Author:
Yang Yan,Huang Xing,Wu Xi-qiao,Liu Chao-rong,Zhao Shi-yong,Zhu Xiao-hua
Abstract
AbstractIn order to study the spatiotemporal variations characteristic of water quality and potential pollution sources of Qujiang River, the water quality data of twelve water quality parameters at three monitoring sections (Tuanbaoling, Baita, and Sailong) from 2015 to 2019 were analyzed by using comprehensive pollution index (CPI) and multivariate statistical techniques (MST). The water quality parameters of Qujiang River basically meet the class 3 value of environmental quality standards for surface water (GB3838-2002, China). CPI varies from 0.62 to 1.06 and the water quality is characterized by slight pollution at the three monitoring sections. Cluster analysis (CA) results show that the months can be divided into three groups on the basis of similarities of the water quality characteristics: Group 1 (dry season), which includes January-April and November–December; Group 2 (flood season), that is, July; Group 3 (flat season), which consists of May–June and August–October. Principal component analysis (PCA) results identify four principal components (PCs) for the dry season and flood season, and five PCs for the flat season, thus explaining 58.23%, 82.94%, and 73.23% of the total variance, respectively. The results of the independent sample t-test show significant differences among the pH, Permanganate index (CODMn), Ammonia nitrogen (NH3–N), Total nitrogen (TN), Fecal coliform (F.coli), and (Flow) Q in the three monitoring sections. Moreover, the pollution is more serious in Baita than Tuanbaoling and Sailong Section and the main problem in the Qujiang River is the high water organic and nitrogen nutrient pollutant content. Hence, monitoring and protection need to be strengthened in the Baita section of Qujiang River.
Funder
the National Natural Science Foundation of China
Science and Technology Planning Projects of Nanchong City
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology
Reference63 articles.
1. Alberto WD, Pilar DMD, Valeria AM, Fabiana PS, Cecilia HA, Ángeles BMDL (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. a case study: Suqua River Basin (Córdoba-Argentina). Water Research 35(12):2881–2894
2. Alizadeh MJ, Kavianpour MR, Danesh M, Adolf J, Shamshirband S, Chau K-W (2018) Effect of river flow on the quality of estuarine and coastal waters using machine learning models. Eng Appl Comput Fluid Mech 12(1):810–823
3. Barakat A, Baghdadi ME, Rais J, Aghezzaf B, Slassi M (2016) Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. Int Soil Water Conserv Res 4(4):284–292
4. Beusen AHW, Bouwman AF, Beek LPHV, Mogollón JM, Middelburg JJ (2015) Global riverine N and P transport to ocean increased during the twentieth century despite increased retention along the aquatic continuum. Biogeosciences Discussions 12(23):20123–20148
5. Bharathi MD, Venkataramana V, Sarma VVSS (2022) Phytoplankton community structure is governed by salinity gradient and nutrient composition in the tropical estuarine system[J]. Cont Shelf Res 234:104643
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献