A Brief Review of the Latest Spectroscopic Research in Environmental Analysis

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Scientist investigating the chemistry of the environment © Michael - stock.adobe.com

Scientist investigating the chemistry of the environment © Michael - stock.adobe.com

Abstract

Spectroscopic analytical techniques are crucial for the analysis of environmental samples. This review emphasizes the latest advancements in several key spectroscopic methods, including atomic, vibrational, molecular, electronic, and X-ray techniques. The applications of these analytical methods in detecting contaminants and other environmental applications are thoroughly discussed.

Introduction

Spectroscopic analytical techniques are vital in environmental sciences, offering powerful tools for the detailed classification and quantification of various environmental samples. Techniques such as inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectroscopy (ICP-OES) are utilized for trace elemental analysis, providing high sensitivity and precision. Raman spectroscopy, including its enhanced variant, surface-enhanced Raman spectroscopy (SERS), is employed for molecular imaging, fingerprinting, and detecting low concentrations of pollutants. Fourier-transform infrared spectroscopy (FT-IR) is used for identifying chemical bonds and functional groups within molecules. X-ray fluorescence (XRF) is applied to assess the presence of various elemental contaminants, and X-ray diffraction (XRD) is used to assess the crystalline identity of solids, like sediments and geological samples. Ultraviolet-visible spectroscopy (UV-vis), packaged as chemosensors, measures absorbance and concentration of analytes, while fluorescence spectroscopy detects the emission of light by substances, often used for tracking molecular interactions, kinetics, and dynamics. Nuclear magnetic resonance (NMR) spectroscopy offers detailed information about molecular structure and conformational subtleties through the interaction of nuclear spin properties following the application of an external magnetic field. This review highlights the most recent advancements and applications of these techniques in environmental analysis.

ICP-MS

The field of air analysis has advanced significantly, particularly with the focus on measuring tyre-wear particle emissions. Researchers are increasingly utilizing unmanned aerial vehicles (UAVs) for sampling, and the use of ICP-MS technology is used for measuring halogenated volatile organics, metals directly from air, and single particles containing a variety of elements and contaminants. This technology continues to reveal its full capabilities in various environmental samples, including the demonstration of the dominance of ICP-MS/MS in air quality analysis (1).

Water analysis remains a crucial research area, with extensive use of ICP-MS/MS. Many studies emphasize preconcentration techniques, with a strong interest in green chemistry. Innovations include the direct introduction of magnetic nanoparticles into flame atomic absorption spectroscopy (FAAS) to enhance sensitivity and the simultaneous dual-drop preconcentration of oxidation state species, eliminating sequential elution steps. Despite advancements in techniques like total reflection X-Ray fluorescence spectrometry (XRFS) and laser-induced breakdown spectroscopy (LIBS) nearing necessary detection limits for contaminated water screening, there are recurring validation issues due to insufficient reference materials (1).

In soil, plant, and related material analysis, sustainable methods for digestion, extraction, and preconcentration are gaining prominence. Development continues in atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), and atomic fluorescence spectroscopy (AFS) for direct solid sample analysis and sensitivity enhancement through analyte enrichment. The nitrogen-microwave inductively coupled atmospheric-pressure plasma shows promise for ICP-MS analyses, suggesting new research directions. Applications of LIBS, including single-chamber laser-ablation LIBS for plant leaf analysis without grinding and pelleting, have increased significantly, although many studies lack validation through certified reference materials or comparisons with other techniques. Additionally, efforts to develop homogeneous reference materials for geological analysis have progressed with microanalytical techniques like laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and secondary ion mass spectrometry (SIMS), which support accurate geological sample analyses. Portable LIBS instruments have gained attention for mineral prospecting and ore processing, with new chemometric models improving data quality and qualitative and quantitative capabilities. The vast quantity of analytical data from modern instrumentation has led to the development of software solutions to facilitate data processing and interpretation (1).

The development of analytical methods at the single-cell level has been driven by the heterogeneous properties of cells and unicellular organisms. Recognizing the significance of trace elements in these biological systems, the use of ICP-MS for single-cell analysis has shown high potential in evaluating cellular elemental composition. Advancements in instrumentation, such as coupling laser ablation to tandem ICP-MS/MS and using alternative mass analyzers like inductively coupled plasma sector field mass spectrometry (ICP-SFMS) and inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS), have greatly enhanced sensitivity, enabled high-resolution imaging, and provided detailed cellular fingerprints (2).

Single-cell ICP-MS has been extensively utilized in diverse fields including oncology and environmental research, yielding crucial findings such as nanoparticle toxicity at the cellular level and contributions to vaccine development. One review delves into the theory behind single-cell ICP-MS analysis; discusses its applications across various disciplines, and highlights the latest advancements in instrumentation that have enhanced these analytical capabilities for environmental samples (2).

ICP-OES

In 2024, the 50th anniversary of ICP-OES is being celebrated, marking its commercial debut in 1974. Over the past five decades, ICP-OES has become a widely used analytical technique, recognized for its capability to determine trace and ultratrace elemental concentrations in various samples, particularly excelling in multielement analysis. Its applications cover a broad array of sample types, including environmental monitoring, food analysis, and medical diagnostics (3).

A published review highlights recent applications of ICP-OES in areas such as food analysis, microplastics, materials, dietary supplements, human tissue, and bodily fluids. As the milestone anniversary approaches, growing interest from both prospective users and current practitioners emphasizes the importance of this review. This review provides an overview of the ICP-OES instrumental technique and significant recent applications in the aforementioned fields (3).

The perennial evergreen tea plant (Camellia sinensis) is used globally for one of the most popular nonalcoholic beverages. However, soil contamination from fertilizers and various industrial, agricultural, and municipal activities can lead to the accumulation of potentially toxic elements (PTEs) in tea plants, necessitating frequent monitoring. One study aimed to determine the levels of PTEs (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in tea leaves and infusions using ICP-OES. Multivariate data analysis methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to identify potential contamination sources, while Pearson's correlation coefficient (PCC) assessed the relationships between the variables (4).

Results showed that the mean concentrations of PTEs in tea infusions decreased in the order: Mn > Fe > Zn > Cu > Co > Ni > Pb > Cr > Cd > Al, with Mn being the most abundant. The concentration trend in tea leaves was similar but with higher PTE levels. Notably, the tolerable dietary intake (TDI) for Ni and provisional tolerable monthly intake (PTMI) for Cd exceeded standards set by the World Health Organization (WHO) and European Food Safety Authority (EFSA). However, calculated hazard index (HI < 1) and cumulative cancer risk (CCR) values indicated negligible exposure risk. The presence of elevated PTE levels in commonly consumed tea products raises public health concerns, highlighting the need for regular monitoring and assessment by regulatory agencies (4).

Raman (SERS)

SERS is a promising technology for the sensitive detection of environmental pollutants in natural waters, but its performance is often hindered by the environmental matrix. A published review examines the impact of natural water matrices on SERS analysis using silver nanoparticles (AgNPs) as a substrate. It was found that natural water matrices could degrade SERS performance and introduce spectral artefacts, primarily due to the presence of natural organic matter (NOM) such as humic substances and proteins, while polysaccharides and inorganic ions had a lesser effect on analyses (5).

This recent study explored the interactions within the ternary system of analyte, NOM, and nanoparticles to understand the matrix effect. The predominant issue affecting SERS detection was the microheterogeneous distribution of analytes caused by NOM, rather than the formation of a NOM-corona or competitive adsorption between NOM and analytes on nanoparticles. This review clarifies the origin and mechanisms of the matrix effect, aiming to improve the practical application of SERS technology in environmental analysis (5).

Another published review emphasizes the critical need for an analytical method capable of detecting nanoplastics with high sensitivity and selectivity to understand their environmental and health impacts. Focusing on Raman spectroscopy, the review discusses challenges in nanoplastic analysis and recent advancements, such as advanced Raman spectroscopy techniques, hyphenation with other instrumental methods, and surface-enhanced Raman spectroscopy (SERS) using conventional substrates and integrated sample preparation methods. Significant efforts have been directed towards improving sensitivity, spatial resolution, accuracy, precision, and feasibility of Raman spectroscopy for nanoplastic analysis. The review concludes by highlighting current challenges and proposing avenues for further research, with the goal of advancing nanoplastic analysis using Raman spectroscopy and filling knowledge gaps in the field (6).

SERS has undergone significant advancements, benefiting from both experimental and theoretical endeavors aimed at understanding its effect and demonstrating its potential. Renowned for its exceptional sensitivity and selectivity, SERS offers detailed molecular fingerprint information, rendering it valuable across diverse fields including surface and interfacial chemistry, energy, materials, biomedicine, and environmental analysis. Another review article comprehensively explores SERS principles, methodologies, and applications, beginning with an elucidation of the fundamental theory behind the SERS enhancement mechanism and the preparation of SERS-active substrates. Recent applications of SERS in energy systems are highlighted, particularly its role in investigating surface reactions and interfacial charge transfer in batteries and electrocatalysts. Furthermore, the review addresses the challenges and future directions of SERS research, offering insights into potential advancements and applications in the field, and also highlights environmental applications (7).

SERS is known to be a powerful tool for detecting molecules at the single-molecule level, offering valuable insights into chemistry, biomolecules, and environmental monitoring. However, creating SERS substrates with high enhancement factors, simplicity in synthesis, stability, and reproducibility remains challenging. To address these challenges, researchers synthesized gold clusters anchored on reduced graphene oxide (Au clusters@rGO) using a co-reduction method. This innovative approach yielded substrates with an ultrahigh enhancement factor of 3.5 × 107, combining the chemical enhancement of reduced graphene oxide with the electromagnetic enhancement of gold clusters. The efficiency of SERS was attributed to the high localized surface plasmon resonance (LSPR) of gold cluster aggregations, the synergistic effects of gold clusters and reduced graphene oxide, and the charge transfer between graphene and the molecules. This study offers a promising strategy for designing and preparing SERS substrates with superior properties (8).

X-ray (XRF and XRD)

The increasing need for on-site, real-time analytical solutions in mining and environmental projects has driven the development of efficient methods to characterize heterogeneous raw materials. Traditional portable methods like portable X-ray fluorescence (pXRF) and portable X-ray diffraction (pXRD) offer rapid screening but often fall short in detecting light elements or complex minerals, necessitating additional laboratory verification for accurate results (9).

One study introduces what is called an ID2B instrument, which is a novel method that combines X-ray fluorescence and X-ray diffraction (XRD-XRF) analysis for rapid in situ chemical and mineralogical characterization. Using a New Caledonia harzburgite sample, a specific type of ultramafic rock, the study compared both laboratory and ID2B analyses, finding identical chemical elements and mineral phases between the two methods in both powderized and as-sawn samples. (As-sawn samples have been cut or sliced using a sawing process to expose a clean surface for analysis.) The chemical proportions obtained from ID2B closely matched those from more complex laboratory XRF analysis, with less than 5% relative errors for key elements. The observed variability was attributed to sample heterogeneity. Overall, the combined XRF-XRD approach provided accurate, lab-comparable results, demonstrating its potential for effective field applications (9).

A 2023 publication aimed to enhance the accuracy of nutrient analysis in soil samples using XRF sensors, which are influenced by matrix differences across various geographic and geological regions. The study evaluated several predictive models—simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF)—for estimating calcium (Ca) and potassium (K) levels in agricultural soils. The RS models were tested both without (RS1) and with (RS2) Compton normalization. Compton normalization is a technique used in X-ray fluorescence (XRF) and other spectroscopic methods to correct for matrix effects by normalizing the intensity of a characteristic X-ray peak to the intensity of the Compton scatter peak, thus improving the accuracy of quantitative elemental analysis. Additionally, the incorporation of soil texture and vis–NIR spectra as auxiliary variables was explored to improve model performance. All approaches managed to reduce the matrix effect to some degree, with the models demonstrating excellent predictive performance (R² ≥ 0.84) (10).

In this study, the best results for Ca prediction were achieved using RS2 (R² = 0.92, RMSE = 48.25 mg/kg), while RF provided the best results for K prediction (R² = 0.84, RMSE = 17.43 mg/kg). Interestingly, more sophisticated models did not consistently outperform simpler linear ones. The inclusion of texture data and vis–NIR spectra improved K prediction accuracy by 10%, whereas Ca prediction saw minimal enhancement. The study highlights the necessity of customized modeling approaches for sensor-based soil analysis, offering valuable insights for future research and practical applications in this field (10).

FT-IR

A study addresses the urgent need for effective identification and classification of microplastics (MPs), which are emerging pollutants requiring robust monitoring and management strategies. Unlike many previous studies that relied on limited datasets and library searches, this research developed and compared four machine learning-based classifiers using two large-scale blended plastic datasets. The classifiers included a 1D convolutional neural network (CNN), decision tree (DT), random forest (RF), and a 2D CNN. Raw spectral data from Fourier transform infrared spectroscopy were fed into the 1D CNN, DT, and RF models, while the 2D CNN utilized corresponding spectral images as input (11).

Results indicated that the 1D CNN outperformed the other models, achieving overall accuracies of 96.43% on a small dataset and 97.44% on a large dataset. The 1D CNN was particularly effective in predicting environmental samples, while the RF model demonstrated robustness with fewer spectral data. Despite the strong performance of the 1D CNN, RF and 2D CNN models remain viable options for plastic identification, especially with limited spectral data. To facilitate rapid and accurate analysis of existing MP samples, the study also developed an open-source MP spectroscopic analysis tool, significantly enhancing the monitoring and management of microplastic pollution (11).

A 2023 review delves into the versatile applications of FT-IR spectroscopy in microbiology, highlighting its importance in elucidating microbial systems. FT-IR spectroscopy facilitates the monitoring of microorganism composition and environmental responses, aiding in microbial identification, process monitoring, cell wall analysis, biofilm examination, stress response assessment, and the investigation of environmental interactions. Although challenges such as sample complexity and data interpretation persist, FT-IR spectroscopy shows significant potential for transformative applications in environmental microbiology (12).

Future prospects for FT-IR in microbiology include the creation of standardized FT-IR libraries for accurate microbial identification, integration with advanced analytical techniques, and the use of high-throughput and single-cell analysis methods. Additionally, real-time environmental monitoring with portable FT-IR systems and the incorporation of FT-IR data into ecological models for predictive insights into microbial responses to environmental changes present exciting opportunities. These advancements promise to significantly enhance our understanding of microorganisms and their interactions within ecosystems (12).

UV-vis

A mini-review published in 2023 summarizes recent advancements in chromofluorogenic and electrochemical sensors for detecting chemically significant ionic species, particularly pollutants. It emphasizes the high sensitivity, selectivity, rapid response time, and versatility of these sensors, drawing on various research findings, including the authors' own work. There are colorimetric chemosensors in the visible spectral range, where the response can be easily seen by the naked eye or more accurately measured using a UV-vis spectrophotometer. There are also fluorometric chemosensors, which have greater sensitivity, with a sub-micromolar analyte detection. The review provides valuable insights and future prospects for designing cost-effective, efficient, and practical sensors for toxic pollutant detection. Additionally, it addresses current limitations and explores future directions for using chromofluorogenic and metal–organic frameworks-based electrochemical sensors in environmental pollutant detection, highlighting promising research and development opportunities in this field (13).

A novel colorimetric and fluorescent probe, NPC-H2S, was developed for detecting hydrogen sulfide (H2S) in environmental, food analysis, and biological imaging applications. Utilizing a corrole chemical derivative (NPC–OH) as the fluorophore and a benzenesulfonyl group (DNBS) as the identification moiety and fluorescence quencher, NPC-H2S exhibited a large Stokes shift at the ultraviolet wavelength of 227 nm. The probe demonstrated high selectivity and sensitivity for H2S with ultra-fast recognition (10 seconds), a low detection limit (61 nM), and a visual (visible) color change from green to yellow. It successfully detected H2S in water samples and monitored food spoilage in various food types, including chicken, beef, pork, fish, and eggs. Additionally, NPC-H2S enabled fluorescent bioimaging of H2S in living cells and zebrafish, highlighting its versatility and potential for diverse environmental and food related applications (14).

Fluorescence

Crop sensing through imaging techniques is crucial for early detection of agricultural stress, helping to minimize yield losses caused by climate change-induced stress. Chlorophyll a fluorescence imaging is a certified method for assessing plant stress, allowing for the evaluation of leaf changes over time and enabling pre-symptomatic monitoring of plant health. A published review highlights how chlorophyll a fluorescence imaging can detect both biotic and abiotic stress, identifying issues as early as 15 minutes after pest feeding, 30 minutes after pathogen application, and at the onset of water-deficit stress. This technique is rapid, non-invasive, cost-effective, and highly sensitive, providing valuable insights into photosynthetic performance and stress impact on plants. Among the chlorophyll fluorescence parameters, the fraction of open photosystem II (PSII) reaction centers (qp) is identified as a key indicator for early stress detection, presenting a promising method for effectively screening the impact of environmental stress on plants (15).

Mercury, with its potent toxicity even at trace levels, demands the urgent development of detection methods that are both rapid and highly sensitive. In this study, nitrogen and sulfur co-doped carbon quantum dots (N,S-CQD) were synthesized using a straightforward hydrothermal process involving chitosan, thiourea, and citric acid, resulting in impressive quantum yield reaching up to 33.0% (16). These carbon dots functioned exceptionally well as a selective fluorescent probe specifically designed for detecting mercury ions (Hg2+), thus presenting promising applications in both environmental monitoring and public health protection. Through meticulous optimization of fluorescence intensity parameters such as pH, ionic strength, and reaction time, the synthesized N,S-CQD exhibited outstanding sensitivity to Hg2+ ions even in the presence of 11 interfering metal ions. With its remarkably low detection limit (∼4 nM) and wide linear range (∼5–160 nM), the N,S-CQD probe emerged as a powerful tool for detecting mercury ions at trace levels, thereby showcasing its potential as a straightforward, rapid, and sensitive assay for environmental analysis (16).

NMR

Liquid water serves as the cornerstone of life on Earth, sculpting a myriad of habitats ranging from terrestrial soils to expansive oceans. In this aqueous environment, organisms continuously release dissolved organic matter (DOM) into their surroundings, traversing through landscapes and seascapes alongside water currents. Astonishingly, the DOM found in oceans and freshwater bodies harbors more carbon than the combined biomass of all living organisms, thereby exerting a significant influence on the intricate mechanisms of the global carbon cycle. Despite its paramount importance, the molecular composition of DOM remains largely elusive due to its inherent complexity (17).

NMR spectroscopy emerges as a promising tool for unraveling the molecular intricacies of DOM, offering insights into its structural diversity and functional properties. Overcoming historical limitations in sensitivity and resolution, recent advancements in NMR spectroscopy, coupled with refined statistical analyses and innovative chemical derivatization strategies, are progressively enhancing our ability to comprehensively characterize marine DOM. This review delineates the application of NMR spectroscopy in explaining the structural complexities of marine DOM, encompassing fundamental NMR principles, contemporary technical innovations, and future research directions. By providing a comprehensive overview, it serves as an indispensable resource for researchers at all levels, facilitating a deeper understanding of marine DOM and its pivotal role in global biogeochemical processes (17).

Fluorine nuclear magnetic resonance (19F-NMR) spectroscopy stands out as a robust technique for quantifying total per- and polyfluoroalkyl substances (PFAS) in intricate sample matrices. By leveraging the distinct terminal −CF3 shift (−82.4 ppm) in the alkyl chain, 19F-NMR allows for precise quantification without succumbing to biases originating from sample preparation or matrix effects. In contrast, conventional analytical methods such as liquid chromatography–mass spectrometry (LC–MS) and combustion ion chromatography (CIC) encounter inherent challenges in total fluorine analysis. A newly developed sensitive 19F-NMR method showcases an impressive limit of detection of 99.97 nM or 50 μg/L perfluorosulfonic acid, exhibiting both high sensitivity and reliability in PFAS detection (18).

Comparative assessments between 19F-NMR and established PFAS analysis techniques, including the total oxidizable precursor (TOP) assay and LC-high resolution MS, consistently demonstrate 19F-NMR's superiority in detecting higher total PFAS quantities (63% higher than TOP assay, 65% higher than LC–MS). Particularly noteworthy is 19F-NMR's ability to identify trifluoroacetic acid at concentrations over five times greater than those quantified by LC–MS in wastewater samples. These findings underscore the potential of 19F-NMR to complement traditional methods, offering improved accuracy in assessing PFAS contamination levels within complex environmental samples (18).

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