Assessing coastal waters of American Samoa: territory-wide water quality data provide a critical “big-picture” view for this tropical archipelago

Administratively Bedukang coastal waters get into the District of Riau Silip Bangka. There is a tin mining activities and farms in the region that directly or indirectly provide significant pressure on water quality in the region. The purpose of this study was to analyze the characteristics of the quality of coastal waters Bedukang and assess the degree of pollution in coastal waters Bedukang using Pollution Index (IP). Methods of data collection was done at 4 stations by purposive sampling. Insitu measurement parameters are parameters of temperature, pH, and salinity and measurement parameters are parameters exsitu TSS, TDS, BOD, Metals Pb and Total Coliform. Analysis of water quality data using Pollution Index (IP). The results show the value of IP, namely at the first station, the station 3 and the station 4, including lightly polluted category with the value of each value of 2.819, 1.025 and 1.022. At station 2 including good water quality category with the pollution index value of 0.919.There are 3 point lightly polluted so it needs special attention from the local government in the utilization of resources in the region.

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CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

Earth System Science Data Discussions ◽

10.5194/essdd-8-173-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 173-258 ◽

Cited By ~ 4

Author(s):

B. Nechad ◽

K. Ruddick ◽

T. Schroeder ◽

K. Oubelkheir ◽

D. Blondeau-Patissier ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Coastal Waters ◽

Quality Parameters ◽

Round Robin ◽

Water Quality Parameters ◽

Quality Data ◽

Ocean Colour ◽

Water Quality Data

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive inter-comparison due to the availability of quality checked in situ databases. The CoastColour project Round Robin (CCRR) project funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). These datasets are available from doi.pangaea.de/10.1594/PANGAEA.841950. The datasets mainly consisted of 6484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

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