Water and Soil

Abstract Introduction
 Overuse of antibiotics and their entry into the environment is a global concern today. Much of the antibiotics taken by animals and humans are excreted unchanged and found in their feces which enter the environment through livestock waste and municipal wastewater disposal. Uncontrolled release and continuous introduction of antibiotics to the environment induced antibiotic resistance in microorganisms living in other habitats which pose a potential hazard to existing aquatic ecosystems and animals. Rivers act as the main sink for the effluents that distribute antibiotics and antibiotic resistant microorganisms in the environment. This study aimed to investigate the antibiotic resistant index (ARI) in Goharrood River. Seasonal variations and source of contamination, focusing on urban surface wastewater of Rasht, were investigated through sampling of river water and sediment in different points along the river during four seasons of a year.
Materials and Methods
The water and sediment of the river were sampled at three points along the river (focused on river course in Rasht city), in autumn, 2016 and in winter, spring and summer, 2017. The number of antibiotic resistant heterotrophic and coliform bacteria were counted via colony count method in the antibiotic supplemented 100 µg/mL Nutrient Agar and Eosine-Methylene Blue agar media respectively. Cephalexin, gentamicin, doxycycline, ciprofloxacin, and trimethoprim antibiotics were tested in this study. ARI was calculated by dividing number of bacteria colonies (heterotrophic and coliform bacteria) in plates supplemented with antibiotics to the number of colonies in control plate (without antibiotic). Escherichia coli as an indicator coliform bacterium was isolated from water and sediment samples (12 strains; 4 season and 3 sampling points) and their resistant pattern to these antibiotic was also tested by disk diffusion (Kirby-Bauer) method in Mueller-Hinton agar medium. The inhibition zone (ZOI) of E. coli growth was measured and its sensitivity/resistant was assessed based on CLSI standard protocol. The calculated ARI of heterotrophic and coliform bacteria of water and sediment of the River and the determined ZOI of E. coli isolated from water and sediment were analyzed by repeated measures of factorial arrangement in a completely randomized design format by SAS software package. Factors included sampling point at 3 levels (before entering river to the Rasht city; A, in the Rasht city; B, and after river exit from the Rasht city; C), and antibiotics at 5 levels (cephalexin, gentamicin, doxycycline, ciprofloxacin, and trimethoprim) as main plot and sampling time at 4 levels (autumn, winter, spring, and summer) as sub-plot.
Results and Discussion
 The highest ARI value of water heterotrophic bacteria was obtained to cephalexin at the sampling point C. Mean ARI of water heterotrophic bacteria to all antibiotics (regardless of type of antibiotic) at three points of A, B and C was 3.77, 4.54 and 7.53%, respectively. The highest ARI levels of heterotrophic and coliform bacteria in water were obtained in the summer season. In fact, the change of seasons and clearly the summer season controlled the ARI in water bacteria rather than the type of antibiotics. So that in this season 30.78% of water heterotrophic bacteria were resistant to ciprofloxacin antibiotic and about half (50.78) of the river water coliforms were resistant to cephalexin. Although ARI for heterotrophic and coliform bacteria was lower in sediment rather than that in water, the highest ARI levels of heterotrophic and coliform bacteria in sediment were obtained against cephalexin in autumn and winter, respectively. In general, the mean ARI in water and sediment bacteria was as follows: cephalexin > gentamicin > ciprofloxacine > doxycycline > trimethoprim. The lowest ZOI value for E. coli was obtained against cephalexin. Therefore, in all three sampling points, isolated E. coli bacteria from water and sediment were resistant to cephalexin. In the study of the sampling time and sampling point interaction, it was also seen that the lowest ZOI of E. coli was in autumn and at sampling point C. Therefore, it seems that E. coli has become resistant to antibiotics when river crosses the city.
Conclusion
 According to the results of this study, Goharrood river is contaminated with antibiotic-resistant, especially cephalexin resistant bacteria and it may distribute pollution downstream. If the river water is used in aquaculture and irrigation of downstream agriculture fields, the antibiotic resistant bacteria may be spread in the other ecosystems and finally may enter the human food chain.

Abstract Introduction: Delineating landscape into homogenous units is fundamental to managing resources and delivering sustainable development. The importance of this has long been recognized as a critical issue in various studies and it has been examined from different aspects. In soil mapping, the geopedologic approach is used for landscape classification, which was defined by Zinck (1989). This approach differentiates landscapes into landforms to increase the purity of soil map units. Therefore, the aim of this study was preparing geopedologic maps of the study area on the level of landform phases intending to make more homogeneous soil units.
Materials and Methods: Honam sub-basin in Lorestan province is one of the most important agricultural areas in the Karkheh River watershed. Soil moisture and temperature regimes of the area were Xeric and Mesic, respectively. After a primary interpretation, a geopedology map of the study area at the landform level was prepared according to the geopedologic approach. After soil surveying, 31 profiles were excavated, described, and sampled in the largest delineation of this map. Ultimately, this landform unit was differentiated to the landform phase units using morphometric features and normalized difference vegetation index. Pedodiversity index was computed for each landform phase unit to investigate the credibility of the geopedological approach for this unit. The conditional probability of each soil family was also measured in each landform phase unit to compare statistical differences between landform phase units. Furthermore, statistical comparisons were made between the Shannon indices of each unit.
Results and Discussion: The soils of the study area were classified into seven soil families according to Soil Survey Staff (2014). Based on the geopedology map, this area includes two landscapes of Piedmont plains and valleys. These two landscapes were differentiated to 6 and 3 relief/molding, respectively. Geologically, the study area has 5 lithologic units. Finally, 22 landform units were identified in this area. The area of the widest landform with the code of Pi461 was 1223.35 ha. With individual use of NDVI, TRI, and aspect map, this landform unit was differentiated into 3 phases, and with the use of these 3 maps collectively, 11 phases were differentiated. The results showed landform map unit of Pi641 with 31 soil profiles and 7 soil families has the highest value of diversity indices, such as 1.59 for the Shannon index. In addition, this map unit is a compound map unit consisting of several soils, where the highest probability of observing soils is related to soils A and B with 32.5% probability. By differentiating this landform unit into phases, the Pi461 map unit is separated into smaller units that are more homogeneous. For example, when it is separated according to the vegetation characteristics, the three phases Pi4611 (N), Pi4612 (N), and Pi4613 (N) were differentiated that have medium, low, and high vegetation, respectively. In this case, Pi4612 (N) map unit with 75% probability of soil C observation and Pi4613 (N) map unit with 87.50% probability of soil B observation are two homogeneous map units. The Shannon index of these two units is 0.56 and 0.37, respectively, which indicates the purity of these map units. The results also showed that diversity indices and conditional probabilities, respectively, were decreased and increased in most of the landform phase map unit compared to the landform map unit. The use of normalized difference vegetation index compared to other environmental features has been effective in separating the landform phase units and preparation of homogeneous map units. So, the most probability of observing the dominant soils of the study area increased from 32.25% in the landform unit to 52.63, 75.75, and 87.50% in the landform phase unit, and the Shannon index decreased from 1.59 in the landform unit to 1.36, 0.56, and 0.37 in the landform phase units. The use of other environmental features to increase the purity of the landform phase map unit is suggested in future studies.
Conclusion: Results of using geopedological approach at landform level in the study area showed that this level is useful at highest levels of soil classification (from order to great group), but due to the complex nature of soils at lower levels of classification (family and soil series) does not have enough efficiency. Therefore, for improving the geopedology method accuracy and to present more uniform map units at lower levels of classification, landform phase maps were presented according to the environmental characteristics of the selected landform. The statistical comparisons between Shannon indices calculated for each map unit in the landform phase map showed a significant difference at the 90% probability level between most of the units, which indicates an increase in the purity of these units at the soil family level.

Abstract Introduction: highly increases in population growth rate, in particular in developing countries, cause high pressure on agricultural resources. In north of Iran, the pressures are compounded by restricted rice paddy fields. Reliable and relevant land suitability evaluations are vital requirements for land use policy and decision making to support sustainable rural development. Therefore, it is necessary to employ and compare the classic model (FAO), new release technique (multi-criteria decision making strategy) and the capabilities of fuzzy systems to assess land suitability. In recent years, multi-criteria evaluations including Boolean overlay operators and weighted linear combination methods have been increasingly used. Also, using the Ordered Weighted Average (OWA) method can improve the above-mentioned techniques. The OWA method is able to calculate the degree of risk taking and risk aversion of individuals and apply them to the selection of the final option. Therefore, the purposes of the current study were to explore the most reliable method of land suitability evaluation for rice by using integrated fuzzy decision making and determine the optimum depth of soil for quantitative land suitability evaluation for rice production in Amol, Mazandaran province.
Materials and Methods: Two-hundred soil samples from 50 observation points at four depths of 0 to 25, 25 to 50, 50 to 75 and 75 to 100 cm with a constant interval were selected. After crop harvesting and taking soil samples from four depths in 50 observation points, and from the genetic horizons of representative pedon excavated in the region, the parameters needed for land suitability evaluation of rice were measured. Then, land suitability classes were calculated using the parametric (square root), Fuzzy-AHP and Fuzzy-AHP-OWA methods and were compared in four depths from 0 to 25, 0 to 50, 0 to 75 and 0 to 100 cm. In Fuzzy-AHP method, Kandel membership functions were used to determine the membership degree and analytic hierarchy process (AHP) was used to determine the weight of each of the effective land properties in crop yield. In Fuzzy-AHP-OWA method, criteria weights were obtained from AHP method and ordered weights using linguistic fuzzy quantifiers.
Results and Discussion: The results showed significant difference between the potential yield (5.5 t/ha) and the average of actual yield (3.9 t/ha) in the study area. With respect to the same and acceptable agricultural management of all plots, this difference might be due to soil limitations and subsequently a decrease in the numerical value of the soil index. Except for 0 to 25 cm soil depth, actual yield for the other soil depths showed a positive significant correlation with all calculated land indices by parametric, Fuzzy AHP and Fuzzy-AHP-OWA methods. The compatibility percentage between the representative pedon and observation points was remarkable for 0-50 and 0-100 cm depths in three studied methods. Considering time and cost consuming for land evaluation, this finding shows that 0 to 50 cm soil depth information might be a relevant alternative for the optimal depth to evaluate land suitability for rice in studied paddy fields. The results of the Fuzzy-AHP method showed that soil texture and organic carbon content were the most important soil properties for rice production. The results of land evaluation using Fuzzy-AHP-OWA method showed that with increasing the levels of risk (decreasing the value of α from 1000 to 0.0001), areas with a higher suitability degree occupy greater area. This can be explained by the fact that the strategy associated with the fuzzy quantifier all (α=1000) represents the worst-case scenario (the lowest criterion value is assigned to each location) and under the strategy associated with the fuzzy quantifier, at least one (α=0.0001), the land suitability pattern is composed of the best possible outcomes. The highest correlation coefficients (R2= 0.37) were obtained for Fuzzy-AHP-OWA (α=1) based on correlation between actual yields and calculated land indices for different depths of each parametric method, Fuzzy-AHP and Fuzzy-AHP-OWA. This is due to the high trade-off among the evaluation criteria in the half fuzzy quantifier (α=1).
Conclusion: The proposed approach based on Fuzzy-AHP-OWA has great potential to model land use suitability evaluation problem. Half fuzzy quantifier is introduced as the best scenario using Fuzzy-AHP-OWA method for rice land suitability evaluation. This is due to the high trade-off among the evaluation criteria in this quantifier. Given the fact that the land suitability studies are often costly and time consuming, the land suitability evaluation by using 0-50 cm results might be a relevant alternative for the optimal soil depth required for land suitability evaluation in paddy fields.

Abstract Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which in turn, determines water-retention and flow characteristics, nutrient-holding capacity, internal drainage, sorption characteristics and long-term soil fertility. High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. Given the soil texture roles in controlling the soil functions, it is necessary to understand the spatial distribution of this feature in regional scale. As soil texture is a staticproperty, regional scale soil texture maps can thus help environmental scientists to predict different soil-related processes. The objective of this study was to develop a soil textural class map using Terra satellite MODIS sensor images. Material and Methods: To achieve this goal, the digital elevation model SRTM radar of the studied area for soil samples from different altitudes and slopes was prepared in foursen consecutive 30 meters time frame. The nearest neighbor method with an error of less than 0.5 pixels was used and the elevation layers were mosaicked and transmitted to the UTM ZON-38 coordinate system and GIS Ready Became. The normalized difference vegetation index of bands 1 and 2 of the matrix was obtained to isolate the reflection of the electromagnetic spectrum of vegetation and soil. This final mosaicked digital elevation model was then divided into different altitudes to accurately evaluate the surface texture. The 60 spatial points were selected to estimate the texture of surface soil in thestudied area with systematic randomized sampling. In the current study, soil texture was determined forthe air-dried samplesusing hydrometer. The SWIR bands of MODIS with resolution of 500 meters were selected for sampling dates. After corrections, DN values of the bands for sampling points were extracted. The Pearson correlation coefficient and step wise regression techniques were used to establish proper relationships between the DN values of the SWIR bands and the soil particles. Kriging and cokriging methods were also employed to create a spatially distributed map of the soil textural classes. Results and Discussion: The results showed that there is a close correlation between the SWIR bands of the terra satellite and the MODIS sensor with band 3, and using this auxiliary variable significantly reduces the estimation error. The best model for fitting semivariogram for clay, silt and sand contents were spherical, spherical and exponential models and the best fitting Cross-semivariogram for clay, silt and sand contents were spherical, exponential and exponential models, respectively. The highest and lowest error estimation was, respectively, related to sand and clay content. The maximum and minimum decrease of estimation error by the auxiliary variables was found for sand and clay content, respectively. The nugget/sill ratio of the kriging semivariograms was greater than 25%for sand and claycontentand lower than 25%for sand and silt content. This indicates that sand and silt contents had a strong spatial dependency, and clay content hada moderate spatial dependency. These ratios for cokriging cross-semivariograms of sand, silt and clay contentsware less than 25%. The interpolation of estimated soil texture was also determined using the cokriging method with 70% of the soil texture measured in the laboratory. Conclusions: Our results indicated thatcokriging method estimated the soil particles more accurately as compared with linear multi-variable stepwise regression and kriging methods. Application of cokriging method also reduces the number of sampling points and the estimation error of soil texture zoning. Therefore, cokriging method seems to be better suited in impact assessments for data-scarceregions such as Iran.