- Email: [email protected]
Rapid analysis of heavy metals in the coal ash with laser-induced breakdown spectroscopy
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.com/locate/ijleo
Original research article
Rapid analysis of heavy metals in the coal ash with laser-induced breakdown spectroscopy
T
⁎
Wenyi Yina,b, Yuzhu Liua,b, , Fengbin Zhoua,b, Ruosong Zhua,b, Qihang Zhanga,b, Feng Jinc a
Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing University of Information Science & Technology, Nanjing 210044, PR China b Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, PR China c Advanced Technology Core, Baylor College of Medicine, Houston, TX 77030, USA
A R T IC LE I N F O
ABS TRA CT
Keywords: Laser-induced breakdown spectroscopy Coal ash Quantitative analysis Pb Aerosols Internal standard method
In this study, laser-induced breakdown spectroscopy (LIBS) was applied for the qualitative elemental analysis of coal ash. The spectrum of the ordinary coal ash indicated that coal ash contains elements of Al, Fe, Cu, Ca, Mn, Ti and so on. Sulfates produced by the reaction of metal oxides in coal ash with the acid gases in the atmosphere are the main causes of haze-fog formation. To simulate the haze, eight solutions with different Pb concentrations were prepared and coal ash samples were soaked in solutions and then dried thoroughly. The spectra were obtained from these contaminated coal ash samples with the LIBS technique. To accurately identify the spectral lines of Pb in the spectrum, the spectrum of pure Pb were selected as a reference and the spectral lines of Pb were determined. At the same time, an internal standard method was used to quantitatively analyze the value of the component of Pb/Al with a calibration curve that had a linear correlation coefficient (R2) of 0.98648, which indicates rough estimation of Pb content can be achieved by the intensity of Pb in the spectrum. The results of the experiments show that LIBS technique can be employed for the rapid detection and analysis of metal elements in coal ash and provide a brand-new method for the detection of atmospheric environment based on the content of Pb.
1. Introduction The coal ash is the product of the complete burning of coal, mainly including various metals and non-metal oxides and salts, which is an important parameter in coal utilization. Coal was widely used for industrial production beginning with the industrial revolution in the late 18th century, which brought great productivity to the society and promoted the development of industrialization [1,2]. Combustion of coal, however, as the largest source of secondary particles and a main emission source of primary particles, a large amount of coal ash reacts with various substances in the atmosphere to form haze, which has caused a huge threat to human health [3,4]. Pb is one of those carcinogenic and toxic heavy metal elements, seriously damaging human health and even causing cancer [5–7]. In addition, it also has negative impact on ecosystem and severely pollutes environment [8]. Nevertheless, due to heavy metal
⁎ Corresponding author at: Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing University of Information Science & Technology, Nanjing 210044, PR China E-mail address: [email protected] (Y. Liu).
https://doi.org/10.1016/j.ijleo.2018.08.110 Received 15 May 2018; Received in revised form 25 August 2018; Accepted 25 August 2018 0030-4026/ © 2018 Elsevier GmbH. All rights reserved.
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
elements such as Pb in haze, it is of great significance to conduct qualitative analysis [9]. Laser-induced breakdown spectroscopy (LIBS) is a type of the atomic emission spectroscopy that applies highly energetic pulses to various samples in solid, liquid, gaseous and aerosol forms [10–12]. In comparison with other techniques, LIBS technique has many advantages such as rapid and precise response, simultaneous muti-element analysis, high sensitivity, cost effective and so on [13–15]. As a new material identification analysis technology, LIBS can be used both in the laboratory and on-line detection in industrial sites [16]. Whatʼs more, LIBS has been widely applied in hydrology, geological prospecting, environmental monitoring, scientific research and other fields of application successfully [17,18]. What’s more, the determination of coal ash content by LIBS technology is of great significance for the correct evaluation of coal quality and processing and utilization [19,20]. In recent years, domestic and foreign researchers have made some attempts on the application of LIBS technology in coal quality analysis and made some progress. Feng et al. [21] proposed a method of PLS quantitative analysis based on dominant factors for low accuracy of LIBS analysis of coal quality components, which improved the measurement accuracy to some extent. Mateo et al. [22] analyzed the influence of different laser wavelengths and sample placement in the measurement process on the measurement of some secondary elements contained in coal and they found that the short wave length laser is more favorable for accurate measurement. These findings provide a favorable reference for the application of LIBS in coal ash. In the present work, a sample of coal ash obtained from a steel company was tested based on laser-induced breakdown spectroscopy and the spectra of the elements were analyzed. To simulate haze containing Pb, the Pb compound was added to the coal ash. To verify the accuracy of LIBS technology, test samples were compared with the ordinary coal ash, which does not contain Pb. In this paper, a calibration curve was obtained and can be taken as a reference line for Pb detection in the future by quantitative analysis of Pb in coal ash. LIBS technology provides a new method for the detection and analysis of coal ash.
2. Experimental 2.1. Experimental setup The schematic diagram of experimental setup is shown in Fig. 1. The Q-switched Nd: YAG laser used as the excitation laser was operated at a fundamental wavelength of 1064 nm, the maximum energy is 600 mJ in a single laser pulse and the pulse energy for the employed laser beam in the current measurement is around 100 mJ per pulse with 10 ns duration at a frequency of 5 Hz. The laser beam was focused onto the sample surface using a focusing lens (f = 300 mm), of which the irradiation energy was collected with a fiber-optical probe. The emission signal from the plasma was transferred via a fiber to a spectrometer system, at the same time, the experimental platform keeps moving to reduce the influence of sample on spectral signal after being bombarded by pulse laser. The spectrum was recorded on a computer, and the PLSUS software was utilized to identify emission lines. The possible elements could be identified according to the spectrum. The spectrometer and wavelength shift were calibrated via the pure metal sample. The optical fiber spectrometer we used is called AVANTES and its model number AvaSPEC-ULSi2048-USB2-SPC-1*. The spectral resolution of spectrometer and the detection delay are 1 nm and 1.5 μs, respectively. To increase the stability and reduce the standard deviation of the spectral intensities, 10 measured spectra were averaged.
2.2. Sample preparation The experimental samples from a steel company are powdered coal ash collected after organized discharge, which were divided into 8 groups based on different concentrations of Pb acetate solution. These solutions with different quantity of (CH3 COO)2 Pb∙3H2 O were prepared and the concentration of these solutions were 100 ppm, 200 ppm, 0.2%, 0.4%, 0.6%, 0.8%, 1%, respectively. These samples were dried at a drying temperature of 120 degrees for 24 h after mixing. To obtain a better LIBS signal, the tablet machine was used to press the coal ash into coal lump of 10 mm in diameter and 10 mm in thickness. Finally, these block samples were performed by LIBS test.
Fig. 1. Schematic diagram of the LIBS setup. 551
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Table 1 The elemental specifications of emission spectra of coal ash. Elements
experimental wavelengths/nm
NIST databases/nm
Elements
experimental wavelengths/nm
NIST databases/nm
AlⅠ AlⅠ MnⅡ CuⅠ AlⅠ AlⅠ CuⅠ AlⅡ CaⅠ FeⅠ CaⅠ TiⅡ TiⅢ TiⅠ TiⅡ
308.14 309.19 343.97 393.11 394.15 395.88 396.58 422.51 428.72 430.44 445.23 497.81 498.82 499.84 500.52
308.22 309.27 343.90 393.30 394.40 396.15 396.42 422.68 428.93 430.45 445.48 497.82 498.84 499.77 500.52
TiⅠ FeⅡ FeⅠ FeⅡ CaⅠ CuⅡ FeⅠ FeⅠ FeⅠ CaⅠ FeⅠ CaⅠ CaⅠ FeⅡ FeⅡ
501.19 526.57 532.63 537.01 555.12 589.83 610.03 612.01 615.97 643.57 670.32 714.55 719.99 766.09 769.55
501.33 526.59 532.61 537.02 551.29 589.80 610.03 612.02 615.94 643.90 670.36 714.81 720.21 766.09 769.63
3. Results and discussion 3.1. LIBS elemental analysis of ordinary coal ash In this paper, the ordinary coal ash was pressed into blocks for LIBS test, and the experimental results and the corresponding data from NIST database are identified in Table 1. As can be seen in the Table 1, the experimental wavelength data were compared with the NIST Atomic Spectra Database [23] and it is found that the experimental wavelength data is in good agreement with the standard NIST databases. However, it is not clear to concentrate the bands of all spectra in one picture. So we divided the 300–800 nm band into Figs. 2–4 based on the obvious characteristic peaks for an intuitive and clear understanding. Figs. 2–4 show the LIBS spectral lines of the samples with the wavelength range of 300 nm–800 nm without adding Pb element. As shown in the figures, some of the metal or nonmetallic elements have been detected by the LIBS experiment, such as the characteristic spectra of Ca, Fe, Cu, Mn, Ti and Al. As far as we known, wavelength shift is an inevitable problem in LIBS technique. To calibrate the spectra that we obtained, the spectra of pure metal elements were obtained to align with sample spectrum. Then the wavelength of the spectral lines in the spectrum was compared with the corresponding wavelength in NIST Atomic Spectra Database and all the spectra in the experiment were calibrated against the error of the wavelength. To verify the accuracy of calibration elements, four kinds of high-purity elements iron, calcium, cuprum and aluminum were separately selected. Under the same experimental conditions, four measured elemental spectral lines were compared with the corresponding wavelengths in the NIST. The samples then can be identified and confirmed when the characteristic line in the elemental spectrum is aligned with the spectrum in the samples. Figs. 5–8 show the comparative spectra of normal coal ash with pure Al, Cu, Ca and Fe, respectively. Al has characteristic peaks at 308.14 nm, 309.19 nm, 394.15 nm, 395.88 nm and 422.51 nm, which can be aligned with the characteristic peaks of the sample. Cu has characteristic peaks at 393.11 nm, 396.58 nm. Ca has characteristic peaks at 428.72 nm and 445.23 nm. Fe has characteristic peaks at 610.03 nm, 612.01 nm, 615.97 nm, 766.09 nm and 769.53 nm. 3.2. Element analysis of LIBS containing Pb in coal ash The above experimental results show that there is no Pb in coal ash. In order to simulate the haze, eight Pb solutions with different
Fig. 2. The LIBS spectrum of coal ash in 300–475 nm. 552
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 3. The LIBS spectrum of coal ash in 475–600 nm.
Fig. 4. The LIBS spectrum of coal ash in 600–800 nm.
Fig. 5. Spectral comparison chart of ordinary coal ash and aluminum.
Pb concentrations were prepared and coal ash samples were soaked in solutions and then dried thoroughly. After analyzing these contaminated coal ash samples with the LIBS technique, the spectrum of coal ash were obtained in comparison with the spectrum of the ash containing 0.8% Pb acetate, which are shown in Fig. 9. Fig. 9 is a comparison diagram of the three spectra of the normal coal ash belt of 300 nm to 495 nm, coal ash in a 0.8% Pb solution and pure Pb solution. As we can see from the Fig. 9, three lines (363.87 nm, 368.42 nm, 405.58 nm) in the samples spectrum can be easily identified as Pb element. To accurately identify the spectral lines of Pb in the spectrum of the coal ash soaked in the Pb solution, the LIBS technique was also employed for the pure Pb and its spectrum was obtained so that the spectral lines of Pb can be easily and accurately determined. The spectrum of pure Pb was selected as reference spectrum and the spectral lines of Pb (357.25 nm, 363.87 nm, 368.42 nm, 373.99 nm, 401.78 nm, 405.58 nm, 500.52 nm, 520.15 nm and 560.83 nm) were determined as is shown in Fig. 10. We can clearly see the characteristic spectra of pure Pb, and according to the NIST database, the characteristic spectrum of 373.993 nm is also detected in the spectrum.
553
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 6. Spectral comparison chart of ordinary coal ash and cuprum.
Fig. 7. Spectral comparison chart of ordinary coal ash and calcium.
Fig. 8. Spectral comparison chart of ordinary coal ash and Iron.
3.3. Quantitative analysis of Pb via internal standard method The internal standard calibration method is to add a certain weight of pure substance as the internal standard to a certain amount of the mixture of the analyzed samples, and then conduct choreographic analysis on the samples containing the internal standard, peak area (or peak height) and relative correction factor of internal standard and component to be measured were determined respectively. According to the formula, the percentage of the tested component in the sample can be obtained. The internal standard calibration method is an accurate quantitative method in chromatographic analysis. For quantitative analysis, the internal standard calibration method was used to determine the concentration of Pb in the solution. The result of the measurement is more accurate, because it is calculated by measuring the relative values of the peak areas of the internal standard substance and the measured component, and thus the error caused by the change of the operating conditions is eliminated to some extent. The sample and internal standard are mixed and injected into the column during operation. Therefore, as long as the ratio of the measured component to the internal standard in the mixed solution is constant, the change in the sample volume will not affect the quantitative result. The 554
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 9. The Spectrum of (a) ordinary coal ash, (b) Pb-containing coal ash and (c) Pure Pb.
Fig. 10. The spectrum of pure Pb.
internal standard method counteracts the influence of the sample volume, even the mobile phase, and the detector. Our quantitative analysis is based on the Lomakin–Scheibe equation: I = a*Cb,
(1)
where I is the observed intensity of the spectral line, ‘a’ is the experiment constant, ‘C’ is the concentration of the objective element, and ‘b’ is the self-absorption coefficient. And if self-absorption is ignored, b = 1. Therefore, in our work the equation could be changed to: IPb=aPb*CPb.
(2)
Since the samples were sliced from the same coal ash, it can be assumed that the concentrations of Al in these eight samples are the same. Thus, the Al I (308.142 nm) was selected as the reference element and the equation above was changed to: IPb/IAl = aPb*CPb/(aAl*CAl).
(3)
Further, the Eq. (3) can be simplified to: I*=AC.
(4) 555
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 11. Calibration curve for Pb in the coal ash.
Where I* is the relative intensity of spectral lines of Pb, A is a constant which equals aPb/aAl*CAl, and C is the concentration of Pb in the coal ash. As demonstrated in Eq. (4), the concentration of Pb is taken as the independent variable, and Σ (IPb/IAl) is used as the dependent variable while fitting a model to establish the calibration curve. As can be seen from the Fig. 11, linear correlation coefficient (R2) of the relative intensity versus the concentration of Pb is 0.98648. It means that the intensity of Pb in the LIBS spectrum of coal ash is proportional to the concentration of the corresponding Pb in coal ash. Thus, we concluded that the Pb concentration of the coal ash can be determined by analyzing the spectral lines of Pb with LIBS. The error bars indicate the uncertainties of spectral calibration causes fluctuations in Fe elements during quantitative analysis. The possible saturation with increasing laser energy and an increasing elemental concentration is very important in the calibration process. For the present measurement, the laser power is kept the same for all the different concentration, and no significant saturation effect is observed. 4. Conclusion Sulfates produced by the reaction of metal oxides in coal ash with the acid gases in the atmosphere are the main causes of haze-fog formation. In this study, LIBS was used for elemental analysis in coal ash. The spectrum of ordinary coal ash is obtained by LIBS and the result of element analysis indicates the ordinary coal ash contains Mn, Ti and other elements. To verify the accuracy of calibration elements, four kinds of high-purity elements Al, Fe, Cu and Ca were separately selected. Under the same experimental conditions, four measured elemental spectral lines were compared with the corresponding wavelengths in the NIST. The samples then can be identified and confirmed when the characteristic line in the elemental spectrum is aligned with the spectrum in the samples. By adding Pb element to the coal ash to simulate the atmospheric haze with Pb content and analyzing its content, a number of characteristic spectra of Pb and coal ash were obtained. The spectrum of pure Pb was selected as reference spectrum in order to accurately identify the spectral lines of Pb in the spectrum, and the spectral lines of Pb were determined. At the same time, an internal standard method was used to quantitatively analyze the concentration of Pb/Al element with a calibration curve that had a linear correlation coefficient (R2) of 0.98648, which indicated that rough estimation of Pb content can be achieved by the intensity of Pb in the spectrum. The experiments show that the LIBS technology can be used to quickly detect the composition and content of the elements in coal ash discharged by the enterprise. After establishing the calibration curve of the elements, the LIBS technique also provides a brand-new method for the accurate quantitative analysis of atmosphere based on the content of Pb, which is beneficial to our environment. Acknowledgments This work was supported by the National Key R&D Program of China (Grant No. 2017YFC0212700), “Six Talent Peaks” project in Jiangsu Province of China (Grant No. 2015-JNHB-011), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX18_1015), College Students’ Practice Innovation Training Program of Nuist (Grant No. 201810300033Z), and the Laboratory Open Project of Nuist (Grant No. 2017kf057). References [1] M. Li, X.W. Lian, J. Wang, et al., Pollution characteristics of metal components in PM2.5 in two districts of Guangzhou, J. Environ. Occup. Med. 33 (2016) 650–656. [2] B.W. Chu, J.M. Hao, J.H. Li, et al., The remarkable effect of FeSO4 seed aerosols on secondary organic aerosol formation from photo oxidation of a-pinene/NOx and toluene/NOx, Atmos. Environ. 55 (2012) 26–34. [3] L.P. Wang, J. Chen, Socio-economic influential factors of haze pollution in China: empirical study by eba model using spatial panel data, Acta Scientiae Circumstantiae 36 (2016) 3833–3839.
556
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
[4] D.N. Yang, C. Wang, Z.Y. Wang, F.C. Fu, et al., Atmospheric corrosion of common metals used in transformer substation and protection measures, Equip. Environ. Eng. 13 (2016) 126–129. [5] X. Wei, R. Zhang, G.S. Zhuang, The impact of coal-fired pollutants through long-range transport on air quality, J. Fudan Univ. (Nat. Sci.) 50 (2011) 547–555. [6] H.H. Chen, A. Laskin, J. Baltrusaitis, A.C. Gorski, et al., Coal fly ash as a source of iron in atmospheric dust, Environ. Sci. Technol. 46 (2012) 2112–2120. [7] X. Wan, P. Wang, Analysis of heavy metals in organisms based on an optimized quantitative LIBS, Optik 126 (2015) 1930–1934. [8] J. Kang, R.H. Li, Y.R. Wang, et al., Ultrasensitive detection of trace amounts of lead in water by LIBS-LIF using a wood-slicesubstrate as a water absorber, Anal. At. Spectrom. 32 (2017) 2292–2299. [9] M. Essien, L.J. Radziemski, Detection of cadmium, lead and zinc in aerosols by laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 3 (1988) 985–988. [10] A.M. Popov, T.A. Labutin, S.M. Zaytsev, et al., Determination of Ag, Cu, Mo and Pb in soils and ores by laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 29 (2014) 1925–1933. [11] J.J. Choi, S.J. Choi, J.J. Standoff, Detection of geological samples of metal, rock and soil at low pressures using laser-induced breakdown spectroscopy, Appl. Spectrosc. 70 (2016) 1411–1419. [12] N. Shaheen, N.M. Irfan, I. Khan, Presence of heavy metals in fruits and vegetables: health risk implications in Bangladesh, Chemosphere 152 (2016) 431–438. [13] S.M. Zaytesv, I.N. Krylov, A.M. Popov, et al., Accuracy enhancement of a multivariate calibration for lead determination in soils by laser-induced breakdown spectroscopy, Spectrochim. Acta Part B 140 (2018) 65–72. [14] D.M. Dong, C.J. Zhao, W.G. Zheng, et al., Spectral characterization of nitrogen in farmland soil by laser-induced breakdown spectroscopy, Spectrosc. Lett. 46 (2013) 421–426. [15] A.C. Samuels, J.F. DeLucia, K.L. McNesby, et al., Laser-induced breakdown spectroscopy of bacterial spores, molds, pollens and protein: initial studies of discrimination potential, Appl. Opt. 42 (2003) 6205–6209. [16] S.L. Lui, Y. Godwal, T.M. Taschuk, et al., Detection of lead in water using laser-induced breakdown spectroscopy and laser-induced fluorescence, Anal. Chem. 80 (2008) 1995–2000. [17] A.A. Muhammad, S.H. Ahmed, M.R. Aslam, et al., Determination of heavy metals in ambient air particulate matter using laser-induced breakdown spectroscopy, Arab. J. Sci. Eng. 38 (2013) 1655–1661. [18] X. Wen, Q.Y. Lin, G.H. Niu, et al., Emission enhancement of laser-induced breakdown spectroscopy for aqueous sample analysis based on Au nanoparticles and solid-phase substrate, Appl. Opt. 55 (2016) 6706–6712. [19] S.C. Yao, J.D. Lu, M.R. Dong, et al., Simultaneous measurements of coal ash composition by laser-induced breakdown spectroscopy in different optical collection, Proc. CESS 33 (2013) 54–60. [20] R.S. Zhu, Y.Z. Liu, Q.H. Zhang, et al., Quantitative analysis of Fe and detection of multiple elements in the coal ash by laser-induced breakdown spectroscopy, Optik 169 (2018) 77–84. [21] F. Jie, W. Zhe, W. Logan, et al., A PLS model based on dominant factor for coal analysis using laser-induced breakdown spectroscopy, Anal. Bioanal. Chem. 400 (2011) 3261–3271. [22] M.P. Mateo, G. Nicolas, A. Yañez, Characterization of inorganic species in coal by laser-induced breakdown spectroscopy using UV and IR radiations, Appl. Surf. Sci. 254 (2007) 868–872. [23] NIST Atomic Spectra Database, http://physics.nist.gov/PhysRefData/ASD/lines_form.html.
557
Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.com/locate/ijleo
Original research article
Rapid analysis of heavy metals in the coal ash with laser-induced breakdown spectroscopy
T
⁎
Wenyi Yina,b, Yuzhu Liua,b, , Fengbin Zhoua,b, Ruosong Zhua,b, Qihang Zhanga,b, Feng Jinc a
Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing University of Information Science & Technology, Nanjing 210044, PR China b Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, PR China c Advanced Technology Core, Baylor College of Medicine, Houston, TX 77030, USA
A R T IC LE I N F O
ABS TRA CT
Keywords: Laser-induced breakdown spectroscopy Coal ash Quantitative analysis Pb Aerosols Internal standard method
In this study, laser-induced breakdown spectroscopy (LIBS) was applied for the qualitative elemental analysis of coal ash. The spectrum of the ordinary coal ash indicated that coal ash contains elements of Al, Fe, Cu, Ca, Mn, Ti and so on. Sulfates produced by the reaction of metal oxides in coal ash with the acid gases in the atmosphere are the main causes of haze-fog formation. To simulate the haze, eight solutions with different Pb concentrations were prepared and coal ash samples were soaked in solutions and then dried thoroughly. The spectra were obtained from these contaminated coal ash samples with the LIBS technique. To accurately identify the spectral lines of Pb in the spectrum, the spectrum of pure Pb were selected as a reference and the spectral lines of Pb were determined. At the same time, an internal standard method was used to quantitatively analyze the value of the component of Pb/Al with a calibration curve that had a linear correlation coefficient (R2) of 0.98648, which indicates rough estimation of Pb content can be achieved by the intensity of Pb in the spectrum. The results of the experiments show that LIBS technique can be employed for the rapid detection and analysis of metal elements in coal ash and provide a brand-new method for the detection of atmospheric environment based on the content of Pb.
1. Introduction The coal ash is the product of the complete burning of coal, mainly including various metals and non-metal oxides and salts, which is an important parameter in coal utilization. Coal was widely used for industrial production beginning with the industrial revolution in the late 18th century, which brought great productivity to the society and promoted the development of industrialization [1,2]. Combustion of coal, however, as the largest source of secondary particles and a main emission source of primary particles, a large amount of coal ash reacts with various substances in the atmosphere to form haze, which has caused a huge threat to human health [3,4]. Pb is one of those carcinogenic and toxic heavy metal elements, seriously damaging human health and even causing cancer [5–7]. In addition, it also has negative impact on ecosystem and severely pollutes environment [8]. Nevertheless, due to heavy metal
⁎ Corresponding author at: Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing University of Information Science & Technology, Nanjing 210044, PR China E-mail address: [email protected] (Y. Liu).
https://doi.org/10.1016/j.ijleo.2018.08.110 Received 15 May 2018; Received in revised form 25 August 2018; Accepted 25 August 2018 0030-4026/ © 2018 Elsevier GmbH. All rights reserved.
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
elements such as Pb in haze, it is of great significance to conduct qualitative analysis [9]. Laser-induced breakdown spectroscopy (LIBS) is a type of the atomic emission spectroscopy that applies highly energetic pulses to various samples in solid, liquid, gaseous and aerosol forms [10–12]. In comparison with other techniques, LIBS technique has many advantages such as rapid and precise response, simultaneous muti-element analysis, high sensitivity, cost effective and so on [13–15]. As a new material identification analysis technology, LIBS can be used both in the laboratory and on-line detection in industrial sites [16]. Whatʼs more, LIBS has been widely applied in hydrology, geological prospecting, environmental monitoring, scientific research and other fields of application successfully [17,18]. What’s more, the determination of coal ash content by LIBS technology is of great significance for the correct evaluation of coal quality and processing and utilization [19,20]. In recent years, domestic and foreign researchers have made some attempts on the application of LIBS technology in coal quality analysis and made some progress. Feng et al. [21] proposed a method of PLS quantitative analysis based on dominant factors for low accuracy of LIBS analysis of coal quality components, which improved the measurement accuracy to some extent. Mateo et al. [22] analyzed the influence of different laser wavelengths and sample placement in the measurement process on the measurement of some secondary elements contained in coal and they found that the short wave length laser is more favorable for accurate measurement. These findings provide a favorable reference for the application of LIBS in coal ash. In the present work, a sample of coal ash obtained from a steel company was tested based on laser-induced breakdown spectroscopy and the spectra of the elements were analyzed. To simulate haze containing Pb, the Pb compound was added to the coal ash. To verify the accuracy of LIBS technology, test samples were compared with the ordinary coal ash, which does not contain Pb. In this paper, a calibration curve was obtained and can be taken as a reference line for Pb detection in the future by quantitative analysis of Pb in coal ash. LIBS technology provides a new method for the detection and analysis of coal ash.
2. Experimental 2.1. Experimental setup The schematic diagram of experimental setup is shown in Fig. 1. The Q-switched Nd: YAG laser used as the excitation laser was operated at a fundamental wavelength of 1064 nm, the maximum energy is 600 mJ in a single laser pulse and the pulse energy for the employed laser beam in the current measurement is around 100 mJ per pulse with 10 ns duration at a frequency of 5 Hz. The laser beam was focused onto the sample surface using a focusing lens (f = 300 mm), of which the irradiation energy was collected with a fiber-optical probe. The emission signal from the plasma was transferred via a fiber to a spectrometer system, at the same time, the experimental platform keeps moving to reduce the influence of sample on spectral signal after being bombarded by pulse laser. The spectrum was recorded on a computer, and the PLSUS software was utilized to identify emission lines. The possible elements could be identified according to the spectrum. The spectrometer and wavelength shift were calibrated via the pure metal sample. The optical fiber spectrometer we used is called AVANTES and its model number AvaSPEC-ULSi2048-USB2-SPC-1*. The spectral resolution of spectrometer and the detection delay are 1 nm and 1.5 μs, respectively. To increase the stability and reduce the standard deviation of the spectral intensities, 10 measured spectra were averaged.
2.2. Sample preparation The experimental samples from a steel company are powdered coal ash collected after organized discharge, which were divided into 8 groups based on different concentrations of Pb acetate solution. These solutions with different quantity of (CH3 COO)2 Pb∙3H2 O were prepared and the concentration of these solutions were 100 ppm, 200 ppm, 0.2%, 0.4%, 0.6%, 0.8%, 1%, respectively. These samples were dried at a drying temperature of 120 degrees for 24 h after mixing. To obtain a better LIBS signal, the tablet machine was used to press the coal ash into coal lump of 10 mm in diameter and 10 mm in thickness. Finally, these block samples were performed by LIBS test.
Fig. 1. Schematic diagram of the LIBS setup. 551
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Table 1 The elemental specifications of emission spectra of coal ash. Elements
experimental wavelengths/nm
NIST databases/nm
Elements
experimental wavelengths/nm
NIST databases/nm
AlⅠ AlⅠ MnⅡ CuⅠ AlⅠ AlⅠ CuⅠ AlⅡ CaⅠ FeⅠ CaⅠ TiⅡ TiⅢ TiⅠ TiⅡ
308.14 309.19 343.97 393.11 394.15 395.88 396.58 422.51 428.72 430.44 445.23 497.81 498.82 499.84 500.52
308.22 309.27 343.90 393.30 394.40 396.15 396.42 422.68 428.93 430.45 445.48 497.82 498.84 499.77 500.52
TiⅠ FeⅡ FeⅠ FeⅡ CaⅠ CuⅡ FeⅠ FeⅠ FeⅠ CaⅠ FeⅠ CaⅠ CaⅠ FeⅡ FeⅡ
501.19 526.57 532.63 537.01 555.12 589.83 610.03 612.01 615.97 643.57 670.32 714.55 719.99 766.09 769.55
501.33 526.59 532.61 537.02 551.29 589.80 610.03 612.02 615.94 643.90 670.36 714.81 720.21 766.09 769.63
3. Results and discussion 3.1. LIBS elemental analysis of ordinary coal ash In this paper, the ordinary coal ash was pressed into blocks for LIBS test, and the experimental results and the corresponding data from NIST database are identified in Table 1. As can be seen in the Table 1, the experimental wavelength data were compared with the NIST Atomic Spectra Database [23] and it is found that the experimental wavelength data is in good agreement with the standard NIST databases. However, it is not clear to concentrate the bands of all spectra in one picture. So we divided the 300–800 nm band into Figs. 2–4 based on the obvious characteristic peaks for an intuitive and clear understanding. Figs. 2–4 show the LIBS spectral lines of the samples with the wavelength range of 300 nm–800 nm without adding Pb element. As shown in the figures, some of the metal or nonmetallic elements have been detected by the LIBS experiment, such as the characteristic spectra of Ca, Fe, Cu, Mn, Ti and Al. As far as we known, wavelength shift is an inevitable problem in LIBS technique. To calibrate the spectra that we obtained, the spectra of pure metal elements were obtained to align with sample spectrum. Then the wavelength of the spectral lines in the spectrum was compared with the corresponding wavelength in NIST Atomic Spectra Database and all the spectra in the experiment were calibrated against the error of the wavelength. To verify the accuracy of calibration elements, four kinds of high-purity elements iron, calcium, cuprum and aluminum were separately selected. Under the same experimental conditions, four measured elemental spectral lines were compared with the corresponding wavelengths in the NIST. The samples then can be identified and confirmed when the characteristic line in the elemental spectrum is aligned with the spectrum in the samples. Figs. 5–8 show the comparative spectra of normal coal ash with pure Al, Cu, Ca and Fe, respectively. Al has characteristic peaks at 308.14 nm, 309.19 nm, 394.15 nm, 395.88 nm and 422.51 nm, which can be aligned with the characteristic peaks of the sample. Cu has characteristic peaks at 393.11 nm, 396.58 nm. Ca has characteristic peaks at 428.72 nm and 445.23 nm. Fe has characteristic peaks at 610.03 nm, 612.01 nm, 615.97 nm, 766.09 nm and 769.53 nm. 3.2. Element analysis of LIBS containing Pb in coal ash The above experimental results show that there is no Pb in coal ash. In order to simulate the haze, eight Pb solutions with different
Fig. 2. The LIBS spectrum of coal ash in 300–475 nm. 552
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 3. The LIBS spectrum of coal ash in 475–600 nm.
Fig. 4. The LIBS spectrum of coal ash in 600–800 nm.
Fig. 5. Spectral comparison chart of ordinary coal ash and aluminum.
Pb concentrations were prepared and coal ash samples were soaked in solutions and then dried thoroughly. After analyzing these contaminated coal ash samples with the LIBS technique, the spectrum of coal ash were obtained in comparison with the spectrum of the ash containing 0.8% Pb acetate, which are shown in Fig. 9. Fig. 9 is a comparison diagram of the three spectra of the normal coal ash belt of 300 nm to 495 nm, coal ash in a 0.8% Pb solution and pure Pb solution. As we can see from the Fig. 9, three lines (363.87 nm, 368.42 nm, 405.58 nm) in the samples spectrum can be easily identified as Pb element. To accurately identify the spectral lines of Pb in the spectrum of the coal ash soaked in the Pb solution, the LIBS technique was also employed for the pure Pb and its spectrum was obtained so that the spectral lines of Pb can be easily and accurately determined. The spectrum of pure Pb was selected as reference spectrum and the spectral lines of Pb (357.25 nm, 363.87 nm, 368.42 nm, 373.99 nm, 401.78 nm, 405.58 nm, 500.52 nm, 520.15 nm and 560.83 nm) were determined as is shown in Fig. 10. We can clearly see the characteristic spectra of pure Pb, and according to the NIST database, the characteristic spectrum of 373.993 nm is also detected in the spectrum.
553
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 6. Spectral comparison chart of ordinary coal ash and cuprum.
Fig. 7. Spectral comparison chart of ordinary coal ash and calcium.
Fig. 8. Spectral comparison chart of ordinary coal ash and Iron.
3.3. Quantitative analysis of Pb via internal standard method The internal standard calibration method is to add a certain weight of pure substance as the internal standard to a certain amount of the mixture of the analyzed samples, and then conduct choreographic analysis on the samples containing the internal standard, peak area (or peak height) and relative correction factor of internal standard and component to be measured were determined respectively. According to the formula, the percentage of the tested component in the sample can be obtained. The internal standard calibration method is an accurate quantitative method in chromatographic analysis. For quantitative analysis, the internal standard calibration method was used to determine the concentration of Pb in the solution. The result of the measurement is more accurate, because it is calculated by measuring the relative values of the peak areas of the internal standard substance and the measured component, and thus the error caused by the change of the operating conditions is eliminated to some extent. The sample and internal standard are mixed and injected into the column during operation. Therefore, as long as the ratio of the measured component to the internal standard in the mixed solution is constant, the change in the sample volume will not affect the quantitative result. The 554
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 9. The Spectrum of (a) ordinary coal ash, (b) Pb-containing coal ash and (c) Pure Pb.
Fig. 10. The spectrum of pure Pb.
internal standard method counteracts the influence of the sample volume, even the mobile phase, and the detector. Our quantitative analysis is based on the Lomakin–Scheibe equation: I = a*Cb,
(1)
where I is the observed intensity of the spectral line, ‘a’ is the experiment constant, ‘C’ is the concentration of the objective element, and ‘b’ is the self-absorption coefficient. And if self-absorption is ignored, b = 1. Therefore, in our work the equation could be changed to: IPb=aPb*CPb.
(2)
Since the samples were sliced from the same coal ash, it can be assumed that the concentrations of Al in these eight samples are the same. Thus, the Al I (308.142 nm) was selected as the reference element and the equation above was changed to: IPb/IAl = aPb*CPb/(aAl*CAl).
(3)
Further, the Eq. (3) can be simplified to: I*=AC.
(4) 555
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
Fig. 11. Calibration curve for Pb in the coal ash.
Where I* is the relative intensity of spectral lines of Pb, A is a constant which equals aPb/aAl*CAl, and C is the concentration of Pb in the coal ash. As demonstrated in Eq. (4), the concentration of Pb is taken as the independent variable, and Σ (IPb/IAl) is used as the dependent variable while fitting a model to establish the calibration curve. As can be seen from the Fig. 11, linear correlation coefficient (R2) of the relative intensity versus the concentration of Pb is 0.98648. It means that the intensity of Pb in the LIBS spectrum of coal ash is proportional to the concentration of the corresponding Pb in coal ash. Thus, we concluded that the Pb concentration of the coal ash can be determined by analyzing the spectral lines of Pb with LIBS. The error bars indicate the uncertainties of spectral calibration causes fluctuations in Fe elements during quantitative analysis. The possible saturation with increasing laser energy and an increasing elemental concentration is very important in the calibration process. For the present measurement, the laser power is kept the same for all the different concentration, and no significant saturation effect is observed. 4. Conclusion Sulfates produced by the reaction of metal oxides in coal ash with the acid gases in the atmosphere are the main causes of haze-fog formation. In this study, LIBS was used for elemental analysis in coal ash. The spectrum of ordinary coal ash is obtained by LIBS and the result of element analysis indicates the ordinary coal ash contains Mn, Ti and other elements. To verify the accuracy of calibration elements, four kinds of high-purity elements Al, Fe, Cu and Ca were separately selected. Under the same experimental conditions, four measured elemental spectral lines were compared with the corresponding wavelengths in the NIST. The samples then can be identified and confirmed when the characteristic line in the elemental spectrum is aligned with the spectrum in the samples. By adding Pb element to the coal ash to simulate the atmospheric haze with Pb content and analyzing its content, a number of characteristic spectra of Pb and coal ash were obtained. The spectrum of pure Pb was selected as reference spectrum in order to accurately identify the spectral lines of Pb in the spectrum, and the spectral lines of Pb were determined. At the same time, an internal standard method was used to quantitatively analyze the concentration of Pb/Al element with a calibration curve that had a linear correlation coefficient (R2) of 0.98648, which indicated that rough estimation of Pb content can be achieved by the intensity of Pb in the spectrum. The experiments show that the LIBS technology can be used to quickly detect the composition and content of the elements in coal ash discharged by the enterprise. After establishing the calibration curve of the elements, the LIBS technique also provides a brand-new method for the accurate quantitative analysis of atmosphere based on the content of Pb, which is beneficial to our environment. Acknowledgments This work was supported by the National Key R&D Program of China (Grant No. 2017YFC0212700), “Six Talent Peaks” project in Jiangsu Province of China (Grant No. 2015-JNHB-011), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX18_1015), College Students’ Practice Innovation Training Program of Nuist (Grant No. 201810300033Z), and the Laboratory Open Project of Nuist (Grant No. 2017kf057). References [1] M. Li, X.W. Lian, J. Wang, et al., Pollution characteristics of metal components in PM2.5 in two districts of Guangzhou, J. Environ. Occup. Med. 33 (2016) 650–656. [2] B.W. Chu, J.M. Hao, J.H. Li, et al., The remarkable effect of FeSO4 seed aerosols on secondary organic aerosol formation from photo oxidation of a-pinene/NOx and toluene/NOx, Atmos. Environ. 55 (2012) 26–34. [3] L.P. Wang, J. Chen, Socio-economic influential factors of haze pollution in China: empirical study by eba model using spatial panel data, Acta Scientiae Circumstantiae 36 (2016) 3833–3839.
556
Optik - International Journal for Light and Electron Optics 174 (2018) 550–557
W. Yin et al.
[4] D.N. Yang, C. Wang, Z.Y. Wang, F.C. Fu, et al., Atmospheric corrosion of common metals used in transformer substation and protection measures, Equip. Environ. Eng. 13 (2016) 126–129. [5] X. Wei, R. Zhang, G.S. Zhuang, The impact of coal-fired pollutants through long-range transport on air quality, J. Fudan Univ. (Nat. Sci.) 50 (2011) 547–555. [6] H.H. Chen, A. Laskin, J. Baltrusaitis, A.C. Gorski, et al., Coal fly ash as a source of iron in atmospheric dust, Environ. Sci. Technol. 46 (2012) 2112–2120. [7] X. Wan, P. Wang, Analysis of heavy metals in organisms based on an optimized quantitative LIBS, Optik 126 (2015) 1930–1934. [8] J. Kang, R.H. Li, Y.R. Wang, et al., Ultrasensitive detection of trace amounts of lead in water by LIBS-LIF using a wood-slicesubstrate as a water absorber, Anal. At. Spectrom. 32 (2017) 2292–2299. [9] M. Essien, L.J. Radziemski, Detection of cadmium, lead and zinc in aerosols by laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 3 (1988) 985–988. [10] A.M. Popov, T.A. Labutin, S.M. Zaytsev, et al., Determination of Ag, Cu, Mo and Pb in soils and ores by laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 29 (2014) 1925–1933. [11] J.J. Choi, S.J. Choi, J.J. Standoff, Detection of geological samples of metal, rock and soil at low pressures using laser-induced breakdown spectroscopy, Appl. Spectrosc. 70 (2016) 1411–1419. [12] N. Shaheen, N.M. Irfan, I. Khan, Presence of heavy metals in fruits and vegetables: health risk implications in Bangladesh, Chemosphere 152 (2016) 431–438. [13] S.M. Zaytesv, I.N. Krylov, A.M. Popov, et al., Accuracy enhancement of a multivariate calibration for lead determination in soils by laser-induced breakdown spectroscopy, Spectrochim. Acta Part B 140 (2018) 65–72. [14] D.M. Dong, C.J. Zhao, W.G. Zheng, et al., Spectral characterization of nitrogen in farmland soil by laser-induced breakdown spectroscopy, Spectrosc. Lett. 46 (2013) 421–426. [15] A.C. Samuels, J.F. DeLucia, K.L. McNesby, et al., Laser-induced breakdown spectroscopy of bacterial spores, molds, pollens and protein: initial studies of discrimination potential, Appl. Opt. 42 (2003) 6205–6209. [16] S.L. Lui, Y. Godwal, T.M. Taschuk, et al., Detection of lead in water using laser-induced breakdown spectroscopy and laser-induced fluorescence, Anal. Chem. 80 (2008) 1995–2000. [17] A.A. Muhammad, S.H. Ahmed, M.R. Aslam, et al., Determination of heavy metals in ambient air particulate matter using laser-induced breakdown spectroscopy, Arab. J. Sci. Eng. 38 (2013) 1655–1661. [18] X. Wen, Q.Y. Lin, G.H. Niu, et al., Emission enhancement of laser-induced breakdown spectroscopy for aqueous sample analysis based on Au nanoparticles and solid-phase substrate, Appl. Opt. 55 (2016) 6706–6712. [19] S.C. Yao, J.D. Lu, M.R. Dong, et al., Simultaneous measurements of coal ash composition by laser-induced breakdown spectroscopy in different optical collection, Proc. CESS 33 (2013) 54–60. [20] R.S. Zhu, Y.Z. Liu, Q.H. Zhang, et al., Quantitative analysis of Fe and detection of multiple elements in the coal ash by laser-induced breakdown spectroscopy, Optik 169 (2018) 77–84. [21] F. Jie, W. Zhe, W. Logan, et al., A PLS model based on dominant factor for coal analysis using laser-induced breakdown spectroscopy, Anal. Bioanal. Chem. 400 (2011) 3261–3271. [22] M.P. Mateo, G. Nicolas, A. Yañez, Characterization of inorganic species in coal by laser-induced breakdown spectroscopy using UV and IR radiations, Appl. Surf. Sci. 254 (2007) 868–872. [23] NIST Atomic Spectra Database, http://physics.nist.gov/PhysRefData/ASD/lines_form.html.
557