Site-specific Models Research Articles

SUMO orchestrates multiple alternative DNA-protein crosslink repair pathways.

Endogenous metabolites, environmental agents, and therapeutic drugs promote formation of covalent DNA-protein crosslinks (DPCs). Persistent DPCs compromise genome integrity and are eliminated by multiple repair pathways. Aberrant Top1-DNA crosslinks, or Top1ccs, are processed by Tdp1 and Wss1 functioning in parallel pathways in Saccharomyces cerevisiae. It remains obscure how cells choose between diverse mechanisms of DPC repair. Here, we show that several SUMO biogenesis factors (Ulp1, Siz2, Slx5, and Slx8) control repair of Top1cc or an analogous DPC lesion. Genetic analysis reveals that SUMO promotes Top1cc processing in the absence of Tdp1 but has an inhibitory role if cells additionally lack Wss1. In the tdp1Δ wss1Δ mutant, the E3 SUMO ligase Siz2 stimulates sumoylation in the vicinity of the DPC, but not SUMO conjugation to Top1. This Siz2-dependent sumoylation inhibits alternative DPC repair mechanisms, including Ddi1. Our findings suggest that SUMO tunes available repair pathways to facilitate faithful DPC repair.

Initialization of thermal models in cold and warm permafrost

Equilibrium modelling, also known as spin-up, is a technique for initializing a stable thermal regime in ground temperature models for permafrost regions. The results act as a baseline for subsequent transient analyses of ground temperature response to climate change or infrastructure. In practice, spin-up procedures are often loosely described or neglected, and the criteria by which a stable thermal regime is evaluated are rarely defined or presented explicitly. In this paper, model results show that no single criterion based on thresholds of inter-cycle temperature change can be used to identify a stable thermal regime in all spin-up scenarios. Results from simulations using a wide range of initialization temperatures and conditions show the number of spin-up cycles can range between 10 and 10 000, and a spin-up criterion as fine as 0.00001 °C/cycle is required to achieve a stable thermal regime suitable for deeper warm permafrost models. The implications of selected threshold criteria are examined in follow-up transient analyses and show that warm permafrost models can be highly sensitive to initial temperature profiles based on the criterion used. The results alert scientists and engineers to the importance of initialization on site-specific and regional permafrost models for transient ground temperature analyses.

Data-driven characterization of the correlation between uniaxial compressive strength and Youngs’ modulus of rock without regression models

The uniaxial compressive strength (UCS) and Youngs’ modulus (E) of rock are important parameters required during design and stability analysis of mining and geotechnical structures. There is correlation between UCS and E of rock, and the proper estimation of such correlation is important for reliable mining engineering analysis. However, limited quantity of UCS and E data pairs often available for most mining project sites makes it difficult to estimate reliable correlation between UCS and E. This study addresses the difficulty by developing Bayesian approach for characterizing the site-specific joint probability distribution of UCS and E that is data-driven, without the use of an empirical model. A major novelty of the proposed approach over previous studies is that it does not require selection or integration of a regression model as input to characterize the correlation between UCS and E. The likelihood function in the proposed approach is directly constructed from only limited UCS and E data pairs and their prior information as inputs. The Bayesian approach is incorporated into Markov Chain Monte Carlo (MCMC) simulation to generate samples pairs of UCS and E, which are then analysed for marginal statistics, marginal probability distribution, joint probability distribution and correlation. Real data of UCS and E obtained from uniaxial compression tests on migmatites at the Sanandaj-Sirjan zone in Iran is used to demonstrate the approach. The marginal statistics, distributions and correlation coefficient from the proposed approach is consistent with those of the measured data from the adopted site. This indicates that the approach is effective in characterizing the correlation between UCS and E, and can be used when there is need for such characterization at a site with limited data. Simulated data are also used in the approach and the results show that the quality and quantity of information available as inputs play an important role in the efficiency of the characterization by the approach. The hallmark of the proposed approach is that it is data-driven, and practitioners do not need to determine and select an appropriate site-specific regression model to evaluate the correlation between UCS and E of rock.

Constructing Quasi-Site-Specific Multivariate Probability Distribution Using Hierarchical Bayesian Model

In geotechnical engineering, it is challenging to construct a site-specific multivariate probability distribution model for soil/rock properties because the site-specific data are usually sparse and incomplete. In contrast, there are abundant generic soil/rock data in the literature for the construction of a generic multivariate probability distribution model, but this model is typically biased and/or imprecise for a specific site. A hybridization method has been proposed to combine these two sources of soil/rock data (site-specific data and a generic database) to produce a quasi-site-specific model, but this method is essentially heuristic. In the current paper, a more rational method that exploits the geologic origin of soil/rock data is proposed. There is a tendency for data to be more similar within a single site and less similar between sites. This is called site uniqueness in geotechnical engineering practice, but no data-driven methods exist to quantify this data feature currently. The hierarchical Bayesian model (HBM) is a natural model to exploit this group information. The grouping criterion can be site localization, soil/rock types, or others. This paper only studies the group criterion based on site localization. This means that a generic database is now viewed as a collection of data groups labeled by qualitative site labels. This site label does not contain any quantitative information such as GPS location, it merely demarcates each group as distinct. The novel contribution is the development of an efficient HBM with closed-form conditional probabilities based on suitably chosen conjugate priors that can handle multivariate, uncertain and unique, sparse, incomplete, and potentially corrupted (MUSIC) data containing site labels. Numerical comparisons between the hybridization method (which cannot incorporate group information) and HBM show that even the simple qualitative knowledge that data belong to a geographically constrained site can improve the estimation of soil/rock properties. The GPS location of each site is not needed.

Responses of Hydrodynamics and Saline Water Intrusion to Typhoon Fongwong in the North Branch of the Yangtze River Estuary

The typhoon impact on an estuarine environment is complex and systematic. A three-dimensional hydrodynamic and salinity transport model with a high-resolution, unstructured mesh and a spatially varying bottom roughness, is applied to investigate the effects of a historical typhoon, Fongwong, which affected Shanghai, on the hydrodynamics and saline water intrusion in the North Branch (NB) of the Yangtze River Estuary (YRE). The model is well validated through observation data of the tidal level, current velocity and direction, and salinity. The numerical results of this typhoon event show that: (1) the tidal level and its range increase toward the upstream part of the NB due to the combined effects of the funnel-shaped plane geometry of the NB and the typhoon; (2) the current velocity and the flow spilt ratio of the NB varies with the tides, with a maximum increase by 0.13 m/s and 26.61% during the flood tide and a maximum decrease by 0.12 m/s and 83.33% during the ebb tide, i.e., the typhoon enhances the flood current and weakens the ebb current; (3) the salinity value increases in the NB to a maximum of 1.40 psu and water is well-mixed in the vertical direction in the typhoon’s stable and falling period. The salinity distribution gradually recovered to the normal salt wedge pattern in 3 days following the typhoon. Although this study is based on a site-specific model, the findings will provide valuable insights into saline water intrusion under typhoon events, and thus assist in implementing more efficient estuarine management strategies for drinking water safety.

Comparison of Evolutionary Selection Acting on the Mitochondrial Protein-Coding Genes Between Intertidal and Deep-Sea Gastropods

Marine molluscs are ecologically and economically important group of organisms that survive in the challenging environments of different oceanic zones. Of all the classes of the phylum Mollusca, gastropods have radiated into marine, freshwater and terrestrial habitats, successfully adapting themselves to thrive in changing environmental conditions. Hence, marine gastropods can be considered as an ideal system to study stress adaptation. In order to withstand the constant fluctuations in temperature, salinity and shifts in oxygen concentration of the intertidal zone, the gastropods inhabiting here rely on a modified and adaptive energy metabolism. The same is applicable for gastropods living in the deep sea environment, which is characterized by high hydrostatic pressure, low oxygen concentrations and abundance of heavy metals. Therefore, survival of these organisms may be correlated to their adaptive mitochondrial genome which serves as the principal site for energy metabolism and production in the cell. Here, we estimated selection pressure acting on the mitochondrial protein-coding genes of 13 intertidal and 2 deep sea gastropods based on site and branch-site specific models. The results exhibited higher number of sites under diversifying selection for the mitochondrial protein-coding genes of intertidal gastropods compared to deep sea species. Overall, this study focusses on the adaptive mitogenome evolution of marine gastropods for survival in the dynamic environments of the intertidal zone as well as deep sea.

A powerful image synthesis and semi-supervised learning pipeline for site-specific weed detection

Precise and efficient weed detection in agricultural fields is the key for robotic weed control. Recent developments in convolutional neural networks (CNNs) have achieved significant success in this regard. CNNs that simultaneously localize and classify objects in images are the predominant forms that have been widely used to detect crops and weeds. However, the use of CNNs in agriculture, particularly for weed detection, has been impeded by a lack of large training dataset with ground truth annotations. Cut-and-paste image synthesis approach and semi-supervised learning are popular methods to alleviate the training data deficiency problem. In this paper, we propose a novel image synthesis and semi-supervised learning pipeline to train site-specific weed detection models without the need for manually labeled images. The CNN models trained by this pipeline achieve performance levels close to that of the supervised models. We investigated the behavior of the proposed pipeline by varying several key components and showed that color match between the training and testing images, training-time color augmentation, and iterative semi-supervised learning largely improve the model performance. These promising results can be used to guide the construction of a weed image database applicable to different weed detection scenarios.

Integrated watershed process model for evaluating mercury sources, transport, and future remediation scenarios in an industrially contaminated site

We used the Soil Water Assessment Tool (SWAT) as a framework to develop an empirical Hg flux model for Upper East Fork Poplar Creek (UEFPC), a Hg-contaminated watershed in Oak Ridge, Tennessee. By integrating long-term Hg monitoring data with simulated flow and suspended solid loads in a site-specific empirical Hg transport model, we (1) quantified the spatial, temporal, and flow regime controls on daily Hg flux (adjusted R2 = 0.82) and (2) made predictions about Hg flux under future climate, land use, and management scenarios. We found that 62.79% of the average daily Hg flux in the watershed is currently driven by base flow, whereas variability in Hg flux is driven by storm and extreme flow. We estimate an average annual Hg flux of 28.82 g day-1 leaving the watershed under baseline precipitation, with an estimated 43.73% reduction in daily Hg flux under drought conditions and a 296% increase in daily Hg flux in extreme precipitation scenarios. We estimated that a new mercury treatment facility would result in a 24.7% reduction in Hg flux under baseline conditions and a 33.4% reduction under extreme precipitation scenarios. The study demonstrated the merit of this approach, which can be replicated for sites where information on flow, suspended solids, and Hg concentrations is available.

Modeling Bioavailability Limitations of Atrazine Degradation in Soils

Pesticide persistence in soils is a widespread environmental concern in agro-ecosystems. One particularly persistent pesticide is atrazine, which continues to be found in soils and groundwater in the EU despite having been banned since 2004. A range of physical and biological barriers, such as sorption and mass-transfer into bacterial cells, might limit atrazine degradation in soils. These effects have been observed in experiments and models working with simplified systems. We build on that work by developing a biogeochemical model of the degradation process. We extended existing engineered system models by including refined representations of mass-transfer processes across the cell membrane as well as thermodynamic growth constraints. We estimated model parameters by calibration with data on atrazine degradation, metabolite (hydroxyatrazine) formation, biomass, and isotope fractionation from a set of controlled retentostat/chemostat experiments. We then produced site-specific model predictions for arable topsoil and compared them with field observations of residual atrazine concentrations. We found that the model overestimated long-term atrazine biodegradation in soils, indicating that this process is likely not limited by bioavailability or energetic constraints of microbial growth. However, sorption-limited bioavailability, could explain the long-term fate and persistence of the main degradation metabolite hydroxyatrazine. Future studies should seek alternative controls that drive the observed atrazine persistence in soil. This work helps to bridge the gap between engineered and natural systems, allowing us to use laboratory setups to gain insight into real environmental systems.

Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data

ABSTRACT In a previous work, the first two authors proposed a data-driven method that can construct a site-specific multivariate probability density function model for soil properties using sparse, incomplete, and spatially variable site investigation data. The spatial variability was limited to the depth direction (horizontal variability was not considered). This data-driven method is referred to as GPR-MUSIC-X. In the current paper, two improvements with respect to GPR-MUSIC-X are made. First, the one-dimensional spatial variability considered by GPR-MUSIC-X is extended to three-dimensional spatial variability (denoted by GPR-MUSIC-3X). Second, a hierarchical Bayesian model (HBM) is adopted to learn the cross-correlation (correlation among different soil parameters) behaviour of generic sites in a soil database accounting for site differences (or uniqueness), and the learning outcome is incorporated into GPR-MUSIC-3X. The resulting model is a quasi-site-specific model (denoted by HBM-MUSIC-3X) because it not only is based on site-specific data but also is informed by the soil database in a manner sensitive to site uniqueness. A case history is used to illustrate the effectiveness of the proposed HBM-MUSIC-3X.

Nurse Staffing Practices and Postoperative Atrial Fibrillation Among Cardiac Surgery Patients: A Multisite Cohort Study

BackgroundPostoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery that is associated with increased morbidity, mortality, and costs. Recent studies suggest that nurse staffing practices are associated with adverse postoperative events, but whether these practices are also related to POAF occurrence is unknown. MethodsTo fill this knowledge gap, a cohort of 6401 cardiac surgery patients admitted to 2 Canadian university health centres (UHC A and UHC B) between 2014 and 2018 was studied. Patients’ cumulative exposure to 4 staffing practices (registered nurse [RN] understaffing, education, experience, and non-RN skill mix) was measured every shift over the first 6 postoperative days, during which 96% of POAF cases occur. The associations of these exposures with in-hospital POAF occurrence were estimated using site-specific multivariable logistic regression models and a mixed-effect model combining data from both sites. ResultsOverall, 563 (27.2%) and 1336 (30.8%) cases of POAF occurred at UHC A and UHC B, respectively. In site-specific models, every 5% increase in the cumulative proportion of understaffed shifts over the first 6 postoperative days was associated with a 3.5% increase in the odds of POAF (adjusted odds ratio [aOR] for UHC A: 1.035; 95% confidence interval [CI]: 1.000-1.070, P = 0.0472; aOR for UHC B: 1.035; 95% CI: 1.013-1.057, P = 0.0019). In the mixed-effect model combining data from both sites, RN understaffing remained significant and was associated with a 3.1% increase in the odds of POAF (aOR: 1.031; 95% CI: 1.014-1.048, P = 0.0003). No other staffing practices were significantly associated with POAF occurrence. ConclusionHigher RN understaffing postoperatively is associated with increased POAF occurrence among cardiac surgery patients.

NGA-subduction global ground motion models with regional adjustment factors

We develop semi-empirical ground motion models (GMMs) for peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral accelerations for periods from 0.01 to 10 s, for the median orientation-independent horizontal component of subduction earthquake ground motion. The GMMs are applicable to interface and intraslab subduction earthquakes in Japan, Taiwan, Mexico, Central America, South America, Alaska, the Aleutian Islands, and Cascadia. The GMMs are developed using a combination of data inspection, data regression with respect to physics-informed functions, ground-motion simulations, and geometrical constraints for certain model components. The GMMs capture observed differences in source and path effects for interface and intraslab events, conditioned on moment magnitude, rupture distance, and hypocentral depth. Site effect and aleatory variability models are shared between event types. Regionalized GMM components include the model constant (that controls ground motion amplitude), anelastic attenuation, magnitude-scaling break point, linear site response, and sediment depth terms. We develop models for the aleatory between-event variability [Formula: see text], within-event variability [Formula: see text], single-station within-event variability [Formula: see text], and site-to-site variability [Formula: see text]. Ergodic analyses should use the median GMM and aleatory variability computed using the between-event and within-event variability models. An analysis incorporating non-ergodic site response should use the median GMM at the reference shear-wave velocity condition, a site-specific site response model, and aleatory variability computed using the between-event and single-station within-event variability models. Epistemic uncertainty in the median model is represented by standard deviations on the regional model constants, which facilitates scaled-backbone representations of model uncertainty in hazard analyses.

Multi-output TCN autoencoder for long-term pollution forecasting for multiple sites

Air pollution is one of the major environmental issues attracting massive attention from researchers and policymakers. Both the developed and developing countries are undergoing a high concentration of pollution levels. Fine particulate matter PM2.5 (particles having a diameter less than 2.5μm) and PM10 (particles having a diameter less than 10μm) can easily penetrate the lungs and respiratory system and causes adverse health issues like heart attacks, cardiovascular diseases, lung function reduction. Real-time pollutant information is of great importance to providing prompt and complete information on air quality. Air pollution forecasting is another significant step of air pollution management, which can help policymakers and citizens make proper decisions to prevent air pollution-related diseases. This research study explores a novel pollutant forecasting model named as Multi-output temporal convolutional network autoencoder (MO-TCNA). The model accumulates each step's predicted values to perform multi-step ahead long-term forecasting for multiple pollutants and multiple sites in a single training model. The MO-TCNA network serves both the PM2.5 and PM10 pollutants forecasting for various locations instead of performing single output and site-specific pollutant forecasting. Consequently, the experimental results show that the MO-TCNA network is time-saving and has better performance than the traditional site-specific forecasting models.

P–668 We aim for one baby, not one embryo: a personalized ET strategy based on embryo score and woman age maximizes LB and minimizes twins

Abstract Study question Is it possible to define a personalized ET model to maximize the chance of live birth (LB) while minimizing the risk of twin pregnancy? Summary answer A model including age and embryo morphological score can inform a personalized ET strategy to maximize LB while minimizing the risk of twin pregnancy. What is known already The morphological score of the transferred embryos affects pregnancy (PR) and LB rates in IVF cycles. Although SET is mainly recommended to avoid multiple pregnancies, DET is still being performed extensively, especially in patients with poor prognosis, with the aim to improve PR per transfer and shorten time to pregnancy. While twin pregnancies are associated with an increased risk of maternal and fetal complications, very low PR may increase patient drop-off, extend time to pregnancy, and increase the cost per successful transfer. A personalized transfer strategy balancing high LB per transfer with low twin pregnancy rates should be defined. Study design, size, duration Retrospective study including 2,470 fresh and frozen embryo transfers (ET) of either one or two embryos at D3 from January 2016 to August 2019 in a single IVF clinic. Biochemical, clinical, multiple pregnancy and live birth rates after SET and DET were analyzed according to the morphological score of the embryos transferred. ETs were divided into 9 groups according to the combinations of their embryo morphological scores. Participants/materials, setting, methods Embryos were assessed on D3 following a national recommended morphological scale. Morphology was categorized as High (H) if A or B+, medium (M) if B or C+, and Low (L) if C or D. The likelihood of biochemical, clinical pregnancy and live birth, and the risk of multiple pregnancy, after SET and DET of embryos of different scores was analyzed. A logistic regression analysis adjusted by age of the woman was ran for each outcome. Main results and the role of chance The distribution of ETs among the 9 groups for SET was: 510 H, 715 M, 346 L; for DET: 142 HH, 148 HM, 29 HL, 268 MM, 164 ML, 148 LL. Mean woman age was similar among groups: 38.7±4.01. Live birth and twin rates increased with embryo score. Considering a SET of category M as reference, the OR of live birth in DET were: 2.76 [1.82, 4.19 95%CI] for HH, and 2.32 [1.51, 3.55 95%CI] for HM, and 1.69 [1.19, 2.40 95%CI] for MM, and in SET: 1.52 [1.12, 2.04 95%CI] for H. Considering a DET of category MM as reference, the OR of twin birth in DET were: 2.8 [1.14, 6.99 95%CI] for HH, 2.5 [0.98, 6.46 95%CI] for HM, and 0.92 [0.11, 7.84 95%CI] for HL. According to this model, a 38y.o. woman with a SET of category M would have a 16% chance of live birth, and no twins. The addition of an M (DET: MM) increases her chance of live birth to 24% with a 2.9% risk of twins. The addition of a H (DET:MH) would increase further her chance of live birth to 30.8%, however, the increase would be due almost exclusively to twins (7%). Limitations, reasons for caution The limitations of this study are its retrospective nature and the small size of some categories. Embryos were classified using a national morphological scale; other morphological classifications could influence the results. The development and validation of site-specific models, using local patients’ data, is recommended before their use in clinical practice. Wider implications of the findings: A personalized assessment of embryo quality and woman age, at a minimum, are necessary to identify the best ET strategy for each patient; this strategy allows to maximize live birth rates while keeping the risk of twin birth as low as possibl. Trial registration number Not applicable

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

Allometric models for predicting aboveground biomass of Combretum-Terminalia woodlands in Amhara, Northwest Ethiopia

Dry woodland ecosystems play an important role in the global carbon balance and these need accurate biomass predictions that can be obtained from tree allometry. However, information on allometric models is limited for Combretum-Terminalia woodlands of the Amhara Region in Northwestern Ethiopia. The objective of the study was to develop an allometric model for the aboveground biomass (AGBind) of the tree species in the study areas. A total of 149 trees were considered to predict the AGBind (stems, branches and leaves) of the selected species of which 101 samples were collected from nine tree species in Metema district, and 48 sample trees were obtained from four species in Quara district. A new model was developed using log-transformed data with linear regression, and their performance was assessed with the mean absolute prediction error (MAPE%) and the mean prediction error (MPE%). The results of the study indicated that Diameter at breast height (DBH) was the best predictor variable of biomass components for all species. For site-specific data, combined DBH, tree height and wood density best explained the stem and AGBind with the MAPE ranged 18.6–19.2% compared with the DBH-alone model (MAPE ranging from 29.8 to 30.2%). A positive validation was observed on all model forms, but M3 and M4 provided highly precise predictions of AGBind for independent data. However, a large bias was observed in the branch and leaf biomass model for site-specific data with the MAPE ranged 66.6–72.8%. A little improvement was observed in the model performance of plant functional type, while general models developed from the compiled data performed well. For a given model form, a comparable performance was found between the site-specific, tree functional type and general model. Generally, the general model with DBH and wood density together followed by the one which includes three predictors was a better AGBind predictor over the site and tree functional type. The species and site-specific models are thus accurate for site-based prediction whereas the general model contributes to the reliable prediction of AGBind in the studied Combretum-Terminalia woodlands. The comparison with the published models showed that most models produced large errors with MPE ranging from 16.10 to 36.60%, but the best model of tropical dry forests performed much better with MPE ranging from -2.24 to 0.34% and these models can probably be used for Combretum-Terminalia woodland.

Adaptive Management of Sustainable Tourism in Antarctica: A Rhetoric or Working Progress?

Growth and diversification of tourism activities in Antarctica have not been matched by proactive strategies for planning or management. Recognizing that the adaptive management approach has been effectively implemented in managing tourism in protected areas, we examine to what extent this approach has been incorporated into the Antarctic tourism research and management, and what constraints exist for its implementation. To better understand the extent of literature contributions, we conducted an appraisal of 72 peer-reviewed journal articles published from 1992 to 2020 and Antarctic management documents. From a scientific perspective, researchers have been advocating for adaptive management approaches to Antarctic tourism and have applied different elements, particularly ecological assessments, design of management measures, monitoring, and regulatory mechanisms. However, these contributions have not been necessarily translated into management policy and regulations. We acknowledge that full implementation of an adaptive management approach is not easily achievable due to the unique Antarctic regime. However, we argue that comprehensive site-specific and regional adaptive management models could be applied as the first step for a more systematic implementation. This incremental approach could contribute to enhanced stakeholder participation and improved decision-making processes, ultimately leading to a more proactive and effective management of Antarctic tourism, essential for the conservation of the continent.

Development & application of Conceptual Framework Model (CFM) for environmental risk assessment of contaminated lands.

Dumping sites are the most common types of contaminated lands as they pollute the environment. Environmental management of contaminated sites cannot be delivered effectively and efficiently without robust holistic & integrated risk assessment. Previous studies reveal the absence of a risk assessment model that holistically integrates all essential factors progressively and categorically. The study aimed to develop a holistic & integrated Conceptual Framework Model (CFM) for environmental risk assessment and to apply developed CFM on real-world existing Mahmood Booti Open Dumping Site (MBODS). CFM developed in this study had three main tiers i.e., baseline study, hazard identification & exposure assessment, and risk estimation. For the application of CFM, baseline data were collected and assessed. Water, leachate & soil samples were collected within 1000 m across the site and analyzed for physio-chemical parameters and heavy metals to estimate risk. Results of applied CFM depicted that Physico-chemical analysis of leachate, water, and soil revealed significant pollution levels. Heavy metal analysis exhibited that Ni, Pb, Mn, and Cr levels exceeded the allowable limits of the “World Health Organization” in leachate, water, and soil samples. It also revealed the existence of metals at the source (dumping site itself), pathway, and receptor of the dumping site. Eir value for Ni, Pb and Cd from the study area manifested a serious probable risk to ecological integrities. Results for PERI from dumpsite demonstrated a serious ecological risk. It can be concluded that although Mahmood Booti dumping site has been at post-closure stage, it is a momentous source of hazardous toxic contaminants to the nearby inhabitants. The work presented in this paper may reproduce repeatedly to create site-specific risk assessment models of other contaminated lands in a cost-effective, consistent and cohesive manner. Application of CFM at Mahmood Booti Dumping site described detailed risk assessment which helps further in risk management.

Linear regression models to predict the tropospheric parameters at the Global Positioning systems’ sites over the East African region

Currently, the East African tropical region has limited information about Precipitable Water Vapour (PWV) data and yet the region has a high potential for its utilization. This is on the grounds that the East African tropical region is profoundly prone to climate change and fluctuation. Existing studies need data on the detailing and performance evaluation of precipitable water vapour models within East Africa. This has been so as a result of the scattered Global Positioning System (GPS) networks and other alternative water vapour measuring equipments, enormous information gaps and the absence of surface meteorological data. The accessibility and precision of surface meteorological estimations is crucial in deriving accurate GPS PWV data. In this study, the daily average, PWV, pressure, temperature and weighted mean temperature () models have been developed utilizing one year (2013) GPS PWV and European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re- Analysis PWV (ERA5 PWV), total column water vapour (TCWV), surface pressure and 2 meter (2m) temperature data. The purpose of the developed models is to predict PWV over regions with data gaps where the computation of GPS Zenith Tropospheric Delays (ZTD) is impossible and in cases of station outages. In addition, the models will provide meteorological parameter where meteorological sensors are missing. The GPS PWV accuracy obtained with the developed models shows an average RMSE of 1.54 mm and MnB of 0.32 mm in comparison to the measured GPS PWV data. The ERA5 PWV accuracy obtained with the developed models shows an average RMSE of 0.33 mm and MnB of 0.01 mm in comparison to the measured ERA5 PWV data. Based on the RMSE, it was observed that the site-specific models developed can be utilized to provide estimates of nearly a similar degree of precision compared to the measured values at the thirteen stations.

Machine learning to discover mineral trapping signatures due to CO2 injection

Mineral trapping is pursued as a geological CO2 sequestration (GCS) mechanism because it permanently stores CO2 in solid phases or minerals. However, CO2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom k-means clustering (NMFk) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO2, and sequestered CO2), six chemical species (K+, Na+, HCO3−, Ca2+, Mg2+, Fe2+), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. The simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMFk, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMFk was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMFk analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na+, K+, Ca2+, Mg2+, pH, and aqeuous CO2. Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.