Grain size,areal thickness distribution and controls on sedimentation of the 1991 Mount Pinatubo tephra layer in the South China Sea

The June 15, 1991 climactic eruption of Mt. Pinatubo produced an extensive, largely co-ignimbrite-derived airfall ash layer on Luzon Island and across the central South China Sea. The layer covers an area of ~4×10⁵ km² with a volume of 5.5 km³. Near the coast of Luzon, the deposit consists of two units: a normally graded basal ash bed, unimodal in grain size, and a finer-grained, internally structureless upper ash bed showing grain size bimodality. With increasing distance from the source, the coarse particle populations of the two units merge and migrate towards a near-constant fine population (~11 m); the distal region is covered by a fine-mode dominated, virtually ungraded single ash layer. The reversal of the winds from easterly directions at upper-tropospheric and stratospheric levels to westerly directions in the middle and lower troposphere indicates that both the coarse- and fine-mode components fell out from high-altitude eruption clouds. The high-velocity upper-level winds, however, would have transported fine-grained ash particles far beyond the South China Sea, which suggests that their settling was accelerated by aggregation. The boundary between the units thus marks a change from fallout of predominantly discrete pyroclasts to simultaneous fallout of aggregated fines and freely falling, coarse-grained particles. The particle populations composing the upper ash bed were almost completely removed from the proximal areas by the upper-level winds. At lower elevations, the counterclockwise circulation of a typhoon over the coastal area advected the ash south and eastward, producing a thickness maximum in the medial region (at about 160 km from source). The strong displacement of fines, possibly aided by wind turbulence, led to a break in bulk tephra thinning rates close to the coastline. In the distal region, outside the influence of the typhoon, southwest monsoonal winds caused a distinct lobe axis inflection and thickness asymmetry. Within this region, at about 420 km from source, fallout of particle aggregates created a second thickness maximum. Comparison of the field data with previous experimental observations and tephra flux records in the deep sea (Wiesner et al. 1995; Carey 1997; McCool 2002) implies that the transport of ash in the water column was largely determined by vertical density currents. Differences in the reaction of coarse and fine particles to turbulence in the descending plumes probably suppressed the segregation of fines but allowed the coarser pyroclasts to maintain their initial order of arrival at the sea surface. Considering typical fall rates of convective plumes, modifications of the initial fallout position of the particles by the South China Sea current system are on the order of only a few kilometers. The results suggest that convective sedimentation processes ensure the preservation of atmospheric particle transport directions, distances, and fallout modes in the deep sea.Editorial responsibility: R. CioniAn erratum to this article can be found at     

Mineralization and deformation of the Malanjkhand terrane (2,490–2,440 Ma) along the southern margin of the Central Indian Tectonic Zone

The Malanjkhand Cu–Mo–Au deposit, located near the northwest margin of the Malanjkhand batholith (terrane), is a strategic and significant porphyry-style deposit that experienced a protracted 50 m.y. deformational history shortly after its formation at 2,490±8 Ma (Stein et al. 2004). In a recent study, Panigrahi et al. (2004) averaged U–Pb SHRIMP zircon data from a pooled set of samples from the Malanjkhand batholith to advocate a meaningless intermediate age of ~2,476 Ma for the Malanjkhand granitoid and its Cu–Mo–Au deposit. In the northwest part of the Malanjkhand batholith, Re–Os dating of occurrence-specific molybdenite captures not only the age of porphyry-style mineralization and associated magmatism, but also elucidates a complex deformational history that extends to ~2,450 Ma. In the central part of the Malanjkhand batholith, Re–Os dating of delicate spindles of accessory molybdenite occurring with pristine muscovite in miarolitic cavities within the undeformed microgranitoid at the Devgaon Mo prospect unequivocally shows that deformation ceased at this location no later than 2,470–2,465 Ma. The deformational history recorded at the Malanjkhand deposit in the northwest most likely reflects prolonged transpressive convergence and docking of the Malanjkhand terrane with units in the poorly understood (proto) Central Indian Tectonic Zone (CITZ) along its southern margin, the Central Indian shear zone. The timing for this convergence is Late Archean–Early Paleoproterozoic.Comment on “Age of granitic activity associated with copper–molybdenum mineralization at Malanjkhand, Central India” by Panigrahi MK et al. (Mineralium Deposita 39:670–677)     

Would you like to know more? The effect of personalized wildfire risk information and social comparisons on information-seeking behavior in the wildland–urban interface

Natural Hazards - Private landowners are important actors in landscape-level wildfire risk management. Accordingly, wildfire programs and policy encourage wildland–urban interface homeowners...     

A machine learning approach for regional geochemical data:Platinum-group element geochemistry vs geodynamic settings of the North Atlantic Igneous Province

Whilst traditional approaches to geochemistry provide valuable insights into magmatic processes such as melting and element fractionation, by considering entire regional data sets on an objective basis using machine learning algorithms(MLAs), we can highlight new facets within the broader data structure and significantly enhance previous geochemical interpretations.The platinum-group element(PGE) budget of lavas in the North Atlantic Igneous Province(NAIP) has been shown to vary systematically according to age, geographic location and geodynamic environment.Given the large multi-element geochemical data set available for the region, MLAs were employed to explore the magmatic controls on these shifting concentrations.The key advantage of using machine learning in analysis is its ability to cluster samples across multi-dimensional(i.e., multi-element)space.The NAIP data set is manipulated using Principal Component Analysis(PCA) and t-Distributed Stochastic Neighbour Embedding(t-SNE) techniques to increase separability in the data alongside clustering using the k-means MLA.The new multi-element classification is compared to the original geographic classification to assess the performance of both approaches.The workflow provides a means for creating an objective high-dimensional investigation on a geochemical data set and particularly enhances the identification of metallogenic anomalies across the region.The techniques used highlight three distinct multi-element end-members which successfully capture the variability of the majority of elements included as input variables.These end-members are seen to fluctuate in prominence throughout the NAIP, which we propose reflects the changing geodynamic environment and melting source.Crucially, the variability of Pt and Pd are not reflected in MLA-based clustering trends, suggesting that they vary independently through controls not readily demonstrated by the NAIP major or trace element data structure(i.e., other proxies for magmatic differentiation).This data science approach thus highlights that PGE(here signalled by Pt/Pd ratio) may be used to identify otherwise localised or cryptic geochemical inputs from the subcontinental lithospheric mantle(SCLM) during the ascent of plume-derived magma, and thereby impact upon the resulting metallogenic basket.     

Distinguishing human from animal faecal contamination in water: A review

Management of faecal contamination of water would be improved if sources could be accurately identified through water analysis. Human faeces are generally perceived as constituting a greater human health risk than animal faeces, but reliable epidemiological evidence is lacking. United States waterborne disease data suggest that human‐specific enteric viruses account for over half the documented outbreaks. However, in New Zealand, where there is a high grazing animal:human ratio (increasing the relative importance of water‐transmissible zoonoses), it seems prudent to assume that human and animal faecal pollution both constitute a risk to human health. Irrespective of the relative risks, the ability to identify sources would assist in overall management of microbial water quality. Faecal streptococci do not appear to provide reliable faecal source identification. Human and animal sources, respectively, maybe distinguishable by two tests on Bifidobacterium spp.—growth at 45°C in trypticase phytone yeast broth and sorbitol fermentation. Different species of Bacteroides tend to be present in humans and animals, but poor survival in water is a problem. Phages of the Bacteroides fragilis strain HSP40 appear to be human specific, but low counts in effluent in some countries, including New Zealand, may limit their usefulness. Different F‐RNA phage subgroups appear to be associated with human and animal faecal sources. The actinomycete Rhodococcus coprophilus has potential as a grazing animal indicator but it is persistent, and existing culturing techniques are time consuming. The development of DNA‐based techniques, such as polymerase chain reaction (PCR), may assist in the assay of some microbial faecal source indicators. Various faecal sterol isomers offer the possibility of distinguishing between human and animal sources, and even between different animals. Washing powder constituents such as fluorescent whitening agents, sodium tripolyphosphate and linear alkyl benzenes, offer useful human source identifiers. It is unlikely that any single determinand will be useful in all situations, but statistical analysis of appropriate “baskets” of microbial and chemical determinands offers the possibility of identifying and apportioning human and animal faecal inputs to natural waters.