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Issue 38, 2020
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Chemometrics for environmental monitoring: a review

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Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

Graphical abstract: Chemometrics for environmental monitoring: a review

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Article information

19 Jul 2020
01 Sep 2020
First published
08 Sep 2020

Anal. Methods, 2020,12, 4597-4620
Article type
Critical Review

Chemometrics for environmental monitoring: a review

M. F. Dupont, A. Elbourne, D. Cozzolino, J. Chapman, V. K. Truong, R. J. Crawford and K. Latham, Anal. Methods, 2020, 12, 4597
DOI: 10.1039/D0AY01389G

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