Zhang Hong-Ze, Cui Wen-Gang, Liu Sui-Hua, Cui Han-Wen, Huang Yue-Mei
School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China.
Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China.
Huan Jing Ke Xue. 2023 Apr 8;44(4):2177-2191. doi: 10.13227/j.hjkx.202205201.
The identification of heavy metal sources in farmland soils is essential for the rational health condition management and sustainable development of soil. Using source resolution results(source component spectrum and source contribution)of a positive matrix factorization(PMF)model, historical survey data, and time-series remote sensing data, integrating a geodetector(GD), an optimal parameters-based geographical detector(OPGD), a spatial association detector(SPADE), and an interactive detector for spatial associations(IDSA)model, this study explored the modifiable areal unit problem(MAUP) of spatial heterogeneity of soil heavy metal sources and identified the driving factors and their interacting effects on the spatial heterogeneity of soil heavy metal sources in categorical and continuous variables, respectively. The results showed that the spatial heterogeneity of soil heavy metal sources at small and medium scales was affected by the spatial scale, and the optional spatial unit was 0.08 km for detecting spatial heterogeneity of soil heavy metal sources in the study region. Considering spatial correlation and discretization level, the combination of the quantile method and discretization parameters with an interruption number of 10 could be implied to reduce the partitioning effects on continuous variables in the detection of spatial heterogeneity of soil heavy metal sources. Within categorical variables, strata(PD 0.12-0.48) controlled the spatial heterogeneity of soil heavy metal sources, the interaction between strata and watersheds explained 27.28%-60.61% of each source, and the high-risk areas of each source were distributed in the lower sinian system, upper cretaceous in strata, mining land in land use, and haplic acrisols in soil types. Within continuous variables, population (PSD 0.40-0.82) controlled the spatial variation in soil heavy metal sources, and the explanatory power of spatial combinations of continuous variables for each source ranged from 61.77% to 78.46%. The high-risk areas of each source were distributed in evapotranspiration (41.2-43 kg·m), distance from the river (315-398 m), enhanced vegetation index (0.796-0.995), and distance from the river (499-605 m). The results of this study provide a reference for the research of the drivers of heavy metal sources and their interactions in arable soils and provide an important scientific basis for the management of arable soil and its sustainable development in karst areas.
识别农田土壤中的重金属来源对于土壤健康状况的合理管理和可持续发展至关重要。本研究利用正定矩阵因子分解（PMF）模型的源解析结果（源成分谱和源贡献）、历史调查数据和时间序列遥感数据，综合运用地理探测器（GD）、基于最优参数的地理探测器（OPGD）、空间关联探测器（SPADE）和空间关联交互探测器（IDSA）模型，探讨了土壤重金属源空间异质性的可变面积单元问题（MAUP），并分别识别了类别变量和连续变量中影响土壤重金属源空间异质性的驱动因素及其交互作用。结果表明，中小尺度下土壤重金属源的空间异质性受空间尺度影响，研究区域内检测土壤重金属源空间异质性的最优空间单元为0.08 km。考虑空间相关性和离散化水平，可采用分位数法和离散化参数相结合，中断数为10，以减少土壤重金属源空间异质性检测中对连续变量的划分影响。在类别变量中，地层（偏相关系数0.12 - 0.48）控制着土壤重金属源的空间异质性，地层与流域之间的交互作用解释了各源的27.28% - 60.61%，各源的高风险区分布在震旦系下部、地层中的白垩系上部、土地利用中的采矿用地以及土壤类型中的黄红壤。在连续变量中，人口（偏决定系数0.40 - 0.82）控制着土壤重金属源的空间变异，连续变量空间组合对各源的解释力在61.77%至78.46%之间。各源的高风险区分布在蒸散量（41.2 - 43 kg·m）、距河流距离（315 - 398 m）、增强植被指数（0.796 - 0.995）以及距河流距离（499 - 605 m）处。本研究结果为耕地土壤重金属源驱动因素及其相互作用的研究提供了参考，为喀斯特地区耕地土壤管理及其可持续发展提供了重要的科学依据。
