- Email: [email protected]
A new way to discriminate polluted wood by vibrational spectroscopies
Author’s Accepted Manuscript A new way to discriminate polluted wood by vibrational spectroscopies Huy Nguyen, Fabienne Lagarde, Guy Louarn, Philippe Daniel www.elsevier.com/locate/talanta
PII: DOI: Reference:
S0039-9140(17)30246-1 http://dx.doi.org/10.1016/j.talanta.2017.02.032 TAL17308
To appear in: Talanta Received date: 6 December 2016 Revised date: 9 February 2017 Accepted date: 15 February 2017 Cite this article as: Huy Nguyen, Fabienne Lagarde, Guy Louarn and Philippe Daniel, A new way to discriminate polluted wood by vibrational spectroscopies, Talanta, http://dx.doi.org/10.1016/j.talanta.2017.02.032 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A new way to discriminate polluted wood by vibrational spectroscopies Huy Nguyena, Fabienne Lagardea, Guy Louarnb, Philippe Daniela,* a
Institut des Molécules et des Matériaux du Mans (IMMM) UMR CNRS 6283, Université du Maine, Av. O. Messiaen 72085 Le Mans Cedex 9 (France) b Institut des Matériaux Jean Rouxel (IMN) – UMR CNRS 6502, Université de Nantes, 2 rue de la Houssinière, 44322 Nantes cedex 3 (France) * Corresponding author Email address: [email protected] (Philippe Daniel)
HIGHLIGHTS Discrimination of different kinds of wood by vibrational spectroscopy and principal component analysis. Quick detection of organic pollutants in wood items. Discrimination of different concentrations and different kinds of organic pollutants in wood. ABSTRACT In this work, two sets of samples were considered: field samples collected from local waste wood and synthetic samples made by mixing clean wood (including oak, beech, poplar) with typical organic pollutants: creosote, polychlorinated byphenils (PCBs), pentachlorophenol (PCP), cypermethrin, dodecyl dimethyl ammonium chloride (DDAC). Vibrational spectroscopy techniques were tested to detect organic pollutants in wood items. Raman and infrared spectroscopies were showed as fast, nondestructive and non-invasive fingerprint techniques for detection of organic molecules. Associated with principal component analysis, we have shown the evidence of quick detection of and discrimination of polluted wood items by kinds and versus concentration. Keywords: Organic pollutants Waste wood Raman spectroscopy Infrared spectroscopy Principal component analysis
1. Introduction Wood has a long history of applications in human life. It can be used widely in both everyday life and industry as tools, construction materials and fuels. It can be considered as a sustainable material due to its environment friendly properties [1]. Wood products, at the end of their service life, can be recycled. Waste wood is an important renewable resource that can be used for particleboard products or energy production [2–7] However, because wood product has a biological origin, its lifespan is affected by attacks of insects, fungus and moisture. It is also flammable. Therefore, in order to protect and increase durability and strength of wood products, raw wood material is often treated by different techniques such as heat treatment [8] and chemical treatment. The second technique has many advantages because of its flexibility and ability to protect wood from different risks by applying a very large panel of molecules. Fungicides, insecticides, pesticides and flame retardant are the most popular preservatives of wood [9–11]. These preservatives can be applied on wood by dipping, brushing and spraying techniques or by pressure treatment in order to put these molecules on wood surface [12]. The added molecules will in low concentrations compared to cellulose, hemicellulose and lignin that are the main constituents of wood. But unfortunately, most of these protecting molecules are known to be toxic, harmful to human and animals health or environment [13–16]. There are two main kinds of chemical molecules those are used for wood preservatives. The first ones are heavy metals, typically chromium – copper – arsenate (CCA) – copper – boron (CCB)[17]). The second group is constituted by organic pollutants such as traditional creosote [18–20]; quaternary ammonium compounds (QACs) [21]; pentachlorophenol (PCP) [22,23]; polychlorinated biphenyls (PCBs) [24]; cypermethrin [25]. Because of their persistency in the environment and their toxicity, some preservatives have been prohibited or at least restricted such as CCA and PCBs. However most of the others are still used for preserving commercial and industrial wood products [26,27]. [28–30].
1
At the end of their life, the presence of these toxic molecules in the wood products hinders their recycling process [31,32]. Moreover, during disposal, the chemical molecules in wood can be washed by rain and end up in the rivers and surface water, which is of course a strong environmental concern [33–35]. Therefore, there is a potential demand to determine the presence of contaminants in waste wood before the recycling process. In order to determine heavy metal, X – ray Fluorescence is a suitable and convenient technique [36]. For organic pollutants, they are mostly detected by chromatographic methods [37–40]. However, the chromatography is not a rapid and non – invasive detection technique and it requires several steps of sample preparation. Indeed, in France, the recycling companies still classify waste wood relying mostly on their sources, without any analytical procedure. Three classes of waste wood are considered: A for clean wood, B for wood treated with non-hazardous material and C for the wood treated with hazardous materials. Nowadays, classes A and B can be recycled mainly for particleboard or combustion fuel and only heavy metals contamination is monitored by X – Fluorescence [41]. The detection of organic pollutants lacks of a really efficient technique In this work, we propose a new approach to detect organic pollutants in wood items. This method relies on vibrational spectroscopies (i.e. Raman and Infrared spectroscopies) associated with principal component analysis (PCA) to discriminate wood items by classes and monitor the presence of organic pollutants, even at low concentrations. Raman and infrared (IR) spectroscopies rely on the interaction of an electromagnetic wave with the vibrations of molecules. Each molecule has its specific modes of vibrations, thus vibrational spectroscopy can be considered as a fingerprint technique for organic compounds detection. These techniques do not require sample preparation, hence it is possible to perform fast, non invasive and non destructive measurements [42–45]. Principal component analysis is a statistical method used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easier to explore and visualize [46] and from the last few years, it has often been applied in spectroscopy data analysis. By employing the advantages of these combined techniques, the discrimination of polluted wood items will be evidenced in this work. 2. Materials and methods 2.1 Sampling protocols In this study, two set of samples were studied. First, field samples were obtained from a local waste wood recycling company. These samples include the three classes of waste wood A, B and C according to the classification of the company itself. The class C means wood that is highly supposed to contain toxic molecules. Three collections of 100 samples of each class were tested directly with vibrational spectroscopy. The second approach uses synthetic samples, which were prepared in the lab by mixing raw wood with organic pollutants. Three typical kinds of clean wood (class A) were used: oak, poplar and beech. They were chipped into small pieces and tested with Raman and Infrared spectroscopies. Organic pollutants were purchased from Sigma Aldrich including: pentachlorophenol (PCP), polychlorinated biphenyls (PCBs), creosote, cypermethrin and Didecyl dimethyl ammonium chloride (DDAC). The as-bought organic pollutants then were diluted in appropriate solvents: PCP and Cypermethrin in acetone, creosote and DDAC in water, PCBs in hexane to get solutions of 0.5%, 1%, 5% and 10% concentrations by volume.
(a)
(b)
2
(c) Figure 1.Wood samples: (a) waste wood on the field; from left to right: (b) raw beech, raw oak, field sample class C; (c) from left to right: oak + creosote 1%, beech + creosote 1%, poplar + creosote 1%
Wood samples were dipped into polluted solution (all concentrations of organic pollutants were tested) for 3 days. They were then dried at room temperature for 2 hours to mimic synthetic polluted wood samples (class C) (figure 1). The moisture contents of samples were around 7-9% (weight) and considering dried. Moisture check was carried on by LaserLiner DampFinder Compact. 40 samples were prepared for each group then were measured with Raman and infrared spectra. 2.2 Vibrational spectrometers Bruker MultiRAM Stand Alone FT-Raman Spectrometer was used to measure Raman spectra. This spectrometer uses Nd:YAG laser excitation (1064nm). Each Raman measurement was carried on by 100 scans in resolution of 4cm-1 and range of 200cm-1 to 3200cm-1, zerofilling number was 2. Bruker Vertex Infrared Spectrometer with platinum ATR module was used to measure infrared spectra. Each measurement was carried on by 20 scans and focus on range of 800cm-1 to 2000cm-1, resolution was 4cm-1, zerofilling number was 2. Three measurements were done on each sample. Room temperature was regulated to 20°C and relative humidity of ambient air was 70% at this moment. 2.3 Statistical analysis Spectra data were baseline corrected and intensity normalized by Opus software (Bruker). Then, they were analysed with principal component analysis (PCA) by using Unscrambler X software (Camo), calculated with 7 components. 3. Results and discussion 3.1 Different kinds of wood In order to estimate the influence of different kinds of wood to Raman and IR spectra and discrimination progress, clean samples of three different kinds of wood: oak, beech and poplar were tested. We have tried to measure Raman spectra of wood item with different laser wavelength (532nm, 685nm and 738nm) however for the shorter wavelength, the fluorescence of wood is too strong and overlap Raman signals. It is only obtainable with the laser 1064nm in Fourier Transform configuration. Wood consists mainly of cellulose, hemicellulose and lignin, hence the IR and Raman spectra must display fingerprint bands for those components. We addressed the previous published IR and Raman band assignments of lignin and cellulose from references (table 1 and 2, supplementary material), those bands were matched with our Raman and IR spectra (figure A, supplementary material). Raman spectra of 40 samples and IR spectra of 52 samples of each kind of wood were obtained for statistical data analysis. Principal component analysis was performed in order to discriminate each group of samples. In PCA calculation, the PC1 is the principal component that the data set has most variance, and PC2 is perpendicular to PC1. Although different kinds of wood share mostly the same components, it is clearly evidenced that the spectra results were distinguished into three groups of wood samples by kinds. PCA was able to emphasize the differences between groups of dataset. PCA scores according to PC1 and PC2 is showed in Figure 2. Each dot represents for one sample spectrum where the colors red, blue, green stand for oak, beech and poplar respectively.
3
(a)
(b) Figure 2. PCA scores of (a) IR and (b) Raman spectra of different kinds of wood: beech; oak and poplar
The discrimination of Raman results was clearer than that of IR results. Because the different kinds of wood can affect to the discrimination of PCA, therefore, only poplar was used to mix with organic pollutants. 3.2 Synthetic polluted wood samples Clean wood samples were then mixed with organic pollutants in order to create polluted wood. Clean wood Clean wood + creosote 1% Clean wood + creosote 5% Clean wood + creosote 10% Creosote
Normalized Intensity
4
2
0 800
1000
1200
1400
1600
1800
2000
Wavenumber (cm-1)
(a)
4
(b) Figure 3. (a) IR spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations
Three groups of poplar mixed with creosote samples in 3 different concentrations: 1%, 5% and 10% by volume in water were tested. The IR spectra obtained for the different creosote concentrations added to poplar in comparison with raw poplar are showed in figure 3a. PCA classification of all the spectra shows that all groups are fairly discriminated from each other, in particular with non-polluted wood. 20
Creosote Clean wood + Creosote 10% Clean wood + Creosote 5% Clean wood + Creosote 0.5% Clean wood
Normalized Intensity
15
10
5
0 400
800
1200
1600
2000
Raman shift (cm
2400
2800
3200
-1
)
(a)
(b) Figure 4. (a) Raman spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations
Similar process was performed for Raman data. As in the case of different kinds of wood, Raman spectra still show clearer results. The differences can be seen directly by eyes in the spectra comparison graph (figure 4a). There are bands in two regions around 750cm-1 and 1050cm-1, which are increased according to the increase of creosote concentration. The differences can be seen clearly from concentration of 5%. As a consequence, PCA score shows very distinguish picture for 3 groups of samples where blue, red and green color represent for groups of poplar – creosote in 0% (clean), 5% and 10% diluted concentration respectively. These results prove that vibrational spectroscopy coupling with PCA can be used to detect creosote in wood item at an appropriate concentration.
(a)
5
(b) Figure 5. PCA scores of IR spectra of different kinds of organic pollutants (a): calculation with clean group; (b) calculation without clean group
Similar protocol was tested for other organic pollutants including PCP, PCBs, Cypermethrin and DDAC (all pollutants solutions were at 10% volume) as they are typical preservatives in wood industry. PCA scores of IR data of 4 groups are shows in figure 5 where the color red, blue, green and light blue indicate groups of poplar mixed with PCBs, PCP, Cypermethrin and DDAC respectively. Groups of different organic pollutants samples were discriminated fairly well. The DDAC group slightly overlaps to other groups. However, groups of PCP, PCBs and cypermethrin samples are discriminated clearly. Figure 6 shows PCA scores for Raman spectra of groups of different organic pollutants (10%). Clean group was totally in one side when Cypermethrin and DDAC groups were discriminated quite well. This is the evidence of ability to detect different organic pollutants with vibrational spectroscopy technique. These first promising results suggest that this technique is not limited to a few organic pollutants, but could also be applied to a large range of organic pollutants that are used in the wood industry.
Figure 6. PCA scores of Raman spectra of groups of poplar mixed with different kinds of organic pollutants: clean samples; cypermethrin; DDAC; PCB and PCB
3.3 Real waste wood samples In case of real waste wood, they are evidently more complex samples. Real waste wood consists not only of the main components of wood and preservatives but also many other compounds as they were collected from garbage and stored on the ground. This problem leads to a natural dispersion of results for the different samples (Figure 7b). 100 samples of each class (A, B and C) were tested with infrared spectroscopy. Three random spectra of each class are showed in Figure 7a.
6
(a)
(b) Figure 7. IR spectra (a) and PCA scores (b) of waste wood samples of class A, B and C
PCA results in this case are shown in Figure 7b. As previously mentioned the complexity of the samples leads to a not so good classification as in the previous case. We can observe that they partially overlap each other but still have different ways of distribution. This result can be explained by the numerous and complex components in real samples. Another reason can be the way samples were collected, as they were obtained and classified in big quantity, including then numerous kinds of woods compared to our study driven only on one kind of wood (poplar). The concentration of pollutants in some samples may be too low to be discarded then only the more polluted ones were discarded from clean one. However in spite of this complexity a tendency could be detected by vibrational spectroscopy coupled to PCA. 4. Conclusion In this work, we have shown the ability of vibrational spectroscopy to detect organic pollutants in wood items qualitatively. Thanks to principal component analysis, the dataset of spectra can be easily and quickly discriminated due to the different kinds of wood, concentration of pollutants as well as the different kinds of pollutants. The results have put in evidence that Raman and infrared spectroscopies are good techniques for quick detection and classification of organic pollutants. They only necessitate an easy sample preparation as no pollutant extraction is needed. They also are non –invasive and non – destructive techniques and Raman spectroscopy does not need any contact with the sample, so it could easily be used as a tool to a fast and direct analysis on wood production. Associated with statistical analysis, these methods can be used as a sensor for quick classification of polluted items. This work is the first step of a more extensive research where new real samples as well as other organic pollutants will be tested and analyzed. Other interesting issue such as effect of sample storage time as well as other chemometrics methods will be considered in further research for deeper and more precise results. Acknowledgements This study is a part of Matieres project, which is financially supported by Pays de la Loire region, France. References [1]
A.H. Buchanan, S.B. Levine, Wood-based building materials and atmospheric carbon emissions, Environ.
7
[2]
[3]
[4] [5] [6] [7]
[8]
[9] [10]
[11] [12]
[13]
[14]
[15] [16]
[17]
[18] [19]
[20]
[21]
[22]
Sci. Policy. 2 (1999) 427–437. doi:10.1016/S1462-9011(99)00038-6. P.F. Sommerhuber, T. Wang, A. Krause, Wood–plastic composites as potential applications of recycled plastics of electronic waste and recycled particleboard, J. Clean. Prod. 121 (2016) 176–185. doi:10.1016/j.jclepro.2016.02.036. E. Ratajczak, G. Bidzinska, A. Szostak, M. Herbec, Resources of post-consumer wood waste originating from the construction sector in Poland, Resour. Conserv. Recycl. 97 (2015) 93–99. doi:10.1016/j.resconrec.2015.02.008. M.H. Kim, H.B. Song, Analysis of the global warming potential for wood waste recycling systems, J. Clean. Prod. 69 (2014) 199–207. doi:10.1016/j.jclepro.2014.01.039. M.U. Hossain, S.Y. Leu, C.S. Poon, Sustainability analysis of pelletized bio-fuel derived from recycled wood product wastes in Hong Kong, J. Clean. Prod. 113 (2015) 400–410. doi:10.1016/j.jclepro.2015.11.069. R.J. Gravelsins, O. Trass, Analysis of grinding of pelletized wood waste with the Szego Mill, Powder Technol. 245 (2013) 189–198. doi:10.1016/j.powtec.2013.04.018. J. Vanneste, T. Van Gerven, E. Vander Putten, B. Van der Bruggen, L. Helsen, Energetic valorization of wood waste: Estimation of the reduction in CO 2 emissions, Sci. Total Environ. 409 (2011) 3595–3602. doi:10.1016/j.scitotenv.2011.04.059. N. Oumarou, D. Kocaefe, Y. Kocaefe, 3D-modelling of conjugate heat and mass transfers: Effects of storage conditions and species on wood high temperature treatment, Int. J. Heat Mass Transf. 79 (2014) 945–953. doi:10.1016/j.ijheatmasstransfer.2014.08.086. A. Donmez Cavdar, Effect of various wood preservatives on limiting oxygen index levels of fir wood, Meas. J. Int. Meas. Confed. 50 (2014) 279–284. doi:10.1016/j.measurement.2014.01.009. S.E. Kinata, K. Loubar, M. Paraschiv, A. Bouslamti, C. Belloncle, M. Tazerout, Slow pyrolysis of CCBtreated wood for energy recovery: Influence of chromium, copper and boron on pyrolysis process and optimization, J. Anal. Appl. Pyrolysis. 104 (2013) 210–217. doi:10.1016/j.jaap.2013.08.002. C. Tascioglu, M. Yalcin, S. Sen, C. Akcay, Antifungal properties of some plant extracts used as wood preservatives, Int. Biodeterior. Biodegradation. 85 (2013) 23–28. doi:10.1016/j.ibiod.2013.06.004. S. Lebow, R. Arango, B. Woodward, P. Lebow, K. Ohno, Efficacy of alternatives to zinc naphthenate for dip treatment of wood packaging materials, Int. Biodeterior. Biodegradation. 104 (2015) 371–376. doi:10.1016/j.ibiod.2015.07.006. Z.G. Sheng, Y. Li, R.M. Fan, X.J. Chao, B.Z. Zhu, Lethal synergism between organic and inorganic wood preservatives via formation of an unusual lipophilic ternary complex, Toxicol. Appl. Pharmacol. 266 (2013) 335–344. doi:10.1016/j.taap.2012.11.013. T.G. Mercer, L.E. Frostick, Evaluating the potential for environmental pollution from chromated copper arsenate (CCA)-treated wood waste: A new mass balance approach, J. Hazard. Mater. 276 (2014) 10–18. doi:10.1016/j.jhazmat.2014.05.006. M. Łebkowska, M. Załeska-Radziwiłł, A. Rutkowska-Narozniak, S. Kobiela, Toxicity assessment of wood preservatives, Environ. Int. 28 (2003) 801–802. doi:10.1016/S0160-4120(02)00113-7. O. Adam, P.M. Badot, F. Degiorgi, G. Crini, Mixture toxicity assessment of wood preservative pesticides in the freshwater amphipod Gammarus pulex (L.), Ecotoxicol. Environ. Saf. 72 (2009) 441–449. doi:10.1016/j.ecoenv.2008.07.017. S.E. Kinata, K. Loubar, A. Bouslamti, C. Belloncle, M. Tazerout, Influence of impregnation method on metal retention of CCB-treated wood in slow pyrolysis process, J. Hazard. Mater. 233–234 (2012) 172–176. doi:10.1016/j.jhazmat.2012.07.011. L. Becker, G. Matuschek, D. Lenoir, A. Kettrup, Leaching behaviour of wood treated with creosote, Chemosphere. 42 (2001) 301–308. doi:10.1016/S0045-6535(00)00071-0. Y. Ikarashi, M.A. Kaniwa, T. Tsuchiya, Monitoring of polycyclic aromatic hydrocarbons and waterextractable phenols in creosotes and creosote-treated woods made and procurable in Japan, Chemosphere. 60 (2005) 1279–1287. doi:10.1016/j.chemosphere.2005.01.054. T. Thierfelder, E. Sandström, The creosote content of used railway crossties as compared with European stipulations for hazardous waste, Sci. Total Environ. 402 (2008) 106–112. doi:10.1016/j.scitotenv.2008.04.035. C. Zhang, F. Cui, G. ming Zeng, M. Jiang, Z. zhu Yang, Z. gang Yu, M. ying Zhu, L. qing Shen, Quaternary ammonium compounds (QACs): A review on occurrence, fate and toxicity in the environment, Sci. Total Environ. 518–519 (2015) 352–362. doi:10.1016/j.scitotenv.2015.03.007. R. Becker, H.G. Buge, T. Win, Determination of pentachlorophenol (PCP) in waste wood - Method comparison by a collaborative trial, Chemosphere. 47 (2002) 1001–1006. doi:10.1016/S0045-6535(02)000048.
8
[23]
[24] [25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34] [35]
[36]
[37] [38]
[39]
[40]
[41] [42]
M.C. Freeman, W.J. O’Dowd, T.D. Brown, R.A. Hargis, R.A. James, S.I. Plasynski, G.F. Walbert, A.F. Lowe, J.J. Battista, Pilot-scale air toxics R&D assessment of creosote-treated and PCP-treated wood cofiring for pulverized coal utility boiler applications, Biomass and Bioenergy. 19 (2000) 447–456. doi:10.1016/S0961-9534(00)00055-6. M. Abb, J. V. Breuer, C. Zeitz, W. Lorenz, Analysis of pesticides and PCBs in waste wood and house dust, Chemosphere. 81 (2010) 488–493. doi:10.1016/j.chemosphere.2010.07.042. M.K.R. Mudiam, R. Jain, S.K. Maurya, H.A. Khan, S. Bandyopadhyay, R.C. Murthy, Low density solvent based dispersive liquid-liquid microextraction with gas chromatography-electron capture detection for the determination of cypermethrin in tissues and blood of cypermethrin treated rats, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 895–896 (2012) 65–70. doi:10.1016/j.jchromb.2012.03.015. L. Lundin, S. Jansson, The effects of fuel composition and ammonium sulfate addition on PCDD, PCDF, PCN and PCB concentrations during the combustion of biomass and paper production residuals, Chemosphere. 94 (2014) 20–26. doi:10.1016/j.chemosphere.2013.08.059. L. Lundin, M.F. Gomez-Rico, C. Forsberg, C. Nordenskjöld, S. Jansson, Reduction of PCDD, PCDF and PCB during co-combustion of biomass with waste products from pulp and paper industry, Chemosphere. 91 (2013) 797–801. doi:10.1016/j.chemosphere.2013.01.090. H.I. Gomes, C. Dias-Ferreira, A.B. Ribeiro, Overview of in situ and ex situ remediation technologies for PCB-contaminated soils and sediments and obstacles for full-scale application, Sci. Total Environ. 445–446 (2013) 237–260. doi:10.1016/j.scitotenv.2012.11.098. S. Kuusisto, O. Lindroos, T. Rantio, E. Priha, T. Tuhkanen, PCB contaminated dust on indoor surfaces Health risks and acceptable surface concentrations in residential and occupational settings, Chemosphere. 67 (2007) 1194–1201. doi:10.1016/j.chemosphere.2006.10.060. C. Rylander, E. Lund, L. Frøyland, T.M. Sandanger, Predictors of PCP, OH-PCBs, PCBs and chlorinated pesticides in a general female Norwegian population, Environ. Int. 43 (2012) 13–20. doi:10.1016/j.envint.2012.02.008. A. Janin, L. Coudert, P. Riche, G. Mercier, P. Cooper, J.F. Blais, Application of a CCA-treated wood waste decontamination process to other copper-based preservative-treated wood after disposal, J. Hazard. Mater. 186 (2011) 1880–1887. doi:10.1016/j.jhazmat.2010.12.094. M. Edo, E. Björn, P.E. Persson, S. Jansson, Assessment of chemical and material contamination in waste wood fuels - A case study ranging over nine years, Waste Manag. 49 (2015) 311–319. doi:10.1016/j.wasman.2015.11.048. L.D. Lin, Y.F. Chen, S.Y. Wang, M.J. Tsai, Leachability, metal corrosion, and termite resistance of wood treated with copper-based preservative, Int. Biodeterior. Biodegrad. 63 (2009) 533–538. doi:10.1016/j.ibiod.2008.07.012. T.G. Mercer, L.E. Frostick, Leaching characteristics of CCA-treated wood waste: A UK study, Sci. Total Environ. 427–428 (2012) 165–174. doi:10.1016/j.scitotenv.2012.04.008. S. Sen, C. Tascioglu, K. Tirak, Fixation, leachability, and decay resistance of wood treated with some commercial extracts and wood preservative salts, Int. Biodeterior. Biodegrad. 63 (2009) 135–141. doi:10.1016/j.ibiod.2008.07.007. R. Ravisankar, S. Sivakumar, A. Chandrasekaran, K. V Kanagasabapathy, Statistical assessment of heavy metal pollution in sediments of east coast of Tamilnadu using Energy Dispersive X-ray Fluorescence Spectroscopy ( EDXRF ), Appl. Radiat. Isot. 102 (2015) 42–47. doi:10.1016/j.apradiso.2015.03.018. R.C. Hale, K.M. Aneiro, Determination of coal tar and creosote constituents in the aquatic environment, J. Chromatogr. A. 774 (1997) 79–95. doi:10.1016/S0021-9673(97)00167-2. T. Hartnik, H.R. Norli, T. Eggen, G.D. Breedveld, Bioassay-directed identification of toxic organic compounds in creosote-contaminated groundwater, Chemosphere. 66 (2007) 435–443. doi:10.1016/j.chemosphere.2006.06.031. E.P. Mateus, M.D.R. Gomes da Silva, A.B. Ribeiro, P.J. Marriott, Qualitative mass spectrometric analysis of the volatile fraction of creosote-treated railway wood sleepers by using comprehensive two-dimensional gas chromatography, J. Chromatogr. A. 1178 (2008) 215–222. doi:10.1016/j.chroma.2007.11.069. J. Šťávová, C. a. Sedgeman, Z.T. Smith, L. a. Frink, J. a. Hart, V.H. Niri, A. Kubátová, Method development for the determination of wood preservatives in commercially treated wood using gas chromatography–mass spectrometry, Anal. Chim. Acta. 702 (2011) 205–212. doi:10.1016/j.aca.2011.06.058. A. Bouslamti, M.. Irle, B. C, V. Salvador, M. Bondu, S. Hulo, B. Caron, Why Simulate a Sample of Recycled Wood?, Maderas. Cienc. Y Tecnol. 14 (2012) 145–153. doi:10.4067/S0718-221X2012000200002. M.R. Almeida, C.H. V Fidelis, L.E.S. Barata, R.J. Poppi, Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation, Talanta. 117 (2013) 305–311.
9
[43]
[44]
[45] [46]
doi:10.1016/j.talanta.2013.09.025. Z. Ji, J.F. Ma, Z.H. Zhang, F. Xu, R.C. Sun, Distribution of lignin and cellulose in compression wood tracheids of Pinus yunnanensis determined by fluorescence microscopy and confocal Raman microscopy, Ind. Crops Prod. 47 (2013) 212–217. doi:10.1016/j.indcrop.2013.03.006. V. Emmanuel, B. Odile, R. Céline, FTIR spectroscopy of woods: A new approach to study the weathering of the carving face of a sculpture, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 136 (2015) 1255–1259. doi:10.1016/j.saa.2014.10.011. A. Sandak, J. Sandak, M. Zborowska, W. Pradzynski, Near infrared spectroscopy as a tool for archaeological wood characterization, J. Archaeol. Sci. 37 (2010) 2093–2101. doi:10.1016/j.jas.2010.02.005. B. Škrbić, K. Szyrwińska, N. Durišić-Mladenović, P. Nowicki, J. Lulek, Principal component analysis of indicator PCB profiles in breast milk from Poland, Environ. Int. 36 (2010) 862–872. doi:10.1016/j.envint.2009.04.008.
Supplementary material Figures
(a)
(b) Figure A. (a) IR and (b) Raman spectra of wood
Tables Cellulose
Lignin
Assignment
10
3347
3392 2924
2900 1720 1670 1640 1598 1506 1458 1419 1385-1370 1352-1330
1455 1425 1368 1333 1314 1280 1230 1200 1160 1109 1058 1032 895
1266 1222 1134 1080 1032 853 812
ν (O-H) νas (CH2) ν (C-H) aliphatic ν (C=O) ν (C=O) δ (H-O-H) δ (C=C) aromatic cycle δ (C=C) aromatic cycle δ (CH2) and (CH3) δ scissoring (CH2) and (CH3) δ (CH) – δs (CH3) γ (CH2) and δ (O-H) δ (CH2) and δ (O-H) ν (C-O) ν (C-O) δ (CH2) and δ (O-H) νas (C-O-C) ν (C-O) and ν (O-H) ν (C-O) νs (C-O-C) ν (C-C) δ (C-H) δ (C-C)
Table 1. Typical IR bands (wavenumber in cm-1) assignment[44]
Frequency (cm−1) 2945
Lignin
2897 1655
Cellulose Lignin
1598 1464 1423
Lignin Lignin and Cellulose Lignin
1378 1330 1274
Cellulose Lignin? Lignin
1140 1121
Lignin? Cellulose, Xylan, and Glucomannan Cellulose, Xylan, and Glucomannan Cellulose
1098
902
Component
Assignment C-H stretching in OCH3 asymmetric C-H and C-H2 stretching Ring conjugated C=C stretching of conifer alcohol; C-O stretching of coniferaldehyde Aryl ring stretching symmetric. HCH and HOC bending O-CH3 deformation; CH2 scissoring; guaiacyl ring vibration HCC, HCO and HOC bending Aryl-OH or aryl-O-CH3 vibration? Aryl-O of ary-OH and aryl O-CH3; guaiacyl ring (with C-O group) mode C-O-C stretching asymmetric? Heavy atom (CC and CO) stretching
Heavy atom (CC and CO) stretching Heavy atom (CC and CO) stretching
Table 2. Typical Raman bands assignment of wood[43]
TABLES (transferred to supplementary material section) Cellulose 3347
Table 1. Typical IR bands (wavenumber in cm-1) assignment(Emmanuel et al., 2015) Lignin Assignment 3392 2924
ν (O-H) νas (CH2)
11
2900 1720 1670 1640
1455 1425 1368 1333 1314 1280 1230 1200 1160 1109 1058 1032 895
1598 1506 1458 1419 1385-1370 1352-1330 1266 1222 1134 1080 1032 853 812
ν (C-H) aliphatic ν (C=O) ν (C=O) δ (H-O-H) δ (C=C) aromatic cycle δ (C=C) aromatic cycle δ (CH2) and (CH3) δ scissoring (CH2) and (CH3) δ (CH) – δs (CH3) γ (CH2) and δ (O-H) δ (CH2) and δ (O-H) ν (C-O) ν (C-O) δ (CH2) and δ (O-H) νas (C-O-C) ν (C-O) and ν (O-H) ν (C-O) νs (C-O-C) ν (C-C) δ (C-H) δ (C-C)
Table 2. Typical Raman bands assignment of wood(Ji et al., 2013) Frequency (cm−1) 2945
Lignin
2897 1655
Cellulose Lignin
1598 1464 1423
Lignin Lignin and Cellulose Lignin
1378 1330 1274
Cellulose Lignin? Lignin
1140 1121
Lignin? Cellulose, Xylan, and Glucomannan Cellulose, Xylan, and Glucomannan Cellulose
1098
902
Component
Assignment C-H stretching in OCH3 asymmetric C-H and C-H2 stretching Ring conjugated C=C stretching of conifer alcohol; C-O stretching of coniferaldehyde Aryl ring stretching symmetric. HCH and HOC bending O-CH3 deformation; CH2 scissoring; guaiacyl ring vibration HCC, HCO and HOC bending Aryl-OH or aryl-O-CH3 vibration? Aryl-O of ary-OH and aryl O-CH3; guaiacyl ring (with C-O group) mode C-O-C stretching asymmetric? Heavy atom (CC and CO) stretching
Heavy atom (CC and CO) stretching Heavy atom (CC and CO) stretching
Graphical abstract : List of figures Figure 1.Wood samples: (a) waste wood on the field; from left to right: (b) raw beech, raw oak, field sample class C; (c) from left to right: oak + creosote 1%, beech + creosote 1%, poplar + creosote 1% Figure 2. PCA scores of (a) IR and (b) Raman spectra of different kinds of wood: beech; oak and poplar
12
Figure 3. (a) IR spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations Figure 4. (a) Raman spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations Figure 5. PCA scores of IR spectra of different kinds of organic pollutants (a): calculation with clean group; (b) calculation without clean group Figure 6. PCA scores of Raman spectra of groups of poplar mixed with different kinds of organic pollutants: clean samples; cypermethrin; DDAC; PCB and PCB Figure 7. IR spectra (a) and PCA scores (b) of waste wood samples of class A, B and C Supplementary material section Figure A. (a) IR and (b) Raman spectra of wood
13
PII: DOI: Reference:
S0039-9140(17)30246-1 http://dx.doi.org/10.1016/j.talanta.2017.02.032 TAL17308
To appear in: Talanta Received date: 6 December 2016 Revised date: 9 February 2017 Accepted date: 15 February 2017 Cite this article as: Huy Nguyen, Fabienne Lagarde, Guy Louarn and Philippe Daniel, A new way to discriminate polluted wood by vibrational spectroscopies, Talanta, http://dx.doi.org/10.1016/j.talanta.2017.02.032 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A new way to discriminate polluted wood by vibrational spectroscopies Huy Nguyena, Fabienne Lagardea, Guy Louarnb, Philippe Daniela,* a
Institut des Molécules et des Matériaux du Mans (IMMM) UMR CNRS 6283, Université du Maine, Av. O. Messiaen 72085 Le Mans Cedex 9 (France) b Institut des Matériaux Jean Rouxel (IMN) – UMR CNRS 6502, Université de Nantes, 2 rue de la Houssinière, 44322 Nantes cedex 3 (France) * Corresponding author Email address: [email protected] (Philippe Daniel)
HIGHLIGHTS Discrimination of different kinds of wood by vibrational spectroscopy and principal component analysis. Quick detection of organic pollutants in wood items. Discrimination of different concentrations and different kinds of organic pollutants in wood. ABSTRACT In this work, two sets of samples were considered: field samples collected from local waste wood and synthetic samples made by mixing clean wood (including oak, beech, poplar) with typical organic pollutants: creosote, polychlorinated byphenils (PCBs), pentachlorophenol (PCP), cypermethrin, dodecyl dimethyl ammonium chloride (DDAC). Vibrational spectroscopy techniques were tested to detect organic pollutants in wood items. Raman and infrared spectroscopies were showed as fast, nondestructive and non-invasive fingerprint techniques for detection of organic molecules. Associated with principal component analysis, we have shown the evidence of quick detection of and discrimination of polluted wood items by kinds and versus concentration. Keywords: Organic pollutants Waste wood Raman spectroscopy Infrared spectroscopy Principal component analysis
1. Introduction Wood has a long history of applications in human life. It can be used widely in both everyday life and industry as tools, construction materials and fuels. It can be considered as a sustainable material due to its environment friendly properties [1]. Wood products, at the end of their service life, can be recycled. Waste wood is an important renewable resource that can be used for particleboard products or energy production [2–7] However, because wood product has a biological origin, its lifespan is affected by attacks of insects, fungus and moisture. It is also flammable. Therefore, in order to protect and increase durability and strength of wood products, raw wood material is often treated by different techniques such as heat treatment [8] and chemical treatment. The second technique has many advantages because of its flexibility and ability to protect wood from different risks by applying a very large panel of molecules. Fungicides, insecticides, pesticides and flame retardant are the most popular preservatives of wood [9–11]. These preservatives can be applied on wood by dipping, brushing and spraying techniques or by pressure treatment in order to put these molecules on wood surface [12]. The added molecules will in low concentrations compared to cellulose, hemicellulose and lignin that are the main constituents of wood. But unfortunately, most of these protecting molecules are known to be toxic, harmful to human and animals health or environment [13–16]. There are two main kinds of chemical molecules those are used for wood preservatives. The first ones are heavy metals, typically chromium – copper – arsenate (CCA) – copper – boron (CCB)[17]). The second group is constituted by organic pollutants such as traditional creosote [18–20]; quaternary ammonium compounds (QACs) [21]; pentachlorophenol (PCP) [22,23]; polychlorinated biphenyls (PCBs) [24]; cypermethrin [25]. Because of their persistency in the environment and their toxicity, some preservatives have been prohibited or at least restricted such as CCA and PCBs. However most of the others are still used for preserving commercial and industrial wood products [26,27]. [28–30].
1
At the end of their life, the presence of these toxic molecules in the wood products hinders their recycling process [31,32]. Moreover, during disposal, the chemical molecules in wood can be washed by rain and end up in the rivers and surface water, which is of course a strong environmental concern [33–35]. Therefore, there is a potential demand to determine the presence of contaminants in waste wood before the recycling process. In order to determine heavy metal, X – ray Fluorescence is a suitable and convenient technique [36]. For organic pollutants, they are mostly detected by chromatographic methods [37–40]. However, the chromatography is not a rapid and non – invasive detection technique and it requires several steps of sample preparation. Indeed, in France, the recycling companies still classify waste wood relying mostly on their sources, without any analytical procedure. Three classes of waste wood are considered: A for clean wood, B for wood treated with non-hazardous material and C for the wood treated with hazardous materials. Nowadays, classes A and B can be recycled mainly for particleboard or combustion fuel and only heavy metals contamination is monitored by X – Fluorescence [41]. The detection of organic pollutants lacks of a really efficient technique In this work, we propose a new approach to detect organic pollutants in wood items. This method relies on vibrational spectroscopies (i.e. Raman and Infrared spectroscopies) associated with principal component analysis (PCA) to discriminate wood items by classes and monitor the presence of organic pollutants, even at low concentrations. Raman and infrared (IR) spectroscopies rely on the interaction of an electromagnetic wave with the vibrations of molecules. Each molecule has its specific modes of vibrations, thus vibrational spectroscopy can be considered as a fingerprint technique for organic compounds detection. These techniques do not require sample preparation, hence it is possible to perform fast, non invasive and non destructive measurements [42–45]. Principal component analysis is a statistical method used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easier to explore and visualize [46] and from the last few years, it has often been applied in spectroscopy data analysis. By employing the advantages of these combined techniques, the discrimination of polluted wood items will be evidenced in this work. 2. Materials and methods 2.1 Sampling protocols In this study, two set of samples were studied. First, field samples were obtained from a local waste wood recycling company. These samples include the three classes of waste wood A, B and C according to the classification of the company itself. The class C means wood that is highly supposed to contain toxic molecules. Three collections of 100 samples of each class were tested directly with vibrational spectroscopy. The second approach uses synthetic samples, which were prepared in the lab by mixing raw wood with organic pollutants. Three typical kinds of clean wood (class A) were used: oak, poplar and beech. They were chipped into small pieces and tested with Raman and Infrared spectroscopies. Organic pollutants were purchased from Sigma Aldrich including: pentachlorophenol (PCP), polychlorinated biphenyls (PCBs), creosote, cypermethrin and Didecyl dimethyl ammonium chloride (DDAC). The as-bought organic pollutants then were diluted in appropriate solvents: PCP and Cypermethrin in acetone, creosote and DDAC in water, PCBs in hexane to get solutions of 0.5%, 1%, 5% and 10% concentrations by volume.
(a)
(b)
2
(c) Figure 1.Wood samples: (a) waste wood on the field; from left to right: (b) raw beech, raw oak, field sample class C; (c) from left to right: oak + creosote 1%, beech + creosote 1%, poplar + creosote 1%
Wood samples were dipped into polluted solution (all concentrations of organic pollutants were tested) for 3 days. They were then dried at room temperature for 2 hours to mimic synthetic polluted wood samples (class C) (figure 1). The moisture contents of samples were around 7-9% (weight) and considering dried. Moisture check was carried on by LaserLiner DampFinder Compact. 40 samples were prepared for each group then were measured with Raman and infrared spectra. 2.2 Vibrational spectrometers Bruker MultiRAM Stand Alone FT-Raman Spectrometer was used to measure Raman spectra. This spectrometer uses Nd:YAG laser excitation (1064nm). Each Raman measurement was carried on by 100 scans in resolution of 4cm-1 and range of 200cm-1 to 3200cm-1, zerofilling number was 2. Bruker Vertex Infrared Spectrometer with platinum ATR module was used to measure infrared spectra. Each measurement was carried on by 20 scans and focus on range of 800cm-1 to 2000cm-1, resolution was 4cm-1, zerofilling number was 2. Three measurements were done on each sample. Room temperature was regulated to 20°C and relative humidity of ambient air was 70% at this moment. 2.3 Statistical analysis Spectra data were baseline corrected and intensity normalized by Opus software (Bruker). Then, they were analysed with principal component analysis (PCA) by using Unscrambler X software (Camo), calculated with 7 components. 3. Results and discussion 3.1 Different kinds of wood In order to estimate the influence of different kinds of wood to Raman and IR spectra and discrimination progress, clean samples of three different kinds of wood: oak, beech and poplar were tested. We have tried to measure Raman spectra of wood item with different laser wavelength (532nm, 685nm and 738nm) however for the shorter wavelength, the fluorescence of wood is too strong and overlap Raman signals. It is only obtainable with the laser 1064nm in Fourier Transform configuration. Wood consists mainly of cellulose, hemicellulose and lignin, hence the IR and Raman spectra must display fingerprint bands for those components. We addressed the previous published IR and Raman band assignments of lignin and cellulose from references (table 1 and 2, supplementary material), those bands were matched with our Raman and IR spectra (figure A, supplementary material). Raman spectra of 40 samples and IR spectra of 52 samples of each kind of wood were obtained for statistical data analysis. Principal component analysis was performed in order to discriminate each group of samples. In PCA calculation, the PC1 is the principal component that the data set has most variance, and PC2 is perpendicular to PC1. Although different kinds of wood share mostly the same components, it is clearly evidenced that the spectra results were distinguished into three groups of wood samples by kinds. PCA was able to emphasize the differences between groups of dataset. PCA scores according to PC1 and PC2 is showed in Figure 2. Each dot represents for one sample spectrum where the colors red, blue, green stand for oak, beech and poplar respectively.
3
(a)
(b) Figure 2. PCA scores of (a) IR and (b) Raman spectra of different kinds of wood: beech; oak and poplar
The discrimination of Raman results was clearer than that of IR results. Because the different kinds of wood can affect to the discrimination of PCA, therefore, only poplar was used to mix with organic pollutants. 3.2 Synthetic polluted wood samples Clean wood samples were then mixed with organic pollutants in order to create polluted wood. Clean wood Clean wood + creosote 1% Clean wood + creosote 5% Clean wood + creosote 10% Creosote
Normalized Intensity
4
2
0 800
1000
1200
1400
1600
1800
2000
Wavenumber (cm-1)
(a)
4
(b) Figure 3. (a) IR spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations
Three groups of poplar mixed with creosote samples in 3 different concentrations: 1%, 5% and 10% by volume in water were tested. The IR spectra obtained for the different creosote concentrations added to poplar in comparison with raw poplar are showed in figure 3a. PCA classification of all the spectra shows that all groups are fairly discriminated from each other, in particular with non-polluted wood. 20
Creosote Clean wood + Creosote 10% Clean wood + Creosote 5% Clean wood + Creosote 0.5% Clean wood
Normalized Intensity
15
10
5
0 400
800
1200
1600
2000
Raman shift (cm
2400
2800
3200
-1
)
(a)
(b) Figure 4. (a) Raman spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations
Similar process was performed for Raman data. As in the case of different kinds of wood, Raman spectra still show clearer results. The differences can be seen directly by eyes in the spectra comparison graph (figure 4a). There are bands in two regions around 750cm-1 and 1050cm-1, which are increased according to the increase of creosote concentration. The differences can be seen clearly from concentration of 5%. As a consequence, PCA score shows very distinguish picture for 3 groups of samples where blue, red and green color represent for groups of poplar – creosote in 0% (clean), 5% and 10% diluted concentration respectively. These results prove that vibrational spectroscopy coupling with PCA can be used to detect creosote in wood item at an appropriate concentration.
(a)
5
(b) Figure 5. PCA scores of IR spectra of different kinds of organic pollutants (a): calculation with clean group; (b) calculation without clean group
Similar protocol was tested for other organic pollutants including PCP, PCBs, Cypermethrin and DDAC (all pollutants solutions were at 10% volume) as they are typical preservatives in wood industry. PCA scores of IR data of 4 groups are shows in figure 5 where the color red, blue, green and light blue indicate groups of poplar mixed with PCBs, PCP, Cypermethrin and DDAC respectively. Groups of different organic pollutants samples were discriminated fairly well. The DDAC group slightly overlaps to other groups. However, groups of PCP, PCBs and cypermethrin samples are discriminated clearly. Figure 6 shows PCA scores for Raman spectra of groups of different organic pollutants (10%). Clean group was totally in one side when Cypermethrin and DDAC groups were discriminated quite well. This is the evidence of ability to detect different organic pollutants with vibrational spectroscopy technique. These first promising results suggest that this technique is not limited to a few organic pollutants, but could also be applied to a large range of organic pollutants that are used in the wood industry.
Figure 6. PCA scores of Raman spectra of groups of poplar mixed with different kinds of organic pollutants: clean samples; cypermethrin; DDAC; PCB and PCB
3.3 Real waste wood samples In case of real waste wood, they are evidently more complex samples. Real waste wood consists not only of the main components of wood and preservatives but also many other compounds as they were collected from garbage and stored on the ground. This problem leads to a natural dispersion of results for the different samples (Figure 7b). 100 samples of each class (A, B and C) were tested with infrared spectroscopy. Three random spectra of each class are showed in Figure 7a.
6
(a)
(b) Figure 7. IR spectra (a) and PCA scores (b) of waste wood samples of class A, B and C
PCA results in this case are shown in Figure 7b. As previously mentioned the complexity of the samples leads to a not so good classification as in the previous case. We can observe that they partially overlap each other but still have different ways of distribution. This result can be explained by the numerous and complex components in real samples. Another reason can be the way samples were collected, as they were obtained and classified in big quantity, including then numerous kinds of woods compared to our study driven only on one kind of wood (poplar). The concentration of pollutants in some samples may be too low to be discarded then only the more polluted ones were discarded from clean one. However in spite of this complexity a tendency could be detected by vibrational spectroscopy coupled to PCA. 4. Conclusion In this work, we have shown the ability of vibrational spectroscopy to detect organic pollutants in wood items qualitatively. Thanks to principal component analysis, the dataset of spectra can be easily and quickly discriminated due to the different kinds of wood, concentration of pollutants as well as the different kinds of pollutants. The results have put in evidence that Raman and infrared spectroscopies are good techniques for quick detection and classification of organic pollutants. They only necessitate an easy sample preparation as no pollutant extraction is needed. They also are non –invasive and non – destructive techniques and Raman spectroscopy does not need any contact with the sample, so it could easily be used as a tool to a fast and direct analysis on wood production. Associated with statistical analysis, these methods can be used as a sensor for quick classification of polluted items. This work is the first step of a more extensive research where new real samples as well as other organic pollutants will be tested and analyzed. Other interesting issue such as effect of sample storage time as well as other chemometrics methods will be considered in further research for deeper and more precise results. Acknowledgements This study is a part of Matieres project, which is financially supported by Pays de la Loire region, France. References [1]
A.H. Buchanan, S.B. Levine, Wood-based building materials and atmospheric carbon emissions, Environ.
7
[2]
[3]
[4] [5] [6] [7]
[8]
[9] [10]
[11] [12]
[13]
[14]
[15] [16]
[17]
[18] [19]
[20]
[21]
[22]
Sci. Policy. 2 (1999) 427–437. doi:10.1016/S1462-9011(99)00038-6. P.F. Sommerhuber, T. Wang, A. Krause, Wood–plastic composites as potential applications of recycled plastics of electronic waste and recycled particleboard, J. Clean. Prod. 121 (2016) 176–185. doi:10.1016/j.jclepro.2016.02.036. E. Ratajczak, G. Bidzinska, A. Szostak, M. Herbec, Resources of post-consumer wood waste originating from the construction sector in Poland, Resour. Conserv. Recycl. 97 (2015) 93–99. doi:10.1016/j.resconrec.2015.02.008. M.H. Kim, H.B. Song, Analysis of the global warming potential for wood waste recycling systems, J. Clean. Prod. 69 (2014) 199–207. doi:10.1016/j.jclepro.2014.01.039. M.U. Hossain, S.Y. Leu, C.S. Poon, Sustainability analysis of pelletized bio-fuel derived from recycled wood product wastes in Hong Kong, J. Clean. Prod. 113 (2015) 400–410. doi:10.1016/j.jclepro.2015.11.069. R.J. Gravelsins, O. Trass, Analysis of grinding of pelletized wood waste with the Szego Mill, Powder Technol. 245 (2013) 189–198. doi:10.1016/j.powtec.2013.04.018. J. Vanneste, T. Van Gerven, E. Vander Putten, B. Van der Bruggen, L. Helsen, Energetic valorization of wood waste: Estimation of the reduction in CO 2 emissions, Sci. Total Environ. 409 (2011) 3595–3602. doi:10.1016/j.scitotenv.2011.04.059. N. Oumarou, D. Kocaefe, Y. Kocaefe, 3D-modelling of conjugate heat and mass transfers: Effects of storage conditions and species on wood high temperature treatment, Int. J. Heat Mass Transf. 79 (2014) 945–953. doi:10.1016/j.ijheatmasstransfer.2014.08.086. A. Donmez Cavdar, Effect of various wood preservatives on limiting oxygen index levels of fir wood, Meas. J. Int. Meas. Confed. 50 (2014) 279–284. doi:10.1016/j.measurement.2014.01.009. S.E. Kinata, K. Loubar, M. Paraschiv, A. Bouslamti, C. Belloncle, M. Tazerout, Slow pyrolysis of CCBtreated wood for energy recovery: Influence of chromium, copper and boron on pyrolysis process and optimization, J. Anal. Appl. Pyrolysis. 104 (2013) 210–217. doi:10.1016/j.jaap.2013.08.002. C. Tascioglu, M. Yalcin, S. Sen, C. Akcay, Antifungal properties of some plant extracts used as wood preservatives, Int. Biodeterior. Biodegradation. 85 (2013) 23–28. doi:10.1016/j.ibiod.2013.06.004. S. Lebow, R. Arango, B. Woodward, P. Lebow, K. Ohno, Efficacy of alternatives to zinc naphthenate for dip treatment of wood packaging materials, Int. Biodeterior. Biodegradation. 104 (2015) 371–376. doi:10.1016/j.ibiod.2015.07.006. Z.G. Sheng, Y. Li, R.M. Fan, X.J. Chao, B.Z. Zhu, Lethal synergism between organic and inorganic wood preservatives via formation of an unusual lipophilic ternary complex, Toxicol. Appl. Pharmacol. 266 (2013) 335–344. doi:10.1016/j.taap.2012.11.013. T.G. Mercer, L.E. Frostick, Evaluating the potential for environmental pollution from chromated copper arsenate (CCA)-treated wood waste: A new mass balance approach, J. Hazard. Mater. 276 (2014) 10–18. doi:10.1016/j.jhazmat.2014.05.006. M. Łebkowska, M. Załeska-Radziwiłł, A. Rutkowska-Narozniak, S. Kobiela, Toxicity assessment of wood preservatives, Environ. Int. 28 (2003) 801–802. doi:10.1016/S0160-4120(02)00113-7. O. Adam, P.M. Badot, F. Degiorgi, G. Crini, Mixture toxicity assessment of wood preservative pesticides in the freshwater amphipod Gammarus pulex (L.), Ecotoxicol. Environ. Saf. 72 (2009) 441–449. doi:10.1016/j.ecoenv.2008.07.017. S.E. Kinata, K. Loubar, A. Bouslamti, C. Belloncle, M. Tazerout, Influence of impregnation method on metal retention of CCB-treated wood in slow pyrolysis process, J. Hazard. Mater. 233–234 (2012) 172–176. doi:10.1016/j.jhazmat.2012.07.011. L. Becker, G. Matuschek, D. Lenoir, A. Kettrup, Leaching behaviour of wood treated with creosote, Chemosphere. 42 (2001) 301–308. doi:10.1016/S0045-6535(00)00071-0. Y. Ikarashi, M.A. Kaniwa, T. Tsuchiya, Monitoring of polycyclic aromatic hydrocarbons and waterextractable phenols in creosotes and creosote-treated woods made and procurable in Japan, Chemosphere. 60 (2005) 1279–1287. doi:10.1016/j.chemosphere.2005.01.054. T. Thierfelder, E. Sandström, The creosote content of used railway crossties as compared with European stipulations for hazardous waste, Sci. Total Environ. 402 (2008) 106–112. doi:10.1016/j.scitotenv.2008.04.035. C. Zhang, F. Cui, G. ming Zeng, M. Jiang, Z. zhu Yang, Z. gang Yu, M. ying Zhu, L. qing Shen, Quaternary ammonium compounds (QACs): A review on occurrence, fate and toxicity in the environment, Sci. Total Environ. 518–519 (2015) 352–362. doi:10.1016/j.scitotenv.2015.03.007. R. Becker, H.G. Buge, T. Win, Determination of pentachlorophenol (PCP) in waste wood - Method comparison by a collaborative trial, Chemosphere. 47 (2002) 1001–1006. doi:10.1016/S0045-6535(02)000048.
8
[23]
[24] [25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34] [35]
[36]
[37] [38]
[39]
[40]
[41] [42]
M.C. Freeman, W.J. O’Dowd, T.D. Brown, R.A. Hargis, R.A. James, S.I. Plasynski, G.F. Walbert, A.F. Lowe, J.J. Battista, Pilot-scale air toxics R&D assessment of creosote-treated and PCP-treated wood cofiring for pulverized coal utility boiler applications, Biomass and Bioenergy. 19 (2000) 447–456. doi:10.1016/S0961-9534(00)00055-6. M. Abb, J. V. Breuer, C. Zeitz, W. Lorenz, Analysis of pesticides and PCBs in waste wood and house dust, Chemosphere. 81 (2010) 488–493. doi:10.1016/j.chemosphere.2010.07.042. M.K.R. Mudiam, R. Jain, S.K. Maurya, H.A. Khan, S. Bandyopadhyay, R.C. Murthy, Low density solvent based dispersive liquid-liquid microextraction with gas chromatography-electron capture detection for the determination of cypermethrin in tissues and blood of cypermethrin treated rats, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 895–896 (2012) 65–70. doi:10.1016/j.jchromb.2012.03.015. L. Lundin, S. Jansson, The effects of fuel composition and ammonium sulfate addition on PCDD, PCDF, PCN and PCB concentrations during the combustion of biomass and paper production residuals, Chemosphere. 94 (2014) 20–26. doi:10.1016/j.chemosphere.2013.08.059. L. Lundin, M.F. Gomez-Rico, C. Forsberg, C. Nordenskjöld, S. Jansson, Reduction of PCDD, PCDF and PCB during co-combustion of biomass with waste products from pulp and paper industry, Chemosphere. 91 (2013) 797–801. doi:10.1016/j.chemosphere.2013.01.090. H.I. Gomes, C. Dias-Ferreira, A.B. Ribeiro, Overview of in situ and ex situ remediation technologies for PCB-contaminated soils and sediments and obstacles for full-scale application, Sci. Total Environ. 445–446 (2013) 237–260. doi:10.1016/j.scitotenv.2012.11.098. S. Kuusisto, O. Lindroos, T. Rantio, E. Priha, T. Tuhkanen, PCB contaminated dust on indoor surfaces Health risks and acceptable surface concentrations in residential and occupational settings, Chemosphere. 67 (2007) 1194–1201. doi:10.1016/j.chemosphere.2006.10.060. C. Rylander, E. Lund, L. Frøyland, T.M. Sandanger, Predictors of PCP, OH-PCBs, PCBs and chlorinated pesticides in a general female Norwegian population, Environ. Int. 43 (2012) 13–20. doi:10.1016/j.envint.2012.02.008. A. Janin, L. Coudert, P. Riche, G. Mercier, P. Cooper, J.F. Blais, Application of a CCA-treated wood waste decontamination process to other copper-based preservative-treated wood after disposal, J. Hazard. Mater. 186 (2011) 1880–1887. doi:10.1016/j.jhazmat.2010.12.094. M. Edo, E. Björn, P.E. Persson, S. Jansson, Assessment of chemical and material contamination in waste wood fuels - A case study ranging over nine years, Waste Manag. 49 (2015) 311–319. doi:10.1016/j.wasman.2015.11.048. L.D. Lin, Y.F. Chen, S.Y. Wang, M.J. Tsai, Leachability, metal corrosion, and termite resistance of wood treated with copper-based preservative, Int. Biodeterior. Biodegrad. 63 (2009) 533–538. doi:10.1016/j.ibiod.2008.07.012. T.G. Mercer, L.E. Frostick, Leaching characteristics of CCA-treated wood waste: A UK study, Sci. Total Environ. 427–428 (2012) 165–174. doi:10.1016/j.scitotenv.2012.04.008. S. Sen, C. Tascioglu, K. Tirak, Fixation, leachability, and decay resistance of wood treated with some commercial extracts and wood preservative salts, Int. Biodeterior. Biodegrad. 63 (2009) 135–141. doi:10.1016/j.ibiod.2008.07.007. R. Ravisankar, S. Sivakumar, A. Chandrasekaran, K. V Kanagasabapathy, Statistical assessment of heavy metal pollution in sediments of east coast of Tamilnadu using Energy Dispersive X-ray Fluorescence Spectroscopy ( EDXRF ), Appl. Radiat. Isot. 102 (2015) 42–47. doi:10.1016/j.apradiso.2015.03.018. R.C. Hale, K.M. Aneiro, Determination of coal tar and creosote constituents in the aquatic environment, J. Chromatogr. A. 774 (1997) 79–95. doi:10.1016/S0021-9673(97)00167-2. T. Hartnik, H.R. Norli, T. Eggen, G.D. Breedveld, Bioassay-directed identification of toxic organic compounds in creosote-contaminated groundwater, Chemosphere. 66 (2007) 435–443. doi:10.1016/j.chemosphere.2006.06.031. E.P. Mateus, M.D.R. Gomes da Silva, A.B. Ribeiro, P.J. Marriott, Qualitative mass spectrometric analysis of the volatile fraction of creosote-treated railway wood sleepers by using comprehensive two-dimensional gas chromatography, J. Chromatogr. A. 1178 (2008) 215–222. doi:10.1016/j.chroma.2007.11.069. J. Šťávová, C. a. Sedgeman, Z.T. Smith, L. a. Frink, J. a. Hart, V.H. Niri, A. Kubátová, Method development for the determination of wood preservatives in commercially treated wood using gas chromatography–mass spectrometry, Anal. Chim. Acta. 702 (2011) 205–212. doi:10.1016/j.aca.2011.06.058. A. Bouslamti, M.. Irle, B. C, V. Salvador, M. Bondu, S. Hulo, B. Caron, Why Simulate a Sample of Recycled Wood?, Maderas. Cienc. Y Tecnol. 14 (2012) 145–153. doi:10.4067/S0718-221X2012000200002. M.R. Almeida, C.H. V Fidelis, L.E.S. Barata, R.J. Poppi, Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation, Talanta. 117 (2013) 305–311.
9
[43]
[44]
[45] [46]
doi:10.1016/j.talanta.2013.09.025. Z. Ji, J.F. Ma, Z.H. Zhang, F. Xu, R.C. Sun, Distribution of lignin and cellulose in compression wood tracheids of Pinus yunnanensis determined by fluorescence microscopy and confocal Raman microscopy, Ind. Crops Prod. 47 (2013) 212–217. doi:10.1016/j.indcrop.2013.03.006. V. Emmanuel, B. Odile, R. Céline, FTIR spectroscopy of woods: A new approach to study the weathering of the carving face of a sculpture, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 136 (2015) 1255–1259. doi:10.1016/j.saa.2014.10.011. A. Sandak, J. Sandak, M. Zborowska, W. Pradzynski, Near infrared spectroscopy as a tool for archaeological wood characterization, J. Archaeol. Sci. 37 (2010) 2093–2101. doi:10.1016/j.jas.2010.02.005. B. Škrbić, K. Szyrwińska, N. Durišić-Mladenović, P. Nowicki, J. Lulek, Principal component analysis of indicator PCB profiles in breast milk from Poland, Environ. Int. 36 (2010) 862–872. doi:10.1016/j.envint.2009.04.008.
Supplementary material Figures
(a)
(b) Figure A. (a) IR and (b) Raman spectra of wood
Tables Cellulose
Lignin
Assignment
10
3347
3392 2924
2900 1720 1670 1640 1598 1506 1458 1419 1385-1370 1352-1330
1455 1425 1368 1333 1314 1280 1230 1200 1160 1109 1058 1032 895
1266 1222 1134 1080 1032 853 812
ν (O-H) νas (CH2) ν (C-H) aliphatic ν (C=O) ν (C=O) δ (H-O-H) δ (C=C) aromatic cycle δ (C=C) aromatic cycle δ (CH2) and (CH3) δ scissoring (CH2) and (CH3) δ (CH) – δs (CH3) γ (CH2) and δ (O-H) δ (CH2) and δ (O-H) ν (C-O) ν (C-O) δ (CH2) and δ (O-H) νas (C-O-C) ν (C-O) and ν (O-H) ν (C-O) νs (C-O-C) ν (C-C) δ (C-H) δ (C-C)
Table 1. Typical IR bands (wavenumber in cm-1) assignment[44]
Frequency (cm−1) 2945
Lignin
2897 1655
Cellulose Lignin
1598 1464 1423
Lignin Lignin and Cellulose Lignin
1378 1330 1274
Cellulose Lignin? Lignin
1140 1121
Lignin? Cellulose, Xylan, and Glucomannan Cellulose, Xylan, and Glucomannan Cellulose
1098
902
Component
Assignment C-H stretching in OCH3 asymmetric C-H and C-H2 stretching Ring conjugated C=C stretching of conifer alcohol; C-O stretching of coniferaldehyde Aryl ring stretching symmetric. HCH and HOC bending O-CH3 deformation; CH2 scissoring; guaiacyl ring vibration HCC, HCO and HOC bending Aryl-OH or aryl-O-CH3 vibration? Aryl-O of ary-OH and aryl O-CH3; guaiacyl ring (with C-O group) mode C-O-C stretching asymmetric? Heavy atom (CC and CO) stretching
Heavy atom (CC and CO) stretching Heavy atom (CC and CO) stretching
Table 2. Typical Raman bands assignment of wood[43]
TABLES (transferred to supplementary material section) Cellulose 3347
Table 1. Typical IR bands (wavenumber in cm-1) assignment(Emmanuel et al., 2015) Lignin Assignment 3392 2924
ν (O-H) νas (CH2)
11
2900 1720 1670 1640
1455 1425 1368 1333 1314 1280 1230 1200 1160 1109 1058 1032 895
1598 1506 1458 1419 1385-1370 1352-1330 1266 1222 1134 1080 1032 853 812
ν (C-H) aliphatic ν (C=O) ν (C=O) δ (H-O-H) δ (C=C) aromatic cycle δ (C=C) aromatic cycle δ (CH2) and (CH3) δ scissoring (CH2) and (CH3) δ (CH) – δs (CH3) γ (CH2) and δ (O-H) δ (CH2) and δ (O-H) ν (C-O) ν (C-O) δ (CH2) and δ (O-H) νas (C-O-C) ν (C-O) and ν (O-H) ν (C-O) νs (C-O-C) ν (C-C) δ (C-H) δ (C-C)
Table 2. Typical Raman bands assignment of wood(Ji et al., 2013) Frequency (cm−1) 2945
Lignin
2897 1655
Cellulose Lignin
1598 1464 1423
Lignin Lignin and Cellulose Lignin
1378 1330 1274
Cellulose Lignin? Lignin
1140 1121
Lignin? Cellulose, Xylan, and Glucomannan Cellulose, Xylan, and Glucomannan Cellulose
1098
902
Component
Assignment C-H stretching in OCH3 asymmetric C-H and C-H2 stretching Ring conjugated C=C stretching of conifer alcohol; C-O stretching of coniferaldehyde Aryl ring stretching symmetric. HCH and HOC bending O-CH3 deformation; CH2 scissoring; guaiacyl ring vibration HCC, HCO and HOC bending Aryl-OH or aryl-O-CH3 vibration? Aryl-O of ary-OH and aryl O-CH3; guaiacyl ring (with C-O group) mode C-O-C stretching asymmetric? Heavy atom (CC and CO) stretching
Heavy atom (CC and CO) stretching Heavy atom (CC and CO) stretching
Graphical abstract : List of figures Figure 1.Wood samples: (a) waste wood on the field; from left to right: (b) raw beech, raw oak, field sample class C; (c) from left to right: oak + creosote 1%, beech + creosote 1%, poplar + creosote 1% Figure 2. PCA scores of (a) IR and (b) Raman spectra of different kinds of wood: beech; oak and poplar
12
Figure 3. (a) IR spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations Figure 4. (a) Raman spectra and (b) PCA scores of poplar mixed with creosote solution in different concentrations Figure 5. PCA scores of IR spectra of different kinds of organic pollutants (a): calculation with clean group; (b) calculation without clean group Figure 6. PCA scores of Raman spectra of groups of poplar mixed with different kinds of organic pollutants: clean samples; cypermethrin; DDAC; PCB and PCB Figure 7. IR spectra (a) and PCA scores (b) of waste wood samples of class A, B and C Supplementary material section Figure A. (a) IR and (b) Raman spectra of wood
13