Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers-Reference-Cited by-同舟云学术

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers

Published:2020-07-22 Issue:17 Volume:54 Page:10630-10637
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Michel Anna P. M.¹^ORCID,Morrison Alexandra E.¹,Preston Victoria L.¹²,Marx Charles T.³,Colson Beckett C.¹⁴,White Helen K.³

Affiliation:

1. Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States

2. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States

4. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

Funder

National Academy of Sciences

Camille and Henry Dreyfus Foundation

W. M. Keck Foundation

Richard Saltonstall Charitable Foundation

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.0c02099

Reference42 articles.

1. GESAMP Guidelines for the Monitoring and Assessment of Plastic Litter in the Ocean; Kershaw, P. J., Turra, A., Galfani, F., Eds. Series, GESAMP Report and Studies 99; IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP/ISA Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, 2019; p 130.

2. Airborne microplastics: Consequences to human health?

3. Microplastics in freshwater and terrestrial environments: Evaluating the current understanding to identify the knowledge gaps and future research priorities

4. Increase of litter at the Arctic deep-sea observatory HAUSGARTEN

5. Marine anthropogenic litter on British beaches: A 10-year nationwide assessment using citizen science data

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