State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Chemosphere. 2022 Jan;287(Pt 4):132402. doi: 10.1016/j.chemosphere.2021.132402. Epub 2021 Sep 28.
Most previous studies have indicated inconsistent relationships between rice cadmium (Cd) and the soil properties of paddy fields at a regional scale under the adverse effects of confounding factors and spatial heterogeneity. In order to reduce these effects, this study integrates Geodetector, a stepwise regression model, and a hierarchical Bayesian method (collectively called GDSH). The GDSH framework is validated in a large typical rice production area in southeastern China. According to the results, significant stratified heterogeneity of the bioaccumulation factor is observed among different subregions and pH strata (q = 0.23, p < 0.01). Additionally, the soil-rice relationships and dominant factors vary by the subregions, and the available soil Cd and pH are found to be the dominant factors in 64% and 50% of subregions, respectively. In the entire region, when the pH < 6, the dominant factors are organic matter and available Cd, and when pH ≥ 6 they are organic matter, pH, and available Cd. Furthermore, these factors presented different sensitivity to the spatial heterogeneity. The results indicate that, at the subregional level, the GDSH framework can reduce the confounding effects and accurately identify the dominant factors of rice Cd. At the regional level, this model can evaluate the sensitivity of the dominant factors to spatial heterogeneity in a large area. This study provides a new scheme for the complete utilization of regional field survey data, which is conducive to formulating precise pollution control strategies.
大多数先前的研究表明，在混杂因素和空间异质性的不利影响下，区域尺度上稻田镉（Cd）与土壤性质之间的关系不一致。为了减少这些影响，本研究整合了地理探测器（Geodetector）、逐步回归模型和分层贝叶斯方法（统称为 GDSH）。该 GDSH 框架在我国东南部一个大型典型水稻生产区进行了验证。结果表明，不同亚区和 pH 层之间的生物累积因子表现出显著的分层异质性（q = 0.23，p < 0.01）。此外，土壤-水稻关系和主导因素因亚区而异，在 64%和 50%的亚区中，可利用土壤 Cd 和 pH 分别被发现是主导因素。在整个区域，当 pH < 6 时，主导因素是有机质和可利用 Cd，当 pH ≥ 6 时，主导因素是有机质、pH 和可利用 Cd。此外，这些因素对空间异质性的敏感性不同。结果表明，在亚区水平上，GDSH 框架可以减少混杂效应并准确识别水稻 Cd 的主导因素。在区域水平上，该模型可以评估主导因素对大面积空间异质性的敏感性。本研究为充分利用区域田间调查数据提供了新方案，有利于制定精确的污染控制策略。
