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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
05/05/2022 |
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Data da última atualização: |
05/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
Near-infrared spectroscopy for prediction of potentially toxic elements in soil and sediments from a semiarid and coastal humid tropical transitional river basin. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Microchemical Journal, v. 179, 107544, Aug. 2022. |
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DOI: |
https://doi.org/10.1016/j.microc.2022.107544 |
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Idioma: |
Inglês |
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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. MenosThe 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 ... Mostrar Tudo |
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Palavras-Chave: |
Aprendizado de máquina; Contamination; Espectroscopia; Gestão de recursos hídricos; Hydrological monitoring; Infravermelho; Machine learning; Monitoramento hidrológico; Soil sensing; Water resources management. |
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Thesagro: |
Contaminação. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02775naa a2200361 a 4500
001 2142678
005 2022-05-05
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.microc.2022.107544$2DOI
100 1 $aMAIA, A. J.
245 $aNear-infrared spectroscopy for prediction of potentially toxic elements in soil and sediments from a semiarid and coastal humid tropical transitional river basin.$h[electronic resource]
260 $c2022
520 $aThe 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.
650 $aContaminação
653 $aAprendizado de máquina
653 $aContamination
653 $aEspectroscopia
653 $aGestão de recursos hídricos
653 $aHydrological monitoring
653 $aInfravermelho
653 $aMachine learning
653 $aMonitoramento hidrológico
653 $aSoil sensing
653 $aWater resources management
700 1 $aNASCIMENTO, R. C.
700 1 $aSILVA, Y. J. A. B. da
700 1 $aNASCIMENTO, C. W. A. do
700 1 $aMENDES, W. de S.
700 1 $aVERAS NETO, J. G.
700 1 $aARAUJO FILHO, J. C. de
700 1 $aTIECHER, T.
700 1 $aSILVA, Y. J. A. B. da
773 $tMicrochemical Journal$gv. 179, 107544, Aug. 2022.
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Registro original: |
Embrapa Solos (CNPS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/04/2023 |
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Data da última atualização: |
04/04/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BERTOLO, L. S.; CALABONI, A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C. |
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Afiliação: |
LÍDIA SANCHES BERTOLO, DEUTSCHE GESELLSCHAFT FÜR INTERNATIONALE ZUSAMMENARBEIT; ADRIANE CALABONI, DEUTSCHE GESELLSCHAFT FÜR INTERNATIONALE ZUSAMMENARBEIT; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA. |
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Título: |
Seleção de amostras e parametrização de modelo para classificação de áreas agrícolas no cerrado usando cubo de dados Sentinel-2. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 928-931. |
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ISBN: |
978-65-89159-04-9 |
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Idioma: |
Português |
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Notas: |
Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão. |
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Conteúdo: |
Este trabalho pretendeu testar, usando soluções inovadoras, como BDC/sits, o desenvolvimento e a sistematização de um método capaz de selecionar amostras e classificar a cobertura e uso da terra do Cerrado. |
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Palavras-Chave: |
Aprendizado de máquina; BDC; Brazil Data Cube; Cultura agrícola; Machine learning; Random forest; Satellite Image Time Series Analysis for Earth Observation Data Cubes; Série temporal; Sits; TerraClass 2020. |
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Thesagro: |
Agricultura. |
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Thesaurus NAL: |
Agriculture; Time series analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152982/1/PC-Selecao-amostras-SBSR-2023.pdf
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Marc: |
LEADER 01503nam a2200337 a 4500
001 2152982
005 2023-04-04
008 2023 bl uuuu u00u1 u #d
020 $a978-65-89159-04-9
100 1 $aBERTOLO, L. S.
245 $aSeleção de amostras e parametrização de modelo para classificação de áreas agrícolas no cerrado usando cubo de dados Sentinel-2.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 928-931.$c2023
500 $aEditores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.
520 $aEste trabalho pretendeu testar, usando soluções inovadoras, como BDC/sits, o desenvolvimento e a sistematização de um método capaz de selecionar amostras e classificar a cobertura e uso da terra do Cerrado.
650 $aAgriculture
650 $aTime series analysis
650 $aAgricultura
653 $aAprendizado de máquina
653 $aBDC
653 $aBrazil Data Cube
653 $aCultura agrícola
653 $aMachine learning
653 $aRandom forest
653 $aSatellite Image Time Series Analysis for Earth Observation Data Cubes
653 $aSérie temporal
653 $aSits
653 $aTerraClass 2020
700 1 $aCALABONI, A.
700 1 $aANTUNES, J. F. G.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
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