Control mechanisms of water chemistry based on long-term analyses of the Yangtze River.

Long-term series data can provide a glimpse of the influence of natural and anthropogenic factors on water chemistry. However, few studies have been conducted to analyze the driving forces of the chemistry of large rivers based on long-term data. This study aimed to analyze the variations and driving mechanisms of riverine chemistry from 1999 to 2019. We compiled published data on major ions in the Yangtze River, one of the three largest rivers in the world. The results showed that Na+ and Cl- concentrations decreased with increasing discharge. Significant differences in riverine chemistry were found between the upper and middle-lower reaches. Major ion concentrations in the upper reaches were mainly controlled by evaporites, especially Na+ and Cl- ions. In contrast, major ion concentrations in the middle-lower reaches were mainly affected by silicate and carbonate weathering. Furthermore, human activities were the drivers of some major ions, notably SO42- ions associated with coal emissions. The increased major ions and total dissolved solids in the Yangtze River in the last 20 years were ascribed to the continuous acidification of the river and the construction of the Three Gorges Dam. Attention should be given to the impact of anthropogenic activities on the water quality of the Yangtze River.

Rare earth elements in soil around coal mining and utilization: Contamination, characteristics, and effect of soil physicochemical properties.

REEs are emerging contaminants, and soils nearby coal and coal ash with high REEs composition are vulnerable to REEs contamination. Besides, coal industry can alter surrounding soil characteristics. However, there is information paucity about REEs contamination and geochemical behaviors along with soil characteristics around coal industrial areas, which are essential for understanding their toxicity and mobilization. The study was conducted in soils surrounding Kriel coal-fired power plant (KCM) and Greenside coal mining in Witbank (GSCM), South Africa. Multivariate statistical analysis, pollution and fractionation indices, and BCR sequential extraction were applied. The ∑REEs in the soils were compared to ∑REEs abundance in the upper earth's crust (UEC), and slightly higher ∑REEs was found in KCM but slightly lower in GSCM. Generally, LREEs are abundant. The soil REEs were normalized using the Post-Archean Australian Shale (PAAS) and Eu and Gd in KCM and Gd in GSCM were >1. Contamination assessment revealed that the soils are slightly to moderately contaminated by REEs. ∑REEs in KCM was significantly correlated with soil particle sizes of 2.00-50.00 µm, Al2O3, Fe2O3, and MnO, while with 2.00-3.00 µm and Al2O3 in GSCM. Fractionation characteristics showed a positive Ce anomaly, with positive linear regressions with Fe2O3 and MnO. In contrast, a negative Eu anomaly was found with positive linear regressions with Al, Ca, and Mg-oxides. Oxidizable fractioned REEs accounted for 32.33% of the ∑REEs in GSCM and 35.85% in KCM, and their high EF suggests enrichment that could be due to coal mining and utilization. Most soil physicochemical properties appear to be negatively correlated with the exchangeable REEs. Overall, the soils are contaminated by REEs and the REEs characteristics are considerably influenced by major elements oxide, U, and Th contents. Therefore, more attention should be paid to REEs contamination and impacts around coal mining and utilization.

A comprehensive method of source apportionment and ecological risk assessment of soil heavy metals: A case study in Qingyuan city, China.

The study combined multiple models to provide a deeper understanding to soil heavy metal contamination and source information, which are essential for controlling pollution and reducing human health risks. In this study, the agricultural soils were collected from the Qingyuan City of China as an example. The multiple models (APCS/MLR, PMF, and GDM) were used to identify and quantitatively apportion the main sources of heavy metal pollution in the area. The results showed that Cu (56.4 %), Ni (70.9 %), B (44.5 %), and Cr (72.8 %) were associated with natural sources, such as soil parent material and soil-forming processes. However, Pb (41.2 %), Zn (61.8 %), Hg (67.0 %), and Cd (69.6 %) were associated with agricultural activities, atmospheric deposition, vehicle exhaust emissions, and vehicle tires, while Mo, Se, and Mn were possibly derived from natural sources, including rock weathering and soil parent materials. Additionally, the network of environmental analysis revealed that soil microbes are far more sensitive to soil heavy metal pollution than herbivores, vegetation, and carnivores. This study can serve as a guideline for reducing the ecological and health risks associated with heavy metals in soil by controlling their preferential sources. Environmental implication Combining multiple models is more effective approach to wide understanding of heavy metal contamination and source information, which is essential for controlling pollution and reducing human health risks. Based on multiple models (APCS/MLR, PMF, and GDM) and network environ analysis, a comprehensive method for apportioning soil heavy metal sources and assessing ecological risk had been provided. Further, the present study can be a guideline for reducing ecological and health risks by heavy metals in soil by controlling preferential sources.

An integrated approach to quantify ecological and human health risks of soil heavy metal contamination around coal mining area.

Soil heavy metals harm ecological biodiversity and human health, and quantifying the risks more accurately is still obscure. In this study, a network environ analysis was applied to quantify risks between ecological communities based on control allocation and human health risk models to calculate human health exposure risks from soil heavy metals around Greenside coal mining in South Africa. Ecological and human health risks were apportioned using PMF model. Results showed assessed heavy metals (mean) exceeded local background content with a cumulative of moderately polluted using pollution load index (PLI). Total initial risk (Ri), the risk to biological organisms from direct soil exposure, was 0.656 to vegetation and 1.093 to soil microorganisms. Risk enters the food web via vegetation and harms the whole system. Integrated risks (initial, direct, and indirect) to vegetation, herbivores, soil microorganisms, and carnivores were 0.656, 0.125, 1.750, and 0.081, respectively, revealing that soil microorganisms are the most risk receptors. Total Hazard Index (HIT) was <1 for adults (0.574) whereas >1 for children (4.690), signifying severe non-cancer effects to children. Total cancer risk (TCR) to children and adults surpassed the unacceptable limit (1.00E-04). Comparatively, Cr is a high-risk metal accounted for 63.24% (adults) and 65.88% (children) of the HIT and 92.98% (adults) and 91.31% (children) of the TCR. Four sources were apportioned. Contributions to Ri (soil microorganisms and vegetation) from F3 (industrial), F4 (atmospheric), F2 (coal mining), and F1 (natural) were 42.20%, 24.56%, 23.55%, and 9.68%, respectively. The non-cancer risk from F3 (37.67% to adults and 38.40% to children) was dominant, and TCR to children from the sources except F1 surpassed the unacceptable limit. An integrated approach of risk quantification is helpful in managing risks and reducing high-risk pollution sources to better protect the environment and human health.

Ecological risk assessment of heavy metal concentrations in sediment and fish of a shallow lake: a case study of Baiyangdian Lake, North China.

The pollution levels of lakes vary in quantity and type of contaminants accumulated in their sediment and water. The second Chinese capital city will be built around Baiyangdian Lake in the near future, and thus, it is important to monitor pollution status of Baiyangdian Lake. This study mainly focused on the accumulated heavy metal concentrations in the surface sediment and in variety of fish bodies. Sediment pollution status and ecological risk were evaluated through geo-accumulation (I-geo), contamination factor (CF), pollution load index (PLI), potential ecological risk ([Formula: see text]), and mean probable effect concentration quotient (mPEC-Q). In addition, human health risks via fish consumption were also evaluated. Based on the results, the average sediment trace As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn concentrations were 9.53, 0.35, 56.37, 32.33, 617.05, 30.18, 19.17, and 84.24 mg/kg dry wt, respectively. Both I-geo and [Formula: see text] inferred low pollution levels and low ecological risks from all assessed trace metals except Cd. Cd posed moderate to high ecological risks. Based on sediment quality guidelines (SQGs), average Ni and Cr concentrations exceeded the threshold effect concentrations (TEC), but their [Formula: see text] are low as their average concentrations is below Hebei province pre-contaminations (30.8 mg/kg for Ni and 68.3 mg/kg for Cr). There is no cumulative toxicity from all the metals through mPEC-Q. Omnivorous fish accumulated statistically insignificantly higher amounts of metals than carnivorous fish, except for Hg. The intake of 12.22 g/person/day fish muscle for the entire life is safe from noncarcinogenic human health problems.