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

• Published in: Environmental science and pollution research international Vol. 22; no. 8; pp. 5850 - 5863
• Main Authors: Del Monego, Maurici, Ribeiro, Paulo Justiniano, Jr, Ramos, Patrícia
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.04.2015; Springer Berlin Heidelberg; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Autonomous underwater vehicles; Catalysis; data collection; Diffusers; Diffusion; Earth and Environmental Science; Ecotoxicology; Effluents; Environment; Environmental Chemistry; Environmental Health; Environmental Monitoring - methods; Geostatistics; guidelines; Kriging; least squares; Least squares method; Least-Squares Analysis; Likelihood Functions; linear models; Maximum likelihood method; Models, Theoretical; monitoring; Plumes; Ports; Portugal; Receiving; Receiving waters; Research Article; Salinity; Salinity effects; Seawater; Sensors; Sewage; Sewage - chemistry; Spatial Analysis; Spatial distribution; Spatial variations; Standard deviation; Underwater vehicles; Variables; Vehicles; Waste Water Technology; Water Management; Water Pollution - analysis; Water Pollution Control; Portugal; United States > US; Autonomous underwater vehicles; Spatial inference; Covariates; Sewage plumes environmental impact assessment; Maximum likelihood; Weighted least squares; Monitoring
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-014-3709-7

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 Matèrn 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.