The rapid socio-economic development around nuclear power plants has led to a significant increase in the surrounding population, rendering the traditional approach of relying solely on the total population within a fixed radius to determine the scale of off-site emergency decontamination stations scientifically inadequate. This paper introduces a novel methodology that integrates grid-based population analysis with probabilistic risk assessment to evaluate the population exceeding dose limits at various probability levels. Using the Daya Bay Nuclear Power Plant as a case study, the study demonstrates that under a 10 mSv threshold, which aligns with the Chinese intervention level for emergency exposure, the affected population at the 90% probability envelope is approximately 50,000. This figure is significantly lower than the total population within the conventional 10 km radius, which stands at around 95,000. Based on the Fukushima nuclear accident scenario, the study estimates that approximately 12,000 people would require decontamination. The existing decontamination station capacity is proven sufficient to handle such emergency scenarios effectively. By incorporating probabilistic risk assessment and grid-based population data, this study offers a cost-effective and data-driven approach to optimize the scale of decontamination stations, ensuring a balance between public safety and resource allocation. The findings provide a robust scientific basis for addressing the dynamic challenges posed by population growth and environmental risks in the vicinity of nuclear power plants.