Monitoring natural hazards

Few subjects have caught the attention of the entire world as much as those dealing with natural hazards. The first decade of this new millennium provides a litany of tragic examples of various hazards that turned into disasters affecting millions of individuals around the globe. The human losses (some 225,000 people) associated with the 2004 Indian Ocean earthquake and tsunami, the economic costs (approximately 200 billion USD) of the 2011 Tohoku Japan earthquake, tsunami and reactor event, and the collective social impacts of human tragedies experienced during Hurricane Katrina in 2005 all provide repetitive reminders that we humans are temporary guests occupying a very active and angry planet. Any examples may have been cited here to stress the point that natural events on Earth may, and often do, lead to disasters and catastrophes when humans place themselves into situations of high risk. Few subjects share the true interdisciplinary dependency that characterizes the field of natural hazards. From geology and geophysics to engineering and emergency response to social psychology and economics, the study of natural hazards draws input from an impressive suite of unique and previously independent specializations. Natural hazards provide a common platform to reduce disciplinary boundaries and facilitate a beneficial synergy in the provision of timely and useful information and action on this critical subject matter. As social norms change regarding the concept of acceptable risk and human migration leads to an explosion in the number of megacities, coastal over-crowding and unmanaged habitation in precarious environments such as mountainous slopes, the vulnerability of people and their susceptibility to natural hazards increases dramatically. Coupled with the concerns of changing climates, escalating recovery costs, a growing divergence between more developed and less developed countries, the subject of natural hazards remains on the forefront of issues that affect all people, nations, and environments all the time.This treatise provides a compendium of critical, timely and very detailed information and essential facts regarding the basic attributes of natural hazards and concomitant disasters. The Encyclopedia of Natural Hazards effectively captures and integrates contributions from an international portfolio of almost 300 specialists whose range of expertise addresses over 330 topics pertinent to the field of natural hazards. Disciplinary barriers are overcome in this comprehensive treatment of the subject matter. Clear illustrations and numerous color images enhance the primary aim to communicate and educate. The inclusion of a series of unique ?classic case study? events interspersed throughout the volume provides tangible examples linking concepts, issues, outcomes and solutions. These case studies illustrate different but notable recent, historic and prehistoric events that have shaped the world as we now know it. They provide excellent focal points linking the remaining terms in the volume to the primary field of study. This Encyclopedia of Natural Hazards will remain a standard reference of choice for many years.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Enterobiasis and strongyloidiasis and associated co-infections and morbidity markers in infants, preschool- and school-aged children from rural coastal Tanzania: a cross-sectional study.

Background. There is a paucity of data pertaining to the epidemiology and public health impact of Enterobius vermicularis and Strongyloides stercoralis infections. We aimed to determine the extent of enterobiasis, strongyloidiasis, and other helminth infections and their association with asymptomatic Plasmodium parasitaemia, anaemia, nutritional status, and blood cell counts in infants, preschool-aged (PSAC), and school-aged children (SAC) from rural coastal Tanzania.MethodsA total of 1,033 children were included in a cross-sectional study implemented in the Bagamoyo district in 2011/2012. Faecal samples were examined for intestinal helminth infections using a broad set of quality controlled methods. Finger-prick blood samples were subjected to filariasis and Plasmodium parasitaemia testing and full blood cell count examination. Weight, length/height, and/or mid-upper arm circumference were measured and the nutritional status determined in accordance with age.Results E. vermicularis infections were found in 4.2% of infants, 16.7%, of PSAC, and 26.3% of SAC. S. stercoralis infections were detected in 5.8%, 7.5%, and 7.1% of infants, PSAC, and SAC, respectively. Multivariable regression analyses revealed higher odds of enterobiasis in children of all age-groups with a reported anthelminthic treatment history over the past six months (odds ratio (OR): 2.15; 95% confidence interval (CI): 1.22 - 3.79) and in SAC with a higher temperature (OR: 2.21; CI: 1.13 - 4.33). Strongyloidiasis was associated with eosinophilia (OR: 2.04; CI: 1.20-3.48) and with Trichuris trichiura infections (OR: 4.13; CI: 1.04-16.52) in children of all age-groups, and with asymptomatic Plasmodium parasitaemia (OR: 13.03; CI: 1.34 - 127.23) in infants. None of the investigated helminthiases impacted significantly on the nutritional status and anaemia, but moderate asymptomatic Plasmodium parasitaemia was a strong predictor for anaemia in children aged older than two years (OR: 2.69; 95% CI: 1.23 ¿ 5.86).Conclusions E. vermicularis and S. stercoralis infections were moderately prevalent in children from rural coastal Tanzania. Our data can contribute to inform yet missing global burden of disease and prevalence estimates for strongyloidiasis and enterobiasis. The association between S stercoralis and asymptomatic Plasmodium parasitaemia found here warrants further comprehensive investigations.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale: some case studies

Every year, debris flows cause huge damage in mountainous areas. Due to population pressure in hazardous zones, the socio-economic impact is much higher than in the past. Therefore, the development of indicative susceptibility hazard maps is of primary importance, particularly in developing countries. However, the complexity of the phenomenon and the variability of local controlling factors limit the use of processbased models for a first assessment. A debris flow model has been developed for regional susceptibility assessments using digital elevation model (DEM) with a GIS-based approach.. The automatic identification of source areas and the estimation of debris flow spreading, based on GIS tools, provide a substantial basis for a preliminary susceptibility assessment at a regional scale. One of the main advantages of this model is its workability. In fact, everything is open to the user, from the data choice to the selection of the algorithms and their parameters. The Flow-R model was tested in three different contexts: two in Switzerland and one in Pakistan, for indicative susceptibility hazard mapping. It was shown that the quality of the DEM is the most important parameter to obtain reliable results for propagation, but also to identify the potential debris flows sources.

Remediation of a marine shore tailings deposit and the importance of water-rock interaction on element cycling in the coastal aquifer

We present the study of the geochemical processes associated with the first successful remediation of a marine shore tailings deposit in a coastal desert environment (Bahia de Ite, in the Atacama Desert of Peru). The remediation approach implemented a wetland on top of the oxidized tailings. The site is characterized by a high hydrauliz gradient produced by agricultural irrigation on upstream gravel terraces that pushed river water (similar to 500 mg/L SO(4)) toward the sea and through the tailings deposit. The geochemical and isotopic (delta(2)H(water) and delta(18)O(water), delta(34)S(sulfate) , delta(18)O(sulfate)) approach applied here revealed that evaporite horizons (anhydrite and halite) in the gravel terraces are the source of increased concentrations of SO(4), Cl, and Na up to similar to 1500 mg/L in the springs at the base of the gravel terraces. Deeper groundwater interacting with underlying marine sequences increased the concentrations of SO(4), Cl, and Na up to 6000 mg/L and increased the alkalinity up to 923 mg/L CaCO(3) eq. in the coastal aquifer. These waters infiltrated into the tailings deposit at the shelf-tailings interface. Nonremediated tailings had a low-pH oxidation zone (pH 1-4) with significant accumulations of efflorescent salts (10-20 cm thick) at the surface because of upward capillary transport of metal cations in the arid climate. Remediated tailings were characterized by neutral pH and reducing conditions (pH similar to 7, Eh similar to 100 mV). As a result, most bivalent metals such as Cu, Zn, and Ni had very low concentrations (around 0.01 mg/L or below detection limit) because of reduction and sorption processes. In contrast, these reducing conditions increased the mobility of iron from two sources in this system: (1) The originally Fe(III)-rich oxidation zone, where Fe(II) was reduced during the remediation process and formed an Fe(II) plume, and (2) reductive dissolution of Fe(III) oxides present in the original shelf lithology formed an Fe-Mn plume at 10-m depth. These two Fe-rich plumes were pushed toward the shoreline where more oxidizing and higher pH conditions triggered the precipitation of Fe(HI)hydroxide coatings on silicates. These coatings acted as a filter for the arsenic, which naturally infiltrated with the river water (similar to 500 mu g/L As natural background) into the tailings deposit.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by processbased modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws.We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25m resolution.

Reconstruction of metal pollution and recent sedimentation processes in Havana Bay (Cuba): A tool for coastal ecosystem management.

Since 1998 the highly polluted Havana Bay ecosystem has been the subject of a mitigation program. In order to determine whether pollution-reduction strategies were effective, we have evaluated the historical trends of pollution recorded in sediments of the Bay. A sediment core was dated radiometrically using natural and artificial fallout radionuclides. An irregularity in the (210)Pb record was caused by an episode of accelerated sedimentation. This episode was dated to occur in 1982, a year coincident with the heaviest rains reported in Havana over the XX century. Peaks of mass accumulation rates (MAR) were associated with hurricanes and intensive rains. In the past 60 years, these maxima are related to strong El Niño periods, which are known to increase rainfall in the north Caribbean region. We observed a steady increase of pollution (mainly Pb, Zn, Sn, and Hg) since the beginning of the century to the mid 90s, with enrichment factors as high as 6. MAR and pollution decreased rapidly after the mid 90s, although some trace metal levels remain high. This reduction was due to the integrated coastal zone management program introduced in the late 90s, which dismissed catchment erosion and pollution.

Preliminary assessment of rockslide and rockfall hazards using a DEM (Oppstadhornet, Norway)

The increasing availability and precision of digital elevation model (DEM) helps in the assessment of landslide prone areas where only few data are available. This approach is performed in 6 main steps which include: DEM creation; identification of geomorphologic features; determination of the main sets of discontinuities; mapping of the most likely dangerous structures; preliminary rock-fall assessment; estimation of the large instabilities volumes. The method is applied to two the cases studies in the Oppstadhornet mountain (730m alt): (1) a 10 millions m3 slow-moving rockslide and (2) a potential high-energy rock falling prone area. The orientations of the foliation and of the major discontinuities have been determined directly from the DEM. These results are in very good agreement with field measurements. Spatial arrangements of discontinuities and foliation with the topography revealed hazardous structures. Maps of potential occurrence of these hazardous structures show highly probable sliding areas at the foot of the main landslide and potential rock falls in the eastern part of the mountain.

Towards a consensus view on understanding nanomaterials hazards and managing exposure

The aim of this article is to present an overview of salient issues of exposure, characterisation and hazard assessment of nanomaterials as they emerged from the consensus-building of experts undertaken within the four year European Commission coordination project NanoImpactNet. The approach adopted is to consolidate and condense the findings and problem-identification in such a way as to identify knowledge-gaps and generate a set of interim recommendations of use to industry, regulators, research bodies and funders. The categories of recommendation arising from the consensual view address: significant gaps in vital factual knowledge of exposure, characterisation and hazards; the development, dissemination and standardisation of appropriate laboratory protocols; address a wide range of technical issues in establishing an adequate risk assessment platform; the more efficient and coordinated gathering of basic data; greater inter-organisational cooperation; regulatory harmonization; the wider use of the life-cycle approaches; and the wider involvement of all stakeholders in the discussion and solution-finding efforts for nanosafety.

Longitudinal analyses of immune responses to Plasmodium falciparum derived peptides corresponding to novel blood stage antigens in coastal Kenya

We have recently described 95 predicted alpha-helical coiled-coil peptides derived from putative Plasmodium falciparum erythrocytic stage proteins. Seventy peptides recognized with the highest level of prevalence by sera from three endemic areas were selected for further studies. In this study, we sequentially examined antibody responses to these synthetic peptides in two cohorts of children at risk of clinical malaria in Kilifi district in coastal Kenya, in order to characterize the level of peptide recognition by age, and the role of anti-peptide antibodies in protection from clinical malaria. Antibody levels from 268 children in the first cohort (Chonyi) were assayed against 70 peptides. Thirty-nine peptides were selected for further study in a second cohort (Junju). The rationale for the second cohort was to confirm those peptides identified as protective in the first cohort. The Junju cohort comprised of children aged 1-6 years old (inclusive). Children were actively followed up to identify episodes of febrile malaria in both cohorts. Of the 70 peptides examined, 32 showed significantly (p<0.05) increased antibody recognition in older children and 40 showed significantly increased antibody recognition in parasitaemic children. Ten peptides were associated with a significantly reduced odds ratio (OR) for an episode of clinical malaria in the first cohort of children and two of these peptides (LR146 and AS202.11) were associated with a significantly reduced OR in both cohorts. LR146 is derived from hypothetical protein PFB0145c in PlasmoDB. Previous work has identified this protein as a target of antibodies effective in antibody dependent cellular inhibition (ADCI). The current study substantiates further the potential of protein PFB0145c and also identifies protein PF11_0424 as another likely target of protective antibodies against P. falciparum malaria

Distribution and risk factors for Plasmodium and helminth co-infections: a cross-sectional survey among children in Bagamoyo district, coastal region of Tanzania.

BACKGROUND: Plasmodium and soil transmitted helminth infections (STH) are a major public health problem, particularly among children. There are conflicting findings on potential association between these two parasites. This study investigated the Plasmodium and helminth co-infections among children aged 2 months to 9 years living in Bagamoyo district, coastal region of Tanzania. METHODS: A community-based cross-sectional survey was conducted among 1033 children. Stool, urine and blood samples were examined using a broad set of quality controlled diagnostic methods for common STH (Ascaris lumbricoides, hookworm, Strongyloides stercoralis, Enterobius vermicularis, Trichuris trichura), schistosoma species and Wuchereria bancrofti. Blood slides and malaria rapid diagnostic tests (mRDTs) were utilized for Plasmodium diagnosis. RESULTS: Out of 992 children analyzed, the prevalence of Plasmodium infection was 13% (130/992), helminth 28.5% (283/992); 5% (50/992) had co-infection with Plasmodium and helminth. The prevalence rate of Plasmodium, specific STH and co-infections increased significantly with age (p < 0.001), with older children mostly affected except for S. stercoralis monoinfection and co-infections. Spatial variations of co-infection prevalence were observed between and within villages. There was a trend for STH infections to be associated with Plasmodium infection [OR adjusted for age group 1.4, 95% CI (1.0-2.1)], which was more marked for S. stercoralis (OR = 2.2, 95% CI (1.1-4.3). Age and not schooling were risk factors for Plasmodium and STH co-infection. CONCLUSION: The findings suggest that STH and Plasmodium infections tend to occur in the same children, with increasing prevalence of co-infection with age. This calls for an integrated approach such as using mass chemotherapy with dual effect (e.g., ivermectin) coupled with improved housing, sanitation and hygiene for the control of both parasitic infections.