New occurrence of potential phosphate resource in northeast Jordan

An exploration program aided by field investigation, exploration drilling, detailed sampling, lithological and petrological studies, geochemical investigation, and resource calculation leads to the discovery of a potential phosphate resource in northeast Jordan close to the international border with Saudi Arabia and Iraq. The studied phosphate is of the Middle Eocene age that belongs to the Wadi Shallala Formation. It is equivalent to the phosphate deposits recorded in the lower part of the Umm Wual Formation in the Turayf region of Saudi Arabia and the Eocene Ratga Formation in the Ethna phosphate deposit west of Iraq. The phosphorites in the region are broadly similar in mineralogical composition and geochemical affinities. X-ray diffraction indicates the presence of francolite with variable amounts of calcite and quartz. Most samples consist of phosphate clasts embedded in carbonate and silica matrix and cement. P₂O₅ content is up to 32.3 % with an average equal to 18.6 %. The impurity is caused by the presence of variable amounts of SiO₂ and CaO. The F% and F/P₂O₅ ratio in studied phosphates is lower compared with that in phosphates from Jordan and Saudi Arabia. The geological and geochemical results were integrated for resource estimation. Three high-grade phosphate layers with ≥23 % P₂O₅ were considered in the calculations. The phosphate resource is classified as an inferred resource. The total volume of the resource is about 649 million tons. The average P₂O₅ content is 24.57, and the stripping ratio is 1:5.8.     

Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia

The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.     

The best fitting distribution of annual maximum rainfall in Peninsular Malaysia based on methods of L-moment and LQ-moment

This paper discusses patterns of annual and monthly precipitation variability at seven weather stations in east central Europe (1851–2007). Precipitation patterns were compared to three simple regional indices of atmospheric circulation, i.e., western circulation, southern circulation and the cyclonicity (C) index and a relationship between precipitation and the North Atlantic Oscillation index was identified. Correlations of the monthly records and multiple regression, using a principal components’ analysis, helped determine the statistical significance of the dependence of precipitation on the circulation indices. The Mann–Kendall test revealed no trend to change in any of the precipitation series, but a certain spatial regularity could be discerned in the phase of the annual periodic component. A common feature of the variability in central European annual precipitation is the dry period identified in the 1980s and the first half of the 1990s. In the northern part of the region, above-average precipitation was noted from the 1960s through to the mid-1970s as a result of the frequent prevalence of depressions. South of the divide, the wettest period was recorded at the turn of 1930s/1940s. After a number of very wet years in the last decade of the twentieth century and the beginning of the twenty-first century, precipitation began to fall at all of the region’s weather stations. The C index is the strongest circulation-linked factor influencing precipitation in central Europe and it accounts for more than 40% of the variance in spatially averaged wintertime precipitation.     

Volatility modeling of rainfall time series

Networks of rain gauges can provide a better insight into the spatial and temporal variability of rainfall, but they tend to be too widely spaced for accurate estimates. A way to estimate the spatial variability of rainfall between gauge points is to interpolate between them. This paper evaluates the spatial autocorrelation of rainfall data in some locations in Peninsular Malaysia using geostatistical technique. The results give an insight on the spatial variability of rainfall in the area, as such, two rain gauges were selected for an in-depth study of the temporal dependence of the rainfall data-generating process. It could be shown that rainfall data are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. The autocorrelation structure of the residuals and the squared residuals derived from autoregressive integrated moving average (ARIMA) models were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, and the Ljung–Box test confirmed the results. A test based on the Lagrange multiplier principle was applied to the squared residuals from the ARIMA models. The results of this auxiliary test show a clear evidence to reject the null hypothesis of no autoregressive conditional heteroskedasticity (ARCH) effect. Hence, it indicates that generalized ARCH (GARCH) modeling is necessary. An ARIMA error model is proposed to capture the mean behavior and a GARCH model for modeling heteroskedasticity (variance behavior) of the residuals from the ARIMA model. Therefore, the composite ARIMA–GARCH model captures the dynamics of daily rainfall in the study area. On the other hand, seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.     

Separation of Vanillin from Oil Palm Empty Fruit Bunch Lignin

The separation of vanillin from the lignin of empty fruit bunch oil palm is presented in this study. The lignin was extracted from soda black liquor derived from oil palm empty fruit bunches (EFB) using 20% sulfuric acid. Nitrobenzene oxidation was carried out by adding 50 mg of dry EFB lignin into a mixture of 7 mL of 2 M NaOH and 4 mL of nitrobenzene in a 15 mL steel autoclave. The autoclave was sealed tightly with a screw cap fitted with a Teflon gasket and heated to 165°C for 3 h in a preheated thermostatic oil bath. High performance liquid chromatography (HPLC) was used to determine the concentration of each compound present in the lignin. There were eight compounds detected in the HPLC chromatogram. The crystallization process was then used as a method to separate vanillin from other compounds. Based on the solubility of vanillin in acetone, 15 mL of acetone was added to the residue of the oxidized products and heated to 60°C for 10 min. A yield of 1.6% vanillin was isolated from a 50 mg lignin sample. The isolated compound was analyzed with HPLC, Fourier transform infrared spectrophotometry (FT‐IR) and nuclear magnetic resonance (¹³C‐NMR) for structural verification. The results of the characterization studies proved that the isolated compound is vanillin.