Technology


Is your environmental laboratory really ready for AI? Talking Point


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Across the environmental sector, AI tools are already being used to identify anomalies in monitoring datasets, support predictive modelling, automate quality control checks, and accelerate reporting workfl ows.


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The potential benefi ts are clear: faster insight, improved consistency, and earlier identifi cation of environmental risk.


However, for environmental laboratories in particular, there is a critical caveat. AI does not succeed because it is advanced or innovative. It succeeds because the data it is trained on are clean, structured, and defensible.


Why environmental laboratories are especially exposed


Environmental laboratories operate under regulatory and scientifi c conditions that place unusually high demands on data integrity.


Results are used to demonstrate compliance with permit limits, inform enforcement decisions, underpin public reporting, and establish long- term environmental trends. In many cases, data must remain defensible years after analysis, sometimes in legal or regulatory contexts.


At the same time, environmental data are inherently complex. Laboratories handle multiple matrices, evolving analytical methods, shifting detection limits, and geographically distributed sampling programmes.


Much of the value lies not in individual measurements, but in their comparability


over time.


When datasets are inconsistent or poorly contextualised, AI systems struggle to separate genuine environmental signals from artefacts introduced by process variation.


Why poor data quality is where AI initiatives fail


In laboratory environments, AI rarely fails because algorithms are inadequate.


More often, it fails because the underlying data are fragmented, inconsistent, or incomplete.


Environmental labs commonly inherit datasets with changing formats, missing metadata, manual transcription errors, or results stored across spreadsheets, PDFs, and disconnected instrument systems.


When AI models are trained on this kind of information, the outputs may appear sophisticated but lack robustness.


Predictions become diffi cult to reproduce, explanations become unclear, and confi dence in the results erodes when they are challenged by regulators, auditors, or clients. In this context, AI can introduce risk rather than reduce it.


What data readiness actually means in practice


For environmental laboratories, being data- ready is not about collecting more data or deploying advanced analytics platforms. It is about ensuring that existing data are usable,


comparable, and traceable.


Data readiness means that samples can be followed consistently from receipt through analysis to reporting, that methods and units are applied uniformly, and that contextual information is captured as part of routine workfl ows rather than as an afterthought.


It also means that quality control data are structured and linked to results, that audit trails are complete, and that historical datasets can be queried and analysed without extensive manual intervention.


In many cases, laboratories discover that the real barrier to AI adoption is not technical capability, but the way data are captured and governed day to day.


The takeaway for environmental laboratory professionals


AI has the potential to transform how environmental laboratories operate, but only when the underlying data are fi t for purpose.


Clean, structured, and accessible data are not optional enhancements; they are the foundation on which credible analytics, automation, and decision-making are built.


Before asking what AI can do for your laboratory, it is worth asking a more fundamental question: is your data ready to support it?


Read the full article online: ilmt.co/TL/b8MG


Environmental Laboratory Tailored gas detection for every lab


Toxic, fl ammable, and asphyxiant gases are among the most common and serious hazards found in lab environments. IGD is committed to protecting the vital work carried out in these critical workplaces by mitigating gas hazards, and the best way to do that is with a gas detection system tailored to your lab’s specifi c needs.


No two lab environments are alike. Differences in size, purpose and confi guration often exist within the same facility. Each lab may present a unique set of gas hazards depending entirely on the processes and materials in use. That’s why it’s important to choose gas detection equipment that’s tailored to the specifi c risks in your lab.


There is no such thing as a “one-size-fi ts-all” solution. That’s why IGD has developed a range of sensors capable of detecting more than 700 gases and vapours. IGD’s sensor technologies offer increased operational lifetimes and industry-leading detection ranges, enabling IGD to design custom gas detection solutions for even the most complex laboratory environments. No matter how diverse your gas hazards are, IGD can help you safeguard your people, processes, and property with precision using a single adaptable system for any lab.


IGD provide a scalable solution for labs of any size or type, from single-room educational suites to huge multilevel research facilities. The Sentinel+ range of fi xed gas detection products offers unparalleled reliability, ultimate safety, and full regulatory compliance on an intuitive, addressable system backed by over a century of innovation.


Sentinel+ is IGD’s exclusive gas detection system. The concept is simple: power and communications are both handled by a single polarity-independent 2-wire cable,


utilising the only communications protocol specifi cally designed and optimised for gas detection. Everyone else uses at least 4 wires to run their systems. By doing a better job with just 2, IGD can offer ultimate protection for a fraction of the cost. This enables customers to save up to 70% on installation, adapt the system to meet any changing requirements, remote monitoring, automatic system setup and a leading 10-year warranty.


The University of Manchester hosts one of the largest engineering campuses in Europe (MecD), where staff and students rely on a variety of gases to conduct vital research. IGD designed a Sentinel+ system comprising 300+ detectors to provide the entire facility with 24/7 monitoring of 10 distinct gas hazards, protecting students and staff and providing the university with a scalable, addressable system. IGD has published a case study explaining how Sentinel+ is protecting staff and students at MecD.


More information online: ilmt.co/PL/vlXD 66180pr@reply-direct.com


Microplastics Analysis


How to analyse microplastics and choose the most suitable method for different samples and research questions?


Given the current lack of standardisation, microplastic analysis requires a high degree of expertise. In this article, Anssi Rajala details the most important considerations for selecting your analytical method, providing practical examples of microplastic-related research questions in increasing order of complexity and explaining how they can be addressed by combining diligent method selection with sample preparation.


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IET - JANUARY / FEBRUARY 2026


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