Environmetrics

Aims & Scope

Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences.

The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems.

Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science.

New methods should be illustrated with recent environmental data.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.7
2024	1.50

Journal Rank

Year	Value
2024	7540

Journal Citation Indicator

Year	Value
2024	248

SJR (SCImago Journal Rank)

Year	Value
2024	0.740

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	70

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Environmental Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values

  Citation: 4605

  Authors: Pentti, Unto

• Bayesian stable isotope mixing models

  Citation: 841

  Authors: Andrew C., Donald L., Stuart, Brice X., Eric J., Jonathan W., Andrew L., Jonathan, David J., Richard

• Large scale wildlife monitoring studies: statistical methods for design and analysis

  Citation: 467

  Authors: Kenneth H., James D., Theodore R., George L., Larissa L., John R.

• Principal component analysis for compositional data with outliers

  Citation: 432

  Authors: Peter, Karel, Clemens

• Characterization of the trophic conditions of marine coastal waters with special reference to the NW Adriatic Sea: proposal for a trophic scale, turbidity and generalized water quality index

  Citation: 422

  Authors: R. A., F., G., A.

• Quantitative tools for perfecting species lists

  Citation: 349

  Authors: Michael W., Peter G., Bruce W., Peter S., Thomas

• Performance of partial Mann–Kendall tests for trend detection in the presence of covariates

  Citation: 344

  Authors: Claudia, Anders

• Spatial modelling using a new class of nonstationary covariance functions

  Citation: 309

  Authors: Christopher J., Mark J.

• Estimating common trends in multivariate time series using dynamic factor analysis

  Citation: 237

  Authors: A. F., R. J., I. T., R., J. J.

• Variance estimation for spatially balanced samples of environmental resources

  Citation: 202

  Authors: Don L., Anthony R.

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