Spatial Patterns Of Heavy Metals Research Articles

Combined impacts of algae-induced variations in water soluble organic matter and heavy metals on bacterial community structure in sediment from Chaohu Lake, a eutrophic shallow lake

Many lakes are suffering from eutrophication and heavy metals-contamination. However, the combined impacts of algae bloom and its induced variations in heavy metals on microbial community in sediment from eutrophic lakes remain unclear. In this study, we performed field experiments to investigate how algae bloom impacted water soluble organic matter (WSOM) and heavy metals in sediment from Chaohu Lake, a eutrophic shallow lake, and probed their combined impacts on sediment bacterial community structure. The results showed that algae bloom increased WSOM quantity, in particular, the soluble microbial by-product-like (SMP) and fulvic acid-like (Fa-L) components markedly enhanced by 203.70 % and 70.17 %, respectively. We also found that algae bloom redistributed the spatial patterns of heavy metals and altered their chemical species in sediment, then promoted contamination degree and potential ecological risk of heavy metals in sediment. Moreover, sediment bacterial community richness and diversity obviously decreased after algae bloom, and the variance partitioning analysis (VPA) results showed that combined impacts of algae-induced changes in WSOM and heavy metals explained 66.56 % of the variations in bacterial community structure. These findings depicted how algae bloom influence sediment WSOM and heavy metals, and revealed the combined impacts of algae-induced variations on microbial community structure in shallow eutrophic lake.

Source Apportionment of Heavy Metal Pollution in Agricultural Soils around the Poyang Lake Region Using UNMIX Model

Rapid urbanization and industrialization have caused the continuous discharge of heavy metals into the soils of China’s Poyang Lake region, where they pose a major threat to human health. Yet, the spatial characteristics of these heavy metals in farmland soils and their pollution sources in this region remain unclear. This study was conducted to document the pollution caused by heavy metals in the Poyang Lake region through sampling that consisted of the collection of 215 soil samples from agricultural fields. The UNMIX model provided identification of the sources causing heavy metal pollution and source contributions to soil pollution. ArcGIS was used to study the spatial distribution of the eleven heavy metals and to validate the apportionment of pollution sources provided by the UNMIX model. Soil concentrations of heavy metals were above the local background concentrations. The average content of eight heavy metals, including Cd, Mo, Zn, Cu, Sb, W, Pb, and Ni, was approximately 1–6 times greater than natural background levels (6.91, 2.0, 1.67, 1.53, 1.23, 1.38, 1.11, and 1.24, respectively), while the average content of V, Cr, and Co was lower than natural background levels. The average contents of Cr, Ni, Cu, Zn, Cd, and Pb were all lower than the screening levels for unacceptable risks in agricultural land soils. The percentage of Cd content exceeded the risk screening value in all sampling sites, up to 55%, indicating that agricultural soils may significantly be affected by cadmium contamination. Five pollution sources of heavy metals were identified: natural sources, copper mine tailings, agricultural activities, atmospheric depositions, and industrial activities. The contribution rates of the pollution sources were 7%, 13%, 20%, 29%, and 31%, respectively. The spatial pattern of heavy metals was closely aligned with the outputs of the UNMIX model. The foregoing supports the utility of the UNMIX model for the identification of pollution sources of heavy metals, apportionment study, and its implementation in agricultural soils in the Poyang Lake region.

The response of benthic foraminifera to heavy metals and grain sizes: A case study from Hainan Island, China

Benthic foraminifera, heavy metals, and sediment grain sizes were studied in three bays of Hainan Island, and canonical correspondence analysis (CCA) and nonparametric regression were used to reveal the relationship between foraminifera and their environment. According to our survey, the three bays were moderately contaminated by Mo and As and uncontaminated to moderately contaminated by Pb, Zn, Cr, Sb, and Hg. The spatial pattern of heavy metals was comparable to sediment transport trends, indicating that their distribution was determined by sediment transport. Both living and dead foraminiferal assemblages were analyzed, and their compositions were similar, although the latter had a higher density and diversity. Based on the CCA method, species were divided into three groups, each of which responded differently to heavy metals and grain sizes. The response curves of individual species to heavy metals and grain sizes were obtained by using the Loess (locally weighted regression) method.

Distribution patterns and source identification for heavy metals in Mirs Bay of Hong Kong in China.

Sediment quality caused by heavy metals was investigated in the Mirs Bay and Tolo Harbor, Hong Kong, China. Samples were collected in January and July, 2010. One-way analysis of variance showed that sediment quality variables (Fe, Zn, Mn, Pb, V, Cu, Cr, Ba, Ni and As) were significantly different (p < 0.05) among the sampling areas, whereas the average concentration of V, Eh and Ba exhibited the significant seasonal variations (p < 0.05) between January and July. The spatial pattern of heavy metals (Pb, Zn and Cu) can probably be attributed to anthropogenic and tidal flushing influence in the harbor. Both geo-accumulation index (Igeo) and enrichment factor (EF) were used to identify the metal pollution level and its related source. Pb, Zn, and Cu are considered as "polluted metal" in Tolo Harbor. Cluster analysis (CA) identified three distinct clusters with the Tolo Habor and Shatou Jiao, the inner bay and the south part of the bay. Principal component analysis (PCA) identified the spatial patterns and their affected parameters in the studying area. Results showed metals distribution in Mirs Bay and its adjacent area is principally affected by human activities such as marineculture, dumping, located mostly in Tolo Harbor and Shatou Jiao, where was closely related with anthropogenic influence. While the monitoring stations including MS13-MS16 and MS8 locating in the south part of the studying area might be corresponded to natural influence.

Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method

Understanding the spatial patterns of heavy metals is important for the protection and remediation of urban soil. Considering that the conventional Geostatistical methods, such as ordinary kriging (OK), are sensitive to dataset outliers, this study converted the identified outliers into a discrete probability density function (PDF). Then, the PDF was used as soft data in the Bayesian maximum entropy (BME) framework to perform a spatial prediction of soil Zn contents in Wuhan City, Central China. By using OK as the reference method, the BME framework was found to produce an overall further accurate prediction, and the PDF of BME predictions was further informative and close to the observed Zn concentrations. An improved BME performance can be expected if soft data with high quality are provided. The BME is a promising method in environmental science, where the so-called outliers that probably carry important information are common.

Spatial Patterns of Heavy Metals in the Sediments of a Municipal Wastewater Treatment Pond System and Receiving Waterbody, Cha am, Thailand

Cha am, a popular beach destination in Thailand, uses an aerated lagoon system with four ponds in series to treat its municipal wastewater. This study investig…

Geo-detection of factors controlling spatial patterns of heavy metals in urban topsoil using multi-source data

Heavy metal contamination has become a serious and widespread problem in urban environment. Understanding its controlling factors is vital for the identification, prevention, and remediation of pollution sources. This study aimed to identify the factors controlling heavy metal accumulation in urban topsoil using the geodetector method and multiple data sources. Environmental factors including geology, relief (elevation, slope, and aspect), and organism (land-use and vegetation) were extracted from a geological thematic map, digital elevation model, and time-series of Landsat images, respectively. Then, the power of determinant (q) was calculated using geodetector to measure the affinity between the environmental factors and arsenic (As) and lead (Pb). Geology was the dominant factor for As distribution in the this study area; it explained 38% of the spatial variation in As, and nonlinear enhancements were observed for the interactions between geology and elevation (q = 0.50) and slope (q = 0.49). Land-use and vegetation bi-enhanced each other and explained 39% of the spatial variation in Pb. These results indicated that geology and relief were the factors controlling the spatial distribution of As, and organism factors, especially anthropogenic activities, were the factors controlling the spatial distribution of Pb in the study area. As was derived from weathering transportation, and deposition processes of original bedrock and subsequent pedogenesis, and anthropogenic activity was the most likely source of Pb contamination in urban topsoil in Shenzhen. Moreover, geodetector provided evidence to explore the factors controlling spatial patterns of heavy metals in soils.

Evaluation of potential health risk of heavy metals in groundwater using the integration of indicator kriging and multivariate statistical methods

The factors and mechanism which control the spatial patterns of heavy metals in groundwater and their effect on human health could be identified with multivariate statistical methods and human health risk assessment. Sampling wells are statistically classified into two cluster based on the similar characters in groundwater quality using Q-mode cluster analysis (Q-mode CA). Two significant factors were extracted by principal component analyses (PCA), explaining 64.19% of the total variance. These factors were in turn described by the clusters 1 and 2, respectively, resulting from the R-mode CA. PCA and CA revealed significant anthropogenic contributions and water-rock interaction effects of the metals in groundwater. Health risk assessment factors including chronic daily intake (CDI) and hazard quotient (HQ) indices were computed for child and adult. The HQ indices of Cd and Pb in the both child and adult cases showed the value greater than the safe limits, which cause the harmful health hazards and potential non-carcinogenic health risks to the human. Spatial variability maps using ordinary kriging show that safe zones are mainly covered the west and south-western parts of the study area, while the contamination zones are found to be concentrated in the east, north, and south-eastern parts of the plain. The indicator kriging maps show highly uneven spatial pattern of Pb and Cd concentrations. The probability maps reveal that more than 50% of the total area possessed the highest probability (0.8–1.0) of exceeding the threshold values for Cd and Pb.

Spatial distribution of heavy metals in soil, water, and vegetables of farms in Sanandaj, Kurdistan, Iran.

BackgroundHeavy metals are ubiquitous elsewhere in nature and their measurement in environment is necessary to develop health management strategies. In this study, we aimed to find out concentrations and spatial patterns of heavy metals in main farms of Sanandaj in Kurdistan, Iran.MethodsOver May to October 2012, six farms were selected to analyze concentrations and spatial patterns of several heavy metals, namely aluminum (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) in their soil, irrigation water, and edible vegetables. Overall, 36 samples of soil and water and 72 samples of vegetables including coriander (Coriandrum sativum), dill (Anethum graveolens), radish (Raphanus sativus) root and radish leaf were collected. The concentrations of metals were determined by inductively coupled plasma optical emission spectrometry. The spatial surfaces of heavy metals were created using geospatial information system.ResultsThe order of metals in soil was Al > Zn > Ni > Cu > Cr > Pb > Co > As > Cd while in water it was Cr > Co > Zn > Pb > Cu > Ni > Al = As = Cd. The order of heavy metals in vegetables was Al > Zn > Cu > Cr > Ni > Pb > Co > As > Cd. Totally, the minimum concentrations of Al, Cu, Pb, and Zn were found in radish root while the maximum of Al, Co, Cr, and Ni were found in radish leaf. The minimum concentrations of Cd and Cr and maximum concentrations of Cu and Zn were also deciphered in dill. Noteworthy, coriander had the minimum concentrations of Co and Ni. The concentrations of Cr and Pb in vegetables were more than maximum allowable limits of the Food and Agriculture Organization (FAO) and the World Health Organization (WHO).ConclusionIn summary, albeit the concentrations of heavy metals in soil and water samples were below FAO and the WHO standards, vegetables were contaminated by chromium and lead.

Spatial variability of heavy metals in surficial sediments: Tajan River Watershed, Iran

In this paper, we assessed the spatial distribution of heavy metal concentrations (Al, As, Cd, Co, Cr, Cs, Fe, Ni, Sn and Zn) in the sediments of the Tajan River Watershed, an inflow to the Caspian Sea in the Northern Iran. Comparison of four interpolation techniques (ordinary kriging, simple kriging, inverse distance weighted and local polynomial interpolation) via cross validation suggested that kriging was the most accurate spatial analysis technique for the majority of the elements. Variogram models obtained by geostatistical procedures indicate existence of spatial correlation between samples, with the exceptions for Al, Cd and Zn. The mapped spatial distributions are showing a clear coincidence of variation pattern for different groups of the heavy metals. This indicates similar origin and affecting factors for these analyzed metals. Possible anthropogenic and natural sources of heavy metals were discussed based on the distribution maps and correlation analysis with other elements and geologic units. The results have shown that the accurate spatial pattern of heavy metals could improve our understanding of possible sources, controlling factors and involving processes. This is essential for monitoring and remediation of the contaminant sites in the watershed.

Spatio-temporal dynamics of heavy metals in sediments of the river estuarine system: Mahanadi basin (India)

Dynamics of heavy metals in the surface sediments of Mahanadi river estuarine system were studied for three different seasons. This study demonstrates that the relative abundance of these metals follows in the order of Fe > Mn > Zn > Pb > Cr > Ni ≥ Co > Cu > Cd. The spatial pattern of heavy metals supported by enrichment ratio data, suggests their anthropogenic sources possibly from various industrial wastes and municipal wastes as well as agricultural runoff. The metal concentrations in estuarine sediments are relatively higher than in the river due to adsorption/accumulation of metals on sediments during saline mixing, while there is a decreasing trend of heavy metal concentrations towards the marine side. The temporal variations for metals, such as Fe, Mn, Zn, Ni and Pb exhibit higher values during monsoon season, which are related to agricultural runoff. Higher elemental concentrations are observed during pre-monsoon season for these above metals (except Ni) at the polluted stations and for metals, such as Cr, Co and Cd at all sites, which demonstrate the intensity of anthropogenic contribution. R-mode factor analysis reveals that “Fe–Mn oxy hydroxide”, “organic matter”, “CaCO3”, and “textural variables” factors are the major controlling geochemical factors for the enrichment of heavy metals in river estuarine sediment and their seasonal variations, though their intensities were different for different seasons. The relationships among the stations are highlighted by cluster analysis, represented in dendrograms to categorize different contributing sites for the enrichment of heavy metals in the river estuarine system.

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EFG) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EFG techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EFG technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.

Spatial Pattern Analysis of Heavy Metals in Beijing Agricultural Soils Based on Spatial Autocorrelation Statistics

This study explored the spatial pattern of heavy metals in Beijing agricultural soils using Moran’s I statistic of spatial autocorrelation. The global Moran’s I result showed that the spatial dependence of Cr, Ni, Zn, and Hg changed with different spatial weight matrixes, and they had significant and positive global spatial correlations based on distance weight. The spatial dependence of the four metals was scale-dependent on distance, but these scale effects existed within a threshold distance of 13 km, 32 km, 50 km, and 29 km, respectively for Cr, Ni, Zn, and Hg. The maximal spatial positive correlation range was 57 km, 70 km, 57 km, and 55 km for Cr, Ni, Zn, and Hg, respectively and these were not affected by sampling density. Local spatial autocorrelation analysis detected the locations of spatial clusters and spatial outliers and revealed that the pollution of these four metals occurred in significant High-high spatial clusters, Low-high, or even High-low spatial outliers. Thus, three major areas were identified and should be receiving more attention: the first was the northeast region of Beijing, where Cr, Zn, Ni, and Hg had significant increases. The second was the southeast region of Beijing where wastewater irrigation had strongly changed the content of metals, particularly of Cr and Zn, in soils. The third area was the urban fringe around city, where Hg showed a significant increase.

Dynamics and quantification of dissolved heavy metals in the Mahanadi river estuarine system, India

Dynamics of heavy metals such as Fe, Mn, Zn, Cr, Cu, Co, Ni, Pb, and Cd in surface water of Mahanadi River estuarine systems were studied taking 31 different stations and three different seasons. This study demonstrates that the elemental concentrations are extremely variable and most of them are higher than the World river average. Among the heavy metals, iron is present at highest concentration while cadmium is at the least. The spatial pattern of heavy metals suggests that their anthropogenic sources are possibly from two major fertilizer plants and municipal sewage from three major towns as well as agricultural runoff. The temporal variations for metals like Fe, Cu, and Pb exhibit higher values during the monsoon season, which are related to agricultural runoff. Concentrations of Ni, Pb, and Cd exceed the maximum permissible limits of surface water quality in some polluted stations and pose health risks. Dissolved heavy metals like Fe, Mn, Cr, Ni, and Pb exhibit a non-conservative behavior during estuarine mixing, while Zn, Cu, and Co distribution is conservative. Distribution of cadmium in the estuarine region indicates some mobilization which may be due to desorption. The enrichment ratio data suggest that various industrial wastes and municipal wastes contribute most of the dissolved metals in the Mahanadi River. The Mahanadi River transports 18.216 × 10(3) t of total heavy metals into the Bay of Bengal and the calculated rate of erosion in the basin is 128.645 kg km(-2) year(-1).

Heavy metals assessment and identification of their sources in agricultural soils of Southern Tehran, Iran

A detailed investigation was conducted to evaluate heavy metal sources and their spatial distribution in agricultural fields in the south of Tehran using statistics, geostatistics, and a geographic information system. The content of Cd, Cu, Co, Pb, Zn, Cr, and Ni were determined in 106 samples. The results showed that the primary inputs of Cr, Co, and Ni were due to pedogenic factors, while the inputs of Zn, Pb, and Cu were due to anthropogenic sources. Cd was associated with distinct sources, such as agricultural and industrial pollution. Ordinary kriging was carried out to map the spatial patterns of heavy metals, and disjunctive kriging was used to quantify the probability of heavy metal concentrations higher than their recommended threshold values. The results show that Cd, Cu, Ni, and Zn exhibit pollution risk in the study area. The sources of the high pollution levels evaluated were related to the use of urban and industrial wastewater and agricultural practices. These results are useful for the development of proper management strategies for remediation practices in the polluted area.

Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS

For many practical problems in environmental management, information about soil heavy metals, relative to threshold values that may be of practical importance is needed at unsampled sites. The Hangzhou–Jiaxing–Huzhou (HJH) Plain has always been one of the most important rice production areas in Zhejiang province, China, and the soil heavy metal concentration is directly related to the crop quality and ultimately the health of people. Four hundred and fifty soil samples were selected in topsoil in HJH Plain to characterize the spatial variability of Cu, Zn, Pb, Cr and Cd. Ordinary kriging and lognormal kriging were carried out to map the spatial patterns of heavy metals and disjunctive kriging was used to quantify the probability of heavy metal concentrations higher than their guide value. Cokriging method was used to minimize the sampling density for Cu, Zn and Cr. The results of this study could give insight into risk assessment of environmental pollution and decision-making for agriculture.

Variability in Contamination by Heavy Metals: Sampling Implications

The choice of a sampling design forassessing soil contamination is a key issue. The leveland spatial pattern of contaminants needs to be takeninto account by those buying, selling or developingsuch land. This paper evaluates sampling design andsize in order to provide satisfactory and efficientestimates of contamination levels. Geostatisticalmethods are used to quantify the spatial dependence ofheavy metals. Semivariance is calculated forlog-transformed data to minimise the effects ofextreme outliers. Several models at different samplingstages are presented for characterising spatialpattern of heavy metals at the investigated site.Variogram models are calculated for a range of samplesizes. The results are assessed by a jack-knifingtechnique and then used for block kriging. The overallconclusion is that a sample size of 50 to 60 isoptimal for characterising variability incontamination levels at an old industrial site of 6.25 ha.