光谱学与光谱分析 |
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Study on the Prediction of Soil Heavy Metal Elements Content Based on Mid-Infrared Diffuse Reflectance Spectra |
WU Deng-wei1, WU Yun-zhao2, MA Hong-rui1* |
1. Department of Resources and Environment, Shaanxi University of Technology and Science, Xi’an 710021, China 2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China |
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Abstract The present paper analyzed the possibility of mid-infrared diffuse reflectance spectra for quick assessment of heavy metal element content in soil quickly. Soil samples were collected from Jiangning District and Baguazhou Island, and the numbers of sample were 103 and 58 separately. Jiangning District samples were used as calibration set while Baguazhou Island samples as validation set. To assess the utility of different pre-treatment process of MIR spectroscopy for soil heavy metal element content analysis, we used PLSR method to develop the calibration between spectral data and soil elements content. Three spectral pre-treating techniques such as smooth, log(1/N), baseline correction, multiplicative scatter correction were used for promotion of predicting performance. The result showed that the progress of (log-BC-MSC) in turn achieved optimal calibration of MIR spectra and better prediction for ex-situ soils. Though the calibration data were treated by different pre-treating schema, the R2 of the 8 elements followed the same law: Ni>0.8> Cr, Cu, Zn, Pb, Hg>0.6> As, Cd. When we applied these calibrations to Baguazhou Island soils, (log-BC-MSC) treated data results in the smallest RMSEp-BGZ. We used the same calibration method to compare the predictive ability of MIR spectra to VNIR spectra. The R2 of 8 elements developed by VNIR spectral calibration are sometime larger than MIR’s, but after we applied these calibrations to validation set, the RSME of MIR data for prediction of BGZ soil samples is 21% to 73% of VNIR’s. This result showed us that for predicting ex-situ soils, MIR analysis substantially outperformed VNIR. These results indicated that MIR spectra can be used to predict soil heavy metal content quickly and non-destructively.
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Received: 2009-05-10
Accepted: 2009-08-20
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Corresponding Authors:
MA Hong-rui
E-mail: mahr@sust.edu.cn
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[1] GAO Tai-zhong, LI Jing-yin(高太忠,李景印). Soil and Environmental Sciences(土壤与环境), 1999, 8(2): 137. [2] REN Hong-yan, ZHUANG Da-fang, QIU Dong-sheng, et al(任红艳, 庄大方, 邱冬生, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(1): 114. [3] Stuart B. Infrared Spectroscopy Fundamentals and Applications. New York: Wiley Press, 2004. [4] Shepherd K D, Walsh M G. Soil Science Society of America Journal, 2002, 66: 988. [5] WANG Lu, LIN Qi-zhong, JIA Dong, et al(王 璐, 蔺启忠, 贾 东, 等). Journal of Remote Sensing(遥感学报), 2007, 11(6): 906. [6] Couteaux M M, Bergb B, Rovirac P. Soil Biology & Biochemistry, 2003, 35: 1587. [7] Cozzolino D, Moron A. Soil & Tillage Research, 2006, 85: 78. [8] HE Xu-sheng(何绪生). Review of China Agricultural Science and Technology(中国农业科技导报), 2004, 6(4): 71. [9] PENG Yu-kui, ZHANG Jian-xin, HE Xu-sheng, et al(彭玉魁, 张建新, 何绪生, 等). Acta Pedologica Sinica(土壤学报), 1998, 35(4): 553. [10] Wu Yunzhao, Chen Jun, Wu Xinmin. Applied Geochemistry, 2005, 20: 1051. [11] McCarty G W, Reeves J B. Soil Science, 2007, 7(2): 94. [12] McCarty G W, Reeves J B, Reeves V B, et al. Soil Science Society of America Journal, 2002, 66: 640. [13] WU Yun-zhao, CHEN Jun, JI Jun-feng, et al. Soil Science Society of America Journal, 2007, 71: 918. [14] Siebielec G, McCarty G W, Stuczynski T I, et al. J. Environ. Qual., 2004, 33: 2056. [15] Boonmung S, Riley M R. Spectroscopy Letters, 2003, 36(3): 251. [16] Jahn B R, Linker R, Upadhyaya S K, et al. Biosystems Engineering, 2006, 94(4): 505. [17] Madari B E, Reeves J B, Machado P, et al. Geoderma, 2006, 136: 245. [18] ZHAO Qiang, ZHANG Gong-li, CHEN Xing-dan(赵 强, 张工力, 陈星旦). Optics and Precision Engineering(光学精密工程), 2005, 13(1): 53. [19] FANG Yong-hua, KONG Chao, LAN Tian-ge(方勇华, 孔 超, 兰天鸽). Optics and Precision Engineering(光学精密工程), 2006, 14(6): 1088. [20] XU Feng-hua(许凤华). The Study of Some Problems in Partial Least Squares Regression(偏最小二乘回归分析中若干问题的研究). Ji’nan:Shandong University of Science and Technology Press(济南:山东科技大学出版社), 2003.
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