Soil science
Saba Bagherifam; Mohammad Amir Delavar; Payman Keshavarz; Parviz Karami
Abstract
Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon ...
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Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.
Materials and Methods
The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.
Results and Discussion
The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.
Conclusion
The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.
Sh. Hassani; Mohammad Babaakbari; M.R. Neyestani; M.A. Delavar
Abstract
Introduction:High concentrations of As in contaminated soils represent a potential risk for groundwater sources and threat the food chain. It has been found that the iron-containing compounds used in remediation of As contaminated soils have distinct effects on the solubility of As and can be used as ...
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Introduction:High concentrations of As in contaminated soils represent a potential risk for groundwater sources and threat the food chain. It has been found that the iron-containing compounds used in remediation of As contaminated soils have distinct effects on the solubility of As and can be used as adsorbents for As removal from aqueous and soil solutions. The objectives of this study were to determine As stabilization in soil, with iron-containing compounds and also to compare the fixation of magnetite nanoparticles, ferrous sulfate, ferrosilicon, magnesium ferrosilicon and iron oxide in fixation of arsenic in contaminated soils. Materials and Methods: A factorial experiment was conducted using a completely randomized design with three replications. The experimental factors were the amendment types and levels. The modifiers used were magnetite nanoparticles, ferrous sulfate, ferrosilicon, magnesium ferrosilicon, Sfordi, and Golgohar iron soil containing 0, 0.1, 0.2 and 0.3% iron. The soil was artificially contaminated with As (20 mg/kg) using Na2HAsO4.7H2O salt and incubated for 1 month. At the end of incubation time, the modifiers were added to the As contaminated soils and after 3 months, the available fractions of arsenic, iron, zinc and copper were extracted using 0.1 M HCl and measured with ICP. Results: The results showed that the type and the amount of the modifiers had a significant effect on the available fraction of arsenic and iron in soil (extractable fraction with 0.1 M hydrochloric acid). The available fraction was reduced due to the addition of all modifiers: Magnetite nanoparticles > iron sulfate > magnesium ferrosilicon > ferrosilicon > Esfordi iron soil and Golgohar iron soil, respectively. The highest decrease in the concentration of available arsenic occurred in the soils treated with 0.3% of modifier. Application of 0.3% levels of magnetite nanoparticles, iron sulfate, ferrosilicon, ferrosilicon magnesium, Golgohar iron soil and Esfordi iron soil stabilized 91, 63, 57, 32 and 48% of arsenic extractable with 0.1 M HCl, respectively. Application of 0.3% of magnetite nanoparticles reduced available arsenic more than other adsorbents. Among the studied modifiers, magnetite nanoparticles showed more efficiency in chemical stabilization of arsenic in soil. The application of magnetite nanoparticles increased the Fe available fraction in soil. Golgohar iron soil, ferrosilicon, Esfordi iron soil, magnesium ferrosilicon, ferrous sulfate and Magnetite nanoparticles, increased the iron extractable with 0.1 M HCl of the soil, respectively. The highest Fe concentrations were observed in 0.3% of Gol Gohar soil, ferrosilicon, Esfordi soil and ferrosilicon. Increasing the modifiers decreased soil copper extractable with 0.1 M hydrochloric acid concentration and increased soil zinc extractable with 0.1 M hydrochloric acid concentration, which was not statistically significant. Conclusion: Application of magnetite nanoparticles reduced arsenic concentration more than other adsorbents and showed more efficiency in chemical stabilization of soil arsenic. Other modifiers have also been able to stabilize the arsenic in the soil, suggesting the possibility of using iron-containing modifiers in arsenic-contaminated soils. The use of modifiers increased the iron concentration in the soil. Due to their reasonable price and availability, iron sulfate and magnesium ferrosilicon are recommended for soil arsenic stabilization. At 0.3% soil level, Gol Gohar and Esfordi iron soil were able to reduce 32% and 48% the arsenic concentration, respectively and are recommended for arsenic stabilization in contaminated soil. Golgohar, ferrosilicon, Esfordi and magnesium iron soils caused the highest increase in soil iron concentration. Due to the concentration of other soil elements and the price of modifiers, the level of 0.2% of iron sulfate, Gol Gohar and Esfordi iron soil, ferrosilicon and magnesium ferrosilicon is recommended for stabilization of arsenic in contaminated soil.
Yaser Safari; Mohammad Amir Delavar; Zahra Noori
Abstract
Introduction: Land suitability evaluation (LSE) may be considered as a worldwide accepted procedure to achieve optimum utilization of the available land resources for sustainable agriculture. The common LSE procedures, like the widely accepted “A framework for land evaluation” presented by FAO, however, ...
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Introduction: Land suitability evaluation (LSE) may be considered as a worldwide accepted procedure to achieve optimum utilization of the available land resources for sustainable agriculture. The common LSE procedures, like the widely accepted “A framework for land evaluation” presented by FAO, however, do not consider variability of soil quality parameters; whereas, the soil quality and its suitability for different uses are influenced by highly variable land management strategies. Therefore, assessing the spatial variability pattern of environmental variables and their accumulative effects on land suitability for specific crops, is the key for achieving to thoughtful land use planning for sustainable use. The present study was done aimed to evaluation of spatial variability of land suitability for irrigated wheat in Zanjan plain using accumulated limitation scores and geostatistics.
Materials and Methods: The study area is located in the southern part of Zanjan city, northwestern Iran, between the latitudes 36° 33′ and 36° 40′ N and the longitudes 48° 23′ and 48° 37′ E, covering an area of about 7000 ha. A total of 85 sampling locations were designed using a systematic sampling grid with an interval of 1000 m and consequently, soil samples at all sampling sites were collected from the depths of 0–25, 25-50, 50–75 and 75–100 cm. The soil samples were taken to the laboratory, where they were air-dried and then passed through a 2 mm sieve. Prepared samples were subsequently analyzed for required soil properties in LSE (Sys et al., 1993) using standard methods. Besides, required climatic data for LSE were obtained from Znajan Synoptic Meteorological Station for a 50 years period (1961– 2011). Then, the limitation degrees for all of the important properties for wheat cropping were determined (Sys et al., 1993). Afterwards, the determined limitation degrees were converted to limitation scores using standard tables presented by Zhang (1989). Then, accumulated limitation scores were calculated for all locations and using an exponential equation, land-suitability membership scores were achieved. Finally, these scores were interpolated using ordinary kriging method in ArcGIS software (ver. 10.2; ESRI) and the final suitability map was produced.
Results and Discussion: The results showed that the climatic conditions for irrigated wheat was relatively good; so that the region received just 1 limitation score arisen from the mean temperature of the growing cycle. On the other hand, among the studied soil properties, the content of coarse fragments made some serious limitations for wheat farming in the studied area; so that more than half of sampling points showed moderate to very severe limitations in respect of this property. This high observed limitation of coarse fragments may be attributed to the youthfulness of studied soils; because according to Soil Taxonomy, the studied soils are mainly classified as Entisols, which are poorly developed and immature soils maintaining their rock structure to some extent. Other studied soil properties, like soil texture and calcium carbonate equivalent content, made no or slight limitations for wheat farming in the studied soils. Accumulated effects of limiting properties led to elevated limitation scores in some sampling locations, especially in northwestern parts of the area and consequently, their suitability classes were decreased. Attributing the specific land suitability classes to each sampling location based on the calculated limitation scores revealed some sharp variability in suitability classes thorough the relatively small distances, which seems to be less compatible with the widely accepted generality of soil continuity. Totally, the spatial distribution map of land-suitability membership scores showed appreciable variability thorough the area. This may suggest that the studied soil properties have relatively high short-range variations, which is originated from the soil substantial characteristics or management practices. Comparison of the interpolated suitability map with the point map revealed that the spatial variability pattern of land suitability for irrigated wheat was more gradual and more obvious in interpolated map.
Conclusions: Compared with common conventional land suitability procedures, continuous pattern of land suitability variation based on the fuzzy viewpoint to the soil variability, lead to more compatible results with the continuous nature of environmental variables. However, due to the long and short-range variations of various soil properties thorough the studied area, appreciable variations in land suitability for wheat farming was observed. Controlling this highly variable suitability of studied lands for irrigated wheat farming needs precise and thoughtful management strategies.
M.A. Delavar; Y. Safari
Abstract
Introduction: The accumulation of heavy metals (HMs) in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. HMs may be considered as the most important soil pollutants, because they are not biodegradable and their physical ...
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Introduction: The accumulation of heavy metals (HMs) in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. HMs may be considered as the most important soil pollutants, because they are not biodegradable and their physical movement through the soil profile is relatively limited. Therefore, root uptake process may provide a big chance for these pollutants to transfer from the surface soil to natural and cultivated plants, which may eventually steer them to human bodies. The general behavior of HMs in the environment, especially their bioavailability in the soil, is influenced by their origin. Hence, source apportionment of HMs may provide some essential information for better management of polluted soils to restrict the HMs entrance to the human food chain. This paper explores the applicability of multivariate statistical techniques in the identification of probable sources that can control the concentration and distribution of selected HMs in the soils surrounding the Zanjan Zinc Specialized Industrial Town (briefly Zinc Town).
Materials and Methods: The area under investigation has a size of approximately 4000 ha.It is located around the Zinc Town, Zanjan province. A regular grid sampling pattern with an interval of 500 meters was applied to identify the sample location, and 184 topsoil samples (0-10 cm) were collected. The soil samples were air-dried and sieved through a 2 mm polyethylene sieve and then, were digested using HNO3. The total concentrations of zinc (Zn), lead (Pb), cadmium (Cd), Nickel (Ni) and copper (Cu) in the soil solutions were determined via Atomic Absorption Spectroscopy (AAS). Data were statistically analyzed using the SPSS software version 17.0 for Windows. Correlation Matrix (CM), Principal Component Analyses (PCA) and Factor Analyses (FA) techniques were performed in order to identify the probable sources of HMs in the studied soils.
Results and Discussion: Comparing the measured HMs contents with their normal range in uncontaminated soils demonstrated the contamination of soils by Pb, Zn and Cd, with average concentrations of 152.8, 294.2 and 5.6 mg kg-1, respectively,whereas Ni and Cu did not show any pollution risk. The total concentration of Zn, Pb and Cd in the soil showed a great degree of variability, indicated by large coefficients of variation (CV) from 228.5 % of Cd to 354.8 % ofPb. These elevated CVs may indicate that these elements’ distribution in the studied area is influenced by an anthropogenic source. In contrast, the relatively low calculated CVs for Ni and Cu may imply that natural sources are responsible for these elements’ distribution in the studied soils. Correlation matrix (CM) analysis revealed high correlation coefficients between Zn-Cd and Ni-Cu, indicating the influence of the same factors in controlling their distribution. On the other hand, Pb contents showed low correlation with Ni and Cu values, whereas its correlation with Zn and Cd was relatively high. Therefore, it seems that Pb distribution in the studied soils is more influenced by the factor which controls the Zn and Cd distribution, rather than another factor that is responsible for accumulation of Ni and Cu in the studied soils. According to the PCA analysis, two significant components were extracted explaining about 84% of total variance. FA analysis showed that studied variables have a relatively high communality with two extracted principal components, indicating that almost all of the measured total variation can be efficiently explained by the extracted principals. Industrial activities in the Zinc Town seem to be the main factor which caused the high concentrations of Pb, Zn and Cd in the soil environment in this area; whereas Ni and Cu were associated with the natural sources including geology of the studied area (parental material’s factor). The obtained results from this study coincide with the prior studies indicating that multivariate statistics is a powerful technique for identification of probable sources of HMs in the soil.
Conclusions: The studied soils are classified as polluted soils with Zn, Pb and Cd,whereas Ni and Cu did not show any pollution risk. PCA and correlation analyses between HMs indicated that HM pollution in the studied area may originate from natural and anthropogenic factors. It can be concluded that Zinc Town controls the distribution of Zn, Pb and Cd in the surrounding soils, but Ni and Cu distribution in the studied area is mainly influenced by natural factors.Totally, industrial activities related to Zn production caused simultaneous entrance of several HMs to the adjacent soils and led to degradation of the lands in the studied area.
S. Abdollahi; Mohammad Amir Delavar; P. Shekari
Abstract
Soil contamination by heavy metals and its long-term detrimental effects on environment and human health is a present-day concern of environmental scientists. The aims of this paper is to present the results of spatial distribution mapping of heavy metals in topsoils (0-10 cm) using 315 georeferenced ...
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Soil contamination by heavy metals and its long-term detrimental effects on environment and human health is a present-day concern of environmental scientists. The aims of this paper is to present the results of spatial distribution mapping of heavy metals in topsoils (0-10 cm) using 315 georeferenced soil samples regularly spaced grid pattern with an interval of 500 meters from Anguran area located in the Zanjan province. Total and available forms of Pb, Zn, and Cd were extracted by HNO3 and DTPA-TEA and measured for the samples. The average for total concentrations of Pb, Zn, and Cd were 109.96, 165.57 and 6.02 mg/kg and for available forms were 46.36, 61.54 and 2.63 mg/kg, respectively. The heavy metal concentration data tended to be positively skewed and outlying values were observed. The Box-Cox transformation technique was applied to normalize the data. Strong positive correlations were observed between the concentrations of heavy metals studied. The results showed that spherical model revealed the best result for describing the spatial variability of Pb, Zn, and Cd. The ranges of influence for variograms of Pb, Zn, and Cd were 4800m, 3987m and 4845m, respectively. The application of the Ordinary Kriging method showed a good performance for estimating heavy metals concentration in the areas not being sampled. The results based on the Kriging Maps showed that the concentration of heavy metals increased around the procreation factories, while decreased in longer distances from the factories. The Kriging Maps of total heavy metals concentration indicate a strong spatial pattern in the Southeast and Center of the study area. These maps can provide valuable information for assessing the pollution hazard.
S. Abdollahi; M. A. Delavar; P. Shekari
Abstract
Increasing soil pollution due to heavy metals is a major concern of present day soil research. This study conducted to know intensity and spatial pattern of soil heavy metals pollution in a 10,000 ha area of Anguran region near Zanjan. A number of 315 surface (0-10 cm) samples collected at nodes of a ...
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Increasing soil pollution due to heavy metals is a major concern of present day soil research. This study conducted to know intensity and spatial pattern of soil heavy metals pollution in a 10,000 ha area of Anguran region near Zanjan. A number of 315 surface (0-10 cm) samples collected at nodes of a 500 meter equilateral grid. Beside HNO3-extracted Pb, Cu, Ni, Cd and Zn content, EC, pH, OC and CaCO3 of the samples were determined. To classify data in taxonomic space, a fuzzy c-means with extragrades clustering algorithm ran on the data using several fuzziness exponents (Φ). Plausible fuzzy clusters obtained at Φ=1.3. To find eight as the optimal number of classes from a 2-10 assemblage, fuzziness validation functions F, H, and S were used. Scrutiny of class centroids and membership values revealed that though number of variables was not numerous, the algorithm clustered data sensitively. Spatial distribution of classes mapped through geostatistical analysis of membership values. Though extragrade class embraced extreme values, still all centroids of regular classes showed severe pollution. Most polluted classes C, E, F and H located at center to southeast, while A, B, D and G covered northern and western parts of the study area. Extragrade class widely spread in the area that confirmed interspersed outliers among all others. Major part of extragrade class lied across southeastern part of the area. Results of the study showed that numerical classification of soil pollution is rather realistic, thus provides a pragmatic approach to the problem.
