Carbon monoxide (CO) is a major atmospheric pollutant with adverse health effects on humans. Moreover, CO can indirectly prolong the lifetime of methane and contribute to global warming. Therefore, understanding the spatial distribution of CO emissions is crucial for designing much-needed strategies to control this pollutant. In this work, a hybrid Weather Research & Forecasting–stochastic time-inverted Lagrangian transport (WRF-STILT)–Bayesian inversion framework was constructed to correct CO emissions over the Greater Bay Area (GBA) for February 2019 and February 2020. After adjusting CO emissions, the average root mean squared error (RMSE), normalized mean error (NME), and correlation coefficient (R) for the simulated CO concentrations in February 2019 and 2020 changed from 0.31 ppm to 0.12 ppm (a 61% reduction), 0.35 to 0.13 (a 63% reduction), and 0.47 to 0.87 (an 85% increase), respectively. The updated CO emissions were then used as input for the Comprehensive Air Quality Model with Extensions (CAMx), a Eulerian model, to further validate the method. The results again indicated that the simulation performance was improved substantially, with a 58% increase in the average R value, a 62% reduction in the RMSE, and a 68% reduction in the NME. This validates the effectiveness of the proposed method in correcting CO emissions. According to the updated emission data, CO emissions over the GBA during the Spring Festival and the COVID-19 lockdown period were 8.3% and 19.6% lower than during normal periods, respectively. These results highlight the importance of accounting for such atypical events in emission estimation and air quality modeling. Analysis of the source areas contributing to CO concentrations in population centers of major GBA cities showed that the average contributions from local emissions and emissions from other GBA cities were 45.5% and 38.8%, respectively. The method developed in this work can be further used for CO adjustment in other regions and contribute to a deeper understanding of the characteristics of this important pollutant.
Environmental sciences, Urban groups. The city. Urban sociology
Gopal Krishan, Vivek Diwakar, S. D. Khobragade
et al.
Abstract Groundwater quality assessment is critical due to its susceptibility to a range of natural and anthropogenic influences, which, if unmanaged, can pose serious environmental and public health risks. This study investigates the hydrogeochemical characteristics and groundwater quality evaluation in the southwestern districts of Punjab, India, with a focus on sustainable resource management. A total of 242 groundwater samples were systematically collected during the summer of 2019 across the districts of Mansa, Fazilka, Muktsar, Bathinda, Firozpur, and Faridkot. The samples were analyzed for almost all major cations, anions and other physicochemical parameters. Relative abundance of cations was Na+ > Mg2+ > Ca2+ > K & anions were SO4 2− > HCO3 − > Cl− > NO3 − > F. Elevated concentrations of sulphate & nitrate were detected, highlighting the impact of agrochemical inputs. The plots of Wilcox and USSL plots revealed a declining trend in groundwater suitability for irrigation, affecting both shallow and deep aquifer sources, due to increasing salinity and sodium hazards. Hydrochemical data was interpreted using Gibbs diagram, Piper’s trilinear plot and Durov diagram to understand the various geochemical processes affecting the groundwater quality. Hydrochemical analysis indicates that rock–water interactions, evaporation & anthropogenic processes predominantly control groundwater composition, as evidenced by high levels of sodium and chloride. This study is significant as the surface water resources are limited and the quality and quantity of groundwater are deteriorating with time due to anthropogenic inputs. These findings underscore the necessity of continuous monitoring and informed groundwater management strategies to mitigate contamination and ensure long-term sustainability.
Water supply for domestic and industrial purposes, Environmental sciences
Michael Petrides, Aliki Peletidi, Evangelia Nena
et al.
Background Approximately 50 million individuals across the globe are impacted by epilepsy, leading to fear, discrimination, psychiatric issues, high costs, and social stigma. Proper diagnosis and treatment could allow up to 70% of those affected to live seizure-free. Community pharmacists have significant potential to actively participate in epilepsy patient care, beyond merely dispensing medications. The objective of this study was to systematically review and assess the roles of pharmacists in epilepsy care, focusing on pharmacist-led interventions and services for patients with epilepsy.Methods Following PRISMA 2020 guidelines, the review included cross-sectional, retrospective cohort, and qualitative/quantitative studies on pharmacist-led epilepsy interventions in community and outpatient settings. Searches were conducted in Scopus, PubMed Central, and Science Direct for studies published through the end of 2023. Two evaluators independently reviewed and chose studies, and the data was analysed using Microsoft Excel®. Quality assessment was performed using the MMAT tool.Results Five eligible studies were included, covering 457 participants. Studies originated from the USA (n = 3), Netherlands (n = 1), and Palestine (n = 1). They evaluated pharmacist-led interventions in epilepsy, including medication adherence, quality of life, and pharmacist’s integration in epilepsy care.Conclusion This review underscores the possible contributions of pharmacists in epilepsy care, stressing the importance of pharmacist-led interventions to enhance medication adherence and the quality of life for individuals with epilepsy. Future research should evaluate the effectiveness and cost-effectiveness of these services, including disease management and patient education. Increasing awareness among pharmacists and patients about pharmacists’ contributions is crucial for improving epilepsy care.
Therapeutics. Pharmacology, Pharmacy and materia medica
Elena Chaboteaux, Pablo Aycart-Lazo, Gustavo Arias-Álvarez
et al.
Abstract Small vertebrate carcasses represent an abundant resource in tropical rainforests, where scavengers play a key role in nutrient recycling, enhancing productivity in nutrient-poor habitats such as Amazonian terra firme forests. However, the contributions of vertebrate and invertebrate taxa to carrion decomposition in Amazonia remain understudied. Using motion-triggered and time-lapse cameras, we documented vertebrate species and arthropod groups interacting with 52 guinea pig carcasses in terra firme and floodplain forests in the Peruvian Amazon during dry and wet seasons. We quantified their contributions to carcass removal and examined variation across habitats and seasons, as well as temporal resource partitioning among insect taxa. A single dung beetle species, Coprophanaeus lancifer, removed 93% of carcasses in terra firme during the wet season, by burying them on-site. In contrast, 92% of floodplain carcasses in the wet season were removed by vertebrates. C. lancifer was absent in the dry season, when vertebrates removed most carcasses. Carcasses buried by C. lancifer lasted less than 24 h, reducing detectability for vertebrates and limiting access for other arthropods and necrophagous flies. To our knowledge, this is the first record of a rainforest invertebrate outcompeting vertebrates for carrion under natural conditions, highlighting C. lancifer as a keystone species shaping carrion trophic interactions.
The objective of this review article was to provide a comprehensive summary and up-to-date overview of the current state of knowledge in heavy metals and decontamination efforts in Nigeria. The sources of heavy metals, both natural and anthropogenic, the toxicity effects, and water contamination in Nigeria have been examined in detail, as well as treatment technologies, primarily through biosorption. The literature has been reviewed, focusing on the removal of heavy metal ions using biological-based biosorbents. Key parameters, such as temperature, pH, and contact time, as well as biosorbent characteristics, have been investigated. Potential future research directions have been proposed.
CI/CD pipelines are widely used in software development, yet their environmental impact, particularly carbon and water footprints (CWF), remains largely unknown to developers, as CI service providers typically do not disclose such information. With the growing environmental impact of cloud computing, understanding the CWF of CI/CD services has become increasingly important. This work investigates the CWF of using GitHub Actions, focusing on open-source repositories where usage is free and unlimited for standard runners. We build upon a methodology from the Cloud Carbon Footprint framework and we use the largest dataset of workflow runs reported in the literature to date, comprising over 2.2 million workflow runs from more than 18,000 repositories. Our analysis reveals that the GitHub Actions ecosystem results in a substantial CWF. Our estimates for the carbon footprint in 2024 range from 150.5 MTCO2e in the most optimistic scenario to 994.9 MTCO2e in the most pessimistic scenario, while the water footprint ranges from 1,989.6 to 37,664.5 kiloliters. The most likely scenario estimates are 456.9 MTCO2e for carbon footprint and 5,738.2 kiloliters for water footprint. To provide perspective, the carbon footprint in the most likely scenario is equivalent to the carbon captured by 7,615 urban trees in a year, and the water footprint is comparable to the water consumed by an average American family over 5,053 years. We explore strategies to mitigate this impact, primarily by reducing wasted computational resources. Key recommendations include deploying runners in regions whose energy production has a low environmental impact such as France and the United Kingdom, implementing stricter deactivation policies for scheduled runs and aligning their execution with periods when the regional energy mix is more environmentally favorable, and reducing the size of repositories.
Zachary K. Garvin, Anaïs Roussel, Luoth Chou
et al.
The ESA/NASA Rosalind Franklin rover, planned for launch in 2028, will carry the first laser desorption ionization mass spectrometer (LDI-MS) to Mars as part of the Mars Organic Molecule Analyzer (MOMA) instrument. MOMA will contribute to the astrobiology goals of the mission through the analysis of potential organic biosignatures. Due to minimal availability of comparable equipment, laboratory analyses using similar techniques and instrumentation have been limited. Until now, the Thermo LTQ-XL platform has been used as the main analog instrument by the MOMA team despite significant differences between the instruments. In this study, we present a series of modifications that bring this commercial benchtop LDI-MS closer to MOMA operating parameters, enabling rapid testing of samples for MOMA validation experiments. We demonstrate that our instrument can detect organic standards in mineral matrices, with MS/MS enabling structural identification even in complex mixtures. Performance was additionally validated against an existing LDI-MS prototype through the comparison of spectra derived from natural samples from a Mars analog site in the Atacama Desert. Lastly, analysis of Mars analog synthetic mineral mixes highlights the capacity of the instrument to characterize both the mineralogical and organic signals in mission-relevant samples. This modified benchtop instrument will serve as a platform for collaborative research to prepare for MOMA operations, test LDI parameters, and generate pre-flight reference data in support of the mission science and astrobiology specific goals.
Many machine learning tasks in the natural sciences are precisely equivariant to particular symmetries. Nonetheless, equivariant methods are often not employed, perhaps because training is perceived to be challenging, or the symmetry is expected to be learned, or equivariant implementations are seen as hard to build. Group averaging is an available technique for these situations. It happens at test time; it can make any trained model precisely equivariant at a (often small) cost proportional to the size of the group; it places no requirements on model structure or training. It is known that, under mild conditions, the group-averaged model will have a provably better prediction accuracy than the original model. Here we show that an inexpensive group averaging can improve accuracy in practice. We take well-established benchmark machine learning models of differential equations in which certain symmetries ought to be obeyed. At evaluation time, we average the models over a small group of symmetries. Our experiments show that this procedure always decreases the average evaluation loss, with improvements of up to 37\% in terms of the VRMSE. The averaging produces visually better predictions for continuous dynamics. This short paper shows that, under certain common circumstances, there are no disadvantages to imposing exact symmetries; the ML4PS community should consider group averaging as a cheap and simple way to improve model accuracy.
Andreas Kamilaris, Chirag Padubidri, Asfa Jamil
et al.
This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.
Ansley J. Brown, Allan A. Andales, Timothy K. Gates
Abstract The global extent of salt‐affected agricultural land, 20% of which is deemed gypsiferous, results in billions of dollars of annual economic loss, a serious problem deserving of attention. However, the analysis of gypsiferous saline soils, such as in the irrigated Lower Arkansas River Valley (LARV) of Colorado, can result in an inflated estimation of soil salinity when using the traditional soil saturated paste extract electrical conductivity (ECe), leading to inaccurate crop yield loss predictions and misguided decisions for remediation. Sparingly soluble gypsum (CaSO4 · 2H2O) in these soils dissolves more readily during laboratory preparation of saturated paste extracts because of excess soil water dilution coupled with sample disturbance. We present a pragmatic linear‐regression approach to correct for this phenomenon, calibrated using two adapted methods for correcting ECe on an individual sample basis. The novel approach used electrical conductivity of pore water samples from saline fields to evaluate the accuracy of the correction methods. The approach was applied on soil samples from two surface‐irrigated, saline fields in the LARV, which were mapped using electromagnetic induction data and analysis of covariance linear regression, calibrated for ECe and ECe corrected for excess gypsum dissoultion (ECeg). Average ECeg values are as much as 26% lower than uncorrected ECe in gypsum‐biased portions of the fields. Estimation of corn salinity hazard in these gypsum‐affected areas using ECeg in lieu of ECe in a traditional yield response function generated mean relative yield values that are higher by up to 13 percentage points. We discuss lessons learned and suggest enhancements to the techniques.
Allotment gardens are an ideal place to analyse the links between plant diversity and social diversity. This study aims to characterise the cultivated and spontaneous plant diversity of allotments and to analyse its links with the socio-demographic characteristics of the gardeners. On the basis of data from 150 allotment garden plots in two cities in the Loire region, we first analyse the complex interweaving of spaces and actors in allotment gardens and describe the socio-demographic diversity of gardeners. We then show the contrast between the high diversity of cultivated plants and the limited diversity of spontaneous flora and micro-habitats in the plots, and identify the effects of gender, social background, residential situation and rural or urban origin of gardeners on plant diversity. The responsibility of the gardening norms transmitted by the managers and the uniqueness of the food function of these spaces is underlined.
Jamison Pinheiro Ribeiro, Andréa Cristina Dorr, Carine Dalla Valle
et al.
A cadeia produtiva do açaí, devido às suas propriedades altamente nutricionais, aos seus benefícios à saúde, ao seu grande valor cultural e potencial de comercialização, tem ganhado destaque no mercado nacional e internacional. Assim, o objetivo desta pesquisa consiste em mapear e caracterizar os agentes que compõem os elos dessa cadeia produtiva no município de Concórdia, estado do Pará. Este estudo caracteriza-se como qualitativo de cunho exploratório-descritivo, realizado através da coleta de dados por meio de entrevistas semi-estruturadas com os atores envolvidos na cadeia produtiva de açaí. Para a análise dos dados, foi utilizada a técnica de análise de conteúdo. Os achados deste artigo evidenciam as interligaçoes entre os elos que compõem a cadeia produtiva dentro do município, destacando a participação dos(as) agricultores(as) familiares no processo de produção, os agentes envolvidos na etapa de distribuição da cadeia, em que os atravessadores possuem certa predominância, e, por fim, o elo de beneficiamento, cujos responsáveis são a cooperativa, as agroindústrias e os batedores artesanais, chegando até os consumidores finais. Diante disso, conclui-se neste estudo que a cadeia produtiva evidenciou como o ambiente institucional permeia o desenvolvimento das atividades econômicas, dentro da base de produção, distribuição e beneficiamento, estabelecendo, assim, relações entre todos os agentes envolvidos na cadeia produtiva do açaí.
Neurological illnesses such as Alzheimer's disease (AD), Parkinson's disease (PD), and epilepsy (EP) have a significant impact on worldwide health. This study uses network pharmacology and genomic analysis to find shared genes and pathways linked to various illnesses.The STRING database was used to identify shared genes between AD, PD, and EP. Associated proteins of common genes were obtained and imported into Cytoscape to design and analyze networks. Gene enrichment analysis was performed using ShinyGO V0.77. AD, PD, and EP share three genes: KIF5A, NDUFB9, and MT-ND1. Network analysis showed relationships between these genes and their associated proteins. Pathway enrichment study revealed major pathways, including Alzheimer's, Parkinson's disease, Neurodegeneration, and oxidative phosphorylation pathways. The current study revealed genetic interconnectivity of AD, PD, and EP, underlining the role of mitochondrial failure, oxidative stress, and synaptic dysfunction in their development. KIF5A, NDUFB9, and MT-ND1 play critical roles in these pathways, making them attractive therapeutic targets. Indirect interactions between these genes via common proteins such as SNCA and MAPT indicate complicated regulatory networks. Identifying common genes and pathways sheds light on shared mechanisms underlying AD, PD, and EP. Drug repurposing opportunities targeting key proteins like SNCA and MAPT may offer novel therapeutic avenues.
Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors as a case study. Data from a long-term field experiment is analyzed to evaluate both sensor performance and environmental changes over time. Our approach employs importance sampling to isolate instrumental drift from environmental variation, providing a more accurate assessment of sensor performance. The results show that failing to account for environmental variation can significantly affect the evaluation of sensor drift, leading to improper calibration processes.
The National Environment Policy Act (NEPA) stands as a foundational piece of environmental legislation in the United States, requiring federal agencies to consider the environmental impacts of their proposed actions. The primary mechanism for achieving this is through the preparation of Environmental Assessments (EAs) and, for significant impacts, comprehensive Environmental Impact Statements (EIS). Large Language Model (LLM)s' effectiveness in specialized domains like NEPA remains untested for adoption in federal decision-making processes. To address this gap, we present NEPA Question and Answering Dataset (NEPAQuAD), the first comprehensive benchmark derived from EIS documents, along with a modular and transparent evaluation pipeline, MAPLE, to assess LLM performance on NEPA-focused regulatory reasoning tasks. Our benchmark leverages actual EIS documents to create diverse question types, ranging from factual to complex problem-solving ones. We built a modular and transparent evaluation pipeline to test both closed- and open-source models in zero-shot or context-driven QA benchmarks. We evaluate five state-of-the-art LLMs using our framework to assess both their prior knowledge and their ability to process NEPA-specific information. The experimental results reveal that all the models consistently achieve their highest performance when provided with the gold passage as context. While comparing the other context-driven approaches for each model, Retrieval Augmented Generation (RAG)-based approaches substantially outperform PDF document contexts, indicating that neither model is well suited for long-context question-answering tasks. Our analysis suggests that NEPA-focused regulatory reasoning tasks pose a significant challenge for LLMs, particularly in terms of understanding the complex semantics and effectively processing the lengthy regulatory documents.
Abstract This paper interrogates influential contemporary accounts of interdisciplinarity, in which it is portrayed as offering new ways of rendering science accountable to society and/or of forging closer relations between scientific research and innovation. The basis of the paper is an eighteen-month empirical study of three interdisciplinary fields that cross the boundaries between the natural sciences or engineering, on the one hand, and the social sciences or arts, on the other. The fields are: 1) environmental and climate change research, 2) ethnography in the IT industry and 3) art-science. In the first part of the paper, in contrast to existing accounts, we question the idea that interdisciplinarity should be understood in terms of the synthesis of two or more disciplines. We stress the forms of agonism and antagonism that often characterize relations between disciplinary and interdisciplinary research, and distinguish between three modes of interdisciplinarity. In the second part we outline three distinctive logics or rationales that guide interdisciplinary research. In addition to the logics of accountability and innovation, we identify the logic of ontology, that is, an orientation apparent in diverse interdisciplinary practices in each of our three fields towards effecting ontological transformation in the objects and relations of research. While the three logics are interdependent, they are not reducible to each other and are differently entangled in each of the fields. We point to the potential for invention in such interdisciplinary practices and, against the equation of disciplinary research with autonomy, to the possibility of forms of interdisciplinary autonomy.