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Toward the deeper water of the fore reef in transect A, a slight increase in the concentrations of Al was observed where they reached ppm in sample However, Fe concentrations showed a markedly increasing tendency across the fore reef reaching a value of ppm in sample 11 Fig. In transect B, the distribution pattern of both elements is different.

The concentrations increased abruptly from the reef flat to the fore reef and reached for Fe and ppm for Al in the sediments of the fore reef Fig. The distribution pattern of Sr generally showed an opposite trend compared with the distribution of Al and Fe. The lowest values of and ppm were obtained close to the shoreline in both transects A and transect B respectively, while the highest values of and were observed in the fore reef sediments of both transects A and B respectively Fig.

Discussion Sediments Texture, Grain Size Distribution and Sorting The reef edge and reef flat are subjected to intensive waves and currents which provide a less permanent depositional environment. The transport of these materials to the fore reef base is normally small, which is enhanced under storm conditions.

The following disintegration processes by wave action reduced most of the shingle to coarse and very coarse sand and most of the fragments to fine grades. In this regard, the reef flat acts as a reservoir of fine materials available for resuspension and is continuously washed out of the reef flat. It is obvious that wave orbital velocity on the reef flat was generally insufficient to resuspend fine materials.

The interaction of waves and current provided the stress capable of resuspending fine materials on the reef flat [3, 8 and 9]. This suggests that the greatest amount of resuspension occurred by the high tide coupled with waves 2 m high generated by the relatively strong southerly winds. Under such conditions, the water becomes milky due to the huge amounts of suspended materials, even over the fore reef zone.

As the tide falls, the sediment-rich water moves offshore to the relatively deep and calm water of the fore reef [8], leaving on the reef flat coarse to very coarse, poorly to moderately sorted and symmetrical sediments. The poorly-sorted sediments of the reef flat indicate that the materials accumulated within this zone are still in the process of breakdown and transportation [29]. The transport of fine materials from the reef flat to the fore reef is the most likely the explanation for the relatively high mud content 3.

While the fore reef zone is affected by calm water conditions, coarsening of its sediments can be observed at depth greater than 10 m. This can only be explained by the contribution of unreworked coarse and very coarse fragments produced mainly by mechanical destruction of corals, mollusks fragments and foraminiferal tests which have a wide range of grain size [11 and 18].

In the fore reef zone, sediments are further from the action of prevailing waves, which improves the sediments’ sorting.

This is in agreement with the results of [30 and 31] that shoaling waves as well as weak and irregular currents are responsible for the poorly sorted sediments. The backreef zone is very shallow and almost barren. The majority of sediments within this zone are medium sand Fig. The coarsening character of sample A1, located close to the shoreline is most probably due to the input of relatively coarse terrigenous materials. The coarsening in sample B4, located far from the shoreline is mostly due to damping of coarse unreworked fragments and grains from the nearby lying reef flat.

The coarsening of sediments at both ends of the backreef zone suggests an input of coarse materials from either the reef flat biogenic skeletal materials or the coastal area terrigenous materials. However, the relatively low carbonate content of sediments in this zone, particularly in transect A, suggests that the transport of coarse materials from the reef flat to the backreef is very limited, whereas the transport of terrigenous materials to the backreef may extend to the reef flat Fig.

This explanation is also supported by the low mud content of the backreef zone sediments. It seems that the prevailing waves over the reef flat are incapable of moving even fine particles onshore to the backreef zone. The enrichment of sample B1 in mud- sized materials is most probably due to the presence of flourishing sea grasses at the site. Marine grasses are believed to be agents with ability to trap and bind silt and clay-sized particles [32].

It is also believed that the input of coarse terrigenous materials control the variations in textural properties of the backreef sediments. The dumping of coarse materials from land is more effective in transect A. It makes the granulometric curve of the effected sediments larger and hence they become poorly sorted. This explanation is supported by the variation in skewness values of sediments across the backreef zone in both transect.

These values remain mostly constant across the backreef zone and increase in tendency to the reef flat, which indicate the influence of coarse terrigenous material influx to the backreef. Geochemistry The behavior of Fe and Al in sediments across the reef complex was governed by the input of terrigenous material from the surrounding basement to the reef.

This is indicated by the highest values of both elements closed to the shoreline Fig. The concentrations decrease across the backreef zone to the reef flat, where calcareous marine sediments dominate and the lowest values were observed. In transect A, sample 6 from the reef flat contained the lowest values of both elements. The concentrations across the fore reef region remain nearly constant and appear to be within the values given by Graf for pure reef limestone and for Fe given by Milliman for an admixture of biogenic reefal sediments.

This suggests that the effect of terrigenous input to Fe and Al concentrations is limited to the backreef zone, whereas the effect becomes minimal in the reef flat and fore reef sediments. The distribution pattern of Fe and Al in transect B is similar to that of A, except across the fore reef zone, where the fore reef sediments are enriched in Fe and Al.

The lowest values of both elements observed on the reef flat sample 5 increase rapidly seaward across the fore reef. This indicates that significant amount of terrigenous materials were supplied to the fore reef. In this area, storms are rare but they contributed a large proportion of the annual rainfall to the area.

Following occasional heavy rains in the region, the normally dry valleys are filled with sediment-laden water which is discharged to the coast and reef system. The evidence of heavy rain is the reddish plume of flood discharged sediments from the adjacent high land, breaking the traffic roads, marking the reddish plume of pools in surface water which extend from the shoreline to the reef flat. The reef is significantly diminished along these wadis even at both sides in the fore reef zone.

The suspended materials delivered to the water of the fore reef may then be transported through the moving current at depth layer of 6 to 12m [23] parallel to the coast. Great delivery of fine materials to the fore reef may lead to low coral coverage along the Jordanian coast, mainly by controlling the substrate, which is in agreement with Larcombe and Woolfe a.

However it is important to monitor the recruitment rates of corals along the Aqaba coast to provide sufficient information for scientific-based decisions. The positive correlation between the concentrations of Fe and Al coupled with the mud content in transect B the correlation becomes negative in transect A , indicate that both elements are associated with the mud content in transect B.

It is obvious that the effect of mud, originated from terrigenous source, on the concentrations of both elements is limited only to the fore reef sediments. The terrigenous origin of Fe and Al is also supported by the distribution of carbonates across the backreef in both transects. The highest values of carbonates in both transects were obtained on the reef flat and on the fore reef, decreased gradually across the backreef to the shoreline and probably reflect the dilution effect of terrigenous input from the coastal area.

Consequently, the concentrations of both elements across the backreef zone as well as the difference in concentrations of both elements between the studied transects are most likely related to the proportion of the admixture of non- biogenic fragments. This may also explain the wide range of the results of many studies carried out in the Gulf of Aqaba and in similar areas around the Red Sea.

It must be noticed that the low values were observed in sediments near the mouth of Wadi Araba. It is obvious that the calcareous sediments at the northern tip of the Gulf are highly diluted by dumping of terrigenous materials via the Wadi Araba.

The behavior of Sr largely reflects the distribution of carbonates across the reef complex. The highest Sr values were obtained in sediments of the fore reef and reef flat Fig. The decreasing values of Sr within the backreef to the shoreline in transect A as well as the positively strong correlation between the carbonate content and Sr, indicate that the carbonate is a major source for Sr in the reefal sediments.

In transect B, Sr values remain nearly constant across the reef complex to the shoreline. However, the relative low values of Sr in the fore reef sediments of transect B as well as the weak positive correlation between carbonate and Sr can only be explained by the contribution of terrigenous materials.

Delivering of terrigenous materials associated with mud to the fore reef may also explain the high mud content 7. This explanation is supported by the correlation between Sr and the mud content as well as between the mud content and carbonate. In transect A, the strong positive correlation between the mud content and Sr as well as the positive correlation between the carbonate and mud strongly indicate that the mud fraction in transect A is mostly originated from a biogenic source, while the negative correlations in transect B suggests a terrigenous origin.

Recent carbonate reef with high Sr content is associated with aragonitic skeletal of corals. The low Sr values obtained in this study can be explained by the proportion of the admixture of non-biogenic fragments. In addition, the varying proportion of the biogenic components, corals, mollusks, foraminefral tests and coralline alge that make up the calcareous sediments, probably largely effect the Sr concentration in space and time.

However, the average concentration of Sr agrees with the results reported by Friedman for reef sediments in the Gulf of Aqaba. Conclusion The studied reef sediments are composed mainly of sand-sized material. The sediments are mostly poorly to moderately sorted and range in skewness from fine skewed to very coarse skewed. The calcium carbonate content is related to the proportion of non-biogenic materials.

The carbonate content generally decreases towards the shoreline, while the terrigenous inputs decrease across the backreef to the reef flat. Fe and Al concentrations are related to the amount of terrigenous materials delivered to the reef. Both elements decrease in concentrations seaward, while Sr which is associated with carbonate increases in the same direction.

The concentrations of Fe and Al can be used to delineate the backreef, while Sr indicate the fore reef and reef flat zones. Acknowledgments We are grateful to the Dept. This manuscript was improved by comments from Prof.

Abdulqader Abed, Dept. Notes Note 1: Al-Fukaha, F. References [1] Larcombe, P. Coral Reefs, 14 Sedimentology, 48 1— Estuarine Coastal and Shelf Science, 52 Geophysical Research, 84 C4 Marine Geology, 1 Coral Reefs, 23 Continental Shelf Research, 24 12 Coral Reefs, 5 — Coral Reefs, 1 Croix, USVI. Coral Reefs, 8 [14] Chester, R. Cambridge University Press. Israel Journal of Earth Sci. In: N. Hulings eds. Hulings, Petroleum, 39 Coral Reef symp.

Deep-Sea Research, 31 A Health Manage. Oceanologia, 46 Ocean Science Journal, 42 2 Limnology and Oceanography, 47 Paper, A, , Atoll Res. Petroleum, 34 2 J Geol. Marine Geology, 22 Survey Circ. Springer Verlag, New York, Australian Journal of Earth Sciences, 46 a In: Dubinsky, Z. Elsevier, New York, — Coastal Mar. In: Miller, R. Arab Gulf J. In: G. Muller and G. Friedman eds. Springer Verlag, Berlin, Developments in Sedimentology.

Elsevier Pub. According to the results obtained through analysis, higher CO, CO2 and HC concentrations are strongly related to lower wind speed, lower degree of wind direction, colder temperatures, lower relative humidity and weakly lower rainfall.

Prediction equations were built for each pollutant concentrations in order to facilitate forecasting air quality using multiple regression analysis. Keywords: Regression analysis; Air pollution; Exhaust emissions; Motor vehicles; Marka area; Meteorological parameters. Introduction Air is an essential component of life on our planet. It supplies us with oxygen that is essential for our bodies to live and carbon dioxide that is essential for plants to make food. In the last years or so, the growth in the world population and the industrial revolution has resulted in an increased demand for energy.

Until now, these energy requirements have been supplied largely by the combustion of fossil fuels, the plant’s resources of convenient carbonaceous fuel, coal and oil, have been used for heating purposes, power industry, transport and synthesis of chemicals.

The by-products of these operations particulates, the oxides of carbon, nitrogen and sulphur have been emitted to the atmosphere in enormous quantities [1]. Air becomes polluted when it contains substances in quantities that could harm the comfort or health of humans and animals, or could damage plants or materials.

These substances are called air pollutants and can be solid particles, liquid droplets or gases and they can occur naturally or as a result of human activity [2]. Odat and Al- Jedaih because increasing the number of vehicles in use has led to getting more and more pollution over the years.

Number of motor vehicles in use of Jordan has increased from vehicles in the year to vehicles in the year One of the important environmental problems in Amman area, situated in central Jordan, has experiencing real air pollution problem. The problem is obvious from the haze that covers the area and from the unpleasant odder. In this paper emission rates from motor vehicle in Marka area will be calculated and emission models will be developed for carbon monoxide CO ,carbon dioxide CO2 and hydrocarbons HC in terms of meteorological factors that affect their concentrations.

Data Collections: Given a set of observations from air monitoring and meteorological station, calculating statistical relationships among the variables is possible by using some statistical techniques such as regression analysis.

Some statistical models, establish how close relationships are between concentration estimates and values actually measured under similar circumstances. Effects of all factors that determine atmospheric pollutant concentrations are implicitly accounted for in the air quality data used to develop and optimize the models. These models also have low development cost and resource requirements [4]. The purpose of this paper is to evaluate the changes of air quality in Marka area caused by motor vehicles and to investigate the correlation of CO, CO2 and HC pollution in this area with meteorological parameters such as wind speed, wind direction, temperature, pressure, rainfall, sun radiation and relative humidity on daily basis during the year of , where severe air pollution episodes occurred.

There have been several studies of the area examples of which the one conducted by the Ministry of Environment, Department of Motor Vehicles —Marka, based on the report issued by the station and Metrological department based on reports of metrological condition in Marka area during the year Concentrations were obtained by inserting a special probe into the exhaust while operating, and then the driver would be asked to increase the rpm.

The data were obtained from unpublished sources conducted by the Ministry of Environment, Department of Motor Vehicles —Amman, and Metrological department based on reports of metrological condition in Marka area during the period of August —December CO, CO2 and HC were considered as dependent variables while meteorological parameters such as wind speed, wind direction, temperature, relative humidity, solar radiation and rainfall were considered as independent variables.

Simple linear regression and multiple linear regressions are related statistical methods for modeling the relationship between two or more random variables using a linear equation. Simple linear regression refers to a regression between two variables while multiple regressions refers to a regression on more than two variables. Linear regression assumes the best estimate of the response is a linear function of some parameters though not necessarily linear on the predictors , [6].

Linear regression analysis was used to quantify the associations among gaseous pollutant, particles and meteorological conditions. However, data which represents a time series need to be transformed and modeled to remove autocorrelation before regression analysis is applied in order to better satisfy the assumption that the error component of regression model is normally distributed and statistically independent.

Multivariate linear — regression models were built to adjust the putative effects of metrological factors on air pollution variables. The forms and assumptions of the models were also analyzed to ensure normality in the distribution of the variables, homoscedasticity of variance, and independence. The main goal of the statistical analysis is to forecast the air pollutants given the metrological condition.

These models would help predicting air pollution status at certain meteorological condition. The first stage of model development was the establishment of a correlation matrix for the different variables included in the study. The correlation matrix is used to select the independent variables that highly correlated with the dependent variable and to investigate the multicolinearity among the independent variables.

The second stage of model development was to perform linear and non-linear regression analysis in order to develop predictable models.

The stepwise regression techniques were performed to select the best variables to enter the model. The stepwise regression procedure is built upon entering the independent variables that most highly correlated with the dependent variable. After that, the next variable is selected to enter the regression model using partial correlation coefficients. By mean of the partial F —test, each variable entered the model is examined at each step, then the regression models were developed as equations for the most independent variables that effect on dependent one.

To overcome this difficulty, analysis have typically used the forms of the Log — normal [8] and gamma[9] distributions to model vehicle emissions data. One graphical tool for analyzing this kind of data is to plot emissions as a function of the cumulative fraction of vehicles [10]. As previously suggested, logarithmic transformation is frequently used to account for the non-normality of the data; yet this may not be the appropriate approach to take.

Since the distribution of raw data significantly deviated from normality, Figure 1. As seen in Table 2,Correlation coefficient for CO concentration shows the strong correlation with relative humidity,temperature, wind direction, rainfall and wind speed as thus: Whereas a strong correlation between the concentrations of CO2 and temperature, where the correlation coefficient mounted up to Relative humidity with a correlation of Variables W. H Rainfall CO2 R – 0.

H: Relative Humidity, W. S:Wind Speed W. D: Wind Direction, T: Temperature. The regression analysis: Regressions Analysis of CO When we used the multiple regression analysis we found that all the independent variables involved in the analysis explain 0.

All variables were statistically significant at P less than 0. The other independent variables temperature, the amount of the clouds, rainfall, wind speed and wind direction contributed a lot in explaining the difference in CO2 concentrations. Using Multiple Regression, meteorological factors explained 0. Total 95 0. Figures 3 show the application of this model graphically.

Table 5: Statistical Characteristics of the model in Equation 3. Total 89 Location of the emission source, height and duration of release, as well as the amount of pollutants released are important.

Experience shows that even when emission rate remain relatively steady for extended periods, a wide variation in gases concentrations from one day to the next can be observed[11]. These variations are generally due to changes in certain meteorological conditions. From the knowledge of studies of various parameters of meteorology, we can predict dispersion of the pollutants in the atmosphere. Therefore, in order to understand air pollution surveillance and dispersion, we must understand the impact of the various meteorological parameters on the dispersion of air pollution.

Such studies include consideration of wind, wind velocity, wind direction, effect of temperature, phenomena of inversion, relative humidity, etc. The effect of metrological parameters on the ambient concentrations of the gases is discussed below: Rainfall The more the rainfall, the lower the concentration. Marka area lacks this metrological factor, which could help in the concentrations of pollutants emitted from motor vehicles. Accordingly, rainfall has no effect on pollution concentrations in the study area.

This process increases the amounts of the up going air currents. When the horizontal and vertical air mixing processes increase, they reduce the concentration of the pollutants in it.

Relative Humidity Humidity is low in Marka area. Also, the process of humidity decrease leads to more concentrations of gas. This paves the way for a suitable environment for certain photochemical interactions of the gases in which they change into different forms.

It has been evidenced from the results of this study that the relation of relative humidity with the concentration of the pollutants is a proportional one. This could be attributed to the role of humidity in causing heat discrepancies as the increase of humidity in the atmosphere will reduce the amount of solar radiation that reaches the earth.

When sunshine collides with these drops, then it will absorb by these scattered drops in the surrounding atmosphere so they start to evaporate and launch its embedded heat in the surrounding air, which contributes in forming heat variations where the air near to the surface of the earth is becoming colder than that in the upper parts, thus reducing the up going air currents and accordingly increasing the pollutants in the atmosphere.

Regarding the inverse relation with CO2 this is due to what is performed by humidity in forming acid rain. Wind Currents Wind currents are caused by pressure differences, consequently leading the differences in temperature in the atmosphere.

They can occur on large scale and lead to significant climatic changes. The intervening valley, trees, buildings can change their direction as well as their speed. Wind currents play a significant role in the distribution of pollutants in the atmosphere.

In view of this, the changes in their velocity or direction and the impact of temperature on the course of their flow must be taken into consideration.

Wind Speed: Pollutants are expected to be carried away and diluted during day times with high wind speeds. Low speed winds are prevailing in Marka area. The annual average speed is 3. This will play a role in having more concentration in gas in this area. It is not possible by any means for low speed winds to carry pollutants for further distance.

The western, and north-western directions are the most predominant wind directions in Marka area [12]. Conclusions: High air pollution took place in Marka area during the year The encountered high CO, CO2 and HC values are due to low temperature, low wind speeds and the shortage of rainfall during most of winter season. The results show a good relationship between the meteorological parameters and CO, CO2 and HC in Marka area within the terms statistically analyzed.

While pollutants concentrations have a strong relation with temperature, they have a significant correlation with wind speed.

In order to predict the CO,CO2 and HC concentrations with regard to meteorological parameters, a statistical model was developed. An understanding of pollution sources and emissions, and their interactions with the atmosphere, is the most important first step in developing appropriate air pollution management plans and action strategies. Without this type of knowledge, incorrect decision making related to air pollution management is possible, creating wasted resources and undesirable results[13].

Due to insufficiency of air quality information, strategic planning on air quality management is non-existent in Marka area. This study is only based on measurements made at one urban location. Undoubtedly, there is need for a more comprehensive study to improve the monitoring and evaluation systems for urban air pollution especially caused from motor vehicle emission, in greater Amman area.

Acknowledgments We thank the Ministry of Environment, Department of Driving and Vehicles Licensing and Department of Meteorology at Marka for providing us with the data used in this study. Duxbury Thomson Learning. Journal of the air and waste management association. Some Issues in the statistical Analysis of vehicles Emissions, Journal of Transportation and Statistics, 3 2 1- New Age International p Ltd.

Davies, T. Jickells, I. Hunova, K. Tovey, K. Bridges and V. The method is based upon the injection of the sample mixture containing iron II and iron III into o-phenanthroline o-phen stream in citrate buffer at pH 3.

The stream passed through the first flow cell is mixed with hydroxylamine hydrochloride to reduce Fe III to Fe II before reaching the second flow cell. Variables such as flow rate of reagents and other flow injection parameters were optimized to produce the most sensitive and reproducible results. Introduction The bioavailability and toxicity of metal species may depend on the lability and the chemical forms in which they are present.

Iron is present as bivalent and trivalent states in natural waters and other terrestrial systems. Changes between these two forms of iron are important in various biological [1] and geochemical [2] processes.

Several procedures and techniques for the determination of Fe II and Fe III in different sample matrices have been reported in literature. However, voltammetric [3,4] and spectrophotometric [] were the most widely used analytical techniques. Many of the reported spectrophotometric methods are based upon the use of packed columns for the simultaneous determination of Fe II and Fe III in mixtures []. Both cation- and anion-exchange columns were used for the on-line separation of the two ions [5,6].

The retained ions were then selectively eluted and detected through the reaction with a certain colorimetric reagent. In other cases, chelates formation were made first and the formed complexes were then separated by high pressure liquid chromatography using C18 columns [7,8]. A flow injection method based upon the use of the same idea but different reagents was also described by Senior et al. In addition, catalytic methods for the determination of iron II and iron III , based on catalysis of various redox reactions, were also reported [].

Iron II and total iron were determined, simultaneously, using a flow injection procedure based on the catalytic effect of iron II , on the oxidation of luminol, with hydrogen peroxide in alkaline medium [13]. This work describes the use of a sequentially arranged double flow cells system for the simultaneous determination of iron II and total iron. This system is based upon the use of both sample and reference cells of the double beam spectrophotometer in the FIA manifold.

The system is much simpler and faster than most of systems described for the simultaneous determination of iron II and iron III. Experimental Reagents Analytical-reagent grade chemicals and deionized water were used to prepare all solutions. Citric acid, ammonium ferric sulphate, ammonium ferrous sulphate, sodium hydroxide, hydroxylamine hydrochloride HAH , and o-phenanthroline were all obtained either from Fluka or from BDH Chemicals. Hydroxylamine hydrochloride HAH : 0.

Citrate buffer: 1. The pH of the solution was then adjusted to 3. Calibration standard solutions containing 0. Soil and rock samples: 2. The mixture was stirred for 10 hours and then allowed to stand for about 1 hour before filtration.

Fractions of these samples were transferred to Pyrex test tubes and irradiated by UV radiation for about 20 hours.

Both irradiated and non-irradiated fractions were then filtered through 0. Apparatus The manifold used in this work is shown in Fig. A Rheodyne 6-way injection valve Type 50 was used to introduce the sample into the carrier stream.

Teflon tubing of 0. The length of the first reaction coil RC1 was 40 cm, while the length of the second reaction coil RC2 was cm. The wavelength was adjusted to nm. General Procedure A double beam spectrophotometer was used in this work. Both the sample and the reference cells were used to measure iron II and total iron, respectively. Samples were injected into o-phenanthroline stream pumped at a rate of 1.

The absorbance of the formed complex was monitored in the first flow cell at nm. After injection, the valve was returned to the load position when the maximum change in absorbance value has been reached. When the base line was reached, another slug of sample was injected. Results and Discussion In this study, the heights of iron II and total iron peaks were measured. The optimal conditions for the simultaneous determination of iron II and total iron were established by varying one variable while keeping other variables constant.

Several parameters including FIA and reaction variables were optimized in this work. The proposed method is based upon the use of both sample and reference cells of the double beam spectrometer for the simultaneous determination of Fe II and total iron. As shown in Fig. Moreover, we should remember that the observed absorbance reading is the result of the absorbance in the first flow cell minus the absorbance in the second flow cell AFC1-AFC2.

Different lengths in the range of cm were tested. Highest signals were obtained when the length of RC2 was cm. The effect of the reaction coil RC1 length on the analytical signal was also investigated. No significant changes were observed on the signals when the coil length changed in the range cm. Similarly, different flow rates in the range 0.

A flow rate of 1. At this flow rate, each injection requires about 80 seconds for the recorder pin to reach the base-line, which means that 45 measurements per hour can be injected using the proposed manifold. Table 1 gives the optimum values found for the studied variables using the same approach. Concentrations of reagents used were in excess compared to the concentration of the iron in the sample to ensure complete and quantitative reaction. The calibration graphs were linear in the range of 0.

To assess the accuracy of the proposed method for the simultaneous determination of iron II and total iron, synthetic mixtures containing known amounts of iron II and iron III were prepared. Iron II and iron III contents in these samples were determined by the proposed method and the results are given in Table 3, together with those obtained by using flame atomic absorption spectroscopy AAS.

As shown, the results obtained for total iron are lower than those obtained by AAS for the same samples. These results were not surprising because it is expected that some of the iron in these samples are not free for the direct reaction with o-phen.

Actually, organic and humic substances are working as efficient chelating agents that hold up iron ions from reacting with the o-phen. Therefore, the observed concentrations are considered to be as the labile or the acid extractable fraction of the iron.

In order to make sure that these results are correct comparing to those obtained by the AAS, these organic and humic substances were destroyed by UV light. A strong UV radiation was used to destroy the humic substances and subsequently to release iron ions. After 20 hours of irradiation, soil and rock samples were then analyzed by the proposed method and results are compared with those obtained using AAS method Table 3.

These results reinforce our previous finding about the accuracy and the applicability of the proposed method. Before irradiation with UV Rock-1 0. After irradiation with UV Rock-1 0. The method is very simple, using minimum number of reagents and reaction sequence.

The speed of analysis and the precision make this method also suitable for the routine analysis of samples containing iron II and iron III , replacing many of the tedious and expensive methods. Such reduction in the coercive field was attributed to the decrease in magnetic anisotropy field. Keywords: Barium hexaferrite; Coercivity; Saturation magnetization; Anisotropy field. Introduction Barium hexaferrite with a chemical formula BaFe12O19 is one of the most important composition for perpendicular magnetic recording.

Barium hexaferrite is suitable for magnetic recording due to its large saturation magnetization, good chemical stability, and low switching field distribution. On the other hand, barium hexaferrite can be used for high density magnetic recording if its particle size and its large anisotropy field were decreased. Large particle size and high anisotropy field cause a poor overwrite modulation [1]. Several techniques can be used to prepare barium ferrite powders such as the sol- gel method [], the glass crystallization method [11], hydrothermal technique [12], and coprecipitation method [13].

The preparation and investigation of barium ferrite doped with Sb -to the knowledge of others- has not been performed yet. So in this work we have investigated the possibility of introducing dopants ions such as Sb by the ball milling route. The ferric ions are distributed among five crystallographic sites, three are octahedral sites 12k , 4f 2 , and 2a , one is tetrahedral site 4f 1 and one trigonal bipyramid 2b [].

The milling experiment was carried out at rpm for 16 h and the ball to powder ratio was The as-milled powders were annealed in air atmosphere at oC for 5 h. It should be noted that XRD analyses of more than 6 samples subjected to different annealing temperatures from oC to oC revealed that the optimum annealing temperature for obtaining barium ferrite doped with Sb was oC. The magnetic measurements were carried out using vibrating sample magnetometer VSM MicroMag , Princeton Measurements Corporation , with 10 kOe maximum applied field.

All magnetic measurements were performed at room temperature. Results and discussion XRD patterns for some samples examined in this work are represented in Fig. All unmarked peaks belong to hexagonal barium ferrite BaFe12O The intensities of XRD peaks of such traces were increased with the increase of Sb content in the sample.

The variation of hexagonal lattice parameters a and c with Sb content for all doping concentration examined in this work are presented in Fig.

Lattice constant a remains constant, whereas hexagonal lattice constant c increases with increasing Sb concentration. This indicates that the change of easy magnetized c-axis is larger than that of a-axis for the substitution with Sb. This change in lattice parameters might change the distance between magnetic ions, which leads to a disturbance in exchange interaction, thus magnetic properties can be alerted by the substitution.

The magnetization curve for the non- substituted sample belongs to hard magnetic material with high coercive field strength of 4 kOe. This value of the coercivity agree with the previous works such as sol-gel method [17], mechanical alloying method [1] and ball milling method [21] of preparing barium ferrite.

Figure 4: Saturation magnetization as a function of Sb concentration of barium ferrite samples. As one might observe the value of the coercivity for pure sample is Oe, which is in good agreement with the literature value, since the coercivity of pure BaFe12O19 prepared by different methods is reported in the range — Oe [3, 24].

It could be noted that, the decrease in anisotropy field might leads to decrease in energy barriers, as a result a smaller field is required to reverse the magnetization, which suppress the coercivity. Figure 7: Anisotropy field as a function of Sb concentration of barium ferrite samples. The IRM curve was obtained by the following procedure: first the sample was demagnetized, second applying positive field, third measuring the remanence magnetization after removing the applied field.

The procedure was repeated with increasing the positive field to reach positive saturation remanence. The DCD curve was obtained by, first, the sample was saturated with a positive field of 10 kOe, second a negative field was applied to the sample, third remanence magnetization was recorded after removing the negative field and at last this procedure was repeated with increasing the negative field until negative saturation remanence was reached.

These curves show the magnitude of particle interaction in each sample. These data suggest that the interaction fields in these samples have a negative values and interaction effects decrease in magnitude with the increase of Sb concentration. Thus it seems that the particles tend to form clusters rather than a column of stacked platelets, i. Solid State Chemistry. A wide-range simulation is conducted to exhibit small sample properties of the estimation and to verify the declaration regarding the drawback of the old specification.

The results of this study indicate that the long memory estimator of important parameters in the FIGARCH model may experience a lower convergence rates. Introduction It has been widely reported that volatility of many financial and macroeconomic time series is highly persistent. While these models appear useful in describing many empirical volatility processes, there is understandably great interest in discerning the reasons and underlying causes for the widespread empirical finding of long memory in volatility.

In particular, Granger and [16] have shown that contemporaneous aggregation of stable GARCH 1,1 processes can result in an aggregate process that exhibits hyperbolically decaying autocorrelations. While this property appears to be consistent with long memory, [34] has shown that the autocorrelation function is assumable, which is inconsistent with it being classified as a long memory process.

A related argument of [1] shows how the contemporaneous aggregation of weakly dependent information flow processes can produce the property of long memory in volatility. A further justification is provided by [29], who suggests that long memory in volatility can arise from the reaction of short-term dealers to the dynamics of a proxy for the expected volatility trend coarse volatility , which causes persistence in the higher frequency volatility or fine volatility process.

Helan and Tashtoush A number of previous papers have observed and provided application of fractional integrated models in many fields, namely; stock returns [5, 6, 15, 31, 28, 26, 25, 32]; exchange rate [2, 14, 12] and inflation rate [3].

However, in the literature, up to this period, there have been little applications of the fractional integrated GARCH class models to commodity futures markets. Baillie [4] examined long memory models in volatility properties of both daily and high frequency intraday futures for six important commodities. They found that the volatility processes were found to be very well described by FIGARCH models, with statistically significant long memory parameter estimates.

Recently, [24] explored a long memory conditional volatility model on international grain markets; namely wheat, corn and soybeans, and compare the performance of the models in capturing dependence of the price volatility and also emphasized suitability of the student-t density intended to account for non-normal, fat-tailed properties of the data.

Conclusions are finally presented in section 4. It is well known e. By contrast, as suggested in the literature, one way of arguing that a process possesses long memory is that its autocorrelation function decreases “slowly”. The usual stationarity and invertability conditions for the ARMA model are assumed. To ensure stationary and invariability of the process y t we assume that d lies between 0 and 0. Hence, the ranges of fractional exponents d are different, and d is permitted to be greater than 0.

Finally, while there is no sign restriction on the ARFIMA model for the conditional mean, the parameters of the FIGARCH model must be subject to additional restrictions to ensure that the resulting conditional variances be all non-negative.

The computations of the approximate log-likelihood function 11 and its first- order derivatives require the values of innovations at and the conditional variances ht , but it is y t that we observe. So before implementing the AMLE, we need to transform y t to at and ht based on 1 and 8. This choice of the pre-sample values may be an issue of concerns. However, the simulation studies for the ARFIMA model by [10] find that even with sample size as small as , using the AMLE causes relatively little bias as long as the value of the parameter d is not too close to 0.

Simulation Results Our simulation study is divided into two parts. In all simulation experiments the data-generation and estimation processes are repeated times. The sample size is fixed at 1, Moreover, it seems that setting N to the small value of in the present model specification produces the least biases. But this observation completely contradicts to the belief that N should be made as large as possible in order to minimize the truncation error.

The 2 simulation results in Table 3 clearly demonstrate that the estimates of the fractional differencing parameter d do follow the pattern suggested by the standard asymptotic theory for the AMLE: The SE are of the same magnitude of the two MASE and all of them exhibit an interesting inverted U-shape pattern as d increases and the speeds at which SE decline as the sample size T increases from to and from to 1, are approximately the T rates.

The 2 small-sample biases are considerably large and almost always positive, especially when d is greater than 0. These simulation results, however, are too inconclusive to indicate what the changing convergence rates really are.

Our simulation results reveal that the estimation may experience difficulties similar to those in estimating unconditional mean of the more familiar fractionally integrated ARMA ARFIMA process. It is well-known that the estimator of the unconditional mean in the ARFIMA model has non-standard asymptotic behavior, our simulation results hint that the estimator of an important parameter in the FIGARCH model may have similar non-standard properties with slower convergence rates.

Aside from this important observation, our simulation results confirm that the FIGARCH model is a viable generalization of the conventional GARCH model that allows us to model and estimate persistent conditional variances. Our simulation study also demonstrates small sample properties of the estimation for the new specification.

In this regard, we have to point out that simulation cannot provide sufficient evidence to pin down the exact range for d and this issue certainly calls for further studies. Journal of Finance, 52 — Bollerslev, and H. Han, and T. Southern Economic Journal, 68 Han, R. Myers, and J.

Journal of Futures Markets, 27 [5] Bollerslev, T. Journal of Econometrics, 73 Crato, and P. Dollery and N. Computational Statistics and Data Analysis 52 KOF Working Papers, Nagler, R. Olsen, and O. Journal of International Money and Finance 12 — Applied Financial Economics, 14 Granger, and R. Calzolari, and L. Journal of Econometrics, 73 — In: Knight, J. Butterworth- Heineman, Oxford, — Journal of Rural Development, 31 South African Journal of Economics, 73 Physica A, Journal of Economic Surveys, 3 Journal of Economic Dynamics and Control, 31 Dave, R.

Olsen, R. Pictet, and J. Journal of Empirical Finance, 4 — Spanish Economic Review, 10 Computational Statistics and Data Analysis, 52 Journal of Time Series Analysis, 28 — Also we will reconsider the topological structure of Menger probabilistic normed spaces briefly PN-spaces under the t-norm T.

We shall give some examples to show that it is possible to construct complete PN-space together with contraction mappings which have no fixed point. Menger in [9]. In this theory, the concept of the distance between two points has a probabilistic nature, i. This theory was extended later to probabilistic normed linear spaces by Serstnev [11] and generalized by several other authors see [1,2,,14]. In this paper, we will consider a Menger probabilistic normed space under the t-norm T, and will obtain some results on its derived topology.

The results presented in this paper generalize and improve many results of S. Chang [4], and K. Menger [], and many others in normed spaces and probabilistic normed spaces. The next definitions give a brief description for the background on which the paper will be built on. Definition 1. We will denote the set of all distribution functions by D. Al-Btoush, Rashid and Masaedeh Defintion1. Here f x denotes the distribution function associated with x.

Recall that a t -norm is a binary operation on [0,1] that is commutative, associative, nondecreasing in each variable, and has 1 as identity. Main Results. Our goal of this work is to introduce the concept of neighborhoods in PN-space and study some properties related to this concept. Definition 2. Thus N p and N q are disjoint. Lemma 2. Theorem 2. As a consequence of Theorem 2. The problem is that the triangle norm in a PN-space is often not strong enough to guarantee that the sequence of iterates of a point under a contraction mapping is a Cauchy sequence.

In what follows, we shall give some examples to show that it is possible to construct complete PN-space together with contraction mappings which have no fixed point.

Case 3 : The proof is similar to the proof given in case 2 and is therefore omitted. Thus Z , f is complete. This completes the Proof. We end this paper by illustrating the following examples which satisfy Theorem 2.

Therefore, we see that the conditions of Theorem 2. Example 2. So, we see that the conditions of Theorem 2. Journal of Math. Analysis, 3 26 — And Mech. J, 9 81 A, 28 A, 37 Nauk SSSR 2 Systems Theory, 6 Organizations must take more creative steps in their use of information technology to make their business more attractive to customers rather than their competitors. The success of these programs rely on keeping track of the activities and accounts of many, perhaps even millions of customers, which would not be without a customer loyalty system.

Thus, this paper introduces the development of a customer loyalty system which can compute, generate, and provide reports to the business stakeholders concerning their work, customers, and their market share growth.

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