reza mohajer; MOHAMMAD HASSAN Salehi
Abstract
Introduction: Increasing demand for an international classification system as a unique language in soil science has caused development of different classification systems. Soil classification is a useful tool for understanding and managing soils. In recent decades, the role of human in soil formation ...
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Introduction: Increasing demand for an international classification system as a unique language in soil science has caused development of different classification systems. Soil classification is a useful tool for understanding and managing soils. In recent decades, the role of human in soil formation has become a matter of great concern among soil scientists. Human is now considered as a soil-forming factor and anthrosolization is recognized as a soil-forming process that consists of a collection of geomorphic and pedological processes resulting from human activities. Industrial developments, mines and their activities and intensive agriculture led to soil changes in urban areas. One of the important missions of soils classification is to identify important properties which have effect on management purposes. In recent years, the importance of human impact on soil properties considered in soil classification systems like American Soil Taxonomy (2014) and World Reference Base (2015) and some revisions and changes have been made in this regard. In this study, the efficiency of American Soil Taxonomy and WRB soil classification systems soils were compared to describe the pollution of soils to heavy metals in Lenjanat region of Isfahan, Iran.
Materials and Methods: Agricultural lands located in Lenjanat region of Isfahan province were selected as the study area. Lenjanat is an industrial region in which intensive agriculture surrounded by different industries like steel and cement making factories and lead mining. Agricultural lands which consisted of five soil map units (Khomeini Shahr, Nekooabad, Isfahan, Lenjan and Zayandehroud) were selected and 400 topsoil samples were randomly collected. Six soil profiles were excavated in each map unit (totally 30 soil profiles) and after describing soil, the classification of soils was determined in the field. Then, representative pedons were chosen for each unit and routine soil morphological, physical and chemical properties were determined using common methods. Finally, the soil profiles were classified according to criteria of Soil Taxonomy up to family level and (WRB) at the second level. The amount of heavy metals was studied in some agricultural crops of the region and livestock muscles in the region. Total Cd and Pb were extracted from the soil samples using concentrated HNO3. Cadmium and lead of plant samples were prepared according to the procedure of Dry-ashing. Heavy metals were extracted by 3 N HCl. The metal contents of soil and plant samples were determined by flame atomic absorption spectrometry (FAAS). Descriptive statistics including mean, variance, maximum, minimum, and coefficient of variation (CV) were calculated using STATISTICA 6.0 software.
Results and Discussion: According to WRB (2015) classification, the soils were classified as 3 reference groups: Cambisols, Gleysols and Calcisols. The soils were also categorized as Aridisols and Inceptisols in Soil Taxonomy system. In this study, the environmental standards based on Swiss Federal Office of Environmental, Forest and Landscape were used for the threshold values of heavy metals pollution in the soils (VBBo). The results also indicate that the amount of cadmium in most of the soil samples was higher than the threshold limit. The amount of lead in soils was below the threshold limit. The results also indicated that all the crops had a lead average higher than the maximum of tolerance. The average of lead in cow and sheep livestock was also above Iran and Europe Union’s permissible limit. Despite American soil taxonomy classification system in the last version has a class (Anthraltic, Anthraquic, Anthrodensic, Anthropic) to show human impacts on soils at family level, it could not show the contamination of soils to heavy metals. However, WRB soil classification system defined qualifier “toxic” (Anthrotoxic, Ecotoxic, Phytotoxic, Zootoxic) which can be used in these conditions. Both systems had serious shortcomings to show poor drained soils in this area. Defining the Aquids suborder for Aridiosols in American Soil Taxonomy and revision of the definition of Gleysols, Anthrosols and also aquic conditions in WRB soil classification system are highly recommended.
Conclusion: The results indicated that WRB soil classification system could explain the soils pollution and also their effects on human health for the studied soils. Definition of some quantitative sub qualifiers for toxic can be useful to improve the efficiency of WRB for classifying polluted soils. Incorporating some criteria for pollution hazards in American Soil Taxonomy should be considered in early future.
Materials and Methods: To find out the effect of geological feature on delineation of homogeneous regions, 73 hydrometric stations at North-East of Iran with arid and semi-arid climate covering an average of 29 years of record length were considered. Initially, all data were normalized. Watersheds were clustered in homogeneous regions adopting Fuzzy c-mean algorithm and two different scenarios, considering and not considering a criterion for geological feature. Three validation criteria for fuzzy clustering, Kwon, Xie-Beni, and Fukuyama-Sugeno, were used to learn the optimum cluster numbers. Homogeneity approval was done based on linear moment’s algorithm for both methods. We adopted 4 common distributions of three parameter log-Normal, generalized Pareto, generalized extreme value, and generalized logistic. Index flood was correlated to physiographic and geographic data for all regions separately. To model index flood, we considered different parameters of geographical and physiological features of all watersheds. These features should be easily-determined, as far as practical issues are concerned. Cumulative distribution functions for all regions were chosen through goodness of fit tests of Z and Kolmogorov-Smirnov.
Results and Discussion: Watersheds were clustered to 6 homogenous regions adopting Fuzzy c-mean algorithm, in which fuzziness parameter was 1.9, under the two different scenarios, considering and not considering a criterion for geological feature. Homogeneity was approved based on linear moment’s algorithm for both methods, although one discordant station with the lowest data was found. For the case with inclusion of genealogic feature, 3-parameter lognormal distribution was selected for all regions, which is a highly practical result. On the other hand, for not considering this feature there were no unique distribution for all regions, which fails for practical usages. As far as index flood estimation is concerned, a logarithmic model with 4 variables of average watershed slope, average altitude, watershed area, and the longest river of the watershed was found the best predicting equation to model average flood discharge. Determination coefficient for one of the regions was low. For this region, however, we merged this region to other regions so that reasonable determination coefficient was found; the resulting equation was used only for that specific region, however. By comparing the distributions of stations and also two evaluation statistics of median relative error and predicted discharge to estimated discharge ration corresponding to 5 different return periods (5, 10, 20, 50, and 100 years). Both perspectives showed acceptable results, and including geological feature was effective for flood frequency studies. With considering the geological feature for regionalization, Besides, Log normal 3 parameters distribution was found appropriate for all of the regions. From this point of view, geological feature was useful. Median of relative error was lower for small return periods and gradually increased as return period was increased. Median of relative error was between 0.21 to 00.45 percentages for the first method, while for the second method it varied between 0.21 to 0.49 percentages. These errors are quite smaller than those reported in literature under the same climatic region of arid and semi-arid. The probable reason may due to the fact that we made a satisfactory regionalization via fuzzy logic algorithm., We considered another mathematical criterion of “predicted discharge to the observed discharge”. The optimum range for this criterion is between 0.5 and 2. While under-estimation and over-estimation are found if this criterion is lower than 0.5 and higher than 2, respectively. Based on this premise, 75 to 95 percentages of stations were categorized as good estimation under the first method of analysis. On the other hand, 78 to 97 percentages of stations were considered good for the second approach.
