菹草对底泥中重金属污染的修复效果

Repairing effect of Potamogeton crispus on heavy metal pollution in sediment

以沉水植物菹草为研究对象,通过生态缸模拟试验,研究菹草的种植密度对底泥中不同重金属富集能力的影响,探索沉水植物菹草对不同重金属污染的修复效果。结果表明:(1)250株·m–2密度组的菹草体内Cd、Cu、Pb元素的平均增量最大,分别达到30.02 mg·kg–1、271.2 mg·kg–1、226.48 mg·kg–1,富集效果较好;167株·m–2密度组菹草体内Zn元素的平均增量最大,达到423.08 mg·kg–1,可见该密度下对Zn的富集效果最好。(2)菹草对四种重金属富集能力而言,在实验的三个密度组中对Zn的富集效果都是最好的,生物富集因子BSAF分别为7.77、9.57、8.16;Cd、Cu、Pb在250株·m–2(每缸30株)密度组下BSAF分别达到最高7.63、6.33、3.93。(3)回归分析的曲线估计,可知菹草植株中的重金属含量(y)与底泥中的重金属含量(x)是一种线性回归的关系。回归方程通式为y=–ax+b,决定系数R2都接近1,回归模型的显著性均达到显著水平。

Submerged plants Potamogeton crispus were used this experiment by outdoor eco-cylinder static simulation test. We studied the influence of planting density on enrichment ability of different heavy metals in sediment, and explored the remediation effect of submerged plants on different heavy metal pollution. The results indicated that： （1） 250 plants·m^-2 （45 plants per aquarium） testing group showed the highest efficiency to remove Cu, Pb, Cd elements in sediment. The increments of Cd, Cu, Pb elements in sample plants achieved to 30.02 mg·kg^-1, 271.2 mg·kg^-l, 226.48 mg·kg^-1 respectively, while the increment of Zn element reached up to a maximum of 423.08 mg·kg^-1 in planting density of 167 plants.m^-2 （30 plants per aquarium）. Thus it could be seen that the plants had the best enrichment effect on Zn under this density. （2） Potamogeton crispus had various enrichment capability on different heavy metals and the BSAF（Biota-sediment accumulation factor） differed in diverse planting densities. In this experiment, the enrichment effect on Zn element was the best; the BSAF were 7.77, 9.57 and 8.16 respectively under the three densities. The highest BSAF of Cd, Cu, Pb elements were 7.63, 6.33 and 3.93 respectively in 250 plants·m^-2 （30 plants per aquarium） group. （3） According to the curve estimation of regression analysis, there was a linear regression relationship between the heavy metal content （y） in Potamogeton crispus and the heavy metal content in sediment （x）. The regression equation formula was y = -ax ＋ b; the coefficient of determination R2 was close to 1, and the significance of regression models achieved to a significant level.