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

<p>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.</p> <p> </p>

Risk assessment, spatial patterns and source apportionment of soil heavy metals in a typical Chinese hickory plantation region of southeastern China

Geoderma ◽  
2020 ◽  
Vol 360 ◽  
pp. 114011 ◽  
Author(s):  
Keli Zhao ◽  
Luyao Zhang ◽  
Jiaqi Dong ◽  
Jiasen Wu ◽  
Zhengqian Ye ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Source Apportionment ◽  
Spatial Patterns ◽  
Southeastern China ◽  
Soil 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.

scholarly journals Assessment of Pollution and Health Risks of Heavy Metals in Particulate Matter and Road Dust Along the Road Network of Dhanbad, India

2021 ◽  
Vol 11 (29) ◽  
Author(s):  
Shweta Kumari ◽  
Manish Kumar Jain ◽  
Suresh Pandian Elumalai
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Heavy Metal ◽  
Particulate Matter ◽  
Health Risk ◽  
Human Health ◽  
Source Apportionment ◽  
Health Risks ◽  
Mass Concentration ◽  
Road Dust

Background. The rise in particulate matter (PM) concentrations is a serious problem for the environment. Heavy metals associated with PM10, PM2.5, and road dust adversely affect human health. Different methods have been used to assess heavy metal contamination in PM10, PM2.5, and road dust and source apportionment of these heavy metals. These assessment tools utilize pollution indices and health risk assessment models. Objectives. The present study evaluates the total mass and average concentrations of heavy metals in PM10, PM2.5, and road dust along selected road networks in Dhanbad, India, analyzes the source apportionment of heavy metals, and assesses associated human health risks. Methods. A total of 112 PM samples and 21 road dust samples were collected from six stations and one background site in Dhanbad, India from December 2015 to February 2016, and were analyzed for heavy metals (iron (Fe), lead (Pb), cadmium (Cd), nickel (Ni), copper (Cu), chromium (Cr), and zinc (Zn)) using atomic absorption spectrophotometry. Source apportionment was determined using principal component analysis. A health risk assessment of heavy metal concentrations in PM10, PM2.5, and road dust was also performed. Results. The average mass concentration was found to be 229.54±118.40 μg m−3 for PM10 and 129.73 ±61.74 μg m−3 for PM2.5. The average concentration of heavy metals was found to be higher in PM2.5 than PM10. The pollution load index value of PM10 and PM2.5 road dust was found to be in the deteriorating category. Vehicles were the major source of pollution. The non-carcinogenic effects on children and adults were found to be within acceptable limits. The heavy metals present in PM and road dust posed a health risk in the order of road dust> PM10> and PM2.5. Particulate matter posed higher health risks than road dust due to particle size. Conclusions. The mass concentration analysis indicates serious PM10 and PM2.5 contamination in the study area. Vehicle traffic was the major source of heavy metals in PM10, PM2.5, and road dust. In terms of non-carcinogenic risks posed by heavy metals in the present study, children were more affected than adults. The carcinogenic risk posed by the heavy metals was negligible. Competing Interests. The authors declare no competing financial interests

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.

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.

Evaluation on Characteristics of Heavy Metal Spatial Pollution in Farmland Soils along the Bijiang River

2013 ◽  
Vol 295-298 ◽  
pp. 1586-1593
Author(s):  
Xiao Qing Zhao ◽  
Hong Hui Yang ◽  
Jian Chen
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Variation ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Pollution Index ◽  
Pollution Level ◽  
Zinc Deposit ◽  
Farmland Soils ◽  
Lead Zinc

Based on the farmland soils along the Bijiang River, a main tributary of the international Lantsang-Mekong River flowing through the Jinding Lead-Zinc Deposit, this dissertation makes analyses on the pollution characteristics of spatial variation in farmland soils by adopting the soil sampling and testing analysis and applying single-factor pollution index (SPI) evaluation and Nemerow composite pollution index (NCPI) evaluation. The results indicate that: (1) In accordance with Environmental Quality Standard for Soils (II), the content of Cd contained in the farmland soils has severely exceeded the standard in a large scale, followed by Pb and Zn. However, the content of As is maintained within the specified standard; (2)The SPI values of soils are in the following sequence: Cd＞Zn＞Pb＞As. The pollution level caused by the heavy metal “Cd” to the farmland soils is extremely heavy in a wide range, and a majority of the farmlands are heavily polluted by Zn. The farmlands with moderate pollution by Pb are centered at Plot 2 in the deposit, and only a few farmland soils are moderately polluted by As at Plot 2 in the deposit;(3) Based on the NCPI, the results indicate that the NCPI of the farmland soils has reached to the degree of heavy pollution; (4) It is indicated based on the RPI evaluation that the RPI values of As, Cd, Pb and Zn contained in the farmland soils have exceeded the standard in the following sequence: Pb＞Zn＞Cd＞As, which illustrates that during the development of Jinding Lead-Zinc Deposit in Lanping County, the heavy metals imposing the most profound influence on the soil pollution are Pb and Zn. The heavy metal pollution in the farmland soils from the upper reaches to the lower reaches of the Bijiang River is not only caused by the development of Jinding Lead-Zinc Deposit in Lanping County, but is also associated with its high soil background value;(5) There is a remarkable spatial variation of heavy metal pollution in farmland soils from the upper reaches to the lower reaches of the Bijiang River. Both the SPI and the NCPI values of heavy metals in the soils within the deposit at the upper reaches of the Bijiang River are the lowest; the pollution index of the soils closest to the deposit are the highest, and the pollution index of the soils with a certain distance from the deposit drops swiftly; the pollution index of Plot 4 rises to a certain degree at the middle reaches, and gradually ascends near the Yunlong County seat at Plot 5, however, with a comparatively small growth rates.

Spatio-temporal impacts of dairy lagoon water reuse on soil: heavy metals and salinity

2015 ◽  
Vol 17 (10) ◽  
pp. 1731-1748 ◽  
Author(s):  
Dennis L. Corwin ◽  
Hamaad Raza Ahmad
Keyword(s):  
Heavy Metals ◽  
Electrical Conductivity ◽  
Heavy Metal ◽  
Water Reuse ◽  
Soil Electrical Conductivity ◽  
Conductivity Measurements ◽  
Lagoon Water ◽  
Soil Heavy Metals ◽  
Spatio Temporal

Heavy metal and salinity impacts on soil from dairy lagoon water reuse are monitored using geospatial apparent soil electrical conductivity measurements.

scholarly journals Surface water isolates of hemolytic and non-hemolytic Acinetobacter with multiple drug and heavy metal resistance ability

2013 ◽  
Vol 12 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Sevilay Akbulut ◽  
Fadime Yilmaz ◽  
Bulent Icgen
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Surface Water ◽  
Pathogenic Bacteria ◽  
Rapid Development ◽  
Heavy Metal Resistance ◽  
Metal Resistance ◽  
Rdna Sequencing ◽  
Wide Range ◽  
Multiple Drugs

Acinetobacter in surface waters are a major concern because of their rapid development of resistance to a wide range of antimicrobials and their ability to persist in these waters for a very long time. Four surface water isolates of Acinetobacter having both multidrug- and multimetal-resistant ability were isolated and identified through biochemical tests and 16S rDNA sequencing. Based on these analyses, two hemolytic isolates were affiliated with Acinetobacter haemolyticus with an accession number of X81662. The other two non-hemolytic isolates were identified as Acinetobacter johnsonii and Acinetobacter calcoaceticus and affiliated with accession numbers of Z93440 and AJ888983, respectively. The antibiotic and heavy metal resistance profiles of the isolates were determined by using 26 antibiotics and 17 heavy metals. Acinetobacter isolates displayed resistance to β-lactams, cephalosporins, aminoglycosides, and sulfonamides. The hemolytic isolates were found to show resistance to higher numbers of heavy metals than the non-hemolytic ones. Due to a possible health risk of these pathogenic bacteria, a need exists for an accurate assessment of their acquired resistance to multiple drugs and metals.

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