Experimental investigation of spark ignition energy in kerosene,hexane, and hydrogen

Quantifying the risk of accidental ignition of flammable mixtures is extremely important in industry and aviation safety. The concept of a minimum ignition energy (MIE), obtained using a capacitive spark discharge ignition source, has traditionally formed the basis for determining the hazard posed by fuels. While extensive tabulations of historical MIE data exist, there has been little work done on ignition of realistic industrial and aviation fuels, such as gasoline or kerosene. In the current work, spark ignition tests are performed in a gaseous kerosene–air mixture with a liquid fuel temperature of 60 °C and a fixed spark gap of 3.3 mm. The required ignition energy was examined, and a range of spark energies over which there is a probability of ignition is identified and compared with previous test results in Jet A (aviation kerosene). The kerosene results are also compared with ignition test results obtained in previous work for traditional hydrogen-based surrogate mixtures used in safety testing as well as two hexane–air mixtures. Additionally, the statistical nature of spark ignition is discussed.     

Assessment of an accidental vapour cloud explosion: Lessons from the Indian Oil Corporation Ltd. accident at Jaipur,India

On 29 October 2009, at 19:30 IST, a devastating vapour cloud explosion occurred in a large fuel storage area at the Indian Oil Corporation (IOC) Depot in Jaipur, India, generating significant blast pressure. As a consequence of this explosion, the entire installation was destroyed, buildings in the immediate vicinity were heavily damaged, and windowpane breakages were found up to 2 km from the terminal. The IOC estimated that the total loss from the fire and explosion was approximately INR 2800 million.Ironically, as a storage site, the Jaipur terminal was not highly congested, and thus was not considered to have adequate potential for a vapour cloud explosion (VCE). Nevertheless, the prima facie evidences indicate that this was a case of VCE. Therefore, the main objective of this study is to quantify the potential overpressures due to vapour cloud explosions (VCEs) using the Process Hazard Analysis DNV Norway based PHAST 6.51 Software. The results are validated by the extent of the damage that had occurred. The estimation of the VCE shows that a maximum 1.0 bar overpressure was generated in the surrounding area. The initial assessment of the accident data roughly estimates the release mode, time, and amount of vaporized fuel. A more accurate estimate has been obtained by modelling the dispersion of vapour clouds in the surrounding atmosphere, which reveals trends and relationships for the occurrence of vapour cloud explosions.     

垃圾渗沥液中氨氮的电化学氧化

针对目前国内外垃圾渗沥液处理中存在的问题,采用电化学氧化与上流式厌氧污泥床（UASB）相结合,研究建立了对香港垃圾渗沥液的二步法处理工艺．本文着重探讨了电化学间接氧化去除渗沥液中氨氮的反应机制及主要影响因素,并通过实验找出了最佳的操作条件：入水初始 pH值为9.0;流速为0.01－0.10cm/s;CI加入量2000mg/L;电流密度 32.3mA/cm²．在此条件下,经过 6h电解后,UASB反应器出水中NH_－N和COD的去除率分别达到100％和87％．对该电化学氧化过程的运行成本进行了评估．     

Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F ) over time based on kinship of possible breeding pairs and to estimate empirically N_e/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.     

Biogeographical and Taxonomic Biases in Tropical Forest Fragmentation Research

Despite several decades of research on the effects of fragmentation and habitat change on biodiversity, there remain strong biases in the geographical regions and taxonomic species studied. The knowledge gaps resulting from these biases are of particular concern if the forests most threatened with modification are also those for which the effects of such change are most poorly understood. To quantify the nature and magnitude of such biases, we conducted a systematic review of the published literature on forest fragmentation in the tropics for the period 1980–2012. Studies included focused on any type of response of single species, communities, or assemblages of any taxonomic group to tropical forest fragmentation and on fragmentation‐related changes to forests. Of the 853 studies we found in the SCOPUS database, 64% were conducted in the Neotropics, 13% in Asia, 10% in the Afrotropics, and 5% in Australasia. Thus, although the Afrotropics is subject to the highest rates of deforestation globally, it was the most disproportionately poorly studied biome. Significant taxonomic biases were identified. Of the taxonomic groups considered, herpetofauna was the least studied in the tropics, particularly in Africa. Research examining patterns of species distribution was by far the most common type (72%), and work focused on ecological processes (28%) was rare in all biomes, but particularly in the Afrotropics and for fauna. We suggest research efforts be directed toward less‐studied biogeographic regions, particularly where the threat of forest fragmentation continues to be high. Increased research investment in the Afrotropics will be important to build knowledge of threats and inform responses in a region where almost no efforts to restore its fragmented landscapes have yet begun and forest protection is arguably most tenuous. Sesgos Biogeográficos y Taxonómicos en la Investigación de la Fragmentación de Bosques Tropicales     

Implications of Macroalgal Isolation by Distance for Networks of Marine Protected Areas

The global extent of macroalgal forests is declining, greatly affecting marine biodiversity at broad scales through the effects macroalgae have on ecosystem processes, habitat provision, and food web support. Networks of marine protected areas comprise one potential tool that may safeguard gene flow among macroalgal populations in the face of increasing population fragmentation caused by pollution, habitat modification, climate change, algal harvesting, trophic cascades, and other anthropogenic stressors. Optimal design of protected area networks requires knowledge of effective dispersal distances for a range of macroalgae. We conducted a global meta‐analysis based on data in the published literature to determine the generality of relation between genetic differentiation and geographic distance among macroalgal populations. We also examined whether spatial genetic variation differed significantly with respect to higher taxon, life history, and habitat characteristics. We found clear evidence of population isolation by distance across a multitude of macroalgal species. Genetic and geographic distance were positively correlated across 49 studies; a modal distance of 50–100 km maintained F_ST < 0.2. This relation was consistent for all algal divisions, life cycles, habitats, and molecular marker classes investigated. Incorporating knowledge of the spatial scales of gene flow into the design of marine protected area networks will help moderate anthropogenic increases in population isolation and inbreeding and contribute to the resilience of macroalgal forests. Implicaciones del Aislamiento por Distancia de Macroalgas para Redes de Áreas Marinas Protegidas     

Soil C and N models that integrate microbial diversity

Industrial agriculture is yearly responsible for the loss of 55–100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influencing C and N cycles. Soil microorganisms play a central role in SOM dynamics. For instance, microbial diversity may explain up to 77 % of carbon mineralisation activities. However, soil microbial diversity is actually rarely taken into account in models of C and N dynamics. Here, we review the influence of microbial diversity on C and N dynamics, and the integration of microbial diversity in soil C and N models. We found that a gain of microbial richness and evenness enhances soil C and N dynamics on the average, though the improvement of C and N dynamics depends on the composition of microbial community. We reviewed 50 models integrating soil microbial diversity. More than 90 % of models integrate microbial diversity with discrete compartments representing conceptual functional groups (64 %) or identified taxonomic groups interacting in a food web (28 %). Half of the models have not been tested against an empirical dataset while the other half mainly consider fixed parameters. This is due to the difficulty to link taxonomic and functional diversity.