Modelling environmental monitoring data coming from different surveys

With this work we propose a spatio-temporal model for Gaussian data collected in a small number of surveys. We assume the spatial correlation structure to be the same in all surveys. In the application concerning heavy metal concentrations in mosses, the data set is dense in the spatial dimension but sparse in the temporal one, thus our model-based approach corresponds to a correlation model depending on survey orders. One advantage of this approach is its computational simplicity. An interpretation for the space-time covariance function, decomposing the overall variance of the process as the product of the spatial component variance by the temporal component variance, is introduced. A simulation study, aiming to validate the model, provided better results in terms of accuracy with the novel covariance function. Maps of predicted heavy metal concentrations and of interpolation error, for the most recent survey, are presented.Data of this kind is recurrent in environmental sciences, which is why we argue that this will be a practical tool to be used very often

Linear latent force models using Gaussian processes.

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

A multivariate statistical and GIS approach to estimate heavy metal(loid)s in contaminated surface soils

In recent decades, there has been a growing concern over the escalating pollution of soil with heavy metal(loid)s, which poses an immediate threat to human health, food safety, and the overall soil environment. This research aimed to assess the extent of contamination, spatial distribution, sources of contamination, potential ecological risks, and health hazards associated with heavy metal(loid)s (specifically As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr, and Zn) by collecting soil samples from the surface soils in the mining region of Cerrito Blanco and Matehuala, San Luis Potosi in central Mexico. In addition to this, another study was conducted on rare trace metal(loid)s (B, Ba, Sb, Sn, and V) and other trace metals (Ca, Mg, Na, and K) in this selected region, which shows a level of contamination for those metals. The contamination levels of these heavy metal(loid)s were determined using various indices such as Igeo (geo-accumulation index), Cf (contamination factor), PLI (pollution load index), Cd (degree of contamination), mCd (modified degree of contamination), PIN (nemerow pollution index), EF (enrichment factor), and PERI (potential ecological risk index). Multivariate statistical techniques, such as principal component analysis (PCA), cluster analysis, or factor analysis, were used to identification of patterns and correlations among different heavy metal(loid)s and soil parameters. The findings indicated a significant degree of contamination in the surface soil due to heavy metal(loid)s. The integrated contamination indices and the potential ecological risk index revealed high levels of contamination and substantial ecological risks in the study areas, with particular emphasis on the need to control As in the surface soils surrounding Matehuala. Source identification of heavy metal(loid)s were performed using the APCS-MLR, PMF, and UNMIX receptor models, which detected three potential sources: mining and smelting activities, natural sources, and anthropogenic activities. The APCS-MLR model appeared to be more suitable for identifying complex contamination sources, demonstrating a better R2 coefficient and P/M (predicted/measured) ratio than the other models. Mining and smelting activities were identified as the primary factors influencing the distribution of heavy metal(loid)s in the surface soils. The most effective GIS interpolation technique was selected to analyse the spatial distribution patterns of heavy metal(loid) content, comparing five different GIS interpolation approaches such as Inverse Distance Weighting (IDW), Local Polynomial (LP), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), and Radial Basis Functions (RBF). The results indicated regions of significant concentrations for all heavy metal(loid)s, with the northern, western, and central parts of the study area exhibiting particularly elevated levels. Ecological risk assessment based on PERI revealed considerable risk for As and moderate risk for the remaining metals. Moreover, a probabilistic evaluation of health risks indicated minimal non-carcinogenic risks (HI) for humans but significant carcinogenic risks (CR) for both adults and children. Notably, children were found to be more vulnerable to the health risks associated with exposure to these heavy metals compared to adults. Consequently, enhanced monitoring efforts are necessary to address the issue of heavy metal(loid)s contamination in the rapidly developing Matehuala regions.James Watt Scholarshi

Methods for High-Dimensional Spatial Data: Dimension Reduction and Covariance Approximation

In spatial statistics, because quantities are correlated based on their relative positions in space, data is modeled as a single realization of a multivariate stochastic process. Spatial data can be high-dimensional either through a large number of observed variables per location, or through a large number of observed locations. The two are often handled differently, with the former addressed through dimension reduction and the latter addressed through appropriate modeling of the spatial correlation between locations. The main body of this dissertation is a three-part work. Parts 2 and 3 pertain to the many variables problem, proposing novel methods of dimension reduction for spatial data. Part 4 pertains to the many locations problem, using state-of-the-art techniques to analyze a massive satellite data set, improving on the current usage of the data

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Convolved Gaussian process priors for multivariate regression with applications to dynamical systems

In this thesis we address the problem of modeling correlated outputs using Gaussian process priors. Applications of modeling correlated outputs include the joint prediction of pollutant metals in geostatistics and multitask learning in machine learning. Defining a Gaussian process prior for correlated outputs translates into specifying a suitable covariance function that captures dependencies between the different output variables. Classical models for obtaining such a covariance function include the linear model of coregionalization and process convolutions. We propose a general framework for developing multiple output covariance functions by performing convolutions between smoothing kernels particular to each output and covariance functions that are common to all outputs. Both the linear model of coregionalization and the process convolutions turn out to be special cases of this framework. Practical aspects of the proposed methodology are studied in this thesis. They involve the use of domain-specific knowledge for defining relevant smoothing kernels, efficient approximations for reducing computational complexity and a novel method for establishing a general class of nonstationary covariances with applications in robotics and motion capture data.Reprints of the publications that appear at the end of this document, report case studies and experimental results in sensor networks, geostatistics and motion capture data that illustrate the performance of the different methods proposed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

European Atlas of Natural Radiation

Natural ionizing radiation is considered as the largest contributor to the collective effective dose received by the world population. The human population is continuously exposed to ionizing radiation from several natural sources that can be classified into two broad categories: high-energy cosmic rays incident on the Earth’s atmosphere and releasing secondary radiation (cosmic contribution); and radioactive nuclides generated during the formation of the Earth and still present in the Earth’s crust (terrestrial contribution). Terrestrial radioactivity is mostly produced by the uranium and thorium radioactive families together with potassium. In most circumstances, radon, a noble gas produced in the radioactive decay of uranium, is the most important contributor to the total dose. This Atlas aims to present the current state of knowledge of natural radioactivity, by giving general background information, and describing its various sources. This reference material is complemented by a collection of maps of Europe displaying the levels of natural radioactivity caused by different sources. It is a compilation of contributions and reviews received from more than 80 experts in their field: they come from universities, research centres, national and European authorities and international organizations. This Atlas provides reference material and makes harmonized datasets available to the scientific community and national competent authorities. In parallel, this Atlas may serve as a tool for the public to: • familiarize itself with natural radioactivity; • be informed about the levels of natural radioactivity caused by different sources; • have a more balanced view of the annual dose received by the world population, to which natural radioactivity is the largest contributor; • and make direct comparisons between doses from natural sources of ionizing radiation and those from man-made (artificial) ones, hence to better understand the latter.JRC.G.10-Knowledge for Nuclear Security and Safet