Tritium (³H) is one of the most critical nuclides for environmental monitoring, yet it is challenging to measure. Its high natural mobility and its potential to enter the human body through the food chain underscore the importance of not overlooking the radiation safety risks associated with tritium. The need for the online measurement of tritium at low concentrations is becoming increasingly apparent. This review examines the two principal stages of current measurement methodologies: sample preparation and radiation signal detection. It provides a summary of the tritium sample preparation and detection techniques, highlighting advances in the research with potential applications in online monitoring. The review concludes with an analysis of the issues inherent in the current techniques and offers perspectives on possible technological enhancements and future trajectories for the development of online monitoring systems for trace tritium levels.

The Monte Carlo (MC) method is the reference approach for particle transport simulation in complex three-dimensional geometries and continuous-energy problems, where it avoids the phase-space discretization required by deterministic methods; yet its $1/\sqrt{N}$ convergence rate renders brute-force computation infeasible for deep-penetration shielding and full-core fixed-source problems. Variance reduction (VR) methods address this bottleneck by modifying particle sampling or transport while preserving estimator unbiasedness, concentrating computational resources on high-contribution phase-space regions. This review systematically surveys VR methods for Monte Carlo particle transport, organized into three categories: classical techniques, advanced local variance reduction (LVR), and advanced global variance reduction (GVR). Using Zero-Variance Theory as the unifying framework, the review elucidates that all VR methods are fundamentally distinct approximations to the optimal importance function. A multi-dimensional classification system is proposed that traces the evolution from manual, heuristic parameter tuning to automated generation, from single-technique application to multi-solver collaborative integration, and from empirical rules to mathematically rigorous efficiency optimization. Key future directions include the rigorization of global efficiency theory, machine-learning-assisted importance function construction, and multi-solver collaborative adaptive frameworks. The field is progressing toward a hierarchical complementarity of local and global VR, jointly advancing VR methodology from academic research to engineering practice.