Diverse classes of contaminants - from persistent industrial chemicals to biologically active pharmaceuticals - require a multi-matrix approach to monitoring and risk assessment.
The current landscape of emerging contaminants (ECs) encompasses a heterogeneous group of synthetic or naturally occurring chemicals that are not commonly monitored.
Micro- and Nano-plastics (MNPs) - These plastic fragments (smaller than 5 mm for micro and 1-1000 nm for nano) originate from primary sources like the cosmetic and fishing industries, or secondary fragmentation via UV radiation and mechanical abrasion. Recent studies show they bioaccumulate in the food chain and can penetrate biological tissues, potentially inducing oxidative damage and lung inflammation (Wu et al., 2023; Boahen et al., 2025).
Per- and Polyfluoroalkyl Substances (PFAS) - Often called "forever chemicals" due to their persistent carbon-fluorine bonds, these synthetic chemicals are used in non-stick cookware, waterproof clothing, and food packaging. They are widely detected in soil, water, air, and human blood, with exposure linked to immunotoxicity, liver damage, and certain cancers (Boahen et al., 2025).
Pharmaceuticals and Personal Care Products (PPCPs) - This diverse collection includes antibiotics, antidepressants, hormones, and cosmetics. Because they are generally polar and biologically active, they often bypass traditional wastewater treatment plants (WWTPs), ultimately entering rivers and oceans where they disrupt biodiversity and ecological balance (Boahen et al., 2025; Mohapatra et al., 2025).
The challenge of detecting contaminants at low nanogram-per-liter levels necessitates sophisticated enrichment and clean-up strategies tailored to the complexity of the sample.
Because emerging contaminants occur at trace levels in highly complex matrices, sample preparation must both enrich the analytes and remove significant interferences (Meher & Zarouri, 2025).
Aqueous Samples
Solid-phase extraction (SPE) is the dominant approach for natural water and wastewater, often utilizing multiresidue formats with sorbent mixes tailored to broad polarity ranges (Câmara et al., 2022; Meher & Zarouri, 2025).
Solid and Biota Matrices
For soil, sediment, and biota, techniques like Pressurized Liquid Extraction (PLE) and Ultrasonic-Assisted Extraction (UAE) are critical for robustly releasing lipophilic contaminants like LCMs from the matrix (Stadelmann et al., 2024; Soriano et al., 2024). The QuEChERS method is also being adapted for broader environmental contaminants in sludge and biota (Santini et al., 2024; Godfrey et al., 2022).
The Role of Automated μSPE Clean-up
Automated micro-Solid Phase Extraction (μSPE) provides a cleaner extract than traditional dispersive SPE (d-SPE). This step significantly increases instrument up-time by preventing MS source contamination and reducing matrix effects (Lehotay et al., 2016; Santini et al., 2024).
Bifarin, O. O., Yelluru, V. S., Simhadri, A., & Fernández, F. M. (2025). A Large Language Model-Powered Map of Metabolomics Research. Analytical Chemistry, 97, 14088-14096.
Boahen, E., Owusu, L., & Adjei-Anim, S. O. (2025). A comprehensive review of emerging environmental contaminants of global concern. Discover Environment, 1:48.
Li, J., Su, G., Letcher, R. J., Xu, W., Yang, M., & Zhang, Y. (2018). Liquid Crystal Monomers (LCMs): A New Generation of Persistent Bioaccumulative and Toxic (PBT) Compounds? Environmental Science & Technology, 52, 5005-5006.
Meher, A. K., & Zarouri, A. (2025). Environmental Applications of Mass Spectrometry for Emerging Contaminants. Molecules, 30(2), 364.
Mohapatra, S., Jong, M. C., Mukherji, S., van Lier, J. B., & Spanjers, H. (2025). Liquid Crystal Monomers (LCMs) of Emerging Concern: Recent Progress and Challenges in Wastewater Treatment. Current Pollution Reports, 11:48.
Stadelmann, B., Leonards, P. E. G., & Brandsma, S. H. (2024). A new class of contaminants of concern? A comprehensive review of liquid crystal monomers. Science of the Total Environment, 947, 174443.
Su, H., Ren, K., Li, R., Li, J., Gao, Z., Hu, G., Fu, P., & Su, G. (2022). Suspect Screening of Liquid Crystal Monomers (LCMs) in Sediment Using an Established Database Covering 1173 LCMs. Environmental Science & Technology, 56.
Wu, P., Wu, X., Huang, Q., Yu, Q., Jin, H., & Zhu, M. (2023). Mass spectrometry-based multimodal approaches for the identification and quantification analysis of microplastics in food matrix. Frontiers in Nutrition, 10:1163823.
Zhao, H., Li, C., Naik, M. Y., Wu, J., Cardilla, A., Liu, M., Zhao, F., et al. (2023). Liquid crystal monomer: a potential PPARgamma antagonist. Environmental Science & Technology.
Technical References
Câmara, J. S., Perestrelo, R., Berenguer, C. V., Andrade, C. F. P., & Gomes, T. M. (2022). Green Extraction Techniques as Advanced Tools for Analysis of Contaminants in Environmental Matrices. Molecules.
Schymanski, E. L., Singer, H. P., Slobodnik, J., Ipolyi, I. M., Oswald, P., Krauss, M., Schulze, T., Haglund, P., Letzel, T., & Grosse, S. (2015). Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis. Analytical and Bioanalytical Chemistry, 407(21), 6237–6255.
Lehotay, S. J., Han, L., & Sapozhnikova, Y. (2016). Automated $\mu$SPE cleanup for GC-MS and LC-MS analysis of pesticides and environmental contaminants in QuEChERS extracts of foods. Chromatographia.
Santini, S., Baini, M., Martellini, T., Bissoli, M., Galli, M., Concato, M., Fossi, M., & Cincinelli, A. (2024). Novel ultrasound assisted extraction and d-SPE clean-up for the analysis of multiple legacy and emerging organic contaminants in edible fish. Food Chemistry, 443, 138582.
Sanusi, I., Olutona, G., Wawata, I., & Onohuean, H. (2023). Occurrence, environmental impact and fate of pharmaceuticals in groundwater and surface water: a critical review. Environmental Science and Pollution Research, 30, 90595-90614.
Soriano, Y., Andreu, V., & Picó, Y. (2024). Pressurized liquid extraction of organic contaminants in environmental and food samples. TrAC Trends in Analytical Chemistry.
Methodology Note
Parts of this Newsroom Article were powered by a literature search using Consensus. It is an AI-powered search engine that extracts and synthesizes findings directly from over 200 million peer-reviewed papers. The system utilizes Large Language Models (LLMs) to scan scientific literature for specific queries, prioritizing high-impact journals and recent data to provide a cross-disciplinary "consensus" of the current state of research. This allows for the rapid identification of analytical trends and sample preparation standards that might otherwise be obscured by the sheer volume of emerging literature.
