Purification of Forest Clear-Cut Runoff Water Using Biochar: A Meso-Scale Laboratory Column Experiment

Biochar can be an effective sorbent material for removal of nutrients from water due to its high specific surface area, porous structure, and high cation and anion exchange capacity. The aim of this study was to test a biochar reactor and to evaluate its efficiency in runoff water purification and consecutive nutrient recycling in clear-cut peatland forests. The goodness of the method was tested in a meso-scale (water volume thousands of liters) reactor experiment by circulating runoff water through wood biochar-filled columns and by determining water nutrient concentrations in the column inlet and outlet. The pseudo-first and second order kinetic models were fitted to the experimental data and the adsorption rate (Kad) and maximum adsorption capacity (Qmax) of the biochar reactor were quantified. The concentration of total nitrogen (TN) decreased by 58% during the 8-week experiment; the majority of TN adsorption occurred within the first 3 days. In addition, NO3-N and NH4-N concentrations decreased below the detection limit in 5 days after the beginning of the experiment. The maximum adsorption capacity of the biochar reactor varied between 0.03–0.04 mg g−1 biochar for NH4-N, and was equal to 0.02 mg g−1 biochar for TN. The results demonstrated that the biochar reactor was not able to adsorb TN when the water TN concentration was below 0.4 mg L−1. These results suggest that a biochar reactor can be a useful and effective method for runoff water purification in clear-cut forests and further development and testing is warranted. Unlike traditional water protection methods in peatland forestry, the biochar reactor can effectively remove NO3-N from water. This makes the biochar reactor a promising water protection tool to be tested in sites where there is the risk of a high rate of nutrient export after forest harvesting or drainage

Biochar as adsorbent in purification of clear‑cut forest runoff water : adsorption rate and adsorption capacity

Peer reviewe

Purification of Forest Clear-Cut Runoff Water Using Biochar: A Meso-Scale Laboratory Column Experiment

Biochar can be an effective sorbent material for removal of nutrients from water due to its high specific surface area, porous structure, and high cation and anion exchange capacity. The aim of this study was to test a biochar reactor and to evaluate its efficiency in runoff water purification and consecutive nutrient recycling in clear-cut peatland forests. The goodness of the method was tested in a meso-scale (water volume thousands of liters) reactor experiment by circulating runoff water through wood biochar-filled columns and by determining water nutrient concentrations in the column inlet and outlet. The pseudo-first and second order kinetic models were fitted to the experimental data and the adsorption rate (Kad) and maximum adsorption capacity (Qmax) of the biochar reactor were quantified. The concentration of total nitrogen (TN) decreased by 58% during the 8-week experiment; the majority of TN adsorption occurred within the first 3 days. In addition, NO3-N and NH4-N concentrations decreased below the detection limit in 5 days after the beginning of the experiment. The maximum adsorption capacity of the biochar reactor varied between 0.03–0.04 mg g−1 biochar for NH4-N, and was equal to 0.02 mg g−1 biochar for TN. The results demonstrated that the biochar reactor was not able to adsorb TN when the water TN concentration was below 0.4 mg L−1. These results suggest that a biochar reactor can be a useful and effective method for runoff water purification in clear-cut forests and further development and testing is warranted. Unlike traditional water protection methods in peatland forestry, the biochar reactor can effectively remove NO3-N from water. This makes the biochar reactor a promising water protection tool to be tested in sites where there is the risk of a high rate of nutrient export after forest harvesting or drainage

Nitrogen Recovery from Clear‑Cut Forest Runoff Using Biochar: Adsorption–Desorption Dynamics Affected by Water Nitrogen Concentration

Forest regeneration operations increase the concentration of nitrogen (N) in watercourses especially outside the growing season when traditional biological water protection methods are inefficient. Biochar adsorption-based water treatment could be a solution for nutrient retention. We studied the total nitrogen (TN) and nitrate-nitrogen (NO3--N) adsorption-desorption properties of spruce and birch biochar. The adsorption test was performed under four different initial concentrations of TN (1, 2, 3, and 4 mg L-1) using forest runoff water collected from ditch drains of boreal harvested peatland. The results showed that the TN adsorption amount increased linearly from the lowest to the highest concentration. The maximum adsorption capacity was 2.4 and 3.2 times greater in the highest concentration (4 mg L-1) compared to the lowest concentration (1 mg L-1) in spruce and birch biochar, respectively. The NO3--N adsorption amount of birch biochar increased linearly from 0 to 0.15 mg NO3--N g biochar(-1) when the initial concentration of NO3--N increased from 0.2 to 1.4 mg L-1. However, in spruce biochar, the initial concentration did not affect NO3--N adsorption amount. The results indicate that concentration significantly affects the biochar's capacity to adsorb N from water. The desorption test was performed by adding biochar extracted from the adsorption test into the forest runoff water with low TN concentration (0.2 or 0.35 mg L-1). The desorption results showed that desorption was negligibly small, and it was dependent on the TN concentration for birch biochar. Therefore, biochar can be a complementary method supporting water purification in peatland areas.Peer reviewe

Long-Lead Rainfall Prediction Based on Climate Patterns of Tele-Connection, A Case Study: Aharchay Basin

This study aims to develop a seasonal rainfall prediction model for the Aharchay Basin, northwest of Iran. The model is based on climate patterns of tele-connection including sea level pressure (SLP) and sea surface temperature (SST) over the period from 1965 to 2005. The models cover both wet (from December to May) and dry (from June to November) seasons. For this purpose, the climatic patterns affecting the climate of the northwest of Iran were initially determined. In the second stage of the study, the correlation coefficient analysis and the Gamma Test (GT) technique were used to select the best predictors and to determine the best combination of the variables. The results revealed that the gamma test model outperformed the other model in determining the required input variables and their best combination. The seasonal rainfall in the basin was also predicted using the Support Vector Machines (SVM) and the results thus obtained were compared with those of the multivariate linear regression model as a benchmark to show the performance of the SVM model in rainfall prediction

Input selection for long-lead precipitation prediction using large-scale climate variables:A case study

In this study, a precipitation forecasting model is developed based on the sea level pressures (SLP), difference in sea level pressure and sea surface temperature data. For this purpose, the effective variables for precipitation estimation are determined using the Gamma test (GT) and correlation coefficient analysis in two wet and dry seasons. The best combination of selected variables is identified using entropy and GT. The performances of the alternative methods in input variables selection are compared. Then the support vector machine model is developed for dry and wet seasonal precipitations. The results are compared with the benchmark models including naïve, trend, multivariable regression, and support vector machine models. The results show the performance of the support vector machine in precipitation prediction is better than the benchmark models.</jats:p

Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran

The present study compares the results of the Soil and Water Assessment Tool (SWAT) with a Support Vector Machine (SVM) to predict the monthly streamflow of arid regions located in the southern part of Iran, namely the Roodan watershed. Data collected over a period of 19 years (1990–2008) was used to predict the monthly streamflow. Calibration (training) and validation (testing) were performed within the same period for both the models after the preparation of the required data. A semi auto-calibration was performed for the SWAT model. Also, the best input combination of the SVM model was identified using the Gamma Test (GT). Finally, the reliability of the SWAT and SVM models were evaluated based on performance criteria such as the Nash-Sutcliffe (NS) model efficiency coefficient and the Root Mean Square Error (RMSE). The obtained results from the development of the SWAT model and SVM model indicated satisfying performance in predicting the monthly streamflow in the large arid region. The SWAT obtained NS and RMSE values of 0.83 and 6.1 respectively, and the SVM obtained NS and RMSE values of 0.84 and 6.75 respectively for the validation (testing) period. Results indicate that for high flows of more than 19 (m3/s), both models predict flow with over and under estimation in the validation (testing) period. Moreover, the SVM has a closer value for the average flow in comparison to the SWAT model; whereas the SWAT model outperformed for total runoff volume with a lower error in the validation period

The application of backpropagation neural network method to estimate the sediment loads

Nearly all formulations of conventional sediment load estimation method were developed based on a review of laboratory data or data field. This approach is generally limited by local so it is only suitable for a particular river typology. From previous studies, the amount of sediment load tends to be non-linear with respect to the hydraulic parameters and parameter that accompanies sediment. The dominant parameter is turbulence, whereas turbulence flow velocity vector direction of x, y and z. They were affected by water bodies in 3D morphology of the cross section of the vertical and horizontal. This study is conducted to address the non-linear nature of the hydraulic parameter data and sediment parameter against sediment load data by applying the artificial neural network (ANN) method. The method used is the backpropagation neural network (BPNN) schema. This scheme used for projecting the sediment load from the hydraulic parameter data and sediment parameters that used in the conventional estimation of sediment load. The results showed that the BPNN model performs reasonably well on the conventional calculation, indicated by the stability of correlation coefficient (R) and the mean square error (MSE)