Hu Bifeng, Chen Songchao, Hu Jie, Xia Fang, Xu Junfeng, Li Yan, Shi Zhou
Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou, China.
INRA, InfoSol Unit, Ardon, Orléans, France.
PLoS One. 2017 Feb 24;12(2):e0172438. doi: 10.1371/journal.pone.0172438. eCollection 2017.
Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification of heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution.
由于传统实验室理化分析成本高、效率低且工作量大,无法对大面积、高采样密度的土壤进行快速重金属调查。本研究利用便携式近地土壤传感器,探索了一种快速评估中国长江三角洲南部浙江省富阳市301份农田土壤重金属污染情况的方法。采用便携式X射线荧光光谱仪(PXRF)测定土壤重金属总量,同时利用便携式可见-近红外光谱仪(PVNIR)预测土壤pH值。PXRF成功预测了锌、铜和铅(R2>0.90且RPD>2.50),而对砷和镍的预测精度较低(R2<0.75且RPD<1.40)。PVNIR对pH值预测良好。基于先前结果对农田土壤重金属污染等级进行分类;Kappa系数为0.87,表明PXRF和PVNIR相结合是一种有效且快速的土壤重金属污染程度测定方法。本研究为评估土壤重金属污染提供了一种新方法;该方法将有助于土壤重金属污染的大规模调查。