Assessing the environmental consequences of CO2 leakage from geological CCS: Generating evidence to support environmental risk assessment

At the start of the industrial revolution (circa 1750) the atmospheric concentration of carbon dioxide (CO2) was around 280 ppm. Since that time the burning of fossil fuel, together with other industrial processes such as cement manufacture and changing land use, has increased this value to 400 ppm, for the first time in over 3 million years. With CO2 being a potent greenhouse gas, the consequence of this rise for global temperatures has been dramatic, and not only for air temperatures. Global Sea Surface Temperature (SST) has warmed by 0.4–0.8 °C during the last century, although regional differences are evident (IPCC, 2007). This rise in atmospheric CO2 levels and the resulting global warming to some extent has been ameliorated by the oceanic uptake of around one quarter of the anthropogenic CO2 emissions (Sabine et al., 2004). Initially this was thought to be having little or no impact on ocean chemistry due to the capacity of the ocean’s carbonate buffering system to neutralise the acidity caused when CO2 dissolves in seawater. However, this assumption was challenged by Caldeira and Wickett (2005) who used model predictions to show that the rate at which carbonate buffering can act was far too slow to moderate significant changes to oceanic chemistry over the next few centuries. Their model predicted that since pre-industrial times, ocean surface water pH had fallen by 0.1 pH unit, indicating a 30% increase in the concentration of H+ ions. Their model also showed that the pH of surface waters could fall by up to 0.4 units before 2100, driven by continued and unabated utilisation of fossil fuels. Alongside increasing levels of dissolved CO2 and H+ (reduced pH) an increase in bicarbonate ions together with a decrease in carbonate ions occurs. These chemical changes are now collectively recognised as “ocean acidification”. Concern now stems from the knowledge that concentrations of H+, CO2, bicarbonate and carbonate ions impact upon many important physiological processes vital to maintaining health and function in marine organisms. Additionally, species have evolved under conditions where the carbonate system has remained relatively stable for millions of years, rendering them with potentially reduced capacity to adapt to this rapid change. Evidence suggests that, whilst the impact of ocean acidification is complex, when considered alongside ocean warming the net effect on the health and productivity of the oceans will be detrimental.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.