How much organic carbon could the soil store? The carbon sequestration potential of Australian soil.

Soil is a huge carbon (C) reservoir, but where and how much extra C can be stored is unknown. Current methods to estimate the maximum amount of mineral-associated organic carbon (MAOC) stabilized in the fine fraction (clay + silt, < 20 µm $$ <20\;\upmu \mathrm{m} $$ ) fit through the MAOC versus clay + silt relationship, not their maxima, making their estimates more uncertain and unreliable. We need a function that 'envelopes' that relationship. Here, using 5089 observations, we estimated that the uppermost 30 cm of Australian soil holds 13 Gt (10-18 Gt) of MAOC. We then fitted frontier lines, by soil type, to the relationship between MAOC and the percentage of clay + silt to estimate the maximum amounts of MAOC that Australian soils could store in their current environments, and calculated the MAOC deficit, or C sequestration potential. We propagated the uncertainties from the frontier line fitting and mapped the estimates of these values over Australia using machine learning and kriging with external drift. The maps show regions where the soil is more in MAOC deficit and has greater sequestration potential. The modelling shows that the variation over the whole continent is determined mainly by climate, linked to vegetation and soil mineralogy. We find that the MAOC deficit in Australian soil is 40 Gt (25-60 Gt). The deficit in the vast rangelands is 20.84 Gt (13.97-29.70 Gt) and the deficit in cropping soil is 1.63 Gt (1.12-2.32 Gt). Management could increase C sequestration in these regions if the climate allowed it. Our findings provide new information on the C sequestration potential of Australian soils and highlight priority regions for soil management. Australia could benefit environmentally, socially and economically by unlocking even a tiny portion of its soil's C sequestration potential.

Spatio-temporal trends of mercury levels in alluvial gold mining spoils areas monitored between rainy and dry seasons in the Peruvian Amazon.

Artisanal and small-scale gold mining (ASGM) in the Amazon has degraded tropical forests and escalated mercury (Hg) pollution, affecting biodiversity, ecological processes and rural livelihoods. In the Peruvian Amazon, ASGM annually releases some 181 tons of Hg into the environment. Despite some recent advances in understanding the spatial distribution of Hg within gold mine spoils and the surrounding landscape, temporal dynamics in Hg movement are not well understood. We aimed to reveal spatio-temporal trends of soil Hg in areas degraded by ASGM.,. We analyzed soil and sediment samples during the dry and rainy seasons across 14 ha of potentially contaminated sites and natural forests, in the vicinities of the Native community of San Jacinto in Madre de Dios, Peru. Soil Hg levels of areas impacted by ASGM (0.02 ± 0.02 mg kg-1) were generally below soil environmental quality standards (6.60 mg kg-1). However, they showed high variability, mainly explained by the type of natural cover vegetation, soil organic matter (SOM), clay and sand particles. Temporal trends in Hg levels in soils between seasons differed between landscape units distinguished in the mine spoils. During the rainy season, Hg levels decreased up to 45.5% in uncovered soils, while in artificial pond sediments Hg increased by up to 961%. During the dry season, uncovered degraded soils were more prone to lose Hg than sites covered by vegetation, mainly due to higher soil temperatures and concomitantly increasing volatilization. Soils from natural forests and degraded soil covered by regenerating vegetation showed a high capacity to retain Hg mainly due to the higher plant biomass, higher SOM, and increasing concentrations of clay particles. Disturbingly, our findings suggest high Hg mobility from gold mine spoil to close by sedimentary materials, mainly in artificial ponds through alluvial deposition and pluvial lixiviation. Thus, further research is needed on monitoring, and remediation of sediments in artificial to design sustainable land use strategies.

Smartphone-Based Methodology Applied to Electromagnetic Field Exposure Assessment.

This study presents the measurements of exposure to electromagnetic fields, carried out comparatively following standard methods from fixed sites using a broadband meter and using a smartphone on which an App designed for this purpose has been installed. The results of two measurement campaigns carried out on the campus of the University of Alcalá over an area of 1.9 km2 are presented. To characterize the exposure, 20 fixed points were measured in the first case and 860 points along the route made with a bicycle in the last case. The results obtained indicate that there is proportionality between the two methods, making it possible to use the smartphone for comparative measurements. The presented methodology makes it possible to characterize the exposure in the area under study in four times less time than that required with the traditional methodology.

A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation.

During the process of seabed terrain exploration using a multi-beam echo system, it is inevitable to obtain a sounding set containing anomalous points. Conventional methods for eliminating outliers are unable to reduce the disruption caused by outliers over the whole dataset. Furthermore, incomplete consideration is given to the terrain complexity, error magnitude, and outlier distribution. In order to achieve both a high-precision terrain quality estimate and quick detection of depth anomalies, this study suggests a dual robust technique. Firstly, a robust polyhedral function is utilized to solve anomaly detection for large errors. Secondly, the robust kriging algorithm is used for refined outlier removal. Ultimately, the process of dual detection and anomaly removal is achieved. The experimental results demonstrate that DRS technology has the most favorable mean square error and error fluctuation range in the test set, with values of 0.8321 and [-2.0582, 1.9209], respectively, when compared to RPF, WT, GF, and WLS-SVM schemes. Furthermore, DRS is able to adjust to various terrain complexities, discrete distribution features, and cluster outlier detection, as shown by objective indicators and visual outcome maps, guaranteeing a high-quality seabed terrain estimate.

Data Reconstruction Using Smart Sensor Placement.

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.

Surrogate-Assisted Differential Evolution for the Design of Multimode Resonator Topology.

The microstrip devices based on multimode resonators represent a class of electromagnetic microwave devices, promising use in tropospheric communication, radar, and navigation systems. The design of wideband bandpass filters, diplexers, and multiplexers with required frequency-selective properties, i.e., bandpass filters, is a complex problem, as electrodynamic modeling is a time-consuming and computationally intensive process. Various planar microstrip resonator topologies can be developed, differing in their topology type, and the search for high-quality structures with unique frequency-selective properties is an important research direction. In this study, we propose an approach for performing an automated search for multimode resonators' conductor topology parameters using a combination of evolutionary computation approach and surrogate modeling. In particular, a variant of differential evolution optimizer is applied, and the model of the target function landscape is built using Gaussian processes. At every iteration of the algorithm, the model is used to search for new high-quality solutions. In addition, a general approach for target function formulation is presented and applied in the proposed approach. The experiments with two microwave filters have demonstrated that the proposed algorithm is capable of solving the problem of tuning two types of topologies, namely three-mode resonators and six-mode resonators, to the required parameters, and the application of surrogated-assisted algorithm has significantly improved overall performance.

A water quality assessment model involving novel fluorescence technology.

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Reliability-Based Design Optimization for Polymer Electrolyte Membrane Fuel Cells: Tackling Dimensional Uncertainties in Manufacturing and Their Effects on Costs of Cathode Gas Diffusion Layer and Bipolar Plates.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. However, the high material costs of the gas diffusion layer (GDL) and bipolar plate (BP) create a barrier for large scale commercial application. This study aims to address this challenge by optimizing the material and design of the cathode, GDL and BP. While deterministic design optimization (DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing reliability-based design optimization (RBDO) to optimize four key PEMFC design variables, i.e., gas diffusion layer thickness, channel depth, channel width and land width. The objective is to maximize cell voltage considering the material cost of the cathode gas diffusion layer and cathode bipolar plate as reliability constraints. The results of the DDO show an increment in cell voltage of 31 mV, with a reliability of around 50% in material cost for both the cathode GDL and cathode BP. In contrast, the RBDO method provides a reliability of 95% for both components. Additionally, under a high level of uncertainty, the RBDO approach reduces the material cost of the cathode GDL by up to 12.25 $/stack, while the material cost for the cathode BP increases by up to 11.18 $/stack Under lower levels of manufacturing uncertainties, the RBDO method predicts a reduction in the material cost of the cathode GDL by up to 4.09 $/stack, with an increase in the material cost for the cathode BP by up to 6.71 $/stack, while maintaining a reliability of 95% for both components. These results demonstrate the effectiveness of the RBDO approach in achieving a reliable design under varying levels of manufacturing uncertainties.

Spatiotemporal modeling of occupational particulate matter using personal low-cost sensor and indoor location tracking data.

Occupational exposure to particulate matter (PM) can result in multiple adverse health effects and should be minimized to protect workers' health. PM exposure at the workplace can be complex with many potential sources and fluctuations over time, making it difficult to control. Dynamic maps that visualize how PM is distributed throughout a workplace over time can help in gaining better insights into when and where exposure occurs. This study explored the use of spatiotemporal modeling followed by kriging for the development of dynamic PM concentration maps in an experimental setting and a workplace setting. Data was collected using personal low-cost PM sensors and an indoor location tracking system, mounted on a moving robot or worker. Maps were generated for an experimental study with one simulated robot worker and a workplace study with four workers. Cross-validation was performed to evaluate the performance and robustness of three types of spatiotemporal models (metric, separable, and summetric) and, as an additional external validation, model estimates were compared with measurements from sensors that were placed stationary in the laboratory or workplace. Spatiotemporal models and maps were generated for both the experimental and workplace studies, with average root mean squared error (RMSE) from 10-fold cross-validation ranging from 7-12 and 73-127 µg/m3, respectively. Workplace models were relatively more robust compared to the experimental study (relative SD ranging from 8-14% of the average RMSE vs. 27-56%, respectively), presumably due to the larger number of parallel measurements. Model estimates showed low to moderate fits compared to stationary sensor measurements (R2 ranging from 0.1-0.5), indicating maps should be interpreted with caution and only used indicatively. Together, these findings show the feasibility of using spatiotemporal modeling for generating dynamic concentration maps based on personal data. The described method could be applied for exposure characterization within comparable study designs or can be expanded further, for example by developing real-time, location-based worker feedback systems, as efficient tools to visualize and communicate exposure risks.

Spatial distribution of Pb and Zn in soils under native vegetation in Southeast Brazil.

Heavy metals can play an important biological role as micronutrients but also as potentially toxic elements (PTEs). Understanding the natural concentrations of PTEs-Pb and Zn included-in soils allows for the identification and monitoring of contaminated areas and their role in environmental risk assessment. In this study, we aim to determine semi-total or natural and available concentrations of Pb and Zn in topsoils (0-20 cm depth) from 337 samples under native vegetation in the State of Minas Gerais, Brazil. Additionally, we sought to interpret the spatial geochemical variability using geostatistical techniques and quality reference values for these elements in soils were established. The semi-total concentrations were determined by flame and graphite furnace atomic absorption after microwave-assisted nitric acid digestion method. The available concentrations were extracted using the Mehlich-I extractor and determined by atomic absorption spectrometer. Spatial variability was modeled using semivariance estimators: Matheron's classic, Cressie and Hawkins' robust, and Cressie median estimators, the last two being less sensitive to extreme values. This allowed the construction of digital maps through kriging of semi-total Pb and Zn contents using the median estimator, as well as other soil properties by the robust estimator. The dominance of acidic pH and low CEC values reflects highly weathered low-fertility soils. Semi-total Pb contents ranged from 2.1 to 278 mg kg-1 (median: 9.35 mg kg-1) whereas semi-total Zn contents ranged from 2.7 to 495 mg kg-1 (median: 7.7 mg kg-1). The available Pb contents ranged from 0.1 to 6.92 mg kg-1 (median: 0.54 mg kg-1) whereas available Zn contents ranged from 0.1 to 78.2 mg kg-1 (median: 0.32 mg kg-1). The highest Pb and Zn concentrations were observed near Januária, in the northern part of the territory, probably on limestone rocks from the Bambuí group. Finally, the QRVs for Pb and Zn in natural soils were lower than their background values from other Brazilian region and below the prevention values suggested by Brazilian environmental regulations.

Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.

The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neural networks (NN) are emerging as a new candidate for performing these tasks as an alternative to conventional geostatistical tools such as Co-Kriging. This study demonstrates the implementation of a NN for estimating radiological values such as ambient dose equivalent (H*(10)), surface activity and activity concentrations of natural radionuclides present in a waste dump of a Cu mine with a high level of natural radionuclides. The results obtained using a NN were compared with those estimated by Co-Kriging. Both models reproduced field measurements equivalently as a function of spatial coordinates. Similarly, the deviations from the reference concentration values obtained in the output layer of the NN were smaller than the deviations obtained from the multiple regression analysis (MRA), as indicated by the results of the root mean square error. Finally, the method validation showed that the estimation of radiological parameters based on their spatial coordinates faithfully reproduced the affected area. The estimation of the activity concentrations was less accurate for both the NN and MRA; however, both methods gave statistically comparable results for activity concentrations obtained by gamma spectrometry (Student's t-test and Fisher's F-test).

Determining climate classifications and producing climate border maps with GIS of Safranbolu district, Karabük, Türkiye.

Knowing climate characteristics enables the detection of particular climate characteristics and their boundaries. This situation is essential in terms of providing sustainable use of areal resources and directing land use plans. For this reason, in this study, climate boundary maps of the Safranbolu district were created based on the need to form a basis for planning. For this purpose, measurement data of all meteorological stations in the district for the last 30 years were obtained; data were associated with the location, and the water balance of each station was calculated using the Thornthwaite climate classification method. In addition, the climate type was determined using different climate classification methods, and the results were compared. All applied methods have shown that Safranbolu has a humid climate; however, the humidity value in the north of Safranbolu is slightly higher than that in the central and southern parts. In addition, water shortage in the north of Safranbolu is observed in July-August, while water shortage in the central and southern parts is observed in July-August-September. Considering the long-term precipitation average of the Safranbolu district, the highest annual precipitation is observed in March and the lowest in August. Etp and Etr throughout the district are highest in July and lowest in January. Surplus water and surface flow occur in the months between December and May, with the highest amount of surface flow occurring in March. There is no month without rain in Safranbolu. Safranbolu, which is on the UNESCO World Heritage List, is a visiting area for local and foreign tourists because of its cultural, architectural, and historical features and geotourism potential. In addition to its current agricultural activities, the cultivation of the "Saffron" plant, which gives its name to the district, and its forest assets cause an increase in both the tourism capacity and population of the district. Considering all of these, studies on climate change risk management and water resources management in Safranbolu have been conducted.

Mapping of on-field soil nutrient variabilities as a guiding force for smart farming: a case study from FarmerZone sentinel-1 from three potato agroecological zones of India.

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming.

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach.

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems.

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.

Spatially adaptive calibrations of airbox PM2.5 data.

The Taiwan air quality monitoring network (TAQMN) and the AirBox network both monitor PM2.5 in Taiwan. The TAQMN, managed by Taiwan's Environmental Protection Administration (EPA), provides high-quality PM2.5 measurements at 77 monitoring stations. The AirBox network launched more recently consists of low-cost, small internet-of-things (IoT) microsensors (i.e., AirBoxes) at thousands of locations. While the AirBox network provides broad spatial coverage, its measurements are unreliable and require calibrations. However, applying a universal calibration procedure to all AirBoxes does not work well because the calibration line varies with local factors, including the chemical compositions of PM2.5 , which are not homogeneous in space. Therefore, different calibrations are needed at different locations to adapt to their local environments. Unfortunately, AirBoxes and EPA locations are misaligned, challenging the calibration task. In this paper, we propose a spatial model with spatially varying coefficients to account for the heterogeneity in the data. Our method gives spatially adaptive calibrations of AirBoxes and produces accurate PM2.5 concentration estimates with their error bars at any location, incorporating two types of measurements. In addition, the proposed method is robust to outliers, requires no colocated data, and provides calibration formulas for new AirBoxes once they are added to the network. We illustrate our approach using hourly PM2.5 data in 2020. After the calibration, the results show that the PM2.5 prediction improves by about 38%-68% in root-mean-squared prediction error. Once the calibration formulas are established, we can obtain reliable PM2.5 values even if we ignore EPA data.

Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite.

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe3O4 nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained <1 for 90% predictions. GFC showed 86.29% NB removal for the given experimental matrix which can be elevated to 96.25% under optimum conditions. The NB adsorption was found to be physical, spontaneous, favorable and obeyed pseudo-2nd order kinetics. The results demonstrate the suitability of GFC as the promising cost-effective and efficient alternative for the decolourization of urban and drinking water streams and elucidate the potential of machine learning models for accurate prediction & elevation of adsorption processes with less experimentation in water purification applications.

A Stabilized Kriging Method for Mapping Disease Rates.

BACKGROUND: Mapping disease rates is an important aspect of epidemiological research because it helps inform public health policy. Disease maps are often drawn according to local administrative areas (LAAs), such as counties, cities, or towns. In LAAs with small populations, disease rates are unstable and are prone to appear extremely high or low. The empirical Bayes methods consider variance differences among different LAAs, thereby stabilizing the disease rates. The methods of kriging break the constraints of geopolitical boundaries and produce a smooth curved surface in the form of contour lines, but the methods lack the stabilizing effect of the empirical Bayes methods. METHODS: An easy-to-implement stabilized kriging method is proposed to map disease rates, which allows different errors in different LAAs. RESULTS: Monte Carlo simulations revealed that the stabilized kriging method had smaller symmetric mean absolute percentage error than three other types of methods (the original LAA-based method, empirical Bayes methods, and traditional kriging methods) in nearly all scenarios considered. Real-world data analysis of oral cancer incidence rates in men from Taiwan demonstrated that the age-standardized rates in the central mountainous sparsely-populated region of Taiwan were stabilized using our proposed method, with no more large differences in numerical values, whereas the rates in other populous regions were not over-smoothed. Additionally, the stabilized kriging map had improved resolution and helped locate several hot and cold spots in the incidence rates of oral cancer. CONCLUSION: We recommend the use of the stabilized kriging method for mapping disease rates.

A Bayesian maximum entropy model for predicting tsetse ecological distributions.

BACKGROUND: African trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information. METHODS: This paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible. RESULTS: Under the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts. CONCLUSIONS: This work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.