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 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.

Spectral Characteristics of Cr and Cu in Agricultural Soil at Beijing Plain Based on SWIR Spectroscopy and PLSR

2013 ◽  
Vol 765-767 ◽  
pp. 3066-3072 ◽  
Author(s):  
Shu Min Li ◽  
Hong Li ◽  
Dan Feng Sun ◽  
Lian Di Zhou
Keyword(s):  
Heavy Metals ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Large Scale ◽  
Spectral Characteristics ◽  
Dynamic Monitoring ◽  
Optimum Number ◽  
Least Squares Regression ◽  
Large Area ◽  
Soil Heavy Metals

Heavy metals pollution in agricultural soils has been an important problem to human health, mapping large-scale spatial distribution of soil heavy metals is urgently needed. Instead of traditional methods, time-consuming and destructive, soil properties predicted by remote sensing technology shows a lot of advantages, which makes large area of real-time dynamic monitoring as possible. However, before achieving prediction using spectra data, the first thing to do is that finding the spectral characteristics of soil heavy metals. In this paper, taking Cr and Cu for example, the correlations between soil heavy metals content and laboratory-measured reflectance is studied using partial least squares regression (PLSR), which is an adaptive method to examine linear between spectrum and concentration. First of all, using the raw spectra, remove outliers of heavy metals concentration by PLSR modeling. Next, though comparing RMSEC and RMSEV against PLSR components, and cumulative explanatory of spectral components to metal content using different pre-precessing methods, find the right pre-pcocessing is CR and optimum number of components to Cr and Cu are 3 and 2 respectively. Simultaneously, with the meaning of PLSR models regression coefficients, we analysis the spectral characteristics of Cr and Cu, although can not to realize the prediction only take use of these spectra, which is still essential to achieve simulating spatial distribution of soil heavy metal by remote sensing.

scholarly journals Spatial distribution of heavy metals in surface sediments of Kalametiya Lagoon in southern Sri Lanka; insight towards pollution status and socio-economic interactions

2021 ◽  
Author(s):  
Sunanda Kodikara ◽  
Hossein Tiemoory ◽  
Mangala Chathura De Silva ◽  
Pathmasiri Ranasinghe ◽  
Sudarshana Somasiri ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Sri Lanka ◽  
Suspended Sediments ◽  
Potential Effect ◽  
Toxic Heavy Metals ◽  
Threshold Effect Level ◽  
Effect Level ◽  
Economic Interactions

Abstract Heavy metal (HM) pollution has become a serious threat to coastal aquatic ecosystems. This study, therefore, aimed at assessing the spatial distribution of selected heavy metals/metalloids including Arsenic (As), Cadmium (Cd), Chromium (Cr), Lead (Pb), and Mercury (Hg) in surface sediment (0–15 cm) samples collected across Kalametiya Lagoon in southern Sri Lanka. Forty-one (41) grid points of the lagoon were sampled and the sediment samples were analyzed for HM content by using ICP-MS. A questionnaire survey was carried out to investigate the possible sources for HM pollution in Kalametiya Lagoon. Water pH and salinity showed significant variation across the lagoon. Overall mean value of pH and salinity were 6.68 ± 0.17 and 2.9 ± 2.2 PSU respectively. The spatial distribution of the heavy metals was not monotonic and showed a highly spatial variation. The kernel density maps of the measured heavy metals demarcated several different areas of the lagoon. The mean contents of As, Cd, Cr, Hg, and Pb were lower than that of threshold effect level (TEL) however, higher for Hg at the North Inlet. Nevertheless, it was still lower than potential effect level (PEL). Socio-economic interactions have dramatically reduced during the past two decades. Industrial sewage, river suspended sediments and agrochemicals such as fertilizers, pesticides were reportedly identified as the possible sources for heavy metal loads. Accumulation of toxic heavy metals can be minimized by detouring the water inflow to the lagoon.

Source apportionment of heavy metal pollution in farmland soils on a regional scale using geographically weighted PCA

2021 ◽  
Author(s):  
Liu Shuyi ◽  
Gao Bingbo
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Source Apportionment ◽  
Rapid Development ◽  
Regional Scale ◽  
Analytical Framework ◽  
Pollution Sources ◽  
Entire Study ◽  
Soil Heavy Metals ◽  
Farmland Soils

&lt;p&gt;Source apportionment of soil heavy metals is an challenge and urgent work as the result of the rapid development of industrialization and urbanization. The common approach is multivariate statistical analysis, such as PCA and APCS/MLR, which infers only a single pattern of sources of heavy metals in entire study area. Due to complicated pathways and processes, patterns of pollution sources in a whole region may include two or more. Hence, we developed an analytical framework based on GWPCA to explore multiple patterns of sources of soil heavy metals on a regional scale. Xiangtan county, an important grain-producing area in China, was taken as a case study, which suffers the problem of heavy metal pollutions. Our results revealed the pollution situations of five soil heavy metals(Pb, Cd, As, Cr and Hg) in farmland soils and suggested that there exists various pollution patterns of these heavy metals in Xiangtan county. In each pattern, the structure of contamination sources is different. Our study also indicates that the analytical framework considering the spatial heterogeneity of pollution sources can help take more precise practices to solve this vital problem.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals SPATIAL DATA INTERPOLATION

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Prof.RAE ZH Aliyev
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Spatial Data ◽  
Movement Speed ◽  
Data Interpolation ◽  
Heavy Metal Concentrations ◽  
Groundwater Movement ◽  
Record Data ◽  
Inverse Distance

During the study and adjustment, techniques revealed our analysis of spatial data in vector format. The latter is best suited for the spatial analysis of discrete objects. However, when the spatial variable is represented as a field of scalar or vector greatness (for example, the spatial distribution of concentrations of heavy metal concentrations in soils or groundwater movement speed field). Convenient ways to record data is bitmap format. This approach is most often used for phenomena of processes that are characterized by considerable anisotropy. However, the characteristic feature of the method of inverse distance is the fact that the interpolated value in measured point is equal to the measured value. Key words: erosion, soil; heavy metals, extremum, spatial data, raster data anti-erosion measures

scholarly journals Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review

2021 ◽  
Vol 1 (7) ◽  
pp. 620-628
Author(s):  
Stefan Daniel Maramis ◽  
Rika Ernawati ◽  
Waterman Sulistyana Bargawa
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Salt Spray ◽  
Regional Scale ◽  
Volcanic Eruptions ◽  
Distribution Analysis ◽  
Metal Contaminants ◽  
Geostatistical Methods ◽  
Heavy Metal Contaminants

Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.

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