Zhu Yuanli, Liu Bo, Jin Gui, Wu Zihao, Wang Dongyan
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China.
Research Center for Land Use & Ecological Security Governance in Mining Area, China University of Mining and Technology, Xuzhou 221116, China.
Toxics. 2024 Mar 21;12(3):229. doi: 10.3390/toxics12030229.
Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran's I of residuals and a higher R (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.
探究土壤砷（As）的局部影响因素和来源对于减少砷污染、保护土壤生态和保障人类健康至关重要。基于地理加权回归（GWR），多尺度地理加权回归（MGWR）考虑了解释变量的不同影响范围，因此采用了自适应带宽。它在许多领域都是一种有效的模型，但尚未用于探究砷的局部影响因素和来源。因此，本研究利用从中国东北黑土区采集的200个样本，检验了MGWR的有效性，揭示了砷与环境变量之间的空间非平稳关系，并确定了砷的局部影响因素和污染源。结果表明，49%的样本砷含量超过背景值，这些样本主要分布在该区域的中部和南部。MGWR在自适应带宽方面优于GWR，残差的莫兰指数较低，R值较高（0.559）。MGWR模型揭示了砷与解释变量之间的空间异质性关系。具体而言，道路密度、总氮、黏土和粉砂含量在大多数点上是主要或次要影响因素。距工业企业的距离仅在少数点上是次要影响因素。因此推断砷的主要污染源为交通和肥料，南部地区也包括工业排放。这些发现突出了考虑自变量自适应带宽的重要性，并证明了MGWR在探究土壤污染物局部来源方面的有效性。
