First nonavian dinosaur from Lebanon: a brachiosaurid sauropod from the Lower Cretaceous of the Jezzine District

Two sauropod teeth from an Early Cretaceous (Neocomian) fluviodeltaic sandstone near Jezzine (Southern Lebanon) are the first nonavian dinosaur remains to be reported from Lebanon. Their distinctive character places them within Brachiosauridae. The sauropod teeth from Lebanon are a significant addition to the very scanty dinosaur record from the Levant, which hitherto consisted mainly of very poorly preserved and not easily identifiable specimens. The Basal Cretaceous Sandstone of Lebanon, thus, appears to be a potentially important source of fossil vertebrate material.     

Real-Time PCR Detection of Norovirus in Mussels Collected from the Bosphorus in Istanbul,Turkey

Norovirus (NoV) is recognised as one of the most common causes of foodborne infections, and shellfish are a well-documented source of this virus. The presence of NoV in shellfish has not previously been investigated in Turkey, and hence the aim of this study was to determine the frequency of human NoV genogroups I and II in mussels collected from the Bosphorus, Istanbul, Turkey. A total of 320 mussels representing 110 samples originating along the Bosphorus coast were collected from fish distributors. RNA was extracted using the RNeasy Mini Kit and real-time RT–PCR performed using primers specific for NoV genogroup I and II. Amongst the 110 samples, 5 (4.5%) were found to be positive for NoV genogroup II by SYBR Green assay; no genogroup I was detected. A positive signal was obtained by SYBR Green for NoV Genogroup II in mussels collected in October, November and December 2008, and February and July 2009. Only four out of five SYBR Green positive samples could be confirmed by the use of a NoV GII probe-based real-time RT–PCR. The average count and SD of Enterobactericaeae, E. coli and sulphide reductase anaerobic bacteria in PCR positive mussels were 3.56 log ± 0.96 log, 2.32 log ± 0.77 log and 1.70 log ± 0.56 log, respectively. This study shows that NoV Genogroup II is present in mussels collected from the Bosphorus, Istanbul, and may constitute a risk to human health.     

Estimation of probabilities and likely consequences of a chain of accidents (domino effect) in Manali Industrial Complex

Chains of accidents (the domino effect) have been occurring with ever increasing frequency in chemical process industries. This is reflected in several accidents ‘J Loss Prevent Process Ind 12 (1999a) 361’; the world's worst industrial accident of the 1990s — the Vishakhpatnam disaster — also involved the domino effect ‘J Loss Prevent Process Ind 12 (1999a) 361; and Process Safety Prog 18 (1999b) 135’. Such chains of accidents have a greater propensity to cause damage than stand-alone accidents ‘Process Safety Prog 17(2) (1998a) 107; and J Loss Prevent Process Ind 12 (1999a) 361’.In order to assess the likelihood of occurrence of the domino effect and its damage potential, use of deterministic models in conjunction with probabilistic analysis is required. Recently we have proposed a systematic methodology called ‘domino effect analysis’ (DEA). A computer-automated tool, DOMIFFECT, has also been developed by us based on DEA ‘Process Safety Prog 17(2) (1998a) 107; Environment Model Software 13 (1998b) 163; and Risk assessment in chemical process industries: advanced techniques. Discovery Publishing House (1998c)’.This paper illustrates the application of DEA and DOMIFFECT to an industrial complex comprising 16 different industries. Out of 12 credible accident scenarios envisaged in three different industries — namely Madras Refineries Limited (MRL), UB Petrochemicals (UBP) and Indian Organic Chemicals Limited (IOCL), eight scenarios are likely to cause the domino effect. A further detailed analysis reveals that accidents in the storage of liquified petroleum gas and propylene and in the reflux drum units of MRL may cause domino effects. Similarly, propylene storage of UBP and monoethylene glycol storage of IOCL are also likely to cause domino effects. The impact of various chains of accidents has been forecast which reveals that in several cases the accidents may be catastrophic, harming the entire industrial complex of 16 industries. The study leads to the identification of ‘hot spots’ — units that pose the greatest risk — in turn forewarning the industries concerned and enabling them to prioritize and augment accident-prevention steps.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

Analysis and evaluation of nitrate levels in groundwater at Al-Hashimiya area, Jordan

Water with high nitrate concentration (NO₃ ⁻) is unfit for human consumption, especially when its concentration exceeded the threshold limit (50 mg/l) recommended by the health authorities such as the World Health Organization (WHO). In Jordan, there is a great concern for determination and monitoring organic and inorganic pollutants that may reach groundwater. Nitrate is highly mobile and present in domestic, agricultural and industrial waste in Jordan, and thus this study focused initially on nitrate as both a contaminant of concern and as an indicator of potential groundwater contamination. The present study determined the extent of nitrate contamination in groundwater in the study area and examined the likely sources of NO₃ ⁻. A total of 248 groundwater samples were collected from 16 wells in different sites of Al-Hashimiya area, Zerqa Governorate, Jordan, and investigated for NO₃ ⁻ concentrations. Moreover, measurements of temperature, electrical conductivity and pH were carried out in the field. Analysis was carried out according to the methods described by the American Public Health Association (APHA). Results showed that there was a dramatic increasing in NO₃ ⁻ concentrations from the year 2001 to 2006 for some selected wells in the present study. NO₃ ⁻ concentration in 2006 was ranged from 10 to 330 mg/l with an average of 77 mg/l. Overall, groundwater had elevated nitrate concentration with 92% of the samples containing more than 20 mg/l NO₃ ⁻, indicating the influence of human activities. This study has shown that there is a strong correlation between the nitrate concentration and the wastewater effluents as a source of pollution.     

Genotoxic potential of diesel exhaust particles from the combustion of first- and second-generation biodiesel fuels—the FuelHealth project

Epidemiological data indicate that exposure to diesel exhaust particles (DEPs) from traffic emissions is associated with higher risk of morbidity and mortality related to cardiovascular and pulmonary diseases, accelerated progression of atherosclerotic plaques, and possible lung cancer. While the impact of DEPs from combustion of fossil diesel fuel on human health has been extensively studied, current knowledge of DEPs from combustion of biofuels provides limited and inconsistent information about its mutagenicity and genotoxicity, as well as possible adverse health risks. The objective of the present work was to compare the genotoxicity of DEPs from combustion of two first-generation fuels, 7% fatty acid methyl esters (FAME) (B7) and 20% FAME (B20), and a second-generation 20% FAME/hydrotreated vegetable oil (SHB: synthetic hydrocarbon biofuel) fuel. Our results revealed that particulate engine emissions from each type of biodiesel fuel induced genotoxic effects in BEAS-2B and A549 cells, manifested as the increased levels of single-strand breaks, the increased frequencies of micronuclei, or the deregulated expression of genes involved in DNA damage signaling pathways. We also found that none of the tested DEPs showed the induction of oxidative DNA damage and the gamma-H2AX-detectable double-strand breaks. The most pronounced differences concerning the tested particles were observed for the induction of single-strand breaks, with the greatest genotoxicity being associated with the B7-derived DEPs. The differences in other effects between DEPs from the different biodiesel blend percentage and biodiesel feedstock were also observed, but the magnitude of these variations was limited.     

Properties of steel foundry electric arc furnace dust solidified/stabilized with Portland cement

Electric arc furnace dust from steel production is generated in considerable amounts worldwide and needs to be treated as hazardous waste. The aim of this study was to investigate the properties of electric arc furnace dust solidified/stabilized by using Portland cement. Mortar and paste samples were prepared with varying waste-to-binder ratios between 0% and 90%. A comprehensive experimental program was designed including XRF characterization, setting time, unconfined compressive strength, and toxicity characteristics leaching procedure (TCLP), synthetic precipitation leaching procedure (SPLP), and acid neutralization capacity (ANC) tests. The results were evaluated in order to determine if the solidified /stabilized product can be disposed of at a landfill site with domestic waste or at a segregated landfill. The effect of using sand on S/S performance was also investigated. The results indicated that the solidification /stabilization process using PC helps the heavy metals to be bound in the cement matrix, but the TCLP leaching results exceeded the EPA landfilling limits. The SPLP leaching results conformed to the limits implying that the waste or S/S products can be disposed of at a segregated landfill; however the low ANC of the S/S products reveals that there may be leaching in the long-term. The sand used in the mortar samples adversely affected the S/S performance, causing higher heavy metal leaching levels, and lower pH levels in the leachate after the TCLP extraction than those measured in the leachate of the paste samples.     

Explosibility of micron- and nano-size titanium powders

Explosibility of micron- and nano-titanium was determined and compared according to explosion severity and likelihood using standard dust explosion equipment. ASTM methods were followed using a Siwek 20-L explosion chamber, MIKE 3 apparatus and BAM oven. The explosibility parameters investigated for both size ranges of titanium include explosion severity (maximum explosion pressure (P_max) and size-normalized maximum rate of pressure rise (K_St)) and explosion likelihood (minimum explosible concentration (MEC), minimum ignition energy (MIE) and minimum ignition temperature (MIT)). Titanium particle sizes were ?100 mesh (<150 μm), ?325 mesh (<45 μm), ≤20 μm, 150 nm, 60–80 nm, and 40–60 nm. The results show a significant increase in explosion severity as the particle size decreases from ?100 mesh with an apparent plateau being reached at ?325 mesh and ≤20 μm. Micron-size explosion severity could not be compared with that for nano-titanium due to pre-ignition of the nano-powder in the 20-L chamber. The likelihood of an explosion increases significantly as the particle size decreases into the nano range. Nano-titanium is very sensitive and can self-ignite under the appropriate conditions. The explosive properties of the nano-titanium can be suppressed by adding nano-titanium dioxide to the dust mixture. Safety precautions and procedures for the nano-titanium are also discussed.     

Quantifying the effect of strong ignition sources on particle preconditioning and distribution in the 20-L chamber

Computational fluid dynamics is used to investigate the preconditioning aspect of overdriving in dust explosion testing. The results show that preconditioning alters both the particle temperature and distribution prior to flame propagation in the 20-L chamber. A parametric study gives the fluid pressure and temperature, and particle temperature and concentration at an assumed flame kernel development time (10 ms) for varying ignitor size and particle diameter. For the 10 kJ ignitor with 50% efficiency, polyethylene particles under 50 μm reach 400 K and may melt prior to flame propagation. Gases from the ignitor detonation displace the dust from the center of the chamber and may increase local particle concentration up to two times the nominal value being tested. These effects have important implications for explosive testing of dusts in the 20-L chamber and comparing to larger 1-m³ testing, where these effects may be negligible.     

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.