Spatial Distribution Of Heavy Metals Research Articles

Geographic distribution of heavy metals and identification of their sources in soils near large, open-pit coal mines using positive matrix factorization

Mining activities are considered the most important factor causing heavy metal accumulation in surface soil and it is important to understand the spatial distribution and source of heavy metals in typical steppes. In this study, the contents, spatial distribution, and sources of heavy metals were determined using geostatistical analyses, multivariate statistical analyses, and a positive matrix factorization (PMF) model using 152 soil samples collected from a grassland near the Sheng-Li coal base. The results shows that the mean concentration of heavy metals is low and does not threaten the quality of the local soil. However, the concentrations of eight heavy metals (Cu 15.04 mg kg-1, Zn 49.30 mg kg-1, Cd 0.11 mg kg-1, Pb 20.00 mg kg-1, Se 0.12 mg kg-1, Ge 1.45 mg kg-1, As 9.06 mg kg-1, and Sn 2.52 mg kg-1) are higher than their mean background values in soil in Inner Mongolia. High coefficients of variation for the heavy metals, especially Ge (1.03), and As (0.56), indicate that the concentrations of the elements are affected by the presence of the open-pit mines. Multivariate statistical and geo-statistical analyses show that Ge and As are highly correlated (R2 = 0.67, P < 0.01), suggesting that they have the same source. Using geostatistical and PMF models, we identified five potential pollution sources in the study area: 1) Industrial pollution (21.2 %), which includes smelting activity and open-pit coal mines, as suggested by elevated levels of Zn, Cd, Ge, and Cu; 2) Germanium mining (7.6 %), as indicated by higher levels of Ge and As; 3) Natural sources (37.2 %), as indicated by higher levels of Mn and Ni; 4) Coal mining activity (8.5 %), as indicated by higher levels of Sn and Cr; 5) Coal conveyor belts and high vehicular traffic, as indicated by elevated levels of Pb and Se. Taken together, the results of this study indicate that the coal base has a significant effect on the heavy metal concentration in the grassland. Therefore, the identification of the spatial distribution of heavy metals in the area may be key to controlling the pollution in the grassland. The results of this study can help to reduce pollution sources, cut down on pollution transport. So that zonal pollution control and ecological protection in the typical steppe region is achieved.

Spatial distribution of heavy metals in surface sediments from the Ganges River basin, Bangladesh

The spatial distribution and source of heavy metals (Ba, Cr, Ni, Pb, V, As, Zn, and Cu) in surface sediments of the Ganges River basin, Bangladesh, were investigated using the geo-accumulation index (Igeo), contamination factor (CF), and pollution load index (PLI). The sequences of mean concentrations of the studied heavy metals are in decreasing order as follows: Ba > V > Cr > Zn > Pb > Ni > As > Cu > Hg. Sediments in the Ganges River basin are characterized by low to moderate Igeo values for Ni, Zn, Pb, and V, while high values for Ba, As, Cu, and Cr, indicating low to moderate/high contamination of the river sediments. The mean CF load in the sediments shows that Ba is highly enriched, having moderate enrichment of As, Cu, and Cr, and relatively low enrichment of Pb, V, Zn, and Ni for the lower river basin reflecting high to moderate degree of metal pollution of the sediments. The PLI values range from 0.11 to 5.89 (average 0.88 ± 1.28) and 0.41 to 8.79 (average 2.13 ± 1.81) for the upper and lower Ganges River basin, respectively, which suggests that the enhanced metal pollution in the latter site is due to the threat of progressive industrialization. The principal component analysis (PCA) and hierarchical clustering (CA) records imply that the sediment samples are primarily polluted by As, Cu, Cr, and Ni resulting from anthropogenic sources. The mean Igeo and PLI values further reflecting high level of metal contamination in the lower Ganges River basin and relatively low level of contamination in the upper basin.

Seasonal, spatial variation, and pollution sources of heavy metals in the sediment of the Saigon River, Vietnam

The current study was conducted to (1) examine seasonal and spatial distribution of heavy metals and metalloid in sediment from the Saigon River and (2) apportion and quantify their pollution sources. Ninety-six sediment samples were taken in the rainy and dry season on 13 sampling sites, distributed over the lower reaches of the River, to analyze for exchangeable concentration of 11 heavy metals and metalloid (Al, B, Cd, Co, Fe, In, Mn, Ni, Pb, Sr, and Zn), pH, EC, organic carbon content, and particle-size distribution. Generally, the concentration of 11 elements was ranked in the order Mn > Al > Fe > Zn > Sr > In > B > Ni > Co > Pb > Cd. Hierarchical cluster analysis grouped 13 sampling sites into two parts based on the similar concentration of the 11 elements. Three-way analysis of variance showed that the total exchangeable concentration of 11 elements was significantly higher in the rainy season than in the dry season and in the upper part than in the lower part of the river. Principal component analysis/factor analysis and correlation analysis revealed that three pollution sources (PS) may contribute to enriching the 11 examined elements in the sediment. These sources included (PS1) from catchment through water erosion over natural areas, explaining 83%, (PS2) mixed sources from catchment through water erosion over agricultural fields and inside Ho Chi Minh City, accounting for 6%, and (PS3) mixed sources from lowland areas, explaining 7.8% of the total variance of the elements. In brief, the sediment concentration of 11 metals and metalloid varied with season and space and three major pollution sources from river catchment, inside Ho Chi Minh City, and lowland contributively enriched the elements in the sediment of the River.

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.

Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest

Hyperspectral imaging, with the hundreds of bands and high spectral resolution, offers a promising approach for estimation of heavy metal concentration in agricultural soils. Using airborne imagery over a large-scale area for fast retrieval is of great importance for environmental monitoring and further decision support. However, few studies have focused on the estimation of soil heavy metal concentration by airborne hyperspectral imaging. In this study, we utilized the airborne hyperspectral data in LiuXin Mine of China obtained from HySpex VNIR-1600 and HySpex SWIR-384 sensor to establish the spectral-analysis-based model for retrieval of heavy metals concentration. Firstly, sixty soil samples were collected in situ, and their heavy metal concentrations (Cr, Cu, Pb) were determined by inductively coupled plasma-mass spectrometry analysis. Due to mixed pixels widespread in airborne hyperspectral images, spectral unmixing was conducted to obtain purer spectra of the soil and to improve the estimation accuracy. Ten of estimated models, including four different random forest models (RF)—standard random forest (SRF), regularized random forest (RRF), guided random forest (GRF), and guided regularized random forest (GRRF)—were introduced for hyperspectral estimated model in this paper. Compared with the estimation results, the best accuracy for Cr, Cu, and Pb is obtained by RF. It shows that RF can predict the three heavy metals better than other models in this area. For Cr, Cu, Pb, the best model of RF yields Rp2 values of 0.75,0.68 and 0.74 respectively, and the values of RMSEp are 5.62, 8.24, and 2.81 (mg/kg), respectively. The experiments show the average estimated values are close to the truth condition and the high estimated values concentrated near several industries, valifating the effectiveness of the presented method.

Impact of Drain Effluent on Surficial Sediments in the Mediterranean Coastal Wetland: Sedimentological Characteristics and Metal Pollution Status at Lake Manzala, Egypt

Surface sediments were collected from Lake Manzala, the Mediterranean coastal wetland located to the east of the Nile Delta, Egypt, to assess the effect of drain effluent on the spatial variations of sedimentary characteristics and heavy metal pollution. Grain-size compositions, textures, and heavy metal distribution patterns in sediments are presented using GIS technique. Results of the analysis of the sediment showed a clear effect of drain effluent, with an increase in fine fractions and homogeneous suspensions in transportation mode. Lake sediments were dominated by sandy mud textures, and mode of transportation was homogeneous suspension and rolling. Spatial distribution of heavy metals (Fe, Mn, Zn, Cu, Ni, Cr, and Pb) was studied in the lake’s surficial sediments, along with their relationship to drain effluent and their contamination status in the ecological system. Heavy metal pollution status was assessed by means of accepted sediment quality guidelines and contamination assessment methods (contamination factor, contamination degree, modified contamination degree, geo-accumulation, and enrichment factor). Among the determined heavy metals, Pb had the most ecological risk. Generally, the heavy metals in the surface sediments indicated pollution risk ranging from moderate to considerable, particularly, in those sites facing drains and inlets that had the highest toxic effluent. The results were interpreted by statistical means. A cluster analysis defined areas facing drain discharge and inlets as separated groups. ANOVA indicated that most of the sedimentation and studied metals directed this clustering.

Spatial distribution of Heavy Metal contents in farmland soils by different spatial interpolation methods: A case study of a farmland in Lanping County, Yunnan Province

The spatial interpolation method is of great sign ificance for the spatial distribution of heavy metals in soil and the prevention and control of pollution. However, different interpolation methods are applied to predict the accuracy of different heavy metal pollution distributions in small-scale areas with a single farmland. There is no relevant argument. Taking a farmland (96000m2) in Lanping County, Yunnan Province, Yunnan Province as the research object, 144 surface soil samples were collected and the contents of Pb and Cd in soil were determined by ICP-MS. The deterministic spatial interpolation method and geostatistical Kriskin interpolation method were used to investigate the accuracy of different interpolation methods and the distribution characteristics of Pb and Cd in soil. The results show that the optimal interpolation methods for heavy metals Pb and Cd in polluted farmland are 0-order ordinary Kristen interpolation method, and the content of Pb and Cd in the soil is relatively accurate. This study will provide important basic information for the identification of spatial distribution characteristics of different heavy metals in small-scale areas with a single farmland, which will help to comprehensively evaluate the function of spatial interpolation to identify spatial distribution information of heavy metals.

Geochemical characterization and spatial distribution of heavy metals from urban dust in Chetumal, Mexico

The first diagnose of heavy metal (Cd, Cr, Cu, Fe, Mn, Ni and Pb) concentrations present in Chetumal, Mexico and its spatial distribution was carried out by analyzing 86 samples of urban dust through atomic absorption spectrophotometry. The assessment of the extent of pollution was undertaken by the use of the Mexican Residential Soil Guideline Values, the calculation of the contamination factor and the pollution load index. The results showed concentrations of heavy metals below the Mexican guidelines in the city, except for chromium and lead in a few samples. However, using the contamination factor the concentrations for chromium, lead and copper are exceeded in some samples. The map of pollution load index shows the areas requiring immediate attention from the decision makers.

Using bivariate linear mixed models to monitor the change in spatial distribution of heavy metals at the site of a historic landfill.

To improve accuracy and efficiency of monitoring remediated sites, the current study proposed the use of bivariate linear mixed modelling and subsequent hypothesis testing to determine significant change in contaminant concentrations over time. The modelling method integrated soil heavy metal (arsenic-As, lead-Pb and zinc-Zn) concentrations obtained from Bicentennial Park, Sydney, Australia, in the years 1990 (n = 144) and 2015 (n = 60), alongside potential influencing factors as predictor variables. Following variable selection, significant predictors included As (1990)-plan curvature, land cover change; As (2015)-multi-resolution ridge top flatness (MRRTF); Pb (1990)-elevation, MRRTF, type of nearest road; Pb (2015)-land cover change; Zn (1990)-distance to the nearest road and road type; and for Zn (2015)-aspect and land cover change. Model quality statistics (standardised squared prediction error; SSPE) indicated relatively good estimates of the prediction variance (mean ~ 1.0 for all metals, median = 0.512 for As (1990), 0.420 for As (2015), 0.417 for Pb (1990), 0.388 for Pb (2015), 0.342 for Zn (1990) and 0.263 for Zn (2015)), however Lin's concordance correlation coefficient indicated poor prediction of point estimates (LCCC = 0.263 for As (1990), 0.414 for As (2015), 0.250 for Pb (1990), 0.166 for Pb (2015), 0.233 for Zn (1990) and 0.408 for Zn (2015)). Pb in 1990 exceeded the Australian guide value of 600mgkg-1 in small, isolated areas of the park, and by 2015, these 'hotspots' had significantly diminished (P < 0.05). Concentrations of As were low in both 1990 and 2015, not exceeding the 300mgkg-1 guide; yet, in 2015, As had significantly increased in the south of the study area (P < 0.2). Zn concentrations in 1990 were elevated but did not exceed the guide value of 30,000mgkg-1. Overall, the models exhibited good estimation of prediction variance and therefore are suitable for hypothesis testing; however, they exhibited poor prediction quality at times. Despite this, bivariate linear mixed modelling is worth exploring as it provides an advantage over modelling single time points and can assist with tracking potential contaminant sources before they cause harm.

Spatial and seasonal characteristics of dissolved heavy metals in the aquaculture areas of Beibu Gulf, South China

To study the contamination of heavy metals in the aquaculture area of Beibu Gulf, the concentrations of seven metals in seawater were investigated. The results showed that significant seasonal and spatial distributions of heavy metals were observed in the four aquaculture areas. Generally, the heavy-metal concentrations in the Maowei Sea were higher than the other three aquaculture areas, which may be related to local industrialization and urbanization development. The concentrations of some metals were obviously higher in wet seasons than in dry seasons, which may be attributed to the seasonal river discharge, rainfall and seawater intrusion. Compared to other regions, some metal levels revealed relatively high contamination. Remarkably, the concentrations of Hg, Cu and Cd were much higher than concentrations found in the past 20 years in the seawater of the Guangxi Beibu Gulf, suggesting that there were increasing anthropogenic inputs of those metals in the aquaculture areas. The assessment of ecological risk index (ERI) indicated that the highest ERI value was found in the Maowei Sea, followed by Pearl Bay, Lianzhou Bay and Hongsha. Hg is the main ecological risk factor, and the heavy-metal contamination in the aquaculture areas must not be ignored.

Spatial Distribution of Heavy Metals in Surface Soil Horizons Surrounding Erbil Steel Company (ESC) Areas

Spatial Distribution of Heavy Metals in Surface Soil Horizons Surrounding Erbil Steel Company (ESC) Areas

Effects of anthropogenic activities on the levels of heavy metals in marine surface sediments of the Thessaloniki Bay, Northern Greece: Spatial distribution, sources and contamination assessment

Surface marine sediments act as important repository of heavy metals (HMs) and play a significant role in their transport and possible release. The anthropogenic contribution to heavy metal contamination and the associated ecological and health risks should be evaluated in studied areas. The objectives of this study were to investigate the presence and spatial distribution of heavy metals (Cr, Ni, Cu, Mn, Cd, Zn, Fe and Pb) in surface marine sediments located in the Northern Thermaikos Gulf, which is an area of increased ecological, financial and social significance, using a sensitive and reliable analytical method and to evaluate the anthropogenic contribution and ecological risks related to the presence of heavy metals. The mean HM concentrations, in the sediments of Thermaikos Gulf exhibited a decreasing order: Fe > Mn > Zn > Cr > Pb ≥ Ni > Cu > Cd. A very strong correlation was demonstrated between Cu, Ni, Pb and Zn indicating that these elements originate from common anthropogenic sources. This was also supported by Enrichment Factors (EF) and Igeo values, which revealed that almost 50% of the sediments were characterized as “heavily to extremely polluted” concerning Cd, while almost 30% and 20% of the sampled sediments were characterized as “moderately to heavily polluted” with regards to Pb and Zn, respectively. Pollution Load Index (PLI) for almost all samples exhibited values >1 indicating anthropogenic pollution. Ecological Risk (Er) values for Cd indicated high to extreme risk for 50% of the samples. Cr, Cu and Pb concentrations for 74%, 95% and 74% of the samples, respectively, indicated occasional association to toxic biological effects on aquatic organisms. For Ni, 87% of the sediments frequent association with adverse biological toxic effects. Contamination Severity Index (CSI) showed that 22% of the samples reflected a moderate to high severity of contamination. Two of the samples near the industrial and shipping zone, exhibited the highest CSI values. The highest total Toxic Units (ΣTUs) were calculated in sediment samples affected by industrial wastewater and shipping activities, with Ni presenting the higher TUs compared to the rest of the HMs and contributed the most (37%) to the overall TUs revealing its high potential toxicity for the marine ecosystems in Thermaikos Gulf. The Total Risk Index (TRI) for each HM exhibited a decreasing order of Ni (3.9) > Cu (3.0) > Cd (2.6) > Pb (2.0) > Cr (1.6) > Zn (1.4). The area exhibiting the highest impact of anthropogenic HMs, coincides with the Thessaloniki's port broad area, where the industrial and shipping activities have been taking place for several years, indicating the cumulative effect of the anthropogenic load from the activities on urban coastal regions.

Geochemical determination and pollution assessment of heavy metals in agricultural soils of south western of Iran.

Soil contamination with heavy metals due to the application of fertilizers and biocides in agricultural activities is a potential threat for human health through the food chain. The present work was designed to study the spatial distribution of heavy metals, pollution level and possible reasons for their contamination in agricultural soils of Aghili plain, Khuzestan, Iran. The median concentrations of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, V, Zn, and Hg were 2.90, 0.29, 8.10, 39.0, 17.75, 354.0, 0.97, 58.35, 5.90, 34.0, 42.0, and 0.01mg/kg, respectively. The results revealed that average concentrations of all studied heavy metals with an exception of Co, Cu, and Ni, were lower than background values. Analysis of source identification showed that Zn, Pb, and Cu (P < 0.01, r > 0.9) and Co, Cr, Mn, Ni, and V (P < 0.01, r > 0.7) were mainly from anthropogenic. In addition, Cd probably was originated from agricultural activities (application of manure and phosphorous fertilizers). Enrichment factor values of all metals (except Ni), were in the range of non to moderate enrichment (EF < 5). According to the degree of contamination (Cd) and ecological risk factor (ERF), all stations were categorized as low to moderate contaminated sites (4.5 < Cd < 17), and biological communities in some locations may be at risk (ERF >65). Results indicate that application of fertilizers, herbicides and pesticides in agricultural soils has led to soil contamination and special management and educational plans are needed for public and farmers to prevent further adverse effects.

Spatial distribution of Fe and Mn in paper mill effluent affected agricultural soils in Morigaon, Assam

The spatial distribution of heavy metals (Fe and Mn) in the paper mill contaminated area of Jagiroad, Assam, India were investigated using statistics, geostatistics and GIS techniques. The total concentration of Fe and Mn were determined for 188 samples collected from the contaminated area. The mean concentration of Fe (7629 mg kg1) was high. The highest and the lowest standard deviation were observed in the Fe (1749) and pH (0.81), respectively. Analysis of the isotropic variogram indicated that the Fe semivariogram was well described with the Gaussian model, with the distance of spatial dependence being 1354 m, while Mn was well described with the spherical model, with the distance of spatial dependence being 833 m. The ordinary kriging estimates of Fe and Mn maps showed that high concentrations of these metals occured in the low-lying areas like bils (lakes). For both the investigated heavy metals the prediction of goodness (G) value was greater than zero. This indicates that spatial prediction is better than assuming mean of observed value as the property value for any unsampled location. Thus the geostatistical method was spatial variability of Fe and Mn.

Spatial Distribution and Health Risk Assessment of Soil Pollution by Heavy Metals in Ijebu-Ode, Nigeria.

Background.Soil pollution by heavy metals in urban areas is of major concern to city planners and policy makers because of the potential threat to human health. Hence, an investigation of soil pollution is crucial to urban environmental assessment and management.Objective.To determine the spatial distribution and health risk assessment of seven heavy metals (cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn)) around Ijebu-ode, southwest Nigeria.Methods.Surface soil samples were analyzed for Cd, Cr, Cu, Mn, Ni, Pb, and Zn levels using standard procedures. Geographic information system (GIS) data, pollution indices (enrichment factor, geo-accumulation index), and the health risk assessment model, respectively, were used to analyze the spatial distribution, pollution level, and potential health risk of heavy metals.Results.Low pH was observed in the urban soils. The average concentrations of the seven heavy metals investigated were in order of Zn > Pb > Mn > Cu > Cd > Ni > Cr. There was high spatial variation in the distribution patterns of the heavy metals. The cancer risks for Cu, Mn, Pb, and Zn for children (1.50 × 10−3 – 2.71 × 10−2) and Mn, Pb, and Zn for adults (7.89 × 10−4 – 2.97 × 10−3) were higher than the acceptable range of 1 × 10−6 - 1 × 10−4.Conclusions.Anthropogenic activities from different urban land uses contribute to the pollution levels and spatial distribution of heavy metals in soils. Increasing pollution of urban soil may contribute to the occurrence of some health risk for residents in the study area.Competing Interests.The authors declare no competing financial interests.

Pollution characteristics, sources, and risk assessment of heavy metals and perfluorinated compounds in PM2.5 in the major industrial city of northern Xinjiang, China

At present, research on PM2.5 has focused on eastern and central China; little attention has been given to Xinjiang, the most northwestern part of China. Ten kinds of heavy metals (HMs) and 12 kinds of perfluorinated compounds (PFCs) in atmospheric PM2.5 are detected by inductively coupled plasma mass spectrometry and high-performance liquid chromatography-mass spectrometry, respectively. Principal component analysis and risk assessment methods are used to study HMs and PFCs and explore their pollution levels and health risks. Results show that HMs and PFCs are prevalent in atmospheric PM2.5 of Shihezi and Urumqi. The spatial distributions of HMs and PFCs in four sampling sites are development zone (B1) = industrial park (A1) > factory (A3) > school (A2) and A1 > A3 > B1 > A2, respectively. In the four sampling sites, copper, lead, zinc, and cadmium (Cd) contents are relatively high. Perfluorooctane sulfonate (PFOS), perfluorohexanoic acid (PFHxA), and perfluorooctanoic acid (PFOA) are the most prevalent PFCs. Risk assessment results show that PFCs in the atmosphere will not harm residents, but the Cd and nickel (Ni) in HMs are harmful to humans, especially Cd, which has both carcinogenic and non-carcinogenic risks. These findings indicate that the carcinogenic risk of HMs and PFCs in children is higher than in adults. In addition, HMs and PFCs may cause joint toxicity.

Prediction of heavy metals in soils of an arid area based on multi-spectral data

With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations. Based on these measured concentrations, as well as rapid and environmentally friendly remote sensing (multi-spectral data), stepwise multiple linear regression (SMLR) and partial least-squares regression (PLS) were combined to predict concentrations and distributions of heavy metals in the soils of the study area. Furthermore, laboratory data were used to assess the accuracy of the prediction results. Obtained results suggest that the SMLR and PLS models were able to predict the metals contents in the study area. The concentrations of Cr, Ni, V and Zn could be predicted by two regression models, while those of Cu and Mn were predicted more accurately when they were attached to the SMLR model. The spatial distribution of heavy metals derived from the two models is consistent with measured values, indicating that it is reasonable to predict the concentrations of heavy metals in the soil of the study area using the multi-spectral data.

Heavy metal imaging in fibrotic human kidney tissue using the synchrotron X-ray fluorescence microprobe.

Abnormally high exposure to heavy metals and their accumulation in some tissues are recognized as causes of many acute and chronic human diseases. Because of the roles many metals have in normal human physiology, proving cause and effect between exposure to heavy metals and pathogenesis of disease is problematic. Therefore, many illnesses that develop through occupational and environmental exposure are not considered directly related to heavy metal toxicity. The high sensitivity and spatial resolution of elements using the synchrotron X-ray fluorescence microprobe (XFM) may give a robust means to investigate spatial distribution of heavy metals in correlation with specific pathologies. For example, proven presence of different heavy metals may correlate spatially with kidney fibrosis, suggesting a mechanistic link between heavy metal-induced fibrosis and chronic kidney disease. One specific example that may benefit from such an analysis relates to a cluster of people with chronic kidney disease of unknown cause (CKDu), in a significant proportion of the population of the North Central Province of Sri Lanka. Here, it was postulated that heavy metal exposure, in particular of cadmium, in foods and agriculture may be one cause of end-stage kidney disease and premature death of patients with CKDu. Synchrotron methods had not been applied previously to this particular problem. This manuscript provides a brief review of the literature and reports some pilot data from an investigation of localization of kidney fibrosis in CKDu with selected heavy metals including cadmium.

Sediment characterisation and spatial distribution of heavy metals in the sediment of a tropical freshwater wetland of Indo-Burmese province

The sediment characterisation of wetlands belonging to the Northeastern Region of India particularly regarding the assessment of sediment carbon stock is very scanty. The presently available literature on the wetlands cannot be employed as a common model for managing the wetlands of the Northeastern Region of India as wetlands are a sensitive ecosystem with a different origin or endogenous interventions. Thereby, this research was conducted on Deepor Beel for investigating the spatial and seasonal variation of sediment parameters, the relationship between the parameters and pollution status of the wetland. Results revealed that the study area is of an acidic nature with a sandy clay loam type texture. Organic carbon, total nitrogen and available nitrogen were higher in sediments in the monsoon period. The mean stock of the sediment carbon pool of Deepor Beel is estimated to be 2.5 ± 0.7 kg m−2. The average non-residual fraction percentage (63.2%) of Pb was higher than the residual fraction. Zn content ∼490 mg kg−1 exceeding its effect range medium (ERM) was determined to suggest frequent biological adverse effects. Highest metal enrichment factor (EF) values were shown by Zn and Pb, which ranged between 78 and 255. Risk assessment code (RAC) values of Pb between 21 and 29% indicated its high bio-accessibility risk. Pearson's coefficient matrix revealed a low degree of positive correlation between organic carbon content and metal concentration. Principal component analysis revealed that the first component comprising of EC, basic cations and metals accounted for 62.3% of variance while the second component (OM, OC, TN, AN, AP) and the third component (pH) accounted for 21.8% and 7.0% of the variance, respectively. The present study revealed the adverse impact of human inputs on the Deepor Beel quality status.

Predicting spatial distribution of heavy metals in an abandoned phosphogypsum pond combining geochemistry, electrical resistivity tomography and statistical methods.

One of the wastes generated in fertiliser production from phosphoric rock is phosphogypsum, whose mismanagement lead to environmental and health risks. Therefore, a detailed evaluation of the chemical composition of phosphogypsum is necessary to determine effective means of its management. Due to the high amount of generated waste, the cost and time consumed for this characterisation by chemical analysis is limiting. Hence, efficient tools should be developed to predict the chemical composition of this waste. Thus, this study aims to: 1) determine the physic-chemical characterisation of phosphogypsum pond using geochemical and geophysical techniques and 2) predict the heavy metals spatial distribution through statistical models. Results show that the most concentrate metal is chromium with a maximum of ≈900 mg.kg-1 and cadmium is the least concentrated (maximum ≈23 mg.kg-1). The Electrical Resistivity Tomography revealed the superposition of two layers. The top one (waste) presents low resistivity (≈17Ω.m) while the bottom layer shows higher resistivity (>124Ω.m). Metal concentrations and resistivities were combined by applying non-linear regression models. Cr showed the strongest correlation (R2 = 0.68), yielding an accurate model that was used for revealing the spatial distribution of the highest Cr concentrations in the pond, with the consequent reduction of expensive traditional methods.