Parisa Lahooti; Seyed Mostafa Emadi; mohammad ali bahmanyar; Mehdi Ghajar Sepanlou
Abstract
Introduction: Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct ...
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Introduction: Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct changes with atmosphere through the photosynthesis and the SOC decomposition. The depletion of C storage not only exacerbates the risk of soil erosion but also reduces agricultural production. An accurate knowledge of regional SOC contents and stocks and their spatial distribution are essential to optimize the soil management and land-use policy for SOC sequestration. Today, digital soil mapping methods such as geostatistics and artificial neural network (ANN) have focused more on SOC contents and stocks mapping. Geostatistics is a robust tool widely applied to model and quantify soil variation and analyze the spatial variability of SOC in large scale. The ANN as a nonlinear technique has been received much less attention for modeling SOC contents and stocks. Therefore, in this study, we aimed to develop and compare the performance of ordinary Kriging, co-kriging, inverse distance weighting (IDW) and artificial neural network models in predicting and mapping the SOC contents and stocks in East and Southeast of the Kohgiluyeh and Boyer-Ahmad province, southern Iran.
Materials and Methods: The composite soil samples were collected randomly from the 0-15 cm soil depths at 204 sampling sites at different land uses in east and southeast of the Kohgiluyeh and Boyer-Ahmad province. The collected soil samples were air-dried, ground, and sieved to pass through a 2 mm mesh. Soil properties such as organic carbon contents and stocks, pH, electrical conductivity (EC), bulk density (BD) and soil texture were determined according to the standard analysis protocols. The normality tests were done according to the Kolmogrov–Smirnov method, and the variability of SOC contents and stocks were analyzed by the classical statistics (mean, maximum, minimum, standard deviation, skewness, and coefficient of variations). The digital elevation model (DEM), slope gradient, precipitation and temperature and Normalized Difference Vegetation Index (NDVI) were used as co-variables (auxiliary data). The NDVI was obtained by the remotely sensed data of LANDSAT 8. The geostatistical parameters were calculated for each soil property as a result of corresponding semivariogram analysis. The spatial prediction maps of soil properties were generated by ordinary kriging (OK), cokriging (Co-K) inverse distance weighting (IDW) with powers of 1, 2, 3, 4 and 5 as well as the Artificial Neural Network (Multilayer Perception model, MLP) methods. The mentioned interpolation methods were used to prepare the SOC spatial distribution maps by using the 80 % of data as the training datasets. The prediction results were then evaluated by the validation data set (20 % of all data). The differences between the observation and prediction values were evaluated by Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (R2) and Concordance Correlation Coefficient (CCC). The spatial distribution maps of the SOC contents and stocks in the study area were finally developed by ArcGIS 10 software.
Results and Discussion: The SOC content for all samples largely varied from 0.20 to 3.96 % .The high coefficient of variation of 53.38 % demonstrates the strong spatial variation of SOC content in the study area. The SOC stocks had also a relatively high variability compared with other soil properties. Such strong variation could be attributed to the diverse soil types, land covers and other environmental conditions across the study area. The average SOC content for forest land use was significantly higher than the other land uses. The intensive tillage in cropland soils appears to have induced the acceleration of organic carbon oxidations leading to the lowest SOC contents and stocks. By increasing the mean precipitation within our study area (in eastern and northeastern regions), the SOC contents and stocks increased significantly. The inverse trend was, however, observed for temperature implying the fact that the higher the temperature, the lower the SOC. Gaussian model was found to be the best model for parameters such as SOC contents and stocks due to the lowest RSS and R2.Overall, the results denoted the higher ability of ANN compared to geostatistical techniques (cokriging, kriging and IDW methods) in estimating both soil organic carbon contents and stocks. According to the results, ANN (MLP) method with one hidden layers with 50 neurons performed better in estimating soil organic carbon contents and stocks atunsampled points, whereas the largest errors were obtained for IDW method.
Conclusions: The good performance of ANN method can be attributed to the division of the study area and the capability of ANN to capture the nonlinear relationships between SOC and environmental factors i.e. slope, DEM, precipitation, temperature and NDVI. The results suggest that the proposed structural method for ANN can play a vital role in improving the prediction accuracy of SOC spatial variability in large scale.
Mohaddese Savasari; Mostafa Emadi; Mohammad Ali Bahmanyar; Puria Biparva4
Abstract
Introduction: Increases in pollution of water resources due to the contaminants have made researchers to develop the various methods in the remediation and the reuses of polluted resources contamination of soils with heavy metals is one of great environmental concerns for the human beings. Cadmium (Cd) ...
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Introduction: Increases in pollution of water resources due to the contaminants have made researchers to develop the various methods in the remediation and the reuses of polluted resources contamination of soils with heavy metals is one of great environmental concerns for the human beings. Cadmium (Cd) as a toxic heavy metal is of significant environmental and occupational concern. Contamination of soils with heavy metals is one of great environmental concerns for the human beings. The numbers of sorbents that have been used for Cd (II) reductive removal are biopolymers, fly ash, activated carbon, metal oxides, clays, zeolites, dried plant parts, microorganisms, and sewage sludge. However, most of the mentioned sorbents had limitations of cost and durability that call a needed approach by cost effective remediation technique with high efficiency. Application of zero valent iron nanoparticles (ZVINs) as a promising technique for remediation of heavy metals are being increasingly considered by researchers. This study was conducted to synthesis and characterize the ZVINs stabilized with ascorbic acid (AAS - ZVIN) in aerobic conditions and to assess their ability for removal efficiency of cadmium (Cd) from the soils and changes in different fraction of Cd in three spiked soils including sandy, acidity and calcareous soils were also studied.
Materials and Methods: The stabilized ZVINs were prepared in cold distilled water by reducing Fe (III) to Fe0 using sodium borohydride in the presence of ascorbic acid as stabilizer and reducing agent. The freshly synthesized AAS-ZVIN washed three times and then used for the subsequent analysis. Characterization of the synthesized AAS-ZVIN was carried out by scanning electron microscope (SEM). X-ray diffraction (XRD) was performed using a Philips D500 diffract meter with Ni-filtered Cu ka radiation. To determine the availability of Cd, the DTPA-extractable amounts of Cd in the spiked soils so sandy, acid and calcareous soils with three replications was studied were studied in an experiment of randomized completely design with a factorial arrangement of treatments consisting of AAS-ZVIN dosage (0, 0.5, 1 and 2 w/w %), Cd contamination levels (15 and 45 mg kg-1) in two time periods of 1 and 4 weeks in the three spiked soils. Moreover, the distribution of the chemical forms of Cd was determined using the sequential extraction method.
Results Discussion: The results of this study show that zero valent iron nanoparticles can be sustained in the future by ascorbic acid under aerobic conditions in a laboratory that is to reduce the cadmium as a useful method, simple, fast and high performance in the decontamination of soils contaminated with lead that require further research to investigate other heavy elements. The results from the obtained SEM and XRD analyses indicated that AAS-ZVINs had the mean size of less than 50 nm, the maximum 2θ peak at 44.8°. Therefore, the particle size of ZVINs produced in this study, measured by SEM images, are less than 100 nm. Chain structure formations have been attributed to the magnetic interactions between the adjacent metal particles. Furthermore, there was an apparent separation between these ZVIN with a little aggregation. Results also showed that the DTPA-extractable Cd in three sandy, acid and calcareous spiked soils decreased with increasing of AAS-ZVIN dosages at both level of contaminations. The availability of Cd in sandy, acid and calcareous spiked soils at 15 and 45 mg kg-1 of contamination were 71 and 49.5 % and 47.52 and 49.47; and 36.05 and 61.3 percentages, respectively. Availability of Cd after four weeks application at two contamination level was also decreased significantly. The results of sequential extraction of sandy, acid and calcareous soils showed that with increasing the level of AAS-ZVIN application from 0 to 2 %, the soluble, exchangeable and carbonate-bound of Cd decreased but organic matter-bound, Fe/Mn oxides bound and residual Cd were increased. Over four weeks after application of AAS-ZVIN in three spiked soils the soluble, exchangeable and carbonate-bound were decreased but organic matter-bound, Fe/Mn oxides bound and residual Cd increased.
Conclusions: The results of this study show zero valent iron nanoparticles can be sustained in the future by ascorbic acid under aerobic conditions in a laboratory that is, To reduce the cadmium as a useful method, simple, fast and high performance in the decontamination of soils contaminated with lead that require further research to investigate other heavy elements. Moreover, the high resolution transmission electronmicroscopy, energy dispersive X-ray analysis, x-ray diffraction spectrophotometer measurements are potentially needed to reveal the accurate morphology, composition, crystal structure, functionality and stability of the prepared stabilized-ZVINs. Moreover, these synthesized ZVINs can also possibly applicable for remediation of soils and wastewater.
