Further Study on the Kinetics of Bioremediation of Soils Contaminated with the Herbicide 2,4‐Dichlorophenoxy Acetic Acid

The bioremediation of soil contaminated with the herbicide 2,4‐dichlorophenoxy acetic acid has been studied applying the bacterium Ralstonia eutropha. The effects of temperature, pH, and moisture content of soils on the rate of degradation of this herbicide have been investigated. The Taguchi experimental design method was applied to determine the relative impacts of the pertinent system variables. According to this approach, four series of experiments were performed under various operating conditions. In all four series the herbicide was degraded efficiently, however, the highest rate was observed at a temperature of 35°C, pH 6, and at 80% moisture content of the soil. A kinetic model for the biodegradation of the herbicide was developed for this particular conditions based on the Monod type growth equation and autocatalytic behavior of the decomposition reaction. A close correlation has been observed between the experimental data and those predicted from the model. The degree of agreement was between 85–95%.     

Spatio-temporal optimisation of agricultural drainage using groundwater models and genetic algorithms: an example from the Murray Irrigation Area,Australia

Drainage schemes for salinity management are aimed at lowering the shallow groundwater to help increase production and reduce ecological risks. Once the groundwater levels are lowered to desired agro-ecological thresholds, the drainage scheme’s operation needs to be optimised according to the spatio–temporal variation in groundwater dynamics. Groundwater systems can be modelled if their behaviour is fully known and understood but a key difficulty in optimisation is dealing with non-linear and non-unique spatio-temporal problems. Such problems can be optimised using genetic algorithms (GA) aimed at finding near optimal solutions to highly non-linear optimisation problems. The major advantages of GAs are their broad applicability, flexibility and their ability to find solutions with relatively modest computational requirements. A surface water/groundwater interaction model has been developed in conjunction with GA based spatio-temporal optimisation of pumping operation of a subsurface drainage scheme. The aim has been to achieve a similar or better than on-going level of service both in space and time domains. The Wakool Tullakool Subsurface Drainage Scheme in the Murray Irrigation Area, Australia is discussed to illustrate the modelling process. The model results are being used to plan the cost-effective operation of the tubewells to control water logging and salinisation.     

Selection of Optimal Thresholds for Estimation and Simulation Based on Indicator Values of Highly Skewed Distributions of Ore Data

This study strives to outline a geostatistics model for estimation and simulation of the Qolqoleh gold ore deposit located in Saqqez, NW of Iran. Considering that this gold deposit contains high-grade values, accurate evaluation of such values is of high importance, and therefore different methods based on indicator values, such as full indicator kriging (FIK) and sequential indicator simulation (SIS), have been employed to improve the accuracy of estimation and simulation of high-grade values. FIK and SIS cover the full range of grades based on several thresholds on the indicator data. The cumulative distribution function (CDF) is typically used for selection of threshold values. Given the highly skewed distribution of gold grade and its intense fluctuations, the number of thresholds is increased using CDF, which in turn results in a whole lot of calculations. To reduce the volume of calculations, the number–size (N–S) fractal model has been used to select thresholds. From such a model, all optimal thresholds are chosen with respect to geology and the unnecessary thresholds are excluded from selection. Thus, a study of the selection of optimal thresholds for estimation and simulation of a gold ore resource by means of FIK and SIS, respectively, based on thresholds selected using the N–S fractal model is presented. Finally, it is proved that results of these geostatistical methods based on thresholds selection from the N–S model appear to be better-positioned to explain ore grade variability compared to thresholds selected from the CDF and threshold selection from the N–S model is more effective for reducing the volume of required calculations.     

High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets

High-resolution precipitation datasets are used for numerous applications. However, depending on the procedures for obtaining these products, such as number of observations, quality checking, error-correction procedures, and interpolation techniques, they include many uncertainties. Therefore, the accuracy of these products needs to be evaluated over different regions. In this study, the Iranian National Dataset (INDS), a new 1?×?1 km precipitation dataset based on precipitation data of 1,441 quality-controlled stations for the climatic period from 1961 to 2005, was constructed using the digital elevation model, correlation method, and Kriging interpolation procedure. Iran's annual precipitation values at grids and stations were extracted from Climatic Research Unit (CRU) CL 2.0, CRU TS 3.10.01, and WorldClim datasets, and differences between corresponding values in each of the three datasets and INDS were calculated and analyzed. The coefficient of determination (R ²) between the national network stations' data and the CRU CL 2.0, CRU TS 3.10.01, and WorldClim datasets were 0.50, 0.13, and 0.62, respectively. Moreover, R ² values between the grids of each dataset and INDS were 0.51, 0.40, and 0.60, respectively. To determine the global datasets' efficiency for displaying temporal patterns of precipitation, the monthly values gathered from them at 11 stations (as representative of Iran's various precipitation regimes) were compared with the real values at these stations. The results showed that in term of temporal patterns, the concurrences among the three global datasets and the INDS was more acceptable, especially in the case of CRU CL 2.0. In general, it is concluded that the global datasets could be deployed for the primary assessment of the annual precipitation distribution; however, for more precise studies, use of local data is highly recommended.     

Adaptive Conditioning of Multiple-Point Statistical Facies Simulation to Flow Data with Probability Maps

Multiple-point statistics (MPS) provides a flexible grid-based approach for simulating complex geologic patterns that contain high-order statistical information represented by a conceptual prior geologic model known as a training image (TI). While MPS is quite powerful for describing complex geologic facies connectivity, conditioning the simulation results on flow measurements that have a nonlinear and complex relation with the facies distribution is quite challenging. Here, an adaptive flow-conditioning method is proposed that uses a flow-data feedback mechanism to simulate facies models from a prior TI. The adaptive conditioning is implemented as a stochastic optimization algorithm that involves an initial exploration stage to find the promising regions of the search space, followed by a more focused search of the identified regions in the second stage. To guide the search strategy, a facies probability map that summarizes the common features of the accepted models in previous iterations is constructed to provide conditioning information about facies occurrence in each grid block. The constructed facies probability map is then incorporated as soft data into the single normal equation simulation (snesim) algorithm to generate a new candidate solution for the next iteration. As the optimization iterations progress, the initial facies probability map is gradually updated using the most recently accepted iterate. This conditioning process can be interpreted as a stochastic optimization algorithm with memory where the new models are proposed based on the history of the successful past iterations. The application of this adaptive conditioning approach is extended to the case where multiple training images are proposed as alternative geologic scenarios. The advantages and limitations of the proposed adaptive conditioning scheme are discussed and numerical experiments from fluvial channel formations are used to compare its performance with non-adaptive conditioning techniques.     

<Emphasis Type="Italic">Salpingoporella</Emphasis> (dasycladalean algae) species from the Lower Cretaceous carbonate facies of Kopet Dagh basin (NE Iran)

Salpingoporella species from algal bearing of Barremian-Aptian limestones in the Kopet Dagh basin (NE of Iran) are described. Different species (S. cemi, S. hasi, S. heraldica, S. hispanica, S. milovanovici, S. muehlmbergi, S. parapiriniae, S. piriniae, S. cf. biokovensis, S. steinhauseri, S. polygonalis) are investigated from different biometrical aspects such as depositional environments and biogeographical distribution as well as their systematic palaeontology from two formations (the Tirgan and Sarcheshmeh formations) in nine stratigraphic sections.