فهرست مطالب پرویز شکاری

Affected by the change of land-use pattern, soil pollution has become an important environmental issue in developed and developing countries.  Heavy element pollution, as a kind of soil pollutant, is really dangerous due to their high toxicity and persistence in the environment. In the current study, the extent of pollution to heavy elements in downstream area of southern waste landfill of Kermanshah city was investigated. In this research, 208 soil samples (from 0 to 20 cm depth), were collected with 50 meters distances systematically from downstream area of waste landfill. The spatial pattern of variables was investigated through calculating experimental variograms and fitted models using geostatistics.  The mean concentrations of Fe, Mn, Cu and Zn in downstream area of waste landfill are 4077, 943, 41.8 and 102 mgkg−1 respectively. The spherical model was recognized as the best model to describe the spatial variability of total heavy metals. Total content of Fe, Mn, Cu and Zn showed a strong spatial dependence. According to the ordinary kriging maps, the total concentration of elements in the central parts of the study area is less than the other parts. This is probably due to the lower slope of the central region and consequently the lower infiltration. The concentration of elements in the southeastern towards the northeast High slope of the area is the result of the adjacency to the waste landfill and also the higher steepness in this part. The findings indicated a long-term progressive trend soil pollution, thus it is necessary to halt latex infiltration either towards the soil or vertically ground water. The results showed a long-term progressive trend in soil pollution. Considering population growth, waste production rate, distance from the city center, and other factors determining the location of landfill, it is suggested to reconsider positioning waste landfill scientifically. Furthermore, some services such as waste separation in the source, waste recycling in the source, and composting would be inevitable.

Digital soil mapping (DSM) can be defined as a production of spatial soil information. Decision tree (DT) algorithm is one of the most popular machine learning methods which was applied in several recent DSM studies. This study was carried out to evaluate the capability of DT in mapping soils in Miandarband region with area of 50,000 ha in Kermanshah province. The C5.0 decision tree algorithm (with and without boosting meta-algorithm) used to establish spatial relationships between known soil taxonomic classes and environmental variables. Using simple systematic sampling, 78 pedons were studied and 6 great groups and 14 subgroups of Soil Taxonomy (ST) were identified. Thirty environmental items were derived from a digital elevation model (DEM) file and a landsat-8 OLI/TIRS (July/Tir 1394) image of the area. Predictions made by C5.0 algorithm showed OA values of 73 percent for great group and subgroup, while comparable values for Kappa Index were 0.61 and 0.63, respectively. Combination of boosting meta-algorithm with C5.0 increased OA values for ST categories 0.80 and 0.76 and Kappa Index values to 72 percent and 66 percent. Results showed a considerable capability for DT in recognition of soil pattern over the study area and the topographic variables seems to be most important. Also, analysis of the produced maps, compared with the observed soil pattern during the field survey, revealed a reasonable agreement of decision tree algorithm predictions with reality.

As a natural phenomenon, dust storm occurs in all climatic conditions, however, as a typical feature of arid and semi-arid climate, causes severe problems in infrastructures and activities of human society such as agriculture, transportation and industries. Dust storm is a phenomena that has a high frequency of occurrence in some parts of the world, especially in the Middle East. Dust storm occurrence in this region has been increased in the past decade, even in the humid months of the year. Studies on dust storms in Iran have shown that many environmental factors contribute to occurrence of this phenomenon and its long-term stability besides the natural factors. The objective of this study was to investigate the dust deposition rate during a one-year period, and its relationship with selected climatic and spatial factors, in Javanrood township.
This study was carried out in an area of about 5500 hectares in Javanrood township, located in Kermanshah province. Since the beginning of August 2015 until the end of June 2016, dust samples were collected at the end of each season, from 35 sampling points randomly distributed throughout the township, including 21 points located in the urban and 14 points located in the rural areas. Dust deposition rate was calculated by dividing the mass of dust on the sampler surface and the sampling period. Dust deposition rate relationship with selected climatic parameters such as prevailing wind speed, wind direction, humidity, rainfall and mean temperature (provided by Meteorological Organization), and spatial parameters such as distance from the western border of the country (assuming that Iraq country is one of the dust resources in this region) and the elevation (above sea level) were examined using Pearson correlation test.
The average rate of dust deposition was 0.38 gr m-2 day-1(1376 kg ha-1 year-1) in the study area. In terms of time, spring season had the highest rate of deposition with an average of 0.47 gr m-2 day-1, and summer had the lowest rate with an average of 0.26 gr m-2 day-1. Considering the spatial aspect, rural areas in winter had the highest rate of deposition with an average of 0.52 gr m-2 day-1, while, urban areas in summer had the lowest amount of deposition rate of 0.23 gr m-2 day-1 in average. Results indicated that the amount of dust deposition in the study area is higher than defined standard levels for most parts of the world. The dust deposition in the rural parts was greater than urban parts, probably because of the adjacency to the dust source areas. The dust deposition rate decreased with increasing in elevation. It had positive and significant correlations with direction and speed of wind (at p It seems that the differences among the values of dust deposition, were resulted from local dust events, the elevation differences, the distance from Iraq border, differences in sampling platforms height, and also adjacency to the farmlands and uncovered pastures. High levels of atmospheric dust deposition in the region and its relation with climatic factors, is probably due to the simultaneous effects of local and regional (which is created in neighboring countries and transported to the area) dust storm events.

Modeling soil variation plays an essential role in sustainable management of the resource. However, discontinuous models used for decades, do not describe soil variation enough as that required in modern agriculture, since lead to basic shortcomings in spatial predictability value of maps. To conquer the problem, new statistical algorithms known as “machine learning” tools are increasingly used to construct and improve soil maps, digitally. As a means of machine learning, SoLIM employs knowledge-based fuzzy approach to realize soil-landscape relations and predict soil pattern in a continuous way. In this work, SoLIM used to predict soil distribution pattern in a 2300 ha area of Miandarband region of Kermanshah province.
Maps of slope gradient and aspect, planform and profile curvature, and wetness index derived from a 10m-resolution digital elevation model (DEM), and along with geological map used in the study as most effective environmental covariates of soil diversity over the area. Based on physiographic analysis, 26 pedons were described and classified in 7 subgroups of Soil Taxonomy (ST) and 16 RSGs of WRB at second-level, respectively. To train the algorithm to recognize relations between covariates and classified soils in both systems, required fuzzy rules defined in SoLIM environment. Following inference, a fuzzy distribution map for each subgroup and RSG constructed. After combining the fuzzy outputs, a non-fuzzy map of predicted soil distribution pattern over the study area obtained for each classification system.
Though results confirmed good learning ability of the algorithm, outputs where different for two the classification systems. As a reflection of its hierarchical structure, map of ST great groups was more contiguous than that of WRB. However, patchy appearance of WRB map interpreted as a sign of better spatial predictability, because of its more flexible two-leveled structure. Thus, probably WRB-based inference leads to more realistic predictions. This indicates how the results are affected by logical structure of soil classification system. To evaluate model performance, results of 25 more pedons aligned in 4 transects and 5 purposive points so that capture most soil variability over the study area, compared to SoLIM predictions. Based on overall map accuracy (OA) and Kappa agreement index (K), SoLIM predictions at ST subgroup level, were correct by 78 and 64 percent, respectively. Same values for WRB were 67 and 62. Inference at family level led to poor results. However, considering transects, correct predictions were 78.3 and 65.2 percent for ST and WRB, but for the random points was 75 for both. Results confirmed good predictions by SoLIM in the study area. At lower categories of ST with hierarchical structure, the model showed a poor ability to identify various soils.
No doubt, increasing sample points is the most effective factor on improving predictability of maps either in traditional or modern soil mapping techniques. However, such viewpoint seems unfeasible and not conforms to economic considerations of DSM. Probably, adopting some other strategies such as identifying most effective environmental covariates, increasing algorithms sensitivity, and better sampling designs to obtain optimal number and distribution of observations over the study area.

This study was performed in order to evaluate the effects of supplementary irrigation and application of Zn and K fertilizers on physiological characteristics of chickpea. The experiment was conducted in a split plot-factorial using randomized complete blocks design with three replications at campus of agriculture and natural resources، Razi University، Kermanshah، Iran. Supplementary irrigation in two levels (W1= non irrigation and W2= supplementary irrigation at podding stage) was considered as main-plot; on the other hand، combination treatments of Zn fertilizer in four levels (Zn1=0، Zn2=20، Zn3=40 and Zn4=60kg/ha) and K fertilizer in three levels (K1=0، K2=50 and K3=100kg/ha) were considered as sub-plots. The results showed that the effects of irrigation on all studied physiological characteristics were significant excluding chlorophyll-a and RWL. Likewise، the effect of Zn fertilizer on all parameters was significant. Moreover، the effect of K fertilizer on all parameters was significant excluding chlorophyll SPAD. Based on the results، supplementary irrigation increased the chlorophyll-a، chlorophyll-b، chlorophyll-ab and relative water content (RWC). Under drought stress condition، proline content and storage protein were more than supplementary irrigation treatment. In the same way، application of K fertilizer improved all parameters excluding RWC. Application of 40kg Zn fertilizer improved all parameters in chickpea. In irrigation condition and already application of Zn and K fertilizers as much as optimum amount plant had a good physiological status and produced highest yield، too.

Hydraulic conductivity (K) is one of the most important parameters in soil sciences, which has highly variation in different direction and depths. Information on spatial and temporal variations of soil hydraulic conductivity is essential to improve soil and water management. The purposes of this study are measuring of the saturated hydraulic conductivity and its depth variation by Guelph permeameter method in the research farm of agricultural faculty, Razi University, Kermanshah. In this regards, 30 boreholes with 8 cm diameter and three depths (60, 90 and 120 cm) were drilled in a grid of 5×5 m. Identification tests such as soil gradation, determination of the liquid and plasticity limits and specific gravity have been done on soil samples and indicated the soil texture was clay. After identification tests, Guelph Permeameter test for three fixed ponding depths of 5, 15 and 25 cm were conducted to determine saturated hydraulic conductivity of soil. The results of statistical analysis showed that hydraulic conductivity values at 60 cm depth have significant difference in 5% level with values about 90 and 120 cm depths and reduction of hydraulic conductivity from 90 to 120 cm was neglected. Between single depth analysis, Laplace’s analysis with 5 cm ponding depth and Richards’s regression basic analysis with 15 cm ponding depth have provided the highest and lowest saturated hydraulic conductivity respectively.

This study was conducted to know the intensity and spatial pattern of soil heavy metals pollution around the national Iranian lead and zinc complex factories in Dizajabad area, Zanjan province. A sum of 272 surface samples (0-10 cm) were collected based on a grid methods with nodes of 250 and 500 meters and the samples were extracted with HNO3 and the concentrations of Pb, Zn and Cd were determined. Average concentrations of Pb, Zn, and Cd in soil were 181.3, 182.28 and 1.48 mgkg-1, respectively. To study spatial pattern of variables, experimental variograms were calculated and best model was fitted in each case, which was exponential for all. Influence ranges for variograms of Pb, Zn, and Cd were 1131, 1073 and 1026 m, respectively. Using ordinary kriging method, interpolation was carried out, which performed well. To classify the data in taxonomic space, a fuzzy c-means with extragrades clustering algorithm was run on the data using several fuzziness exponents (Φ). Plausible fuzzy clusters were obtained at Φ=1.2. To find 7 as the optimal number of classes from a 2-10 assemblage, fuzziness validation functions F, H and S were used. Most polluted classes A, B, E and G are located at center and around the factory, while C, D and F are covered northern, northeast and southeast parts of the study area. Major parts of extragrade class was the location of factory and landfill compliance.

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.

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.

Chenges in the soil characteristics is rather continuously. A method that takes this continuity into account would present a realistic pattern of soil distribution either in taxonomic or geographical space. The fuzzy set theory provides such an approach. In this study, the robustness of fuzzy clustering in soil pattern recognition was evaluated in a subcatchment of western Iran. The clustering carried out on the basis of minimization of an objective function in assigning membership values to each pedon in each fuzzy class. Fuzziness exponent values from 1.15 to 1.5 were used. The following validation of the resulted clusters (classes), optimal number of classes in whole, morphological and particle-size subsets were determined 8, 4, and 5 respectively. Plots of membership values across the landscape indicated class overlap and considerable contiguity. Considering low differentiation of these young soils and the high similarity among their properties, the method indicated a high capacity in recognizing different soil types over the study area. Furthermore, there was relationships between the soil fuzzy classes and landform. Thus, the method is capable in continuous classification, which could be so important in construction of continuous soil maps at low aggregation levels, e. g., pedon.