a. landi

Parent material and topography are among the soil-forming factors that affect soil evolution by influencing different parameters. This study was conducted to compare the effect of marl and calcareous parent materials in different slope positions, including the summit, shoulder, foot-, and toe-slopes on soil clay mineralogy in the Karoon 3 Basin, east of Khuzestan Province. Four soil profiles in each of the two topo-sequences were dug. They were sampled based on their genetic horizons and some physical, chemical properties, and clay mineralogy were measured. The results showed that the type and abundance of clay minerals identified for both parent materials were more affected by topo-sequence position. The composition of minerals identified in the topo-sequence with marl parent materials included kaolinite, palygorskite, smectite, chlorite, mica, and quartz, and in the topo-sequence with calcareous parent materials, palygorskite, smectite, chlorite, mica minerals, and quartz, and most of the identified minerals were also observed in all positions in the C horizon. However, in marl parent materials kaolinite, and calcareous parent materials, smectite seems to have been formed pedogenically. The result of the association between Weaver and Beck indicated that most of the clay minerals are in the equilibrium of Palygorskite.

In recent years, soil contamination with potentially toxic elements (PTEs) has become a major problem in most parts of the world. PTEs are naturally generated from the pedogenesis in the soil and are formed mainly by rock weathering. Nevertheless, the natural content of metals, i.e., Cr, Zn, Ni, Pb, Cd, used to be low in the soil, but due to anthropogenic activities such as industrial emissions, atmospheric transportation, sewage irrigation, and application of pesticides and fertilizers, there is an increase in the content of PTEs. PTEs in soil are one of the most important environmental pollutants due to their toxicity, durability, easy absorption by plants and long half-life. Therefore, the assessment of soil health is very important for the sustainable development of agriculture and the rehabilitation of soils contaminated with PTEs. The present study was conducted to quantify PTEs pollution for soil environmental assessment using a flexible approach based on multivariate analysis and using pollution indicators in a part of the central lands of Khuzestan province.
 
For this purpose, in February 2021, 200 surface soil samples (0-10 cm) were taken using stratified random sampling. The collected soil samples were cleaned by removing plant materials and other pebbles, and air dried, powdered, and sieved by using a 2 mm sieve size. The interest in soil's physical and chemical properties i.e., pH was determined with a digital pH meter. Soil textural particles were measured by the hydrometer method, soil organic carbon (SOC) content was estimated by following Walkley and Black method, bulk density (BD) was measured by the Clod method, and total metal content was determined using the aqua-regia solution digestion method and analyzed using Inductively Coupled Plasma-Optical Emission spectrometry (ICP-OEC). The level of Pb, Ni, Zn, Cr pollution was estimated based on environmental indicators including contamination factor (CF), enrichment factor (EF), geo-accumulation index (Igeo), pollution index of individual metals (PI), and modified pollution index of individual metals (MPI). Multivariate statistical methods including correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to find the source of metals in the soil. All statistical methods were performed using SPSS (26 version) software.
 
Measurement of soil pH showed that the soil of the studied area tends to alkalinity. Also, the soil texture in this area is loam. The results showed that the SOC in these soil samples is 0.71%, and the range of EC (between 0.18 and 60.5 dS/m) indicates the distribution of saline and non-saline soils in the studied area. The total average concentration of Zn, Ni, Cr, and Pb were 60.26, 50.96, 50.38, and 12.67 mg/kg, respectively. The order of average for heavy metals was Zn> Ni> Cr> Pb. The highest amount of standard deviation and concentration changes were observed in Zn and Pb elements. These two elements also showed a high degree of variation coefficient in the studied area, which can indicate the high impact of human activities on the content of these elements. The results obtained from the application of multivariate statistics showed that there is a positive correlation between the elements such as Zn, Ni, and Pb in the study area, indicating that these metals probably have the same source. Whereas the absence of correlation of Cr with these elements indicates a separate source for this element compared to Pb, Zn, and Ni. There was also a strong relationship among these elements based on the PCA and CA classification. Based on the multivariate statistical analysis the source of pollution for the metals studied was mainly from both anthropogenic and geogenic activities. The results showed that the soil samples taken from the study area are in the low pollution category based on the individual element indices of CF and Igeo, but in the moderate pollution class based on the EF index. In addition, the evaluation based on the cumulative and multi-element indices of PI and MPI showed that 100% of samples have high pollution.
 
The present study concludes that the average values of Zn, Ni, Cr, and Pb were found to be below the guidelines set by the IEPA (Iran Environmental Protection Agency) as well as the Earth's crust values. The results indicate existing relationships among the studied variables, revealing that the heavy metals Zn, Ni, and Zn share the same source in the study area. Additionally, it was observed that the source of Cr is primarily geogenic in nature. These findings highlight the significance of utilizing multivariate statistical methods and pollution indicators in tandem, as they prove to be valuable tools for evaluating and quantitatively determining the potential pollution risk.

 Estimating soil properties on large scales using experimental methods requires specialized equipments and can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-InfraRed (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, non-destructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used for more than 30 years to predict an extensive variety of soil properties like organic and inorganic carbon, nitrogen, organic carbon, moisture, texture and salinity. The objectives of this study were to estimate soil properties (carbonate calcium equivalent (CCE), electrical conductivity (EC), pH, and organic carbon (OC)) using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm). In this study, the best predictions of all the soil properties, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Artificial Neural Network, Support Vector Machine Regression and Principal Component Regression (PCR) models were also compared to estimate soil properties.

 A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom region (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. The samples were air dried and passed through a 2 mm sieve, and using standard procedures soil properties were determined in the laboratory. Accordingly, soil pH and the EC contents of soil samples were determined in saturated pastes and extracts, respectively. The CCE content of the soils were measured using back titration, and the OC contents of the samples were measured using Walkley-Black method. The Reflectance spectra of all samples were measured using an ASD field spectrometer. The selection of the best model was done according to the value of the Ratio of Performance to Deviation (RPD), the coefficient of determination (R2), and the Root Mean Square Eerror (RMSE).

 Once the models were constructed using PLSR, ANN, SVMR and PCR approaches, descriptive analysis was carried out for each property, for the data measured in the laboratory. The parameters calculated for the properties were mean, coefficient of variation (CV), minimum and maximum, standard deviation and range. Coefficient of variation for the organic carbon, CCE, pH, and EC values were 21.7, 12.4, 1.34, and 28.74, respectively. Wilding (1985) proposed low, medium, and high variability for the CV values less than 15%, 15-35%, and greater than 35%, respectively. Accordingly, the organic carbon and EC of soils could be classified in the group with moderate variability. However, the calcium carbonate equivalent and pH are in the group with low variability. Since spectral data preprocessing has an effective role on improving the calibration, in order to perform spectral preprocessing, two first nodes at the first (350-400 nm) and the end (2450-2500 nm) of each spectrum were removed. In addition, two interruptions were eliminated, due to the change in the detector in the range of 900 to 1700 nm. Different preprocessing methods i.e., Standard Normal Variable (SNV) and First (FD) and Second Derivatives (SD) and Savitzky-Golay preprocessing techniques were performed on spectral data. Then, using PLSR, the cross‐validation method was used to evaluate soil properties calibration and validation. According to Stenberg (2002), for agricultural applications, The values of RPD greater than 2 indicate that the models provide precise predictions, the values of RPD between 1.5 and 2 are considered to be reasonably representative, and the values of RPD less than 1.5 indicate poor predictive performance. The results indicated the desirable capability of the PLSR method in estimating the EC (RPD > 2, R2 = 0.94), CCE (RPD > 2, R2 = 0.88), and OC (RPD > 2, R2 = 0.89). The best results of the pH (RPD > 2, R2 = 0.79) were estimated by the SVMR method. In this study the best methods of preprocessing techniques were First (FD) and Second Derivatives (SD) and Savitzky-Golay filter.

 In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil properties and showed its potential for substituting laboratory analyses. Moreover, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties. The PLSR model with First and Second derivatives and Savitzky-Golay pre-processing techniques seems to be more robust algorithm for estimating EC, OC, and CCE. The best results of the pH were estimated by the SVMR method with First and Second derivatives and Savitzky-Golay pre-processing techniques.

Nowadays, to mitigate global warming, soil can play a significant role as a pool of terrestrial carbon. Therefore, the objective of the study was to investigate the effect of climate on the radiocarbon Mean Resistance Time (MRT) in surface and subsurface soils in some of rangelands of Khuzestan Province, Iran. This way, rangelands were selected under different climates in two regions called Izeh and Ramhormoz. In both regions, the control profiles were studied to determine physical and chemical properties of the soil, carbon forms and 14C (carbon-14 isotopic ratio). The results showed that the MRT of the corresponding depths of Ramhormoz profile (with lower mean annual rainfall) was far higher than those of the Izeh profile, due to higher turnover of fresh organic matters in Izeh rangeland. Unlike Izeh profile, the MRT in Ramhormoz profile did not increase with depth owing to clay content and topographic position of the region i.e. being a depositional site. Overall, the age of carbon-14 in the top horizons of the study areas was mostly affected by factors such as climate, land management and the presence of decomposition sensitive forms of carbon. While, in deep soil the age of carbon was affected by other factors, especially soil taxa, clay content, topographic position (erosional or depositional sites) and discontinuity in soil parameters such as bulk density than by climate.

Recently, air pollution due to the occurrence of dust storms is one of the worst environmental problems in Western and Southwestern Iran, especially the Khuzestan Province (12, 13). According to the reports of the Meteorological Organization of Iran the average number of dusty days in the cities of Ahvaz and Abadan in the Khuzestan Province reaches 68 and 76 days each year, respectively (6). Previous studies have shown that the yearly damage costs of wind erosion and occurrence of dust storms in the Khuzestan Province reach about 30 Billion Rials (5). However, very few studies have been conducted on the characterization of dust particles and also the identification of their origins in Iran, especially the Khuzestan Province. Hojati et al. (10) reported that dust deposition rate, mean particle diameter, and concentration of soluble ions in samples taken from Isfahan and Chaharmahal and Bakhtiari Province decrease with altitude, with a significantly lower gradient in periods with dust storms. They reported three factors that control the rate and characteristics of dust deposited across the study transect: 1) climatic conditions at the deposition sites, 2) distance from the dust source, and 3) differences between local and transboundary sources of dust.Therefore, this study was conducted to investigate the effects of dust storms on deposition rate, mineralogy and size distribution patterns of dust particles from twelve localities around the Houralazim lagoon.

Dust samples were collected monthly during a 6 month experiment from August 2011 to February 2012. In order to differentiate between the contribution of dust production by local soils and other sources, surface soils were also sampled from the vicinity of the dust sampling sites. The collection trays were made of a glass surface (100 × 100 cm) covered with a 2 mm-sized PVC mesh on the top to form a rough area for trapping the saltating particles (Fig. 2). Dust samples were collected by scraping materials adhered to the glass trays using a spatula. All the trays were wet cleaned before the next collection. The collected dust and soil samples were examined for their grain size distribution using a Malvern Hydro 2000g laser particle size analyzer, as well as their mineral compositions by a Philips PW1840 X-ray diffractometer and a LEO 906 E transmission electron microscope (TEM).

The results showed that wind speed and direction patterns during the periods with dust storms and those without dust storms were different. Accordingly, in periods with dust storms (3, 5 and 6) the contribution of winds with speeds greater than 11.1 m/sec, especially from the Northwest direction, increased when compared with those from the periods without dust storms (1, 2 and 4). Besides, the direction of prevailing winds in periods without dust storms were mainly from the West and the Northwest. However, in periods with dust storms East-directed winds were also observed (Fig. 3). These show that the source areas of dust particles in these periods are probably different. The results also illustrated that the average amount of deposited particles in the periods with dust storms (12.5 g m-2 month-1) was considerably more than that of the periods without dust storms (7.5 g m-2 month-1) (Figs. 4 and 5). The difference in dust deposition rate between periods having dust storms and those without dust storms seems to be due to dust input from a source outside the study area. Particle size distribution analysis showed that dust particles collected from the study area in both periods (with and without dust storms) are mainly silt-sized particles. This fraction contributes to 60 to 76 % of the particles collected from periods without dust storms and 66 to 82 % of particles affected by dust storms (Table 2). The results also imply that in both periods (with and without dust storms), dust particles collected from the study area had a bimodal distribution pattern which suggests mixing of settled particles from different sources and/or deposition processes (Fig. 6). Mineralogical composition of dust particles were collected from both periods (with and without dust storms) and those from the soils contained quartz, calcite, feldspar, halite, dolomite and palygorskite (Figs. 7 and 8). Moreover, the TEM images of dust particles collected in periods with dust storms showed higher amounts of palygorskite than in periods without dust storms (Fig. 9).

The similarity in the physical properties of local soils and deposited particles of the periods with and without dust storms implies that the contribution of local soils and sediments in producing dust particles is high. However, it seems that in periods with dust storms the contribution of a transboundary origin such as Iraqi arid lands in producing dust particles increases.