Insight into structural composition of dissolved organic matter in saline-alkali soil by fluorescence spectroscopy coupled with self-organizing map and structural equation modeling.

Classification and regression tree Fluorescence spectroscopy Self-organizing map Soil dissolved organic matter Structural equation modeling

Journal

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
ISSN: 1873-3557
Titre abrégé: Spectrochim Acta A Mol Biomol Spectrosc
Pays: England
ID NLM: 9602533

Informations de publication

Date de publication:
15 Oct 2022
Historique:
received: 24 02 2022
revised: 20 04 2022
accepted: 23 04 2022
pubmed: 27 5 2022
medline: 28 6 2022
entrez: 26 5 2022
Statut: ppublish

Résumé

Soil salinization has been occurring all over the world, which severely affected crop production and threatened the life of mankind. It is necessary to take serious steps to improve soil fertility for the sustainability and productive capacity of agriculture. Soil samples of different depths were collected from native vegetation communities (Comm. Phragmites communis (CPC) and Comm. Populus alba (CPA)) and irrigated crops (corn fields (CFD) and seed melon fields (SMF)) in Hetao irrigation area of China. Three dimensional excitation-emission matrix (EEM) fluorescence technology combined with self-organizing map were used to analyze the dissolved organic matter (DOM) composition and structural characteristics in saline-alkali soils and its spatial distribution under different vegetation covers. Critical factors were recognized by classification and regression tree (CART) for distinguishing soil samples, and latent factors were revealed with structural equation modeling (SEM) for improving the humification degree of DOM from saline soils in Hetao irrigation area. Five components were obtained in the DOM substances, i.e., tyrosine-like (C1), tryptophan-like (C2), UV fulvic-like (C3), visible fulvic-like (C4) and humic-like (C5). The protein-like peaks were all obvious, and the fulvic-like peaks (600-735 a.u.) were conspicuous in the CPC soil than in others, except CFD1 and SMF1. C1 was the critical factor to distinguish native vegetation from irrigated crops, and C1 and C2 were the critical factors to distinguish CFD from SMF. Contrary to the HA/FA (0.20) and A/C (0.25), the path coefficient (-0.15) of sources with T/H was negative, indicating that the incremental contents of fluorenscense substances were in the sequences of protein-like > visible fulvic-like > UV fulvic-like > humic-like, affecting by the allochthonous. C1 (1.00) and C4 (1.00) were the primary components for improving the humification degree of DOM, which were principally originated from plant debris. EEM combined with self-organizing map, CART and SEM is an efficient way to distinguish different salinized soils and reveal the latent factors for improving the soil fertility.

Identifiants

pubmed: 35617840
pii: S1386-1425(22)00460-7
doi: 10.1016/j.saa.2022.121311
pii:
doi:

Substances chimiques

Alkalies 0
Dissolved Organic Matter 0
Humic Substances 0
Soil 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

121311

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Auteurs

Dongping Liu (D)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China.

Huibin Yu (H)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China. Electronic address: yhbybx@163.com.

Hongjie Gao (H)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China.

Xueyu Liu (X)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China. Electronic address: liuxueyuyu@163.com.

Weining Xu (W)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China; College of Water Sciences, Beijing Normal University, Beijing 100875, PR China.

Fang Yang (F)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, PR China.

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Classifications MeSH