| 引用本文: | 张金兰,陈克海,郑雪云,王河,黄秋鑫.2024.不同来源类型土壤重金属铬空间分布预测插值模型研究[J].地球环境学报,15(5):780-789 |
| ZHANG Jinlan,CHEN Kehai,ZHENG Xueyun,WANG He,HUANG Qiuxin.2024.Predicting interpolation models for spatial distribution of heavy metal chromium in soils basing different source types[J].Journal of Earth Environment,15(5):780-789 |
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| 摘要: |
| 通过空间聚类分析法解析目标区域内耕地土壤重金属铬的来源类型，确定了点状、线状和面状三种来源区。针对不同来源类型区，通过反距离权重（IDW）、径向基函数（RBF）、普通克里金（OK）、简单克里金（SK）四种插值方法模型，进行土壤重金属铬空间分布预测分析，结果表明：四种不同插值方法模型预测结果的平均标准差和空间分布总趋势相似，但在数据极值的保留上有所差异；不同来源类型区域中使用不同空间插值方法的效果有所不同，应根据空间分析的目的，考虑极值、平均值等关键数据，有针对性地选择空间插值方法。对于点状来源区，宜使用RBF或OK进行空间插值分析；对于线状来源区，宜使用IDW进行空间插值分析；对于面状来源区，宜使用RBF进行空间插值分析。研究结果初步探明基于重金属铬来源类型进行空间插值方法模型优化是有效的，为后续应用提供了方向性参考。 |
| 关键词: 土壤重金属 铬 空间聚类分析 来源类型 插值模型 |
| DOI：10.7515/JEE222077 |
| CSTR：32259.14.JEE222077 |
| 分类号: |
| 基金项目:广东省普通高校青年创新人才类项目（2019GKQNCX021）；广东工贸职业技术学院培英育才计划项目（粤工
贸院[2019]142号）；广东工贸职业技术学院科研项目（2022-ZKT-04）；广州市基础与应用基础研究项目
（202201010080） |
| 英文基金项目:outh Innovative Talents Program of Guangdong Universities (2019GKQNCX021); Peiying Education Program
of Guangdong Polytechnic of Industry and Commerce ([2019]142); Research Project of Guangdong Polytechnic of
Industry and Commerce (2022-ZKT-04); Guangzhou Basic and Applied Basic Research Project (202201010080) |
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| Predicting interpolation models for spatial distribution of heavy metal chromium in soils basing different source types |
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ZHANG Jinlan1, CHEN Kehai1, ZHENG Xueyun1, WANG He1, HUANG Qiuxin2*
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1. Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China
2. Guangdong Academy of Sciences, Guangzhou 510070, China
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| Abstract: |
| Background, aim, and scope Using an appropriate interpolation model is crucial for enhancing the accuracy of interpolation results and ensuring the rationality, scientific rigor, and validity of the predictions regarding the spatial distribution of heavy metals in soil. Numerous studies have focused on the validation of interpolation models applied on regional pollution investigations. This study investigates the occurrence and distribution of heavy metal chromium (Cr) in farmland soil within a typical region of Guangzhou, aiming to analyze the accuracy of four common spatial interpolation methods across different pollution source areas. Materials and methods A total of 648 farmland soil samples were collected and analyzed in the study area. The pollution source types of Cr were determined by Anselin Local Moran’s I index spatial variable clustering analysis, complemented by local geographical and hydrological conditions. The source types of Cr in soil were predicted and analyzed using inverse distance weight (IDW), radial basis function (RBF), ordinary Kriging interpolation (OK), and simple Kriging (SK) interpolation models. Results Four source areas of Cr in soil were determined, including one planar source area, one linear source area, and two point source areas. The concentrations of Cr in the planar source area were significantly higher than those in linear source area and point source area. The highest concentration of Cr in soil was observed in linear source area. Coefficients of variation of Cr in soil ranged from 59.00% to 68.50%, notably higher in both linear source area and point source area. The prediction results of the four interpolation methods were similar in spatial distributions and equivalent in the interpolation accuracy. Discussion The planar source area, linear source area, and the point source area may be polluted by agriculture activities, river discharges, and industrial activities, respectively. The selection of interpolation methods directly influences the accuracy of interpolation results, considering the differences in soil quality, complexity of heavy metal sources, representativeness of sampling points, and limitations in sample size. It should be also noted that different interpolation methods exhibit significant differences in retaining “extreme values” of data, with OK and SK showing an obvious “compression” effect, resulting in information loss on maximum and minimum values. In comparison to Kriging interpolation, IDW and RBF better retain “extreme values”. Although the prediction results of the four interpolation methods show similar trends in spatial distribution, discrepancies exist in small areas with low and high values. The efficacy of different spatial interpolation methods varies in different source type regions and is affected by the uniform distribution of sample points. When the sample points were less distributed, the difference of several interpolation methods was great. The findings of this study suggested that when selecting a spatial interpolation method, key factors such as extreme values and average values, as well as the uniform distribution of sampling points should be considered. Conclusions This study provides preliminarily evidence that selecting spatial interpolation models based on heavy metal source types is effective. For point source area, RBF or OK should be chosen for spatial interpolation analysis. IDW should be selected for linear source area, while RBF should be chosen for planar source area. Recommendations and perspectives The model parameters can be further optimized in subsequent applications by combining the mathematical principles of the model. The accuracy and applicability of the model can be further improved by the integration of the models. |
| Key words: soil heavy metal chromium spatial cluster analysis different types of sources the interpolation model |
