Abstract
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.

Abstract
The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron's classical estimator was used the compute the experimental semivariogram, which was fitted to three theoretical models: spherical, exponential and Gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the nearby beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume's dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

Abstract
Ocean sewage outfalls are major sources of contaminants to coastal ocean ecosystems. This method of disposal has advantages in terms of economy and relative societal impact, but it also raises important concerns about public health and ecosystem preservation. Autonomous Underwater Vehicles have already been shown to be very useful for monitoring routine of ocean outfalls. The major advantage of this technology over traditional methods is the ability to collect high-resolution data which can be very valuable for environmental impact assessment and comparison with plume prediction models. Once the data has been collected in the field it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information, for example, regarding the spatial distribution of soil properties. In this work geostatistics is used to model and map the spatial distribution of temperature and salinity measurements gathered by MARES AUV in a monitoring campaign to Foz do Arelho outfall, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology provides good estimates of the dispersion of effluent and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.

Abstract
Several monitoring approaches have been used to understand the physical, chemical, and biological processes associated with coastal sewage discharges. However, these efforts have not improved the understanding of the interaction of effluent plume/coastal ocean processes. Autonomous underwater vehicles (AUVs) have already been shown to be very useful for performing high-resolution surveys of small features such as outfall plumes. Some of the advantages of these platforms include easier field logistics, low cost per deployment, good spatial coverage, sampling over repeated sections, and the ability to perform feature based or adaptive sampling. Once the data have been collected in the field, it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information; for example, regarding the spatial distribution of soil 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, geostatistics is used to model and map the spatial distribution of temperature measurements gathered by an AUV in a sea ouffall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology can provide good estimates of the dispersion of effluent, and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.

Abstract
The wastewater plumes show to be very difficult to observed in detail. The several studies already conducted exhibit very complex and patchy structures both in vertical and horizontal sections. It is not clear if this plume patchiness is due to physical processes or measurement limitations. Rapid tow-yo sampling is expected to reduce the time variability during and between transects. The AUVs may be a useful instrument to map and detect wastewater plumes. This paper presents several prediction studies using time series files of actual in-situ measurements integrated in a near field model. The model predictions of the plume characteristics at the end of near field support the definition of the best sampling strategy for an AUV monitoring mission in a Portuguese west coast outfall.

Abstract
Near field model predictions can be used to reduce the uncertainty about outfall sewage plume location during a monitoring mission. In this paper we present an innovative methodology that uses these models to predict the plume location, establishing a sensing strategy applicable to a monitoring mission using an AUV. The paper describes two applications that implement this methodology, accomplishing the automatic mission definition using real-time oceanographic data.