Reclamation of calcareous saline–sodic soil using different amendments: Time changes of soluble cations in leachate

Soil salinity and sodicity are escalating problems worldwide, especially in arid and semiarid regions. A laboratory experiment was conducted using soil column to investigate leaching of soluble cations during reclamation process of a calcareous saline–sodic soil (CaCO₃?=?20.7%, electrical conductivity (EC)?=?19.8 dS m^?1, sodium absorption ratio (SAR)?=?32.2[meq L^?1]^0.5). The amendments consisted of control, cattle manure (50 g kg^?1), pistachio residue (50 g kg^?1), gypsum (5.2 g kg^?1; equivalent of gypsum requirement), manure + gypsum and pistachio residue + gypsum, in three replicates which were mixed thoroughly with the soil, while sulfuric acid as an amendment was added to irrigation water. To reflect natural conditions, after incubation period, an intermittent irrigation method was employed every 30 days. The results showed that EC, SAR, and soluble cations of leachate for the first irrigation step were significantly higher than those of the subsequent leaching runs. Moreover, the concentration of removed soluble cations was lower for the control and gypsum-treated soils. It was found that among applied amendments, treatments containing cattle manure showed higher concentrations of sodium, calcium, and magnesium in the leachate, while due to pistachio residue application, further amount of potassium was removed out of soil column. The addition of pistachio residue resulted in the highest reduction in soil salinity and sodicity since the final EC and exchangeable sodium percentage dropped to 18.0% and 11.6% of their respective initial values, respectively. In the calcareous soil, solubility of gypsum found to be limited, in contrast, when it was added in conjunction with organic amendments, greater amounts of sodium were leached.     

Application of multivariate statistical methods to indicate the origin and geochemical behavior of potentially hazardous elements in sediment around the Sarcheshmeh copper mine,SE Iran

Multivariate statistical techniques, i.e., correlation coefficient analysis, principal components analysis (PCA), and hierarchical cluster analysis (CA), were applied to the total and water-soluble concentrations of potentially hazardous metals in sediments associated with the Sarcheshmeh mine, one of the largest Oligo-Miocene porphyry copper deposits in the world. The samples were analyzed for hazardous metal concentration levels by inductively coupled plasma mass spectrometry method. Results indicate that the contaminant metals As, Cd, Cu, Mo, S, Sb, Sn, Se, Pb, and Zn were positively correlated with the total concentrations. These hazardous metals also have strong association in the PCA and CA results. Different anthropic versus natural sources of contaminant metals were distinguished by using CA method. Water-soluble fraction of hazardous metals showed that the hydro-geochemical behavior of these metals in sediments is different considerably. Elements such as Cd, Co, Cr, Cu, Fe, Mn, Ni, S, and Zn are readily water soluble from contaminated samples, especially from evaporative mineral phases, while the release of As, Mo, Sb, and Pb into the water is limited by adsorption processes. Results obtained from the application of multivariate techniques on the water-soluble fraction data set show that the hazardous metals are categorized into three groups including (1) Ni, S, Co, Cu, Cr, and Fe; (2) Se, Mn, Cd, and Zn; and (3) Sb, As, Mo, and Sn. This classification describes the hydro-geochemical behavior of hazardous metals in water–sediment environments of the Sarcheshmeh porphyry copper mine and can be used as a basis in remedial and treatment strategies.     

Hydrochemistry, mineralogy and chemical fractionation of mine and processing wastes associated with porphyry copper mines: A case study from the Sarcheshmeh mine, SE Iran

The Sarcheshmeh is one of the largest Oligo-Miocene porphyry Cu deposits in the world. Comparative hydrochemical, mineralogical and chemical fractionation associated with mining efflorescence salts and processing wastes of this mine are discussed. Hydrochemical results showed that rock waste dumps, reject wastes and old impoundments of tailings are the main sources of acid mine drainage waters (AMD) that contain potentially toxic metals such as Cd, Co, Cu, Mn, Ni and Zn as well as Al. Episodic fluxes of highly contaminated acidic waters were produced in a tailings dam over a short period of time. Secondary soluble minerals provide important controls on the quality of AMD produced, especially in old, dry tailings impoundments. Secondary sulfate minerals such as gypsum, magnesiocopiapite, hydronium jarosite, kornelite and coquimbite were found in rock waste drainages and in old weathered reject wastes. Highly soluble secondary minerals such as gypsum, eriochalcite, and bonattite are also observed in an evaporative layer on old tailings impoundments. Chemical fractionation patterns of potentially toxic elements showed that the geochemical behavior of metals is primarily controlled by the mineralogical composition of waste samples. Elements such as Co, Cr, Cu, Mn, Ni and Zn are readily released into the water soluble fraction from efflorescence salts associated with rock waste drainages, as well as from the evaporative layer of old tailings. Potentially toxic elements, such as As, Mo and Pb, are principally adsorbed or co-precipitated with amorphous and crystalline Fe oxides, but they may also be associated with oxidizing, primary sulfides and residual fractions. Following the development of the dammed tailings pond, the secondary minerals were dissolved, producing acidic waters contaminated by Al (154 mg L⁻¹), Cu (150 mg L⁻¹), Cd (0.31 m gL⁻¹), Co (2.13 mg L⁻¹), Mn (73.7 mg L⁻¹), Ni (1.74 mg L⁻¹), Zn (20.3 mg L⁻¹) and Cl (1690 mg L⁻¹). Therefore, the potential use of recycled water from the Sarcheshmenh dammed tailings pond is diminished by the presence of corrosive ions like Cl⁻ in highly acidic fluids that promote corrosion of pipes and pumps in the water recycling system.     

Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND

Abstract

The main objective of this study is to assess the relative contribution of the state-of-the-art topo-hydrological factor, known as height above the nearest drainage (HAND), to landslide susceptibility modellling using three novel statistical models: weights-of-evidence (WofE), index of entropy and certainty factor. In total, 12 landslide conditioning factors that affect the landslide incidence were used as input to the models in the Ziarat Watershed, Golestan Province, Iran. Landslide inventory was randomly divided into a ratio of 70:30 for training and validating the results of the models. The optimum combination of conditioning factors was identified using the principal components analysis (PCA) method. The results demonstrated that HAND is the defining factor among hydrological and topographical factors in the study area. Additionally, the WofE model had the highest prediction capability (AUPRC = 74.31%). Therefore, HAND was found to be a promising factor for landslide susceptibility mapping.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.