Investigation of ammonia removal from polluted waters by Clinoptilolite zeolite

Ammonia nitrogen compounds in the wastewaters and effluents have harmful effects on water resources. Ion exchange with zeolites is a separation process for ammonia removal from effluents. The objective of this research was to study the efficiency of an ammonia removal and the factor affecting to this process. The Clinoptilolite was obtained from Semnan mines at the north part of Iran. The samples were grounded and sieved based on the U.S. standard mesh number 20, 30, 40 and conditioned by ammonia sulfate and sodium chloride solutions. The characteristics of samples for ammonia removal and the selectivity sequence for adsorbing interfering cations were then determined. Results shown that the average ion exchange capacity of zeolite in batch and continuous systems were 6.65–16 and 16.31–19.5 mg NH₄ ⁺ /g zeolite weight, respectively. In study on the zeolite for selective cations showed the ranking of K⁺, NH₄ ⁺, Na⁺, Ca²⁺ and Mg²⁺ respectively. Results indicated that high level of regeneration (95–98%) might be achieved with NaCl solution. Based on the results, Clinoptilolite may be effective applied in wastewater treatment, both from technical and economical aspects.     

Assessment of reference evapotranspiration across an arid urban environment having poor data monitoring system

Estimation of reference evapotranspiration (ET₀) in urban areas is challenging but essential in arid urban climates. To evaluate ET₀ in an urban environment and non-urban areas, air temperature and relative humidity were measured at five different sites across the arid city of Isfahan, Iran, over 4 years. Wind speed and sunshine hours were obtained from an urban surrounding weather station over the same period and used to estimate ET₀. Calculated ET₀ was compared with satellite-based ET₀ retrieved from the MOD16A2 PET product. Although MODIS PET was strongly correlated with the Valiantzas equation, it overestimated ET₀ and showed average accuracy (r = 0.93–0.94, RMSE = 1.18–1.28 mm/day, MBE = 0.73–0.84 mm/day). The highest ET₀ differences between an urban green space and a non-urban area were 1.1 and 0.87 mm/day, which were estimated by ground measurements and MODIS PET, respectively. The sensitivity of ET₀ to wind speed and sunshine hours indicated a significant effect on cumulative ET₀ at urban sites compared to the non-urban site, which has a considerable impact on the amount of irrigation required in those areas. Although MODIS PET requires improvement to accurately reflect field level microclimate conditions affecting ET₀, it is beneficial to hydrological applications and water resource managers especially in areas where data is limited. In addition, our results indicated that using limited data methods or meteorological data from regional weather stations, leads to incorrect estimation of ET₀ in urban areas. Therefore, decision-makers and urban planners should consider the importance of precisely estimating ET₀ to optimize management of urban green space irrigation, especially in arid and semi-arid climates such as the city of Isfahan.     

Evaluating the support vector machine for suspended sediment load forecasting based on gamma test

Due to the various influencing factors on river suspended sediment transportation, determining an appropriate input combination for developing the suspended sediment load forecasting model is very important for water resources management. The influence of pre-processing of input variables by Gamma Test (GT) was investigated on performance of Support Vector Machine (SVM) with two kernels; Radial Basis Function (RBF) and polynomial in order to forecast daily suspended sediment amount in the period between 1983 and 2014 at Korkorsar basin, northern Iran. The best input combination was identified using GT and correlation coefficient analysis. Then, the SVM model was developed and the suspended sediment amount was forecasted with RBF and polynomial kernels. The obtained results in testing phase showed that GT-SVM (RBF kernel) model can estimate suspended sediment more accurately with the lowest RMSE (14.045 ton/day), highest correlation coefficient (0.88) and highest NSEC coefficient (0.88) than SVM (RBF kernel) model (RMSE?=?18.36ton/day, \( {R}^2=0.79, \) \( NSEC=0.73 \)) and had a better performance than the other models. The results indicated that in forecasting the first nine maximum values of suspended sediment load, GT-SVM (RBF) had a higher capability than the other models and could provide a more accurate estimation from the maximum rate of suspended sediment. The results of this study showed the capability of identifying the priority of the input parameters can change GT to a useful and technical test for input variables pre-processing to forecast the amount of suspended sediments.     

Proton production in relativistic heavy ion collisions; comparison with a thermodynamical model

Experimental results concerning proton production in nuclear collisions, obtained at Saturne with the Diogene 4 pi facility, are compared with the predictions of a thermodynamical model, using collective velocity distributions combined with a statistical thermodynamics in local rest frames. Experimental differential cross sections for alpha + nucleus and Neon + nucleus central collisions at incident energies between 200 and 800 MeV per nucleon are well reproduced by the model, for an angular range 30-110 degrees in the laboratory system. Extracted values of the temperatures are compared with those given by other authors.     

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity

ABSTRACT

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.     

Carbon monoxide prediction using novel intelligent network

This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer (CMAC), having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron (MLP) are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of carbon monoxide as one of primary air pollutants. Carbon monoxide data used in this evaluation were recorded and averaged at Villa station in Tehran, Iran from October 3^rd. 2001 to March 14^th. 2002 at one-hour intervals. The results show that the errors made by TD-CMAC is fewer than those made by other models.     

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area.     

Delineation of potential recharge area using a hybrid model,case of Djelfa Hadjia watershed

Groundwater potential map is important for environmental assessment and water resources management. In this work, a groundwater recharge potential map was established for the watershed of Oued Djelfa Hadjia in Algeria, based on new multiparameters hybrid model. The model has hydroclimatic parameters, geological settings, slope factor, and stream network density factor as inputs. The groundwater recharge estimated by the model range from 0.71 to 14 mm. The model allows delineation of potential area of recharge. The total water abstraction in Djelfa city is about of 14 hm³; however, the calculated groundwater recharge is about 3 mm/year (min 0.71 mm and max 14 mm), which correspond to an average recharge volume of 3.9 hm³ which mean that the aquifer is under over exploitation.     

Spectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields

This paper proposes a spectral–spatial method for classification of hyperspectral images. The proposed method, called SSC, consists of two steps. In the first step, to overcome the computation complexity, a wavelet-based classifier is designed. In the second step, to enhance the classification accuracy, a novel hidden Markov random field called NHMRF technique in spatial domain is suggested. In NHMRF, we convert two-dimensional energies of traditional hidden Markov random field to three-dimensional energies and then we apply edge preserving regularization terms on each two-dimensional energy of this cube. The class label of each test pixel is fixed based on minimum three-dimensional energy achieved by edge preserving regularization terms. Experimental results show that the classification accuracy of the proposed approach based on three-dimensional energies and edge preserving regularization terms is effectively improved in comparison with the state-of-the-art methods.     

The origin of oil in the Cretaceous succession from the South Pars Oil Layer of the Persian Gulf

The origin of the oil in Barremian–Hauterivian and Albian age source rock samples from two oil wells (SPO-2 and SPO-3) in the South Pars oil field has been investigated by analyzing the quantity of total organic carbon (TOC) and thermal maturity of organic matter (OM). The source rocks were found in the interval 1,000–1,044 m for the Kazhdumi Formation (Albian) and 1,157–1,230 m for the Gadvan Formation (Barremian–Hauterivian). Elemental analysis was carried out on 36 samples from the source rock candidates (Gadvan and Kazhdumi formations) of the Cretaceous succession of the South Pars Oil Layer (SPOL). This analysis indicated that the OM of the Barremian–Hauterivian and Albian samples in the SPOL was composed of kerogen Types II and II–III, respectively. The average TOC of analyzed samples is less than 1 wt%, suggesting that the Cretaceous source rocks are poor hydrocarbon (HC) producers. Thermal maturity and Ro values revealed that more than 90 % of oil samples are immature. The source of the analyzed samples taken from Gadvan and Kazhdumi formations most likely contained a content high in mixed plant and marine algal OM deposited under oxic to suboxic bottom water conditions. The Pristane/nC₁₇ versus Phytane/nC₁₈ diagram showed Type II–III kerogen of mixture environments for source rock samples from the SPOL. Burial history modeling indicates that at the end of the Cretaceous time, pre-Permian sediments remained immature in the Qatar Arch. Therefore, lateral migration of HC from the nearby Cretaceous source rock kitchens toward the north and south of the Qatar Arch is the most probable origin for the significant oils in the SPOL.