Using network to enhance the insights on correlation and pollution assessment of co-occurring metals in marine sediments, the East China Sea

Bioaccumulation, biomagnification, and ecological risk of trace metals in the ecosystem around oilfield production area: A case study in Shengli Oilfield

The production for crude oil usually leads to contamination of the soil with trace metals and organic contaminants from spilled petroleum. Organic contaminants were generally paid more attention than trace metals in the oilfield pollution. Many studies have investigated the impacts of some petroleum hydrocarbon pollutants, however, the impacts and risk assessment of trace metals remain largely unexplored. Moreover, under some circumstances, the risks associated with trace metals are not necessarily lower than those associated with organic contaminants. This study aimed to investigate methods to evaluate the possible risks associated with 11 trace metals (Ti, Ba, Sr, Rb, V, Li, Mo, Co, Cs, Bi, and Tl) in soil and biota samples from the Shengli Oilfield using ICP-MS. The results showed that 11 trace metals in the surface soils exceeded the local background levels. The geo-accumulation index (Igeo) indicated that the soils had light-moderate to moderate contamination levels, with higher Igeo value of Ba, V, Li, Mo, Co, and Cs. The individual potential ecological risk indices ([Formula: see text]) demonstrated moderate Bi and Tl pollution in soils. Comparatively, the [Formula: see text] is recommended for the risk assessment of trace metals on the ecosystem around the oilfield area. Mo, Bi, and Sr easily accumulate in plants, as reflected by their bioaccumulation factor. Ti, Ba, V, Li, Co, Cs, Bi, and Tl exhibited considerable biomagnification, particularly in birds. In this study, trace metals showed considerable bioaccumulation and biomagnification, and the risks of these trace metals on the ecosystem around oilfield production area need more attention.

Metals profile in deep-sea sediment from an active tectonic region around Simeulue Island, Aceh, Indonesia

Simeulue waters are adjacent to the northern part of Sumatra Island, which is undergoing massive land-use transformations; moreover, the waters are located in an active tectonic region. Land changes and tectonic activity might affect the metal pollution profile in this deep sea area. Thus, this study aimed to investigate the vertical profile and assess the sediment quality from the deep-sea marine sediment around Simeulue Island based on metal concentration. Seventy-six bottom sediment samples were collected from eight cores at a water depth of up to 2800 m in the Simeulue waters, Indonesia, in November 2017. Metals Cd, Cu, Fe, Ni, Pb, and Zn were quantified from the cores and multivariate analyses were carried out to understand the process. Metals distributions are analogous to the grain size parameters and LOI₅₅₀ distribution pattern, while Sumatra and Simeulue islands influenced grain size and LOI₅₅₀ spatial distribution. The vertical grain size profile exhibited no extreme oscillation in the investigated cores. Thus, sediment transport from the Island was the main suspect for these metals' profiles in the deep water, and the tectonic activity had a minor impact. Cu, Ni, Pb, and Zn tend to rise in the collected cores, suggesting that the accumulation of the metals is growing. While Fe tended to be stable and Cd oscillated in the cores. Indices were computed to assess the metal contamination profile. The cores were dominated by EF class 1 (none to slight enrichment) status and I_geo class 1 (unpolluted). Cd was the metal of concern in the study since a high Cd was observed in some layers (maximum EF = 26.45 and maximum I_geo = 3.81). Thus, this study can be used as a database to improve the regulation formulation for improved environmental managerial efforts in the region.

Functional diversity of macrofaunal assemblages as indicators to assess heavy metal pollution in the Bohai Sea, China

Functional diversity of macrofaunal assemblages can reflect the composition and differences of functional traits, indicating their response to various contaminants, especially heavy metal pollution. We explored the effects of environment variables over gradients of heavy metal pollution on macrofaunal assemblages, using biological traits analysis, generalized linear model (GLM), AZTI marine biotic index (AMBI), and various biodiversity indexes. The RLQ (co-inertia analysis) and fourth-corner approaches were used to investigate the specific response of functional traits to heavy metal pollution. Most sites were environmentally degraded by heavy metal pollution and macrofaunal body size had a miniaturization trend. There was a significant correlation between functional diversity indexes and AMBI. The RLQ and fourth-corner analysis and GLM models showed that heavy metal and natural environmental gradients had a profound effect on functional diversity. The functional divergence and dispersion indexes, along with the abundance of some specific species, were appropriate indexes for heavy metal pollution.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl^-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.