Integrated environmental monitoring and multivariate data analysis—A case study
Authors: Eide Ingvar , Westad Frank , Nilssen Ingunn , de Freitas Felipe Sales , dos Santos Natalia Gomes , dos Santos Francisco , Cabral Marcelo Montenegro , Bicego Marcia Caruso , Figueira Rubens , Johnsen Ståle 
Published: August 2016

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

Key Points

The present article demonstrates the essential benefit of multivariate data analysis for optimized data interpretation in integrated environmental monitoring. Seven campaigns during 2010–2012 were carried out with sensors and sediment traps placed on the seabed at the Peregrino oil field, and in addition, data on discharges of drill cuttings were provided on a daily basis. Principal component analysis (PCA) was used to evaluate similarities and differences among samples and correlations between variables, and partial least squares (PLS) regression was used to obtain correlations between predictors and responses. According to the multivariate data analyses, chemical indicators, and numerical modeling of dispersion and deposition using the dose-related risk and effect assessment model (DREAM), the main conclusion is that the majority of the sediment trap samples had a natural and biogenic origin.