LIBS Sensing for In-Situ Iron Detection and Quantification in Wastewater Outfall

doi:10.21203/rs.3.rs-2733388/v1

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LIBS Sensing for In-Situ Iron Detection and Quantification in Wastewater Outfall

https://doi.org/10.21203/rs.3.rs-2733388/v1

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Iron measurement in water is important to ensure its quality. Although iron is essential for good health, when its level in water exceeds the permitted limit, it can cause health- and environmental-related issues. In this study, a spectroscopic methodology based on laser-induced breakdown spectroscopy (LIBS) has been developed for in-situ detection and quantification of iron in water. Wastewater from the foundation of a historic building at the National Energy Technology Laboratory (NETL) Morgantown site was collected for ten continual days and analyzed with the LIBS benchtop system and NETL’s LIBS probe. Spectral signatures of iron in the LIBS spectra were identified which demonstrated the significant presence of iron in the test samples. Machine learning tools, principal component analysis (PCA) and linear discriminant analysis (LDA), were used to distinguish the ten liquid samples. For quantitative study, univariate and multivariate analyses were performed. While two strong emission lines of iron, Fe I 371.9 nm and Fe I 373nm, were used to develop the calibration curve for univariate analysis, whole spectra in the selected wavelength range were utilized to plot the partial least square regression (PLS-R) curve. These calibration curves were then used to estimate the iron content in water samples, and the estimated values were compared to inductively coupled plasma-mass spectrometry (ICP-MS) measurements. The relative difference was found to be less than 15%, which shows that the iron content in liquid can be reliably detected and quantified by the LIBS-based spectroscopic technique.

Iron detection by LIBS

LIBS

Underwater LIBS

Iron measurement in water

LIBS sensor

When iron content in water exceeds a certain level, it causes a number of detrimental effects on both humans and the environment, depending on concentration. Water with higher iron concentration can have an unpleasant odor and a metallic taste, and can affect food items prepared using it. Likewise, staining of household fixtures such as bathtubs, faucets, and sinks is also observed when the water has high iron content and, in extreme cases, water supply lines can also become restricted or clogged due to scaling. Thus, the U.S. Environmental Protection Agency (EPA) has restricted iron in drinking water to a maximum of 0.3 mg/l (Marson, 1949). Iron is essential for most living organisms due to its role in metabolic processes. However, it should be kept within an appropriate level. Regarding human health, while iron deficiency may lead to anemia, its overconsumption may contribute to damage to the liver, pancreas, and heart, and may lead to diabetes, hemochromatosis, stomach problems, and nausea (Abbaspour, Hurrell, & Kelishadi, 2014; Swanson, 2003). Likewise, iron level in surface waters is important for aquatic plants and animals, and can be altered by industrial wastewater or acid mine drainage. Therefore, regular monitoring of iron content in drinking water, wastewater, and natural water is important. Moreover, for drinking water purification, an initial estimate of iron concentration is useful to choose the proper filtration method.

Typically, iron content is determined using analytical methods such as ICP-MS or ICP-OES (inductively coupled plasma- mass spectrometry or -optical emission spectrometry), as these methods are considered reliable. However, since they require time consuming sample preparation, processing, and data analysis, these methods are inappropriate for in-situ and online detection and quantification of iron contaminants in water. A spectroscopic technique known as laser-induced breakdown spectroscopy (LIBS) may be a robust alternative for field use or in-line for continuous quantification of iron. In contrast to ICP-based methods, LIBS requires little to no sample pretreatment, and results can be obtained in near real time (Radziemski & Cremers, 2013; Singh & Thakur, 2020).

In LIBS, a laser pulse is focused into or onto the test sample where a micro plasma is produced. This plasma emits light which is collected and analyzed to reveal the characteristic spectral signatures of the elemental species present in the samples. LIBS is a versatile technique and has been used for analysis of samples in solid, liquid, and gaseous states for multiple applications. It has been studied for application in the steel industry, pharmaceuticals, agriculture, geological study, space exploration, forensic science, cultural heritage, carbon storage, quality monitoring of food materials, etc. (Anglos & Detalle, 2014; Bhatt et al., 2015; Bhatt, Alfarraj, Ghany, Yueh, & Singh, 2017; Bhatt, Jain, & McIntyre, 2018; Galmed, Maaza, Mothudi, & Harith, 2021; C. Goueguel, McIntyre, Singh, Jain, & Karamalidis, 2014; Hark & East, 2014; Knight, Scherbarth, Cremers, & Ferris, 2000; Noll et al., 2001; Sezer, Bilge, & Boyaci, 2017; Tiwari, Rai, Kumar, Parigger, & Rai, 2019; K. Yu, Ren, & Zhao, 2020). Since Cremers et al. (Cremers, Radziemski, & Loree, 1984) started LIBS for liquid analysis in 1984, a number of journal articles have been published on LIBS applications for elemental analysis in aqueous solutions (Kuwako, Uchida, & Maeda, 2003; Lazic, Colao, Fantoni, Spizzichino, & Jovićević, 2007; Michel & Chave, 2008; Michel, Lawrence-Snyder, Angel, & Chave, 2007; Nakane et al., 2000; Pace, D'Angelo, Bertuccelli, & Bertuccelli, 2006; Rai, Yueh, & Singh, 2008; Wachter & Cremers, 1987) and colloidal suspensions (Bundschuh, Yun, & Knopp, 2001; Ito, Ueki, & Nakamura, 1995; Nakamura, Ito, Sone, Hiraga, & Kaneko, 1996; Yun, 2007; Yun, Bundschuh, Neck, & Kim, 2001). Elements such as Ca, K, Na, Mg, Mn, and Li, which are easily detected in liquids due to their low ionization and excitation energies, have been studied extensively using LIBS (Bhatt, Goueguel, Jain, McIntyre, & Singh, 2020; Lazic & Jovićević, 2014; X. Yu et al., 2014). Additionally, many research groups have reported successful detection and quantification of heavy metals in liquids (Lee, Oh, & Han, 2012; Rai et al., 2008; Zhao, Chen, Zhang, Li, & Zhou, 2010). Both underwater (i.e., with the laser spark submerged in the bulk fluid) and surface measurements have been used for liquid analysis by LIBS. Since spectral signals are comparatively weaker in underwater LIBS due to strong quenching, it is more useful for the elements which have comparatively low excitation energy and have strong resonance emission lines such as the alkali and alkaline earth elements. For elements with higher excitation energies, such as the transition metals, focusing the laser pulse onto the surface of the liquid results in a hotter plasma due to less quenching. Consequently, the signals are significantly stronger. However, absorption of the high-energy laser pulse by the liquid surface often results in splashing, thus contaminating optical elements and hindering the efficiency of LIBS measurements on the surface. To overcome the splashing issue, researchers can produce a laminar jet of the liquid analyte and focus laser pulse on the jet to ablate the sample.

Monitoring iron concentration is not only important for ensuring drinking water quality; it can also be used to evaluate the health of the water supply system. For example, an increasing iron concentration can be an indication of corrosion. Strategically placed LIBS sensors could provide continuous, or at least high frequency, compositional measurements and quickly identify such a change in concentration. A few researchers have evaluated LIBS for iron detection in liquids (Golik et al., 2015; Loudyi et al., 2009; Nakamura et al., 1996). Golik et al. (Golik et al., 2015) studied the possibility of femtosecond LIBS for quantitative analysis of iron in water by focusing laser pulse on the surface of the liquid sample and achieving a limit of detection (LOD) of 2.6 ppm. An iron suspension in water was also studied by dual LIBS achieving a LOD of 16 ppb pulses (Nakamura et al., 1996). A dramatically improved LOD for iron (and lead) was reported by Loudyi et al. (Loudyi et al., 2009) with aid of laser-induced fluorescence (LIF) augmented LIBS (i.e., LIF-LIBS). In previous experiments, the LIBS technique was used to study dissolved minerals and critical elements such as rare earth elements (REEs), toxic elements such as, Se, Hg, and S in liquids, and even CO₂-induced dissolution of carbonates (Bhatt, Hartzler, Jain, & McIntyre, 2021; Bhatt, Jain, Edenborn, & McIntyre, 2019; Bhatt, Jain, Goueguel, McIntyre, & Singh, 2017; Bhatt et al., 2018; C. L. Goueguel, Bhatt, Jain, Lopano, & McIntyre, 2018).

In this study, samples collected from the foundation drain of a building built in the 1980s on top of coal ash have been analyzed to determine iron content over a period of ten days. In the past, the outflow from the foundation drain reached a nearby creek causing discoloration of the creek bed. To remedy this, a filtration system was installed in the mid-nineties to remove dissolved minerals, although, staining resulting from iron oxide remains today. The iron content of this outflow has been monitored from 1992 to the present day. Over this period, the iron level in the (unfiltered) outflow has dropped from 650 ppm to 30 ppm. No iron was detected in the filtered outflow to the creek in the course of this study. To study these samples, two experimental systems were used: 1) a traditional benchtop LIBS setup and 2) an NETL-designed miniature LIBS probe. Sample measurements were performed on both the liquid surface using a laminar jet (benchtop system) and under the liquid surface (miniature probe).

Two experimental LIBS systems used in this study are briefly explained below.

2.1. Benchtop Setup with Jet System

Benchtop setup used for the analysis is shown in Fig. 1. A 1064 nm Nd:YAG laser (New Wave Research) producing pulses of 5 ns width was used as the excitation source. The laser pulses were focused with a 10 cm focal length lens onto the surface of the liquid jet. The pulse energy delivered to the sample was adjusted using a half wave plate and a polarizing beam splitter. A laminar jet of the test samples was produced by using a peristaltic pump to deliver liquid to a glass Pasture pipette having a fine tip that had been drawn down in diameter to ~ 0.5 mm. The top of the pipette was open to the atmosphere and was continuously fed by the peristaltic pump whose rate was set to maintain a constant fluid level in the pipette body (approx. 50–100 mm). This arrangement (i.e., a gravity fed jet), evened out pulsations due to the pump and produced an unvarying laminar jet approximately 1 cm long and 0.5 mm in diameter. Focusing of the laser pulse onto the jet was optimized to produce maximum emission signals. The plasma emission was collected in a confocal mode through the same focusing lens and reflected by a dichroic mirror towards the collection lens of 5 cm focal length. The light was collected and directed through an optical fiber (Andor SR-OPT-8024) and was provided to the Czerny-Turner spectrograph (Andor Shamrock SR303i-A) with an intensified charge-coupled device (ICCD) camera (Andor iStar DH320T-25F-03).

2.2. Miniaturized Probe for Underwater LIBS

The schematic of the miniature LIBS probe sensor head is shown in Fig. 2. Details about the probe’s construction and operation can be found in Hartzler et al. (2019) (Hartzler, Jain, & McIntyre, 2019). The sensor head is built using a low-cost passively Q-switched (PQSW) laser which is fiber coupled to a pump laser and a gated spectrometer (Andor Shamrock SR303i-A and iStar DH320T-25F-0). The Nd:YAG laser is pumped by a quasi-CW, 808 nm diode laser (Apollo Instruments F700-808-6) with a 250 µs 83 mJ pulse at 5 Hz. The system produces an output beam at 1064 nm with a 3 ns pulse width and an energy of 4 mJ per pulse. The laser pulse is focused into the test samples contained in a cuvette approximately 1 cm deep from the surface. Similar to the benchtop setup, plasma emission was collected in a confocal manner and directed to the spectrometer via lens, dichroic mirror, and a fiber optic.

The test samples used in this experiment were collected from the wastewater outfall of a building at the NETL Morgantown site. Samples were collected for ten continuous days from September 29 to October 8, 2020. One additional sample collected on December 19, 2019, was also used as a test sample. The sample collected in 2019 was labelled as S0 and the ten samples collected in September and October 2020 were labelled as S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, respectively. To prevent iron precipitation, concentrated nitric acid (70% v/v HNO₃) was added to the samples resulting in a final concentration of 10% v/v nitric acid. For calibration, a stock solution was prepared by dissolving iron nitrate (Fe(NO₃)₃·9H₂O) in 10% v/v nitric acid in deionized (DI) water. This stock solution was further diluted with 10% nitric acid to make a range of Fe cation concentration from 1000 ppm to 8 ppm. Univariate and multivariate calibration curves were developed from the LIBS data collected from these calibration samples, which were later used to predict the iron content in the test water samples.

4.1. LIBS Spectra and Iron Detection

LIBS spectra were recorded from the calibration samples and test samples using both benchtop setup and the miniature LIBS probe. In LIBS, spectral signals are the emission lines of the analytes of interest, each of which can have a different ionization energy and persistence time (i.e., emission lifetime). Therefore, experimental parameters such as laser energy and detector gate time delay and width are crucial to ensure the desired analytes are excited and detected. These three parameters were varied to obtain strong iron signals in the spectra. For benchtop setup, laser energy in the range of 15−30 mJ, gate time delay in 1−5 µs, and gate width in 5−10 µs range were observed to give identifiable iron lines in the 360−380 nm wavelength range with the following lines detected: Fe I 363.1 nm, Fe I 371.9 nm, Fe I 373 nm, Fe I 374.5 nm, Fe I 374.9 nm, and Fe I 375.9 nm, see Fig. 3(a).

In the benchtop setup, the laser pulse was focused onto the surface of the water jet of the sample; thus, plasma formation, expansion, and emission took place in air at atmospheric pressure. However, for the miniature LIBS probe system, the laser pulse was focused into the water below the surface, which is known as underwater LIBS. In underwater LIBS, signals are comparatively weaker than those in air due to the quenching effect, resulting in lower plasma temperatures and significantly shorter plasma persistence time. While evaluating the experimental parameters, a detector gate time delay in the range of 100−400 ns and gate width in the range of 0.5−3 µs were found to produce detectable iron emission lines in the LIBS spectra. Unfortunately, with a PQSW laser, the pulse energy, 4 mJ for this system, is essentially fixed. With underwater measurements by the LIBS probe, the following iron lines were observed: Fe I 371.9 nm, Fe I 373 nm, Fe I 374.5 nm, Fe I 374.9 nm, and Fe I 385.99 nm, see Fig. 3(b). In addition to iron, some other analytes were also detected in these test samples, specifically, Ca, Mg, Sr, Na, and K (data not shown). Note that the optical components used in the benchtop setup and miniature LIBS probe were different, where most significantly, the miniature probe optics strongly attenuated parts of the UV spectrum. Therefore, there are different wavelength ranges in Fig. 3 as no lines were observable below ~ 370 nm for the miniature probe.

4.2. Principal Component and Linear Discriminant Analyses

Machine learning tools, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were employed to see if the samples collected over ten days were identifiably different from each other. PCA is an unsupervised machine learning method used to reduce the dimensionality of large datasets and it maximizes the variance of the data. It transforms a large set of variables into a smaller one preserving most of the information held by original data. These smaller number of latent variables are called principal components (PC) and each PC contains a certain portion of the original information. The first component carries the largest amount of information and subsequent PCs hold lesser amounts of information than the previous one. Unlike PCA, LDA is a supervised classification technique, and its goal is to project the features provided in higher dimensional space onto a lower-dimensional space. While PCA maximizes the variance of the data, LDA maximizes the distance between the groupings of the data. LDA computes the directions called linear discriminants representing the axes, which maximizes the separation between multiple classes.

In this study, PCA and LDA were combined to see clustering of the eleven water samples. PCA was performed using the whole spectral data in the wavelength range of 360 to 377 nm. One of the major attributes of PCA is the scores, which describe the properties of the samples and are represented as a map of one PC plotted against another, showing similarities or differences of the samples. Each sample has a score on each PC which reflects the location of the sample along that PC. For these water samples, the first two principal components accounted for more than 99% of the explained variance of the data as shown in Fig. 4(a) and their scatter plot is shown in Fig. 4(b). Explained variance is a measurement of the proportion of variation in the data accounted for by any PC. PCA demonstrated clustering of the samples S0 and S10 and some others, however, clustering of others was not clearly distinguishable.

To analyze clustering of the samples more precisely, LDA was performed. LDA uses the labels associated with the values, and the algorithm knows which value corresponds to which sample. Since the intensity values at different wavelengths are not independent, the data has a collinearity issue. Therefore, PCA was performed first, and the principal components were used as input for LDA. The individual principal components are always independent from each other, and linearity issue will be resolved. Ten principal components from PCA were extracted and used for LDA. The scatter plot of the first two linear discriminants is shown in Fig. 4(c). LDA showed slightly better clustering for the samples S0 and S10 compared to PCA, but for others, distinguishable clustering was not observed. Hence, from both PCA and LDA, it can be estimated that the samples S0 and S10 are likely different from each other as well as different from others (S1 to S9), in terms of composition and/or concentration level of species. However, other samples (S1 to S9) appeared to be similar. With this, it is anticipated that iron content in these samples will be in a comparable range.

4.3. Quantification of Iron Content

To quantify the iron content in the water samples, univariate and multivariate analyses were performed. LIBS spectra from the calibration samples with known iron concentration (range 0−1000 ppm) and the test samples were obtained with the benchtop system using a laser energy of 20 mJ, gate time delay of 4 µs, and gate width of 10 µs. The values of the experimental parameters were selected as they produced a maximum signal to background ratio for the iron emission lines. For univariate analysis, two strong emission lines of iron at wavelengths 371.9 nm and 373 nm were selected. Linear calibration models were developed by plotting the background subtracted intensity of these analyte lines against iron concentration in 0 to 200 ppm range. Linear coefficients were found to be 0.99 for both the lines as shown in Fig. 5(a). These calibration models were used to predict the concentration of iron in the water samples, and the obtained results are presented in Table 1.

For multivariate analysis (MVA), partial least square regression (PLS-R) was performed using a python programming tool where the entire spectral data in the wavelength range of 360 to 377 nm was used. LIBS data obtained from the calibration samples with Fe concentration range of 0 to 1000 ppm was split into training and test data sets (80:20 ratio) to evaluate the prediction capability of the developed PLS-R model. The PLS-R calibration and validation plot is shown in Fig. 5(b). Later, this PLS-R calibration model was also used to predict the iron content in the water samples and the results are presented in Table 1. To evaluate the correctness of the predicted concentration of iron in water samples, ICP-MS measurements were performed. Relative difference of PLS-R and ICP-MS measurements for iron content was less than 15%, and that was less than 20% when comparing ICP-MS and univariate analysis results. This shows that the LIBS technique is useful for iron detection and quantification.

Using the calibration and test samples, underwater measurements were performed using the miniature LIBS probe system. For data acquisition, a gate delay of 300 ns and gate width of 3 µs were used while laser pulse energy was 4 mJ. Both univariate and multivariate analyses were performed with the data obtained and it was found that the predicted concentration of iron in all the samples was within a similar range compared to benchtop setup measurements.

Table 1

Iron concentration measured by univariate, multivariate, and ICP-MS.
Samples		Predicted Fe (ppm)
Samples	Sample Collection Date	Univariate Analysis	Multivariate Analysis	ICP-MS Analysis
S0	Dec 19, 2019	34	32	28.60
S1	Sept 29, 2020	25	31	27.53
S2	Sept 30, 2020	27	24	23.05
S3	Oct 1, 2020	20	21	23.06
S4	Oct 2, 2020	21	21	24.41
S5	Oct 3, 2020	29	25	24.40
S6	Oct 4, 2020	28	26	24.35
S7	Oct 5, 2020	31	24	25.93
S8	Oct 6, 2020	25	27	24.86
S9	Oct 7, 2020	32	32	28.59
S10	Oct 8, 2020	28	26	24.70

4.4. Correlation of Iron Content with Local Rainfall

When the variation in measured iron content in the water samples and local rainfall were evaluated, a correlation was found as shown in Fig. 6. The local rainfall data was obtained from the Morgantown municipal airport station. A decrease in iron content was observed with the rainfall which is likely due to the dilution of the local groundwater, demonstrating the connection between surface events and subsurface water chemistry.

LIBS appears to be a good analytical technique for iron content analysis in aqueous samples. Iron emission signals were detected in the LIBS spectra obtained by both benchtop setup and miniature LIBS probe. Iron lines Fe I 363.1 nm, Fe I 371.9 nm, Fe I 373 nm, Fe I 374.5 nm, Fe I 374.9 nm, and Fe I 375.9 nm were identified in the spectra obtained from the benchtop setup in the 360−380 nm wavelength range. Similarly, lines Fe I 371.9 nm, Fe I 373 nm, Fe I 374.5 nm, Fe I 374.9 nm, and Fe I 385.99 nm were detected in the spectra obtained with miniature LIBS probe. In addition to significant presence of iron, elements commonly found in groundwater such as Ca, Mg, Sr, Na, and K were also detected in the water samples. PCA and LDA were performed with the LIBS data obtained from the eleven water samples collected as drainage from the building foundation. PCA and LDA were performed to evaluate if the samples collected on different days vary with each other and it was found that most of the samples were similar in terms of composition and concentration level. To quantify the iron content in the water samples, univariate calibration curves were developed using two selected strong iron lines, and PLS-R curves were produced with the entire spectral dataset. Iron content in the samples was found in the 20−32 ppm range. When the results obtained with LIBS measurements were compared with ICP-MS measurements, relative difference was found to be less than 20%.

Disclaimer

This project was funded by the U.S. Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Availability of data and materials All data and materials support their published claims and comply with field standards.

Authors Contribution All authors contributed to this study. Chet R. Bhatt performed the experiments, recorded measurement data, performed data analysis, and wrote a manuscript. Daniel Hartzler supported in experiment running, results interpretation, and manuscript writing. Dustin McIntyre is the principal investigator of the project. He managed the overall project, reviewed the results, and manuscript.

Funding This project was funded by the U.S. Department of Energy, National Energy Technology Laboratory, in part, through a site support contract.

Conflict of interest The authors declare no competing interests.

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No competing interests reported.

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LIBS Sensing for In-Situ Iron Detection and Quantification in Wastewater Outfall

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Abstract

Figures

1. Introduction

2. Experimental Setup

2.1. Benchtop Setup with Jet System

2.2. Miniaturized Probe for Underwater LIBS

3. Materials And Methods

4. Result And Discussion

4.1. LIBS Spectra and Iron Detection

4.2. Principal Component and Linear Discriminant Analyses

4.3. Quantification of Iron Content

4.4. Correlation of Iron Content with Local Rainfall

5. Conclusions

Declarations

References

Additional Declarations

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