Multivariate geostatistical analyses of heavy metals in soils: spatial multi-scale variations in Wulian, Eastern China

Impact of manganese mining on potentially toxic elements pollution and bioaccumulation in Spirogyra varians and Hydrilla verticillata in the Xiaojiang River

Potentially toxic elements (PTEs) pose a significant threat to aquatic ecosystems. This study investigated the content and potential sources of PTEs (Cr, Mn, Ni, Cu, Zn, Cd, Pb) in water, sediment, and dominant aquatic plants (Hydrilla verticillata and Spirogyra varians) in the Xiaojiang River, located near the Zhaiying manganese mine in Guizhou Province, China. Correlation analysis, principal component analysis (PCA), and cluster analysis were employed to assess PTE distribution and potential sources. Water PTE concentrations complied with the Class II standard (GB3838-2002), indicating no water pollution. However, sediment PTE levels exceeded background values, particularly Mn, which exhibited moderate to strong contamination. Cd also showed moderate contamination, posing a considerable ecological risk. Cd was the main potential pollutant with the highest contribution rate. Mn and Cd were therefore identified as priority pollutants requiring targeted abatement strategies. Mining activities likely represent the primary source, but combined pollution from vehicle traffic and agriculture might also contribute. Hydrilla verticillata demonstrated a higher capacity for PTE enrichment from sediment compared to Spirogyra varians, suggesting its potential for sediment remediation (except for Cu). A significant correlation existed between both plant species and sediment PTE content. PCA supported the association between S. varians and sediment PTEs. Linear regression analyses revealed better correlations between S. varians and sediment Mn, Ni, Cu, and Zn (0.77, 0.68, 0.82, and 0.79, respectively). Taken together, these findings suggest that S. varians serves as an effective bioindicator for monitoring sediment contamination with PTEs.

Pollution evaluation and source identification of heavy metals in soil around steel factories located in Lanshan District, Rizhao City, eastern China

More than 40 enterprises have settled in the constructed steel-supporting industrial park adjacent to the Yellow Sea in Lanshan District, Rizhao City, eastern China. The concentration of heavy metals in the soil around steel factories often exceeds the limit specified by the national environmental agency. In this study, nine metals (Cu, Zn, Pb, Cd, Cr, Ni, Mn, Fe, and Mg) in the soil around the steel-supporting industrial park were examined, and 100 soil samples were analyzed. The pollution characteristics and sources of these heavy metals were obtained via pollution index analysis, potential ecological risk evaluation, geostatistical analysis, and multivariate statistical analysis combined with a positive matrix factorization (PMF) model. The results indicated that the heavy metals showed varying accumulation levels, among which Cd, Ni, and Pb were the major heavy metals greatly influencing the soil quality. The area around the steel factories exhibited heavy pollution and a high ecological risk, and Ni and Cd were the main risk factors. The soil at the steel factories and that in the southeastern and southwestern parts of the study area attained higher heavy metal element contents than those in the soil in other parts. PMF analysis confirmed that Cu, Pb, and Cd originated from mixed agricultural and traffic sources. Mn was related to natural sources. Cr and Ni likely resulted from atmospheric deposition, and Zn, Cd, Fe, and Mg were mainly associated with industrial materials, with these four sources accounting for 32.68%, 12.2%, 27.57%, and 27.54%, respectively, of the total metal content. This study could facilitate the investigation, evaluation, and source identification of soil heavy metal pollution in industrial regions and surrounding areas of Lanshan District, Rizhao City.

Assessment of Chemical Properties, Heavy Metals, and Metalloid Contamination in Floodplain Soils under the Influence of Copper Mining: A Case Study of Sibay, Southern Urals

The ecotoxicological condition of soils around mining areas is most often unsatisfactory, which affects entire ecosystems and human health. This research sought to analyze the morphological, agrochemical properties, and content of heavy metals (Cd, Cu, Zn) and metalloids (As) of soils located in a floodplain. The study was conducted within the city of Sibay (Republic of Bashkortostan, Russia). The soil samples were collected from the floodplains of the rivers Karagayly and Khudolaz. According to morphological studies, the soil cover was represented by the Lithic Leptosols, Stagnic Phaeozems, and Fluvisols. The results showed that the soils were characterized by high values of organic matter, potassium, and low levels of phosphorus. Soils that were located away from the city in the Karagayly River were not contaminated. However, the floodplain areas pertaining to the urban district and located near the quarries were characterized by severe anthropogenic soil pollution, disrupted integrity of the soil cover, decreased vegetation, and accumulating labile forms of heavy metals and metalloids. The highest degree of pollution was observed in the floodplain soil of the river Khudolaz where all elements exceeded the maximum permissible concentration (MPC) level. Soils in the floodplain of the Karagayly river were marked by an increased degree of contamination of Zn: exceeding MPC by 1.6 times. With the trend toward an arid climate, the ecotoxicological condition of floodplain soils is an important challenge.

Source appointment of potentially toxic elements (PTEs) at an abandoned realgar mine: Combination of multivariate statistical analysis and three common receptor models

Identifying pollution sources and quantifying their contributions are of great importance for proposing management and control strategies of potentially toxic elements (PTEs) in soil. In this study, multivariate statistical analysis and receptor models were combined to identify potential pollution sources and apportion their contributions at an abandoned realgar mine. Principal component analysis (PCA) result shows that three factors are responsible for PTEs, which is also supported by cluster analysis (CA). Correlation analysis and spatial analysis also show that the heavy metals from the same pollution source are of higher correlation coefficients and similar spatial distribution. Three receptor models were combined to apportion contributions of pollution sources. Three pollution sources were detected by absolute principal component analysis-multiple linear regression (APCA-MLR). In contrast, four sources were identified by positive matrix factorization (PMF) and UNMIX. Soil parent material was heavily loaded on Cr, Cu, Ni and Zn, occupying the largest average contribution (30%-43%). Cadmium was mainly derived from agricultural activities with contribution higher than 60%. Arsenic accumulation was mainly associated with mining and smelting activity with contribution higher than 80%. PMF and UNMIX models showed that more than half of Pb concentrations were influenced by industrial activities. Comparatively speaking, APCA-MLR was a well-performing model for all PTEs even though it only detected three pollution sources. The study showed that it was a good choice to apply multiple receptor models in order to achieve more reliable and objective conclusions of source appointment.

Soil Metal Immobilization in Agricultural Land Contaminated with Cadmium and Lead: A Case Study of Effectiveness Evaluation in Lanping, Southwest China

The growth of edible crops on land that is highly polluted with potentially toxic elements is prohibited in many developed countries, but the growth of fiber or energy crops may be permitted. Here, we have evaluated metal immobilization in a maize field polluted with cadmium (Cd) and lead (Pb) to determine the thresholds of soil CaCl₂-extractable Cd and Pb and to assess management options designed to maximize food safety. Based on geographical and statistical methods we found that when the soil pH was increased from 5.24 to 6.24, the soil CaCl₂-extractable Cd and Pb values decreased by 47.8 and 74.7%, respectively. Soil CaCl₂-extractable Pb concentrations need to be < 2.14 mg kg^-1 in order to comply with the Chinese maximum permissible grain Pb concentration (< 0.2 mg kg^-1). Immobilization increased the percentage of samples that were below permissible levels from 77.4% to 96.2% (grain Cd) and 90.6% to 96.2% (grain Pb) during the period 2017 to 2019. To avoid excessive or inadequacy immobilization, the spatial distribution of correlation coefficients of soil pH, CaCl₂-extractable or grain Cd/Pb may be helpful in the precise management of immobilization for long-term remediation.

Research trends and frontiers on source appointment of soil heavy metal: a scientometric review (2000-2020)

In recent years, source appointment of soil heavy metal has attracted growing attention. However, few studies have attempted to make a comprehensive and systematical review on this topic. For this reason, a total of 1051 publications were retrieved from the Web of Science (WOS) database between 2000 and 2020. A scientometric analysis was carried out to reveal the characteristics of publications, research power, and research hotspots. CiteSpace was used to visualize and summarize the information about the development in this field. The results showed that (1) the number of publications in source appointment of soil heavy metal had increased rapidly; Environmental science and ecology and environmental sciences were top 2 most popular subject categories; (2) Research power was mainly distributed in Asia, Europe, and North America. China and Chinese Academy of Sciences were the most productive country and institution in terms of publications in this field. Biao Huang (China) was the most productive author. However, Hakanson L (Sweden) was the most influential author in terms of citation frequency; (3) Heavy metal, source identification, and contamination were the most frequent keywords. Keyword clustering analysis showed that the research hotspots mainly concentrated on air pollution, bioremediation, spatial distribution, soil, PCA, and so on; (4) Keyword bursts analysis showed that the research frontiers mainly focused on spatial analysis of soil heavy metal and exposure risk to human health.

Spatial assessment models to evaluate human health risk associated to soil potentially toxic elements

Quantifying source apportionment of potentially toxic elements (PTEs) in soils and associated human health risk (HHR) is essential for soil environment regulation and pollution risk mitigation. For this purpose, an integrated method was proposed, and applied to a dataset consisting of As, Cd, Cr, Cu, Hg, Ni, Pb, Se, and Zn in 273 soil surface samples. Positive matrix factorization (PMF) was used to quantitatively examine sources contributions of PTEs in soils; and the HHR arising from the identified source was determined by combining source profiles and health risk assessment; at last, sequential Gaussian simulation (SGS) was used to identify the areas with high HHR. Four sources were identified by PMF. Natural and agricultural sources affected all 9 PTEs contents with contributions ranging from 19.2% to 62.9%. 41.9% of Cd, 40.8% of Pb, 58.6% of Se, and 29.8% of Zn were controlled by industrial and traffic emissions. Metals smelting and mining explained 35.5%, 30.5%, and 24.9% of Cr, Cu, and Ni variations, respectively. Hg was dominated by atmospheric deposition from coal combustion and coking (58.7%). The mean values of the total non-carcinogenic risks of PTEs were 1.55 × 10^-1 and 9.40 × 10^-1 for adults and children, and the total carcinogenic risk of PTEs had an average value of 8.86 × 10^-5. Based on source-oriented HHR calculation, natural and agricultural sources were the most important factor influencing HHR, explaining 51.0% and 49.1% of non-carcinogenic risks for children and adults, and 44.2% of carcinogenic risk. SGS indicated that 1.1% of the total area was identified as hazardous areas with non-carcinogens risk for children.

Enhancing apportionment of the point and diffuse sources of soil heavy metals using robust geostatistics and robust spatial receptor model with categorical soil-type data

Soil-type data usually contain valuable information about soil heavy metal (HM) concentrations; however, they were rarely considered in the apportionment of point or diffuse sources in previous studies. In this study, the spatial variations of the soil HM concentrations in Jintan County, China were partitioned into two portions - the soil-type effects and the corresponding residuals, using analysis of variance (ANOVA). Standardized robust kriging error (SRKE) with soil-type data as auxiliary information (SRKE-ST) was proposed to identify the high-value spatial outliers of soil HMs, and the performance of SRKE-ST was compared with that of commonly-used SRKE. Robust absolute principal component scores/robust geographically weighted regression (RAPCS/RGWR) with soil-type data as auxiliary information (RAPCS/RGWR-ST) was proposed to apportion the diffuse sources of soil HMs, and the performance of RAPCS/RGWR-ST was compared with those of RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR). Results showed that (i) RSKE-ST effectively excluded high-value spatial outliers resulting from the effects of complex soil-type polygons on soil HM concentrations; (ii) RAPCS/RGWR-ST generated higher estimation accuracy in source contributions and less negative contributions than RAPCS/RGWR and APCS/MLR did. It is concluded that the proposed RSKE-ST and RAPCS/RGWR-ST could effectively use categorical soil-type data to enhance, respectively, the identification of high-value spatial outliers (i.e., potential point sources) and the apportionment of diffuse sources of soil HMs in large-scale areas.

Soil from an Abandoned Manganese Mining Area (Hunan, China): Significance of Health Risk from Potentially Toxic Element Pollution and Its Spatial Context

This study assessed the significance and potential impact of potentially toxic element (PTE) (i.e., Mn, Pb, Cu, Zn, Cr, Cd, and Ni) pollution in the surface soil from an abandoned manganese mining area in Xiangtan City, Hunan Province, China, on the health of residents. The risks were sequentially evaluated using a series of protocols including: the geo-accumulation index (I_geo), pollution load index (PLI), potential ecological risk index (RI), and implications for human health from external exposures using the hazard quotient (HQ), hazard index (HI) and carcinogenic risk (CR). The results revealed that Mn and Cd were the major pollutants in the soil samples. The ecological risk assessment identified moderate risks, which were mainly derived from Cd (82.91%). The results of the health risk assessment revealed that generally across the area, the non-carcinogenic risk was insignificant, and the carcinogenic risk was at an acceptable level. However, due to local spatial fluctuation, some of the sites presented a non-carcinogenic risk to children. The soil ingestion pathway is the main route of exposure through both non-carcinogenic and carcinogenic risks, with Mn being the major contributor to non-carcinogenic risk, with Cr and Cd the major contributors to carcinogenic risk. In addition, three pollution sources were identified through the Pearson correlation coefficient and principal component analysis (PCA), which included: a. mining activities and emissions from related transportation; b. natural background; c. agricultural management practices and municipal sewage discharge. The study provides information on the effects of spatial variation for the development of the abandoned mining areas and a useful approach to the prioritization of locations for the development and utilization of soil in these areas in China.

Mineralogical and Geochemical Nature of Calcareous Vineyard Soils from Alcubillas (La Mancha, Central Spain)

The mineralogical and geochemical patterns of calcareous vineyard soils located in Alcubillas (La Mancha, Central Spain) have been evaluated; also their variability has been studied. The information provided by this study supports the assessment of geochemical spatial variability, the origin of these soils, their elements and the factors that control their distribution. The presence of quartz, calcite, feldspar and, in particular, illite and kaolinite is due to their inheritance from surrounding lithologies (and pedological processes), which mostly include limestones, marls and other sedimentary rocks, as well as metasedimentary rocks of Hercynian origin. Furthermore, since the presence and accumulation of certain trace elements in vineyard soils is a relevant global hazard (in particular with respect to wine production quality), the spatial distributions of Ba, Cr, Cu, Pb, Rb, Sr, V and Zr (carried out using geostatistical techniques and geometry-based interpolation methods) were investigated in order to determine the origin of these trace elements. The presence of these elements can be interpreted as being due to geogenic, pedogenic and, in certain cases, anthropic influences. The nature of certain agricultural practices, including the use of fertilizers, phytosanitary products and machinery, could explain the local increases in some trace element contents.

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 0.01 mg/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 (C_d) and ecological risk factor (ERF), all stations were categorized as low to moderate contaminated sites (4.5 < C_d < 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.

Heavy metal contamination assessment of surface sediments of the Subei Shoal, China: Spatial distribution, source apportionment and ecological risk

This study investigated heavy metals (Ni, Zn, Cr, Cu, As, Pb, Cd and Hg) concentrations in surface sediment of Subei Shoal, China, to illustrate their spatial distribution characteristics, sources and potential ecological risk of pollution. Contents of total organic carbon (TOC), clay, silt and sand were 1.7 ± 0.8%, 3.3 ± 3.2%, 13.6 ± 14.2% and 83.1 ± 17.4%, respectively. The spatial distribution of TOC, clay and silt were similar; however, distribution of Hg was inverse. Concentrations of Ni, Zn, Cr, Cu, As, Pb, Cd and Hg were 47.88 ± 8.93, 38.18 ± 8.86, 19.22 ± 5.14, 11.32 ± 5.07, 6.97 ± 2.45, 0.13 ± 0.72, 0.56 ± 0.77 and 0.06 ± 0.02 mg kg^-1 sediment, respectively. Principal component analysis suggested that Cu, Zn, Cd, Cr and Ni were mainly derived from natural sources, whereas Pb, Cd, As and Hg from industrial and agricultural sources. Results of geo-accumulation index, potential ecological risk index (RI), pollution load index (PLI), toxic risk index (TRI) and contamination severity index (CSI) demonstrated that pollution levels of Cd and Hg were moderate, which should attract more attention as main pollution factors. The pollution was mainly distributed in the central and northern parts, and the southern part had a good ecological environment. Moreover, the contaminated stations accounted respective for 33.4%, 25.9%, 33.3% and 70.4% of RI, PLI, TRI and CSI, of which 70.4% of the contamination severity index stations contained 66.7% of much lower severity stations. These findings could contribute to more effective exploitation of tidal flat resources, and the prevention and treatment of tidal marsh environment.

An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Identifying quantitative sources and hazardous areas of heavy metals is a crucial issue for soil management. For this purpose, an integrated approach composed of finite mixture distribution modeling (FMDM), positive matrix factorization (PMF) and sequential Gaussian co-simulation (SGCS) was proposed. FMDM was used to establish background standards and pollution thresholds. PMF supported by FMDM background standards was applied to estimate the source apportionment. Hazardous areas of single metals were delineated using SGCS with FMDM pollution thresholds and uncertainty analysis, and overall hazardous areas were defined by the presence of multiple metals. This integrated approach was applied to a dataset of seven metals as a case study. FMDM indicated that the distributions of Cr, Cu, Ni, and Zn were fitted to two-dimensional mixture distributions, representing a background distribution and a moderately polluted distribution. The distributions of Cd, Hg, and Pb were composed of a three-component lognormal mixture distribution, corresponding to the background, moderate, and high pollution distributions. Three sources were apportioned. Cr, Cu, Ni, and Zn were dominated by parent materials. Parent materials contributed 52.6%, 45.8%, and 81.9% of Cd, Hg, and Pb concentrations, respectively. Human emissions from coal combustion, industrial work and traffic had significant influences on Hg, Cd, and Pb, with contributions of 49.8%, 26.9%, and 15.6%, respectively. Agricultural practices were exclusively associated with 20.5% of Cd. Overall, hazardous areas exceeding moderate pollution thresholds covered 17.4% of the total area, corresponding to urban areas and industrial sites, whereas overall hazardous areas above high pollution thresholds were limited to 0.01% of the total area.

Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils

Absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) were applied to a dataset consisting of 10 heavy metals in 300 surface soils samples. Robust geostatistics were used to delineate and compare the factors derived from these two receptor models. Both APCS/MLR and PMF afforded three similar source factors with comparable contributions, but APCS/MLR had some negative and unidentified contributions; thus, PMF, with its optimal non-negativity results, was adopted for source apportionment. Experimental variograms for each factor from two receptor models were built using classical Matheron's and three robust estimators. The best association of experimental variograms fitted to theoretical models differed between the corresponding APCS and PMF-factors. However, kriged interpolation indicated that the corresponding APCS and PMF-factor showed similar spatial variability. Based on PMF and robust geostatistics, three sources of 10 heavy metals in Guangrao were determined. As, Co, Cr, Cu, Mn, Ni, Zn, and partially Hg, Pb, Cd originated from natural source. The factor grouping these heavy metals showed consistent distribution with parent material map. 43.1% of Hg and 13.2% of Pb were related to atmosphere deposition of human inputs, with high values of their association patterns being located around urban areas. 29.6% concentration of Cd was associated with agricultural practice, and the hotspot coincided with the spatial distribution of vegetable-producing soils. Overall, natural source, atmosphere deposition of human emissions, and agricultural practices, explained 81.1%, 7.3%, and 11.6% of the total of 10 heavy metals concentrations, respectively. Receptor models coupled with robust geostatistics could successfully estimate the source apportionment of heavy metals in soils.

Eliminating redundant spatial variation to better understand the variance of interest of soil potentially toxic elements at different sampling scales in different soil types south of Nanjing, China

Sampling scale and prediction of spatial distribution are essential in surveys of soil metal pollution. Sufficient sampling density encompassing the principal spatial sources of variance and prediction of polluted areas with the help of soil maps makes pollution evaluation more reliable and subsequent soil remediation assessment more efficient. Two soil sampling schemes, using 232 points at 2-km intervals in 2002 for sampling at county scale and 109 points at 200-1000-m intervals in 2012 at town scale, were used to study the potentially toxic metals Cu, Cd, Cr, Hg, Ni, Pb, Zn, and the metalloid As in an urban-rural hinge area. We focused on finding characteristics of the explanatory power of soil type toward different sampling scales from 200 to 2000 m, a routine sampling scale in practice for remediation of soil potentially toxic elements (PTEs). We also attempted to eliminate the redundant spatial variation to better understand the variance of soil PTEs. Spatial variation of PTEs at different scales was compared and estimated using soil map units based on geostatistical methods. The explanatory power of the soil map units selected at different scales was significantly different at P < 0.01 and the smaller scales better explained the spatial variance. Anthropic activities profoundly affected the contents of PTEs in soils and the amounts of anthropogenic pollutants released often exceed the contribution from natural sources. Variances of interest of Cr and Cu were underestimated by 72.4 and 32.8%, respectively, due to soil type as a factor but were overestimated for other elements by percentages following the sequence Zn (45.4%) > Hg (28.6%) > Pb (28.8%) > Ni (26.73%) > As (13.7%) > Cd (10.5%). Eliminating variances of zero interest would be helpful in increasing the effectiveness of remediation of metal-contaminated soils.

Pollution assessment of heavy metal accumulation in the farmland soils of Beijing's suburbs

In this study, we used GIS to assess heavy metal concentrations and their spatial variations in Beijing's suburban farmlands. The Beijing soil heavy metal background values (background values), the primary standard of the Chinese Environmental Quality Standard of Soil (primary standard), and the Environmental Quality Standard of Green Food Production Site (green standard) were used as the standards for assessing soil heavy metals. Results showed that the average Hg value was higher than that of the primary standard. The Ni and Pb values were lower than the corresponding background values. From the percentage area, the concentration is 100% area with As, more than 93% area with Cr, Cu, Pb and Zn, and 67% area with Cd and Hg, meeting the primary standard; over 97% area met the green standard. The calculated Nemerow index showed that less than 3% of the samples were slightly polluted, indicating good environmental quality. But the accumulated pollution of Cd and Hg was relatively high in some areas which was mainly affected by the high density of the industry. The farmland soils in Beijing suburbs were found to be suitable for farming, but a small percentage of the soils exceeded the green standard; Cd and Hg levels require special attention.

Source identification and spatial distribution of metals in soils in a typical area of the lower Yellow River, eastern China

In this study, 234 soil samples were recently collected from Gaoqing County (a typical area of the lower Yellow River) to determine the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. Multivariate statistical analyses such as correlation analysis, principal components analysis, and one-way ANOVA were applied to identify the source of metals in the soil. Geostatistical methods were used to analyze the spatial structure and distribution of the metals. The results indicated that the mean contents of all metals exceeded the background value of the lower Yellow River, especially for As, Cu, and Hg (1.23, 1.20, and 1.29 times that of the BV, respectively), indicating that these metals were enriched in the study area to different degrees. The results derived from multivariate analysis suggested that As, Cd, Cr, Cu, Ni, Pb, and Zn were mainly controlled by the combination of human activities and soil parent material, and the human activities included industrial emissions, traffic emissions, and agricultural practices. In addition, Hg mainly originated from anthropogenic inputs, such as textile printing, plastics processing, and petrochemical engineering. The contents of metals in different types of land use and parent materials are clearly different. The mean content for eight elements in urban construction land was significantly higher than that of the other land use types; in addition to Hg, the mean content of the other elements was the highest in the lacustrine deposit. The elements of As, Cd, Cr, Cu, Ni, Pb, and Zn had similar hotspots in the urban area, indicating the significant human influence. In addition, these seven metals showed high values in the southeast lacustrine deposit area. The high-value areas of Hg were concentrated in the southwest and northeast study area, which were consistent with the spatial pattern of the industrial sites.

Spatial distribution and sources of heavy metals in natural pasture soil around copper-molybdenum mine in Northeast China

The characterization of the content and source of heavy metals are essential to assess the potential threat of metals to human health. The present study collected 140 topsoil samples around a Cu-Mo mine (Wunugetushan, China) and investigated the concentrations and spatial distribution pattern of Cr, Ni, Zn, Cu, Mo and Cd in soil using multivariate and geostatistical analytical methods. Results indicated that the average concentrations of six heavy metals, especially Cu and Mo, were obviously higher than the local background values. Correlation analysis and principal component analysis divided these metals into three groups, including Cr and Ni, Cu and Mo, Zn and Cd. Meanwhile, the spatial distribution maps of heavy metals indicated that Cr and Ni in soil were no notable anthropogenic inputs and mainly controlled by natural factors because their spatial maps exhibited non-point source contamination. The concentrations of Cu and Mo gradually decreased with distance away from the mine area, suggesting that human mining activities may be crucial in the spreading of contaminants. Soil contamination of Zn were associated with livestock manure produced from grazing. In addition, the environmental risk of heavy metal pollution was assessed by geo-accumulation index. All the results revealed that the spatial distribution of heavy metals in soil were in agreement with the local human activities. Investigating and identifying the origin of heavy metals in pasture soil will lay the foundation for taking effective measures to preserve soil from the long-term accumulation of heavy metals.

Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy

Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R ²) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD₁) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal spectra for Zn, while PCR equation employed RD₁ values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area.

Multivariate statistical and lead isotopic analyses approach to identify heavy metal sources in topsoil from the industrial zone of Beijing Capital Iron and Steel Factory

Heavy metals are considered toxic to humans and ecosystems. In the present study, heavy metal concentration in soil was investigated using the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the geoaccumulation index (Igeo) to determine metal accumulation and its pollution status at the abandoned site of the Capital Iron and Steel Factory in Beijing and its surrounding area. Multivariate statistical (principal component analysis and correlation analysis), geostatistical analysis (ArcGIS tool), combined with stable Pb isotopic ratios, were applied to explore the characteristics of heavy metal pollution and the possible sources of pollutants. The results indicated that heavy metal elements show different degrees of accumulation in the study area, the observed trend of the enrichment factors, and the geoaccumulation index was Hg > Cd > Zn > Cr > Pb > Cu ≈ As > Ni. Hg, Cd, Zn, and Cr were the dominant elements that influenced soil quality in the study area. The Nemerow index method indicated that all of the heavy metals caused serious pollution except Ni. Multivariate statistical analysis indicated that Cd, Zn, Cu, and Pb show obvious correlation and have higher loads on the same principal component, suggesting that they had the same sources, which are related to industrial activities and vehicle emissions. The spatial distribution maps based on ordinary kriging showed that high concentrations of heavy metals were located in the local factory area and in the southeast-northwest part of the study region, corresponding with the predominant wind directions. Analyses of lead isotopes confirmed that Pb in the study soils is predominantly derived from three Pb sources: dust generated during steel production, coal combustion, and the natural background. Moreover, the ternary mixture model based on lead isotope analysis indicates that lead in the study soils originates mainly from anthropogenic sources, which contribute much more than the natural sources. Our study could not only reveal the overall situation of heavy metal contamination, but also identify the specific pollution sources.

Source identification and spatial distribution of heavy metals in tobacco-growing soils in Shandong province of China with multivariate and geostatistical analysis

Samples of surface soil from tobacco (Nicotiana tabacum L.) fields were analysed for heavy metals and showed the following concentrations (mean of 246 samples, mg/kg): As, 5.10; Cd, 0.11; Cr, 49.49; Cu, 14.72; Hg, 0.08; Ni, 19.28; Pb. 20.20 and Zn, 30.76. The values of the index of geoaccumulation (I geo) and of the enrichment factor indicated modest enrichment with As, Cd, Cr, Hg, Ni or Pb. Principal component analysis and cluster analysis correctly allocated each investigated element to its source, whether anthropogenic or natural. The results were consistent with estimated inputs of heavy metals from fertilizers, irrigation water and atmospheric deposition. The variation in the concentrations of As, Cd, Cu, Pb and Zn in the soil was mainly due to long-term agricultural practises, and that of Cr and Ni was mainly due to the soil parent material, whereas the source of Hg was industrial activity, which ultimately led to atmospheric deposition. Atmospheric deposition was the main exogenous source of heavy metals, and fertilizers also played an important role in the accumulation of these elements in soil. Identifying the sources of heavy metals in agricultural soils can serve as a basis for appropriate action to control and reduce the addition of heavy metals to cultivated soils.

Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter

Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination.

Heavy metals in soils from a typical county in Shanxi Province, China: Levels, sources and spatial distribution

The concentrations of As, Cd, Cr, Cu, Pb, Ni, Zn, and Hg in 128 surface soil samples from Xiangfen County, Shanxi Province, China were measured. The concentrations of these eight heavy metals were lower than the critical values in the national soil quality standard. However, these concentrations were found to be slightly higher than their background values in soils in Shanxi Province, indicating enrichment of these metals in soils in Xiangfen County, especially for Hg and Cd. Principal component analysis coupled with cluster analysis was used to analyze the data and identify possible sources of these heavy metals; the results showed that the eight heavy metals in soils from Xiangfen County came from three different sources. Lead, Cd, Cu and Zn mainly arose from agricultural practices and vehicle emissions. Arsenic and Ni arose mainly from parent materials. Industrial practices were the main sources of Cr and Hg. The spatial distribution of the heavy metals varied greatly, and was closely correlated to local anthropogenic activities. This study will be helpful not only for improving local soil environmental quality but will also provide a basis for effectively targeting policies to protect soils from long-term heavy metal accumulation.

Distinguishing anthropogenic and natural sources of trace elements in soils undergoing recent 10-year rapid urbanization: a case of Donggang, Eastern China

A total of 59 samples consisting of 45 topsoils samples and 14 subsoils samples were collected from urban soils of Donggang and were analyzed for soil properties and 12 trace elements. The mean contents of As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Se, V, and Zn in topsoils were 5.76, 0.20, 9.88, 44.82, 17.69, 0.05, 578.96, 19.98, 28.38, 0.21, 58.88, and 58.32 mg kg(-1), respectively. The mean enrichment factor results suggested that Hg, Cd, Pb, Cu, Se, and Zn were enriched in topsoils compared with subsoils. Spatial distribution maps of trace elements indicated that Hg, Cd, Pb, Cu, Se, and Zn had similar patterns, with the highest values in the industrial region. There were no significant associations displayed between spatial distributions of As, Co, Cr, Mn, Ni, and V and the industrial region. Through correlation analysis, stepwise regression analysis, and redundancy analysis, three main sources of 12 trace elements were identified. Cd, Hg, and Se originated from industrial emissions and coal combustion, and As, Co, Cr, Mn, Ni, and V had a lithogenic origin. The combination of human activities and natural sources contributed to the contents of Cu, Pb, and Zn, and the human activities included industrial and traffic emissions.

Spatial distribution of lead in Sacramento, California, USA

Chronic exposure to lead remains a health concern in many urban areas; Sacramento, California is one example, with state surveillance data showing nearly 3% of screened children reported with blood lead levels over 4.5 μg/dL in 2009. To investigate the environmental exposure, 91 soil samples were collected and analyzed by ICP-AES and ICP-MS for 14 elements. An additional 28 samples were collected from areas of focus and analyzed by hand-held X-ray fluorescence spectrometry for Pb and Zn. Analysis of the metals data revealed non-normal distributions and positive skewness, consistent with anthropogenic input. In addition, high correlation coefficients (≥0.75) of metal concentrations in Cd-Pb, Cd-Zn, Pb-Zn, and Sb-Sn pairs suggest similarities in the input mechanisms. Semivariograms generated from Pb and associated metals reveal these metals to exhibit spatial correlation. A prediction map of lead concentrations in soil was generated by ordinary kriging, showing elevated concentrations in soil located in the central, older area of Sacramento where historic traffic density and industrial activity have been historically concentrated. XRF analysis of Pb and Zn from additional samples verifies elevated concentrations in the central areas of Sacramento as predicted.

Impact of 70 years urban growth associated with heavy metal pollution

Historical trends in trace element deposition were analyzed using herbaria specimens. We determined Al, Fe, Mg, Mn, Ca, Na, P, K, S, As, Cd, Cr, Cu, Ni, Pb and Zn contents in leaves of eight specimens collected in 1941. To assess changes, we collected the same plants from a botanical garden in 2012. The concentrations of major elements showed large species variability. However, temporal trends were predominately detected for heavy metals. The Cd, Ni and Cr contents in the 2012 leaves were 10, 13 and 16 times higher, respectively, than in 1941. Urban activities have substantially raised the levels of these metals in urban atmospheres due to changes in human activities over 70 years of urban growth. Nevertheless, Pb has decreased (126%) in recent decades thanks to controlled lead fuel combustion. In short, metal deposition trend to increase Cr, Ni and Cd levels.