Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

Geostatistics has been successfully used to analyze and characterize the spatial variability of environmental properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work universal block kriging is novelty used to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents that are very valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies might be very helpful in the future to validate dispersion models.

Characterization of soils in an area of prescribed fire

The prescribed fire is a technique that is often used, it has several advantages. Pedological and hydropedological techniques were tested to assess the prescribed fire changes may cause in soils. This work was performed in Tresminas area (Vila Pouca de Aguiar, Northern Portugal), during February and March 2011. In the present study we applied several techniques. For the field sampling was followed the ISO 10381-1[1], ISO 10381-2[2], and FAO rules [3], as well as were used a grid with 17 points for measuring the soil parameters. During the fire, we have tried to check, with the assistance of the Portuguese Forestry Authority, some important parameters such as, the propagation speed, the size of the flame front and the intensity of energy emitted per unit area. Before the fire, was collected carefully soil disturbed and undisturbed samples for laboratory analysis, and measured soil water content; we also have placed four sets of thermocouples for measuring soil temperature. After the fire, were collected the thermocouples and new soil samples; the water content were measured in the soil and collected ashes. In the laboratory, after preparing and sieving the samples, were determined the soil particle size. The soil pH and electrical conductivity in water was also determined. The total carbon (TC) and inorganic carbon (IC)[4] was measured by a Shimadzu TOC-Vcsn. The water content in soil has not varied significantly before and after the fire, as well as soil pH and soil electrical conductivity. The TC and IC did not change, which was expected, since the fire not overcome the 200° C. Through the various parameters, we determined that the prescribed fire didn’t affect the soil. The low temperature of the fire and its rapid implementation that lead to the possible adverse effects caused by the wild fire didn’t occurred.

Fate Hazardous elements in soils surrounding a coal-fired power plant complex

Proceedings of the 13th International UFZ-Deltares Conference on Sustainable Use and Management of Soil, Sediment and Water Resources - 9–12 June 2015 • Copenhagen, Denmark

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

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 Mat`ern 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.