Spatial distribution and multiple sources of heavy metals in the water of Chaohu Lake, Anhui, China

Four antibiotics and copper interactive effects on the growth and physiological characteristics of Hydrilla verticillata (L.f.) Royle

Co-pollution of antibiotics and heavy metal copper (Cu) is common in freshwater environments because of their wide use as antimicrobial agents, especially in aquaculture. However, the toxic effects of coexisting antibiotics and heavy metals on aquatic plants remain unclear. This study investigated the effect of four antibiotics (i.e., enrofloxacin, ENR; tetracycline, TC; sulfamethoxazole, SMX; erythromycin, ERY), Cu, and their mixture on the growth and physiological responses of Hydrilla verticillata (L.f.) Royle. Results showed that the four antibiotics exhibited toxic effects on the growth and physiological indicators of H. verticillata, and root elongation was the most sensitive endpoint of the phytotoxicity test. The median effect concentration (EC50) of root elongation indicated that TC (EC50 = 10.05 mg/L) has the highest level of growth toxicity, and the toxicity of ENR to aquatic plants was close to TC (EC50 = 10.44 mg/L), followed by SMX (EC50 = 20.08 mg/L). However, there was no significant toxic effect of 20 mg/L ERY on the root elongation. Hydrophobicity may be a key factor affecting the phytotoxicity of antibiotics. Moreover, antagonistic toxic effects were observed under ENR + Cu, TC + Cu, SMX + Cu, and ERY + Cu co-exposures at all the experimental concentrations (0.01-20 mg/L). Due to the concentrations of antibiotics in natural waters usually with ng/L levels, our results suggested that environmental antibiotic concentrations probably pose low ecological risk to aquatic plants and indicated the H. verticillata could be used as phytoremediation candidate to remove antibiotic or antibiotic-Cu pollutions in general nature water.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (COD_Mn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R² value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.

Indices and models of surface water quality assessment: Review and perspectives

Many technologies have been designed to monitor, evaluate, and improve surface water quality, as high-quality water is essential for human activities including agriculture, livestock, and industry. As such, in this study, we investigated water quality indices (WQIs), trophic status indices (TSIs), and heavy metal indices (HMIs) for assessing surface water quality. Based on these indices, we summarised and compared water assessment models using expert system (ES) and machine learning (ML) methods. We also discussed the current status and future perspectives of water quality management. The results of our analyses showed that assessment indices can be used in three aspects of surface water quality assessment: WQIs are aggregated from multiple parameters and commonly used in surface water quality classification; TSIs are calculated from the concentrations of different nutrients required for algae and bacteria, and employed to evaluate the eutrophication levels of lakes and reservoirs; HMIs are mainly applied for human health risk assessment and the analysis of correlation of heavy metal sources. ES- and ML-based assessment models have been developed to efficiently generate assessment indices and predict water quality status based on big data obtained from new techniques. By implementing dynamic monitoring and analysis of water quality, we designed a next-generation water quality management system based on the above indices and assessment models, which shows promise for improving the accuracy of water quality assessment.

Occurrence and spatial distribution of heavy metals in landfill leachates and impacted freshwater ecosystem: An environmental and human health threat

Municipal landfill leachates are a source of toxic heavy metals that have been shown to have a detrimental effect on human health and the environment. This study aimed to assess heavy metal contamination in leachates, surface water, and sediments from non-sanitary landfills in Uyo, Nigeria, and to identify potential health and environmental effects of leachate contamination. Over the wet and dry seasons, surface water and sediment samples were collected from an impacted freshwater ecosystem, and leachates samples from six monitoring wells. Elemental analyses of samples were conducted following standard analytical procedures and methods. The results indicated that leachate, surface water, and sediment samples all had elevated levels of heavy metals, implying a significant impact from landfills. Pollution indices such as the potential ecological risk index (PERI), pollution load index (PLI), degree of contamination (Cd), modified degree of contamination (mCd), enrichment factor (EF), geoaccumulation index (Igeo), and Nemerov pollution index (NPI) were used to assess the ecological impacts of landfill leachates. The following values were derived: PERI (29.09), PLI (1.96E-07), Cd (0.13), mCd (0.16), EF (0.97-1.79E-03), Igeo (0), and NPI (0.74). Pollution indicators suggested that the sediment samples were low to moderately polluted by chemical contaminants from the non-sanitary landfills, and may pose negative risks due to bioaccumulation. Human health risks were also assessed using standard risk models. For adults, children, and kids, the incremental lifetime cancer rate (ILCR) values were within the acceptable range of 1.00E-06-1.00E-04. The lifetime carcinogenicity risks associated with oral ingestion exposure to heavy metals were 9.09E-05, 1.21E-05, and 3.60 E-05 for kids, adults, and children, respectively. The mean cumulative risk values for dermal exposures were 3.24E-07, 1.89E-06, and 1.17E-05 for adults, children, and kids, respectively. These findings emphasized the risks of human and biota exposure to contaminants from landfills.

Concentration, distribution, and assessment of dissolved heavy metals in rivers of Lake Chaohu Basin, China

This study aimed at establishing the spatial and seasonal distribution patterns of dissolved metals, and assessing the water quality and potential human health risk, in rivers of Lake Chaohu Basin (LCB, China). Four seasonal samplings were conducted at 83 sites from April to December in 2018. The water quality was assessed using heavy metal evaluation index (HEI), while hazard index (HI) and carcinogenic risks indicated potential human risk, according to 12 metals (Cr, Mn, Fe, Ni, Cu, Zn, As, Mo, Cd, Sb, Ba, and Pb). Spatially, sites were effectively classified into Group I and II using cluster analysis. Generally, dissolved metals were low in rivers of LCB at whole basin scale. Total metals concentrations, as well as HEI and HI, were significantly higher in Group II compared with Group I. The mean total concentration was 496.38 μg L^-1, with the highest mean of Zn (233.39 μg L^-1), followed by Ba (170.66 μg L^-1). The pollution status was generally classified as "slightly affected" by HEI, with a mean of 1.51. According to HI, there were 6.02% and 10.84% of all the 83 sites (main in Nanfei River) with greater chances of harmful health risks for adults and children, respectively. Furthermore, a high risk was observed of Cr, As, and Ni, which was listed in the decreasing order. Although the dissolved metals were relatively low, the potential risk for human health still existed in rivers of LCB, which the local manager should pay more attention to in future.

Heavy metals in water and surface sediments of the Fenghe River Basin, China: assessment and source analysis

This paper combines environmental science, inorganic chemistry, water quality monitoring and other disciplines to analyze and assess the heavy metals in the water bodies and sediments of the Fenghe River Basin (FRB) in Shaanxi Province, and reveal their sources. The Water Quality Index (WQI), Nemero Index (Pn), Geological Accumulation Index (I-geo) and Potential Ecological Risk Index (RI) are used to assess heavy metals in water and sediments. Pearson correlation analysis (CA), hierarchical cluster analysis (HCA), principal component analysis (PCA) and positive matrix factorization (PMF) models are used to study the relationship and source of heavy metals. The results show that most of the residual heavy metals in the water are below the corresponding environmental quality standards for surface water. Most of the heavy metals in the sediment exceed the background value of the soil. The factors or sources of heavy metals in water and sediment are revealed in detail through PMF models. The main sources of pollution in the region are urban construction and transportation, the electronics industry, machinery manufacturing and tourism. In water, the average contribution rates of these four sources to heavy metals were 36.8%, 11.7%, 9.4% and 42.0%, and in sediments were 8.0%, 29.2%, 23.9% and 38.9%. Therefore, these sectors should be given sufficient attention.

Rapid monitoring of heavy metal pollution in lake water using nitrogen and phosphorus nutrients and physicochemical indicators by support vector machine

A novel method of predicting heavy metal concentration in lake water by support vector machine (SVM) model was developed, combined with low-cost, easy to obtain nutrients and physicochemical indicators as input variables. 115 surface water samples were collected from 23 sites in Chaohu Lake, China, during different hydrological periods. The particulate concentrations of heavy metals in water were much higher than the dissolved concentrations. According to Nemerow pollution index (Pi), pollution degrees by Fe, V, Mn and As ranged from heavy (2 ≤ Pi < 4) to serious (Pi ≥ 4). The concentrations of most heavy metals were the highest during the medium-water period and the lowest during the dry season. Non-metric Multidimensional Scaling Analysis confirmed heavy metal concentrations had slight spatial difference but relatively large seasonal variation. Redundancy Analysis indicated the close associations of heavy metals with nutrient and physicochemical indicators. When both nutrient and physicochemical indicators were used as input variables, the simulation effects for most elements in total and particulate were relatively better than those obtained using only nutrient or only physicochemical indicators. The simulation effects for As, Ba, Fe, Ti, V and Zn were generally good, based on their training R values of 0.847, 0.828, 0.856, 0.867, 0.817 and 0.893, respectively, as well as their test R values of 0.811, 0.836, 0.843, 0.873, 0.829 and 0.826, respectively; and meanwhile, in both the training and test stages, these metals also had relatively lower errors. The spatial distribution of heavy metals in Chaohu Lake was then predicted using the fully trained SVM models.

Magnetic properties and its application in the prediction of potentially toxic elements in aquatic products by machine learning

Magnetic measurement was provided to substitute for time-consuming conventional methods for determination of potentially toxic elements. Both the concentrations of 12 elements and 9 magnetic parameters were determined in 700 muscle tissue samples from the snail Bellamya aeruginosa, shrimp species Exopalaemon modestus and Macrobrachium nipponense, and fish species Hemisalanx prognathous Regan, Coilia ectenes taihuensis, and Culer alburnus Basilewsky collected from Chaohu Lake during different hydrological periods. Spherical and irregular iron oxide particles were observed in the muscle tissues of the studied aquatic products. A field survey of the exposure parameters in humans, such as per capita intake dose of local aquatic products, found no evidence that consumption of the tested species poses a potential health risk. Redundancy analysis revealed different degrees of correlation between the magnetic parameters and concentrations of elements in aquatic products. Back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) models were applied to predict elemental concentrations in aquatic products, using magnetic parameters as input. SVM models performed well in predicting the presence of Cr and Ni, with R and index of agreement values of >0.8 in both training and validation stages as well as relatively low errors. The BP-ANN and SVM models both performed relatively poorly in predicting the presence of Cd and Zn in aquatic products, with R values between 0.333 and 0.718 for Cd and between 0.454 and 0.664 for Zn in training and validation stages. For most of the elements, a better R value was obtained with the SVM than with BP-ANN model. The R of Co, Cr, Cu, Ni, and Ti in the training and validation stages of snail in the SVM model were >0.8. This study is a first step in developing a novel approach allowing the rapid monitoring of potentially toxic elements concentrations in aquatic products.

Algal Organic Matter Drives Methanogen-Mediated Methylmercury Production in Water from Eutrophic Shallow Lakes

Algal blooms bring massive amounts of algal organic matter (AOM) into eutrophic lakes, which influences microbial methylmercury (MeHg) production. However, because of the complexity of AOM and its dynamic changes during algal decomposition, the relationship between AOM and microbial Hg methylators remains poorly understood, which hinders predicting MeHg production and its bioaccumulation in eutrophic shallow lakes. To address that, we explored the impacts of AOM on microbial Hg methylators and MeHg production by characterizing dissolved organic matter with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy and quantifying the microbial Hg methylation gene hgcA. We first reveal that the predominance of methanogens, facilitated by eutrophication-induced carbon input, could drive MeHg production in lake water. Specifically, bioavailable components of AOM (i.e., CHONs such as aromatic proteins and soluble microbial byproduct-like materials) increased the abundances (Archaea-hgcA gene: 438-2240% higher) and activities (net CH₄ production: 16.0-44.4% higher) of Archaea (e.g., methanogens). These in turn led to enhanced dissolved MeHg levels (24.3-15,918% higher) for three major eutrophic shallow lakes in China. Nevertheless, our model results indicate that AOM-facilitated MeHg production could be offset by AOM-induced MeHg biodilution under eutrophication. Our study would help reduce uncertainties in predicting MeHg production, providing a basis for mitigating the MeHg risk in eutrophic lakes.

Metal concentrations in fish from nine lakes of Anhui Province and the health risk assessment

In the present study, to comprehensively investigate the metal contamination in the fish of Anhui Province, four fish species, Ctenopharyngodon idella, Cyprinus carpio, Hypophthalmichthys molitrix, and Hypophthalmichthys nobilis, were collected from nine lakes, and the levels of Zn, Pb, Cr, Cu, Ni, As, Hg, and Cd in the fish muscle were determined. The results showed that the highest concentrations of Zn (7.791 mg/kg), Pb (0.522 mg/kg), Cr (0.030 mg/kg), and Cu (0.767 mg/kg) were found in Tiangang Lake, Xifei Lake, Tiangang Lake and Baidang Lake, respectively. However, metals Ni, As, Hg, and Cd were not detected in all fish samples. In the fish species, the metal bioaccumulation ability was decreased with the following order: C. idellus > H. molitrix > H. nobilis > C. carpio. Furthermore, the target hazard quotient (THQ) was used to assess the health risk via fish consumption. The results indicated for co-exposure; C. idellus would pose a health risk to children at high exposure level (95th) as THQ value was higher than 1. It should be pointed out that Pb contributes most to the total THQs (the ratio was 88%); thus, the contamination of Pb should be paid more attention. This field investigation combined with health risk assessment would provide useful information on the heavy metal pollution in Anhui Province.

Health Risk Assessment and Source Apportionment for Heavy Metals in a Southern Chinese Reservoir Impacted by Stone Mining Activities

Metal contaminants in drinking water pose a potential threat to human health. Metal elements (Fe, Mn, Cu, Cr, Cd, As, and Pb) in Shanzi Reservoir, China, a drinking water source for nearby cities, were measured in 2013 and 2014. The distribution characteristics of metal elements in water were identified and a health risk assessment model was used to evaluate potential harm. Principal component analysis and cluster analysis were used to determine the main sources of metal pollutants. The results showed that Pb and As exceeded the standard at some sampling sites, whereas other metal elements met the drinking water standards. The spatial distribution of metal elements was extremely uneven and might be affected by either the geochemical environment or human activities in the study region. The total risk value of metals (5 × 10^-5  a^-1 ) was below the recommended value of the United States Environmental Protection Agency (USEPA), the total cancer risk was higher than the total noncancer risk, and both risks were higher for children than for adults. Arsenic was the priority control pollutant, and the priority control site was located upstream of the reservoir. Source analysis showed that Fe, Mn, and Cu were mainly from soil formation and stone mining and processing industries; Pb and As were mainly from agricultural activities, free dumping and burning of domestic garbage, and atmospheric deposition from transportation emissions; Cd was mainly from agricultural application of fertilizers and pesticides; and Cr was from the stone mining and processing industry and from the electroplating and metal manufacturing industries. Integr Environ Assess Manag 2020;16:342-352. © 2019 SETAC.

Spatial and geochemical aspects of heavy metal distribution in lacustrine sediments, using the example of Lake Wigry (Poland)

Heavy metals which pollute aquatic environments typically bond with bottom sediments and the analysis of the spatial distribution of metals allows to assess the geochemical purity of deposits and to identify the potential pollution sources. Research carried out on the Wigry Lake involved the collection of almost 500 samples of sediments, and the specification of the depth of their residence (0.2-71.4 m) as well as the level of concentration of three metals: Fe (80.3-32 857 mg kg^-1), Mn (17.8-1698 mg kg^-1) and Zn (3.14-632 mg kg^-1). The geochemical and bathymetric data was interpolated using geostatistical methods and mapped with the consideration of 5 types of sediments: lacustrine chalk, carbonate gyttja, fluvial-lacustrine sediment, organic gyttja and clastic sediment. As a result, a significant increase in the concentration of metals was revealed in deeper zones, at a considerable distance from the lake shore, wherein the respective values of correlation coefficients were as follows: depth-Mn 0.77; depth-Fe 0.60; depth-Zn 0.58. A strong dependency between the concentration of analysed metals and the type of sediment, attributed to the granular and chemical composition of sediments, was also revealed. Correlations between individual metallic pairs (Fe-Mn 0.77; Fe-Zn 0.80; Mn-Zn 0.75) indicated that similar factors influence spatial distribution of metals in sediments. The implementation of 3 different geochemical backgrounds allowed to conclude that the Wigry Lake is slightly polluted with the analysed metals, and that the origin of Mn is mainly natural, while in the case of Fe and Zn anthropogenic influence can also be identified.

Rapid screening and quantification of multi-class antibiotic pollutants in water using a planar waveguide immunosensor

Antibiotics are commonly used in livestock-related agriculture and aquaculture, but they also remain in water and potentially threaten human health. Immunosensors are attractive tools for the rapid detection of antibiotics in water due to their high sensitivity and low costs. However, the simultaneous detection of multi-class antibiotics remains a challenge due to the limited number of detection sites on the immunochip. Also, matrix effects hinder the practical application of these sensors. This paper presents a method for multi-class antibiotic detection in real water using a planar waveguide immunosensor (PWI). We integrate the screening and quantitive detection sites on the same immunochip, and a single screening detection site could detect multi-class antibiotics from the same family, increasing the detection types of analytes. In addition, to eliminate the matrix effects, we develop a testing buffer for real water detection, so that complex pretreatments of the samples can be omitted. Using our sensor and testing buffer, we detect 14 different antibiotics in real water. Lincomycin can be detected with a detection limit of 0.01 μg L⁻¹, and 13 quinolones can be screened in a single assay. These results demonstrate that this planar waveguide immunosensor is capable of simultaneous screening and quantification of multi-class antibiotic pollutants and is expected to be applied for practical environmental monitoring.

We present a method for simultaneous screening and quantitative detection of multi-class antibiotics in real water using planar waveguide immunosensors.

Diagnosis of the unexpected fluorescent contaminants in quantifying dissolved organic matter using excitation-emission matrix fluorescence spectroscopy

Dissolved organic matter (DOM) is widely present in aqueous environments and plays a significant role in pollutant mitigation and transformation. So far, excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) has been widely applied to quantify fluorescent DOM. However, this approach fails to provide accurate concentration of DOM when fluorescent contaminants exist. In this work, a new method, prior linear decomposition (PLD), is developed to solve this problem by introducing prior information, i.e., EEMs of DOM, into data decomposition. First, EEM of humic acid (HA) with different numbers of random Gaussian peaks are tested to confirm the robustness of PLD. The percentages for the relative errors within 5% are found to be 97.7% and 69% using PLD and PARAFAC, respectively. Then, the determination of mixture of HA with several contaminants is performed, validating the feasibility of DOM quantification and capability of contaminant diagnosis using PLD for synthetic water samples. Finally, DOM-containing natural water samples collected from a polluted lake, river and wastewater treatment plant (WWTP) are measured. The testing results confirm that PLD provides an accurate result with less evaluated error than PARAFAC and the EEMs of the contaminants can be inferred precisely. This work clearly demonstrates that PLD offers a robust approach for quantifying fluorescent DOM, which is of great significance in both natural and engineered aqueous environments.

An overview of heavy metal pollution in Chaohu Lake, China: enrichment, distribution, speciation, and associated risk under natural and anthropogenic changes

An exhaustive overview of heavy metal pollution in Chaohu Lake illustrating enrichment intensity, temporal and spatial distribution, chemical speciation, and ecological risk under natural and anthropogenic changes was conducted. Low concentrations of heavy metals excluding Hg were found in water whereas high Hg might be ascribed to surrounding coal-fired power plants. Copper, Pb, Zn, Cd, and Hg were enriched in sediment whereas Cr and Ni were comparable to background values. Besides, As demonstrated an equal accumulation from natural and anthropogenic fluxes. Heavy metals were at a low level prior to the 1950s; it increased gradually during the 1950s-1960s owing to population growth and agricultural expansion; then it displayed abrupt increase since the late 1970s due to rapid modern urbanization and industrialization and agricultural intensification. Spatial distribution of heavy metals was a good indicator of natural and anthropogenic changes, where higher enrichment was found in the western lake. Apart from fluvial input, anthropogenic disturbances such as land use changes, atmospheric deposition, and algae-derived organic matter, along with natural stressors including climate change, hydrological alteration, and soil erosion, made significant contribution to the biogeochemical cycle of heavy metals in the lake. Heavy metals mainly from anthropogenic sources were dominantly partitioned in non-residual fractions, whereas those mainly from natural sources were predominantly distributed in residual form. Mercury and Cd were below the threshold effect concentration (TEC) indicating that adverse effects were excluded. However, result of chemical speciation demonstrated Cd would pose a considerable potential ecological risk. Besides, most of the heavy metals were in the range of TEC-PEC suggesting possible toxicity.

Distribution, bioaccumulation and trophic transfer of trace metals in the food web of Chaohu Lake, Anhui, China

Chaohu Lake is one of the five largest freshwater lakes in China situated in Anhui Province. Water, sediment and aquatic organisms were collected from Chaohu Lake. Trace metals were measured to investigate their bioaccumulation pattern and trophic transfer in the food web as well as potential health risk assessment through fish consumption. Trophic interactions were investigated by stable nitrogen isotope. Linear regression of log metal concentration versus δ¹⁵N was used to determine whether there is biomagnification or biodilution. Results showed that concentrations of trace metals in water were rather low except Hg, some of which surpassed the scope of quality standard. Trace metals in sediment exceeded background values nevertheless within the range for the protection of aquatic life. Therein, geochemical fractionation showed that Cd would pose a considerable potential ecological risk. Trace metals were higher in plankton except for Cu and Zn was higher in shrimp due to metabolic needs. Decreasing trend was observed in Pb, Cr, Cd, As and Hg levels with increasing trophic level whereas increasing trend was observed in Zn. Trace metals in fish were lower than legislation thresholds except for Cr in two samples that exceeded the threshold value. Nonetheless, total target hazard quotient values and target cancer risk were lower than unit and within acceptable range, indicating there was no health risk for inhabitants from trace metals through fish consumption.

Dissolved trace elements in Hooghly (Ganges) River Estuary, India: Risk assessment and implications for management

The study presents a spatio-seasonal distribution of 13 trace elements in the surface water (0-5 cm) along the north-south gradient of Hooghly River Estuary, India, and subsequently evaluates the human health risk by adopting USEPA standards. An overall homogeneous spatial distribution of elements was pronounced, whereas an irregular and inconsistent seasonal pattern were recorded for the majority of the elements. The concentration range (μg/l) of the elements and their relative variability were obtained as follows in the decreasing order: Al (55,458-104,955) > Fe (35,676-78,427) > Mn (651.76-975.78) > V (85.15-147.70) > Si (16.0-153.88) > Zn (26.94-105.32) > Cr (21.61-106.02) > Ni (19.64-66.72) > Cu (34.70-65.80) > Pb (26.40-37.48) > Co (11.16-23.01) > As (0.10-8.20) > Cd (1.19-5.53). Although Pb, Ni, Cr, Al, Fe, and Mn exceeded the WHO prescribed threshold limit for drinking water, Metal Pollution Index values (8.02-11.86) superseded the upper threshold limit endorsing adverse impact on biota. The studied elements were justified to have a non-carcinogenic risk as derived from hazard quotient and hazard index values. However, the trace elements As, Cd, Pb, and Cr exceeded the upper limit of cancer risk (10^-4), thereby leading to carcinogenic risk concern for both children and adult population groups, where children are more susceptible than the adults. Hence, evaluation of bioavailable fractions of the elements is required for proper management of this stressed fluvial system.

Copper impairs zebrafish swimbladder development by down-regulating Wnt signaling

Copper nanoparticles (CuNPs) are used widely in different fields due to their attractive and effective abilities in inhibiting bacteria and fungi, but little information is available about their biological effects and potential molecular mechanisms on fish development. Here, CuNPs and copper (II) ions (Cu²⁺) were revealed to inhibit the specification and formation of three layers of zebrafish embryonic posterior swimbladder and impair its inflation in a stage-specific manner. CuNPs and Cu²⁺ were also revealed to down-regulate Wnt signaling in embryos. Furthermore, Wnt agonist 6-Bromoindirubin-3'-oxime (BIO) was found to neutralize the inhibiting effects of CuNPs or Cu²⁺ or both on zebrafish swimbladder development. The integrated data here provide the first evidence that both CuNPs and Cu²⁺ act on the specification and growth of the three layers of swimbladder and inhibit its inflation by down-regulating Wnt signaling in a stage-specific manner during embryogenesis.

Heavy metal contamination, sources, and pollution assessment of surface water in the Tianshan Mountains of China

In order to gain insight into heavy metal contamination occurring in the surface water of the Tianshan Mountains in northwest China, we collected surface water samples from there, tested heavy metals Pb, Ni, Cd, Co, Hg, As, Cu, Mn, Zn, and Cr, and then we analyzed the data using typical analysis, multivariate statistical, and pollution index methods. Results showed that (1) the order of the average values of the ten kinds of heavy metals in all the water samples was as follows: Zn > Mn > Cu > Co > Ni > Pb > Cr > As > Hg > Cd. The maximum variation coefficients of Zn and Pb were 138.96 and 145.86 %, respectively, indicating that these heavy metal concentrations varied largely between different sampling locations. (2) Research showed the average concentrations of Pb, Cd, As, Cu, Zn, and Cr were all within the national surface water standard of class IV and those of As, Cu, Mn, and Cr were all within the range of the Drinking Water Guidelines from the WHO, indicating the surface water of the Tianshan Mountains is clean. (3) Multivariate statistical analysis showed that Cu, Cd, Mn, Hg, Zn, and Pb have close correlations, and they mainly came from artificial sources; while Ni, As, Co, Cu, and Cr mainly came from natural sources. The results of correlation analysis, principal component analysis, and cluster analysis are consistent. (4) Pollution evaluation showed the values of comprehensive pollution index (WQI) of ten kinds of heavy metals in three sections were all lower than 2, suggesting the low levels of pollution, while the over-limit ratios of Pb and Zn in water samples of the middle Urumqi-Akesu section, As in the western Zhaosu-Tekesi section, and Pb, Hg, and Zn in the eastern Balikun-Yiwu section were all above 10 %. This research shows that recent economic development of the Tianshan Mountains has negatively influenced the heavy metal concentrations in the surface water, although the concentrations of the ten kinds of tested heavy metals are relatively low.

Metal concentrations in surface water and sediments from Pardo River, Brazil: human health risks

Pardo River (Brazil) is suffering from an important anthropogenic impact due to the pressure of highly populated areas and the influence of sugarcane cultivation. The objective of the present study was to determine the levels of 13 trace elements (As, Be, Cd, Cr, Cu, Pb, Mn, Hg, Ni, Tl, Sn, V and Zn) in samples of surface water and sediments from the Pardo River. Furthermore, the human health risks associated with exposure to those metals through oral intake and dermal absorption were also evaluated. Spatial and seasonal trends of the data were closely analyzed from a probabilistic approach. Manganese showed the highest mean concentrations in both water and sediments, remarking the incidence of the agricultural activity and the geological characteristics within the basin. Thallium and arsenic were identified as two priority pollutants, being the most important contributors to the Hazard Index (HI). Since non-carcinogenic risks due to thallium exposure slightly exceeded international guidelines (HI>1), a special effort should be made on this trace element. However, the current concentrations of arsenic, a carcinogenic element, were in accordance to acceptable lifetime risks. Nowadays, there is a clear increasing growth in human population and economic activities in the Pardo River, whose waters have become a serious strategic alternative for the potential supply of drinking water. Therefore, environmental monitoring studies are required not only to assure that the current state of pollution of Pardo River does not mean a risk for the riverside population, but also to assess the potential trends in the environmental levels of those elements.

A comparative study of metal pollution and potential eco-risk in the sediment of Chaohu Lake (China) based on total concentration and chemical speciation

Total and extractable concentrations of Cu, Pb, and Zn were determined in surface sediments of west Chaohu Lake (China) by HCl-HNO3-HF-HClO4 digestion and an optimized BCR sequential extraction procedure, respectively. The metal pollution was evaluated by the enrichment factor approach, and the potential eco-risk was evaluated by the sediment quality guideline (SQG) and risk assessment code (RAC) assessments. The results indicated that both total and extractable metal concentrations were highly variable and were affected by sediment properties, even though the sediments were predominantly composed of <63-μm particles (>89%). Enrichment factors of the metals based on the total and extractable concentrations all showed higher values in the northern lake area and decreasing values towards the south. This distribution indicated an input of anthropogenic metals via the Nanfei River. Anthropogenic Cu, Pb, and Zn in surface sediments showed comparable values for each metal based on the total and extractable concentrations, suggesting that anthropogenic Cu, Pb, and Zn resided predominantly in the extractable fractions. Sediment Cu had low eco-risk, and Pb and Zn had medium eco-risk by the SQG assessment, whereas the eco-risk rankings of Cu, Pb, and Zn were medium, low, and low-high, respectively, by the RAC assessment. Referencing to the labile (dilute acid soluble) metal concentrations, we deduced that the eco-risk of Cu may be largely overestimated by the RAC assessment, and the eco-risk of Pb may be largely overestimated by the SQG assessment. Overall, sediments Cu and Pb may pose low eco-risk, and Zn may pose low-high eco-risk.

Water quality of the Ribeirão Preto Stream, a watercourse under anthropogenic influence in the southeast of Brazil

It is known that Brazil still has a privileged position of water quantity and quality, but water use has not proceeded in a responsible manner and often results in impairment of quality. This study aims to evaluate limnological parameters, parasites and bacteria, and concentrations of heavy metals (Cd, Pb, Cu, Cr, Mn, Hg, and Zn) in surface water of Ribeirão Preto Stream. The Ribeirão Preto Stream is located in urban areas under anthropogenic influence. The results showed that the levels of dissolved oxygen values were lower than those established by the National Environmental Council (CONAMA Resolution No 357/2005). The reading of electrical conductivity showed values typical of impacted environments. The parasitological analysis revealed the presence of nematode larvae. The bacteriological analysis showed higher values for total coliform and Escherichia coli than those set by the Brazilian National Environment Council (CONAMA). The heavy metals Cd, Pb, Cu, Cr, Mn, Hg, and Zn showed concentrations in accordance with the guidelines established by CONAMA. The results provide data on the quality of these waters and showed the necessity to protect the watercourse from point sources of contamination, recommending their continued monitoring.