Environmental Science Research Articles

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Defining future cities: A comparative review of future cities’ definitions and characteristics across multiple fields

The rapid interest in future cities within academia is driven by the rising complexity of urban development and challenges such as rapid urbanization, population growth, and inequality. Researchers across various fields have different understandings of future cities and their characteristics, causing confusion and divergence between implementation goals and theories. This research addresses this gap by providing a unified, multidisciplinary definition of future cities, critically analyzing their intellectual and practical characteristics across fields including philosophy, computer science, engineering, environmental science, management, economics, social science, health sciences, architecture and planning. This research employed a mixed-method approach of a hybrid systematic-narrative literature review to identify key characteristics, followed by a multidisciplinary focus group of experts to synthesize the definition and key characteristics. It concluded that despite the rising interest in academia about future cities in recent years, there are differences in how various fields perceive future cities, particularly in areas like infrastructure, resource management, innovation, adaptability, technology, sustainability, and human needs. Despite these differences, future cities share common goals of meeting the well-being and justice needs of both current and future residents. They rely on technology, sustainability practices, community involvement, responsible citizenship, and efficient governance and leadership to ensure sustainable and effective long-term outcomes.

Application of additional dose of N could sustain rice yield and maintain plant nitrogen under elevated ozone (O3) and carbon dioxide (CO2) condition

IntroductionGlobal food security is challenged by the increasing levels of air pollutants like ozone (O3) through their impacts on crop productivity. The present study was conducted to quantify the interactive effect of elevated ozone (O3) and carbon dioxide (CO2), on different rice varieties in northern India.MethodsAn experiment was conducted in Genetic H field, Environment science, IARI for two consecutive years (2020 and 2021) during the kharif season, to quantify the impact of elevated O3 and CO2 interaction on productivity, and plant N in three rice varieties (Pusa basmati 1121, Nagina 22, IR64 Drt1) under different nitrogen (N) management practices. Rice crop was grown in Free Air Ozone-Carbon dioxide Enrichment rings (FAOCE) rings with two levels of O3 (elevated 60 ±10ppb and ambient) and two levels of CO2 (elevated, 550±25 ppm and ambient) concentration and their interaction with two N fertilizer treatments i.e., 100% RDN (recommended dose of N) and 125% RDN.Results and discussionElevated O3 significantly decreased physiological parameters like photosynthesis rate, stomatal conductance and transpiration rate of the crop. Grain yield reduced by 7.2-7.5%, in Pusa Basmati 1121 and from 6.9-9% in IR64 Drt1 varieties in elevated O3 treatment as compared to ambient treatment. Yield reduction in Nagina 22 variety was not significant in elevated O3 treatment. Elevated CO2 concentration of 550 ppm was able to fully compensate the yield loss in Nagina 22 variety and partially compensate (3.9-8.0%) in Pusa Basmati 1121 and IR64 Drt1 varieties. Grain N concentration in rice varieties decreased by 10.8-14.7% during first year and by 7.8-20.6% during second year in elevated O3 plus CO2 interaction treatment than ambient. Grain N uptake also decreased (13.2-17.1% in first year and 4.5-22.8% in second year) in elevated O3 plus CO2 interaction treatment as compared to ambient. Application of additional 25% of recommended dose of N improved grain N concentration, grain N uptake as well as available N of soil as compared to 100% RDN treatment in elevated O3 plus CO2 interaction treatment. Additional 25% N dose could help in sustaining rice productivity and quality under elevated O3 and CO2 condition.

Self-Enhanced Electrochemiluminesence of Dye-Doped Polymer Dots for Coreactant-Free Visualized Detection of Iodide Ions.

The monitoring of radioactive iodide levels is of great significance in environmental science and cancer radiotherapy. In this work, a high-throughput, radiation-resistant, and visualized electrochemiluminescence (ECL) strategy was developed for detection of iodide ions. Herein, the hydrophobic ruthenium derivative (Ru(bpy)3[B(C6F5)4]2) (bpy = bipyridyl) was doped in tertiary amine-coupled polymer dots (N-PFO Pdots) to synthesize self-enhanced Pdots (Ru@Pdots), which showed extremely high ECL intensity in absence of coreactant. Due to the efficient ECL resonance energy transfer between Ru(bpy)3[B(C6F5)4]2 and N-PFO, the Ru@Pdots exhibited 18 times higher ECL intensity compared with bare N-PFO Pdots. Besides, Ru@Pdots also showed 220-times higher ECL intensity compared with Ru(bpy)3[B(C6F5)4]2 doped coreactant-dependent Pdots (Ru@PFO Pdots). Using Ru@Pdots as ECL emitters, an ECL imaging array was designed for iodide ion detection, which exhibited a detection range of 0.8 nM-4 μM and a limit of detection of 0.1 nM. In this strategy, iodide ions were oxidized as iodide free radicals on the surface of the electrode, which could further consume the nitrogen radical of Ru@Pdots and effectively quench the ECL signal. This method also showed good specificity, radiation-resistant performance, and accuracy in actual seawater sample testing, which indicated its value in marine environmental monitoring, nuclear security, and cancer radiotherapy.

A bibliometric analysis of 100 years of research on Himalayan cedar: research trends, gaps, and future implications

Himalayan Cedar (Cedrus deodara), a member of the family Pinaceae is well-known for its ecological, economic, and cultural significance. It is native to the Western Himalayan region and listed as Least Concern in the IUCN list of threatened species. In the present study, a bibliometric analysis of more than a hundred years of research on C. deodara is carried out. Total 616 documents published from 1916 to 2024 were retrieved from the Scopus database and analyzed using biblioshiny and VoS viewer. A comprehensive overview of publication trends, country-wise publications, bibliographic coupling, citation analysis, keyword analysis, and collaborative research networks are presented. The research findings revealed that publications have increased significantly in recent decades and primarily multidisciplinary. Forest ecology, pharmacology, phytochemistry, climate change, environmental science, and taxonomy were among the major thrust areas. However, recent studies are mostly focused on carbon stock, biomass, dendrochronology, and climate change. Out of the 362 publication sources, Bradford’s law identified six journals as core sources for publication. Lotka’ law revealed that only 8% of authors have published more than two documents on C. deodara. The present study provides a comprehensive evaluation and visualization of C. deodara based bibliometric research carried out during the past 100 years. Further, the study provides collective information and a research framework for scholars, the general public, and decision-makers by identifying research gaps and future research areas.

The role of eco-anxiety in the presentation of bulimia nervosa: a case report

BackgroundDespite a growing literature demonstrating the significant impacts of climate change on mental health, research is urgently needed to investigate how climate change-related concerns may contribute to the development, exacerbation, or re-emergence of eating disorders, as well as affect the effectiveness of existing interventions. This case report contributes to this scant knowledge base by offering empirical evidence for how responses to climate change can influence eating disorder symptoms and, importantly, limit the effectiveness of evidence-based treatments such as Cognitive Behavior Therapy-Enhanced (CBT-E).Case presentationA 24-year-old female graduate student studying environmental science presented to a specialized eating disorder clinic with worsening bulimia nervosa. Her symptoms initially improved with CBT-E; however, after three months, concerns about food waste significantly impeded further progress. The therapist, identifying symptoms of eco-anxiety, adapted standard CBT-E strategies to include psychoeducation about eco-anxiety, cognitive restructuring of beliefs about food waste and other eating-related eco-concerns, relevant exposures related to such concerns, and problem-solving to increase social support. These adaptations led to resumed progress, with the patient achieving nutritional adequacy by treatment end (38 sessions) and maintaining treatment gains through one year follow-up.ConclusionsTo our awareness, this is the first case report on the co-occurrence of eco-anxiety and eating disorders. This case underscores the importance of screening for concurrent eco-anxiety, suggests ways in which eating disorders and eco-anxiety can influence one another longitudinally, describes how coexisting eco-anxiety can limit standard CBT-E’s effectiveness, and provides examples of successful treatment adaptations tailored to address eco-anxiety-related concerns.

Perceived Convenience, Social Influence and Household Waste Sorting Behavior of Young Residents: An Empirical Study From a Developing Country

Waste management remains a challenge, especially for developing countries and effective waste separation plays a critical role of reduction in the amount of waste to ensure sustainable development goals. Aiming to investigate the waste sorting behavior of young residents in the second most populous city such as Hanoi, hosting numerous universities and colleges in Vietnam, we conducted an online survey with students from 10 universities in Hanoi via online platform and a sample size of 331 valid responses have been collected. Since we wish to provide insights into young residents’ awareness, knowledge, and behavior about environmental protection and waste separation, the convenience sampling method is employed. The result suggests that five influential factors positively affect young residents’ sorting behavior among which environmental attitude and perceived convenience have a greater impact on young residents’ sorting behavior. The underlying reasons are then discussed to provide policy implications which include environmental science courses as well as regulations on waste sorting at source.

Oil biomarkers: main types, value and applications fields

Recent advances in modern chemistry provide environmental scientists with the ability to identify and track spilled oil residues in various environments. The compounds commonly used to identify the source of spilled oil are called biomarkers, they are universal in crude oil and petroleum products and are generally more stable to atmospheric environmental influences than most other oil constituents. The distribution of biomarker compounds is unique to each oil. Fingerprint indices calculated from oil fingerprints provide a stable and useful tool for determining the correspondence or non-correspondence of various oil residues present in some environmental samples. This paper highlights the main biomarkers of oil, their role in the study of oil and its deposits, and also shows the results of the authors’ own research.

Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques

Addressing the imperative demand for accurate water quality assessment, this paper delves into the application of deep learning techniques, specifically leveraging IoT sensor datasets for the classification and prediction of water quality parameters. The utilization of LSTM (Long Short-Term Memory) models navigates the intricacies inherent in environmental data, emphasizing the balance between model accuracy and interpretability. This equilibrium is achieved through the deployment of interpretability methods such as LIME, SHAP, Anchor, and LORE. Additionally, the incorporation of advanced parameter optimization techniques focuses on fine-tuning essential parameters like learning rates, batch sizes, and epochs to optimize model performance. This comprehensive approach ensures not only precise predictions but also enhances the transparency and interpretability of the model, addressing the critical need for actionable information in water quality management. The research significantly contributes to the convergence of deep learning, IoT, and environmental science, offering valuable tools for informed decision-making while highlighting the importance of fine-tuning parameters for optimal model performance

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

A scoping review to determine if adverse human health effects are associated with use of organophosphates for mosquito control.

Organophosphate insecticides are widely used for adult mosquito control. Although proven effective in reducing mosquito populations and limiting arbovirus transmission, public concern exists regarding potential human health effects associated with organophosphate exposure. The aim of this scoping review was to describe any reported human health conditions associated with organophosphates during their use for adult mosquito control in the United States and Canada. Original peer-reviewed articles published in English language journals from 1 January 2000 to 22 May 2024, were obtained by searching from the databases MEDLINE, EMBASE, Agricultural & Environmental Science Collection, CAB Abstracts, and Scopus. The search identified 6,154 screened articles. Following an independent review, 10 studies were identified that described human health conditions associated with organophosphate exposure during adult mosquito control applications. Of the 10 included studies, only two articles were published within the last 11 years (2013 to 22 May 2024). Three types of study design were represented in the included studies: cohort (n = 5), case study (n = 1), and risk assessment (n = 4). The included studies could not determine causality between exposure to adulticides and development of illness or adverse impacts. Exposure to organophosphates did not contribute to an observed increase in metabolic toxicity, hospitalization rates, or self-reported symptoms and exposure. The available and limited evidence indicates that organophosphates can be used safely to control nuisance mosquitoes or mosquitoes that transmit arboviruses. Continued research regarding the human health effects associated with organophosphate applications for adult mosquito control could help evaluate the basis of the public's concerns and inform public health decision-making.

Predicting metastable oxide-to-hydroxide phase transformations by bulk and interface thermodynamics: Application to the phase stability of aluminium oxides and hydroxides in water

Fundamental understanding of the phase stability of oxides in aqueous environments is of eminent importance for a broad range of disciplines, such as corrosion, tribology, catalysis, medical implants, biosensing and environmental sciences. Most oxide-to-hydroxide phase transformation sequences proceed by consecutive formation of different metastable hydroxide phases towards the most stable bulk hydroxide phase, in accord with Ostwald's Rule of Stages and thus in contradiction with bulk thermodynamics. In this work, a novel unified thermodynamic model is presented for predicting such metastable oxide-to-hydroxide phase transformation sequences by accounting for the energy barrier(s) associated with the creation of new interface(s) between the competing parent and product phase(s). To this end, semi-empirical expressions for the estimation of the interface energies between metals, oxides and/or hydroxides were derived based on the macroscopic atom approach. Application of the model to the phase stability of aluminium in water (at neutral pH) for 298 ≤ T ≤ 500 K predicts a solid-state phase transformation sequence from Al → am-Al2O3 → (pseudo)boehmite, in accord with experimental observations. Subsequent competing formation of the trihydroxide phases bayerite and gibbsite by a dissolution-precipitation on the (pseudo)boehmite surface is equally favoured and critically depends on the hydrolysis of dissolved Al3+ species in solution, as governed by e.g. pH, temperature and the presence of foreign ions in solution.

Enhancing sensitivity in fluorescence measurements across an ultra-wide concentration range through optimizing combined-segments strategy

Fluorescence measurement technology is crucial in analytical instrumentation. However, when applied in fields such as environmental science and medical research for quantification purposes, it requires prior estimation of sample concentration and sample dilution to ensure measurement precision. This requirement presents challenge for its broader application in online fluorescence sensing. This study addresses the challenge of achieving high-precision measurements across an ultra-wide concentration range, which is critical for applications ranging from environmental monitoring to clinical diagnostics. The binary segmentation method can initially achieve the goal of ultra-wide range fluorescence measurement, but it fails to maintain sensitivity and precision across the entire range, with relative errors exceeding ±15%. Therefore, we propose an optimizing combined-segments strategy, which examines the effects of adjusting the fluorescence reception position, range, and region interval parameters of the fluorescence reception probes on measurement sensitivity limits. This strategy aims to find the optimal measurement sensitivity limit (lmopt) and the best combined-segments configuration based on more than one quantitative relationship curves. Experimental results demonstrate that within a concentration range 20 times broader than the linear range, it can effectively maintain relative errors within ±5%. This enhances the applicability of fluorescence sensing techniques in various practical settings and advances online and direct fluorescence measurement technologies across an ultra-wide concentration range.

Unveiling the Potential of Protein-Based Sustainable Antibacterial Materials.

The surge in bacterial growth and the escalating resistance against a multitude of antibiotic drugs have burgeoned into an alarming global threat, necessitating urgent and innovative interventions. In response to this peril, scientists have embarked on the development of advanced biocompatible antibacterial materials, aiming to counteract not only bacterial infections but also the pervasive issue of food spoilage resulting from microbial proliferation. Protein-based biopolymers and their meticulously engineered composites are at the forefront of this endeavor. Their potential in combating this severe global concern presents an approach that intersects the domains of biomedicine and environmental science. The present review article delves into the intricate extraction processes employed to derive various proteins from their natural sources, unraveling the complex biochemical pathways that underpin their antibacterial properties. Expanding on the foundational knowledge, the review also provides a comprehensive synthesis of functionalized proteins modified to enhance their antibacterial efficacy, unveiling a realm of possibilities for tailoring solutions to specific biomedical and environmental applications. The present review navigates through their antibacterial applications; from wound dressings to packaging materials with inherent antibacterial properties, the potential applications underscore the versatility and adaptability of these materials. Moreover, this comprehensive review serves as a valuable roadmap, guiding future research endeavors in reshaping the landscape of natural antibacterial materials on a global scale.

Sustainable development of electric vehicles in China: A bibliometric analysis and systematic review, 2011 onward

To address the challenge of climate change, China declared that its carbon dioxide emissions would peak in 2030 and become carbon neutral by 2060. China is the largest energy supplier and electric vehicles market worldwide and will stop selling combustion vehicles by 2035 and completely phase them out by 2050. To provide a overview of the current research, a bibliometric analysis and systematic review were conducted. The bibliometric analysis utilized data from Web of Science and Scopus, selected based on the PRISMA guidelines. Focusing on studies conducted in China and related to social sciences, environmental sciences, business, or economics. A total of 188 articles met the inclusion criteria. The analysis was performed using VOS viewer software to map co-citations and keyword co-occurrences, identifying core research themes and prominent clusters in the field. However, most researchers have primarily focused on one or two aspects of sustainability, with the hot spots being subsidies, charging costs, charging infrastructure, charging stations, batteries, and emissions. Life cycle assessment is also the most applied theory. This study recommended future research to focus on the usage stage of EVs, incorporating all three sustainability pillars. Additionally, regional assessment should be conducted to consider regional characteristics such as temperature, how policy influences affect electric vehicle emissions, social impacts, and economic advantages. Before promoting electric vehicles, regional assessments should be conducted, so that stakeholders can make decisions.

Bayesian Ensemble Kalman Filter for Gaussian Mixture Models

AbstractInverse theory and data assimilation methods are commonly used in earth and environmental science studies to predict unknown variables, such as the physical properties of underground rocks, from a set of measured geophysical data, like geophysical seismic or electromagnetic data. A new Bayesian approach based on the ensemble Kalman filter using Gaussian mixture models is presented to overcome the assumption of Gaussian distribution of the unknown variables commonly used in the data assimilation literature and to generalize the algorithm to inverse problems with multimodal probability distributions. In applications of subsurface characterization, the multimodality of the unknown variables is generally due to the presence of different rock types, also known as geological facies. In the proposed method, the weights of the Gaussian mixture model represent the facies proportions, and they follow a Markov chain model. The proposed Bayesian model generates the unknown model parameters conditioned on measured data using a Markov chain Monte Carlo sampler. The validity of the method is demonstrated on a data assimilation problem where the goal is to estimate the posterior distribution of the unknown rock density from a set of repeated measurements of acoustic wave velocity measured at different times. The proposed method provides accurate estimates with efficient computational times.

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

BackgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts.We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.ResultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.ConclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

Indigenous Perspectives on Dismantling the Legacies of Settler Colonialism in Rural Sociology☆

AbstractRural Sociology has failed to incorporate Settler‐colonialism and Indigenous theory in studying rural social relations. This presents a serious gap in the discipline's conceptualization of land as the foundation of social reproduction. Indigenous theory provides rich insights about humans' relations among themselves and with the more‐than‐human that inform our understanding of Settler colonialism as a driver of social formation, and human–environmental interactions more generally. This paper, written by four Indigenous environmental social scientists and one Settler rural sociologist, invites the discipline to engage Indigeneity and Settler colonialism in methodologically, theoretically, and ethically appropriate ways.

Engaging Beyond Academia: A Call to Act for Environmental Scientists

Engaging Beyond Academia: A Call to Act for Environmental Scientists

Financial Strategies for Sustainability: Examining the Circular Economy Perspective

Circular economy (CE) research has advanced significantly in recent years in areas like environmental science, engineering, and technology. Governance presents CE strategies as imperative and compelling, often linked to efforts to achieve sustainable business development. However, the financial quantification of a CE is still underexplored and heterogeneous, with multiple research streams exploring the financial dimension. The aims of this study are twofold: (i) to analyze the productivity of the scientific literature and the conceptual evolution of circularity as it relates to financial strategies; and (ii) to formulate future research directions addressing a CE in close connection with financial sustainability knowledge. Bilbiometrix R-package 2023.12.1 and VOSviewer version 1.6.20 were used to analyze a sample of 279 articles retrieved from WoS and Scopus using the PRISMA protocol. The analysis covered the period 2010–2023, with the diachronic thematic analysis revealing four key themes: (1) CE and green financial performance; (2) environmental impact of CE; (3) performance approach of CE and metrics; (4) CE and sustainable development. Building on previous knowledge, we developed an integrated and multidisciplinary framework of CE embedded in financial performance. Future research agenda shaped by the emerging themes and research gaps can support managers, shareholders, or governance in incorporating CE policies into operational activities and strengthening sustainable circularity knowledge.