Effects of calcium hydroxide and calcium chloride addition to bentonite in iron ore pelletization.

Pyrite ash is created as waste from the roasting of pyrite ores during the production of sulphuric acid. These processes generate great amounts of pyrite ash waste that is generally land filled. This creates serious environmental pollution due to the release of acids and toxic substances. Pyrite ash waste can be utilized in the iron production industry as a blast furnace feed to process this waste and prevent environmental pollution. The essential parameters affecting the pelletization process of pyrite ash were studied using bentonite as a binder. Experiments were then carried out using bentonite and a mixture of bentonite with calcium hydroxide and calcium chloride in order to make the bentonite more effective. The metallurgical properties of pyrite ash, bentonite, calcium hydroxide, calcium chloride, a mixture of these and sintered pellets were studied using X-ray analysis. The crushing strength tests were carried out to investigate the strength of pyrite ash waste pellets. The results of these analyses showed that pyrite ash can be agglomerated to pellets and used in the iron production industry as a blast furnace feed. The crushing strength of the pellets containing calcium hydroxide and calcium chloride in addition to bentonite was better than the strength of pellets prepared using only bentonite binder.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

Determining Ecoregions for Environmental and GMO Monitoring Networks

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.     

Chemical Composition and Acidity of Rain at the Gulf of Iskenderun,North-East Mediterranean

The purpose of the present work was to investigate the chemistry of rain in the Gulf of Iskenderun, North East Mediterranean, Turkey. The Gulf region has a large number of industries. Main industries existing in the region are iron and steel works, fertilizer plants, a cement plant, and several medium and mini size steel mills in addition to the international pipe line terminals. This study aims to apportion the local sources contributing to the overall pollution of the region. To this end a precipitation sampling program was started in January 2000, and over 48 precipitation samples were collected from each of 5 stations located at Iskenderun city center, Iskenderun industrial zone, Payas city center, the campus of Iskenderun Technical College and the campus of Mustafa Kemal University. Samples were analysed for pH, NO₃ ⁺, Cl^-. Ca, Al, Ba, Na, Cd, Co, Cr, Cu, Pb, Li, Mg, Mn, Ni, Zn, Fe, K. Concentrations of metal ions were determined by ICP-AES. NO₃ ^- ions and pH were determined by using NO₃ ^- selective electrode and pH meter, respectively.pH values of the collected samples at the industrial zone and at Payas city center, ranged between 5.02 and 7.38, respectively. NO₃ ^- and metal ions concentrations were highly variable. Concentrations of Ca and Fe ions were higher in the industrial zone and Payas city center. In the other three stations, concentrations of metal ions and NO₃ ^- ion were lower than that of industrial zone and the values of pH ranged between 6 and 7.4. The average pH values at Iskenderun Gulf showed that the precipitation was not acidic, because of the high concentration of Ca. The highest concentrations of Na and Cl ions were recorded in the University campus because the campus is located by the Mediterranean Sea.     

Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

Mineral contents and proximate composition of Pistacia vera kernels

The mineral contents of Pistacia vera kernels were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). The minimum and maximum values of K, P, Ca, Mg, and S elements ranged from 6,333 to 8,064 mg/kg, 3,630 to 5,228 mg/kg, 1,614 to 3,226 mg/kg, 1,716 to 2,402 mg/kg, and 1,417 to 1,825 mg/kg, respectively. In addition, the mean values of Fe, Zn, Cu, Mn, B, Mo, Cr and Ni elements were determined as 42.48, 20.52, 12.81, 7.48, 11.31, 0.106, 0.511 and 1.67 mg/kg, respectively. Ash levels of kernels were found between 2.28 % (Urfa) and 2.79 % (Halebi). In addition, crude oil and protein contents were determined between 48.8 % (Halebi) to 55.3 % (Siirt) and 23.33 % (Uzun) to 27.16 % (Halebi), respectively.     

Assessment of irrigation system sustainability using the Theil–Sen estimator of slope of time series

Agriculture, especially the irrigated sector, is the mainstay of Sudan’s economy as it accounts for 40 % of gross domestic product (GDP) and employs 70 % of the workforce. The economic viability of irrigated schemes is dependent on three factors: crop yield, water management and cropped area. The research question of this study was whether or not the current status of these factors can be sustained in order to maintain the economic viability of irrigation systems? To answer this question, a new (to the best of the author’s knowledge) approach was developed based on time series analysis, and on the Theil–Sen estimator of slope. The study defined sustainability conceptually as “the ability of an irrigation system to sustain crop yields using the optimum cropped area and water consumption to realize the economic viability of the irrigation system without a decline in soil quality and environment”. Time series datasets of crop yields, cropped area and irrigation water consumption are collected routinely by statistical departments. Any abrupt years in the development of trends were detected and related to their driving forces/causes, of which climatic conditions and marketing policies were found to be the most important. The simple approach developed proved its suitability for quantifying the progress of irrigated schemes’ towards sustainability development as tested under the conditions of Gezira irrigated scheme in Sudan—the largest singly managed irrigation scheme in the world. The scheme was found to be sustainable under the condition that the crop yield is considered as the top priority; otherwise, the sustainability of the scheme is jeopardized.