The Effectiveness of Radiation Portal Monitors in Detecting Smuggled Rhinoceros Horns

The illegal trade of rhinoceros horns continues to threaten wildlife populations and undermine conservation efforts worldwide. This study explores the feasibility of using existing radiation portal monitors to detect rhinoceros horns implanted with radioactive sources during international sea transportation. By leveraging the Gamma Detector Response and Analysis Software, a series of simulated pass-bys were conducted under controlled variables, including fill capacities, material densities, and 60Co source strengths. The models incorporated a 6.10-m (20-ft) shipping container with a keratin horn configuration, simulating realistic concealment scenarios. Background environments were modeled after Denver, CO, to reflect elevated natural radiation conditions. Detection performance was assessed using theoretical probability of detection, incorporating International Atomic Energy Agency Nuclear Security Series No. 1 standards for operational consistency. Results show that increased cargo capacity and denser shielding materials, such as aluminum and stainless steel, reduce probability of detection, though such solid-block shielding is unlikely in real-world cargo. Shadow shielding from empty or lightly filled containers enhanced detection capability by reducing background noise. Furthermore, modeling scenarios with multiple horns improved probability of detection, aligning with seizure patterns observed in sea transport trafficking cases. While the study does not determine the minimum detectable activity due to law enforcement sensitivities, it demonstrates the plausibility of radiation portal monitor systems identifying radiologically tagged horns under realistic shipping conditions. The findings support the concept of radiation tagging as a cost-effective, scalable strategy to deter trafficking, increase seizure rates, and reinforce international conservation efforts without disrupting trade flow or requiring infrastructure overhauls.