Potential Sources Of Heavy Metals Research Articles

Distribution patterns, risk assessment and potential sources of heavy metals in sediment in the Qiongzhou Strait, China

Risk assessment and pollutant source analysis are crucial tools for the management and protection of coastal ecosystems. The distribution patterns, risk assessment, and potential sources of heavy metals (Cd, Cr, Fe, Ni, Cu, Zn, As, Hg, and Pb) in surface sediment were analyzed in the Qiongzhou Strait, China, in summer and autumn of 2022. Heavy metals in autumn showed higher ecological risk than that in summer. Seasonal shifts in ocean currents may result in variations in heavy metal accumulation and dispersion. Cd and Hg were the priority heavy metals found, and according to the Positive Matrix Factorization results, the study area contains five sources of pollution, with natural sources, shipping-related activities, and industrial activities being the primary contributors. This study indicated that pollutants from adjacent areas should be considered for managing the environmental quality of Qiongzhou Strait.

Influencing Factors, Risk Assessment, and Source Identification of Heavy Metals in Purple Soil in the Eastern Region of Guang’an City, Sichuan Province, China

Soil heavy metal contamination poses a significant threat to both environmental health and ecological safety. To investigate the influencing factors, ecological hazards, and sources analysis of heavy metals in purple soil, 27 sets of soil samples were collected from varying genetic horizons within Guang’an City, and the contents of As, Cd, Cu, Cr, Hg, Ni, Pb, and Zn were analyzed. The results indicated higher concentrations of heavy metals in soil A horizon, compared to that of C horizon. The relevance analysis indicated that the soil’s heavy metals were strongly correlated with the soil’s physicochemical properties. The enrichment factor, pollution load index, and potential risk index highlighted slightly to severely polluted levels of soil Cd and Hg, which significantly contribute to the ecological hazards posed by soil heavy metals. The potential source of heavy metals analyzed using the APCS-MLR model identified both anthropogenic inputs and natural sources as primary contributors to heavy metal presence in soils. The Cu, Cr, Ni, Pb, and Zn contents in the samples from different genetic horizons were chiefly influenced by natural sources, such as soil matrix erosion and weathering, while the concentrations of Cd and Hg were largely affected by anthropogenic activities, specifically coal combustion and agriculture. Conversely, the As content was found to be influenced by a combination of both factors. Anthropogenic activities greatly impacted soil heavy metals at various depths within the study area, thereby underscoring the importance of monitoring these heavy metals. The findings gained from this research can give a scientific basis for the potential utilization of purple soil.

Characteristics and correlation analysis of heavy metal distribution in China's freshwater aquaculture pond sediments

The concentration of heavy metals (HMs) in aquaculture pond sediments significantly affects aquatic food safety and environmental quality. The contamination characteristics, drivers and potential sources of HMs in typical bulk freshwater aquaculture pond sediments in major provinces of China were systematically investigated using a variety of methods and models. Specifically, 130 surface sediment samples were collected from the study area, and the geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to jointly evaluate the characteristics of the HMs. Spearman's correlation and redundancy analysis revealed the main drivers of the HMs. Additionally, the positive matrix factorization (PMF) model and absolute principal component score-multiple linear regression (APCS-MLR) model were used to identify the sources of HMs. The results revealed that the pond sediments were safe for fish culture in most of the study areas. Aquafeed protein content is an important driver of HM concentrations in sediments. The total organic carbon (TOC) content, percentage of clay particles, and pH of the aquaculture pond sediments determined the sediment HMs enrichment abilities as 13.6 %, 52 %, and 9.8 %, respectively. Cd, a significantly enriched pollutant, posed a greater ecological risk than the other five HMs (Cr, Cu, Zn, As, and Pb). Three sources of HMs were identified, including agricultural activity (e.g., aquafeeds, pesticides, and fertilizers), industrial production, and natural sources, with contributions of 44.29 %, 36.66 %, and 19.05 %, respectively. This study provides a scientific basis for minimizing the input and accumulation of HMs in freshwater aquaculture pond sediments, and this can provide insights into the prevention and control of the ecological risks posed by HMs.

Effects of land use/cover change on heavy metal distribution of soils in wetlands and ecological risk assessment

This study aimed to determine the impact of land use/cover changes on the heavy metal content in the Sultan Marshland and surrounding area and assess the pollution status. 54 topsoil samples (0–20 cm) were collected from the Rangeland, Farmland, Scrubland, Southern Marshland, Northern Marshland, and Dry Lake areas. The heavy metal contents of the soil samples (Cr, Pb, Fe, Zn, Cu, Co, Mn, Cd, Mo, As, and Ni) were determined using ICP-MS and ICP-OES devices. The impact of land use/cover change on soil heavy metal content was evaluated using variance analysis, while differences between groups were identified using the Duncan test. Principal Component Analysis (PCA) was conducted to identify potential sources of heavy metals. The contamination status of the soils was evaluated based on land use/cover using the Contamination Factor (Cf), Pollution Load Index (PLI), Ecological Risk Factor (Er), and Potential Ecological Risk Index (PERI).Changes in land use/cover around the Sultan Marshlands affected heavy metal distribution of the soils except for Cd. Among all land use/cover types, Fe concentration was the highest in the soils, while Cd concentration was the lowest. Soils in Southern Marshland exhibited higher average concentrations of Cr, Fe, Zn, Co, Cu, and Ni compared to other land uses/covers. Farmlands and rangelands had higher concentrations of Cd, As and Pb. Land use/cover was ranked based on the total heavy metal load in the following order in terms of average values: Southern Marshland > Scrubland > Farmland > Rangeland > Northern Marshland > Dry Lake. According to Cf, the soils in the Dry Lake were exposed to considerable levels of As contamination. Based on PLI, half of the soil sampling points in the Southern Marshland soils showed a degradation in environmental quality. Er indicated that all land uses moderately polluted with Cd. According to the average PERI, all soils under different land use/cover types were categorized as having a low ecological risk. It was believed that heavy metals originated from both natural and human activities. To ensure the sustainability of the ecosystem and to mitigate the risk of heavy metal pollution entering the food chain, it is recommended to manage farming and mining activities and land use habits.

A study of environmental pollution and risk of heavy metals in the bottom water and sediment of the Chaohu Lake, China.

Most of the existing research for heavy metals in water at present is focusing on surface water. However, potential environmental risk of heavy metals in the bottom water of lakes cannot be ignored. In this study, the content, distribution, and speciation of nine heavy metals (As, V, Cr, Co, Ni, Cu, Zn, Cd, and Pb) in the bottom water and sediment of Chaohu Lake were studied. Some pollution assessment methods were used to evaluate the environmental effect of heavy metals. Positive matrix factorization was conducted to investigate the potential sources of heavy metals in sediment. The contents of heavy metals in the bottom water of Chaohu Lake mean that its environmental pollution can be ignored. In sediment, Cd and Zn have showed stronger ecological risk. pH and redox potential are more likely to affect the stability of heavy metals in the bottom water of Chaohu Lake during the dry reason. Industrial sources (16%) are no longer the largest source of heavy metal pollution; traffic sources (33.6%) and agricultural sources (23.4%) have become the main sources of pollution at present. This study can provide some support and suggestions for the treatment of heavy metals in lakes.

Risk assessment of heavy metals in the freshwater lake sediments around Eppawala phosphate deposit, Sri Lanka

The Eppawala area in Sri Lanka has an agricultural-based economy. As a result, the recent agricultural intensification could increase the risk of heavy metal contamination in lakes in the area as the main water canal in the area, i.e., Jaya Ganga, flows across these lakes. Therefore, this study focuses on the risk assessment of heavy metals in the freshwater lake sediments in the Eppawala area and the identification of potential sources for heavy metal contamination in the lakes. Nine heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) were investigated in surface sediments (n = 22) of the upstream and downstream lakes of the Eppawala Phosphate Deposit (EPD). The average heavy metal concentrations in the upstream lake sediments were higher than those downstream. Eppawala lake sediments were heavily polluted by As along with moderate to high Cr pollution. However, only As and Cd indicated considerable to moderate ecological risk levels to the local environment. The downstream lake sediments showed lower heavy metal contents compared to those upstream and had negative correlations between heavy metals and P2O5 contents. This reveals that the EPD does not contribute to the heavy metal contamination in the Eppawala lake sediments. However, the statistical analysis showed that heavy metals were mostly derived from similar pollution sources. Agrochemicals used in paddy cultivation in the vicinity might be a potential source of heavy metals. This study highlights the importance of implementing remediation to control the heavy metal pollution prevailing in the Eppawala lakes.

Grain size and heavy metal assessment in barchan dunes surrounding the Talatan PV power generation area, Qinghai Province

Heavy metal contamination in sediments near photovoltaic (PV) power generation areas poses potential environmental risks, requiring detailed characterization and source apportionment to facilitate sustainable management. This research explores the characteristics of sediment grains and the concentrations of heavy metals present in surface sediments from barchan dunes proximal to the Talatan Photovoltaic (PV) power generation area, located within Gonghe County, Qinghai Province. The sediments displayed an average grain size within the medium sand range, with marginal differences discernible between the windward and leeward slopes. With the exception of Mn, Cu, Zn, V, Pb, and Ba, concentrations of the remaining metals were found to surpass the baseline levels established by Chinese aeolian soil data, alluding to potential anthropogenic influences. Through employing coefficients of variation in multivariate statistical analysis, it was identified that the concentrations of Cr and Co were significantly elevated, suggesting potential anthropogenic contamination, which may be associated with photovoltaic industrial activities. Specifically, the elevated concentrations of Cr and Co suggested anthropogenic contamination, potentially associated with photovoltaic industrial activities. Utilizing a combination of Correlation Coefficient Analysis, Principal Component Analysis, and Cluster Analysis, three potential sources of heavy metals were identified: (1) industrial origin for elements such as Cu, Cr, Ni, Zn, and As; (2) elements with limited direct application in the photovoltaic industry but associated with materials and energy storage, namely Ba, V, and Mn; (3) anthropogenic inputs related to construction materials and battery storage systems in the photovoltaic park, specifically Co and Pb. The findings offer a significant understanding of the heavy metal characteristics and sources in proximity to the Talatan PV power generation area, emphasizing the impact of human activities on environmental quality. These insights underscore the necessity for enhanced monitoring and management of industrial activities to mitigate potential environmental impacts. Further research is recommended on a broader spatial scale to yield a more comprehensive understanding of this subject.

Spatial and variety distributions, risk assessment, and prediction model for heavy metals in rice grains in China.

In this study, 6229 brown rice grains from three major rice-producing regions were collected to investigate the spatial and variety distributions of heavy metals in rice grains in China. The potential sources of heavy metals in rice grains were identified using the Pearson correlation matrix and principal component analysis, and the health risks of dietary exposure to heavy metals via rice consumption were assessed using the hazard index (HI) and total carcinogenic risk (TCR) method, respectively. Moreover, 48 paired soil and rice samples from 11 cities were collected to construct a predicting model for Cd accumulation in rice grains using the multiple linear stepwise regression analysis. The results indicated that Cd and Ni were the main heavy metal pollutants in rice grains in China, with approximately 10% of samples exceeding their corresponding maximum allowable limits. The Yangtze River basin had heavier pollution of heavy metals than the Southeast Coastal Region and Northeast Plain, and the indica rice varieties had higher heavy metal accumulation abilities compared with the japonica rice. The Cu, Pb, and Cd mainly originated from anthropogenic sources, while As, Hg, Cr, and Ni originated from both natural and anthropogenic sources. The mean HI and TCR values of dietary exposure to heavy metals via rice consumption ranged from 2.92 to 4.31 and 9.74 × 10-3 to 1.44 × 10-2, respectively, much higher than the acceptable range, and As and Ni were the main contributor to the HI and TCR for Chinese adults and children, respectively. The available Si (ASi), total Cd (TCd), available Mo (AMo), and available S (AS) were the main soil factors determining grain Cd accumulation. A multiple linear stepwise regression model was constructed based on ASi, TCd, AMo, and AS in soils with good accuracy and precision, which could be applied to predict Cd accumulation in rice grains and guide safe rice production in contaminated paddy fields.

Increasing trends in heavy metal risks in the Caohai Lake sediments from 2011 to 2022

The accumulation of heavy metals (HMs) in lakes can pose a serious risk to aquatic ecosystems. Caohai Lake is the largest plateau freshwater lake in Guizhou Province, China. However, the spatial and temporal dynamics and ecological risks of HM contamination in Caohai sediments in recent years remain unclear. Here, we analyzed the HM content of sediments in Caohai Lake in 2022 and collected historical (2011–2021) data from published articles to explore the spatiotemporal distribution characteristics, toxicity risks and potential sources of HMs. The mean concentrations (2011–2022) of Ni, As, Cd, Zn, Hg and Pb in the Caohai Lake sediments were all higher than the background values, and the values of As (32.0 mg/kg), Cd (14.19 mg/kg), Pb (87.6 mg/kg) and Zn (406.2 mg/kg) were 1.9, 4.0, 2.4 and 1.3 times their probable effect level (PEL) values, respectively. Sediment As and Cd concentrations in Caohai Lake showed significant decreasing and increasing trends from 2011 to 2022, with mutation points occurring in 2018 and 2020, respectively. The Caohai Lake sediment aquatic life risk index (TRI) for HMs showed a gradual increase until 2019, when it reached a sudden change point, leading to a very high toxicity risk in 2020, with Cd contributing the most to the TRI (43.66). Cd, Pb and Zn, the main pollutants in the sediments of Caohai Lake, originate from traffic, mining and agricultural sources. Our integrated results demonstrated the severity of HM pollution in Caohai Lake and can be helpful for formulating reasonable pollution prevention and control measures.

Spatial and temporal distribution of heavy metals pollution and risk indices in surface sediments of Gomal Zam Dam Basin, Pakistan.

Sediments were considered a sink and potential source of heavy metals in the aquatic system. For this purpose, the present study examined surface sediments for spatial and temporal variation of heavy metals pollution and risk indices in the Gomal Zam Dam Basin (GZDB), Pakistan. Sediment samples (n = 20) were collected from the GZDB, i.e., Gomal Zam Dam, its inlets, and outlets in the winter and summer seasons of 2020, and examined for heavy metals such as zinc (Zn), nickel (Ni), manganese (Mn), lead (Pb), chromium (Cr), copper (Cu), iron (Fe), and cobalt (Co) concentrations. Among GZDB, results showed that the Zhob River Inlet had a higher levels of heavy metals in both seasons. The results revealed that pollution load index values were < 1, observing no pollution in the aquatic system. The risk indices values revealed that sampling sites showed no or very low risk during the summer, 84% of samples showed no or very low risk during the winter, and the rest noted withreasonable risks. Winter seasonshowed higher average values of contamination and risk indices than summer. Statistical analyses revealed that the heavy metals contaminations were mainly due to geogenic sources of rock weathering and ore deposits, with minor contributions from anthropic activities. This study recommends regular monitoring of temporalstudies on heavy metals contamination in the water of the GZDB.

Analyzing topsoil heavy metal pollution sources and ecological risks around antimony mine waste sites by a joint methodology

Studies on soil contamination caused by waste sites near antimony mining areas are scarce. For environmental protection, it is critical to investigate the contamination levels, spatial distribution, and ecological risk assessment of heavy metals (HMs) in the surface soils of different land use types around waste sites and to identify related potential sources. In this study, the pollution status, spatial distribution, sources and ecological risks of Hg, Pb, Cd, Cr, As and Sb in the surface soils of built-up areas, woodlands and croplands around the waste sites of antimony mining areas were resolved for the first time by combining self-organizing maps (SOM), K-means clustering, geographic information systems (GIS) and positive matrix factorization (PMF). According to the analysis results of soil samples, the average abundance order of HMs is: Sb > As > Cd > Pb > Cr > Hg. The cumulative geological index defines Sb as a severe pollution level. The pollution index shows that Sb and Cd in all samples are above the severe pollution level. The pollution load index shows that 67.7% of soil samples are heavily polluted. SOM and K-means divided the studied elements into three clusters and were combined with GIS analysis to from Cd as cluster 1, Sb and As as cluster 2, and Pb, Hg and Cr as cluster 3. The PMF analysis yielded three potential sources of heavy metals: natural sources dominated by Hg, Pb and Cr (48.6%), direct sources caused by the accumulation of waste sites represented by Sb and As (32.9%) and mixed sources caused by human activities and transportation represented by Cd (18.5%). Ecological risk shows that the risk of construction area is prominent, and Sb is the main contribution element of high risk. This study combines multiple methods to investigate the current status of heavy metal pollution in the soil environment around antimony mine slag sites, providing a theoretical basis for better research on the pollution of heavy metals in the soil around antimony mine slag sites and source control, and contributing to the scarcity of antimony mine slag site pollution research.

Variability and Heavy Metal Pollution Levels in Water and Bottom Sediments of the Liwiec and Muchawka Rivers (Poland)

In recent years, human impact on the Earth’s ecological environment has become increasingly visible, with serious negative consequences. One of the most important pollutants are heavy metals which can easily bind to sediments. Due to their toxic behavior, persistence, lack of biodegradability and bioaccumulation, they are considered key river pollutants that need to be controlled. This study examined two rivers: the Liwiec and Muchawka rivers located in south-eastern Poland. The mouth of the Liwiec River is the Bug River, which is partly the border between Poland and Belarus. In turn, the mouth of the Muchawka River is the Liwiec River. The objectives of the study were the following: (1) To complete a qualitative analysis of heavy metals (Cd, Pb, Cu, Ni, Zn) in the waters and bottom sediments of the Liwiec and Muchawka rivers; (2) To assess the degree of heavy metal contamination; (3) To identify the sources of contamination. The analysis included samples of surface water and bottom sediments collected (16 water and 16 bottom sediment samples were taken from the Muchawka River and 32 water and 32 bottom sediment samples were taken from the Liwiec River) in June and September 2022. The variability of characteristics, such as temperature, precipitation and humidity, contributes to seasonal changes in the distribution characteristics and sources of heavy metals. The study showed that only a small part of the heavy metals entering rivers are present in the water depth in the dissolved state, and most of them enrich the sediment, resulting in much higher concentrations of heavy metals in the sediment than in the water column. The differences in the distribution of some elements in water and sediment are due to the fact that surface sediments retain long-term records. Therefore, sediment can be considered a potential source of heavy metals in the aquatic environment. In general, the content of heavy metals determined in bottom sediments was not high but indicative of anthropogenic human activity. There is a possibility of re-release of heavy metals from the sediment into the water when hydrodynamic conditions or environmental factors (pH, redox potential, etc.) change, which could lead to secondary water pollution. The data obtained will be of great importance to both researchers studying river systems and the population living in the area.

Contamination of selected heavy metals in Limnothrissa miodon (Boulenger, 1906) in the four strata of Lake Kariba Zambia: are the consumers at risk?

Pollution of aquatic ecosystems is one of the major challenges affecting many countries of the world. Heavy metal pollutants, in particular, threaten the life of aquatic organisms (fauna and flora) and, more importantly, humans who consume aquatic products as a critical source of proteins. In the present study, the concentrations of selected heavy metals (cobalt- Co, Chromium-Cr, nickel- Ni and manganese- Mn) in Limnothrissa miodon, locally known as ‘Kapenta’ were assessed using the Microwave Plasma Atomic Emission Spectrometer (MP AES) 4200 at the Zambia Agricultural Research Institute (ZARI). The fish was collected from Lake Kariba, Zambia, which is divided into four fisheries management strata (I, II, III, and IV). The health risks to consumers were evaluated using the Estimated Daily Intake (EDI), Target Hazard Quotient (THQ) and Hazard Index (HI). Analysis of variance (ANOVA) was used to assess the difference in the means of heavy metal concentration across the four strata for each element. The concentrations of all the heavy metal elements were within the permissible limits considered to be safe for human consumption based on the Food and Agriculture Organization (FAO) standards. However, the concentration of individual heavy metal elements varied significantly across the strata with stratum I and II showing higher levels in general except for Mn which was highest in stratum II and III compared with the other strata. Furthermore, Mn concentration was the highest in all the strata and the highest concentration was observed in the fish from stratum II. The EDIs, THQs and HIs of each heavy metal element did not show any threat to consumers of the fish from the lake. Further studies are required to better understand the potential sources of heavy metals and to regularly monitor existing activities that may elevate the concentration levels.

The Concentrations, Sources, Ecological, and Human Health Risk Assessment of Heavy Metals in Roadside Soils of Six Cities in Shanxi Province, China.

Roadside soils are regarded as a reservoir for heavy metal pollution, which potentially leads to ecosystem deterioration as well as serious hazard to human health. A comprehensive investigation was conducted for the levels, relationship with soil properties, and potential sources of heavy metals (Hg, Cu, Zn, Pb, Cd, and Cr) in roadside soils in six cities (Changzhi, Jincheng, Yuncheng, Linfen, Xinzhou, Datong) of Shanxi Province; and the corresponding ecological risk and human health risk associated with the concentrations of heavy metals were addressed. Heavy metal concentrations of 112 roadside soil samples in the surveyed cities were, in decreasing order, Zn, Cu, Cr, Pb, Cd, and Hg, which were higher than corresponding background values. The highest concentrations were in Changzhi. The results of Pearson correlation analysis demonstrated that positive correlations in varying degrees existed between soil properties such as electrical conductivity, total nitrogen, total phosphorus, and total organic carbon with specific heavy metals and that negative correlations were observed for clay and electrical conductivity. Anthropogenic sources related to traffic emissions and industrialization were the main sources of heavy metals in roadside soils according to principal component analysis. The ecological risk assessments were achieved by pollution index and potential risk index, indicating that contamination with Hg was the most serious, which posed the highest risk to the ecosystems in the surveyed cities; and the ecological risk in Changzhi ranked at the top compared with other cities. For the human health risk assessment, the results demonstrated that the noncarcinogenic and carcinogenic risks were in the acceptable range in the surveyed cities. However, there was a higher health risk from heavy metal exposure for children than adults, and the main exposure pathway was soil ingestion. In addition, Changzhi was the city with the highest noncarcinogenic and carcinogenic risks, and the main human health risks were posed by Cr contamination in roadside soil, which was different from the results of ecological risks. Both results of ecological and health risk assessment demonstrated that the higher risk exhibited in southern and southeastern cities than northern cities in Shanxi Province. Environ Toxicol Chem 2023;42:1485-1500. © 2023 SETAC.

Source Analysis and Ecological Risk Assessment of Heavy Metals in the Arable Soil at the Geological High Background, Based on the Township Scale

Due to the extensive development of carbonate rocks in southwest China, heavy metals are naturally occurring elements that have high natural background levels in the environment. Therefore, it is important to conduct ecological risk assessments and identify potential sources of heavy metals in the geological high background area. Based on the township scale, a total of 307 surface soil samples were collected in Qinglong Town, Fengjie County, Chongqing. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn and pH were analyzed and determined. The spatial distribution and source of soil heavy metals were discussed using the geostatistical analysis and an absolute principal component score-multiple linear regression (APCS-MLR) model in the studied area. The results showed that the average values of seven heavy metals (As, Cd, Cr, Cu, Hg, Ni, and Zn) in the arable soil exceeded the background values of Chongqing, and the cumulative effect of Cd and As was obvious. The concentrations of Cd significantly exceededthe screening values in The Risk Control Standard for Soil Environmental Quality and Soil Pollution in Agricultural Land (GB 15618-2018), with the over-standard rates of 52.12%. The spatial characteristics of soil heavy metal contents exhibited a pattern of high in the south and low in the north. PCA and APCS-MLR modeling revealed that the contributions of natural sources to Cr, Cu, Ni, and Zn were 86.62%, 64.34%, 76.44%, and 85.46%, respectively. As, Pb, and Hg mainly derived from industrial activities, which accounted for 74.63%, 61.90%, and 73.49%, respectively, and Cd was affected by both natural sources and industrial activities (accounting for 47.74% and 39.56% of the total Cd content, respectively). The evaluation of the soil by the Nemerow comprehensive index (P) showed that Cd pollution was relatively serious, accounting for 27.04% of soil pollution. The potential ecological hazard index showed that Cd and Hg were the main ecological hazard elements, and the distribution range of RI was 51.77 to 2228, indicating mainly mild and moderate risks, and the moderate and above risk areas in the study area were mainly located around the southern industrial source area. Altogether, our results revealed that in the study area, the heavy metal pollution was mainly caused by industrial activities, and the heavy metal pollution caused by geological background was mainly light to moderate. In conclusion, the medium and above risk areas in the study area were mainly caused by mineral and industrial activities, whereas the heavy metal pollution caused by geological background was mainly light to moderate pollution.

Spatial Distribution Characteristics and Source Analysis of Heavy Metals in Urban River Surface Sediments from the Lanzhou Reach of the Yellow River

In order to investigate the contamination characteristics and potential sources of heavy metals from the urban river surface sediments in the Yellow River Basin, we selected the Lanzhou reach of the Yellow River as the object of investigation. A total of 46 surface sediment samples were collected along the Lanzhou reach of the Yellow River, and the contents of eight heavy metals, Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb, were determined by using inductively coupled plasma mass spectrometry and an atomic fluorescence spectrometer. Contamination indexes including single factor pollution index (Pi) and geo-accumulation index (Igeo), together with the sediment pollution index (SPI), were used to assess heavy metal pollution characteristics and ecological risk levels in the urban river surface sediments of the Lanzhou reach of the Yellow River. Pearson's correlation analysis (CA), positive matrix factorization (PMF), and principal component analysis/absolute principal component score (PCA/APCS) were jointly employed to quantitatively analyze pollution sources of heavy metals. The results showed that the mean concentrations of the majority of heavy metals exceeded their corresponding background values of Gansu Province and Lanzhou City with the exception of As, and the spatial distribution of high concentrations of heavy metals was mainly concentrated in the corner of the river. Based on the single factor pollution and geo-accumulation indexes of the eight heavy metals, in the Lanzhou reach of the Yellow River, Cr was the dominant pollution element in the urban river surface sediments, followed by Cd and Ni. Additionally, the SPI values for the eight heavy metals in the surface sediments ranged from 0.48 to 8.56, presenting natural to low ecological risk level. Furthermore, source apportionment revealed that a mixture source of industrial and agricultural activities (77.6%) was the largest contributor of Cr, Ni, Zn, As, Cd, and Pb in the urban river surface sediments, followed by natural sources (11.4%) and a mixed source of industrial and traffic activities (11%).

Potential risks, source apportionment, and health risk assessment of dissolved heavy metals in Zhoushan fishing ground, China

Dissolved heavy metal pollution in the ocean is one of the most severe environmental concerns; however, the potential sources of heavy metals and the resulting health risks are not fully understood. To explore the distribution characteristics, source apportionment, and health risks of dissolved heavy metals (As, Cd, Cu, Hg, Pb, and Zn) in the Zhoushan fishing ground, this study analyzed heavy metals in surface seawater during the wet and dry seasons. The concentrations of heavy metals varied greatly between seasons, and the mean concentration in the wet season was generally higher than that in the dry season. A positive matrix factorization model coupled with correlation analysis was applied to identify promising sources of heavy metals. Four potential sources (agricultural, industrial, traffic, atmospheric deposition, and natural sources) were identified as the determinants of the accumulation of heavy metals. The health risk assessment results revealed that non-carcinogenic risk (NCR) for adults and children were acceptable (HI < 1), and carcinogenic risk (CR) were at a low level (1 × 10−6 < TCR ≤ 1 × 10−4). The source-oriented risk assessment indicated that industrial and traffic sources were the main sources of pollution, contributing 40.7 % of NCR and 27.4 % of CR, respectively. This study proposes forming reasonable, effective policies to control industrial pollution and improve the ecological environment of Zhoushan fishing grounds.

Ecological and environmental risks of heavy metals in sediments in Dingzi Bay, South Yellow Sea

As a special geographical location between rivers and oceans, coastal estuaries always face severe heavy metal contaminations, especially in semi-closed bay. In this study, the spatial distribution, chemical fraction, ecological risks, and potential sources of heavy metals (Pb, Cr, Cu, As, Cd, Zn, and Ni) in surface sediments and sediment cores were investigated in Dingzi Bay, Shandong Peninsula. The Igeo values and modified potential ecological risk index (MRI) indicated that Cd and As presented high environmental risks in the surface and sediment cores. The high concentration sites were mainly located in the middle and the mouth of the Dingzi Bay. The source identification indicated that most heavy metals in surface sediments originated from shipping and aquaculture, while As and Ni from industrial pollution. The correlation coefficients showed that high proportion of fine particle, TN, TOC, TP, and AVS in surface sediments could significantly elevate the bioavailability of most heavy metals.

Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0-20 and 20-40cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4-5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2-3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution.

A spatial distribution – Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil

With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution.