Analysis of trends and variability of toxic concentrations in the Niagara River using the Hilbert-Huang transform method |
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| Institution: | 1. Department of Civil Engineering, National Taiwan University, Taipei, Taiwan;2. 207 Jarvis Hall, Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, NY 14260, United States of America;1. Università degli Studi della Tuscia, Viterbo, Dipartimento di Scienze Agrarie e Forestali (DAFNE), Via San Camillo de Lellis snc, 01100 Viterbo, Italy;2. Università degli Studi della Tuscia, Viterbo, Dipartimento di Scienze Ecologiche e Biologiche (DEB), Loc. Riello snc, 01100 Viterbo, Italy;1. Department of Life Sciences, The Natural History Museum, Cromwell Road, London SW7 5BD, United Kingdom;2. Department of Physical-Chemistry, University of Cadiz, Poligono Industrial Rio San Pedro s/n, 11,510 Puerto Real, Cadiz, Spain;3. Division of Diabetes and Nutritional Science, King''s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, United Kingdom;1. Shandong Forestry Research Academy, Jinan 250014, China;2. Northeast Normal University of China, Changchun 130024, China;3. Haoshan Forest Farm, Yiyuan, Zobo 256100, China;1. Department of Animal Physiology and Ecotoxicology, Faculty of Biology and Environmental Protection, University of Silesia, Bankowa 9, Katowice 40-007, Poland;2. Laboratory of Scanning Electron Microscopy, Faculty of Biology and Environmental Protection, University of Silesia, Jagiellońska 28, Katowice 40-007, Poland;3. Bioengineering Laboratory, Heart Prosthesis Institute FRK, Wolności 345a, Zabrze 41-800, Poland |
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| Abstract: | This study introduces a more recent data analysis method, Hilbert Huang Transform method (HHT), to describe contaminant concentration data of a non-stationary and non-linear nature. In order to improve the modeling of the contaminant concentrations, it is proposed to first process the data using the Empirical mode decomposition (EMD) method from HHT to obtain a collection of intrinsic mode functions (IMFs) which can then be modeled separately using either autoregressive moving average (ARMA) models expanded with a seasonal term, or linear regression analysis, depending on the nature of the IMF. Three priority contaminants measured at Niagara-on-the-Lakes are selected for this study. It is found that the trend of fluoranthene concentrations from April of 1986 to March of 1997 is decreasing and then beginning to increase; the 1,2,4-trichlorobenzene concentrations are decreasing; while the dieldrin concentrations are decreasing. With HHT, appropriate time series models can be identified and constructed for the studied contaminant concentrations to better illustrate the variability of each IMF (and thus the contaminant concentrations) for the studied period. For all data sets modeled in this study, pre-processing the data with HHT allowed for higher R2 values, correlation coefficients and lower sum of squared errors when compared to modeling without HHT. It is thus confirmed that pre-processing the data with HHT and modeling with time series analysis will provide a more effective means of the studied data sets when identifying and analyzing the trends and variability of studied contaminant concentrations in the Niagara River. |
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