Surface Water Quality Assessment and Contamination Source Identification Using Multivariate Statistical Techniques: A Case Study of the Nanxi River in the Taihu Watershed, China

Abstract

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

Temporal Seasonal Variations and Source Apportionment of Water Pollution in Melaka River Basin using Multivariate Statistical Techniques

“…Meanwhile, the raw data are also subjected to be treated by logarithmatic transformation (logscaling), column scaling and column auto-scaling. After log-transformation, the raw data will be valued within 0 to 0.060 in resulted for kurtosis and skewness, while this allow z-scale standardized for every variables of log-transformed to be minimize in the effects of different units as well as variance of variables and equally to transform the data dimensionless [6,18]. Data transformation enables to normalize all set of data in order to fulfil the prediction of cluster plus factor analysis [24].…”

“…Different variations of wet and dry seasons will lead to the different attribute of water pollution in the river [5]. Hence, water resources can only be managed by having the frequent monitoring and assessment activities on the river water quality [6]. In the purposes of monitoring the river water quality, sampling stations become an important of information sources on the local area as well as temporal status of water quality in the river.…”

Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

“…The rigorous and systematic investigation of drinking 2 water quality determination is essential for the protection of public health and the development of more accurate and efficient testing methods. The effective and efficient pursuit of knowledge on drinking water quality determination is critical to ensuring the safety and sustainability of our water resources [18][19][20]. Commonly, assessing WQ entails collecting water samples from various sites at different time intervals and evaluating them in laboratories.…”

Surface water quality and geogenic process in the quillcayhuanca and shallap streams, huaraz-Peru

“…Likewise, Sheng et al 3 applied the Principal Component Analysis (PCA), considering water quality parameters, and integral contamination indices, identifying contaminated tributaries. Multivariate statistical techniques were also applied by Zhang et al 4 managing to identify types of pollution sources, and better understanding the Spatiotemporal variations in water quality. In addition to PCA analysis, Xu et al 5 applied Fuzzy Comprehensive Evaluation (FCE) in the water quality of a lake in China.…”