Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China.

Jiabo Chen,Yanjie Wang,Fayun Li,Zhiping Fan

doi:10.3390/ijerph13101035

Abstract

Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May–October, February–April and November–January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (CODMn), 5-day biochemical oxygen demand (BOD5), NH4+–N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.

Highlights

Surface water quality and aquatic ecosystems have been seriously impacted by complex human activities and natural processes at both river and basin scales, including domestic wastewater, industrial sewage, runoff, land reclamation, oil development, mining exploitation, atmospheric deposition and climate change [1,2,3,4]
Hierarchical applied to group the water quality dataset basedbased on the on temporal and spatial
CAwas was applied to group the water quality dataset the temporal and variation

Summary

Introduction

Surface water quality and aquatic ecosystems have been seriously impacted by complex human activities and natural processes at both river and basin scales, including domestic wastewater, industrial sewage, runoff, land reclamation, oil development, mining exploitation, atmospheric deposition and climate change [1,2,3,4]. Because of the complexity of river water environments [5,6] and obvious differences in regional pollution characteristics [2,4,5], regulators and experts of environmental protection face severe challenges in preventing and controlling water pollution. Understanding the spatial and temporal patterns in the hydrochemistry of river water [1,7], extracting the most useful information from complicated monitoring data [2] and identifying the major sources of regional water pollution [3,5] can aid regulators in establishing priority measures for the efficient conservation and restoration of river water resources and aquatic ecosystems. Public Health 2016, 13, 1035; doi:10.3390/ijerph13101035 www.mdpi.com/journal/ijerph

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