摘要
本文依据1995—2010年12个站位13个水质指标的7488个实测数据,利用聚类分析、判别分析、主成分分析和因子分析方法,研究了渤海湾近岸海域表层水质时空变化特征及潜在污染源。渤海湾近岸海域水质在16年间被聚为三类:早期(1995—2001年,不包括1998年和1999年)主要是营养盐的污染;第二时期(2007—2008年)以有机污染为主;近期(1998—1999年,2002—2006年,2009—2010年)主要是陆源污染和人类活动的影响较大。空间上的特征显示,近岸且于河口附近的站位,受到人类活动的影响较大;处于海水养殖区的站位,其油类和营养盐污染相对较严重。多种多元统计方法的综合应用对于渤海湾表层水质的研究是有效的,本结果在渤海湾水生态环境监测和治理方面可提供科学可行的理论依据和指导价值。
This study examined spatial-temporal variations of water quality and potential pollutant sources in the surface water layer in the offshore areas of Bohai Bay, China, based on 7,488 sets of experimental data collected for 13 water quality indicators at 12 sites three times yearly from 1995 to 2010. A multivariate statistical approach was comprehensively applied, including cluster analysis, discriminant analysis, principal component analysis, and factor analysis. The data series of 16 years were grouped into three clusters: cluster A at early stage of nutrient pollution dominant(1995-2001 with 1998-1999 excluded), cluster B at middle stage of organic contamination dominant(2007-2008), and cluster C at late stage of land-based pollution and human activities dominant(1998-1999, 2002-2006 and 2009-2010). In spatial variation, human activities had more serious impact on the nearshore stations; oil and nutrients pollution was more serious in the marine culture areas. This study shows that the comprehensive approach of multivariate statistical methods is effective and useful in water quality investigation of the Bohai Bay offshore areas and our findings would help further study of water ecological environment in this area.
作者
赵海萍
李清雪
陶建华
ZHAO Haiping LI Qingxue TAO Jianhua(School of Environmental Science and Engineering, Tianjin University, Tianjin 300072 School of Urban Construction, Hebei University of Engineering, Handan, Hebei 056038)
出处
《水力发电学报》
EI
CSCD
北大核心
2016年第10期21-30,共10页
Journal of Hydroelectric Engineering
基金
水利部公益项目(201401030)
河北省重点基础研究项目(14964206D-8)
关键词
水环境
渤海湾
聚类分析
判别分析
主成分分析
源解析
water environment
Bohai Bay
cluster analysis
discriminant analysis
principal component analysis
source identification