Due to human activities and industrial production, heavy metals accumulate continuously in soils, resulting in environmental ecological risks. Thus, it is critical to reveal the spatial patterns of the increments in soil heavy metals and their influencing factors to prevent the continuous deterioration of soil due to heavy metal pollution. In this study, based on soil samples collected in 2016 and 2019 at the same sites in the southern part of Daye city, the spatial distributions of increments in soil heavy metals were obtained using spatial interpolation and overlap methods. Then, the geographically weighted regression (GWR) model was used to analyze the influence of various environmental factors in three categories (location characteristics, topographical factors, and soil properties) on the increments in soil heavy metals. The results showed the following: (1) The soils in the study region were severely polluted with Cd, Cu, Pb, and Zn. Throughout almost the whole study region, the concentrations of these four heavy metals in soil exceeded local background values. (2) The concentrations of Cd, Cu, Pb, and Zn increased from 2016 to 2019 in 77.38%, 59.71%, 68.42%, and 49.21% of the study region, respectively. According to the spatial distribution of comprehensive change index values, soil heavy metal pollution continued to deteriorate in 74.4% of the study region from 2016 to 2019. (3) The GWR model revealed spatially varying relationships between the increases in soil heavy meals and environmental factors, and the results indicated that location characteristics and topographical factors had the largest and smallest influences, respectively, on the spatiotemporal increments in soil heavy metals. The influences of soil properties on the increments in soil heavy metals were similar to the influences on their concentrations. The GWR model had a higher R² and lower AICc than the ordinary least square regression model, indicating that GWR had a stronger ability to explain the relationships between the increments in soil heavy metals and environmental factors.

由于人类活动和工业生产，重金属在土壤中不断积累，导致环境生态风险。因此，揭示土壤中重金属增量的空间格局及其影响因素对于防止由于重金属污染造成的土壤持续恶化至关重要。在这项研究中，基于大冶市南部相同地点2016年和2019年收集的土壤样本，使用空间插值和重叠方法获得了土壤重金属增量的空间分布。然后，使用地理加权回归（GWR）模型分析了三种环境因素（位置特征，地形因素和土壤特性）对土壤重金属增量的影响。结果表明：（1）研究区土壤被Cd，Cu，Pb和Zn严重污染。在几乎整个研究区域中，土壤中这四种重金属的浓度均超过了局部背景值。（2）Cd，Cu，Pb和Zn的浓度从2016年到2019年分别增加了研究区域的77.38％，59.71％，68.42％和49.21％。根据综合变化指数值的空间分布，从2016年到2019年，研究区域的土壤重金属污染持续恶化，占74.4％。（3）GWR模型揭示了土壤重金属粉尘增加与环境因素之间的空间变化关系。 ，结果表明，位置特征和地形因素分别具有最大和最小的影响，土壤重金属的时空增量 土壤性质对土壤重金属增量的影响与其对浓度的影响相似。GWR模型具有更高的与普通最小二乘回归模型相比，R ²和AICc较低，这表明GWR具有更强的能力来解释土壤重金属的增加与环境因素之间的关系。