Characterizing and explaining spatio-temporal variation of water quality in a highly disturbed river by multi-statistical techniques.

Lei Zou,Xiang Zhang,Dunxian She,Shaofei Wu,Jun Xia,Jianfeng Liu

doi:10.1186/s40064-016-2815-z

Abstract

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran’s index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.

Highlights

Rivers are the important source of fresh water for agriculture, industry, drinking supplies, and for recreational, navigational and hydropower activities
Group C indicates a highest pollution of chemical oxygen demand (COD) during the period of late summer to early autumn and has the lowest concentration during the winter, while NH3-N shows a little change in concentration for the period under investigation
It is evident from the figure that for COD more than half of the stations are grouped into group B, while for NH3-N most of the stations are found in group A

Summary

Introduction

Rivers are the important source of fresh water for agriculture, industry, drinking supplies, and for recreational, navigational and hydropower activities. It offers a wide range of habitats for the aquatic flora and fauna (Varol et al 2012). The chemical composition of the water varies with time and space because of natural factors like climate and topography. The assessment of water quality is a major concern for an effective river management, especially in densely populated regions (Vega et al 1998). In North America, the strategies for improving surface water quality have been initiated in the early 1970s followed by Europe in the 2000s (Hawkins 2015; Hering et al 2010). Considering the complex variation in water quality across time and space, an effective management of the river water quality requires two key types of information (1) spatial and temporal characteristics of the pollutants, and (2) information about the driving factors influencing the water quality, which have been a core task of water environment research around the world (e.g., Mostafaei 2014; Ogwueleka 2015; Phung et al 2015)

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Conclusion