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ESTIMATION OF COASTAL WATER CONTAMINATION LONG‐TERM TREND USING SEASONAL LINEAR MODELS 1
Author(s) -
Beliaeff B.,
Smith P. J.
Publication year - 1996
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1996.tb04057.x
Subject(s) - mytilus , environmental science , seasonality , estuary , sampling (signal processing) , hydrology (agriculture) , ecology , biology , geology , geotechnical engineering , filter (signal processing) , computer science , computer vision
Bivalves are used as bioindicators to assess trends of the chemical quality of coastal and marine environments due to their ability to concentrate chemicals. These shellfish are subject to seasonal physiological changes influencing the chemical concentration. Using quarterly data, we model concentration via linear regression with a biologically based seasonal component. This was applied to cadmium concentration measured in the blue mussel ( Mytilus edulis ) at three sites in the Seine estuary (Normandy, France). In this case we have a high concentration season from January to June and a “low concentration” season from July to December. This season definition was checked a posteriori , using box‐and‐whisker plots and a statistical test of comparison of pair‐wise adjusted least‐squares mean differences, and it appears to be very reasonable. We averaged data by season and across sites. Our final model (R 2 = 0.846 with N = 27 observations) includes highly significant terms: a season effect, which accounts for 45% of the total variability, a linear and a quadratic time term. Outliers were identified by high Studentized residual values and attributed to bias in the temporal sampling schemes. The methodology developed will further be used with other shellfish and/or other trace elements and organic chemicals.