Comparative assessment of soil fertility across varying elevations.

Land elevation exerts a significant influence on soil fertility through affecting macro and micro climatic conditions and geomorphological processes. To evaluate the soil fertility at different elevation classes, namely 1600-2000, 2000-2400, 2400-2800, and > 2800 m, 350 surface soil samples (0-30 cm) were collected from the agricultural lands of northwestern Iran. Soil properties, including soil texture, calcium carbonate (CaCO3), pH, electrical conductivity (EC), organic matter (OM), and soil macronutrients (TN, P, and K) and micronutrients (Fe, Mn, Zn, and Cu), were measured. Finally, the interpretation and classification of the soil samples were made using the nutritional value index (NIV). The comparison of the NIV index based on elevation changes showed that the Gomez method classifies the soil properties in an optimal order as evidenced by its tendency towards the center of the data. However, the Common method is more consistent with the observed trend. After classifying the NIV index using the Common method, it was determined that CaCO3 and soil salinity are not the limiting factor for soil fertility in different elevation classes. However, in all elevations, high pH, low OM at elevations > 2800 m, total nitrogen (TN), available phosphorous (AP), and micronutrients deficiencies, except Zn at the elevation of 1600-2000 m, are the main limiting factors for soil fertility of agricultural lands. The results provide further insight into the elevation-based land evaluation and may supports grower's decision on nutrient management and crop selection strategies.

Digital mapping of soil erodibility factor in northwestern Iran using machine learning models.

Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1 km spacing. To calculate the K-factor, the distribution of particle size and organic carbon (OC) were determined. In addition, 21 terrain attributes were calculated by Digital Elevation Model (DEM) to add value to the soil data. Then, K-factor was modeled using Random Forest (RF) and Artificial Neural Network (ANN) models. In the next step, a non-linear Multiple Logistic Regression (NMLR) was used to obtain low and high classes of K-factor. The results showed that the performance of RF is superior to ANN with a high coefficient of determination [R2 = 0.85] and good accuracy [RMSE = 0.003 (Mg ha h/ha MJ mm)]. Therefore, the RF was employed for predicting the K-factor spatial distribution. Finally, using the NMLR model, the study area was divided into low and high classes of K-factor with good correlation [R2 Cox and Snell = 0.78, R2 Nagelkerke = 0.65]. The areas of these two classes were 60.4% for low class and 39.6% for the high class of K-factor. Based on these results, it was concluded that the resultant map of low and high classes of K-factor could be used by farmers and managers for managing soil water erosion risks in the study area.

Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran).

Soil organic matter (SOM) is one of the important factors in arid and semiarid areas, which describes the soil quality. Spatial estimation of SOM is important to understand the SOM storage and the emphasis of the SOM in the global carbon cycle and environmental issues. Mapping of SOM content can have significant uses in environmental modeling. In the current study, various methods have been evaluated for estimating the SOM content through soil samples and using auxiliary variables in the west of Eastern Azerbaijan province, Iran. In this study, support vector machine (SVM), multi-factor regression (MFR), and multi-factor weighted regression model (MWRM) approaches have been suggested for predicting and investigating the spatial distribution of SOM. In total, 155 surface soil samples (from the depth of 0 to 30 cm) were obtained. These soil samples were randomly divided into training data set (105 soil samples) and testing data set (50 samples). According to the results, SOM is affected by soil properties as well as environmental factors (normalized difference vegetation index (NDVI)). In this study, clay, silt/sand, NDVI, and soil moisture were used as auxiliary variables in the estimation of SOM. Three methods were compared to determine a suitable method for spatial estimation of SOM, and results showed that SVM has the lowest estimation error (RMSE = 0.100, MAE = 0.07, and MRE = 3.32) and highest regression coefficient (R2 = 0.719) during SOM estimation. The present results show the indirect effect of elevation by controlling auxiliary variables and confirm the importance of auxiliary variables in spatial distribution patterns of SOM.

Predicting the spatial distribution of soil mineral particles using OLI sensor in northwest of Iran.

Texture is one of the most important soil properties that knowledge of the spatial distribution is essential for land-use planning and other activities related to agriculture and environment protection. So, this study was performed to supply the soil texture spatial distribution using standardized spectral reflectance (ZPC1) index of Landsat 8 satellite images in the northwest of Iran. The soil sampling was performed using a random method in 145 points. Mineral soil particles including clay, silt, and sand were determined, and soil texture was calculated. In this study, Landsat 8 satellite images were used to interpolate the soil texture spatial distribution. In the first step, the principal component analysis (PCA) was obtained. Then, PCA1 was standardized using a z-score (ZPC1), and regression techniques were used to create proper relationships between ZPC1 and the primary soil particles. Then, spatial distribution of soil particles was used to create a spatially distributed map of the soil textural classes. The results showed that the standardization of the first component reduced the standard deviation of PCA1 from 23.6 to 10.8. The results of comparing ZPC1 with soil mineral components showed that with increasing the amounts of soil clay and sand, the ZPC1 value decreases and increases, respectively. The results showed that the ranges of the spatial distribution of clay and sand were similar to the laboratory-measured amounts. The results of texture class prediction using the soil texture triangle showed that the amount of similarity between the measured and predicted classes was 53.79%.

Understanding deforestation impacts on soil erosion rates using 137Cs, 239+240Pu, and 210Pbex and soil physicochemical properties in western Iran.

To investigate the effects of converting forests into vineyards typical to Zarivar Lake watershed, Iran, which occurred mainly in the 1970s and 80s, on soil erosion,137Cs and 210Pbex, being mid-and-long-term soil loss tracers, were applied. In Chernobyl-contaminated areas like those found in some parts of Europe and Asia, the proportion of 137Cs Chernobyl fallout needs to be determined to convert 137Cs inventories into soil erosion rates. To do so, Pu radioisotopes were applied for the first time in Iran. The soil samples were gathered from two adjacent, almost similar hillslopes under natural forest (slope length: 250 m; slope gradient: 20%) and rainfed vineyard (slope length: 200 m; slope gradient: 17%). 137Cs/239+240Pu ratios indicated that 49.8 ± 10.0% of 137Cs originated from Chernobyl. The net soil erosion rates derived by 137Cs, and 210Pbex approaches were 5.0 ± 1.1 and 5.9 ± 2.9 Mg ha-1 yr-1 in the forested hillslope, and 25.9 ± 5.7 and 32.5 ± 14.5 Mg ha-1 yr-1 in the vineyard hillslope, respectively. Both 137Cs and 210Pbex highlighted that deforestation increased soil erosion by around five times. Moreover, the impacts of deforestation on soil physicochemical properties were investigated in surface and subsurface soils. Compared to forested hillslope, soil organic carbon stock in the upper 40 cm of the vineyard reduced by 14 Mg C ha-1 (29%), 8 Mg C ha-1 of which was removed by erosion within 35 years, and the remaining have likely been lost via emissions (6 Mg C ha-1). The vineyard topsoil experienced the most dramatic drops in percolation stability (PS), sealing index, and organic matter by about 55, 51, and 49%, respectively. Among all measured physicochemical properties, PS showed the greatest sensitivity to land-use change. Overall, the present study's findings confirmed that deforestation for agricultural purposes triggered soil loss, deteriorated soil quality and possibly contributed to the reduction of the lake's water quality and climate change.

Distribution, source apportionment, and risk analysis of heavy metals in river sediments of the Urmia Lake basin.

The anthropogenic heavy metal dissemination in the natural environment through riverine sediments is a major ecological and public health concern around the world. This study gives insight into the source apportionment and potential ecological and health risks of heavy metals in river sediments of the Urmia Lake basin, a natural world heritage located in northwestern Iran. A comprehensive sediment sampling was conducted in seven major rivers feeding the basin during the summer and winter of 2021. Samples were analyzed for zinc (Zn), copper (Cu), cadmium (Cd), lead (Pb), and nickel (Ni) contents and a suite of chemical and physical properties. Subsequently, Pollution Index (PI), Pollution Load Index (PLI), Ecological Risk (ER), Hazard Quotients (HQ), Hazard Index (HI), and Carcinogenic Risk (CR) indices were determined. The mean concentration of heavy metals in all rivers' sediments exhibited the descending order of Ni > Zn > Pb > Cu > Cd during both summer and winter. Multivariate analysis suggested that Zn was primarily initiated from natural processes, Cd and Pb were affected by human activities, and Cu along Ni were derived from natural and anthropogenic factors. The PI unveiled that most sediment samples were unpolluted to slightly polluted by Zn, Cu, and Pb, and slightly to moderately polluted by Cd. PLI and ER indices demonstrated that the sediment poses non to moderate pollution and low to moderate ecological risk, respectively. Using a human health risk approach, we found that the HI values of all heavy metals and THI were less than one for children and adults implying non-carcinogenic risk in the analyzed sediments. Carcinogenic effects of Cd and Pb at all rivers sediments via ingestion, inhalation, and dermal contact were almost within tolerable risks (1 × 10-6 to 1 × 10-4) for children and adults. PI, PLI, ER, HQ, HI, and CR index values of sediment samples during the summer were higher than those during the winter. This is attributed to the greater heavy metal concentrations and the lower water flow during summer. Our results provide practical information for better management and control of heavy metal pollution in aquatic-sedimentary ecosystems.

Specific Removal of Nitrite from Lake Urmia Sediments by Biohydrogel Based on Isolated Soy Protein/Tragacanth/Mesoporous Silica Nanoparticles/Lycopene.

In this study, a biodegradable biohydrogel based on isolated soy protein/tragacanth containing mesoporous silica nanoparticles and lycopene pigment (ISP/TG/MPS/Lyc) is prepared. The physicochemical characteristics and structure of the biohydrogel are investigated by scanning electron microscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction, and thermal gravimetry analysis (TGA) techniques. Mechanical properties (tensile strength and elongation at break point), antioxidant activity, water solubility, water absorption capacity (WAC), and the humidity content of the biohydrogels are studied. Five optimal biohydrogels including pure ISP, ISP/TG, ISP/MPS, ISP/Lyc, and ISP/TG/MPS/Lyc are used for chemical treatment of Lake Urmia sediments. For this purpose, biochemical oxygen demand (BOD), chemical oxygen demand (COD), nitrate, and nitrite of sediments are examined before and after treatment with biohydrogels. According to the FTIR results, there is only physical interaction between lycopene and isolated soy protein. According to the TGA results, adding silica mesoporous to biohydrogel increases its thermal stability. Tragacanth gum and lycopene pigment reduce water solubility and increase the WAC of biohydrogel. The biohydrogel significantly reduces the BOD and COD of the sediments. The biohydrogel reduces nitrite content up to 90%, while reducing nitrate content by almost 30%. The results show that the biohydrogel containing lycopene selectively purifies nitrite from the sediment solution of Lake Urmia.

Central Composite Design Optimization of Zinc Removal from Contaminated Soil, Using Citric Acid as Biodegradable Chelant.

Citric acid (CA) was evaluated in terms of its efficiency as a biodegradable chelating agent, in removing zinc (Zn) from heavily contaminated soil, using a soil washing process. To determine preliminary ranges of variables in the washing process, single factor experiments were carried out with different CA concentrations, pH levels and washing times. Optimization of batch washing conditions followed using a response surface methodology (RSM) based central composite design (CCD) approach. CCD predicted values and experimental results showed strong agreement, with an R2 value of 0.966. Maximum removal of 92.8% occurred with a CA concentration of 167.6 mM, pH of 4.43, and washing time of 30 min as optimal variable values. A leaching column experiment followed, to examine the efficiency of the optimum conditions established by the CCD model. A comparison of two soil washing techniques indicated that the removal efficiency rate of the column experiment (85.8%) closely matching that of the batch experiment (92.8%). The methodology supporting the research experimentation for optimizing Zn removal may be useful in the design of protocols for practical engineering soil decontamination applications.

Distribution of phosphorous pools in western river sediments of the Urmia Lake basin, Iran.

Impact of anthropogenic loading of phosphorous (P) to an aquatic ecosystem can be qualitatively assessed by measuring the buildup and distribution of P in sediments and by differentiating bioavailable and recalcitrant P pools. Distribution of P pools in sediments is affected by the physico-chemical properties including specific elements, particle size distribution, pH, electrical conductivity (EC), and carbonate content. We applied X-ray fluorescence and scanning electron microscopy (SEM) methods to characterize sediments from western rivers in the Urmia Lake basin in Iran with a particular focus on properties that are relevant to P speciation. Phosphorous pools were sequentially extracted into operationally defined exchangeable (EXCH-P), iron and aluminum oxide-bound (Fe/Al-P), calcium-bound (Ca-P), and residual (RES-P) P pools. In river sediments, the size of P pool was found to be in the order of Ca-P > RES-P > Fe/Al-P > EXCH-P indicating small fraction of bioavailable P pool and Ca-P minerals being the most dominant P sink. Carbonate-related properties had an inverse relationship with bioavailable P pools in the river sediments studied. The principal component analysis (PCA) of the sequential extraction data with sediment properties revealed that four principal components described 82.7% of total variation. Similarly, particle size-related properties were found to have the highest eigenvalues in the first PC. Electron diffraction spectra (EDS) and X-ray fluorescence (XRF) analyses showed a largely uniform distribution of P in the upstream sediment. However, limited evidence of local enrichment of P with Fe, Al, and Ca contents was observed in the downstream river sediments. Correlation of Fe/Al-P pool size with Al2O3 and SiO2 contents indicated that P was associated with Al oxide and clay minerals in the sediment matrix. Overall, the results from this study provide insights into the variability of upstream and downstream river processes and their relationship with P pools with regard to their bioavailability. These results are expected to be useful in assessing the potential impact of P loading on the aquatic ecosystem in the Urmia Lake basin.