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.

scholarly journals Inoculation of paddy soils with Rhodopseudomonas palustris enhanced heavy metal immobilisation

2021 ◽  
Vol 67 (No. 1) ◽  
pp. 55-60
Author(s):  
Xian Xiao ◽  
Yan Zhu ◽  
Yuexiang Gao ◽  
Jing Fu ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Bacillus Subtilis ◽  
Rhodopseudomonas Palustris ◽  
Paddy Soils ◽  
Optimal Dosage ◽  
Soil Heavy Metal ◽  
Rice Grains ◽  
Pot Experiments ◽  
Metal Contents

To investigate the effect of microbial inoculum on soil heavy metal immobilisation, pot experiments were conducted with paddy soils contaminated by cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg), respectively. The results showed that the inoculation of Rhodopseudomonas palustris was more effective in the immobilisation of Pb and Cd in soils than the composite of R. palustris and Bacillus subtilis. Interestingly, a lower dosage of inoculum immobilised significantly more heavy metals than the higher dosage, potentially due to the competition of bacteria with limited nutrients. The heavy metal contents in rice grains also supported this finding, as less Pb and Cd were accumulated under the lower dosage. However, there were limited effects of microbial inoculations on the immobilisation of Hg and As. In general, our study indicated the effectiveness of R. palustris in immobilising Pb and Cd in soils and highlighted the importance of determining the optimal dosage of inoculum in bioremediation.  

scholarly journals Estimation of Soil Heavy Metal Content Using Hyperspectral Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1464 ◽  
Author(s):  
Zhenhua Liu ◽  
Ying Lu ◽  
Yiping Peng ◽  
Li Zhao ◽  
Guangxing Wang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Content ◽  
Regional Scale ◽  
Soil Samples ◽  
Hyperspectral Data ◽  
Heavy Metal Content ◽  
Soil Heavy Metal ◽  
Relationship Of ◽  
The Relationship

Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

Analysis of the Change of Heavy Metal Contents in Farmland in Mengcheng

2012 ◽  
Vol 178-181 ◽  
pp. 773-776
Author(s):  
Guo Wei Xu ◽  
Xue Wu ◽  
Su Ling Huang ◽  
Xin Tian Yuan ◽  
Yang Gao ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Content ◽  
Heavy Metal Content ◽  
National Standard ◽  
Soil Heavy Metal ◽  
Metal Contents ◽  
Survey Results ◽  
Changing Rate

In order to find out the variations of soil heavy metal contents in Mengcheng, the heavy metal of the soil was tested in the same way in 2010, based on the survey results of 2001. The results showed that the contents of the 8 kinds of heavy metal in Mengcheng County were lower than those of the national standard, but the heavy metal content of Mengcheng County in 2010 were significantly higher than those in 2001, especially Pb, and the content of Hg, Ni, As also increased greatly; The increased of changing rate of various heavy metals contents are in the following descending order: Pb> Hg> Ni> As> Cu> Cd> Cr> Zn. The uneven dispersion of various heavy metals element in different sections of Mengcheng County also increased.

scholarly journals Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

2019 ◽  
Vol 11 (23) ◽  
pp. 2731 ◽  
Author(s):  
Mirzaei ◽  
Verrelst ◽  
Marofi ◽  
Abbasi ◽  
Azadi
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Heavy Metal Stress ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Regression Methods ◽  
Field Spectroscopy ◽  
Metal Contents ◽  
Metal Monitoring

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

scholarly journals A Back Propagation Neural Network Model Optimized by Mind Evolutionary Algorithm for Estimating Cd, Cr, and Pb Concentrations in Soils Using Vis-NIR Diffuse Reflectance Spectroscopy

2019 ◽  
Vol 10 (1) ◽  
pp. 51 ◽  
Author(s):  
Xi Wang ◽  
Shi An ◽  
Yaqing Xu ◽  
Huping Hou ◽  
Fuyao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metal ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Metal Content ◽  
Diffuse Reflectance Spectroscopy ◽  
Back Propagation ◽  
Heavy Metal Content ◽  
Estimation Accuracy

Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R2) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.

Distribution Characteristic and Assessment of Soil Heavy Metal Pollution around Baotou Tailings in Inner Mongolia, China

2011 ◽  
Vol 356-360 ◽  
pp. 2730-2736 ◽  
Author(s):  
Ren Xin Zhao ◽  
Wei Guo ◽  
Wen Hui Sun ◽  
Shi Lei Xue ◽  
Bo Gao ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Inner Mongolia ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Pollution Index ◽  
Soil Heavy Metals ◽  
Soil Heavy Metal

The pollution status and total concentration of soil heavy metals were analyzed around Baotou tailing reservoir located in Inner Mongolia grassland and desert transition zone. Aim of the study is to control soil heavy metal pollution of Baotou tailings and provide the basic data information. The results indicated that concentrations of Pb, Zn and Mn from different directions of the tailing reservoir changed significantly with distance and were higher than the background values of Inner Mongolia. According to the single factor pollution index, soils from different directions were contaminated by Pb, Cu, Zn and Mn. The pollution degree was in order: Mn > Pb > Zn > Cu> Cr > Ni > As. According to Nemerow’s synthetical pollution index, soils collected from the southeast of the tailing reservoir had the most serious heavy metal contamination, the index was 11.1. The order of pollution level in different directions was southeast > northeast > southwest > northwest, which was mainly affected by the dominant wind of northwest. In general, the pollution characteristic and the elements of heavy metal contamination were corresponding with the concentrations of iron tailings. The health and stabilization of environmental quality are being threatened by soil heavy metals.

scholarly journals The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1227
Author(s):  
Huihui Zhao ◽  
Peijia Liu ◽  
Baojin Qiao ◽  
Kening Wu
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Interpolation Method ◽  
Average Error ◽  
Accuracy Evaluation ◽  
Least Squares Regression ◽  
Soil Heavy Metals ◽  
Tai Lake

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p < 0.01), and of Hg was 0.318 (p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.

scholarly journals Heavy Metal Contents and Assessment of Soil Contamination in Different Land-Use Types in the Qaidam Basin

2021 ◽  
Vol 13 (21) ◽  
pp. 12020
Author(s):  
Bayan Nuralykyzy ◽  
Pan Wang ◽  
Xiaoqian Deng ◽  
Shaoshan An ◽  
Yimei Huang
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Land Use ◽  
Soil Contamination ◽  
Qaidam Basin ◽  
Rapid Development ◽  
Quality Standard ◽  
Soil Heavy Metal ◽  
Land Use Types ◽  
Metal Contents

Due to the unique geographical location and rapid development in the agricultural industry, heavy metals’ risk of soil contamination in the Qaidam Basin is gradually increasing. The following study was conducted to determine the soil heavy metal contents under different types of land use, contamination levels, and the physicochemical properties of soil. Soil samples were collected from facility lands, orchards, farmlands, and grasslands at 0–10 and 10–20 cm soil layers. Heavy metals including copper (Cu), chromium (Cr), nickel (Ni), zinc (Zn), lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) were analyzed using inductively coupled plasma mass spectrometry and the soil was evaluated with different methods. Overall, the average Cu (25.07 mg/kg), Cr (45.67 mg/kg), Ni (25.56 mg/kg), Zn (71.24 mg/kg), Pb (14.19 mg/kg), Cd (0.17 mg/kg), As (12.54 mg/kg), and Hg (0.05 mg/kg) were lower than the environmental quality standard. However, the Cu, Cr, Ni, and As were highest in farmland, and Zn and Hg were highest in the facility land. The Pb content was highest in orchards, and the Cd content was the same in facility land, orchards, and farmland. Among the different land-use types, the soil heavy metal concentrations decreased in the order of facility land > farmland > grassland > orchards. The pH was alkaline, the content of SOC (soil organic carbon) 15.76 g/kg in grassland, TN (total nitrogen) 1.43 g/kg, and TP (total phosphorus) 0.97 g/kg in facility land showed the highest result. The soil BD (bulk density) had a significant positive correlation with Cu, Cr, Ni, Zn, Pb, Cd, and the TP positively correlated with Cu, Zn, Cd, and Hg. The soil evaluation results of the comprehensive pollution index indicated that the soil was in a clean condition. The index of potential environmental risk indicates that heavy metals are slightly harmful to the soil.

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