Water, Air, and Soil Pollution

, Volume 194, Issue 1–4, pp 111–139

Pollution Bioindicators: Statistical Analysis of a Case Study

Article

Abstract

In this paper a three-step procedure is proposed to deal with ecological data, usually very complex in their treatment. The three steps – exploratory, confirmatory, and modelling phases – reflect the different methodological approaches necessary in each phase of the study. To illustrate the methodology, a case study is proposed, concerning the suitability of plants as pollution bioindicators. Samples of differently aged Pinus pinea L. needles were collected throughout 1 year in three different locations, whose human disturbance was known to be different. In the samples some morphological and functional parameters were measured, whose relation with the stress was already known. The exploratory analysis suggested pollution with human origin, the needle’s age, and the environmental conditions as the main factors of influence of damage. The confirmatory analysis confirmed both site and age as main factors and occasionally the sampling date. On this basis, some models were estimated separately for each site: models that best described the damage as function of age resulted non-linear and some of them with seasonal fluctuations. As a result, whereas the models described well enough the pollution temporal variation, the difference of pollution in the sites was best described by the different values of the models parameters in the different sites. In short, different pollution conditions are described better by the damage trend than by the individual measures. The three-step procedure resulted of high utility in outlining the most interesting relations to investigate through the modelling, the opportunity to model the indicators variation along time separately for each site, and to introduce the seasonal variation in some models.

Keywords

Pollution Plant ecology Leaves damage Pinus pinea Bioindicators Data analysis Principal components analysis Cluster analysis Analysis of variance Linear regression Nonlinear regression Models 

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Dipartimento di Matematica Guido CastelnuovoSapienza Università di RomaRomeItaly
  2. 2.Laboratorio di Indagini BiologicheIstituto Centrale del Restauro Ministero Beni e Attività CulturaliRomeItaly
  3. 3.Dipartimento di Biologia VegetaleSapienza Università di RomaRomeItaly

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