Exchanging uncertainty: interoperable geostatistics?

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed systems, typically using XML (eXtensible Markup Language) and Web services. Web services provide a mechanism for a user to discover and consume a particular process, often as part of a larger process chain, with minimal knowledge of how it works. Wrapping current geostatistical algorithms with a Web service layer would thus increase their accessibility, but raises several complex issues. This paper discusses a solution to providing interoperable, automatic geostatistical processing through the use of Web services, developed in the INTAMAP project (INTeroperability and Automated MAPping). The project builds upon Open Geospatial Consortium standards for describing observations, typically used within sensor webs, and employs Geography Markup Language (GML) to describe the spatial aspect of the problem domain. Thus the interpolation service is extremely flexible, being able to support a range of observation types, and can cope with issues such as change of support and differing error characteristics of sensors (by utilising descriptions of the observation process provided by SensorML). XML is accepted as the de facto standard for describing Web services, due to its expressive capabilities which allow automatic discovery and consumption by ‘naive’ users. Any XML schema employed must therefore be capable of describing every aspect of a service and its processes. However, no schema currently exists that can define the complex uncertainties and modelling choices that are often present within geostatistical analysis. We show a solution to this problem, developing a family of XML schemata to enable the description of a full range of uncertainty types. These types will range from simple statistics, such as the kriging mean and variances, through to a range of probability distributions and non-parametric models, such as realisations from a conditional simulation. By employing these schemata within a Web Processing Service (WPS) we show a prototype moving towards a truly interoperable geostatistical software architecture.

Parallel geostatistics for sparse and dense datasets

Very large spatially-referenced datasets, for example, those derived from satellite-based sensors which sample across the globe or large monitoring networks of individual sensors, are becoming increasingly common and more widely available for use in environmental decision making. In large or dense sensor networks, huge quantities of data can be collected over small time periods. In many applications the generation of maps, or predictions at specific locations, from the data in (near) real-time is crucial. Geostatistical operations such as interpolation are vital in this map-generation process and in emergency situations, the resulting predictions need to be available almost instantly, so that decision makers can make informed decisions and define risk and evacuation zones. It is also helpful when analysing data in less time critical applications, for example when interacting directly with the data for exploratory analysis, that the algorithms are responsive within a reasonable time frame. Performing geostatistical analysis on such large spatial datasets can present a number of problems, particularly in the case where maximum likelihood. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively. Most modern commodity hardware has at least 2 processor cores if not more. Other mechanisms for allowing parallel computation such as Grid based systems are also becoming increasingly commonly available. However, currently there seems to be little interest in exploiting this extra processing power within the context of geostatistics. In this paper we review the existing parallel approaches for geostatistics. By recognising that diffeerent natural parallelisms exist and can be exploited depending on whether the dataset is sparsely or densely sampled with respect to the range of variation, we introduce two contrasting novel implementations of parallel algorithms based on approximating the data likelihood extending the methods of Vecchia [1988] and Tresp [2000]. Using parallel maximum likelihood variogram estimation and parallel prediction algorithms we show that computational time can be significantly reduced. We demonstrate this with both sparsely sampled data and densely sampled data on a variety of architectures ranging from the common dual core processor, found in many modern desktop computers, to large multi-node super computers. To highlight the strengths and weaknesses of the diffeerent methods we employ synthetic data sets and go on to show how the methods allow maximum likelihood based inference on the exhaustive Walker Lake data set.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

Influence of La-doping on phase transformation and photocatalytic properties of ZnTiO3 nanoparticles synthesized via modified sol-gel method

Non-doped and La-doped ZnTiO3 nanoparticles were successfully synthesized via a modified sol–gel method. The synthesized nanoparticles were structurally characterized by PXRD, UV-vis DRS, FT-IR, SEM-EDS, TEM, Raman and photoluminescence spectroscopy. The results show that doping of La into the framework of ZnTiO3 has a strong influence on the physico-chemical properties of the synthesized nanoparticles. XRD results clearly show that the non-doped ZnTiO3 exhibits a hexagonal phase at 800 °C, whereas the La-doped ZnTiO3 exhibits a cubic phase under similar experimental conditions. In spite of the fact that it has a large ionic radius, the La is efficiently involved in the evolution process by blocking the crystal growth and the cubic to hexagonal transformation in ZnTiO3. Interestingly the absorption edge of the La-doped ZnTiO3 nanoparticles shifted from the UV region to the visible region. The photocatalytic activity of the La-doped ZnTiO3 nanoparticles was evaluated for the degradation of Rhodamine B under sunlight irradiation. The optimum photocatalytic activity was obtained for 2 atom% La-doped ZnTiO3, which is much higher than that of the non-doped ZnTiO3 as well as commercial N-TiO2. A possible mechanism for the degradation of Rhodamine B over La-doped ZnTiO3 was also discussed by trapping experiments. More importantly, the reusability of these nanoparticles is high. Hence La-doped ZnTiO3 nanoparticles can be used as efficient photocatalysts for environmental applications.

Synthesis of Cr and La-codoped SrTiO3 nanoparticles for enhanced photocatalytic performance under sunlight irradiation

In this study, we report a facile polymeric citrate strategy for the synthesis of Cr,La-codoped SrTiO3 nanoparticles. The synthesized samples were well characterized by various analytical techniques. The UV-vis DRS studies reveal that the absorption edge shifts towards the visible light region after doping with Cr, which is highly beneficial for absorbing the visible light in the solar spectrum. More attractively, codoping with La exhibits greatly enhanced photocatalytic activity for the degradation of Rhodamine B under sunlight irradiation. The optimum photocatalytic activity at 1 atom% of Cr,La-codoped SrTiO3 nanoparticles is almost 6 times higher than that of pure SrTiO3 nanoparticles and 3 times higher than that of Cr-doped SrTiO3 nanoparticles. The high photocatalytic performance in the present photocatalytic system is due to codoping with La, which acts as a most effective donor for stabilizing Cr3+ in Cr,La-codoped SrTiO3 nanoparticles. More importantly, the synthesized photocatalysts possess high reusability. A proposed mechanism for the enhanced photocatalytic activity of Cr,La-codoped SrTiO3 nanoparticles was also investigated by trapping experiments. Therefore, our results not only demonstrate the highly efficient visible light photocatalytic activity of the Cr,La-codoped SrTiO3 photocatalyst, but also enlighten the codoping strategy in the design and development of advanced photocatalytic materials for energy and environmental applications.

Surface plasmon resonance-induced photocatalysis by Au nanoparticles decorated mesoporous g-C3N4 nanosheets under direct sunlight irradiation

In recent years, surface plasmon-induced photocatalytic materials with tunable mesoporous framework have attracted considerable attention in energy conversion and environmental remediation. Herein we report a novel Au nanoparticles decorated mesoporous graphitic carbon nitride (Au/mp-g-C3N4) nanosheets via a template-free and green in situ photo-reduction method. The synthesized Au/mp-g-C3N4 nanosheets exhibit a strong absorption edge in visible and near-IR region owing to the surface plasmon resonance effect of Au nanoparticles. More attractively, Au/mp-g-C3N4 exhibited much higher photocatalytic activity than that of pure mesoporous and bulk g-C3N4 for the degradation of rhodamine B under sunlight irradiation. Furthermore, the photocurrent and photoluminescence studies demonstrated that the deposition of Au nanoparticles on the surface of mesoporous g-C3N4 could effectively inhibit the recombination of photogenerated charge carriers leading to the enhanced photocatalytic activity. More importantly, the synthesized Au/mp-g-C3N4 nanosheets possess high reusability. Hence, Au/mp-g-C3N4 could be promising photoactive material for energy and environmental applications.

g-C3N4/NaTaO3 organic–inorganic hybrid nanocomposite:high-performance and recyclable visible light driven photocatalyst

Novel g-C3N4/NaTaO3 hybrid nanocomposites have been prepared by a facile ultrasonic dispersion method. Our results clearly show the formation of interface between NaTaO3 and g-C3N4 and further loading of g-C3N4 did not affect the crystal structure and morphology of NaTaO3. The g-C3N4/NaTaO3 nanocomposites exhibited enhanced photocatalytic performance for the degradation of Rhodamine B under UV–visible and visible light irradiation compared to pure NaTaO3 and Degussa P25. Interestingly, the visible light photocatalytic activity is generated due to the loading of g-C3N4. A mechanism is proposed to discuss the enhanced photocatalytic activity based on trapping experiments of photoinduced radicals and holes. Under visible light irradiation, electron excited from the valance band (VB) to conduction band (CB) of g-C3N4 could directly inject into the CB of NaTaO3, making g-C3N4/NaTaO3 visible light driven photocatalyst. Since the as-prepared hybrid nanocomposites possess high reusability therefore it can be promising photocatalyst for environmental applications.

Biotechnological, nutritional and therapeutic uses of Pleurotus spp. (Oyster mushroom) related with its chemical composition: A review on the past decade findings

The particular characteristics of growth and development of mushrooms in nature result in the accumulation of a variety of secondary metabolites, several of them with biological activities. The genus Pleurotus is a cosmopolitan group of mushrooms with high nutritional value and therapeutic properties, besides a wide array of biotechnological and environmental applications. Scope and approach: The present report aims to provide a critical review on aspects related to chemical compounds isolated from the genus Pleurotus with possible biotechnological, nutritional and therapeutic uses. Investigations on the genus have immensely accelerated during the last ten years, so that only reports published after 2005 have been considered. Key findings and conclusions: The most important Pleurotus species cultivated in large scale are P. ostreatus and P. pulmonarius. However, more than 200 species have already been investigated to various degrees. Both basidiomata and mycelia of Pleurotus are a great renewable and easily accessible source of functional foods/nutraceuticals and pharmaceuticals with antioxidant, antimicrobial, anti-inflammatory, antitumor and immunomodulatory effects. A series of compounds have already been precisely defined including several polysaccharides, phenolics, terpenes and sterols. However, intensification of structure determination is highly desirable and demands considerable efforts. Further studies including clinical trials need to be carried out to ascertain the safety of these compounds as adequate alternatives to conventional drugs. Not less important is to extend the search for novel bioactives to less explored Pleurotus species.

Finite difference calculations of permeability in large domains in a wide porosity range.

Determining effective hydraulic, thermal, mechanical and electrical properties of porous materials by means of classical physical experiments is often time-consuming and expensive. Thus, accurate numerical calculations of material properties are of increasing interest in geophysical, manufacturing, bio-mechanical and environmental applications, among other fields. Characteristic material properties (e.g. intrinsic permeability, thermal conductivity and elastic moduli) depend on morphological details on the porescale such as shape and size of pores and pore throats or cracks. To obtain reliable predictions of these properties it is necessary to perform numerical analyses of sufficiently large unit cells. Such representative volume elements require optimized numerical simulation techniques. Current state-of-the-art simulation tools to calculate effective permeabilities of porous materials are based on various methods, e.g. lattice Boltzmann, finite volumes or explicit jump Stokes methods. All approaches still have limitations in the maximum size of the simulation domain. In response to these deficits of the well-established methods we propose an efficient and reliable numerical method which allows to calculate intrinsic permeabilities directly from voxel-based data obtained from 3D imaging techniques like X-ray microtomography. We present a modelling framework based on a parallel finite differences solver, allowing the calculation of large domains with relative low computing requirements (i.e. desktop computers). The presented method is validated in a diverse selection of materials, obtaining accurate results for a large range of porosities, wider than the ranges previously reported. Ongoing work includes the estimation of other effective properties of porous media.

Assessing the safety of Pseudomonas putida introduction in the environment: an overview of ecotoxicological tests.

Risk assessment guidelines for the environmental release of microbial agents are performed in a tiered sequence which includes evaluation of exposure effects on non target organisms. However, it becomes important to verify whether environmental risk assessment from temperate studies is applicable to tropical countries, as Brazil. Pseudomonas putida is a bacteria showing potential to be used for environmental applications as bioremediation and plant disease control. This study investigates the effects of this bacteria exposure on rodents and aquatic organisms (Daphnia similes) that are recommended to be used as non-target organism in environmental risk assessments. Also, the microbial activity in three different soils under P. putida exposure was evaluated. Rats did not show clinical alterations, although the agent was recovered 16 h after the exposure in lung homogenates. The bacteria did not reduce significantly the reproduction and survival of D. similis. The soil enzymatic activities presented fluctuating values after inoculation with bacteria. The measurement of perturbations in soil biochemical characteristics is presented as an alternative way of monitoring the overall effects of the microbial agent to be introduced even in first stage (Tier I) of the risk assessment in tropical ecosystems.

Planning for smart urban ecosystems : information technology applications for capacity building in environmental decision making

Since the industrial revolution, our world has experienced rapid and unplanned industrialization and urbanization. As a result, we have had to cope with serious environmental challenges. In this context, an explanation of how smart urban ecosystems can emerge, gains a crucial importance. Capacity building and community involvement have always been key issues in achieving sustainable development and enhancing urban ecosystems. By considering these, this paper looks at new approaches to increase public awareness of environmental decision making. This paper will discuss the role of Information and Communication Technologies (ICT), particularly Webbased Geographic Information Systems (Web-based GIS) as spatial decision support systems to aid public participatory environmental decision making. The paper also explores the potential and constraints of these webbased tools for collaborative decision making.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Robots for environmental monitoring : significant advancements and applications

Robotic systems are increasingly being utilised as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed.

ICT Applications in the Research for Environmental Sustainability

26 p.