Study on Resistivity Characteristics and Evaluation Model of Cadmium Contaminated Laterite

doi:10.13304/j.nykjdb.2023.0490

Abstract

Abstract:

To investigate the influence characteristic of cadmium on the resistivity of laterite， the laterite samples with different water content， dry density and cadmium content were tested to analyze the relationships between the influence factors and the resistivity of laterite using the two-electrode method，and finally the resistivity evaluation model of the cadmium contaminated laterite was proposed. The results showed that the resistivity of cadmium-contaminated laterite decreased with the increasing of dry density， water content and temperature. The resistivity sharply decreased in the range of dry density less than 1.30 g·cm^-3， then gradually decreased and further stabilized. On the condition of different cadmium content， the resistivity gradually decreased with the increasing water content， and the most significant decrease was observed when the dry density was 1.20 g·cm^-3. Under the same dry density and water content， the effect of temperature above 0 ℃ on resistivity was not significant. The increasing cadmium gave rise to the decreasing resistivity gradually， and the change in resistivity was significant when the cadmium content was less than 100 mg·kg^-1. Considering the characteristics of dry density， water content and temperature comprehensively， the resistivity evaluation model of cadmium-contaminated laterite was established by the introduction of the volume water content，which had high fitting accuracy（R²=0.939 9）， and the measured resistivity values were in good agreement with the model calculated resistivity values. The average mean absolute percentage error and root mean square error were 4.77% and 0.07，respectively. It could provided a theoretical model for the rapid electrical detection of heavy metal cadmium pollution in laterite areas， and it was beneficial for the quality evaluation of regional laterite cultivated land as a convenient analytical method.

Key words: laterite, cadmium pollution, impact factor, resistivity, model

摘要：

为探究重金属污染物镉对红黏土电阻率特性的影响特征，制备不同含水率、干密度、镉含量的红黏土试样，采用二极法进行电阻率测试，分析不同因素与红黏土电阻率的关系，建立镉污染红黏土电阻率评价模型。结果表明，镉污染红黏土电阻率随着干密度、含水率和温度的增加均呈下降趋势，干密度小于1.30 g·cm^-3范围内电阻率急剧下降，随后电阻率逐渐减小并趋于稳定；在不同镉含量条件下随着含水率增加电阻率逐渐减小，在干密度为1.20 g·cm^-3时电阻率下降最显著；在干密度、含水率相同条件下，0 ℃以上温度变化对电阻率的影响不明显，镉溶液掺入量增加导致电阻率逐渐减小，当镉含量小于100 mg·kg^-1时电阻率变化幅度较大。综合考虑电阻率随干密度、含水率和温度变化特征，引入体积含水量建立镉污染红黏土电阻率评价模型，拟合精度较高（R²=0.939 9），实测值与模型值较吻合，平均绝对百分比误差和均方根误差分别为4.77%、0.07。该模型可为红黏土地区重金属镉污染度的电法快速检测提供理论模型参考，为区域性红黏土耕地质量评价提供便捷分析手段。

关键词: 红黏土, 镉污染, 影响因素, 电阻率, 模型

CLC Number:

Xiaoshuang CHEN, Xingqian XU, Xi ZHAO, Xin QU, Haijun WANG, Guangcan PENG. Study on Resistivity Characteristics and Evaluation Model of Cadmium Contaminated Laterite[J]. Journal of Agricultural Science and Technology, 2024, 26(4): 164-173.

陈小双, 徐兴倩, 赵熹, 屈新, 王海军, 彭光灿. 镉污染红黏土电阻率特性及其评价模型研究[J]. 中国农业科技导报, 2024, 26(4): 164-173.

Figures/Tables 8

References 43

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序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	电阻率Resistivity		相对误差 Relative error/%
序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	实测值 Actual measured value/（Ω·m）	预测值 Predictive value/（Ω·m）	相对误差 Relative error/%
1	0.33	50	412.38	442.20	7.23
		100	282.72	293.49	3.81
		150	184.91	195.83	5.91
		200	140.34	136.51	2.73
2	0.35	50	329.02	351.19	6.74
		100	225.17	233.09	3.52
		150	142.26	155.53	9.33
		200	107.70	108.42	0.67
3	0.37	50	223.65	231.66	3.58
		100	141.86	153.76	8.39
		150	93.87	102.59	9.29
		200	82.17	71.52	12.96
4	0.39	50	182.50	178.17	2.37
		100	123.45	118.26	4.21
		150	85.43	78.91	7.64
		200	56.45	55.00	2.56
5	0.41	50	158.84	144.55	9.00
		100	104.31	95.94	8.03
		150	61.33	64.01	4.37
		200	46.87	44.62	4.80

序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	电阻率Resistivity		相对误差 Relative error/%
序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	实测值 Actual measured value/（Ω·m）	预测值 Predictive value/（Ω·m）	相对误差 Relative error/%
1	0.33	50	412.38	442.20	7.23
		100	282.72	293.49	3.81
		150	184.91	195.83	5.91
		200	140.34	136.51	2.73
2	0.35	50	329.02	351.19	6.74
		100	225.17	233.09	3.52
		150	142.26	155.53	9.33
		200	107.70	108.42	0.67
3	0.37	50	223.65	231.66	3.58
		100	141.86	153.76	8.39
		150	93.87	102.59	9.29
		200	82.17	71.52	12.96
4	0.39	50	182.50	178.17	2.37
		100	123.45	118.26	4.21
		150	85.43	78.91	7.64
		200	56.45	55.00	2.56
5	0.41	50	158.84	144.55	9.00
		100	104.31	95.94	8.03
		150	61.33	64.01	4.37
		200	46.87	44.62	4.80

序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	电阻率Resistivity		相对误差 Relative error/%
序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	实测值 Actual measured value/（Ω·m）	预测值 Predictive value/（Ω·m）	相对误差 Relative error/%
6	0.43	50	111.78	113.05	1.13
		100	71.98	75.03	4.24
		150	47.28	50.06	5.89
		200	32.55	34.90	7.21
7	0.45	50	84.08	89.96	6.99
		100	60.89	59.70	1.94
		150	39.87	39.84	0.08
		200	25.94	27.77	7.05
8	0.47	50	74.81	75.93	1.51
		100	48.35	50.40	4.25
		150	36.80	33.63	8.60
		200	16.14	16.99	5.25

序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	电阻率Resistivity		相对误差 Relative error/%
序号 Number	体积含水量 Volumetric water content/%	镉含量 Cadmium content/（mg·kg^-1）	实测值 Actual measured value/（Ω·m）	预测值 Predictive value/（Ω·m）	相对误差 Relative error/%
6	0.43	50	111.78	113.05	1.13
		100	71.98	75.03	4.24
		150	47.28	50.06	5.89
		200	32.55	34.90	7.21
7	0.45	50	84.08	89.96	6.99
		100	60.89	59.70	1.94
		150	39.87	39.84	0.08
		200	25.94	27.77	7.05
8	0.47	50	74.81	75.93	1.51
		100	48.35	50.40	4.25
		150	36.80	33.63	8.60
		200	16.14	16.99	5.25

[1]	Yanfang WANG, Ruixue ZHAO. Bidirectional Empowerment Mechanism of “Data+AI” and Practical Path in Agriculture [J]. Journal of Agricultural Science and Technology, 2025, 27(9): 11-20.
[2]	Feng CUI, Jingzhou LIU, Yixin QIAN, Guiying CHEN. Spatiotemporal Evolution and Driving Factors of Marine Fishery Ecological Security in China [J]. Journal of Agricultural Science and Technology, 2025, 27(9): 224-239.
[3]	Haitao XU, Hongzhen MA, Wenwen WANG, Wenxiang FAN, Bo XU, Jungang ZHANG, Haibin GUO, Youhua WANG. Research on Dynamic Development and Accumulated Temperature Model of Maize Plant Height and Stem Diameter Based on Effective Accumulated Temperature [J]. Journal of Agricultural Science and Technology, 2025, 27(8): 187-201.
[4]	Zheng WU, Hongyun YANG, Aizhen SUN, Jie KONG, Shumei HUANG. Diagnosis of Potassium Nutrition in Rice Based on CA_MobileViT Model [J]. Journal of Agricultural Science and Technology, 2025, 27(8): 80-88.
[5]	Min SHI, Zhenyan XU, Jian LUO. Study on Identification of Future Industries Based on Interdisciplinary [J]. Journal of Agricultural Science and Technology, 2025, 27(7): 10-19.
[6]	Jianwei WU, Lin ZHANG, Wengang ZHENG, Xiangyang QIN, Zhonggao WU, Xihong FANG, Yongquan WANG, Tiangang LU, Jian WANG, Xiangshu PIAO, Fang WANG, Qiaoyun YUE, Haihe ZHANG, Jing JI, Xiaoman CONG, Qifeng LI. Research Progress on Key Application Scenario of Intelligent Orchard [J]. Journal of Agricultural Science and Technology, 2025, 27(3): 12-23.
[7]	Danyi SHI, Yu QIU, Chengzhen HUANG, Juan WANG. Effect of Acid Modified Biochar on Infiltration Characteristics of Coastal Saline Soil [J]. Journal of Agricultural Science and Technology, 2024, 26(9): 183-192.
[8]	Ziqin LI, Jiaqiang WANG, Zhen LI, Deqiu ZOU, Xiaogong ZHANG, Xiaoyu LUO, Weiyang LIU. Estimation of Chlorophyll Density of Cotton Leaves Based on Spectral Index [J]. Journal of Agricultural Science and Technology, 2024, 26(8): 103-111.
[9]	Lijun FU, Xiaoyu LIN, Jianhua LIN, Huinan SHEN, Yongzhen WU. Research on Processing Technology and Shelf Life of Red Matsutake Beef Sauce [J]. Journal of Agricultural Science and Technology, 2024, 26(6): 148-158.
[10]	Yue HUANG, Yanfen XIE, Xuanquan ZHU, Meng JIA, Ge WANG, Yuxiang BAI, Yu DU, Peng ZHOU, Yuting ZHAO, Hongqiong ZHU, Fan YANG, Zhiwen XIAO, Wenbo WANG, Zhipeng FANG, Jiabao HAN, Na WANG. Risk Assessment and Influencing Factors Analysis of Chlorine Content in Tobacco Leaves in Tobacco Planting Areas [J]. Journal of Agricultural Science and Technology, 2024, 26(6): 206-213.
[11]	Yue PAN, Baoqing WANG, Jijiao WANG, Yong MA, Yalan LI. CO₂ Response Model Fitting and Evaluation of Vitis amurensis [J]. Journal of Agricultural Science and Technology, 2024, 26(4): 58-66.
[12]	Jiaqiang ZHENG, Huichun ZHANG, Youlin XU, Hongping ZHOU. Research Review on Modeling and Simulation for Pesticide Spraying System [J]. Journal of Agricultural Science and Technology, 2024, 26(3): 76-90.
[13]	Wei WANG, Hongyu FU, Jianning LU, Yunkai YUE, Ruifang YANG, Guoxian CUI, Wei SHE. Research on Interpretation of Ramie Lodging Information Based on Unmanned Aerial Vehicles [J]. Journal of Agricultural Science and Technology, 2024, 26(3): 91-97.
[14]	Wencheng CONG, Limin YUAN, Zhongju MENG, Yu YANG. Effects of Corncob on Capillary Water Transport and Evaporation Characteristics of Sandy Soil [J]. Journal of Agricultural Science and Technology, 2024, 26(2): 198-207.
[15]	Meixia LIANG, Xinqiang ZHANG, Bingqing LIN. Simulation of Sediment Reduction Effect Under Different Scenarios in Red Soil Erosion Watershed in South China [J]. Journal of Agricultural Science and Technology, 2024, 26(10): 177-185.