摘要
随着社会现代化进程的迈进,愈加频繁的人类活动加剧了土壤重金属污染。当土壤中重金属元素含量超过风险筛选值时,会经过食物链摄入人体,过量的重金属累积对人体健康造成损害。筛选出具有重金属污染风险的土壤是治理土壤污染的重要环节。采用X射线荧光(XRF)光谱仪获取了59份国家标准土壤样品的光谱数据,然后对其进行小波阈值去噪和迭代离散小波变换本底扣除等预处理;运用基于竞争性自适应重加权采样(CARS)算法对土壤中的重金属元素进行谱线筛选;将筛选后的结果作为模型的输入,通过建立1D-CNN模型预测土壤样本是否具有重金属污染的风险。实验结果显示,通过CARS算法采样后的特征通道数大幅度减少,Ni、Cu、As、Pb元素从原来的2048个特征点分别减少为37、53、37、45个,为原来通道数的1.81%~2.59%。相较于不筛选和连续投影(SPA)筛选方法,结合CARS算法的1D-CNN模型在判断土壤样品是否有Ni、Cu、As、Pb元素污染风险时的准确率分别可以达到96.67%,93.22%,91.67%,88.33%。经CARS筛选,1D-CNN比偏最小二乘回归(PLSR)方法在预测准确性方面有明显优势。提出的CARS-1D-CNN算法在提高模型预测准确率的同时减少了模型的计算量,对于XRF光谱土壤重金属元素污染风险筛选具有较好的理论指导和应用价值。
The more frequent human activities with the modernization of the society intensify the soil heavy metal pollution.When the content of heavy metal elements in the soil exceeds its risk screening value,there may be risks to human health.Therefore,screening out the soil with the risk of heavy metal pollution is an important part of soil pollution control.The spectral data of 59 national standard soil samples were obtained by X-ray fluorescence(XRF)spectroscopy,and then pre-processed by wavelet soft threshold denoising and iterative discrete wavelet transform background deduction.Moreover,the competing adaptive reweighted sampling(CARS)algorithm was applied to screen the heavy metals in the soil.Finally,the screened results were input to the one-dimensional convolutional neural network(1D-CNN)model to predict whether soil samples were at risk of heavy metal contamination.The results showed that the number of feature channels sampled by the CARS algorithm was significantly reduced from 2048 to 37,53,37 and 45 for Ni,Cu,As and Pb respectively,which is 1.81%~2.59%of the original number of channels.Compared with the no screening(i.e.original data)and successive projections algorithm(SPA),the accuracy of the CARS-1D-CNN model can reach 96.67%,93.22%,91.67%and 88.33%,respectively in determining whether the soil samples are at risk of contamination with Ni,Cu,As and Pb.Based on CARS screening,1D-CNN has a significant advantage over traditional partial least squares regression(PLSR)methods regarding predictive accuracy.Therefore,the CARS combined with the 1D-CNN method proposed in this paper improves the model prediction accuracy while reducing its computing complexity,which is a good theoretical guidance for soil heavy metal elemental contamination risk screening.
作者
杨婉琪
李智琪
李福生
吕树彬
樊佳婧
YANG Wan-qi;LI Zhi-qi;LI Fu-sheng;L Shu-bin;FAN Jia-jing(School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Yangtze River Delta Research Institute,University of Electronic Science and Technology of China(Huzhou),Huzhou 313001,China;Division of Advanced Manufacturing,Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2024年第3期670-674,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(62075028)资助。
关键词
X射线荧光光谱
重金属
竞争性自适应重加权采样
一维卷积神经网络
X-ray fluorescence spectroscopy
Heavy metals
Competing adaptive reweighted sampling
One-dimensional convolutional neural network
