Polycyclic aromatic hydrocarbon characterization and prediction in coastal sediments using regression modeling and machine learning

Freitag, Amy; Regan, Seann; Leight, A. K.; Kimbrough, Kimani; Edwards, Michael; Burkart, Heidi; Rider, Mary

doi:10.25923/r7k5-vj14

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Polycyclic aromatic hydrocarbon characterization and prediction in coastal sediments using regression modeling and machine learning

2021
By Freitag, Amy ; Regan, Seann ; Leight, A. K. ; ...
Series: NOAA technical memorandum NOS NCCOS ; 293

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Freitag, Amy ; Regan, Seann ; Leight, A. K. ; Kimbrough, Kimani ; Edwards, Michael ; ... More +

Freitag, Amy ; Regan, Seann ; Leight, A. K. ; Kimbrough, Kimani ; Edwards, Michael ; Burkart, Heidi ; Rider, Mary Less -
Corporate Authors:

National Centers for Coastal Ocean Science (U.S.)
NOAA Program & Office:

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) Less -
Description:

Here we utilize spatial human dimensions data to further characterize polycyclic aromatic hydrocarbon (PAH) sediment results to: 1) Characterize the relationship between PAH sediment contamination and human dimensions data. 2) Characterize and predict sediment PAH concentrations nationally based on these relationships. 3) Address data gaps to increase monitoring and assessment efficiency.
Keywords:

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Coastal Zone Management Machine Learning Marine Sediments Mathematical Models Polycyclic Aromatic Hydrocarbons
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NOAA technical memorandum NOS NCCOS ; 293
DOI:

https://doi.org/10.25923/r7k5-vj14
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Technical Memorandum
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United States
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Public Domain
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Submitted
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urn:sha256:b77d6a9820a1f27d872c586e12c854d677b18187b18ac3e2ceaea069f6217183
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https://repository.library.noaa.gov/view/noaa/33208/noaa_33208_DS1.pdf
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Polycyclic aromatic hydrocarbon characterization and prediction in coastal sediments using regression modeling and machine learning

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