School of Geographical Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, Jiangsu 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China.
School of Geographical Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, Jiangsu 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China.
Sci Total Environ. 2017 Feb 15;580:1518-1529. doi: 10.1016/j.scitotenv.2016.12.137. Epub 2016 Dec 29.
Multivariate statistical analyses combined with geographically weighted regression (GWR) were used to identify spatial variations of heavy metals in sediments and to examine relationships between metal pollution and land use practices in watersheds, including urban land, agriculture land, forest and water bodies. Seven metals (Cu, Zn, Pb, Cr, Ni, Mn and Fe) of sediments were measured at 31 sampling sites in Sheyang River. Most metals were under a certain degree enrichment based on the enrichment factors. Cluster analysis grouped all sites into four statistically significant cluster, severely contaminated areas were concentrated in areas with intensive human activities. Correlation analysis and PCA indicated Cu, Zn and Pb were derived from anthropogenic activities, while the sources of Cr and Ni were complicated. However, Fe and Mn originated from natural sources. According to results of GWR, there are stronger association between metal pollution with urban land than agricultural land and forest. Moreover, the relationships between land use and metal concentration were affected by the urbanization level of watersheds. Agricultural land had a weak associated with heavy metal pollution and the relationships might be stronger in less-urbanized. This study provided useful information for the assessment and management of heavy metal hazards in studied area.
采用多元统计分析与地理加权回归(GWR)相结合的方法,识别了沉积物中重金属的空间变化,并研究了流域土地利用方式与重金属污染之间的关系,包括城市土地、农业用地、森林和水体。在射阳河的 31 个采样点测量了沉积物中的 7 种金属(Cu、Zn、Pb、Cr、Ni、Mn 和 Fe)。根据富集因子,大多数金属都处于一定程度的富集状态。聚类分析将所有站点分为四个具有统计学意义的聚类,严重污染区集中在人类活动密集的地区。相关性分析和 PCA 表明 Cu、Zn 和 Pb 来源于人为活动,而 Cr 和 Ni 的来源较为复杂。然而,Fe 和 Mn 则来源于自然源。根据 GWR 的结果,重金属污染与城市土地的关联比农业土地和森林更强。此外,土地利用与金属浓度之间的关系受到流域城市化水平的影响。农业用地与重金属污染的关联较弱,在城市化程度较低的地区可能更强。本研究为研究区域重金属危害的评估和管理提供了有用的信息。