Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran.

Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid 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.

Evaluation of heavy metal contamination and ecological risk of soil adjacent to Saravan municipal solid waste disposal site, Rasht, Iran.

This study was performed on the soil of the Hyrcanian forests near Saravan municipal solid waste dumpsite, Rasht, Iran. In this research, the contents of metals (As, Pb, Cr, Cd, Cu, Hg, and Zn) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The geoaccumulation index (Igeo), contamination factor (CF), and enrichment factor (EF), as well as pollution load index (PLI), were used to evaluate the metals contamination. The ecological risk factor ([Formula: see text]) and the potential ecological risk index (PERI) were applied to assess ecological risk. Pearson's correlation coefficients and the principal component analysis (PCA) were used to determine the possible origin of the metals. The metal concentrations were as follows: Zn > Pb > Cu > Cr > As > Cd > Hg. The results of the statistical tests showed that, except for Cr, the other elements had a significant difference with the control station (P < 0.05). The results of the Pearson's correlation coefficients, the PCA, and the Igeo revealed that the possible source of As, Hg, and Pb was the waste dumpsite activities and other anthropogenic origins, while Cd, Cu, Zn, and Cr probably have geogenic sources. The PLI was < 1, in unpolluted grade for all stations. The [Formula: see text] of the metals ranged as follows Hg > Cd > As > Pb > Zn, Cu > Cr, which implies that Cd and Hg play a key role in determining the ecological risk. The mean value of the PERI was 192.11 that represented a moderate ecological risk.