不同耕作方式对路旁土壤重金属分布的影响——以黄淮平原国道310开封段为例

doi:10.13249/j.cnki.sgs.2014.03.377

摘要/Abstract

摘要：

在G310开封段的杜良和杏花营分别布设采样断面,共采集320个样品。用ICP-MS测定土壤Pb、Cu、Zn、Cd、Cr和Ni含量,用泛克里格插值法分析路旁土壤重金属含量空间分布特征。结果表明,不同耕作方式对路旁土壤重金属含量空间分布影响很大。水旱轮作农田在淹水期间,由于水分的流动性以及土壤Eh下降等原因,加速了重金属的迁移,其分布无明显规律性。旱旱轮作农田的土壤重金属含量呈现出与道路平行的带状分布,其中土壤Cr和Cu含量随着距路基距离增加而递减;土壤Pb、Ni、Zn和Cd含量随着距路基距离增加而先增大然后减小,峰值含量出现在距路基30~50 m间。

关键词: 路旁土壤, 重金属, 空间分布, 耕作方式, 黄淮平原

Abstract:

Two soil sampling transects in Xinghuaying and Duliang on Kaifeng section along the National Highway 310 were chosen to investigate effects of upland-upland rotation and paddy-upland rotation on spatial distribution of heavy metals in roadside soils. Five sampling sub-transects of 150 m were designed to be perpendicular to the road within 100 m on both sides of the highway. On each sub-transect, mixed topsoil samples (0-15 cm) were collected at 10 m intervals from the roadbed to the outside end. The total samples of 324 were collected, then, the concentrations of Pb, Cu, Ni, Cr, Cd and Zn in soils were detected by the inductively coupled plasma mass spectrometry (ICP-MS) respectively according to the recommended standard method. The spatial distribution of heavy metals concentrations in roadside soils was discussed by Universal Kriging Interpolation model. The results show that the different tillage methods have great effects on spatial distribution of heavy metals in roadside soils. The distribution patterns of metal concentrations are not strips parallel to the road in paddy-upland rotation field on Duliang Transect (DT), because the water in rice field changes pH, Eh of soils, and then accelerates the migration of soil heavy metals when the rice is grown. In contrast, the distribution patterns of metal concentrations in upland-upland rotation field on Xinghuaying Transect (XT) are strips parallel to the highway. The concentrations of Cr and Cu decrease exponentially with the distance from the roadbed, however, the concentrations of Cd, Pb, Ni and Zn reveal a skew distribution with the distance, which increase firstly, reach their highest values at 30 m or 50 m away from the roadbed, and then decrease gradually to the control values.

Key words: roadside soils, heavy metals, spatial distribution, tillage methods, Huanghuai Plain

中图分类号:

仝致琦, 陈太政, 段海静, 谷蕾, 马建华. 不同耕作方式对路旁土壤重金属分布的影响——以黄淮平原国道310开封段为例[J]. 地理科学, 2014, 34(3): 377-384.

Zhi-qi TONG, Tai-zheng CHEN, Hai-jing DUAN, Lei GU, Jian-hua MA. Effects of Different Tillage Methods on Spatial Distribution of Heavy Metals in Roadside Soils: A Case Study of Kaifeng Section Along the National Highway 310 in Huanghuai Plain[J]. SCIENTIA GEOGRAPHICA SINICA, 2014, 34(3): 377-384.

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采样断面及项目		重金属(mg/kg)
采样断面及项目		Cd	Cr	Cu	Pb	Zn	Ni
DT	范围	0.01~0.47	10.79~62.59	3.81~51.44	14.64~147.94	40.33~404.20	25.22~97.89
	均值	0.16	26.35	26.43	70.86	124.75	53.52
	标准差	0.08	8.00	7.70	22.23	56.33	11.97
	变异系数（%）	50.00	30.36	29.13	31.37	45.15	22.37
	对照样平均浓度^*	0.13	14.23	23.87	14.85	44.83	25.35
XT	范围	0.11~0.29	7.87~48.80	5.09~20.58	14.43~26.85	18.04~67.66	6.17~21.48
	均值	0.18	27.73	10.79	18.19	39.84	12.06
	标准差	0.03	10.02	4.13	2.71	12.10	3.66
	变异系数（%）	16.67	36.13	38.28	14.90	30.37	30.35
	对照样平均浓度^*	0.14	13.43	6.63	15.77	22.40	7.74

半变异模型及参数			Cd	Cr	Cu	Pb	Zn	Ni
DT	南侧	趋势类型	一阶	常量	一阶	一阶	二阶	二阶
		模型	指数	高斯	二次方程式	二次方程式	四球	孔洞效应
		块金值(C₀)	0	52.07	0.048	105.69	1993.06	72.86
		基台值(C₀+C)	0.013	100.55	48.542	295.28	3268.80	103.23
		C₀/(C₀+C) （%）	0	51.78	0.10	35.79	60.98	70.58
	北侧	趋势类型	一阶	常量	一阶	一阶	二阶	二阶
		模型	指数	高斯	二次方程式	二次方程式	四球	孔洞效应
		块金值(C₀)	0.001 2	28.56	0.29	0.53	617.40	55.56
		基台值(C₀+C)	0.002 5	42.26	69.24	534.33	2716.61	85.80
		C₀/(C₀+C)（%）	48.00	67.58	0.42	0.10	22.73	64.76
XT	南侧	趋势类型	一阶	常量	一阶	一阶	二阶	二阶
		模型	指数	高斯	二次方程式	二次方程式	四球	孔洞效应
		块金值(C₀)	9.91	25.44	0.80	3.73	27.51	0.54
		基台值(C₀+C)	9.91	237.58	4.68	6.30	60.17	2.84
		C₀/(C₀+C) （%）	100	10.71	17.09	59.20	45.72	19.01
	北侧	趋势类型	一阶	常量	一阶	一阶	二阶	二阶
		模型	指数	高斯	二次方程式	二次方程式	四球	孔洞效应
		块金值(C₀)	0.000 22	18.17	0.56	0.42	11.30	1.39
		基台值(C₀+C)	0.000 91	114.72	3.92	4.59	68.65	4.38
		C₀/(C₀+C) (%)	24.18	15.84	14.28	9.15	16.46	31.17

采样断面及交叉检验参数			Cd	Cr	Cu	Pb	Zn	Ni
DT	南侧	平均值（n=80）(mg/kg)	0.004 1	-0.055	-0.43	-0.75	5.51	-0.77
		标准平均值	0.041	-0.008 2	-0.035	-0.051	0.043	-0.057
		均方根预测误差(mg/kg)	0.097	8.30	7.18	14.75	64.03	13.35
		平均预测标准差(mg/kg)	0.093	8.41	7.47	14.10	69.81	11.50
		标准均方根预测误差	1.06	0.99	0.96	1.05	1.00	1.11
	北侧	平均值（n=80）(mg/kg)	0.004 0	0.049	0.20	0.36	-0.93	0.138
		标准平均值	0.10	0.007 3	0.030	0.036	-0.019	0.0043
		均方根预测误差(mg/kg)	0.048	5.77	7.33	21.14	49.63	8.82
		平均预测标准差(mg/kg)	0.041	5.62	6.24	16.73	50.34	9.20
		标准均方根预测误差	1.12	1.03	1.15	1.26	1.00	0.97
XT	南侧	平均值（n=80）(mg/kg)	0.000 62	-0.009 0	0.056	-0.15	-0.32	-0.018
		标准平均值	0.004 6	-0.002 5	0.041	-0.057	-0.067	0.014
		均方根预测误差(mg/kg)	0.018	5.05	0.98	2.32	7.47	1.20
		平均预测标准差(mg/kg)	0.018	5.47	1.13	2.38	7.68	1.39
		标准均方根预测误差	0.97	0.93	0.87	0.98	1.14	0.92
	北侧	平均值（n=80）(mg/kg)	-0.001 9	0.13	0.046	-0.043	2.86	0.10
		标准平均值	-0.073	0.029	0.031	-0.020	0.009 3	0.050
		均方根预测误差(mg/kg)	0.022	4.56	1.24	1.22	5.63	1.38
		平均预测标准差(mg/kg)	0.023	4.57	1.09	1.39	6.03	1.49
		标准均方根预测误差	0.94	0.99	1.17	0.90	0.97	0.96