How to separate long-term trends from periodic variation in water quality monitoring

Abstract : Modelling and multivariate analyses processed on multiple time series usually encounter some difficulties for three reasons: (1) sampling dates may be not equally spaced; (2) several values may be missing; and (3) the usual multivariate analyses may not succeed in separating long-term trends from regular periodic variations on an annual scale within the time series. To circumvent these difficulties, we propose a statistical approach based on the modelling of data by the non-parametric smoother Loess and the application of functional principal components analysis (FPCA). FPCA thereby facilitates the typology of variables based on their long-term trends and/or their periodic variation. We applied this approach to a long-term study over nine years (1983–1991) of the water quality of the Seine river (France) conducted downstream of a plant for wastewater treatment.
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Stéphane Champely, Sylvain Dolédec. How to separate long-term trends from periodic variation in water quality monitoring. Water Research, IWA Publishing, 1997, 31 (11), pp.2849-2857. ⟨10.1016/S0043-1354(97)00136-X⟩. ⟨hal-02352141⟩

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