Abstract
BackgroundInsight into temporal–spatial variations of dissolved organic matter (DOM) fractions were undertaken to trace potential factors toward a further understanding aquatic environment in Lake Shahu, a brackish-water lake in northwest China, using synchronous fluorescence spectroscopy (SFS) combined with principal component analysis (PCA), second derivative and canonical correlation analysis (CCA).ResultFive fluorescence peaks were extracted from SFS by PCA, including tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), fulvic-like fluorescence (FLF), and humic-like fluorescence (HLF), whose relative contents were obtained by second derivative synchronous fluorescence spectroscopy. The increasing order of total fluorescence components contents was July (11,789.38 ± 12,752.61) < April (12,667.58 ± 15,246.91) < November (19,748.87 ± 17,192.13), which was attributed to tremendous enhancement in TYLF content from April (1615.56 ± 258.56) to November (5631.96 ± 634.82). The PLF (the sum of TYLF and TRLF) dominated the fluorescence components, whose proportion was 40.55, 37.09, or 46.91% in April, July, or November. DOM fractions in November were distinguished from April and July, which could be attributed to that water of the Yellow River was continuously loaded into the lake as water replenishment from April to September. From the replenishment period to non-replenishment, the contents of the five components gradually changed from low in the middle and high around the lake to high throughout entire lake. Based on the CCA results, the potential factors included TYLF, TRLF, MHLF, SD, and BOD5 in April, which were relative to organic matter pollution. The potential factors contained TYLF, TRLF, FLF, Chl-a, TP, CODCr, and DO in July, indicating the enrichment of TP lead algae and plants growth. The potential factors in November consisted of TYLF, TRLF, CODCr, SD, TN, and FLF, representing residue of the algae and plants have been deeply degraded.ConclusionThe replenishment of water led to enrichment of TP, resulting in growth of algae and plants, and was the key factor of water quality fluctuations. This work provided a workflow from perspective of DOM to reveal causes of water quality fluctuations in a brackish-water lake and may be applied to other types of waterbodies.
Highlights
Insight into temporal–spatial variations of dissolved organic matter (DOM) fractions were undertaken to trace potential factors toward a further understanding aquatic environment in Lake Shahu, a brackish-water lake in northwest China, using synchronous fluorescence spectroscopy (SFS) combined with principal component analysis (PCA), second derivative and canonical correlation analysis (CCA)
DOM from the lake contained five fluorescence components: tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), fulvic-like fluorescence (FLF), and humic-like fluorescence (HLF), which in November were distinguished from April and July
The TRLF dominated in DOM fractions during the period of water replenishment, while TYLF was the predominant component during the period of water non-replenishment
Summary
Insight into temporal–spatial variations of dissolved organic matter (DOM) fractions were undertaken to trace potential factors toward a further understanding aquatic environment in Lake Shahu, a brackish-water lake in northwest China, using synchronous fluorescence spectroscopy (SFS) combined with principal component analysis (PCA), second derivative and canonical correlation analysis (CCA). The study of the variation of DOM composition and distribution is significant for understanding aquatic environments and evaluating water quality Various characteristic techniques, such as HPLC, FTIR, UV–vis, and fluorescence spectroscopy, have been applied to determine the structure, composition, and functionalities of DOM [5–8]. SFS provides better structure and resolved peaks, which can be analyzed and differentiate the fluorescence spectra of samples of various origins and is suitable for a small number of samples Statistical methods such as principal component analysis (PCA) could be used in SFS in order to acquire more information to assist in further analysis. Derivatives are applied in SFS to reduce extensive spectroscopic overlap and eliminate matrix interference [16]
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