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 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.

Fault Diagnosis of Nuclear Power Equipment Based on HMM-SVM and Database Development

2010 ◽  
Vol 139-141 ◽  
pp. 2532-2536 ◽  
Author(s):  
Hou Yao Zhu ◽  
Chun Liang Zhang ◽  
Xia Yue
Keyword(s):  
Nuclear Power ◽  
System Development ◽  
Small Samples ◽  
Support Vector ◽  
Classification Problems ◽  
Advantages And Disadvantages ◽  
Good Ability ◽  
Svm Model ◽  
Vector Machines ◽  
Timing Pattern

This paper mainly introduced the basic theory of Hidden Markov Model (HMM) and Support Vector Machines (SVM). HMM has strong capability of handling dynamic process of time series and the timing pattern classification, particularly for the analysis of non-stationary, poor reproducibility signals. It has good ability to learn and re-learn and high adaptability. SVM has strong generalization ability of small samples, which is suitable for handling classification problems, to a greater extent, reflecting the differences between categories. Based on the advantages and disadvantages between the two models, this paper presented a hybrid model of HMM-SVM. Experiments showed that the HMM-SVM model was more effective and more accurate than the two single separate models. The paper also explored the application of its database system development, which could help the managers to get and handle the data quickly and effectively.

scholarly journals Heavy metal remediation technologies in Latvia: possible applications and preliminary case study results / Technologie remediacji obszarów zanieczyszczonych metalami ciężkimi na Łotwie: możliwe zastosowania i wstępne wyniki badań

2012 ◽  
Vol 19 (4) ◽  
pp. 533-547
Author(s):  
Juris Burlakovs ◽  
Magnuss Vircavs
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Heavy Metal Contamination ◽  
Anthropogenic Activities ◽  
Residential Areas ◽  
Territorial Planning ◽  
Heavy Metal Remediation ◽  
Study Results ◽  
Polluted Sites

Abstract Environmental contamination with heavy metals as a result of anthropogenic activities is not a recent phenomenon. Contaminated sites with heavy metals can be found in functioning as well as abandoned industrial (brownfield) territories, landfills, residential areas with historical contamination, road sides and rarely in polluted sites by natural activities. Pollution data on its amount and concentrations is known from historical studies and monitoring nowadays, but it should be periodically updated for the use of territorial planning or in case of a change of the land use. A special attention should be paid to heavy metal contamination, because in many cases this contamination is most problematic for remediation. 242 territories now are numbered as contaminated and fixed in the National Register of contaminated territories - at least 56 of them are known as contaminated with heavy metals in different amount and concentration. Legislative aspects are discussed as well as an overview of soil and groundwater contamination research and the possible remediation technologies in Latvia are given. Two case studies are described in order to give the inside look in pre-investigations done before potential start of heavy metal remediation works.

An Accurate Prediction Method for Budgets of Large Construction Project

2014 ◽  
Vol 513-517 ◽  
pp. 4139-4142
Author(s):  
Ling Jiang ◽  
Juan Du
Keyword(s):  
Prediction Method ◽  
Processing Method ◽  
Support Vector ◽  
Redundant Information ◽  
Post Processing ◽  
Contribution Rate ◽  
Estimation Model ◽  
The Core ◽  
Model Based ◽  
Svm Model

Accurate budget estimation is an important prerequisite to guide the project. The traditional method using a linear estimation model can not accurately reflect the contribution of each component to the budget estimation of the entire system, leading to poor estimating results. This paper proposes an accurate project budget estimation model based on chaotic post-processing SVM-PCA (Support Vector Machine-principle Component Analysis). On basis of SVM model, the model filters redundant information in the system to ensure the input information data contribution rate. Then after output the data, chaotic post-processing method is adopted to smooth irregular characteristics of the data, in order to ensure the accuracy of the budget estimating model. Finally, five projects in a group of 10 categories elements are used to conduct estimating budget experiments. Experimental results show that the project budget estimation model based on chaotic post-processing SVM-PCA can accurately estimate the core consumes of each project, therefore has great value in engineering.

scholarly journals Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra

2020 ◽  
Vol 12 (11) ◽  
pp. 4441
Author(s):  
Yun Xue ◽  
Bin Zou ◽  
Yimin Wen ◽  
Yulong Tu ◽  
Liwei Xiong
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Support Vector Machine ◽  
Sustainable Development ◽  
Environmental Conditions ◽  
Metal Content ◽  
Chromium Content ◽  
Principal Component ◽  
Support Vector ◽  
Farmland Soil

Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.

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 Impact of Industrial Wastewater Irrigation on Heavy Metal Deposition in Farm Soils of Bhaluka Area, Bangladesh

2020 ◽  
pp. 19-31 ◽  
Author(s):  
Faisal Islam ◽  
H. M. Zakir ◽  
A. Rahman ◽  
Shaila Sharmin
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Soil Quality ◽  
Contaminated Soils ◽  
Industrial Wastewater ◽  
Industrial Area ◽  
Pollution Level ◽  
Study Results ◽  
Wastewater Samples ◽  
Farm Soils

The study was conducted to determine heavy metal contents in industrial wastewater and contaminated soils of Bhaluka, Mymensingh and to assess their pollution level. A total of 9 industrial wastewater and 12 contaminated farm soil samples were collected directly from the farmers’ fields of Bhaluka area and analysed for this study. Considering EC, salinity and TDS, 56 to 89% wastewater samples were found problematic for long term irrigation. The concentration of CO3, HCO3 and Cl in wastewater ranged from 0.20-1.60, 2.0-11.2 and 1.30-4.79 me L-1, respectively and the content of Ca, Mg, Na and K in wastewater ranged from 16.03-52.10, 4.86-21.87, 101.98-678.90 and 5.59-48.63 mg L-1, respectively. The study results revealed that all wastewater samples were found unsuitable for irrigation in respect of CO3, HCO3 and K. Among the heavy metals studied, Pb, Cd and Fe concentrations in all wastewater samples and Mn content in 5 samples were found above than the acceptable limit for irrigation. The concentration of Zn, Cr, Cu, Pb, Ni, Cd, Mn and Fe in wastewater irrigated soils of Bhaluka industrial area ranged from 50.48 to 448.56, 47.22 to 83.65, 19.13 to 328.23, 42.37 to 77.96, 22.93 to 43.86, 0.70 to 1.40, 161.5 to 341.7 and 38105 to 65399 μg g-1, respectively. Considering geoaccumulation index, the Igeo values for Pb and Cd for all locations of the study area exhibited positive values (0.495< Igeo <1.624), that means Igeo class: 1-2, indicate moderately polluted soil quality. On the other hand, as regards to enrichment factor (EFc), 9 locations for Pb, 5 for Cd, 1 for Zn and 1 for Cu had EFc values > 5.0, indicate contaminated soil quality. The study concluded that industrial wastewater used for irrigation was directly linked with the heavy metals deposition in the farm soils.

scholarly journals Electronic cigarettes

2015 ◽  
Author(s):  
Kelsey Hynes ◽  
BCIT School of Health Sciences, Environmental Health ◽  
Helen Heacock ◽  
Fred Shaw ◽  
Jaymar Bisente ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Environmental Health ◽  
Electronic Cigarettes ◽  
Statistical Significance ◽  
Health And Safety ◽  
Average Concentration ◽  
P Value ◽  
The Public ◽  
Study Results

  Background: Since 2011, the popularity of electronic cigarettes in North America has increased dramatically. However, with a lack of scientific data performed on long term health effects and the limited number of short term studies, it is difficult for Environmental Health Officers to effectively educate the public on concerns relating to the health and safety of the general public. The increase of teenage users demonstrates the need for better government legislation and enforcement, in order to prevent the re-glamorization of smoking in younger generations. Therefore, the following study conducted a chemical analysis on artificially inhaled vapor from two different types of e-cigarettes (disposable and rechargeable), to determine if any heavy metal concentrations; specifically cadmium, chromium, lead and arsenic, are detectable. Methods: The vapor from one of two e-cigarette types was artificially inhaled through a cellulose filter cassette by a personal sampling pump. A two tailed t-test was performed to determine if there were any differences between the heavy metals and the type of e-cigarette used in the study. Results: There was no statistical significant difference in heavy metal concentration by the type of e-cigarette used (for cadmium the p-value was 0.00, and power was 0.00, for chromium the p-value was 0.181220, and power was 0.008976342, for lead the p-value was 0.333711, and power was 0.001825742, for arsenic the p-value was 0.00, and power was 0.00). Conclusion: Based on the results, it was determined that there was no statistical significance between disposable e-cigarettes and rechargeable e-cigarettes with respect to concentration of the four heavy metals of interest (eg. cadmium, chromium, lead and arsenic). Although there was no statistical significance between the types of e-cigarettes used, the average concentration of chromium (IV) from the rechargeable e-cigarette was 0.13mg/m3, which is ten times the recommended 8-hour time weighted average (TWA) set by the BC Occupational Health and Safety Regulations. Hence, further studies must be conducted to determine if the average concentration found in this study truly reflects the concentration found in inhaled vapor from rechargeable e-cigarettes. Furthermore, environmental health officers can provide the public with the concentration found in this study and warn of potential health risks associated with e-cigarettes until further studies are released.  

scholarly journals Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7945
Author(s):  
Yinlong Zhu ◽  
Fujie Zhang ◽  
Lixia Li ◽  
Yuhao Lin ◽  
Zhongxiong Zhang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Panax Notoginseng ◽  
Image Feature ◽  
Classification Model ◽  
Support Vector ◽  
Classification Methods ◽  
Test Set ◽  
Model Based ◽  
Svm Model ◽  
Study Results

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

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.

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