycyclic aromatic hydrocarbons, and polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/Fs) is plausible under certain thermal and catalytic conditions. Notably, anomalous increases in outlet concentrations (e.g., 3.5-fold for PCDD/Fs downstream of WSS) serve as critical indicators supporting the hypothesis of significant memory-driven re-emission. Rather than presenting definitive mechanistic proofs for all UP-POPs, this work establishes a framework to explore potential memory-effect-driven mechanism for UP-POPs beyond PCDD/Fs. It identifies critical knowledge gaps concerning mass balance, storage capacities, and release fluxes across APCD chains. Ultimately, this framework aims to guide future experimental design to elucidate migration pathways and optimize operational parameters (e.g., temperature, pressure), facilitating synergistic strategies to mitigate memory effects.