Data/hora: 18/06/2024 13:35:01

Biblioteca(s): | Embrapa Solos.
Data corrente: | 05/05/2022
Data da última atualização: | 05/05/2022
Autoria: | MAIA, A. J.; NASCIMENTO, R. C.; SILVA, Y. J. A. B. da; NASCIMENTO, C. W. A. do; MENDES, W. de S.; VERAS NETO, J. G.; ARAUJO FILHO, J. C. de; TIECHER, T.; SILVA, Y. J. A. B. da.
Afiliação: | ANGELO JAMIL MAIA, UFRPE; RENNAN CABRAL NASCIMENTO, UFRPE; YGOR JACQUES AGRA BEZERRA DA SILVA, UFRPE; CLÍSTENES WILLIAMS ARAÚJO DO NASCIMENTO, UFRPE; WANDERSON DE SOUSA MENDES, LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH (ZALF); JOSÉ GERMANO VERAS NETO, UFPB; JOSE COELHO DE ARAUJO FILHO, CNPS; TALES TIECHER, UFRGS; YURI JACQUES AGRA BEZERRA DA SILVA, UFPI.
Título: | Near-infrared spectroscopy for prediction of potentially toxic elements in soil and sediments from a semiarid and coastal humid tropical transitional river basin.
Ano de publicação: | 2022
Fonte/Imprenta: | Microchemical Journal, v. 179, 107544, Aug. 2022.
DOI: | https://doi.org/10.1016/j.microc.2022.107544
Idioma: | Inglês
Conteúdo: | The input of potentially toxic elements (PTE) in river basins is a major environmental problem. PTE concentrations are determined using traditional analytical methods, which are mostly time consuming, expensive, and reliant on hazardous reagents. An alternative method to traditional chemical analysis is near-infrared (NIR) spectroscopy, which allows for the quantification of several PTEs through chemometric models. The aim of this study is to apply NIR spectroscopy for the prediction of PTE concentrations in the Ipojuca river basin, an area exposed to potential pollutant activities. We collected 145 soil samples and 33 bed sediments samples. All samples were ground and sieved at <- 100-um, and then analyzed for Al, Ce, Co, Cr, Fe, La, Mn, Mo, Ni, Pr, Sc, Sm, Sn, Sr, Th, Ti, V, and Y concentrations by inductively coupled plasma optical emission spectroscopy. Spectral data were retrieved from all samples using an FT-IR/NIR spectrometer in the range of 1000 - 2500 nm. The samples were subdivided into two sets: (i) soil and (ii) soil and bed sediments. Prediction models were built using the random forest algorithm (RF) and partial least squares regression (PLS) combined with different spectral preprocessing methods. Satisfactory results were obtained for Al, Ti, Sc, and V, and reasonable results for Fe, La, Mn, Pr, Sm, Sr, and Th. Our findings indicate that RF models obtains generally better results than PLS, and also that NIR spectroscopy can be a viable alternative assessment tool even in large areas with geochemical and pedological heterogeneity.
Palavras-Chave: | Aprendizado de máquina; Contamination; Espectroscopia; Gestão de recursos hídricos; Hydrological monitoring; Infravermelho; Machine learning; Monitoramento hidrológico; Soil sensing; Water resources management.
Thesagro: | Contaminação.
Categoria do assunto: | P Recursos Naturais, Ciências Ambientais e da Terra
Registro original: | Embrapa Solos (CNPS)
Exemplares: | CNPS - (21069-1) - UPC - AP - DD -  -  -  - DIS