News|Articles|February 17, 2026

Validated ICP-OES and ICP-MS Workflow Advances Sustainable, Multi-element Monitoring of Heavy Metals in Wastewater

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Key Takeaways

  • Full validation across wastewater matrices confirms performance for 25 analytes, including As, Cd, Cr, Hg, and Pb, supporting routine surveillance under international analytical quality criteria.
  • AGREE greenness scoring demonstrates reduced reagent consumption, energy use, and waste through microwave digestion and automated sample handling without compromising metrological rigor.
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Validated green ICP-OES/ICP-MS workflow quantifies 25 toxic metals in wastewater at trace levels, guiding labs on tiered monitoring and compliance.

A recent chapter in a published book, titled, “Heavy Metals in Air, Soil and Water, and Plant Responses – Recent Research Updates,” described a newly validated analytical workflow combining inductively coupled plasma–optical emission spectroscopy (ICP-OES) and inductively coupled plasma–mass spectrometry (ICP-MS). This new method demonstrates reliable, trace-level quantification of 25 metals in complex wastewater matrices while meeting green chemistry benchmarks while providing laboratories and regulators with a verified, sustainability-assessed approach to routine surveillance of toxic elements, which include arsenic, cadmium, chromium, mercury, and lead.1

ICP-MS and ICP-OES are both atomic techniques, but they are not interchangeable. The method to use depends on the number of samples and element concentrations being measured. For example, if elements are at concentrations above 10 ppb, then ICP-OES is the best technique to use.2 If the elements in a sample are lower than 10 ppb, then ICP-MS is the better method to use.2

In the book chapter, both ICP-OES and ICP-MS methods were fully validated against international performance criteria, which were defined as specificity, linearity, accuracy, precision, and measurement uncertainty.1 The study also applied the Analytical GREEness (AGREE) metric to quantify environmental impact, assigning comparable greenness scores (0.72 for ICP-OES; 0.71 for ICP-MS).1 These scores reflect reduced reagent use, optimized energy consumption, and minimized waste achieved through microwave-assisted digestion and automated workflows.

The study also confirms the complementary performance profiles for the two ICP platforms. ICP-MS provides ultra-trace sensitivity, high throughput, and collision/reaction-cell control of spectral interferences, making it well suited to regulatory monitoring and investigations of metal mobility, partitioning, and fate in aquatic environments.1 Meanwhile, ICP-OES delivers better performance at higher concentration ranges with lower instrumental complexity and cost.1

By validating both methods on the same wastewater matrices and across 25 analytes, the authors show laboratories can deploy a tiered strategy that utilizes the strengths of both techniques.1 ICP-OES can be used for higher-level screening and process monitoring, while ICP-MS can be used for confirmatory or ultra-trace determinations.1

Why is this study unique?

This study shows that green analytical chemistry objectives can be realized in multi-element analysis. As the AGREE assessment shows, optimized digestion, automation, and resource management materially reduce environmental footprint without sacrificing analytical rigor.1 For laboratories facing tightening sustainability targets and accreditation demands, the work offers a documented pathway to modernize metal analysis workflows.

Compliance testing for water-quality standards is a mandatory part of the process, and sustainable ICP methods can help deliver reliable quantification at trace levels. This supports earlier detection of contamination events and more accurate mass-balance assessments across treatment trains.1 Utilities can benchmark treatment efficacy for metals removal, while regulators gain defensible data for enforcement and policy development.1 Meanwhile, contract laboratories can leverage automation and reduced consumables to improve throughput and cost efficiency.

The authors believe that numerous developments will take place in environmental monitoring as ICP capabilities improve. For example, the authors believe hyphenated methods, such as HPLC–ICP-MS, will become more prominent to enable metal speciation at higher sensitivity.1 Advances in sample introduction and high-throughput platforms may also help reduce analysis times and lower detection limits, while less-toxic digestion chemistries and smaller sample volumes could further reduce acid consumption and waste.1 And finally, real-time monitoring systems and digital analytics are expected to be integrated with these analytical methods to support predictive assessment of heavy-metal trends and faster intervention in water-resource management.1

This study comes at a time when industrialization continues to play a major role in the global economy. We are also seeing how green chemistry practices are influencing scientific methods. This study reflected both these realities, as the validated ICP-OES/ICP-MS workflow presented here is backed by green-chemistry metrics.1 As a result, the method offers environmental laboratories a practical, evidence-based framework to meet regulatory, operational, and environmental performance goals while safeguarding aquatic ecosystems and public health.1

References

  1. Chevidenkandy, A.; Bhasi, D.; Thayyil, S. et al. Quantification of Heavy Metals in Wastewater: A Critical Appraisal of Sophisticated Analytical Tools. In Heavy Metals in Air, Soil and Water, and Plant Responses – Recent Research Updates; Anjum, N. A.; Masood, A.; Umar, S.; Khan, N. A., Eds. IntechOpen: New York, 2025; pp 45–78. Available at: https://www.intechopen.com/online-first/1236563
  2. Agilent, Elemental Analysis by ICP-OES or ICP-MS. Agilent. Available at: https://www.agilent.com/en/product/atomic-spectroscopy/icp-oes-vs-icp-ms?Campaign_Source=PAN_PSM_AtomicSpec_G&gclsrc=aw.ds&gad_source=1&gad_campaignid=22031712651&gbraid=0AAAAADSHcWcSzyktVXazkPxk0n2cfVDRd&gclid=CjwKCAiAtLvMBhB_EiwA1u6_PsgCRsoewKwooLjGmhqt9amByaDkV8IBliAPU8kTysnAaE2H0_FTxxoCk3YQAvD_BwE (accessed 2026-02-13).