基于光谱技术的土壤环境污染物成分检测方法
A Method of Soil Environmental Pollutant Composition Detection Based on Spectral Technology
-
摘要: 土壤污染会严重威胁人类生活，因此有必要对土壤环境污染物成分进行检测，以寻求正确的处理方法。但目前检测手段较为落后，使得污染物成分检测精度较低。为解决这一问题，本文提出了一种基于光谱技术的土壤环境污染物成分检测方法。首先使用光谱仪器扫描土壤样品，再利用差分吸收光学光谱技术测量土壤环境污染物含量，将该含量作为改进深度学习网络的输入向量；然后将粒子群算法优化权值与Softmax分类器相结合，获得的深度学习自动编码器，对输入向量编码进行解码，并训练输入向量样本合集，至此，完成基于深度神经网络的土壤污染物检测模型的构建。实验结果证明，该方法具有较强的样本数据分类能力，具有较高的污染物成分检测精度。
-
关键词:
- 土壤环境污染物 /
- 光谱技术 /
- 成分检测 /
- Softmax分类器 /
- 差分吸收
Abstract: Soil pollution will seriously threaten human life. Therefore, it is necessary to detect the components of pollutants in soil environment in order to find correct treatment methods. However, the current detection method are relatively backward, which leads to the lower detection accuracy for pollutant components. In order to solve this problem, a method for detecting the components of soil environmental pollutants based on spectral technology is proposed in this paper. Firstly, the soil samples were scanned using a spectral instrument, and then the soil environmental pollutants was measured using differential absorption optical spectroscopy, which was used as the input vector for the improved deep learning network. In addition, the optimized weight of particle swarm optimization algorithm is combined with Softmax classifier to obtain the depth learning automatic encoder, decode the input vector compilation, and train the input vector sample set. Therefore, the construction of soil pollutant detection model based on depth neural network is completed. The experimental results show that this method has strong sample data classification ability and high accuracy of pollutant components. -
-
杨仁杰, 王斌, 董桂梅, 等. 基于二维相关荧光谱土壤中PAHs检测方法研究[J]. 光谱学与光谱分析, 2019, 39(3):818-822 Yang R J, Wang B, Dong G M, et al. Detection of PAHs in soil based on two-dimensional correlation fluorescence spectroscopy[J]. Spectroscopy and Spectral Analysis, 2019, 39(3):818-822(in Chinese)
李爱民, 连增艳, 杨仁杰, 等. 基于三维荧光光谱直测土壤中的多环芳烃[J]. 环境化学, 2018, 37(4):910-912 Li A M, Lian Z Y, Yang R J, et al. Direct determination of polycyclic aromatic hydrocarbons in soil based on three-dimensional fluorescence spectrum[J]. Environmental Chemistry, 2018, 37(4):910-912(in Chinese)
陈至坤, 郭蕊, 程朋飞. 基于激光诱导荧光的油类污染物检测系统研究[J]. 光散射学报, 2020, 32(1):84-89 Chen Z K, Guo R, Cheng P F. Research on oil contaminant detection system based on laser induced fluorescence[J]. The Journal of Light Scattering, 2020, 32(1):84-89(in Chinese)
曹明月, 李贤, 黄好强, 等. 近红外光谱检测青贮饲料的营养成分[J]. 现代牧业, 2020, 4(1):42-45 Cao M Y, Li X, Huang H Q, et al. Study on the nutritional components of silage with near infrared spectroscopy[J]. Modern Animal Husbandry, 2020, 4(1):42-45(in Chinese)
王翔, 赵南京, 俞志敏, 等. 土壤有机污染物激光诱导荧光光谱检测方法研究进展[J]. 光谱学与光谱分析, 2018, 38(3):857-863 Wang X, Zhao N J, Yu Z M, et al. Detection method progress and development trend of organic pollutants in soil using laser-induced fluorescence spectroscopy[J]. Spectroscopy and Spectral Analysis, 2018, 38(3):857-863(in Chinese)
范俊楠, 郭丽, 张明杰, 等. 湖北省重点区域及周边表层土壤重金属污染现状及评价[J]. 中国环境监测, 2020, 36(1):96-104 Fan J N, Guo L, Zhang M J, et al. Present situation and evaluation of heavy metals pollution in surface soils of key areas and surrounding areas in Hubei[J]. Environmental Monitoring in China, 2020, 36(1):96-104(in Chinese)
Liu F, Wu H Y, Zhao Y G, et al. Mapping high resolution National Soil Information Grids of China[J]. Science Bulletin, 2022(3):328-340 余璇, 吴劲, 宋柳霆, 等. 基于健康风险评价的土壤优先控制污染物筛选研究[J]. 环境污染与防治, 2018, 40(4):473-478 , 483 Yu X, Wu J, Song L T, et al. Priority control pollutants screening based on health risk assessment in soil[J]. Environmental Pollution & Control, 2018, 40(4):473-478, 483(in Chinese)
王琦, 李芳柏, 黄小追, 等. 一种基于风险管控的稻田土壤重金属污染分级方法[J]. 生态环境学报, 2018, 27(12):2321-2328 Wang Q, Li F B, Huang X Z, et al. A classification approach of heavy metal pollution of paddy soil based on risk management[J]. Ecology and Environmental Sciences, 2018, 27(12):2321-2328(in Chinese)
曹冉, 孜比布拉·司马义, 斯琴. 乌鲁木齐市北郊农田土壤重金属污染及生态风险评价[J]. 河北农业大学学报, 2019, 42(3):57-63 Cao R, Zibibulam Simayi, Si Q. Evaluation of heavy metals pollution and ecological risk of farmland soils in north Urumqi of Xinjiang[J]. Journal of Hebei Agricultural University, 2019, 42(3):57-63(in Chinese)
左兆陆, 赵南京, 孟德硕, 等. 基于迭代逼近算法的土壤中机油和柴油混合物荧光信号重叠特性研究(英文)[J]. 光谱学与光谱分析, 2020(1):310-315 Zuo Z L, Zhao N J, Meng D S, et al. Study on the overlapping characteristics of fluorescence signals of machine oil and diesel mixtures in soil based on iterative approximation algorithm[J]. Spectroscopy and Spectral Analysis, 2020 (1):310-315(in English)
李克伟, 凌永生, 张皓嘉, 等. 填埋场中有害元素成分原位检测方法[J]. 强激光与粒子束, 2018, 30(2):135-141 Li K W, Ling Y S, Zhang H J, et al. In-situ detection method of harmful elements in landfill[J]. High Power Laser and Particle Beams, 2018, 30(2):135-141(in Chinese)
Gupta T, Singh S P, Rajput P, et al. Introduction of measurement, analysis and remediation of environmental pollutants[J]. Energy, Environment, and Sustainability, 2020(1):1-5 Coster K, Adekunle A S, Mamba B B, et al. Electrochemical detection of environmental pollutants based on graphene derivatives:A Review[J]. Frontiers in Materials, 2021, 7:616787 Amine G M. Nanocomposites for electrochemical detection of environmental pollutants[J]. Micro and Nano Technologies, 2020, 1:555-581 孔德明, 宋乐乐, 崔耀耀, 等. 结合平行因子分析算法和模式识别方法的三维荧光光谱技术用于石油类污染物的检测[J]. 光谱学与光谱分析, 2020, 40(9):2798-2803 Kong D M, Song L L, Cui Y Y, et al. Three-dimensional fluorescence spectroscopy coupled with parallel factor and pattern recognition algorithm for characterization and classification of petroleum pollutants[J]. Spectroscopy and Spectral Analysis, 2020, 40(9):2798-2803(in Chinese)
赵静晓, 陈烽. 基于激光光谱技术的运动员大量运动后气体成分检测[J]. 激光杂志, 2019, 40(12):23-26 Zhao J X, Chen F. Detection of gas composition after massive exercise based on laser spectroscopy[J]. Laser Journal, 2019, 40(12):23-26(in Chinese)
周昆鹏, 刘双硕, 崔健, 等. 基于荧光发射光谱的水质化学需氧量的检测[J]. 光谱学与光谱分析, 2020, 40(4):1143-1148 Zhou K P, Liu S S, Cui J, et al. Detection of chemical oxygen demand (COD) of water quality based on fluorescence emission spectra[J]. Spectroscopy and Spectral Analysis, 2020, 40(4):1143-1148(in Chinese)
王照国, 张红云, 苗夺谦. 基于F1值的非极大值抑制阈值自动选取方法[J]. 智能系统学报, 2020, 15(5):1006-1012 Wang Z G, Zhang H Y, Miao D Q. Automatic selection method of non-maximum suppression threshold based on F1 score[J]. CAAI Transactions on Intelligent Systems, 2020, 15(5):1006-1012(in Chinese)
杨红梅, 李琳, 杨日东, 等. 基于双向LSTM神经网络电子病历命名实体的识别模型[J]. 中国组织工程研究, 2018, 22(20):3237-3242 Yang H M, Li L, Yang R D, et al. Named entity recognition based on bidirectional long short-term memory combined with case report form[J]. Chinese Journal of Tissue Engineering Research, 2018, 22(20):3237-3242(in Chinese)
-
点击查看大图
计量
- 文章访问数: 2588
- HTML全文浏览数: 2588
- PDF下载数: 83
- 施引文献: 0
下载:
