River beds are usually untouched by human feet, unless the rivers are dry. However, the heavy metals in river beds carry the footprints of various human activities, such as urbanization, industrialization, and agriculture. After heavy metals generated from these activities are discharged into rivers, they are adsorbed by suspended matters, and then suspended matters carrying heavy metals will eventually sink into river sediments.
Heavy metals in river sediments can be taken in by aquatic plants and animals and accumulate, then ultimately enter the human body via the food chain and lead to health issues. Thus, a comprehensive analysis of the contamination levels and contributing sources of heavy metals in river sediments is an effective way to understand the impact of human activities on the river ecosystem and is crucial for watershed management, pollution control, and human health protection.
How Scientists Trace the Footprints of Human Activities in River Sediments
Scientists evaluate the impact of human activities on river beds through field sampling of river sediments, laboratory chemical analysis of heavy metals in the sediments, and statistical and spatial analyses of the concentrations of heavy metals. Heavy metals in river sediments are contributed to by both anthropogenic (e.g., agricultural, residential, and industrial activities) and natural sources (e.g. parent materials in rocks and soil), while the change in their concentrations is usually affected by the changing anthropogenic activities. The concentrations and sources of heavy metals are associated with the intensity and types of anthropogenic activities and the pollution control policies in the local areas. Thus, in order to understand and control the heavy metal pollution in river sediments for a specific area, it is necessary to understand the contamination levels and sources of heavy metals in the area.
A wide range of studies around the world has analyzed the contamination level of heavy metals in river sediments and identified their sources. However, most of the previous studies identified either natural or anthropogenic sources but did not determine the specific anthropogenic activities (e.g. industrial, residential, and agricultural) that might be responsible for different heavy metals. Distinguishing the specific activities for different heavy metals is necessary for effective pollution control.
In addition, the sources identified by these studies are only potential sources assumed by the researchers based on the general knowledge of the chemical and physical properties of the heavy metals and literature reviews. Usually, the potential sources were not verified by comparing them to the actual pollution sources in the local areas, which might reduce the reliability of the findings.
In order to more accurately and precisely trace the footmarks of human activities in river sediments, we recently conducted a comprehensive study to assess the contamination levels and to identify the sources of heavy metals in river sediments at 134 samplings sites distributed along the entire river network in Nantong, eastern China, using systemic sampling, laboratory chemical analysis, and statistical and spatial analyses.
The contamination levels of heavy metals were assessed using descriptive statistics, geo-accumulation index (Igeo), and ecological risk index (RI), and the potential sources of heavy metals were identified using principal component analysis (PCA) and cluster analysis (CA). The potential sources were further verified by comparing their spatial distributions and the locations of the actual local sources using GIS (Geographic Information System)-based spatial analysis.
Why Conduct the Research in This Area?
Figure 1: Map of the study area and sampling sites. Figure republished with permission from Springer from https://link.springer.com/article/10.1007%2Fs41742-018-0097-8
Nantong is located in the Yangtze River Delta in eastern China (Figure 1). It has been experiencing more rapid industrialization and urbanization than the majority of areas in the world over the past nearly four decades. This area is also one of the most populous regions in China and is an important agricultural region. The conflict between the rising demand for clean water and food associated with population growth and the increasing farmland loss and water pollution due to urbanization and industrialization has become a pressing issue in this area.
Nantong is a typical plain river network region with numerous crisscross rivers, which conveniently connect urban and rural areas, as well as the irrigation (or drainage) system of farmland. With this kind of river network, heavy metals from industrial and domestic waste discharges are more likely to be transported into the river ecosystem and are of great harmfulness. Therefore, it is necessary to obtain valuable information on concentrations, contamination levels, and sources of various metals in river sediments in this area.
How Serious is the Heavy Metal Contamination in River Sediments
Figure 2 Spatial patterns in contamination levels of Pb, Zn, Hg, and Cd, reflected by the Geo-accumulation Index (Igeo). Figure republished with permission from Springer from https://link.springer.com/article/10.1007%2Fs41742-018-0097-8
The results of descriptive statistics and risk assessment show the contamination level of heavy metals is in the order of Pb＞Zn＞Cd＞As＞Cu＞Cr＞Hg＞Ni. The river sediments are heavily contaminated with Pb and Zn adjacent to the towns where lots of textile plants and wire rope enterprises are located. The geo-accumulation index (Igeo) indicates the metal concentration in the sediments compared to the global standard shale values. The spatial patterns in the contamination levels reflected by Igeo for Pb, Zn, Hg, and Cd are illustrated in Figure 2.
Moderate to heavy contamination levels for Pb and Zn are located in the southern part of the study area. A few spots, mainly in the southern part, also show moderate to heavy contaminations for Cd. It is practically uncontaminated, or uncontaminated to moderately-contaminated for Hg in the majority of the study area.
Figure 3 Spatial distribution of Ecological Risk Index (RI), representing the ecological risks caused by multiple heavy metals. Figure republished with permission from Springer from https://link.springer.com/article/10.1007%2Fs41742-018-0097-8
The heavy metal contamination in the study area is verified by the Ecological Risk Index (RI) values, which considers the ecological risks caused by multiple heavy metals. The low-risk level is observed in only a small part of the north. The moderate risk level occupies almost half of the study area, mainly located in the north. The majority of the central and southern parts are covered by considerable risk or severe risk levels (Figure 3).
Where Heavy Metals Come from
Figure 4: Loadings of factors on heavy metals from PCA, showing the common sources of heavy metals. Figure republished with permission from Springer from https://link.springer.com/article/10.1007%2Fs41742-018-0097-8
Principal component analysis (PCA) is a regular method used to analyze the major factors that affect the concentrations of multiple heavy metals. If a factor has a high loading on several heavy metals, it means the concentrations of these heavy metals are mainly affected by this factor, so this factor is a major common source of these heavy metals. Factor 1 (F1) has high positive loadings on As, Cr, Pb, and Zn (Figure 4a).
This factor might be associated with the effluents of industry (e.g. textile, metal processing) because these metals have been reported to come from discharges from textile and metal processing in many previous studies, so F1 represents the source of industrial wastewater discharge.
Factor 2 (F2) has high positive loadings on Ni, Al, and Cu ((Figure 4b). Combined with the results of descriptive statistics, Igeo, EF calculation, cluster analysis, and correlation analysis, the PCA result indicates that F2 represents the natural source of parent material (sandy alluvial deposit), which contributed these three metals to the river sediments. In addition, Cu also has a relatively higher loading in F1, and it has significant positive correlations with As, Cr, Pb, and Zn, which suggests it is also slightly influenced by industrial sources.
Factor 3 (F3) has a high positive loading for Hg, significantly higher than the loadings for other metals (Figure 4c). This factor might represent domestic effluents and waste incineration sources. Spatial variability in Hg concentrations in river sediments shows that they are evidently controlled by the effluent sources. As seen during the sampling and field survey, domestic solid wastes from individual homes were piled up in rectangular chambers in the open air by the residents along the river. The wastes contain high Hg items such as batteries and fluorescent lamps. After they are simply incinerated without proper classification, some Hg remains in the ashes. During heavy rains, the mercury-contained particles are washed out from incineration chambers and discharged into nearby rivers. Thus, F3 represents municipal and domestic waste sources.
Factor 4 (F4) has a significantly high positive loading on Cd (Figure 4d). Cd tends to have higher content in phosphatic fertilizers because it is inherited from the phosphatic rocks. The increasing application of phosphatic fertilizers in farmlands over years has accelerated the accumulation of Cd in soils, which then resulted in a greater overall movement of Cd to aquatic ecosystems, which ultimately increased Cd concentrations in river sediments to some extent.
Figure 5: Spatial patterns of the factor scores from PCA, showing the match between the sources identified by PCA and the locations of actual sources. Figure republished with permission from Springer from https://link.springer.com/article/10.1007%2Fs41742-018-0097-8
The potential contributing sources of heavy metals identified in PCA were verified by the good match between the spatial distributions of the factor scores and the locations of actual sources (Figure 5). High scores of F1 (representing industrial sources) appear at samplings sites in the area with a high density of textile mills, especially in Jiangzhao, which contains over 20 small textile and leather mills, and also in Zhuhang, where many metal processing plants are distributed (Figure 5a). The factor scores of F2 (representing natural sources) are generally low all over the study area, which is consistent with the natural origin ((Figure 5b). However, the score is slightly higher in Jiangzhao due to the high loading of F2 on Cu. Cu is contributed by mixed natural and industrial sources. High scores of F3 (representing municipal and domestic sources) are located at the sampling sites that are adjacent to discharge outlets from Nantong city and towns, such as Xianfeng, Xiaohai, and Zhangzhishan (Figure 5c). The scores of F4 (representing fertilizer application) are more homogeneously distributed in the study area (Figure 5d). This spatial pattern matches the spatial distribution of farmlands, which exist all over the study area.
In Closing
Our study revealed that human activities, including urbanization, industrialization, and agriculture, have left deep footmarks on river beds in the study area, because rivers sediments have been considerably contaminated in heavy metals, especially for Pb and Zn. Four main sources of heavy metals were identified. They are (1) industrial sources contributing As, Cr, Pb, Zn, and partly Cu, (2) parent materials contributing Al, Ni, and partly Cu, (3) municipal and domestic wastes associated with Hg, and (4) excessive fertilizer application responsible for Cd. The results are useful for environmental protection agencies to target corresponding pollution sources for the contaminations of specific heavy metals.
The results and findings of this study can provide useful information for governments, environmental protection agencies, and other decision-makers to implement appropriate policies and regulations to control the heavy metal pollution in the river sediments caused by the rapid urbanization and industrialization in the study area. The approaches that integrate descriptive statistics, correlation analysis, multivariate analysis, contamination and risk assessments, and spatial analysis can be also useful for environmental scientists to examine various environmental issues beyond the study area.
View Comments
Be careful using the term "heavy metal", arsenic is a metalloid, see my letter on the subject https://www.mdpi.com/1660-4601/16/22/4446