Abstract: This study addresses the issue of unevenly distributed sampling points, which leads to inaccurate characterization of pollutant diffusion ranges. Using ArcGIS spatial interpolation, the distribution of Mn²⁺ ions in a chemical park was analyzed, revealing discrepancies due to uneven sampling. To overcome this, two neural network models—GA-BP and standard BP—were applied to predict Mn²⁺ concentrations at unsampled locations. The GA-BP neural network, optimized with a Genetic Algorithm, showed the best performance, filling gaps in data and allowing for a more accurate concentration distribution map. This revised map was used to delineate the Mn²⁺ diffusion range, which was further validated with the known production and migration mechanisms of Mn²⁺. The results demonstrate that the GA-BP model significantly improves the accuracy of pollutant diffusion mapping and offers a more reliable method for environmental pollution assessment, especially in areas with limited sampling data. The BRIEF REPORT is available for this paper at http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202409280204.