Geostatistical Models Research Articles

Impact of fluvial meander-belt sedimentary heterogeneity on the efficiency of low-enthalpy geothermal doublets: heat-transport simulations of forward stratigraphic models

In the present study, different geocellular models of meander-belt stratigraphic architectures were produced that are representative of the sedimentary products of sand-bed meandering rivers and their petrophysical heterogeneity. The static models were created combining a rule-based stratigraphic modelling approach with geostatistical modelling, and were applied in groundwater-flow and heat-transport simulations using MODFLOW-2005 and MT3D-USGS. Overall, histories of injected cold-water plume propagation were examined considering (i) three architectural frameworks as representative of different river morphodynamics, (ii) four scenarios of facies architecture, and (iii) alternative well layouts. The presence, size and spatial distribution of sedimentary heterogeneities related to river hydrodynamics, channel-form abandonment, or to modes of meander-transformation are seen to control the shape of the thermal plume, thereby affecting well-doublet performance. The considered scenarios of facies make-up for point-bar deposits have a modest influence on temperature decline near the abstraction well. The presence of sandstone beds in the lower heterolithic parts of abandoned-channel fill does not facilitate significant thermal-plume expansion beyond mud-plug. The effect of basal lags made of open-framework conglomerates on heat advection depends on their geometry, but is effectively negligible when it makes up less than 1% of deposits. Relatively thin mud drapes lying on point-bar accretion surfaces are seen to act as baffles to flow, but their impact is minimal in view of their small number. The study provides useful and novel insight on the potential impact of sedimentary heterogeneity in fluvial reservoirs, which can be applied to the design of well doublets, and highlights areas deserving further investigations.

Assessing the overlap between fishing and chondrichthyans exposes high‐risk areas for bycatch of threatened species

AbstractChondrichthyans (sharks, rays, skates, and chimaeras) make up one of the oldest and most ecologically diverse vertebrate groups, yet they face severe threats from fishing, necessitating improved management strategies. To effectively manage these species, we need to understand their spatial interactions with fisheries. However, this understanding is often challenged by limited data on chondrichthyan catches and species identification. In such cases, assessing potential risks from fishing activities can provide valuable insights into these spatial interactions. Here, we propose a method combining geostatistical models fitted to a fishery‐independent dataset with vessel monitoring system (VMS) data to estimate the spatial overlap between chondrichthyans and fishing. Our case study focuses on the western Adriatic Sea in the Mediterranean, examining the overlap between bottom trawling (including otter bottom trawling and beam trawling) and demersal chondrichthyans. We find that the northwestern part of the basin is a hotspot where threatened chondrichthyans (classified as Vulnerable, Endangered, or Critically Endangered by the International Union for Conservation of Nature Red List) greatly overlap with bottom trawling activities. Moreover, some areas, such as the northernmost part of the Adriatic and the “area dei fondi sporchi” in the north‐central offshore part, exhibit minimal overlap between threatened chondrichthyans and bottom trawling, potentially serving as refuges. We recommend prioritizing the management of otter bottom trawling in the northwestern basin to protect these threatened species, while also paying attention to the possible impacts of beam trawling on skates and chondrichthyan habitats. Despite certain limitations, our findings demonstrate that combining geostatistical models of species distributions with VMS data is a promising method for identifying areas of concern for species vulnerable to fishing. This approach can inform targeted management measures and cost‐effective onboard monitoring programs.

Air leakage mechanism and hole sealing technology in directional long-drilled perimeter rock-borehole composite fissures: for gas enrichment in varying coal-seam thickness change areas

The issue of gas enrichment in the tectonic zone of coal seams, which poses significant threats to coal mine safety and leads to gas disasters, is primarily addressed through borehole extraction. The quality of this extraction is notably influenced by air leakage and sealing technologies used during drilling. To tackle the problem of gas leakage in directional long boreholes within the floor, a composite fissure leakage model for the surrounding rock and borehole is developed. This model inverts the characteristics of coal seam thickness variations and abnormal gas enrichment through heterogeneous geostatistical modeling. It enables the quantitative characterization of the air leakage mechanism in directional long boreholes of the bottom plate and proposes an effective sealing process. Due to damage and disturbance, fissures and irregular plastic zones form around the excavated roadway, which are major contributors to air leakage. Over time, the air content in the coal and rock seams gradually increases while the gas content and concentration decrease continuously. Compared to the polyurethane "two plugs and one injection" sealing process, the multi-stage grouting and sealing process provides a more stable gas extraction concentration and flow rate in the sealed drill holes. This process effectively seals the fissure zones of the roadway, reduces air leakage from the boreholes, and improves gas extraction concentration.

APPLICATION OF SUPPORT VECTOR MACHINE AND ORDINARY KRIGING (SVM-OK) TO SPATIAL INTERPOLATION OF POROSITY ATTRIBUTE OF AGBABU BITUMEN DEPOSIT

The lack of geostatistical modelling of volumetric characteristics of Nigeria's long-discovered heavy oil and bitumen deposits in Agbabu was accountable for the discrepancies in the hydrocarbon-in-place calculations. The absence of spatial variability evaluation in such efforts is responsible for the inconsistency and divergence that define the impacts of the existing attempts to estimate the porosity attribute. Due to its optimum features and simplicity of estimate, Kriging is reported as the most generally used approach to evaluate the connection between variables (inputs and output). However, Kriging reveals that the relationship between variables requires a linear form, which is frequently not the case. Thus, the necessity for a procedure with no fixed shape is built based on information acquired directly from the data. Consequently, support vector regression, a form of the support vector machine, is employed as a choice in contrast to kriging. This research is a comparative application of support vector machine and kriging (SVM-OK) to the spatial interpolation of the porosity property of the Agbabu bitumen field. The porosity values from existing data are trained, cross-validated, and verified with the help of the support vector machine, and they are interpolated by ordinary kriging. The geographical patterns of the predictions have been checked visually using graphs based on these three diagnostics: variance of the error terms and correlation of the error terms. The findings of this study illustrate the performance of the combination of the Support vector machine and conventional kriging in the spatial interpolation of volumetric characteristics in the Agbabu bitumen deposit. The approach exhibited great predictive performance. SVM-kriging will be employed in reservoir characterisation with the least mean absolute error (MAE) and mean squared error (MSE).

Predicting Soil Organic Carbon in Forest Soils of Telangana, India: A Comparative Analysis of Deterministic and Geostatistical Interpolation Models

Predicting Soil Organic Carbon in Forest Soils of Telangana, India: A Comparative Analysis of Deterministic and Geostatistical Interpolation Models

3D stochastic simulation of a deep-water turbidite system: An example from the Los Molles Formation, southern region of the Neuquén Basin, Argentina

A recurring challenge in geological modeling is bridging the gap between different scales. For example, difficulties arise when connecting outcrop data through production to exploration scale models. An object-based stochastic simulation was performed using outcrop data from the Arroyo La Jardinera area in the southern region of the Neuquén Basin, Argentina. This simulation involved four depositional sequences in a vertical succession that includes turbidites and associated deep marine facies. The aims of this study are (a) to determine the best geological model consistent with field and aerial image data; (b) to validate the application of object modeling to determine facies distribution; (c) to evaluate uncertainties from models based on scarce data. The studied interval covers a transgressive (J1) to regressive succession (J21, J22, and J23) of basin plain to slope depositional settings, featuring sandy and gravelly turbidite channels, turbidite lobes and interlobes, lobe fringes, and muddy slope and basin plain. Each depositional sequence model was constructed using specific input parameters for architectural elements, with lithological proportions based on sedimentary logs. The J1 sequence includes basin plain, lobe fringe, and minor lobe deposits; J21 features turbidite lobes, fringes, and subordinate channels; J22 accommodates turbidite channels scoured into muddy slope facies; and J23 encompasses gravelly and sandy turbidite channels carved on muddy slope facies. The geostatistical modeling of outcrop data has allowed building a quantitative sedimentological model useful for understanding subsurface facies heterogeneity in both exploration (vertical) and production (horizontal) scales.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020

BackgroundThe burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes.MethodsBayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6–59 months and 5–14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km2 grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated.ResultsThere was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5–14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk.ConclusionIncreased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings.

Geo-statistical assessment of spatial variability of soil water-holding capacity for optimal irrigation under semi-arid vertisols in South India

Based on a field survey conducted in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, details about the crops grown, cropping pattern, land utilization and soil samples were collected from 20 locations under seven different soil textures of semi-arid vertisols. Using the data, efficient geostatistical models have been explored to study the spatial variation of irrigation water requirement of paddy, sugarcane and banana grown in the study area. Geostatistics was applied to identify the best interpolation method to acquire the spatial map of the available water holding capacity. Ordinary kriging and Disjunctive kriging were explored with six models viz., Circular, Spherical, Exponential, Gaussian, Penta-spherical and Sine-Hole effect models and were used for spatial prediction. The cross-validation statistics indicated that the Sine-Hole effect model with disjunctive kriging of available water holding capacity was superior for interpolation with a minimum value of RMSE and moderate spatial dependency. With the available water holding capacity map of Lalgudi block, total available water, readily available water and irrigation interval of paddy, sugarcane and banana were estimated. The irrigation interval of paddy, sugarcane and banana were found to vary between 1-2, 3-5 and 2-4 days respectively within Lalgudi block. The adoption of spatial algorithms for estimating crop water requirements would greatly help irrigation planners and water policymakers create efficient regional plans for precision irrigation under semi-arid vertisols.

Geostatistical Inversion of Frequency-Domain Electromagnetic Data for Near Surface Modelling

The detailed characterization of near-surface deposits is important for both environmental and economic reasons. These shallow subsurface systems can be very complex and heterogenous due to natural dynamics and anthropogenic interferences. Modeling techniques based exclusively on direct sampling generate limited informed three-dimensional models of the near-surface. Geophysical methods provide valuable and additional information to model the spatial distribution of the near-surface for locations where direct observations are not available. From this set of methods, frequency-domain electromagnetic induction (FDEM) has been successfully applied to image complex near-surface deposits. Yet, predicting the spatial distribution of relevant subsurface properties from geophysical data, and the integration of direct observations, is not straightforward. It requires solving a challenging geophysical inversion problem. Geostatistical modelling tools have been effectively applied to couple direct observations with geophysical data such as seismic reflection. We propose an iterative geostatistical FDEM inversion method able to integrate data from direct measurements of the near-surface with surface loop-loop FDEM measurements to simultaneously predict high-resolution models of electrical conductivity and magnetic susceptibility, and their associated uncertainty. The iterative geostatistical inversion method is based on stochastic sequential simulation and co-simulation as model perturbation and update techniques. The iterative optimization is based on the local data misfit between observed and simulated FDEM data, weighted by the sensitivity of the acquisition equipment. The proposed method is first demonstrated for a synthetic landfill data set created based on real data collected at a mine tailing disposal site in Portugal, and on a real data set collected at a site with archaeological features in Knowlton, UK. The results show the ability of the proposed method to accurately predict and characterize the spatial distribution of electrical conductivity and magnetic susceptibility down to the depth of interest while reproducing the recorded FDEM data.

Support Vector Machines With Uncertainty Option and Incremental Sampling for Kriging

ABSTRACTThis paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring.

An assessment of the coverage of live-birth registration across space in Nigeria

ABSTRACT Birth registration is an important channel that allows children to be documented, providing them with legal identity and nationality early in life and protecting them against violence, abuse and exploitation. However, this privilege has been denied to millions of children, particularly in developing countries. Registration is limited in Nigeria with about three in every five births unregistered. Using data from three waves of the Nigeria Demographic and Health Survey, we adopt a geostatistical model to assess the space-time variations in registration coverage across Nigeria and compute the exceedance maps at different probabilities. The findings indicate a North-South divide in coverage but with improved coverage over time across the country. The probability of attaining about 60% coverage is low in the northern part of the country. The generated maps provide guiding tools to the Nigerian government to set priorities in order to scale up birth registration coverage in the country.

Bayesian feedback in the framework of ecological sciences

In ecological studies, it is not uncommon to encounter scenarios where the same phenomenon (e.g., species occurrence, species abundance) is observed using two different types of samplers. For example, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale watching from commercial fishing vessels or bird watching from citizen science), where observers tend to look for particular species in areas where they expect to find them.Species Distribution Models (SDMs) are widely used tools for analysing this type of ecological data. In particular, two models are available for the aforementioned data: a geostatistical model (GM) for data collected where the sampling design is not directly related to the observations, and a preferential model (PM) for data obtained from opportunistic sampling.The integration of information from disparate sources can be addressed through the use of expert elicitation and integrated models. This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. This sequential approach has been evaluated through the simulation of various scenarios and the subsequent comparison of the results from sharing information between models using a variety of criteria. The procedure has also been exemplified on a real dataset.The primary findings indicate that, in general, it is preferable to transfer information from the independent (with a completely random sampling) model to the preferential model rather than in the alternative direction. However, this depends on several factors, including the spatial range and the spatial arrangement of the sampling locations.

Impact of a national tsetse control programme to eliminate Gambian sleeping sickness in Uganda: a spatiotemporal modelling study.

Tsetse flies (Glossina) transmit Trypanosoma brucei gambiense, which causes gambiense human African trypanosomiasis (gHAT). As part of national efforts to eliminate gHAT as a public health problem, Uganda implemented a large-scale programme of deploying Tiny Targets, which comprise panels of insecticide-treated material which attract and kill tsetse. At its peak, the programme was the largest tsetse control operation in Africa. Here, we quantify the impact of Tiny Targets and environmental changes on the spatial and temporal patterns of tsetse abundance across North-Western Uganda. We leverage a 100-month longitudinal dataset detailing Glossina fuscipes fuscipes catches from monitoring traps between October 2010 and December 2019 within seven districts in North-Western Uganda. We fitted a boosted regression tree (BRT) model assessing environmental suitability, which was used alongside Tiny Target data to fit a spatiotemporal geostatistical model predicting tsetse abundance across our study area (~16 000 km2). We used the spatiotemporal model to quantify the impact of Tiny Targets and environmental changes on the distribution of tsetse, alongside metrics of uncertainty. Environmental suitability across the study area remained relatively constant over time, with suitability being driven largely by elevation and distance to rivers. By performing a counterfactual analysis using the fitted spatiotemporal geostatistical model, we show that deployment of Tiny Targets across an area of 4000 km2 reduced the overall abundance of tsetse to low levels (median daily catch=1.1 tsetse/trap, IQR=0.85-1.28). No spatial-temporal locations had high (>10 tsetse/trap/day) numbers of tsetse compared with 18% of locations for the counterfactual. In Uganda, Tiny Targets reduced the abundance of G. f. fuscipes and maintained tsetse populations at low levels. Our model represents the first spatiotemporal geostatistical model investigating the effects of a national tsetse control programme. The outputs provide important data for informing next steps for vector control and surveillance.

Groundwater Level Prediction Using Machine Learning and Geostatistical Interpolation Models

Given the vulnerability of surface water to the direct impacts of climate change, the accurate prediction of groundwater levels has become increasingly important, particularly for dry regions, offering significant resource management benefits. This study presents the first statewide groundwater level anomaly (GWLA) prediction for Arizona across its two distinct aquifer types—unconsolidated sand and gravel aquifers and rock aquifers. Machine learning (ML) models were combined with empirical Bayesian kriging (EBK) geostatistical interpolation models to predict monthly GWLAs between January 2010 and December 2019. Model evaluations were based on the Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) metrics. With average NSE/R2 values of 0.62/0.63 and 0.72/0.76 during the validation and test phases, respectively, our multi-model approach demonstrated satisfactory performance, and the predictive accuracy was much higher for the unconsolidated sand and gravel aquifers. By employing a remote sensing-based approach, our proposed model design can be replicated for similar climates globally, and hydrologically data-sparse and remote areas of the world are not left out.

Geostatistical evaluation of the impact of climate variability on malaria incidence In the South-West of Nigeria

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

Effective application of geological process modeling for unravelling carbonate build-up complexity: A case study from the EX-Carbonate build-up in central Luconia Province, Malaysia

In the dynamic field of carbonate reservoir Exploration, the need for specialized models is vital, assisting as a key tool to facilitate interdisciplinary collaboration and enhance strategies for predicting reservoir behavior. This study examines the use of geological process (GPM), a standard tool to simulate forward stratigraphic modelling, particularly focusing on the Central Luconia Province in the South China Sea. Central Luconia Province stands out for its extensive carbonate build-ups and is recognized as a prolific hydrocarbon reservoir. Forward Stratigraphic Modelling (FSM) leads to a paradigmatic change in geological modeling. Unlike geostatistical models controlled by geometrical parameters, FSM relies on mathematical representations of the physical rules determining erosion, transport, and sedimentation in carbonate growth. This tool, supported by various researchers, has proven its ability in quantitative sedimentary system analysis across diverse temporal and spatial scales. This study located in EX Field showcases the ability of stratigraphic forward modeling in replicating sedimentary complexities and resulting stratigraphic architecture of the EX isolated carbonate platform. The methodology links parameters from existing literature with multiscale data. Additionally, carbonate production laws, contingent on water depth, play a pivotal role in the meticulous modeling process. This work illustrates the efficacy of geological process modelling as a transformative approach for unraveling the complexities of carbonate systems, particularly in regions with prolific hydrocarbon reservoirs like Central Luconia. The incorporation of literature-derived parameters and multiscale data serves as evidence of the model's ability to offer nuanced understandings of the dynamic evolution of carbonate platforms, thereby advancing our understanding of reservoir prediction and future management strategies.

A Quantitative Particle-Based Approach for the Geometallurgical Assessment of Tailings Deposits

Tailings generated during ore processing may host significant residual contents of valuable commodities, including critical metals. The particle properties of the tailings, such as mineralogy, particle size, and the surface liberation of ore minerals, strongly control processing behaviour. This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. This was achieved through binning of different particle types, geostatistical modelling of particle bin frequencies, and bootstrap resampling to reconstruct particle populations. The spatial distributions of processing-relevant particle properties throughout the tailings deposit were predicted with corresponding uncertainties. There are clear systematic trends in the spatial distributions of different particle types, resulting from the sedimentary-style deposition of the tailings. For instance, the tailings nearer the dam walls comprise coarser, silicate-rich particles, while fine-grained and well-liberated sulphide mineral particles are more abundant in the centre of the tailings deposit. As a result, robust models could be developed for the spatial distributions of particle size and mineralogy, which strongly control the sorting of particles during deposition, and other related properties, such as sulphide mineral grain sizes. Finally, a bulk sulphide flotation process was simulated and acid mine drainage potential estimated using the interpolated particle data. Around 58% of the sulphide minerals present could be recoverable by flotation, with the recoverable sulphide portion decreasing towards the centre of the TSF due to the fine-grained nature of the sulphide minerals. The acid mine drainage potential of the tailings is estimated to be moderate to high, indicating that the carbonate minerals present are not sufficient to neutralise the high acid-generating potential of the sulphide minerals. Overall, this study demonstrates how particle-based geometallurgical models can be developed and utilised for practical applications, with the aim of improving the accuracy of resource and reserve estimations of tailings deposits and the sustainable and responsible management of anthropogenic resources. The methodology proposed here can be easily transferred to other tailings deposits.

Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model

The air in the Lombardy Plain, Italy, is one of the most polluted in Europe due to limited atmosphere circulation and high emission levels. There is broad scientific consensus that ammonia (NH3) emissions have a primary impact on air quality, and in Lombardy, the agricultural sector and livestock activities are widely recognised as being responsible for approximately 97% of regional ammonia emissions due to the high density of livestock.In this paper, we quantify the relationship between ammonia emissions and PM2.5 concentrations in the Lombardy Plain and evaluate PM2.5 changes due to the reduction of ammonia emissions through a ‘what-if’ scenario analysis. The information in the data is exploited using a spatiotemporal statistical model capable of handling spatial and temporal correlation as well as missing data. To do this, we propose a new heteroskedastic extension of the well-established Hidden Dynamic Geostatistical Model. Maximum likelihood parameter estimates are obtained by the expectation–maximisation algorithm and implemented in a new version of the D-STEM software.Considering the years between 2016 and 2020, the scenario analysis is carried out on high-resolution PM2.5 maps of the Lombardy Plain. As a result, it is shown that a 26% reduction in NH3 emissions in the wintertime could reduce the PM2.5 average by 1.44 μg/m3 while a 50% reduction could reduce the PM2.5 average by 2.76 μg/m3 which corresponds to a reduction close to 3.6% and 7% respectively. Finally, results are detailed by province and land type.

A combined approach to geostatistical models and spatial information systems to evaluate the role of spatial parameters in the prevalence of cancer in Iran: a case study: bladder cancer

A combined approach to geostatistical models and spatial information systems to evaluate the role of spatial parameters in the prevalence of cancer in Iran: a case study: bladder cancer