Soil science
H. Emami; M. Memarzadeh
Abstract
Introduction
Wind Erosion is the natural process of transportation and deposition of soil by wind. It is a common phenomenon occurred mostly in dry, sandy soils or anywhere the soil is loose, dry, and finely granulated. Heavy metals are found in the environment and soils may become contaminated by accumulation ...
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Introduction
Wind Erosion is the natural process of transportation and deposition of soil by wind. It is a common phenomenon occurred mostly in dry, sandy soils or anywhere the soil is loose, dry, and finely granulated. Heavy metals are found in the environment and soils may become contaminated by accumulation of heavy metals through emissions from the rapidly expanding industrial areas, mine tailings, disposal of high metal wastes, leaded gasoline and paints, land application of fertilizers, animal manures, sewage sludge, pesticides, wastewater irrigation, coal combustion residues, spillage of petrochemicals, and atmospheric deposition. Soils are the major sink for heavy metals released into the environment by the aforementioned anthropogenic activities and their total concentration in soils persists for a long time after their introduction. The heavy metal contamination of soil and its potential risks to humans and the ecosystem is a significant concern. Windy deposition, which is the process of heavy metals being transported by erosive winds and deposited onto soil, is one of the sources of heavy metal contamination. Due to the geographical situation and climatic conditions such as arid soil, erosive winds are blown in periods of year in Tabas. Since wind are erosion is severe in this area, huge amounts of wind deposition accompanied with erosive winds entered into this town. Heavy metals through the windy deposition are suspended, translated and finally deposited in residential regions, which can create some problems for human health. Therefore, the knowledge of wind erosion and the human risk of these deposits is essential. The aim of this research was to determine the rate of wind erosion and the concentration of some heavy metals in these deposits.
Materials and Methods
For this purpose, the rate of suspended load was measured monthly from February 2021 to January 2022. Based on previous information from the erosive winds and storms, suspended depositions were gathered in some directions (north, northwest, northeast, west and southwest) of the Tabas entrance. In addition, the suspended load in the city center of Tabas was also measured. The cumulative load of suspended depositions was measured monthly and the concentration of some heavy metals such as manganese (Mn), iron (Fe), cupper (Cu), and zinc (Zn) were measured in these suspended particles. Soil digestion was made by Aqua regia (nitric acid and chloridric acid; ratio of 3:1), and after then atomic absorption was used to measure the total concentration of above heavy metals.
Results and Discussion
The results indicate that Tabas experiences significant wind deposition of suspended loads, with the highest rates entering from the northeast direction and the lowest rates from the southwest direction. This pattern aligns with the wind rose of Tabas, which illustrates the prevailing wind directions in the region. Additionally, substantial suspended loads are observed in the northwest and north directions. The variations in suspended load discharge reveal that the maximum discharge occurs in the city center of Tabas during the months of June and July 2021. This corresponds to the arid climate conditions of these months, where plant growth is limited, soil cohesion is low, and loose soil particles on the surface are susceptible to wind forces. As a result, these loose particles are easily detached by the wind, contributing to the high levels of suspended load. Regarding the spatial variation of heavy metals in suspended particles, the cumulative concentrations of Mn, Fe, Cu, and Zn are found to be higher in the west, northwest, north, and west directions, respectively. This suggests that these heavy metals are transported and deposited in specific areas within Tabas due to the prevailing wind patterns. In terms of temporal variation, the highest concentrations of Mn and Fe in suspended particles are observed in April 2021, predominantly in the northeast and west directions, respectively. On the other hand, the highest concentrations of Cu and Zn are found in May 2021, with the southwest and northeast directions being the primary deposition areas for each metal, respectively. These findings highlight the spatial and temporal dynamics of suspended load and heavy metal deposition in Tabas, emphasizing the influence of wind patterns and climatic conditions on these processes. Understanding these variations is crucial for assessing the potential risks associated with heavy metal contamination and implementing appropriate mitigation measures in the region.
Conclusion
The results of this research showed that most contents of the suspended load are entered from the northeast direction into Tabas. In addition, the spatial variation of heavy metals indicated that the concentrations of studied heavy metals (Mn, Fe, Cu, and Zn) in suspended particles, especially in the western, northwestern, and northern in spring, are very high and they can cause carcinogenic effects on human life. Therefore, the management practices should be mostly made in these directions to control or reduce soil erosion and reduce its damage effects.
Y. Ataie; M. R. Nikpour; A. Kanooni; Y. Hoseini
Abstract
Introduction: Suspended load estimation is utilized to study and investigate many problems of water engineering sciences such as dam reservoir design, transportation of sediments and pollution in the rivers, creation of stable channels, estimation of erosion and sedimentation around bridge piers, and ...
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Introduction: Suspended load estimation is utilized to study and investigate many problems of water engineering sciences such as dam reservoir design, transportation of sediments and pollution in the rivers, creation of stable channels, estimation of erosion and sedimentation around bridge piers, and watershed management. The purpose of this study was to estimate the suspended load in the Dareh-Roud watershed in Ardabil province using the rivers discharge values and the physiographic characteristics of the sub-basins. Moreover, annual suspended load and sediment specific discharge were calculated for the whole of the watershed. Materials and Methods: In this study, the Dareh-Roud watershed in Ardebil province was considered as the study area. The flow discharge and suspended load data were collected from 16 hydrometric stations with a statistical period of 15 years from 2001-2015. The physiographic characteristics of sub-basins, including area (A), slope (S), shape factor (Sf), and curve number (CN), were achieved using ArcGIS and WMS. Five different input combinations were defined based on the effect of flow discharge variables and physiographic properties on the suspended load. Also, considering the area and slope parameters, the sub-basins were divided into two groups (i.e., the first and second groups). The performance of data-intelligent models, including Artificial Neural Networks (ANN), Adaptive Neural-Fuzzy Interference System (ANFIS), and Gene Expression Programming (GEP) models were investigated in the predict of the suspended load in the study area. Several statistical indicators, including determination coefficient (R2), root mean square error (RMSE), and Nash- Sutcliffe efficiency (NS), were utilized to evaluate the model’s efficiency. Results and Discussion: According to the results, estimation of suspended load without using the physiographic characteristics resulted in a high error, and in contrast, the suspended load estimation was most accurate by using a combined scenario involving all physiographic aspects and flow discharge. The scatterplots indicated that in the first group, the points were concentrated around the 1:1 axis for the values of less than 20 (ton/day). However, for the greater amounts, the scattering of issues around the one-to-one line was not appropriate, which means that the models were in the condition of underestimation. Similar conditions were observed for the second group, the excellent dispersion was seen for the values of less than 1000 (ton/day), and in general, the models had underestimation conditions. However, in both groups, the dispersion of the GEP model was somewhat better than the other models. Based on the values of R2 and NS, ANN and ANFIS models had the acceptable and satisfactory accuracy for the first group. The GEP model was more reliable and efficient in estimating the suspended load of the first group. On the other hand, the efficiency of ANN and ANFIS was not acceptable for the second group. Comparison of the results of different models using the best input combination indicated that the GEP model with the highest determination coefficient (R2 = 0.68), the lowest root mean square error (RMSE = 7.69 ton/day). The NS equal to 0.55 in the validation step has shown better performance than the other models in estimating the suspended load for the first group. Similarly, for the second group, the GEP model with the highest determination coefficient (R2 = 0.72), the lowest root means square error (RMSE = 975.26 ton/day). The NS equal to 0.43 in the validation step has shown better performance than other models in estimating the suspended load. Conclusion: In the present study, the efficiency of different intelligent models was investigated in the suspended load estimation of Dareh-roud watershed. In this regard, an extended period (i.e., during 15 years) of measured data, including flow discharge and sediment at the hydrometric stations located on the mentioned watershed, were used. In order to simulate the suspended load, five different input combinations were considered. For all models, the accuracy of suspended load estimation was improved by combining the physiographic characteristics and discharge values. Due to the higher accuracy of the GEP model, regional sediment models were achieved for the first and second groups, separately. Also, annual suspended load and sediment specific discharge were calculated for all sub-basins. According to the results, most of the suspended load of the Dareh-Roud watershed is produced and transported in its old rivers (i.e., Dareh-Roud and Qarah-Su). Based on the results of this research, in the Dareh-Roud watershed, 6.33 million tons of suspended sediments were transported during 2001-2015.
aboalhasan fathabadi; hamed rouhani
Abstract
Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating ...
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Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE) resampling techniques were used to calculate suspended sediment loads by using sediment rating curves.
Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent) and a test set (20 percent) by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE) and the highest correlation of coefficient (R2). In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times). With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to 3000) times. Sediment rating curves equation was fitted to each sampled suspended sediment and discharge data sets. Using these sediment rating curve and their residual suspended sediment concentration were calculate for test data. Finally using the 2.5 and 97.5 percentile of the B bootstrap realizations, 95% bootstrap prediction intervals were predicted.
Results and Discussion: Results showed that Motorkhane and MiyaneTonelShomare 7 stations were best fitted by a sigmoid function and Stor and Glinak stations were best fitted by second order polynomial and liner function, respectively The first 50 of the B bootstrapped curves were plotted for all stations.with respect to these plots implied that bootstrapped curves more scattered whereas observed data were less. The suspended sediment curve parameters estimated more accurately where, the suspended sediments were sampled more, as a result of reduced uncertainty in estimated suspended sediment concentration due to parameter uncertainty. In addition to sampling density bootstrapped curves, uncertainty depends on the curve shape. For GLUE methodology to assess the impact of threshold values on the uncertainty results, threshold values systematically changed from 0.1 to 0.45. Study results showed that 95% confidence intervals are sensitive to the selected threshold values and higher threshold values will result in an increasing 95% confidence interval. However, the highest 95% confidence intervals obtained by GLUE method (when threshold value was set to 0.1) was little than those values obtained by Bootstrap.
Conclusions: The uncertainty of sediment rating curves was addressed in this study by considering two different procedures based on the GLUE and bootstrap methods for four stations in Sefidrod watershed.Results showed that nonlinear equation fitted log-transformed values of sediment concentration and discharge better than linear equation. Uncertainty result using GLUE depend on chosen threshold values. As threshold values increased, 95% confidence intervals decreased. Uncertainty results showed that 95% confidence intervals estimated by bootstrap were higher than the biggest 95% confidence intervals (when threshold value set to 0.1) estimated by GLUE method. Overall, in all stations, 95% confidence intervals arising from suspended sediment curve shapes (e.g, linear, second order polynomial and sigmoid function), data sampling density and uncertainty estimation methods (here were GLUE and Bootstrap).
