scholarly journals Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network

2021 ◽  
Vol 13 (14) ◽  
pp. 2718
Author(s):  
Xitong Xu ◽  
Shengbo Chen ◽  
Liguo Ren ◽  
Cheng Han ◽  
Donglin Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Mathematical Models ◽  
Fractional Order ◽  
Agricultural Soils ◽  
Nir Spectroscopy ◽  
Generalized Regression Neural Network ◽  
Fractional Order Derivative ◽  
Band Combination

With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils.

scholarly journals ESTIMATION OF SOIL HEAVY METAL COMBINING FRACTIONAL ORDER DERIVATIVE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1439-1444
Author(s):  
L. Chen ◽  
K. Tan
Keyword(s):  
Heavy Metal ◽  
Fractional Order ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Order Derivative ◽  
Least Squares Regression ◽  
Estimation Model ◽  
Fractional Order Derivative ◽  
Soil Heavy Metal ◽  
Xuzhou City

Abstract. It is important for the sustainable development of soil and monitoring the soil quality to obtain the heavy metal contents. Visible and near-infrared (Vis–NIR) spectroscopy provides an alternative method for soil heavy metal estimation. A total of 80 soil samples collected in Xuzhou city of China were utilized as data sets for calibration and validation to establish the relationship between the soil reflectance and soil heavy metal content. To amplify the weak spectral characteristic, improve the estimation ability, and explore the characteristic band regions, the preprocessing method of fractional order derivative (FOD) (intervals of 0.25, range of 0–2) and the wavebands selection method of interval partial least squares regression (IPLS) are introduced in this paper. Combining these two methods, for Chromium (Cr), the best estimation model yields Rp2 and RMSRp values of 0.97 and 2.20, respectively, when fractional order is 0.5. This paper explores the potential that FOD conducts the most appropriate order to preprocess spectra and IPLS selects the feature band regions in estimating soil heavy metal of Cr. The results show that FOD and IPLS can strengthen the soil information and improve the accuracy and stability of soil heavy metal estimation effectively.

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

2002 ◽  
Vol 11 (4) ◽  
pp. 285-300 ◽  
Author(s):  
V. MÄNTYLAHTI ◽  
P. LAAKSO
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Agricultural Soils ◽  
Mineral Soil ◽  
Soil Samples ◽  
Potential Hazard ◽  
Metal Concentrations ◽  
Limit Values ◽  
Heavy Metal Concentrations ◽  
Target Values

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.;

scholarly journals Diversity of Arbuscular Mycorrhizal Fungus Populations in Heavy-Metal-Contaminated Soils

1999 ◽  
Vol 65 (2) ◽  
pp. 718-723 ◽  
Author(s):  
C. Del Val ◽  
J. M. Barea ◽  
C. Azcón-Aguilar
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Contaminated Soils ◽  
Agricultural Soils ◽  
Mycorrhizal Fungus ◽  
Arbuscular Mycorrhizal ◽  
Am Fungi ◽  
Wiener Index ◽  
Life Cycles ◽  
The European Union

ABSTRACT High concentrations of heavy metals have been shown to adversely affect the size, diversity, and activity of microbial populations in soil. The aim of this work was to determine how the diversity of arbuscular mycorrhizal (AM) fungi is affected by the addition of sewage-amended sludge containing heavy metals in a long-term experiment. Due to the reduced number of indigenous AM fungal (AMF) propagules in the experimental soils, several host plants with different life cycles were used to multiply indigenous fungi. Six AMF ecotypes were found in the experimental soils, showing consistent differences with regard to their tolerance to the presence of heavy metals. AMF ecotypes ranged from very sensitive to the presence of metals to relatively tolerant to high rates of heavy metals in soil. Total AMF spore numbers decreased with increasing amounts of heavy metals in the soil. However, species richness and diversity as measured by the Shannon-Wiener index increased in soils receiving intermediate rates of sludge contamination but decreased in soils receiving the highest rate of heavy-metal-contaminated sludge. Relative densities of most AMF species were also significantly influenced by soil treatments. Host plant species exerted a selective influence on AMF population size and diversity. We conclude based on the results of this study that size and diversity of AMF populations were modified in metal-polluted soils, even in those with metal concentrations that were below the upper limits accepted by the European Union for agricultural soils.

scholarly journals Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

2019 ◽  
Vol 11 (2) ◽  
pp. 419 ◽  
Author(s):  
Piao Liu ◽  
Zhenhua Liu ◽  
Yueming Hu ◽  
Zhou Shi ◽  
Yuchun Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Estimation Accuracy ◽  
Soil Heavy Metals ◽  
Soil Heavy Metal ◽  
Metal Contents

Soil heavy metals affect human life and the environment, and thus, it is very necessary to monitor their contents. Substantial research has been conducted to estimate and map soil heavy metals in large areas using hyperspectral data and machine learning methods (such as neural network), however, lower estimation accuracy is often obtained. In order to improve the estimation accuracy, in this study, a back propagation neural network (BPNN) was combined with the particle swarm optimization (PSO), which led to an integrated PSO-BPNN method used to estimate the contents of soil heavy metals: Cd, Hg, and As. This study was conducted in Guangdong, China, based on the soil heavy metal contents and hyperspectral data collected from 90 soil samples. The prediction accuracies from BPNN and PSO-BPNN were compared using field observations. The results showed that, 1) the sample averages of Cd, Hg, and As were 0.174 mg/kg, 0.132 mg/kg, and 9.761 mg/kg, respectively, with the corresponding maximum values of 0.570 mg/kg, 0.310 mg/kg, and 68.600 mg/kg being higher than the environment baseline values; 2) the transformed and combined spectral variables had higher correlations with the contents of the soil heavy metals than the original spectral data; 3) PSO-BPNN significantly improved the estimation accuracy of the soil heavy metal contents, with the decrease in the mean relative error (MRE) and relative root mean square error (RRMSE) by 68% to 71%, and 64% to 67%, respectively. This indicated that the PSO-BPNN provided great potential to estimate the soil heavy metal contents; and 4) with the PSO-BPNN, the Cd content could also be mapped using HuanJing-1A Hyperspectral Imager (HSI) data with a RRMSE value of 36%, implying that the PSO-BPNN method could be utilized to map the heavy metal content in soil, using both field spectral data and hyperspectral imagery for the large area.

Quantification of heavy metals in agricultural soils: the influence of sieving in standard analytical methods

2020 ◽  
Author(s):  
Anne Karine Boulet ◽  
Adelcia Veiga ◽  
Carla Ferreira ◽  
António Ferreira
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Soil Sample ◽  
Analytical Methods ◽  
Agricultural Soils ◽  
Soil Samples ◽  
Metal Concentrations ◽  
Aqua Regia ◽  
Heavy Metal Concentrations ◽  
Pre Treatment

<p>Conservation of agriculture soils is a topic of major concern, namely through the increase of soil organic matter. SoilCare project (https://www.soilcare-project.eu/) aims to enhance the quality of agricultural soils in Europe, through the implementation and testing of Soil Improving Cropping Systems in 16 study sites. In Portugal, the application of urban sewage sludge amendments in agriculture soils has been investigated. However, this application is a sensitive topic, due to the risk of long term accumulation of heavy metals and consequent contamination of the soil. The recent Portuguese legislation (Decret-Law 103/2015) is more restrictive than the precedent one (Decret-Law 276/2009) in terms of maximum concentrations of heavy metals in agricultural soils. The analytical quantification of heavy metals, however, raises some methodological questions associated with soil sample pre-treatment, due to some imprecisions in standard analytical methods. For example, the ISO 11466 regarding the extraction in Aqua Regia provides two pre-treatment options: (i) sieve the soil sample with a 2 mm mesh (but if mass for analyses is <2g, mill and sieve the sample <250µm is required), or (ii) mill and sieve the soil sample through a 150µm mesh. On the other hand, the EN 13650 requests soil samples to be sieved at 500µm. Since heavy metals in the soil are usually associated with finer particles, the mesh size used during the pre-treatment of soil samples may affect their quantification.</p><p>This study aims to assess the impact of soil particle size on total heavy metal concentrations in the soil. Soil samples were collected at 0-30cm depth in an agricultural field with sandy loam texture, fertilized with urban sludge amendment for 3 years. These samples were then divided in four subsamples and sieved with 2mm, 500µm, 250µm and 106µm meshes (soil aggregates were broken softly but soil wasn’t milled). Finer and coarser fractions were weighted and analyzed separately. Heavy metals were extracted with Aqua Regia method, using a mass for analyze of 3g, and quantified by atomic absorption spectrophotometer with graphite furnace (Cd) and flame (Cu, Ni, Pb, Zn and Cr).</p><p>Except for Cu, heavy metals concentrations increase linearly with the decline of the coarser fraction. This means that analyzing heavy metals content only in the finest fractions of the soil leads to an over estimation of their concentrations in the total soil. Results also show that coarser fractions of soil comprise lower, but not negligible, concentrations of heavy metals. Calculating heavy metal concentrations in the soil based on the weighted average of both fine and coarse fractions and associated concentrations, provide similar results to those driven by the analyses of heavy metals in the <2mm fraction. This indicates that milling and analyzing finer fractions of the soil did not influence the quantification of heavy metals in total soil. Clearer indications on analytical procedures should be provided in analytical standards, in order to properly assess heavy metal concentrations and compare the results with soil quality standards legislated.  </p>

scholarly journals Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas

2021 ◽  
Author(s):  
Jihong Dong ◽  
Wenting Dai ◽  
Jiren Xu ◽  
Songnian Li
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spectral Estimation ◽  
Small Samples ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Estimation Model ◽  
Svm Model ◽  
Study Results ◽  
Mlr Model

The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R2 of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R2 between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R2 value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R2 and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible.

scholarly journals Communities of oribatid mites and heavy metal accumulation in oribatid species in agricultural soils in Egypt impacted by waste water

2010 ◽  
Vol 46 (No. 4) ◽  
pp. 159-170 ◽  
Author(s):  
H.M. El-Sharabasy ◽  
A. Ibrahim
Keyword(s):  
Waste Water ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Agricultural Land ◽  
Heavy Metal Contamination ◽  
Agricultural Soils ◽  
Diversity Index ◽  
Heavy Metal Accumulation ◽  
Oribatid Mites ◽  
The Body

The continued use of waste water for irrigation of agricultural fields in Egypt may lead to accumulation of heavy metals in soils and adverse effects on soil-living communities. We investigated responses of oribatid communities to heavy metal contamination in mango plantations irrigated by the Ismailia canal in the Suez region. Mean concentrations of heavy metals determined in irrigation water were considerably above the recommended levels. Concentrations of metals in agricultural soil were however below the permissible levels. A comparison with concentrations of a typical uncontaminated soil in this area revealed that the Ismailia water canal used for irrigation of agricultural land has elevated levels of heavy metals. The results of our ecological survey showed that the abundance and structure of the soil oribatid communities were not influenced by levels of heavy metals in the soil. We also showed that the diversity index can be a valuable tool for assessing the possible impact of pollutants on different species of oribatid mites. The oribatid species appeared to be accumulating different amounts of heavy metals when characterised by their bioconcentration factors. Most species were poor zinc accumulators. The accumulation of heavy metals in the body of oribatids was not strictly determined by their body size or by the trophic level. In conclusion, our study showed that mango plantations impacted by waste water from the Ismailia canal are accumulating heavy metals in their soils above the background concentrations, but ecological effects on soil-living communities are not apparent yet.

scholarly journals Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Pingguo Yang ◽  
Miao Yang ◽  
Renzhao Mao ◽  
Hongbo Shao
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Agricultural Soils ◽  
Pollution Index ◽  
Northern China ◽  
Second Grade ◽  
Multivariate Statistical ◽  
Soil Heavy Metal ◽  
Average Value ◽  
Relative Safety

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area.

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

2021 ◽  
Vol 13 (9) ◽  
pp. 5272
Author(s):  
Yanhong Li ◽  
Huifen Kuang ◽  
Chunhua Hu ◽  
Gang Ge
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Agricultural Soils ◽  
Poyang Lake ◽  
Pollution Sources ◽  
Average Content ◽  
Natural Background ◽  
Poyang Lake Region ◽  
Lake Region

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.

Sign in / Sign up

Export Citation Format

Share Document