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2017 | 26 | 5 |

Tytuł artykułu

Simultaneous rapid analysis of multiple nitrogen compounds in polluted river treatment using near-infrared spectroscopy and a support vector machine

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
An intermittent aerobic process has been developed to effectively remove nitrogen from polluted rivers. In addition, a chemometric model was developed to achieve simultaneous rapid analysis of total nitrogen, ammonia nitrogen, and nitrite nitrogen based on near-infrared spectroscopy data combined with a support vector machine. An intermittent aeration process showed that total nitrogen decreased from 54.25 mg·L⁻¹ to 0.64 mg·L⁻¹. Ammonia nitrogen decreased significantly in the aeration stage, but increased in the non-aeration stage. Eventually, ammonia nitrogen decreased from 45.04 mg·L⁻¹ to 0.57 mg·L⁻¹. Nitrite nitrogen and nitrate nitrogen increased in the aeration stage, but decreased in the non-aeration stage. The concentration ranges of nitrite nitrogen and nitrate nitrogen were, respectively, 0.05~31.40 mg·L⁻¹ and 0~0.38 mg·L⁻¹. The 138 water samples were collected during the intermittent aeration process, of which 116 samples were used as the calibration set and the remaining 22 samples were used as a test set in modeling. The actual concentration values and the near-infrared spectroscopy data were used as input of the models. Then the corresponding calibration values and predication values were output by the models. All the samples were scanned with near-infrared spectroscopy from 4,000~12,500 cm⁻¹ and measured by chemical methods. Principal component analysis of raw near-infrared spectral data showed that the matrix dimension of spectral data was significantly reduced, which decreased from 2,203×106 to 6×106. Support vector machine models of total nitrogen, ammonia nitrogen, and nitrite nitrogen showed that the calibration correlation coefficient (R²) of calibration values and actual values were, respectively, 0.9561, 0.9661, and 0.9702, with the root mean square error of cross validation (RMSECV) being 0.09372, 0.04749, and 0.03187. The test results of support vector machine models of total nitrogen, ammonia nitrogen, and nitrite nitrogen showed that the predication correlation coefficient (R²) of prediction values and actual values were, respectively, 0.9616, 0.9410, and 0.9284, with the root mean square error of prediction (RMSEP) being 0.09420, 0.08227, and 0.06770. This study indicated that nitrogen in a polluted river can be removed through the intermittent aerobic process. Moreover, simultaneous rapid determination of total nitrogen, ammonia nitrogen, and nitrite nitrogen may be achieved with near-infrared spectroscopy and a support vector machine. The results showed that the proposed methods provided effective treatment and detection technology for a polluted river.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

5

Opis fizyczny

p. 2013-2019,fig.

Twórcy

autor
  • School of Resources and Environmental Engineering, Anhui University, Hefei, 230601, China
  • Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University Hefei, 230601, China
autor
  • Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University Hefei, 230601, China
autor
  • School of Resources and Environmental Engineering, Anhui University, Hefei, 230601, China
autor
  • Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University Hefei, 230601, China
autor
  • Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University Hefei, 230601, China
autor
  • Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University Hefei, 230601, China

Bibliografia

  • 1. WANG Z.Q., LI B., LIANG R.J.,WANG L.Z. Comparative study on endogenous release of nitrogen and phosphorus in Nansi Lake, China. Acta Scientiae Circumstantiae. 33 (2), 487, 2013.
  • 2. SMALL G.E., COTNER J.B., FINLAY J.C., STARK R.A., STERNER R.W. Nitrogen transformations at the sediment-water interface across redox gradients in the Laurentian Great Lakes. Hydrobiologia. 731 (1), 95, 2014.
  • 3. PAN M., ZHAO J., ZHEN S.C., HENG S., WU J.Effects of the combination of aeration and biofilm technology on transformation of nitrogen in black-odor river. Water Sci. Technol. 74 (3), 655. 2016.
  • 4. ZHANG H.H., BI C.J.,CHEN Z.L., WANG X.P. Pollution characteristics of nitrogen and its influence factors in water and sediments of Dishui Lake system. China Environmental Science. 34 (10), 2646, 2014.
  • 5. WANG M.,YAN H., JIAO L.X.,WANG S.R., LIU W.B., LUO J., LUO Z.Q. Characteristics of internal nitrogen loading and influencing factors in Dianchi Lake sediment. China Environmental Science. 35 (1), 218, 2015.
  • 6. KUWABARA J.S., CARTER J.L., TOPPING B.R., FEND S.V., WOODS P.F., BERELSON W., BALISTRIERI L. JR22-Importance of Sediments-Water Interactions in Coeur d'Alene Lake, Idaho,USA-Management Implications. Environ. Manage. 32 (3), 348, 2015.
  • 7. WU Y.P., CHEN J. Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China. Ecol. Indic. 32 (9), 294, 2013.
  • 8. ZHAO Y., SHAN B.Q., TANG W.Z., ZHANG H. Nitrogen mineralization and geochemical characteristics of amino acids in surface sediments of a typical polluted area in the Haihe River Basin, China. Environ. Sci. Pollut. R. 22 (22), 17975, 2015.
  • 9. HE Y., SHEN S.Y., HUANG M.S., ZHANG B., YAO L. Research of nitrification-denitrification regarding endogenous nitrogen from urban malodorous river sediments: A review. Ecology & Environmental Sciences. 1 (6), 1166, 2012.
  • 10. SHEN S.Y., HE Y., HUANG M.S., GUP.D., RAO Y.F., YAO L.P., ZHAN Y.F. Effects of aerating disturbances on nitrification at sediment-water interface. Chinese Journal of Environmental Engineering. 8 (10), 4153, 2014.
  • 11. CHEN J.Y., XU Z.C., LUO Q.J., LIAO B.H., FU Q.W., HUANG B. Effect of aeration on the Transport and transformation of pollutants from the sediments in the tributary of Dianchi Lake. Ecology and Environmental Sciences. 17 (6), 2154, 2008.
  • 12. RUAN A.D., HE R., XU S.Y., LIN T. Effect of dissolved oxygen on nitrogen purification of microbial ecosystem in sediments. J. Environ. Scie. Heal. A. 44 (4), 397,2009.
  • 13. BERZAGHI P., RIOVANTO R. Near infrared spectroscopy in animal science production: principles and applications. Ital. J. Anim. Sci. 8 (1), 3, 2016.
  • 14. ROMAN M., BALABIN R.S. Biodiesel classification by base stock type (vegetable oil) using near-infrared spectroscopy data. Anal. Chim. ACTA. 689 (2), 190, 2011.
  • 15. MELENDEZ-PASTOR I., ALMENDRO-CANDEL M.B., NAVARRO-PEDRERO J., GOMEZ I., LILLO M.G., HERNANDEZ E.I. Monitoring urban wastewaters’ characteristics by visible and short wave near-infrared spectroscopy. Water. 5 (4), 2026, 2013.
  • 16. MENG Y., WANG S.S., CAI H., JIANG B.H., ZHAO W.J. Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy. J. Anal. Methods Chem. 2015 (1), 101, 2015.
  • 17. LIU Y.Y., WAN W.B., SHEN J.T., JIANG Y., ZHANG L., JIANG H. Establishment of universal quantitative models for determination of acyclovir tables. Chinese Pharmaceutical Journal. 27 (2), 191, 2013.
  • 18. NIU X.Y., ZHAO Z.L., JIA K.J., LI X.T. A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics. Food Chem. 133 (2), 592, 2012.
  • 19. DING S.F., QI B.J., TAN H.Y. An overview on theory and algorithm of support vector machines. Journal of Electronic Science and Technology of China. 40 (1), 2, 2011.
  • 20. HUANG Y., ZHANG L., LIAN G., ZHAN R., XU R., HUANG Y., MITRA B., WU J., LUO G. A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine. Burns Journal of the International Society for Burn Injuries. 42 (2), 291, 2016.
  • 21. XIN N., GU X.F., WU H., Hu Y.Z., YANG Z.L. Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 89 (4) 18, 2012.
  • 22. YANG F., TIAN J., XIANG Y.H., ZHANG Z.Y, HARRINGTON P.D.B. Near infrared spectroscopy combined with least squares support vector machines and fuzzy rule-building expert system applied to diagnosis of endometrial carcinoma. Cancer Epidemiol. 36 (3), 317, 2012.
  • 23. MORA C.R., SCHIMLECK L.R. Kernel regression methods for the prediction of wood properties of Pinus taeda using near infrared spectroscopy. Wood Sci. Technol. 44 (44), 561, 2010.
  • 24. SHEN T.T., ZOU X.B., SHI J.Y., LI Z.H., HUANG X.W., XU Y.W., CHEN W. Determination Geographical Origin and Flavonoids Content of Goji Berry Using Near-Infrared Spectroscopy and Chemometrics. Food Anal. Method. 9 (1), 68, 2016.
  • 25. MI Y.P., WANG X.P., JIN X. Water COD prediction based on machine learning. Journal of Zhejiang University (Engineering Science). 42 (5), 790, 2008.
  • 26. ZHANG G.Z.,QIAO G.L, WU F.P., YANG H., NIU Y. Application of principal component analysis to an evaluation of water quality in water cellar. Environ. Sci. Technol. 37 (4), 181, 2014.
  • 27. ECKERT-GALLUP A.C., SALLABERRY A.J., DALLDAN A.R., NEARY V.S. Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states. Ocean Eng. 112 (15), 307, 2016.
  • 28. DENG X.L., KONG C., WU W.B., MEI H.L., LI Z. Detection of citrus HuangLongBing based on principal component analysis and back propagation neural network. Acta Photonica Sinica. 43 (4), 142, 2014.
  • 29. ZHANG H., WANG K., HUANG J., ZHANG Y., SONG J. Characterization of dissolved organic matter with intermittent aeration by fluorescence. Analysis Letters, 49 (12), 1874. 2016.
  • 30. HUPPERT T.J. Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics. 3 (1), 2016. doi: 10.1117/1.NPh.3.1.010401.
  • 31. HE J.C., YANG X.L., WANGL.R., PAN J.M. Rapid determination of chemical oxygen demand (COD) biochemical oxygen demand and (BOD₅) and pH in wastewater using near-infrared spectroscopy. Acta Scientiae Circumstantiae. 27 (12), 2105, 2007.
  • 32. DERNONCOUNT D., HANCZAR B., ZUCKER J.D. Analysis of feature selection stability on high dimension and small sample data. Comput. Stat. Data An. 71 (1), 1, 2014.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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