title: Integrated environmental monitoring and multivariate data analysis-A case study.
abstract: The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC.
authors: Statoil ASA, Research Centre, Trondheim, Norway.
authors: Ingvar
authors: I
authors: Eide
authors: CAMO Software AS, Oslo, Norway.
authors: Frank
authors: F
authors: Westad
authors: Statoil ASA, Research Centre, Trondheim, Norway.|Department of Biology, Trondhjem Biological Station, Norwegian University of Science and Technology, Trondheim, Norway.
authors: Ingunn
authors: I
authors: Nilssen
authors: Oceanographic Institute, University of São Paulo, Praça do Oceanográfico, São Paulo, Brazil.
authors: Felipe Sales
authors: FS
authors: de Freitas
authors: PROOCEANO Serviço Oceanográfico, Rio de Janeiro, Brazil.
authors: Natalia Gomes
authors: NG
authors: Dos Santos
authors: PROOCEANO Serviço Oceanográfico, Rio de Janeiro, Brazil.
authors: Francisco
authors: F
authors: Dos Santos
authors: PROOCEANO Serviço Oceanográfico, Rio de Janeiro, Brazil.
authors: Marcelo Montenegro
authors: MM
authors: Cabral
authors: Oceanographic Institute, University of São Paulo, Praça do Oceanográfico, São Paulo, Brazil.
authors: Marcia Caruso
authors: MC
authors: Bicego
authors: Oceanographic Institute, University of São Paulo, Praça do Oceanográfico, São Paulo, Brazil.
authors: Rubens
authors: R
authors: Figueira
authors: Statoil ASA, Research Centre, Trondheim, Norway.
authors: Ståle
authors: S
authors: Johnsen
mesh_terms: D001938:Brazil; D004784:Environmental Monitoring / Q000379:methods*; D019015:Geologic Sediments / Q000737:chemistry; D015999:Multivariate Analysis; D061329:Oil and Gas Fields; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical
keywords:  Drill cuttings; Partial least squares regression; Peregrino; Principal component analysis (PCA); Sediment traps