河流底泥DOM、营养盐与重金属空间分异特征及响应关系

为探究河流底泥中的溶解性有机物(DOM)和营养盐对重金属迁移转化的影响,以沈阳市新穆河、南小河和老背河为研究对象,利用三维荧光光谱,结合平行因子(PARAFAC)、主成分分析(PCA)和结构方程模型(SEM)研究了底泥中的DOM、营养盐与重金属的空间分异特征及DOM与营养盐对重金属的响应关系.结果表明,3条河流底泥中的总氮(TN)和总磷(TP)呈重度污染状态.南小河底泥中的ω(Zn)、ω(As)、ω(Cu)、ω(Ni)和ω(Cd)分别是辽宁省土壤背景值的2.48、 1.02、 3.45、 3.00和39.05倍,老背河的ω(Zn)、ω(Cu)和ω(Cd)分别是背景值的2.56、 1.92和8.00倍,新穆河的ω(Zn)和ω(Cu)分别是背景值的1.40和1.10倍,总体上3条河流均存在重金属累积现象,累积严重程度为：南小河>老背河>新穆河,其中南小河和老背河Cd污染严重.应用PARAFAC共解析出5类DOM组分,分别为类色氨酸、酚类络合物、微生物代谢产物、类富里酸和类胡敏酸,其中类富里酸和类胡敏酸丰度较高;基于PCA分析,新穆河底泥污染的主控因子为TN、 TP、类富里酸和类...

Spatial Differentiation Characteristics and Response Relationship of DOM, Nutrients, and Heavy Metals in River Sediments

In order to investigate the environmental impact of dissolved organic matter (DOM) and nutrients on heavy metals in river sediments, this study examined the spatial differentiation characteristics of DOM, nutrients, and heavy metals and the relationship between DOM and nutrients in response to heavy metals. Focusing on three important tributaries, the Xinmu River, Nanxiao River, and Laobei River in the Shenyang section of the Hunhe River Basin, which have obvious differences in the dominant ways of human production and living activities, this experiment employed three-dimensional excitation-emission matrices (EEMs), combined with parallel factor analysis (PARAFAC), principal component analysis (PCA), and structural equation modeling (SEM). The results indicated that total nitrogen (TN) and total phosphorus (TP) in the three rivers showed a severe contamination state. Mean heavy metal contents showed a change trend of Zn>Cu>Ni>As>Pb>Cd. Among them, the contents of Zn, As, Cu, Ni, and Cd in the sediments of the Nanxiao River were 2.48, 1.02, 3.45, 3.00, and 39.05 times the soil background value in Liaoning Province. The contents of Zn, Cu, and Cd in the Laobei River were 2.56, 1.92, and 8.00 times the background value, respectively, and the contents of Zn and Cu in the Xinmu River were 1.40 and 1.10 times the background value. This indicated that there was an accumulation of heavy metals in the three rivers, and the accumulation severity was Nanxiao River>Laobei River>Xinmu River. Additionally, the Cd content of the Nanxiao River and Laobei River strongly exceeded the standard. Five types of DOM components were analyzed using PARAFAC, which were tryptophan-like, phenolic complexes, microbial metabolites, fulvic-like acid, and humic-like acid. Among them, the abundance of humus-like substances (fulvic-like acid and humic-like acid) was relatively high, followed by tryptophan-like, phenolic complexes, and microbial metabolites, with average proportions of 65.88%, 14.22%, 10.07%, and 9.82%, respectively. The results showed that the DOM composition of the three rivers was affected by both external input and endogenous sources, and the proportion of humic acid-like components was significantly higher than that of protein-like components, and the degree of humification was higher. Based on PCA analysis, the main controlling factors of Xinmu River sediment pollution were TN, TP, fulvic-like acid, and humic-like acid, mainly from agricultural non-point source pollution and rural domestic sewage; the main controlling factors of the Nanxiao River were TP, Zn, Cu, As, and humic acid, mainly from industrial wastewater and urban domestic sewage; the main controlling factors of the Laobei River were TN, TP, Zn, fulvic-like acid, and humic-like acid, which mainly came from urban domestic sewage and construction waste. Based on SEM analysis, the path coefficients of the influence of DOM, nutrients, and pH on heavy metals were 0.52, 0.36, and 0.11, respectively. DOM had the largest impact, followed by nutrients and pH. The order of the influence of DOM components on heavy metals was fulvic-like acid (0.97)>tryptophan (0.82)>microbial metabolites (0.78)>humic-like acid (0.73)>phenolic complexes (0.55). The order of the influence of nutrients on heavy metals was TP (0.54)>TN (0.51). DOM and nutrients had a greater impact on the migration of heavy metals. Thus, it is recommended to strengthen the source control and emission reduction of organic matter, nutrients, and heavy metals to avoid the potential ecological risk of "external input+internal release" of heavy metals in sediments.