以我国三江平原为研究区域，采用水稻、玉米和大豆种植面积作为替代数据，通过估算噻虫嗪使用量建立其网格化使用清单；应用改进后充分考虑环境行为过程的网格化农药排放和残留模型（simplified gridded pesticide emission and residue model，SGPERM）建立噻虫嗪网格化残留清单，并以此估算土壤中噻虫嗪残留的质量分数。同时，使用风险熵法（risk quotient，RQ）评估新烟碱类杀虫剂的潜在生态风险。结果表明：2011—2020年三江平原累计使用噻虫嗪约85.6 t，通过喷洒飘移和径流排放造成的噻虫嗪损失量约为0.97 t；2020年三江平原噻虫嗪的总残留量为13.1 t，基于残留量估算得到各网格内表层土壤中噻虫嗪的空间质量分数范围为0~3.45 ng/g；三江平原土壤中噻虫嗪的残留会对研究区域内典型生物——赤子爱胜蚓（Eisenia foetida）产生潜在风险。使用清单的建立方法及改进的排放/残留清单模型能够有效揭示使用和残留情况的时空演变规律，为农药的污染控制措施及相关政策与行动计划的制定提供基础数据和科学依据，也可为相关农药网格化清单的建立提供技术方法。

Agricultural non-point source pollution compromises the security of water and soil resources in China, making it imperative to understand the distribution characteristics of such pollution in specific areas to effectively implement environmental protection measures. As a principal grain-producing area in China, the Sanjiang Plain is critical for ensuring national food security and stability, boasting an annual grain output of approximately 2.5×107t. However, the body of research addressing the use and pollution characteristics of neonicotinoid insecticides in this region remains limited, with inadequate in systematic studies on the residual mass fraction and distribution of these substances in the soil.This study concentrates on the Sanjiang Plain, a significant grain-producing region in China, and selects the widely used neonicotinoid insecticide, imidacloprid, as the subject for conducting related inventory research. An estimation method for neonicotinoid insecticides was formulated and applied to calculate the thiamethoxam use in the Sanjiang Plain from 2011 to 2020. Additionally, rice, maize, and soybean cultivation areas in the Sanjiang Plain served as surrogate data to establish a gridded use inventory of thiamethoxam in the region with a resolution of 10 km × 10 km. Furthermore, the usage inventory served as input data, and an enhanced simplified gridded pesticide emission and residue model, which thoroughly incorporated environmental behavior processes, was employed to develop residue and soil mass fraction inventories. Finally, the risk quotient methodology was employed to assess the potential ecological risks of neonicotinoid pesticides in soil to non-target species.The results showed that: (1) the cumulative usage of thiamethoxam in the Sanjiang Plain from 2011 to 2020 was about 86 t, and the loss through spray drift and runoff discharge was about 1.3 t. In terms of spatial distribution, hotspots of thiamethoxam usage were identified in the central, southwestern, and southeastern parts of the Sanjiang Plain. In contrast, the northwestern, eastern, and southern parts of the cultivated land, being smaller in area, exhibited a lower demand for pesticides. (2) Years of consistent usage resulted in the accumulation of neonicotinoid insecticides in the soil, with the cumulative residue of thiamethoxam in the Sanjiang Plain's soil increasing from 7.0 to 13.1 tons between 2011 and 2020, marking an approximate 0.8-fold increase. To address potential uncertainties in the thiamethoxam residue calculation process, an analysis of these uncertainties and the sensitivity of the parameters is undertook, aiming to identify the primary factors influencing residue levels and to refine and optimize the calculation process, thereby reducing result uncertainties. (3) Spatial mass fraction of neonicotinoid insecticides in the surface soil of each grid were estimated, based on residue levels, ranging from 0 to 3.45 ng/g. To assess the accuracy of the residue inventory established herein, the simulated values were juxtaposed with actual soil monitoring concentration data, affirming the inventory's reliability. (4) Typical organisms of the Sanjiang Plain are already under threat from soil thiamethoxam residues, and the regional extent of this risk, along with the potential threat to the ecosystem, is gradually escalating, necessitating consideration.The establishment of the usage inventory and the refined emission/residue inventory model effectively elucidates the spatial and temporal dynamics of pesticide use and residue. This provides fundamental data and a scientific foundation for formulating pesticide pollution control strategies, relevant policies, and action plans, and also offers a technical methodology for developing a gridded pesticide inventory.