Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Del Monego, M (Author) Other; Ribeiro, PJ (Author) Other; Patrícia Ramos (Author) FEUP

Journal

Title: Environmental Science and Pollution Research

Vol. 22 No. 9

Pages: 5850-5863

ISSN: 0944-1344

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00A-BKS

DOI: 10.1007/s11356-014-3709-7

Abstract (EN): In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the MatSrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14