![Predicting organic loading in natural water using spectral fluorescent signatures [An article from: Journal of Hazardous Materials]](https://www.ebooknetworking.net/books/B00/0RR/bigB000RR130W.jpg)
Predicting organic loading in natural water using spectral fluorescent signatures [An article from: Journal of Hazardous Materials]
Book Details
Author(s)K. Bengrai@?ne, T.F. Marhaba
PublisherElsevier
ISBN / ASINB000RR130W
ISBN-13978B000RR1306
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Journal of Hazardous Materials, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Spectral fluorescent signature (SFS) is a rapid, reagent free and inexpensive technique, which has great potential for environmental monitoring of aqueous systems, especially for predicting dissolved organic carbon (DOC) along natural waters. This technical note aimed to examine the possibility to use SFS associated with partial least squares regression (PLS) to assess the organic loading in natural water. A model was built using samples of water collected between October 1999 and February 2002 on the Passaic River at Little Falls, NJ, USA. A correlation was established between measured DOC, SFS, and the corresponding daily registered flow from United States Geological Survey (USGS) New Jersey's streamflow database. The methodology presented herein looks promising in making use of the significant organic characteristics information contained in a SFS for application and use in spatial and temporal water quality management and treatment.
Description:
Spectral fluorescent signature (SFS) is a rapid, reagent free and inexpensive technique, which has great potential for environmental monitoring of aqueous systems, especially for predicting dissolved organic carbon (DOC) along natural waters. This technical note aimed to examine the possibility to use SFS associated with partial least squares regression (PLS) to assess the organic loading in natural water. A model was built using samples of water collected between October 1999 and February 2002 on the Passaic River at Little Falls, NJ, USA. A correlation was established between measured DOC, SFS, and the corresponding daily registered flow from United States Geological Survey (USGS) New Jersey's streamflow database. The methodology presented herein looks promising in making use of the significant organic characteristics information contained in a SFS for application and use in spatial and temporal water quality management and treatment.
