Objective Although industrial sectors involving semiconductors; memory and storage technologies; display, optical, and photonic technologies; energy; biotechnology; and health care produce the most products that contain nanomaterials, nanotechnology is also used as an environmental technology to protect the environment through pollution prevention, treatment, and cleanup. In this review, we focus on environmental cleanup and provide a background and overview of current practice; research findings; societal issues; potential environment, health, and safety implications; and future directions for nanoremediation. We do not present an exhaustive review of chemistry/engineering methods of the technology but rather an introduction and summary of the applications of nanotechnology in remediation. We also discuss nanoscale zerovalent iron in detail. Data sources We searched the Web of Science for research studies and accessed recent publicly available reports from the U.S. Environmental Protection Agency and other agencies and organizations that addressed the applications and implications associated with nanoremediation techniques. We also conducted personal interviews with practitioners about specific site remediations. Data synthesis We aggregated information from 45 sites, a representative portion of the total projects under way, to show nanomaterials used, types of pollutants addressed, and organizations responsible for each site. Conclusions Nanoremediation has the potential not only to reduce the overall costs of cleaning up large-scale contaminated sites but also to reduce cleanup time, eliminate the need for treatment and disposal of contaminated soil, and reduce some contaminant concentrations to near zero—all in situ. Proper evaluation of nanoremediation, particularly full-scale ecosystem-wide studies, needs to be conducted to prevent any potential adverse environmental impacts.
Childcare is a ubiquitous issue for economic development across Canada, a challenge exacerbated by rural characteristics like low population density, workforce shortages (e.g., early childcare educators), and limited data. New early learning and childcare regulation has and continues to change the policy environment with the goal of increasing the accessibility and affordability of high-quality childcare. These changes have resulted in successes; however, policy changes have also had unintentional consequences for the viability of rural childcare facilities.
This article presents the results of a rural case study in the province of British Columbia that aimed to understand the business needs and challenges of rural childcare providers and to use these findings to contribute to the creation of constructive business solutions. Results also identified that the changing policy environment is presenting unanticipated challenges to childcare viability, resulting in a largely unrecognized risk of increasing sector precarity and potential loss of existing rural childcare providers.
We identify interventions, both at the regional and provincial scale, that may help in the short term to improve business operations and alleviate unintentional sector vulnerabilities related to changing policy. Interventions include proactive outreach to childcare providers to build relationships and increase knowledge of available support programs, offering in-person one-on-one support, enhancing short-term stability and predictability by providing timelines for policy change, and the creation of on-call or casual workforce services that can easily serve multiple small childcare providers.
Keywords: Childcare, rural economic development, policy change
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Précarité rurale face à l'évolution des politiques : Garde d’enfants en Colombie-Britannique rurale
Résumé
Les services de garde d’enfants constituent un enjeu omniprésent pour le développement économique du Canada. Ce défi est exacerbé par les caractéristiques du milieu rural, telles que la faible densité de population, la pénurie de main-d'oeuvre (par exemple, d'éducateurs à la petite enfance) et le manque de données. La nouvelle réglementation en matière d'apprentissage et de garde des jeunes enfants a modifié et continue de modifier le contexte politique afin d'accroître l'accessibilité et l'abordabilité de services de garde de qualité. Ces changements ont donné lieu à des succès; toutefois, ils ont également eu des conséquences imprévues sur la viabilité des services de garde en milieu rural.
Cet article présente les résultats d'une étude de cas menée dans une région rurale de la province de la Colombie-Britannique. Cette étude visait à comprendre les besoins et les défis des fournisseurs de services de garde d’enfants en milieu rural et à utiliser ces résultats pour contribuer à l'élaboration de solutions commerciales constructives. Les résultats ont également révélé que l'évolution du contexte politique pose des défis imprévus à la viabilité des services de garde d’enfants, ce qui entraîne un risque largement méconnu d'accroissement de la précarité du secteur et de disparition potentielle des prestataires de services de garde d’enfants existants en milieu rural.
Nous avons recensé des interventions, tant à l'échelle régionale que provinciale, susceptibles d'améliorer à court terme les activités commerciales et d'atténuer les vulnérabilités involontaires du secteur liées aux changements de politiques. Ces interventions comprennent des démarches proactives auprès des prestataires de services de garde d’enfants afin d'établir des relations et d'accroître leur connaissance des programmes de soutien disponibles, l’offre d’un soutien individualisé en personne, une meilleure stabilité et prévisibilité à court terme grâce à la mise en place d'échéanciers pour les changements de politiques, et la création de services de main-d'oeuvre occasionnelle ou sur appel pouvant facilement desservir plusieurs petits prestataires de services de garde d’enfants.
Mots-clés : garde d'enfants, développement économique rural, changement de politiques
Childhood obesity remains a major public health challenge in the United States, strongly influenced by a combination of individual-level, household-level, and environmental-level risk factors. Traditional epidemiological studies typically analyze these levels independently, limiting insights into how structural environmental conditions interact with individual-level characteristics to influence health outcomes. In this study, we introduce a micro-macro machine learning framework that integrates (1) individual-level anthropometric and socioeconomic data from NHANES and (2) macro-level structural environment features, including food access, air quality, and socioeconomic vulnerability extracted from USDA and EPA datasets. Four machine learning models Logistic Regression, Random Forest, XGBoost, and LightGBM were trained to predict obesity using NHANES microdata. XGBoost achieved the strongest performance. A composite environmental vulnerability index (EnvScore) was constructed using normalized indicators from USDA and EPA at the state level. Multi-level comparison revealed strong geographic similarity between states with high environmental burden and the nationally predicted micro-level obesity risk distribution. This demonstrates the feasibility of integrating multi-scale datasets to identify environment-driven disparities in obesity risk. This work contributes a scalable, data-driven, multi-level modeling pipeline suitable for public health informatics, demonstrating strong potential for expansion into causal modeling, intervention planning, and real-time analytics.
Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.
Triaxial vibration sensor are widely used used in various application. Recently, low-cost sensors based on micro electro mechanical system (MEMS) technology are also becoming more widely adopted. However, their measurement accuracy can be affected by environmental factors such as temperature. In this study, we developed an environmental testing system integrated with a triaxial vibration exciter. The system can reproduce long-stroke, low-frequency triaxial vibrations -- such as those caused by huge earthquakes -- under temperatures ranging from $-30~^\circ\mathrm{C}$ to $+80~^\circ\mathrm{C}$. Using this system, the measurement accuracy of vibration sensors can be evaluated under different environmental conditions. The system provides highly accurate reference measurements using a laser interferometer and reference accelerometers that are primarily calibrated within the system. The overall accuracy of the reference vibration measurement is estimated to be approximately 0.23~\%. Based on these reference measurements, we investigated the accuracy of earthquake observations using a MEMS accelerometer as a demonstration. The system configuration and testing procedures are presented in this paper.
Rodrigo de Souza Bulhões, Marina Silva Paez, Dani Gamerman
In this paper, we propose a flexible matrix-variate spatiotemporal model for analyzing multiple response variables observed at spatially distributed locations over time. Our approach relaxes the restrictive assumption of spatial isotropy, which is often unrealistic in environmental and ecological processes. We adopt a deformation-based method that allows the covariance structure to adapt to directional patterns and nonstationary behavior in space. Temporal dynamics are incorporated through dynamic linear models within a fully Bayesian framework, ensuring coherent uncertainty propagation and efficient state-space inference. Additionally, we introduce a strategy for handling missing observations across different variables, preserving the joint data structure without discarding entire time points or stations. Through a simulation study and an application to real-world air quality monitoring data, we demonstrate that incorporating spatial deformation substantially improves interpolation accuracy in anisotropic scenarios while maintaining competitive performance under near-isotropy. The proposed methodology provides a general and computationally tractable framework for multivariate spatiotemporal modeling with incomplete data.
Galaxy properties correlate with their position within the cosmic web. While galaxies are observed in an environment today, they may have experienced different environments in the past. The environmental history, linked to pre-processing, leaves an imprint on the properties of galaxies. We use the GAEA semi-analytic model and IllustrisTNG to reconstruct the environmental histories of galaxies between $z=0$ and $z=4$ that today reside in filaments. Our goal is to understand how galaxy properties are related to their past environments, and the role of the cosmic web in shaping their properties. We find that filament galaxies at $z=0$ are a heterogeneous mix of populations with distinct environmental histories. The vast majority of them have experienced group processing, with only $\sim$20\% remaining centrals throughout their life. For $\rm 9 < \log_{10}(M_{star}/M_{sun}) < 10$ galaxies, models confirm that the environmental effects are primarily driven by group processing: satellites stop growing stellar mass and exhibit elevated quenched fractions, whereas filament galaxies remain centrals have properties that are similar to field galaxies. Massive galaxies ($\rm \log_{10}(M_{star}/M_{sun}) > 10$) that have never been satellites and entered filaments more than 9 Gyr ago show accelerated stellar mass assembly and higher quenched fractions relative to the field, due to a higher frequency of merger events inside filaments, even at fixed mass. The most massive $\rm \log ((M_{star} / M_{sun}) > 11$) galaxies accreted onto filaments over 9 Gyr ago, highlighting the role of filaments in building up the high-mass end of the galaxy population. Filaments regulate galaxy evolution in a mass-dependent way: group environments regulate low-mass galaxies, while filaments favour the growth of massive galaxies.
H. O. Fatoyinbo, P. Tiwari, P. O. Olanipekun
et al.
Highly pathogenic avian influenza (HPAI), especially the H5N1 strain, remains a major threat to animal health, food security, and public health. Recent spillover events in dairy cattle in the United States, linked to wild birds, highlight the critical importance of understanding transmission pathways at the cattle--wild bird--environment interface. In this work, we formulate and analyze a deterministic compartmental model that captures the transmission of HPAI between dairy cattle and wild birds, incorporating both direct and indirect (environmental) routes. The model combines an $SEIR$ framework for cattle with an $SIR$ structure for wild birds, coupled through an environmental compartment. We derive the basic reproduction number, $\mathcal{R}_{0}$, using the next-generation matrix approach, decomposing it into cattle-to-cattle, bird-to-bird, and environmental contributions. Qualitative analysis establishes positivity, boundedness, and global stability of equilibria through Lyapunov functions. Numerical simulations confirm the results of the theoretical analyses, illustrating outbreak trajectories, extinction thresholds, and persistence dynamics. A global sensitivity analysis, based on Latin hypercube sampling and partial rank correlation coefficients, identifies key parameters, particularly transmission among cattle, environmental contamination, and recovery rate as critical drivers of epidemic outcomes. Our results show that disease elimination is achievable when $\mathcal{R}_{0} < 1$, while persistence is inevitable for $\mathcal{R}_{0} > 1$. These findings provide a comprehensive mathematical framework for assessing HPAI risks and offer guidance for biosecurity strategies aimed at mitigating spillover and controlling outbreaks in livestock populations.
Shubham Kejriwal, Enrico Barausse, Alvin J. K. Chua
The upcoming Laser Interferometer Space Antenna (LISA) will detect up to thousands of extreme-mass-ratio inspirals (EMRIs). These sources will spend $\sim 10^5$ cycles in band, and are therefore sensitive to tiny changes in the general-relativistic dynamics, potentially induced by astrophysical environments or modifications of general relativity (GR). Previous studies have shown that these effects can be highly degenerate for a single source. However, it may be possible to distinguish between them at the population level, because environmental effects should impact only a fraction of the sources, while modifications of GR would affect all. We therefore introduce a population-based hierarchical framework to disentangle the two hypotheses. Using simulated EMRI populations, we perform tests of the null vacuum-GR hypothesis and two alternative beyond-vacuum-GR hypotheses, namely migration torques (environmental effects) and time-varying $G$ (modified gravity). We find that with as few as $\approx 20$ detected sources, our framework can statistically distinguish between these three hypotheses, and even indicate if both environmental and modified gravity effects are simultaneously present in the population. Our framework can be applied to other models of beyond-vacuum-GR effects available in the literature.
This paper introduces a deep learning framework for generating point clouds from WiFi Channel State Information data. We employ a two-stage autoencoder approach: a PointNet autoencoder with convolutional layers for point cloud generation, and a Convolutional Neural Network autoencoder to map CSI data to a matching latent space. By aligning these latent spaces, our method enables accurate environmental point cloud reconstruction from WiFi data. Experimental results validate the effectiveness of our approach, highlighting its potential for wireless sensing and environmental mapping applications.
Generative AI is spreading rapidly, creating significant social and economic value while also raising concerns about its high energy use and environmental sustainability. While prior studies have predominantly focused on the energy-intensive nature of the training phase, the cumulative environmental footprint generated during large-scale service operations, particularly in the inference phase, has received comparatively less attention. To bridge this gap this study conducts a scoping review of methodologies and research trends in AI carbon footprint assessment. We analyze the classification and standardization status of existing AI carbon measurement tools and methodologies, and comparatively examine the environmental impacts arising from both training and inference stages. In addition, we identify how multidimensional factors such as model size, prompt complexity, serving environments, and system boundary definitions shape the resulting carbon footprint. Our review reveals critical limitations in current AI carbon accounting practices, including methodological inconsistencies, technology-specific biases, and insufficient attention to end-to-end system perspectives. Building on these insights, we propose future research and governance directions: (1) establishing standardized and transparent universal measurement protocols, (2) designing dynamic evaluation frameworks that incorporate user behavior, (3) developing life-cycle monitoring systems that encompass embodied emissions, and (4) advancing multidimensional sustainability assessment framework that balance model performance with environmental efficiency. This paper provides a foundation for interdisciplinary dialogue aimed at building a sustainable AI ecosystem and offers a baseline guideline for researchers seeking to understand the environmental implications of AI across technical, social, and operational dimensions.
Johnathan Daniel Maxey, Neil D. Hartstein, Dane Dickinson
et al.
Abstract Aquaculture’s contribution to global N2O emissions is poorly constrained and often reliant on supply chain/industrial emissions/life-cycle analyses which generalise system responses to farm-derived inputs and contain few examples of direct measurements made in situ. Among the studies that do report aquaculture associated N2O emissions the focus has been on pond culture and wetlands systems rather than open marine systems. Our study examined the effects of open system aquaculture culture on water column N2O cycling in two hydrodynamically contrasting southern hemisphere systems: the heavily stratified Macquarie Harbour, Tasmania, Australia and the semi-enclosed but well-mixed Big Glory Bay, New Zealand. Significant, but localised, N2O undersaturation was observed under the active salmon farm in the heavily stratified Macquarie Harbour during the peak feeding season, but not under fallowed salmon farms or the non-farmed areas. This was observed in a low-oxygen but not anoxic water column. Water column N2O was either in equilibrium with the atmosphere or supersaturated in all other instances. In Big Glory Bay N2O undersaturation was observed during winter and spring sampling surveys that generally persisted across the bay and resulted in removal of atmospheric N2O. The specific mechanisms of N2O loss are still uncertain but is likely driven by a combination of particle associated denitrification activity in farm waste plumes, denitrification/DNRA in sediments and on the detritus covered mussel shells and lines. Overall, this study demonstrates that industry impacts to N2O cycling can include loss dynamics which have previously been unreported. Therefore, global estimates of N2O emissions from aquaculture may be significantly overestimated.
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three caricature models from different areas of the life sciences: (i) a predator-prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and, (iii) an advection-diffusion-reaction model describing transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods, but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.
Hongzheng Zhu, Kieran Khamis, David M. Hannah
et al.
Abstract In‐situ dissolved organic matter (DOM) monitoring frequencies have often been chosen for convenience or based on perceived wisdom, without fully assessing their impact on representation of DOM dynamics. To address this gap, we collected 5‐min fluorescence data in an urban headwater and resampled it at coarser intervals to investigate the impact of monitoring frequencies on the detectability of DOM dynamics during storms. Expecting hydrometeorological conditions to modify the impact of monitoring frequency, we categorized 85 storm events into groups: Group A (low intensity, short duration), Group B (high intensity, short duration), and Group C (low intensity, long duration). Surprisingly, our analysis indicated that monitoring frequency has minimal influence on commonly used biogeochemical indexes (e.g., maximum, hysteresis and flushing index), which are employed to characterize solute behavior, regardless of storm type. To facilitate a direct comparison between monitoring frequencies, we back‐interpolated coarser data into 5‐min intervals and calculated mean squared errors by comparing them with original high‐resolution data. Our findings indicated that in colder periods with predominately Type A and C storms, a coarser monitoring frequency (>30 min) can capture DOM dynamics. Conversely, in warmer periods when Type B storms dominate, a finer frequency (≤15 min) is necessary to capture key solute chemograph processes (e.g., first flush and dilution). Generally, we suggest a 15‐min monitoring frequency as optimal for similar urban headwater systems, and advocate an adaptive approach based on seasonal variations to improve efficiency, especially when power, data transfer, and storage are constraints.
Khurram Shahzad, Waqar Shoukat Ali, Sohaib Muhammad
et al.
The tropical dry forests (TDF) have an enormously rich flora and fauna that offer various ecological services to the surrounding human societies. Biodiversity assessment is mandatory for implementing any sustainable forest management policy, which is why it is one of the important criteria and indicators currently used. Threats to TDF biodiversity are the primary challenges arising from environmental concerns caused by anthropogenic activity leading to global warming issues. The study aimed to investigate the vegetation assessment and several environmental and anthropogenic variables influencing forest biodiversity from 5 threatened forest sites of District Sialkot (Ghalotian, Kishan Garh, Daburgi Chanda Singh, Pir Kot, and Ghulab Garh), Pakistan. We collected 170 distinct plant species, including 135 dicots, 27 monocots, seven pteridophytes, and one bryophyte, categorized into 138 genera and 62 families, divided into 114 herbs, 32 trees, and 24 shrubs. The phytosociological analysis described the quantitative characteristics, including % frequency, % density, % cover, and importance Value Index (IVI) of all forest areas. Gulab Garh forest has the richest biodiversity forest area, and herbs are the dominant species that have been documented. Environmental factors such as temperature, precipitation, organic matter, soil pH, Ca+2, Mg+2, Na+, Cl−, and electric conductivity (EC) strongly affect forest vegetation investigated by principal coordinate analysis. Shannon and Simpson’s diversity indexes reveal that all sites contain loamy and sandy soil and display a significant relationship between alpha diversity and richness. Increasing trends in temperature and decreasing trends in rainfall suggested that climate significantly affects the Sialkot region’s plant biodiversity. SWOT analysis highlighted that population growth leads to increasing anthropogenic activities such as constructing housing societies and roads, inadequate farming, and excessive grazing, impacting the forest vegetation and altering TDF ecosystem properties/services and functioning. Our findings reinforce the vegetational assessment and importance of local forest biodiversity and significant environmental drivers that influence the plant species diversity in TDF areas. Future conservation strategies are suggested to reduce unlawful resource consumption, restore plant biodiversity in designated protected areas, and conserve rare species locally.
Yuningsih Erwin, Biyantoro Gigih Adjie, Sugiharto Eko
et al.
Optimization of student achievement is carried out with various efforts, including participating in training for preparation for achievement matches. Students of the Faculty of Sports and Health Sciences (FIKK) Surabaya State University (Unesa) who attend training often apply for dispensation not to attend lectures. This has an impact on the quality of learning because students are constrained by place and time so they cannot attend lectures and are only given assignments. So, a system is needed so that student-athletes can take online lectures that are more flexible in time and place. But, FIKK Unesa does not yet have an online lecture system for students. Therefore, this study aims to design and build a Moodle learning management system for student-athletes at FIKK Unesa. The method used in designing and creating the system is waterfall using the Moodle LMS framework. The results showed that the Moodle LMS that had been designed and built could run and function properly.
James Lee Wei Shung, Andrea Piazzoni, Roshan Vijay
et al.
Autonomous Vehicles (AVs) being developed these days rely on various sensor technologies to sense and perceive the world around them. The sensor outputs are subsequently used by the Automated Driving System (ADS) onboard the vehicle to make decisions that affect its trajectory and how it interacts with the physical world. The main sensor technologies being utilized for sensing and perception (S&P) are LiDAR (Light Detection and Ranging), camera, RADAR (Radio Detection and Ranging), and ultrasound. Different environmental parameters would have different effects on the performance of each sensor, thereby affecting the S&P and decision-making (DM) of an AV. In this publication, we explore the effects of different environmental parameters on LiDARs and cameras, leading us to conduct a study to better understand the impact of several of these parameters on LiDAR performance. From the experiments undertaken, the goal is to identify some of the weaknesses and challenges that a LiDAR may face when an AV is using it. This informs AV regulators in Singapore of the effects of different environmental parameters on AV sensors so that they can determine testing standards and specifications which will assess the adequacy of LiDAR systems installed for local AV operations more robustly. Our approach adopts the LiDAR test methodology first developed in the Urban Mobility Grand Challenge (UMGC-L010) White Paper on LiDAR performance against selected Automotive Paints.