resumo
Widespread microplastic pollution is raising growing concerns as to its detrimental effects upon living organisms. A realistic risk assessment must stand on representative data on the abundance, size distribution and chemical composition of microplastics. Raman microscopy is an indispensable tool for the analysis of very small microplastics (<20 mu m). Still, its use is far from widespread, in part due to drawbacks such as long measurement time and proneness to spectral distortion induced by fluorescence. This review discusses each drawback followed by a showcase of interesting and easily available solutions that contribute to faster and better identification of microplastics using Raman spectroscopy. Among discussed topics are: enhanced signal quality with better detectors and spectrum processing; automated particle selection for faster Raman mapping; comprehensive reference libraries for successful spectral matching. A last section introduces non-conventional Raman techniques (non-linear Raman, hyper spectral imaging, standoff Raman) which permit more advanced applications such as real-time Raman detection and imaging of microplastics. (C) 2018 Elsevier Ltd. All rights reserved.
palavras-chave
3 GORGES RESERVOIR; FRESH-WATER; ANTHROPOGENIC PARTICLES; MARINE-ENVIRONMENT; PLASTIC PARTICLES; ATLANTIC-OCEAN; RIVER THAMES; NILE RED; MU-M; SEDIMENTS
categoria
Engineering; Environmental Sciences & Ecology; Water Resources
autores
Araujo, CF; Nolasco, MM; Ribeiro, AMP; Ribeiro-Claro, PJA
nossos autores
Antonio Ribeiro
Estudante de doutoramento
Catarina F. Araújo
Investigador
Mariela Martins Nolasco
Investigador Auxiliar
Paulo Ribeiro-Claro
Professor Associado com AgregaçãoProjectos
CICECO - Aveiro Institute of Materials (UID/CTM/50011/2013)
Projeto de Investigação Exploratória: Mariela Nolasco (IF/01468/2015)
Other
Capa
Mediaagradecimentos
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT Ref. UID/CTM/50011/2013), financed by portuguese funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement. FCT is gratefully acknowledged for funding a PhD grant to CFA (SFRH/BD/129040/2017) and a researcher contract under the program IF 2015 to MMN (IF/01468/2015).