The role of tourism in rural development: Evidence from Iran

GeoJournal - Tourism can play an important role in diversifying the rural economy and its expanding in rural areas contributes to the sustainability of the population and the economy of these areas...     

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.     

Mooring optimization of floating platforms using a genetic algorithm

This paper presents a new procedure for the optimization of the mooring design of floating platforms, in which an automatic design sequence is also established. Regarding the optimization philosophy, the following aspects are dealt with:

• The optimization of the platform heading and its mooring pattern, taking into account the environmental force spreading;

• optimum line length or line tension for each mooring line, associated to the optimization of the mooring line materials and sizes.

Basically, the main goal of this paper is to introduce a new method, which will provide the quickest way to find the best mooring system, defined here as that which minimizes platform responses.A genetic algorithm (GA) is applied in this contribution, and this paper describes exactly the procedure of developing a GA code directed toward the solution of mooring design optimization problems. In order to prove the efficiency and the vast potential of the proposed algorithm as a design tool, sample moorings are analyzed for different environmental conditions and the final results, including the time required to run them, are presented.     

Temporal distribution of earthquakes using renewal process in the Dasht-e-Bayaz region

Temporal distribution of earthquakes with M _w > 6 in the Dasht-e-Bayaz region, eastern Iran has been investigated using time-dependent models. Based on these types of models, it is assumed that the times between consecutive large earthquakes follow a certain statistical distribution. For this purpose, four time-dependent inter-event distributions including the Weibull, Gamma, Lognormal, and the Brownian Passage Time (BPT) are used in this study and the associated parameters are estimated using the method of maximum likelihood estimation. The suitable distribution is selected based on logarithm likelihood function and Bayesian Information Criterion. The probability of the occurrence of the next large earthquake during a specified interval of time was calculated for each model. Then, the concept of conditional probability has been applied to forecast the next major (M _w > 6) earthquake in the site of our interest. The emphasis is on statistical methods which attempt to quantify the probability of an earthquake occurring within a specified time, space, and magnitude windows. According to obtained results, the probability of occurrence of an earthquake with M _w > 6 in the near future is significantly high.     

Determination of gravity anomaly at sea level from inversion of satellite gravity gradiometric data

Gravity gradients can be used to determine the local gravity field of the Earth. This paper investigates downward continuation of all elements of the disturbing gravitational tensor at satellite level using the second-order partial derivatives of the extended Stokes formula in the local-north oriented frame to determine the gravity anomaly at sea level. It considers the inversion of each gradient separately as well as their joint inversion. Numerical studies show that the gradients T_zz, T_xx, T_yy and T_xz have similar capability of being continued downward to sea level in the presence of white noise, while the gradient T_yz is considerably worse than the others. The bias-corrected joint inversion process shows the possibility of recovering the gravity anomaly with 1 mGal accuracy. Variance component estimation is also tested to update the observation weights in the joint inversion.     

Salinisation de l’aquifère libre de la Chaouia côtière (Azemmour-Tnine Chtouka), Maroc

L’aquifère libre de la Chaouia côtière constitue un exemple des aquifères les plus exploités au Maroc. Ce travail est consacré à l’étude des processus de la salinisation des eaux souterraines par l’analyse physico-chimique de 39 puits répartis dans la zone. Deux types de faciès ont été révélés, l’un est chloruré-sodique dans la frange côtière ; l’autre faciès est de type chloruré bicarbonaté-calcique caractérisant les eaux exploitées dans les calcaires marneux du Crétacé. L’influence marine (aérosols et intrusion marine), la dissolution/précipitation de la roche aquifère et l’infiltration des eaux d’irrigation, sont parmi les causes de l’augmentation de la salinité des eaux souterraines, en plus de l’exploitation excessive de l’eau souterraine.     

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.     

A study of crude oil‐degrading bacteria from mangrove forests in the Persian Gulf

Mangroves are coastal ecosystems, found in tropical and subtropical regions around the world. They are found in the transitional zones between land, sea, and rivers. Petroleum hydrocarbons are the most common environmental pollutants, and oil spills pose a great hazard to mangroves forests. This research was focused on the isolation and characterization of crude oil‐degrading bacteria from mangrove ecosystems at the Persian Gulf. Sixty‐one crude oil‐degrading bacteria were isolated from mangrove samples (plant, sediment, and seawater) that enriched in ONR7a medium with crude oil as only carbon source. Some screening tests such as growth at high concentration of crude oil, bioemulsifier production, and surface hydrophobicity were done to select the most efficient strains for crude oil degradation. Molecular identification of strains was carried out by amplification of the 16S rRNA gene by PCR. The results of this study were indicated that the quantity of crude oil‐degrading bacteria was higher in the root of mangrove plants compare to other mangrove samples (sediment and seawater). Also, identification results confirmed that these isolated strains belong to Vibrio sp. strain NW4, Idiomarina sp. strain BW32, Kangiella sp. strain DP40, Marinobacter sp. strain DW44, Halomonas sp. strain BS53, and Vibrio sp. strain DS35. The application of bioremediation strategies with these bacteria can reduce crude oil pollution in this important marine environment.     

Constraints on Mercury’s Na exosphere: Combined MESSENGER and ground-based data

We have used observations of sodium emission obtained with the McMath-Pierce solar telescope and MESSENGER’s Mercury Atmospheric and Surface Composition Spectrometer (MASCS) to constrain models of Mercury’s sodium exosphere. The distribution of sodium in Mercury’s exosphere during the period January 12-15, 2008, was mapped using the McMath-Pierce solar telescope with the 5″ × 5″ image slicer to observe the D-line emission. On January 14, 2008, the Ultraviolet and Visible Spectrometer (UVVS) channel on MASCS sampled the sodium in Mercury’s anti-sunward tail region. We find that the bound exosphere has an equivalent temperature of 900-1200 K, and that this temperature can be achieved if the sodium is ejected either by photon-stimulated desorption (PSD) with a 1200 K Maxwellian velocity distribution, or by thermal accommodation of a hotter source. We were not able to discriminate between the two assumed velocity distributions of the ejected particles for the PSD, but the velocity distributions require different values of the thermal accommodation coefficient and result in different upper limits on impact vaporization. We were able to place a strong constraint on the impact vaporization rate that results in the release of neutral Na atoms with an upper limit of 2.1 × 10⁶ cm⁻² s⁻¹. The variability of the week-long ground-based observations can be explained by variations in the sources, including both PSD and ion-enhanced PSD, as well as possible temporal enhancements in meteoroid vaporization. Knowledge of both dayside and anti-sunward tail morphologies and radiances are necessary to correctly deduce the exospheric source rates, processes, velocity distribution, and surface interaction.