Abstract In recent years significant breakthroughs in exploring big data, recognition of complex patterns, and predicting intricate variables have been made. One efficient way of analyzing big data, recognizing complex patterns, and extracting trends is through machine-learning (ML) algorithms. The field of porous media, and more generally geoscience, have also witnessed much progress, and recent progress in developing various ML techniques have benefitted various problems in porous media and geoscience across disparate scales. Thus, it is becoming increasingly clear that it is imperative to adopt advanced ML methods for the problems in porous media and geoscience because they enable researchers to solve many difficult problems. At the same time, one can use the already existing extensive knowledge of porous media to endow ML algorithms and develop novel physics-guided methods. The goal of this review paper is to provide the first comprehensive review of the recently developed methods in the ML algorithms and describe their application to porous media and geoscience. Thus, we review the basic concept of the ML and describe more advanced methods, known as deep-learning algorithms. Then, the application of such methods to various problems in porous media and geoscience, such as hydrological modeling, fluid flow in porous media, and (sub)surface characterization, are reviewed. We also provide a discussion of future directions in this rapidly developing field.
Shinichi Nakagawa, Yefeng Yang, Erin L. Macartney
et al.
Meta-analysis is a quantitative way of synthesizing results from multiple studies to obtain reliable evidence of an intervention or phenomenon. Indeed, an increasing number of meta-analyses are conducted in environmental sciences, and resulting meta-analytic evidence is often used in environmental policies and decision-making. We conducted a survey of recent meta-analyses in environmental sciences and found poor standards of current meta-analytic practice and reporting. For example, only ~ 40% of the 73 reviewed meta-analyses reported heterogeneity (variation among effect sizes beyond sampling error), and publication bias was assessed in fewer than half. Furthermore, although almost all the meta-analyses had multiple effect sizes originating from the same studies, non-independence among effect sizes was considered in only half of the meta-analyses. To improve the implementation of meta-analysis in environmental sciences, we here outline practical guidance for conducting a meta-analysis in environmental sciences. We describe the key concepts of effect size and meta-analysis and detail procedures for fitting multilevel meta-analysis and meta-regression models and performing associated publication bias tests. We demonstrate a clear need for environmental scientists to embrace multilevel meta-analytic models, which explicitly model dependence among effect sizes, rather than the commonly used random-effects models. Further, we discuss how reporting and visual presentations of meta-analytic results can be much improved by following reporting guidelines such as PRISMA-EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Ecology and Evolutionary Biology). This paper, along with the accompanying online tutorial, serves as a practical guide on conducting a complete set of meta-analytic procedures (i.e., meta-analysis, heterogeneity quantification, meta-regression, publication bias tests and sensitivity analysis) and also as a gateway to more advanced, yet appropriate, methods.
Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains limited. Here, we present the first global assessment of gully density's spatial patterns. Using mapped observations from over 17,000 representative study sites worldwide, we trained random forest models that simulate both the susceptibility to gullying at a 1 km2 resolution and the corresponding gully head density (GHD). Through an interpretable machine learning framework, we demonstrate that global GHD patterns result from a combination of environmental factors with non-linear interactions, leading to significant regional variations in the dominant factors controlling GHD. We distinguish between gully hotspots driven primarily by natural factors such as topography, geomorphology, tectonics, pedology or climate and those where land use and land cover play a dominant role. Based on these insights, we identified critical global areas of gully erosion, i.e., hotspots where gully occurrence is likely highly sensitive to anthropogenic drivers. These include the Chinese Loess Plateau, the Ethiopian Highlands, and large parts of the Mediterranean and Sahel regions. Also desert regions are often characterized by high GHDs. However, in these cases, their occurrence is mainly driven by natural factors. The insights we provide are valuable to inform land management and targeted erosion mitigation strategies.
Nanozymes, next‐generation enzyme‐mimicking nanomaterials, have entered an era of rational design; among them, Co‐based nanozymes have emerged as captivating players over times. Co‐based nanozymes have been developed and have garnered significant attention over the past five years. Their extraordinary properties, including regulatable enzymatic activity, stability, and multifunctionality stemming from magnetic properties, photothermal conversion effects, cavitation effects, and relaxation efficiency, have made Co‐based nanozymes a rising star. This review presents the first comprehensive profiling of the Co‐based nanozymes in the chemistry, biology, and environmental sciences. The review begins by scrutinizing the various synthetic methods employed for Co‐based nanozyme fabrication, such as template and sol‐gel methods, highlighting their distinctive merits from a chemical standpoint. Furthermore, a detailed exploration of their wide‐ranging applications in biosensing and biomedical therapeutics, as well as their contributions to environmental monitoring and remediation is provided. Notably, drawing inspiration from state‐of‐the‐art techniques such as omics, a comprehensive analysis of Co‐based nanozymes is undertaken, employing analogous statistical methodologies to provide valuable guidance. To conclude, a comprehensive outlook on the challenges and prospects for Co‐based nanozymes is presented, spanning from microscopic physicochemical mechanisms to macroscopic clinical translational applications.
Elizabeth Costello, Brittney O Baumert, Zhenjiang Li
et al.
Abstract Objectives Bariatric surgery is an effective treatment for severe obesity and associated metabolic comorbidities. Exposure to polyfluoroalkyl substance (PFAS) before bariatric surgery may attenuate improvements in glucose metabolism and explain some of the heterogeneity in post-surgery outcomes. Design This is an observational cohort study. Methods Adolescents (n = 186) enrolled in the Teen-Longitudinal Assessment of Bariatric Surgery study were included. Eight-PFAS congeners were measured in plasma before surgery. Linear and logistic regressions were used to examine cross-sectional associations between log2-transformed PFAS (ng/mL) and fasting glucose, insulin, hemoglobin A1c (HbA1c), and homeostatic model assessment for insulin resistance (HOMA-IR). Linear mixed models were used to examine the longitudinal associations between PFAS and outcomes measured at baseline and 6-, 12-, 36-, and 60-months post-surgery. Polyfluoroalkyl substance mixture associations at each visit were assessed using quantile g-computation. All models were adjusted for demographics, study site, and use of diabetes medication. Results Perfluorohexanesulfonic acid (PFHxS) was associated with greater increases in fasting glucose and HbA1c in the 1- to 5-year post-operative period: for instance, a PFHxS level of 1.95 log2-ng/mL was associated with a 3.30 mg/dL (95% CI: 1.23, 5.37) increase over 4 years, while a PFHxS level of −0.16 log2-ng/mL was associated with a 1.19 mg/dL (95% CI: −0.91, 3.29) increase. PFHxS, perfluoroheptanesulfonic acid, and perfluoroheptanoic acid were positively associated with insulin and HOMA-IR at baseline, but not in the 1- to 5-year post-operative period. Each simultaneous quartile increase in the PFAS mixture was associated with higher insulin and HOMA-IR at baseline, but this association did not persist at follow-up visits. Conclusions Perfluorohexanesulfonic acid exposure may attenuate improvement in fasting glucose and HbA1c after bariatric surgery. Improvements in insulin resistance after surgery were not associated with PFAS exposure.
High-quality, naturally protected seeds prior to sowing, along with growth activation of seedlings, represent a promising approach to stabilising crop yield and quality. Enhancing plant resistance to dynamic environmental stresses, including harmful organisms, is one of the strategies for realising the biological potential of crop yields in breeding and seed production. This research aimed to experimentally evaluate a preparation based on humic substances, film formers, a nanocomposite, succinic acid, and microbiological carotene. Experiments were conducted using spring barley and wheat seeds. A seed encrustation technology employing a functional preparation was applied. Laboratory and field experiments were conducted at V. Dokuchaev Kharkiv National Agrarian University, Department of Plant Growing, over two years. The experimental design and economic efficiency assessment of the functional preparation in enhancing yield was carried out according to established methodologies. Pre-sowing seed treatment with the preparation resulted in improved field germination, synchronised seedling emergence, and increased yield. Comprehensive studies revealed that the preparation was compatible with fungicides, demonstrating a synergistic effect of their joint protective effect. Experimental results confirmed that seed incrustation with protective and stimulating formulations based on water-soluble polymers is an effective method for protecting plants from seed- and soil-borne infections while reducing the level of environmental pollution. The extended and enhanced fungicidal activity of film-forming protective and stimulating compositions was also demonstrated. Agricultural production tests indicated that the developed preparation was user-friendly, environmentally safe, and economically efficient, contributing to increased crop yields. The positive test results support practical recommendations for its application in both seed encrustation and grain crop spraying during the tillering and milky-wax ripeness phases
<p>We used the CAM5 model to examine how different particle-bound polycyclic aromatic hydrocarbon (PAH) degradation approaches affect the spatial distribution of benzo(a)pyrene (BaP). Three approaches were evaluated: NOA (no effect of OA coatings state on BaP), shielded (viscous OA coatings shield BaP from oxidation under cool and dry conditions) and ROI-T (viscous OA coatings slow BaP oxidation in response to temperature and humidity). Results show that BaP concentrations vary seasonally, influenced by emissions, deposition, transport and degradation approach, all of which are influenced by meteorological conditions. All simulations predict higher population-weighted global average (PWGA) fresh BaP concentrations during December–January–February (DJF) compared to June–July–August (JJA), due to increased emissions from household activities and reduced removal processes during colder months. The shielded and ROI-T approaches, which account for OA coatings, result in 2–6 times higher BaP concentrations in DJF compared to NOA. The shielded simulation predicts the highest PWGA fresh BaP concentration (1.3 <span class="inline-formula">ng m<sup>−3</sup></span>), with 90 % of BaP protected from oxidation. In contrast, the ROI-T approach forecasts lower concentrations in middle to low latitudes, as it assumes less effective OA coatings under warmer, more humid conditions. Evaluations against observed BaP concentrations show the shielded approach performs best, with a normalized mean bias (NMB) within <span class="inline-formula">±</span> 20 %. The combined incremental lifetime cancer risk (ILCR) for both fresh and oxidized PAHs is similar across simulations, emphasizing the importance of considering both forms in health risk assessments. This study highlights the critical role of accurate degradation approaches in PAH modeling.</p>
Kristine Godziuk, Mary Forhan, Flavio T. Vieira
et al.
ABSTRACT Background Treatments aimed at improving physical function and body composition, including reducing fat mass (FM) and increasing muscle mass, may benefit individuals with advanced knee osteoarthritis (OA) and obesity. We investigated the feasibility and efficacy of a multimodal behavioural intervention compared to usual care to enhance physical function and muscle mass in this population. Methods The POMELO (Prevention Of MusclE Loss in Osteoarthritis) study is a two‐arm pilot randomized controlled trial; NCT05026385. Participants aged 40–75 years, with a BMI ≥ 35 kg/m2 and knee OA were randomized 1:1 to either the intervention group (POMELO) or usual care (UC). The 3‐month POMELO intervention incorporated progressive resistance exercise (3 sessions/week), individualized nutrition counselling targeted for OA, and 12 group education sessions on nutrition and arthritis self‐management. The UC group received standard clinical care. After the 3‐month supervised intervention, both groups were followed for 6 months without support. Assessments at baseline, 3 months and 9 months (primary endpoint) included body composition (DXA, measuring FM and appendicular lean soft tissue [ALST]), physical function (chair‐sit‐to‐stands [CSTS], 6‐min walk [6MWT], maximal handgrip strength [HGS]), and health‐related quality of life (Euroqol visual analog scale [EQ‐5D VAS]). Co‐primary outcomes were feasibility (intervention completion ≥ 80% and per‐protocol adherence ≥ 60% [i.e., attendance at 12 education sessions and exercise 3 ×/week]) and acceptability (4‐item Likert‐scale satisfaction survey, and open‐ended questions). Secondary outcomes included changes in physical function and ALST. Results Fifty participants were randomized (POMELO = 25, UC = 25), with 32 completing the study (69% female, mean age 64.9 ± 1.2 years, BMI 42.1 ± 1.0 kg/m2). The POMELO intervention group had 80% completion and 74% adherence, confirming feasibility. Higher satisfaction rates were observed in POMELO compared to UC (3.5 vs. 2.2, p < 0.001) indicating greater acceptability. The POMELO group had improvements in CSTS (mean difference [MD] 3.96, ES 1.2, p < 0.001), 6MWT (MD 31.6 m, ES 0.4, p = 0.039) and EQ‐5D VAS (MD 7.9 points, ES = 0.4, p = 0.01) compared to UC. Both groups experienced FM loss, but only the UC group lost ALST and HGS. Conclusion The POMELO intervention, combining personalized nutrition, resistance exercise and self‐management support, was feasible, acceptable and showed greater efficacy than usual care to improve physical function in patients with knee OA and obesity. Our pilot study of this intervention showed potential benefits on body composition and quality of life without focusing on weight reduction. A larger study is needed to confirm these results, as this approach may offer advantages over usual care, potentially leading to better mobility and health outcomes.
Diseases of the musculoskeletal system, Human anatomy
Lisa Dellmuth, Maria-Therese Gustafsson, Suanne Mistel Segovia-Tzompa
Abstract There is a lively debate about the legitimacy of the international climate regime, as represented by the United Nations Framework Convention on Climate Change, and the quality of non-state actor participation in the regime. This commentary examines perceptions of involved non-state actors from 2021–2022 regarding their participation and regime legitimacy. The findings reveal no legitimacy crisis for the adaptation and mitigation regimes, but the surveyed NSAs are divided in their legitimacy beliefs. NSAs also express significant disappointment about their opportunities for participation.
Replicability takes on special meaning when researching phenomena that are embedded in space and time, including phenomena distributed on the surface and near surface of the Earth. Two principles, spatial dependence and spatial heterogeneity, are generally characteristic of such phenomena. Various practices have evolved in dealing with spatial heterogeneity, including the use of place-based models. We review the rapidly emerging applications of artificial intelligence to phenomena distributed in space and time and speculate on how the principle of spatial heterogeneity might be addressed. We introduce a concept of weak replicability and discuss possible approaches to its measurement.
This article examines the interaction between gentrification and environmental sciences in Colombia, using a hermeneutic desk review approach to unravel how this urban phenomenon influences and is influenced by environmental factors. Through the hermeneutic circle method, adapted for a single researcher, a deep understanding of the existing literature is achieved and main lines of research are identified. Areas explored include green gentrification, climate change impacts, health and well-being effects, environmental justice, and associated public policies. The study highlights the need for policies that balance environmental improvement with social protection, and underlines the importance of including local communities in urban planning processes to prevent displacement and increase social equity. This interdisciplinary approach provides valuable insights to understand the complexity of gentrification and its multiple dimensions in an urban and environmental context
Ann Bostrom, J. Demuth, Christopher D. Wirz
et al.
Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human–AI teaming perspectives on AI development similarly underscore. Co‐development strategies may also help reconcile efforts to develop performance‐based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.
Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).
Kenneth S. Tramm, Jason T. Minter, Catherine A. Seaton
Abstract Risk‐based corrective action (RBCA) programs employ conservative models to develop default values for soil screening, which simplify the risk assessment process. However, for several naturally occurring metals (e.g., arsenic and lead), these published screening values are often unrealistic and well below the documented background levels in soil. This can lead to confusion among the regulated community and inexperienced regulators, as it will inappropriately identify naturally occurring conditions as a release (false positive or Type I error). An effective RBCA program requires the incorporation of defensible background threshold values (BTVs) in the screening process. Recent datasets and BTV development methods are available to enhance existing RBCA programs and reduce the occurrence of Type I errors. This review evaluated the role “background” currently plays in the Texas Risk Reduction Program (TRRP) and offers defensible approaches in minimizing Type I errors estimated by one Texas municipality to directly result in an unnecessary expenditure of over $250,000 annually to address this confusion in the form of additional assessment, remediation, soil management, and even disposal requirements. The same BTV development process demonstrated in this Texas case study can also inform risk assessment efforts in other areas where BTVs can supplement existing RBCA programs.
Nazario Tartaglione, Fabien Desbiolles, Anna delMoral‐Méndez
et al.
Abstract Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect.
Abstract Background Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. Methods Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016–2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children’s sex-specific associations were explored. Results After controlling for covariates, one standard deviation (SD) (11.5 μg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 – 26%) and 14% (95% CI: 1 – 29%) increase in SGA, respectively; and one SD (9.6 μg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 – 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22–32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. Conclusion Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.