Metal Concentrations In Agricultural Soils Research Articles

Estimating of heavy metal concentration in agricultural soils from hyperspectral satellite sensor imagery: Considering the sources and migration pathways of pollutants

The evolution of hyperspectral remote sensing and artificial intelligence technologies has led to a surge in their application for predicting Soil Heavy Metal Concentrations (SHMC). Nevertheless, the preponderance of existing research within this sphere centers around data procured from ground-based and airborne hyperspectral sources. Studies that employ satellite-based methodologies typically rely on medium spatial resolution hyperspectral or multispectral satellite data. The application of high spatial and spectral resolution satellite data, such as that obtained from GaoFen-5 (GF-5), remains conspicuously underexplored. Furthermore, the impact of geographical environmental factors (GEFs) on the accuracy of predictions has been infrequently considered. In the context of this backdrop, the present study introduces stacking models designed to estimate SHMC. This approach integrates reflectance spectral features (SFs) derived from GF-5 hyperspectral imagery and GEFs, including topography and pollution sources. The results demonstrate a notable improvement in the predictive accuracy of SHMC using our Stacking model, as compared to single models. The incorporation of GEFs into the method results in a varying degree of reduction in the Root Mean Square Error (RMSE), along with an enhancement in the R2 on the training set. The predictive performance improvement is most prominent for Cd and As, with the RMSE decreasing by 52% and 48%, respectively. Notably, apart from Pb, there is an improvement in performance for all elements within the test set. This study confirms the effectiveness of integrating GEFs into SHMC prediction models to enhance accuracy. Applying this technique to predict soil pollution at a regional scale and to demarcate heavily polluted areas can yield satisfactory results. In the future, we plan to apply this technique to other research areas or datasets to expand its universality. Furthermore, we aim to delve more deeply into the potential of GEFs to enhance the predictive capacity of SHMC.

Spatial and Temporal Distribution and Source Analysis of Heavy Metals in Agricultural Soils of Ningxia, Northwest of China

The spatial and temporal dynamic monitoring of the heavy metal concentration in agricultural soils can help us to understand the extent and changes of regional soil heavy metal pollution, allowing us safeguard food safety and human health and provide basic data for the prevention and control of heavy metal pollution in agricultural soils. The heavy metals’ concentrations, including Cd, Cr, Cu, Hg, Ni, Pb, Zn, and As, were measured in surface (0–20 cm) soil samples collected in 2017 and 2021 from agriculture land to evaluate their pollution levels in the Ningxia Hui Autonomous Region (Hereinafter referred to as Ningxia), northwest of China. A correlation analysis, principal component analysis, and positive matrix factorization were used jointly to identify possible sources. The results showed that, for most soils, the concentrations of As, Cr, Cu, Ni, Pb, and Zn were lower than the contamination thresholds stipulated by the national guideline. The average concentrations of As, Cd, Zn, and Ni in 2021 were significantly lower than the levels in 2017 (p < 0.05), but the average concentration of Cu was significantly higher in 2021 than in 2017 (p < 0.05), and the average concentrations of Cr, Pb, and Hg did not change significantly during the last five years. According to the pollution assessment index (mean Igeo values), Cd and Hg were found to accumulate in the farmland environment and were the major pollutants in the region, and the hotspots with high concentrations of heavy metals were mainly located in the Yellow River irrigation area in Northern Ningxia. Four main sources of heavy metals in the soils were identified: As mainly originated from natural sources; Pb, Cd, and Ni from atmospheric deposition and industrial activities; Cr, Zn, and Cu from agricultural activities; and Hg from the coal-related industrial activities. The concentration of heavy metals from anthropogenic sources accounted for 77.10% in agricultural soils, indicating the strong influence of this source on soil heavy metal accumulation. These findings provide scientific evidence and valuable information to prevent heavy metal contamination and control of farmland.

Heavy Metals Pollution of Agricultural Soils near Industrial Complexes in Jeon-Nam Regions of Korea

Monitoring heavy metal concentrations in agricultural soils near industrial area is very important due to food safety. The aim of this study was to assess the heavy metal concentrations, pollution index (PI), and pollution load index (PLI) in agricultural soils close to industrial complexes in Jeon-Nam province. Fifteen industrial complexes in Jeon-Nam province were selected (Yeongam, Muan, Yeonggwang, Hampyeong, Gangjin, Wando, Haenam, Jindo, Jangseong, Damyang, Boseong, Goheung, Hwasun, Gokseong, and Gurye). Five agricultural fields each were selected at each industrial complex within 500 meter and 1,000 meter radius at each industrial complexe boundaries, respectively. Two depth of soil samples (0 - 15 cm depth for top soil and 15 - 30 cm depth for sub soil) were collected in spring of 2020 before planting. Soil samples were analyzed for seven heavy metals, including arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn). Heavy metal concentrations for both top and sub soil ranged from 1.7 to 8.6 mg kg-1 for As, from 0.3 to 0.9 mg kg-1 for Cd, from 13.3 to 67.8 mg kg-1 for Cr, from 8.9 to 20.7 mg kg-1 for Cu, from 7.0 to 28.1 mg kg-1 for Ni, from 7.0 to 31.8 mg kg-1 for Pb, from 62.6 to 143.9 mg kg-1 for Zn. Heavy metal concentrations in studied soils were not higher than the levels of Soil Contamination Warning Standard (SCWS). Pollution index (PI) and pollution load index (PLI) values of all seven heavy metals including As, Cd, Cr, Cu, Ni, Pb, and Zn ranged from 0.1 to 0.7 for PI and from 0.14 to 0.30 for PLI. Both values were less than 1. Therefore, these soils are classified uncontaminated for PI and unpolluted for PLI. Overall, all soil samples did not exceed the level limits of Soil Environment Conservation Law (SECL).Descriptive statistics of heavy metals concentration in agricultural soils near the industrial complexes in Jeon-Nam province in Korea. Element† As (mg kg-1) Cd (mg kg-1) Cr (mg kg-1) Cu (mg kg-1) Ni (mg kg-1) Pb (mg kg-1) Zn (mg kg-1) Soil depth Top Sub Top Sub Top Sub Top Sub Top Sub Top Sub Top Sub Average 4.5 4.8 0.6 0.6 27.7 27.7 13.9 13.6 12.8 13.1 13.3 13.7 96.9 95.2 Min. 0.0 0.6 0.1 0.2 1.7 2.3 1.5 2.2 1.2 1.5 0.0 2.9 32.6 33.9 Max. 13.8 16.9 1.3 1.3 128.8 116.1 33.9 40.0 52.6 46.1 65.9 82.5 206.8 187.0 Stdev. 2.4 2.7 0.2 0.2 17.4 16.5 6.3 6.2 7.2 6.9 8.3 9.5 34.8 33.6 C.V. (%) 52.9 56.9 36.2 38.5 62.9 59.4 45.3 45.9 56.0 53.0 62.3 69.4 35.9 35.3 Kurtosis 1.6 3.9 0.9 0.8 9.1 6.8 0.7 2.4 8.2 5.3 13.7 22.5 0.4 0.2 Skweness 1.0 1.4 0.7 0.8 2.3 2.0 0.9 1.2 2.2 1.8 3.0 3.9 0.8 0.8 SCWS limit 25 4 n/a 150 100 200 300 Number of above SCWS 0 0 0 0 0 0 0 †Min., minimum; Max., maximum; Stdev., standard deviation; C.V., coefficients variations. Soil Contamination Warning Standard (SCWS) from Ministry of Environment (MOE) in Korea.

Evaluating Pollution Indexes using Heavy Metal Concentrations in Agricultural Soils around Industrial Complexes in the Jeon-Nam Regions of Korea

Evaluating Pollution Indexes using Heavy Metal Concentrations in Agricultural Soils around Industrial Complexes in the Jeon-Nam Regions of Korea

Heavy metal concentration in the agricultural soils under the different climatic regions: a case study of Iran

Contamination of agricultural soils with heavy metals is a growing global concern with an increase in anthropogenic activities. Despite local studies conducted, no accurate and comprehensive results about agricultural soils of Iran entirely and regarding the climate have been observed regarding heavy metal concentration. Therefore, the present study is aimed at determining a reliable heavy metal concentration in agricultural soils and evaluate the effect of climate on the average concentrations. To provide harmonized and comparable data, 711 soil surface samples were randomly taken from all of the agricultural soils of Iran during 2016–2018. Also, to evaluate the effect of climate, samples were divided into three parts according to major climates of studied area. Results revealed that the average concentration of Cd, Cr, Co, Cu, Mn, Ni, Pb, Zn, and V in agricultural soils was 0.31, 101, 27.9, 37.2, 702, 68.0, 28.2, 73.0, and 101 mg/kg, respectively. The concentration of Cr, Ni, Co, V, and Pb in, respectively, 28.4, 11.3, 8.72, 1.60, and 1.34% of soil samples was higher than the national standard in contrast to the concentration of the other element, which was lower. There were no significant differences between the average concentrations of heavy metals in agricultural soils with respect of climate, indicating the minimum effects of soil forming factors on soil heavy metals. Comparing the mean concentration of heavy metals in three major climatic zones of the country, it seems that geochemistry is the key factor controlling the concentration of metals in agricultural soils of Iran.

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.

Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data.

Heavy metals in the agricultural soils of reclaimed mining areas can contaminate food and endanger human health. The objective of this study is to effectively estimate the concentrations of heavy metals, such as zinc, chromium, arsenic, and lead, using hyperspectral sensor data and the random forest (RF) algorithm in the study area of Xuzhou, China. The RF's built-in feature selection ability and modeling expressive ability in heavy metal estimation of soil were explored. After the preprocessing of the spectrum obtained by an ASD (analytical spectral device) field spectrometer, the random forest algorithm was carried out to establish the estimation model based on the correlation-selected features and the full-spectrum features respectively. Results of all the different processes were compared with classical approaches, such as partial least squares (PLS) regression and support vector machine (SVM). In all the experimental results, from the perspective of models, the best estimation model for Zn (R2 = 0.9061; RMSE = 6.5008) is based on the full-spectrum data of continuum removal (CR) pretreatment, and the best models for Cr (R2 = 0.9110; RMSE = 4.5683), As (R2 = 0.9912; RMSE = 0.5327), and Pb (R2 = 0.9756; RMSE = 1.1694) are all derived from the correlation-selected features. And these best models of these heavy metals are all established by the RF method. The experiments in this paper show that random forests can make full use of the input spectral data in the estimation of four kinds of heavy metals, and the obtained models are superior to those established by traditional methods.

Assessment of Zn pollution sources and apportionment in agricultural soils impacted by a Zn smelter in South Korea.

This study was conducted to assess the anthropogenic impact on the metal concentration in agricultural soils in the vicinity of a Zn smelter. We determined Pb isotope ratios to trace source of metals and calculate source apportionment. 19.7% of the agricultural soil samples had Zn concentrations exceeding the Korean worrisome level (WL). The isotopic variation in the contaminated agricultural soils reflected the input of contaminants derived from the Zn smelter. The spatial distribution of Zn concentrations and Pb isotopes suggested that the Zn smelter dust fallout was the major source of heavy metal pollution in the agricultural soils. Lead isotope compositions of soil horizon I was comparable to those of soil horizon II, indicating that smelter-origin Zn had migrated vertically. Binary mixing between Zn concentrates and background soils could explain the variations in Pb isotope ratios in the contaminated agricultural soils. Source apportionment calculations showed that for agricultural soils that were found to exceed the Korean countermeasure standard (CS), the average proportion of smelter-origin Zn was 45.8%-83.3% while for agricultural soils that exceeded the WL but were below the CS, the average proportion of smelter-origin Zn was 36.3%-68.1%. The remainder was derived from a geogenic source.

Visible near infrared reflectance spectroscopy prediction of soil heavy metal concentrations in paper mill biosolid- and liming by-product-amended agricultural soils

Continuous assessment of soil heavy metal concentrations in agricultural soils is critical for ensuring ecosystem health and minimizing potential adverse effects. However, conventional laboratory determinations are time-consuming, expensive and involve hazardous chemicals. We evaluated the potential of visible near infrared reflectance spectroscopy (VNIRS) to rapidly and simultaneously predict total, Mehlich-3 (M3), DTPA, and water-soluble (H2O) soil heavy metal concentrations. Soil samples were collected from a loam soil (n=64) and a loamy sand (n=32) after repeated annual applications (3–9years) of paper mill biosolids (PB) and liming by-products. Models were developed using modified partial least squares regression on 75% of the total sample set and also for each soil, after removal of the spectral outliers (n=3), with the remainder used for validation. The coefficient of determination in validation (R2V) and the ratio of performance to deviation (RPD) were used to assess model accuracy. Predictions for the total set were excellent for total Zn and Cd, M3-Cu and -Cd, and DTPA-Cd (R2V>0.90, RPD>3.0), good for total Cu, Ni and Mo, M3-Ni, DTPA-Cu and -Ni, and H2O-Cu and -Ni (R2V=0.70 to 0.90, RPD=1.70 to 3.0), but poor for M3-Zn, DTPA-Zn, and H2O-Zn, -Cd and -Mo (R2V<0.70, RPD<1.70). Except for one case, site-specific models were either more accurate or provided similar results with the model of the total set in predicting site-specific concentrations. The relationship with soil organic matter (SOM) was the major predictive mechanism for Cd in our dataset, while the prediction of other soil heavy metals was due to the combined influence of SOM and Fe oxides. We conclude that VNIRS has the potential to predict total, M3, DTPA and to a lesser extent water-soluble soil heavy metals, especially when developed using site-specific samples.

Assessment of Selected Heavy Metal Concentrations in Agricultural Soils around Industrial Complexes in Southwestern Areas of Korea

Agricultural soils near or around industrial complexes can contain a certain amount of heavy metals that readily enter the food chain and negatively affect human health. Therefore, we conducted the study to investigate the distribution of selected heavy metals, including arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), lead (Pb), mercury (Hg), and zinc (Zn), in farm-land soils around fifteen industrial complexes in the southwestern provinces, Korea. The concentrations of heavy metals in the soil samples were determined by the pseudo-total aqua regia (3 HCl : 1 HNO₃) digestion procedure. The heavy metal concentrations in most soils examined did not exceed the levels of Soil Contamination Warning Standard (SCWS) for agricultural lands (Region 1) presented in Soil Environment Conservation Law (SECL) established by Ministry of Environment (MOE), Korea. However, only one sampling site showed higher As amount (27.1 mg kg -1 ) than the SCWS level of As(25 mg kg -1 ). Pollution index (PI) for heavy metals did not exceed 1.0. The PI values were significantly positively correlated (p < 0.01) with the heavy metal concentrations. In particular, the values of correlation coefficient between the Cd and Pb concentrations and the PI values were higher than those estimated from other combinations, and thus the amounts of Cd and Pb in the agricultural soils highly affected the PI values for the heavy metals.

Characteristics of municipal landfill leachate and its impact on surrounding agricultural land

An investigation was carried out to assess the characteristics of leachate and its impact on surrounding agricultural land of the Matuail landfill site, Dhaka, Bangladesh. Leachate samples were collected from active dumping areas; soil and plant samples from three different sites of surrounding agricultural areas. Water quality parameters like pH, DO, BOD, COD, TDS were measured for leachate samples and heavy metals like Cu, Zn, Pb, Cd, Ni were analyzed for leachate, soil and plant samples. The results showed that untreated leachate concentrations of DO, BOD, COD and TDS were 1.34, 96, 1343 and 7120 mg/l, respectively that exceeded inland surface water standard but the concentrations of DO (7.49 mg/l), BOD (10 mg/l) and TDS (790/l) in the treated leachate pond were found within the permissible limits. The leachate samples are not contaminated with heavy metals as these are present below the toxic limits. The heavy metal concentrations in agricultural soils are below the permissible limits except Pb; but in plants the concentrations of Cu, Zn and Pb exceeded the critical limits.Bangladesh J. Sci. Res. 29(1): 31-39, June-2016

Spatial distribution and environmental factors of catchment-scale soil heavy metal contamination in the dry-hot valley of Upper Red River in southwestern China

The severe soil erosion in the dry-hot valley (DHV) in southwestern China may result in heavy metal pollution in the Red River. However, little is known concerning the level of contamination, spatial distribution and environmental controls of the heavy metals in the soils of the DHV. Heavy metal concentrations in agricultural soils in a typical DHV catchment were investigated. Soil samples from both the topsoil (0–20cm) and subsoil (20–40cm) were collected at sixty-two sites, and the soil physical and chemical properties of sand, silt, clay, and organic matter contents; soil pH; bulk density; and the concentrations of Cr, Ni, Cu, Zn, and Pb were determined. Topographic factors of slope gradient, slope aspect, elevation, and topographic wetness index at each site were calculated using DEM. Kriging and canonical correspondence analyses were used to assess the spatial distribution of and environmental controls on heavy metals. The results demonstrated that the concentrations of Cr, Ni, Cu, Zn, and Pb in the DHV were higher than most of the reported values for agricultural soils worldwide and that the DHV may constitute a potential source of heavy metal pollution in the Red River. Considering each heavy metal individually, the vertical distribution in the topsoil and subsoil was similar at all of the sampling sites. High concentrations of Cr, Ni, Zn and Pb were observed at low elevations and low slope areas, and the spatial distribution pattern of Cu was contrary to that of Cr, Ni, Zn and Pb. The concentrations of heavy metals in the topsoil were controlled by both soil and topographical factors, while in the subsoil, they were primarily controlled by soil-related factors. It is concluded that high background concentrations, uncontrolled use of chemical fertilizers, and mining are the sources responsible for heavy metal contamination in the DHV region.

Effects of landscape heterogeneity on the elevated trace metal concentrations in agricultural soils at multiple scales in the Pearl River Delta, South China

Based on multiple geo-accumulation indices and correlation and partial redundancy analyses, we examined the spatial patterns of agricultural soil contaminations for As, Pb, Cd, Cr, and Ni in the Pearl River Delta, South China and their relations with landscape heterogeneity at small, medium and large spatial scales. We found that the concentrations of trace elements were slightly elevated, and most trace metals had a geogenic origin. Landscape variables explained 21–53% of the variation of elevated trace metal concentrations with an increasing explanatory power from the small to the large scale. The three variable groups representing parent materials, distance density characteristics and land use had different contributions to the elevated trace metals among scales. Both the distance density variables and land use pattern had a stronger influences on trace metal concentrations at a small scale than at a larger scale, while the parent materials was important at all the scales.

Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy

In order to monitor the accumulation of heavy metals effectively and avoid the damage to the health of agricultural soils, a promising approach is to predict low concentrations of heavy metals in soils using visible and near-infrared (VNIR) reflectance spectroscopy coupled with calibration techniques. This study aimed to (i) compare the performance of a combination of partial least squares regression with genetic algorithm (GA-PLSR) against a general PLSR for predicting low concentrations of four heavy metals (i.e., As, Pb, Zn and Cu) in agricultural soils; (ii) explore the transferability of GA-PLSR models defined on one subset of land-use types to the other types; and (iii) to investigate the predictive mechanism for the prediction of the metals. One hundred soil samples were collected in the field locating at Yixing in China, and VNIR reflectance (350–2500nm) spectra were measured in a laboratory. With the entire soil samples, GA-PLSR and PLSR models were calibrated for the four heavy metals using a leave-one-out cross-validation procedure. The GA-PLSR models achieved better cross-validated accuracies than the PLSR models. For the transferability of GA-PLSR models, the soil samples were divided into three pairs of training sets and test sets from different land-use types. Three GA-PLSR models defined on the training sets had good transferability to the test sets, but nine GA-PLSR models were not successful. As for the predictive mechanism, besides the widely-used correlation analysis between OM and the metals, the relationship between the content of OM and the prediction accuracy of the metals was investigated and the similarity of the important wavelengths for OM and the metals was compared. The three methods verified that OM had a significant correlation with the predictions of the spectrally-featureless metals (Pb, Zn and Cu) from VNIR reflectance. We conclude that GA-PLSR modeling has a better capability for the prediction of the low heavy metal concentrations from VNIR reflectance, and it has a potential of transferability between different land-use types, and its accuracy is fundamentally influenced by OM.

Long-term assessment of the environmental fate of heavy metals in agricultural soil after cessation of organic waste treatments

The current study examined the anthropogenic accumulation and natural decrease in metal concentrations in agricultural soils following organic waste application. Three common organic wastes, including municipal sewage sludge, alcohol fermentation processing sludge, and pig manure compost (PMC), were applied annually to an agricultural soil under field conditions over 7 years (1994-2000) at a rate of 12.5, 25, and 50 ton ha(-1) year(-1) and the soil accumulation of three metals of concern (Cu, Pb, and Zn) was monitored. Subsequently, organic waste amendments ceased and the experimental plots were managed using conventional fertilization for another 10 years (2001-2010) and the natural decrease in metal concentrations monitored. Although Cu and Zn concentrations in all experimental plots did not exceed the relevant guideline values (150 mg kg(-1) for Cu and 300 mg kg(-1) for Zn), significant increases in metal concentrations were observed from cumulative application of organic wastes over 7 years. For instance, PMC treatment resulted in an increase in Cu and Zn from 9.8 and 72 mg kg(-1) to 108.2 and 214.3 mg kg(-1), respectively. In addition, the natural decrease in Cu and Zn was not significant as soils amended with PMC showed only a 16 and 19 % decline in Cu and Zn concentrations, respectively, even 10 years after amendment ceased. This research suggested that more attention must be paid during production of organic waste-based amendments and at the application stage.

Neutron activation analysis of wheat samples

The deficiency of essential micronutrients and excess of toxic metals in cereals, an important food items for human nutrition, can cause public health risk. Therefore, before their consumption and adoption of soil supplementation, concentrations of essential micronutrients and metals in cereals should be monitored. This study collected soil and two varieties of wheat samples-Triticum aestivum L. (Jordão/bread wheat), and Triticum durum L. (Marialva/durum wheat) from Elvas area, Portugal and analyzed concentrations of As, Cr, Co, Fe, K, Na, Rb and Zn using Instrumental Neutron Activation Analysis (INAA) to focus on the risk of adverse public health issues. The low variability and moderate concentrations of metals in soils indicated a lower significant effect of environmental input on metal concentrations in agricultural soils. The Cr and Fe concentrations in soils that ranged from 93-117 and 26,400-31,300mg/kg, respectively, were relatively high, but Zn concentration was very low (below detection limit <22mg/kg) indicating that soils should be supplemented with Zn during cultivation. The concentrations of metals in roots and straw of both varieties of wheat decreased in the order of K>Fe>Na>Zn>Cr>Rb>As>Co. Concentrations of As, Co and Cr in root, straw and spike of both varieties were higher than the permissible limits with exception of a few samples. The concentrations of Zn in root, straw and spike were relatively low (4-30mg/kg) indicating the deficiency of an essential micronutrient Zn in wheat cultivated in Portugal. The elemental transfer from soil to plant decreases with increasing growth of the plant. The concentrations of various metals in different parts of wheat followed the order: Root>Straw>Spike. A few root, straw and spike samples showed enrichment of metals, but the majority of the samples showed no enrichment. Potassium is enriched in all samples of root, straw and spike for both varieties of wheat. Relatively to the seed used for cultivation, Jordão presented higher transfer coefficients than Marialva, in particular for Co, Fe, and Na. The Jordão and Marialva cultivars accumulated not statistically significant different concentrations of different metals. The advantages of using INAA are the multielementality, low detection limits and use of solid samples (no need of digestion).

Soil organic carbon buffers heavy metal contamination on semiarid soils: Effects of different metal threshold levels on soil microbial activity

Traditionally, three threshold levels have been accepted for heavy metal concentrations in agricultural soils, depending on soil pH. The aim of this work was to ascertain how the three threshold values proposed for Cd (3, 6.5, and 12.5 mg kg −1) and Zn (300, 650, and 1300 mg kg −1) really affect soil microbial activity. Two soils, a scrubland soil and a forest soil, differing widely in their organic C content, were used in this study. Despite the different soil characteristics, the fractions of Cd and Zn extracted with a solution of diethylenetriaminepentaacetic acid (DTPA) showed little difference between soils. Parameters, such as microbial biomass C (C mic), soil basal respiration (BR), adenosine triphosphate (ATP) content, dehydrogenase activity (DHA), urease activity (UA), alkaline phosphatase activity (APA), and β-glucosidase (β-GA), were less affected by heavy metals in the forest soil than in the scrubland soil. In general, the simultaneous addition of both metals had a synergistic effect on microbial activity, and this treatment produced a significant decrease of microbial activity of both soils with respect to control. The highest level (L3) of Cd, Zn and Cd + Zn treatments produced significant decrease of microbial and biochemical parameters in both soils.

Arsenic and Heavy Metal Concentrations in Agricultural Soils Around Tin and Tungsten Mines in the Dai Tu district, N. Vietnam

This study assessed the arsenic and heavy metal contaminations of agricultural soils around the tin and tungsten mining areas in Dai Tu district in northern Vietnam. Among the examined elements, high total contents of As and Cu were found in the agricultural fields at both tin and tungsten mining sites. Although the major part of the accumulated As and Cu were bound by various soil constituents such as Fe and Mn oxides, organic matter, and clay minerals, increases in water soluble As and Cu were observed, especially for the paddy fields. The results suggest that, in the studied area, As and Cu dispersion from their pollution sources into farmlands is mainly via fluvial transportation of mine waste through streams that cross the paddy fields around the tin mining area, and soil erosion at the tea fields located at lower positions of the slope in the tungsten mining area.

Assessment and mapping of environmental quality in agricultural soils of Zhejiang Province, China

Heavy metal concentrations in agricultural soils of Zhejiang Province were monitored to indicate the status of heavy metal contamination and assess environmental quality of agricultural soils. A total of 908 soil samples were collected from 38 counties in Zhejiang Province and eight heavy metal (Cd, Cr, Pb, Hg, Cu, Zn, Ni and As) concentrations had been evaluated in agricultural soil. It was found 775 samples were unpolluted and 133 samples were slightly polluted and more respectively, that is approximately 14.65% agricultural soil samples had the heavy metal concentration above the threshold level in this province by means of Nemerow's synthetical pollution index method according to the second grade of Standards for Soil Environmental Quality of China (GB15618-1995). Contamination of Cd was the highest, followed by Ni, As and Zn were lower correspondingly. Moreover, Inverse Distance Weighted (IDW) interpolation method was used to make an assessment map of soil environmental quality based on the Nemerow's pollution index and the soil environmental quality was categorized into five grades. Moreover, ten indices were calculated as input parameters for principal component analysis (PCA) and the principal components (PCs) were created to compare environmental quality of different soils and regions. The results revealed that environmental quality of tea soils was better than that of paddy soils, vegetable soils and fruit soils. This study indicated that GIS combined with multivariate statistical approaches proved to be effective and powerful tool in the mapping of soil contamination distribution and the assessment of soil environmental quality on provincial scale, which is beneficial to environmental protection and management decision-making by local government.

Arsenic and heavy metal concentrations in agricultural soils in South Savo province

Increasing concentrations of arsenic and heavy metals in agricultural soils are becoming a growing problem in industrialized countries. These harmful elements represent the basis of a range of problems in the food chain, and are a potential hazard for animal and human health. It is therefore important to gauge their absolute and relative concentrations in soils that are used for crop production. In this study the arsenic and heavy metal concentrations in 274 mineral soil samples and 38 organogenic soil samples taken from South Savo province in 2000 were determined using the aqua regia extraction technique. The soil samples were collected from 23 farms.The elements analyzed were arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc. The median concentrations in the mineral soils were:As 2.90 mg kg –1, Cd 0.084 mg kg –1, Cr 17.0 mg kg –1, Cu 13.0 mg kg –1, Hg 0.060 mg kg –1, Ni 5.4 mg kg –1, Pb 7.7 mg kg –1, Zn 36.5 mg kg –1. The corresponding values in the organogenic soils were:As 2.80 mg kg –1, Cd 0.265 mg kg –1, Cr 15.0 mg kg –1, Cu 29.0 mg kg –1, Hg 0.200 mg kg –1, Ni 5.9 mg kg –1, Pb 11.0 mg kg –1, Zn 25.5 mg kg –1. The results indicated that cadmium and mercury concentrations in the mineral and organogenic soils differed. Some of the arsenic, cadmium and mercury concentrations exceeded the normative values but did not exceed limit values. Most of the agricultural fields in South Savo province contained only small amounts of arsenic and heavy metals and could be classified as “Clean Soil”. A draft for the target values of arsenic and heavy metal concentrations in “Clean Soil” is presented.;