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Statistical Analysis of Environmental Data

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An Introduction to Water Quality Science

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Abstract

This chapter describes different statistical techniques to assimilate environmental data and explored the advantages and disadvantages of such techniques. The objectives of environmental statistics are (i) to improve the knowledge of the environment; (ii) to provide information for the general public and specific user groups about the state of the environment and the main factors that influence it; and (iii) to support evidence-based policy and decisions making. Moreover, this chapter also explains general environmental statistics.

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References

  • Akhand, A., Chanda, A., Watanabe, K., Das, S., Tokoro, T., Hazra, S., & Kuwae, T. (2021). Reduction in riverine freshwater supply changes inorganic and organic carbon dynamics and air-water CO2 fluxes in a tropical mangrove dominated estuary. Journal of Geophysical Research: Biogeosciences, 126(5), e2020JG006144.

    Article  Google Scholar 

  • Akhand, A., Chanda, A., Watanabe, K., Das, S., Tokoro, T., Hazra, S., & Kuwae, T. (2022). Drivers of inorganic carbon dynamics and air-water CO2 fluxes in two large tropical estuaries: Insights from coupled radon (222Rn) and p CO2 surveys. Limnology and Oceanography, 67, S118–S132.

    Article  Google Scholar 

  • Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26(1), 32–46.

    Google Scholar 

  • Anderson, M. J., & Walsh, C. I. (2013). PERMANOVA, ANOSIM and the Mantel test in the face of heterogeneous: What null hypothesis are you testing? Ecological Monographs, 83(4), 557–574.

    Article  Google Scholar 

  • Baeten, L., Warton, D. I., Van Calster, H., De Frenne, P., Verstraeten, G., Bonte, D., Bernhardt-Römermann, M., Cornelis, J., Decocq, G., Eriksson, O., Hédl, R., Heinken, T., Hermy, M., Hommel, P., Kirby, K., Naaf, T., Petřík, P., Walther, G. R., Wulf, M., & Verheyen, K. (2014). A model-based approach to studying changes in compositional heterogeneity. Methods in Ecology and Evolution, 5, 156–164.

    Article  Google Scholar 

  • Borg, L., & Groenen, P. J. F. (2005). Modern multidimensional scaling: Theory and applications. Springer.

    Google Scholar 

  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.

    Article  Google Scholar 

  • Breiman, L. (1998). Arcing classifiers (with discussion and a rejoinder by the author). The Annals of Statistics, 26(3), 801–824.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

    Article  Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman & Hall.

    Google Scholar 

  • Chanda, A., Das, S., Bhattacharyya, S., Akhand, A., Das, I., Samanta, S., et al. (2020). CO2 effluxes from an urban tidal river flowing through two of the most populated and polluted cities of India. Environmental Science and Pollution Research, 27, 30093–30107.

    Article  Google Scholar 

  • Clarke, K. R. (1993). Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology (Austral Ecology), 18, 117–143.

    Article  Google Scholar 

  • Clarke, K. R., & Ainsworth, M. (1993). A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series, 92(3), 205–219.

    Article  Google Scholar 

  • Clarke, K. R., Chapman, M. G., Somerfield, P. J., & Needham, H. R. (2006a). Dispersion-based weighting of species counts in assemblage analyses. Marine Ecology Progress Series, 320, 11–27.

    Article  Google Scholar 

  • Clarke, K. R., Somerfield, P. J., & Chapman, M. G. (2006b). On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. Journal of Experimental Marine Biology and Ecology, 330, 55–80.

    Article  Google Scholar 

  • Clarke, K. R., Somerfield, P. J., & Gorley, R. N. (2008). Testing of null hypotheses in exploratory community analyses: Similarity profiles and biota-environment linage. Journal of Experimental Marine Biology and Ecology, 366, 56–69.

    Article  Google Scholar 

  • Das, S., & Hazra, S. (2019). Vertical distribution of optically active substances (OAS) in the coastal sea off Indian Sundarban during post-monsoon. Marine Biology Research, 15(2), 163–169.

    Article  Google Scholar 

  • Das, S., Giri, S., Das, I., Chanda, A., Ghosh, A., Mukhopadhyay, A., et al. (2017). Nutrient dynamics of northern Bay of Bengal (nBoB)—Emphasizing the role of tides. Regional Studies in Marine Science, 10, 116–134.

    Article  Google Scholar 

  • Death, G. (2002). Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology, 83, 1105–1117.

    Google Scholar 

  • Driver, H. E., & Kroeber, A. L. (1932). Quantitative expression of cultural relationships. The University of California Publications in American Archaeology and Ethnology, 31, 211–256.

    Google Scholar 

  • Faith, D. P., Minchin, R. M., & Belbin, L. (1987). Compositional dissimilarity as a robust measure of ecological distance. Plant Ecology, 69, 57–68.

    Article  Google Scholar 

  • Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Saitta, L., & Kaufmann, M. (eds), Machine learning: Proceedings of the thirteenth international conference, Bari, Italy.

    Google Scholar 

  • Han, F., & Liu, H. (2012). Transelliptical component analysis. Advances in Neural Information Processing Systems, 25, 171–179.

    Google Scholar 

  • Hastie, T. J., & Stuetzle, W. (1989). Principal curves. Journal of the American Statistical Association, 84(406), 502–516.

    Article  Google Scholar 

  • Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. Chapman & Hall/CRC.

    Google Scholar 

  • Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D., & Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods in Ecology and Evolution, 6, 399–411.

    Article  Google Scholar 

  • Johnstone, I. M., & Lu, A. Y. (2009). On consistency and sparsity for principal components analysis in high dimensions. Journal of the American Statistical Association, 104(486), 682–693.

    Article  Google Scholar 

  • Joliffe, N. T., Trendafilov, N. T., & Uddin, M. (2003). A modified principal component technique based on the lasso. Journal of Computational and Graphical Statistics, 12(3), 531–547.

    Article  Google Scholar 

  • Kuhnert, P. M., Henderson, B., Lewis, S. E., Bainbridge, Z. T., Wilkinson, S., & Brodie, J. E. (2012). Quantifying total suspended sediment export from the Burdekin River catchment using the load’s regression estimator tool. Water Resources Research, 48(4), 1–18.

    Article  Google Scholar 

  • Larsen, D. R., & Speckman, P. L. (2004). Multivariate regression trees for analysis of abundance data. Biometrics, 60, 543–549.

    Article  Google Scholar 

  • Legendre, P., & Legendre, L. (2012). Numerical ecology (3rd ed.). Elsevier.

    Google Scholar 

  • Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27(2), 209–220.

    Google Scholar 

  • McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Chapman & Hall/CRC.

    Book  Google Scholar 

  • Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11), 559–572.

    Google Scholar 

  • Sinha, R., Das, S., & Ghosh, T. (2020). Pollution and its consequences at Ganga Sagar mass bathing in India. Environment, Development and Sustainability, 22, 1413–1430.

    Article  Google Scholar 

  • Torgerson, W. S. (1952). Multidimensional scaling: I. Theory and method. Psychometrika, 17, 401–419.

    Article  Google Scholar 

  • Warton, D. I., Wright, T. W., & Wang, Y. (2012). Distance-based multivariate analyses confound location and dispersion effect. Methods in Ecology and Evolution, 3, 89–101.

    Article  Google Scholar 

  • Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J., & Dunstan, P. K. (2015). Model-based thinking in ecology. Plant Ecology, 216(5), 669–682.

    Article  Google Scholar 

  • Witten, D. M., Tibshirani, R., & Hastie, T. J. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10(3), 515–534.

    Article  Google Scholar 

  • Wood, S. (2017). Generalized additive models: An introduction with R. Chapman & Hall/CRC.

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Chemistry, Brainware University, Kolkata, West Bengal, India

    Sourav Das

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  1. Sourav Das
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Das, S. (2023). Statistical Analysis of Environmental Data. In: An Introduction to Water Quality Science. Springer, Cham. https://doi.org/10.1007/978-3-031-42137-2_9

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