Source apportionment, contamination levels, and spatial prediction of potentially toxic elements in selected soils of the Czech Republic

2020 ◽  
Author(s):  
Prince Chapman Agyeman ◽  
Samuel Kudjo Ahado ◽  
John Kingsley ◽  
Ndiye Michael Kebonye ◽  
James Kobina Mensah Biney ◽  
...  
Keyword(s):  
Czech Republic ◽  
Source Apportionment ◽  
Toxic Elements ◽  
Spatial Prediction ◽  
Potentially Toxic Elements ◽  
The Czech Republic ◽  
Contamination Levels

scholarly journals National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons

2020 ◽  
Author(s):  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon ◽  
Eyal Ben Dor ◽  
Raphael A. Viscarra Rossel ◽  
Lubos Boruvka
Keyword(s):  
Czech Republic ◽  
Spectral Data ◽  
Forest Soils ◽  
Toxic Elements ◽  
Potentially Toxic Elements ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
The Czech Republic ◽  
Organic Horizons

Forest ecosystems are among the main parts of the biosphere; however, they have been endangered from the significant elevation and harmful effects of air and soil pollutants, including potentially toxic elements (PTEs). The concentration of PTEs in forest soils varies not only laterally but also vertically with depth. Forest surface organic horizons are of particular interest in forest ecosystem monitoring due to their role as stable adsorbents of the deposited atmospheric substances. Therefore, the main purpose of this study was to conduct rapid examinations of forest soils PTEs (Cr, Cu, Pb, Zn, and Al), testing the capability of VIS--NIR spectroscopy coupled with machine learning (ML) techniques (partial least square regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and fully connected neural network (FNN), a deep learning (DL) approach, in forest organic horizons. One-thousand-and-eighty forested sites across the Czech Republic at two soil layers, defining the fragmented (F) and humus (H) organic horizons, were investigated (total 2160 samples). PTEs as well as total Fe and SOC, as auxiliary data, were conventionally and spectrally determined and modelled in the combined organic horizons (F + H) and in each individual horizon using the ML and DL algorithms. Results indicated that the concentration of all PTEs was higher in the horizon H compared to the F horizon. Although the spectral reflectance of samples tended to decrease with increased PTEs concentration. Strongly significant positive correlations between all PTEs and total Fe in all horizons were obtained, which were higher in the H and F + H horizons than the F horizon. The highest correlations of PTEs with the spectra were at 460--590~nm, which is mostly linked to the presence of Fe-oxide. These results show the importance of Fe for spectral prediction of PTEs. Cr and Al were the most accurately predicted elements, regardless of the applied learning technique. SVMR provided the best results in assessing the H horizon (e.g., R\(^2\) = 0.88 and root mean square error (RMSE) = 3.01~mg/kg, and R\(^2\) = 0.82 and RMSE = 1682.25~mg/kg for Cr and Al, respectively); however, FNN predicted the combined F + H horizons the best (R\(^2\) = 0.89 and RMSE = 2.95~mg/kg, and R\(^2\) = 0.86 and RMSE = 1593.64~mg/kg for Cr and Al, respectively) due to the larger number of samples. In the F horizon, almost no parameters were predicted adequately. This study shows that given the availability of larger sample sizes, FNN can be a more promising technique compared to ML methods for assessment of Cr and Al concentration based on national spectral data in the forests of the Czech Republic.

Contamination levels, health risks and source apportionment of potentially toxic elements in road dusts of a densely populated African City

2021 ◽  
Vol 15 ◽  
pp. 100445
Author(s):  
Emmanuel Toluwalope Odediran ◽  
Jamiu Adetayo Adeniran ◽  
Rafiu Olasunkanmi Yusuf ◽  
Khadijat Abdulkareem Abdulraheem ◽  
Olusola Adedayo Adesina ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Health Risks ◽  
Toxic Elements ◽  
Potentially Toxic Elements ◽  
African City ◽  
Contamination Levels ◽  
Road Dusts

Characterization and source apportionment of particulate matter in the Czech Republic

1996 ◽  
Vol 27 ◽  
pp. S685-S686 ◽  
Author(s):  
R.K. Stevens ◽  
J.P. Pinto ◽  
Y. Mamane ◽  
R.D. Willis
Keyword(s):  
Czech Republic ◽  
Particulate Matter ◽  
Source Apportionment ◽  
The Czech Republic

scholarly journals Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238513
Author(s):  
Cao Jianfei ◽  
Li Chunfang ◽  
Zhang Lixia ◽  
Wu Quanyuan ◽  
Lv Jianshu
Keyword(s):  
Source Apportionment ◽  
Toxic Elements ◽  
Eastern China ◽  
Potentially Toxic Elements ◽  
Mining City
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