Investigative monitoring within the European Water Framework Directive: a coastal blast furnace slag disposal, as an example

The European Water Framework Directive (WFD) establishes a framework for the protection of estuarine and coastal waters, with the most important objective being to achieve 'good ecological status' for all waters, by 2015. Hence, Member States are establishing programmes for the monitoring of water quality status, through the assessment of ecological and chemical elements. These monitoring programmes can be of three types: surveillance monitoring; operational monitoring (both undertaken on a routine basis); and investigative monitoring (carried out where the reason of any exceedance for ecological and chemical status is unknown). Until now, nothing has been developed in relation to investigative monitoring and no clear guidance exists for this type of monitoring, as it must be tackled on a 'case-by-case' basis. Consequently, the present study uses slag disposal from a blast furnace, into a coastal area, as a case-study in the implementation of investigative monitoring, according to the WFD.In order to investigate the potential threat of such slags, this contribution includes: a geophysical study, to determine the extent of the disposal area; sediment analysis; a chemical metal analysis; and an ecotoxicological study (including a Microtox test and an amphipod bioassay). The results show that metal concentrations are several times above the background concentration. However, only one of the stations showed toxicity after acute toxicological tests, with the benthic communities being in a good status. The approaches used here show that contaminants are not bioavailable and that no management actions are required with the slags.     

A comparative assessment of heavy metal accumulation in soft parts and byssus of mussels from subarctic, temperate, subtropical and tropical marine environments

Existing data on metal concentrations in mussels from subarctic, temperate, subtropical and tropical waters were analyzed using multivariate statistics in order to assess regional variations in metal contamination. Potential errors were reduced by only analyzing data from surveys that employed the same protocols, analytical methodologies and analysts. Factor analysis demonstrated that mussels inhabiting extremely contaminated areas (e.g. from Japanese and Swedish metallurgy sources) could be separated from mussels from other contaminated areas, and that metals such as Cd, Pb, Cu and Zn could be used to identify heavily contaminated samples while Co, Fe, Cr and Ni concentrations were good markers for exposure to inputs from different industrial sources. Furthermore byssus, like soft tissue, selectively and sensitively reflects variations of certain metal concentrations in ambient waters and thus serves as a reliable biomonitor for these contaminants in a variety of coastal and estuarine areas.     

Using sediment quality guidelines for dredged material management in commercial ports from Spain

Dredged material contamination was assessed in different commercial ports from Spain: Port of Cádiz and Huelva, South West; Bilbao and Pasajes, North; Cartagena and Barcelona, East; Coru?a, North West. Sediment from different locations of these ports was sampled and was characterized following the Spanish recommendations for dredged material management. This characterization included grain size distribution, organic matter content and concentration of the chemical compounds included in the list of pollutants and hazardous substances (As, Cd, Cu, Cr, Hg, Ni, Pb and Zn; PCB congeners IUPAC number 28, 52, 101, 118, 138, 153 and 180; PAHs were also analyzed). The results were compared to the limit values of Spanish Action Levels that define the different categories for assessment and management. A set of empirically derived sediment quality guidelines (SQG) was used to assess the possible toxicity of the dredged materials and to improve the use of the chemical approach to characterize dredged material for its management.     

Life history and production of <Emphasis Type="Italic">Corophium urdaibaiense</Emphasis> (Crustacea: Amphipoda) in the Urdaibai estuary (NE Spain)

The population of species Corophium urdaibaiense has been studied for a year in the Urdaibai estuary (NE Spain). The new species is characterised by its preference for muddy sand sediments (15% of mud) and the long body size (with a maximum length of 10.24 mm). The maximum abundance occurs in November (11,600 ind m⁻²); it almost disappears at the end of spring. The juveniles represent 79% of the population and the recruitment is concentrated within the October–December period. The breeding period begins in May, when the temperature is above 15°C, and it ceases in November, when the temperature is below 15°C. Bivoltinism is probable, taking into account the latitude (43°), temperature range (12–25°C) and breeding period (May and August–October), but the data are not sufficiently consistent to confirm the hypothesis. The production ranges from 2.93 to 5.85 g m⁻² year⁻¹, considering one and two generations per year, respectively. Some aspects concerning the food availability, predation and intraspecific competition could have an influence on the dynamics of the population and are discussed.     

On conditional skewness with applications to environmental data

The statistical literature contains many univariate and multivariate skewness measures that allow two datasets to be compared, some of which are defined in terms of quantile values. In most situations, the comparison between two random vectors focuses on univariate comparisons of conditional random variables truncated in quantiles; this kind of comparison is of particular interest in the environmental sciences. In this work, we describe a new approach to comparing skewness in terms of the univariate convex transform ordering proposed by van Zwet (Convex transformations of random variables. Mathematical Centre Tracts, Amsterdam, 1964), associated with skewness as well as concentration. The key to these comparisons is the underlying dependence structure of the random vectors. Below we describe graphical tools and use several examples to illustrate these comparisons.