Environmental Impact Assessment and Management of Sewage Outfall Discharges Using AUV'S

Ramos, Patrícia; Neves, Mário V.

doi:10.5772/6714

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…When taking the standard errors into account, at the 2 m depth, the plume may be identified by the regions where salinity is less than 35.4 psu and, at the 4 m depth, by the regions where salinity is less than 35.5 psu. The plume exhibits a considerably more complex structure than the compact shape of the classical picture of the buoyant plume and as patchy as in previous studies (Petrenko et al 1998;Ramos and Neves 2009). The small plume-related anomalies in the local salinity of Figs.…”

Section: Mappingmentioning

confidence: 54%

“…The physical and biological coastal processes that determine the values of environmental variables are complex and still poorly understood (Niu et al 2007;Rogowski et al 2012). Usually, the observations are so unpredictable that the spatial distribution of these variables appears to be random (Washburn et al 1992;Wu et al 1994;Carvalho et al 2002;Petrenko et al 1998;Ramos et al 2007;Ramos and Neves 2009). This randomness makes deterministic solutions adequate to describe only the simplest components (Goovaerts 1997;Webster and Oliver 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Monego

Ribeiro

Ramos

2014

Environ Sci Pollut Res

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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 Matèrn 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.

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Section: Mappingmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

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

Monego

Ribeiro

Ramos

2014

Environ Sci Pollut Res

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“…The study site is shown in Figure 2. The S. Jacinto outfall is located off the Portuguese west coast near the Aveiro estuary [9, 10]. The total length of the outfall, including the diffuser, is 3378m (the first 3135m section has a diameter of 1600mm and the last 243m section has a diameter of 1200mm).…”

Section: Resultsmentioning

confidence: 99%

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

Ramos

2013

International Journal of Advanced Robotic Systems

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Geostatistics has been successfully used to analyse and characterize the spatial variability of environmental properties. Besides providing estimated values at unsampled locations, geostatistics measures the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. This work uses universal block kriging to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign. The aim is to distinguish 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, which are valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume's dilution are rare, these studies may be very helpful in the future to validate dispersion models.

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“…Two major types of vehicles provide similar, but complementary types of observations discharge plumes ( Fig.22.1). Propelled vehicles, such as the Hydroid Remus, Isurus, or Mares, provide relatively synoptic surveys discharge plumes (Rogowski et al 2012Ramos and Neves 2008). Buoyancy propelled ocean gliders, while not synoptic over their entire survey, can provide a longer term monitoring tool that is capable of weeks to months of sustained monitoring (Todd et al 2009;Seegers et al 2014).…”

Section: Vehiclesmentioning

confidence: 99%

Intakes and Outfalls for Seawater Reverse-Osmosis Desalination Facilities

Missimer¹,

Jones²,

Maliva³

2015

Environmental Science and Engineering

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Environmental Impact Assessment and Management of Sewage Outfall Discharges Using AUV'S

Cited by 6 publications

References 16 publications

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

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

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

Intakes and Outfalls for Seawater Reverse-Osmosis Desalination Facilities

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